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CHAPTER 25 Indicator Species Analysis

CHAPTER 25 Indicator Species Analysis. Tables, Figures, and Equations. From: McCune, B. & J. B. Grace. 2002. Analysis of Ecological Communities . MjM Software Design, Gleneden Beach, Oregon http://www.pcord.com. How it works

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CHAPTER 25 Indicator Species Analysis

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  1. CHAPTER 25 Indicator Species Analysis Tables, Figures, and Equations From: McCune, B. & J. B. Grace. 2002. Analysis of Ecological Communities.MjM Software Design, Gleneden Beach, Oregon http://www.pcord.com

  2. How it works • 1. Calculate the proportional abundance of a particular species in a particular group relative to the abundance of that species in all groups. • Let A = sample unit  species matrix • aijk= abundance of species j in sample unit i of group k • nk= number of sample units in group k • g = total number of groups • First calculate the mean abundance xkj of species j in group k:

  3. Then calculate the relative abundance RAkj of species j in group k (this measures exclusiveness, the concentration of abundance into a particular group):

  4. Table 25.1. Relative abundance (%) of each species in each group defined by topographic position. The data matrix contains 54 plots and 85 species. Each group contains 18 items. “Sequence” is the sequence of occurrence of the group in the data; “Max” is the maximum relative abundance of the species.

  5. 2. Calculate the proportional frequency of the species in each group (the proportion of sample units in each group that contain that species). First transform A to a matrix of presence-absence, B: Then calculate relative frequency RFkj of species j in group k:

  6. Table 25.2. Relative frequency (%) of each species in each group defined by topographic position. The data matrix contains 54 plots and 85 species. “Max” is the maximum relative frequency of each species.

  7. 3. Combine the two proportions calculated in steps 1 and 2 by multiplying them. Express the result as a percentage, yielding an indicator value (IVkj) for each species j in each group k. Because the component terms are multiplied, both indicator criteria must be high for the overall indicator value to be high. Conversely, if either term is low, then the species is considered a poor indicator.

  8. 4. The highest indicator value (IVmax) for a given species across groups is saved as a summary of the overall indicator value of that species.

  9. Table 25.3. Indicator values (% of perfect indication) of each species for each group, rounded to the nearest whole percentage. These values were obtained by combining the relative abundances and relative frequencies in Tables 25.1 and 25.2. The data matrix contains 54 plots and 85 species. “Max” is the maximum indicator value of the species across the three groups.

  10. 5. Evaluate statistical significance of IVmax by randomly reassigning SUs to groups 1000 times. Each time, calculate IVmax. H0: IVmax is no larger than would be expected by chance (i.e., that the species has no indicator value). p (type I error) = proportion of times that the IVmax from the randomized data set equals or exceeds the IVmax from the actual data set.

  11. Table 25.4. Monte Carlo test of significance of observed maximum indicator value (IV) for each species, based on 1000 randomizations. The means and standard deviations of the IV from the randomizations are given along with p-values for the hypothesis of no difference between groups. The p-value is based on the proportion of randomized trials with indicator value equal to or exceeding the observed indicator value.

  12. Figure 25.1. Portion of an indicator species hierarchy for freshwater zooplankton, based on Warncke (1998). Only statistically significant indicator species are shown. Groups and subgroups were based on a hierarchical division of sample units. The numbers for each species represent the percentage of perfect indication (IV) of that species in that subgroup.

  13. Figure 25.2. Dendrogram from cluster analysis of the Bison Range data set. Is there an optimum number of clusters?

  14. Figure 25.3. Use of indicator species analysis as an objective criterion for pruning a dendrogram. Left: change in p-value from the randomization tests, averaged across species at each step in the clustering. Right: number of species with p 0.05 for each step of clustering.

  15. Figure 25.4. Ordination of species based on indicator species analysis, contrasting association with two mountain ranges, the Cascades and the Coast Range (from Peterson & McCune 2001). See text for details.

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