OUP CORRECTED PROOF – FINAL, 18/10/2010, SPi

Biological Diversity
Frontiers in Measurement
and Assessment
EDITED BY

Anne E. Magurran
Professor of Ecology & Evolution,
University of St Andrews, UK
and
Brian J. McGill
Assistant Professor, School of Biology and Ecology
& Sustainability Solutions Initiative, University of Maine, USA

1
OUP CORRECTED PROOF – FINAL, 18/10/2010, SPi

3
Great Clarendon Street, Oxford OX2 6DP
Oxford University Press is a department of the University of Oxford.
It furthers the University’s objective of excellence in research, scholarship,
and education by publishing worldwide in
Oxford New York
Auckland Cape Town Dar es Salaam Hong Kong Karachi
Kuala Lumpur Madrid Melbourne Mexico City Nairobi
New Delhi Shanghai Taipei Toronto
With offices in
Argentina Austria Brazil Chile Czech Republic France Greece
Guatemala Hungary Italy Japan Poland Portugal Singapore
South Korea Switzerland Thailand Turkey Ukraine Vietnam
Oxford is a registered trademark of Oxford University Press
in the UK and in certain other countries
Published in the United States
by Oxford University Press Inc., New York
© Oxford University Press 2011
The moral rights of the authors have been asserted
Database right Oxford University Press (maker)
First published 2011
All rights reserved. No part of this publication may be reproduced,
stored in a retrieval system, or transmitted, in any form or by any means,
without the prior permission in writing of Oxford University Press,
or as expressly permitted by law, or under terms agreed with the appropriate
reprographics rights organization. Enquiries concerning reproduction
outside the scope of the above should be sent to the Rights Department,
Oxford University Press, at the address above
You must not circulate this book in any other binding or cover
and you must impose the same condition on any acquirer
British Library Cataloguing in Publication Data
Data available
Library of Congress Cataloging-in-Publication Data
Biological diversity : frontiers in measurement and assessment / edited by
Anne E. Magurran and Brian J. McGill.
p. cm.
ISBN 978–0–19–958067–5
1. Biodiversity. 2. Biodiversity—Monitoring. 3. Biodiversity conservation.
I. Magurran, Anne E., 1955– II. McGill, Brian J.
QH541.15.B56B587 2010
578.7—dc22 2010029049
Typeset by SPI Publisher Services, Pondicherry, India
Printed in Great Britain
on acid-free paper by
CPI Antony Rowe, Chippenham, Wiltshire

ISBN 978–0–19–958066–8 (Hbk.)

978–0–19–958067–5 (Pbk.)

1 3 5 7 9 10 8 6 4 2
 OUP CORRECTED PROOF – FINAL, 18/10/2010, SPi

CHAPTER 4

Estimating species richness

Nicholas J. Gotelli and Robert K. Colwell

4.1 Introduction studies continue to ignore some of the fundamental

sampling and measurement problems that can com-
Measuring species richness is an essential objec- promise the accurate estimation of species richness
tive for many community ecologists and conserva- (Gotelli & Colwell 2001).
tion biologists. The number of species in a local In this chapter we review the basic statisti-
assemblage is an intuitive and natural index of cal issues involved with species richness estima-
community structure, and patterns of species rich- tion. Although a complete review of the subject is
ness have been measured at both small (e.g. Blake beyond the scope of this chapter, we highlight sam-
& Loiselle 2000) and large (e.g. Rahbek & Graves pling models for species richness that account for
2001) spatial scales. Many classic models in commu- undersampling bias by adjusting or controlling for
nity ecology, such as the MacArthur–Wilson equi- differences in the number of individuals and the
librium model (MacArthur & Wilson 1967) and number of samples collected (rarefaction) as well as
the intermediate disturbance hypothesis (Connell models that use abundance or incidence distribu-
1978), as well as more recent models of neutral tions to estimate the number of undetected species
theory (Hubbell 2001), metacommunity structure (estimators of asymptotic richness).
(Holyoak et al. 2005), and biogeography (Gotelli
et al. 2009) generate quantitative predictions of the
number of coexisting species. To make progress in 4.2 State of the field
modelling species richness, these predictions need 4.2.1 Sampling models for biodiversity data
to be compared with empirical data. In applied
ecology and conservation biology, the number of Although the methods of estimating species rich-
species that remain in a community represents the ness that we discuss can be applied to assemblages
ultimate ‘scorecard’ in the fight to preserve and of organisms that have been identified by genotype
restore perturbed communities (e.g. Brook et al. (e.g. Hughes et al. 2000), to species, or to some
2003). higher taxonomic rank, such as genus or family (e.g.
Yet, in spite of our familiarity with species rich- Bush & Bambach 2004), we will write ‘species’ to
ness, it is a surprisingly difficult variable to mea- keep it simple. Because we are discussing estima-
sure. Almost without exception, species richness tion of species richness, we assume that one or more
can be neither accurately measured nor directly samples have been taken, by collection or observa-
estimated by observation because the observed tion, from one or more assemblages for some speci-
number of species is a downward-biased estimator fied group or groups of organisms. We distinguish
for the complete (total) species richness of a local two kinds of data used in richness studies: (1) inci-
assemblage. Hundreds of papers describe statistical dence data, in which each species detected in a sam-
methods for correcting this bias in the estimation ple from an assemblage is simply noted as being
of species richness (see also Chapter 3), and spe- present, and (2) abundance data, in which the abun-
cial protocols and methods have been developed dance of each species is tallied within each sample.
for estimating species richness for particular taxa Of course, abundance data can always be converted
(e.g. Agosti et al. 2000). Nevertheless, many recent to incidence data, but not the reverse.

39
OUP CORRECTED PROOF – FINAL, 18/10/2010, SPi

40 B I O L O G I C A L DI V E R S I T Y

Box 4.1 Observed and estimated richness

Sobs is the total number of species observed in a sample, or ACE (for abundance data)
in a set of samples.
Sest is the estimated number of species in the 
10
Srare = fk is the number of rare species in a sample (each
assemblage represented by the sample, or by the set of k =1
samples, where est is replaced by the name of an estimator. with 10 or fewer individuals).
Abundance data. Let fk be the number of species each S
obs
Sabund = fk is the number of abundant species in a
represented by exactly k individuals in a single sample. k =11
Thus, f0 is the number of undetected species (species sample (each with more than 10 individuals).
present in the assemblage but not included in the sample), 
10

f1 is the number of singleton species, f2 is the number of nrare = k fk is the total number of individuals in the
k =1
doubleton species, etc. The total number of individuals in rare species.
S
obs The sample coverage estimate is C AC E = 1 − nrfar1 e , the
the sample is n = fk .
k =1
proportion of all individuals in rare species that are not
Replicated incidence data. Let qk be the number of singletons. Then the ACE estimator of species richness is
f1
species present in exactly k samples in a set of replicate SACE = Sabund + CSrACar eE + C AC „2 , where „2ACE is the
E ACE
incidence samples. Thus, q0 is the number of undetected coefficient of variation,
species (species present in the assemblage but not included ⎡ 
10 ⎤
in the set of samples), q1 is the number of unique species, k(k − 1)fk
⎢S ⎥
⎢ rare k=1 ⎥
q2 is the number of duplicate species, etc. The total number „2ACE = max ⎢ − 1, 0⎥
S ⎣ CACE (nrare ) (nrare − 1) ⎦
obs
of samples is m = qk .
k =1
The formula for ACE is undefined when all rare species
Chao 1 (for abundance data) are singletons (f1 = nrare , yielding CACE = 0). In this case,
compute the bias-corrected form of Chao1 instead.
f2
SChao1 = Sobs + 2 1f2 is the classic form, but is not defined
when f2 = 0 (no doubletons). ICE (for incidence data)
−1)
SChao1 = Sobs + f2(1 ( ff21+1) is a bias-corrected form, always 
10
Sinfr = qk is the number of infrequent species in a
obtainable.         k =1
2 3 4
1 f1 f1 1 f1
var(SChao1 ) = f2 2 f2
+ f2
+ 4 f2
for sample (each found in 10 or fewer samples).
S
f1 > 0 and f2 > 0 (see Colwell 2009, Appendix B of Sfreq =
obs
qk is the number of frequent species in a
EstimateS User’s Guide for other cases and for asymmetrical k =11
confidence interval computation). sample (each found in more than 10 samples).

10
ninfr = kqk is the total number of incidences in the
Chao 2 (for replicated incidence data) k =1
infrequent species.
q12
SChao2 = Sobs + is the classic form, but is not defined The sample coverage estimate is CICE = 1 − niqnf1 r , the
2q2
when q2 = 0 (no duplicates).
  q1 (q1 −1) proportion of all incidences of infrequent species that are
SChao2 = Sobs + m−1 m 2(q2 +1)
is a bias-corrected form, not uniques. Then the ICE estimator of species richness is
S nf r
always obtainable. 
 2  3  4  CICE = Sfreq + Ci ICE + CqICE
1
„2ICE , where „2ICE is the coefficient
var(SChao2 ) = q2 12 qq12 + qq12 + 14 qq12 for of variation,
⎡ ⎤
q1 > 0 and q2 > 0 (see Colwell 2009, Appendix B of 10
⎢S k(k − 1)qk ⎥
EstimateS User’s Guide for other cases and for asymmetrical ⎢ infr minfr k=1 ⎥
„2ICE = max ⎢ − 1, 0⎥
confidence interval computation). ⎣C ICE (minfr − 1) (ninfr )
2 ⎦
 OUP CORRECTED PROOF – FINAL, 18/10/2010, SPi

ESTIMATING SPECIES RICHNESS 41

The formula for ICE is undefined when all infrequent Jackknife estimators (for incidence data)
species are uniques (q1 = ninfr , yielding CICE = 0). In this
case, compute the bias-corrected form of Chao2 The first-order jackknife richness estimator is
instead.  
m−1
Sjackknife1 = Sobs + q1
m
Jackknife estimators (for abundance data)
The second-order jackknife richness estimator is
The first-order jackknife richness estimator is
 
Sjackknife1 = Sobs + f1 q (2m − 3) q2 (m − 2)2
Sjackknife2 = Sobs + 1 −
The second-order jackknife richness estimator is m m (m − 1)
Sjackknife2 = Sobs + 2f1 − f2

By their nature, sampling data document only inferences about the number of colours (species) in
the verified presence of species in samples. The the entire jar. This process of statistical inference
absence of a particular species in a sample may depends critically on the biological assumption that
represent either a true absence (the species is not the community is ‘closed,’ with an unchanging total
present in the assemblage) or a false absence (the number of species and a steady species abundance
species is present, but was not detected in the distribution. Jellybeans may be added or removed
sample; see Chapter 3). Although the term ‘pres- from the jar, but the proportional representation of
ence/absence data’ is often used as a synonym for colours is assumed to remain the same. In an open
incidence data, the importance of distinguishing metacommunity, in which the assemblage changes
true absences from false ones (not only for rich- size and composition through time, it may not be
ness estimation, but in modelling contexts, e.g. Elith possible to draw valid inferences about community
et al. 2006) leads us to emphasize that incidence structure from a snapshot sample at one point in
data are actually ‘presence data’. Richness esti- time (Magurran 2007). Few, if any, real communities
mation methods for abundance data assume that are completely ‘closed’, but many are sufficiently
organisms can be sampled and identified as dis- circumscribed that that richness estimators may be
tinct individuals. For clonal and colonial organisms, used, but with caution and caveats.
such as many species of grasses and corals, indi- For all of the methods and metrics (Box 4.1) that
viduals cannot always be separated or counted, but we discuss in this chapter, we make the closely
methods designed for incidence data can nonethe- related statistical assumption that sampling is with
less be used if species presence is recorded within replacement. In terms of collecting inventory data
standardized quadrats or samples (e.g. Butler & from nature, this assumption means either that indi-
Chazdon 1998). viduals are recorded, but not removed, from the
Snacking from a jar of mixed jellybeans provides assemblage (e.g. censusing trees in a plot) or, if
a good analogy for biodiversity sampling (Longino they are removed, the proportions remaining are
et al. 2002). Each jellybean represents a single indi- unchanged by the sampling.
vidual, and the different colours represent the dif- This framework of sampling, counting, and iden-
ferent species in the jellybean ‘assemblage’—in a tifying individuals applies not only to richness esti-
typical sample, some colours are common, but most mation, but also to many other questions in the
are rare. Collecting a sample of biodiversity data study of biodiversity, including the characterization
is equivalent to taking a small handful of jelly- of the species abundance distribution (see Chap-
beans from the jar and examining them one by ter 9) and partitioning diversity into α and β com-
one. From this incomplete sample, we try to make ponents (see Chapters 6 and 7).
 OUP CORRECTED PROOF – FINAL, 18/10/2010, SPi

42 B I O L O G I C A L DI V E R S I T Y

40
Figure 4.1 Species accumulation and rarefaction curves. The Individual-based
jagged line is the species accumulation curve for one of many 35 rarefaction curve
possible orderings of 121 soil seedbank samples, yielding a total
of 952 individual tree seedlings, from an intensive census of a plot 30

Number of species
of Costa Rican rainforest (Butler & Chazdon 1998). The cumulative
number of tree species (y-axis) is plotted as a function of the 25 Sample-based rarefaction curve
cumulative number of samples (upper x-axis), pooled in random
20
order. The smooth, solid line is the sample-based rarefaction curve
for the same data set, showing the mean number of species for all Species accumulation curve
15
possible combinations of 1, 2, . . . , m∗ , . . . , 121 actual
samples from the dataset—this curve plots the statistical 10
expectation of the (sample-based) species accumulation curve.
The dashed line is the individual-based rarefaction curve for the 5
same data set—the expected number of species for
(m∗ ) (952/121) individuals, randomly chosen from all 952 0
individuals (lower x-axis). The black dot indicates the total 0 20 40 60 80 100 120
richness for all samples (or all individuals) pooled. The Number of samples
sample-based rarefaction curve lies below the individual-based
rarefaction curve because of spatial aggregation within species. 0 200 400 600 800 1000
This is a very typical pattern for empirical comparisons of Number of individuals
sample-based and individual-based rarefaction curves.

4.2.2 The species accumulation curve Regardless of the species abundance distribu-
tion, this curve increases monotonically, with a
Consider a graph in which the x-axis is the num-
decelerating slope. For a given sample, different
ber of individuals sampled and the y-axis is the
stochastic realizations of the order in which the
cumulative number of species recorded (Fig. 4.1,
individuals in the sample are added to the graph
lower x-axis). Imagine taking one jellybean at a time
will produce species accumulation curves that dif-
from the jar, at random. As more individuals (jelly-
fer slightly from one another. The smoothed aver-
beans) are sampled, the total number of species
age of these individual curves represents the sta-
(colours) recorded in the sample increases, and a
tistical expectation of the species accumulation
species accumulation curve is generated. Of course,
curve for that particular sample, and the variabil-
the first individual drawn will represent exactly one
ity among the different orderings is reflected in
species new to the sample, so all species accumu-
the variance in the number of species recorded for
lation curves based on individual organisms origi-
any given number of individuals. However, this
nate at the point [1,1]. The next individual drawn
variance is specific, or conditional, on the particu-
will represent either the same species or a species
lar sample that we have drawn because it is based
new to the sample. The probability of drawing a
only on re-orderings of that single sample. Suppose,
new species will depend both on the complete num-
instead, we plot the smoothed average of several
ber of species in the assemblage and their relative
species accumulation curves, each based on a dif-
abundances. The more species in the assemblage
ferent handful of jellybeans from the same jar, each
and the more even the species abundance distribu-
handful having the same number of beans. Varia-
tion (see Chapter 9), the more rapidly this curve will
tion among these smoothed curves from the several
rise. In contrast, if the species abundance distribu-
independent, random samples represents another
tion is highly uneven (a few common species and
source of variation in richness, for a given number
many rare ones, for example), the curve will rise
of individuals. The variance among these curves is
more slowly, even at the outset, because most of the
called an unconditional variance because it estimates
individuals sampled will represent more common
the true variance in richness of the assemblage. The
species that have already been added to the sample,
unconditional variance in richness is necessarily
rather than rarer ones that have yet to be detected.
 OUP CORRECTED PROOF – FINAL, 18/10/2010, SPi

ESTIMATING SPECIES RICHNESS 43

larger than the variance conditional on any single autocorrelation is taken into account, the samples
sample. themselves may be only partially independent.
Nevertheless, the inevitable non-independence of
individuals within samples can be overcome by
4.2.3 Climbing the species accumulation
plotting a second kind of species accumulation
curve
curve, called a sample-based species accumulation
In theory, finding out how many species character- curve, in which the x-axis is the number of samples
ize an assemblage means sampling more and more and the y-axis is the accumulated number of species
individuals until no new species are found and the (Fig. 4.1, upper x-axis). Because only the identity
species accumulation curve reaches an asymptote. but not the number of individuals of each species
In practice, this approach is routinely impossible for represented within a sample is needed to construct
two reasons. First, the number of individuals that a sample-based species accumulation curve, these
must be sampled to reach an asymptote can often be curves plot incidence data. This approach is there-
prohibitively large (Chao et al. 2009). The problem fore also suitable for clonal and colonial species that
is most severe in the tropics, where species diversity cannot be counted as discrete individuals.
is high and most species are rare. For example, after
nearly 30 consecutive years of sampling, an ongo-
ing inventory of a tropical rainforest ant assemblage
4.2.4 Species richness versus species density
at La Selva, Costa Rica, has still not reached an
asymptote in species richness. Each year, one or two The observed number of species recorded in a sam-
new species are added to the local list. In some cases ple (or a set of samples) is very sensitive to the
these species are already known from collections at number of individuals or samples observed or col-
other localities, but in other cases they are new to lected, which in turn is influenced by the effec-
science (Longino et al. 2002). In other words, bio- tive area that is sampled and, in replicated designs,
diversity samples, even very extensive ones, often by the spatial arrangement of the replicates. Thus,
fall short of revealing the complete species richness many measures reported as ‘species richness’ are
for an assemblage, representing some unspecified effectively measures of species density: the number
milestone along a slowly rising species accumula- of species collected in a particular total area. For
tion curve with an unknown destination. quadrat samples or other methods that sample a
A second reason that the species accumula- fixed area, species density is expressed in units of
tion curve cannot be used to directly determine species per specified area. Even for traps that col-
species richness is that, in field sampling, ecolo- lect individuals at a single point (such as a pitfall
gists almost never collect random individuals in trap), there is probably an effective sampling area
sequence. Instead, individual plants or mobile ani- that is encompassed by data collection at a single
mals are often recorded from transects or points point.
counts, or individual organisms are collected in pit- Whenever sampling is involved, species density
fall and bait traps, sweep samples, nets, plankton is a slippery concept that is often misused and
tows, water, soil, and leaf litter samples, and other misunderstood. The problem arises from the non-
taxon-specific sampling units that capture multi- linearity of the species accumulation curve. Con-
ple individuals (Southwood & Henderson 2000). sider the species accumulation curve for rainforest
Although these samples can, under appropriate seedlings (Butler & Chazdon 1998) in Fig. 4.2, which
circumstances, be treated as independent of one plots the species of seedlings grown from dormant
another, the individuals accumulated within a sin- seed in 121 soil samples, each covering a soil surface
gle sample do not represent independent observa- area of 17.35 cm2 and a depth of 10 cm. The x-axis
tions. Although individuals contain the biodiver- plots the cumulative surface area of soil sampled.
sity ‘information’ (species identity), it is the sam- The slopes of lines A, B, and C represent species
ples that represent the statistically independent density: number of species observed (y), divided by
replicates for analysis. When spatial and temporal area-sampled (x). You can see that species density
 OUP CORRECTED PROOF – FINAL, 18/10/2010, SPi

44 B I O L O G I C A L DI V E R S I T Y

40

35 Sample-based rarefaction curve

Figure 4.2 Species richness and species density are not the same 30

Number of species
thing. The solid line is the sample-based rarefaction curve for the C
25
same data set as in Fig 4.1, showing the expected species richness of
B
rainforest tree seedlings for 1, 2, . . . , m∗ , . . . , 121 soil samples,
20
each covering a soil surface area of 17.35 cm2 and a depth of
10 cm. Species richness (y-axis) is plotted as a function of the total A
15
soil surface area sampled (x-axis). Because species density is the ratio
of richness (y-coordinate) to area (x-coordinate) for any point in the 10
graph, the slopes of lines A, B, and C quantify species density for
500, 1000, and 2000 cm2 , respectively. Clearly, species density 5
estimates depend on the particular amount of area sampled. All of
the species density slopes over-estimate species number when 0
extrapolated to larger areas, and species density estimates based on 0 500 1000 1500 2000
differing areas are not comparable. Soil area sampled (cm2)

depends critically not just on area, but on the spe- (James & Wamer 1982). This decomposition demon-
cific amount of area sampled. For this reason, it strates that the number of species per sampling unit
never works to ‘standardize’ the species richness reflects both the underlying species richness and
of samples from two or more assemblages by sim- the total number of individuals sampled. If two
ply dividing observed richness by area sampled (or samples differ in species density, is it because of
by any other measure of effort, including number differences in underlying species richness, differ-
of individuals or number of samples). Estimating ences in abundance, or some combination of both?
species density by calculating the ratio of species In other words, how do we meaningfully com-
richness to area sampled will always grossly over- pare the species richness of collections that prob-
estimate species density when this index is extrap- ably differ in both the number of individuals and
olated to larger areas, and the size of that bias will the number of samples collected? Until recently,
depend on the area sampled. many ecologists have not recognized this prob-
Sometimes, however, ecologists or conservation lem. The distinction between species density and
biologists are interested in species density, for some species richness has not always been appreciated,
particular amount of area, in its own right. For and many papers have compared species density
example, if only one of two areas, equal in size and using standard parametric statistics, but without
cost per hectare, can be purchased to establish a accounting for differences in abundance or sam-
reserve, species density at the scale of the reserve is pling effort.
clearly a variable of interest. Because species density One statistical solution is to treat abundance,
is so sensitive to area (and, ultimately, to the num- number of samples, or sample area as a covariate
ber of individuals observed or collected), it is useful that can be entered into a multiple regression analy-
to decompose it into the product of two quanti- sis or an analysis of covariance. If the original data
ties: species richness (number of species represented (counts and identities of individuals) are not avail-
by some particular number, N, of individuals) and able, this may be the best that we can do. For exam-
total individual density (number of individuals N, ple, Dunn et al. (2009) assembled a global database
disregarding species, in some particular amount of of ant species richness from a number of published
area A): studies. To control for sampling effects, they used
      the area, number of samples, and total number of
species species N individuals
= × individuals from each sample location as statisti-
ar ea A N individuals ar ea A
cal covariates in regression analyses. However, they
 OUP CORRECTED PROOF – FINAL, 18/10/2010, SPi

ESTIMATING SPECIES RICHNESS 45

did not make the mistake of trying to ‘standard- based on all n individuals, does not differ from the
ize’ the richness of different samples by dividing richness of a subsample of size n∗ from the larger
the species counts by the area, the number of indi- sample cannot be rejected at P ≤ 0.05. If this null
viduals sampled, or any other measure of effort. hypothesis is not rejected, and the original, unrar-
As we have repeatedly emphasized, this rescaling efied samples differed in species density, then this
produces serious distortions: extrapolations from difference in species density must be driven by
small sample ratios of species density inevitably differing numbers of individuals between the two
lead to gross over-estimates of the number of samples. Alternatively, if s is not contained within
species expected in larger sample areas (Fig. 4.2 and the confidence interval of s ∗ , the two samples differ
Figure 4–6 in Gotelli & Colwell 2001). in species richness in ways that cannot be accounted
for entirely by differences in abundance and/or
sampling effort (at P ≤ 0.05).
4.2.5 Individual-based rarefaction
Rarefaction can be used not only to calculate a
The species accumulation curve itself suggests an point estimate of s ∗ , but also to construct an entire
intuitive way to compare the richness of two sam- rarefaction curve in which the number of individuals
ples (for the same kind of organism) that differ in randomly subsampled ranges from 1 to N. Rarefac-
the number of individuals collected. Suppose one tion can be thought of as a method of interpolating
of the two samples has N individuals and S species, E(s ∗ |n∗ ) the expected number of species, given n∗
and the other has n individuals and s species. The individuals (1 ≤ n∗ ≤ N), between the point [1, 1]
samples differ in the number of individuals present and the point [S, N] (Colwell et al. 2004). With pro-
(N > n) and will usually differ in the number of gressively smaller subsamples from N – 1 to 1, the
species present (typically S > s). In the procedure resulting individual-based rarefaction curve, in a sense,
called rarefaction, we randomly draw n∗ individuals, is the reverse of the corresponding species accumu-
subsampling without replacement from the larger lation curve, which progressively builds larger and
of the two original samples, where n∗ = n, the size larger samples.
of the smaller original sample. (This re-sampling, Because this individual-based rarefaction curve
without replacement, of individuals from within is conditional on one particular sample, the vari-
the sample does not violate the assumption that the ance in s ∗ , among random re-orderings of indi-
process of taking the sample itself did not change viduals, is 0 at both extremes of the curve: with
the relative abundance of species). Computing the the minimum of only one individual there will
mean number of species, s̄ ∗ , among repeated sub- always be only one species represented, and with
samples of n∗ individuals estimates E(s ∗ |n∗ ), the the maximum of N individuals, there will always be
expected number of species in a random subsam- exactly S species represented. Hurlbert (1971) and
ple of n∗ individuals from the larger original sam- Heck et al. (1975) give analytical solutions for the
ple (Fig. 4.1, lower x-axis). The variance of (s ∗ ), expectation and the conditional variance of s ∗ , which
among random re-orderings of individuals, can also are derived from the hypergeometric distribution.
be estimated this way along with a parametric 95% In contrast, treating the sample (one handful of
confidence interval, or the confidence interval can jellybeans) as representative of a larger assemblage
be estimated from the bootstrapped values (Manly (the jar of jellybeans) requires an estimate of the
1991). unconditional variance (the variance in s ∗ |n∗ among
A simple test can now be conducted to ask replicate handfuls of jellybeans from the same jar).
whether s, the observed species richness of the com- The unconditional variance in richness, S, for the
plete smaller sample, falls within the 95% confi- full sample of N individuals, must be greater than
dence interval of s ∗ , the expected species richness zero to account for the heterogeneity that would
based on random subsamples of size n from the be expected with additional random samples of
larger sample (Simberloff 1978). If the observed the same size taken from the entire assemblage.
value falls within the confidence interval, then the Although Smith & Grassle (1977) derived an esti-
hypothesis that the richness of the smaller sample, mator for the unconditional variance of E(s ∗ |n∗ ),
OUP CORRECTED PROOF – FINAL, 18/10/2010, SPi

46 B I O L O G I C A L DI V E R S I T Y

it is computationally complex and has been little 

S
 
used. R.K. Colwell and C.X. Mao (in preparation) E (s ∗ ) = 1 − (1 − n∗ /N)ni , (4.1)
i=1
have recently derived an unconditional variance
estimator for individual-based rarefaction that is in which i indexes species from 1 to S, and ni is the
analogous to the unconditional variance estimator abundance of species i in the full sample. As a null
for sample-based rarefaction described in Colwell model for the species–area relationship (see Chap-
et al. (2004), and discussed below. ter 20), the Coleman curve assumes that islands of
Regardless of how the variance is estimated, the different area randomly intercept individuals and
statistical significance of the difference in rarefied accumulate different numbers of species (Coleman
species richness between two samples will depend, et al. 1982). The individual-based rarefaction curve
in part, on n, the number of individuals being com- is very closely analogous to the Coleman curve
pared. This sample-size dependence arises because (and, although mathematically distinct, differs only
all rarefaction curves based on individuals con- slightly from it) because relative island area is a
verge at the point [1,1]. Therefore, no matter how proxy for the proportion n∗ /N of individuals sub-
different two assemblages are, rarefaction curves sampled from the pooled distribution of all individ-
based on samples of individuals drawn at ran- uals in the original sample (Gotelli 2008).
dom will not appear to differ statistically if n is
too small. In some cases, rarefaction curves may
4.2.6 Sample-based rarefaction
cross at higher values of n, making the results of
statistical tests even more dependent on n (e.g. Individual-based rarefaction computes the
Raup 1975). expected number of species, s ∗ , in a subsample
To compare multiple samples, each can be rar- of n∗ individuals drawn at random from a single
efied down to a common abundance, which will representative sample from an assemblage. In
typically be the total abundance for the smallest of contrast, sample-based rarefaction computes the
the samples. At that point, the set of s ∗ values, one expected number of species s ∗ when m∗ samples
for each sample, can be used as a response variable (1 ≤ m∗ ≤ M) are drawn at random (without
in any kind of statistical analysis, such as ANOVA replacement) from a set of samples that are,
or regression. This method assumes that the rarefac- collectively, representative of an assemblage
tion curves do not cross (which may be assessed (Fig. 4.1, upper x-axis) (Gotelli & Colwell 2001;
visually), so that their rank order remains the same Colwell et al. 2004). (This re-sampling, without
regardless of the abundance level used. Alterna- replacement, of samples from within the sample
tively, multiple samples from the same assemblage set does not violate the assumption that the process
can be used in a sample-based rarefaction, which we of taking the sample itself did not change the
describe below. relative abundance of species.) The fundamental
Rarefaction has a long history in ecology and evo- difference is that sample-based rarefaction, by
lution (Sanders 1968; Hurlbert 1971; Raup 1975; Tip- design, preserves the spatial structure of the
per 1979; Järvinen 1982; Chiarucci et al. 2008).The data, which may reflect processes such as spatial
method was proposed in the 1960s and 1970s to aggregation or segregation (see Chapter 12)
compare species number when samples differed both within and between species. In contrast,
in abundance (Tipper 1979), but the same statisti- individual-based rarefaction does not preserve the
cal problem had been solved many decades ear- spatial structure of the data and assumes complete
lier by biogeographers who wanted to estimate random mixing among individuals of all species.
species/genus ratios and other taxonomic diversity Thus, for sample-based rarefaction, E (s ∗ |m∗ ) is the
indices (Järvinen 1982). expected number of species for m∗ pooled samples
Brewer & Williamson (1994) and Colwell & Cod- that express the same patterns of aggregation,
dington (1994) pointed out that a very close approx- association, or segregation as the observed set of
imation for the rarefaction curve is the Coleman samples. For this reason, sample-based rarefaction
‘passive sampling’ curve, is a more realistic treatment of the independent
 OUP CORRECTED PROOF – FINAL, 18/10/2010, SPi

ESTIMATING SPECIES RICHNESS 47

sampling units used in most biodiversity studies. test on the overlap of traditional 95% CIs is overly
Because sample-based rarefaction requires only conservative: richness values that would differ sig-
incidence data, it can also be used for clonal nificantly with the 84% interval would often be
organisms or for species in which individuals declared statistically indistinguishable because the
in a sample cannot be easily distinguished or 95% intervals for the same pair of samples would
counted. overlap (Payton et al. 2003).
Operationally, sample-based rarefaction can be An important pitfall to avoid in using sample-
carried out by repeatedly selecting and pooling based rarefaction to compare richness between
m∗ samples at random from the set of samples, sample sets is that the method does not directly con-
and computing the mean and conditional (on the trol for differences in overall abundance between
particular set of samples) variance and 95% confi- sets of samples. Suppose two sets of samples are
dence interval for s ∗ . On the other hand, E (s ∗ |m∗ ) recorded from the same assemblage, but they dif-
is more easily and accurately computed from com- fer in mean number of individuals per sample
binatorial equations based on the distribution of (systematically or by chance). When plotted as a
counts, the number of species found in exactly 1, function of number of samples (on the x-axis) the
2, . . . , m∗ samples in the set (Ugland et al. 2003; sample-based rarefaction curve for the sample set
Colwell et al. 2004; see Chiarucci et al. 2008 for with a higher mean abundance per sample will lie
a history of this approach). Colwell et al. 2004 above the curve for the sample set with lower mean
also introduced a sample-based version of the abundance because more individuals reveal more
Coleman rarefaction model, the results of which species. The solution suggested by Gotelli & Col-
closely approximate the true sample-based rarefac- well (2001) is to first calculate sample-based rarefac-
tion curve. tion curves and their variances (or CIs) for each set
Ugland et al. (2003) provide an expression for of samples in the analysis. Next, the curves are re-
the conditional variance in richness estimates from plotted against an x-axis of individual abundance,
sample-based rarefaction. Colwell et al. (2004) rather than number of samples. This re-plotting
derived an unconditional variance estimator for effectively shifts the points of each individual-based
sample-based rarefaction that treats the observed rarefaction curve to the left or the right, depending
set of samples, in turn, as a sample from some on the average number of individuals that were
larger assemblage, so that the variance in S for collected in each sample. Ellison et al. (2007) used
all M samples, pooled (the full set of samples), this method to compare the efficacy of ant sam-
takes some non-zero value. This unconditional vari- pling methods that differed greatly in the average
ance (and its associated confidence interval (CI)) number of individuals per sample (e.g. 2 ants per
accounts for the variability expected among repli- pitfall trap, versus > 89 ants per plot for standard-
cate sets of samples. Based on unconditional vari- ized hand sampling). Note that if sample-based rar-
ances for two sample-based rarefaction curves, rich- efaction is based on species occurrences rather than
ness can be compared for any common number of abundances, then the rescaled x-axis is the number
samples (or individuals, as explained below). Using of species occurrences, not the number of individu-
eigenvalue decomposition, Mao & Li (2009) devel- als.
oped a computationally complex method for com-
paring two sample-based rarefaction curves in their
entirety. A much simpler, but approximate, method 4.2.7 Assumptions of rarefaction
is to assess, for a desired value of m∗ , whether or To use rarefaction to compare species richness of
not the two (appropriately computed) confidence two (or more) samples or assemblages rigorously,
intervals overlap. If the two CIs (calculated from the the following assumptions should be met:
unconditional variance) are approximately equal,
for a type I error rate of P < 0.05, the appropriate 1. Sufficient sampling. As with any other statis-
CI is about 84% (Payton et al. 2003; the z value tical procedure, the power to detect a dif-
for 84% CI is 0.994 standard deviations). Basing the ference, if there is one, depends on having
OUP CORRECTED PROOF – FINAL, 18/10/2010, SPi

