Forecasting deforestation in the Brazilian Amazon to prioritize conservation efforts
As Amazon deforestation rates reach the highest levels observed in the past decade, it is extremely important to direct conservation efforts to regions containing preserved forests with a high risk of deforestation. This requires forecasting deforestation, a complex endeavor due to the interplay of...
| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IOP Publishing
2021-01-01
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| Series: | Environmental Research Letters |
| Subjects: | |
| Online Access: | https://doi.org/10.1088/1748-9326/ac146a |
| _version_ | 1797747578869121024 |
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| author | Rodolfo Jaffé Samia Nunes Jorge Filipe Dos Santos Markus Gastauer Tereza C Giannini Wilson Nascimento Jr Marcio Sales Carlos M Souza Jr Pedro W Souza-Filho Robert J Fletcher Jr |
| author_facet | Rodolfo Jaffé Samia Nunes Jorge Filipe Dos Santos Markus Gastauer Tereza C Giannini Wilson Nascimento Jr Marcio Sales Carlos M Souza Jr Pedro W Souza-Filho Robert J Fletcher Jr |
| author_sort | Rodolfo Jaffé |
| collection | DOAJ |
| description | As Amazon deforestation rates reach the highest levels observed in the past decade, it is extremely important to direct conservation efforts to regions containing preserved forests with a high risk of deforestation. This requires forecasting deforestation, a complex endeavor due to the interplay of multiple socioeconomic, political, and environmental factors across different spatial and temporal scales. Here we couple high-resolution land-cover maps with Bayesian hierarchical spatial models to identify the main drivers of recent deforestation in the Brazilian Amazon and predict which areas are likely to lose a larger proportion of forest in the next 3 years. Recent deforestation was positively associated with forest edge density, the length of roads and waterways, elevation and terrain slope; and negatively associated with distance to urban areas, roads, and indigenous lands, area designated as protected or indigenous territory, and municipality GDP per capita. From these variables, forest edge density and distance to roads showed the largest effect sizes and highest predictive power. Predictive accuracy was highest for shorter time windows and larger grid sizes. Predicted deforestation was largely concentrated in the North-Eastern portions of the Brazilian Amazon, and amounted to roughly 3, 5, and 6 million hectares for 2020, 2021, and 2022, respectively. About 50% of this predicted deforestation is expected to occur inside protected areas or indigenous lands. Our short-term forecasts can help plan preventive measures to limit deforestation while meeting the specific needs of local areas. |
| first_indexed | 2024-03-12T15:52:38Z |
| format | Article |
| id | doaj.art-356e30ace04b42a2a178559d74772851 |
| institution | Directory Open Access Journal |
| issn | 1748-9326 |
| language | English |
| last_indexed | 2024-03-12T15:52:38Z |
| publishDate | 2021-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | Environmental Research Letters |
| spelling | doaj.art-356e30ace04b42a2a178559d747728512023-08-09T15:03:59ZengIOP PublishingEnvironmental Research Letters1748-93262021-01-0116808403410.1088/1748-9326/ac146aForecasting deforestation in the Brazilian Amazon to prioritize conservation effortsRodolfo Jaffé0https://orcid.org/0000-0002-2101-5282Samia Nunes1Jorge Filipe Dos Santos2Markus Gastauer3Tereza C Giannini4Wilson Nascimento Jr5Marcio Sales6Carlos M Souza Jr7https://orcid.org/0000-0002-0205-6134Pedro W Souza-Filho8Robert J Fletcher Jr9Instituto Tecnológico Vale , Rua Boaventura da Silva 955, 66055-090 Belém, PA, Brazil; Departamento de Ecologia, Instituto de Biociências, Universidade de São Paulo , Rua do Matão 321—Trav. 14, 05508-090 São Paulo, SP, BrazilInstituto Tecnológico Vale , Rua Boaventura da Silva 955, 66055-090 Belém, PA, BrazilInstituto