The course presents an overview of the theory behind biological diversity evolution and dynamics and of methods for diversity calculation and estimation. We will become familiar with the major alpha, beta, and gamma diversity estimation techniques.
Understanding how biodiversity evolved and is evolving on Earth and how to correctly use and interpret biodiversity data is important for all students interested in conservation biology and ecology, whether they pursue careers in academia or as policy makers and other professionals (students graduating from our programs do both). Academics need to be able to use the theories and indices correctly, whereas policy makers must be able to understand and interpret the conclusions offered by the academics.
The course has the following expectations and results:
- covering the theoretical and practical issues involved in biodiversity theory,
- conducting surveys and inventories of biodiversity,
- analyzing the information gathered,
- and applying their analysis to ecological and conservation problems.
Needed Learner Background:
- basics of Ecology and Calculus
- good understanding of English

From the lesson

Species-abundance distributions and comparisons

In this module we will talk about the most common species-abundance distribution models and I will show you how to compare different communities and samples in order to achieve a quantitative and statistical measure of the changes in biological diversity due to treatments.
I will explain some Evenness measures and how to represent them in form of curves of biodiversity. This will help to discriminate communities’ diversity and to better analyse the anthropogenic impacts on biodiversity.

Ph.D., Associate Professor in Ecology and Biodiversity Biological Diversity and Ecology Laboratory, Bio-Clim-Land Centre of Excellence, Biological Institute

[MUSIC]

Hi guys, welcome to the 19th lecture of the course, Biological Diversity, Theory,

Measure and Other Sampling Techniques.

Today I'm going to explain to you how to compare communities diversity.

To be able to compare community, we first need to understand what to count as

an abundance, because we can count different things.

For instance, we can count the number of individuals, but in some cases, one for

instance, we have trees and spruces from trees.

It's very difficult to understand what is an individual and

what is a different individuals.

So, in that case we can count, for instance, modular unities or densities

instead of individuals or biomass or, for instance, percentage of coverage.

We can use squares, we can use different sampling techniques.

In any case, we need a surrogate of [INAUDIBLE] to account for

the differences in biodiversity between different samples.

Which method we have to compare communities?

One of this is the rank-abundance plot.

As I show you, the rank-abundance plot is very interesting because you can

use two different courses to understand which one is which richer?

Which one is more species?

Which one is even?

The Kolmogorov-Smirnov test is another way to have a statistical variable

meter to understand the difference.

So if the difference is statistically relevant or not.

Richness estimators also use, for instance, Chao 1, alpha Fisher,

Jackknife because they estimate the affected number of species and

are very useful to understand.

Which community is richer than the other one.

Another way is to use the species-area curves as a cumulation curve or

rarefaction curve to understand the differences in community.

Or we can use the Species abundance distribution and

calculate the alpha-Fisher.

We could have three different scenarios.

The first one is that we have areas of same dimensions sampled from similar

(ecologically) communities.

The second one is that we have areas of different dimensions or

samples from different (ecologically) communities.

The third different scenario is that we can have a small dimensional samples

that is a scenario very often that, and

we have number of individuals that is minor of 100 or 200.

In the first scenario, I mean, again,

the areas of the same measured samples from similar ecological communities.

We can use richness and abundance such as S or N.

To compare communities are very useful index to compare

evenness of the two samples is the EH and the slope of ECDF as a graph

as they intercept if n of each samples is measured than 100 Individuals.

And if you want to understand differences in dominance and rarity,

we can just use the relative dominance and the percentage of rare number of

individuals divided by number of species, so PCTRare N divided by S.

For the second scenario so if we have areas of different dimension or

samples from different ecologically community we can use for to estimate

the differences in their richness and abundance the Margalef Index or

the Alpha Index if n of each sample is measured in a 100 or

there the Smith-Wilson index to compare abundances.

If we want to understand the difference in the evenness we

can use evenness of Shannon or the M of ECDF.

So there's no, and to understand the difference between dominance and

rarity we can use absolute or relative dominance.

And percentage rare at 1 or 5% In the third case where the samples are of

more dimension means that the number of individuals are less than 100 or 200.

We can use to evaluate the differences in diversity between the two samples

the Hulbert Simpson index that is very scalable in cases more dimension samples

And we can use 1- D, so 1 minus the Hulbert/Simpson Index.

To evaluate the differences in evenness instead we can just use this evenness.

For the evaluation of dominance Dominance and rarity, relative dominance or

McNaughton index is very useful.

And for rare species, the PctRare5% or

the PctRareN/S are very useful indicators.

Moreover, we can use statistical tests to understand if these differences

between communities are more or less statistically evident.

And one of these is the T-test or the ANOVA test to compare two or

more communities in case of normality of data distribution.

If normality cannot be ensured, we can use the jackknifing process.

To use the jackknifing process is very simple.

We need just to calculate diversity, the Shannon or Simpson Not very important.

We can use one or these two.

And of all add samples together to obtain a kind of aesthetic,

so the original diversity estimation.

Then we calculate n times the diversity excluding

each time only one sample to obtain.

Minus one, or minus j.

Then, we calculate the set of values for

each n samples that is just nst, minus n, minus one.

Of sti, deity or j, depends on our sample.

So, we will remove from the original diversity estimation.

The diversity of each sample we removed and we estimate the diversity

jackknifing as the sum of this pseudo-values divided by n.

That is the total number of samples.

In this way, we can also calculate the standard error That is simple.

The standard error of the estimation of jackknifing process, and

we have a variable statistics to provide information about

the evidence in differences in statistical communities.

So this lecture tried to show you how to compare different communities based

on based on [INAUDIBLE], based on [INAUDIBLE], based on [INAUDIBLE].

And I hope you will use these tools to compare your data and