48 B I O L O G I C A L DI V E R S I T Y

large enough individuals or samples, especially 5. Random placement. Individual-based rarefaction

since rarefactions curves necessarily converge assumes that the spatial distribution of individ-
towards the origin. Although it is difficult to give uals sampled is random. If individuals within
specific recommendations, our experience has species are spatially aggregated, individual-
been that rarefaction curves should be based on based rarefaction will over-estimate species rich-
at least 20 individuals (individual-based rarefac- ness because it assumes that the rare and com-
tion) or 20 samples (sample-based rarefaction), mon species are perfectly intermixed. Some
and preferably many more. authors have modified the basic rarefaction
2. Comparable sampling methods. Because all sam- equations to include explicit terms for spatial
pling methods have inherent and usually clumping (Kobayashi & Kimura 1994). However,
unknown sampling biases that favour detection this approach is rarely successful because the
of some species but not others (see Chapter 3), model parameters (such as the constants in the
rarefaction cannot be used to compare data from negative binomial distribution) cannot be eas-
two different assemblages that were collected ily and independently estimated for all of the
with two different methods (e.g. bait samples vs species in the sample. One way to deal with
pitfall traps, mist-netting vs point-sampling for aggregation is to increase the distance or timing
birds). However, rarefaction can be used mean- between randomly sampled individuals so that
ingfully to compare the efficacy of different sam- patterns of spatial or temporal aggregation are
pling methods that are used in the same area not so prominent. An even better approach is to
(Longino et al. 2002; Ellison et al. 2007). Also, use sample-based rarefaction, again employing
data from different sampling methods may be sampling areas that are large enough to over-
pooled in order to maximize the kinds of species come small-scale aggregation.
that may be sampled with different sampling 6. Independent, random sampling. Individuals or
methods (e.g. ants in Colwell et al. (2008)). How- samples should be collected randomly and
ever, identical sampling and pooling procedures independently. Both the individual-based and
must to be employed to compare two composite sample-based methods described in this chap-
collections. ter assume that sampling, from nature, does not
3. Taxonomic similarity. The assemblages repre- affect the relative abundance of species (statis-
sented by the two samples should be taxonom- tically, sampling with replacement). However,
ically ‘similar’. In other words, if two samples if the sample is relatively small compared to
that differ in abundance but have rarefaction the size of the underlying assemblage (which is
curves with identical shapes do not share any often the case), the results should be similar for
taxa, we would not want to conclude that the samples collected with or without replacement.
smaller collection is a random subsample of the More work is needed to derive estimators that
larger (Tipper 1979). Rarefaction seems most use- can be used for sampling without replacement,
ful when the species composition of the smaller which will be important for cases in which the
sample appears to be a nested or partially nested sample represents a large fraction of the total
subset of the larger collection. Much more pow- assemblage. Unfortunately, as we have noted
erful methods are now available to test directly earlier, biodiversity data rarely consist of col-
for differences in species composition (Chao lections of individuals that were sampled ran-
et al. 2005). domly. Instead, the data often consist of a series
4. Closed communities of discrete individuals. The of random and approximately independent sam-
assemblages being sampled should be well cir- ples that contain multiple individuals.
cumscribed, with consistent membership. Dis-
crete individuals in a single sample must
4.2.8 Estimating asymptotic species richness
be countable (individual-based rarefaction) or
species presence in multiple samples must be Consider the species richness of a single biodiver-
detectable (sample-based rarefaction). sity sample (or the pooled richness of a set of sam-
 OUP CORRECTED PROOF – FINAL, 18/10/2010, SPi

ESTIMATING SPECIES RICHNESS 49

ples) as the starting point in a graph of richness Menten function; Keating & Quinn (1998)) to a
versus abundance or sample number (the dot at the rarefaction or species accumulation curve. This
right-hand end of the curves in Fig. 4.1). Rarefac- approach dates back at least to Holdridge et al.
tion amounts to interpolating ‘backward’ from the (1971), who fitted a negative binomial function to
endpoint of a species accumulation curve, yielding smoothed species accumulation curves to compare
estimates of species richness expected for smaller the richness of Costa Rica trees at different local-
numbers of individuals or samples. In contrast, ities. Many other asymptotic functions have since
using this starting point to estimate the complete been explored (reviewed by Colwell & Coddington
richness of the assemblage, including species that (1994), Flather (1996), Chao (2005), and Rosenzweig
were not detected by the sample, can be visualized et al. (2003)). Unfortunately, this strictly phenom-
as extrapolating ‘forward’ along a hypothetical pro- enological method, despite the advantage that it
jection the accumulation curve (Colwell et al. 2004, makes no assumptions about sampling schemes or
their Figure 4). Two objectives of extrapolation can species abundance distributions, does not seem to
be distinguished: (1) estimating the richness of a work well in practice. Two or more functions may
larger sample and (2) estimating the complete rich- fit a dataset equally well, but yield drastically dif-
ness of the assemblage, visualized as the asymptote ferent estimates of asymptotic richness (Soberón &
of the accumulation curve. Once this asymptote is Llorente 1993; Chao 2005), and variance estimates
reached, the species accumulation curve is flat and for the asymptote are necessarily large. Residual
additional sampling will not yield any additional analysis often reveals that the popular functions
species. do not correctly fit the shape of empirical species
Why should the species accumulation curve have accumulation curves (O’Hara 2005), and this curve-
an asymptote? On large geographical scales, it does fitting method consistently performs worse than
not: larger areas accumulate species at a constant other approaches (Walther & Moore 2005; Walther
or even an increasing rate because expanded sam- & Morand 2008). For these reasons, we do not rec-
pling incorporates diverse habitat types that sup- ommend fitting asymptotic mathematical functions
port distinctive species assemblages (see Chap- as a means of estimating complete species richness
ter 20). As a consequence, the species accumulation of local assemblages.
curve continues to increase, and will not reach a Mixture models, in which species abundance
final asymptote until it approaches the total area or occurrence distributions are modelled as a
of the biosphere. The subject of species turnover is weighted mixture of statistical distributions, offer
covered by Jost et al. and Magurran (Chapters 6 a completely different, non-parametric approach to
and 7) and species–area relationships are the subject of extrapolating an empirical rarefaction curve to a
Chapter 20. In this chapter, we focus on the estima- larger sample sizes (or a larger set of samples)
tion of species richness at smaller spatial scales— (reviewed by Mao et al. (2005), Mao & Colwell
scales at which an asymptote is a reasonable sup- (2005), and Chao (2005)). Colwell et al. (2004), for
position and sampling issues are substantially more example, modelled the sample-based rarefaction
important than spatial turnover on habitat mosaics curve as a binomial mixture model. However, these
or gradients (Cam et al. 2002). In statistical terms, models are effective only for a doubling or tripling
we assume that samples were drawn independently of the observed sample size. Beyond this point, the
and at random from the local assemblage, so that variance of the richness estimate increases rapidly.
the ordering of the samples in time or space is not Unless the initial sample size is very large, pro-
important. In fact, unimportance of sample order is jecting the curve to an asymptotic value usually
diagnostic of the kinds of sample sets appropriately requires much more than a doubling or tripling of
used by ecologists to assess local species richness the initial sample size (Chao et al. 2009), so this
(Colwell et al. 2004). method is not always feasible, especially for hyper-
The most direct approach to estimating the diverse taxa (Mao & Colwell 2005).
species richness asymptote is to fit an asymp- Another classical approach to estimating asymp-
totic mathematical function (such as the Michaelis– totic richness is to fit a species abundance
 OUP CORRECTED PROOF – FINAL, 18/10/2010, SPi

50 B I O L O G I C A L DI V E R S I T Y

100

80

Species (frequency)
60

Figure 4.3 Estimation of asymptotic species richness by fitting a

log-normal distribution to a species abundance distribution. The graph 40
shows the number of species of ants in each of seven
logarithmically-scaled abundance categories (a total of 435 species 20
collected) in a long-term rainforest inventory in Costa Rica (Longino
et al. 2002). The number of undetected species (21 additional species) is 0
estimated by the area marked with horizontal hatching, yielding a 1 2–3 4–7 8–15 16–31 32–63 64–128
predicted complete richness of 456 species. Abundance (individuals)

distribution (see Chapter 9), based on a single sam- biodiversity surveys and conservation issues, and a
ple, to a truncated parametric distribution, then subject of basic research on the causes and conse-
estimate the ‘missing’ portion of the distribution, quences of species richness in natural ecosystems.
which corresponds to the undetected species in In Box 4.1, we have listed six of the most widely
an assemblage. Fisher et al. (1943) pioneered this used and best-performing indices. All the estima-
approach by fitting a geometric series to a large tors in Box 4.1 depend on a fundamental principle
sample of moths captured at light traps. Relative discovered during World War II by Alan Turing and
incidence distributions from replicated sets of sam- I.J. Good (as reported by Good (1953, 2000)), while
ples can be treated in the same way (Longino et al. cracking the military codes of the German Wehrma-
2002). The most widely used species abundance cht Enigma coding machine: the abundances of the
distribution for this approach is the log-normal very rarest species or their frequencies in a sample
(Fig. 4.3) and its variants (from Preston (1948) to or set of samples can be used to estimate the fre-
Hubbell (2001)), but other distributions (geometric quencies of undetected species. All of the estima-
series, negative binomial, γ, exponential, inverse tors in Box 4.1 correct the observed richness Sobs by
Guassian) have also been used. The challenges of adding a term based on the number of species rep-
fitting the log-normal have been widely discussed resented in a single abundance sample by only one
(e.g. Colwell & Coddington 1994; Chao 2004; Dor- individual (singletons), by two (doubletons), or by a
nelas et al. 2006; Connolly et al. 2009). One of few individuals. For incidence data, the added term
the limitations of this approach is shared with the is based on the frequencies of species represented in
extrapolation of fitted parametric functions: two or only one (uniques) sample, in two (duplicates), or in
more species abundance distributions may fit the a few replicate incidence samples.
data equally well, but predict quite different assem- Fig. 4.4 shows how well one of these estima-
blage richness. In addition, the species abundance tors, Chao2, estimates the asymptotic richness of the
distribution that fits best may be one that cannot seedbank dataset of Figure 4.1, based on sets of m∗
be used to estimate undetected species, such as the samples chosen at random. The estimator stabilizes
widely used log-series distribution (Chao 2004). after about 30 samples have been pooled. When all
The limitations of parametric methods inspired 121 samples have been pooled, the estimator sug-
the development of non-parametric richness esti- gests that 1–2 additional species still remain unde-
mators, which require no assumptions about an tected.
underlying species abundance distribution and do Only four of the estimators in Box 4.1 (Chao1,
not require the fitting of either a priori or ad hoc ACE, and the two individual-based jackknife esti-
models (Chao 2004). These estimators have experi- mators) are appropriate for abundance data; the
enced a meteoric increase in usage in the past two rest require replicated incidence data. Most of the
decades, as species richness has become a focus of incidence-based estimators were first developed, in
 OUP CORRECTED PROOF – FINAL, 18/10/2010, SPi

ESTIMATING SPECIES RICHNESS 51

40
Chao2 species richness estimator
35

30

Number of species
25 Sample-based rarefaction curve

20

15

Figure 4.4 Asymptotic species richness estimated by the Chao2 10

non-parametric richness estimator for the seedbank dataset of
Fig. 4.1. Plotted values for Chao2 are means of 100 randomizations 5
of sample order. The estimator stabilizes after only about 30 samples
have been pooled. When all 121 samples have been pooled (34 0
species detected), the estimator suggests that one or two additional 0 20 40 60 80 100 120
species still remain undetected. Number of samples

biological applications, for capture–recapture meth- additional individuals would be needed to sample
ods of population size estimation. The number of 100% (or any other percentage) of the asymptotic
samples that include Species X in a set of bio- species richness of a region based on the samples
diversity samples corresponds to the number of already in hand. Pan et al. (2009) have recently
recaptures of marked Individual X in a capture– extended the Chao1 and Chao2 indices to provide
recapture study. In species richness estimation, the an estimate of the number of shared species in mul-
full assemblage of species, including those species tiple assemblages.
not detected in the set of samples (but susceptible The jackknife is a general statistical technique for
to detection), corresponds, in population size esti- reducing the bias of an estimator by removing sub-
mation, to the total population size, including those sets of the data and recalculating the estimator with
individuals never captured (but susceptible to cap- the reduced sample. In this application of the tech-
ture) (Boulinier et al. 1998; Chao 2001, 2004). nique, the observed number of species is a biased
Behind the disarming simplicity of Chao1 and (under-) estimator of the complete assemblage rich-
Chao2 lies a rigorous body of statistical theory ness (Burnham & Overton 1979; Heltshe & Forrester
demonstrating that both are robust estimators of 1983; Chao 2004). For a set of m replicate incidence
minimum richness (Shen et al. 2003). ACE and ICE samples, the kth order jackknife reduces the bias by
are based on estimating sample coverage—the pro- estimating richness from all sets of m–k samples.
portion of assemblage richness represented by the The first-order jackknife (Jackknife1) thus depends
species in a single abundance sample (ACE) or in a only on the uniques (species found in only one sam-
set of replicated incidence samples (ICE). The esti- ple) because the richness estimate is changed only
mators are adjusted to the ‘spread’ of the empirical when a sample that contains one of these species
species abundance (or incidence) distribution by a is deleted from a subset of samples. Likewise, the
coefficient of variation term (Chao 2004). The Chao1 second-order jackknife (Jackknife2) depends only
and Chao2 estimators also provide a heuristic, intu- on the uniques and the duplicates (species found
itive ‘stopping rule’ for biodiversity sampling: no in exactly two samples). Similar expressions for
additional species are expected to be found when all abundance-based jackknife estimators are based on
species in the sample are represented by at least two the number of singletons (species represented by
individuals (or samples). Extending this approach, exactly one individual) and doubletons (species
Chao et al. (2009) provide equations and simple represented by exactly two individuals; Burnham &
spreadsheet software for calculating how many Overton (1979)). These estimators can be derived by
OUP CORRECTED PROOF – FINAL, 18/10/2010, SPi

52 B I O L O G I C A L DI V E R S I T Y

letting the number of samples m tend to infinity in plot (a method first suggested by Pielou (1975),
the equations for the incidence-based estimators. but popularized by Colwell & Coddington (1994)).
For example, Butler & Chazdon (1998) collected
seeds from 121 soils samples from a 1 ha plot, on
4.2.9 Comparing estimators of asymptotic
a 10 × 10 m grid in tropical rainforest in Costa
species richness
Rica, yielding 952 individual seedlings represent-
Given the diversity of asymptotic estimators that ing a total of 34 tree species (Figure 4.1). Col-
have been proposed, which one(s) should ecologists well & Coddington (1994) randomly rarefied these
use with their data? The ideal estimator would be data, by repeatedly pooling m∗ samples (1 ≤ m∗ ≤
unbiased (it neither over- or under-estimates asymp- M), and found that the Chao2 index (illustrated
totic species richness), precise (replicates samples in Fig. 4.4) and the second-order jackknife estima-
from the same assemblage produce similar esti- tors were least biased for small m∗ , followed by
mates), and efficient (a relatively small number of the first-order jackknife and the Michaelis–Menten
individuals or samples is needed). Although there estimator. Walther & Morand (1998) used a similar
are many ways to estimate bias, precision, and approach with nine parasite data sets and found
efficiency (Walther & Moore 2005), none of the that Chao2 and the first-order jackknife performed
available estimators meet all these criteria for all best. Walther & Moore (2005), using different quan-
datasets. Most estimators are biased because they titative measures of bias, precision, and accuracy,
chronically under-estimate true diversity (O’Hara compiled the results of 14 studies that compared
2005). The Chao1 estimator was formally derived estimator performance, and concluded that, for
as a minimum asymptotic estimator (Chao 1984), most data sets, non-parametric estimators (mostly
but all of the estimators should be treated as esti- the Chao and jackknife estimators) performed bet-
mating the lower bound on species richness. Esti- ter than extrapolated asymptotic functions or other
mators of asymptotic species richness are often parametric estimators.
imprecise because they typically have large vari- In a second strategy for comparing diversity esti-
ances and confidence intervals, especially for small mators, the investigator specifies the true species
data sets. This imprecision is inevitable because, by richness, the pattern of relative abundance, and
necessity, these estimators represent an extrapola- the spatial pattern of individuals in a computer-
tion beyond the limits of the data. In contrast, rar- simulated landscape. The program then randomly
efaction estimators usually have smaller variances samples individuals or plots, just as an ecologist
because they are interpolated within the range of would do in a field survey. The estimators are then
the observed data. However, as noted earlier, the calculated and compared on the basis of their abil-
unconditional variance of richness as estimated by ity to estimate the ‘true’ species richness of the
rarefaction is always larger than the variance that is region. This kind of simulation can also be used
conditional on a single sample (or set of samples). to explore the effects of spatial aggregation and
Finally, most estimators are not efficient and often segregation, sampling efficiency, and the size and
exhibit ‘sampling creep’: the estimated asymptote placement of sampling plots. Brose et al. (2003) car-
itself increases with sample size, suggesting that the ried out the most extensive analysis of this kind
sample size is not large enough for the estimate to to date. In their analyses, which estimator per-
stabilize (e.g. Longino et al. (2002)). formed best depended on the relative evenness
Two strategies are possible to compare the per- of the rank abundance distribution, the sampling
formance of different estimators. The first strategy intensity, and the true species richness. As in the
is to use data from a small area that has been empirical surveys (Walther & Moore 2005), non-
exhaustively sampled (or nearly so), and to define parametric estimators performed better in these
that assemblage as the sampling universe. As in model assemblages than extrapolated asymptotic
rarefaction, a random subsample of these data can curves (parametric estimators based on truncated
then be used to calculate asymptotic estimators distributions were not considered). One encourag-
and compare them to the known richness in the ing result was that environmental gradients and
 OUP CORRECTED PROOF – FINAL, 18/10/2010, SPi

ESTIMATING SPECIES RICHNESS 53

spatial autocorrelation (which characterize all bio- r SPADE: http://chao.stat.nthu.edu.tw/software

diversity data at some spatial scales) did not have a CE.html
serious effect on the performance of the estimators. r VEGAN (for R): http://cc.oulu.fi/∼jarioksa/
These results are consistent with the findings of softhelp/vegan.html.
Hortal et al. (2006), who aggregated empirical data
sets at different spatial grains and found that non-
parametric estimators were not greatly affected by 4.3 Prospectus
the spatial scale of the sampling.
Estimates of species richness require special sta-
O’Hara (2005) took a hybrid approach that used
tistical procedures to account for differences in
both empirical data and simulated assemblages. He
sampling effort and abundance. For comparing
first fit negative binomial and Poisson log-normal
species richness among different assemblages, we
distributions to two very extensive (but incomplete)
recommend sample-based rarefaction using uncon-
sets of survey data for moths. He used these fitted
ditional variances, with adjustments for the number
models to generate sample data for comparing non-
of individuals sampled. Rarefaction methods for
parametric estimators, parametric estimators, and
data that represent sampling from nature with-
extrapolated asymptotic curves. As in other studies,
out replacement are still needed, for small assem-
true species richness was greater than predicted by
blages, as are additional estimators for the number
the estimators. In each comparison, only one of the
of shared species in multiple samples (A. Chao,
parametric estimators had a 95% confidence inter-
personal communication). For many datasets, all
val that encompassed the true richness. The catch is
existing methods for estimating undetected species
that this method worked well only when the ‘cor-
seem to substantially under-estimate the number
rect’ species abundance distribution was used. In
of species present, but the best methods nonethe-
other words, the investigator would need to know
less reduce the inherent undersampling bias in
ahead of time that the negative binomial, Poisson
observed species counts. Non-parametric estima-
log-normal, or some other distribution was the cor-
tors (e.g. Chao1, Chao2) perform best in empirical
rect one to use (which rather defeats the value of
comparisons and benchmark surveys, and have a
using non-parametric estimators). Unfortunately, in
more rigorous framework of sampling theory than
spite of decades of research on this topic, there is
parametric estimators or curve extrapolations.
still no agreement on a general underlying form of
the species abundance distribution, and there are
difficult issues in the fitting and estimation of these
distributions from species abundance data (see 4.4 Key points
Chapter 10). We hope that future work may lead to 1. Biodiversity sampling is a labour-intensive activ-
better species richness estimators. At this time, the ity, and sampling is often not sufficient to detect
non-parametric estimators still give the best perfor- all or even most of the species present in an
mance in empirical comparisons, and they are also assemblage.
simple, intuitive, and relatively easy to use. 2. Species richness counts are highly sensitive to
the number of individuals sampled, and to
the number, size, and spatial arrangement of
4.2.10 Software for estimating species samples.
richness from sample data 3. Sensitivity to sampling effort cannot be
Free software packages with tools for estimating accounted for by scaling species richness as a
species richness from sample data include: ratio of species counts to individuals, samples,
or any other measure of effort.
r EstimateS (Colwell 2009): http://purl.oclc.org/ 4. Sample-based and individual-based rarefaction
estimates methods allow for the meaningful comparison of
r EcoSim (Gotelli & Entsminger 2009): http:// diversity samples based on equivalent numbers
garyentsminger.com/ecosim/index.htm of individuals and samples.
OUP CORRECTED PROOF – FINAL, 18/10/2010, SPi

54 B I O L O G I C A L DI V E R S I T Y

5. Non-parametric estimators of species richness, Acknowledgements

which use information on the rare species in
an assemblage to adjust for the number species N.J.G. acknowledges US National Science Founda-
present but not detected, are the most promising tion grants DEB-0107403 and DEB 05-41936 for sup-
avenue for estimating the minimum number of port of modelling and null model research. R.K.C.
species in the assemblage. was supported by NSF DEB-0072702.
 OUP CORRECTED PROOF – FINAL, 18/10/2010, SPi

References

Abbot, I. (1983) The meaning of z in species/area regres- species diversity in jawed vertebrates. Proceedings of
sion and the study of species turnover in island biogeog- the National Academy of Sciences, 106, 13410–13414.
raphy. Oikos, 41, 385–390. Alldredge, M. W., Pollock, K. H., Simons, T. R., & Shriner,
Abella, P., Bilton, N. D., Millan, A., Sanchez-Fernandez, S. A. (2007) Multiple-species analysis of point count
D., & Ramsay, P. M. (2006) Can taxonomic distinctness data: a more parsimonious modelling framework. Jour-
assess anthropogenic impacts in inland waters? A case nal of Applied Ecology, 44, 281–290.
study from a Mediterranean river basin. Freshwater Allen, A. P., & E. P. White. 2003. Effects of range size
Biology, 51, 1744–1756. on species–area relationships. Evolutionary Ecology
Abrams, P. A. (2001) A world without competition. Research, 5, 493–499.
Nature, 412, 858–859. Allen, B., Kon, M., & Bar-Yam, Y. (2009) A new phyloge-
Adler, P. B. (2004) Neutral models fail to reproduce netic diversity measure generalizing the Shannon index
observed species-area and species-time relationships in and its application to phyllostomid bats. The American
Kansas grasslands. Ecology, 85, 1265–1272. Naturalist, 174, 236–243.
Adler, P. B. & Lauenroth, W. K. (2003) The power of time: Alonso, D. & McKane, A. J. (2004) Sampling Hubbell’s
spatiotemporal scaling of species diversity. Ecology Let- neutral theory of biodiversity. Ecology Letters, 7,
ters, 6, 749–756. 901–910.
Adler, P. B., White, E. P., Lauenroth, W. K., Kaufman, Alroy, J. (1992) Conjunction among taxonomic distribu-
D. M., Rassweiler, A., & Rusak, J. A. (2005) Evidence tions and the Miocene mammalian biochronology of the
for a general species-time-area relationship. Ecology, 86, Great Plains. Paleobiology, 18, 326–343.
2032–2039. Alroy, J. (1994) Appearance event ordination: a new
Agosti, D., Majer, J., Alonso, E., & Schultz, T. R. (eds) (2000) biochronologic method. Paleobiology, 20, 191–207.
Ants: Standard Methods for Measuring and Monitor- Alroy, J. (1996) Constant extinction, constrained diver-
ing Biodiversity. Smithsonian Institution Press, Wash- sification, and uncoordinated stasis in North Amer-
ington, DC. ican mammals. Palaeogeography, Palaeoclimatology,
Akaike, H. (1973) Information theory and an extension of Palaeoecology, 127, 285–311.
the maximum likelihood principle. International Sym- Alroy, J. (2000) New methods for quantifying macroevo-
posium on Information Theory, 2, 267–281. lutionary patterns and processes. Paleobiology, 26,
Akçakaya, H. R., Radeloff, V. C., Mladenoff, D. J., & He, 707–733.
H. S. (2004) Integrating landscape and metapopula- Alroy, J., Marshall, C. R., Bambach, R. K., Bezusko, K.,
tion modeling approaches: viability of the sharp-tailed Foote, M., Fürsich, F. T., Hansen, T. A., Holland, S. M.,
grouse in a dynamic landscape. Conservation Biology, Ivany, L. C., Jablonski, D., Jacobs, D. K., Jones, D. C.,
18, 526–537. Kosnik, M. A., Lidgard, S., Low, S., Miller, A. I., Novack-
Albrecht, M., Duelli, P., Schmidm, B., & Muller, C. B. Gottshall, P. M., Olszewski, T. D., Patzkowsky, M. E.,
(2007) Interaction diversity within quantified insect Raup, D. M., Roy, K., John, J., Sepkoski, J., Sommers,
food webs in restored and adjacent intensively M. G., Wagner, P. J., & Webber, A. (2001) Effects of
managed meadows. Journal of Animal Ecology, 76, sampling standardization on estimates of Phanerozoic
1015–1025. marine diversity. Proceedings of the National Academy
Alfaro, M. E., Santini, F., Brock, C., Alamillo, H., Dornburg, of Sciences USA, 98, 6261–6266.
A., Rabosky, D. L., Carnevale, G., & Harmon, L. J. (2009) Alroy, J., Aberhan, M., Bottjer, D. J., Foote, M., Fürsich,
Nine exceptional radiations plus high turnover explain F. T., Harries, P. J., Hendy, A. J. W., Holland, S. M.,

295
OUP CORRECTED PROOF – FINAL, 18/10/2010, SPi

296 REFERENCES

Ivany, L. C., Kiessling, W., Kosnik, M. A., Marshall, Anselin, L. (1995) Local indicators of spatial association-
C. R., McGowan, A. J., Miller, A. I., Olszewski, T. D., LISA. Geographical Analysis, 27, 93–115.
Patzkowsky, M. E., Peters, S. E., Villier, L., Wagner, Ansorge, W. J. (2009) Next-generation DNA sequencing
P. J., Bonuso, N., Borkow, P. S., Brenneis, B., Clapham, techniques. New Biotechnology, 25, 195–203.
M. E., Fall, L. M., Ferguson, C. A., Hanson, V. L., Anthony, K. R. N., Hoogenboom, M. O., Maynard, J. A.,
Krug, A. Z., Layou, K. M., Leckey, E. H., Nürnberg, S., Grottoli, A. G., & Middlebrook, R. (2009) Energetics
Powers, C. M., Sessa, J. A., Simpson, C., Tomasovych, approach to predicting mortality risk from environmen-
A., & Visaggi, C. C. (2008) Phanerozoic trends in the tal stress: a case study of coral bleaching. Functional
global diversity of marine invertebrates. Science, 321, Ecology, 23, 539–550.
97–100. Arita, H. T., Christen, J. A, Rodríguez, P., & Soberón, J.
Altermatt, F., Baumeyer, A., & Ebert, D. (2009) Experimen- (2008) Species diversity and distribution in presence-
tal evidence for male biased flight-to-light behavior in absence matrices: mathematical relationships and bio-
two moth species. Entomologia experimentalis et appli- logical implications. The American Naturalist, 172,
cata, 130, 259–265. 519–532.
Altschul, S. F. & Lipman, D. J. (1990) Equal animals. Arntz, W. E. & Rumohr, H. (1982) An experimental study
Nature, 348, 493–494. of macrobenthic colonization and succession, and the
Alvarez, L. W., Alvarez, W., Asaro, F., & Michel H. V. importance of seasonal variation in temperate latitudes.
(1980) Extraterrestrial cause for the Cretaceous–Tertiary Journal of Experimental Marine Biology and Ecology,
extinction. Science, 208, 1095–1108. 64, 17–46.
Amann, R. I., Ludwig, W., & Schleifer, K. H. (1995) Phy-
Barker, G. M. (2002) Phylogenetic diversity: a quantitative
logenetic identification and in-situ detection of individ-
framework for measurement of priority and achieve-
ual microbial-cells without cultivation. Microbiological
ment in biodiversity conservation. Biological Journal of
Reviews, 59, 143–169.
the Linnean Society, 76, 165–194.
Amaro, A. M., Chamorro, D., Seeger, M., Arredondo, R.,
Baselga, A., Jiménez-Valverde, A., & Niccolini, G. (2007)
Peirano, I., & Jerez, C. A. (1991) Effect of external pH
A multiple-site similarity measure independent of rich-
perturbations on invivo protein-synthesis by the aci-
ness. Biology Letters, 3, 642–645.
dophilic bacterium thiobacillus-ferrooxidans. Journal of
Bawa K. S. & Seidler R. (1998) Natural forest management
Bacteriology, 173, 910–915.
and conservation of biodiversity in tropical forests. Con-
Anderson, S. (1977) Geographic ranges of North Ameri-
servation Biology, 12, 46–55.
can terrestrial mammals. American Museum novitates,
Bayley P. B. & Herendeen R. A. (2000) The efficiency of a
2629, 1–15.
seine net. Transactions of the American Fisheries Soci-
Anderson, N. H. & Sedell, J. R. (1979) Detritus processing
ety, 129, 901–923.
by macroinvertebrates. Annual Review of Entomology,
24, 351–357. Bazzaz, F. A. (1975) Plant species diversity in old-field
Anderson, M. J., Ellingsen, K. E., & McArdle, B. H. (2006) successional ecosystems in southern Illinois. Ecology,
Multivariate dispersion as a measure of beta diversity. 56, 485–488.
Ecology Letters, 9, 683–693. Beals, E. W. (1984) Bray–Curtis ordination: An effective
Andrewartha, H. G. & Birch, L. C. (1954) The Distribu- strategy for analysis of multivariate ecological data.
tion and Abundance of Animals. University of Chicago Advances in Ecological Research, 15, 1–55.
Press, Chicago. Begon, M., Harper, J. L., & Townsend, C. R. (2006) Ecol-
Andrewartha, H. G. & Birch, L. C. (1984) The Eco- ogy: From Individuals to Ecosystems, 4th edn. Sinauer
logical Web: More on the Distribution and Abun- Associates, Sunderland, MA.
dance of Animals. University of Chicago Press, Beja, O., Spudich, E. N., Spudich, J. L., Leclerc, M., &
Chicago. DeLong, E. F. (2001) Proteorhodopsin phototrophy in
Angilletta, M. J. (2009) Thermal Adaptation. A Theoret- the ocean. Nature, 411, 786–789.
ical and Empirical Synthesis. Oxford University Press, Bell, G. (2000) The distribution of abundance in neutral
Oxford. communities. The American Naturalist, 155, 606–617.
Anonymous (1999) The World at Six Billion. United Bell, G. (2001) Neutral macroecology. Science, 293,
Nations Population Division, New York. 2413–2418.
Anscombe, F. J. (1950) Sampling theory of the nega- Bell, G. (2003) The interpretation of biological sur-
tive binomial and logarithmic series distributions. Bio- veys. Proceedings of the Royal Society London, 270,
metrika, 37, 358–382. 2531–2542.
 OUP CORRECTED PROOF – FINAL, 18/10/2010, SPi

REFERENCES 297

Bender, E. A., Case, T. J., & Gilpin, M. E. (1984) Pertur- form of the interspecific relationship between abun-
bation experiments in community ecology: theory and dance and distribution. Journal of Animal Ecology, 75,
practice. Ecology, 65, 1–13. 1426–1439.
Ben-Moshe, A., Dayan, T., & Simberloff, D. (2001) Conver- Blake, J. & Loiselle, B. (2000) Diversity of birds along
gence in morphological patterns and community orga- an elevational gradient in the Cordillera Central, Costa
nization between Old and New World rodent guilds. Rica. The Auk, 117, 663–686.
The American Naturalist, 158, 484–495. Blaxter, M., Mann, J., Chapman, T., Thomas, F., Whitton,
Bent, S. J., Pierson, J. D., & Forney, L. J. (2007) Measuring C., Floyd, R., & Eyualem-Abebe (2005) Defining opera-
species richness based on microbial community finger- tional taxonomic units using DNA barcode data. Philo-
prints: The emperor has no clothes. Applied and Envi- sophical Transactions of the Royal Society London B,
ronmental Microbiology, 73, 2399–2399. 360, 1935–1943.
Berger, W. H. & Parker, F. L. (1970) Diversity of plank- Blow, M. J., Zhang, T., Woyke, T., Speller, C. F.,
tonic foraminifera in deep-sea sediments. Science, 168, Krivoshapkin, A., Yang, D. Y., Derevianko, A., & Rubin,
1345. E. M. (2008) Identification of ancient remains through
Bersier, L. F. & Sugihara, G. (1997) Species abundance genomic sequencing. Genome Research, 18, 1347–1353.
patterns: the problem of testing stochastic abundance Bockstaller, C. & Girardin, P. (2003) How to validate
models. Journal of Animal Ecology, 66, 179–774. environmental indicators. Agricultural Systems, 76,
Bettoli, P. W. & Maceina, M. J. (1996) Sampling with toxi- 639–653.
cants. In: Fisheries Techniques, Murphy, B. R. & Willis, Boik, R. J. (2004) Commentary on: Why Likelihood? In:
D. W. (eds). American Fisheries Society Bethesda, MD, The Nature of Scientific Evidence: Statistical, Phili-
pp. 303–333. sophical, and Empirical considerations, Taper, M. L. &
Bhaya, D., Grossman, A. R., Steunou, A.-S., Khuri, N., Lele, S. R. (eds). University of Chicago Press, Chicago,
Cohan, F. M., Hamamura, N., Melendrez, M. C., Bate- pp. 167–180.
son, M. M., Ward, D. M., & Heidelberg, J. F. (2007) Pop- Bonar, S. A. & Hubert, W. A. (2002) Standard sampling of
ulation level functional diversity in a microbial com- inland fish: Benefits, challenges, and a call for action.
munity revealed by comparative genomic and metage- Fisheries, 27, 10–16.
nomic analyses. The ISME Journal, 1, 703–713. Bonar, S. A., Thomas, G. L., Thiesfeld, S. L., & Pauley,
Bianchi, G., Gislason, H., Graham, K., Hill, L., Jin, X., G. B. (1993) Effect of triploid grass carp on the aquatic
Koranteng, K., Manickchand-Heileman, S., Paya, I., macrophyte community of Devils Lake, Oregon. North
Sainsbury, K., Sanchez,F., & Zwanenburg, K. (2000) American Journal of Fisheries Management, 13, 757–
Impact of fishing on size composition and diversity of 765.
demersal fish communities. ICES Journal of Marine Sci- Bonar, S. A., Divens, M., & Bolding, B. (1997) Methods
ence, 57, 558–571. for sampling the distribution and abundance of bull
Bianchi, F., Booij, C. J. H., & Tscharntke, T. (2006) Sustain- trout and Dolly Varden. Washington Department of
able pest regulation in agricultural landscapes: a review Fish and Wildlife, Fish Management Program, Inland
on landscape composition, biodiversity and natural pest Fisheries Investigations, Resource Assessment Division,
control. Proceedings of the Royal Society London B, 273, Olympia, WA.
1715–1727. Bonar, S. A., Hubert, W. A., & Willis, D. W. (2009a)
Bibby, C. J. (1999) Making the most of birds as environ- The North American freshwater fish standard sampling
mental indicators. Ostrich, 70, 81–88. project: Improving fisheries communication. Fisheries,
Biggs, R., Carpenter, S. R., & Brock, W. A. (2009) Turning 34, 340–344.
back from the brink: detecting an impending regime Bonar, S. A., Hubert, W. A., & Willis, D. W. (2009b) Stan-
shift in time to avert it. Proceedings of the National dard methods for sampling North American freshwater
Academy of Sciences USA, 106, 826–831. fishes. American Fisheries Society, Bethesda.
Bivand, R. S., Pebesma, E. J., & Gómez-Rubio, V. Bonham, C. D. (1989) Measurements for terrestrial vegeta-
(2008) Applied spatial data analysis with R. Springer, tion. Wiley, New York.
Düsseldorf. Borcard, D., Legendre, P., & Drapeau, P. (1992) Partialling
Blackburn, T. M. & Gaston, K. J. (1998) Some methodolog- out the spatial component of ecological variation. Ecol-
ical issues in macroecology. The American Naturalist, ogy, 5, 1045–1055.
151, 68–83. Borchers, D. L., Buckland, S. T., & Zucchini, W. (2002)
Blackburn, T. M., Cassey, P., & Gaston, K. J. (2006) Vari- Estimating animal abundance: closed populations.
ations on a theme: sources of heterogeneity in the Springer, London.
OUP CORRECTED PROOF – FINAL, 18/10/2010, SPi

