Drivers of deforestation in the basin of the Usumacinta River inference on process from pattern analysis using generalised additive models
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.
| Main Authors: | , , , , , |
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| Format: | Texto biblioteca |
| Language: | eng |
| Subjects: | Deforestación, Análisis multivariante, Degradación ambiental, Factores socioeconómicos, Artfrosur, |
| Online Access: | https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0222908 |
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