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

Statistics applied to the analysis of biodiversity

The last module (n° 6) of this course will be dedicated to statistics applied to the analysis of biodiversity. We will see how to apply the information gathered in the previous modules to obtain a statistical significance. We will explore parametric and non-parametric tests, the useful chi-square test, the correct application of correlation and the regression analysis, and some hints about the multivariate analysis techniques, such as ANOVA.

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 27 lecture of the course Biological Diversity, Theories, Measure,

and Data sampling techniques.

Today, I will show you the fourth part of the statistics applied to the study of

biological diversity.

When we are conducting statistical comparisons between two samples,

beside to be interested in the association between the observed frequencies and

the effectiveness of the sample itself.

So trap, sampling techniques, and so on, as explained in the previous lecture,

one of the questions to answer in order to demonstrate the validity of

our hypothesis is if the sample size is significantly different,

from a statistical point of view, of course.

For example, if the biodiversity of a contaminated site is statistically

different from that of a non-contaminated site, it can be, in fact, that samples

with a similar mean or median, can have completely different variances.

In general, the comparison tests between the samples are divided into nonparametric

tests and parametric tests, depending on if they are comparing the means or

the variances using actual values, or the medians using ranks respectively.

I would describe you some simple parametric test.

This test assumes the observation are at scale of internal ratio, so

they are continuous,

otherwise the data should be transforming with the logging being transformation.

And that they are deprived from the population normally distributed.

If the distribution is not symmetrical, or

you are not sure about that, you must use a nonparametric test.

Generally biometric measurements, weight, height, length can be considered normal

distributed when applied to homogenous categories, or male or female or adults or

young people, for instance, while the are likely to be symmetrical.

Before proceeding to the comparison of samples with a parametric test,

you must make sure that the two samples are actually or

probably extracted from the two different populations.

Because although they can have similar means,

at least the variances have to be different.

Otherwise, you would have immediately confirmed the new hypothesis.

To check this issue, proceed to conduct a preliminary test that's

called F-test which evaluates the deviation of the ratio of two variances.

So the F-test is just calculating that's the maximum

variance divided the minimum variance.

After calculating the variances of the two samples,

as described in the previous lectures, derive the degrees of freedom.

So for the sample one is just degree of freedom minus one,

and sample two the same, where n1 and

n2 correspond to the number of sampling units of the samples 1 and 2.

Subsequently, check into the distribution of the F-probability table the value

at the intersection of the two degree of freedoms.

If the obtained value of F is lower than that in the table,

it is not possible to reject the new hypothesis immediately.

So the variances are similar, because probably the two samples come from

the same population, that is no statistical significant differences.

We need to conclude that at this level of analysis, the two variances are similar

and to accurately reject the null hypothesis and so

confirm the statistical significance, we must proceed to perform a parametric test.

Even the calculated value of F is higher than the critical value in the table for

the respective degrees of freedom, is not necessary to proceed further, and

it can be concluded that the null hypothesis is

reject because the samples are significantly different.

They do not derive from the same population.

So only in the first case, the following test should be carried out.

We can conduct the Z-test to compare the means of large samples.

It means more than 25 sampling units for both samples,

or we can carry the T-test for comparing the means of small samples.

It means less than 25 sampling units for both samples.

Z-test to compare the means of large samples as the ratio between

the difference of the two samples means, and

the standard error of the difference estimated from the variance of two samples

can be calculated with the following formula.

If the calculated value of that is greater than 1.96 or 2.58,

which is the normal distribution corresponding to P 0.05 or

P 0.01, you can reject the null hypothesis and confirm that the two means

are statistically significant or highly significant respectively.

The T-test instead is useful to compare the means of small samples, and

that's used at the variance of the two samples being small, may be similar, and

the sample units of both can be added up.

So this test introduces in the formula, the common variance, therefore,

the formula becomes using the square root of the common variance, the following.

Checking in the probability table of these distribution,

if the calculated value is larger than that in the table for the related degrees

of freedom of a two table test, you can reject the new hypothesis, and

it can be concluded that the difference between the two means is significant.

T is more than the value in the table for P 0.05, or highly significant,

T is greater than the value in the table to P 0.01.

So guys, today I show you how to calculate parametric test for

normally distributed data.

But in case you have no normal distributed data,

you need a nonparametric test that I will explain to you during the next lecture.

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