Resource selection in an apex predator and variation in response to local landscape characteristics

Morato, Ronaldo Gonçalves; Connette, Grant M.; Stabach, Jared A.; Paula, Rogério Cunha de; Ferraz, Kátia Maria Paschoaletto Micchi de Barros; Kantek, Daniel Luis Zanella; Miyazaki, Selma Samiko; Pereira, Thadeu Deluque Costa; Silva, L.C.; Paviolo, Agustin Javier; Angelo, Carlos De; Bitetti, Mario S. Di; Cruz, Paula; Lima, Fernando; Cullen, Laury; Sana, Dênis Aléssio; Ramalho, Emiliano Esterci; Carvalho, Marina Mouzinho; Silva, M.X. da; Moraes, M.D.F.; Vogliotti, Alexandre; May, Joares; Haberfeld, Mario; Rampim, Lilian Elaine; Sartorello, Leonardo; Araújo, Gediendson Ribeiro de; Wittemyer, George; Ribeiro, Milton Cézar; Leimgruber, Peter

doi:10.1016/j.biocon.2018.10.022

Cited by 51 publications

(59 citation statements)

References 61 publications

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“…In addition, individuals F3 and M1, although not resident, remained in flooded areas, evidencing the suitability of this population for this type of environment. The data obtained in this study are complementary to the positive association between jaguars and water bodies detected by several researchers (Crawshaw-Jr. & Quigley, 1991;Cullen-Jr. et al, 2013;De Angelo et al, 2011;Morato et al, 2018a). In addition, the animals in these localities are installed in one of the wettest regions of the Pantanal (Padovani, 2010) and apparently are already habituated to this situation, which makes these populations differentiated in relation to the other populations of jaguars already registered in the scientific literature.…”

Section: Discussionmentioning

confidence: 91%

“…In addition, there are no livestock in our study area. These differences in the behaviour of jaguars, both between environments and between time of the year, reflect the opportunistic nature of the species, which seems to take advantage of constantly changing environments and the variation of food resources in time and space (Emmons, 1987;Cavalcanti & Gese, 2009;Morato et al, 2018a). More than 85 species of prey (wild and livestock/domestic), including mammals, reptiles and birds, were recorded in their diet throughout their geographical distribution (Seymour, 1989).…”

Section: Discussionmentioning

confidence: 99%

“…There is wide variation in home range used by jaguars, which varies according to the type of environment, the availability of prey, seasonality, the rainfall regime and differences associated with sex (Schaller & Crawshaw-Jr., 1980;Crawshaw-Jr. & Quigley, 1991;Cavalcanti & Gese, 2009;Cullen-Jr. et al, 2013;Morato et al, 2018a).…”

Section: Introductionmentioning

confidence: 98%

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Effectiveness of protected areas for jaguars: the case of the Taiamã Ecological Station in Brazil

Cardoso¹,

Morato²,

Miyazaki³

et al. 2020

Pap. Avulsos Zool.

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The largest feline in the Americas and the third largest in the world, the jaguar is an apex predator in the food chain and a key species in the ecosystems where it occurs, developing important ecological functions in maintaining ecosystem balance. In Brazil, the Pantanal is considered an important refuge for the species, and protected areas such as the Taiamã Ecological Station (TES) are relevant for conservation of pristine ecosystem where the species persist. Thus, considering that this area is located in one of the regions with the highest concentration of this large cat in the Pantanal, the objective of this study was to evaluate the effectiveness of the protection given by the TES for the jaguar population. The station is a flooded island in the middle of the Paraguay river and surrounded by extensive wetlands. We monitored ten jaguars using GPS collar at the TES and its surroundings. The samples were separated into high-water season (January to June) and low-water season (July to December), and the estimated home ranges were grouped as: 1) residents only and 2) all monitored individuals. The stabilization of the home ranges of eight jaguars, considered residents, was determined through variograms. When all jaguars were analysed together there was 55% overlap between the clustered areas of the two analysed seasons. In the analysis excluding non-resident individuals there was 72% overlap between the clustered areas. The type of land cover inside these areas was very similar between these periods. The range of this protected area is not sufficient to effectively protect these jaguar populations, since the grouped home ranges of the resident animals studied are 3.5(wet)/2.5(dry) times larger than the area of the TES. However, the surroundings of the TES have low human impact and no deforestation and, in this way, the landscape context of the station may have a significant effect on the persistence of a large carnivore like the jaguar. However, it is necessary to create more protected areas in order to prevent possible future environmental changes in the areas surrounding TES.