298 REFERENCES

Borregaard, M. K. & Rahbek, C. (2006) Prevalence of Buckland, S. T., Magurran, A. E., Green, R. E., & Few-
intraspecific relationships between range size and abun- ster, R. M. (2005) Monitoring change in biodiversity
dance in Danish birds. Diversity & Distributions, 12, through composite indices. Philosophical Transactions
417–422. of the Royal London B, 360, 243–254.
Boswell, M. T. & Patil, G. P. (1970) Chance mechanisms Buckley, L. B. & Jetz, W. (2008) Linking global turnover of
generating the negative binomial distribution. In: Ran- species and environments. Proceedings of the National
dom Counts in Scientific Work, Vol 1, Patil, G. P. (ed). Academy of Sciences. USA, 105, 17836–17841.
Pennsylvania State, University Press, University Park, Buckley-Beason, V. A., Johnson, W. E., Nash, W. G.,
pp. 3–22. Stanyon, R., Menninger, J. C., Driscoll, C. A., Howard, J.,
Botta-Dukát, Z. (2005) Rao’s quadratic entropy as a mea- Bush, M., Page, J. E., Roelke, M. E., Stone, G., Martelli,
sure of functional diversity based on multiple traits. P. P., Wen, C., Ling, L., Duraisingam, R. K., Lam, P. V.,
Journal of Vegetation Science, 16, 33–540. O’Brien, S. J. (2006) Molecular evidence for species-level
Boulinier, T., Nichols, J., Sauer, J., Hines, J., & Pollock, distinctions in clouded leopards. Current Biology, 16,
K. (1998) Estimating species richness: the importance 2371–2376.
of heterogeneity in species detectability. Ecology, 79, Bulla, L. (1994) An index of evenness and its associated
1018–1028. diversity measure. Oikos, 70, 167–171.
Bowring, S. A., Grotzinger, J. P., Isachsen, C. E., Knoll, Bulmer, M. G. (1974) Fitting Poisson Lognormal Dis-
A. H., Pelechaty, S. M., & Kolosov, P. (1993) Calibrat- tribution to species-abundance data. Biometrics, 30,
ing rates of early Cambrian evolution. Science, 261, 101–110.
1293–1298. Bunge, J., Epstein, S. S., & Peterson, D. G. (2006) Comment
Bray, J. R. & Curtis, J. T. (1957) An ordination for the on ‘Computational improvements reveal great bacterial
upland forest communities of southern Wisconsin. Eco- diversity and high metal toxicity in soil’. Science, 313,
logical Monographs, 27, 325–349. 918.
Brewer, A. & Williamson, M. (1994) A new relationship for Burnham, K. P. & Anderson, D. R. (1998) Model Selec-
rarefaction. Biodiversity and Conservation, 3, 373–379. tion and Inference, a Practical Information-Theoretic
Brook, B., Sodhi, N., & Ng, P. (2003) Catastrophic extinc- Approach. Springer, New York.
tions follow deforestation in Singapore. Nature, 424, Burnham, K. P. & Anderson, D. R. (2002) Model Selec-
420–426. tion and Inference: A Practical Information-Theoretic
Brose, U., Martinez, N. D., & Williams, R. J. (2003) Estimat- Approach, 2nd edn. Springer, New York.
ing species richness: sensitivity to sample coverage and Burnham, K. P. & Overton. W. S. (1979) Robust estima-
insensitivity to spatial patterns. Ecology, 84, 2364–2377. tion of population size when capture probabilities vary
Brown, J. H. (1987) Variation in desert rodent guilds: pat- among animals. Ecology, 60, 927–936.
terns, processes, and scales. In: Organization of Com- Bush, A. & Bambach, R. (2004) Did alpha diversity
munities: Past and Present, Gee, J. H. R. & Giller, P. S. increase during the Phanerozoic? Lifting the veils of
(eds). Blackwell, London, pp. 185–203. taphonomic, latitudinal, and environmental biases in
Brown, J. H. (1999) Macroecology: progress and prospect. the study of paleocommunities. Journal of Geology, 112,
Oikos, 87, 3–14. 625–642.
Brown, J. H. & Kodric-Brown, A. (1977) Turnover rates in Butler, B. J. & Chazdon, R. L. (1998) Species richness, spa-
insular biogeography: effect of immigration on extinc- tial variation, and abundance of the soil seed bank of a
tion. Ecology, 58, 445–449. secondary tropical rain forest. Biotropica, 30, 214–222.
Brown, J. H. & West, G. B. (2000) Scaling in Biology. Oxford Buzas, M. A. & Hayek, L. -A. C. (1996) Biodiversity res-
University Press, Oxford. olution: an integrated approach. Biodiversity Letters, 3,
Brown, J. H., Mehlman, D. H., & Stevens, G. C. (1995) 40–43.
Spatial variation in abundance. Ecology, 76, 2028–2043. Buzas, M. A. & Hayek, L. -A. C. (1998) SHE Analy-
Buckland, S. T., Anderson, D. R., Burnham, K. P., Laake, sis for biofacies identification. Journal of Foraminiferal
J. L., Borchers, D. L., & Thomas, L. (2001) Introduc- Research, 28, 233–239.
tion to Distance Sampling. Oxford University Press, Buzas, M. A., Koch, C. F., Culver, S. J., & Sohl, N. F. (1982)
Oxford. On the distribution of species occurrence. Paleobiology,
Buckland, S. T., Anderson, D. R., Burnham, K. P., Laake, 8, 143–150.
J. L., Borchers, D. L., & Thomas, L. (2004) Advanced Byrd, I. B. (1973) Homer Scott Swingle, 1902–1973. Wildlife
Distance Sampling. OUP, Oxford. Society Bulletin, 1, 157–159.
 OUP CORRECTED PROOF – FINAL, 18/10/2010, SPi

REFERENCES 299

Cadotte, M. W., Cardinale, B. J., & Oakley, T. H. (2008) Evo- Cavender-Bares, J., Ackerly, D. A., Baum, D., & Bazzaz,
lutionary history and the effect of biodiversity on plant F. A. (2004) Phylogenetic overdispersion in Floridean
productivity. Proceedings of the National Academy of oak communities. The American Naturalist, 163,
Sciences. USA, 105, 17012–17017. 823–843.
Cadotte, M. W., Cavender-Bares, J., Tilman, D., & Oakley, Cavender-Bares, J., Kozak, K. H., Fine, P. V.A., & Kembel,
T. H. (2009) Using phylogenetic, functional, and trait S. W. (2009) The merging of community ecology and
diversity to understand patterns of plant community phylogentic biology. Ecology Letters, 12, 693–715.
productivity. PLoS One, 4, e5695. CBOL Plant Working Group. (2009) A DNA barcode for
Cadotte, M. W., Davies, T. J., Regetz, J., Kembel, S. W., land plants. Proceedings of the National Academy of
Clevand, E., & Oakley, T. (2010) Phylogenetic diversity Sciences, 106, 12794–12797.
metrics for ecological communities: integrating species Chao, A. (1984) Non-parametric estimation of the number
richness, abundance and evolutionary history. Ecology of classes in a population. Scandinavian Journal of Sta-
Letters, 13(1), 96–105. tistics, 11, 265–270.
Cam, E., Nichols, J., Hines, J., Sauer, J., Alpizar-Jara, R., & Chao, A. (1987) Estimating the population-size for capture
Flather, C. (2002) Disentangling sampling and ecologi- recapture data with unequal catchability. Biometrics, 43,
cal explanations underlying species-area relationships. 783–791.
Ecology, 83, 1118–1130. Chao, A. (2001) An overview of closed capture-recapture
Camargo, J. A. (1993) Must dominance increase with the models. Journal of Agricultural, Biological and Environ-
number of subordinate species in competitive interac- mental Statistics, 6, 158–175.
tions? Journal of Theoretical Biology, 161, 537–542. Chao, A. (2005) Species estimation and applications. In:
Cardinale, B. J., Palmer, M. A., & Collins, S. L. Encyclopedia of Statistical Sciences, Balakrishnan, N.,
(2002) Species diversity enhances ecosystem func- Read, C. B., & Vidakovic, B. (eds), 2nd edn. Wiley, New
tioning through interspecific facilitation. Nature, 415, York, Vol. 12, pp. 7907–7916.
426–429. Chao, A. & Bunge, J. (2002) Estimating the number of
Cardinale, M., Brusetti, L., Quatrini, P., Borin, S., Puglia, species in a Stochastic abundance model. Biometrics, 58,
A. M., Rizzi, A., Zanardini, E., Sorlini, C., Corselli, C., 531–539.
& Daffonchio, D. (2004) Comparison of different primer Chao, A. & Lee, S. -M. (1992) Estimating the number of
sets for use in automated ribosomal intergenic spacer classes via sample coverage. Journal of the American
analysis of complex bacterial communities. Applied and Statistical Association, 87, 210–217.
Environmental Microbiology, 70, 6147–6156. Chao, A. & Shen, T. -J. (2003a) SPADE: Species Prediction
Carroll, C. (2006) Interacting effects of climate change, And Diversity Estimation. Program and user’s guide at
landscape conversion, and harvest on carnivore pop- http://chao.stat.nthu.edu.tw/softwareCE.html.
ulations at the range margin: marten and lynx in Chao, A. & Shen, T. -J. (2003b) Nonparametric estimation
the northern Appalachians. Conservation Biology, 21, of Shannon’s index of diversity when there are unseen
1092–1104. species. Environment and Ecological Statistics, 10,
Caruso, T. & Migliorini, M. (2006) A new formulation of 429–443.
the geometric series with applications to oribatid (Acari, Chao, A., Yip, P., & Lin, H. S. (1996) Estimating the number
Oribatida) species assemblages from human-disturbed of species via a martingale estimating function. Statis-
Mediterranean areas. Ecological Modelling, 195, tica Sinica, 6, 403–418.
402–406. Chao, A., Chazdon, R. L., Colwell, R. K., & Shen, T. -J.
Casas, F., Mougeot, F., Viñuela, J., & Bretagnolle, V. (2009) (2005) A new statistical approach for assessing simi-
Effects of hunting on the behaviour and spatial distri- larity of species composition with incidence and abun-
bution of farmland birds: importance of hunting-free dance data. Ecology Letters, 8, 148–159.
refuges in agricultural areas. Animal Conservation, 12, Chao, A., Chazdon, R. L., Colwell, R. K., & Shen, T. -J.
346–354. (2006) Abundance-based similarity indices and their
Castoe, T. A., Poole, A. W., Gu, W., Jason de Koning, estimation when there are unseen species in samples.
A. P., Daza, J. M., Smith, E. N., & Pollock, D. D. (2009) Biometrics, 62, 361–371.
Rapid identification of thousands of copperhead snake Chao, A., Jost, L., Chiang, S. -C., Jiang, Y. -H., & Chaz-
(Agkistrodon contortrix) microsatellite loci from modest don, R. (2008) A two-stage probabilistic approach to
amounts of 454 shotgun genome sequence. Molecular multiple-community similarity indices. Biometrics, 64,
Ecology Resources, 341–347. 1178–1186.
OUP CORRECTED PROOF – FINAL, 18/10/2010, SPi

300 REFERENCES

Chao, A., Colwell, R. K., Lin, C. -W., & Gotelli, N. (2009) CIA Fact Book. (2009) https://www.cia.gov/library/
Sufficient sampling for asymptotic minimum species publications/the-world-factbook/geos/XX.html.
richness estimators. Ecology, 90, 1125–1133. Accessed 12 July 2009 12h17 UTC.
Chapin III, F. S., Zavaleta, E. S., Eviner, V. T., Naylor, R. L., Cianciaruso, M. V., Batalha, M. A., Gaston, K. J., & Petchey,
Vitousek, P. M., Reynolds, H. L., Hooper, D. U., Lavorel, O. L. (2009) Including intraspecific variability in func-
S., Sala, O. E., Hobbie, S. E., Mack, M. C., & Díaz, S. tional diversity. Ecology, 90, 81–89.
(2000) Consequences of changing biodiversity. Nature, Clarke, K. R. (1990) Comparisons of dominance curves.
405, 234–242. Journal of Experimental Marine Biology and Ecology,
Chave, J., Muller-Landau, H. C., & Levin, S. A. (2002) 138, 143–157.
Comparing classical community models: theoretical Clarke, J. A, & May, R. M. (2002) Taxonomic bias in con-
consequences for patterns of diversity. The American servation research. Science, 297, 191–192.
Naturalist, 159, 1–23. Clarke, K. R. & Warwick, R. M. (1998) A taxonomic dis-
Chen, I. C., Shiu, H., Benedick, S., Holloway, J. D., Chey, tinctness index and its statistical properties. Journal of
V. K., Barlow, H. S., Hill, J. K., & Thomas, C. D. (2009) Applied Ecology, 35, 523–531.
Elevation increases in moth assemblages over 42 years Clarke, K. R. & Warwick, R. M. (2001) Change in Marine
on a tropical mountain. Proceedings of the National Communities: An Approach to Statistical Analysis and
Academy of Sciences. USA, 106, 1479–1483. Interpretation (PRIMER-E). Plymouth Marine Labora-
Chiarucci, A., Wilson, J. B., Anderson, B. J., & De Domini- tory, Plymouth.
cis, V. (1999) Cover versus biomass as an estimate of Clifford, H. T. & Stephenson, W. (1975) An Intro-
species abundance: does it make a difference to the con- duction to Numerical Classification. Academic Press,
clusions? Journal of Vegetation Science, 10, 35–42. New York.
Chiarucci, A., Bacaro, G., Rocchini, D., & Fattorini, L. Clough, Y., Holzschuh, A., Gabriel, D., Purtauf, T.,
(2008) Discovering and rediscovering the sample-based Kleijn, D., Kruess, A., Steffan-Dewenter, I., & Tscharn-
rarefaction formula in the ecological literature. Commu- tke, T. (2007) Alpha and beta diversity of arthropods
nity Ecology, 9, 121–123. and plants in organically and conventionally man-
Chivian, D., Brodie, E. L., Alm, E. J., Culley, D. E., Dehal, aged wheat fields. Journal of Applied Ecology, 44,
P. S., DeSantis, T. Z., Gihring, T. M., Lapidus, A., Lin, 804–812.
L. -H., Lowry, S. R., Moser, D. P., Richardson, P. M., Cochran, W. G. (1977) Sampling Techniques. Wiley, New
Southam, G., Wanger, G., Pratt, L. M., Andersen, G. L., York.
Hazen, T. C., Brockman, F. J., Arkin, A. P., & Onstott, Coddington, J. A., Agnarsson, I., Miller, J. A., Kuntner,
T. C. (2008) Environmental genomics reveals a single- M., & Hormiga, G. (2009) Undersampling bias: the null
species ecosystem deep within Earth. Science, 322, 275– hypothesis for singleton species in tropical arthropod
278. surveys. Journal of Animal Ecology, 78, 573–584.
Chown, S. L. & Gaston, K. J. (2008) Macrophysiology for a Cohen, J. E. (1995) How Many People Can the Earth Sup-
changing world. Proceedings of the Royal Society Lon- port? W. W. Norton, New York.
don, B, 275, 1469–1478. Coleman, B. D., Mares, M. A., Willig, M. R., & Hsieh, Y. -H.
Chown, S. L. & Gaston, K. J. (2010) Body size varia- (1982) Randomness, area, and species richness. Ecology,
tion in insects: a macroecological perspective. Biological 63, 1121–1133.
Reviews, 85, 139–169. Collins, S. L. & Glenn, S. M. (1997) Effects of organis-
Chown, S. L. & Terblanche, J. S. (2007) Physiological diver- mal and distance scaling on analysis of species dis-
sity in insects: ecological and evolutionary contexts. tribution and abundance. Ecological Applications, 7,
Advances in Insect Physiology, 33, 50–152. 543–551.
Chown, S. L., Gaston, K. J., & Williams, P. H. (1998) Global Collins, S. L., Micheli, F., & Hartt, L. (2000) A method to
patterns in species richness of pelagic seabirds: the Pro- determine rates and patterns of variability in ecological
cellariiformes. Ecography, 21, 342–350. communities. Oikos, 91, 285–293.
Chown, S. L., van Rensburg, B. J., Gaston, K. J., Rodrigues, Collins, S. L., Suding, K. N., Cleland, E. E., Batty, M.,
A. S. L., & van Jaarsveld, A. S. (2003) Energy, species Pennings, S. C., Gross, K. L., Grace, J. B., Gough, L.,
richness, and human population size: conservation Fargione, J. E., & Clar, C. M. (2008) Rank clocks and
implications at a national scale. Ecological Applications, plant community dynamics. Ecology, 89, 3534–3541.
13, 1233–1241. Colwell, R. K. (2009) Estimates: Statistical Estima-
Chytrý, M., Sedláková, I., & Tichý, L. (2009) Species rich- tion of Species Richness and Shared Species from
ness and species turnover in a successional heathland. Samples User’s Guide and application published at:
Applied Vegetation Science, 4, 89–96. http://purl.oclc.org/estimates.
 OUP CORRECTED PROOF – FINAL, 18/10/2010, SPi

REFERENCES 301

Colwell, R. K. & Coddington, J. A. (1994) Estimating ter- Constanza, R. et al. (1997) The value of the world’s
restrial biodiversity through extrapolation. Philosophi- ecosystem services and natural capital. Nature, 387,
cal Transactions of the Royal Society, London, Series B, 253–257 .
345, 101–118. Cornelissen, J. H. C., Lavorel, S., Garnier, E., Díaz, S., Buch-
Colwell, R. K., Mao, C. X., & Chang, J. (2004) mann, N., Gurvich, D. E., Reich, P. B., Ter, Morgan, H. D.,
Interpolating, extrapolating, and comparing incidence- van der Heijden, M. G. A., Pausas, J. G., and Poorter, H.
based species accumulation curves. Ecology, 85, (2003). A handbook of protocols for standardised and
2717–2727. easy measurement of plant functional traits worldwide.
Colwell, R. K., Brehm, G., Cardelús, C., Gilman, A. C., & Australian Journal of Botany, 51, 335–380.
Longino, J. T. (2008) Global warming, elevational range Cornwell, W. K. & Ackerly, D. D. (2009) Community
shifts, and lowland biotic attrition in the wet tropics. assembly and shifts in plant trait distributions across an
Science, 322, 258–261. environmental gradient in coastal California. Ecological
Condit, R., Hubbell, S. P., LaFrankie, J. V., Sukumar, R., Monographs, 79, 109–126.
Manokaran, N., Foster, R. B., & Ashton P. S. (1996) Cornwell, W. K., Schwilk, D. W., & Ackerly, D. D. (2006) A
Species-area and species-individual relationships for trait-based test for habitat filtering: convex hull volume.
tropical trees: a comparison of three 50-ha plots. Journal Ecology, 87, 1465–1471.
of Ecology, 84, 549–562. Costello, M. J., Pohle, G., & Martin, A. (2004) Evaluating
Condit, R., Ashton, P. S., Baker, P., Bunyavejchewin, S., biodiversity in marine environmental assessments. In:
Gunatilleke, S., Gunatilleke, N., Hubbell, S. P., Foster, Research and Development Monograph Series, Series
R. B., Itoh, A., LaFrankie, J. V., Lee, H. S., Losos, E., RaDM (ed.), Ottawa.
Manokaran, N., Sukumar, R., & Yamakura, T. (2000) Cowley, M. J.R., Thomas, C. D., Wilson, R. J., León-Cortés,
Spatial patterns in the distribution of tropical tree J. L., Gutiérrez, D., & Bulman, C. R. (2001) Density-
species. Science, 288, 1414–1418. distribution relationships in British butterflies: II. An
Conlisk, E., Bloxham, M., Conlisk, J., Enquist, B., & Harte, assessment of mechanisms. Journal of Animal Ecology,
J. (2007) A new class of models of spatial distribution. 70, 426–441.
Ecological Monographs, 77, 269–284. Crame, J. A. (2001) Taxonomic diversity gradients through
Conlisk, E., Conlisk, J., Enquist, B., Thompson, J., & geological time. Diversity & Distributions, 7, 175–189.
Harte, J. (2009) Improved abundance prediction from Cressie, N. (1992) Statistics for Spatial Data. Wiley Inter-
presence-absence data. Global Ecology and Biogeogra- science, Chichester.
phy, 18, 1–10. Crisp, M. D. & Cook, L. G. (2009) Explosive radiation
Connell, J. H. (1978) Diversity in tropical rain forests and or cryptic mass extinction? Interpreting signatures in
coral reefs. Science, 199, 1302–1310. molecular phylogenies. Evolution, 63, 2257–2265.
Connell, J. H. & Sousa, W. P. (1983) On the evidence Crist, T. O. & Veech, J. A. (2006) Additive partitioning of
needed to judge ecological stability or persistence. The rarefaction curves and species-area relationships: unify-
American Naturalist, 121, 789–824. ing alpha, beta, and gamma diversity with sample size
Connolly, S. R. & Miller, A. I. (2001a) Global Ordovi- and area. Ecology Letters, 9, 923–932.
cian faunal transitions in the marine benthos: proximate Crist, T. O., Veech, J. A., Gering, J. C., & Summerville, K. S.
causes. Paleobiology, 27, 779–795. (2003) Partitioning species diversity across landscapes
Connolly, S. R. & Miller, A. I. (2001b) Joint estimation and regions: a hierarchical analysis of alpha, beta,
of sampling and turnover rates from fossil databases: and gamma diversity. The American Naturalist, 162,
capture-mark-recapture methods revisited. Paleobiol- 734–743.
ogy, 27, 751–767. Crozier, R. H. (1997) Preserving the information content of
Connolly, S. R., Hughes, T. P., Bellwood, D. R., & species: genetic diversity, phylogeny, and conservation
Karlson R. H. (2005) Community structure of corals worth. Annual Review of Ecology and Systematics, 28,
and reef fishes at multiple scales. Science, 309, 243–268.
1363–1365. Cunningham, S. A., Summerhayes, G., & Westoby, M.
Connolly, S. R., Dornelas, M., Bellwood, D. R., & Hughes, (1999) Evolutionary divergences in leaf structure and
T. P. (2009) Testing species abundance models: a new chemistry, comparing rainfall and soil nutrient gradi-
bootstrap approach applied to Indo-Pacific coral reefs. ents. Ecology, 69, 569–588.
Ecology, 90, 3138–3149. Curtis, T. P. & Sloan, W. T. (2004) Prokaryotic diversity and
Connor, E. F. & Simberloff, D. (1979) The assembly of its limits: microbial community structure in nature and
species communities: chance or competition? Ecology, implications for microbial ecology. Current Opinion in
60, 1132–1140. Microbiology, 7, 221–226.
OUP CORRECTED PROOF – FINAL, 18/10/2010, SPi

302 REFERENCES

Curtis, T. P., Sloan, W. T., & Scannell, J. W. (2002) Estimat- Death, R. G. & Zimmerman, E. M. (2005) Interac-
ing prokaryotic diversity and its limits. Proceedings of tion between disturbance and primary productivity in
the National Academy of Sciences of the United States determining stream invertebrate diversity. Oikos, 111,
of America, 99, 10494–10499. 392–402.
Daily, G. C., Alexander, S., Ehrlich, P. R., Goulder, L., de Bello, F., Thuiller, W., Lepš, J., Choler, P., Clément,
Lubchenco, J., Matson, P. A., Mooney, H. A., Postel, S., J. -C., Macek, P., Sebastià, M. T., & Lavorel, S. (2009)
Schneider, S. H., & Tilman, D. (1997) Ecosystem services: Partitioning of diversity reveals the scale and extent of
benefits supplied to human societies by natural ecosys- trait convergence and divergence. Journal of Vegetation
tems. Issues in Ecology, 1, 1–18. Science, 20, 475–486.
Dale, M. R.T. & Fortin, M. J. (2002) Spatial autocorre- DeLong, E. E. & Pace, N. R. (2001) Environmental diver-
lation and statistical tests in ecology. Ecoscience, 9, sity of Bacteria and Archaea. Systematic Biology, 50,
162–167. 470–478.
Dale, M. R.T., Dixon, P., Fortin, M.-J., Legendre, P., Myers, de Mazancourt, C., Johnson, E., & Barraclough, T. G. (2008)
D. E., & Rosenberg, M. S. (2002) Conceptual and mathe- Biodiversity inhibits species’ evolutionary responses to
matical relationships among methods for spatial analy- changing environments. Ecology Letters, 11, 380–388.
sis. Ecography, 25, 558–577. DeQuiroz, K. (2007) Species concepts and species delimi-
Dalton, H. (1920) The measurement of the inequality of tations. Systematic Biology, 56, 879–886.
incomes. Economic Journal, 119, 348–361. Dewdney, A. K. (1998) A general theory of the sampling
Dampier, J. E. E., Luckai, N., Bell, F. W., & Towill, W. D. process with applications to the “veil line”. Theoretical
(2007) Do tree-level monocultures develop following Population Biology, 54, 294–302.
Canadian boreal silviculture? Tree-level diversity tested Dewdney, A. K. (2000) A dynamical model of commu-
using a new method. Biodiversity and Conservation, 16, nities and a new species-abundance distribution. The
2933–2948. Biological Bulletin, 198, 152–165.
Damschen, E. I., Haddad, N. M., Orrock, J. L., Tewks- Diamond, J. M. & May, R. M. (1977) Species turnover rates
bury, J. J., & Levey, D. J. (2006) Corridors increase on islands: dependence on census intervals. Science,
plant species richness at large scales. Science, 313, 197, 266–270.
1284–1286. Díaz, S. & Cabido, M. (2001) Vive la différence: plant func-
Darwin, C. (1859) On the origin of species by means of tional diversity matters to ecosystem processes. Trends
natural selection, or the preservation of favoured races in Ecology and Evolution, 16, 646–655.
in the struggle for life. John Murray, London. Díaz, S., Lavorel, S., de Bello, F., Quétier, F., Grigulis, K.,
Dauvin, J. C. (1984) Dynamique d’écosystèmes macroben- & Robson, T. M. (2007) Incorporating plant functional
thiques des fonds sédimentaires de la baie de Mor- diversity effects in ecosystem service assessments. Pro-
laix et leur perturbation par les hydrocarbures de ceedings of the National Academy of Sciences, 104,
l’Amoco Cadiz’. Thèse d’Etat, Université de Paris, Paris, 20684–20689.
456pp. Diggle, P. J. (1983) Statistical Analysis of Spatial Point Pat-
Davis, M. B. & Shaw, R. G. (2001) Range shifts and adap- terns. Academic Press, London.
tive responses to Quaternary climate change. Science, Diniz, J. A. F., Bini, L. M., & Hawkins, B. A. (2003)
292, 673–679. Spatial autocorrelation and red herrings in geograph-
Davison, A., Blackie, R. L. E., & Scothern, G. P. (2009) ical ecology. Global Ecology and Biogeography, 12,
DNA barcoding of stylommatophoran land snails: a test 53–64.
of existing sequences. Molecular Ecology Research, 9, Diserud, O. H. & Engen, S. (2000) A general and dynamic
1092–1101. species abundance model, embracing the lognormal
Dawson, W., Burslem, D. F. R. P., & Hulme, P. E. (2009) The and the gamma models. The American Naturalist, 155,
suitability of weed risk assessment as a conservation 497–511.
tool to identify invasive plant threats in East African Diserud, O. H. & Ødegaard, F. (2007) A multiple-site sim-
rainforests. Biological Conservation, 142, 1018–1024. ilarity measure. Biology Letters, 3, 20–22.
Dayan, T. & Simberloff, D. (2005) Ecological and Dobyns, J. R. (1997) Effects of sampling intensity on the
community-wide character displacement: the next gen- collection of spider (Araneae) species and the estima-
eration. Ecology Letters, 8, 875–894. tion of spider richness. Environmental Entomology, 26,
Deagle, B. E., Kirkwood, R., & Jarman, S. N. (2009) Analy- 150–162.
sis of Australian fur seal diet by pyrosequencing prey Donaldson, J., Nänni, I., Zachariades, C., & Kemper, J.
DNA in faeces. Molecular Ecology, 18, 2022–2038. (2002) Effects of habitat fragmentation on pollinator
 OUP CORRECTED PROOF – FINAL, 18/10/2010, SPi

REFERENCES 303

diversity and plant reproductive success in renosterveld B., Gibb, H., Gotelli, N., Gove, A., Guénard, B., Janda,
shrublands of South Africa. Conservation Biology, 16, M., Kaspari, M., Longino, J. T., Majer, J., McGlynn, T. P.,
1267–1276. Menke, S. B., Parr, C., Philpott, S., Pfeiffer, M., Retana, J.,
Dormann, C. F. (2007a) Assessing the validity of autologis- Suarez, A., & Vasconcelos, H. (2009) Climatic drivers of
tic regression. Ecological Modelling, 207, 234–242. hemispheric asymmetry in global patterns of ant species
Dormann, C. F. (2007b) Effects of incorporating spatial richness. Ecology Letters, 12, 324–333.
autocorrelation into the analysis of species distribution Dutilleul, P., Clifford, P., Richardson, S., & Hemon, D.
data. Global Ecology and Biogeography, 16, 129–138. (1993) Modifying the t test for assessing the corre-
Dormann, C. F., McPherson, J. M., Araújo, M. B., Bivand, lation between two spatial processes. Biometrics, 49,
R., Bolliger, J., Carl, G., Davies, R. G., Hirzel, A., Jetz, W., 305–314.
& Kissling, W. D. (2007) Methods to account for spatial DuToit, J. T. (2003) Large herbivores and savanna hetero-
autocorrelation in the analysis of species distributional geneity. In: The Kruger Experience: Ecology and Man-
data: a review. Ecography, 30, 609. agement of Savanna Heterogeneity, DuToit, J. T., Rogers,
Dornelas, M. & Connolly, S. R. (2008) Multiple modes in K. H., & Biggs, H. C. (eds). Island Press, Washington,
a coral species abundance distribution. Ecology Letters, pp. 292–309.
11, 1008–1016. Dykhuizen, D. E. (1998) Santa Rosalia revisited: Why
Dornelas, M., Connolly, S. R., & Hughes, T. P. (2006) Coral are there so many species of bacteria? Antonie Van
reef diversity refutes the neutral theory of biodiversity. Leeuwenhoek International Journal of General and
Nature, 440, 80–82. Molecular Microbiology, 73, 25–33.
Dornelas, M., Moonen, A. C., Magurran, A. E., & Barberi, EASAC. (2009) Ecosystem Services and Biodiversity in
P. (2009) Species abundance distributions reveal envi- Europe. The Royal Society, London.
ronmental heterogeneity in modified landscapes. Jour- Economo, E. & Kiett, T. (2008) Species diversity in neutral
nal of Applied Ecology, 46, 666–672. metacommunities: a network approach. Ecology. Letters
Dornelas, M., Phillip, D. A. T., and Magurran, A. E. (2010) 11, 52–62.
Abundance and dominance become less predictable as Edwards, A. W.F. (1992) Likelihood – Expanded Edition.
species richness decreases. Global Ecology and Biogeog- Johns Hopkins University Press, Baltimore.
raphy, in press. Efron, B. & Thisted, R. (1976) Estimating the number
Doroghazi, J. R. & Buckley, D. H. (2008) Evidence from of unseen species: how many words did Shakespeare
GC-TRFLP that bacterial communities in soil are lognor- know? Biometrika, 63, 35–41.
mally distributed. Plos One, 3, e2910. Efron, B. & Tibshirani, R. J. (1993) An Introduction to the
Dray, S. & Legendre, P. (2008) Testing the species traits- Bootstrap. Chapman & Hall/CRC, Boca Raton, FL.
environment relationships: the fourth-corner problem Elith, J., Graham, C. H., Anderson, R. P., Dudik, M., Ferrier,
revisited. Ecology, 89, 3400–3412. S., Guisan, A., Hijmans, R. J., Huettmann, F., Leathwick,
Dray, S., Legendre, P., & Peres-Neto, P. R. (2006) Spa- J. R., Lehmann, A., Li, J., Lohmann, L. G., Loiselle, B. A.,
tial modelling: a comprehensive framework for princi- Manion, G., Moritz, C., Nakamura, M., Nakazawa, Y.,
pal coordinate analysis of neighbour matrices (PCNM). Overton, J. M. M., Peterson, A. T., Phillips, S. J., Richard-
Ecological Modelling, 196, 483–493. son, K., Scachetti-Pereira, R., Schapire, R. E., Soberon, J.,
Drayton, B. & Primack, R. B. (1996) Plant species lost in an Williams, S., Wisz, M. S., & Zimmermann, N. E. (2006)
isolated conservation area in metropolitan Boston from Novel methods improve prediction of species’ distribu-
1894 to 1993. Conservation Biology, 10, 30–39. tions from occurrence data. Ecography, 29, 129–151.
Driscoll, C. A., Menotti-Raymond, M., Roca, A., Hupe, K., Ellis, E. C. & Ramankutty, N. (2008) Putting people in the
Johnson, W. E., Geffen, E., Harley, E. H., Delibes, M., map: anthropogenic biomes of the world. Frontiers in
Pontier, D., Kitchener, A. C., Yamaguchi, N., O’Brien, Ecology and the Environment, 6, 439–447.
S. J., & Macdonald, D. W. (2007) The near eastern origin Ellison, A. M., Record, S., Arguello, A., & Gotelli, N. J.
of cat domestication. Science, 317, 519–523. (2007) Rapid inventory of the ant assemblage in a
Drobner, U., Bibby, J., Smith, B., & Wilson, J. B. (1998) temperate hardwood forest: species composition and
The relation between community biomass and even- assessment of sampling methods. Environental Ento-
ness: What does community theory predict, and can mology, 36, 766–775.
these predictions be tested? Oikos, 82, 295–302. Elton, C. (1946) Competition and the structure of ecologi-
Dunn, R. R., Sanders, N. J., Menke, S. B., Weiser, M. D., cal communities. Journal of Animal Ecology, 15:54–68.
Fitzpatrick, M. C., Laurent, E., Lessard, J. -P., Agosti, D., Elzinga, C. L., Salzer, D. W., & Willoughby, J. W. (1998)
Andersen, A., Bruhl, C., Cerda, X., Ellison, A., Fisher, Measuring & Monitoring Plant Populations. US Dept.
OUP CORRECTED PROOF – FINAL, 18/10/2010, SPi

