species distribution model as a conservation tool
KATIA M. P. M. B. FERRAZ1, BEATRIZ M. BEISIEGEL2, ROGÉRIO C. DE PAULA2, DÊNIS A. SANA3, Environmental suitability models have been CLÁUDIA B. DE CAMPOS2, TADEU G. DE OLIVEIRA4, ARNAUD L. J. DESBIEZ5 produced for jaguar distribution in Bra- zil during the Jaguar National Action Plan How species distribution mo- Workshop, facilitated by IUCN/SSC CBSG Brazil and organized and funded by CENAP/ dels can improve cat conser- ICMBio, Pró Carnívoros and Panthera, in No- vember 2009, Atibaia, São Paulo state, Bra- vation - jaguars in Brazil zil. During the workshop, jaguar specialists provided occurrence point data for species Modeling species distribution is a promising field of research for improving conser- distribution modeling. A jaguar database was vation efforts and setting priorities. The aim of this study was to produce an environ- composed only by recent (less than five years) mental suitability map for jaguar distribution in two biomes in Brazil – Caatinga and and confirmed records (e.g., signs, telemetry, Atlantic Forest – , where the species is Critically Endangered as part of the Jaguar camera-trapping, chance observations). All National Action Plan workshop (Atibaia, São Paulo state). Species occurrence (N = models and detailed information about the 57 for Caatinga and N = 118 for Atlantic Forest), provided by jaguar specialists, and procedure and the results are included in ten environmental predictors (elevation, land cover, distance from water and biocli- the Jaguar National Action Plan. Background matic variables) were used to generate species distribution models in Maxent. Both information on SDM and necessary consi- models presented high predictive success (AUC = 0.880 ± 0.027 for Caatinga and AUC derations are summarized in the Supporting = 0.944 ± 0.022 for Atlantic Forest) and were highly significant (p < 0.001), predicting Online Material Appendix I (www.catsg.org/ only 18.64% of Caatinga and 10.32% of Atlantic Forest as suitable for jaguar occur- catnews). Here, to illustrate the potential of rence. The species distribution models revealed the low environmental suitability of the use of the SDM for cat conservation, we both biomes for jaguar occurrence, emphasizing the urgency of setting conservation presented the environmental suitability mo- priorities and strategies to improve jaguar conservation such as the implementation dels for jaguar in two biomes (Caatinga and of new protected areas and corridors for species dispersal. Atlantic Forest, Fig. 1), where the species is considered Critically Endangered in Brazil (de Predicting species distribution has made en- Leathwick 2009). The geographic projection Paula et al. 2012, this issue; Beisiegel et al. ormous progress during the past decade. A of these conditions (i.e., where both abiotic 2012, this issue). 38 wide variety of modeling techniques (see Gui- and biotic requirements are fulfilled) repre- san & Thuiller 2005) have been intensively ex- sents the potential distribution of the species. Methods plored aiming to improve the comprehension Finally, those areas where the potential distri- Jaguar distribution was modeled for each of species-environment relationships (Peter- bution is accessible to the species are likely biome separately considering the differences son 2001). The species distribution modeling to approximate the actual distribution of it. between the environmental spaces (i.e., con- (SDM) relates species distribution data to The jaguar, the largest felid in the Americas, ceptual space defined by the environmental information on the environmental and/or spa- has been heavily affected by retaliation killing variables to which the species responds). The tial characteristics of those locations. Combi- for livestock predation, fear, skin trade, prey biome map used was obtained from a Land nations of environmental variables most clo- depletion, trophy hunting (e.g. Smith 1976, Cover Map of Brazil (1:5.000.000), 2004, by sely associated to presence points can then Conforti & Azevedo 2003) and habitat loss the Brazilian Institute of Geography and Sta- be identified and projected onto landscapes (Sanderson et al. 2002). As a consequence, tistics, IBGE (available for download at http:// to identify areas of predicted presence on it is now restricted to ca. 46% of its former www.ibge.gov.br/). the map (Soberón & Peterson 2005, Elith & range (Sanderson et al. 2002). Predictive distribution models were formu- lated considering the entire available jaguar dataset as the dependent variable (presence Table 1. Environmental predictor variables used in jaguar distribution model. points) and the selected environmental varia- Variables Description bles as the predictors (Table 1). Jaguar data Land cover Land cover map from GlobCover Land Cover version V2.3, 2009 available for modeling (N = 57 for Caatinga; Elevation Elevation map by NASA Shuttle Radar Topography Mission N = 118 for Atlantic Forest; Fig. 2) were plot- Distance from water Map of gradient distance from water obtained from vector map ted as lat/long coordinates on environmental of rivers from IBGE maps with a grid cell size of 0.0083 decimal Bioclimatic variables Maps of bioclimatic variables from Worldclim: degree2 (~1 km2). Bio1 = Annual mean temperature Models were obtained by Maxent 3.3.3e Bio2 = Mean diurnal range (mean of monthly (max temp - min (Phillips & Dudík 2008) using 70% of the data temp)) for training (N = 40 for Caatinga and N = 66 Bio5 = Max temperature of warmest month for Atlantic Forest) and 30% for testing the Bio6 = Min temperature of coldest month models (N = 17 for Caatinga and N = 28 for Bio12 = Annual precipitation Atlantic Forest; Pearson 2007). Data were Bio13 = Precipitation of wettest month sampled by bootstrapping with 10 random Bio14 = Precipitation of driest month partitions with replacements. All runs were set with a convergence threshold of 1.0E–5
CATnews Special Issue 7 Spring 2012 potential jaguar distribution in Caatinga and Atlantic Forest with 500 iterations, with 10,000 background points. The logistic threshold output format was used resulting in continuous values for each grid cell in the map from 0 (unsuitable) to 1 (most suitable). These values can be inter- preted as the probability of presence of sui- table environmental condition for the target species (Veloz 2009). The logistic threshold used to “cut-off” the models converting the continuous probability model in a binary mo- del was the one that assumed 10 percentile training presence provided by the Maxent outputs 0.300 for Caatinga; 0.100 for Atlantic forest. These thresholds were selected by the specialists as the best one to represent the suitable areas for recent jaguar distribution in Fig. 1. Biomes of Brazil with both biomes. Caatinga and Atlantic Forest Models were evaluated by the AUC value, the biomes with protected areas omission error and by the binomial probability (green). (Pearson 2007).
Results and Discussion than 50% of its natural cover and greatly open (>15%) broadleaved evergreen or semi- The SDM for Caatinga and Atlantic Forest impacted and fragmented by human activi- deciduous forest (55.26%), while unsuitable biomes presented high predictive success ties (Leal et al. 2005). Most of the protected areas encompassed mainly mosaic cropland and were highly statistically significant (AUC areas found in this biome (Fig. 3) presented (50-70%)/ vegetation (grassland/shrubland/ = 0.880 ± 0.027, omission error = 0.206, p < large areas as suitable for jaguar occurrence, forest) (20-50%). 0.001; AUC = 0.944 ± 0.022, omission error such as Serra Branca Ecological Station (ES) Most of the continuous forest remains indica- 39 = 0.129, p < 0.001, respectively; SOM Fig. 1, and Serra da Capivara National Park (NP) with ted as suitable for the jaguars at the Atlantic 2), predicting about 18.64% of the Caatinga 100%, Morro do Chapéu State Park (SP) with Forest biome correspond to the Brazilian pro- (Fig. 3) and 10.32% of the Atlantic Forest 91.29% and Serra das Confusões NP with tected areas (Fig. 4) such as Morro do Diabo (Fig. 4) as suitable for jaguar occurrence. 71.51%. Nevertheless Serra das Confusões SP, Mico Leão Preto ES, Caiuá ES, Carlos Bo- Much of the Caatinga biome (844,453 km2) and Chapada Diamantina NPs (with 62.63%) telho SP, Intervales SP, Alto Ribeira Touristic predicted as suitable (54.77%) for jaguar are the only two protected areas that are lo- SP and Xitué ES, Iguaçu NP, Serra da Bocaina occurrence encompassed the closed to open cated in transitional areas with the Cerrado NP, Tinguá Biological Reserve (BR) and Serra (>15%) shrubland. Meanwhile, much of the biome, hence the lower suitability within the dos Órgãos NP, besides surroundings areas unsuitable area (26.62%) for the species also Caatinga. Serra das Confusões