[MUSIC] Hi guys, welcome to the 16th lecture of the course Biological Diversity, Theories, Measures, and Data Sampling Techniques. Today, we will talk about evenness. First of all, we need to define what is evenness, even if it's just the answer to the question how equally abundant are each of the species. A simple way to combine abundance averages is just evenness, so it's the homogeneity within the sample. This is rare that all of the species are equally abundant, and so, some of them are more abundant in general than the others. So the distribution of individuals amongst species is just the measure of evenness. So, at the open two extremes of this evenness is just the dominance, or rarity. Dominance, of course, is the number of species that dominate the ecosystem. The rarity is the very rare number of species in the place than in the area where we are sampling. There are some indexes to measure the absolute dominance, and one of these is just simple N1. N1 is the abundance of the most abundant species in the sample. There are relative abundance indexes. One of these is N1 divided by N, where N is the total abundance on the samples. Another measure is the McNaughton Index of dominance that is N1 + N2 divided 2N multiply it by 100. It means that N1 is the number of individuals or the most abundant species. N2 is the number of individuals or the second abundant species divided by the total number of individuals in all species. The percentage index of singletons is another way to measure rarity, and in this case, we just measure S1 divided by S, where S1 is the number of singletons that our species represented by only one individual, and S is the total number of species in the sample. If we want to measure the rarity, we have another way that the PctRare 1%. This index is just the sum of S1 + S2 + etc, until ST, divided by S, where T is the biggest Integral number less than 0.01 multiply it by N, that is the total abundance. We can use this only if total abundance number is more than 100. But if N>20 we can use PctRare5%. In this case, T is the biggest integer number that formalize the same or the previous one, but where T is the biggest integer number less than 0.05 multiplied by N. The last index of rarity that we can use is the PctRareN/S Index, where we use on the exponent, we use S1 + S2 + ST/S, where T is the biggest integer number less than N/S, where N is the total abundance and S is the total richness. A way to represent the dominance in rarity is the relative abundance graph. In this graph, we put on that [INAUDIBLE] species number and [INAUDIBLE] cumulative relative abundance. And what we can find in the curve, the inflection point, and this point, is the point that's split in two parts, species that are dominant, and species that are rare. So, we see that as much the line, the imaginary line that we can build on the inflection point, is shifted to the left, the less rare species we have in the sample. On this graph that you see in the picture, this inflection point reaches almost 25 species. Evenness increases diversity. In fact, when we have more evenness, we have greater diversity. This is true for all indices. For instance, in the example in the picture, you see that we have the same number of species four and the same number of individuals, but in the sample where the homogeneity, so the evenness, is higher, also the diversity is higher. The evenness can be used as an indicator of diversity, and for many ecosystems, high evenness is a sign of ecosystem health. It means that we don't have a single species that is dominating the ecosystem. Often, only invasive species are dominant and this means that in this sample size, dominance is very high because of a lot of invasive species. There is also a kind of paradox, the paradox of enrichment, where a polluted side, for instance, that are enriched by nutrients show a higher biodiversity. But in a general case, this are more aged species. So, it means that they have simple biodiversity and with the dominance of a few ecological species. That means that numerically abundant in dominance and in the number of individuals, but not in the number of species. It is important to understand that between ecosystems, comparability is usually not possible. It means that some areas have lower biodiversity naturally than others, and this is normal. For instance, that is not really much less even than the deciduous forest, and that's dominated by a single species, for instance, blue spruce. Seasonality also may confound the comparison as well. Earlier in temperature growing season for instance, it means that less even than later. So, it's important to keep in mind that we cannot compare different ecological areas in this case. But if we want to have kind of comparison anyway, we need to try to prioritize areas for conservation. But they are based largely on bio-diversity so not psychological uniqueness. So, evenness, in this case, could be a good representation that we can use different ways to represent this. For instance, we can use log 2 as the number of individuals in a graph in a histogram. While we put on our shifts of the number of individuals, according to log 2 and inordinate numbers of species, of same time, we can see that if you don't use log 2, but just we use just the number of individuals, these around will be shifted with the high abundance species on the left. This was not first time by a person suggested that suggested the idea of wayline. Meanwhile, the way line is just a line that hides the rare part of the species, so all the left of this graph is completely hidden because of these rare species are not sampled. Use it for a presentation of the evenness, or the rank abundance plot, or dominance diversity curve. But, they're also called with the curved plot. The rank abundance plot, that I will explain you later, is just the species rank put it on the [INAUDIBLE]. And the reality of abundance interpreted on the ordinate. From the trend of the Q, we can understand genus of the specifies, in particular, the central part of the Q with more or less slope, we can understand that there is more or less homogeneity in the samples. So, thank you for your attention and see you at the next lecture.