Tecnológico Vale , Rua Boaventura da Silva 955, 66055-090 Belém, PA, BrazilInstituto Tecnológico Vale , Rua Boaventura da Silva 955, 66055-090 Belém, PA, BrazilInstituto Tecnológico Vale , Rua Boaventura da Silva 955, 66055-090 Belém, PA, BrazilInstituto Tecnológico Vale , Rua Boaventura da Silva 955, 66055-090 Belém, PA, BrazilInstituto do Homem e do Meio Ambiente da Amazônia , Trav. Dom Romualdo de Seixas 1698, Ed. Zion Business 11° andar, 66055-200 Belém, PA, BrazilInstituto do Homem e do Meio Ambiente da Amazônia , Trav. Dom Romualdo de Seixas 1698, Ed. Zion Business 11° andar, 66055-200 Belém, PA, BrazilInstituto Tecnológico Vale , Rua Boaventura da Silva 955, 66055-090 Belém, PA, Brazil; Instituto de Geociências, Universidade Federal do Pará , Av. Augusto Correa 1, Belém, PA 66075-110, BrazilDepartment of Wildlife Ecology and Conservation, University of Florida , PO Box 110430, 110 Newins-Ziegler Hall, Gainesville, FL 32611-0430, United States of AmericaAs Amazon deforestation rates reach the highest levels observed in the past decade, it is extremely important to direct conservation efforts to regions containing preserved forests with a high risk of deforestation. This requires forecasting deforestation, a complex endeavor due to the interplay of multiple socioeconomic, political, and environmental factors across different spatial and temporal scales. Here we couple high-resolution land-cover maps with Bayesian hierarchical spatial models to identify the main drivers of recent deforestation in the Brazilian Amazon and predict which areas are likely to lose a larger proportion of forest in the next 3 years. Recent deforestation was positively associated with forest edge density, the length of roads and waterways, elevation and terrain slope; and negatively associated with distance to urban areas, roads, and indigenous lands, area designated as protected or indigenous territory, and municipality GDP per capita. From these variables, forest edge density and distance to roads showed the largest effect sizes and highest predictive power. Predictive accuracy was highest for shorter time windows and larger grid sizes. Predicted deforestation was largely concentrated in the North-Eastern portions of the Brazilian Amazon, and amounted to roughly 3, 5, and 6 million hectares for 2020, 2021, and 2022, respectively. About 50% of this predicted deforestation is expected to occur inside protected areas or indigenous lands. Our short-term forecasts can help plan preventive measures to limit deforestation while meeting the specific needs of local areas.https://doi.org/10.1088/1748-9326/ac146aAmazon deforestationBayesian hierarchical spatial modelsINLA-SPDEpredictive modelingprotected areas |
| spellingShingle | Rodolfo Jaffé Samia Nunes Jorge Filipe Dos Santos Markus Gastauer Tereza C Giannini Wilson Nascimento Jr Marcio Sales Carlos M Souza Jr Pedro W Souza-Filho Robert J Fletcher Jr Forecasting deforestation in the Brazilian Amazon to prioritize conservation efforts Environmental Research Letters Amazon deforestation Bayesian hierarchical spatial models INLA-SPDE predictive modeling protected areas |
| title | Forecasting deforestation in the Brazilian Amazon to prioritize conservation efforts |
| title_full | Forecasting deforestation in the Brazilian Amazon to prioritize conservation efforts |
| title_fullStr | Forecasting deforestation in the Brazilian Amazon to prioritize conservation efforts |
| title_full_unstemmed | Forecasting deforestation in the Brazilian Amazon to prioritize conservation efforts |
| title_short | Forecasting deforestation in the Brazilian Amazon to prioritize conservation efforts |
| title_sort | forecasting deforestation in the brazilian amazon to prioritize conservation efforts |
| topic | Amazon deforestation Bayesian hierarchical spatial models INLA-SPDE predictive modeling protected areas |
| url | https://doi.org/10.1088/1748-9326/ac146a |
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