[MUSIC] Hi guys, welcome to the 11th lecture of my course, Biological Diversity Theories, Measure, and Data sampling techniques. Today I will explain you how to analyze, and measure biodiversity. First of all, we need to start to understand what is a community. We have some different possibilities. One of them was proposed by Crabs in 1985. He said that a community is a group of population in a single place. Later Bagon and other authors in 1986 proposed that the communities is just an assemblage of population which accrue together in space and time. We need to summarize this definition and we can express just the community as a group of interacting population in a single time in a single definite place. There are some implications of this definition. First of all, species in a community needs to interact with each other. This can include all species, so I mean the community can include all species or can be limited to a single guild. More common and more tractable, of course, because we reduce the number of species included. And a community can also be defined by a consistent special boundary. So definition of community depends so how we design the studies, and the sampling aimed as we want to understand and we want to calculate. There are different aspects of bio diversity. Before starting our research study, we need to ask ourselves what can we measure? There are different possibilities. We can measure richness of species, abundances of species, diversity that is a combination or relationship between richness and abundances. Guild, perfect structure, evolutionary diversity with each species diversity. Such as genetic morphological diversity, and other features. Then we need to ask how to summarize and describe nature, because we got near infinite number of things to record. So we need to simplify. The simplification is dictated by the experimental question, the location, the taxon, and he sample or real the sub sample from nature. It depending on to choose an aspect of biodiversity, to choose a location, to choose a life stage etc. The sampling effort is just the number of samples we need to collect to insure a correct sampling of the community. It depends on the economic resources we have available, the human resources, time and question to answer. It has been suggested to kind of rule of ten. So it means that collectedly stem samples, plot, grades, etc, for each treatment would be enough, sufficient to provide a very good research study. Three, of course, is the magic minimal number in statistics, but is usually is a too low for ecology. So how to sample the data? We need first to keep data separately because merger data cannot be separated later. That's very important point. If you look for viability, you need statistic. That's the second very important point. But for doing statistic, if you merge raw data, you cannot perform any kind of statistic. So, for instance, if your sample builds per day, you need to take information on the hour. Otherwise, later, you can have this information but only together with day by day. To sample diversity, you need to remember that less sample you get, more biases you of course you have. So biases is just systematical differet from the population parameter of interest. So it's important to get increase the number of samples to approach the real numbers of species. If you are evaluating treatments, you need less samples, but if you are evaluating the total diversity, you need more samples. So how to compare communities based on samples? You need to use abundances. That's the basic information for each taxon for instances if you are collecting species, genes, family, etc. All for any operational taxonomic unit. Then you need to show this biodiversity data. And tere are different ways to show. I will not explain in details now, because this is argument of next lecture. But I was just to introduce which system we have to explain and to show biodiversity data. First is the rarefaction and accumulation curves. These curves just compare different samples each other, and makes information, and provide information how different are these samples to each other. And which one is the richest, which one is the poorest, etc. Another way is the log normal of distribution of data. We can have different distribution of our abundance per species, and these can be shifted on the left. It means that we are missing array of species. They can be balanced. So they can follow a log normal curve, it means that we have a good representation of common and rare species. Another way to show biodiversity data is the rank abundance plot. The rank abundance plot is a plot that only shows the abundance ranks of species, and on the ordinate, the proportional abundance of species. And this is another way to compare different samples. A very useful tool is the species area curves. These curves as the curves that represent the area sampled on the epsisal on the x-axis and on the epsinal axis represent the number of species. So, as soon as we get a kind of plateau, we can underhand when the sampling effort, the minimal sampling effort, is complete.