Dataset Information

Governance regime and location influence avoided deforestation success of protected areas in the Brazilian Amazon.

ABSTRACT: Protected areas in tropical countries are managed under different governance regimes, the relative effectiveness of which in avoiding deforestation has been the subject of recent debates. Participants in these debates answer appeals for more strict protection with the argument that sustainable use areas and indigenous lands can balance deforestation pressures by leveraging local support to create and enforce protective regulations. Which protection strategy is more effective can also depend on (i) the level of deforestation pressures to which an area is exposed and (ii) the intensity of government enforcement. We examine this relationship empirically, using data from 292 protected areas in the Brazilian Amazon. We show that, for any given level of deforestation pressure, strictly protected areas consistently avoided more deforestation than sustainable use areas. Indigenous lands were particularly effective at avoiding deforestation in locations with high deforestation pressure. Findings were stable across two time periods featuring major shifts in the intensity of government enforcement. We also observed shifting trends in the location of protected areas, documenting that between 2000 and 2005 strictly protected areas were more likely to be established in high-pressure locations than in sustainable use areas and indigenous lands. Our findings confirm that all protection regimes helped reduce deforestation in the Brazilian Amazon.

SUBMITTER: Nolte C

PROVIDER: S-EPMC3612687 | biostudies-other | 2013 Mar

REPOSITORIES: biostudies-other

ACCESS DATA

Publications

Governance regime and location influence avoided deforestation success of protected areas in the Brazilian Amazon.

Nolte Christoph C Agrawal Arun A Silvius Kirsten M KM Soares-Filho Britaldo S BS

Proceedings of the National Academy of Sciences of the United States of America 20130311 13

Protected areas in tropical countries are managed under different governance regimes, the relative effectiveness of which in avoiding deforestation has been the subject of recent debates. Participants in these debates answer appeals for more strict protection with the argument that sustainable use areas and indigenous lands can balance deforestation pressures by leveraging local support to create and enforce protective regulations. Which protection strategy is more effective can also depend on ( ...[more]

PMID: 23479648

Similar Datasets

Project description:Tropical forests are diminishing in extent due primarily to the rapid expansion of agriculture, but the future magnitude and geographical distribution of future tropical deforestation is uncertain. Here, we introduce a dynamic and spatially-explicit model of deforestation that predicts the potential magnitude and spatial pattern of Amazon deforestation. Our model differs from previous models in three ways: (1) it is probabilistic and quantifies uncertainty around predictions and parameters; (2) the overall deforestation rate emerges "bottom up", as the sum of local-scale deforestation driven by local processes; and (3) deforestation is contagious, such that local deforestation rate increases through time if adjacent locations are deforested. For the scenarios evaluated-pre- and post-PPCDAM ("Plano de Ação para Proteção e Controle do Desmatamento na Amazônia")-the parameter estimates confirmed that forests near roads and already deforested areas are significantly more likely to be deforested in the near future and less likely in protected areas. Validation tests showed that our model correctly predicted the magnitude and spatial pattern of deforestation that accumulates over time, but that there is very high uncertainty surrounding the exact sequence in which pixels are deforested. The model predicts that under pre-PPCDAM (assuming no change in parameter values due to, for example, changes in government policy), annual deforestation rates would halve between 2050 compared to 2002, although this partly reflects reliance on a static map of the road network. Consistent with other models, under the pre-PPCDAM scenario, states in the south and east of the Brazilian Amazon have a high predicted probability of losing nearly all forest outside of protected areas by 2050. This pattern is less strong in the post-PPCDAM scenario. Contagious spread along roads and through areas lacking formal protection could allow deforestation to reach the core, which is currently experiencing low deforestation rates due to its isolation.

Dataset Information

Governance regime and location influence avoided deforestation success of protected areas in the Brazilian Amazon.

Publications

Governance regime and location influence avoided deforestation success of protected areas in the Brazilian Amazon.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets