Ecology Laboratory Field Manual (3144L)

Fall 2018
 Pretty cool introductory field manual stuff:
Average total reading time: 62 minutes

So let’s go ahead and say it: most texts you read for science
classes are generally boring. Despite this, for us to learn the
subjects we so love we grit our teeth and read them anyway-at least
parts of them. That’s why I’ve made every effort to make this field
manual entertaining and still be just as informative as other
textbooks and manuals. This way you can read it and actually enjoy it!
You may have already noticed it’s a bit more casual than you’re used
to. Also, I know time is precious to all of us so I’ve included
average total reading time (ATRT) for each section from a sample of
three people. Now that’s quite the sample size isn’t it? You may be
slower or faster so find a quiet spot and read straight through
because….

Ecology is totally awesome. This semester you’re going to learn

exactly why. Few subjects are as all-encompassing as ecology. It
relies on information collected using techniques from almost all other
sciences to accomplish its main goal- to study the interactions of
organisms to each other and their environment. This is why it’s
called an interdisciplinary science.

If you’re like me every science class you’ve ever had explains

how their origin dates back to the beginning of time. “We’ve been
selectively breeding livestock for thousands of years!”-so therefore
genetics has been around for forever. Yeah right. So this is where I’m
supposed to tell you that the ancient Egyptians and Greeks studied
ecology. This isn’t true. What these cultures did do was take notice
of what was going on around them. For example, Herodotus (a pretty
awesome ancient Greek dude) observed that crocodiles in the Nile River
open their mouths to allow sandpipers to pick out the leeches. In
Ecology, we call this mutualism: no leeches for the crocs and a good
snack for the birds. However, it wasn’t until 1866 that the term
Ecology was coined by a famous biologist named Ernst Haeckel. In
addition, the first American ecology book was published in 1905 by
Frederic Clements. We will be learning more about Clements later in
the semester. Of course ecological concepts were discussed before this
but systematized ecology (this is going out in the field to count bugs
and trees and such) wasn’t developed until around this time.

So what’s Ecology Lab all about? Well, it’s about developing a

few main skills that are necessary to be a successful scientist:
scientific thinking and awesome writing skills. You’ll be surprised
what this entails - I was. It doesn’t mean using every “50 cent” word
you know or sounding as eloquent as possible. It’s about conducting a
study and writing about it in as simple and concise a manner as
possible. You will also relearn any statistics you may have forgotten
or never learned and apply them to biological situations.
 The general format of the lab involves your developing and
creating ideas on what you think the outcomes of our experiments will
be, followed by conducting experiments and gathering field data,
analyzing this data (usually using statistics), and writing about all
of the cool stuff you learned and did in and out of the field. We will
be conducting these studies using tried and true ecology techniques
with a blend of modern technology that our generation has become so
adept at using.

By the end of Ecology Lab you will be able to write well in a

scientific format and you will have developed field skills with which
to impress your friends. For example, I found that early on in my
biological career I couldn’t name more than 10 things I saw outside
(be it animals or plants), let alone know specific attributes about
them. If this sounds like you, don’t worry, because after this class
you’ll be a regular Jack Hanna, or at the very least a decent safari
guide.

Take Notice
In this section you’ll be reading a scientific article by a
group of ecologists, one of which is a current ecologist that used to
work for UNCC. His name is Dr. Richard Bierregaard or more often
called Dr. B. Dr. B went to Yale for his undergraduate studies and
University of Pennsylvania for graduate school. He kept it ivy league
the whole way through. After that he moved to the Amazon rain forest
near Manaus, Brazil and conducted a bunch of studies for the next 12
years. That’s right; he lived in the rain forest for over a decade!
Pretty sweet, huh? Many of these studies can be found in the book
Lessons from Amazonia: the Ecology and Conservation of a Fragmented
Forest. The study you will be reading involves trapping birds over a
great deal of time and seeing how the fragmentation of forests (that
is cutting some of them down while leaving patches of forest standing)
affects the populations in the remnant patches. While you read this
study don’t worry if you don’t completely understand everything, just
take notice of what I bring to your attention. Read through the paper
(I know it’s slightly long-but this is really important stuff) and
then read the subsequent comments when you come to a highlighted
section. It’s busy but it keeps you from having to flip back and
forth. A .pdf file of the paper will also be provided for ease of
reading. This exercise is meant to bring your attention to the
structure and consistency of scientific writing-so follow the
hummingbird!:
Brief list of latin terms often used in scientific writing:

terra firme -- solid earth

in vitro -- “in a test tube” or outside the body of the organism

under study

in vivo -- inside the body

 in situ -- in place. This means studying something where it is
found

de novo -- from the start, anew, or beginning again

a priori – literally “before”, most often used in science papers

in reference to hypotheses. “a priori” hypotheses are
constructed before one begins a study.

a posteriori – “after” – in our context, hypotheses, or

statistical tests, designed after we’ve collected some data.

ad nauseam -- going on about something for so long you’re likely

to make other people sick or nauseous. (Okay so I just threw
this one in for fun, you probably won’t see it in the
literature.

So did you pay attention to all of the formatting? Did you also
interpret and analyze the content at the same time? This is the most
challenging part. What is the purpose of Dr. B’s paper? Does he
accomplish his goals in the study? What surprising results were found?
Will all the species in the rain forest die if we continue to clear
cut it? What other questions do you have about the study that were not
addressed?

When you’re interpreting scientific papers, it’s important to be

a skeptic. Never assume (despite the fact that the people writing
these studies usually have their PhDs) that they’re right. Imagine
spending years on a study. You might imagine that you want to find
good repeatable results. Sometimes this doesn’t happen but in a world
where there is a great deal of pressure to publish your research,
scientists come up with their own interpretations of their data. It’s
your job to determine if these interpretations are valid.
 Statistics - A Chi2 - Crash Course
ATRT: 52min
“When you have eliminated the impossible, whatever remains, however
improbable, must be the truth.”-Sherlock Holmes

So you’re sitting outside on campus waiting for your next class

to start and you notice some beetles sitting on a plant. You count them.
There are 12 beetles on this plant. You think to yourself that’s a lot
of beetles. Maybe there is an invasion of some sort. Upon closer
examination you notice two of the same beetles on a different plant a
few feet away. You think to yourself they must like the one plant
better. I mean after all there are more beetles on that plant right?
Maybe! You might be right—the beetles do like plant A better than B, or
maybe they really don’t care and it’s just by chance that there were 12
on one plant and 2 on the other. This is simply a hypothesis and
hypotheses must be rigorously tested. You need more data and then you
need some way to tell if what you find is any different than what would
occur by chance alone.

