A Bayesian method for assessing multi-scale species-habitat relationships
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Scientists face several theoretical and methodological challenges in appropriately describing fundamental wildlife-habitat relationships in models. The spatial scales of habitat relationships are often unknown, and are expected to follow a multi-scale hierarchy. Typical frequentist or information theoretic approaches often suffer under collinearity in multi-scale studies, fail to converge when models are complex or represent an intractable computational burden when candidate model sets are large.
Our objective was to implement an automated, Bayesian method for inference on the spatial scales of habitat variables that best predict animal abundance.
We introduce Bayesian latent indicator scale selection (BLISS), a Bayesian method to select spatial scales of predictors using latent scale indicator variables that are estimated with reversible-jump Markov chain Monte Carlo sampling. BLISS does not suffer from collinearity, and substantially reduces computation time of studies. We present a simulation study to validate our method and apply our method to a case-study of land cover predictors for ring-necked pheasant (Phasianus colchicus) abundance in Nebraska, USA.
Our method returns accurate descriptions of the explanatory power of multiple spatial scales, and unbiased and precise parameter estimates under commonly encountered data limitations including spatial scale autocorrelation, effect size, and sample size. BLISS outperforms commonly used model selection methods including stepwise and AIC, and reduces runtime by 90%.
Given the pervasiveness of scale-dependency in ecology, and the implications of mismatches between the scales of analyses and ecological processes, identifying the spatial scales over which species are integrating habitat information is an important step in understanding species-habitat relationships. BLISS is a widely applicable method for identifying important spatial scales, propagating scale uncertainty, and testing hypotheses of scaling relationships.
KeywordsAbundance Bayesian model selection Habitat selection Model uncertainty Spatial scale
Funding for this project was received from Federal Aid in Wildlife Restoration projects W-98-R, administered by the Nebraska Game and Parks Commission. We would like to thank Chelsea Forehead, Caitlyn Gillespi, Anthony Jenniges, Amanda Lipinski, and Lindsey Messinger for their assistance in collecting the data presented here, Annie Madsen and Matthew Strassburg for assistance in conducting the literature review, and two anonymous reviewers for their valuable comments on earlier versions of this manuscript. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government. The Nebraska Cooperative Fish and Wildlife Research Unit is supported by a cooperative agreement among the U.S. Geological Survey, the Nebraska Game and Parks Commission, the University of Nebraska, the U.S. Fish and Wildlife Service, and the Wildlife Management Institute. The authors declare no conflicts of interest.
- Bini LM, Diniz JAF, Rangel TFLVB, Akre TSB, Albaladejo RG, Albuquerque FS, Aparicio A, Araújo MD, Baselga A, Beck J, Isabel Bellocq M, Böhning-Gaese K, Borges PAV, Castro-Parga I, Chey VK, Chown SL, De Marco Paulo Jr, Dobkin DS, Ferrer-Castán D, Field R, Filloy J, Fleishman E, Gómez JF, Hortal J, Iverson JB, Kerr JT, Daniel Kissling W, Kitching IJ, León-Cortés JL, Lobo JM, Montoya D, Morales-Castilla I, Moreno JC, Oberdorff T, Olalla-Tárraga MÁ, Pausas JG, Qian H, Rahbek C, RodrÍguez MÁ, Rueda M, Ruggiero A, Sackmann P, Sanders NJ, Terribile LC, Vetaas OR, Hawkins BA (2009) Coefficient shifts in geographical 565 ecology: an empirical evaluation of spatial and non-spatial regression. Ecography 32(2):193–204CrossRefGoogle Scholar
- Bishop A, Barenberg A, Volpe N, Riens J, Grosse R (2011) Nebraska land cover development. Rainwater Basin Joint Venture Report, Landcover Accuracy Assessment ReportGoogle Scholar
- Hutto RL, Pletschet SM, Hendricks P (1986) A fixed-radius point count method for nonbreeding and breeding season use. Auk 12:593–602Google Scholar
- Kuhn I (2007) Incorporating spatial autocorrelation may invert observed patterns. Divers Distrib 13(1):66–69Google Scholar
- Plummer M (2003) JAGS: a program for analysis of Bayesian graphical models using Gibbs sampling. In: Proceedings of the 3rd international workshop on distributed statistical computing, vol 124, p 125Google Scholar
- Plummer M (2013) rjags: Bayesian graphical models using MCMC. R package version 3Google Scholar
- Robbins CS, Bystrak D, Geissler PH (1986) The breeding bird survey: its first fifteen years, 1965–1979. Report, DTIC DocumentGoogle Scholar
- Thornton DH, Fletcher RJ (2014) Body size and spatial scales in avian response to landscapes: a meta-analysis. Ecography 37(5):454–463Google Scholar
- Watanabe S (2013) A widely applicable Bayesian information criterion. J Mach Learn Res 14:867–897Google Scholar
- Williams BK, Nichols JD, Conroy MJ (2002) Analysis and management of animal populations. Academic Press, New YorkGoogle Scholar