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Forecasting conservation impact to pinpoint spatial priorities in the Brazilian Cerrado
Biological Conservation 240 (2019) 108283
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Biological Conservation journal homepage: www.elsevier.com/locate/biocon
Forecasting conservation impact to pinpoint spatial priorities in the Brazilian Cerrado
T
Fernanda T. Bruma,*, Robert L. Presseyb, Luis Mauricio Binic, Rafael Loyolac,d a
Programa de Pós-Graduação em Ecologia e Conservação, Universidade Federal do Paraná, Curitiba, PR, Brazil Australian Research Council Centre of Excellence for Coral Reef Studies, James Cook University, Townsville, Australia c Departamento de Ecologia, Universidade Federal de Goiás, Goiânia, GO, Brazil d Fundação Brasileira para o Desenvolvimento Sustentável - FBDS, Rio de Janeiro, RJ, Brazil b
A R T I C LE I N FO
A B S T R A C T
Keywords: Counterfactual thinking Conservation policy Land-use model Matching Protected area effectiveness Research-implementation spaces Vegetation loss
Proper assessing the impacts of conservation interventions can create interaction spaces between researcher and implementation. For example, protected areas (PAs) are the main strategy to conserve biodiversity, but there is a widespread bias in their location towards unproductive and inaccessible lands. Thus, investments on PAs are likely to have been allocated to areas that did not need protection, at least in the short term, creating communication noise to the society. Here, we estimate the likely conservation impact of the recently established (2002–2012) PAs and indigenous lands (ILs) in a future scenario of land use projected to 2050. We selected areas that were similar to the PAs/ILs with positive conservation impact to propose spatial priorities aiming to minimize loss of Cerrado vegetation in the future. In our analyses, PAs in general and those of strict protection had significantly lower conversion rates than control areas, while sustainable use PAs and ILs showed no difference between control and protected areas. We did not find differences in impact values between PAs and ILs, but impact values were higher for strict protection than for sustainable use areas. We found a high density of potential priority areas to maximize impact in northern Cerrado. This region is the next agricultural frontier in the biome, having extensive vegetation cover that can be legally converted according to national legislation. By pinpointing conservation priorities based on impact, we can improve the benefit from land protection and increase the space of interactions between science, policymaking and society at large.
1. Introduction The major goal of protected areas (PAs) is to halt the loss of species, ecosystems, and other aspects of the natural environment (Pressey et al., 2015). Nonetheless, several assessments have highlighted the residual nature of the current PA network (Pressey, 1994; Pressey et al., 2002; Venter et al., 2018; Vieira et al., 2019). Protected areas are biased towards unproductive and inaccessible lands (Margules and Pressey, 2000; Pressey et al., 2002; Vieira et al., 2019), which could compromise their role in conserving and guaranteeing the sustainable use of biodiversity for future generations. Considering the finite budget available for conservation actions, evaluating the return on investment from the current PA system is essential to conservation science (Ferraro and Pattanayak, 2006; Hockings and Phillips, 1999; Parrish et al., 2003). The success of PAs can be assessed in terms of conservation impact, i.e. the difference that protected areas make to environmental outcomes
relative to the counterfactual situation of no intervention (Ferraro, 2009; Pressey et al., 2015). There are several methods to evaluate conservation impact, by evaluating reduction in vegetation loss relative to unprotected areas or using conversion rates before and after creation of PAs. However, those “before-and-after” and “in-and-out” approaches can be inadequate because the environmental indicators (such as vegetation loss rates) might be affected by covariates that are not being considered under such approaches (Andam et al., 2008; Ferraro and Pattanayak, 2006). Location bias associated with PAs might influence impact estimates because covariates such as slope and fertility can affect the probability of an area being converted. Further, “in-and-out” approaches can provide biased estimates due to spillover effects whereby the presence of a PA influences the likelihood of areas outside being deforested (Ewers and Rodrigues, 2008). In general, disregarding confounding covariates in impact evaluation might create misleading results by overestimating the conservation impact of PAs (Andam et al.,
⁎ Corresponding author at: Programa de Pós-Graduação em Ecologia e Conservação, Universidade Federal do Paraná, Caixa Postal 19031, CEP 81531-990, Curitiba, Brazil. E-mail address: [email protected] (F.T. Brum).
https://doi.org/10.1016/j.biocon.2019.108283 Received 18 April 2019; Received in revised form 17 September 2019; Accepted 6 October 2019 0006-3207/ © 2019 Elsevier Ltd. All rights reserved.
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conservation impact “would allow decision- and policy-makers to have more confidence in what is being proposed by scientists and ease the communication and collaboration between stakeholders” (Maas et al., 2019, this issue). Here we aim to forecast the conservation impact of the recently established PAs/ILs in the Brazilian Cerrado to verify whether the current strategies of land protection will be effective in avoiding future vegetation loss. We address the following questions: (1) What would be the likely impact of SP and SU PAs and ILs in avoiding vegetation loss under a probable land-use scenario for the Brazilian Cerrado? (2) Where are priority unprotected sites in the Brazilian Cerrado that would maximize conservation impact in terms of avoiding vegetation loss and, therefore, qualify as good conservation investments? Given that research-policy implementation space can arise because conservation impact is not properly evaluated, we expect that by selecting conservation priorities based on impact we can improve the conservation benefit from land protection and help to create spaces of interaction between research and policymaking.
2008; Carranza et al., 2014; Joppa and Pfaff, 2009). To correctly estimate the conservation impact of PAs in reducing vegetation loss, one needs to investigate counterfactual outcomes while controlling for confounding covariates. If the counterfactual is estimated to experience more vegetation loss than a PA, then protection has a positive conservation impact (Pressey et al., 2015). Hence, there is need to select areas to build the counterfactual that are not protected but are similar to the protected areas in terms of characteristics that are related to both the potential for biodiversity loss and likelihood of protection. Matching methods are gaining visibility in impact evaluation because they use rigorous statistical techniques to build a control (unprotected) group of sites for comparison that are as similar as possible to the treatment (protected) group (Khandker et al., 2010). In Brazil, PAs are governed by the Sistema Nacional de Unidade de Conservação (SNUC, MMA, 2003), which classifies the PAs into two different management regimes: strictly protected areas (SPs) and sustainable use areas (SUs). Beside PAs, indigenous lands (ILs) also have been regarded as important for biodiversity conservation in Brazil (Carranza et al., 2014; Ribeiro et al., 2018). Although Brazil has already achieved the 17% target of land protection established by CBD for 2020 (Ferreira and Valdujo, 2014; Vieira et al., 2019), this achievement hides uneven protection of biomes, for those biomes other than the Amazon remain poorly protected (Vieira et al., 2019). One of these biomes is the Brazilian Cerrado, globally recognized as a key area for conservation given its high species richness, high plant endemicity and also high rates of native vegetation loss (Mittermeier et al., 2004). The Cerrado has the second largest area protected in Brazil, although this represents only 8.6% of the region (Vieira et al., 2019). Besides its conservation prominence, the region is also known for its high potential for agribusiness, with almost 47% of the Cerrado already converted, mainly to agriculture and cattle ranching (MMA, 2014; Strassburg et al., 2017; Vieira et al., 2019). The Brazilian government has programs to slow down vegetation loss in the Cerrado, such as the Action Plan for Prevention and Control of Deforestation and Fire in the Cerrado (PPCerrado, MMA, 2014), but the main strategy for Cerrado