304 REFERENCES

of the Interior, Bureau of Land Management; Nature species richness in South Africa. Biology Letters, 2,
Conservancy, Denver, CO. 184–188.
Engen, S. & Lande, R. (1996a) Population dynamic Evans, K. L., van Rensburg, B. J., Gaston, K. J., & Chown,
models generating species abundance distributions of S. L. (2006b) People, species richness and human pop-
the gamma type. Journal of Theoretical Biology, 178, ulation growth. Global Ecology and Biogeography, 15,
325–331. 625–636.
Engen, S. & Lande, R. (1996b) Population dynamic models Evans, K. L., Gaston, K. J., Sharp, S. P., McGowan, A.,
generating the lognormal species abundance distribu- & Hatchwell, B. J. (2009) The effect of urbanisation on
tion. Mathematical Biosciences, 132, 169–183. avian morphology and latitudinal gradients in body
Engen, S., Lande, R., Walla, T., & DeVries, P. J. (2002) size. Oikos, 118, 251–259.
Analyzing spatial structure of communities using the Evans, M., Hastings, N., & Peacock, B. (1993) Statistical
two-dimensional Poisson lognormal species abundance Distributions, 2nd edn. Wiley, New York.
model. The American Naturalist, 160, 60–73. Faith, D. P. (1992) Conservation evaluation and phyloge-
Engen, S., Saether, B. E., Sverdrup-Thygeson, A., Grotan, netic diversity. Biological Conservation, 61, 1–10.
V., & Odegaard, F. (2008) Assessment of species diver- Fargione, J., Brown, C. S., & Tilman, D. (2003) Community
sity from species abundance distributions at different assembly and invasion: an experimental test of neu-
localities. Oikos, 117, 738–748. tral versus niche processes. Proceedings of the National
Epperson, B. K. (2003) Geographical Genetics. Princeton Academy of Sciences of the United States of America,
University Press, Princeton. 100, 8916–8920.
Erickson, R. O. (1945) The Clematis fremontii var. riehlii Farrell, L. E., Roman, J., & Sunquist, M. E. (2000) Dietary
population in the Ozarks. Annals of the Missouri Botan- separation of sympatric carnivores identified by molec-
ical Garden, 32, 413–460. ular analysis of scats. Molecular Ecology, 9, 1583–1590.
Erwin, D. H. (2006) Extinction: How life on Earth Nearly Felsenstein, J. (1985) Phylogenies and the comparative
Ended 250 Million Years Ago. Princeton University method. The American Naturalist, 125, 1–15.
Press, Princeton. Felsenstein, J. (2004) Inferring Phylogenies. Sinauer Asso-
Etienne, R. S. (2005) A new sampling formula for neutral ciates, Sunderland, MA, USA.
biodiversity. Ecology Letters, 8, 253–260. Feng, M. C., Nowierski, R. M., & Zeng, Z. (1993) Popu-
Etienne, R. S. (2007) A neutral sampling formula for mul- lations of Sitobion avenae and Aphidius ervi on sprint
tiple samples and an ‘exact’ test of neutrality. Ecology wheat in the northwestern United States. Entomologia
Letters, 10, 608–618. Experimentalis et Applicata, 67, 109–117.
Etienne, R. S. & Alonso, D. (2005) A dispersal-limited sam- Fewster, R. M., Buckland, S. T., Siriwardena, G. M., Baillie,
pling theory for species and alleles. Ecology Letters, 8, S. R., & Watson, J. D. (2000) Analysis of population
1147–1156. trends for farmland birds using generalized additive
Etienne, R. S. & Olff, H. (2004) A novel genealogical models. Ecology, 81, 1970–1984.
approach to neutral biodiversity theory. Ecology Let- Fiedler, K. & Schulze, C. H. (2004) Forest modification
ters, 7, 170–175. affects diversity (but not dynamics) of speciose tropical
Etienne, R. S. & Olff, H. (2005) Confronting different mod- pyraloid moth communities. Biotropica, 36, 615–627.
els of community structure to species-abundance data: Figueiredo, M. S. L. & Grelle, C. E. V. (2009) Predicting
a Bayesian model comparison. Ecology Letters, 8, 493– global abundance of a threatened species from its occur-
504. rence: implications for conservation planning. Diversity
Etienne, R. S., Latimer, A. M., Silander, J. A., & Cowling, & Distributions, 15, 117–121.
R. M. (2006) Comment on “Neutral ecological theory Filippi-Codaccioni, O., Clobert, J., & Julliard, R.. (2009)
reveals isolation and rapid speciation in a biodiversity Urbanization effects on the functional diversity of
hot spot”. Science, 311, 610. avian agricultural communities. Acta Oecologia, 35,
Etienne, R. S., Alonso, D., & McKane, A. J. (2007a) The 705–710.
zero-sum assumption in netural biodiversity theory. Finch, S., Skinner, G., & Freeman, G. H. (1975) The distrib-
Journal of Theoretical Biology, 248, 522–536. ution and analysis of cabbage root fly egg populations.
Etienne, R. S., Apol, M. E. F., Olff, H., & Weissing, F. J. The Annals of Applied Biology, 79, 1–18.
(2007b) Modes of speciation and the neutral theory of Fisher, J. A. D. & Frank, K. T. (2004) Abundance-
biodiversity. Oikos, 116, 241–258. distribution relationships and conservation of exploited
Evans, K. L., Rodrigues, A. S. L., Chown, S. L., & Gas- marine fishes. Marine Ecology Progress Series, 279,
ton, K. J. (2006a) Protected areas and regional avian 201–213.
 OUP CORRECTED PROOF – FINAL, 18/10/2010, SPi

REFERENCES 305

Fisher, M. M. & Triplett, E. W. (1999) Automated Foote, M. (2001b) Evolutionary rates and the age distribu-
approach for ribosomal intergenic spacer analysis of tion of living and extinct taxa. In: Evolutionary Patterns
microbial diversity and its application to freshwater – Growth, Form, and Tempo in the Fossil Record, Jack-
bacterial communities. Applied and Environmental son, J. B. C., Lidgard, S., & McKinney, F. K. (eds). The
Microbiology, 65, 4630–4636. University of Chicago Press, Chicago, pp. 245–294.
Fisher, R. A., Corbet, A. S., & Williams, C. B. (1943) The Foote, M. (2001c) Inferring temporal patterns of preser-
relation between the number of species and the number vation, origination, and extinction from taxonomic sur-
of individuals in a random sample of an animal popu- vivorship analysis. Paleobiology, 27, 602–630.
lation. Journal of Animal Ecology, 12, 42–58. Foote, M. (2003) Origination and extinction through the
Fisher, J. A. D., Frank, K. T., Petrie, B., Leggett, W. C., & Phanerozoic: a new approach. The Journal of Geology,
Shackell, N. L. (2008) Temporal dynamics within a con- 111, 125–148.
temporary latitudinal diversity gradient. Ecology Let- Foote, M. (2005) Pulsed origination and extinction in the
ters, 11, 883–897. marine realm. Paleobiology, 31, 6–20.
Flaten, G. R., Botnen, H., Grung, B., & Kvalheim, O. M. Foote, M. (2007a) Extinction and quiescence in marine ani-
(2007) Quantifying disturbances in benthic communi- mal genera. Paleobiology, 33, 261–272.
ties – comparison of the community disturbance index Foote, M. (2007b) Symmetric waxing and waning of
(CDI) to other multivariate methods. Ecological Indica- marine invertebrate genera. Paleobiology, 33, 517–529.
tors, 7, 254–276. Foote, M. & Raup, D. M. (1996) Fossil preservation
Flather, C. (1996) Fitting species-accumulation functions and the stratigraphic ranges of taxa. Paleobiology, 22,
and assessing regional land use impacts on avian diver- 121–140.
sity. Journal of Biogeography, 23, 155–168. Foote, M., Crampton, J. S., Beu, A. G., & Cooper, R. A.
Fleishman, E., Thomson, J. R., Mac Nally, R., Murphy, (2008) On the bidirectional relationship between geo-
D. D., & Fay, J. P. (2005) Using indicator species to graphic range and taxonomic duration. Paleobiology,
predict species richness of multiple taxonomic groups. 34, 421–433.
Conservation Biology, 19, 1125–1137. Foran, D. R., Crooks, K. R., & Minta, S. C. (1997) Species
Folch, J., Cocero, M. J., Chesné, P., Alabart, J. L., identification from scat: an unambiguous genetic
Dominguez, V., Cognié , Y., Roche, A., Vernández-Áriaz, method. Wildlife Society Bulletin, 25, 835–839.
A., Marti, J. I., Sánchez, P., Echegoyen, E., Beckers, Ford, N. B. & Lancaster, D. L. (2007) The species-
J. F., Sánchez Bonastre, A., & Vignon, X. (2009) First abundance distribution of snakes in a bottomland hard-
birth of an animal from an extinct subspecies (Capra wood forest of the southern United States. Journal of
pyrenaica pyrenaica) by cloning. Theriogenology, 71, Herpetology, 41, 385–393.
1026–1034. Forest, F., Grenyer, R., Rouget, M., Davies, T. J., Cowling,
Folke, C., Carpenter, S., Walker, B., Scheffer, M., Elmqvist, R.M, Faith, D. P., Balmford, A., Manning, J. C., Proches,
T., Gunderson, L., & Holling, C. S. (2004) Regime shifts, S., van der Bank, M., Reeves, G., Hedderson, T. A. J.,
resilience, and biodiversity in ecosystem management. & Salvolainen, V. (2007) Preserving the evolutionary
Annual Review of Ecology Evolution and Systematics, potential of floras in biodiversity hotspots. Nature, 445,
35, 557–581. 757–760.
Foote, M. (1988) Survivorship analysis of Cambrian and Fornara, D. A. & Tilman, D. (2009) Ecological mechanisms
Ordovician trilobites. Paleobiology, 14, 258–271. associated with the positive diversity – productivity
Foote, M. (1994) Temporal variation in extinction risk and relationship in an N-limited grassland. Ecology, 90,
temporal scaling of extinction metrics. Paleobiology, 20, 408–418.
424–444. Fortin, M. J. & Dale, M. R.T. (2005) Spatial Analysis:
Foote, M. (1997) Estimating taxonomic durations and A Guide for Ecologists. Cambridge University Press,
preservation probability. Paleobiology, 23, 278–300. Cambridge.
Foote, M. (2000) Origination and extinction components Foster, S. D. & Dunstan, P. K. (2009) The analysis of biodi-
of taxonomic diversity: general problems. In: Deep time versity using rank abundance distributions. Biometrics,
– Paleobiology’s perspective, Erwin, D. H. & Wing, 661, 186–195.
S. L. (eds). Paleobiology Memoir, Paleontological Soci- Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2002)
ety and University of Chicago Press, pp. 74–102. Geographically Weighted Regression: The Analysis of
Foote, M. (2001a) Estimating completeness of the fossil Spatially Varying Relationships. Wiley, Chichester.
record. In: Paleobiology II, Briggs, D. E. G. & Crowther, Foxcroft, L. C., Rouget, M., Richardson, D. M., & Mac-
P. R. (eds). Blackwell, Oxford, pp. 500–504. Fadyen, S. (2004) Reconstructing 50 years of Opun-
OUP CORRECTED PROOF – FINAL, 18/10/2010, SPi

306 REFERENCES

tia stricta invasion in the Kruger National Park, South Garnier, E., Laurent, G., Bellmann, A., Debain, S., Berthe-
Africa: environmental determinants and propagule lier, P., Ducout, B., Roumet, C., & Navas, M.-L. (2001)
pressure. Diversity & Distributions, 10, 427–437. Consistency of species ranking based on functional leaf
Foxcroft, L. C., Richardson, D. M., Rouget, M., & Mac- traits. The New Phytologist, 152, 69–83.
Fadyen, S. (2009) Patterns of alien plant distribution Garnier, E., Lavorel, S., Ansquer, P., Castro, H., Cruz,
at multiple spatial scales in a large national park: P., Dolezal, J., Eriksson, O., Fortunel, C., Freitas, H.,
implications for ecology, management and monitoring. Golodets, C., Grigulis, K., Jouany, C., Kazakou, E., Kigel,
Diversity & Distributions, 15, 367–378. J., Kleyer, M., Lehsten, V., Lepš, J., Meier, T., Pakeman,
Frankham, R., Ballou, J. D., & Briscoe, D. A. (eds) 2002. R., Papadimitriou, M., Papanastasis, V. P., Quested, H.,
Introduction to Conservation Genetics. Cambridge Uni- Quétier, F., Robson, M., Roumet, C., Rusch, G., Skarpe,
versity Press. C., Sternberg, M., Theau, J.-P., Thébault, A., Vile, D.,
Freckleton, R. P., Gill, J. A., Noble, D., & Watkinson, A. R. & Zarovali, M. P. (2006) Assessing the effects of land-
(2005) Large-scale population dynamics, abundance- use change on plant traits, communities and ecosystem
occupancy relationships and the scaling from local to functioning in grasslands: a standardized methodology
regional population size. Journal of Animal Ecology, 74, and lessons from an application to 11 European Sites.
353–364. Annals of Botany, 99, 967–985.
Freeman, S. N., Noble, D. G., Newson, S. E., & Baillie S. R. Gaston, K. J. (1991) How large is a species’ geographic
(2007) Modelling population changes using data from range? Oikos, 61, 434–438.
different surveys: the Common Birds Census and the Gaston, K. J. (1994) Rarity. Chapman & Hall, London.
Breeding Bird Survey. Bird Study, 54, 61–72. Gaston, K. J. (ed) (1996) Biodiversity: A biology of num-
Freese, L., Auster, P. J., Heifetz, J., & Wing, B. L. (1999) bers and difference. Wiley, New York.
Effects of trawling on seafloor habitat and associated Gaston, K. J. (1996a) Species-range size distributions: pat-
invertebrate taxa in the Gulf of Alaska. Marine Ecology- terns, mechanisms and implications. Trends in Ecology
Progress Series, 182, 119–126. and Evolution, 11, 197–201.
Frezal, L. & Leblois, R. (2008) Four years of DNA barcod- Gaston, K. J. (1996b) Biodiversity – latitudinal gradients.
ing: current advances and prospects. Infection, Genetics Progress in Physical Geography, 20, 466–476.
and Evolution, 8, 727–736. Gaston, K. J. (2003) The Structure and Dynamics of Geo-
Frontier, S. (1985) Diversity and structure in aquatic graphic Ranges. Oxford University Press, Oxford.
ecosystems. Oceanography and Marine Biology – An Gaston, K. J. (2006) Biodiversity and extinction: macroeco-
Annual Review, 23, 253–312. logical patterns and people. Progress in Physical Geog-
Gans, J., Wolinsky, M., & Dunbar, J. (2005) Computational raphy, 30, 258–269.
improvements reveal great bacterial diversity and high Gaston, K. J. (2009) Geographic range limits: achieving
metal toxicity in soil. Science, 309, 1387–1390. synthesis. Proceedings of the Royal Society London, B,
Garcia, L. V. (2004) Escaping the Bonferroni iron claw in 276, 1395–1406.
ecological studies. Oikos, 105, 657. Gaston, K. J. & Blackburn, T. M. (2000) Pattern and Process
Garcia-Martinez, J., Acinas, S. G., Anton, A. I., & in Macroecology. Blackwell, Oxford.
Rodriguez-Valera, F. (1999) Use of the 16S-23S ribo- Gaston, K. J. & Fuller, R. A. (2008) Commonness, pop-
somal genes spacer region in studies of prokary- ulation depletion and conservation biology. Trends in
otic diversity. Journal of Microbiological Methods, 36, Ecology and Evolution, 23, 14–19.
55–64. Gaston, K. J. & Fuller, R. A. (2009) The sizes of species’
Gardner, T. A., Cote, I. M., Gill, J. A., Grant, A., & Watkin- geographic ranges. Journal of Applied Ecology, 46, 1–9.
son, A. R. (2003) Long-term region-wide declines in Gaston, K. J. & Lawton, J. H. (1989) Insect herbivores on
Caribbean corals. Science, 301, 958–960. bracken do not support the core-satellite hypothesis.
Gardner, T. A., Barlow, J., Aruajo, I. S., Ávila-Pires, T. C., The American Naturalist, 134, 761–777.
Bonaldo, A. B., Costa, J. E., Esposito, M. C., Ferreira, Gaston, K. J. & Lawton, J. H. (1990) Effects of scale and
L. V., Hawes, J., Hernandez, M. I. M., Hoogmoed, M. S., habitat on the relationship between regional distribu-
Lieite, R. N., Lo-Man-Hung, N. F., Malcolm, J. R., Mar- tion and local abundance. Oikos, 58, 329–335.
tins, M. B., Mestre, L. A. M., Miranda-Santos, R., Overal, Gaston, K. J. & He, F. (2002) The distribution of species
W. L., Parry, L., Peters, S. L., Roberio-Junior, M. A., da range size: a stochastic process. Proceedings of the
Silva, M. N. F., Motta, C. D. S., & Peres, C. A. (2007) Royal Society London, B, 269, 1079–1086.
The cost-effectiveness of biodiversity surveys in tropical Gaston, K. & May, R. M. (1992) The taxonomy of taxono-
forests. Ecology Letters, 11, 139–150. mists. Nature, 356, 281–283.
 OUP CORRECTED PROOF – FINAL, 18/10/2010, SPi

REFERENCES 307

Gaston, K. J. & McArdle, B. H. (1994) The temporal vari- Gelman, A. (2003) A Bayesian formulation of exploratory
ability of animal abundances: measures, methods and data analysis and goodness-of-fit testing. International
patterns. Philosophical Transactions of the Royal Soci- Statistical Review, 71, 369–382.
ety, London Lond. B, 345, 335–358. Genome 10K Community of Scientists. (2009) Genome
Gaston, K. J. & Warren, P. H. (1997) Interspecific 10K: a proposal to obtain whole-genome sequence
abundance-occupancy relationships and the effects of for 10000 vertebrate species. Journal of Heredity, 100,
disturbance: a test using microcosms. Oecologia, 112, 659–674.
112–117. Gerrodette, T. (1993) TRENDS: Software for a power
Gaston, K. J., Blackburn, T. M., & Lawton, J. H. (1997) analysis of linear regression. Wildlife Society Bulletin,
Interspecific abundance-range size relationships: an 21, 515–516.
appraisal of mechanisms. Journal of Animal Ecology, 66, Gibbs, J. E. (1995) MONITOR: Software for power analysis
579–601. in population monitoring programs. In. USGS Pautux-
Gaston, K. J., Blackburn, T. M., & Gregory, R. D. (1998a) ent Wildlife Research Center Laurel, Maryland.
Interspecific differences in intraspecific abundance- Gibbs, J. P., Droege, S., & Eagle, P. (1998) Monitor-
range size relationships of British breeding birds. Ecog- ing populations of plants and animals. BioScience, 48,
raphy, 21, 149–158. 935–940.
Gaston, K. J., Blackburn, T. M., Gregory, R. D., & Gienapp, P., Leimu, R., & Merilä, J. (2007) Responses to
Greenwood, J. J.D. (1998b) The anatomy of the inter- climate change in avian migration time – microevolu-
specific abundance-range size relationship for the tion versus phenotypic plasticity. Climate Research, 35,
British avifauna: I. Spatial patterns. Ecology Letters, 1, 25–35.
38–46. Gienapp, P., Teplitsky, C., Alho, J. S., Mills, J. A., & Merilä,
Gaston, K. J., Blackburn, T. M., & Gregory, R. D. J. (2008) Climate change and evolution: disentangling
(1999a) Does variation in census area confound den- environmental and genetic responses. Molecular Ecol-
sity comparisons? Journal of Applied Ecology, 36, ogy, 17, 167–178.
191–204. Gilchrist, G. W., Huey, R. B., Balanyá, J., Pascual, M., &
Gaston, K. J., Blackburn, T. M., & Gregory, R. D. (1999b) Serra, L. (2004) A time series of evolution in action: a lat-
Intraspecific abundance-range size relationships: case itudinal cline in wing size in South American Drosophila
studies of six bird species in Britain. Diversity & Dis- subobscura. Evolution, 58, 768–780.
tributions 5, 197–212. Gill, S. R., Pop, M., DeBoy, R. T., Eckburg, P. B., Turnbaugh,
Gaston, K. J., Blackburn, T. M., Greenwood, J. J.D., P. J., Samuel, B. S., Gordon, J. I., Relman, D. A., Fraser-
Gregory, R. D., Quinn, R. M., & Lawton, J. H. Liggett, C. M., & Nelson, K. E. (2006) Metagenomic
(2000) Abundance-occupancy relationships. Journal of analysis of the human distal gut microbiome. Science,
Applied Ecology, 37 (Suppl. 1), 39–59. 312, 1355–1359.
Gaston, K. J., Borges, P. A.V., He, F., & Gaspar, C. (2006) Gillespie, T. W., Foody, G. M., Rocchini, D., Giorgi, A. P.,
Abundance, spatial variance, & occupancy: species dis- & Saatchi S. (2008) Measuring and modelling biodiver-
tribution in the Azores. Journal of Animal Ecology, 75, sity from space. Progress in Physical Geography, 32,
646–656. 203–221.
Gaston, K. J., Chown, S. L., & Evans, K. L. (2008a) Eco- Gislason, H. & Rice, J. (1998) Modelling the response of
geographic rules: elements of a synthesis. Journal of size and diversity spectra of fish assemblages to changes
Biogeography, 35, 483–500. in exploitation. ICES Journal of Marine Science, 55,
Gaston, K. J., Jackson, S. F., Cantú-Salazar, L., & Cruz- 362–370.
Piñón, G. (2008b) The ecological performance of pro- Gleason, H. A. (1929) The significance of Raunkiaer’s law
tected areas. Annual Review of Ecology, Evolution and of frequency. Ecology, 10, 406–408.
Systematics, 39, 93–113. Golicher, D. J., O’Hara, R. B., Ruiz-Montoya, L., &
Gaston, K. J., Chown, S. L., Calosi, P., et al. (2009) Macro- Cayuela, L. (2006) Lifting a veil on diversity: a Bayesian
physiology: a conceptual re-unification. The American approach to fitting relative-abundance models. Ecologi-
Naturalist, 174, 595–612. cal Applications, 16, 202–212.
Gebremedhin, B., Ficetola, G. F., Naderi, S., Rezaei, H. R., Good, I. J. (1953) The population frequencies of species
Maudet, C., Rioux, D., Luikart, G., Flagstad, Ø., Thuiller, and the estimation of population parameters. Bio-
W., & Taberlet, P. (2009) Frontiers in identifying conser- metrika, 40, 237–264.
vation units: from neutral markers to adaptive genetic Good, I. J. (2000) Turing’s anticipation of empirical Bayes
variation. Animal Conservation, 12, 107–109. in connection with the cryptanalysis of the naval
OUP CORRECTED PROOF – FINAL, 18/10/2010, SPi

308 REFERENCES

Enigma. Journal of Statistical Computation and Simu- Graham, J. H., Hughie, H. H., Jones, S., Wrinn, K., Krzysik,
lation, 66, 101–111. A. J., Duda, J. J., Freeman, D. C., Emlen, J. M., Zak, J. C.,
Goßner, M., Chao, A., Bailey, R., & Prinzing, A. (2009) Kovacic, D. A., Chamberlin-Graham, C., & Balbach, H.
Native fauna on exotic trees: phylogenetic conservatism (2004) Habitat disturbance and the diversity and abun-
and geographic contingency in two lineages of phy- dance of ants (Formicidae) in the Southeastern Fall-
tophages on two lineages of trees. The American Nat- Line Sandhills – art. no. 30. Journal of Insect Science, 4,
uralist, 173, 599–614. 30–30.
Gotelli, N. J. (1991) Metapopulation models: the rescue Grant, P. R. & Schluter, D. (1984) Interspecific competition
effect, the propagule rain, and the core-satellite hypoth- inferred from patterns of guild structure. In: Ecological
esis. The American Naturalist, 138, 768–776. Communities: Conceptual Issues and the Evidence,
Gotelli, N. J. (2008) A Primer of Ecology, 4th edn. Sinauer Strong, D. R., Simberloff, D., Abele, L. G., & Thistle,
Associates, Sunderland, MA. A. B. (eds). Princeton University Press, Princeton, USA,
Gotelli, N. J. & Colwell, R. K. (2001) Quantifying bio- pp. 201–233.
diversity: procedures and pitfalls in the measurement Grassle, J. F. & Smith, W. (1976) A similarity measure sen-
and comparison of species richness. Ecology Letters, 4, sitive to the contribution of rare species and its use in
379–391. investigation of variation in marine benthic communi-
Gotelli, N. & Entsminger, G. L. (2009) EcoSim: Null ties. Oecologia, 25, 13–22.
Models Software for Ecology. Version 7. Acquired Gray, J. S. (1979) Pollution-induced changes in popula-
Intelligence Inc. & Kesey-Bear, Jericho, VT 05465. tions. Philosophical Transactions of the Royal Society of
http://garyentsminger.com/ecosim.htm. London B, 286, 545–561.
Gotelli, N. J. & Graves, G. R. (1996) Null Models in Ecol- Gray, J. S. (1981) Detecting pollution induced changes in
ogy. Smithsonian Institution Press, WA, USA. communities using the log-normal distribution of indi-
Gotelli, N., Anderson, M. J., Arita, H. T., Chao, A., Colwell, viduals among species. Marine Pollution Bulletin, 12,
R. K., Connolly, S. R., Currie, D. J., Dunn, R. R., Graves, 173–176.
G. R., Green, J. L., Grytnes, J. A., Jiang, Y.-H., Jetz, W., Gray, J. S. (1983) Use and misuse of the log-normal plotting
Lyons, S. K., McCain, C. M., Magurran, A. E., Rahbek, method for detection of effects of pollution – a reply.
C., Rangel, T. F.L. V.B., Soberon, J., Webb, C. O., & Willig, Marine Ecology-Progress Series, 11, 203–204.
M. R. (2009) Patterns and causes of species richness: Gray, J. S. (1987) Species-abundance patterns. In: Orga-
a general simulation model for macroecology. Ecology nization of communities – past and present, Gee,
Letters, 12, 873–886. J. H. R. & Giller, P. S. (eds). Blackwell, Oxford, pp.
Govender, N., Trollope, W. S. W., & van Wilgen, B. W. 53–67.
(2006) The effect of fire season, fire frequency, rainfall Gray, J. S. & Mirza, F. B. (1979) Possible method for the
and management on fire intensity in savanna vegeta- detection of pollution-induced disturbance on marine
tion in South Africa. Journal of Applied Ecology, 43, benthic communities. Marine Pollution Bulletin, 10,
748–758. 142–146.
Gower, J. C. (1971) A general coefficient of similarity and Gray, J. S., Clarke, K. R., Warwick, R. M., & Hobbs,
some of its properties. Biometrics, 27, 857–871. G. (1990) Detection of the initial effects of pollution
Gower, J. C. (1985) Measures of similarity, dissimilarity on marine benthos: an example from the Ekofisk and
and distance. In Enclopedia of Statistical Sciences, Kotz, Eldfisk oilfields, North Sea. Marine Ecology Progress
S. & Johnson, N. L. (eds). Wiley, New York, Vol. 5, Series, 66, 285–299.
pp. 397–405. Gray, J. S., Bjogesaerter, A., & Ugland, K. I. (2005) The
Grace, J. B. (2006) Structural Equation Modeling and Nat- impact of rare species on natural assemblages. Journal
ural Systems. Cambridge University Press, Cambridge. of Animal Ecology, 74, 1131–1139.
Gradstein, F., Ogg, J., & Smith, A. (2005) A geolog- Gray, J. S., Bjorgesaeter, A., & Ugland, K. I. (2006) On plot-
ical Times Scale 2004. Cambridge University Press, ting species abundance distributions. Journal Of Animal
Cambridge. Ecology, 75, 752–756.
Graham, C. H. & Fine, P. V. A. (2008) Phylogenetic beta Green, J. L. & Plotkin, J. B. (2007) A statistical theory
diversity: linking ecological and evolutionary processes for sampling species abundances. Ecology Letters, 10,
across space in time. Ecology Letters, 11, 1265–1277. 1037–1045.
Graham, C. H. & Hijmans, R. J. (2006) A comparison of Gregorius, H. R. (1987) The relationship between the con-
methods for mapping species ranges and species rich- cepts of genetic diversity and differentiation. Theoreti-
ness. Global Ecology and Biogeography, 15, 578–587. cal and Applied Genetics, 74, 397–401.
 OUP CORRECTED PROOF – FINAL, 18/10/2010, SPi

REFERENCES 309

Gregorius, H. R. (1996) Differentiation between popu- Hannah, L. & Kay, J. A. (1977) Concentration in the mod-
lations and its measurement. Acta Biotheoretica, 44, ern industry: theory, measurement, and the U. K. expe-
23–36. rience. MacMillan, London.
Gregorius, H. R. (2010) Linking diversity and differentia- Hanski, I. (1982) Dynamics of regional distribution: the
tion. Diversity, 2, 370–394. core and satellite species hypothesis. Oikos, 38, 210–221.
Gregory, R. D., Noble, D., Field, R., Marchant, J., Raven, Hanski, I. (1994) A practical model of metapopulation
M., & Gibbons, D. W. (2003) Birds as indicators of biodi- dynamics. Journal of Animal Ecology, 63, 151–162.
versity. Ornis Hungarica, 12–13, 11–24. Hanski, I. (1997) Metapopulation dynamics, from concepts
Greig-Smith, P. (1957) Quantitative Plant Ecology. Butter- and observations to predictive models. In: Metapopula-
worth, London. tion Biology, Hanski, I. & Gilpin, M. E. (eds). Academic
Greig-Smith, P. (1983) Quantitative Plant Ecology, 3rd edn. Press, San Diego, pp. 69–91.
Blackwell, London. Hanski, I. & Gyllenberg, M. (1997) Uniting two general
Greve, M. (2007) Avifaunal responses to environmental patterns in the distribution of species. Science, 275,
conditions and land-use changes in South Africa: diver- 397–400.
sity, composition and body size. M.Sc. Thesis, Stellen- Hardy, O. J. (2008) Testing the spatial phylogenetic struc-
bosch University, 185pp. ture of local communities: statistical performances of
Greve, M., Gaston, K. J., van Rensburg, B. J., & Chown, different null models and test statistics on a locally neu-
S. L. (2008) Environmental factors, regional body size tral community. The Journal of Ecology, 96, 914–926.
distributions, and spatial variation in body size of local Hardy, O. J. & Jost, L. (2008) Interpreting and estimating
avian assemblages. Global Ecology and Biogeography, measures of community phylogenetic structuring. The
17, 514–523. Journal of Ecology, 96, 849–852.
Grime, J. P. (1973a) Control of species density in herba- Hardy, O. J. & Senterre, B. (2007) Characterizing the phylo-
ceous vegetation. Journal of Environmental Manage- genetic structure of communities by additive partition-
ment, 1, 151–167. ing of phylogenetic diversity. The Journal of Ecology, 95,
Grime, J. P. (1973b) Competitive exclusion in herbaceous 493–506.
vegetation. Nature, 242, 344–347. Harms, K. E., Condit, R., Hubbell, S. P., & Foster, R. B.
Grinnell, J. (1922) The role of the ‘accidental’. Auk, 39, (2001) Habitat associations of trees and shrubs in a
373–380. 50-ha neotropical forest plot. Journal of Ecology, 89,
Groves, R. M. (1989) Survey Errors and Survey Costs. 947–959.
Wiley, New York. Harper, J. L. (1981) The meanings of rarity. In: The Biolog-
Gunnarsson, T. (2006) A Mirror of Nature: Nordic Land- ical Aspects of Rare Plant Conservation, Synge, H. (ed).
scape Painting 1840–1910. Statens Museum for Kunst, Wiley, New York, pp. 189–203.
Copenhagen, pp. 11–37. Harrison, P. (1992) The Third Revolution. Population,
Guo, Q., Brown, J. H., & Valone, T. J. (2000) Abun- Environment and a Sustainable World. Penguin Books,
dance and distribution of desert annuals: are spatial and London.
temporal patterns related? The Journal of Ecology, 88, Harrison, S., Ross, S. J., & Lawton, J. H. (1992) Beta diver-
551–560. sity on geographic gradients in Britain. Journal of Ani-
Gurd, D. B. 2007. Predicting resource partitioning and mal Ecology, 61, 151–158.
community organization of filter-feeding dabbling Harte, J. (2008) From spatial pattern in the distribution and
ducks from functional morphology. The American Nat- abundance of species to a unified theory of ecology: the
uralist, 169, 334–343. role of maximum entropy methods. Applied Optimiza-
Halfpenny, J. 1986. A Field Guide to Mammal Track- tion, 102, 243.
ing in Western America. Johnson Books, Boulder, CO, Harte, J., Kinzig, A., & Green, J. (1999) Self-similarity in
pp. 134–148. the distribution and abundance of species. Science, 284,
Hall, S. J. & Greenstreet, S. P. (1998) Taxonomic dis- 334–336.
tinctness and diversity measures: responses in marine Harte, J., Conlisk, E., Ostling, A., Green, J. L., & Smith,
fish communities. Marine Ecology Progress Series, 166, A. B. (2005) A theory of spatial structure in ecologi-
227–229. cal communities at multiple spatial scales. Ecological
Hallam, S. J., Putnam, N., Preston, C. M., Detter, J. C., Monographs, 75, 179–197.
Rokhsar, D., Richardson, P. M., & DeLong, E. F. (2004) Harte, J., Zillio, T., Conlisk, E., & Smith, A. B. (2008) Maxi-
Reverse methanogenesis: testing the hypothesis with mum entropy and the state variable approach to macro-
environmental genomics. Science, 305, 1457–1462. ecology. Ecology, 89, 2700–2711.
OUP CORRECTED PROOF – FINAL, 18/10/2010, SPi

310 REFERENCES

Hartley, S., Kunin, W. E., Lennon, J. J., & Pocock, M. J. O. Hector, A., Schmid, B., Beierkuhnlein, C., Caldeira,
(2004) Coherence and discontinuity in the scaling of M. C., Diemer, M., Dimitrakopoulos, P. G., Finn, J. A.,
species’ distribution patterns. Proceedings of the Royal Freitas, H., Giller, P. S. Good, J., Harris, R., Högberg,
Society London, B, 271, 81–88. P., Huss-Danell, K., Joshi, J., Jumpponen, A., Körner,
Hassell, M. P., Southwood, T. R. E., & Reader, P. M. (1987) C., Leadley, P. W., Loreau, M., Minns, A., Mulder,
The dynamics of the viburnum whitefly (Aleurotrachelus C. P.H., O’Donovan, G., Otway, S. J., Pereira, J. S., Prinz,
jelinekii): a case study of population regulation. Journal A., Read, D. J., Scherer-Lorenzen, M., Schulze, E.-D.,
of Animal Ecology, 56, 283–300. Siamantziouras, A.-S. D., Spehn, E. M., Terry, A. C.,
Hauer, R. F. & Resh, V. H. (2006) Macroinvertebrates. In: Troumbis, A. Y., Woodward, F. I., Yachi, S., & Lawton,
Methods in Stream Ecology, Hauer, F. R. & Lamberti, J. H. (1999) Plant diversity and productivity in European
G. A. (eds). Academic Press/Elsevier San Diego, CA, grasslands. Science, 286, 1123–1127.
pp. 435–463. Heemsbergen, D. A., Berg, M. P., Loreau, M., van Hal, J. R.,
Hawkins, C. P., Norris, R. H., Hogue, J. N., & Feminella, Faber, J. H., & Verhoef, H. A. (2004) Biodiversity effects
J. W. (2000) Development and evaluation of predic- on soil processes explained by interspecific functional
tive models for measuring the biological integrity of dissimilarity. Science, 306, 1019–1020.
streams. Ecological Applications, 10, 1456–1477. Heino, J. (2005) Positive relationship between regional
Hawkins, B. A., Diniz-Filho, J. A. F., Bini, L. M., De Marco, distribution and local abundance in stream insects: a
P., & Blackburn, T. M. (2007) Red herrings revisited: spa- consequence of niche breadth or habitat niche position?
tial autocorrelation and parameter estimation in geo- Ecography, 28, 345–354.
graphical ecology. Ecography, 30, 375. Heino, J. (2008) Temporally stable abundance-occupancy
Hayden, E. C. (2009) 10,000 genomes to come. Nature, relationships and occupancy frequency patterns in
462, 21. stream insects. Oecologia, 157, 337–347.
He, F. & Condit, R. (2007) The distribution of species: occu- Helmus, M. R., Bland, T. J., Williams, C. K., & Ives,
pancy, scale, and rarity. In: Scaling Biodiversity, Storch, A. R. (2007) Phylogenetic measures of biodiversity. The
D., Marquet, P. A., & Brown, J. H. (eds). Cambridge American Naturalist, 169, E68–E83.
University Press, Cambridge, pp. 32–50. Heltshe, J. & Forrester, N. E. (1983) Estimating species
He, F. & Gaston, K. J. (2000a) Estimating species abun- richness using the jackknife procedure. Biometrics, 39,
dance from occurrence. The American Naturalist, 156, 1–11.
553–559. Henderson, P. A. (2007) Discrete and continuous change in
He, F. & Gaston, K. J. (2000b) Occupancy-abundance rela- the fish community of the Bristol Channel in response to
tionships and sampling scales. Ecography, 23, 503–511. climate change. Journal of the Marine Biological Associ-
He, F. & Gaston, K. J. (2003) Occupancy, spatial variance ation, 87, 589–598.
and the abundance of species. The American Naturalist, Henderson, P. A. & Holmes, R. H. A. (1991) On
162, 366–375. the population dynamics of dab, sole and flounder
He, F. & Tang, D. (2008) Estimating the niche preemption within Bridgwater bay in the lower severn Estuary,
parameter of the geometric series. Acta Oecologica, 33, England. Netherlands Journal of Sea Research, 27,
105–107. 337–344.
He, F., Gaston, K. J., & Wu, J. (2002) On species occupancy- Henderson, P. A. & Magurran, A. E. (2010) Linking species
abundance models. Écoscience, 9, 119–126. abundance distributions in numerical abundance and
Heard, S. B. (1992) Patterns in tree balance among cladis- biomass through simple assumptions about commu-
tic, phenetic, and randomly generated phylogenetic nity structure. Proceedings of the Royal Society London,
trees. Evolution, 46, 1818–1826 published online, 277, 1561–1570.
4pt plus4pt minus4pt Hebert, P. D. N., Cywinsky, A., Ball, Hendry, A. P. & Kinnison, M. T. (1999) The pace of modern
S. L., & deWaard, J. R. (2003) Biological identifications life: measuring rates of contemporary microevolution.
through DNA barcodes. Proceedings of the Royal Soci- Evolution, 53, 1637–1653.
ety London, B, 270, 313–321. Hendry, A. P., Farrugia, T. J., & Kinnison, M. T.
Hebert, P. D. N., Stoeckle, M. Y., Zemlak, T. S., & Francis, (2008) Human influences on rates of phenotypic
C. M. (2004) Identification of birds through DNA bar- change in wild populations. Molecular Ecology, 17,
codes. PLoS, Biology, 2, 1657–1663. 20–29.
Heck, K. L., Jr., van Belle, G., & Simberloff, D. (1975) Hengeveld, R. (1990). Dynamic Biogeography. Cambridge
Explicit calculation of the rarefaction diversity measure- University Press, Cambridge.
ment and the determination of sufficient sample size. Hesse, R., Allee, W. C., & Schmidt, K. P. (1937) Ecological
Ecology, 56, 1459–1461. Animal Geography. Wiley, New York.
 OUP CORRECTED PROOF – FINAL, 18/10/2010, SPi