NP is indeed a and some isolated forest remains (e.g., Rio encompassed this land cover. This discrepan- very important area for jaguars as it is large Doce SP and Itatiaia NP). The marshlands in cy is due especially to human development (5,238 km2), connected to Serra da Capiva- the Upper Paraná River, in the west portion of or simply occupation that leads to medium to ra NP/Serra Branca ES and also somehow the Atlantic Forest biome, are as important as high level of disturbance in the environment. bridges the Caatinga jaguar population with forest areas to jaguar conservation. The most These habitat alterations are especially due those of the Nascentes do Rio Parnaíba pro- suitable areas in the region includes continu- to mining activities, agriculture, timber ex- tected areas complex, likely the most impor- ous protected areas such the Ilha Grande NP, traction, firewood production, and lowering tant of the Cerrado domain. The bulk of prime Várzeas do Rio Ivinhema SP and Ilhas e Vár- of prey items due to excessive hunting acti- areas for jaguars, located within the center zeas do Rio Paraná Environmental Protection vities. The closed to open shrubland covers of the Caatinga domain are being proposed Area (EPA). about 40.67% of total biome area. The closed as a new NP, created to protect one of the Some suitable areas indicated by the model formations have 60% to 80% of plant cover, most important populations of the Critically such as Cantareira SP and its surrounding did whereas the open formations have only 40 Endangered Caatinga jaguar, Boqueirão da not present any recent record of the species to 60% (Chaves et al. 2008). The vegeta- Onça NP (Fig. 3). The creation of this new pro- presence. The depauperate quality of forest tion type is deciduous, generally with thorny tected area should be of utmost importance cover of these areas with high human pres- woody species > 4.5 m tall, interspersed with for jaguar conservation in the Caatinga. If the sure probably explains the absence of the succulent plants, especially cacti. The trees NP will be created according to the proposed species there. This clearly illustrates the over- are 7-15 m high, with thin trunks. Several limits, it will encompass 24.66% of the highly prediction (i.e., commission error), frequently have tiny leaves where others have spines or suitable area for jaguars. observed in SDM. In this particular situation, thorns (Andrade-Lima 1981). Much of the Atlantic Forest biome (1,110,182 the degraded vegetation and human pressure The semi-arid Caatinga domain is one of the km2) predicted as suitable (27.44%) for ja- are not contemplated in the environmental most threatened biomes in Brazil with less guar occurrence encompassed the closed to variables input in the modeling, decreasing
Jaguar in Brazil Ferraz et al.
a)
b)
Fig. 2. Jaguar presence points for (a) Caa- Fig. 3. Potential distribution model for jaguar in Caatinga biome with some protected tinga (N = 57) and (b) Atlantic Forest (N = areas highlighted. 40 118) biomes in Brazil. its predictive power. On the other hand, some areas with recent records of the species (not included in the modeling) were not indicated as suitable by the model such as the Juréia- Itatins ES and Caraguatatuba area of Serra do Mar SP. The omission and commission errors are common and frequent in SDM (Fielding & Bell 1997, Pearson 2007), emphasizing the need of cautious interpretation as local cha- racteristics could decrease the model predic- tive success. Most of the cropland areas (rainfed crop- lands, mosaic croplands/vegetation, mosaic croplands/forest; 64.67%) were considered unsuitable for the species occurrence. Jagu- ars depend on large prey such as peccaries, which are very susceptible to environmental degradation and poaching (e.g. Cullen Jr. et al. 2000), which is intense throughout the At- lantic forest, with the exception of a few well preserved areas. Accordingly, Cullen Jr. et al. (2005) had already verified that jaguars dis- play a strong selection for primary and secon- dary forests, a strong avoidance of pastures and a weak use of agricultural areas. The probability of jaguar presence was asso- ciated differently to the environmental pre- dictor variables. Elevation (19.03%), the pre- Fig. 4. Potential distribution model for jaguar in Atlantic Forest biome with some pro- cipitation of driest month (Bio14; 18.08%) and tected areas highlighted.
