A novel statistical method for classifying habitat generalists and specialists

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.     

A process-based model for the partitioning of soluble, suspended particulate and bed sediment fractions of plutonium and caesium in the eastern Irish Sea

A dynamic model of plutonium behaviour in the marine environment has been developed, representing the oxidation state distribution and partitioning of plutonium between the soluble, colloidal, suspended particulate and seabed sediment fractions. With simple re-parameterisation, this model can also be applied to (137)Cs. The model, which is calibrated and validated against field data, has been used to predict concentrations of Pu(alpha) and (137)Cs in both water and seabed sediments from the vicinity of the Sellafield Ltd. reprocessing plant in Cumbria, UK. The model predicts that sediment reworking and transport are the key environmental processes as the Sellafield Pu(alpha) and (137)Cs discharge continues to decline. Inventory calculations generated by the model are consistent with previous estimations. For a hypothetical post-discharge scenario, the concentrations of these radionuclides in both seawater and surface sediments are predicted to decrease sharply, concurrent with a downward vertical migration of the activity retained in sediments.     

Extending null scenarios with Faddy distributions in a probabilistic randomization protocol for presence-absence data

Environmental and Ecological Statistics - Navarro and Manly (Popul Ecol 51:505–512, 2009) (NM) have proposed a randomization protocol for null model analysis of species occurrences at...     

Non-catalytic and catalytic wet oxidation of landfill leachate over CuO and MoO3

Journal of Material Cycles and Waste Management - Leachate, a toxic and complex liquid, is a growing problem for municipal solid waste landfills. This study investigated non-catalytic and...