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Section: Discussionmentioning

confidence: 91%

Section: Discussionmentioning

confidence: 99%

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Effectiveness of protected areas for jaguars: the case of the Taiamã Ecological Station in Brazil

Cardoso¹,

Morato²,

Miyazaki³

et al. 2020

Pap. Avulsos Zool.

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“…Given the lack of GPS telemetry-based home range estimates from Colombia and the Tumbes-Chocó-Magdalena hotspot [45], we included the multiple buffer sizes to assess jaguar habitat use at varying scales. Species interact with the environment at different scales [46–48] and jaguars exhibit distinct responses to landscape variables at the home range versus foraging scale [49].…”

Section: Methodsmentioning

confidence: 99%

Wetlands are keystone habitats for jaguars in an intercontinental biodiversity hotspot

et al. 2019

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Agricultural development was the major contributor to South America’s designation as the continent with the highest rates of forest loss from 2000–2012. As the apex predator in the Neotropics, jaguars (Panthera onca) are dependent on forest cover but the species’ response to habitat fragmentation in heterogeneous agricultural landscapes has not been a subject of extensive research. We used occupancy as a measure of jaguar habitat use in Colombia’s middle Magdalena River valley which, as part of the intercontinental Tumbes-Chocó-Magdalena biodiversity hotspot, is exceedingly fragmented by expanding cattle pastures and oil palm plantations. We used single-season occupancy models to analyze 9 months of data (2015–2016) from 70 camera trap sites. Given the middle Magdalena’s status as a “jaguar corridor” and our possible violation of the occupancy models’ demographic closure assumption, we interpreted our results as “probability of habitat use (Ψ)” by jaguars. We measured the associations between jaguar presence and coverage of forest, oil palm, and wetlands in radii buffers of 1, 3, and 5 km around each camera trap. Our camera traps recorded 77 jaguar detections at 25 of the camera trap sites (36%) during 15,305 trap nights. The probability of detecting jaguars, given their presence at a site, was 0.28 (0.03 SE). In the top-ranked model, jaguar habitat use was positively influenced by wetland coverage (β = 7.16, 3.20 SE) and negatively influenced by cattle pastures (β = -1.40, 0.63 SE), both in the 3 km buffers. We conclude that wetlands may serve as keystone habitats for jaguars in landscapes fragmented by cattle ranches and oil palm plantations. Greater focus on wetland preservation could facilitate jaguar persistence in one of the most important yet vulnerable areas of their distribution.

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“…Modeling methods rely on niche habitat concepts for environmental covariates to construct accurate models of jaguar distributions on the landscape (27). In other studies, jaguars exist in primary forest habitat, or areas with high forest cover, far from deforested patches or other human activities like cattle pastures, roads, or croplands.…”

Section: Study Speciesmentioning

confidence: 99%

Multi-Level, Multi-Scale Modeling and Predictive Mapping for Jaguars in the Brazilian Pantanal.

Bohnett

Hoctor

Hulse

et al. 2020

Preprint

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Background Machine learning (ML) methods and remote sensing data were used to build multi-level multi-scale resource selection models and predictive maps onto the extended landscape for jaguars (Panthera onca) in the Brazilian Pantanal. Objectives were to compare multiple predictive modeling and exploratory modeling approaches. Included in the analysis, multi-scale raster grains (30m, 90m, 180m, 360m, 720m, 1440m), GPS collaring temporal levels (point, path, and step) and model data structural levels (group, individual, case-control).Methods Multi-scale multi-level data subsets were fit with explanatory and predictive statistical methods. Conditional logistic regression, generalized additive modeling (GAM), and classification regression trees, such as random forests (RF) and gradient boosted regression tree (GBM) were compared for their utility to the study. Model evaluation, using training and testing data in a k-fold cross-validation approach, determined the AUC, Kappa, and TSS for model evaluation and comparison. · Results Results indicated that the multi-level, multi-scale techniques improved model outputs. Overall, larger level models and those that used multi-scale raster grains showed the best model evaluation. The highest-ranked model was the multi-scale path selection function GBM and was one of the broadest levels of data. ·Conclusions Results indicated that multi-level, multi-scale models produced mixed results of applicability across models and levels. The identification of the appropriate temporal scale and statistical model needs careful consideration in predictive mapping efforts.

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Resource selection in an apex predator and variation in response to local landscape characteristics

Cited by 51 publications

References 61 publications

Effectiveness of protected areas for jaguars: the case of the Taiamã Ecological Station in Brazil

Effectiveness of protected areas for jaguars: the case of the Taiamã Ecological Station in Brazil

Wetlands are keystone habitats for jaguars in an intercontinental biodiversity hotspot

Multi-Level, Multi-Scale Modeling and Predictive Mapping for Jaguars in the Brazilian Pantanal.

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