conservation is still the establishment of PAs. Therefore, it is paramount that the conservation impact of the Cerrado’s PAs is maximized and impact evaluation is performed meticulously. The application of adequate analyses allows us to better plan actions to maximize conservation return (Ferraro and Pattanayak, 2006). Some work has been done to evaluate the impact of PAs on nature conservation in Brazil, specifically on their avoidance of native vegetation loss in the Amazon (Nolte et al., 2013) and the Cerrado (Carranza et al., 2014; Françoso et al., 2015; Paiva et al., 2015). Those studies showed the general importance of PAs (and ILs, Carranza et al., 2014; Nolte et al., 2013) in reducing conversion of native vegetation into anthropogenic landscapes. However, they found that impact varies with management regimes (SP or SU) and jurisdiction (federal, state or municipal). Likewise, all of these studies evaluated the conservation impact of PAs in biodiversity outcomes retrospectively, i.e. looking at how the PAs affected land-use dynamics in the past. Nevertheless, the threats to biodiversity are dynamic in space and time, posing an extra challenge for conservation planning and decision-making (Pressey et al., 2007). Forward projection of the impact of a conservation intervention is therefore a critical component of success in conservation management (Ferraro and Pattanayak, 2006; Law et al., 2017; Monteiro et al., 2018). Several models of future land use have been developed globally (Câmara et al., 2015) and nationally (Soares-Filho et al., 2016), and they might be helpful to evaluate the future impact of the current PA network in relation to land-use scenarios. By using the knowledge of what strategies are already working in conservation, we can better inform future conservation priorities and maximize the impact of conservation actions. Indeed, impact evaluation could be helpful to navigate the complexity in which the spaces between science and implementation are immersed (Maas et al., 2019, this issue). Measuring (or forecasting, e.g. Monteiro et al., 2018)
2. Materials and methods 2.1. Protected areas in Cerrado The location, jurisdiction (federal, state or municipal) and information on year of creation of the PAs and ILs in the Cerrado (Fig. 1a) were obtained from the database of the Ministry of Environment (CNUC, 2016) and the National Indian Foundation (FUNAI, 2016), respectively. PAs and ILs were analyzed separately because they are governed by different legal regimes. We included in the impact analyses only the PAs and ILs created between 2002 and 2012 (Fig. 1a) because PAs created up to 2001 had their conservation impact evaluated by Carranza et al. (2014). Also, by limiting the timeframe we could better infer the relationship between the future change in the landscape with the presence of the PAs (Andam et al., 2008). We analyzed the conservation impact of 71 PAs (27 of SP and 44 of SU) and 29 ILs. 2.2. Land use data For the baseline year (2012) and future land use (2050) we estimated the amount and location of native vegetation (forest and savanna) using the land-use models built by Soares-Filho et al. (2016) for Brazil. We cropped the models to the Cerrado’s extent, with a resolution of 500 × 500 m. The land-use map for 2050 was built from projections of agricultural expansion for 2024 and then extrapolated to 2050 based on historical trends between 1994 and 2013 (MAPA(Ministério da Agricultura Pecuária e Abastecimento), 2014) using the OTIMIZAGRO model (Soares-Filho et al., 2016). OTIMIZAGRO is a spatially explicit model that simulates nine annual crops and plantation forests based on information on climatic suitability, and uses probabilities of vegetation loss, which were estimated as a function of spatial determinants of habitat conversion, such as distance to roads and previously converted areas. Projected native vegetation loss rates were based on 2009–2014 averages in the Cerrado, and the location of land conversions was constrained to areas where conversion is legal in accordance with the Brazilian Native Vegetation Protection Law (a.k.a. the new forest code). Also, to constrain land conversion the model also considers the limits of protected areas (including indigenous lands), and vegetation loss inside those areas followed the management regime allowed by law and the presence of inholdings (privately owned land inside the boundary of a publicly protected area) inside the PAs/ILs (for further details see Soares-Filho et al., 2016). 2.3. Impact evaluation All the spatial analyses were conducted at 500 × 500 m resolution (hereafter “cells”), the same as the land-use data across the entire 2
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Fig. 1. The Brazilian Cerrado. (a) Location of protected areas and indigenous lands. (b) Land cover of forest and savanna native vegetation and the projected native vegetation loss in 2050.
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was positive. However, if the protected cells lost the same amount of vegetation or more vegetation than the matched control cells, the impact was zero or negative, respectively. Using the unpaired Wilcoxon rank sum test, we tested whether the median values of conservation impact of all PAs and those of SPs/SUs and ILs were significantly positive (i.e. higher than 0) and we also compared conservation impact values between PAs/ILs and SPs/SUs, and between federal and state jurisdictions (only for PAs). We did not include areas protected under municipal jurisdiction due to the small sample size (n = 1). Additionally, we obtained the combined impact of all PAs/ILs by pooling together all the protected and control samples, regardless of the information on the matching pairs. We then subtracted the mean conversion rate of the matched control cells by the mean conversion rate in the protected cells and divided it by the mean conversion in the matched control cells. We computed the pooled impact to compare a previous assessment of PA/IL conservation impact in the Cerrado in the timeframe of 2002–2009 (Carranza et al., 2014).
Cerrado. First, we selected all the cells that contained Cerrado native vegetation (savanna or forest) in the baseline year (2012). Then, we identified if the cells were under any management regime (SP, SU or IL) at any time in the baseline year (i.e. protected cells) or not (i.e. control cells). A cell was considered protected if the polygon of a PA or IL covered the center of the 500 × 500 m cell. Cells that belong to pre2002 or post- 2012 PAs and ILs were removed from analysis, along with cells within a buffer of 10 km around all extant PAs and ILs to avoid spillover effects on the impact estimates. Hence, there remained 161 682 cells within PAs and ILs from the 2002–2012 set, and 2 712 569 unprotected cells for further analyses. Then, by looking at the projected land use map from 2050, we computed a binary conversion score indicating if each cell would have been converted in 2050 (1) or if its native vegetation remained intact (0) (Fig. 1b). To evaluate the projected impact of the PAs and ILs established in 2002–2012 in avoiding Cerrado native vegetation loss, we compared the proportion of vegetation loss of the protected areas against control unprotected cells. We matched the protected and control cells to assess the conservation impact by using the matching approach. Matching uses statistical methods to construct the best possible artificial comparison group (control sample) for a given treatment group (protected sample) (Khandker et al., 2010). We selected the control cells for the comparison to each protected cell based on covariates that could influence the probability of conversion. The matching selection was based on commonly used covariates (Andam et al., 2008; Carranza et al., 2014; Nolte et al., 2013), i.e. distance to paved roads (IBGE, 2016), distance to the nearest state or federal capital city (IBGE, 2016), travel time to the nearest city with population > 50 000 individuals (Nelson, 2008), mean annual rainfall (Hijmans et al., 2005), altitude (Hijmans et al., 2005), agricultural suitability (IBGE, 2016), and vegetation type (forest or savanna) based on the land-use data. For the matching analysis, we used the package “Matchit” (Ho et al., 2011) in R (R Core Team, 2016). We matched cells to evaluate the impact of the protected areas by using propensity score matching (Rosenbaum and Rubin, 1983). For this, we fitted a logistic generalized linear model, in which the response was a binary variable indicating whether (1) or not (0) the cell was protected, and the predictors of this logistic model were the covariates. The predicted values from this model were propensity scores, and they represented the probability of a given cell being protected based