This is just an example of an observation you made. We make

observations all the time. Sometimes we get technical with our
observations and we take measurements or create mathematical models to
help with our analysis. Now bear with me. When analyzed, these
individual observations are known as samples. Statistics are quantities
that we calculate from samples to test our hypotheses. Statistics are
just likely estimates of parameters. Confused yet? Parameters are the
characteristics of the entire population. Statistics estimate
parameters. And you thought the only definition of the word parameter
was guideline. Statistics use roman letters like 𝑦̅ (pronounced y bar)to
represent the mean. Remember this mean is just an estimation of a
parameter or the actual population mean, symbolized by µ (pronounced
mew). What about now? So, in the case of our beetles we could use
statistics to analyze the probability that the plant with more beetles
really is the favorite of these particular beetles. What do you think
we’d need to do this? Probably a lot more beetles and way more plants.
This increases the sample size and we all known there are probably a
lot of beetles and plants that represent the entire population.

There are a few things about terminology that should be mentioned.

You’ve probably already noticed that in Biology there are about 100
words that are often used interchangeably. Think of biome, ecosystem,
habitat, etc. Well, the same goes for statistics. For example,
observations (that are part of your sample) are also known as traits.
They’re called traits because they are characteristics of what you are
studying. Moreover, often times these traits are referred to as
dependent variables. This is because there is something affecting the
observation/trait/dependent variable you are studying. In the beetle
example maybe the number of beetles on the plants is affected by the
flavor of the foliage (leaves) on the plant. This is known as a factor
or independent variable and affects the trait/dependent variable. Does
that make sense? Remember, just because words are used in exchange for
one another doesn’t mean that they necessarily have the same meaning.
It’s just more practical to interchange them. But we’ll have to save
this argument for your linguistic class…..

So we take samples and in these samples we analyze traits to see

how certain factors affect these traits. These traits have categories.
In our beetle example the number of beetles is non-continuous. That is,
you can only have whole numbers. Unless, of course you tear beetles in
half in which case you were the weird kid on the playground. Traits can
also be continuous where you have 14 or 15 or every number in between.
An example of a continuous trait is weight. You can also have nominal
traits which are categorical and can be placed into a finite number of
groups like gender: usually you only have male and female.

Has any of this jogged your memory yet? There are a few things
that I know you haven’t forgotten. That’s descriptive statistics. These
include mean, median, and mode. Are you familiar with sample size? We
use the letter ‘n’ to denote sample size so that mathematically the
∑𝑦
mean of a sample is calculated by using the formula 𝑦̅ = 𝑛 where the
big E-shaped symbol (known as sigma) is the sum of all your y’s (traits,
measurements, etc) divided by n (how many of them you measured) to give
you your average or y bar.

How about standard deviation and standard error? I’m sure you
remember that they’re measurements of variance or the variability of
your data. Do you known how to calculate standard deviation by hand?
Few activities in statistics require calculation by hand. After all, do
any of us have any idea how a calculator works on the inside? Probably
not. Is that important? Not really. As long as we know basic math the
only thing we need to be able to do is use the calculator properly.
That is why we will not be delving deeply into statistical theorems. We
will simply learn how to calculate statistical tests as well as do basic
statistical math. Being able to calculate standard deviation by hand is
one calculation you should be able to do. This ability also helps to
understand exactly what standard deviation is. So let’s try an example:

First let’s say we have a population. That’s right, the entire

population of whatever we’re studying. To help follow all of the formula
stuff you may want to write it down so that you can follow more easily.
So, let’s say there are red colored mites on your hand-the ones that
when you squash them make it look like you’re bleeding. These are called
clover mites. They’re actually related to spiders so they have eight
legs. Now, let’s say you were playing in the grass and we’ll call all
the ones on your body the population of clover mites. You find a total
of eight of them. Some are big and some are small so you decide to
measure their length to see how their size varies. You measure them in
millimeters and find the following measurements: 2, 4, 4, 4, 5, 5, 7,
and 9mm for each of them. So how do you calculate the standard deviation?
Well, first we need a formula. The formula or standard deviation is:
∑(𝑦−𝑦̅)2
√ with y being each measurement, 𝑦̅ as your mean and n as your
𝑛
population size. The calculated mean of our population is 5. Now take
each observation and subtract 5 from it. For example, 2-5 is -3. Square
that number and add all those together and divide by 8, the number in
your population. The calculations would look like this:

2-5=-3, -32=9.

4-5=-1, -12=1

4-5=-1, -12=1

4-5=-1, -12=1

5-5=0, 02=0

5-5=0, 02=0

7-5=2, 22=2

Then take the square root of all of these and divide by n, which
9+1+1+1+0+0+2
is 8 and the √ 8
= 2. Two is your standard deviation. That’s a
lot of work for such a small population of clover mites huh? Well, don’t
worry most statistic programs will calculate this for you but it’s
important to understand how to do this so you can understand what it
means. Without going into much theoretical background, this means that
95 percent of your data points lie within one standard deviation of the
mean. That is about 95 percent of your mites measure between 3 and 7 mm
in length. On a side note, when calculating the standard deviation of
a sample, not an entire population, your formula changes to n-1 in the
denominator. Standard error is also a measurement of variance. It is
the standard deviation of the sample mean’s estimate of the population
mean. That’s a lot of words I know. But remember you would likely be
taking many samples for an experiment and by calculating all of their
means you estimate the population size. Take the standard deviation of
these and voila!-you have standard error. Don’t worry you won’t need to
calculate this by hand.

Assumptions, assumptions. Everything we do involves assumptions.

