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Choosing Between Diversity Indices James A. Danoff-Burg Dept. Ecol., Evol., & Envir. Biol. Columbia University.

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Presentation on theme: "Choosing Between Diversity Indices James A. Danoff-Burg Dept. Ecol., Evol., & Envir. Biol. Columbia University."— Presentation transcript:

1 Choosing Between Diversity Indices James A. Danoff-Burg Dept. Ecol., Evol., & Envir. Biol. Columbia University

2 Lecture 5 – Choosing Between Diversity Indices© 2003 Dr. James A. Danoff-Burg, Alpha Diversity Indices  A diversity of diversities  Log Alpha  Log-Normal Lambda  Q-Statistic  Simpson  McIntosh  Berger-Parker  Shannon-Wiener  Brillouin  How to choose between these?

3 Lecture 5 – Choosing Between Diversity Indices© 2003 Dr. James A. Danoff-Burg, Bases for Choice  Appropriateness of each index for your data  Discriminant ability of the index  Statistical Comparability  Widespread utility of the index  Your Question

4 Lecture 5 – Choosing Between Diversity Indices© 2003 Dr. James A. Danoff-Burg, Bases for Choice  Appropriateness of each index for your data  Discriminant ability of the index  Statistical Comparability  Widespread utility of the index  Your Question

5 Lecture 5 – Choosing Between Diversity Indices© 2003 Dr. James A. Danoff-Burg, Appropriateness  Index assumptions need to be met  Abundance model of data  Sensitivity to sample size  Each index needs to be considered for all of these aspects  determines whether can be used for your data

6 Lecture 5 – Choosing Between Diversity Indices© 2003 Dr. James A. Danoff-Burg, Assumptions  Alpha diversity indices do not make many assumptions  No assumptions made about species abundance distributions Cause of distribution  not needed –species abundance models have assumptions about these »Geometric – niche pre-emption, regular arrivals »Log – niche pre-emption, irregular arrival intervals »Log-Normal – successively apportioning available niche space of all resources in proportion to abundance »Broken Stick – simultaneously apportioning available niche space of one resource in proportion to abundance Shape of curve  “Non-parametric” Normality is not needed

7 Lecture 5 – Choosing Between Diversity Indices© 2003 Dr. James A. Danoff-Burg, Abundance Model  Some indices perform better under a specific abundance model  Example: Simpson – probability that two individuals are of the same species  Geometric Underestimate Simpson diversity value  Log Underestimate Simpson diversity value  Log-Normal Best for Simpson diversity analysis analysis  Broken Stick May overestimate Simpson diversity value

8 Lecture 5 – Choosing Between Diversity Indices© 2003 Dr. James A. Danoff-Burg, Abundance Model Fitting  Main problem: Often have multiple models that fit the data  Occasionally because of low number of abundance classes  Log 2 has only 11 classes (octaves) even possible Most data have less than 11 E.g., less than 256 individuals in a species –Resulting in only 8 classes  Fewer classes, mean fewer opportunities for departures from fit  Small data sets  fit many models Few spp in each abundance class  decreased discriminability

9 Lecture 5 – Choosing Between Diversity Indices© 2003 Dr. James A. Danoff-Burg, Abundance Model Fitting  Secondary problem: Log-Normal is a frequent consequence  Often because of the central limit tendency of large data sets  If a data set has many species  often log-normal distribution results  Does not necessarily mean that the community has assembled by a successive breaking of the available nice space As is the assumption with the log-normal distribution

10 Lecture 5 – Choosing Between Diversity Indices© 2003 Dr. James A. Danoff-Burg, Appropriateness – Sample Effort  Some indices are tremendously sensitive to sample size  Low replication  skewed values Idiosyncratic results Not truly representative of the environment  Indices sensitive to inadequate sampling  S = very sensitive to sampling effort  Dominance indices (Simpson, Berger-Parker, McIntosh)  Information statistics indices (Shannon)  Evenness indices  Indices insensitive to sampling effort  Always: Log series a, 1/d (influenced by abd of most abd sp)  If more than 50% of spp represented: Q

11 Lecture 5 – Choosing Between Diversity Indices© 2003 Dr. James A. Danoff-Burg, Appropriateness - Sampling Effort  How to determine when you have completely sampled the environment?  Assuming prior information Leveling off of S with adding more samples –If interest is largely richness Leveling off of Pielou’s t point –If interest is proportional abundance  Leveling off point = adequate sample size

12 Lecture 5 – Choosing Between Diversity Indices© 2003 Dr. James A. Danoff-Burg, Appropriateness, Sample Size - When is Enough Enough?  Leveling off of S with adding more samples  If interest is largely richness  S is more sensitive to sample size than diversity indices  Need more samples  Leveling off of Pielou’s t point  If interest is proportional abundance  Any diversity index can be used H’ Shannon S Diversity Index Value Sample Addition Sequence 1/D Simpson 1/d Berger-Parker Pielou’s t

