Building better wildlife-habitat models
Wildlife-habitat models are an important tool in wildlife management today, and by far the majority of these predict aspects of species distribution (abundance or presence) as a proxy measure of habitat quality. Unfortunately, few are tested on independent data, and of those that are, few show usefu...
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Veröffentlicht in: | Ecography (Copenhagen) 1999-04, Vol.22 (2), p.219-219 |
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container_title | Ecography (Copenhagen) |
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creator | Beutel, T. S. Beeton, R. J. S. Baxter, G. S. |
description | Wildlife-habitat models are an important tool in wildlife management today, and by far the majority of these predict aspects of species distribution (abundance or presence) as a proxy measure of habitat quality. Unfortunately, few are tested on independent data, and of those that are, few show useful predictive skill. We demonstrate that six critical assumptions underlie distribution based wildlife-habitat models, all of which must be valid for the model to predict habitat quality. We outline these assumptions in a meta-model, and discuss methods for their validation. Even where all six assumptions show a high level of validity, there is still a strong likelihood that the model will not predict habitat quality. However, the meta-model does suggest habitat quality can be predicted more accurately if distributional data are ignored, and variables more indicative of habitat quality are modelled instead. |
doi_str_mv | 10.1111/j.1600-0587.1999.tb00471.x |
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S.</creatorcontrib><creatorcontrib>Beeton, R. J. S.</creatorcontrib><creatorcontrib>Baxter, G. S.</creatorcontrib><title>Building better wildlife-habitat models</title><title>Ecography (Copenhagen)</title><description>Wildlife-habitat models are an important tool in wildlife management today, and by far the majority of these predict aspects of species distribution (abundance or presence) as a proxy measure of habitat quality. Unfortunately, few are tested on independent data, and of those that are, few show useful predictive skill. We demonstrate that six critical assumptions underlie distribution based wildlife-habitat models, all of which must be valid for the model to predict habitat quality. We outline these assumptions in a meta-model, and discuss methods for their validation. Even where all six assumptions show a high level of validity, there is still a strong likelihood that the model will not predict habitat quality. 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Psychology</subject><subject>General aspects</subject><subject>Habitat conservation</subject><subject>Habitat preferences</subject><subject>Modeling</subject><subject>Predictive modeling</subject><subject>Riverine habitats</subject><subject>Statistical models</subject><subject>Wildlife habitats</subject><issn>0906-7590</issn><issn>1600-0587</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>1999</creationdate><recordtype>article</recordtype><recordid>eNqVkFtLwzAYhoMoOA__wIsholetX9Lm5I244TZhHhDFy5BkqbZ260w6nP_elo55bW5CeJ-8LzwInWKIcXMuixgzgAio4DGWUsa1AUg5jtc7qLeNdlEPJLCIUwn76CCEAgATyUQPXQxWeTnLF-994-ra-f538yzzzEUf2uS1rvvzaubKcIT2Ml0Gd7y5D9Hr6PZlOImmj-O74c00smnCZES1lQ5EKrNUEqo1EUQYClYLxgy1jBiTwiwTBvMGNDalAqjNNNMZJ46y5BCdd71LX32tXKjVPA_WlaVeuGoVFOaEQkp4A151oPVVCN5launzufY_CoNq3ahCtQJUK0C1btTGjVo3n882KzpYXWZeL2we_hq4pDLFDXbdYY0U9_OPAXU7fBwTLJuGk66hCHXltw0JEwkR7UDUxXmo3Xoba_-pGE84VW8PY_X0_DYik8G9mia_SqGQtQ</recordid><startdate>199904</startdate><enddate>199904</enddate><creator>Beutel, T. 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Psychology</topic><topic>General aspects</topic><topic>Habitat conservation</topic><topic>Habitat preferences</topic><topic>Modeling</topic><topic>Predictive modeling</topic><topic>Riverine habitats</topic><topic>Statistical models</topic><topic>Wildlife habitats</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Beutel, T. S.</creatorcontrib><creatorcontrib>Beeton, R. J. S.</creatorcontrib><creatorcontrib>Baxter, G. S.</creatorcontrib><collection>Istex</collection><collection>Pascal-Francis</collection><collection>CrossRef</collection><collection>Ecology Abstracts</collection><collection>Environmental Sciences and Pollution Management</collection><jtitle>Ecography (Copenhagen)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Beutel, T. S.</au><au>Beeton, R. J. S.</au><au>Baxter, G. S.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Building better wildlife-habitat models</atitle><jtitle>Ecography (Copenhagen)</jtitle><date>1999-04</date><risdate>1999</risdate><volume>22</volume><issue>2</issue><spage>219</spage><epage>219</epage><pages>219-219</pages><issn>0906-7590</issn><eissn>1600-0587</eissn><abstract>Wildlife-habitat models are an important tool in wildlife management today, and by far the majority of these predict aspects of species distribution (abundance or presence) as a proxy measure of habitat quality. Unfortunately, few are tested on independent data, and of those that are, few show useful predictive skill. We demonstrate that six critical assumptions underlie distribution based wildlife-habitat models, all of which must be valid for the model to predict habitat quality. We outline these assumptions in a meta-model, and discuss methods for their validation. 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source | Wiley Online Library Journals Frontfile Complete; Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals; Jstor Complete Legacy |
subjects | Agricultural management Animal, plant and microbial ecology Applied ecology Biological and medical sciences Conservation, protection and management of environment and wildlife Ecological modeling Forest habitats Forum Fundamental and applied biological sciences. Psychology General aspects Habitat conservation Habitat preferences Modeling Predictive modeling Riverine habitats Statistical models Wildlife habitats |
title | Building better wildlife-habitat models |
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