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ADVANCES IN ECOLOGICAL
RESEARCH

Series Editor

GUY WOODWARD
Imperial College London
Silwood Park Campus
Ascot, Berkshire, United Kingdom
Academic Press is an imprint of Elsevier
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CONTRIBUTORS

Donald J. Baird
Environment Canada @ Canadian Rivers Institute, Department of Biology, University of
New Brunswick, Fredericton, New Brunswick, Canada
Mark V. Brown
Evolution and Ecology Research Centre, School of Biological, Earth and Environmental
Sciences, University of New South Wales, Sydney, New South Wales, Australia
James M. Bullock
NERC Centre for Ecology and Hydrology, Wallingford, Oxfordshire, United Kingdom
Anthony A. Chariton
CSIRO Oceans and Atmosphere, Lucas Heights, New South Wales, Australia
Steve Cinderby
Environment, University of York, Heslington, York, United Kingdom
Katherine A. Dafforn
Evolution and Ecology Research Centre, School of Biological, Earth and Environmental
Sciences, University of New South Wales, Sydney, and Sydney Institute of Marine Sciences,
Mosman, New South Wales, Australia
Isabelle Durance
Cardiff School of Biosciences, Cardiff, United Kingdom
Bridget Emmett
NERC Centre for Ecology & Hydrology, Environment Centre Wales, Bangor, Gwynedd,
United Kingdom
Stephanie Gardham
Department of Environment and Geography, Macquarie University, Sydney, and CSIRO
Oceans and Atmosphere, Lucas Heights, New South Wales, Australia
Jim Harris
Environmental Science and Technology Department, School of Applied Sciences,
University of Cranfield, Cranfield, United Kingdom
Kevin Hicks
Environment, University of York, Heslington, York, United Kingdom
Grant C. Hose
Department of Biological Sciences, Macquarie University, Sydney, New South Wales,
Australia
Emma L. Johnston
Evolution and Ecology Research Centre, School of Biological, Earth and Environmental
Sciences, University of New South Wales, Sydney, and Sydney Institute of Marine Sciences,
Mosman, New South Wales, Australia

vii
viii Contributors

Brendan P. Kelaher
National Marine Science Centre, Southern Cross University, Coffs Harbour, New South
Wales, Australia
Tom H. Oliver
NERC Centre for Ecology and Hydrology, Wallingford, Oxfordshire, United Kingdom
Dave Paterson
Scottish Oceans Institute, East Sands, University of St. Andrews, St. Andrews, Scotland,
United Kingdom
Dave Raffaelli
Environment, University of York, Heslington, York, United Kingdom
Stuart L. Simpson
CSIRO Land and Water, Lucas Heights, New South Wales, Australia
Sarah Stephenson
CSIRO Oceans and Atmosphere, Lucas Heights, New South Wales, Australia
Melanie Y. Sun
Evolution and Ecology Research Centre, School of Biological, Earth and Environmental
Sciences, University of New South Wales, Sydney, and Sydney Institute of Marine Sciences,
Mosman, New South Wales, Australia
Piran C.L. White
Environment, University of York, Heslington, York, United Kingdom
PREFACE

Guy Woodward*, Alex J. Dumbrell†, Donald J. Baird{,

Mehrdad Hajibabaei}
*Imperial College London, United Kingdom
†
University of Essex, United Kingdom
{
Environment Canada, Canada
}
University of Guelph, Canada

Ecology is entering previously uncharted waters, in the wake of the huge

growth in “Big Data” approaches that are beginning to dominate the field.
Previously, the rate at which ecology advanced, especially when dealing
with large scales and multispecies systems, was limited by the paucity of
empirical data, which was often collected in a painstaking and labour-
intensive manner by a few dedicated individuals. We are now entering a
phase where the polar opposite situation is the norm and the new rate-
limiting step is the ability to process the vast quantities of data that are being
generated on an almost industrial scale and, more importantly, to interpret
their ecological significance. This ecoinformatics revolution is happening
simultaneously on many fronts: from the exponential increases in sequenc-
ing power using novel molecular techniques, to the increased capacity for
remote sensing and high-resolution GIS, and the marshalling of huge vol-
umes of metadata collected by both the scientific community and the rapidly
swelling ranks of Citizen Scientists. This latter group will account for a size-
able portion of the Big Data that needs to be handled in future: Citizen
Scientists are already starting to eclipse the capacity of official bodies to carry
out large-scale and long-term routine data collection and biomonitoring, as
the traditional boundaries between natural and social sciences and data own-
ership become evermore blurred. This democratisation and sharing of data
among scientists, across disciplines, and with the lay public that has gone
hand in hand with Big Data approaches is altering the very nature of scien-
tific discourse in a profound manner, and in ways that we do not yet fully
comprehend. This volume highlights three examples of some of the main
Big Data trends and their potential to address the big questions in ecology
in this new multidisciplinary era.
In addition to geospatial data series and large federated databases that are
becoming commonplace, particularly in the field of biomonitoring and
remote sensing, ecogenomics represents both one of the greatest informatics
resources and one of the biggest emerging challenges in ecology. This is a

ix
x Preface

rapidly growing field, and the recent explosion of molecular ecology

embraces a plethora of terms that were barely on the horizon a decade
ago, including metasystematics, metranscriptomics, and functional geno-
mics, among others. These terms are entering the day-to-day lexicon of
ecologists at an accelerating rate, and they are now frequently seen in both
grant proposals and peer-reviewed publications. Even so, most ecological
studies that use such approaches are still restricted to descriptive “fishing
expeditions”, rather than being used for explicit hypothesis generation or
testing. Thus, although countless recent papers have revealed previously
unguessed-at levels of biodiversity in even the most remote and hostile envi-
ronments, particularly in the microbial world, very few have been couched
in the rigorous hypothetico-deductive framework that is the bread and but-
ter of the more established fields of mainstream ecology. In the light of this, it
is critically important that in the heady rush to adopt Big Data approaches,
we must take care to corroborate them with more traditional techniques, if
only to enable a degree of handshaking before jettisoning obsolete technol-
ogies: otherwise, we run the risk of creating a schism in ecology that could
lead to huge inefficiencies in the future, where we simply end up asking the
same old questions but with different data, rather than truly advancing
the field.
Before ecogenomics techniques and data are widely applied, they must
therefore first provide credible evidence that they can do at least what exis-
ting techniques can do, but with added value. In the paper by Dafforn et al.
(2014), the authors describe a case study that applies a metagenomics
approach in estuarine ecosystems in Australia, while comparing the results
with a parallel approach using traditional taxonomic analysis. The authors
demonstrate convincingly that, despite the bioinformatics challenges, the
ecogenomics approaches clearly provide data far more rapidly and effi-
ciently, with benthic assemblages resolved at higher levels of taxonomic res-
olution. Perhaps even more importantly, though, they provide far stronger
insights into the major environmental drivers of composition across a range
of contrasting estuarine ecosystem conditions. In the second paper in the
volume, by Gardham et al. (2014), a comparable metagenomics approach
is applied to analyse mesocosm experiments studying the effects of metal pol-
lution on freshwater benthic assemblages. When focused on the microbial
community in particular, the exploratory power of multivariate approaches
is greatly enhanced, in terms of exploring assemblage pattern-driver rela-
tionships, and this offers a huge new potential means of ecological indicator
development. While metagenomics approaches are now being more widely
applied in ecosystem research, both studies illustrate the opportunities
Preface xi

created through the application of these new techniques, and also the emer-
gence of the new generation of studies that are starting to embed Big Data
into more explicitly hypothesis-focused frameworks. They also illustrate
how Big Data processing requirements make it more crucial than ever to
understand the complex analytical pathways that turn terabytes of DNA
sequence into trustworthy ecological information.
The mushrooming of such sequence-based databases provides a vast and
potentially invaluable resource for current and future generations of ecolo-
gists (Fig. 1), but increasingly concerns have been raised about the stringency
of quality assurance and ground-truthing of the underlying data, which
could seriously undermine the field if errors are being propagated unwit-
tingly and repeatedly and on a potentially grand scale: i.e. there can be a
world of difference between Big Data and Good Data. Notwithstanding