REFERENCES 311

Hewitt, J. E., Anderson, M. J., & Thrush, S. F. (2005) occupancy relationships. Journal of Animal Ecology, 71,
Assessing and monitoring ecological community health 846–854.
in marine systems. Ecological Applications, 15, 942–953. Holt, A. R., Warren, P. H., & Gaston, K. J. (2004a) The
Hickerson, M. J., Meyer, C. P., & Moritz, C. (2006) DNA importance of habitat heterogeneity, biotic interactions
barcoding will often fail to discover new animal species and dispersal in abundance-occupancy relationships.
over broad parameter space. Systems Biology, 55, Journal of Animal Ecology, 73, 841–851.
729–739. Holt, R. D., Knight, T. M., & Barfield, M. (2004b) Allee
Hicks, G. R. F. (1980) Structure of phytal harpacticoid effects, immigration, and the evolution of species’
copepod assemblages and the influence of habitat com- niches. The American Naturalist, 163, 253–262.
plexity and turbidity. Journal of experimental marine Holyoak, M., Jarosik V., & Novak, I. (1997) Weather-
Biology and Ecology, 44, 157–192. induced changes in moth activity bias measurement
Hilborn, R. & Mangel, M. (1997) The Ecological Detective, of long-term population dynamics from light trap
Confronting Models with Data. Princeton University samples. Entomologia Experimentalis et Applicata, 83,
Press, New Jersey. 329–335.
Hill, M. O. (1973) Diversity and evenness: a uni- Holyoak, M., Leibold, M., & Holt, R. (2005) Metacommu-
fying notation and its consequences. Ecology, 54, nities: Spatial Dynamics and Ecological Communities.
427–432. University of Chicago Press, Chicago.
Hillebrand, H. (2004) On the generality of the latitudi- Hong, S. H., Bunge, J., Jeon, S. O., & Epstein, S. S. (2006)
nal diversity gradient. The American Naturalist, 163, Predicting microbial species richness. Proceedings of
192–211. the National Academy of Sciences of the United States
Hillebrand, H., Bennett, D. M., & Cadotte, M. W. (2008) of America, 103, 117–122.
Consequences of dominance: a review of evenness Hooper, D. U. & Vitousek, P. M. (1997) The effects of plant
effects on local and regional ecosystem processes. Ecol- composition and diversity on ecosystem processes. Sci-
ogy, 89, 1510–1520. ence, 277, 1302–1305.
Hilt, N., Brehm, G., & Fiedler, K. (2006) Diversity and Hooper, D. U., Chapin, F. S., Ewel, J. J., Hector, A.,
ensemble composition of geometrid moths along a suc- Inchausti, P., Lavorel, S., Lawton, J. H., Lodge, D. M.,
cessional gradient in the Ecuadorian Andes. Journal of Loreau, M., Naeem, S., Schmid, B., Setala, H., Symstad,
Tropical Ecology, 22, 155–166. A. J., Vandermeer, J., & Wardle, D. A. (2005) Effects
Hinsley, S. A., Pakeman, R., Bellamy, P. E. & New- of biodiversity on ecosystem functioning: a consen-
ton, I. (1996) Influences of habitat fragmentation on sus of current knowledge. Ecological Monographs, 75,
bird species distributions and regional population size. 3–35.
Proceedings of the Royal Society London, B, 263, Horn, H. S. (1966) Measurement of “overlap” in compar-
307–313. ative ecological studies. The American Naturalist, 100,
Hobbs, R. J., Arico, S., Aronson, J., et al. (2006) Novel 419–424.
ecosystems: theoretical and management aspects of the Horn, H. I. (ed.) (1975) Markovian Properties of Forest
new ecological world order. Global Ecology and Bio- Succession. Harvard University Press, Cambridge, MA.
geography, 15, 1–7. Horner-Devine, M. C., Lage, M., Hughes, J. B., & Bohan-
Hoehn, P., Tscharntke, T., Tylianakis, J. M., & Steffan- nan, B. J. M. (2004) A taxa – area relationship for bacte-
Dewenter, I. (2008) Functional group diversity of bee ria. Nature, 432, 750–753.
pollinators increases crop yield. Proceedings of the Hortal, J., Borges, P. A. V., & Caspar, C. (2006) Evaluating
Royal Society London, B, 275, 2283–2291. the performance of species richness estimators: sensitiv-
Holdridge, L. R., Grenke, W. C., Hatheway, W. H., Liang, ity to sample grain size. Journal of Animal Ecology, 75,
T., & Tosi, J. A. (1971) Forest Environments in Tropical 274–287.
Life Zones. Pergamon Press, Oxford. Houchmandzadeh, B. (2008) Neutral clustering in a
Holmes, S. (2003) Bootstrapping phylogenetic trees, the- simple experimental ecological community. Physical
ory and methods. Statistical Science, 18, 241–255. Review Letters, Aug 15, 101(7), 078103. Epub 2008.
Holt, R. (2008) Theoretical perspectives on resource Huang, D., Meier, R., Todd, P. A., & Chou, L. M. (2008)
pulses. Ecology, 89, 671–681. Slow mitochondrial COI evolution at the base of the
Holt, A. R., Gaston, K. J., & He, F. (2002a) Occupancy- metazoan tree and its implications for DNA barcoding.
abundance relationships and spatial distribution. Basic Journal of Molecular Evolution, 66, 167–174.
and Applied Ecology , 3, 1–13. Hubalek, Z. (1982) Coefficients of association and similar-
Holt, A. R., Warren, P. H., & Gaston, K. J. (2002b) ity, based on binary (presence-absence) data: an evalua-
The importance of biotic interactions in abundance- tion. Biological Reviews, 57, 669–689.
OUP CORRECTED PROOF – FINAL, 18/10/2010, SPi

312 REFERENCES

Hubbell, S. P. (2001) A Unified Theory of Biodiversity and Hurlbert, S. H. (1990) Spatial distribution of the montane
Biogeography. Princeton University Press, Princeton. unicorn. Oikos, 58, 257–271.
Hubbell, S. P., Foster, R. B., O’Brien, S. T., Harms, K. E., Hurlbert, A. H. & Jetz, W. (2007) Species richness,
Condit, R., Wechsler, B., Wright, S. J., & De Lao, S. L. hotspots, and the scale dependence of range maps in
(1999) Light-gap disturbances, recruitment limitation, ecology and conservation. Proceedings of the National
and tree diversity in a neotropical forest. Science, 283, Academy of Sciences. USA, 104, 13384–13389.
554. Hurlbert, A. H. & White, E. P. (2005) Disparity between
Huber, J. A., Mark Welch, D., Morrison, H. G., Huse, S. M., range map – and survey-based analyses of species rich-
Neal, P. R., Butterfield, D. A., & Sogin, M. L. (2007) ness: patterns, processes and implications. Ecology Let-
Microbial population structures in the deep marine ters, 8, 319–327.
biosphere. Science, 318, 97–100. Huston, M. A. (1979) A general hypothesis of species
Huelsenbeck, J. P. & Rannala, B. (1997) Maximum like- diversity. The American Naturalist, 113, 81–101.
lihood estimation of topology and node times using Huston, M. A. (1994) Biological Diversity: The Coexistence
stratigraphic data. Paleobiology, 23, 174–180. of Species on Changing Landscapes. Cambridge Uni-
Hugenholtz, P. & Pace, N. R. (1996) Identifying micro- versity Press, Cambridge.
bial diversity in the natural environment: a molecu- Huston, M. L. (1997) Hidden treatments in ecological
lar phylogenetic approach. Trends in Biotechnology, 14, experiments: re-evaluating the ecosystem function of
190–197. biodiversity. Oecologia, 110, 449–460.
Hughes, R. G. (1986) Theories and models of species abun- Hutchings, J. A. (2000) Collapse and recovery of marine
dance. The American Naturalist, 128, 879–899. fishes. Nature, 406, 882–885.
Hughes, J. B. (2000) The scale of resource specialization Hutchinson, G. E. (1957) Homage to Santa Rosalia; or why
and the distribution and abundance of lycaenid butter- are there so many kinds of animals? The American Nat-
flies. Oecologia, 123, 375–383. uralist, 93, 145–159.
Hughes, J. B., Hellmann, J. J., Ricketts, T. H., & Bohan- Isaac, N. J. B., Turvey, S. T., Collen, B., Waterman, C., &
nan, B. J. M. (2000) Counting the uncountable: statistical Baillie, J. E. M. (2007) Mammals on the EDGE: conser-
approaches to estimating microbial diversity. Applied vation priorities based on threat and phylogeny. PLoS
and Environmental Microbiology, 67, 4399–4406. ONE, 2, e296.
Hui, C. & McGeoch, M. A. (2007a) A self-similarity model Izsak J. (2006) Some practical aspects of fitting and testing
for the occupancy frequency distribution. Theoretical the Zipf-Mandelbrot model – a short essay. Scientomet-
Population Biology, 71, 61–70. rics, 67, 107–120.
Hui, C. & McGeoch, M. A. (2007b) Modeling species dis- Jablonski, D. (2000) Micro- and macroevolution: scale and
tributions by breaking the assumption of self-similarity. hierarchy in evolutionary biology and paleobiology. In:
Oikos, 116, 2097–2107. Deep time – Paleobiology’s Perspective, Erwin, D. H.
Hui, C. & McGeoch, M. A. (2007c) Capturing the “droopy- & Wing, S. L. (eds). Paleobiology Memoir, Paleontolog-
tail” in the occupancy-abundance relationship. Eco- ical Society and University of Chicago Press, Chicago,
science, 14, 103–108. pp. 15–52.
Hui, C. & McGeoch, M. A. (2008) Does the self-similar dis- Jaccard, P. (1900) Contribution an Problème de l’immi-
tribution model lead to unrealistic predictions? Ecology, gration post-glaciaire de la flore alpine. Bulletin de la
89, 2946–2952. Société Vaudoise des Sciences Naturelles, 36, 87–130.
Hui, C., McGeoch, M. A., & Warren, M. (2006) A spatially Jaccard, P. (1901) Etude compararative de la distribution
explicit approach to estimating species occupancy and florale dans une portion des Alpes et du Jura. Bulletin de
spatial correlation. The Journal of Animal Ecology, 75, la Société Vaudoise des Sciences Naturelles, 37, 647–579.
140–147. Jackson, S. F. & Gaston, K. J. (2008) Land use change
Hui, C., Veldtman, R., & McGeoch, M. A. (2010) Measures, and the dependence of national priority species on
perceptions and scaling patterns of aggregated species protected areas. Global Ecology and Biogeography, 14,
distributions. Ecography, 33, 95–102. 2132–2138.
Hurlbert, S. H. (1971) The nonconcept of species diver- Jackson, J. B. C., Kirby, M. X., Berger, W. H., et al. (2001)
sity: a critique and alternative parameters. Ecology, 52, Historical overfishing and the recent collapse of coastal
577–586. ecosystems. Science, 293, 629–638.
Hurlbert, S. H. (1984) Pseudoreplication and the design James, F. C. & Wamer, N. O. (1982) Relationships between
of ecological field experiments. Ecological Monographs, temperate forest bird communities and vegetation
54, 187–211. structure. Ecology, 63, 159–171.
 OUP CORRECTED PROOF – FINAL, 18/10/2010, SPi

REFERENCES 313

Janson, S. & Vegelius, J. (1981) Measures of ecological asso- Karjalainen, J., Rahkola, M., Viljanen, M., Andronikova,
ciation. Oecologia, 49, 371–376. I. N., & Avinskii, V. A. (1996) Comparison of methods
Jansen G, Savolainen R, & Versäläinen K. (2009) DNA used in zooplankton sampling and counting in the joint
barcoding as a heuristic tool for classifying unde- Russian-Finnish evaluation of the trophic state of Lake
scribed Nearctic Myrmica ants (Hymenoptera: Formici- Ladoga. Hydrobiologia, 322, 249–253.
dae). Zoologica Scripta, 38, 527–536. Kearney, M. & Porter, W. P. (2009) Mechanistic niche mod-
Janzen, D. H., Hallwachs, W., Blandin, P., Burns, J. M., elling: combining physiological and spatial data to pre-
Cadiou, J.- M., Chacon, I., Dapkey, T., Deans, A. R., dict species’ ranges. Ecology Letters, 12, 334–350.
Epstein, M. E., Espinoza, B., Franclemont, J. G., Haber, Keating, K. A. & Quinn, J. F. (1998) Estimating species
W. A., Hajibabei, M., Hall, J. P. W., Hebert, P. D. N., richness: the Michaelis-Menten model revisisted. Oikos,
Gauld, I. D., Harvey, D. J., Hausmann, A., Kitching, I. J., 81, 411–416.
Lafontaine, D., Landry, J. -F., Lemaire, C., Miller, J. Y., Keddy, P. A. (1992) Assembly and response rules: two
Montero, J., Munroe, E., Green, C. R., Ratnasingham, S., goals for predictive community ecology. Journal of Veg-
Rawlins, J. E., Robbins, R. K., Rodriguez, J. J., Rougerie, etation Science, 3, 157–164.
R., Sharkey, M. J., Smith, M. A., Solis, M. A., Sullivan, Kelly, A. E. & Goulden, M. L. (2008) Rapid shifts in
J. B., Thiaucourt, .P, Wahl, D. B., Weller, S. J., Whitfield, plant distribution with recent climate change. Proceed-
J. B., Willmott, K. R., Wood, D. M., Woodley, N. E., & ings of the National Academy of Sciences. USA, 105,
Wilson, J. J. (2009) Integration of DNA barcoding into 11823–11826.
an ongoing inventory of complex tropical biodiversity. Kembel, S. W. & Hubbell, S. P. (2006) The phylogenetic
Molecular Ecology Research, 9(Suppl. 1), 1–26. structure of a neotropical forest tree community. Ecol-
Järvinen, O. (1982) Species-to-genus ratios in biogeog- ogy, 87, S86–S99.
raphy: a historical note. Journal of Biogeography, 9, Kempton, R. A. (1979) The structure of species abundance
363–370. and measurement of diversity. Biometrics, 35, 307–321.
Jetz, W. & Rahbek, C. (2002) Geographic range size and Kempton, R. A. & Taylor, L. R. (1974) Log-series and log-
determinants of avian species richness. Science, 297, normal parameters as diversity discriminants for Lepi-
1548–1551. doptera. Journal of Animal Ecology, 43, 381–399.
Johnson, J. B. & Omland, K. S. (2004) Model selection in Kempton, R. A. & Taylor, L. R. (1976) Models and statistics
ecology and evolution. Trends in Ecology and Evolu- for species diversity. Nature, 262, 818–820.
tion, 19, 101–108. Kempton, R. A. & Taylor, L. R. (1978) The Q-statistic and
Jones, M. M., Tuomisto, H., Borcard, D., Legendre, P., the diversity of floras. Nature, 275, 252–253.
Clark, D. B., & Olivas, P. C. (2008) Explaining variation Kempton, R. A. & Wedderburn, R. W.M. (1978) A compar-
in tropical plant community composition: influence of ison of three measures of species diversity. Biometrics,
environmental and spatial data quality. Oecologia, 155, 34, 25–37.
593–604. Kendrick, G. A., Holmes, K. W., & Van Niel, K. P. (2008)
Jorgensen, S. E., Xu, F. -L., Salas, F., & Marques, J. C. (eds.) Multi-scale spatial patterns of three seagrass species
(2005) Application of indicators for the assessment of with different growth dynamics. Ecography, 31, 191.
ecosystem health. CRC Press, Boca Raton, FL. Kennedy, C. E. J. & Southwood, T. R. E. (1984) The num-
Jost, L. (2006) Entropy and diversity. Oikos, 113, 363–375. ber of species of insects associated with British trees: a
Jost, L. (2007) Partitioning diversity into independent reanalysis. Journal of Animal Ecology, 53, 455–478.
alpha and beta components. Ecology, 88, 2427–2439. Kevan, P. G., Greco, C. F., & Belaoussoff, S. (1997) Log-
Jost, L. (2008) GST and its relatives do not measure differ- normality of biodiversity and abundance in diagnosis
entiation. Molecular Ecology, 17, 4015–4026. and measuring of ecosystemic health: pesticide stress
Jost, L. (2009) Mismeasuring biological diversity: response on pollinators on blueberry heaths. Journal of Applied
to Hoffman and Hoffman (2008). Ecological Economics, Ecology, 34, 1122–1136.
68, 925–927. King, T. A., Williams, J. C., Davies, W. D., & Shelton, W. L.
Jost, L., DeVries, P., Walla, T., Greeney, H., Chao, A., & (1981) Fixed versus random sampling of fishes in a large
Ricotta, C. (2010) Partitioning diversity for conservation reservoir. Transactions of the American Fisheries Soci-
analyses. Diversity and Distributions, 16, 65–76. ety, 110, 563–568.
Jurasinski, G., Retzer, V., & Beierkuhnlein, C. (2009) Inven- Kinzig, A., Tilman, D., & Pacala, S. (2001) The Func-
tory, differentiation, and proportional diversity: a con- tional Consequences of Biodiversity: Empirical Progress
sistent terminology for quantifying species diversity. and Theoretical Extensions. Princeton University Press,
Oecologia, 159, 15–26. Princeton, NJ.
OUP CORRECTED PROOF – FINAL, 18/10/2010, SPi

314 REFERENCES

Kissling, W. D. & Carl, G. (2008) Spatial autocorrelation Krausman, P. R. (2002) Introduction to Wildlife Manage-
and the selection of simultaneous autoregressive mod- ment: The Basics. Prentice-Hall, Upper Saddle River, NJ.
els. Global Ecology and Biogeography, 17, 59–71. Krebs, C. J. (1972) Ecology. Harper & Row, New York.
Kitchener, A. C., Beaumont, M. A., & Richardson, D. (2006) Krebs, C. J. (1989) Ecological Methodology. Harper and
Geographical variation in the clouded leopard, Neofe- Row, New York.
lis nebulosa, reveals two species. Current Biology, 16, Krebs, C. J. (1999) Ecological Methodology, 2nd edn. Addi-
2377–2383. son Wesley Longman, Menlo Park, CA.
Kleijn, D., Baquero, R. A., Clough, Y., et al. (2006) Mixed Kreft, H. & Jetz, W. (2007) Global patterns and determi-
biodiversity benefits of agri-environment schemes in nants of vascular plant diversity. Proceedings of the
five European countries. Ecology Letters, 9, 243–254. National Academy of Sciences, 104, 5925.
Klein Goldewijk, K. (2001) Estimating global land use Kress, W. J., Erickson, D. L., Jones, F. A., Swenson,
change over the past 300 years: the HYDE database. N. G., Perez, R., Sanjur, O., & Bermingham, E. (2009)
Global Biogeochemical Cycles, 15, 417–33. Plant DNA barcodes and a community phylogeny
Knight, T. M., McCoy, M. W., Chase, J. M., McCoy, K. A., of a tropical forest dynamics plot in Panama. Pro-
& Holt, R. D. (2005) Trophic cascades across ecosystems. ceedings of the National Academy of Sciences, 106,
Nature, 437, 880–883. 18621–18626.
Kobayashi, S. & Kimura, K. (1994) The number of Kruskal, J. (1964) Nonmetric multidimensional scaling: a
species occurring in a sample of a biotic community numerical method. Phychometrika, 29, 115–129.
and its connections with species-abundance relation- Kulczynski, S. (1928) Die Pflanzenassoziationen der Pieni-
ship and spatial distribution. Ecological Research, 9, nen. Bulletin international de l’Academie Polonaise des
281–294. Sciences et des Lettres, Classe des sciences mathéma-
Koch, L. F. (1957) Index of biotal dispersity. Ecology, 38, tiques et naturelles, Série B Suppl 2, 57–203.
145–148. Kunin, W. E. (1998) Extrapolating species abundance
Kohn, M., Knauer, F., Stoffella, A., Schröder, W., & Pääbo, across spatial scales. Science, 281, 1513–1515.
S. (1995) Conservation genetics of the European brown Kunin, W. E. & Gaston, K. J. (eds) (1997) The Biology
bear – a study using excremental PCR of nuclear and of Rarity: Causes and Consequences of Rare-Common
mitochondrial sequences. Molecular Ecology, 4, 95–103. Differences. Chapman & Hall, London.
Kohn, M. H., Murphy, W. J., Ostrander, E. A., & Wayne, Kuno, E. (1986) Evaluation of statistical precision and
R. K. (2006) Genomics and conservation genetics. design of efficient sampling for the population estimates
Trends in Ecology and Evolution, 21, 629–637. based on frequency of sampling. Research in Population
Kolasa, J. (1989) Ecological systems in hierarchical per- Ecology, 28, 305–319.
spective: breaks in community structure and other con- Kvitrud, M. A., Riemer, S. D., Brown, R. F., Bellinger, M. R.,
sequences. Ecology, 70, 36–47. & Banks, M. A. (2005) Pacific harbor seals (Phoca vit-
Kolb, A., Barsch, F., & Diekmann, M. (2006) Determinants ulina) and salmon: genetics presents hard numbers for
of local abundance and range size in forest vascular elucidating predator-prey dynamics. Marine Biology,
plants. Global Biogeochemical Cycles, 15, 237–247. 147, 1459–1466.
Koleff, P., Gaston, K. J., & Lennon, J. J. (2003) Measuring Lack, D. L. (1947) Darwin’s Finches. Cambridge Univer-
beta diversity for presence – absence data. Journal of sity Press, Cambridge.
Animal Ecology, 72, 367–382. Lahaye, R., van der Bank, M., Bogarin, D., Warner, J.,
Konig, G. (1835) Die Forst-Mathematik. Beckersche Buch- Pupulin, F., Gogot, G., Maurin, O., Duthoit, S., Barra-
handlung, Gotha. clough, T. G., & Savolainen, V. (2008) DNA barcoding
Kosso, P. (1992) Reading the book of nature: an introduc- in floras of biodiversity hotspots. Proceedings of the
tion to the philosophy of science. Cambridge University National Academy of Sciences, 105, 2923–2928.
Press, Cambridge. Laliberté, E. & Legendre, P. (2010) A distance-based frame-
Kraft, N. J. B. & Ackerly, D. D. (2009) Response to Com- work for measuring functional diversity from multiple
ment on “Functional Traits and Niche-Based Tree Com- traits. Ecology, 91, 299–305.
munity Assembly in an Amazonian Forest”. Science, Laliberté, E. and Shipley, W. (2009) http://ftp3.ie.
324, 1015. freebsd.org/pub/CRAN/web/packages/FD/FD.pdf.
Kraft, N. J. B., Cornwell, W. K., Webb, C. O., & Ackerly, Lambshead, P. J. D., Platt, H. M., & Shaw, K. M. (1983) The
D. D. (2007) Trait evolution, community assembly, and detection of differences among assemblages of marine
the phylogenetic structure of ecological communities. benthic species based on an assessment of dominance
The American Naturalist, 170, 271–283. and diversity. Journal of Natural History, 17, 859–874.
 OUP CORRECTED PROOF – FINAL, 18/10/2010, SPi

REFERENCES 315

Lamouroux, N., Doledec, S., & Gayraud, S. (2004) Bio- Lawton, J. H. & Gaston, K. J. (2001) Indicator species. In:
logical traits of stream macroinvertebrate commu- Encyclopedia of Biodiversity, Levin, S. A. (ed). Acad-
nities: effects of microhabitat, reach, and basin fil- emic Press, New York, Vol. 3, pp. 437–450.
ters. Journal of North American Benthic Society, 23, Lawton, J. H., Bignell, D. E., Bolton, B., Bloemers,
449–466. G. F., Eggleton, P., Hammond, P. M., Hodda, M.,
Lance, G. N. & Williams, W. T. (1967) Mixed-data classifi- Holt, R. D., Larsen, T. B., Mawdsley, N. A., Stork,
catory programs. I. Agglomerative systems. Australian N. E., Srivastava, D. S., & Watt, A. D. (1998) Bio-
Computational Journal, 1, 15–20. diversity inventories, indicator taxa and effects of
Lande, R. (1996) Statistics and partitioning of species habitat modification in tropical forest. Nature, 391,
diversity, and similarity among multiple communities. 72–76.
Oikos, 76, 5–13. Legendre, P. & Legendre, L. (1998) Numerical Ecology
Lande, R., Engen, S., & Saether, B. -E. (2003) Stochas- 2nd edn. Elsevier, Amsterdam.
tic Population Dynamics in Ecology and Conservation. Legendre, P., Galzin, R., & Harmelin-Vivien, M. L. (1997)
Oxford University Press, Oxford. Relating behaviour to habitat: solutions to the fourth-
Lankau, R. A. & Strauss, S. Y. (2007) Mutual feedbacks corner problem. Ecology, 78, 547–562.
maintain both genetic and species diversity in a plant Legendre, P., Borcard, D., & Peres-Neto, P. R. (2008) Ana-
community. Science, 317, 1561–1563. lyzing or explaining beta diversity? Comment. Ecology,
Larsen, D. P., Kincaid, T. M., Jacobs, S. E., & Urquhart, N. S. 89, 3238–3244.
(2001) Designs for evaluating local and regional scale Leger, E. A. & Forister, M. L. (2009) Colonization, abun-
trends. BioScience, 51, 1069–1078. dance, and geographic range size of gravestone lichens.
Larsen, D., Kaufmann, P., Kincaid, T., & Urquhart, N. Basic and Applied Ecology, 10, 279–287.
(2004) Detecting persistent change in the habitat of Leitner, W. A. & Rosenzweig, M. L. (1997) Nested species-
salmon-bearing streams in the Pacific Northwest. Cana- area curves and stochastic sampling: a new theory.
dian Journal of Fisheries and Aquatic Sciences, 61, Oikos, 79, 503–512.
283–291. Lekve, K., Boulinier, T., Stenseth, N. C., Gøsaeter, J., Fro-
La Sorte, F. A. & Boecklen, W. J. (2005) Tempo- mentin, J. -M., Hines, J. E., & Nichols, J. D. (2002) Spatio-
ral turnover of common species in avian assem- temporal dynamics of species richness on coastal fish
blages in North America. Journal of Biogeography, 32, communities. Proceedings of the Royal Society London,
1151–1160. 269, 1781–1789.
Latimer, A. M., Silander, J. A., Gelfand, A. E., Rebelo, Lennon, J. J. (2000) Red-shifts and red herrings in geo-
A. G., & Richardson, D. M. (2004) Quantifying threats graphical ecology. Ecography, 23, 101–113.
to biodiversity from invasive alien plants and other fac- Lennon, J. J., Koleff, P., Greenwood, J. J.D., & Gaston, K. J.
tors: a case study from the Cape Floristic Region. South (2001) The geographical structure of British bird distri-
African Journal of Science, 100, 81–86. butions: diversity, spatial turnover and scale. Journal of
Lauer, T. E. & Spacie, A. (2004) Space as a limiting Animal Ecology, 70, 966–979.
resource in freshwater systems: competition between Lennon, J. J., Kunin, W. E., Hartley, S., & Gaston, K. J.
zebra mussels (Dreissena polymorpha) and freshwater (2007) Species distribution patterns, diversity scaling
sponges (Porifera). Hydrobiologia, 517, 137–145. and testing for fractals in southern African birds. In:
Lawes, J., Gilbert, J., & Masters, M. (1882) Agricultural, Scaling Biodiversity, Storch, D., Marquet, P. A., &
botanical and chemical results of experiments on the Brown, J. H. (eds). Cambridge University Press, Cam-
mixed herbage of permanent meadow, conducted for bridge, pp. 51–76.
more than twenty years on the same land. II. The botani- Leprieur, F., Beauchard, O., Blanchet, S., Oberdorff, T.,
cal results. Philosophical Transactions of the Royal Soci- & Brosse, S. (2008) Fish invasions in the world’s river
ety, London B., 173, 1181–1413. systems: when natural processes are blurred by human
Lawton, J. H. (1990) Species richness and population activities. PLoS Biology, 6, e28 (1–7).
dynamics of animal assemblages. Patterns in body Lepš, J. & Smilauer, P. (2003) Multivariate Analysis of Eco-
size: abundance space. Philosophical Transactions of the logical Data using CANOCO. Cambridge University
Royal Society, London, Series B, 330, 283–291. Press, Cambridge.
Lawton, J. H. (1999a) http://www.worries. Oikos, 85, Lepš, J., de Bello, F., Lavorel, S., & Berman, S. (2006) Quan-
190–192. tifying and interpreting functional diversity of natural
Lawton, J. H. (1999b) Are there general laws in ecology? communities: practical considerations matter. Preslia,
Oikos, 84, 177–192. 78, 481–501.
OUP CORRECTED PROOF – FINAL, 18/10/2010, SPi

316 REFERENCES

le Roux, P. C. & McGeoch, M. A. (2008) Rapid range Lynch, M. & Lande, R. (1993) Evolution and extinction
expansion and community reorganization in response in response to environmental change. In: Biotic Inter-
to warming. Global Change Biology, 14, 2950–2962. actions and Global Change, Kareiva, P. M., Kingsolver,
Lichstein, J. W., Simons, T. R., Shriner, S. A., & Franzreb, J. G., & Huey, R. B. (eds). Sinauer Associates, Sunder-
K. E. (2003) Spatial autocorrelation and autoregres- land, pp. 234–250.
sive models in ecology. Ecological Monographs, 72, Lyons, J. (1986) Capture efficiency of a beach seine for
445–463. seven freshwater fishes in a north-temperate lake. North
Limpert, E., Stahel, W. A., & Abbt, M. (2001) Log-normal American Journal of Fisheries Management, 6, 288–289.
distributions across the sciences: keys and clues. Bio- Mabunda, D., Pienaar, D. J., & Verhoef, J. (2003) The
Science, 51, 341–352. Kruger National Park: a century of management and
Liu, W. T., Marsh, T. L., Cheng, H., & Forney, L. J. (1997) research. In: The Kruger Experience: Ecology and Man-
Characterization of microbial diversity by determining agement of Savanna Heterogeneity, DuToit, J. T., Rogers,
terminal restriction fragment length polymorphisms of K. H., & Biggs, H. C. (eds). Island Press, Washington,
genes encoding 16S rRNA. Applied and Environmental pp. 3–21.
Microbiology, 63, 4516–4522. MacArthur, R. (1957) On the relative abundance of bird
Locke, J. W. (1994) Statistical measurement control. In: species. Proceedings of the National Academy of Sci-
Quality and Statistics: Total Quality Management, ences, 43, 293–295.
Kowalewski, M. J. (ed). ASTM Philadelphia, PA, MacArthur, R. (1960) On the relative abundance of species.
pp. 30–42. The American Naturalist, 94, 25–36.
Loehle, C. (2006) Species abundance distributions result MacArthur, R. H. (1965) Patterns of species diversity. Bio-
from body size-energetics relationships. Ecology, 87, logical Reviews, 40, 510–533.
2221–2226. MacArthur, R. H. (1972) Geographical Ecology: Patterns in
Loehle, C. & Hansen, A. (2005) Community structure and the Distribution of Species. Princeton University Press,
scaling relations for the avifauna of the US pacific and Princeton, NJ.
inland northwest. Ecological Complexity, 2, 59–70. MacArthur, R. & Levins, R. (1967) The limiting similarity,
Loh, J., Green, R. E., Ricketts, T., Lamoreux, J., Jenkins, M., convergence, and divergence of coexisting species. The
Kapos, V., & Randers, J. (2005) The Living Planet Index: American Naturalist, 101, 377–385.
using species population time series to track trends MacArthur, R. H. & Wilson, E. O. (1967) The Theory
in biodiversity. Philosophical Transactions of the Royal of Island Biogeography. Princeton University Press,
Society, London, Series B, 360, 289–295. Princeton.
Longino, J. T., Coddington, J., & Colwell, R. K. (2002) The Mace, G.M & Baillie, J. E. M. (2007) The 2010 biodiversity
ant fauna of a tropical rain forest: estimating species indicators: challenges for science and policy. Conserva-
richness three different ways. Ecology, 83, 689–702. tion Biology, 21, 1406–1413.
Lopez, J. V., Culver, M., Stephens, J. C., Johnson, W. E., Mace, G. M., Collar, N. J., Gaston, K. J., Hilton-Taylor, C.,
& O’Brien, S. J. (1997) Rates of nuclear and cytoplasmic Akçakaya, H. R., Leader-Williams, N., Milner-Gulland,
mitochondrial DNA sequence divergence in mammals. E. J., & Stuart, S. N. (2008) Quantification of extinction
Molecular Biology and Evolution, 14, 277–286. risk: IUCN’s system for classifying threatened species.
Loreau, M. (2010) Linking biodiversity and ecosystems: Conservation Biology, 22, 1424–1442.
towards a unifying ecological theory. Philosophical MacKenzie, D. I., Nichols, J. D., Lachman, G. B., Droege, S,
Transactions of the Royal Society, London, Series B, 365, Royle, J. A, & Langtimm, C. A. (2002) Estimating site
49–60. occupancy rates when detection probabilities are less
Loreau, M., Naeem, S., & Inchausti, P. (2002) Biodiver- than one. Ecology, 83, 2248–2255.
sity and Ecosystem Functioning: Synthesis and Perspec- MacKenzie, D. I., Nichols, J. D., Hines, J. E., Knutson,
tives. Oxford University Press, USA. M. G., & Franklin, A. B. (2003) Estimating site occu-
Luck, G. W. (2007) A review of the relationships between pancy, colonization, and local extinction when a species
human population density and biodiversity. Biological is detected imperfectly. Ecology, 84, 2200–2207.
Reviews, 82, 607–645. MacKenzie, D. I., Nichols, J. D., Royle J. A., Pollock, K. H.,
Lukhtanov, V. A., Sourakov, A., Zakharov, E. V., & Hebert, Bailey, L. L., & Hines, J. E. (2006) Occupancy Estima-
P. D.N. (2009) DNA barcoding Central Asian butter- tion and Modeling: Inferring Patterns and Dynamics of
flies: increasing geographical dimension does not sig- Species Occurrence. Academic Press, San Diego.
nificantly reduce the success of species identification. MacNally, R. (2007) Use of the abundance spectrum and
Molecular Ecology and Research, 9, 1302–1310. relative-abundance distributions to analyze assemblage
 OUP CORRECTED PROOF – FINAL, 18/10/2010, SPi

