Role of Brazilian Amazon protected areas in climate change mitigation

Soares‐Filho, Britaldo; Moutinho, Paulo; Nepstad, Daniel C.; Anderson, Anthony Β.; Rodrigues, Henrique; Garcia, Ricardo Alexandrino; Dietzsch, Laura; Merry, Frank; Bowman, Maria; Hissa, Letícia de Barros Viana; Silvestrini, Rafaella Almeida; Maretti, Cláudio C.

doi:10.1073/pnas.0913048107

Cited by 598 publications

(469 citation statements)

References 35 publications

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“…Protected areas are also integral parts of strategies to reduce carbon emissions from deforestation and degradation, such as the REDD+* program (21,22). Recent studies have estimated the effects of terrestrial protected area networks on deforestation (23)(24)(25)(26)(27), fire (28), regrowth (29), and poverty (30-32), but not their effects on ecosystem services.…”

Section: Significancementioning

confidence: 99%

Estimating the impacts of conservation on ecosystem services and poverty by integrating modeling and evaluation

Ferraro

Hanauer

Miteva

et al. 2015

Proc. Natl. Acad. Sci. U.S.A.

102

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Scholars have made great advances in modeling and mapping ecosystem services, and in assigning economic values to these services. This modeling and valuation scholarship is often disconnected from evidence about how actual conservation programs have affected ecosystem services, however. Without a stronger evidence base, decision makers find it difficult to use the insights from modeling and valuation to design effective policies and programs. To strengthen the evidence base, scholars have advanced our understanding of the causal pathways between conservation actions and environmental outcomes, but their studies measure impacts on imperfect proxies for ecosystem services (e.g., avoidance of deforestation). To be useful to decision makers, these impacts must be translated into changes in ecosystem services and values. To illustrate how this translation can be done, we estimated the impacts of protected areas in Brazil, Costa Rica, Indonesia, and Thailand on carbon storage in forests. We found that protected areas in these conservation hotspots have stored at least an additional 1,000 Mt of CO 2 in forests and have delivered ecosystem services worth at least $5 billion. This aggregate impact masks important spatial heterogeneity, however. Moreover, the spatial variability of impacts on carbon storage is the not the same as the spatial variability of impacts on avoided deforestation. These findings lead us to describe a research program that extends our framework to study other ecosystem services, to uncover the mechanisms by which ecosystem protection benefits humans, and to tie cost-benefit analyses to conservation planning so that we can obtain the greatest return on scarce conservation funds.T o inform decision makers about how they can best allocate resources to maintain and enhance ecosystem services, scholars need to develop a better understanding of how policy interventions actually affect the supply of ecosystem services. In the last two decades, scholars have made important advances in defining, measuring, and valuing ecosystem services across time and space (1-7). These measures and values have in turn been used as conservation planning and resource management decision tools (8-11). The degree to which policies and programs have affected these services and values in the past remains poorly understood, however (12, 13). Without an evidence base for the impacts of real policies and programs on ecosystem services, the insights from modeling and valuation are not as informative to decision makers as they could be.A separate and more recent literature focuses on estimating the impacts of conservation policies and programs on environmental and social outcomes (reviewed in refs. 14 and 15). Scholars working in this impact evaluation literature strive to eliminate rival explanations for the empirical patterns observed, so that any correlations between programs and outcomes can be confidently attributed to causal relationships; however, these studies do not estimate impacts on ecosystem services, but rather estima...

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Section: Significancementioning

confidence: 99%

Estimating the impacts of conservation on ecosystem services and poverty by integrating modeling and evaluation

Ferraro

Hanauer

Miteva

et al. 2015

Proc. Natl. Acad. Sci. U.S.A.

102

View full text Add to dashboard Cite

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“…in developing countries it is important to understand the dynamics of land-use change and predict which areas are at highest risk of forest loss. Multiple studies have attempted to quantify and predict future deforestation, most notably in the Amazon basin (Soares-Filho et al 2006;Rosa et al 2013), and in other forests around the world (Rideout et al 2013;Vieilledent et al 2013); however, to date we know of no analyses that predict future forest loss across the full extent of Borneo.…”

Section: Introductionmentioning

confidence: 99%

“…Published analyses of forest loss have employed a range of statistical techniques including logistic regression (Chowdhury 2006;Echeverria et al 2008), genetic algorithms (Venema et al 2005;Soares-Filho et al 2013), weights of evidence (Soares-Filho et al 2010;Maeda et al 2011) and cellular automata (Thapa et al 2013). These models are based on a number of environmental and anthropogenic landscape features that are thought to drive deforestation rates.…”

Section: Introductionmentioning

confidence: 99%

Multiple-scale prediction of forest loss risk across Borneo

et al. 2017

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Context The forests of Borneo have among the highest biodiversity and also the highest forest loss rates on the planet. Objectives Our objectives were to: (1) compare multiple modelling approaches, (2) evaluate the utility of landscape composition and configuration as predictors, (3) assess the influence of the ratio of forest loss and persistence points in the training sample, (4) identify the multiple-scale drivers of recent forest loss and (5) predict future forest loss risk across Borneo. Methods We compared random forest machine learning and logistic regression in a multi-scale approach to model forest loss risk between 2000 and 2010 as a function of topographical variables and landscape structure, and applied the highest performing model to predict the spatial pattern of forest loss risk between 2010 and 2020. We utilized a naïve model as a null comparison and used the total operating characteristic AUC to assess model performance. Results Our analysis produced five main results. We found that: (1) random forest consistently outperformed logistic regression and the naïve model; (2) including landscape structure variables substantially improved predictions; (3) a ratio of occurrence to nonoccurrence points in the training dataset that does not match the actual ratio in the landscape biases the predictions of both random forest and logistic regression; (4) forest loss risk differed between the three nations that comprise Borneo, with patterns in Kalimantan highly related to distance from the edge of the previous frontier of forest loss, while Malaysian Borneo showed a more diffuse pattern related to the structure of the landscape; (5) we predicted continuing 123Landscape Ecol (2017) 32:1581-1598 DOI 10.1007 very high rates of forest loss in the 2010-2020 period, and produced maps of the expected risk of forest loss across the full extent of Borneo. Conclusions These results confirm that multiplescale modelling using landscape metrics as predictors in a random forest modelling framework is a powerful approach to landscape change modelling. There is immense immanent risk to Borneo's forests, with clear spatial patterns of risk related to topography and landscape structure that differ between the three nations that comprise Borneo.

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“…It has been estimated that the recent expansion of protected areas (conservation units and indigenous lands) in the Brazilian Amazon is responsible for as much as 37% of the significant reduction in deforestation rates between 2004(Soares-Filho et al 2008. Furthermore, it has been calculated that the combined protected areas of the Amazon may represent a reduction in carbon emissions until 2050 in the order of 8 billion tonnes, or 3 times the target of the Kyoto Protocol (Soares-Filho et al 2010). Such estimates provide compelling arguments for including protected areas among the beneficiaries of REDD+ funds.…”

Section: Traditional Populations and Forest Conservationmentioning

confidence: 99%