Few ecological studies have explored landscape suitability using the gradient concept of landscape structure for wildlife species. Identification of conditions influencing the landscape ecology of endangered species allows for development of more robust recovery strategies. Our objectives were to (i) identify the range of landscape metrics (i.e., mean patch area; patch and edge densities; percent land cover; shape, aggregation, and largest patch indices) associated with woody vegetation used by ocelots (Leopardus pardalis), and (ii) quantify the potential distribution of suitable woody cover for ocelots across southern Texas. We used the gradient concept of landscape structure and the theory of slack combined with GPS telemetry data from 10 ocelots. Spatial distribution of high suitable woody cover is comprised of large patches, with low shape-index values (1.07–2.25), patch (27.21–72.50 patches/100 ha), and edge (0–191.50 m/ha) densities. High suitability landscape structure for ocelots occurs in 45.27% of woody cover in southern Texas. Our study demonstrates a new approach for measuring landscape suitability for ocelots in southern Texas. The range of landscape values identified that there are more large woody patches containing the spatial structure used by ocelots than previously suspected, which will aid in evaluating recovery and road planning efforts.
The competitive exclusion principle states that ecologically similar species will be unable to coexist due to competition for resources, however, similar species coexist across a variety of ecosystems. Understanding mechanisms of coexistence is essential for managing a target species. Advances in monitoring technology have provided the ability to obtain reliable, high-frequency data on wildlife. From these data, behavioral states can be approximated by analyzing turning angles and distances between locations. We monitored 8 ocelots Leopardus pardalis, 13 bobcats Lynx rufus and 5 coyotes Canis latrans on the East Foundation's El Sauz Ranch and the Yturria San Francisco Ranch in south Texas, USA, which were fitted with GPS collars that collected locations every 30 min. We characterized behavioral states using hidden Markov models. We assumed low turning angles and longer steps to represent patrolling territory, larger turning angles with shorter steps would represent hunting behavior, and low angles and minimal movement would indicate periods of rest. If differences in timing and space use exist between species, these differences may help facilitate coexistence. We predicted 1) each species exhibits three behavioral states: resting, hunting and territory patrolling; 2) ocelots moved farther (i.e. territory patrolling) in open areas and rested in dense cover; and 3) bobcats and coyotes would remain in more open areas than ocelots. We found ocelots and bobcats remained closer to heavy cover when resting and foraging and used open areas more when patrolling territory while coyotes rested in the open and selected for cover when hunting or patrolling. Further, we found evidence of temporal partitioning of behaviors both within and across species. Our study provides a novel approach to examining coexistence and identifies behaviorally mediated spatial and temporal differences in habitat use that may facilitate coexistence between ocelots, bobcats and coyotes.
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.
Managing wildlife populations in the face of global change requires regular data on the abundance and distribution of wild animals, but acquiring these over appropriate spatial scales in a sustainable way has proven challenging. Here we present the data from Snapshot USA 2020, a second annual national mammal survey of the USA. This project involved 152 scientists setting camera traps in a standardized protocol at 1485 locations across 103 arrays in 43 states for a total of 52,710 trap‐nights of survey effort. Most (58) of these arrays were also sampled during the same months (September and October) in 2019, providing a direct comparison of animal populations in 2 years that includes data from both during and before the COVID‐19 pandemic. All data were managed by the eMammal system, with all species identifications checked by at least two reviewers. In total, we recorded 117,415 detections of 78 species of wild mammals, 9236 detections of at least 43 species of birds, 15,851 detections of six domestic animals and 23,825 detections of humans or their vehicles. Spatial differences across arrays explained more variation in the relative abundance than temporal variation across years for all 38 species modeled, although there are examples of significant site‐level differences among years for many species. Temporal results show how species allocate their time and can be used to study species interactions, including between humans and wildlife. These data provide a snapshot of the mammal community of the USA for 2020 and will be useful for exploring the drivers of spatial and temporal changes in relative abundance and distribution, and the impacts of species interactions on daily activity patterns. There are no copyright restrictions, and please cite this paper when using these data, or a subset of these data, for publication.
Pressure from hunting can alter the behavior and habitat selection of game species. During hunting periods, cervids such as elk (Cervus canadensis) typically select for areas further from roads and closer to tree cover, while altering the timing of their daily activities to avoid hunters. Our objective was to determine the habitat characteristics most influential in predicting harvest risk of elk. We captured 373 female elk between January 2015 and March 2017 in the Uinta-Wasatch-Cache National Forest and surrounding area of central Utah, USA. We determined habitat selection during the hunting season using a resource selection function (RSF) for 255 adult cow elk. Additionally, we used a generalized linear mixed model to evaluate risk of harvest based on habitat use within home ranges (3rd order selection) as well as the location of the home range on the landscape to evaluate vulnerability on a broader scale. Female elk selected for areas that reduced hunter access (rugged terrain, within tree cover, on private land). Age, elevation and distance to roads within a home range were most influential in predicting harvest risk (top model accounted for 36.2% of AIC weight). Elevation and distance to trees were most influential in predicting risk when evaluating the location of the home range (top model accounted for 42.1% of AIC weight). Vulnerability to harvest was associated with proximity to roads. Additionally, survival in our landscape decreased with age of femaleelk.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.