Predictors of deforestation in the Brazilian Amazon
Aim and Location. We assessed the effects of biophysical and anthropogenic predictors on deforestation in Brazilian Amazonia. This region has the world's highest absolute rates of forest destruction and fragmentation. Methods. Using a GIS, spatial data coverages were developed for deforestation and...
| Autor principal: | Laurance, William F. |
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| Outros Autores: | Albernaz, Ana Luísa Kerti Mangabeira, Schroth, G?otz, Fearnside, Philip Martin, Bergen, Scott, Venticinque, Eduardo Martins, Costa, Carlos da |
| Grau: | Artigo |
| Idioma: | English |
| Publicado em: |
Journal of Biogeography
2020
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| Assuntos: | |
| Acesso em linha: |
https://repositorio.inpa.gov.br/handle/1/19048 |
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oai:repositorio:1-19048 |
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oai:repositorio:1-19048 Predictors of deforestation in the Brazilian Amazon Laurance, William F. Albernaz, Ana Luísa Kerti Mangabeira Schroth, G?otz Fearnside, Philip Martin Bergen, Scott Venticinque, Eduardo Martins Costa, Carlos da Deforestation Gis Prediction Brasil Bos Taurus Aim and Location. We assessed the effects of biophysical and anthropogenic predictors on deforestation in Brazilian Amazonia. This region has the world's highest absolute rates of forest destruction and fragmentation. Methods. Using a GIS, spatial data coverages were developed for deforestation and for three types of potential predictors: (1) human-demographic factors (rural-population density, urban-population size); (2) factors that affect physical accessibility to forests (linear distances to the nearest paved highway, unpaved road and navigable river), and (3) factors that may affect land-use suitability for human occupation and agriculture (annual rainfall, dry-season severity, soil fertility, soil waterlogging, soil depth). To reduce the effects of spatial autocorrelation among variables, the basin was subdivided into >1900 quadrats of 50 × 50 km, and a random subset of 120 quadrats was selected that was stratified on deforestation intensity. A robust ordination analysis (non-metric multidimensional scaling) was then used to identify key orthogonal gradients among the ten original predictor variables. Results. The ordination revealed two major environmental gradients in the study area. Axis 1 discriminated among areas with relatively dense human populations and highways, and areas with sparse populations and no highways; whereas axis 2 described a gradient between wet sites having low dry-season severity, many navigable rivers and few roads, and those with opposite values. A multiple regression analysis revealed that both factors were highly significant predictors, collectively explaining nearly 60% of the total variation in deforestation intensity (F2,117 = 85.46, P < 0.0001). Simple correlations of the original variables were highly concordant with the multiple regression model and suggested that highway density and rural-population size were the most important correlates of deforestation. Main conclusions. These trends suggest that deforestation in the Brazilian Amazon is being largely determined by three proximate factors: human population density, highways and dry-season severity, all of which increase deforestation. At least at the spatial scale of this analysis, soil fertility and waterlogging had little influence on deforestation activity, and soil depth was only marginally significant. Our findings suggest that current policy initiatives designed to increase immigration and dramatically expand highway and infrastructure networks in the Brazilian Amazon are likely to have important impacts on deforestation activity. Deforestation will be greatest in relatively seasonal, south-easterly areas of the basin, which are most accessible to major population centres and where large-scale cattle ranching and slash-and-burn farming are most easily implemented. 2020-06-15T22:04:55Z 2020-06-15T22:04:55Z 2002 Artigo https://repositorio.inpa.gov.br/handle/1/19048 10.1046/j.1365-2699.2002.00721.x en Volume 29, Número 5-6, Pags. 737-748 Restrito Journal of Biogeography |
| institution |
Instituto Nacional de Pesquisas da Amazônia - Repositório Institucional |
| collection |
INPA-RI |
| language |
English |
| topic |
Deforestation Gis Prediction Brasil Bos Taurus |
| spellingShingle |
Deforestation Gis Prediction Brasil Bos Taurus Laurance, William F. Predictors of deforestation in the Brazilian Amazon |
| topic_facet |
Deforestation Gis Prediction Brasil Bos Taurus |
| description |
Aim and Location. We assessed the effects of biophysical and anthropogenic predictors on deforestation in Brazilian Amazonia. This region has the world's highest absolute rates of forest destruction and fragmentation. Methods. Using a GIS, spatial data coverages were developed for deforestation and for three types of potential predictors: (1) human-demographic factors (rural-population density, urban-population size); (2) factors that affect physical accessibility to forests (linear distances to the nearest paved highway, unpaved road and navigable river), and (3) factors that may affect land-use suitability for human occupation and agriculture (annual rainfall, dry-season severity, soil fertility, soil waterlogging, soil depth). To reduce the effects of spatial autocorrelation among variables, the basin was subdivided into >1900 quadrats of 50 × 50 km, and a random subset of 120 quadrats was selected that was stratified on deforestation intensity. A robust ordination analysis (non-metric multidimensional scaling) was then used to identify key orthogonal gradients among the ten original predictor variables. Results. The ordination revealed two major environmental gradients in the study area. Axis 1 discriminated among areas with relatively dense human populations and highways, and areas with sparse populations and no highways; whereas axis 2 described a gradient between wet sites having low dry-season severity, many navigable rivers and few roads, and those with opposite values. A multiple regression analysis revealed that both factors were highly significant predictors, collectively explaining nearly 60% of the total variation in deforestation intensity (F2,117 = 85.46, P < 0.0001). Simple correlations of the original variables were highly concordant with the multiple regression model and suggested that highway density and rural-population size were the most important correlates of deforestation. Main conclusions. These trends suggest that deforestation in the Brazilian Amazon is being largely determined by three proximate factors: human population density, highways and dry-season severity, all of which increase deforestation. At least at the spatial scale of this analysis, soil fertility and waterlogging had little influence on deforestation activity, and soil depth was only marginally significant. Our findings suggest that current policy initiatives designed to increase immigration and dramatically expand highway and infrastructure networks in the Brazilian Amazon are likely to have important impacts on deforestation activity. Deforestation will be greatest in relatively seasonal, south-easterly areas of the basin, which are most accessible to major population centres and where large-scale cattle ranching and slash-and-burn farming are most easily implemented. |
| format |
Artigo |
| author |
Laurance, William F. |
| author2 |
Albernaz, Ana Luísa Kerti Mangabeira Schroth, G?otz Fearnside, Philip Martin Bergen, Scott Venticinque, Eduardo Martins Costa, Carlos da |
| author2Str |
Albernaz, Ana Luísa Kerti Mangabeira Schroth, G?otz Fearnside, Philip Martin Bergen, Scott Venticinque, Eduardo Martins Costa, Carlos da |
| title |
Predictors of deforestation in the Brazilian Amazon |
| title_short |
Predictors of deforestation in the Brazilian Amazon |
| title_full |
Predictors of deforestation in the Brazilian Amazon |
| title_fullStr |
Predictors of deforestation in the Brazilian Amazon |
| title_full_unstemmed |
Predictors of deforestation in the Brazilian Amazon |
| title_sort |
predictors of deforestation in the brazilian amazon |
| publisher |
Journal of Biogeography |
| publishDate |
2020 |
| url |
https://repositorio.inpa.gov.br/handle/1/19048 |
| _version_ |
1787143752080424960 |
| score |
11.755432 |