|Document Type:||Journal Article|
|Title:||Joint dynamic species distribution models: a tool for community ordination and spatiotemporal monitoring|
|Author:||James T. Thorson, J. N. Ianelli, Elise Larsen, M. D. Scheuerell, C. Szuwalski, Elise Zipkin|
|Journal:||Global Ecology and Biogeography|
|Keywords:||Species distribution model, geostatistics, flight curve, Bering Sea, spatiotemporal model, species co-occurrence,|
Spatial analysis of the distribution and density of species is of continued interest within theoretical and applied ecology. Species distribution models (SDM) are increasingly used to analyze count, presence/absence, and presence-only data sets. There is a growing literature regarding dynamic SDM (which incorporate temporal variation in species distribution), joint SDM (which simultaneously analyze the correlated distribution of multiple species), and geostatistical models (which account for similarity between nearby sites caused by unobserved covariates). However, no previous study has combined all three attributes within a single framework. We therefore develop spatial dynamic factor analysis for use as a “joint, dynamic SDM” (JDSDM), which uses Gaussian random fields to account for spatial similarity when estimating one or more “factors.” Each factor evolves over time following a density-dependent (Gompertz) process, and the log-density of each species is approximated as a linear combination of different factors. We demonstrate JDSDM using two multispecies case studies (an annual survey of bottom-associated species in the Bering Sea, and a seasonal survey of butterfly density in the continental USA) and show that that JDSDM can be used for species ordination, i.e., as a model-based estimator of which species have similar spatiotemporal dynamics. We also demonstrate how JDSDM can rapidly identify dominant patterns in community dynamics, including the partitioning of the Bering Sea into inner, middle, and outer domains, and the “flight curves” typical of early or late-emerging butterflies. We conclude by suggesting future research that could incorporate phylogenetic relatedness or functional similarity, and propose that our approach could be used to monitor community dynamics at large spatial and temporal scales.
|Full Text URL:||http://onlinelibrary.wiley.com/doi/10.1111/geb.12464/abstract|
|Theme:||Ecosystem approach to improve management of marine resources|
Assess ecosystem status and trends.
Provide scientific support for the implementation of ecosystem-based management