on their covariate values. The protected and control cells were then matched based on their propensity score values to pair cells with similar conditions. We used exact matching for vegetation type (forest or savanna) and agricultural suitability (very low, low, medium, medium/high) for both categorical covariates. For the other covariates, we used a caliper of 0.25 standard deviations around the propensity score as limits within which control cells were considered acceptable. We were able to match 139 209 protected cells with control cells (one control cell per protected cell without replacement), representing approximately 87% of the protected cells from PAs and ILs established from 2002 to 2012. To measure the effect of land protection in reducing vegetation loss in the future scenario of land use (2050), we calculated the proportion of lost vegetation for protected cells and their matched control cells, and then we calculated the conservation impact for each PA/IL, in order to estimate its individual impact. Firstly, we obtained the proportion of vegetation loss for each PA/IL and its matched control. This was calculated by the sum of converted cells in protected and matched control cells, divided by the total area of the PA, giving then the proportion of vegetation loss in protected and matched unprotected cells. We tested whether the proportion of vegetation loss differed between matched control and protected samples for each category of protection (PA, SP, SU and IL) by using paired Wilcoxon signed rank test (Wilcoxon, 1945). Then, the conservation impact of each PA/IL was calculated as the difference between the conversion rate in the matched control cells and the protected cells. If the vegetation loss for each PA/IL was smaller than in its matched control cells, the conservation impact of the PA/IL
2.4. Priority cells for conservation impact To select priority cells for conservation impact, we selected all the control cells that were matched to PAs and ILs with positive conservation impact and had loss of vegetation in the future land-use maps. Then, because the cells were small, numerous, and scattered, we produced a kernel density map to highlight the regions in the Cerrado with high densities of priority cells for conservation impact. The high-density areas indicated regions where conservation actions such as the establishment of protected areas are predicted to have high potential to slow the future loss of Cerrado native vegetation. 3. Results In the baseline year of 2012, 49% of the Cerrado was still covered by native vegetation (savanna = 30%, forest = 19%), but the projection for 2050 was for a decrease to 31% (savanna = 19%, forest = 12%) (Fig. 2). The land-use model predicted that the main threat to remaining vegetation would be conversion to pasture (2012 = 38%, 2050 = 43%) and soybean croplands (2012 = 6%, 2050 = 12%) (Fig. 2). The loss of native vegetation in protected cells was significantly lower than in the matched control cells for PAs in general (V = 736.5, P = 0.009) and in SP PAs (V = 1471.5, P = 0.006) (Fig. 3a), while for SU PAs (V = 330.5, P = 0.144) and ILs (V = 151, P = 0.0758) there was no significant difference with control cells detected (Fig. 3a). The Wilcoxon test showed that PAs in general (V = 1471.5, P = 0.009) and SP PAs (V = 273, P = 0.006) had median impact values significantly higher than zero, while median values for SU PAs (V = 488.5, P = 0.140) and ILs (V = 284, P = 0.070) did not differ significantly from 0. The impact of PAs and ILs did not differ (W = 881, P = 0.871), while the impact of SP PAs was significantly higher than that of SU PAs (W = 737, P = 0.046) (Fig. 3b). The conservation impact of PAs also did not differ according to jurisdiction (W = 578, P = 0.418). Among all the PAs and ILs analyzed, 66% had a positive projected impact in avoiding native vegetation loss (21 SPs, 24 SUs, 21 ILs) and were mostly distributed in the central and northern parts of the Cerrado (Fig. 4). We selected as priority 40 849 cells that can maximize the impact of conservation action in reducing native vegetation loss, representing almost 30% of the matched cells and totaling an area of 1 021 225 ha. The heatmap showed a high density of priority cells in the middle and northern region of Cerrado (Fig. 4). Compared to the analysis of the impact of PAs in the Cerrado for the timeframe 2002–2009 (Carranza et al., 2014), we found similar results for PAs in general (2002–2009 = 60%, 2012–2050 = 48%) and for SP PAs (2002–2009 = 97%, 2012–2050 = 93%), but much lower values for SU PAs (2002–2009 = 28%, 2012–2050 = 13%) and ILs (2002–2009 = 87%, 2012–2050 = 38%). 4
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assessment for the same region (Carranza et al., 2014). Paiva et al. (2015) observed that, in general, the older protected areas showed greater conservation impact than the more recently created ones, which might explain the lower values that we found in comparison to the set (PAs/ILs created up to 2001) analyzed by Carranza et al. (2014) in the same region. We found that management regime is an important factor influencing future PA impact. Specifically, we found that SU PAs and ILs had a minor impact in general. The low conservation impact of SU PAs areas has already been reported by past analyses for the Cerrado (Carranza et al., 2014) and the Brazilian state of Goiás (Paiva et al., 2015; but see Ribeiro et al., 2018). Even though some studies have shown that SU PAs might be effective in decreasing fires in tropical forests (Nelson and Chomitz, 2011; Porter-Bolland et al., 2012), this management regime allows economic activities inside the PA, which might hinder conservation effectiveness in reducing vegetation loss. Since the mid-1990s in Brazil, SU areas have surpassed strict protection areas in number and extent, now being almost two thirds of the total extent of the PA network (Vieira et al., 2019). By relying mostly on SU PAs, Brazil might be compromising the integrity of its natural ecosystems. In addition to fulfilling their main goals (Indigenous nations' right to land and the protection of their cultural heritage), ILs also have been observed to play an important role in avoiding deforestation in tropical ecosystems (Carranza et al., 2014; Nepstad et al., 2006; Nolte et al., 2013). Unfortunately, our results did not support the view that these areas also have an important conservation role, even though we observed that 21 ILs had positive projected impact in reducing deforestation. However due to our analysis focusing only on recently established ILs (2002–2012) and age of these ILs influence on conservation impact (Andam et al., 2008; Paiva et al., 2015), the absence of significant conservation impact might be explained due to lack of time for regularization on land ownership of those areas; although this is still a question to be tested in further studies. The main threat to remaining Cerrado vegetation is predicted to be conversion of natural areas to pasture and soybeans fields. The Cerrado is known as the “barn of Brazil” and, in the last 10 years, the government has encouraged the occupation of the northern and more pristine region of the Cerrado, known as MATOPIBA region (Vieira et al., 2018). As a result, soy production in the region doubled from 4.3 to 8.6 million tons in 2014 (Bolfe et al., 2016). The legally sanctioned loss of native vegetation in the Cerrado might impact 35% of the natural remnants,
Fig. 2. Land-use categories in the Cerrado in 2012 and 2050, based on the model from Soares-Filho et al. (2016).
4. Discussion Our analysis showed that the recently established PAs in the Brazilian Cerrado are projected to have a positive impact in reducing native vegetation loss in the future, but much depends on their management regime. Further, our results of lower conversion rates inside PAs than outside are consistent with past trend assessments in tropical forest (Andam et al., 2008; Nelson and Chomitz, 2011; Nolte et al., 2013) and in the Cerrado (Carranza et al., 2014; Paiva et al., 2015). However, we found lower overall values than in the previous
Fig. 3. Estimates of future conservation impact of protection in the Cerrado. (a) Vegetation loss in protected and matched control cells in different categories of protected areas. (b) Conservation impact values in reducing vegetation loss in the Cerrado. PA: Protected Areas. SP: Strict Protection. SU: Sustainable Use. IL: Indigenous lands. * P < 0.05, ** P < 0.001, ns = P > 0.05. SP and SU are subsets of PA. Results in both graphs are shown for individual PAs and ILs. 5
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Fig. 4. Conservation impact values of protected areas and indigenous lands and the kernel density of priority cells for conservation impact.