REFERENCES 317

change in massively altered landscapes. The American multiple covariate distance sampling. The Auk, 124,
Naturalist, 170, 319–330. 1229–1243.
Magurran, A. E. (1988) Ecological Diversity and its Mea- Marquet, P. A., Keymer, J. A., & Hernan, C. (2003) Breaking
surement. Princeton University Press, Princeton, NJ. the stick in space: of niche models, metacommunities
Magurran, A. E. (2004) Measuring Biological Diversity. and patterns in the relative abundance of species. In:
Blackwell Science, Oxford. Macroecology: Concepts and Consequences, Blackburn,
Magurran, A. E. (2005) Evolutionary Ecology: The Trinida- T. M. & Gaston, K. J. (eds). Blackwell Science, Oxford,
dian Guppy. Oxford University Press, Oxford. pp. 64–86.
Magurran, A. E. (2007) Species abundance distributions Marriott, E. (2002) The Plague Race. A Tale of Fear, Science
over time. Ecology Letters, 10, 347–354. and Heroism. Picador, London.
Magurran, A. E. (2008) Diversity over time. Folia Geob- Marshall, C. R. (1990) Confidence intervals on strati-
otanica, 43, 319–327. graphic ranges. Paleobiology, 16, 1–10.
Magurran, A. E. (2009) Threats to freshwater fish. Science, Marshall, C. R. (1994) Confidence intervals on strati-
325, 1215–1216. graphic ranges: partial relaxation of the assumption of
Magurran, A. E., Baillie, S. R., Buckland, S. T., Dick, J. McP., randomly distributed fossil horizons. Paleobiology, 20,
Elston, D. A., Scott, E. M., Smith, R. I., Somerfield, P. J., 459–469.
Watt, A. D. (2010) Long-term data sets in biodiversity Marshall, C. R. (1995) Distinguishing between sudden and
research and monitoring: assessing change in ecological gradual extinctions in the fossil record: predicting the
communities through time. Trends in Ecology and Evo- position of the iridium anomaly using the ammonite
lution, in press. fossil record on Seymour Island, Antarctica. Geology,
Magurran, A. E. & Henderson, P. A. (2003) Explaining 23, 731–734.
the excess of rare species in natural species abundance Marshall, C. R. (1997) Confidence intervals on strati-
distributions. Nature, 422, 714–716. graphic ranges with nonrandom distributions of fossil
Magurran, A. E. & Phillip, D. A. T. (2001) Implications of horizons. Paleobiology, 23, 165–173.
species loss in freshwater fish assemblages. Ecography, Marshall, C. R. & Ward, P. D. (1996) Sudden and gradual
24, 645–650. molluscan extinctions in the latest Cretaceous of west-
Mandelbrot, B. B. (1963) New methods in statistical eco- ern European Tethys. Science, 274, 1360–1363.
nomics. Journal of Political Economy, 71, 421–440. Martin, J., Runge, M. C., Nichols, J. D., Lubow, B. C., &
Mandelbrot, B. B. (1982) The fractal geometry of nature. Kendall, W. L. (2009) Structured decision making as a
W. H. Freeman and Co, New York. conceptual framework to identify thresholds for con-
Manly, B. F. J. (1991) Randomisation and Monte Carlo servation and management. Ecological Applications, 19,
Methods in Biology. Chapman & Hall, London. 1079–1090.
Manly, B. F. J. (2004) Multivariate Statistical Methods: A Martinez, W. L. & Martinez, A. R. (2002) Computa-
Primer. Chapman & Hall/CRC. tional Statistics Handbook with MATLAB. Chapman &
Manley, P. N., Zielinski, W. J., Schlesinger, M. D., & Mori, Hall/CRC, Boca Raton.
S. R. (2004) Evaluation of a multiple-species approach to Mason, N. W.H., MacGillivray, K., Steel, J. B., & Wilson,
monitoring species at the ecoregional scale. Ecological J. B. (2003) An Index of functional diversity. Journal of
Applications, 14, 296–310. Vegetation Science, 14, 571–578.
Mantel, N. (1967) The detection of disease clustering and Mason, N. W.H., Mouillot, D., Lee, W. G., & Wilson,
a generalized regression approach. Cancer Research, 27, J. B. (2005) Functional richness, functional evenness and
209–220. functional divergence: the primary components of func-
Mao, C. X. & Colwell, R. K. (2005) Estimation of species tional diversity. Oikos, 111, 112–118.
richness: mixture models, the role of rare species, and Maurer, B. A. (1999) Untangling Ecological Complexity.
inferential challenges. Ecology, 86, 1143–1153. University of Chicago Press, Chicago.
Mao, C. X. & Li, J. (2009) Comparing species assem- May, R. M. (1975) Patterns of species abundance and diver-
blages via species accumulation curves. Biometrics, 65, sity. In: Ecology and Evolution of Communities, Cody,
1063–1067. M. L. & Diamond, J. M. (eds). Harvard University Press
Mao, C. X., Colwell, R. K., & Chang, J. (2005) Estimating Cambridge, MA, pp. 81–120.
species accumulation curves using mixtures. Biomet- May, R. M. (1990) Taxonomy as destiny. Nature, 347,
rics, 61, 433–441. 129–130.
Marques, T. A., Thomas, L., Fancy, S. G., & Buckland, May, R. M. (2007) Unanswered questions and why
S. T. (2007) Improving estimates of bird density using they matter. In Theoretical Ecology: Principles and
OUP CORRECTED PROOF – FINAL, 18/10/2010, SPi

318 REFERENCES

Applications, 3rd edn, May, R. M. & McLean, A. R. (eds), McGill, B. J. & Nekola, J. C. (2010) Mechanisms in macro-
pp. 205–215. Oxford University Press, Oxford. ecology: AWOL or purloined letter? Towards a prag-
Mayfield, M., Boni, M., Daily, G., & Ackerly, D. D. matic view of mechanism. Oikos, 119, 591–603.
(2005) Species and functional diversity of native and McGill, B. J., Hadly, E. A., & Maurer, B. A. (2005) Commu-
human-dominated plant communities. Ecology, 86, nity inertia of quaternary small mammal assemblages
2365–2372. in North America. Proceedings of the National Acad-
Mazancourt, C. (2001) Consequences of community drift. emy of Sciences of the United States of America, 102,
Science, 293, 1772. 16701–16706.
McDonald, R. I., Kareiva, P., & Forman, R. T. T. (2008) McGill, B. J., Maurer, B. A., & Weiser, M. D. (2006a)
The implications of current and future urbanization for Empirical evaluation of the neutral theory. Ecology, 87,
global protected areas and biodiversity conservation. 1411–1423.
Biological Conservation, 141, 1695–1703. McGill, B. J., Enquist, B. J., Weiher, E., & Westoby,
McElwain, J. C., Wagner, P. J., & Hesselbo, S. P. (2009) M. (2006b) Rebuilding community ecology from func-
Fossil plant relative abundances indicate sudden loss tional traits. Trends in Ecology and Evolution, 21,
of Late Triassic biodiversity in Greenland. Science, 324, 178–185.
1554–1556. McGill, B. J., Etienne, R. S., Gray, J. S., Alonso, D., Ander-
McGeoch, M. A. (1998) The selection, testing and appli- son, M. J., Benecha, H. K., Dornelas, M., Enquist, B. J.,
cation of terrestrial insects as bioindicators. Biological Green, J. L., He, F., Hurlbert, A. H., Magurran, A. E.,
Reviews, 73, 181–201. Marquet, P. A., Maurer, B. A., Ostling, A., Soykan, C. U.,
McGeoch, M. A. (2007) Insects and bioindication: theory Ugland, K. I., & White, E. P. (2007) Species abundance
and practice. In: Insect Conservation Biology, Stewart, distributions: moving beyond single prediction theories
A. J., New, T. R., & Lewis, O. T. (eds), CABI, Wallingford, to integration within an ecological framework. Ecology
pp. 144–174. Letters, 10, 995–1015.
McGeoch, M. A. & Gaston, K. J. (2002) Occupancy fre- McGowan, A. J. & Smith, A. B. (2008) Are global Phanero-
quency distributions: patterns, artefacts and mecha- zoic marine diversity curves truly global? A study
nisms. Biological Reviews, 77, 311–331. of the relationship between regional rock records and
McGeoch, M. A., Van Rensburg, B. J., & Botes, A. (2002) global Phanerozoic marine diversity. Paleobiology, 34,
The verification and application of bioindicators: a case 80–103.
study of dung beetles in a savanna ecosystem. Journal McIntire, E. J. B. & Fajardo, A. (2009) Beyond description:
of Applied Ecology, 39, 661–672. the active and effective way to infer processes from spa-
McGeoch, M. A., Kalwij, J. M., & Rhodes, J. I. (2009) A tial patterns. Ecology, 90, 46–56.
spatial assessment of Brassica napus gene flow potential McIntosh, R. P. (1962) Raunkiaer’s “Law of Frequency”.
to wild and weedy relatives in the Fynbos Biome. South Ecology, 43, 533–535.
African Journal of Science, 105, 109–115. McIntosh, R. P. (1967) An index of diversity and the
McGeoch, M. A., Schroeder, M., Ekbom, B., & Larsson, S. relation of certain concepts of diversity. Ecology, 48,
(2007) Saproxylic beetle diversity in a managed boreal 392–404.
forest: importance of stand characteristics and forestry McKinney, M. L. (2002) Influence of settlement time,
conservation measures. Diversity & Distributions, 13, human population, park shape and age, visitation and
418–429. roads on the number of alien plant species in pro-
McGill, B. (2003a) Does Mother Nature really prefer rare tected areas in the USA. Diversity & Distributions, 8,
species or are log-left-skewed SADs a sampling arte- 311–318.
fact? Ecology Letters, 6, 766–773. McKinney, M. L. (2006) Urbanization as a major cause
McGill, B. J. (2003b) A test of the unified neutral theory of of biotic homogenization. Biological Conservation, 127,
biodiversity. Nature, 422, 881–885. 247–260.
McGill, B. J. (2003c) Strong and weak tests of macroecolog- McKinney, M. L. & Lockwood, J. L. (1999) Biotic homog-
ical theory. Oikos, 102, 679–685. enization: a few winners replacing many losers in the
McGill, B. J. (2006) A renaissance in the study of abun- next mass extinction. Trends in Ecology and Evolution,
dance. Ecology, 314, 770–772. 14, 450–453.
McGill, B. J. (2010) Towards a unification of unified theo- McNaughton, S. J. & Wolf, L. L. (1970) Dominance and the
ries of biodiversity. Ecology Letters, 13, 627–642. niche in ecological systems. Science, 167, 131–139.
McGill, B. & Collins, C. (2003) A unified theory for macro- McPeek, M. A. (2007) The macroevolutionary conse-
ecology based on spatial patterns of abundance. Evolu- quences of ecological differences among species. Pale-
tionary Ecology Research, 5, 469–492. ontology, 50, 111–129.
 OUP CORRECTED PROOF – FINAL, 18/10/2010, SPi

REFERENCES 319

McPeek, M. A. (2008) The ecological dynamics of clade Montroll, E. & Shlesinger, M. F. (1982) On 1/f noise and
diversification and community assembly. The American other distributions with long tails. Proceedings of the
Naturalist, 172, E270–E284. National Academy of Sciences, 79, 3380–3383.
McPherson, J. M. & Jetz, W. (2007) Type and spatial struc- Moodley, Y. & Bruford, M. W. (2007) Molecular biogeogra-
ture of distribution data and the perceived determinants phy: towards an integrated framework for conserving
of geographical gradients in ecology: the species rich- pan-African biodiversity. PLoS ONE, 5, e454.
ness of African birds. Global Ecology and Biogeography, Mooers, A. O. & Heard, S. B. (1997) Evolutionary process
16, 657–667. from phylogenetic tree shape. The Quarterly Review of
McRae, B. H., Schumaker, N. H., McKane, R. B., Busing, Biology, 72, 31–54.
R. T., Solomon, A. M., & Burdick, C. A. (2008) A multi- Mooers, A. O., Heard, S. B., & Chrostowski, E. (2005) Evo-
moldel framework for simulating wildlife population lutionary heritage as a metric for conservation. In: Phy-
response to land-use and climate change. Ecological logeny and Conservation, Purvis, A., Brooks, T. L., &
Modelling, 219, 77–91. Gittleman, J. L. (eds). Oxford University Press, Oxford,
Meier, R., Shiyang, K., Vaidya, G., & Ng, P. K. L. (2006) pp. 120–138.
DNA barcoding and taxonomy in Diptera: a tale of high Mora, C., Tittensor, D. P., & Myers, R. A. (2008) The
intraspecific variability and low identification success. completeness of taxonomic inventories for describ-
Systematic Biology, 55, 715–728. ing the global diversity and distribution of marine
Meier, R., Zhang, G., & Ali, F. (2008) The use of mean fishes. Proceedings of the Royal Society London, B, 275,
instead of smallest interspecific distances exaggerates 149–155.
the size of the “Barcoding Gap” and leads to misiden- Moreno, C. E. & Halffter, G. (2001) Spatial and temporal
tification. Systematic Biology, 57, 809–813. analysis of α, β and γ diversity of bats in a fragmented
Mercado-Silva, N. & Escandon-Sandoval, D. S. (2008) A landscape. Biodiversity and Conservation, 10, 367–382.
comparison of seining and electrofishing for fish com- Morin, P. J. (1999) Community Ecology. Wiley-Blackwell,
munity bioassessment in a Mexican Atlantic slope mon- Malden, PA.
tane river. North American Journal of Fisheries Manage- Morisita, M. (1959) Measuring of interspecific association
ment, 28, 1725–1732. and similarity between communities. Memoires of the
Milesi, C., Hashimoto, H., Running, S. W., & Nemani, Faculty of Science, Kyushu University, Series E (Biolol-
R. W. (2005) Climate variability, vegetation productivity ogy), 3, 65–80.
and people at risk. Global and Planetary Change, 47, Morlon, H., Chuyong, G., Condit, R., Hubbell, S., Ken-
221–231. fack, D., Thomas, D., Valencia, R., & Green, J. L. (2008)
Millar, C. D., Huynen, L., Subramanian, S., Mohandesan, A general framework for the distance – decay of sim-
E., & Lambert, D. M. (2008) New developments in ilarity in ecological communities. Ecology Letters, 11,
ancient genomics. Trends in Ecology and Evolution, 23, 904.
386–393. Morlon, H., White, E. P., Etienne, R. S., Green, J. L.,
Millennium Ecosystem Assessment. (2005) Ecosystems Ostling, A., Alonso, D., Enquist, B. J., He, F., Hurlbert,
and Human Well-being: Biodiversity Synthesis. World A., Magurran, A. E., Maurer, B. A., McGill, B. J., Olff, H.,
Resources Institute, Washington, DC. Storch, D., & Zillio T. (2009) Taking species abundance
Miller, A. I. & Foote, M. (1996) Calibrating the distributions beyond individuals. Ecology Letters, 12,
Ordovician Radiation of marine life: implications 488–501.
for Phanerozoic diversity trends. Paleobiology, 22, Morris, P. J., Ivany, L. C., Schopf, K. M., & Brett, C. E.
304–309. (1995) The challenge of paleoecological stasis: reassess-
Minchin, P. R. (1987) An evaluation of the relative robust- ing sources of evolutionary stability. Proceedings of the
ness of techniques for ecological ordination. Vegetatio, National Academy of Sciences, USA, 92, 11269–11273.
69, 89–107. Moss, D., Furse, M. T., Wright, J. F., & Armitage, P. D.
Misra, M. K. & Misra, B. N. (1981) Species diversity and (1987) The prediction of the macroinvertebrate fauna of
dominance in a tropical grassland community. Folia unpolluted running-water sites in Great Britain using
Geobotanica, 16, 309–316. environmental data. Freshwater Biology, 17, 41–52.
Monaghan, M. T., Wild, R., Elliot, M., Fujisawa, T., Balke, Motomura, I. (1932) On the statistical treatment of com-
M., Inward, D. J. G., Lees, D. C., Ranaivosolo, R., Eggle- munities. Zoological Magazine, Tokyo, 44, 379–383.
ton, P., Barraclough, T. G., & Vogler, A. P. (2009) Acceler- Mouchet, M., Guilhaumon, F., Villéger, S., Mason,
ated species inventory on Madagascar using coalescent- N. W. H., Tomasini, J. A., & Mouillot, D. (2008) Towards
based models of species delineation. Systematic Biology, a consensus for calculating dendrogram-based func-
58, 298–311. tional diversity indices. Oikos, 117, 794–800.
OUP CORRECTED PROOF – FINAL, 18/10/2010, SPi

320 REFERENCES

Mouillot, D., Mason, N. W. H., Dumay, O., & Wilson, J. B. Nee, S., Harvey, P. H., & Cotgreave, P. (1992) Population
(2005) Functional regularity: a neglected aspect of func- persistence and the natural relationship between body
tional diversity. Oecologia, 142, 353–359. size and abundance. In Conservation of Biodiversity for
Moulton, M. P. & Pimm, S. L. (1987) Morphological assort- Sustainable Development, Sandlund, O. T., Hindar, K.,
ment in introduced Hawaiian passerines. Evolutionary & Brown, A. H. D. (eds). Scandavanian University Press,
Ecology, 1, 113–124. Oslo, pp. 124–136.
Mulder, C. P. H., Uliasi, D. D., & Doak, D. F. (2001) Phys- Nemani, R., Keeling, C. D., Hashimoto, H., Jolly, W. M.,
ical stress and diversity-productivity relationships: the Piper, S. C., Tucker, C. J., Myneni, R. B., & Running,
role of positive interactions. Proceedings of the National S. W. (2003) Climate-driven increases in global terres-
Academy of Sciences USA, 98, 6704–6708. trial net primary production from 1982 to 1999. Science,
Murphy, B. R. & Willis, D. W. (1996) Fisheries Techniques. 300, 1560–1563.
American Fisheries Society, Bethesda, MD, USA. Newson, S. E., Woodburn, R., Noble, D. G., & Baillie, S. R.
Murray, R. D., Holling, M., Dott, H. E. M., & Vandome, (2005) Evaluating the breeding bird survey for produc-
P. (1998) The Breeding Birds of South-East Scotland. A ing national population size and density estimates. Bird
tetrad atlas 1988–1994. The Scottish Ornithologists Club, Study, 52, 42–54.
Edinburgh. Newson, S. E., Evans, K. L., Noble, D. G., Greenwood,
Murray, B. R., Rice, B. L., Keith, D. A., Myerscough, P. J., J. J. D., & Gaston, K. J. (2008) Use of distance sampling
Howell, J., Floyd, A. G., Mills, K., & Westoby, M. (1999) to improve estimates of national population sizes for
Species in the tail of rank-abundance curves. Ecology, common and widespread breeding birds in the UK.
80, 1806–1816. Journal of Applied Ecology, 45, 1330–1338.
Muyzer, G., Dewaal, E. C., & Uitterlinden, A. G. (1993) Nicholls, H. (2009) Darwin 200: Let’s make a mammoth.
Profiling of complex microbial-populations by dena- Nature, 456, 310–314.
turing gradient gel-electrophoresis analysis of poly- Nixon, K. C. & Wheeler, Q. D. (1992) Measures of phyloge-
merase chain reaction-amplified genes-coding for 16s netic diversity. In: Extinction and Phylogeny, Novacek,
ribosomal-RNA. Applied and Environmental Microbi- M. J. & Wheeler, Q. D. (eds). Columbia University Press,
ology, 59, 695–700. New York, pp. 216–234.
Nachman, G. (1981) A mathematical model of the func- Norden, N., Chazdon, R., Chao, A., Jiang, Y. -H., &
tional relationship between density and spatial distri- Vilchez-Alvarado, B. (2009) Resilience of tropical rain
bution of a population. Journal of Animal Ecology, 50, forests: rapid tree community reassembly in secondary
453–460. forests. Ecology Letters, 12, 385–394.
Nachman, G. (1984) Estimates of mean population den- Nusslein, K. & Tiedje, J. M. (1999) Soil bacterial commu-
sity and spatial distribution of Tetranychus urticae (Aca- nity shift correlated with change from forest to pasture
rina: Tetranychidae) and Phytoseiulus persimilis (Aca- vegetation in a tropical soil. Applied and Environmental
rina: Phytoseiidae) based upon the proportion of Microbiology, 65, 3622–3626.
empty sampling units. Journal of Applied Ecology, 21, Ochiai, A. (1957) Zoogeographic studies on the soleoid
903–913. fishes found in Japan and its neighboring regions. Bul-
Naeem, S., Bunker, D. E., Hector, A., Loreau, M., & Per- letin of the Japanese Society of Scientific Fisheries, 22,
rings, C. (2009) Biodiversity, Ecosystem Functioning, 526–530.
and Human Wellbeing: An Ecological and Economic O’Dwyer, J. P., Lake, J. K., Ostling, A., Savage, V. M., &
Perspective. Oxford University Press, Oxford. Green, J. L. (2009) An integrative framework for stochas-
Nakatsu, C. H. (2007) Soil microbial community analysis tic size-structured community assembly. Proceedings of
using denaturing gradient gel electrophoresis. Soil Sci- the National Academy of Science, 106, 6170–6175.
ence Society of America Journal, 71, 562–571. Ogutu, J. O. & Owen-Smith, N. (2003) ENSO, rainfall and
Nanney, D. L. (2004) No trivial pursuit. BioScience, 54, temperature influences on extreme population declines
720–721. among African savanna ungulates. Ecology Letters, 6,
National Research Council. (2000) Ecological Indicators 412–419.
for the Nation. National Academy Press, Washington, O’Hara, R. B. (2005) Species richness estimators: how
DC. many species can dance on the head of a pin. Journal
Nee, S. (2003) The unified phenomenological theory of of Animal Ecology, 74, 375–386.
biodiversity. In: Macroecology: Concepts and Conse- Oksanen,J.,Kindt,R.,Legendre,P.,O’Hara,B.,Simpson,G.L.,
quences, Blackburn, T. M. & Gaston, K. J. (eds). Black- & Stevens, M. H. H. (2008) vegan: Community Ecology
well Science, Oxford, pp. 31–44. Package. In: R package version. http://cran.r-project.org.
 OUP CORRECTED PROOF – FINAL, 18/10/2010, SPi

REFERENCES 321

Olden, J. D. (2006) Biotic homogenization: a new research Palumbi, S. R. & Cipriano, F. (1998) Species identification
agenda for convervation biogeography. Journal of Bio- using genetic tools: the value of nuclear and mitochon-
geography, 33, 2027–2039. drial gene sequences in whale conservation. The Journal
Olsen, G. J., Lane, D. J., Giovannoni, S. J., Pace, N. R., & of Heredity, 89, 459–464.
Stahl, D. A. (1986) Microbial ecology and evolution – a Pan, H. Y., Chao, A., & Foissner, W. (2009) A non-
ribosomal-RNA approach. Annual Review of Microbi- parametric lower bound for the number of species
ology, 40, 337–365. shared by multiple communities. Journal of Agri-
Olson, V. A., Davies, R. G., Orme, C. D. L., Thomas, cultural, Biological and Environmental Statistics, 14,
G. H., Meiri, S., Blackburn, T. M., Gaston, K. J., Owens, 452–468.
I. P. F., & Bennett, P. M. (2009) Global biogeography Pardo, L. (2006) Statistical Inference Based on Divergence
and ecology of body size in birds. Ecology Letters, 12, Measures. Chapman & Hall/CRC, Taylor & Francis
249–259. Group, Boca Raton, FL.
Oremland, R. S., Capone, D. G., Stolz, J. F., & Fuhrman, Parmesan, C. (2006) Ecological and evolutionary
J. (2005) Whither or wither geomicrobiology in the era responses to recent climate change. Annual Review of
of ‘community metagenomics’. Nature Reviews Micro- Ecology, Evolution and Systematics, 37, 637–669.
biology, 3, 572–578. Parr, C. L. & Chown, S. L. (2003) Burning issues for con-
Osborn, A. M., Moore, E. R. B., & Timmis, K. N. (2000) servation: a critique of faunal fire research in Southern
An evaluation of terminal-restriction fragment length Africa. Austral Ecology, 28, 384–395.
polymorphism (T-RFLP) analysis for the study of micro- Parr, C. L., Robertson, H. G., Biggs, H. C., & Chown,
bial community structure and dynamics. Environmen- S. L. (2004) Response of African savanna ants to long-
tal Microbiology, 2, 39–50. term fire regimes. Journal of Applied Ecology, 41,
Øvreås, L. (2000) Population and community level 630–642.
approaches for analyzing microbial diversity in natural Parsons, R. F. & Cameron, D. G. (1974) Maximum plant
environments. Ecology Letters, 3, 236–251. species diversity in terrestrial communities. Biotropica,
Ovreas L. & Torsvik, L. Microbial diversity and commu- 6, 202–203.
nity structure in two different agricultural soil commu- Parsons, K. M., Piertney, S. B., Middlemas, S. J., Ham-
nities. Microbial Ecology, 36, 303–315. mond, P. S., & Armstrong, J. D. (2005) DNA-based iden-
Øvreås, L., Daae, F. L., Torsvik, V., & Rodriguez-Valera, F. tification of salmonid prey species in seal faeces. Journal
(2003) Characterization of microbial diversity in hyper- of Zoology, 266, 275–281.
saline environments by melting profiles and reasso- Passmore, A. J., Jarman, S. N., Swadling, K. M.,
ciation kinetics in combination with terminal restric- Kawaguchi, S., McMinn, A., & Nicol, S. (2006)
tion fragment length polymorphism (T-RFLP). Micro- DNA as a dietary biomarker in Antarctic krill,
bial Ecology, 46, 291–301. Euphausia superba. Journal of Marine Biotechnology, 8,
Owen-Smith, N., Kerley, G. I. H., Page, B., Slotow, R., & 686–696.
van Aarde, R. J. (2006) A scientific perspective on the Patrício, J., Salas, F., Pardal, M. A., Jørgensen, S. E., &
management of elephants in the Kruger National Park Marques, J. C. (2006) Ecological indicators performance
and elsewhere. South African Journal of Science, 102, during a re-colonisation field experiment and its com-
389–394. pliance with ecosystem theories. Ecological Indicators,
Pace, N. R. (1997) A molecular view of microbial diversity 6, 43–57.
and the biosphere. Science, 276, 734–740. Patuxent Wildlife Research Center (2001) Breeding Bird
Pace, N. R., Stahl, D. A., Lane, D. J., & Olsen, G. J. Survey FTP site. URL ftp://www.mp2-pwrc.usgs.gov/
(1986) The analysis of natural microbial-populations by pub/bbs/Datafiles/
ribosomal-RNA sequences. Advances in Microbial Ecol- Pausas, J. G. & Verdú, M. (2008) Fire reduces mor-
ogy, 9, 1–55. phospace occupation in plant communities. Ecology, 89,
Packer, L., Gibbs, J., Sheffield, C., & Hanner, R. (2009) DNA 2181–2186.
barcoding and the mediocrity of morphology. Molecu- Pautasso, M. & Gaston, K. J. (2005) Resources and global
lar Ecology and Research, 9(Suppl. 1), 42–50. avian assemblage structure in forests. Ecology Letters,
Palomares, F., Godoy, J. A., Piriz, A., O’Brien, S. J., & John- 8, 282–289.
son, W. E. (2002) Faecal genetic analysis to determine the Pavoine, S., Ollier, S., & Pontier, D. (2005a) Measur-
presence and distribution of elusive carnivores: design ing diversity from dissimilarities with Rao’s quadratic
and feasibility of the Iberian Lynx. Molecular Ecology, entropy: are any dissimilaries suitable? Theoretical Pop-
11, 2171–2182. ulation Biology, 67, 231–239.
OUP CORRECTED PROOF – FINAL, 18/10/2010, SPi

322 REFERENCES

Pavoine, S., Ollier, S., & Dufour, A. B. (2005b) Is the Petchey, O. L. & Gaston, K. J. (2006) Functional diversity:
originality of a species measurable? Ecology Letters, 8, back to basics and looking forward. Ecology Letters, 9,
579–586. 741–758.
Pavoine, S., Love, M., & Bonsall, M. B. (2009) Hierarchical Petchey, O. L. & Gaston, K. J. (2007) Dendrograms and
partitioning of evolutionary and ecological patterns in measuring functional diversity. Oikos, 116, 1422–1426.
the organization of phylogenetically-structured species Petchey, O. L., O’Gorman, E. J., & Flynn, D. F. B. (2009)
assemblages: application to rockfish (genus: Sebastes) A functional guide to functional diversity measures.
in the Southern California Bight. Ecology Letters, 12, In: Biodiversity, Ecosystem Functioning, and Human
898–908. Wellbeing: An Ecological and Economic Perspective,
Payton, M. E., Greenstone, M. H., & Schenker, N. (2003) Naeem, S., Bunker, D. E., Hector, A., Loreau, M., &
Overlapping confidence intervals or standard error Perrings, C. (eds). Oxford University Press, Oxford,
intervals: What do they mean in terms of statistical sig- pp. 49–59.
nificance? 6 pp. Journal of Insect Science, 3, 34, available Peters, S. E. (2006) Genus extinction, origination, and the
online: insectscience.org/3.34. durations of sedimentary hiatuses. Paleobiology, 32,
Pearson, T. H. (1975) The benthic ecology of Loch Linnhe 387–407.
and Loch Eil, a sea-loch system on the west coast of Peters, S. E. & Foote, M. (2002) Determinants of extinction
Scotland. IV. Changes in the benthic fauna attributable in the fossil record. Nature, 416, 420–424.
to organic enrichment. Journal of Experimental Marine Peterson, J. T. & Paukert, C. P. (2009) Converting non-
Biology and Ecology, 20, 1–41. standard fish sampling data to standardized data. In:
Pease, C. M. (1988) Biases in the survivorship curves of Standard Methods for Sampling North American Fresh-
fossil taxa. Journal of Theoretical Biology, 130, 31–48. water Fishes, Bonar, S. A., Hubert, W. A., & Willis, D. W.
Peet, R. K. (1974) The measurement of species diversity. (eds). American Fisheries Society, Bethesda, MD.
Annual Review of Ecology and Systematics, 5, 285–307. Phillips, J. (1860) Life on Earth: its Origin and Succession.
Perry, J. N., Liebhold, A. M., Rosenberg, M. S., Dun- Macmillan, Cambridge.
gan, J., Miriti, M., Jakomulska, A. & Citron-Pousty, S. Phillip, D. A. (1998) Biodiversity of freshwater fishes of
(2002) Illustrations and guidelines for selecting statisti- Trinidad and Tobago, West Indies. In: School of Biology.
cal methods for quantifying spatial pattern in ecological University of St Andrews, St Andrews, p. 99.
data. Ecography, 25, 578. Pianka, E. R. (1989) Latitudinal gradients in species diver-
Perry, J. N. & Woiwod, I. P. (1992) Fitting Taylor’s power sity. Trends in Ecology and Evolution, 4, 223.
law. Oikos, 65, 538–542. Pielou, E. C. (1975) Species abundance distributions. In:
Perry, J. N. & Taylor, L. R. (1985) Adès: new ecologi- Ecological Diversity. Wiley Interscience, New York,
cal families of species-specific frequency distributions pp. 19–31.
that describe repeated spatial samples with an intrinsic Pielou, E. C. (1977) Mathematical Ecology. Wiley, New
power-law variance-mean property. Journal of Animal York.
Ecology, 54, 931–953. Pillans, S., Ortiz, J.-C., Pillans, R. D., & Possingham, H. P.
Perry, J. N. & Taylor, L. R. (1986) Stability of real interacting (2007) The impact of marine reserves on nekton diver-
populations in space and time: implications, alterna- sity and community composition in subtropical eastern
tives and the negative binomial kc. Journal of Animal Australia. Biological Conservation, 136, 455–469.
Ecology, 55, 1053–1068. Piña-Alguilar, R. E., Lopez-Saucedo, J., Sheffield, R., Ruiz-
Pertoldi, C., Wójcik, J. M., Malgorzata, T., Kawalko, A., Galaz, L. I., Barroso-Padilla, J. J., & Gutiérrez-Gutiérrez,
Kristensen, T. N., Loeschcke, V., Gregersen, V. R., Colt- A. 2009. Revival of extinct species using nuclear trans-
man, D., Wilson, G. A., Randi, E., Henryon, M., & fer: hope for the mammoth, true for the Pyrenean ibex,
Bendixen, C. (2009) Genome variability in European but is it time for “conservation cloning”?. Cloning and
and American bison detected using the BovineSNP50 Stem Cells, 11, 341–346.
BeadChip. Conservation Genetics, 11, 627–634. doi: Piñeyro-Nelson, A., Van Heerwaarden, J., Perales, H. R.
10.1007/s10592–009–9977-y. et al. (2009) Transgenes in Mexican maize: molecular
Petchey, O. L. & Gaston, K. J. (2002) Functional diver- evidence and methodological considerations for GMO
sity (FD), species richness and community composition. detection in landrace populations. Molecular Ecology,
Ecology Letters, 5, 402–411. 18, 750–761.
Petchey, O. L. & Gaston, K. J. (2004) How do different Platt, J. R. (1964) Strong inference. Science, 146, 347–353.
measures of functional diversity perform? Ecology, 85, Platt, H. M., Shaw, K. M., & Lambshead, P. J.D. (1984)
847–857. Nematode species abundance patterns and their use in
 OUP CORRECTED PROOF – FINAL, 18/10/2010, SPi

