Density of wild felids in Sonora, Mexico: a comparison of spatially explicit capture-recapture methods

Multiscale assessment of habitat selection and avoidance of sympatric carnivores by the endangered ocelot

Habitat selection by animals is a complex, dynamic process that can vary across spatial and temporal scales. Understanding habitat selection is a vital component of managing endangered species. Ocelots (Leopardus pardalis), a medium-sized endangered felid, overlap in their northern range with bobcats (Lynx rufus) and coyotes (Canis latrans), with all three species sharing similar space and resource use. As the potential for competition between these three carnivores is high, understanding differences in habitat use and the effect of these potential competitors on habitat selection of ocelots is essential to conservation. Our objective was to compare habitat selection between species and examine if ocelots avoided areas used by competitors at broad and fine scales. We captured and collared 8 ocelots, 13 bobcats, and 5 coyotes on the East Foundation's El Sauz Ranch and the Yturria San Francisco Ranch in South Texas, USA from 2017 to 2021. We compared 2nd (position of home range) and 3rd (use within the home range) order selection across species and examined whether ocelots avoided areas categorized as high probability of use by bobcats and coyotes across both orders of selection. We found a preference for heterogeneous landscapes by bobcats and coyotes while ocelots were strongly tied to woody cover across both orders. At the 2nd order, ocelots selected areas with higher probability of use by bobcats and showed no response to higher probability of use by coyotes, suggesting ocelots did not avoid either species. However, at the 3rd order, ocelots avoided areas used by coyotes. Ocelots selected for areas of use by bobcats at the 2nd order and 3rd order. Results suggest that at the broader scale, placement of the home range is not affected by the presence of sympatric carnivores, however, at a finer scale, ocelots are avoiding coyotes but not bobcats. Our study emphasizes the importance of woody and herbaceous cover at the broad scale and dense vegetation at the finer scale to sustain ocelots. In addition, we show differing patterns of interspecific avoidance by ocelots across species and scales.

Spatial capture-recapture and LiDAR-derived vegetation metrics reveal high densities of ocelots on Texas ranchlands

Reliable estimates of population density and size are crucial to wildlife conservation, particularly in the context of the Endangered Species Act. In the United States, ocelots (Leopardus pardalis pardalis) were listed as endangered in 1982, and to date, only one population density estimate has been reported in Texas. In this study, we integrated vegetation metrics derived from LiDAR and spatial capture-recapture models to discern factors of ocelot encounter rates and estimated localized population estimates on private ranchlands in coastal southern Texas. From September 2020 to May 2021, we conducted a camera trap study across 42 camera stations on the East Foundation’s El Sauz Ranch, which was positioned within a larger region of highly suitable woody and herbaceous cover for ocelots. We observed a high density of ocelots (17.6 ocelots/100 km2) and a population size of 36.3 ocelots (95% CI: 26.1–58.6) with the 206.25 km2 state space area of habitat. The encounter probability of ocelots increased with greater canopy cover at 1-2 m height and decreasing proximity to woody cover. These results suggest that the incorporation of LiDAR-derived vegetative canopy metrics allowed us to understand where ocelots are likely to be detected, which may aid in current and future population monitoring efforts. These population estimates reflect the first spatially explicit and most recent estimates in a portion of the northernmost population of ocelots in southern Texas. This study further demonstrates the importance of private working lands for the recovery of ocelots in Texas.

Estimating animal abundance at multiple scales by spatially explicit capture-recapture

Information about how animal abundance varies across landscapes is needed to inform management action but is costly and time-consuming to obtain; surveys of a single population distributed over a large area can take years to complete. Surveys employing small, spatially replicated sampling units improve efficiency, but statistical estimators rely on assumptions that constrain survey design or become less reasonable as larger areas are sampled. Efficient methods that avoid assumptions about similarity of detectability or density among replicates are therefore appealing. Using simulations and data from >3500 black bears sampled on 73 independent study areas in Ontario, Canada, we (1) quantified bias induced by unmodeled spatial heterogeneity in detectability and density; (2) evaluated novel, design-based estimators of average density across replicate study areas; and (3) evaluated two estimators of the variance of average density across study areas: an analytic estimator that assumed an underlying homogeneous spatial Poisson point process for the distribution of animals' activity centers, and an empirical estimator of variance across study areas. In simulations where detectability varied in space, assuming spatially constant detectability yielded density estimates that were negatively biased by 20% to 30%; estimating local detectability and density from local data and treating study areas as independent, equal replicates when estimating average density across study areas using the design-based estimator yielded unbiased estimates at local and landscape scales. Similarly, detectability of black bears varied among study areas and estimates of bear density at landscape scales were higher when no information was shared across study areas when estimating detectability. This approach also maximized precision (relative SEs of estimates of average black bear density ranged from 7% to 18%) and computational efficiency. In simulations, the analytic variance estimator was robust to threefold variation in local densities but the empirical estimator performed poorly. Conducting multiple, similar SECR surveys and treating them as independent replicates during analyses allowed us to efficiently estimate density at multiple scales and extents while avoiding biases caused by pooling spatially heterogeneous data. This approach enables researchers to address a wide range of ecological or management-related questions and is applicable with most types of SECR data.