Figure 1 The number of DNA sequences contained within the GenBank database (the
principal non-NGS sequence repository) as a function of time (open symbols). This acts
as a proxy for publication quantity as you can't publish DNA sequences without first provid-
ing them to GenBank. These data include non-ecological DNA sequences. The solid symbol
at the top is current number of DNA sequences contained within the MG-RAST repository,
which only stores metagenome data, i.e. whole-community ecological data. Note the
change in axis scales and how metagenomic approaches over the course of a couple of
years has now produced more DNA sequences than the entire GenBank collection.
xii Preface

these underlying issues, the rate of data generation that can now be achieved
at relatively little cost is breathtaking and would have been inconceivable just
a few years ago. It is also the sophistication of the data and the fact that mul-
tiple forms of information are being synthesised and compiled simulta-
neously that form the hallmarks of the most recent advances in this area.
Collated databases containing outputs from multiple ecological studies will
soon surpass single studies in terms of data breadth, and emerging molecular
(e.g. next-generation sequencing) approaches will dwarf other ecological
data in terms of depth and breadth of coverage of multispecies systems: in
fact, it could be argued that this revolution has already happened (Fig. 1).
There is another major source of large ecological datasets that are becom-
ing increasingly prevalent, which also present associated Big Data challenges,
and this comes in the form of the outputs of large-scale multi-institutional
(often multi-national) research programmes. Within the UK, the Natural
Environment Research Council recently launched the Biodiversity and
Ecosystem Service Sustainability Programme (BESS; 2011–2017), a
multimillion pound investment that represents a UK-wide effort to charac-
terise the links between biodiversity stocks and flows of ecosystem services
across a broad spectrum of terrestrial and aquatic landscapes (http://www.
nerc-bess.net/). This ambitious programme is led by Professor Dave
Raffaelli (University of York), and the paper he leads in this volume
(Raffaelli et al., 2014) highlights the Big Data challenges faced by BESS
and the approaches being used to overcome these. Raffaelli et al. begin with
lessons that can be learnt from history and draw the readers’ attention to the
pioneering International Biological Programme (IBP), which ran from 1964
to 1974 and was one of the first to attempt what we now call Big Data ecol-
ogy. The IBP was in many ways too far ahead of its time, and it was beset by
numerous problems resulting from its own huge complexity and scale of
ambition, and it was abandoned long before its full potential could be rea-
lised. Raffaelli et al. highlight how half a century later we are only now
finally starting to be able to deal with the size and scope of this style of
research programme. It is only in the last few years that we have been able
to wield the necessary tools for such a complex and challenging undertaking,
and these were unfortunately lacking in the 1960s. To illustrate this, Raffaelli
et al. explore the different approaches taken by the four main projects within
BESS, which work to answer similar ecological questions, but in very dif-
ferent systems: remote upland streams, lowland agricultural landscapes,
urban areas, and coastal environments. They then demonstrate how data
from each of these can be integrated before looking to the future to address
Preface xiii

emerging challenges as the datasets continue to expand in both volume and

scope. This form of large-scale and multidisciplinary research programme is
increasingly becoming the norm, and indeed, it is a prerequisite for many
research funding schemes, especially in Europe, as it is widely seen as being
essential for understanding and predicting the behaviour of seemingly com-
plex ecosystems in the human-dominated twenty-first century. The days of
the lone researcher working in splendid isolation on a narrowly focussed
problem are fading fast, as the need to develop broad collaborations that span
traditional disciplinary boundaries means that “science by committee” has
become the norm in the age of Big Data: this is especially true at the interface
of the natural and social sciences, where the impacts of humans on ecosystem
services have become a huge focus of research activity in a matter of just a
few years. Whether this fundamental shift in the way ecology is conducted is
entirely healthy is a question that merits further debate, as there is a real dan-
ger that the gifted auteurs that have previously driven many of the field’s
biggest advances may be left behind in this very different future landscape.
Nonetheless, it seems inevitable that at least in the foreseeable future, the
impetus will continue to be with ambitious, large-scale science, as the renais-
sance of the IBP’s legacy continues to gather strength, underpinned by
advances in Big Data. Given the rapidly accelerating rate at which ecology
is now progressing, it seems certain that dramatic revolutionary advances lie
ahead in the near future that we cannot yet even imagine, and we hope that
this volume helps to move us a little further and a little faster forwards
towards that goal.

REFERENCES
Dafforn, K.A., Baird, D.J., Chariton, A.A., Sun, M.Y., Brown, M., Simpson, S.L.,
Kelaher, B.P., Johnston, E.L., 2014. Faster, higher and stronger? The pros and cons
of molecular faunal data for assessing ecosystem condition. Adv. Ecol. Res. 51, 1–40.
Gardham, S., Hose, G., Stephenson, S., Chariton, A., 2014. DNA metabarcoding meets
experimental ecotoxicology: advancing knowledge on the ecological effects of copper
in freshwater ecosystems. Adv. Ecol. Res. 51, 79–104.
Raffaelli, D., Bullock, J.M., Cinderby, S., Durance, I., Emmett, B., Harris, J., Hicks, K.,
Oliver, T.H., Paterson, D., White, P.C.L., 2014. Big data and ecosystem research
programmes. Adv. Ecol. Res. 51, 41–78.
 CHAPTER ONE

Faster, Higher and Stronger? The

Pros and Cons of Molecular Faunal
Data for Assessing Ecosystem
Condition
Katherine A. Dafforn*,†,1, Donald J. Baird{, Anthony A. Chariton},
Melanie Y. Sun*,†, Mark V. Brown*, Stuart L. Simpsonk,
Brendan P. Kelaher}, Emma L. Johnston*,†
*Evolution and Ecology Research Centre, School of Biological, Earth and Environmental Sciences, University
of New South Wales, Sydney, New South Wales, Australia
†
Sydney Institute of Marine Sciences, Mosman, New South Wales, Australia
{
Environment Canada @ Canadian Rivers Institute, Department of Biology, University of New Brunswick,
Fredericton, New Brunswick, Canada
}
CSIRO Oceans and Atmosphere, Lucas Heights, New South Wales, Australia
}
National Marine Science Centre, Southern Cross University, Coffs Harbour, New South Wales, Australia
k
CSIRO Land and Water, Lucas Heights, New South Wales, Australia
1
Corresponding author: e-mail address: k.dafforn@unsw.edu.au

Contents
1. Introduction 2
1.1 Bioassessment and monitoring of ecosystem change 2
1.2 Application of molecular tools in biomonitoring 6
1.3 Assessing estuarine condition 6
1.4 Case study: Contrasting molecular big data with traditional morphological
tools 8
2. Methods 9
2.1 Estuarine survey design 9
2.2 Benthic sediment sampling 9
2.3 Morphological biomonitoring 11
2.4 Molecular biomonitoring 11
2.5 Measuring anthropogenic stressors 12
2.6 Contrasting morphological and molecular tools 13
3. Results 14
3.1 Morphological and molecular community composition 14
3.2 Relating anthropogenic stressors to sediment communities 19
3.3 Diversity measures 22
4. Discussion 27
4.1 Characterising ecological systems 27
4.2 Distinguishing the effects of multiple stressors 29

Advances in Ecological Research, Volume 51 # 2014 Elsevier Ltd 1

ISSN 0065-2504 All rights reserved.
http://dx.doi.org/10.1016/B978-0-08-099970-8.00003-8
2 Katherine A. Dafforn et al.