REFERENCES 323

the detection of environmental perturbations. Hydrobi- Preston, F. W. (1962) The canonical distribution of com-
ologia, 118, 59–66. monness and rarity: Parts 1 and 2. Ecology, 43, 185–215,
Plotkin, J. B. & Muller-Landau, H. C. (2002) Sampling 410–432.
the species composition of a landscape. Ecology, 83, Price, P. W., Diniz, I. R., Morais, H. C., & Marques, E. S. A.
3344–3356. (1995) The abundance of insect herbivore species in the
Podani, J. (2005) Multivariate exploratory data analysis of tropics: the high local richness of rare species. Biotrop-
ordinal data in ecology: pitfalls, problems and solutions. ica, 27, 468–478.
Journal of Vegetation Science, 16, 497–510. Primack, R. B. (1998). Essentials of Conservation Biology,
Podani, J. & Schmera, D. (2006) On dendrogram-based 2nd edn. Sinauer Associates, Sunderland, MA.
measures of functional diversity. Oikos, 115, 179–185. Pueyo, S. (2006) Diversity: between neutrality and struc-
Podani, J. & Schmera, D. (2007) How should a den- ture. Oikos, 112, 392–405.
drogram based measure of functional diversity func- Pueyo, Y., Alados, C. L., & Ferrer-Benimeli, C. (2006) Is the
tion? A rejoinder to Petchey and Gaston. Oikos, 116, analysis of plant community structure better than com-
1427–1430. mon species-diversity indices for assessing the effects of
Poff, N. L., Olden, J. D., Vieira, N. K. M., Finn, D. S., livestock grazing on a Mediterranean arid ecosystem?
Simmons, M. P., & Kondratieff, B. C. (2006) Functional Journal of Arid Environments, 64, 698–712.
trait niches of North American lotic insects: traits-based Purvis, A. & Hector, A. (2000) Getting the measure of
ecological applications in light of phylogenetic relation- biodiversity. Nature, 405, 212–219.
ships. Journal of North American Benthic Society, 25, Pybus, O. G. & Harvey, P. H. (2000) Testing macro-
730–755. evolutionary models using incomplete molecular
Pollard, E. (1979) A national scheme for monitoring the phylogenies. Proceedings of the Royal Society London,
abundance of butterflies. The first three years. Proceed- B, 267, 2267–2272.
ings and Transactions of the British Entomological and Quince, C., Curtis, T. P., & Sloan, W. T. (2008) The rational
Natural History Society, 12, 77–90. exploration of microbial diversity. The ISME Journal, 2,
Poon, E. L. & Margules, C. R. (2004) Searching for new 997–1006.
populations of rare plant species in remote locations. Quince, C., Lanzen, A., Curtis, T. P., Davenport, R. J.,
In: Sampling Rare or Elusive Species, Thompson, W. L. Hall, N., Head, I. M., Read, L. F., & Sloan, W. T. (2009)
(ed). Island Press, Washington, DC, pp. 189–207. Accurate determination of microbial diversity from 454
Poos, M. S., Walker, S. C., & Jackson, D. A. (2009) pyrosequencing data. Nature Methods 6, 639–641.
Functional-diversity indices can be driven by method- Quinn, G. P. & Keough, M. J. (2002) Experimental Design
ological choices and species richness. Ecology, 90, and Data Analysis for Biologists. Cambridge University
341–347. Press, Cambridge.
Popper, K. R. (1959) The Logic of Scientific Discovery. R Development Core Team. (2005) R: A Language and
Hutchinson. Enviornment for Statistical Computing. R Foundation
Porter, W. P., Sabo, J. L., Tracy, C. R., Reichman, O. J., & for Statistical Computing, Vienna, Austria.
Ramankutty, N. (2002) Physiology on a landscape scale: Rabeni, C. F., Peterson, J. T., Lyons, J., & Mercado-Silva, N.
plant-animal interactions. Integrative and Comparative (2009) Sampling fish in warmwater wadeable streams.
Biology, 42, 431–453. In: Standard Methods for Sampling North American
Pradel, R. (1996) Utilization of Capture-Mark-Recapture Freshwater Fishes, Bonar, S. A., Hubert, W. A., & Willis,
for the study of recruitment and population growth D. W. (eds). American Fisheries Society, Bethesda.
rate. Biometrics, 52, 703–709. Rabinowitz, D. (1981) Seven forms of rarity. In: Biological
Prendergast, J. R., Quinn, R. M., Lawton, J. H., Eversham, Aspects of Rare Plant Conservation, Synge, H. (ed.).
B. C., & Gibbons, D. W. (1993) Rare Species, the Coin- Wiley, Chichester, pp. 205–217.
cidence of Diversity Hotspots and Conservation Strate- Rabinowitz, D., Cairns, S., & Dillon, T. (1986) Seven forms
gies. Nature, 365, 335–337. of rarity and their frequency in the flora of the British
Press, W. H., Teukolsky, S. A., Vetterling, W. T., & Flannery, Isles. In: Conservation Biology: The Science of Scarcity
B. P. (2007) Numerical Recipes: The Art of Scientific and Diversity, Soulé, M. J. (ed). Sinauer, Sunderland,
Computing. Cambridge University Press, Cambridge. MA, pp. 182–204.
Preston, F. W. (1948) The commonness and rarity of Rahbek, C. & Graves, G. R. (2001) Multiscale assess-
species. Ecology, 29, 254–283. ment of patterns of avian species richness. Proceed-
Preston, F. W. (1960) Time and space and the variation of ings of the National Academy of Sciences. USA, 98,
species. Ecology, 41, 612–627. 4534–4539.
OUP CORRECTED PROOF – FINAL, 18/10/2010, SPi

324 REFERENCES

Rand, T. A., Tylianakis, J. M., & Tscharntke, T. (2006) distinctive species often capture more phylogenetic
Spillover edge effects: the dispersal of agriculturally diversity than expected. Journal of Theoretical Biology,
subsidized insect natural enemies into adjacent natural 251, 606–615.
habitats. Ecology Letters, 9, 603–614. Regan, H. M., Hierl, L. A., Franklin, J., Deutschman,
Rangel, T., Diniz-Filho, J. A. F., & Bini, L. M. (2006) D. H., Schmalbach, H. L., Winchell, C. S., & Johnson,
Towards an integrated computational tool for spatial B. S. (2008) Species prioritization for monitoring and
analysis in macroecology and biogeography. Global management in regional multiple species conservation
Ecology and Biogeography, 15, 321–327. plans. Diversity & Distributions, 14, 462–471.
Ranjard, L., Poly, F., Combrisson, J., Richaume, A., Gour- Reid, W. V. (1998) Biodiversity hotspots. Trends in Ecology
biere, F., Thioulouse, J., & Nazaret, S. (2000) Hetero- and Evolution, 13, 275–280.
geneous cell density and genetic structure of bacte- Renkonen, O. (1938) Statistisch-ökologische Untersuchun-
rial pools associated with various soil microenviron- gen über die terrestrische Käferwelt der finnischen
ments as determined by enumeration and DNA fin- Bruchmoore. Annale Zoologici Societatis Zoologicae-
gerprinting approach (RISA). Microbial Ecology, 39, Botanicae Fennicae Vanamo, 6, 1–231.
263–272. Rice, J. C. (2000) Evaluating fishery impacts using metrics
Rao, C. R. (1982) Diversity and dissimilarity coefficients: of community structure. ICES Journal of Marine Sci-
a unified approach. Theoretical Population Biology, 21, ence, 57, 682–688.
24–43. Richards, P. W. 1969. Speciation in the tropical rain forest
Rapoport, E. H. 1982. Areography: Geographical Strate- and the concept of the niche. Biological Journal of the
gies of Species. Pergamon, Oxford. Linnean Society, 1, 149–153.
Ratnasingham, S. & Hebert, P. D. N. (2007) BOLD: the bar- Richardson, D. M., Holmes, P. M., Esler, K. J., Galatow-
code of life data system (www.barcodinglife.org). Mole- itsch, S. M., Stromberg, J. C., Kirkman, S. P., Pyšek, P.,
cular Ecology Notes 7:355–364. & Hobbs, R. J. (2007) Riparian vegetation: degradation,
Raunkaier, C. (1909) Formationsundersogelse og Forma- alien plant invasions, and restoration prospects. Diver-
tionsstatistik. Svensk Botanisk Tidskrift, 30, 20–132. sity & Distributions, 13, 126–139.
Raunkaier, C. (1934) Life Forms and Statistical Plant Geog- Ricklefs, R. E. 2008. Disintegration of the ecological com-
raphy. Oxford University Press, Oxford. munity. The American Naturalist, 172, 741–750.
Raup, D. M. (1975) Taxonomic diversity estimation using Ricklefs, R. E. & Travis, J. (1980) A morphological
rarefaction. Paleobiology, 1, 333–342. approach to the study of avian community organiza-
Raup, D. M. (1978) Cohort analyses of generic survivor- tion. Auk, 97, 321–338.
ship. Paleobiology, 4, 1–15. Ricotta, C. (2004) A parametric diversity measure combin-
Raup, D. M. (1979) Size of the Permo-Triassic bottleneck ing the relative abundances and taxonomic distinctive-
and its evolutionary implications. Science, 206, 217–218. ness of species. Diversity & Distributions, 10, 143–146.
Raup, D. M. (1991) A kill curve for Phanerozoic marine Ricotta, C. (2005) A note on functional diversity measures.
species. Paleobiology, 17, 37–48. Basic and Applied Ecology, 6, 479–486.
Raup, D. M. & Boyajian, G. E. (1988) Patterns of Ricotta, C. & Moretti, M. (2008) Quantifying functional
generic extinction in the fossil record. Paleobiology, 14, diversity with graph-theoretical measures: advantages
109–125. and pitfalls. Community Ecology, 9, 11–16.
Raup, D. M. & Sepkoski, J. J., Jr. (1982) Mass extinctions in Ricotta, C. & Szeidl, L. (2009) Diversity partitioning of
the marine fossil record. Science, 215, 1501–1503. Rao’s quadratic entropy. Theoretical Population Biol-
Raxworthy, C. J., Pearson, R. G., Rabibisoa, N., Rakoton- ogy, 76, 299–302.
drazafy, A. M., Ramanamanjato, J.-B., Raselimanana, Riitters, K. H., O’Neill, R. V., Hunsaker, C. T., Wickham,
A. P., Wu, S., Nussbaum, R. A., & Stone, D. A. (2008) J. D., Yankee, D. H., Timmins, S. P., Jones, K. B., & Jack-
Extinction vulnerability of tropical montane endemism son, B. L. (1995) A factor analysis of landscape pattern
from warming and upslope displacement: a preliminary and structure metrics. Landscape Ecology, 10, 23–39.
appraisal for the highest massif in Madagascar. Global Robbins, C. S., Bystrak, D., & Geissler, P. H. (1986) The
Change Biology, 14, 1703–1720. Breeding Bird Survey: Its First Fifteen Years, 1965–1979.
Redding, D. W. & Mooers, A. O. (2006) Incorporating US Dept of the Interior, Fish and Wildlife Service, Wash-
evolutionary measures into conservation prioritisation. ington, DC.
Conservation Biology, 20, 1670–1678. Roca. A. L., Georgiadis, N., Pecon-Slattery, J., & O’Brien,
Redding, D. W., Hartmann, K., Mimoto, A., Bokal, D., S. J. (2001) Genetic evidence for two species of elephant
DeVos, M., & Mooers, A. O. (2008) Evolutionarily in Africa. Science, 293, 1747–1477.
 OUP CORRECTED PROOF – FINAL, 18/10/2010, SPi

REFERENCES 325

Roche Diagnostics. (2009) Using Multiplex Identifier J. A. (eds). Columbia University Press New York,
(MID) Adaptors for the GS FLX Titanium Chemistry pp. 311–348.
– Basic MID Set. Technical Bulletin Genome Sequencer Rosenzweig, M. L. (2003) Reconciliation ecology and the
FLX System, Mannheim, Germany, pp. 1–11. future of species diversity. Oryx, 37, 194–205.
Rodrigues, A. S. L., Gregory, R. D., & Gaston, K. J. (2000) Rosenzweig, M. L. (2004) Applying species-area relation-
Robustness of reserve selection procedures under tem- ships to the conservation of species diversity. In: Fron-
poral species turnover. Proceedings of the Royal Society tiers of Biogeography: New Directions in the Geogra-
London, 267, 49–55. phy of Nature, Lomolino, M. V. & Heany, L. (eds). Sin-
Roe, A. D. & Sperling, F. A. H. (2007) Patterns of evolution auer Associates, Sunderland, MA, pp. 325–343.
of mitochondrial cytochrome c oxidase I and II DNA Rosenzweig, M. L., Turner, W. R. Cox, J. G., & Ricketts,
and implications for DNA barcoding. Molecular Phylo- T. H.. (2003) Estimating diversity in unsampled habitats
genetics and Evolution, 44, 325–345. of a biogeographical province. Conservation Biology,
Roesch, L. F., Fulthorpe, R. R., Riva, A., Casella, G., Had- 17, 864–874.
win, A. K. M., Kent, A. D., Daroub, S. H., Camargo, Rosewell, J., Shorrocks, B., & Edwards, K. (1990) Competi-
F. A. O., Farmerie, W. G., & Triplett, E. W. (2007) Pyrose- tion on a divided and ephemeral resource: testing the
quencing enumerates and contrasts soil microbial diver- assumptions. I. Aggregation. Journal of Animal Ecol-
sity. The ISME Journal, 1, 283–290. ogy, 59, 977–1001.
Romanov, M. N., Tuttle, E. M., Houck, M. L., Modi, W. S., Rosing, M. T. & Frei, R. (2004) U-rich Archaean sea-floor
Chemnick, L. G., Karody, M. L., Stremel Mork, E. M., sediments from Greenland – indications of > 3700 Ma
Otten, C. A., Renner, T., Jones, K. C., Dandekar, S., Papp, oxygenic photosynthesis. Earth and Planetary Science
J. C., Da, Y., NISC Comparative Sequencing Program, Letters, 217, 237–244.
Green, E. D., Magrini, V., Hickenbotham, M. T., Glass- Rossi, R. E., Mulla, D. J., Journel, A. G., & Franz, E. H.
cock, J., McGrath, S., Mardis, E. R., & Ryder, O. A. (1992) Geostatistical tools for modeling and interpreting
(2009) The value of avian genomics to the conservation ecological spatial dependence. Ecological Monographs,
of wildlife. BMC Genomics, 10: doi:10.1186/1471–2164– 62, 277–314.
10-S2-S10. Roughgarden, J. (2009) Is there a general theory of
Romanuk, T. N. & Kolasa, J. (2001) Simplifying the com- community ecology? Biology and Philosophy, 24,
plexity of temporal diversity dynamics: a differentiation 521–529.
approach. Ecoscience, 8, 259–263. Routledge, R. (1979) Diversity indices: which ones are
Rondinini, C., Wilson, K. A., Boitani, L., Grantham, H., & admissible? Journal of Theoretical Biology, 76, 503–515.
Possingham, H. P. (2006) Tradeoffs of different types of Routledge, R. D. & Swartz, T. B. (1991) Taylor’s power law
species occurrence data for use in systematic conserva- reexamined. Oikos, 60, 107–112.
tion planning. Ecology Letters, 9, 1136–1145. Rovito, S. M., Parra-Olea, S., Vásquez-Almazán, C. R.,
Rondon, M. R., August, P. R., Bettermann, A. D., Brady, Papenfuss, T. J., & Wake, D. B. (2009) Dramatic declines
S. F., Grossman, T. H., Liles, M. R., Loiacono, K. A., in neotropical salamander populations are an impor-
Lynch, B. A., MacNeil, I. A., Minor, C., Tiong, C. L., tant part of the global amphibian crisis. Proceed-
Gilman, M., Osburne, M. S., Clardy, J., Handelsman, J., ings of the National Academy of Sciences. USA, 106,
& Goodman, R. M. (2000) Cloning the soil metagenome: 3231–3236.
a strategy for accessing the genetic and functional diver- Royal Society. (2003) Measuring Biodiversity for Conser-
sity of uncultured microorganisms. Applied and Envi- vation. The Royal Society, London.
ronmental Microbiology, 66, 2541–2547. Royle, R. A., Nichols, J. D., & Kéry, M. (2005) Modelling
Rosenfeld, J. S. (2002) Functional redundancy in ecology occurrence and abundance of species when detection is
and conservation. Oikos, 98, 156–162. imperfect. Oikos, 110, 353–359.
Rosenzweig, M. L. (1992) Species diversity gradients: we Rubinoff, D., Cameron, S., & Will, K. (2006) A genomic
know more and less than we thought. Journal of Mam- perspective on the shortcomings of mitochondrial DNA
malogy, 73, 715–730. for “barcoding” identification. The Journal of Heredity,
Rosenzweig, M. L. (1995) Species Diversity in Space and 97, 581–594.
Time. Cambridge University Press, Cambridge. Li, R., Fan, W., Tian, G., Zhu, H., He, L., Cai, J., Huang, Q.,
Rosenzweig, M. L. (1998) Preston’s ergodic conjec- Cai, Q., Li, B., Bai, Y., Zhang, Z., Zhang, Y., Wang, W., Li,
ture: the accumulation of species in space and time. J., Wei, F., Li, H., Jian, M., Li, J., Zhang, Z., Nielsen, R.,
In: Biodiversity Dynamics: Turnover of Populations, Li, D., Gu, W., Yang, Z., Xuan, Z., Ryder, O. A., Leung, F.
Taxa, and Communities, McKinney, M. L. & Drake, C.-C., Zhou, Y., Cao, J., Sun, X., Fu, Y., Fang, X., Guo, X.,
OUP CORRECTED PROOF – FINAL, 18/10/2010, SPi

326 REFERENCES

Wang, B., Hou, R., Shen, F., Mu, B., Ni, P., Lin, R., Qian, community analysis. Journal of Applied Ecology, 44,
W., Wang, G., Yu, C., Nie, W., Wang, J., Wu, Z., Liang, 330–339.
H., Min, J., Wu, Q., Cheng, S., Ruan, J., Wang, M., Shi, Salas, F., Marcos, C., Neto, J. M., Patrıcio, J., Perez-
Z., Wen, M., Liu, B., Ren, X., Zheng, H., Dong, D., Cook, Ruzafa, A., & Marques, J. C. (2006a) User-friendly guide
K., Shan, G., Zhang, H., Kosiol, C., Xie, X., Lu, Z., Zheng, for using benthic ecological indicators in coastal and
H., Li, Y., Steiner, C., Lam, T., Lin, S., Zhang, Q., Li, G., marine quality assessment. Ocean and Coastal Manage-
Tian, J., Gong, T., Liu, H., Zhang, D., Fang, L., Ye, C., ment, 49, 308–331.
Zhang, J., Hu, W., Xu, A., Ren, Y., Zhang, G., Bruford, Salas, F., Patrıcio, J., Marcos, C., Pardal, M. A., Perez-
M. W., Li, Q., Ma, L., Guo, Y., An, N., Hu, Y., Zheng, Y., Ruzafa, A., & Marques, J. C. (2006b) Are taxonomic dis-
Shi, Y., Li, Z., Liu, Q., Chen, Y., Zhao, J., Qu, N., Zhao, tinctness measures compliant to other ecological indi-
S., Tian, F., Wang, X., Wang, H., Xu, L., Liu, X., Vinar, cators in assessing ecological status? Marine Pollution
T., Wang, Y., Lam, T.-W., Yiu, S.-M., Liu, S., Zhang, H., Bulletin, 52, 162–174.
Li, D., Huang, Y, Wang, X., Yang, G., Jiang, Z., Wang, J., Samways, M. J. (1989) Insect conservation and the distur-
Qin, N., Li, L., Li, J., Bolund, L., Kristiansen, K., Wong, bance landscape. Agriculture, Ecosystems & Environment,
G. K.-S., Olson, M., Zhang, X., Li, S., Yang, H., Wang, J., 27, 183–194.
& Wang, J. (2010). The sequence and de novo assembly Sanders, H. (1968) Marine benthic diversity: a compara-
of the giant panda genome. Nature, 463, 311–317. tive study. The American Naturalist, 102, 243.
Rusch, D. B., Halpern, A. L., Sutton, G., Heidelberg, Sara, M. (2008) Breeding abundance of threatened rap-
K. B., Williamson, S., Yooseph, S., Wu, D., Eisen, J. A., tors as estimated from occurrence data. The Ibis, 150,
Hoffman, J. M., Remington, K., Beeson, K., Tran, B., 776–778.
Smith, H., Baden-Tillson, H., Stewart, C., Thorpe, J., Sawyer, A. J. (1989) Inconstancy of Taylor’s b: simulated
Freeman, J., Andrews-Pfannkoch, C., Venter, J. E., Li, K., sampling with different quadrat sizes and spatial distri-
Kravitz, S., Heidelberg, J. F., Utterback, T., Rogers, Y. -H., butions. Research in Population Ecology, 31, 11–24.
Falcón, L. I., Souza, V., Bonilla-Rosso, G., Eguiarte, L. E., Scharff, N., Coddington, J. A., Griswold, C. E., Hormiga,
Karl, D. M., Sathendranath, S., Platt, T., Bermingham, E., G., & De Place Bjørn, P. (2003) When to quit? Estimating
Gallardo, V., Tamayo-Castillo, G., Ferrari, M. R., Straus- spider species richness in a northern European decidu-
berg, R. L., Nealson, K., Friedman, R., Frazier, M., & ous forest. Journal of Arachnology, 31, 246–273.
Venter, J. C. (2007) The Sorcerer II global ocean sampling Scheaffer, R. L., Mendenhall, W., & Ott, L. (2006) Elemen-
expedition: Northwest Atlantic through Eastern tropical tary Survey Sampling. Thomson Brooks/Cole, South-
Pacific. PLoS Biology, 5, 398–431. bank, Vic., Belmont, CA.
Russell, G. J., Diamond, J. M., Pimm, S. L., & Reed, Schechtman, E. & Wang, S. (2004) Jackknifing two-sample
T. M. (1995) A century of turnover: community dynam- statistics. Journal of statistical Planning and Inference,
ics at three timescales. Journal of Animal Ecology, 64, 119, 329–340.
628–641. Scheiner, S. M., Cox, S. B., Willig, M., Mittelbach, G. G.,
Rust, K. F. & Rao, J. N. K. (1996) Variance estimation for Osenberg, C., & Kaspari, M. (2000) Species richness,
complex surveys using replication techniques. Statisti- species-area curves and Simpson’s paradox. Evolution-
cal Methods in Medical Research, 5, 283–310. ary Ecology Research, 2, 791–802.
Ryti, R. T. & Case, T. J. (1986) Overdispersion of Schiermeier, Q. (2005) Hurricane link to climate change is
ant colonies: a test of hypotheses. Oecologia, 69, hazy. Nature, 437, 461–461.
446–453. Schleper, C., Jurgens, G., & Jonuscheit, M. (2005) Genomic
Sachs, J. D. (2008) Common Wealth: Economics for a studies of uncultivated archaea. Nature Reviews Micro-
Crowded Planet. Penguin Press, London. biology, 3, 479–488.
Sadler, P. M. & Cooper, R. A. (2003) Best-fit intervals Schloss, P. D. & Handelsman, J. (2006a) Introducing
and consensus sequences: a comparison of the resolv- SONS, a tool for operational taxonomic unit-based com-
ing power of traditional biostratigraphy and computer parisons of microbial community memberships and
assisted correlation. In: High-Resolution Stratigraphic structures. Applied Environmental Microbiology, 72,
Approaches in Paleontology, Harries, P. (ed). Plenum 6773–6779.
Press, New York, pp. 49–94. Schloss, P. D. & Handelsman, J. (2006b) Toward a census of
Saint-Germain, M., Buddle, C. M., Larrivée, M., Mercado, bacteria in soil. Plos Computational Biology, 2, 786–793.
A., Motchula, T., Reichert, E., Sackett, T. E., Sylvain, Schmera, D., Erös, T., & Podani, J. (2009) A measure for
Z., & Webb, A. (2007) Should biomass be considered assessing functional diversity in ecological communi-
more frequently as a currency in terrestrial arthropod ties. Aquatic Microbial Ecology, 43, 157–167.
 OUP CORRECTED PROOF – FINAL, 18/10/2010, SPi

REFERENCES 327

Schulze, E. -D. & Mooney, H. A. (1994) Biodiversity and Shimida, A. (1984) Whittaker’s plant diversity sampling
Ecosystem Function. Springer, Berlin, Germany. method. Israel Journal of Botany, 33, 41–46.
Schulze, C. H., Waltert, M., Kessler, P. J. A., et al. (2004) Shimitani, K. (2001) On the measurement of species
Biodiversity indicator groups of tropical land-use sys- diversity incorporating species differences. Oikos, 93,
tems: comparing plants, birds, and insects. Ecological 135–147.
Applications, 14, 1321–1333. Shorrocks, B. & Rosewell, J. (1986) Guild size in
Schwarz, G. (1978) Estimating the dimension of a model. drosophilids: a simulation model. Journal of Animal
Annals of Statistics, 6, 461–464. Ecology, 55, 527–541.
Schwieger, F. & Tebbe, C. C. (1998) A new approach to Shurin, J. B. (2007) How is diversity related to species
utilize PCR-single-strand-conformation polymorphism turnover through time? Oikos, 116, 957–965.
for 16s rRNA gene-based microbial community analy- Siddall, M. E., Fontanella, F. M., Watson, S. C., Kvist,
sis. Applied and Environmental Microbiology, 64, S., & Erséus, C. (2009) Barcoding bamboozled by bac-
4870–4876. teria: convergence to metazoan mitochondrial primer
Schweiger, O., Klotz, S., Durka, W., & Kühn, I. (2008) targets by marine microbes. Systematic Biology, 58,
A comparative test of phylogenetic diversity indices. 445–451.
Oecologia, 157, 485–495. Signor, P. W., III & Lipps, J. H. (1982) Sampling bias,
Seeger, M. & Jerez, C. A. (1992) Phosphate limita- gradual extinction patterns and catastrophes in the fos-
tion affects global gene-expression in Thiobacillus- sil record. Geological Society of America Special Paper,
ferrooxidans. Geomicrobiology Journal, 10, 227–237. 190, 291–296.
Seigel, A. F. & German, R. Z. (1982) Rarefaction and taxo- Sileshi, G., Hailu, G., & Mafongoya, P. L. (2006)
nomic diversity. Biometrics, 38, 235–2411. Occupancy-abundance models for predicting densities
Selmi, S. & Boulinier, T. (2004) Distribution-abundance of three leaf beetles damaging the multipurpose tree
relationship for passerines breeding in Tunisian oases: Sesbania sesban in eastern and southern Africa. Bulletin
test of the sampling hypothesis. Oecologia, 139, of Entomological Research, 96, 61–69.
440–445. Simberloff, D. S. (1972) Properties of the rarefaction
Sepkoski, J. J., Jr. (1975) Stratigraphic biases in the analysis diversity measurement. The American Naturalist, 106,
of taxonomic survivorship. Paleobiology, 1, 343–355. 414–418.
Sepkoski, J. J., Jr. (1979) A kinetic model of Phanerozoic Simberloff, D. (1978) Use of rarefaction and related meth-
taxonomic diversity. II. Early Phanerozoic families and ods in ecology. In: Biological Data in Water Pollu-
multiple equilibria. Paleobiology, 5, 222–251. tion Assessment: Quantitative and Statistical Analyses,
Sepkoski, J. J., Jr. (1982) A compendium of fossil marine Dickson, K. L., Cairns, J., Jr., & Livingston, R. J. (eds).
families. Milwaukee Public Museum Contributions in American Society for Testing and Materials, Philadel-
Biology and Geology, 51, 139. phia, pp. 150–165.
Sepkoski, J. J. Jr. (1988) Alpha, beta, or gamma: where does Simberloff, D. & Connor, E. F. (1979) Q-mode and R-mode
all the diversity go? Paleobiology, 14, 221–234. analyses of biogeographic distributions: null hypothe-
Sepkoski, J. J., Jr. (2002) A compendium of fossil marine ses based on random colonization. In: Contemporary
animal genera. Bulletins of American Paleontology, 363, Quantitative Ecology and Related Econometrics, Patil,
1–563. G. P. & Rosenzweig, M. L. (eds), International Coopera-
Shaw, A. B. (1964) Time in Stratigraphy. McGraw-Hill, tive Publishing House, Fairland, pp. 123–128.
New York. Simková, A., Kadlec, D., Gelnar, M., & Morand, S. (2002)
Shaw, J., Lickey, E. B., Schilling, E. E., & Small, R. L. (2007) Abundance-prevalence relationship of gill congeneric
Comparison of whole chloroplast genome sequences to ectoparasites: testing the core satellite hypothesis and
choose noncoding regions for phylogenetic studies in ecological specialisation. Parasitology Research, 88,
angiosperms: the tortoise and the hare III. American 682–686.
Journal of Botany, 94, 275–288. Simpson, E. H. (1949) Measurement of diversity. Nature,
Shen, T., Chao, A., & Lin, C. (2003) Predicting the number 163, 688.
of new species in further taxonomic sampling. Ecology, Simpson, G. G. (1944) Tempo and Mode in Evolution.
84, 798–804. Columbia University Press, New York.
Sherwin, W. B., Jabot, F., Rush, R., & Rossetto, M. (2006) Simpson, G. G. (1953) The Major Features of Evolution.
Measurement of biological information with applica- Columbia University Press, New York.
tions from genes to landscapes. Molecular Ecology, 15, Singh, J., Behal, A., Singla, N., Joshi, A., Birbian, N., Singh,
2857–2869. S., Bali, V., & Batra, N. (2009) Metagenomica: concept,
OUP CORRECTED PROOF – FINAL, 18/10/2010, SPi

328 REFERENCES

methodology, ecological inference and recent advances. Soberón, J. (2007) Grinnellian and Eltonian niches and
Biotechnology Journal, 4, 480–494. geographic distributions of species. Ecology Letters, 10,
Sitran, R., Bergamasco, A., Decembrini, F., & Guglielmo, 1115–1123.
L. (2009) Microzooplankton (tintinnid ciliates) diver- Soberón, J. & Llorente, J. (1993) The use of species accumu-
sity: coastal community structure and driving mecha- lation functions for the prediction of species richness.
nisms in the southern Tyrrhenian Sea (Western Mediter- Conservation Biology, 7, 480–488.
ranean). Journal of Plankton Research, 31, 153–170. Sogin, M. L., Morrison, H. G., Huber, JA., Welch, D. M.,
Šizling, A. L. & Storch, D. (2004) Power-law species – Huse, S. M., Neal, P. R., Arrieta, J. M., & Herndl, G. J.,
area relationships and self-similar species distributions (2006) Microbial diversity in the deep sea and the under-
within finite areas. Ecology Letters, 7, 60–68. explored “rare biosphere”. Proceedings of the National
Šizling, A. L. & Storch, D. (2007) Geometry of species Academy of Sciences, 103, 12115–12120.
distributions: random clustering and scale invariance. Solow, A. R. (1993a) Inferring extinction from sighting
In: Scaling Biodiversity, Storch, D., Marquet, P. A., & data. Ecology, 74, 962–963.
Brown, J. H. (eds). Cambridge University Press, Cam- Solow, A. R. (1993b) Inferring extinction in a declin-
bridge, pp. 77–100. ing population. Journal of Mathematical Biology, 32,
Šizling, A. L., Šizlingová, E., Storch, D., Reif, J., & Gaston, 79–82.
K. J. (2009) Rarity, commonness and the contribution Solow, A. R. (1996) Tests and confidence intervals for a
of individual species to species richness patterns. The common upper endpoint in fossil taxa. Paleobiology, 22,
American Naturalist, 174, 82–93. 406–410.
Sloan, W. T., Quince, C., & Curtis, T. P. (2008) The Uncount- Solow, A. R. & Smith, W. K. (1997) On fossil preservation
ables. In: Accessing Uncultivated Microorganisms: from and the stratigraphic ranges of taxa. Paleobiology, 23,
the Environment to Organisms and Genomes and Back, 271–277.
Zengler, K. (ed). ASM Press: Washington, DC, pp. 35–54. Song, H., Buhay, J. E., Whiting, M. F., & Crandall,
Smith, M. D. & Knapp, A. K. (2003) Dominant species K. A. (2008) Many species in one: DNA barcoding
maintain ecosystem function with non-random species overestimates the number of species when nuclear,
loss. Ecology Letters, 6, 509–517. mitochondrial pseudogenes are coamplified. Proceed-
Smith, W. & Grassle, F. (1977) Sampling properties of a ings of the National Academy of Sciences, 105,
family of diversity measures. Biometrics, 33, 283–292. 13486–13491.
Smith, B. & Wilson, J. B. (1996) A consumer’s guide to Sørensen, T. (1948) A method of establishing groups of
evenness indices. Oikos, 76, 70–82. equal amplitude in plant sociology based on similarity
Smith, E. P. & Zaret, T. M. (1982) Bias in estimating niche of species content and its application to analyses of the
overlap. Ecology, 63, 1248–1253. vegetation on Danish commons. Biologiske Skrifter, 5,
Smith, K. W., Dee, C. W., Fearnside, J. D., Fletcher, E. W., & 1–34.
Smith, R. N. (1993) The Breeding Birds of Hertfordshire. Sorensen, L. L., Coddington, J. A., & Scharff, N. (2002)
TheHertfordshireNaturalHistorySociety,Hertfordshire. Inventorying and estimating subcanopy spider diver-
Smith, W., Solow, A. R., & Preston, P. E. (1996) An estima- sity using semiquantitative sampling methods in an
tor of species overlap using a modified beta-binomial Afromontane forest. Environmental Entomology, 31,
model. Biometrics, 52, 1472–1477. 319–330.
Smith, A. B., Gale, A. S., & Monks, N. E. (2001) Sea- Soule, M. E. (1986) Conservation Biology: The Science of
level change and rock-record bias in the Cretaceous: a Scarcity and Diversity. Sinauer Associates, Sunderland,
problem for extinction and biodiversity studies. Paleo- MA.
biology, 27, 241–253. Sousa, W. P. (1979) Disturbance in marine intertidal boul-
Smith, D. R., Brown, J. A., & Lo, N. C.H. (2004) Appli- der fields: the nonequilibrium maintenance of species
cations of adaptive sampling to biological populations. diversity. Ecology, 60, 1225–1239.
In: Sampling for Rare or Elusive Species: Concepts, Sousa, W. P. (1984) The role of disturbance in natural com-
Designs, and Techniques for Estimating Population munities. Annual Review of Ecology and Systematics,
Parameters, Thompson, W. L. (ed). Island Press, Wash- 15, 353–391.
ington, pp. 77–122. Southwood, T. R. E. (1977) Habitat: the templet for
Sobek S., Steffan-Dewenter I., Scherber C., & Tscharntke ecological strategies. Journal of Animal Ecology, 46,
T. (2009) Spatiotemporal changes of beetle communities 337–365.
across a tree diversity gradient. Diversity & Distribu- Southwood, T. R. E. (1978) Ecological Methods. Chapman
tions, 15, 660–670. & Hall, London.
 OUP CORRECTED PROOF – FINAL, 18/10/2010, SPi

REFERENCES 329

Southwood, T. R. E. (1996) The Croonian Lecture 1995. Storfer, A., Eastman, J. M., & Spear, S. F. (2009) Modern
Natural communities: structure and dynamics. Philo- molecular methods for amphibian conservation. Bio-
sophical Transactions of the Royal Society, London Science, 59, 559–571.
Lond. B., 351, 1113–1129. Strauss, D. & Sadler, P. M. (1989) Classical confidence
Southwood, R. & Henderson, P. A. (2000) Ecological Meth- intervals and Bayesian probability estimates for ends of
ods. 3rd edn. Blackwell Science, Oxford. local taxon ranges. Mathematical Geology, 21, 411–427.
Soykan, C., McGill, B., Magurran, A., Dornelas, M., Bahn, Strong, D. R., Simberloff, D., Abele, L. G., & Thistle, A. B.
V., Ugland, K., & Gray, J. S. (in prep.) An Assessment (1984) Ecological Communities: Conceptual Issues and
of Indicator Performance along Human Disturbance the Evidence. Princeton University Press, Princeton,
Gradients. USA.
Spiegelhalter, D., Best, N., Calin, B., & van der Linde, Sugihara, G. (1980) Minimal community structure: an
A. (2002) Bayesian measures of model complexity and explanation of species abundance patterns. The Amer-
fit. Journal of the Royal Statistical Society, Series B, 64, ican Naturalist, 116, 770–787.
583–639. Sugihara, G., Bersier, L. F., Southwood, T. R. E., Pimm,
Staley, J. T. & Konopka, A. (1985) Measurement of in- S. L., & May, R. M. (2003) Predicted correspondence
situ activities of nonphotosynthetic microorganisms between species abundances and dendrograms of niche
in aquatic and terrestrial habitats. Annual Review of similarities. Proceedings of the National Academy of
Microbiology, 39, 321–346. Sciences of the United States of America, 100, 5246–5251.
Steffan-Dewenter, I., Muenzenberg, U., Buerger, C., Thies, Sutherland, W. J., Bailey, M. J., Bainbridge, I. P., et al.
C., & Tscharntke, T. (2002) Scale-dependent effects of (2008) Future novel threats and opportunities facing UK
landscape context on three pollinator guilds. Ecology, biodiversity identified by horizon scanning. Journal of
83, 1421–1432. Applied Ecology, 45, 821–833.
Stein, B. A., Scott, C., & Benton, N. (2008) Federal lands Swenson, N. G. & Enquist, B. J. (2007) Ecological and evo-
and endangered species: the role of military and other lutionary determinants of a key plant functional trait:
federal lands in sustaining biodiversity. Bioscience, 58, wood density and its community-wide variation across
339–347. latitude and elevation. American Journal of Botany, 94,
Steinke, D., Vences, M., Salzburger, W., & Meyer, A. (2005) 451–459.
TaxI: a software tool for DNA barcoding using distance Swingle, H. S. (1950) Relationships and Dynamics of Bal-
methods. Philosophical Transactions of the Royal Soci- anced and Unbalanced Fish Populations. Agricultural
ety, London B, 360, 1075–1980. Experiment Station of the Alabama Polytechnic Insti-
Stewart, J. G., Schieble, C. S., Cashner, R. C., & Barko V. A. tute, Auburn, AL.
(2005) Long-term Trends in the Bogue Chitto River Fish Swingle, H. S. (1952) Farm pond investigations in
Assemblage: a 27 Year Perspective. Southeastern Natu- Alabama. Journal of Wildlife Management, 16, 243–249.
ralist, 4, 261–272. Taberlet, P. & Fumagalli, L. (1996) Owl pellets as a source
Stirling, G. & Wilsey, B. (2001) Empirical relation- of DNA for genetic studies of small mammals. Molecu-
ships between species richness, evenness, and pro- lar Ecology, 5, 301–305.
portional diversity. The American Naturalist, 158, Taper, M. L. & Lele, S. R. (2004) The Nature of Scientific
286–299. Evidence: Statistical, Philosophical, and Empirical Con-
Stohlgren, T. J. (2007) Measuring Plant Diversity: Lessons siderations.. University of Chicago Press, Chicago.
from the Field. Oxford University Press, Oxford, New Tavares, E. S. & Baker. A. J. (2008) Single mitochondrial
York. gene barcodes reliably identify sister-species in diverse
Stohlgren, T. J., Jarnevich, C., Chong, G. W., & Evangelista, clades of birds. BMC Evolutionary Biology, 8, 81.
P. H. (2006) Scale and plant invasions: a theory of biotic Taylor, L. R. (1961) Aggregation, variance and the mean.
acceptance. Preslia, 78, 405–426. Nature, 189, 732–735.
Storch, D. & Šizling, A. L. (2002) Patterns in commoness Taylor, L. R. (ed.) (1978) Bates, Williams, Hutchinson – A
and rarity in Central European birds: reliability of the Variety of Diversities. Blackwell Publishing, Oxford.
core-satellite hypothesis. Ecography, 25, 405–416. Taylor, L. R. (1984) Assessing and interpreting the spatial
Storch, D., Šizling, A., Reif, J., Polechová, J., Šizlingová, distributions of insect populations. Annual Review of
E., & Gaston, K. J. (2008) The quest for a null model Entomology, 29, 321–357.
for macroecological patterns: geometry of species dis- Taylor, L. R., Kempton, R. A., & Woiwod, I. P. (1976)
tributions at multiple spatial scales. Ecology Letters, 11, Diversity statistics and the log series model. Journal of
771–784. Animal Ecology, 45, 255–271.
OUP CORRECTED PROOF – FINAL, 18/10/2010, SPi