ecotourism might have generated up to 133 480 jobs. These figures suggest that there are ways to keep native vegetation intact and still profit from sustainable activities (Young and Medeiros, 2018). With PAs expected to achieve not only biodiversity protection, but also contribute to the social development of local communities, stimulate economic growth through tourism revenues and play a key part in the mitigation and adaptation to climate change, PAs must have adequate funding, comprehensive planning, and effective management (Watson et al., 2014). The erosion of the support of protected areas around the globe, together with the increase of PADDD (protected area downgrading, downsizing and degazettement), especially in the developing countries (Mascia et al., 2014), could compromise even more the conservation impact of PAs in protecting biodiversity and ecosystem services (Resende et al., 2019). Our approach presents some limitations. First, the land use projection used here is only one possibility of future, based on legal compliances with deforestation and protection . However, due to the weakening of the Brazilian environmental legislation this projection may be significantly altered by 2050 (Loyola, 2014; Dobrovolski et al., 2018; Vieira et al., 2018). Second, even though vegetation, as used in our study, has been verified as a good proxy for biodiversity with regards to spatial planning in the Cerrado (Monteiro et al., 2018), it might be not be the best measure for other ecosystem services, such as scenic beauty for ecotourism for example (see Resende et al., 2019). Recently, Monteiro et al. (2018) showed using spatial planning with the goal of minimizing vegetation loss in the Cerrado, it was possible to cover large proportions of species’ ranges inside PAs and other priority areas, indicating that vegetation information could represent a reliable surrogate for overall biodiversity. Our study also demonstrates that spatial conservation prioritization can aid in locating those natural landscapes most at threat to conversion by croplands and pastures, especially in the most pristine regions of the Cerrado. Most of the conservation priorities that we identified were clustered in the northern part of the Cerrado, a region that is highly threatened. Then, our impact-based priorities for biodiversity conservation might be effective in
which would unleash an unprecedented environmental crisis involving species extinctions (recent estimates show that the projected deforestation for 2050 could compromise the entire range of the Xyris uninervis, a threatened endemic plant species, for example; see Strassburg et al., 2017), threats to water supply, and increased carbon emissions (Strassburg et al., 2017; Vieira et al., 2018). Even though agriculture is a fundamental activity for Brazil’s economy (Strassburg et al., 2014), the unsustainable expansion of agricultural frontiers might pose a threat not only for wildlife, but also compromise agricultural production itself and human welfare (Strassburg et al., 2017, 2019; Vieira et al., 2018). For example, the Cerrado provides essential ecosystem services related to food security, including production of livestock forage, carbon storage, and water quality by contributing to eight of the twelve major watersheds in Brazil (Overbeck et al., 2015). It is possible to reconcile agriculture production and biodiversity conservation by improving the use of current agricultural lands, especially pasturelands (Strassburg et al., 2014, 2017) or by redirecting agricultural expansion to suitable areas but with minimum impact on biodiversity (Lemes et al., 2019). Such a strategy coupled with large-scale restoration may allow for substantial agriculture expansion with a net-zero vegetation loss (Strassburg et al., 2014, 2017). To succeed in this context spatial planning is essential, not only to maximize biodiversity protection, but also to maximize agricultural production and regional development (Dobrovolski et al., 2013, 2011; Kiesecker et al., 2010; Lemes et al., 2019; Strassburg et al., 2017, 2019). Beyond the debate between agriculture expansion and conservation, several other mechanisms could provide income and economic impact leading to sustainable development. Those include carbon pricing initiatives, green finance (especially green bonds), economic incentives such as payment for ecosystem services, and sustainable rural development based on non-timber products and manufacturing. On the top of those mechanisms is ecotourism. Young and Medeiros (2018) have recently estimated that ecotourism in national parks alone (among other types of PAs in Brazil) was responsible for 4–12 billion dollars of economic impact in 2016. They also estimate that, in this same year, 6
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reducing vegetation loss and maximizing biodiversity conservation. In addition, by combining conservation priorities with social and economic needs of local communities (Porter-Bolland et al., 2012; Scarano, 2017), we could generate a more resilient and robust conservation strategy, promoting not only biodiversity and ecosystem services, but also economic growth in the region.
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Declaration of Competing Interest We declare no conflict of interest. Acknowledgements This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) Finance Code 001. FTB received a postdoctoral scholarship from CNPq (Grant #152172/2016-5), a technological development scholarship (DTI-A) by CNPq (Grant #381106/2017-9) and currently holds a postdoctoral fellowship grant from Programa Nacional de Pós-Doutorado from Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (PNPD/CAPES, Grant #88882.306081/2018-1). RL and LMB research is funded by CNPq (Grants #306694/2018-2 and #304314/2014‐5, respectively). RLP acknowledges the support of the Australian Research Council. This paper is a contribution of the INCT in Ecology, Evolution and Biodiversity Conservation founded by MCTIC/CNPq (Grant #465610/2014-5) and FAPEG (Grant #201810267000023). We thank Bea Maas, Andrew Knight, James Oakleaf and one anonymous reviewer for their valuable suggestions. References Andam, K.S., Ferraro, P.J., Pfaff, A., Sanchez-Azofeifa, G.A., Robalino, Ja, 2008. Measuring the effectiveness of protected area networks in reducing deforestation. Proc. Natl. Acad. Sci. U. S. A. 105, 16089–16094. https://doi.org/10.1073/pnas. 0800437105. Bolfe, É.L., Victória, D., de, C., Contini, E., Silva, G.B., Araujo, L.S., Gomes, D., 2016. MATOPIBA em Crescimento Agrícola. Rev. Política Agrícola 1, 38–62. Câmara, G., Soterroni, A., Ramos, F., Carvalho, A., Andrade, P., Souza, R.S., Mosnier, A., Mant, R., et al., 2015. Modelling Land Use Change in Brazil: 2000-2050, 1st edition. Laxenbrug, Cambridge. INPE, IPEA, ILASA, UNEP-WCMC, São José dos Campos, Brasília. https://doi.org/10.22022/REDD/08-2016.12115. Carranza, T., Balmford, A., Kapos, V., Manica, A., 2014. Protected area effectiveness in reducing conversion in a rapidly vanishing ecosystem: the Brazilian Cerrado: protected area effectiveness in Cerrado. Conserv. Lett. 7, 216–223. https://doi.org/10. 1111/conl.12049. CNUC, 2016. Cadastro Nacional de Unidades de Conservação/National Register of Conservation Units. Accessed date: November 2016. http://www.mma.gov.br/ areasprotegidas/cadastro-nacional-de-ucs. Dobrovolski, R., Diniz-Filho, J.A.F., Loyola, R.D., Marco Júnior, P., 2011. Agricultural expansion and the fate of global conservation priorities. Biodivers. Conserv. 20, 2445–2459. https://doi.org/10.1007/s10531-011-9997-z. Dobrovolski, R., Loyola, R.D., Guilhaumon, F., Gouveia, S.F., Diniz-Filho, J.A.F., 2013. Global agricultural expansion and carnivore conservation biogeography. Biol. Conserv. 165, 162–170. https://doi.org/10.1016/j.biocon.2013.06.004. Dobrovolski, R., Loyola, R., Rattis, L., Gouveia, S.F., Cardoso, D., Santos-Silva, R., Gonçalves-Souza, D., Bini, L.M., Diniz-Filho, J.A.F., 2018. Science and democracy must orientate Brazil’s path to sustainability. Perspect. Ecol. Conserv. 16, 121–124. https://doi.org/10.1016/j.pecon.2018.06.005. Ewers, R.M., Rodrigues, A.S.L., 2008. Estimates of reserve effectiveness are confounded by leakage. Trends Ecol. Evol. 23, 113–116. https://doi.org/10.1016/j.tree.2007.11. 008. Ferraro, P.J., 2009. Counterfactual thinking and impact evaluation in environmental policy. New Dir. Eval. 2009, 75–84. https://doi.org/10.1002/ev.297. Ferraro, P.J., Pattanayak, S.K., 2006. Money for nothing? A call for empirical evaluation of biodiversity conservation investments. PLoS Biol. 4, 482–488. https://doi.org/10. 1371/journal.pbio.0040105. Ferreira, M.N., Valdujo, P.H., 2014. Observatório de UC’s: Biodiversidade em Unidades de Conservação. WWF, Brasília 64p. Françoso, R., Brandão, R., de Campos Nogueira, C., Salmona, Y., Machado, R.B., Colli, G.R., 2015. Habitat loss and the effectiveness of protected areas in the Cerrado biodiversity hotspot. Nat. Conserv. 3, 35–40. https://doi.org/10.1016/j.ncon.2015. 04.001. FUNAI, 2016. Fundação Nacional do Índio. Accessed date: November 2016. http:// www.funai.gov.br/index.php/i3geo. Hijmans, R.J., Cameron, S.E., Parra, J.L., Jones, P.G., Jarvis, A., 2005. Very high resolution interpolated climate surfaces for global land areas. Int. J. Climatol. 25,
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Biological Conservation journal homepage: www.elsevier.com/locate/biocon
Forecasting conservation impact to pinpoint spatial priorities in the Brazilian Cerrado
T
Fernanda T. Bruma,*, Robert L. Presseyb, Luis Mauricio Binic, Rafael Loyolac,d a
Programa de Pós-Graduação em Ecologia e Conservação, Universidade Federal do Paraná, Curitiba, PR, Brazil Australian Research Council Centre of Excellence for Coral Reef Studies, James Cook University, Townsville, Australia c Departamento de Ecologia, Universidade Federal de Goiás, Goiânia, GO, Brazil d Fundação Brasileira para o Desenvolvimento Sustentável - FBDS, Rio de Janeiro, RJ, Brazil b
A R T I C LE I N FO
A B S T R A C T
Keywords: Counterfactual thinking Conservation policy Land-use model Matching Protected area effectiveness Research-implementation spaces Vegetation loss
Proper assessing the impacts of conservation interventions can create interaction spaces between researcher and implementation. For example, protected areas (PAs) are the main strategy to conserve biodiversity, but there is a widespread bias in their location towards unproductive and inaccessible lands. Thus, investments on PAs are likely to have been allocated to areas that did not need protection, at least in the short term, creating communication noise to the society. Here, we estimate the likely conservation impact of the recently established (2002–2012) PAs and indigenous lands (ILs) in a future scenario of land use projected to 2050. We selected areas that were similar to the PAs/ILs with positive conservation impact to propose spatial priorities aiming to minimize loss of Cerrado vegetation in the future. In our analyses, PAs in general and those of strict protection had significantly lower conversion rates than control areas, while sustainable use PAs and ILs showed no difference between control and protected areas. We did not find differences in impact values between PAs and ILs, but impact values were higher for strict protection than for sustainable use areas. We found a high density of potential priority areas to maximize impact in northern Cerrado. This region is the next agricultural frontier in the biome, having extensive vegetation cover that can be legally converted according to national legislation. By pinpointing conservation priorities based on impact, we can improve the benefit from land protection and increase the space of interactions between science, policymaking and society at large.