4.3 The ‘new diversity’ 30

4.4 Conclusion 32
Acknowledgements 32
Appendix A. Metal Contaminant Concentrations (mg/kg dry wt) in Benthic Sediments 33
Appendix B. Priority PAH Contaminant Concentrations (μg/kg dry wt) in Benthic
Sediments 34
Appendix C. Sediment Quality (Silt Content (% <63 μm)) and Enrichment Measures
(Chlorophyll a (μg/g) and Total Organic Carbon (%)) in Benthic Sediments 36
References 37

Abstract
Ecological observation of global change processes is dependent on matching the scale
and quality of biological data with associated geophysical and geochemical driver infor-
mation. Until recently, the scale and quality of biological observation on natural assem-
blages has often failed to match data generated through physical or chemical platforms
due to constraints of cost and taxonomic resolution. With the advent of next-generation
DNA sequencing platforms, creating ‘big data’ scale observations of biological assem-
blages across a wide range of phylogenetic groups are now a reality. Here we draw from
a variety of studies to illustrate the potential benefits and drawbacks of this new data
source for enhancing our observation of ecological change compared with traditional
methods. We focus on a key habitat—estuaries—which are among the most threat-
ened by anthropogenic change processes. When community composition data derived
using morphological and molecular approaches were compared, the increased level of
taxonomic resolution from the molecular approach allowed for greater discrimination
between estuaries. Apart from higher taxonomic resolution, there was also an order of
magnitude more taxonomic units recorded in the molecular approach relative to the
morphological. While the morphological data set was constrained to traditional
macroinvertebrate sampling, the molecular tools could be used to sample a wide range
of taxa from the microphytobenthos, e.g., diatoms and dinoflagellates. Furthermore, the
information provided by molecular techniques appeared to be more sensitive to a
range of well-established drivers of benthic ecology. Our results indicated that molec-
ular approaches are now sufficiently advanced to provide not just equivalent informa-
tion to that collected using traditional morphological approaches, but rather an order of
magnitude bigger, better, and faster data with which to address pressing ecological
questions.

1. INTRODUCTION
1.1. Bioassessment and monitoring of ecosystem change
The ecological measurement of global change processes is dependent on
matching the scale and quantity of biological data with associated
The Pros and Cons of Molecular Faunal Data 3

geophysical and geochemical driver information (Baird and Hajibabaei,

2012). Until recently, the scale and quality of biological observation on nat-
ural assemblages has failed to match data generated through physical and
chemical platforms due to constraints of cost and taxonomic resolution
(Friberg et al., 2011). With the advent of next-generation DNA sequencing
platforms, generating ‘big data’ scale observations on biological assemblages
across a wide range of phylogenetic groups is now a reality (Baird and
Hajibabaei, 2012; Brown et al., 2009; Chariton et al., 2010a; Hajibabaei
et al., 2011; Kohli et al., 2014; Sogin et al., 2006; Sun et al., 2013). Big data
can be defined as large volumes of data that require novel data processing
tools and strategies (Hampton et al., 2013). Dealing with big data can be
challenging, but presents great opportunity for data-intensive bio-
monitoring approaches. Here, we illustrate the potential advantages of this
new data source in observation of ecological change, illustrating the pros and
cons of this new approach, focusing on estuaries which are among the most
anthropogenically disturbed marine habitats (Kennish, 2002).
Observing natural ecosystems, particularly at large scales, requires a con-
sistent approach to data collection (Birk et al., 2013). A major current con-
straint is the necessity of limiting the phylogenetic breadth of observation to
what is practical in terms of timely data generation (Friberg et al., 2011). For
this reason, studies have tended to converge on particular groups of well-
studied and taxonomically tractable species (e.g. fish, macroinvertebrates)
(Chariton et al., 2010b; Dafforn et al., 2012, 2013; McKinley et al.,
2011), which are characterised by ease of collection and identification, as
well as their importance to industry (e.g. fisheries) and ecosystem processes.
However, despite the widespread collection of such data in ecological studies
and environmental monitoring programmes, data integration to link com-
mon responses across taxonomic groups remains challenging.
Biomonitoring science focuses on using patterns in the occurrence and
characteristics of individual taxa and/or biological assemblages to interpret
ecological change. This normally takes the form of simple binary analysis
(divergent/non-divergent), or a ‘shades of grey’ classification. Most bio-
monitoring programmes employ sets of phylogenetically constrained obser-
vations to bolster a lack of comprehensive biological coverage. For example,
in river monitoring, separate sampling approaches are employed to study
fish, macroinvertebrates and attached algae (periphyton) (Birk et al.,
2012; Bonada et al., 2006). Generally, these observational approaches have
evolved in parallel, but inevitably suffer from divergent spatio-temporal
sampling approaches and the amount of cost and effort expended to obtain
4 Katherine A. Dafforn et al.

samples (Cao and Hawkins, 2011). Their compatibility for integrated anal-
ysis of ecosystem-level change is therefore questionable. It should also be
noted that this incompatibility is also driven by the vagaries arising from par-
allel research traditions resulting in divergent communities of scientific
practice.
Observing ecological change requires careful and clear formulation of
research questions. For example, Magurran et al. (2010) noted that the spa-
tial and temporal properties of the observation units (e.g. taxa groups, habitat
units) should be pertinent to the question being asked. For example, if a
migratory species is being studied, it is important to ensure that the species
is present when seasonally intermittent stressors are the subject of study in a
specified habitat area. Moreover, with an increased focus on improved
observational quality in terms of taxon occurrence at local scales, with
increased frequency (e.g. as suggested by Harris and Heathwaite, 2012), then
separation of driver–response signals from noise should be possible, and, ide-
ally, quantifiable in either an absolute or probabilistic sense (Baird and
Hajibabaei, 2012).
Comparisons of relevant chemical contaminant concentrations and eco-
logical health measures across estuaries are challenging, due to large natural
variation. To overcome this, comparisons over multiple estuaries require
substantial spatial and temporal replication to provide adequate statistical
power to detect human impacts (Underwood, 1991). Recent efforts to
monitor these impacts have focused on integrating information collected
from chemical and ecological monitoring into a more holistic understanding
of estuarine condition (Borja et al., 2008; Chariton et al., 2010b; Dafforn
et al., 2012). However, we still lack quantitative information at multiple
scales, which can be summarised for comparison across whole estuaries or
coastal regions, which are essential if they are to be broadly implemented
for assessment and management purposes.
In situations where prevailing environmental drivers/stressors are man-
ifold, and where there is a desire to separate specific drivers, it is useful to
have rich taxonomic information to allow discrimination (Baird and
Hajibabaei, 2012; Burton and Johnston, 2010; Olsgard et al., 1998).
However, biological observations remain constrained by a general focus
on limited phylogenetic groupings due to the difficulties of obtaining
high-resolution taxonomic information. Thus, the interpretation of patterns
observed at ecosystem scales are necessarily constrained to a limited number
of ‘observable receptors’, leading to weak inference. Moreover, when cross-
ecosystem comparisons are being made, it is valuable to clearly separate
The Pros and Cons of Molecular Faunal Data 5

system-specific patterns manifested at the local community scale from those

occurring at the metacommunity scale (Heino, 2013). For this reason,
increasing the number of ‘observable receptors’ is one potential route
towards the development of stressor-specific diagnostic responses at ecosys-
tem scale: the ability to observe hundreds to thousands of entities offers
greater potential to observe unique, taxon-stressor responses which can
be aggregated and interpreted at the assemblage scale (see Fig. 1.1, for further
details). Moreover, analysing the relative contribution of multiple drivers to
biological patterns observed at the ecosystem scale using multivariate statis-
tics (Friberg et al., 2011; Lücke and Johnson, 2009) is constrained by the
number of simultaneous observations, which are available to include in
the analysis. Where these are similar in magnitude to the number of driver

