A novel statistical method for classifying habitat generalists and specialists

Abstract

We develop a novel statistical approach for classifying generalists and specialists in two distinct habitats. Using a multinomial model based on estimated species relative abundance in two habitats, our method minimizes bias due to differences in sampling intensities between two habitat types as well as bias due to insufficient sampling within each habitat. The method permits a robust statistical classification of habitat specialists and generalists, without excluding rare species a priori. Based on a user-defined specialization threshold, the model classifies species into one of four groups: (1) generalist; (2) habitat A specialist; (3) habitat B specialist; and (4) too rare to classify with confidence. We illustrate our multinomial classification method using two contrasting data sets: (1) bird abundance in woodland and heath habitats in southeastern Australia and (2) tree abundance in second-growth (SG) and old-growth (OG) rain forests in the Caribbean lowlands of northeastern Costa Rica. We evaluate the multinomial model in detail for the tree data set. Our results for birds were highly concordant with a previous nonstatistical classification, but our method classified a higher fraction (57.7%) of bird species with statistical confidence. Based on a conservative specialization threshold and adjustment for multiple comparisons, 64.4% of tree species in the full sample were too rare to classify with confidence. Among the species classified, OG specialists constituted the largest class (40.6%), followed by generalist tree species (36.7%) and SG specialists (22.7%). The multinomial model was more sensitive than indicator value analysis or abundance-based phi coefficient indices in detecting habitat specialists and also detects generalists statistically. Classification of specialists and generalists based on rarefied subsamples was highly consistent with classification based on the full sample, even for sampling percentages as low as 20%. Major advantages of the new method are (1) its ability to distinguish habitat generalists (species with no significant habitat affinity) from species that are simply too rare to classify and (2) applicability to a single representative sample or a single pooled set of representative samples from each of two habitat types. The method as currently developed can be applied to no more than two habitats at a time.

Trait-mediated assembly processes predict successional changes in community diversity of tropical forests

“…We used a recently developed multinomial model to classify 66 species as second-growth specialists and 133 species as old-growth specialists based on relative abundances in each habitat; the remaining 189 species were either generalists or too rare to classify (38). Oldgrowth specialists had significantly greater WSG (mean = 0.57) compared with second-growth specialists (mean = 0.44, Wilcoxon test: P < 10 −7 ).…”

“…We tested for trait differences among species classified as second-vs. old-growth specialists. Classifications were taken from a multinomial analysis of species relative abundances in second-vs. old-growth stands (38). We tested for differences in species traits between the two categories using Wilcoxon rank sum tests.…”

Structure of bird communities in eucalyptus plantations: nestedness as a pattern of species distribution

“…To determine habitat specificity of species, bird species were classified according to a method developed to determine habitat specialists and generalists (Chazdon et al, 2011). This method uses a multinomial model based on a species' relative abundance in two environments, and classifies species into one of the following categories: (1) generalist; (2) habitat "A" specialist; (3) habitat "B" specialist; and (4) too rare to classify (Chazdon et al, 2011).…”

“…This method uses a multinomial model based on a species' relative abundance in two environments, and classifies species into one of the following categories: (1) generalist; (2) habitat "A" specialist; (3) habitat "B" specialist; and (4) too rare to classify (Chazdon et al, 2011). The major advantage of this method is the classification of habitat specialists and generalists without exclusion of rare species a priori, and the ability to distinguish habitat generalists from species that are simply too rare to classify (Chazdon et al, 2011). As recommended by Chazdon et al…”

“…Species were classified as habitat specialists or generalists with the software CLAM (Chao and Lin, 2011). As this software aims to compare only two different environments, species recorded in eucalyptus plantations of different ages were quantified in only one group by their relative abundance, i.e., we evaluated plantations x native forest.…”