In statistics, we assume that our data are distributed normally. You
probably remember the normal distribution. It looks like a bell curve:
 This simply means that the most frequent values are found in the
middle of the curve. That is, the mean of the data is in the middle
and the highest and lowest values are the least frequent. Makes sense
right? Most statistical tests assume your data are normally
distributed. But, if they’re not there are plenty of tests brilliant
statisticians have developed to test data that are non-normally
distributed.

How do we figure out whether the data we find are significant?

You probably remember that whatever test we’re using you’re looking
for the number .05. If it’s less than .05, you have a significant
finding. How in the world did we get to this point? Let’s approach it
in a stepwise manner. What do you do first when you’re conducting an
experiment? You form a hypothesis about what you think you might find.
In statistics these hypotheses have mathematical symbols. Remember µ?
Well, in many cases you are using samples to find the estimated
population difference (if any) between two or more groups.

At this point let’s say you’ve made it to class and you’ve

stopped thinking about beetles or mites. You’re a cool kid so you sit
in the back row and you start to notice the differences in height
between the people who sit up front and those who sit in the back. (If
you haven’t done this, then you’re probably just not cool. It’s ok. It
happens.) It seems like more short people sit up front. Do you think
it’s like this in all classrooms? How would you test this
statistically? So, you think more short people sit up front. Whether
you know it or not, you’ve just formed a hypothesis. This is called
the alternate hypothesis, or Ha, where you think the average height is
different between the front and back of the room. In mathematical
terms you think that µ1 ≠ µ2. Now, you need something to compare this
to. This is called the null hypothesis (HO). This is what you test
your idea against. What did you predict? That there is no difference
in height between the front and the back of the room or that µ1 = µ2.
At this point you’ve already done the first few steps necessary for
forming and statistically testing a hypothesis. You’ve

1. Created a biological hypothesis and

2. Created a statistical hypothesis.

3. Next, you need to pick your level of significance. This is also
known as the alpha level. For now we’ll just go with the classic less
than .05.

4. Now you’ll need to carry out the experiment. In this case you’ll
need to awkwardly interrupt class one day with your measuring tape and
measure the height of everyone once you’ve split the class down the
middle.

5. Once you’ve gathered the data, you’ll need to know which

statistical test to choose. I’ll give you this one and we’ll go over
others. For this, you have two sample means that are normally
distributed and you want to test the difference between them. For
this, you’ll use an unpaired t-test. A paired t-test tests the same
samples at different times. That is, there is a reason to “pair” the
data. Graphically testing the differences between two means appears
like this:

We can see that the bigger the difference in the means the more likely
there is a significant difference.

6. Now you’ll need a formula if you’re going to calculate your

statistic by hand or know how to plug your numbers into a statistics
program. In this example, we’ll do one by hand. For any of the more
complicated tests we will use computer programs. The t-test formula is
(𝑦̅1−𝑦̅2)
t=𝑠 (𝑦̅1−𝑦̅2). We’re looking at the difference in the averages divided by
the difference in standard deviation. The formula for the difference
𝑆𝑝12 +𝑆𝑝22
in standard deviation is as follows: 𝑠 (𝑦̅1-𝑦̅2)= √ 𝑛1+𝑛2
. That is, the
square root of the pooled variance of each sample squared divided by
the total sample size. Lastly, sp2, or the pooled variance of each
(𝑆12 ∗𝑑𝑓1)+(𝑆22 ∗𝑑𝑓2)
sample is equal to sp2= 𝑑𝑓1+𝑑𝑓2
, or the standard deviation of each
sample squared times the degrees of freedom (for t-tests this is just
the sample size minus 1) divided by the total degrees of freedom. Are
you following all of this?

7. Let’s say we’ve measured and we have the measurements. Now I’m just
making these measurements up for simplicity. Maybe the people in your
class are super tall. We have the following data:
Back of the class: n1=11 people, 𝑦̅1=32 decimeters (dm)-you’ve chosen to
use dm because you’ve always wanted to and never had the chance
before, s1=5dm, and front of the class: n2=11, 𝑦̅2=30dm, s2=4dm. So now
you just plug in the numbers to find the t score. The overall
(𝑦̅1−𝑦̅2)
ridiculously looking formula is t= , if it helps
(𝑆12 ∗𝑑𝑓1)+(𝑆22 ∗𝑑𝑓2) (𝑆12 ∗𝑑𝑓1)+(𝑆22 ∗𝑑𝑓2)
√ +
𝑑𝑓1+𝑑𝑓2 𝑑𝑓1+𝑑𝑓2
𝑛1+𝑛2
(32−30)
you can write out each individually. So, t= = 1.036.
(25∗10)+(16∗10) (25∗10)+(16∗10)
√ +
10+10 10+10
11+11

Next, you compare this value to one in a t-score chart (usually in the
back of a statistics book) to what is significant for an alpha level
of .05 with a total degrees of freedom of 20 (total sample size minus
two-one for each sample.) This t score is 2.086. It is also known as
the critical value. In other words it’s critical for you to reach it
to reach significance.

8. Now you compare your calculated value to the critical value. There
are some general rules that apply here. If it is greater than or equal
to this value you reject your null hypothesis (HO) and accept your
original alternate hypothesis (Ha).

9. If your calculated value does not reach your critical value like in
our example you accept HO.

10. Lastly you interpret your results biologically. So what do these

numbers tell us? Well they tell us that even though it seemed like
shorter people sit closer to the front of the classroom, it just seems
that way and there are not significantly shorter people towards the
front.

That was a lot for one test huh? Well don’t worry because beyond
t-tests we will be using statistics programs just like we use
calculators to do advanced math. You will however be using the same
methodology for approaching an experiment. So, what you will need to
know are the concepts behind other statistical tests so you will know
when to use which one.

Next on the list is ANOVAs, also known as analysis of variance.