Integrating presence-only and occupancy data to model habitat use for the northernmost population of jaguars

Species distribution models (SDMs) have become an essential tool for the management and conservation of imperiled species. However, many at-risk species are rare and characterized by limited data on their spatial distribution and habitat relationships. This has led to the development of SDMs that integrate multiple types and sources of data to leverage more information and provide improved predictions of habitat associations. We developed a novel integrated species distribution model to predict habitat suitability for jaguars (Panthera onca) in the border region between northern Mexico and the southwestern USA. Our model combined presence-only and occupancy data to identify key environmental correlates, and we used model results to develop a probability of use map. We adopted a logistic regression modeling framework, which we found to be more straightforward and less computationally intensive to fit than Poisson point process-based models. Model results suggested that high terrain ruggedness and the presence of riparian vegetation were most strongly related to habitat use by jaguars in our study region. Our best model, on average, predicted that there is currently 25,463 km² of usable habitat in our study region. The United States portion of the study region, which makes up 38.6% of the total area, contained 40.6% of the total usable habitat. Even though there have been few detections of jaguars in the southwestern USA in recent decades, our results suggest that protection of currently suitable habitats, along with increased conservation efforts, could significantly contribute to the recovery of jaguars in the USA.

Landscape patterns in the occupancy of jaguars (Panthera onca) and their primary prey species in a disturbed region of the Selva Maya in Mexico

In order to prioritize the conservation and management efforts to protect jaguars (Panthera onca), it is of utmost importance to determine their tolerance in face of human disturbances, habitat modifications and varying degrees of prey availability. We assessed the occupancy probability of jaguars and five of their most common prey species throughout a heterogeneous landscape in the Selva Maya in southern Mexico: armadillo (Dasypus novemcinctus), coati (Nasua narica), paca (Cuniculus paca), white-tailed deer (Odocoileus virginianus), and collared peccary (Dicotyles tajacu). Additionally, we projected prey and Jaguar occupancies onto a 5993 km2 landscape based on the habitat type in the area. We averaged the best prey models ranked by QAICc and found that white-tailed deer had the highest average occupancy probability of 0.72 ± 0.06 and paca the lowest with 0.14 ± 0.04. The average occupancy probability for jaguars was 0.35 ± 0.07 and the strongest predictor of jaguar occupancy was a positive effect of collared peccary occupancy. These findings support previous studies that show that predator distribution is largely influenced by their prey availability, even in the midst of degraded habitats, and underlies the essential need to incorporate protection plans for prey species in jaguar conservation strategies.

Density and activity patterns of bobcat in its southernmost distribution

Estimating density and activity patterns is useful for management and conservation of species. Data for Mexican bobcat (Lynx rufus) populations are scarce. Here we estimated the density of a bobcat population in Oaxaca, southern Mexico, and evaluated its daily activity patterns. We also evaluated macroecological patterns of bobcat density across its distribution range to determine any geographical (latitudinal, longitudinal, elevation, or range centroid) or climatic effects on the population density. Camera–trap data were divided into four 60–day periods (two in the dry season and two in the rainy season). Density was calculated using the random encounter model and daily activity patterns were analyzed fitting a kernel density function. The mean estimated density for the four periods was 17.3 bobcats/100 km2, with the highest densities occurring during the dry periods. Bobcat daily activity pattern presented two peaks, one after midnight and the other after dawn, with very slight changes between seasons. In the study area, density and activity patterns were associated with anthropogenic perturbation and prey availability. Bobcats increased their population density in the dry season, and showed a preference for activity at night and early morning hours when it is cooler and there are likely fewer competitors but more prey. Across its range, bobcat density was mainly related to annual precipitation and mean temperature of the driest quarter at 100 km radius buffers, and between annual precipitation and longitude on a smaller scale (50 km radius buffers). These findings support their preference for the arid or mesic environments that enabled them to reach southern areas of the Neartic region.

