Habitat suitability criteria via parametric distributions: estimation, model selection and uncertainty

Nicholas A. Som; Damon H. Goodman; Russell W. Perry; Thomas B. Hardy

doi:10.1002/rra.2900

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Habitat suitability criteria via parametric distributions: estimation, model selection and uncertainty

Nicholas A. Som·Damon H. Goodman·Russell W. Perry·Thomas B. Hardy

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Abstract

Previous methods for constructing univariate habitat suitability criteria (HSC) curves have ranged from professional judgement to kernel-smoothed density functions or combinations thereof. We present a new method of generating HSC curves that applies probability density functions as the mathematical representation of the curves. Compared with previous approaches, benefits of our method include (1) estimation of probability density function parameters directly from raw data, (2) quantitative methods for selecting among several candidate probability density functions, and (3) concise methods for expressing estimation uncertainty in the HSC curves. We demonstrate our method with a thorough example using data collected on the depth of water used by juvenile Chinook salmon ( Oncorhynchus tschawytscha ) in the Klamath River of northern California and southern Oregon. All R code needed to implement our example is provided in the appendix. Published 2015. This article is a U.S. Government work and is in the public domain in the USA.

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Source-reported bounding extent: 41.77336007442078° to 42.259016415705766° latitude; -123.57421875° to -121.72302246093749° longitude. This indicates report coverage, not an exact sampling location. View area on OpenStreetMap.

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Nicholas A. Som, Damon H. Goodman, Russell W. Perry, Thomas B. Hardy. 2015-03-27. Habitat suitability criteria via parametric distributions: estimation, model selection and uncertainty. https://doi.org/10.1002/rra.2900

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