1. Introduction The major goal of protected areas (PAs) is to halt the loss of species, ecosystems, and other aspects of the natural environment (Pressey et al., 2015). Nonetheless, several assessments have highlighted the residual nature of the current PA network (Pressey, 1994; Pressey et al., 2002; Venter et al., 2018; Vieira et al., 2019). Protected areas are biased towards unproductive and inaccessible lands (Margules and Pressey, 2000; Pressey et al., 2002; Vieira et al., 2019), which could compromise their role in conserving and guaranteeing the sustainable use of biodiversity for future generations. Considering the finite budget available for conservation actions, evaluating the return on investment from the current PA system is essential to conservation science (Ferraro and Pattanayak, 2006; Hockings and Phillips, 1999; Parrish et al., 2003). The success of PAs can be assessed in terms of conservation impact, i.e. the difference that protected areas make to environmental outcomes
relative to the counterfactual situation of no intervention (Ferraro, 2009; Pressey et al., 2015). There are several methods to evaluate conservation impact, by evaluating reduction in vegetation loss relative to unprotected areas or using conversion rates before and after creation of PAs. However, those “before-and-after” and “in-and-out” approaches can be inadequate because the environmental indicators (such as vegetation loss rates) might be affected by covariates that are not being considered under such approaches (Andam et al., 2008; Ferraro and Pattanayak, 2006). Location bias associated with PAs might influence impact estimates because covariates such as slope and fertility can affect the probability of an area being converted. Further, “in-and-out” approaches can provide biased estimates due to spillover effects whereby the presence of a PA influences the likelihood of areas outside being deforested (Ewers and Rodrigues, 2008). In general, disregarding confounding covariates in impact evaluation might create misleading results by overestimating the conservation impact of PAs (Andam et al.,
⁎ Corresponding author at: Programa de Pós-Graduação em Ecologia e Conservação, Universidade Federal do Paraná, Caixa Postal 19031, CEP 81531-990, Curitiba, Brazil. E-mail address: [email protected] (F.T. Brum).
https://doi.org/10.1016/j.biocon.2019.108283 Received 18 April 2019; Received in revised form 17 September 2019; Accepted 6 October 2019 0006-3207/ © 2019 Elsevier Ltd. All rights reserved.
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conservation impact “would allow decision- and policy-makers to have more confidence in what is being proposed by scientists and ease the communication and collaboration between stakeholders” (Maas et al., 2019, this issue). Here we aim to forecast the conservation impact of the recently established PAs/ILs in the Brazilian Cerrado to verify whether the current strategies of land protection will be effective in avoiding future vegetation loss. We address the following questions: (1) What would be the likely impact of SP and SU PAs and ILs in avoiding vegetation loss under a probable land-use scenario for the Brazilian Cerrado? (2) Where are priority unprotected sites in the Brazilian Cerrado that would maximize conservation impact in terms of avoiding vegetation loss and, therefore, qualify as good conservation investments? Given that research-policy implementation space can arise because conservation impact is not properly evaluated, we expect that by selecting conservation priorities based on impact we can improve the conservation benefit from land protection and help to create spaces of interaction between research and policymaking.
2008; Carranza et al., 2014; Joppa and Pfaff, 2009). To correctly estimate the conservation impact of PAs in reducing vegetation loss, one needs to investigate counterfactual outcomes while controlling for confounding covariates. If the counterfactual is estimated to experience more vegetation loss than a PA, then protection has a positive conservation impact (Pressey et al., 2015). Hence, there is need to select areas to build the counterfactual that are not protected but are similar to the protected areas in terms of characteristics that are related to both the potential for biodiversity loss and likelihood of protection. Matching methods are gaining visibility in impact evaluation because they use rigorous statistical techniques to build a control (unprotected) group of sites for comparison that are as similar as possible to the treatment (protected) group (Khandker et al., 2010). In Brazil, PAs are governed by the Sistema Nacional de Unidade de Conservação (SNUC, MMA, 2003), which classifies the PAs into two different management regimes: strictly protected areas (SPs) and sustainable use areas (SUs). Beside PAs, indigenous lands (ILs) also have been regarded as important for biodiversity conservation in Brazil (Carranza et al., 2014; Ribeiro et al., 2018). Although Brazil has already achieved the 17% target of land protection established by CBD for 2020 (Ferreira and Valdujo, 2014; Vieira et al., 2019), this achievement hides uneven protection of biomes, for those biomes other than the Amazon remain poorly protected (Vieira et al., 2019). One of these biomes is the Brazilian Cerrado, globally recognized as a key area for conservation given its high species richness, high plant endemicity and also high rates of native vegetation loss (Mittermeier et al., 2004). The Cerrado has the second largest area protected in Brazil, although this represents only 8.6% of the region (Vieira et al., 2019). Besides its conservation prominence, the region is also known for its high potential for agribusiness, with almost 47% of the Cerrado already converted, mainly to agriculture and cattle ranching (MMA, 2014; Strassburg et al., 2017; Vieira et al., 2019). The Brazilian government has programs to slow down vegetation loss in the Cerrado, such as the Action Plan for Prevention and Control of Deforestation and Fire in the Cerrado (PPCerrado, MMA, 2014), but the main strategy for Cerrado conservation is still the establishment of PAs. Therefore, it is paramount that the conservation impact of the Cerrado’s PAs is maximized and impact evaluation is performed meticulously. The application of adequate analyses allows us to better plan actions to maximize conservation return (Ferraro and Pattanayak, 2006). Some work has been done to evaluate the impact of PAs on nature conservation in Brazil, specifically on their avoidance of native vegetation loss in the Amazon (Nolte et al., 2013) and the Cerrado (Carranza et al., 2014; Françoso et al., 2015; Paiva et al., 2015). Those studies showed the general importance of PAs (and ILs, Carranza et al., 2014; Nolte et al., 2013) in reducing conversion of native vegetation into anthropogenic landscapes. However, they found that impact varies with management regimes (SP or SU) and jurisdiction (federal, state or municipal). Likewise, all of these studies evaluated the conservation impact of PAs in biodiversity outcomes retrospectively, i.e. looking at how the PAs affected land-use dynamics in the past. Nevertheless, the threats to biodiversity are dynamic in space and time, posing an extra challenge for conservation planning and decision-making (Pressey et al., 2007). Forward projection of the impact of a conservation intervention is therefore a critical component of success in conservation management (Ferraro and Pattanayak, 2006; Law et al., 2017; Monteiro et al., 2018). Several models of future land use have been developed globally (Câmara et al., 2015) and nationally (Soares-Filho et al., 2016), and they might be helpful to evaluate the future impact of the current PA network in relation to land-use scenarios. By using the knowledge of what strategies are already working in conservation, we can better inform future conservation priorities and maximize the impact of conservation actions. Indeed, impact evaluation could be helpful to navigate the complexity in which the spaces between science and implementation are immersed (Maas et al., 2019, this issue). Measuring (or forecasting, e.g. Monteiro et al., 2018)