Taxon 1 2 3 4 5 6 7 8 9 10 11 12

STRESSOR X

Specific diagnostic indicator taxon

STRESSOR Y
(for Stressor X)

STRESSOR Z

STRESSOR X

STRESSOR Y

STRESSOR Z

STRESSOR X

General stress indicator taxon

STRESSOR Y

STRESSOR Z

Figure 1.1 Boxes represent the responses of hypothetical taxon assemblages (e.g. from
survey samples and mesocosm experiments) to multiple stressors X, Y and Z. In each
case, taxa responding are indicated in red (dark grey in print version); those not
responding are indicated in green (light grey in print version). One taxon responds
equally to all stressors—and can be classed as a 'general stress indicator', but its indis-
criminate response provides no diagnostic value. On the other hand, one taxon clearly
responds only to Stressor X and can be classed as a 'potential diagnostic indicator of
Stressor X'. Following this logic, expanding the range of taxa (¼receptors) increases
the likelihood that diagnostic indicator taxa can be identified and thus add diagnostic
value to ecological assessments.
6 Katherine A. Dafforn et al.

variables, over-fitting can result in potential erroneous inferences regarding

driver–assemblage responses (Green, 1991; Quinn and Keough, 2002).
A method is therefore needed which can generate large numbers of consis-
tent observations of taxon occurrence, thus moving the diagnosis of cause in
multiple stressor scenarios towards big data approaches (Woodward
et al., 2014).

1.2. Application of molecular tools in biomonitoring

With the advent of high-throughput sequencing platforms, it is now possible
to consider a comprehensive analysis of the biological structure of environ-
mental samples (Creer et al., 2010; Shokralla et al., 2012; Zinger et al.,
2012). By using a combination of multiple gene markers, carefully selected
primers and a dedicated bioinformatics pipeline, it is possible to generate a
more phylogenetically complete snapshot of the biodiversity of a commu-
nity (Coissac et al., 2012; Hajibabaei et al., 2011; Morgan et al., 2013; Stoeck
et al., 2010).
Baird and Hajibabaei (2012) introduced the concept of ‘Biomonitoring
2.0’ to describe the shift towards causal analysis in ecological assessment.
A key tenet of this approach is that the increase in the numbers of taxa
observed using DNA-based molecular identification results in a similar
increase in the numbers of ‘receptors’ responding to specific sets of environ-
mental variables. In this way, the step-change in the numbers of unique
‘receptor-entities’ offers significant potential for statistical discrimination
of cause (Fig. 1.1).

1.3. Assessing estuarine condition

Estuaries can be broadly defined as the interface between fresh and marine
waters (Kennish, 2002). These systems are inherently spatially and temporally
complex, making the development of protocols founded on predictability,
e.g., routine biomonitoring programmes, challenging (Akin et al., 2003;
Chapman and Wang, 2001). At the semi-diurnal scale, large variations in
the physico-chemical properties of the overlying waters can occur with
the ebb and flow of the tides, with the extent of these variations being driven
by many factors, including channel and mouth morphology, tidal regime
and distance from the mouth. In high energy areas, the overlying waters
may shift from being marine to freshwater dominated (Rogers, 1940).
In addition, expanses of inter-tidal sediments may be directly exposed to the
air during the lower phase of tide. Such large and rapid changes in
The Pros and Cons of Molecular Faunal Data 7

environmental conditions undoubtedly place considerable physiological

pressure on estuarine residing biota (Elliott and Quintino, 2007). This is
especially the case when considering the impact of a rapid change in salinity,
where organisms have developed a range of behavioural and physiological
strategies to cope with such challenges (Charmantier et al., 2001). Clearly,
such adaptations are not universal, and the biological diversity within the
more physiologically challenging areas (e.g. poikilohaline areas where salin-
ity variation is of biological significance) is generally lower than that of more
stable areas, such as the predominately marine waters (euhaline) at the front
of estuaries. The underpinning view is that for both macro (>500 μm) and
meiofauna (0.1–500 μm) biological diversity is appreciably lower in low
salinity environments (Reizopoulou et al., 2013). However, in contrast
to macrofauna, meiofaunal biomass does not decline with salinity
(Remane, 1934). Changes in biological compositions of estuaries are not
solely driven by salinity, with large variations in community composition
observed along gradients of sediment grain size, nutrients and organic mate-
rial loading (Chariton et al., 2010b; Dafforn et al., 2013; Elliott and
Quintino, 2007).
The ecology of estuaries is also driven by marked changes in environ-
mental conditions which occur over more protracted periods. The most
obvious of these is seasonal variation, which can force massive changes in
productivity and biomass particularly in temperate systems (e.g. Kelaher
and Levinton, 2003). Other examples include periods of high rainfall and
freshwater inflow that can limit the influence of euhaline and even brackish
waters to the mouth of the estuaries. Conversely, during drier periods, the
influence of saltwater may extend further upstream. In common with rivers
and lakes, the physico-chemical and biological characteristics of an estuary
are strongly shaped by the surrounding catchment and its land-use. With
approximately 60% of the human population residing within 100 km of
the coast (Vitousek et al., 1997), the ecological foot print of anthropogenic
activities on estuarine system is often marked. The primary direct and indi-
rect anthropogenic stressors vary greatly across systems, and often include a
range of point and diffuse sources. For many systems, the key stressors
include alterations in the proportions of fresh and marine waters due to water
extraction and changes in mouth morphology; eutrophication from excess
nutrients; over harvesting of commercial species; increased rates of sedimen-
tation due to run-off and a loss of riparian vegetation, seagrass beds and man-
grove stands; as well as contaminants, including legacy contaminants which
persist due to their absorption to the sediments (Kennish, 2002). In addition,
8 Katherine A. Dafforn et al.

environmental stressors associated with climate change, e.g., decrease in pH

and an increase in saltwater intrusion, are also becoming increasing apparent
(Elliott et al., 2014; Kennish, 2002). For scientist and environmental man-
agers, one of the great challenges is being able to identify whether changes in
biological communities and ecosystem processes are being driven by natural
phenomena, specific anthropogenic activities, or a combination of both
(Elliott and Quintino, 2007).
While it is apparent that anthropogenic contaminants such as metals (e.g.
Cd, Cu, Pb, Zn) and organics (e.g. polycyclic aromatic hydrocarbons
(PAHs)) have an impact on benthic communities (Burton and Johnston,
2010), there remain great challenges in quantifying the degree of impact
caused by individual contaminants or even class of contaminants (metals,
organics). A frequent outcome of benthic ecology studies with matching
environmental contaminants data is that, in combination but not individu-
ally, increased contaminant concentrations often explain a large portion of
the ecological change. This occurs because the concentrations of many of
the contaminants and physico-chemical factors that increase the accumula-
tion of contaminants (e.g. particle size and organic carbon) are strongly
correlated.
The more comprehensive ecological data sets provided by molecular
tools may potentially allow for greater discrimination of the effects of indi-
vidual contaminants.

