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Managers, modelers, and measuring the impact of species distribution model uncertainty on marine zoning decisions
Marine managers routinely use spatial data to make decisions about their marine environment. Uncertainty associated with this spatial data can have profound impacts on these management decisions and their projected outcomes. Recent advances in modeling techniques, including species distribution mode...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6179233/ https://www.ncbi.nlm.nih.gov/pubmed/30304038 http://dx.doi.org/10.1371/journal.pone.0204569 |
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author | Costa, Bryan Kendall, Matthew McKagan, Steven |
author_facet | Costa, Bryan Kendall, Matthew McKagan, Steven |
author_sort | Costa, Bryan |
collection | PubMed |
description | Marine managers routinely use spatial data to make decisions about their marine environment. Uncertainty associated with this spatial data can have profound impacts on these management decisions and their projected outcomes. Recent advances in modeling techniques, including species distribution models (SDMs), make it easier to generate continuous maps showing the uncertainty associated with spatial predictions and maps. However, SDM predictions and maps can be complex and nuanced. This complexity makes their use challenging for non-technical managers, preventing them from having the best available information to make decisions. To help bridge these communication and information gaps, we developed maps to illustrate how SDMs and associated uncertainty can be translated into readily usable products for managers. We also explicitly described the potential impacts of uncertainty on marine zoning decisions. This approach was applied to a case study in Saipan Lagoon, Commonwealth of the Northern Mariana Islands (CNMI). Managers in Saipan are interested in minimizing the potential impacts of personal watercraft (e.g., jet skis) on staghorn Acropora (i.e., Acropora aspera, A. formosa, and A. pulchra), which is an important coral assemblage in the lagoon. We used a recently completed SDM for staghorn Acropora to develop maps showing the sensitivity of zoning options to three different prediction and three different uncertainty thresholds (nine combinations total). Our analysis showed that the amount of area and geographic location of predicted staghorn Acropora presence changed based on these nine combinations. These dramatically different spatial patterns would have significant zoning implications when considering where to exclude and/or allow jet skis operations inside the lagoon. They also show that different uncertainty thresholds may lead managers to markedly different conclusions and courses of action. Defining acceptable levels of uncertainty upfront is critical for ensuring that managers can make more informed decisions, meet their marine resource goals and generate favorable outcomes for their stakeholders. |
format | Online Article Text |
id | pubmed-6179233 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-61792332018-10-19 Managers, modelers, and measuring the impact of species distribution model uncertainty on marine zoning decisions Costa, Bryan Kendall, Matthew McKagan, Steven PLoS One Research Article Marine managers routinely use spatial data to make decisions about their marine environment. Uncertainty associated with this spatial data can have profound impacts on these management decisions and their projected outcomes. Recent advances in modeling techniques, including species distribution models (SDMs), make it easier to generate continuous maps showing the uncertainty associated with spatial predictions and maps. However, SDM predictions and maps can be complex and nuanced. This complexity makes their use challenging for non-technical managers, preventing them from having the best available information to make decisions. To help bridge these communication and information gaps, we developed maps to illustrate how SDMs and associated uncertainty can be translated into readily usable products for managers. We also explicitly described the potential impacts of uncertainty on marine zoning decisions. This approach was applied to a case study in Saipan Lagoon, Commonwealth of the Northern Mariana Islands (CNMI). Managers in Saipan are interested in minimizing the potential impacts of personal watercraft (e.g., jet skis) on staghorn Acropora (i.e., Acropora aspera, A. formosa, and A. pulchra), which is an important coral assemblage in the lagoon. We used a recently completed SDM for staghorn Acropora to develop maps showing the sensitivity of zoning options to three different prediction and three different uncertainty thresholds (nine combinations total). Our analysis showed that the amount of area and geographic location of predicted staghorn Acropora presence changed based on these nine combinations. These dramatically different spatial patterns would have significant zoning implications when considering where to exclude and/or allow jet skis operations inside the lagoon. They also show that different uncertainty thresholds may lead managers to markedly different conclusions and courses of action. Defining acceptable levels of uncertainty upfront is critical for ensuring that managers can make more informed decisions, meet their marine resource goals and generate favorable outcomes for their stakeholders. Public Library of Science 2018-10-10 /pmc/articles/PMC6179233/ /pubmed/30304038 http://dx.doi.org/10.1371/journal.pone.0204569 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 Costa, Bryan Kendall, Matthew McKagan, Steven Managers, modelers, and measuring the impact of species distribution model uncertainty on marine zoning decisions |
title | Managers, modelers, and measuring the impact of species distribution model uncertainty on marine zoning decisions |
title_full | Managers, modelers, and measuring the impact of species distribution model uncertainty on marine zoning decisions |
title_fullStr | Managers, modelers, and measuring the impact of species distribution model uncertainty on marine zoning decisions |
title_full_unstemmed | Managers, modelers, and measuring the impact of species distribution model uncertainty on marine zoning decisions |
title_short | Managers, modelers, and measuring the impact of species distribution model uncertainty on marine zoning decisions |
title_sort | managers, modelers, and measuring the impact of species distribution model uncertainty on marine zoning decisions |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6179233/ https://www.ncbi.nlm.nih.gov/pubmed/30304038 http://dx.doi.org/10.1371/journal.pone.0204569 |
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