Multivariate random forest models of estuarine-associated fish and invertebrate communities

摘要

Models that evaluate species-habitat relationships at the community level have been gaining attention with increasing interest in ecosystem management. Developing models that can incorporate both a large number of predictor variables and a multivariate response (a vector of individual species occurrences or abundances) is challenging. One promising new approach is multivariate random forests (MRF), a method that combines multivariate regression trees with bootstrap resampling and predictor subsampling from traditional random forests. Random forest models have been shown to be highly accurate and powerful in their predictive ability in a wide variety of applications. They can effectively model nonlinear and interacting variables. Our research evaluated change in estuarine assemblage composition along habitat gradients in Southeast Alaska using landscape-scale habitat variables and MRF. For 541 estuaries, we identified 24 predictor variables describing the geomorphic and habitat environment on land and in the estuary. MRF models were constructed in R software for combined fish and invertebrate assemblages. Cluster analysis of model proximities revealed strong spatial variation in community composition in relation to differences in tidal range, precipitation, percent of eelgrass, and amount of intertidal habitat. This research presents a new science-based management template that can be used to inform and assess species management and protection strategies, as well as to guide future research on species distributions.