In this case you’re analyzing the effect of one factor or independent
variable on a particular trait (dependent variable). This could be the
effect of a certain energy drink and how various amounts affect heart
rate. Statistically, it would look similar to the t-test but let’s say
we’re testing more than two quantities. Well then, how does it look?
Rather than testing a difference in only two samples you’re testing
the difference in more than two. Mathematically your statistical
hypotheses would look like HO: µ1 = µ2 = µ3… and Ha: µ1 ≠ µ2 ≠ µ3…. So there is
a difference in the effect of varying amounts of energy drink on heart
rate. An ANOVA tells us whether or not there is a difference among all
of these means, but does it tell us where? Between the first and the
second? No, it does not. We have to run post-hoc tests after we run an
ANOVA to find where the difference lies. A popular post-hoc test often
run after an ANOVA is called a Tukeys test. Remember ANOVAs also
assume normality. So, if your data are not distributed normally you
need to use the non-parametric (non-normal) equivalent known as a
Krukall-Wallis test.

Correlation is another statistical test used to test the

association between two continuous variables. However, this test only
indicates an association and does not imply causative effects on each
other. Rather than testing the differences in averages like mew (µ),
this test uses the roman letter r of a sample to estimate the greek
letter rho (P)of the population. r is known as the correlation
coefficient and estimates the strength of the correlation. It can
either be -1 thus having no correlation at all, 1 showing a 100
percent correlation, or somewhere in between showing that both
variables have either a positive or negative association with each
other. For example, do you think the number of trees in an area
correlates with the amount of rainfall that area sees? How about the
amount of studying and the grade one receives on an exam. Surely they
correlate but probably not 100 percent. Other variables probably
affect tree concentration and test performance right? A Parametric
correlation test is known as Pearsonian correlation while the non-
parametric equivalent is the Spearman correlation.

Do you remember slope from your basic math classes? y=mx+b? In

statistics this is known as regression. Y represents the dependent
variable or trait and x is the independent variable or factor. b is
the regression coefficient. Like correlation regression calculates a
coefficient of two continuous variables but in this case you’re
looking at how one affects the other. The three possible outcomes are
as follows:

You could have data that when graphed has a positive slope (the red
line). What does this mean? This means that with every increase in
your factor your trait increases. This could be that every increase in
caffeine increases your heart rate. The opposite relationship also
exists Perhaps also, with increases in blood pressure medication your
blood pressure decreases (the blue line). There could also be no
relationship (the green line) between your independent and dependent
variables. Moreover, when you do regressional analysis on a statistics
program you calculate a coefficient of determination (r2). This number
indicates to what degree your factor(s) is affecting your trait.

Lastly, we end this section with Chi squared (𝑋2). For some
reason I always remembered what this test was about, maybe not
necessarily how to use it but what to use it for. So, do you remember?
We use a Chi square test to test whether the data we’ve collected is
significantly different from previously known proportions of similar
data. That is, whether our observed values are significantly different
from proportions we’d expect to get for that type of data. For
example, we know that in genetics certain crosses between parents only
give certain ratios of offspring. To keep it simple we can use humans
as an example. There is a 50:50 chance of having a boy or a girl
right? So let’s say we’ve sampled 100 people and end up with 44 men
and 56 women. Then all we need to know is the formula for chi squared
∑(𝑂−𝐸)2
and we do the same thing we did with the t-test. So 𝑋2 = 𝐸
.
(44−50)2 (56−50)2
Plugging in the numbers gives 50
+ 50
= 1.44. So now all we do
is look up our value in the chi square distribution chart. This chart
works differently than the t-test chart. We look up 1.44 and see that
the probability is .23 percent. Is this below .05? No it isn’t. What
does this mean? It means there is no significant difference between
what we observed and what we expected. So, therefore our ratio and
that expected of the general population are the same.

Have you refreshed your knowledge of statistics? Good enough to

ace a quiz? Given a scenario would you know when to use what test?
Could you apply these tests in the right situation? Throughout the
semester it will be important to understand why we use certain tests
to properly answer the hypotheses you form. Remember key words for
each one. Like more than two groups for ANOVAs, two continuous
variables for correlation and regression, proportion for chi squared,
and be sure to know what test you would use if your data are not
normally distributed. At least if it’s listed here.
 Rainforests and Deserts-Could You Survive?
ATRT:8 minutes
"The world is full of obvious things which nobody by any chance ever
observes."- Sherlock Holmes

One morning you wake up and you find a red bump on your forearm.
It’s quite sore and it itches. You think to yourself “Did I get bit by
a spider?” You decide to ignore it and go about your day. First, you
grab the line of static rope that attaches you to the nearby tree
you’ve been sleeping in. Pulling yourself to some decking you get out
of your hammock that hangs about 20 feet above the ground. You take a
mango out of your pack for breakfast. You show your friend who just
woke up the bite on your arm. He tells you that it’s a botfly and it’s
living in your arm. It’s your first week of research in the
rainforest. You’re not sure if you can make it. Check out this youtube
link to see how you remove a botfly (don’t do it if you’re squeamish):
http://www.youtube.com/watch?v=EqV1MCDLcMM&oref=http%3A%2F%2Fwww.youtube.com%2Fresu
lts%3Fsearch_query%3Dbotfly%2Bextraction%26oq%3Dbotfly%26gs_l%3Dyoutube.3.1.0l10.967.1821.0.
4481.6.5.0.1.1.0.203.697.0j4j1.5.0...0.0...1ac.Z_CeIDR2Joo

Do you think you could survive and live in the rainforest? How about
conduct research there? What about other extreme environments? Could
you live in the desert? What do you know about either environment? For
starters, what’s a desert? You know it’s a hot place that doesn’t get
much rain. Did you know that they’re cold deserts? Look at the
following chart:
You see that biomes (including rainforests and deserts) are classified
based on both temperature and rainfall. Tropical rainforests receive a
great deal of rain and are warm year round. However, the mountains of
North Carolina include temperate rainforests because they have a great
deal of rain but are not warm all year.

So how have plants adapted differently between hot deserts and

tropical rainforests? If you’re not sure think of each environment.
Would plants be shorter or taller in one environment over another?
Thicker or thinner? Any defenses that might have evolved? What about
the leaves of plants in these environments? What do the leaves of
desert plants look like? Are they thicker than rainforest plants so
they don’t lose too much water, or does being too thick cause too much
water loss? Well, guess what? We happen to have both a desert and
rainforest on campus. They’re in the McMillan Greenhouse. First things
first we’ve just finished the section on statistics so make some
predictions about the plant leaves below:

What you will do: Form groups of two and measure five randomly
selected plants in each biome.