330 REFERENCES

Taylor, L. R., Woiwod, I. P., & Perry, J. N. (1978) The Tiedje, J. M., Asuming-Brempong, S., Nusslein, K., Marsh,
density-dependence of spatial behaviour and the rarity T. L., & Flynn, S. J. (1999) Opening the black box of soil
of randomness. Journal of Animal Ecology, 47, 383–406. microbial diversity. Applied Soil Ecology, 13, 109–122.
Taylor, L. R., Woiwod, I. P., & Perry, J. N. (1979) The Tilman, D. (2001) Functional diversity. In: Encyclopaedia
negative binomial as a dynamic ecological model for of Biodiversity, Levin, S. A. (ed). Academic Press, San
aggregation and the density dependence of k. Journal Diego, CA, pp. 109–120.
of Animal Ecology, 48, 289–304. Tilman, D., Lehman, C. L., & Kareiva, P. (1997a) Popula-
Taylor, C. M., Millican, D. S., Roberts, M. E., & Slack, W. T. tion dynamics in spatial habitats. In: Spatial Ecology,
(2008) Long-term change to fish assemblages and the Tilman, D. & Kareiva, P. (eds). Princeton University
flow regime in a southerneastern U. S. river system after Press, New Jersey, pp. 3–20.
extensive aquatic ecosystem fragmentation. Ecography, Tilman, D., Knops, J., Wedin, D., Reich, P., Ritchie, M., &
31, 787–797. Siemann, E. (1997b) The influence of functional diver-
Teplitsky, C., Mills, J. A., Alho, J. S., Yarrall, J. W., & Merilä, sity and composition on ecosystem processes. Science,
J. (2008) Bergmann’s rule and climate change revisited: 277, 1300.
disentangling environmental and genetic responses in Tipper, J. C. (1979) Rarefaction and rarefiction—the use
a wild bird population. Proceedings of the National and abuse of a method in paleoecology. Paleobiology,
Academy of Sciences. USA, 105, 13492–13496. 5, 423–434.
Terborgh, J. T. (1983) Five New World Primates: a Study Tobler, W. R. (1970) A computer movie simulating urban
in Comparative Ecology. Princeton University Press, growth in the Detroit region. Economic Geography, 46,
Princeton, NJ. 234–240.
Terborgh, J., Foster, R. B., & Núñez, V. P. (1996) Tropical Todd, S. W. (2006) Gradients in vegetation cover, structure
tree communities: a test of the nonequilibrium hypoth- and species richness of Nama-Karoo shrublands in rela-
esis. Ecology, 77, 561–567. tion to distance from livestock watering points. Journal
The H. John Heinz III Center for Science, Economics, of Applied Ecology, 43, 293–304.
and the Environment (2008) The Nation’s Ecosystems: Tokeshi, M. (1990) Niche apportionment or random
core Indicators, Pn: The State of the Nation’s Ecosys- assortment: species abundance patterns revisited. Jour-
tems 2008; Measuring the Land, Waters, and Living nal of Animal Ecology, 59, 1129–1146.
Resources of The United States. Island Press, Washing- Tokeshi, M. (1993) Species abundance patterns and com-
ton, DC., 13–62. munity structure. Advances in Ecological Research, 24,
Thibault, K., White, E., & Ernest, S. K.M. (2004) Temporal 112–186.
dynamics in the structure and composition of a desert Tokeshi, M. (1996) Power fraction: a new explanation
rodent community. Ecology, 85, 2649–2655. of relative abundance patterns in species-rich assem-
Thomas, C. D. & Mallorie, H. C. (1985) Rarity, species rich- blages. Oikos, 75, 543–550.
ness and conservation: butterflies of the Atlas Moun- Tokeshi, M. (1999) Species Coexistience. Blackwell Sci-
tains of Morocco. Biological Conservation, 33, 95–117. ences Ltd, Oxford.
Thomas, L., Buckland, S. T., Rexstad, E. R., Laake, J. L., Tomašových, A. & Kidwell, S. M. (2009) Fidelity of vari-
Strindberg, S., Hedley, S. L., Bishop, J. R. B., Marques, ation in species composition and diversity partition-
T. A., & Burnham, K. P. (2010) Distance software: design ing by death assemblages: time-averaging transfers
and analysis of distance sampling surveys for estimating diversity from beta to alpha levels. Paleobiology, 35,
population size. Journal of Applied Ecology, 47, 5–14. 94–118.
Thomaz, D., Guiller, A., & Clarke, B. (1996) Extreme diver- Torsvik, V., Goksoyr, J., & Daae, F. L. (1990a) High diver-
gence of mitochondrial DNA within species of pul- sity in DNA of soil bacteria. Applied and Environmen-
monate land snails. Proceedings of the Royal Society tal Microbiology, 56, 782–787.
London, B, 263, 363–368. Torsvik, V., Salte, K., Sorheim, R., & Goksoyr, J.
Thompson, W. L. (2004) Sampling Rare or Elusive Species: (1990b) Comparison of phenotypic diversity and
Concepts, Designs, and Techniques for Estimating Pop- DNA heterogeneity in a population of soil bacte-
ulation Parameters. Island Press, Washington. ria. Applied and Environmental Microbiology, 56,
Thrush, S. F., Hewitt, J. E., Dayton, P. K., Coco, G., Lohrer, 776–781.
A. M., Norkko, A., Norkko, J., & Chiantore, M. (2009) Torsvik, V., Daae, F. L., Sandaa, R. A., & Ovreas, L.
Forecasting the limits of resilience: integrating empirical (1998) Novel techniques for analysing microbial diver-
research with theory. Proceedings of the Royal Society sity in natural and perturbed environments. Journal of
London, 276, 3209–3217. Biotechnology, 64, 53–62.
 OUP CORRECTED PROOF – FINAL, 18/10/2010, SPi

REFERENCES 331

Torsvik, V., Ovreas, L., & Thingstad, T. F. (2002) Prokary- Ugland, K. I. & Gray, J. S. (1982) Lognormal distributions
otic diversity – magnitude, dynamics, and controlling and the concept of community equilibrium. Oikos, 39,
factors. Science, 296, 1064–1066. 171–178.
Tosh, C. A., Reyers, B., & van Jaarsveld, A. S. (2004) Ugland, K. I., Gray, J. S., & Ellingsen, K. E. (2003) The
Estimating the abundances of large herbivores in the species – accumulation curve and estimation of species
Kruger National Park using presence-absence data. Ani- richness. Journal of Animal Ecology, 72, 888–897.
mal Conservation, 7, 55–61. Ugland, K. I., Lambshead, F. J. D., McGill, B., Gray, J. S.,
Toth, M. 2008. A new noninvasive method for detecting O’Dea, N., Ladle, R. J., & Whittaker, R. J. (2007) Mod-
mammals from birds’ nests. Journal of Wildlife Manage- elling dimensionality in species abundance distribu-
ment, 72, 1237–1240. tions: description and evaluation of the Gambin model.
Tratalos, J., Fuller, R. A., Evans, K. L., Davies, R. G., New- Evolutionary Ecology Research, 9, 313–324.
son, S. E., Greenwood, J. J.D., & Gaston, K. J. (2007) Ulrich, W. & Buszko, J. (2003) Self-similarity and the
Bird densities are associated with household densities. species-area relation of Polish butterflies. Basic and
Global Change Biology, 13, 1685–1695. Applied Ecology, 4, 263–270.
Travis, J. & Ricklefs, R. E. (1983) A morphological compar- Ulrich, W. & Ollik, M. (2004) Frequent and occasional
ison of island and mainland assemblages of Neotropical species and the shape of relative-abundance distribu-
birds. Oikos, 41, 434–441. tions. Diversity & Distributions, 10, 263–269.
Tringe, S. G., von Mering, C., Kobayashi, A., Salamov, Ulrich, W. & Zalewski, M. (2006) Abundance and co-
A. A., Chen, K., Chang, H. W., Podar, M., Short, J. M., occurrence patterns of core and satellite species of
Mathur, E. J., Detter, J. C., Bork, P., Hugenholtz, P., ground beetles on small lake islands. Oikos, 114,
& Rubin, E. M. (2005) Comparative metagenomics of 338–348.
microbial communities. Science, 308, 554–557. Umina, P. A., Weeks, A. R., Kearney, M. R., McKechnie,
Tscharntke, T., Gathmann, A., & Steffan-Dewenter, I. S. W., & Hoffmann, A. A. (2005) A rapid shift in a classic
(1998) Bioindication using trap-nesting bees and wasps clinal pattern in Drosophila reflecting climate change.
and their natural enemies: community structure and Science, 308, 691–693.
interactions. Journal of Applied Ecology, 35, 708–719. Urich, T., Lanzen, A., Qi, J., Huson, D. H., Schleper, C., &
Tscharntke, T., Klein, A., Kruess, A., Steffan-Dewenter, I., Schuster, S. C. (2008) Simultaneous assessment of soil
& Thies, C. (2005) Landscape perspectives on agricul- microbial community structure and function through
tural intensification and biodiversity – ecosystem ser- analysis of the Meta-Transcriptome. PLoS ONE, 3,
vice management. Ecology Letters, 8, 857–874. e2527.
Tscharntke, T., Sekercioglu, C. H., Dietsch, T. V., Sodhi, Urquhart, N. S. & Kincaid, T. M. (1999) Designs for detect-
N. S., Hoehn, P., & Tylianakis, J. M. (2008) Landscape ing trend from repeated surveys of ecological resources.
constraints on functional diversity of birds and insects Journal of Agricultural, Biological, and Environmental
in tropical agroecosystems. Ecology, 89, 944–951. Statistics, 4, 404–414.
Tuomisto, H. (2010) A diversity of beta diversities: Urquhart, N. S., Paulsen, S. G., & Larsen, D. P. (1998) Mon-
straightening up a concept gone awry. Part 1. Defining itoring for policy-relevant regional trends over time.
beta diversity as a function of alpha and gamma diver- Ecological Applications, 8, 246–257.
sity. Ecography, 33, 2–22. Valentini, A., Miquel, C., Nawaz, M. A., Bellemain,
Tuomisto, H., Ruokolainen, K., Kalliola, R., Linna, A., E., Coissac, E., Pompanon, F., Gielly, L., Cruaud, C.,
Danjoy, W., & Rodriguez, Z. (1995) Dissecting Ama- Nascetti, G., Wincker, P., Swenson, J. E., & Taber-
zonian biodiversity. Science, 269, 63–66. let, P. (2009) New perspectives in diet analysis based
Turner, W., Leitner, W. A., & Rosenzweig, M. L. (2000) on DNA barcoding and parallel pyrosequencing:
Ws2m.exe. http://eebweb.arizona.edu/diversity. the trnL approach. Molecular Ecology Research, 9,
Tylianakis, J. M., Klein, A. -M., & Tscharntke, T. (2005) Spa- 51–60.
tiotemporal variation in the diversity of hymenopters Vamosi, S. M., Heard, S. B., Vamosi, J. C., & Webb, C. O.
across a tropical habita gradient. Ecology, 86, 3296–3302. (2009) Emerging patterns in the comparative analysis of
Tyson, G. W., Chapman, J., Hugenholtz, P., Allen, E. E., phylogenetic community structure. Molecular Ecology,
Ram, R. J., Richardson, P. M., Solovyev, V. V., Rubin, 18, 572–592.
E. M., Rokhsar, D. S., & Banfield, J. F. (2004) Commu- van der Gast, C. J., Ager, D., & Lilley, A. K. (2008) Temporal
nity structure and metabolism through reconstruction scaling of bacterial taxa is influences by both stochas-
of microbial genomes from the environment. Nature, tic and deterministic ecological factors. Environmental
428, 37–43. Microbiology, 10, 1411–1418.
OUP CORRECTED PROOF – FINAL, 18/10/2010, SPi

332 REFERENCES

Vandermeer, J., Granzow de la Cerda, I., Perfecto, I., Venter, J. C., Remington, K., Heidelberg, J. F., Halpern,
Boucher, D., Ruiz, J. & Kaufmann, A. (2004) Multi- A. L., Rusch, D., Eisen, J. A., Wu, D. Y., Paulsen, I.,
ple basins of attraction in a tropical forest: evidence Nelson, K. E., Nelson, W., Fouts, D. E., Levy, S., Knap,
for a nonequilibrium community structure. Ecology, 85, A. H., Lomas, M. W., Nealson, K., White, O., Peterson, J.,
575–579. Hoffman, J., Parsons, R., Baden-Tillson, H., Pfannkoch,
van Rensburg, B. J., McGeoch, M. A., Matthews, W., C., Rogers, Y. H., & Smith, H. O. (2004) Environmental
Chown, S. L., & van Jaarsveld, A. S. (2000) Testing gen- genome shotgun sequencing of the Sargasso Sea. Sci-
eralities in the shape of patch occupancy frequency dis- ence, 304, 66–74.
tributions. Ecology, 81, 3163–3177. Vera, M., Guiliani, N., & Jerez, C. A. (2003) Proteomic and
van Rensburg, B. J., Chown, S. L., & Gaston, K. J. (2002) genomic analysis of the phosphate starvation response
Species richness, environmental correlates, and spatial of Acidithiobacillus ferrooxidans. Hydrometallurgy, 71,
scale: a test using South African birds. The American 125–132.
Naturalist, 159, 566–577. Vera, J. C., Wheat, C. W., Fescemyer, H. W., Frilander, M. J.,
van Straalen, N. M. (1998) Evaluation of bioindicator Crawford, D. L., Hanski, I., & Marden, J. H. (2008) Rapid
systems derived from soil arthropod communities. transcriptome characterization for a nonmodel organ-
Applied Soil Ecology, 9, 429–437. ism using 454 pyrosequencing. Molecular Ecology, 17,
van Straalen, N. M. & Verhoef, H. A. (1997) The devel- 1636–1647.
opment of a bioindicator system for soil acidity based Vile, D., Shipley, B., & Garnier, E. (2006) Ecosystem
on arthropod pH preferences. The Journal of Applied productivity can be predicted from potential relative
Ecology, 34, 217–232. growth rate and species abundance. Ecology Letters, 9,
Van Valen, L. (1973) A new evolutionary law. Evolutionary 1061–1067.
Theory, 1, 1–30. Villéger, S., Mason, N. W.H., & Mouillot, D. (2008) New
Van Valen, L. (1979) Taxonomic survivorship curves. Evo- multidimensional functional diversity indices for a mul-
lutionary Theory, 4, 129–142. tifaceted framework in functional ecology. Ecology, 89,
Vane-Wright, R. I., Humphries, C. J., & Williams, P. H. 2290–2301.
(1991) What to protect? – Systematics and the agony of Violle, C. & Jiang, L. (2009) Towards a trait-based quan-
choice. Biological Conservation, 55, 235–254. tification of species niche. Journal of Plant Ecology, 2,
Veech, J. A., Summerville, K. S., Crist, T. O., & Gering, 87–93.
J. C. (2002) The additive partitioning of species diver- Virginia Natural Heritage Program. (2006) DCR-DNH
sity: recent revival of an old idea. Oikos, 99, 3–9. vegetation plots database, ver. 3.0. Virginia Department
Veldtman, R. & McGeoch, M. A. (2004) Spatially explicit of Conservation and Recreation, Division of Natural
analyses unveil density dependence. Proceedings of the Heritage, Richmond.
Royal Society London, B, 271, 2439–2444. Vitousek, P. M., Ehrlich, P. R., Ehlrich, A. H., & Matson,
Vellend, M. (2001) Do commonly used indices of β- P. A. (1986) Human appropriation of the products of
diversity measure species turnover? Journal of Vegeta- photosynthesis. BioScience, 36, 368–373.
tion Science, 12, 545–552. Vitousek, P. M., Mooney, H. A., Lubchenco, J., & Melillo,
Vellend, M., Harmon, L. J., Lockwood, J. L., Mayfield, J. M. (1997) Human domination of Earth’s ecosystems.
M. M., Hughes, A. R., Wares, J. P., & Sax, D. F. (2007) Science, 277, 494–499.
Effects of exotic species on evolutionary diversification. Volkov, I., Banavar, J. R., Hubbell, S. P., & Maritan, A.
Trends in Ecology and Evolution, 22, 481–488. (2003) Neutral theory and relative species abundance in
Vences, M., Thomas, M., Bonett, R. M., & Vieites, ecology. Nature, 424, 1035–1037.
D. R. (2005a) Deciphering amphibian diversity through Volkov, I., Banavar, J. R., Hubbell, S. P., & Maritan, A.
DNA barcoding: chances and challenges. Philosophi- (2007) Patterns of relative species abundance in rain-
cal Transactions of the Royal Society, London B 360: forests and coral reefs. Nature, 450, 45–49.
1859–1868. Vrba, E. S. (1985) Environment and evolution: alter-
Vences, M., Thomas, M., van der Meijden, A., Chiari, Y., native causes of the temporal distribution of evolu-
& Vieites, D. R. (2005b) Comparative performance of tionary events. South African Journal of Science, 81,
the 16S rRNA gene in DNA barcoding of amphibians. 229–236.
Frontiers in Zoology, 2, 5. Wagner, P. J. (2000) Likelihood tests of hypothesized dura-
Venier, L. A. & Fahrig, L. (1998) Intraspecific abundance- tions: determining and accommodating biasing factors.
distribution relationships. Oikos, 82, 438–490. Paleobiology, 26, 431–449.
 OUP CORRECTED PROOF – FINAL, 18/10/2010, SPi

REFERENCES 333

Walker, B., Kinzig, A., & Langridge, J. (1999) Plant nity structure. Journal of the Marine Biological Associa-
attribute diversity, resilience, and ecosystem function: tion of the United Kingdom, 71, 225–244.
the nature and significance of dominant and minor Warwick, R. M. & Clarke, K. R. (1993) Comparing the
species. Ecosystems, 2, 95–113. severity of disturbance: a meta-analysis of marine mac-
Walker, S. C., Poos, M. S., & Jackson, D. A. (2008) Func- robenthic community data. Marine Ecology Progress
tional rarefaction: estimating functional diversity from Series, 92, 221–231.
field data. Oikos, 117, 286–296. Warwick, R. M. & Clarke, K. R. (1994) Relearning the
Walther, B. A. & Moore, J. L. (2005) The concepts of bias, ABC – taxonomic changes and abundance biomass rela-
precision and accuracy, and their use in testing the per- tionships in disturbed benthic communities. Marine
formance of species richness estimators, with a litera- Biology, 118, 739–744.
ture review of estimator performance. Ecography, 28, Warwick, R. M. & Clarke, K. R. (1995) New ‘biodiversity’
815–829. measures reveal a decrease in taxonomic distinctness
Walther, B. A. & Morand, S. (1998) Comparative perfor- with increasing stress. Marine Ecology Progress Series,
mance of species richness estimation method. Parasitol- 129, 301–305.
ogy, 116, 395–405. Warwick, R. M. & Clarke, K. R. (1998) Taxonomic distinct-
Wang, S. C. (2003) On the continuity of background and ness and environmental assessment. Journal of Applied
mass extinction. Paleobiology, 29, 455–467. Ecology, 35, 532–543.
Wang, S. C. & Everson, P. J. (2007) Confidence intervals Watling, L. & Norse, E. A. (1998) Disturbance of the seabed
for pulsed mass extinction events. Paleobiology, 33, by mobile fishing gear: a comparison to forest clearcut-
324–336. ting. Conservation Biology, 12, 1180–1197.
Ward, R. D. (2009) DNA barcoding divergence among Webb, C. O. (2000) Exploring the phylogenetic structure of
species and genera of birds and fishes. Molecular Ecol- ecological communities: an example for rain forest trees.
ogy Research, 9, 1077–1085. The American Naturalist, 156, 145–155.
Ward, S. A., Sunderland, K. D., Chambers, R. J. & Dixon Webb, C. O., Ackerly, D. D., McPeek, M. A., &
A. F. G. (1986) The use of incidence counts for estimation Donoghue, M. J. (2002) Phylogenies and community
of cereal aphid populations. 3. Population development ecology. Annual Review of Ecology and Systematics, 33,
and the incidence-density relation. Netherlands Journal 475–505.
of Plant Pathology, 92, 175–183. Webb, T. J., Noble, D., & Freckleton, R. P. (2007)
Ware, D. M. & Thomson, R. E. (2005) Bottom-up ecosys- Abundance-occupancy dynamics in a human-
tem trophic dynamics determine fish production in the dominated environment: linking interspecific and
northeast Pacific. Science, 308, 1280–1284. intraspecific trends in British farmland and woodland
Ware, S. J., Rees, H. L., Boyd, S. E., & Birchenhough, S. N. birds. Journal of Animal Ecology, 76, 123–134.
(2008) Performance of selected indicators in evaluating Webb, C. O., Ackerly, D. D., & Kembel, S. W. (2008) Phy-
the consequences of dredged material relocation and locom: software for the analysis of phylogenetic com-
marine aggregate extraction. Ecological Indicators, 9, munity structure and trait evolution. Bioinformatics, 24,
704–718. 2098–2100.
Warming, E. 1909. Oecology of Plants. Clarendon Press, Weiher, E. (2004) Why should we constrain stress
Oxford. and limitation? – Why conceptual terms deserve
Warren, P. H. & Gaston, K. J. (1997) Interspecific broad definitions. Journal of Vegetation Science, 15,
abundance-occupancy relationships: a test of mecha- 569–571.
nisms using microcosms. Journal of Animal Ecology, 66, Weiher, E. & Keddy, P. A. (1995) Assembly rules, null
730–742. models, and trait dispersion, new questions from old
Wartenberg, D., Ferson, S., & Rohlf, F. J. (1987) patterns. Oikos, 74, 159–164.
Putting things in order: a critiqye of detrended cor- Weiher, E., Clarke, G. D.P., & Keddy, P. A. (1998) Commu-
respondence analysis. The American Naturalist, 129, nity assembly rules, morphological dispersion, and the
434–448. coexistence of plant species. Oikos, 81, 309–322.
Warwick, R. M. (1986) A new method for detecting pol- Weiher, E., van der Werf, A., Thompson, K., Roderick,
lution effects on marine macrobenthic communities. M., Garnier, E., & Eriksson, O. (1999) Challenging
Marine Biology, 92, 557–562. Theophrastus: a common core list of plant traits for
Warwick, R. M. & Clarke, K. R. (1991) A comparison of functional ecology. Journal of Vegetation Science, 10,
some methods for analysing changes in benthic commu- 609–620.
OUP CORRECTED PROOF – FINAL, 18/10/2010, SPi

334 REFERENCES

Weiss, R. E. & Marshall, C. R. (1999) The uncertainty in the Wiegand, T. & Moloney, K. A. (2004) Rings, circles, and
true end point of a fossil’s stratigraphic ranges when null-models for point pattern analysis in ecology. Oikos,
stratigraphic sections are sampled discretely. Mathe- 104, 209.
matical Geology, 31, 435–453. Wilcox, B. A. (1978) Supersaturated island faunas: a
Weitzman, M. L. (1992) On diversity. Quarternary Journal species-age relationship for lizards on post-pleistocene
of Economics, 107, 363–405. land-bridge islands. Science, 199, 996–998.
Wellington, E. M. H., Berry, A., & Krsek, M. (2003) Resolv- Williams, C. B. (1964) Patterns in the Balance of Nature
ing functional diversity in relation to microbial commu- and Related Problems in Quantitative Ecology. Acad-
nity structure in soil: exploiting genomics and stable emic Press, London.
isotope probing. Current Opinion in Microbiology, 6, Williams, B. K., Nichols, J. D., & Conroy, M. J. (2002)
295–301. Analysis and Management of Animal Populations. Aca-
Westoby, M. (1999) A leaf-height-seed (LHS) plant ecology demic Press, San Diego.
strategy scheme. Plant and Soil, 199, 213–227. Williamson, M. & Gaston, K. J. (2005) The lognormal dis-
White, E. P. (2007) Spatiotemporal scaling of species rich- tribution is not an appropriate null hypothesis for the
ness: patters, processes, and implications. In: Scaling species-abundance distribution. Journal of Animal Ecol-
Biodiversity, Storch, D., Marquet, P. A., & Brown, J. H. ogy, 74, 409–422.
(eds). Cambridge University Press, Cambridge, pp. 325– Willis, J. C. (1922) Age and Area: A Study in Geographical
346. Distribution and Origin of Species. Cambridge Univer-
White, G. C., Burnham, K. P., & Anderson, D. R. (2001) sity Press, Cambridge.
Advanced features of Program MARK. In: Wildlife, Willis, D. W. & Murphy, B. R. (1996) Planning for sam-
Land, and People: Priorities for the 21st Century, Field, pling. In: Fisheries Techniques, Murphy, B. R. & Willis,
R., Warren, R. J., Okarma, H., & Sievert, P. R. (eds). The D. W. (eds). American Fisheries Society, Bethesda, MD,
Wildlife Society, Bethesda, MD, pp. 368–377. USA, pp. 1–15.
White, E. P., Ernest, S. K. M., & Thibault, K. M. (2004) Wilsey, B. J., Chalcraft, D. R., Bowles, C. M., & Willig,
Trade-offs in community properties through time in M. R. (2005) Relationships among indices suggest that
a desert rodent community. The American Naturalist, richness is an incomplete surrogate for grassland biodi-
164, 670–676. versity. Ecology, 86, 1178–1184.
White, E. P., Adler, P. B., Lauenroth, W. K., Gill, R. A., Wilson, J. B. (1991) Methods for fitting domi-
Greenberg, D., Kaufman, D. M., Rassweiler, A., Rusak, nance/diversity curves. Journal of Vegetation Science,
J. A., Smith, M. D., Steinbeck, J. R., Waide, R. B., & Yao, 2, 35–46.
J. (2006) A comparison of the species-time relationship Wilson, J. B. (1993) Would we recognise a broken-
across ecosystems and taxonomic groups. Oikos, 112, stick community if we found one? Oikos, 67, 181–
185–195. 183.
White, E. P., Enquist, B. J., & Green, J. L. (2008) On estimat- Wilson, L. T. & Room, P. M. (1983) Clumping patterns
ing the exponent of power-law frequency distributions. of fruit and arthropods in cotton, with implications
Ecology, 89, 905–912. for binomial sampling. Environmental Entomology, 12,
Whitman, W. B., Coleman, D. C., & Wiebe, W. J. (1998) 296–302.
Prokaryotes: the unseen majority. Proceedings of the Wilson, J. B., Wells, T. C. E., Trueman, I. C., Jones, G.,
National Academy of Sciences of the United States of Atkinson, M. D., Crawley, M. J., Dodd, M. E., & Sil-
America, 95, 6578–6583. vertown, J. (1996a) Are there assembly rules for plant
Whittaker, R. H. (1952) A study of summer foliage insect species abundance? An investigation in relation to soil
communities in the Great Smoky Mountains. Ecological resources and successional trends? Journal of Ecology,
Monographs, 22, 1–44. 84, 527–538.
Whittaker, R. H. (1960) Vegetation of the Siskiyou moun- Wilson, D. E., Nichols, J. D., Rudran, R., & Southwell,
tains, Oregon and California. Ecological Monographs, C. (1996b) Introduction. In: Measuring and Monitoring
30, 279–338. Biological Diversity, Standard Methods for Mammals,
Whittaker, R. H. (1965) Dominance and diversity in land Wilson, D. E., Cole, R. F., Nichols, J. D., Rudran, R., &
plant communities. Science, 147, 250–260. Foster, M. S. (eds). Smithsonian Institution Press, Wash-
Whittaker, R. H. (1972) Evolution and measurement of ington, DC, pp. 1–7.
species diversity. Taxon, 12, 213–251. Wilting, A., Buckley-Beason, V. A., Feldhaar, H., Gadau, J.,
Whittaker, R. H. (1975) Communities and Ecosystems. 2nd O’Brien, S. J., & Linsenmair, K. E. (2007) Clouded leop-
edn. MacMillan Publishers, New York. ard phylogeny revisited: support for species recognition
 OUP CORRECTED PROOF – FINAL, 18/10/2010, SPi

REFERENCES 335

and population division between Borneo and Sumatra. Tjoelker, M. G., Veneklaas, E. J., & Villar, R. (2004)
Frontiers in Zoology, 4, 15. The worldwide leaf economics spectrum. Nature, 428,
Wimberly, M. C., Yabsley, M. J., Baer, A. D., Dugan, V. G., 821–827.
& Davidson, W. R. (2008) Spatial heterogeneity of cli- Wright, J. F. (2000) An introduction to RIVPACS. In:
mate and land-cover constraints on distributions of tick- Assessing the Biological Quality of Fresh Waters: RIV-
borne pathogens. Global Ecology and Biogeography, 17, PACS and Other Techniques, Wright, J. F., Sutcliffe,
189–202. D. W., & Furse, M. T. (eds). Freshwater Biological Asso-
Winemiller, K. O. (1990) Spatial and temporal variation in ciation, Ambleside, Cumbria, pp. 1–24.
tropical fish trophic networks. Ecological Monographs, Yang, L. H. (2004) Periodical cicadas as resource pulses in
60, 331–367. North American forests. Science, 306, 1565–1567.
Winker, K. (2009) Reuniting phenotype and genotype in Yin, Z. Y., Ren, H., Zhang, Q. M., Peng, S. L., Guo, Q. F.,
biodiversity research. BioScience, 59, 657–665. & Zhou, G. Y. (2005) Species abundance in a forest
Woese, C. R. (1987) Bacterial evolution. Microbiological community in south China: a case of Poisson lognormal
Reviews, 51, 221–271. distribution. Journal of Integrative Plant Biology, 47,
Wolda, H. (1981) Similarity indices, sample size and diver- 801–810.
sity. Oecologia, 50, 296–302. Yoccoz, N. G., Nichols, J. D., & Boulinier, T. (2001) Moni-
Wolda, H. (1983) Diversity, diversity indices and tropical toring of biological diversity in space and time. Trends
cockroaches. Oecologia, 58, 290–298. in Ecology and Evolution, 16, 446–453.
Wolf, J. H. D. (2005) The response of epiphytes to anthro- Zahariev, M., Dahl, V., Chen, W., & Lévesque, C. A. (2009)
pogenic disturbance of pine-oak forests in the highlands Efficient algorithms for the discovery of DNA oligonu-
of Chiapas, Mexico. Forest Ecology and Management, cleotide barcodes from sequence databases. Molecular
212, 376–393. Ecology Research, 9(Suppl. 1), 58–64.
Woodcock, S., van der Gast, C. J., Bell, T., Lunn, Zamora, J., Verdú, J. R., & Galante, E. (2007) Species rich-
M., Curtis, T. P., Head, I. M., & Sloan, W. T. ness in Mediterranean agroecosystems: spatial and tem-
(2006) Neutral assembly of bacterial communities. In: poral analysis for biodiversity conservation. Biological
Joint Symposium of the Environmental-Microbiology- Conservation, 134, 113–121.
Group/British-Ecological-Society/Society-for- General- Zapiola, M. L., Campbell, C. K., Butler, M. D., & Mallory-
Microbiology, York, pp. 171–180. Smith, C. A. (2008) Escape and establishment of trans-
Woodcock, S., van der Gast, C. J., Bell, T., Lunn, M., Curtis, genic glyphosate-resistant creeping bentgrass Agrostis
T. P., Head, I. M., & Sloan, W. T. (2007) Neutral assembly stolonifera in Oregon, USA: a 4-year study. Journal of
of bacterial communities. FEMS Microbiology Ecology, Applied Ecology, 45, 486–494.
62, 171–180. Zar, J. H. (1996) Biostatistical Analysis, 3rd edn. Prentice-
Wootton, J. T. (2005) Field parameterization and experi- Hall, Upper Saddle River, New Jersey, USA
mental test of the neutral theory of biodiversity. Nature, Zhang, D. A., Brecke, P., Lee, H. F., He, Y.-Q., & Zhang,
433, 309–312. J. (2007) Global climate change, war, and population
Wright, S. (1951) The genetic structure of populations. decline in recent history. Proceedings of the National
Annals of Eugenics, 15, 323–354. Academy of Sciences. USA, 104, 19214–19219.
Wright, D. H. (1991) Correlations between incidence and Zillio, T. & Condit, R. (2007) The impact of neutrality,
abundance are expected by chance. Journal of Biogeog- niche differentiation and species input on diversity and
raphy, 18, 463–466. abundance distributions. Oikos, 116, 931–940.
Wright, I. J., Reich, P. B., Westoby, M., Ackerly, D. D., Zuckerberg, B., Porter, W. F., & Corwin, K. (2009) The
Baruch, Z., Bongers, F., Cavender-Bares, J., Chapin, T., consistency and stability of abundance-occupancy rela-
Cornelissen, J. H., Diemer, M., Flexas, J., Garnier, E., tionships in large-scale population dynamics. Journal of
Groom, P. K., Gulias, J., Hikosaka, K., Lamont, B. B., Animal Ecology, 78, 172–181.
Lee, T., Lee, W., Lusk, C., Midgley, J. J., Navas, M. L., Zuckerka, E. & Pauling, L. (1965) Molecules as documents
Niinemets, U., Oleksyn, J., Osada, N., Poorter, H., Poot, of evolutionary history. Journal of Theoretical Biology,
P., Prior, L., Pyankov, V. I., Roumet, C., Thomas, S. C., 8, 357.