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Community Ecology BDC331

Community Ecology BDC331. Mark J Gibbons, Room 4.102, BCB Department, UWC Tel: 021 959 2475. Email: mgibbons@uwc.ac.za. Image acknowledgements – http://www.google.com. B. A. COLOUR. A. B. RED. 18. 41. LILAC. 14. 3. YELLOW. 4. 2. ORANGE. 6. 3. LIGHT GREEN. 5. 4. DARK BLUE.

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Community Ecology BDC331

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  1. Community Ecology BDC331 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. B A COLOUR A B RED 18 41 LILAC 14 3 YELLOW 4 2 ORANGE 6 3 LIGHT GREEN 5 4 DARK BLUE 4 2 BLACK 9 7 APPLE GREEN 3 2 LIGHT BLUE 2 1 TOTAL 65 65 Description of Communities Guild Taxocene Measures of Community Diversity Species Richness - S Same Number Species – 9 Same Number individuals - 65 Different Distribution of individuals amongst species

  3. Species Density Botanists Number of Species Observed Total Number of Individuals Counted Determining Species Richness 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. Species-Area or Accumulation Curves Asymptote considered to represent the number of species occurring in the community Issues of Sample Area or Number

  6. There are a number of ways of determining this: 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

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

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

  9. ( ) 1 N! ln = n1! n2! n3! n4! ...... N N = total number of individuals in the entire collection n1 = Number of individuals of species 1 n2 = Number of individuals of species 2 Best used where data not random Sensitive to the abundance of rare species ^ H ^ H can only use count data Brillouin Index

  10. 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 [ ] s ^ ni (ni – 1) ∑ D = N (N – 1) i = 1 ni = Number of individuals of species i in the sample N = Total number of individuals in the sample s = Number of species in the sample D can use biomass, cover, productivity & count data. ^ D can only use count data. Species Evenness Measures ∑ D = pi2 Simpson’s Index pi = Proportion of the ith species This Index actually determines the probability of two organisms at random that are the same species Sensitive to the abundance of common species

  11. Diversity Maximum Diversity H’ Shannon (J) Hmax S = Number of Species Hmax = ln(S) Simpson’s 1 / D S E1/D = Evenness

  12. DSt = 0.1612 1 / DSt = 6.20473 Example using: Simpson’s Index of Diversity 1 D [ ] s ^ ni (ni – 1) ∑ D = N (N – 1) i = 1 Putting Confidence Intervals around Estimates Jackknifing – the generation of pseudo-means Numbers of beetles in 16 hedgerow samples

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

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

  15. Example Data sets to calculate all measures

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

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