What you will need: A pair of calipers and maybe a clipboard.

One person can measure the plants while the other records the data
here. Make sure you both have it written down later.

Group Leaf
Thickness (mm)
Desert Tropical

Average

While you walk around the greenhouse observe the differences in plants
among the two biomes. Try to write down a few features you notice.
We’ll even discuss a few more that you may not notice.

Desert:

1.

2.

3.
4.

5.

Rainforest:

1.

2.

3.

4.

5.

Next we will combine the averages for each group into a table
summarizing the class data:

Class Leaf Thickness (mm)

Desert Tropical

Before writing your first report, we need to test our biome data
statistically. How would you do this? What was your biological
hypothesis? What is your statistical hypothesis? What is your null and
alternate? What test would you use for this data? How many biomes are
there? What trait are we measuring?
 Animal Sightings: Urban vs. Rural
ATRT:17 min
Whisky, frisky,

Hippity hop;

Up he goes

To the tree top!

Whirly, twirly,

Round and round,

Down he scampers

To the ground.

Furly, curly

What a tail!

Tall as a feather

Broad as a sail!

Where's his supper?

In the shell,

Snappity, crackity,

Out it fell.

-Anonymous

Look! It’s a crow, no it’s a goose, no it’s a squirrel! If you’ve

recently walked around on campus you may have noticed we have quite
the number of birds and a few mammals on campus-often times you’ll see
a flock of geese. I’ve even seen geese chase students on campus to
protect their eggs. We also have a few mammals that can be seen.
Squirrels are the most abundant but we also have rabbits and even deer
on campus. Where do you think the greatest abundance of animals can be
found? On the developed more urbanized area of campus where leftover
food abounds or in the UNCC Ecological Reserve where they’re protected
by addition tree cover and have access to a constant water source.

There are many methods used to estimate animal populations.

These include catch and release, tagging, tracking, and animal
sightability models. Using animal sightings to estimate population
size is tricky. Why might this be? Often times, helicopters or planes
are used and aerial photographs are taken but this method is limited
by forest density. In the field we’ll be analyzing the difference in
animal population abundance and diversity between the urbanized area
of campus and the wooded reserve. We’ll do this by going to the field
and counting animals. However, first and foremost, a lesson in
identification is necessary. Below is a list of animals (birds and
mammals) you may see on campus:

Eastern Gray Squirrel, Eastern Cottontail, White-tailed Deer, Canada

Goose, Mourning Dove, American Crow, Northern Mockingbird, Northern
Cardinal, Eastern Bluebird, Tufted Titmouse, Carolina Chickadee,
Sparrow, Turkey Vulture, European Starlings, and even Hawks!

What you will do: Your group will be assigned two different routes on
two separate days. You will follow the route and identify and count
all species.

What you will need: Binoculars, animal guide (this will be provided),
Clipboard and chart.

Animal Rural Urban

Next we will be calculating the Shannon Diversity Index (SDI) of all

the animals you found. This will tell us how diverse your areas were.
 Calculating the Index

1. Divide the number of a species (n) in one area by the total

number of all species in that area to get relative abundance. We
will call this number p.
2. Calculate the natural log (ln) of this number. Don’t worry excel
will do it for you.
3. Multiply the relative abundance (p) by the ln(p). That is
multiply number 1 and 2.
4. Multiply this number by -1.
5. Then raise e to this number. E is just a constant at
2.71828183…..
Your table should look something like this:

Group Number: n p ln(p) p*ln(p)

Area:

x1 x1/y

Total y n/ n/ H’= sum((p*ln(p))*-

a a 1

Shannon Diversity e^(H’)

Index

Next you’ll do this for the other area you studied!

 Treehuggers: Old School Meets New School

ATRT: 38 min
I think that I shall never see
A billboard lovely as a tree.
Perhaps, unless the billboards fall,
I'll never see a tree at all.
-Ogden Nash, "Song of the Open Road," 1933

Beetles, mites, and trees oh my! In this section you’ll be

forming your own hypotheses about how trees are naturally grouped.
Have you heard the expressions “not the only pebble on the beach”? or
“the apple doesn’t fall far from the tree”? These are called idioms
and are analogies referring to the behavior of people. Do they hold
true in nature? For some time in the ecological literature it has been
debated whether trees are grouped together and that their fruit
doesn’t fall far from the tree so to speak, or if they’re widespread
like pebbles and not stringently limited by specific ecological
factors. Don’t overthink the analogies; the real question is whether
the same kind of trees grow together in similar areas. Remember
Clements, - one of the fathers of ecology? Originally, Frederic
Clements hypothesized that tree species were bound into whole units
that formed communities in a kind of “superorganism.” This was called
the community unit hypothesis. A few years later, a budding naturalist
by the name of Henry Gleason countered with his Individualistic
Hypothesis claiming that the location and distribution of different
trees depended on certain conditions and many species could coexist
and overlap in their dispersal. The following picture depicts these
debates:

On the left is Clements hypothesis depicting clear cut boundaries in

tree distribution as one travels along an environmental gradient. The
right depicts Gleason’s Individualistic hypothesis with a lack of
clear cut boundaries in vegetation. A gradient is simply a change in
steepness or grade. This is also known as slope and can mean a change
in other environmental features such as temperature or pressure.
 Luckily, nearly 50 years after this debate started, as is the
case with most hypotheses, scientists figured out that both Clements
and Gleason were right. I mean wrong. I mean right. The truth it seems
is that trees and other vegetation exhibit both clumping and wide
dispersal in different environments along different gradients. So
depending on how you look at it each of them was slightly right and
slightly wrong.

In this experiment we will be analyzing how tree species are

grouped (or not) along our own gradient as we go up in elevation from
193 meters above sea level in the flood plain near Toby Creek to the
top of the slope at 215 meters above sea level at the top of the
slope:

Will trees in the UNCC ecological reserve follow more of

Clement’s, Gleason’s, or be a blending of both hypotheses?
What you will do: You will go out to the plots along the slope in the
reserve and identify all of the trees therein, test the soil pH in the
corresponding area, collect a soil sample later used to calculate
moisture content, and run statistics on all of this.