1.4. Case study: Contrasting molecular big data with traditional

morphological tools
Next-generation DNA sequencing platforms allow us to generate “big data”
scale observations of biological assemblages, but the advantages of these
techniques over traditional morphological tools require detailed analysis.
Rarely are observational studies designed to comprehensively co-sample
for both sequencing and morphological analyses (e.g. Chariton et al.,
2014; Gardham et al., 2014). We used co-sampled sediments from a
large-scale field study of estuary health to assess the advantages of new
molecular techniques over traditional morphological tools for ecological
observation. Different techniques to quantify ecological impact have utilised
changes to community composition as well as changes to diversity and abun-
dance with the prediction that negative effects of anthropogenic contami-
nants would manifest themselves as compositional changes or reductions
in species diversity, potentially indicating reduced function (Chariton
et al., 2010a). Indeed Johnston and Roberts (2009) found that species
The Pros and Cons of Molecular Faunal Data 9

richness was reduced by and average of 40% across a range of contaminated

marine systems compared to reference sites. Here, we compare traditional
morphological data against molecular sequencing data with a variety of
indices commonly used to examine estuarine condition. These were
(1) the sediment community composition (a) sub-sampled to include only
taxa found using both approaches and pooled to the same level of tax-
onomic resolution; (b) including all taxa identified and analysed at the
highest taxonomic level;
(2) the relationships among the sediment community identified using each
approach and a variety of individual and grouped anthropogenic
stressors; and
(3) the richness of individual taxa, polychaete families and crustacean orders.

2. METHODS
2.1. Estuarine survey design
Field surveys in multiple estuaries were used to compare information
provided by molecular techniques with that provided by traditional mor-
phological techniques for assessing benthic sediment health. Six sites
(between 1 and 2 km apart) were sampled from each of eight estuaries along
the coast of New South Wales, Australia (Fig. 1.2). Port Kembla, Hunter
River, Port Jackson and Georges River are urbanised estuaries with histories
of industrialisation. Hacking River, Clyde River, Hawkesbury River and
Karuah River are estuaries that are relatively less modified by urbanisation
and have no history of major industry. Furthermore, Clyde River estuary is a
Marine Protected Area, and sites in Hacking River and Karuah River were
also in, or adjacent to, Marine Protected Areas (Fig. 1.2).

2.2. Benthic sediment sampling

Benthic sediments were collected subtidally (5 m depth) between February
and March 2011 using a Van Veen sediment grab. Three sediment grabs
were collected at each site and sub-sampled for the surface microbial com-
munity (<1 cm depth) and for chlorophyll-a analysis using separate sterile
50-mL Falcon tubes. Each grab sample was homogenised in a clean tray
and sub-sampled for infauna community analysis using a 250-mL plastic
jar. Sub-samples were also collected to assess anthropogenic contamination
(metals and PAHs) and organic enrichment (total organic carbon (TOC) and
silt content (% <63 μm)). Plasticware used to collect sediment for metals
10 Katherine A. Dafforn et al.

Figure 1.2 Map of study sites along the New South Wales coastline, SE Australia. Port
Kembla, Hunter River, Port Jackson and Georges River are heavily modified estuaries.
Karuah River, Hawkesbury River, Hacking River and Clyde River are relatively unmodified
estuaries.

analyses was previously soaked in 5% HNO3 for a minimum of 24 h and

then rinsed in deionised water (Milli-Q™). Samples were kept in the dark
on ice for transport to the laboratory and then samples for chemical analyses
were frozen at 20  C. Details of chemical analyses are included in
The Pros and Cons of Molecular Faunal Data 11

Appendices A–C. Sediment deposition was estimated from a sediment trap

(30  5 cm Perspex cylinders) deployed at each site for 3 months.

2.3. Morphological biomonitoring

Infaunal sub-samples (125-mL) were stained with Rose Bengal and pre-
served in a 7% formalin solution then passed through a 2-mm mesh
(to remove large debris) and onto a 500-μm sieve. The remaining organisms
were sorted with a dissecting microscope and identified to the lowest feasible
taxonomic level (mostly order for the crustaceans or family for the poly-
chaetes). A reference collection was deposited at the Australian Museum.

2.4. Molecular biomonitoring

Total genomic DNA was extracted from 8 g of each surface sediment sample
(n ¼ 144) using the PowerMax™ Soil DNA Isolation Kit (Mo Bio Laboratories
Inc., Carlsbad, CA, USA). Eukaryotic microbial community composition was
determined using 454 ribosomal tag pyrosequencing targeting 18S rRNA
genes. 18S primers all18SF (50 -TGGTGCATGGCCGTTCTTAGT-30 )
and all18SR (50 -CATCTAAGGGCATCACAGACC-30 ) were used to
amplify between 200 and 500 base pair product corresponding to the 18S
rRNA gene-v9 hypervariable region (Hardy et al., 2010). Amplicons were
sequenced on a whole plate by the Australian Genome Research Facility
Ltd. (Brisbane, Australia) using a Roche GSFLX pyrosequencer with short-
read chemistry (Roche Applied Science, Indianapolis, IN, USA).
Sequences were filtered for mismatching primers, ambiguous bases,
homopolymers and low-quality score windows using QIIME to ensure
sequence fidelity (Caporaso et al., 2010) and 1 080 222 quality 18S reads
remained. Using the Usearch algorithm for de novo chimaera detection
<1% of 18S sequences were identified as chimaeras and discarded. Opera-
tional taxonomic units (OTUs) were aligned and assigned species level taxon-
omy using the SILVA small subunit ribosomal v115 release for 18S sequences
(method: BLAST). Clustering was performed at a sequence similarity cut-off
of 97% for OTU generation (Huse et al., 2010). The number of OTUs was
sub-sampled to standardise sequencing effort (1828 18S OTUs) leaving a total
of 10,857 unique 18S OTUs. Sequences unclassified at the Phylum level were
removed. Finally, rare sequences with <4 occurrences or those found in only
one sample were discarded leaving a final 18S data set of 3575 OTUs.
To determine potential cross contamination between samples and the
appropriateness of the chosen OTU generation, two control assemblages
Exploring the Variety of Random
Documents with Different Content
 o p eac e t e gospe p ay e;
For otherwise certayne
Your laboure is in vayne;
For all your crueltye,
I knowe that you and we
Shall never well agree:
Ye may in no wise se
Sutch as disposed be
Of ther charitye
To preach the verytye;
Ye stope them with decrees,
And with your veritees,
Unwritten, as ye saye;
Thus ye make them stay:
But God, that all do may,
I do desire and pray,
To open us the day,
Which is the very kaye
Of knowledge of his way,
That ye have stolen awaye!
And then, my lordes, perfay,
For all your popishe play,
Not all your gold so gay,
Nor all your riche araye,
Shall serve youe to delaye
But some shall go astraye,
And lerne to swyme or sinke;
For truly I do thinke,
Ye may well wake or wynke,
For any meat or drinke
Ye geitt, without ye swynke.
But that wold make youe wrothe;
For, I trowe, ye be lothe
To do eyther of both,
That is, yourself to cloth
With laboure and with sweate
And faste till youe eate
 d aste t youe eate
But that youe erne and geate;
Like verlettes and pages,
To leve your parsonages,
Your denns and your cages,
And by[605] dayly wages:
God blesse us, and Sainct Blase!
This were a hevy case,
A chaunce of ambesase,
To se youe broughte so base,
To playe without a place:
Now God send better grace!
And loke ye lerne apase
To tripe in trouthes trace,
And seke some better chaunce
Yourselves to avaunce,
With sise synke or synnes;
For he laughe[s] that wynnes,
As ye haue hetherto,
And may hereafter do;
Yf ye the gospell preche,
As Christ hymself did teche,
And in non other wise
But after his devise,
Ye may with good advyse
Kepe your benefise
And all your dignite,
Without malignite,
In Christes name, for me;
I gladely shall agre
It ever may so be.
But this I say and shall,
What happ soeuer fall,
I pray and call
The Kinge celestiall,
Ones to give youe grace
To se his worde haue place;
To se his worde haue place;
And then within shorte space
We shall perceyve and se
Howe euery degre
Hath his auctorite
By the lawe of Christ,
The lay man and the prest,
The poore man and the lorde;
For of that monocorde
The scripture doth recorde;
And then with good accorde,
In love and in Concorde
We shall together holde;
Or elles ye may be bolde,
For heate or colde
Say ye what ye will,
Yt were as good be still;
For thoughe ye glose and frase
Till your eyes dase,
Men holde it but a mase
Till Godes worde haue place,
That doth include more grace
Then all erthly men
Could ever knowe or ken.

