Interacting gradients of selection and survival probabilities to estimate habitat quality: An example using the Gray Vireo (Vireo vicinior)

Common approaches to estimating habitat quality are habitat suitability indices, which are often subjective. We propose a quantitative definition of habitat quality as the percentage of selected habitat that has a high probability of contributing to population growth. Using this definition, we creat...

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Autores principales: Jonathan P. Harris, Loren M. Smith, Scott T. McMurry
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Publicado: Elsevier 2021
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spelling oai:doaj.org-article:2f0f0144dff04e0a803994e9803479b62021-12-01T05:00:43ZInteracting gradients of selection and survival probabilities to estimate habitat quality: An example using the Gray Vireo (Vireo vicinior)1470-160X10.1016/j.ecolind.2021.108210https://doaj.org/article/2f0f0144dff04e0a803994e9803479b62021-11-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S1470160X2100875Xhttps://doaj.org/toc/1470-160XCommon approaches to estimating habitat quality are habitat suitability indices, which are often subjective. We propose a quantitative definition of habitat quality as the percentage of selected habitat that has a high probability of contributing to population growth. Using this definition, we created a habitat quality index based on spatial projections of habitat selection and survival probabilities, and the interaction of those probability gradients. We used nest-site selection and nest survival of Gray Vireos (Vireo vicinior) to represent habitat selection and survival probabilities of a sensitive life stage that is likely to increase population size. We used data from 173 Gray Vireo nests in central New Mexico to estimate selection and survival probabilities. Generalized linear mixed-effect models (GLMM) and logistic exposure models (LEM) were used to estimate nest-site selection and daily nest survival, respectively. We projected top-ranked GLMM and LEM models in a geographic information system (GIS) to spatially display relative probabilities of selection and survival. We converted these continuous probabilities into binary rasters representing high and low selection and survival. Multiplying these two rasters in ArcGIS provided us with a raster of four categories: areas with i) low selection and low survival, ii) low selection and high survival, iii) high selection and low survival, and iv) high selection and high survival. We determined the spatial area of each category and calculated percentage of selected habitat where survival was likely to occur, which we termed a habitat quality index (HQI). At our study site, Gray Vireos had a HQI of 0.85, suggesting that approximately 85% of the highly selected habitat had a high probability of contributing to Gray Vireo population growth through nest survival, and that few ecological traps exist in this landscape. These methods provide a quantifiable, continuous index of habitat quality, with spatial projections of potential ecological trap locations.Jonathan P. HarrisLoren M. SmithScott T. McMurryElsevierarticleGray VireoHabitat selectionHabitat suitability indexHabitat quality indexVireo viciniorNest survivalEcologyQH540-549.5ENEcological Indicators, Vol 131, Iss , Pp 108210- (2021)
institution DOAJ
collection DOAJ
language EN
topic Gray Vireo
Habitat selection
Habitat suitability index
Habitat quality index
Vireo vicinior
Nest survival
Ecology
QH540-549.5
spellingShingle Gray Vireo
Habitat selection
Habitat suitability index
Habitat quality index
Vireo vicinior
Nest survival
Ecology
QH540-549.5
Jonathan P. Harris
Loren M. Smith
Scott T. McMurry
Interacting gradients of selection and survival probabilities to estimate habitat quality: An example using the Gray Vireo (Vireo vicinior)
description Common approaches to estimating habitat quality are habitat suitability indices, which are often subjective. We propose a quantitative definition of habitat quality as the percentage of selected habitat that has a high probability of contributing to population growth. Using this definition, we created a habitat quality index based on spatial projections of habitat selection and survival probabilities, and the interaction of those probability gradients. We used nest-site selection and nest survival of Gray Vireos (Vireo vicinior) to represent habitat selection and survival probabilities of a sensitive life stage that is likely to increase population size. We used data from 173 Gray Vireo nests in central New Mexico to estimate selection and survival probabilities. Generalized linear mixed-effect models (GLMM) and logistic exposure models (LEM) were used to estimate nest-site selection and daily nest survival, respectively. We projected top-ranked GLMM and LEM models in a geographic information system (GIS) to spatially display relative probabilities of selection and survival. We converted these continuous probabilities into binary rasters representing high and low selection and survival. Multiplying these two rasters in ArcGIS provided us with a raster of four categories: areas with i) low selection and low survival, ii) low selection and high survival, iii) high selection and low survival, and iv) high selection and high survival. We determined the spatial area of each category and calculated percentage of selected habitat where survival was likely to occur, which we termed a habitat quality index (HQI). At our study site, Gray Vireos had a HQI of 0.85, suggesting that approximately 85% of the highly selected habitat had a high probability of contributing to Gray Vireo population growth through nest survival, and that few ecological traps exist in this landscape. These methods provide a quantifiable, continuous index of habitat quality, with spatial projections of potential ecological trap locations.
format article
author Jonathan P. Harris
Loren M. Smith
Scott T. McMurry
author_facet Jonathan P. Harris
Loren M. Smith
Scott T. McMurry
author_sort Jonathan P. Harris
title Interacting gradients of selection and survival probabilities to estimate habitat quality: An example using the Gray Vireo (Vireo vicinior)
title_short Interacting gradients of selection and survival probabilities to estimate habitat quality: An example using the Gray Vireo (Vireo vicinior)
title_full Interacting gradients of selection and survival probabilities to estimate habitat quality: An example using the Gray Vireo (Vireo vicinior)
title_fullStr Interacting gradients of selection and survival probabilities to estimate habitat quality: An example using the Gray Vireo (Vireo vicinior)
title_full_unstemmed Interacting gradients of selection and survival probabilities to estimate habitat quality: An example using the Gray Vireo (Vireo vicinior)
title_sort interacting gradients of selection and survival probabilities to estimate habitat quality: an example using the gray vireo (vireo vicinior)
publisher Elsevier
publishDate 2021
url https://doaj.org/article/2f0f0144dff04e0a803994e9803479b6
work_keys_str_mv AT jonathanpharris interactinggradientsofselectionandsurvivalprobabilitiestoestimatehabitatqualityanexampleusingthegrayvireovireovicinior
AT lorenmsmith interactinggradientsofselectionandsurvivalprobabilitiestoestimatehabitatqualityanexampleusingthegrayvireovireovicinior
AT scotttmcmurry interactinggradientsofselectionandsurvivalprobabilitiestoestimatehabitatqualityanexampleusingthegrayvireovireovicinior
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