Effective and cost-efficient monitoring of biological invasions under global change: A model-based framework
Autor(a) principal: | |
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Data de Publicação: | 2016 |
Outros Autores: | , , , , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10174/28005 https://doi.org/10.1111/1365-2664.12631 |
Resumo: | Ecological monitoring programmes are designed to detect and measure changes in biodiversity and ecosystems. In the case of biological invasions, they can contribute to anticipating risks and adaptively managing invaders. However, monitoring is often expensive because large amounts of data might be needed to draw inferences. Thus, careful planning is required to ensure that monitoring goals are realistically achieved. Species distribution models (SDM s) can provide estimates of suitable areas to invasion. Predictions from these models can be applied as inputs in optimization strategies seeking to identify the optimal extent of the networks of areas required for monitoring risk of invasion under current and future environmental conditions. A hierarchical framework is proposed herein that combines SDM s, scenario analysis and cost analyses to improve invasion assessments at regional and local scales. We illustrate the framework with Acacia dealbata Link. (Silver‐wattle) in northern Portugal. The framework is general and applicable to any species. We defined two types of monitoring networks focusing either on the regional‐scale management of an invasion, or management focus within and around protected areas. For each one of these two schemes, we designed a hierarchical framework of spatial prioritization using different information layers (e.g. SDM s, habitat connectivity, protected areas). We compared the performance of each monitoring scheme against 100 randomly generated models. In our case study, we found that protected areas will be increasingly exposed to invasion by A. dealbata due to climate change. Moreover, connectivity between suitable areas for A. dealbata is predicted to increase. Monitoring networks that we identify were more effective in detecting new invasions and less costly to management than randomly generated models. The most cost‐efficient monitoring schemes require 18% less effort than the average networks across all of the 100 tested options. Synthesis and applications . The proposed framework achieves cost‐effective monitoring networks, enabling the interactive exploration of different solutions and the combination of quantitative information on network performance with orientations that are rarely incorporated in a decision support system. The framework brings invasion monitoring closer to European legislation and management needs while ensuring adaptability under rapid climate and environmental change. |
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Effective and cost-efficient monitoring of biological invasions under global change: A model-based frameworkAcacia dealbataClimate changeConnectivityMonitoring networksNorthern PortugalOptimizationRisk managementScale dependenceSpecies distribution modelsSurveillance effortEcological monitoring programmes are designed to detect and measure changes in biodiversity and ecosystems. In the case of biological invasions, they can contribute to anticipating risks and adaptively managing invaders. However, monitoring is often expensive because large amounts of data might be needed to draw inferences. Thus, careful planning is required to ensure that monitoring goals are realistically achieved. Species distribution models (SDM s) can provide estimates of suitable areas to invasion. Predictions from these models can be applied as inputs in optimization strategies seeking to identify the optimal extent of the networks of areas required for monitoring risk of invasion under current and future environmental conditions. A hierarchical framework is proposed herein that combines SDM s, scenario analysis and cost analyses to improve invasion assessments at regional and local scales. We illustrate the framework with Acacia dealbata Link. (Silver‐wattle) in northern Portugal. The framework is general and applicable to any species. We defined two types of monitoring networks focusing either on the regional‐scale management of an invasion, or management focus within and around protected areas. For each one of these two schemes, we designed a hierarchical framework of spatial prioritization using different information layers (e.g. SDM s, habitat connectivity, protected areas). We compared the performance of each monitoring scheme against 100 randomly generated models. In our case study, we found that protected areas will be increasingly exposed to invasion by A. dealbata due to climate change. Moreover, connectivity between suitable areas for A. dealbata is predicted to increase. Monitoring networks that we identify were more effective in detecting new invasions and less costly to management than randomly generated models. The most cost‐efficient monitoring schemes require 18% less effort than the average networks across all of the 100 tested options. Synthesis and applications . The proposed framework achieves cost‐effective monitoring networks, enabling the interactive exploration of different solutions and the combination of quantitative information on network performance with orientations that are rarely incorporated in a decision support system. The framework brings invasion monitoring closer to European legislation and management needs while ensuring adaptability under rapid climate and environmental change.Jouenal of Applied Ecology2020-08-10T14:39:24Z2020-08-102016-03-14T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://hdl.handle.net/10174/28005http://hdl.handle.net/10174/28005https://doi.org/10.1111/1365-2664.12631enghttps://besjournals.onlinelibrary.wiley.com/doi/epdf/10.1111/1365-2664.12631ndalagador@uevora.ptndndndndndndmba@uevora.ptnd221Vicente, Joana R.Alagador, DiogoGuerra, CarlosAlonso, Joaquim M.Kueffer, ChristophVaz, Ana S.Fernandes, Rui F.Cabral, João A.Araújo, Miguel B.Honrado, João P.info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-01-03T19:23:54Zoai:dspace.uevora.pt:10174/28005Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T01:17:57.102251Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Effective and cost-efficient monitoring of biological invasions under global change: A model-based framework |
