Drivers of deforestation in the basin of the Usumacinta River: Inference on process from pattern analysis using generalised additive models

This data was imported from PubMed:

Authors: Vaca, R.A., Golicher, D.J., Rodiles-Hernández, R., Castillo-Santiago, M.Á., Bejarano, M. and Navarrete-Gutiérrez, D.A.

http://eprints.bournemouth.ac.uk/32836/

Journal: PLoS One

Volume: 14

Issue: 9

Pages: e0222908

eISSN: 1932-6203

DOI: 10.1371/journal.pone.0222908

Quantifying patterns of deforestation and linking these patterns to potentially influencing variables is a key component of modelling and projecting land use change. Statistical methods based on null hypothesis testing are only partially successful for interpreting deforestation in the context of the processes that have led to their formation. Simplifications of cause-consequence relationships that are difficult to support empirically may influence environment and development policies because they suggest simple solutions to complex problems. Deforestation is a complex process driven by multiple proximate and underlying factors and a range of scales. In this study we use a multivariate statistical analysis to provide contextual explanation for deforestation in the Usumacinta River Basin based on partial pattern matching. Our approach avoided testing trivial null hypotheses of lack of association and investigated the strength and form of the response to drivers. As not all factors involved in deforestation are easily mapped as GIS layers, analytical challenges arise due to lack of a one to one correspondence between mappable attributes and drivers. We avoided testing simple statistical hypotheses such as the detectability of a significant linear relationship between deforestation and proximity to roads or water. We developed a series of informative generalised additive models based on combinations of layers that corresponded to hypotheses regarding processes. The importance of the variables representing accessibility was emphasised by the analysis. We provide evidence that land tenure is a critical factor in shaping the decision to deforest and that direct beam insolation has an effect associated with fire frequency and intensity. The effect of winter insolation was found to have many applied implications for land management. The methodology was useful for interpreting the relative importance of sets of variables representing drivers of deforestation. It was an informative approach, thus allowing the construction of a comprehensive understanding of its causes.

This data was imported from Scopus:

Authors: Vaca, R.A., Golicher, D.J., Rodiles-Hernández, R., Castillo-Santiago, M.Á., Bejarano, M. and Navarrete-Gutiérrez, D.A.

http://eprints.bournemouth.ac.uk/32836/

Journal: PLoS ONE

Volume: 14

Issue: 9

eISSN: 1932-6203

DOI: 10.1371/journal.pone.0222908

© 2019 Vaca et al. 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 credited. Quantifying patterns of deforestation and linking these patterns to potentially influencing variables is a key component of modelling and projecting land use change. Statistical methods based on null hypothesis testing are only partially successful for interpreting deforestation in the context of the processes that have led to their formation. Simplifications of cause-consequence relationships that are difficult to support empirically may influence environment and development policies because they suggest simple solutions to complex problems. Deforestation is a complex process driven by multiple proximate and underlying factors and a range of scales. In this study we use a multivariate statistical analysis to provide contextual explanation for deforestation in the Usumacinta River Basin based on partial pattern matching. Our approach avoided testing trivial null hypotheses of lack of association and investigated the strength and form of the response to drivers. As not all factors involved in deforestation are easily mapped as GIS layers, analytical challenges arise due to lack of a one to one correspondence between mappable attributes and drivers. We avoided testing simple statistical hypotheses such as the detectability of a significant linear relationship between deforestation and proximity to roads or water. We developed a series of informative generalised additive models based on combinations of layers that corresponded to hypotheses regarding processes. The importance of the variables representing accessibility was emphasised by the analysis. We provide evidence that land tenure is a critical factor in shaping the decision to deforest and that direct beam insolation has an effect associated with fire frequency and intensity. The effect of winter insolation was found to have many applied implications for land management. The methodology was useful for interpreting the relative importance of sets of variables representing drivers of deforestation. It was an informative approach, thus allowing the construction of a comprehensive understanding of its causes.

This data was imported from Web of Science (Lite):

Authors: Abel Vaca, R., Golicher, D.J., Rodiles-Hernandez, R., Angel Castillo-Santiago, M., Bejarano, M. and Alejandro Navarrete-Gutierrez, D.

http://eprints.bournemouth.ac.uk/32836/

Journal: PLOS ONE

Volume: 14

Issue: 9

ISSN: 1932-6203

DOI: 10.1371/journal.pone.0222908

The data on this page was last updated at 05:23 on February 23, 2021.