CATnews Special Issue 7 Spring 2012 potential jaguar distribution in Caatinga and Atlantic Forest
the mean diurnal range (Bio2; 17.25%) were of paramount importance for predicting thre- 1 0.9 the highest contributor variables for jaguar atened species range in a world of climatic 0.8 model at the Caatinga biome. The probabili- change. In fact, this kind of prediction could 0.7 0.6 ty of jaguar presence increased as elevation be vital for setting proper and effective action 0.5 0.4 and the mean diurnal range increased, but de- plans for critically endangered populations/ 0.3
Probability Probability of presence 0.2 creased as the precipitation of driest month species. 0.1 0 increased (Fig. 5). The presence of jaguar in In practice, one of the most useful contri- 0 250 500 750 1000 1250 1500 1750 2000 Elevation Caatinga is associated with higher areas pro- butions from SDMs could be the prediction bably because of the lower human pressure of suitable areas for species occurrence as 1 0.9 and more pristine vegetation (e.g., Boqueirão well as helping to delineate potential corri- 0.8 0.7 da Onça NP). Although variables Bio14 and dors which link populations on a continental 0.6 0.5 Bio2 had important contributions to the model scale. The environmental suitability maps in 0.4 its relationships with jaguar presence were a modeling framework could be used as a 0.3
Probability Probability of presence 0.2 not so clear. basis to improve the already existing extra- 0.1 0 Land cover (41.29%) was the highest con- ordinary initiatives that seek to create such 0 10 20 30 40 50 60 70 Bio14 tributor variable for the jaguar model in the linkages (e.g. jaguar corridor initiative). This, 1 Atlantic Forest biome. The high probability of in turn, has been considered one of the most 0.9 0.8 jaguar presence was related to the closed to effective conservation strategies to guaran- 0.7 0.6 open (>15%) grassland or woody vegetation tee cat species conservation (Macdonald et 0.5 0.4 regularly flooded (Fig. 6). Wetland areas and al. 2010). 0.3 riparian vegetation (Fig. 7) are core areas and The assessment of conservation priorities Probability of presence 0.2 0.1 0 dispersal corridors for jaguars (Cullen Jr. et for felids should consider the environmental 0 25 50 75 100 125 150 175 al. 2005). However, only 30% of the original suitability of landscape in a modeling frame- Bio2 area of the Paraná River is left because of the work. Suitability maps could be considered Fig. 5. Marginal response curves of the construction of hydroelectric power stations by stakeholders for defining priority areas predicted probability of jaguar occurrence (Agostinho & Zalewski 1996). for the establishment of new protected are- at the Caatinga biome for the environmen- as or corridors. However, conservation infe- tal predictor variables that contributed Future for SDM as a tool for cat conser- rences should rely on robust models, avoi- substantially to the SDM. 41 vation ding omission and overprediction in species The field of SDM is promising for impro- distribution range. with distribution information and clear re- ving conservation efforts and priorities (e.g. The modeling exercise defining priority are- sults to discuss, and it stimulated debates Thorn et al. 2009, Costa et al. 2010, Marini as for conservation efforts should be a use- and discussions which otherwise may not et al. 2010). SDM is a useful tool for resolving ful first evaluation. In this workshop one of have occurred. However, for reliable conser- practical questions in applied ecology and the most valuable contributions of this exer- vation decisions suitability models must rely conservation biology, but also in fundamental cise was the participatory manner in which on well-delineated field inventories (Costa sciences (e.g. biogeography and phylogeogra- this model was constructed. Furthermore et al. 2010) and model results must be va- phy) (Guisan & Thuiller 2005). It represents the resulting maps provided stakeholders lidated. an empirical method to draw statistical infe- 1 rences about the drivers of species’ ranges 0.9 under different conservation, ecological and 0.8 evolutionary processes (Zimmermann et al. 0.7 2010). 0.6 The SDM approach can improve our know- 0.5 0.4 ledge about cat species worldwide by 1) high- 0.3 Probability of presence lighting areas where the species might occur 0.2 but confirmed observation is missing, 2) iden- 0.1 tifying gaps in data collection and guiding the 0 sampling efforts, 3) identifying key areas for conservation efforts and potential corridors linking protected areas and/or populations, 4) contributing for the assessment of IUCN red list categories, 5) helping to reduce con- flicts (e.g., zoning), among others. Moreover, this modeling technique can provide a com- prehensive understanding of the historical, current and future ranges of cat species, pro- Fig. 6. . Marginal response curve of the predicted probability of jaguar occurrence at the viding insights to conservation planning (e.g., Atlantic Forest biome for the environmental predictor variable that contributed substan- Marini et al. 2010). Modeling should also be tially to the species distribution model.
