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Drivers of Wetland Conversion: a Global Meta-Analysis

Meta-analysis of case studies has become an important tool for synthesizing case study findings in land change. Meta-analyses of deforestation, urbanization, desertification and change in shifting cultivation systems have been published. This present study adds to this literature, with an analysis o...

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Autores principales: van Asselen, Sanneke, Verburg, Peter H., Vermaat, Jan E., Janse, Jan H.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3840019/
https://www.ncbi.nlm.nih.gov/pubmed/24282580
http://dx.doi.org/10.1371/journal.pone.0081292
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author van Asselen, Sanneke
Verburg, Peter H.
Vermaat, Jan E.
Janse, Jan H.
author_facet van Asselen, Sanneke
Verburg, Peter H.
Vermaat, Jan E.
Janse, Jan H.
author_sort van Asselen, Sanneke
collection PubMed
description Meta-analysis of case studies has become an important tool for synthesizing case study findings in land change. Meta-analyses of deforestation, urbanization, desertification and change in shifting cultivation systems have been published. This present study adds to this literature, with an analysis of the proximate causes and underlying forces of wetland conversion at a global scale using two complementary approaches of systematic review. Firstly, a meta-analysis of 105 case-study papers describing wetland conversion was performed, showing that different combinations of multiple-factor proximate causes, and underlying forces, drive wetland conversion. Agricultural development has been the main proximate cause of wetland conversion, and economic growth and population density are the most frequently identified underlying forces. Secondly, to add a more quantitative component to the study, a logistic meta-regression analysis was performed to estimate the likelihood of wetland conversion worldwide, using globally-consistent biophysical and socioeconomic location factor maps. Significant factors explaining wetland conversion, in order of importance, are market influence, total wetland area (lower conversion probability), mean annual temperature and cropland or built-up area. The regression analyses results support the outcomes of the meta-analysis of the processes of conversion mentioned in the individual case studies. In other meta-analyses of land change, similar factors (e.g., agricultural development, population growth, market/economic factors) are also identified as important causes of various types of land change (e.g., deforestation, desertification). Meta-analysis helps to identify commonalities across the various local case studies and identify which variables may lead to individual cases to behave differently. The meta-regression provides maps indicating the likelihood of wetland conversion worldwide based on the location factors that have determined historic conversions.
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spelling pubmed-38400192013-11-26 Drivers of Wetland Conversion: a Global Meta-Analysis van Asselen, Sanneke Verburg, Peter H. Vermaat, Jan E. Janse, Jan H. PLoS One Research Article Meta-analysis of case studies has become an important tool for synthesizing case study findings in land change. Meta-analyses of deforestation, urbanization, desertification and change in shifting cultivation systems have been published. This present study adds to this literature, with an analysis of the proximate causes and underlying forces of wetland conversion at a global scale using two complementary approaches of systematic review. Firstly, a meta-analysis of 105 case-study papers describing wetland conversion was performed, showing that different combinations of multiple-factor proximate causes, and underlying forces, drive wetland conversion. Agricultural development has been the main proximate cause of wetland conversion, and economic growth and population density are the most frequently identified underlying forces. Secondly, to add a more quantitative component to the study, a logistic meta-regression analysis was performed to estimate the likelihood of wetland conversion worldwide, using globally-consistent biophysical and socioeconomic location factor maps. Significant factors explaining wetland conversion, in order of importance, are market influence, total wetland area (lower conversion probability), mean annual temperature and cropland or built-up area. The regression analyses results support the outcomes of the meta-analysis of the processes of conversion mentioned in the individual case studies. In other meta-analyses of land change, similar factors (e.g., agricultural development, population growth, market/economic factors) are also identified as important causes of various types of land change (e.g., deforestation, desertification). Meta-analysis helps to identify commonalities across the various local case studies and identify which variables may lead to individual cases to behave differently. The meta-regression provides maps indicating the likelihood of wetland conversion worldwide based on the location factors that have determined historic conversions. Public Library of Science 2013-11-25 /pmc/articles/PMC3840019/ /pubmed/24282580 http://dx.doi.org/10.1371/journal.pone.0081292 Text en © 2013 Asselen et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
van Asselen, Sanneke
Verburg, Peter H.
Vermaat, Jan E.
Janse, Jan H.
Drivers of Wetland Conversion: a Global Meta-Analysis
title Drivers of Wetland Conversion: a Global Meta-Analysis
title_full Drivers of Wetland Conversion: a Global Meta-Analysis
title_fullStr Drivers of Wetland Conversion: a Global Meta-Analysis
title_full_unstemmed Drivers of Wetland Conversion: a Global Meta-Analysis
title_short Drivers of Wetland Conversion: a Global Meta-Analysis
title_sort drivers of wetland conversion: a global meta-analysis
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3840019/
https://www.ncbi.nlm.nih.gov/pubmed/24282580
http://dx.doi.org/10.1371/journal.pone.0081292
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