1. Community Ecology BDC321 Mark J Gibbons, Room 4.102, BCB Department, UWC Tel: 021 959 2475. Email: mgibbons@uwc.ac.za Image acknowledgements – http://www.google.com

2. Measures of Community Diversity Species Richness - S Description of Communities A B 65 TOTAL 12LIGHT BLUE 23APPLE GREEN 79BLACK 24DARK BLUE 45LIGHT GREEN 36ORANGE 24YELLOW 314LILAC 4118RED BACOLOUR Same Number Species – 9 Same Number individuals - 65 Different Distribution of individuals amongst species Guild Taxocene

3. Determining Species Richness Species Density Number of Species Observed Total Number of Individuals Counted Botanists Numerical Species Richness Sample-based Samples taken: all individuals within identified & counted Individual-based Individuals sampled sequentially Focus of Community Studies PROBLEM: Number of species reflects number of samples or individuals

4. COMMUNITIES vs SAMPLES

5. Issues of Sample Area or Number Species-Area or Accumulation Curves Asymptote considered to represent the number of species occurring in the community

8. Randomised 999 times Rarefaction Curves The absolute number of species likely to be found in the pool is obtained when the curve flattens out WHEN IDENTIFYING or COMPARING COMMUNITIES, ARE YOU INTERESTED IN ESTIMATING ABSOLUTE RICHNESS? DEPENDS ON THE QUESTION BEING ASKED There are a number of ways of determining this:

9. Effort (area or number of samples or individuals) is important STANDARDIZE a priori HOW?

10. Species Diversity Indices Heterogeneity Measures Shannon Index (H’ ) H’ = - p i ln(p i ) ∑ p i = Proportion of the ith species Varies between 1.5 and 4. Should ONLY really be used for datasets where absolute richness known – otherwise Brillouin Index Sensitive to the abundance of rare species

11. Brillouin Index H ^ 1 N ln N! ( ) n 1 ! n 2 ! n 3 ! n 4 !...... = n 1 = Number of individuals of species 1 n 2 = Number of individuals of species 2 N = total number of individuals in the entire collection ^ H can only use count data Best used where data not random Sensitive to the abundance of rare species

12. Simpson’s Index D = p i 2 ∑ p i = Proportion of the ith species This Index actually determines the probability of two organisms at random that are the same species [ ] D = ^ ∑ n i (n i – 1) N (N – 1) n i = Number of individuals of species i in the sample N = Total number of individuals in the sample s = Number of species in the sample i = 1 s D can use biomass, cover, productivity & count data. ^ D can only use count data. Sensitive to the abundance of common species Species Evenness Measures 1 - D Simpson’s Index of Diversity The probability that two organisms drawn at random are different species 1 D Strictly speaking, D can only be used for an infinite population - Estimator

13. Evenness Diversity Maximum Diversity H’ H max Shannon (J) H max = ln(S) S = Number of Species Simpson’s 1 / D S E 1/D =

14. Putting Confidence Intervals around Estimates Jackknifing – the generation of pseudo-means [ ] D = ^ ∑ n i (n i – 1) N (N – 1) i = 1 s Simpson’s Index of Diversity 1 D Example using: Numbers of beetles in 16 hedgerow samples D St = 0.16121 / D St = 6.20473

15. Repeat calculations n times, where n = number of samples, missing out each sample i in turn Record D (St-1) value – calculate reciprocal

16. Ф = n. 1 / D (St) – [(n-1). 1 / D (St-1) ] Calculate pseudo-values ( Ф) Calculate mean pseudo-value ( Ф) Calculate variance, standard error and 95% CI

17. Example Data sets to calculate all measures

18. THE END Image acknowledgements – http://www.google.com