title |
Effective and cost-efficient monitoring of biological invasions under global change: A model-based framework |
spellingShingle |
Effective and cost-efficient monitoring of biological invasions under global change: A model-based framework Vicente, Joana R. Acacia dealbata Climate change Connectivity Monitoring networks Northern Portugal Optimization Risk management Scale dependence Species distribution models Surveillance effort |
title_short |
Effective and cost-efficient monitoring of biological invasions under global change: A model-based framework |
title_full |
Effective and cost-efficient monitoring of biological invasions under global change: A model-based framework |
title_fullStr |
Effective and cost-efficient monitoring of biological invasions under global change: A model-based framework |
title_full_unstemmed |
Effective and cost-efficient monitoring of biological invasions under global change: A model-based framework |
title_sort |
Effective and cost-efficient monitoring of biological invasions under global change: A model-based framework |
author |
Vicente, Joana R. |
author_facet |
Vicente, Joana R. Alagador, Diogo Guerra, Carlos Alonso, Joaquim M. Kueffer, Christoph Vaz, Ana S. Fernandes, Rui F. Cabral, João A. Araújo, Miguel B. Honrado, João P. |
author_role |
author |
author2 |
Alagador, Diogo Guerra, Carlos Alonso, Joaquim M. Kueffer, Christoph Vaz, Ana S. Fernandes, Rui F. Cabral, João A. Araújo, Miguel B. Honrado, João P. |
author2_role |
author author author author author author author author author |
dc.contributor.author.fl_str_mv |
Vicente, Joana R. Alagador, Diogo Guerra, Carlos Alonso, Joaquim M. Kueffer, Christoph Vaz, Ana S. Fernandes, Rui F. Cabral, João A. Araújo, Miguel B. Honrado, João P. |
dc.subject.por.fl_str_mv |
Acacia dealbata Climate change Connectivity Monitoring networks Northern Portugal Optimization Risk management Scale dependence Species distribution models Surveillance effort |
topic |
Acacia dealbata Climate change Connectivity Monitoring networks Northern Portugal Optimization Risk management Scale dependence Species distribution models Surveillance effort |
description |
Ecological monitoring programmes are designed to detect and measure changes in biodiversity and ecosystems. In the case of biological invasions, they can contribute to anticipating risks and adaptively managing invaders. However, monitoring is often expensive because large amounts of data might be needed to draw inferences. Thus, careful planning is required to ensure that monitoring goals are realistically achieved. Species distribution models (SDM s) can provide estimates of suitable areas to invasion. Predictions from these models can be applied as inputs in optimization strategies seeking to identify the optimal extent of the networks of areas required for monitoring risk of invasion under current and future environmental conditions. A hierarchical framework is proposed herein that combines SDM s, scenario analysis and cost analyses to improve invasion assessments at regional and local scales. We illustrate the framework with Acacia dealbata Link. (Silver‐wattle) in northern Portugal. The framework is general and applicable to any species. We defined two types of monitoring networks focusing either on the regional‐scale management of an invasion, or management focus within and around protected areas. For each one of these two schemes, we designed a hierarchical framework of spatial prioritization using different information layers (e.g. SDM s, habitat connectivity, protected areas). We compared the performance of each monitoring scheme against 100 randomly generated models. In our case study, we found that protected areas will be increasingly exposed to invasion by A. dealbata due to climate change. Moreover, connectivity between suitable areas for A. dealbata is predicted to increase. Monitoring networks that we identify were more effective in detecting new invasions and less costly to management than randomly generated models. The most cost‐efficient monitoring schemes require 18% less effort than the average networks across all of the 100 tested options. Synthesis and applications . The proposed framework achieves cost‐effective monitoring networks, enabling the interactive exploration of different solutions and the combination of quantitative information on network performance with orientations that are rarely incorporated in a decision support system. The framework brings invasion monitoring closer to European legislation and management needs while ensuring adaptability under rapid climate and environmental change. |
publishDate |
2016 |
dc.date.none.fl_str_mv |
2016-03-14T00:00:00Z 2020-08-10T14:39:24Z 2020-08-10 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10174/28005 http://hdl.handle.net/10174/28005 https://doi.org/10.1111/1365-2664.12631 |
url |
http://hdl.handle.net/10174/28005 https://doi.org/10.1111/1365-2664.12631 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
https://besjournals.onlinelibrary.wiley.com/doi/epdf/10.1111/1365-2664.12631 nd alagador@uevora.pt nd nd nd nd nd nd mba@uevora.pt nd 221 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.publisher.none.fl_str_mv |
Jouenal of Applied Ecology |
publisher.none.fl_str_mv |
Jouenal of Applied Ecology |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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RCAAP |
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RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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1799136661724987392 |