2. Materials and methods 2.1. Protected areas in Cerrado The location, jurisdiction (federal, state or municipal) and information on year of creation of the PAs and ILs in the Cerrado (Fig. 1a) were obtained from the database of the Ministry of Environment (CNUC, 2016) and the National Indian Foundation (FUNAI, 2016), respectively. PAs and ILs were analyzed separately because they are governed by different legal regimes. We included in the impact analyses only the PAs and ILs created between 2002 and 2012 (Fig. 1a) because PAs created up to 2001 had their conservation impact evaluated by Carranza et al. (2014). Also, by limiting the timeframe we could better infer the relationship between the future change in the landscape with the presence of the PAs (Andam et al., 2008). We analyzed the conservation impact of 71 PAs (27 of SP and 44 of SU) and 29 ILs. 2.2. Land use data For the baseline year (2012) and future land use (2050) we estimated the amount and location of native vegetation (forest and savanna) using the land-use models built by Soares-Filho et al. (2016) for Brazil. We cropped the models to the Cerrado’s extent, with a resolution of 500 × 500 m. The land-use map for 2050 was built from projections of agricultural expansion for 2024 and then extrapolated to 2050 based on historical trends between 1994 and 2013 (MAPA(Ministério da Agricultura Pecuária e Abastecimento), 2014) using the OTIMIZAGRO model (Soares-Filho et al., 2016). OTIMIZAGRO is a spatially explicit model that simulates nine annual crops and plantation forests based on information on climatic suitability, and uses probabilities of vegetation loss, which were estimated as a function of spatial determinants of habitat conversion, such as distance to roads and previously converted areas. Projected native vegetation loss rates were based on 2009–2014 averages in the Cerrado, and the location of land conversions was constrained to areas where conversion is legal in accordance with the Brazilian Native Vegetation Protection Law (a.k.a. the new forest code). Also, to constrain land conversion the model also considers the limits of protected areas (including indigenous lands), and vegetation loss inside those areas followed the management regime allowed by law and the presence of inholdings (privately owned land inside the boundary of a publicly protected area) inside the PAs/ILs (for further details see Soares-Filho et al., 2016). 2.3. Impact evaluation All the spatial analyses were conducted at 500 × 500 m resolution (hereafter “cells”), the same as the land-use data across the entire 2
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Fig. 1. The Brazilian Cerrado. (a) Location of protected areas and indigenous lands. (b) Land cover of forest and savanna native vegetation and the projected native vegetation loss in 2050.
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was positive. However, if the protected cells lost the same amount of vegetation or more vegetation than the matched control cells, the impact was zero or negative, respectively. Using the unpaired Wilcoxon rank sum test, we tested whether the median values of conservation impact of all PAs and those of SPs/SUs and ILs were significantly positive (i.e. higher than 0) and we also compared conservation impact values between PAs/ILs and SPs/SUs, and between federal and state jurisdictions (only for PAs). We did not include areas protected under municipal jurisdiction due to the small sample size (n = 1). Additionally, we obtained the combined impact of all PAs/ILs by pooling together all the protected and control samples, regardless of the information on the matching pairs. We then subtracted the mean conversion rate of the matched control cells by the mean conversion rate in the protected cells and divided it by the mean conversion in the matched control cells. We computed the pooled impact to compare a previous assessment of PA/IL conservation impact in the Cerrado in the timeframe of 2002–2009 (Carranza et al., 2014).
Cerrado. First, we selected all the cells that contained Cerrado native vegetation (savanna or forest) in the baseline year (2012). Then, we identified if the cells were under any management regime (SP, SU or IL) at any time in the baseline year (i.e. protected cells) or not (i.e. control cells). A cell was considered protected if the polygon of a PA or IL covered the center of the 500 × 500 m cell. Cells that belong to pre2002 or post- 2012 PAs and ILs were removed from analysis, along with cells within a buffer of 10 km around all extant PAs and ILs to avoid spillover effects on the impact estimates. Hence, there remained 161 682 cells within PAs and ILs from the 2002–2012 set, and 2 712 569 unprotected cells for further analyses. Then, by looking at the projected land use map from 2050, we computed a binary conversion score indicating if each cell would have been converted in 2050 (1) or if its native vegetation remained intact (0) (Fig. 1b). To evaluate the projected impact of the PAs and ILs established in 2002–2012 in avoiding Cerrado native vegetation loss, we compared the proportion of vegetation loss of the protected areas against control unprotected cells. We matched the protected and control cells to assess the conservation impact by using the matching approach. Matching uses statistical methods to construct the best possible artificial comparison group (control sample) for a given treatment group (protected sample) (Khandker et al., 2010). We selected the control cells for the comparison to each protected cell based on covariates that could influence the probability of conversion. The matching selection was based on commonly used covariates (Andam et al., 2008; Carranza et al., 2014; Nolte et al., 2013), i.e. distance to paved roads (IBGE, 2016), distance to the nearest state or federal capital city (IBGE, 2016), travel time to the nearest city with population > 50 000 individuals (Nelson, 2008), mean annual rainfall (Hijmans et al., 2005), altitude (Hijmans et al., 2005), agricultural suitability (IBGE, 2016), and vegetation type (forest or savanna) based on the land-use data. For the matching analysis, we used the package “Matchit” (Ho et al., 2011) in R (R Core Team, 2016). We matched cells to evaluate the impact of the protected areas by using propensity score matching (Rosenbaum and Rubin, 1983). For this, we fitted a logistic generalized linear model, in which the response was a binary variable indicating whether (1) or not (0) the cell was protected, and the predictors of this logistic model were the covariates. The predicted values from this model were propensity scores, and they represented the probability of a given cell being protected based on their covariate values. The protected and control cells were then matched based on their propensity score values to pair cells with similar conditions. We used exact matching for vegetation type (forest or savanna) and agricultural suitability (very low, low, medium, medium/high) for both categorical covariates. For the other covariates, we used a caliper of 0.25 standard deviations around the propensity score as limits within which control cells were considered acceptable. We were able to match 139 209 protected cells with control cells (one control cell per protected cell without replacement), representing approximately 87% of the protected cells from PAs and ILs established from 2002 to 2012. To measure the effect of land protection in reducing vegetation loss in the future scenario of land use (2050), we calculated the proportion of lost vegetation for protected cells and their matched control cells, and then we calculated the conservation impact for each PA/IL, in order to estimate its individual impact. Firstly, we obtained the proportion of vegetation loss for each PA/IL and its matched control. This was calculated by the sum of converted cells in protected and matched control cells, divided by the total area of the PA, giving then the proportion of vegetation loss in protected and matched unprotected cells. We tested whether the proportion of vegetation loss differed between matched control and protected samples for each category of protection (PA, SP, SU and IL) by using paired Wilcoxon signed rank test (Wilcoxon, 1945). Then, the conservation impact of each PA/IL was calculated as the difference between the conversion rate in the matched control cells and the protected cells. If the vegetation loss for each PA/IL was smaller than in its matched control cells, the conservation impact of the PA/IL