13 Lecture 5 – Choosing Between Diversity Indices© 2003 Dr. James A. Danoff-Burg, Sampling Effort  Need consistency in sampling effort  Need to use the same effort throughout experiment Helps to ensure comparability of indices All would then be equal(ly biased)  When sample sizes are unequal?  Rarefy the larger sample to the smaller  More next week on rarefaction (WE 1)  Better to have many small samples than few large samples  Increases replication  Decreases thoroughness of each replicate

14 Lecture 5 – Choosing Between Diversity Indices© 2003 Dr. James A. Danoff-Burg, Bases for Choice  Appropriateness of each index for your data  Discriminant ability of the index  Statistical Comparability  Widespread utility of the index  Your Question

15 Lecture 5 – Choosing Between Diversity Indices© 2003 Dr. James A. Danoff-Burg, Discriminant Ability of the Index  Differences are usually very subtle  Need analytical rigor to differentiate  Assuming differences exist, how best to see them?  No two sites are identical in terms of S, N, relative abundance  All sites will differ, how can we detect this?

16 Lecture 5 – Choosing Between Diversity Indices© 2003 Dr. James A. Danoff-Burg, Studies of Discriminability I  Taylor (1978)  Using 8 indices on moths, 9 sites, over 4 years Rothamsted Insect Survey, England  Best: Log a (by far)  Next: H’, S, log-normal l, 1/D, log biomass  Useless: log-normal S* and s  Kempton (1979)  Using 4 indices on same data, 14 sites, 7 years  Best: S, H’  Useless: 1/D, 1/d

17 Lecture 5 – Choosing Between Diversity Indices© 2003 Dr. James A. Danoff-Burg, Studies of Discriminability II  Kempton & Taylor (1976)  Transformed indices > untransformed form  exp H’ > H’  1 / D > D  Kempton & Wedderburn (1978)  a and Q > any H’ and any D  Magurran (1981)  Best: Margalef (D mg = (S-1) / ln N); U, S  Less well: HB > H’, exp H’  Worst: 1/d, D or 1/D, McIntosh D, H’ E, HB E  Morris & Lakhani (1979)  H’ > D or 1/D

18 Lecture 5 – Choosing Between Diversity Indices© 2003 Dr. James A. Danoff-Burg, Diversity Aspects Measured  Not just discriminability, but what is the index measuring?  Richness, Dominance, Evenness, Abundance…  What most affects the index?  Rare species or species richness? Type 1 Measures log a, log-normal l, Q, S, H’, HB, D mg, McU  Abundance of the most common species or dominance? Type 2 Measures 1/D or D, 1/d, McD, H’E, HBE  Within each type  significant correlation

19 Lecture 5 – Choosing Between Diversity Indices© 2003 Dr. James A. Danoff-Burg, Bases for Choice  Appropriateness of each index for your data  Discriminant ability of the index  Statistical Comparability  Widespread utility of the index  Your Question

20 Lecture 5 – Choosing Between Diversity Indices© 2003 Dr. James A. Danoff-Burg, Statistical Comparability  Historically, statistical comparisons not made  Mostly descriptive comparisons in past  Statistical Options  H’, Var H’, t-test  Replication Most sets of replicated estimates are normally distributed Non-normal data can be transformed for normality  Jackknife data Provides standard error and confidence limits as well

21 Lecture 5 – Choosing Between Diversity Indices© 2003 Dr. James A. Danoff-Burg, Bases for Choice  Appropriateness of each index for your data  Discriminant ability of the index  Statistical Comparability  Widespread utility of the index  Your Question

22 Lecture 5 – Choosing Between Diversity Indices© 2003 Dr. James A. Danoff-Burg, Widespread Utility of the Index  Important for ensuring comparability  Between studies  Between sites  Between researchers  Most commonly used  S, H’, 1/D, log a  Less commonly used  Log-Normal l, Q Even though highly valuable as discussed above  D mg, McU, McE, HB, 1/d

23 Lecture 5 – Choosing Between Diversity Indices© 2003 Dr. James A. Danoff-Burg, Widespread Utility - Cautions  Be careful with the H’ Shannon  Heavily criticized, despite widespread use  “no direct biological interpretation” (Goodman 1975)  Be careful with the log a  Based only on S & N Insensitive to changes when both stay constant Uncommon situation  “there can be no universal best buy but there are rich opportunities for inappropriate usages” (Southwood 1978)

24 Lecture 5 – Choosing Between Diversity Indices© 2003 Dr. James A. Danoff-Burg, Indices Performance Summary Index Discriminant Ability Sample Size Sensititivy Richness, Evenness, DominanceCalculation Widely used? Sensitivity to Abd models Log aGoodLowRichnessSimpleYesN (?) Log Normal lGoodModerateRichnessComplexNYes QGoodLowRichnessComplexNN SGoodHighRichnessSimpleYesN MargalefGoodHighRichnessSimpleNN ShannonModerate RichnessIntermediateYesN BrillouinModerate RichnessComplexNN McIntosh UGoodModerateRichnessIntermediateNN SimpsonModerateLowDominanceIntermediateYes Berger-ParkerPoorLowDominanceSimpleNN Shannon EPoorModerateEvennessSimpleNN Brillouin EPoorModerateEvennessComplexNN McIntosh DPoorModerateDominanceSimpleNN = Desirable Traits in an Index