Thuse endith the thirde parte of this present treatise called the
Image of Ypocresye.

Nowe with sondry sectes

The world sore infectes,
As in Christes dayes
Amonge the Pharisees,
In clothinge and in names;
For some were Rhodyans,
And Samaritans,
Some were Publicanes,
Some were Nazarenes,
 ,
Bisshops and Essenes,
Preestes and Pharisees;
And so of Saducees,
Prophetes and preachers,
Doctours and teachers,
Tribunes and tribes,
Lawers and scribes,
Deacons and levytes,
With many ipocrites;
And so be nowe also,
With twenty tymes[606] mo
Then were in Christes dayes
Amonge the Pharisees:
The Pope, whom first they call
Ther lorde and principall,
The patriarke withall;
And then the Cardinall
With tytles all of pride,
As legates of the side,
And some be cutt and shorne
That they be legates borne;
Then archebisshops bold,
And bisshops for the folde,
They metropolitannes,
And these diocysanyes,
That haue ther suffraganyes
To blesse the prophanyes;
Then be ther curtisanes
As ill as Arrianes
Or Domicianes,
Riall residentes,
And prudent presidentes;
So be their sensors,
Doughty dispensors,
Crafty inventors,
And prevy precentors,
And prevy precentors,
With chaplaynes of honour
That kepe the Popes bower;
Then allmoners and deanes,
That geit by ther meanes
The rule of all reames;
Yett be ther subdeanes,
With treasorers of trust,
And chauncelours iniust,
To scoure of scab and rust,
With vicars generalls,
And ther officialles,
Chanons and chaunters,
That be great avaunters;
So be ther subchaunters,
Sextons and archedeakons,
Deakons and subdeakons,
That be ypodeakons,
Parsonnes and vicars,
Surveyors and sikers,
Prevy pursepikers,
Provostes and preachers,
Readers and teachers,
With bachilers and maysters,
Spenders and wasters;
So be ther proctors,
With many dull doctors,
Proude prebendaryes,
Colde commissaries,
Synfull secundaries,
Sturdy stipendaries,
With olde ordinaryes,
And penytencyaryes,
That kepe the sanctuaries;
So be ther notaries,
And prothonotaries,
Lawers and scribes,
Lawers and scribes,
With many quibibes,
Redy regesters,
Pardoners and questers,
Maskers and mummers,
Deanes and sumners,
Apparatoryes preste
To ride est and weste;
Then be ther advocates,
And parum litterates,
That eate vpp all estates,
With wyly visitors,
And crafty inquisitors,
Worse then Mamalokes,
That catche vs with ther crokes,
And brenne vs and oure bokes;
Then be ther annivolors,
And smalle benivolers,
With chauntry chapleynes,
Oure Ladyes chamberleynes;
And some be Jesu Christes,
As be oure servinge pristes,
And prestes that haue cure
Which haue ther lyvinge sure,
With clerkes and queresters,
And other smale mynisters,
As reders and singers,
Bedemen and bellringers,
That laboure with ther lippes
Ther pittaunce out of pittes,
With Bennet and Collet,
That bere bagg and wallett;
These wretches be full wely,
They eate and drinke frely,
Withe salve, stella cœli,[607]
And ther de profundis;
They lye with immundis
They lye with immundis,
And walke with vacabundis,
At good ale and at wynne
As dronke as any swynne;
Then be ther grosse abbottes,
That observe ther sabbottes,
Fayer, ffatt, and ffull,
As gredy as a gull,
And ranke as any bull,
With priors of like place,[608]
Some blacke and some white,
As channons be and monkes,
Great lobyes and lompes,
With Bonhomes and brothers,
Fathers and mothers,
Systers and nonnes,
And littell prety bonnes,
With lictors and lectors,
Mynisters and rectors,
Custos and correctors,
With papall collectors,
And popishe predagoges,[609]
Mockinge mystagoges,
In straunge array and robes,
Within ther sinagoges;
With sectes many mo,
An hundreth in a throo
I thinke to name by roo,
As they come to my mynde,
Whom, thoughe they be vnkind,
The lay mens labor finde;
For some be Benedictes
With many maledictes;
Some be Cluny,
And some be Plumy,
With Cistercyences,
d
Grandimontences,
Camaldulences,
Premonstratences,
Theutonycences,
Clarrivallences,
And Easiliences:
Some be Paulines,
Some be Antonynes,
Some be Bernardines,
Some be Celestines,
Some be Flamynes,
Some be Fuligines,
Some be Columbines,
Some be Gilbertines,
Some be Disciplines,
Some be Clarines,
And many[610] Augustines,
Some Clarissites,
Some be Accolites,
Some be Sklavemytes,
Some be Nycolites,
Some be Heremytes,
Some be Lazarites,
Some be Ninivites,
Some be Johannytes,
Some be Josephites,
Some be Jesuytes,
Servi and Servytes,
And sondry Jacobites;
Then be ther Helenytes,
Hierosolymites,
Magdalynites,
Hieronimytes,
Anacorites,
And Scenobites;
So be ther Sophrans,
Constantinopolitanes,
Holy Hungarians,
Purgatorians,
Chalomerians,
And Ambrosians;
Then be ther Indianes,
And Escocyanes,
Lucifrans,
Chartusyanes,
Collectanes,
Capusianes,
Hispanians,
Honofrianes,
Gregorianes,
Vnprosianes,
Winceslanes,
With Ruffianes,
And with Rhodianes;
Some be Templers,
And Exemplers,
Some be Spitlers,
And some be Vitlers,
Some be Scapelers,
And some Cubiculers,
Some be Tercyaris,
And some be of St. Marys,
Some be Hostiaris,
And of St. Johns frarys,
Some be Stellifers,
And some be Ensefers,
Some Lucifers,
And some be Crucyfers,
Some haue signe of sheres,
And some were shurtes of heres,
Some be of the spone,
And some be crossed to Rome,
Some daunte and daly
In Sophathes valley,
And in the blak alley
Wheras it ever darke is,
And some be of St Markis
Mo then be good clarkes,
Some be Mysiricordes,
Mighty men and lordes,
And some of Godes house
That kepe the poore souse,
Minimi and Mymes,
And other blak devines,
With Virgins and Vestalles,
Monkes and Monyalles,
That be conventualles,
Like frogges and todes;
And some be of the Rhodes,
Swordemen and knightes,
That for the [faith] fightes
With sise, sinke, and quatter.
But nowe never the latter
I intend to clatter
Of a mangye matter,
That smelles of the smatter,
Openly to tell
What they do in hell,
Wheras oure ffryers dwell
Everich in his sell,
The phane and the prophane,
The croked and the lame,
The mad, the wild, and tame,
Every one by name:
The formest of them all
Is ther Generall;
And the next they call
Ther hie Provincyall,
With Cvstos and Wardyn
That lye next the gardeyn;
Then oure father Prior,
With his Subprior
That with the covent comes
To gather vpp the cromes;
Then oure fryer Douche
Goeth by a crouche,
And slouthfull ffryer Slouche
That bereth Judas pouche;
Then ffryer Domynike
And ffryer Demonyke,
Fryer Cordiler
And ffryer Bordiler,
Fryer Jacobine,
Fryer Augustyne,
And ffryer Incubyne
And ffryer Succubine,
Fryer Carmelyte
And ffryer Hermelite,
Fryer Mynorite
And ffryer Ipocrite,
Frier ffranciscane
And ffrier Damiane,
Frier Precher
And ffrier Lecher,
Frier Crusifer
And ffrier Lusifer,
Frier Purcifer
And ffrier Furcifer,
Frier Ferdifer
And ffrier Merdifer,
Fryer Sacheler
And ffryer Bacheler,
Fryer Cloysterer
And ffrier Floysterer,
Frier Pallax
And ffrier Fallax,
Frier Fugax
And ffrier Nugax,
Frier Rapax
And ffrier Capax,
Frier Lendax
And ffrier Mendax,
Frier Vorax
And ffrier Nycticorax,[611]
Fryer Japax,
Frier Furderer
And ffrier Murderer,
Frier Tottiface
And ffrier Sottiface,
Frier Pottiface
And frier Pockyface,
Frier Trottapace
And ffrier Topiace,
Frier Futton
And ffrier Glotton,
Frier Galiard
And ffrier Paliard,
Frier Goliard
And ffrier Foliard,
Frier Goddard
And ffrier Foddard,
Frier Ballard
And ffrier Skallard,
Frier Crowsy
And ffrier Lowsy,
Frier Sloboll
And ffrier Bloboll,
Frier Toddypoll
And ffrier Noddypoll,
Frier fflaphole
Frier fflaphole
And ffrier Claphole,
Frier Kispott
And ffrier Pispott,
Frier Chipchop
And ffrier Likpott,
Frier Clatterer
And ffrier fflatterer,
Frier Bib, ffrier Bob,
Frier Lib, ffrier Lob,
Frier Fear, ffrier Fonde,
Frier Beare, ffrier Bonde,
Frier Rooke, ffrier Py,
Frier Flooke, ffrier Flye,
Frier Spitt, ffrier Spy,
Frier Lik, ffrier Ly,
With ffrier We-he
Found by the Trinytye,
And frier Fandigo,
With an hundred mo
Could I name by ro,
Ne were for losse of tyme,
To make to longe a ryme:
O squalidi laudati,
Fœdi[612] effeminati,
Falsi falsati,
Fuci fucati,
Culi cacati,[613]
Balbi braccati,
Mimi merdati,[614]
Larvi larvati,[615]
Crassi cathaphi,[616]
Calvi cucullati,
Curvi curvati,
Skurvi knavati,
Spurci spoliati,
Spu c spo at ,
Hirci armati,
Vagi devastati,
Devii debellati,
Surdi sustentati,
Squalidi laudati,
Tardi terminati,
Mali subligati,
Inpii conjurati,
Profusi profugi,
Lapsi lubrici,
Et parum pudici!
Oth ye drane bees,
Ye bloody flesheflees,
Ye spitefull spittle spyes,
And grounde of herisees,
That dayly without sweat
Do but drinke and eate,
And murther meat and meat,
Ut fures et latrones!
Ye be incubiones,[617]
But no spadones,
Ye haue your culiones;
Ye be histriones,
Beastely balatrones,[618]
Grandes thrasones,[619]
Magni nebulones,
And cacodæmones,[620]
That [eat] vs fleshe and bones
With teeth more harde then stones;
Youe make hevy mones,
As it were for the nones,
With great and grevous grones,
By sightes and by sobbes
To blinde vs with bobbes;
Oh ye false faytours,
Youe theves be and tratours,
The devils dayly wayters!
Oh mesell Mendicantes,
And mangy Obseruauntes,
Ye be vagarantes!
As persers penitrantes,
Of mischef ministrantes,[621]
In pillinge postulantes,
In preachinge petulantes,
Of many sycophantes,[622]
That gather, as do antes,
In places wher ye go,
With in principio
Runnynge to and ffro,
Ye cause mikle woo
With hie and with loo;
Wher youe do resorte,
Ye fayne and make reporte
Of that youe never harde,
To make foles aferde
With visions and dremes,[623]
Howe they do in hevens,
And in other remes
Beyonde the great stremes
Of Tyger and of Gange,
Where tame devils range,
And in the black grange,
Thre myle out of hell,
Where sely sowles dwell,
In paynes wher they lye,
Howe they lament and cry
Vnto youe, holy lyars,
And false fflatteringe ffriers,
For Dirige and masses;
Wherwith, like very asses,
We maynteyn youe and your lasses;
We maynteyn youe and your lasses;
But in especiall
Ye say, the sowles call
For the great trentall;
For some sely sowles
So depe ly in holes
Of ffier and brennyng coles,
That top and tayle is hid;
For whom to pray and bid
Thens to haue them rid,
Ye thinke it but a foly;
Althoughe the masse be holy,
The fendes be wyly;
Till masse of scala cœli,[624]
At Bathe or at Ely,
Be by a ffrier saide
That is a virgine mayde,
These sowles may not away,
As all yow ffriers say;
So trowe I without doubte
These sowles shall never out;
For it is rara avis,
Ye be so many knaves;
I swere by crosses ten,
That fewe be honest men;
So many of youe be
Full of skurrilite,
That throughly to be sought
The multitude is noughte:
Ye be nothinge denty;
Ye come among vs plenty
By coples in a peire,
As sprites in the heire,
Or dogges in the ffayre;
Where yow do repayre,
Ye ever ride and rune,
A ift
As swifte as any gune,
With nowe to go and come,
As motes in the sonne,
To shrive my lady nonne,
With humlery hum,
Dominus vobiscum!
God knoweth all and some,
What is and hath bene done,
Syns the world begone,
Of russett, gray, and white,
That sett ther hole delighte
In lust and lechery,
In thefte and trecherey,
In lowsy lewdenes,
In synne and shrodenes,
In crokednes acurst,
Of all people the worste,
Marmosettes and apes,
That with your pild pates
Mock vs with your iapes:
Ye holy caterpillers,
Ye helpe your wellwillers
With prayers and psalmes,
To devoure the almes
That Christians should give
To meynteyne and releve
The people poore and nedy;
But youe be gredy,
And so great a number,
That, like the ffier of thunder,
The worlde ye incomber:
But hereof do I wonder,
Howe ye preache in prose,
And shape therto a glose,
Like a shipmans hose,
To fayne yourse[l]ves ded,
Whi h th l b f d
Whiche nathelesse be fed,
And dayly eate oure bred,
That ye amonge vs beg,
And gett it spite of oure hede:
It wonder is to me,
Howe ye maye fathers be
Your sede to multiply,
But yf yow be incubi,[625]
That gender gobolynes:
Be we not bobolynes,
Sutch lesinges to beleve,
Whiche ye amonge vs dry[ve]?
Because ye do vs shrive,
Ye[626] say we must youe call
Fathers seraphicall
And angelicall,
That be fantasticall,
Brute and bestiall,
Yea, diabolicall,
The babes of Beliall,
The sacrifise of Ball,
The dregges of all durte,
Fast bounde and girte
Vnder the devils skyrte;
For pater Priapus,
And frater Polpatus,
With doctor Dulpatus,
Suffultus fullatus,[627]
Pappus paralyticus,[628]
And pastor improvidus,
Be false and frivolus,
Proude and pestiferous,
Pold and pediculous,
Ranke and ridiculous,
Madd and meticulous,
Ever invidious
Ever invidious,
Never religious,
In preachinge prestigious,
In walkinge prodigious,
In talkinge sedicious,
In doctrine parnicious,
Haute and ambicious,
Fonde and supersticious,
In lodginge prostibulus,
In beddinge promiscuous,
In councells myschevous,
In musters monstrous,
In skulkinge insidicious,
Vnchast and lecherous,
In excesse outragious,
As sicknesse contagious,[629]
The wurst kind of edders,
And stronge sturdy beggers:
Wher one stande and teaches,
An other prate and preches,
Like holy horseleches:
So this rusty rable
At bourd and at table
Shall fayne and fable,
With bible and with bable,
To make all thinge stable,
By lowringe and by lokinge,
By powrynge and by potinge,
By standinge and by stopinge,
By handinge and by ffotinge,
By corsy and by crokinge,
With their owne pelf promotinge,
With ther eyes alweyes totinge
Wher they may haue shotinge
Ther and here ageyne:
Thus the people seyne,[630]
With wordes true and playne,
Howe they jest and ioll
With ther nody poll,
With rownynge and rollinge,
With bowsinge and bollinge,
With lillinge and lollinge,
With knyllinge and knollinge,
With tillinge and tollinge,
With shavinge and pollinge,
With snyppinge and snatchinge,
With itchinge and cratchinge,
With kepinge and katchinge,
With wepinge and watchinge,
With takinge and catchinge,
With peltinge and patchinge,
With findinge and fatchinge,
With scriblinge and scratchinge,
With ynkinge and blatchinge;
That no man can matche them,
Till the devill fatche them,
And so to go together
Vnto their denne for ever,
Wher hens as they never
Hereafter shall dissever,
But dy eternally,
That lyve so carnally;
For that wilbe ther ende,
But yf God them sende
His grace here to amend:
And thus I make an ende.

