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Advances in global sensitivity analyses of demographic-based species distribution models to address uncertainties in dynamic landscapes

Developing a rigorous understanding of multiple global threats to species persistence requires the use of integrated modeling methods that capture processes which influence species distributions. Species distribution models (SDMs) coupled with population dynamics models can incorporate relationships...

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Autores principales: Naujokaitis-Lewis, Ilona, Curtis, Janelle M.R.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: PeerJ Inc. 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4958004/
https://www.ncbi.nlm.nih.gov/pubmed/27547529
http://dx.doi.org/10.7717/peerj.2204
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author Naujokaitis-Lewis, Ilona
Curtis, Janelle M.R.
author_facet Naujokaitis-Lewis, Ilona
Curtis, Janelle M.R.
author_sort Naujokaitis-Lewis, Ilona
collection PubMed
description Developing a rigorous understanding of multiple global threats to species persistence requires the use of integrated modeling methods that capture processes which influence species distributions. Species distribution models (SDMs) coupled with population dynamics models can incorporate relationships between changing environments and demographics and are increasingly used to quantify relative extinction risks associated with climate and land-use changes. Despite their appeal, uncertainties associated with complex models can undermine their usefulness for advancing predictive ecology and informing conservation management decisions. We developed a computationally-efficient and freely available tool (GRIP 2.0) that implements and automates a global sensitivity analysis of coupled SDM-population dynamics models for comparing the relative influence of demographic parameters and habitat attributes on predicted extinction risk. Advances over previous global sensitivity analyses include the ability to vary habitat suitability across gradients, as well as habitat amount and configuration of spatially-explicit suitability maps of real and simulated landscapes. Using GRIP 2.0, we carried out a multi-model global sensitivity analysis of a coupled SDM-population dynamics model of whitebark pine (Pinus albicaulis) in Mount Rainier National Park as a case study and quantified the relative influence of input parameters and their interactions on model predictions. Our results differed from the one-at-time analyses used in the original study, and we found that the most influential parameters included the total amount of suitable habitat within the landscape, survival rates, and effects of a prevalent disease, white pine blister rust. Strong interactions between habitat amount and survival rates of older trees suggests the importance of habitat in mediating the negative influences of white pine blister rust. Our results underscore the importance of considering habitat attributes along with demographic parameters in sensitivity routines. GRIP 2.0 is an important decision-support tool that can be used to prioritize research, identify habitat-based thresholds and management intervention points to improve probability of species persistence, and evaluate trade-offs of alternative management options.
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spelling pubmed-49580042016-08-19 Advances in global sensitivity analyses of demographic-based species distribution models to address uncertainties in dynamic landscapes Naujokaitis-Lewis, Ilona Curtis, Janelle M.R. PeerJ Biogeography Developing a rigorous understanding of multiple global threats to species persistence requires the use of integrated modeling methods that capture processes which influence species distributions. Species distribution models (SDMs) coupled with population dynamics models can incorporate relationships between changing environments and demographics and are increasingly used to quantify relative extinction risks associated with climate and land-use changes. Despite their appeal, uncertainties associated with complex models can undermine their usefulness for advancing predictive ecology and informing conservation management decisions. We developed a computationally-efficient and freely available tool (GRIP 2.0) that implements and automates a global sensitivity analysis of coupled SDM-population dynamics models for comparing the relative influence of demographic parameters and habitat attributes on predicted extinction risk. Advances over previous global sensitivity analyses include the ability to vary habitat suitability across gradients, as well as habitat amount and configuration of spatially-explicit suitability maps of real and simulated landscapes. Using GRIP 2.0, we carried out a multi-model global sensitivity analysis of a coupled SDM-population dynamics model of whitebark pine (Pinus albicaulis) in Mount Rainier National Park as a case study and quantified the relative influence of input parameters and their interactions on model predictions. Our results differed from the one-at-time analyses used in the original study, and we found that the most influential parameters included the total amount of suitable habitat within the landscape, survival rates, and effects of a prevalent disease, white pine blister rust. Strong interactions between habitat amount and survival rates of older trees suggests the importance of habitat in mediating the negative influences of white pine blister rust. Our results underscore the importance of considering habitat attributes along with demographic parameters in sensitivity routines. GRIP 2.0 is an important decision-support tool that can be used to prioritize research, identify habitat-based thresholds and management intervention points to improve probability of species persistence, and evaluate trade-offs of alternative management options. PeerJ Inc. 2016-07-19 /pmc/articles/PMC4958004/ /pubmed/27547529 http://dx.doi.org/10.7717/peerj.2204 Text en ©2016 Naujokaitis-Lewis and Curtis http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ) and either DOI or URL of the article must be cited.
spellingShingle Biogeography
Naujokaitis-Lewis, Ilona
Curtis, Janelle M.R.
Advances in global sensitivity analyses of demographic-based species distribution models to address uncertainties in dynamic landscapes
title Advances in global sensitivity analyses of demographic-based species distribution models to address uncertainties in dynamic landscapes
title_full Advances in global sensitivity analyses of demographic-based species distribution models to address uncertainties in dynamic landscapes
title_fullStr Advances in global sensitivity analyses of demographic-based species distribution models to address uncertainties in dynamic landscapes
title_full_unstemmed Advances in global sensitivity analyses of demographic-based species distribution models to address uncertainties in dynamic landscapes
title_short Advances in global sensitivity analyses of demographic-based species distribution models to address uncertainties in dynamic landscapes
title_sort advances in global sensitivity analyses of demographic-based species distribution models to address uncertainties in dynamic landscapes
topic Biogeography
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4958004/
https://www.ncbi.nlm.nih.gov/pubmed/27547529
http://dx.doi.org/10.7717/peerj.2204
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