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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...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2017
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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. |
format | Online Article Text |
id | pubmed-5728484 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
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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