Individual Identification of Large Felids in Field Studies: Common Methods, Challenges, and Implications for Conservation Science

Large felids represent some of the most threatened large mammals on Earth, critical for both tourism economies and ecosystem function. Most populations are in a state of decline, and their monitoring and enumeration is therefore critical for conservation. This typically rests on the accurate identification of individuals within their populations. We review the most common and current survey methods used in individual identification studies of large felid ecology (body mass > 25 kg). Remote camera trap photography is the most extensively used method to identify leopards, snow leopards, jaguars, tigers, and cheetahs which feature conspicuous and easily identifiable coat patterning. Direct photographic surveys and genetic sampling are commonly used for species that do not feature easily identifiable coat patterning such as lions. We also discuss the accompanying challenges encountered in several field studies, best practices that can help increase the precision and accuracy of identification and provide generalised ratings for the common survey methods used for individual identification.

Ocelot density and habitat use in Tamaulipan thornshrub and tropical deciduous forests in Northeastern México

Northeastern México is one of the most diverse regions in the country with high mammal richness. This region also sits on the northern periphery of the geographic distribution of ocelots (Leopardus pardalis), which are listed as endangered in México. Ocelot ecology in northeastern México is poorly known at local and landscape levels, especially in the ecologically rich temperate sierras, a perceived stronghold for ocelots. We used an occupancy approach to estimate ocelot-habitat use in Tamaulipan thornshrub and tropical deciduous forests and a spatially explicit capture–recapture (SECR) framework to estimate density of ocelots in the northern edge of the Sierra Tamaulipas, México. From May to December 2009, we conducted two camera trap surveys (summer: 20 camera stations; fall: 58 camera stations) on Rancho Caracol and Rancho Camotal, north of the Rio Soto de La Marina. We found ocelot detections were higher in areas with increasing patch density of tropical deciduous forest and habitat use was greater in Tamaulipan thornshrub and tropical deciduous forests with lower edge densities. Ocelot densities varied by sex, with females achieving greater densities (7.88 ocelots/100 km2 [95% CI: 4.85–12.81]) than males (3.81 ocelots/100 km2 [95% CI: 1.96–7.43]). Ocelots were averse to areas with high densities of edge cover in each woody community, supporting the notion of a forest patch interior species. Despite the study occurring 11 years ago, population densities were also among the highest reported in México using spatially explicit capture–recapture methods, The high ecological integrity of the habitat within the Sierra de Tamaulipas, recent protection as a Biosphere Reserve, and remote rugged terrain suggest long-term security of the ocelot population in this region.

Environmental and anthropogenic factors synergistically affect space use of jaguars

Large terrestrial carnivores have undergone some of the largest population declines and range reductions of any species, which is of concern as they can have large effects on ecosystem dynamics and function.^1-4 The jaguar (Panthera onca) is the apex predator throughout the majority of the Neotropics; however, its distribution has been reduced by >50% and it survives in increasingly isolated populations.⁵ Consequently, the range-wide management of the jaguar depends upon maintaining core populations connected through multi-national, transboundary cooperation, which requires understanding the movement ecology and space use of jaguars throughout their range.^6-8 Using GPS telemetry data for 111 jaguars from 13 ecoregions within the four biomes that constitute the majority of jaguar habitat, we examined the landscape-level environmental and anthropogenic factors related to jaguar home range size and movement parameters. Home range size decreased with increasing net productivity and forest cover and increased with increasing road density. Speed decreased with increasing forest cover with no sexual differences, while males had more directional movements, but tortuosity in movements was not related to any landscape factors. We demonstrated a synergistic relationship between landscape-scale environmental and anthropogenic factors and jaguars' spatial needs, which has applications to the conservation strategy for the species throughout the Neotropics. Using large-scale collaboration, we overcame limitations from small sample sizes typical in large carnivore research to provide a mechanism to evaluate habitat quality for jaguars and an inferential modeling framework adaptable to the conservation of other large terrestrial carnivores.