What you will need: Measuring tape, tree guide (this will be
provided), collection bowl, a soil pH kit, and a shovel.

George, George, watch out for that tree!

 Now you go out and identify all the trees in your area right?
It’s not that easy is it? Identifying trees takes skill and it takes
time to develop that skill. I remember my first time out in the field
trying to identify trees. I asked the professor to identify just about
every tree I saw. Is this an oak? What kind? To try and simplify this
task I’ve made the following dichotomy key (flow chart) of most of the
local trees on campus:

All you need to know to use it are a few simple facts about
trees. Pictures say it all when it comes to tree identification. Look
at the following graphic:

(from 4H tree guide for NC)

You may have never noticed before but leaves branch in multiple ways.
A single leaf can come from a bud or multiple leaves can come from a
single bud (these are actually called leaflets). The first is termed
simple and the latter is compound.

Another important aspect of tree leaves are their arrangement on

the branch. Are they directly across from each other or staggered? The
first is called opposite and the second is called alternate. Next are
their edges. Are they smooth or toothed? Are the teeth large or small?
Are they single or double toothed? Evenly spaced? Lastly, what is
their general shape? Are they skinny, uniquely shaped, or
symmetrically oval?
 Now that you know the general features of the deciduous trees
(non-evergreens) you can narrow the tree down using the flow chart and
then look it up in the tree guide. Each tree will have a
distinguishing feature to help further characterize it. If you have
You can also use Leafsnap ®, an app that Cornell university created
for leaf identification. We’ll discuss this more in lab.

Remember, trees like people are highly variable. Imagine trying

to describe what an American looked like to an alien. Could you do it?
After all of this is it important to memorize all the trees? Read the
information beside each tree as you come across the species. You’ll be
quizzed on some of them and one day if you’re lost in the wilderness
it will be useful to know which tree’s berries are edible and which
one’s would make the best canoe.

Once you’ve got the trees identified in each plot, you’ll need
to measure what’s called the diameter at breast height (d.b.h.), the
diameter of the trees measured approximately in front of your chest.
To be exact it’s 1.4 meters off the ground. Fill out the following
chart for your area:

Species Dbh(cm)
You will later combine all of the class data for all plots in all
areas to calculate an index. You will then test this index
statistically. Also, be sure to collect a sample of soil in your
circular plot. Later we will use this to calculate soil moisture.

Lastly, you will need to test the pH of the soil in each area
(not just the one you are measuring trees in) using a test kit. Fill
out the following chart:

Soil pH
at 3
Different
Levels
in the
Forest
Team FP MS TP

Average
 Drip, Drip - Using an IV
What is the IV 300? Simply put, it is an index representing how
dominant or representative a certain tree species is within a certain
area. This index includes 3 important calculations: basal area,
frequency, and density of each tree species. Each of these is
calculated relative to the other tree species in a particular area.

First you will need to calculate tree basal area (B.A.).

Multiply the d.b.h. by .00007854 (that’s four zeros-don’t worry it’s a
constant-we won’t go into its derivation). Add this to the chart
you’re creating. You will be creating multiple charts – one for each
area. Sort first by species and then by plot. It should look something
like this:

Flood Plain

Plot Species Dbh Tree BA Species Species Species

(cm) (m2 tree-1) Frequency Density Basal
Area
Stems ha-1 (m2 ha-
1
)

1 Oak x x x x x
1 Oak x x
2 Oak x x
2 Oak x x
1 Pine x x x x x
1 Pine x x
Total x x x

Next, calculate frequency by taking the number of plots you

found a particular species in divided by the total number of plots in
that area.

Density is measured by taking the total number of each tree

species divided by the total area surveyed. Our plots were .01 hectare
(ha, this is 10,000 square meters or roughly the size of a football
field). This gives us trees per ha. In this example it would be 4
oaks/.02ha as we have two .01 ha plots.
 Next is species B.A. calculated by summing the tree B.A. and
dividing by the total area surveyed.

Now you have to calculate all values relative to each other.

This is done by taking the absolute values as a percentage. For
frequency divide each species frequency by the total frequency of all
species. For density, divide each species density by the total density
of all species. Are you getting it? For B.A. divide each species B.A.
by the total B.A. of all species. It should look something like this:

Flood Plain

Species Species Species Species Relative Relative Relative Importance

Frequency Density Basal Frequency Density Basal Value
Area Area
Stems m2 ha-1
ha-1

Oak x x x x x x x
Pine x x x x x x x
Total x x x x x x x

The IV 300 is simply the sum of the relative frequency, density

and basal area.

In the la-bore-a-tory (to be said like a mad scientist), as the

last step of this lab we will be measuring soil moisture. This is done
by weighing the soil collected in your plot, drying it and weighing it
again. Then just take the difference between the two. Divide the
difference by the weight of the dry portion to get the percent of
moisture initially in the soil. Do you know the difference between
weight and mass? Record your measurements here:

Soil
moisture
(% dry wt)
=100*(water
weight) /
(soil dry
weight)
Team FP MS TOP

Average
 The Dirt on Dirt
ATRT: 32 min

“A pig used to dirt turns its nose up at rice” – Japanese proverb

Have you ever heard of geophagy? It means eating dirt. Believe

it or not some cultures eat dirt including people of the Appalachian
mountains. Traditionally geophagy served as a supplement for some
nutrition especially during pregnancy. It’s also thought that it aids
in the treatment of nausea. So let’s say you’re a geophagist (not sure
if that’s a word) and you want to make the perfect soil smoothie. The
terms soil and dirt are often confused. Dirt includes dust (most
indoor dust is human skin), grime, soot, and soil. What would you need
for this smoothie? You would need three main mineral particulates
called sand, silt, or clay. What proportions of each exist in soil
depends on the size of the particle. Sand has the largest particle
size with clay having the smallest.

