Northwest Fisheries Science Center

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Document Type: Journal Article
Center: NWFSC
Document ID: 7654
Title: Spatial factor analysis: a new tool estimating multispecies spatial distributions and correlated ranges
Author: James T. Thorson, M. D. Scheuerell, K. E. See, Andrew O. Shelton, Hans Skaug, Kasper Kristensen
Publication Year: 2015
Journal: Methods in Ecology and Evolution
Volume: 6
Issue: 6
Pages: 627-637
DOI: 10.1111/2041-210X.12359
Keywords: species distribution models, factor analysis, geostatistics, Gaussian random field,

 Predicting and explaining the distribution and density of species is one of the oldest concerns in ecology.  Species distributions are increasingly estimated using geostatistical methods, which estimate a latent spatial variable explaining observed variation in densities, but geostatistical methods may be imprecise for species with low densities or few observations.  Additionally, simple geostatistical methods fail to acount for correlations in distribution among species, and generally estimate such cross-correlations as a post-hoc exercise.  We therefore introduce spatial factor analysis (SFA), a spatial model for estimating a low-rank approximation to multivariate data, and use it to jointly estimate the distribution of multiple species simultaneously.  As a first example, we show that distributions for 10 bird species in the breeding bird survey in 2013 can be parsimoniously represented using only 4 spatial factors.  As a second case study, we show that forward-prediction of catches for 20 rockfishes (Sebastes spp.) off the U.S. West Coast is more accurate using SFA than analyzing each species individually.  Finally, we derive an analytic estimate of cross-correlations among species from SFA parameters, and show that single-species models may give a different picture of cross-correlations than joint estimation using SFA.  We conclude by proposing future research that would model species cross-correlations using dissimilarity of species’ traits, and the development of spatial dynamic factor analysis for a low-rank approximation to spatial time-series data

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Theme: Ecosystem approach to improve management of marine resources
Foci: Provide scientific support for the implementation of ecosystem-based management