Model based grouping of species across environmental gradients

By P. Dunstan, Scott D. Foster, R. Darnell. 2011

109 citations recorded by the source corpus.

We present a novel approach to the statistical analysis and prediction of multispecies data. The approach allows the simultaneous grouping and quantification of multiple species’ responses to environmental gradients. The underlying statistical model is a finite mixture model, where mixing is performed over the individual species’ responses to environmental gradients. Species with similar responses are grouped with minimal information loss. We term these groups species archetypes. Each species archetype has an associated GLM that can be used to predict distributions with appropriate measures of uncertainty. Initially, we illustrate the concept and method using artificial data and then with application to real data comprising 200 species from the Great Barrier Reef (GBR) lagoon on 13 oceanographic and geological gradients from 12°S to 24°S. The 200 species from the GBR are well represented by 15 species archetypes. The model is interpreted through maps of the probability of presence for a fine scale set of locations throughout the study area. Maps of uncertainty are also produced to provide statistical context. The presence of each species archetype was strongly influenced by oceanographic gradients, principally temperature, oxygen and salinity. The number of species in each group ranged from 4 to 34. The method has potential application to the analysis of multispecies distribution patterns and for multispecies management.

Subfields: Ecological Modeling, Genetics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/bd3421e877d401277f48affaee935238d55be17c

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Citations in the corpus

Using species richness and functional traits predictions to constrain assemblage predictions from stacked species distribution models (2015, methodology): “An innovative way to model species categories would be the species archetypes model (SAM; Dunstan et al., 2011; Hui et al., 2013), which predicts communities using a finite mixture of regression model, on the basis of common responses to environmental gradients.”
A comprehensive evaluation of predictive performance of 33 species distribution models at species and community levels (2019, background): “Methodological advances in multiple-species distribution modeling have lagged behind, but are recently experiencing a rapid expansion (Leathwick et al. 2006, Dunstan et al. 2011, Guisan and Rahbek 2011, Warton et al. 2015, Wilkinson et al. 2019).”
Statistical solutions for error and bias in global citizen science datasets (2014, background): “More recently, mixed models that can model the response of groups of 380 species simultaneously have been developed (Dunstan et al. 2011) (Table 2).”