2.4. Priority cells for conservation impact To select priority cells for conservation impact, we selected all the control cells that were matched to PAs and ILs with positive conservation impact and had loss of vegetation in the future land-use maps. Then, because the cells were small, numerous, and scattered, we produced a kernel density map to highlight the regions in the Cerrado with high densities of priority cells for conservation impact. The high-density areas indicated regions where conservation actions such as the establishment of protected areas are predicted to have high potential to slow the future loss of Cerrado native vegetation. 3. Results In the baseline year of 2012, 49% of the Cerrado was still covered by native vegetation (savanna = 30%, forest = 19%), but the projection for 2050 was for a decrease to 31% (savanna = 19%, forest = 12%) (Fig. 2). The land-use model predicted that the main threat to remaining vegetation would be conversion to pasture (2012 = 38%, 2050 = 43%) and soybean croplands (2012 = 6%, 2050 = 12%) (Fig. 2). The loss of native vegetation in protected cells was significantly lower than in the matched control cells for PAs in general (V = 736.5, P = 0.009) and in SP PAs (V = 1471.5, P = 0.006) (Fig. 3a), while for SU PAs (V = 330.5, P = 0.144) and ILs (V = 151, P = 0.0758) there was no significant difference with control cells detected (Fig. 3a). The Wilcoxon test showed that PAs in general (V = 1471.5, P = 0.009) and SP PAs (V = 273, P = 0.006) had median impact values significantly higher than zero, while median values for SU PAs (V = 488.5, P = 0.140) and ILs (V = 284, P = 0.070) did not differ significantly from 0. The impact of PAs and ILs did not differ (W = 881, P = 0.871), while the impact of SP PAs was significantly higher than that of SU PAs (W = 737, P = 0.046) (Fig. 3b). The conservation impact of PAs also did not differ according to jurisdiction (W = 578, P = 0.418). Among all the PAs and ILs analyzed, 66% had a positive projected impact in avoiding native vegetation loss (21 SPs, 24 SUs, 21 ILs) and were mostly distributed in the central and northern parts of the Cerrado (Fig. 4). We selected as priority 40 849 cells that can maximize the impact of conservation action in reducing native vegetation loss, representing almost 30% of the matched cells and totaling an area of 1 021 225 ha. The heatmap showed a high density of priority cells in the middle and northern region of Cerrado (Fig. 4). Compared to the analysis of the impact of PAs in the Cerrado for the timeframe 2002–2009 (Carranza et al., 2014), we found similar results for PAs in general (2002–2009 = 60%, 2012–2050 = 48%) and for SP PAs (2002–2009 = 97%, 2012–2050 = 93%), but much lower values for SU PAs (2002–2009 = 28%, 2012–2050 = 13%) and ILs (2002–2009 = 87%, 2012–2050 = 38%). 4
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assessment for the same region (Carranza et al., 2014). Paiva et al. (2015) observed that, in general, the older protected areas showed greater conservation impact than the more recently created ones, which might explain the lower values that we found in comparison to the set (PAs/ILs created up to 2001) analyzed by Carranza et al. (2014) in the same region. We found that management regime is an important factor influencing future PA impact. Specifically, we found that SU PAs and ILs had a minor impact in general. The low conservation impact of SU PAs areas has already been reported by past analyses for the Cerrado (Carranza et al., 2014) and the Brazilian state of Goiás (Paiva et al., 2015; but see Ribeiro et al., 2018). Even though some studies have shown that SU PAs might be effective in decreasing fires in tropical forests (Nelson and Chomitz, 2011; Porter-Bolland et al., 2012), this management regime allows economic activities inside the PA, which might hinder conservation effectiveness in reducing vegetation loss. Since the mid-1990s in Brazil, SU areas have surpassed strict protection areas in number and extent, now being almost two thirds of the total extent of the PA network (Vieira et al., 2019). By relying mostly on SU PAs, Brazil might be compromising the integrity of its natural ecosystems. In addition to fulfilling their main goals (Indigenous nations' right to land and the protection of their cultural heritage), ILs also have been observed to play an important role in avoiding deforestation in tropical ecosystems (Carranza et al., 2014; Nepstad et al., 2006; Nolte et al., 2013). Unfortunately, our results did not support the view that these areas also have an important conservation role, even though we observed that 21 ILs had positive projected impact in reducing deforestation. However due to our analysis focusing only on recently established ILs (2002–2012) and age of these ILs influence on conservation impact (Andam et al., 2008; Paiva et al., 2015), the absence of significant conservation impact might be explained due to lack of time for regularization on land ownership of those areas; although this is still a question to be tested in further studies. The main threat to remaining Cerrado vegetation is predicted to be conversion of natural areas to pasture and soybeans fields. The Cerrado is known as the “barn of Brazil” and, in the last 10 years, the government has encouraged the occupation of the northern and more pristine region of the Cerrado, known as MATOPIBA region (Vieira et al., 2018). As a result, soy production in the region doubled from 4.3 to 8.6 million tons in 2014 (Bolfe et al., 2016). The legally sanctioned loss of native vegetation in the Cerrado might impact 35% of the natural remnants,
Fig. 2. Land-use categories in the Cerrado in 2012 and 2050, based on the model from Soares-Filho et al. (2016).
4. Discussion Our analysis showed that the recently established PAs in the Brazilian Cerrado are projected to have a positive impact in reducing native vegetation loss in the future, but much depends on their management regime. Further, our results of lower conversion rates inside PAs than outside are consistent with past trend assessments in tropical forest (Andam et al., 2008; Nelson and Chomitz, 2011; Nolte et al., 2013) and in the Cerrado (Carranza et al., 2014; Paiva et al., 2015). However, we found lower overall values than in the previous
Fig. 3. Estimates of future conservation impact of protection in the Cerrado. (a) Vegetation loss in protected and matched control cells in different categories of protected areas. (b) Conservation impact values in reducing vegetation loss in the Cerrado. PA: Protected Areas. SP: Strict Protection. SU: Sustainable Use. IL: Indigenous lands. * P < 0.05, ** P < 0.001, ns = P > 0.05. SP and SU are subsets of PA. Results in both graphs are shown for individual PAs and ILs. 5
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Fig. 4. Conservation impact values of protected areas and indigenous lands and the kernel density of priority cells for conservation impact.