What else would you need besides sand, silt and clay? Well,
you’d also need water, decomposing organic matter, air, and other
minerals. Mix them all up and you have your smoothie.

So, should you eat soil if you’re ever hungry and nothing else
is around? Probably not. There are lots of organisms in dirt which
we’ll be studying in this lab including parasites. Besides, in nature
all of these ingredients are not already mixed together and it’s not
likely you’ll be carrying a blender through the woods.

Soil is actually split into visible layers. The topmost or O

layers contains the organic material and detritus that are beginning
to decompose. The next layer is the A layer where you find some of the
sand and silt and maybe some clay. The A layer is actually split into
A1 and A2. A1 has some organic material mixed in while A2 is
completely eluviated (depleted) of most organic material leaving only
minerals behind. For this reason it is also called the E layer Lastly,
the B layer contains mostly clay and iron. It is termed illuviated
(accumulates material).

In this lab we will be looking at the depth of each of these

layers as well as the diversity of organisms we find in the soil as we
go up in elevation along our transect in the UNCC ecological reserve.
See below for information on the transect. Why do you think the soil
forms horizons (layers) in the first place? Rain? Flooding? Do you
think the depth of the layers are significantly different at different
locations along the slope? Don’t worry you’ll find out soon.

What you will do: You will go out to the transect in the UNCC
Ecological reserve and take soil cores to measure the depths of each
layer at each area along the slope. In addition, you will collect
soil samples from which we will characterize and count the organisms
therein. Lastly, we will also use these soil samples to calculate the
percentage of sand in the soil and the percentage of moisture.

In this experiment we will be analyzing how organism abundance varies

along our own gradient as we go up in elevation from 193 meters above
sea level in the flood plain near Toby Creek to the top of the slope
at 215 meters above sea level at the top of the slope:

What you will need: Measuring tape, tree guide (this will be
provided), collection bowl, a soil pH kit, and a shovel.

O Marks the Spot:

Each section is labeled according to their location:

Pictured above is each area along the transect, including the flood
plain (FP), middle slope (MS), and the top of the slope (TOP). Near
the middle of each plot two circular areas are measured out with a
radius of 5.7 meters.

What you will need: A soil corer, a ruler, a shovel, and a metal bowl.
Amazing how you can do complex experiments with such simple tools
right?

How to determine depth of soil layers

Look at the picture of a soil core below:

The O layer is usually no more than 25 mm. It is dark and made of

mostly plant litter. A1 is dark organic matter while in A2 (E) begins
to become lighter in color. Measure the layers and fill out the chart
below:

Depth O layer (mm)

Team FP MS TP
Average

What type of test will we use to analyze this data? Always be thinking
of this.

Dirty Bugs - Who’s Who?

Next you will collect a sample of soil and bring it to the lab.
This sample will be placed in a metal funnel. You will then place a
light bulb above the funnel and a beaker containing water and alcohol
beneath it. This will encourage the soil organisms to exit the funnel
and go into the beaker of fluid to be analyzed the next week.

The next week pour the liquid containing your soil organisms
into a petri dish and use the following chart and a field microscope
to identify them:
 Fill out the following table on abundance:

ORGANISMS FP MS TP
ANT
APHID
BEETLE
BEETLE LARVAE
BEETLE MITE
CENTIPEDE
COLEMBOLLA
DIPLURAN
EARTHWORM
FLY
HARVESTMAN
ISOPOD
JAPYGIG
LARVAE
MILLIPEDE
NEMATODE
PAUROPOD
PREDACIOUS MITE
PROTURAN
PSEUDOSCORPION
PSOCID
ROACH
ROVE BEETLE
SPIDER
SYMPHALID
TERMITE
THRIP
WORM
SPP. RICHNESS
TOTAL

Remember we will need to combine group data and you’ll need to be sure
to get the entire class’s data for all slopes.

What do you think we’re going to do next? If you guessed create

an index you’re right! Yeah indices! Did you know that’s the plural of
index? We will be calculating the Shannon Diversity Index (SDI) of all
organisms at each place along the transect.
 Calculating the Index

6. Divide the number of a species (n) in one area by the total

number of all species in that area to get relative abundance. We
will call this number p.
7. Calculate the natural log (ln) of this number. Don’t worry excel
will do it for you.
8. Multiply the relative abundance (p) by the ln(p). That is
multiply number 1 and 2.
9. Multiply this number by -1.
10. Then raise e to this number. e is just a constant at
2.71828183…..

Your table should look something like this:

Flood Plain n p ln(p) p*ln(p)

Ant x1 x1/y
Aphid
beetle
Beetle Larvae
Beetle Mite
Centipede
Colembolla
Dipluran
earthworm
Fly
Harvestman
Isopod
Japygig
Larvae
Millipede
Nematode
Pauropod
Predacious Mite
Proturan
Pseudoscorpion
Psocid
Roach
Rove Beetle
Spider
Symphalid
Termite
Thrip
Worm
 Total y n/ n/ H'= sum((p*ln(p))*-
a a 1
Shannon Diversity e^(H')
Index

Remember you will need to make tables for each area in the slope
including the middle slope and top. We will then statistically
analyze these indices.

A Gram of Sand

To conclude, we will use our soil samples to calculate the percentage

of sand they contain.

1. Weigh out 50 grams of soil

2. Add 100 milliliters of calgon solution
3. Pour sediment through sieve into large bowl
4. Scoop what is retained into a large pre-weighed beaker
5. Put in drying oven
6. In the next class you will weigh the dry sand and divide by the
original 50 grams of total soil weight to calculate percent sand
from each slope:

Percent Sand
Team FP MS TP

Average
 The Pons of Ponds
ATRT: 28 min
"A lake is the landscape's most beautiful and expressive feature. It is
Earth's eye; looking into which the beholder measures the depth of his own
nature."- Henry David Thoreau Walden

Have you ever heard of “Walden pond”? Henry David Thoreau wrote
it. It’s an essay (a book to most people) on Thoreau’s thoughts about
life and society. This happened way back in the 1890s. He came up with
all these thoughts whilst living in a cabin and sitting by a pond all
day. Maybe you’ll have similar thoughts too! We’ll be spending quite a
bit of time at the ponds on campus during the next few labs. Have you
been to any of the ponds on campus? Did you know there were three?