Thus endeth the ffourthe and laste parte of this treatise called the
Image of Ypocresy.

The grudge of ypocrites conceyved ageynst the auctor of this

treatise.
These be as knappishe knackes
As ever man made,
For javells and for iackes,
A jymiam for a iade.

Well were we, yf we wist

What a wight he were
That starred vpp this myst,
To do vs all this dere:

Oh, yf we could attayne hym,

He mighte be fast and sure
We should not spare to payne hym,
While we mighte indure!

The awnswer of the auctor.

Ego sum qui sum,

My name may not be told;
But where ye go or come,
Ye may not be to bold:

For I am, is, and was,

And ever truste to be,
Neyther more nor las
Then asketh charite.

This longe tale to tell

Hathe made me almost horse:
I trowe and knowe right well
That God is full of force,

And able make the dome

And defe men heare and speake,
And stronge men overcome
By feble men and weke:
So thus I say my name is;
Ye geit no more of me,
Because I wilbe blameles,
And live in charite.

Thuse endith this boke called the Image of Ypocresye.

 [468] The Image of Ipocrysy] Is now printed from MS.
Lansdown 794. The original has very considerable alterations and
additions by a different hand: the first page is here and there
illegible, partly from the paleness of the ink, and partly from the
notes which Peter Le Neve (the possessor of the MS. in 1724) has
unmercifully scribbled over it. I give the title here as it stands at
the end of the First Part.
Hearne and others have attributed this remarkable production
to Skelton. The poem, however, contains decisive evidence that
he was not its author: to say nothing of other passages,—the
mention of certain writings of Sir Thomas More and of “the
mayde of Kent” (Elizabeth Barton), which occurs in the Third Part,
would alone be sufficient to prove that it was the composition of
some writer posterior to his time.
[469] Vp to the clowdy skye] Originally “Vp into the skye.”
[470] Our parsons and curates] This line (now pasted over in
the MS.) has been obtained from a transcript of the poem made
by Thomas Martin of Palgrave.
[471] Glottons] Originally “Prelates.”
[472] And] Substituted for “To,” when the preceding line was
added.
[473] him] Originally “vs.”
[474] Take] Originally “haue.”
[475] Dothe] Originally “Or.”
[476] Doo] Originally “That.”
[477] seem] Is the substitution of a somewhat later hand, the
original word being faded: qy. “self?”
[478] runne in att the rove] Originally “runnynge at the masse.”
[479] prove] Originally “presse.”
[480] Wher they may be sure] Followed by a deleted line, now
partly illegible,—

“ ... wayte to haue wynnynge.”

 [481] To fyshe for any gayne] Followed by a deleted line which
seems to have been,—

“With shotinge or with singinge.”

[482] Shall pryck, &c.] The position of this line, and of the next
but one, was originally different.
[483] Chafyng] Which seems to be the reading intended, was
originally preceded by “Wyll.”
[484] And then] Originally “At lenghe.”
[485] Thoughe] MS. “Throughe”
[486] Which] Qy. “With?”
[487] bowes] Qy. “vowes?”
[488] of ther] Qy. “other?”
[489] backe] Something wanting here.
[490] No man wyll they spare] Originally,—

“They passe not of a sparre.”

[491] Your] Originally “For.”

[492] In] Originally “And.”
[493] Youre] Originally “And.”
[494] Wher God his gyfte or grace] Originally,

“Wher god of his grace.”

[495] And all his kingdom, whan] Originally,

“At the good tyme whan.”

[496] Ye] Originally “That.”

[497] Lordely, &c.] On the outer margin of the MS., opposite
this verse, are the following lines, partly cut off by the binder;
 “Thes be the knavysh
knackes that ever w ...
o ...
ffor Javelles and for J[ackes].”

[498] And worldly welth to haue] Originally “And possession to

haue.”
[499] chippe] Qy. “clippe?”
[500] When masse and all is done] Followed by a deleted line;

“The paynes to release.”

[501] as] Originally “that.”

[502] All] Originally “All ys.”
[503] For lust fyndes no lett] Occupies the place of the
following three deleted lines;

“be she ffayre or fowle

for vnderneth an amys
alyke ther hart is.”

[504] or] MS. “as.”

[505] Or owgly] Over this is the deleted word “blobcheked.”
[506] pretens] Originally “the bande.”
[507] not I] Originally “for why.”
[508] Lest here you] Originally “Here lest youe.”
[509] with vs] Originally “your.”
[510] treuth] Originally “the treuth.”
[511] That all the falt doth lye] Originally “But all the falt do
lye.”
[512] oure] Qy. “youre?” but compare 6th line of next column.
In the following line, “sanguinolently” should perhaps be printed
as Latin,—“sanguinolenti.”
[513] cokold foles] Originally “loutes and knaves.”
 [514] We wer an oxes fether] Originally “And in oure hoode a
fether.”
[515] Oure hedes for to gnob] Followed by two deleted lines;

“And make vs soch a lob

To vse one lyke a lob.”

[516] For your] Originally “With.”

[517] Through] Originally “With.”
[518]

And small, &c.

...
To make soch recompens

This passage is substituted for two deleted lines;

“To your possessyon

Without discretion.”

[519]

By gyvyng, &c.
...
Of harty penytens

This passage is substituted for three deleted lines;

“S ... fonde affection

To oure correccion
Without protection.”

[520] yowe] Originally “them.”

[521] that] Originally “an.”
[522] be] Originally “to be.”
[523] For you on] Originally “For on.”
[524] Can suffre or abyde] Originally “Ye cane here abide.”
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