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Introduction to the analysis of community data Vojtech Novotny Czech Academy of Science, University of South Bohemia & New Guinea Binatang Research Center.

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Presentation on theme: "Introduction to the analysis of community data Vojtech Novotny Czech Academy of Science, University of South Bohemia & New Guinea Binatang Research Center."— Presentation transcript:

1 Introduction to the analysis of community data Vojtech Novotny Czech Academy of Science, University of South Bohemia & New Guinea Binatang Research Center

2 Ecological analysis of community samples typical data format:

3 Some of the questions you can ask about the samples: How many species? How many individuals? What species are common / rare? How different are the sites in their species composition? How different are the species in their distribution?

4 Presence – absence characteristics: number of species and sites

5 Species accumulation curve

6 How many species? Corrected estimate for missing species Chao1 S + singletons 2 /(2*doubletons) S – number of species sampled

7 Courtesy Jonathan Coddington.

8 Courtesy Jonathan Coddington

9 No. of species often depends on the number of individuals: samples with more individuals have also more species Rarefraction: Comparing the number of species in a random selection of the same number of individuals from each sample

10 Diversity measures: describing distribution of individuals among species Simpson’s index: the probability that two individuals chosen from your sample will belong to the same species Berger-Parker’s index: share of the most common species

11 Diversity estimate: Simpson’s diversity: 1- ∑[ni(ni-1)/N(N-1)] ni – number of individuals from species i, N – total number of individ. Berger-Parker’s Index: n max /N nmax = abundance of the most common species, N – total no. of individ.

12 alpha diversity beta diversity gamma diversity α β γ  =  avg +   = 20  avg = 16.6  = = 3.4 Alpha, beta and gamma diversity

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14 Community similarity estimate: Jaccard similarity: shared species/[total species X + Y] Jaccard similarity = A/(A+B+C) X, Y - samples X Y

15 Koleff et al J anim Ecol 72:367 Similarity indices

16 Sorensen Lennon et al. Koleff et al J anim Ecol 72:367 Jaccard "Broad sense" measures incorporate differences in species richness as well as differences in composition "Narrow sense" measures independent of differences in species richness Example 1 a = 10, b = 10, c = 100 Jaccard = 10/120 = 0.08 Sorensen = 20/130 = 0.15 Lennon = 1- 10/20 = Example 2 a = 10, b = 10, c = 1000 Jaccard = 10/1020 = Sorensen = 20/1030 = Lennon = 1- 10/20 = 0.5

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18 EstimateS data format, saved as TXT file

19 Chao1 S + singletons 2 /(2*doubletons) S = number of species sampled Simpson's Index (D) measures the probability that two individuals randomly selected from a sample will belong to the same species Jaccard C J C J = a / (a + b + c) a = richness in first site, b = richness in second site, j = shared species Sorenson C S C S = 2a / (2a + b +c)

20 SU 2 PresentAbsent SU 1 Presentab Absentcd Jaccard Coefficient number of shared species as proportion of total number of species in the two SUs ranges from 0 (no species in common) to 1 (the SUs have identical species lists)

21 SU 2 PresentAbsent SU 1 Presentab Absentcd Sørenson Coefficient like Jaccard, ignores shared absences

22 Quantitative Version of Sørenson (Bray-Curtis) Similarity

23 Morisita-Horn C mH –Not influenced by sample size & richness –Highly sensitive to the abundance of common spp. –C mH = 2  (an i * bn i ) / (da + db)(aN)(bN) aN = total # of indiv in site A an i = # of individuals in ith species in site A da =  an i 2 / aN 2


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