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Translating statistical species-habitat models to interactive decision support tools

Understanding species-habitat relationships is vital to successful conservation, but the tools used to communicate species-habitat relationships are often poorly suited to the information needs of conservation practitioners. Here we present a novel method for translating a statistical species-habita...

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Autores principales: Wszola, Lyndsie S., Simonsen, Victoria L., Stuber, Erica F., Gillespie, Caitlyn R., Messinger, Lindsey N., Decker, Karie L., Lusk, Jeffrey J., Jorgensen, Christopher F., Bishop, Andrew A., Fontaine, Joseph J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5728484/
https://www.ncbi.nlm.nih.gov/pubmed/29236707
http://dx.doi.org/10.1371/journal.pone.0188244
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author Wszola, Lyndsie S.
Simonsen, Victoria L.
Stuber, Erica F.
Gillespie, Caitlyn R.
Messinger, Lindsey N.
Decker, Karie L.
Lusk, Jeffrey J.
Jorgensen, Christopher F.
Bishop, Andrew A.
Fontaine, Joseph J.
author_facet Wszola, Lyndsie S.
Simonsen, Victoria L.
Stuber, Erica F.
Gillespie, Caitlyn R.
Messinger, Lindsey N.
Decker, Karie L.
Lusk, Jeffrey J.
Jorgensen, Christopher F.
Bishop, Andrew A.
Fontaine, Joseph J.
author_sort Wszola, Lyndsie S.
collection PubMed
description Understanding species-habitat relationships is vital to successful conservation, but the tools used to communicate species-habitat relationships are often poorly suited to the information needs of conservation practitioners. Here we present a novel method for translating a statistical species-habitat model, a regression analysis relating ring-necked pheasant abundance to landcover, into an interactive online tool. The Pheasant Habitat Simulator combines the analytical power of the R programming environment with the user-friendly Shiny web interface to create an online platform in which wildlife professionals can explore the effects of variation in local landcover on relative pheasant habitat suitability within spatial scales relevant to individual wildlife managers. Our tool allows users to virtually manipulate the landcover composition of a simulated space to explore how changes in landcover may affect pheasant relative habitat suitability, and guides users through the economic tradeoffs of landscape changes. We offer suggestions for development of similar interactive applications and demonstrate their potential as innovative science delivery tools for diverse professional and public audiences.
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spelling pubmed-57284842017-12-22 Translating statistical species-habitat models to interactive decision support tools Wszola, Lyndsie S. Simonsen, Victoria L. Stuber, Erica F. Gillespie, Caitlyn R. Messinger, Lindsey N. Decker, Karie L. Lusk, Jeffrey J. Jorgensen, Christopher F. Bishop, Andrew A. Fontaine, Joseph J. PLoS One Research Article Understanding species-habitat relationships is vital to successful conservation, but the tools used to communicate species-habitat relationships are often poorly suited to the information needs of conservation practitioners. Here we present a novel method for translating a statistical species-habitat model, a regression analysis relating ring-necked pheasant abundance to landcover, into an interactive online tool. The Pheasant Habitat Simulator combines the analytical power of the R programming environment with the user-friendly Shiny web interface to create an online platform in which wildlife professionals can explore the effects of variation in local landcover on relative pheasant habitat suitability within spatial scales relevant to individual wildlife managers. Our tool allows users to virtually manipulate the landcover composition of a simulated space to explore how changes in landcover may affect pheasant relative habitat suitability, and guides users through the economic tradeoffs of landscape changes. We offer suggestions for development of similar interactive applications and demonstrate their potential as innovative science delivery tools for diverse professional and public audiences. Public Library of Science 2017-12-13 /pmc/articles/PMC5728484/ /pubmed/29236707 http://dx.doi.org/10.1371/journal.pone.0188244 Text en https://creativecommons.org/publicdomain/zero/1.0/ This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 (https://creativecommons.org/publicdomain/zero/1.0/) public domain dedication.
spellingShingle Research Article
Wszola, Lyndsie S.
Simonsen, Victoria L.
Stuber, Erica F.
Gillespie, Caitlyn R.
Messinger, Lindsey N.
Decker, Karie L.
Lusk, Jeffrey J.
Jorgensen, Christopher F.
Bishop, Andrew A.
Fontaine, Joseph J.
Translating statistical species-habitat models to interactive decision support tools
title Translating statistical species-habitat models to interactive decision support tools
title_full Translating statistical species-habitat models to interactive decision support tools
title_fullStr Translating statistical species-habitat models to interactive decision support tools
title_full_unstemmed Translating statistical species-habitat models to interactive decision support tools
title_short Translating statistical species-habitat models to interactive decision support tools
title_sort translating statistical species-habitat models to interactive decision support tools
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5728484/
https://www.ncbi.nlm.nih.gov/pubmed/29236707
http://dx.doi.org/10.1371/journal.pone.0188244
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