ecotourism might have generated up to 133 480 jobs. These figures suggest that there are ways to keep native vegetation intact and still profit from sustainable activities (Young and Medeiros, 2018). With PAs expected to achieve not only biodiversity protection, but also contribute to the social development of local communities, stimulate economic growth through tourism revenues and play a key part in the mitigation and adaptation to climate change, PAs must have adequate funding, comprehensive planning, and effective management (Watson et al., 2014). The erosion of the support of protected areas around the globe, together with the increase of PADDD (protected area downgrading, downsizing and degazettement), especially in the developing countries (Mascia et al., 2014), could compromise even more the conservation impact of PAs in protecting biodiversity and ecosystem services (Resende et al., 2019). Our approach presents some limitations. First, the land use projection used here is only one possibility of future, based on legal compliances with deforestation and protection . However, due to the weakening of the Brazilian environmental legislation this projection may be significantly altered by 2050 (Loyola, 2014; Dobrovolski et al., 2018; Vieira et al., 2018). Second, even though vegetation, as used in our study, has been verified as a good proxy for biodiversity with regards to spatial planning in the Cerrado (Monteiro et al., 2018), it might be not be the best measure for other ecosystem services, such as scenic beauty for ecotourism for example (see Resende et al., 2019). Recently, Monteiro et al. (2018) showed using spatial planning with the goal of minimizing vegetation loss in the Cerrado, it was possible to cover large proportions of species’ ranges inside PAs and other priority areas, indicating that vegetation information could represent a reliable surrogate for overall biodiversity. Our study also demonstrates that spatial conservation prioritization can aid in locating those natural landscapes most at threat to conversion by croplands and pastures, especially in the most pristine regions of the Cerrado. Most of the conservation priorities that we identified were clustered in the northern part of the Cerrado, a region that is highly threatened. Then, our impact-based priorities for biodiversity conservation might be effective in
which would unleash an unprecedented environmental crisis involving species extinctions (recent estimates show that the projected deforestation for 2050 could compromise the entire range of the Xyris uninervis, a threatened endemic plant species, for example; see Strassburg et al., 2017), threats to water supply, and increased carbon emissions (Strassburg et al., 2017; Vieira et al., 2018). Even though agriculture is a fundamental activity for Brazil’s economy (Strassburg et al., 2014), the unsustainable expansion of agricultural frontiers might pose a threat not only for wildlife, but also compromise agricultural production itself and human welfare (Strassburg et al., 2017, 2019; Vieira et al., 2018). For example, the Cerrado provides essential ecosystem services related to food security, including production of livestock forage, carbon storage, and water quality by contributing to eight of the twelve major watersheds in Brazil (Overbeck et al., 2015). It is possible to reconcile agriculture production and biodiversity conservation by improving the use of current agricultural lands, especially pasturelands (Strassburg et al., 2014, 2017) or by redirecting agricultural expansion to suitable areas but with minimum impact on biodiversity (Lemes et al., 2019). Such a strategy coupled with large-scale restoration may allow for substantial agriculture expansion with a net-zero vegetation loss (Strassburg et al., 2014, 2017). To succeed in this context spatial planning is essential, not only to maximize biodiversity protection, but also to maximize agricultural production and regional development (Dobrovolski et al., 2013, 2011; Kiesecker et al., 2010; Lemes et al., 2019; Strassburg et al., 2017, 2019). Beyond the debate between agriculture expansion and conservation, several other mechanisms could provide income and economic impact leading to sustainable development. Those include carbon pricing initiatives, green finance (especially green bonds), economic incentives such as payment for ecosystem services, and sustainable rural development based on non-timber products and manufacturing. On the top of those mechanisms is ecotourism. Young and Medeiros (2018) have recently estimated that ecotourism in national parks alone (among other types of PAs in Brazil) was responsible for 4–12 billion dollars of economic impact in 2016. They also estimate that, in this same year, 6
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reducing vegetation loss and maximizing biodiversity conservation. In addition, by combining conservation priorities with social and economic needs of local communities (Porter-Bolland et al., 2012; Scarano, 2017), we could generate a more resilient and robust conservation strategy, promoting not only biodiversity and ecosystem services, but also economic growth in the region.
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Declaration of Competing Interest We declare no conflict of interest. Acknowledgements This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) Finance Code 001. FTB received a postdoctoral scholarship from CNPq (Grant #152172/2016-5), a technological development scholarship (DTI-A) by CNPq (Grant #381106/2017-9) and currently holds a postdoctoral fellowship grant from Programa Nacional de Pós-Doutorado from Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (PNPD/CAPES, Grant #88882.306081/2018-1). RL and LMB research is funded by CNPq (Grants #306694/2018-2 and #304314/2014‐5, respectively). RLP acknowledges the support of the Australian Research Council. This paper is a contribution of the INCT in Ecology, Evolution and Biodiversity Conservation founded by MCTIC/CNPq (Grant #465610/2014-5) and FAPEG (Grant #201810267000023). We thank Bea Maas, Andrew Knight, James Oakleaf and one anonymous reviewer for their valuable suggestions. References Andam, K.S., Ferraro, P.J., Pfaff, A., Sanchez-Azofeifa, G.A., Robalino, Ja, 2008. Measuring the effectiveness of protected area networks in reducing deforestation. Proc. Natl. Acad. Sci. U. S. A. 105, 16089–16094. https://doi.org/10.1073/pnas. 0800437105. Bolfe, É.L., Victória, D., de, C., Contini, E., Silva, G.B., Araujo, L.S., Gomes, D., 2016. MATOPIBA em Crescimento Agrícola. Rev. Política Agrícola 1, 38–62. Câmara, G., Soterroni, A., Ramos, F., Carvalho, A., Andrade, P., Souza, R.S., Mosnier, A., Mant, R., et al., 2015. Modelling Land Use Change in Brazil: 2000-2050, 1st edition. Laxenbrug, Cambridge. INPE, IPEA, ILASA, UNEP-WCMC, São José dos Campos, Brasília. https://doi.org/10.22022/REDD/08-2016.12115. Carranza, T., Balmford, A., Kapos, V., Manica, A., 2014. Protected area effectiveness in reducing conversion in a rapidly vanishing ecosystem: the Brazilian Cerrado: protected area effectiveness in Cerrado. Conserv. Lett. 7, 216–223. https://doi.org/10. 1111/conl.12049. CNUC, 2016. Cadastro Nacional de Unidades de Conservação/National Register of Conservation Units. Accessed date: November 2016. http://www.mma.gov.br/ areasprotegidas/cadastro-nacional-de-ucs. Dobrovolski, R., Diniz-Filho, J.A.F., Loyola, R.D., Marco Júnior, P., 2011. Agricultural expansion and the fate of global conservation priorities. Biodivers. Conserv. 20, 2445–2459. https://doi.org/10.1007/s10531-011-9997-z. Dobrovolski, R., Loyola, R.D., Guilhaumon, F., Gouveia, S.F., Diniz-Filho, J.A.F., 2013. Global agricultural expansion and carnivore conservation biogeography. Biol. Conserv. 165, 162–170. https://doi.org/10.1016/j.biocon.2013.06.004. Dobrovolski, R., Loyola, R., Rattis, L., Gouveia, S.F., Cardoso, D., Santos-Silva, R., Gonçalves-Souza, D., Bini, L.M., Diniz-Filho, J.A.F., 2018. Science and democracy must orientate Brazil’s path to sustainability. Perspect. Ecol. Conserv. 16, 121–124. https://doi.org/10.1016/j.pecon.2018.06.005. Ewers, R.M., Rodrigues, A.S.L., 2008. Estimates of reserve effectiveness are confounded by leakage. Trends Ecol. Evol. 23, 113–116. https://doi.org/10.1016/j.tree.2007.11. 008. Ferraro, P.J., 2009. Counterfactual thinking and impact evaluation in environmental policy. New Dir. Eval. 2009, 75–84. https://doi.org/10.1002/ev.297. Ferraro, P.J., Pattanayak, S.K., 2006. Money for nothing? A call for empirical evaluation of biodiversity conservation investments. PLoS Biol. 4, 482–488. https://doi.org/10. 1371/journal.pbio.0040105. Ferreira, M.N., Valdujo, P.H., 2014. Observatório de UC’s: Biodiversidade em Unidades de Conservação. WWF, Brasília 64p. Françoso, R., Brandão, R., de Campos Nogueira, C., Salmona, Y., Machado, R.B., Colli, G.R., 2015. Habitat loss and the effectiveness of protected areas in the Cerrado biodiversity hotspot. Nat. Conserv. 3, 35–40. https://doi.org/10.1016/j.ncon.2015. 04.001. FUNAI, 2016. Fundação Nacional do Índio. Accessed date: November 2016. http:// www.funai.gov.br/index.php/i3geo. Hijmans, R.J., Cameron, S.E., Parra, J.L., Jones, P.G., Jarvis, A., 2005. Very high resolution interpolated climate surfaces for global land areas. Int. J. Climatol. 25,
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