25 Lecture 5 – Choosing Between Diversity Indices© 2003 Dr. James A. Danoff-Burg, Index Choice Guidelines  Clearly formulate question you are studying  Ensure equal sample sizes  Draw a Rank Abundance graph  Calculate Margalef (Richness) and Berger-Parker (Dominance) indices  Determine Log a and Q  Test fit to abundance models  Use ANOVA to test for treatment differences  Use Jackknife to improve estimate of indexes  Be consistent in choice of index across your studies

26 Lecture 5 – Choosing Between Diversity Indices© 2003 Dr. James A. Danoff-Burg, Bases for Choice  Appropriateness of each index for your data  Discriminant ability of the index  Statistical Comparability  Widespread utility of the index  Your Question

27 Lecture 5 – Choosing Between Diversity Indices© 2003 Dr. James A. Danoff-Burg, Your Question  What you want to know determines how you analyze your data  How important is each aspect of diversity?  Richness?  Evenness?  Dominance?  Abundance?  Per-species (relative) abundance?  Taxon diversity?  Trophic structuring?  Guild diversity?

28 Lecture 5 – Choosing Between Diversity Indices© 2003 Dr. James A. Danoff-Burg, Your Question  What answers your question?  What are the most important aspects of diversity?  What data directly addresses your question? How should it be presented? How should it be emphasized?

29 Lecture 5 – Choosing Between Diversity Indices© 2003 Dr. James A. Danoff-Burg, Your Question  Work through the gardens example  Abundance model?  Diversity aspects?  Form of the data?  Appropriate analyses?  Answer for a few subcomponent questions  Assignment:  Do above using your thesis (or the gardens data)  3-5 pages  Due 2 nd April before class

30 Lecture 5 – Choosing Between Diversity Indices© 2003 Dr. James A. Danoff-Burg, Studies of Discriminability III – Gotelli & Colwell 2001  Purpose / Goal  To discuss the different ways of presenting richness  To discuss the ways in which we can approximate total species richness  To discuss the difficulties encountered when using richness Proportional abundances Species Density Standardizing number of species across different sized areas Species / Genus –Important in biogeography

31 Lecture 5 – Choosing Between Diversity Indices© 2003 Dr. James A. Danoff-Burg, Gotelli & Colwell 2001  Differentiates between Individual-based and Sample-based assessment methods  Individual: life lists, Christmas bird counts, collector’s curves  Sample: replicated quadrats, mist nets, trap data  Hybrid: m-species lists (observing to a point)  Differentiates between accumulation and rarefaction curves (either individual or sample based)  Accumulation – total # of spp during process of data collection  Rarefaction – repeatedly subsampling without replacement from the data (progressive data reduction)

32 Lecture 5 – Choosing Between Diversity Indices© 2003 Dr. James A. Danoff-Burg, Gotelli & Colwell 2001  Samples always below Individuals  Individuals are clumped  Random assortments of traps through time unclumped  Rarefaction smoothed curves  Replicated data removal process (like Pielou)  Built “right to left”  Accumulation stepped line  Observed fact  Built “left to right” Sample Addition Sequence Richness Samples: Accumulation Samples: Rarefaction Individuals: Accumulation Individuals:Rarefaction

33 Lecture 5 – Choosing Between Diversity Indices© 2003 Dr. James A. Danoff-Burg, Gotelli & Colwell 2001  Factors influencing richness estimates  Underlying species richness  Relative abundance distributions  Sampling effort

34 Lecture 5 – Choosing Between Diversity Indices© 2003 Dr. James A. Danoff-Burg, Gotelli & Colwell 2001  Cautions when using richness  Scaling different sized areas to species density Susceptible to non-linearity of increasing area and richness Leads to an inability to extrapolate from smaller to larger areas  Species per comparable unit area Problem with non-linear relationship between sampling efforts and richness  Species per genus

35 Lecture 5 – Choosing Between Diversity Indices© 2003 Dr. James A. Danoff-Burg, Gotelli & Colwell 2001  Solution?  Use rarefaction on your data to standardize sampling effort  Bring larger sample down to size of smallest one

36 Lecture 5 – Choosing Between Diversity Indices© 2003 Dr. James A. Danoff-Burg,

37 Lecture 5 – Choosing Between Diversity Indices© 2003 Dr. James A. Danoff-Burg, Hypothetical Model Curves Geometric Series Log Series Log-Normal Series Broken Stick Model Per Species Abundance Species Addition Sequence


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