A Bayesian method for assessing multi-scale species-habitat relationships

A Bayesian method for assessing multi-scale species-habitat relationships Landscape Ecol (2017) 32:2365–2381 https://doi.org/10.1007/s10980-017-0575-y RESEARCH AR TIC L E A Bayesian method for assessing multi-scale species-habitat relationships . . Erica F. Stuber Lutz F. Gruber Joseph J. Fontaine Received: 9 December 2016 / Accepted: 15 September 2017 / Published online: 3 October 2017 Springer Science+Business Media B.V. 2017 Abstract spatial scales of predictors using latent scale indicator Context Scientists face several theoretical and variables that are estimated with reversible-jump methodological challenges in appropriately describing Markov chain Monte Carlo sampling. BLISS does fundamental wildlife-habitat relationships in models. not suffer from collinearity, and substantially reduces The spatial scales of habitat relationships are often computation time of studies. We present a simulation unknown, and are expected to follow a multi-scale study to validate our method and apply our method to a hierarchy. Typical frequentist or information theoretic case-study of land cover predictors for ring-necked approaches often suffer under collinearity in multi- pheasant (Phasianus colchicus) abundance in scale studies, fail to converge when models are Nebraska, USA. complex or represent an intractable computational Results Our method returns accurate descriptions of burden when candidate model sets are large. the explanatory power of multiple spatial scales, and Objectives Our objective was to implement an http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Landscape Ecology Springer Journals

A Bayesian method for assessing multi-scale species-habitat relationships

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Publisher
Springer Journals
Copyright
Copyright © 2017 by Springer Science+Business Media B.V.
Subject
Life Sciences; Landscape Ecology; Ecology; Nature Conservation; Landscape/Regional and Urban Planning; Sustainable Development; Environmental Management
ISSN
0921-2973
eISSN
1572-9761
D.O.I.
10.1007/s10980-017-0575-y
Publisher site
See Article on Publisher Site

Abstract

Landscape Ecol (2017) 32:2365–2381 https://doi.org/10.1007/s10980-017-0575-y RESEARCH AR TIC L E A Bayesian method for assessing multi-scale species-habitat relationships . . Erica F. Stuber Lutz F. Gruber Joseph J. Fontaine Received: 9 December 2016 / Accepted: 15 September 2017 / Published online: 3 October 2017 Springer Science+Business Media B.V. 2017 Abstract spatial scales of predictors using latent scale indicator Context Scientists face several theoretical and variables that are estimated with reversible-jump methodological challenges in appropriately describing Markov chain Monte Carlo sampling. BLISS does fundamental wildlife-habitat relationships in models. not suffer from collinearity, and substantially reduces The spatial scales of habitat relationships are often computation time of studies. We present a simulation unknown, and are expected to follow a multi-scale study to validate our method and apply our method to a hierarchy. Typical frequentist or information theoretic case-study of land cover predictors for ring-necked approaches often suffer under collinearity in multi- pheasant (Phasianus colchicus) abundance in scale studies, fail to converge when models are Nebraska, USA. complex or represent an intractable computational Results Our method returns accurate descriptions of burden when candidate model sets are large. the explanatory power of multiple spatial scales, and Objectives Our objective was to implement an

Journal

Landscape EcologySpringer Journals

Published: Oct 3, 2017

References

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