Jaguar in Brazil Ferraz et al.
Peterson A. T. 2001. Predicting species’ geographic distributions based on ecological niche mode- ling. Condor 103, 599-605. Phillips S. J. & Dudík M. 2008. Modeling of spe- cies distributions with Maxent: new extensions and a comprehensive evaluation. Ecography 31, 161-175. Sanderson E., Redford K., Chetkiewicz C., Medellin R., Rabinovitz A. R., Robinson J. G. & Taber A. 2002. Planning to save a species: the jaguar as a model. Conservation Biology 16, 58-72. Smith N. J. H. 1976. Spotted cats and the Amazon skin trade. Oryx 13, 362-371. Soberón J. M. & A. T. Peterson. 2005. Interpretati- on of models of fundamental ecological niches and species‘ distributional areas. Biodiversity Informatics 2, 1-10. Thorn J. S., Nijman V., Smith D. & Nekaris K. A. I. 2009. Ecological niche modelling as a tech- Fig. 7. Riparian vegetation is an important part of jaguar core areas and corridors (Photo nique for assessing threats and setting con- A. Gambarini), servation priorities for Asian slow lorises (Pri- mates: Nycticebus). Diversity and Distributions Acknowledgments Cullen Jr. L., Abreu C. K., Sana D. & Nava A. F. D. 15, 289-298. Paper # 01 of the SISBIOTA - Top Predators net- 2005. As onças-pintadas como detetives da Veloz S. D. 2009. Spatially autocorrelated sam- work. The authors thank Conselho Nacional de paisagem no corredor do Alto Paraná, Brasil. pling falsely inflates measures of accuracy for Desenvolvimento Científico e Tecnológico (CNPq) Natureza e Conservação 3, 43-58. presence-only niche models. Journal of Bioge- and Fundação de Amparo à Pesquisa do Estado de Cullen Jr. L., Bodmer R. E. & Pádua C. V. 2000. Ef- ography 36, 2290-2299. 42 São Paulo (FAPESP) for financial support. We thank fects of hunting in habitat fragments of the At- Zimmermann N. E., Edwards Jr. T. C., Graham C. H., all participants of the Jaguar National Action Plan lantic forests, Brazil. Biol. Conserv. 95, 49-56. Pearman P. B. & Svenning J. 2010. New trends Workshop (Atibaia, São Paulo, Brazil) who provi- Elith J. & Leathwick J. R. 2009. Species distributi- in species distribution modelling. Ecography ded recent jaguar presence points. We also thank on models: ecological explanation and predic- 33, 985-989. the CENAP (The National Center for Research and tion across space and time. Annual Review of Conservation of Mammalian Carnivores) for pro- Ecology and Evolution Systematics 40, 677-97. Supporting Online Material SOM available at viding useful information for the study. We are Fielding A. H. & Bell J. F. 1997. A review of me- www.catsg.org/catnews grateful to Fridolin Zimmermann and Christine thods for the assessment of prediction errors Breitenmoser from KORA for valuable contribu- in conservation presence/absence models. En- 1 Departamento de Ciências Florestais, Escola tions to the manuscript. vironmental Conservation 24, 38-49. Superior de Agricultura ‘Luiz de Queiroz”, Uni- Guisan A. & Thuiller W. 2005. Predicting species versidade de São Paulo, 13418-900, Piracicaba, References distribution: offering more than simple habitat São Paulo, Brasil Agostinho A.A. & Zalewski M. 1996. A planície models. Ecology Letters 8, 993-1009.
CATnews Special Issue 7 Spring 2012