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231226s2023 xx |||||o 00| ||eng c |
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|a 10.1111/cobi.14058
|2 doi
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|a pubmed24n1172.xml
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|a (DE-627)NLM351718060
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|a (NLM)36661056
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|a DE-627
|b ger
|c DE-627
|e rakwb
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|a eng
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| 100 |
1 |
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|a Powlen, Kathryn A
|e verfasserin
|4 aut
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| 245 |
1 |
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|a Identifying socioeconomic and biophysical factors driving forest loss in protected areas
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|c 2023
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|a Text
|b txt
|2 rdacontent
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|a ƒaComputermedien
|b c
|2 rdamedia
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| 338 |
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|a ƒa Online-Ressource
|b cr
|2 rdacarrier
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| 500 |
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|a Date Completed 31.07.2023
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|a Date Revised 01.08.2023
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|a published: Print-Electronic
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|a Citation Status MEDLINE
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|a © 2023 The Authors. Conservation Biology published by Wiley Periodicals LLC on behalf of Society for Conservation Biology.
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|a Protected areas (PAs) are a commonly used strategy to confront forest conversion and biodiversity loss. Although determining drivers of forest loss is central to conservation success, understanding of them is limited by conventional modeling assumptions. We used random forest regression to evaluate potential drivers of deforestation in PAs in Mexico, while accounting for nonlinear relationships and higher order interactions underlying deforestation processes. Socioeconomic drivers (e.g., road density, human population density) and underlying biophysical conditions (e.g., precipitation, distance to water, elevation, slope) were stronger predictors of forest loss than PA characteristics, such as age, type, and management effectiveness. Within PA characteristics, variables reflecting collaborative and equitable management and PA size were the strongest predictors of forest loss, albeit with less explanatory power than socioeconomic and biophysical variables. In contrast to previously used methods, which typically have been based on the assumption of linear relationships, we found that the associations between most predictors and forest loss are nonlinear. Our results can inform decisions on the allocation of PA resources by strengthening management in PAs with the highest risk of deforestation and help preemptively protect key biodiversity areas that may be vulnerable to deforestation in the future
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| 650 |
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|a Journal Article
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| 650 |
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4 |
|a Mexico
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| 650 |
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4 |
|a México
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| 650 |
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4 |
|a aprendizaje automático
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| 650 |
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4 |
|a bosque aleatorio
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| 650 |
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4 |
|a conservación
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| 650 |
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4 |
|a conservation
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| 650 |
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4 |
|a deforestación
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| 650 |
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4 |
|a deforestation
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| 650 |
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4 |
|a machine learning
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| 650 |
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4 |
|a random forest
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| 650 |
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4 |
|a 保护
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| 650 |
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4 |
|a 墨西哥
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| 650 |
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4 |
|a 机器学习
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| 650 |
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4 |
|a 森林砍伐
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| 650 |
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4 |
|a 随机森林
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| 700 |
1 |
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|a Salerno, Jonathan
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Jones, Kelly W
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Gavin, Michael C
|e verfasserin
|4 aut
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| 773 |
0 |
8 |
|i Enthalten in
|t Conservation biology : the journal of the Society for Conservation Biology
|d 1999
|g 37(2023), 4 vom: 16. Aug., Seite e14058
|w (DE-627)NLM098176803
|x 1523-1739
|7 nnns
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| 773 |
1 |
8 |
|g volume:37
|g year:2023
|g number:4
|g day:16
|g month:08
|g pages:e14058
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| 856 |
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|u http://dx.doi.org/10.1111/cobi.14058
|3 Volltext
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