So what do you know about ponds? They’re like lakes right?

What’s the difference? Size mainly. Ponds are smaller although
scientists argue over how small a body of water has to be before it’s
called a pond. Generally, ponds aren’t so deep that light cannot reach
the bottom. However, no standard definition has been acknowledged by
ecologists around the world.

Nonetheless, many types of plants, animals, and protists live in

ponds. How many and what kind depends on many characteristics such as
pH, turbidity (remember turbidity?), dissolved oxygen, temperature,
light, and other nutrient concentrations. In fact, all of these
attributes affect each other. For example, cooler water can hold more
oxygen. Moreover, the organisms therein are dependent upon and affect
these same attributes. Remember, most organisms eat, breathe (undergo
respiration of some kind) and eliminate waste.

What kind of organisms do we find in ponds? Well, it depends

what we’re looking with. At the microscopic level we find plankton.
Plankton is just a general term referring to numerous microscopic
organisms including cyanobacteria and eukaryotic protists. Protists
are complicated. Basically, some algae are protists and some protists
are algae. Some protists eat other protists and bacteria. Some
photosynthesize and others are heterotrophic. Some do both.

Zooplankton, or the animal-like plankton, are the consumers in

pond environments feeding primarily on the phytoplankton. This group
not only includes protists but other multicellular organisms such as
water fleas and insect larvae.

Most likely, at the macroscopic level, you already know what you
can find in ponds. These include crayfish, snails, worms, and fish.

Where do you find all these organisms in a pond? On the bottom?

Close to shore? It actually depends on their energy requirements. Some
organisms require more light, more oxygen, etc. Some live close to
shore in the littoral zone where the action of waves is minimal. The
photic zone refers to the depth that light penetrates, at least to one
percent up to its surface intensity. The aphotic or profundal zone
receives little or no light. Finally, the benthic zone is the bottom
of the pond. Pond organisms could be found in one or multiple zones or
even in District 12 in Panem. These zones change day to day and season
to season and are dependent upon both sunlight and consequently
temperature.

Temperature also forms its own set of zones - or at least the

scientist who named them thinks so. The epilimnion consists of the
less dense warmer water near the surface. In the middle is the
thermocline where the temperature changes rapidly leading to the
hypolimnion, or area with more dense water concentrated with nutrients
such as nitrogen and phosphorus.

In this next lab we will be looking at almost all of these pond

attributes to see how they affect each other.

What you will do: You will go to multiple ponds on the UNCC campus.
You will collect plankton and measure temperature, dissolved oxygen,
and turbidity. You will collect a water sample to later be tested for
nitrogen, and phosphorus concentration.

What you will need: A dissolved oxygen and temperature meter (YSI
EcoSense DO200), turbidometer (Hack 2100A), plankton net, cup, bottle,
and tube for water sample.

When arriving at the pond you will need to test the dissolved
oxygen, temperature, and turbidity. Record your results here for all
but nitrogen and phosphate, we will test these later back in the lab
so bring back a sample of water:
Hospital Pond

Team Turbidity Temp DO NO3 PO4

# NTU ºC ppm ppm ppm

Average

Brocker Pond

Team Turbidity Temp DO NO3 PO4

# NTU ºC ppm ppm

Average
 Filter approximately 40 liters of water using a plankton net.
When finished be sure to rinse net fully into a collection bottle.
This will later be analyzed using a microscope

Lastly you will use the water sample you’ve collected to test
for both nitrate and phosphate levels in the ponds. For this you will
use a calorimeter.

How to use a calorimeter:

1. Shake your sample like polaroid picture

2. Put 10 mL of tap water into calorimeter tube
3. Put 5 mL of sample water into glass flask using pipet
4. Add 5 mL of mixed acid reagent to this tube. Swirl and incubate
for two minutes
5. Add two .1 gram spoons of Nitrate Reducing Reagent. Swirl for
three minutes, incubate for 10.
6. Pipet this sample into calorimeter tube.
7. Turn on calorimeter, press enter twice, select all tests and
then select 064 Nitrate-N L
8. Insert blank tube and select scan blank
9. Put in sample tube and select scan sample.
10. Multiply results by 4.4 to get nitrate in parts per million
(ppm)-Add data to chart

Follow same steps for testing phosphate, except as you might have
guessed you’ll need to use phosphate reducing agent, etc.

Finally, since you were bored just staring into the water at
Walden Pond (or your version of it at least) you will now prepare a
microscope slide using the plankton you collected. Use the
following chart to identify the plankton:
Fill out the table. This will be for your group:

Organisms at ______Pond #

Green Algae

Blue-Green Algae

Vorticella

Volvox

Rotifer
 Ceratium/Dinoflagellate

Copepod

Ostracod

Nauplius

Daphnia/Cladocera

Ciliated Protozoa

Pseudopod Protozoa

Flagellated Protozoa

Now we will combine all of the data:

Organism Hospital Heck

Pond Pond

Green Algae

Blue-Green Algae

Vorticella

Volvox

Rotifer

Ceratium/Dinoflagellate

Copepod

Ostracod

Nauplius

Daphnia/Cladocera

Ciliated Protozoa

Pseudopod Protozoa

Flagellated Protozoa
Now to further analyze the data we will do the same actions as with
the soil organisms. You love indices (multiple indexes) so much at
this point you will be calculating the shannon diversity index
again on your pond organisms. Fill out the chart for each pond:

Hospital Pond N p ln(p) p*ln(p)

Green Algae x1 x1/

y

Blue-Green Algae

Vorticella

Volvox

Rotifer

Ceratium/Dinoflagellat
e

Copepod

Ostracod

Nauplius

Daphnia/Cladocera

Ciliated Protozoa

Pseudopod Protozoa

Flagellated Protozoa

Total y n/a n/ H'=sum((p*ln(p)

a ) *-1

Shannon Diversity Index e^(H')

How will you analyze this data? What tests will you use? This will
be your last report for Ecology Lab. How did you do? How much did
you learn? Do you love ecology now? 