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Crab-eating foxes (Cerdocyon thous) and pampas foxes (Lycalopex gymnocercus) are very similar in body size and food habits, with distributional ranges that overlap extensively in South America. We used camera-trap records of both species obtained at the Iberá Nature Reserve (INR), northeastern Argentina, to test the hypothesis that, when living in sympatry, they reduce competition by using different habitats and by being active at different times. Camera-trap records obtained at 2 additional sites inhabited by only 1 of these species, the Atlantic Forest of Misiones (AF) and Lihué Calel National Park (LCNP), were used to determine the activity patterns of these foxes when living alone. At INR, we set 41 camera-trap stations in 2 habitats (shrubland forest and flooded grassland), and in 2 treatments per habitat (with or without cattle). Three stations also were set in gallery forests. We obtained 540 photographs of crab-eating foxes (289 records) and 175 photographs of pampas foxes (115 records) in 1,521 camera-trap days. At LCNP, 27 camera-trap stations (1,002 camera-trap days) provided 109 records of pampas foxes. At AF, 195 camera-trap stations (11,689 camera-trap days) provided 103 records of crab-eating foxes. At INR, crab-eating foxes were more frequently recorded in forest habitats, whereas pampas foxes preferred opened grasslands. However, both species were found in all habitats and their recording rates were not negatively correlated. At INR, crab-eating foxes were nocturnal, with peaks of activity after dusk and before dawn, a pattern similar to that observed at AF and elsewhere. At INR, pampas foxes showed a peak of activity between 0000 and 0400 h and another between 1000 and 1300 h, a pattern that differed from that observed at LCNP and other places, where the species is mostly nocturnal. At INR, pampas foxes reduced their activity at times when activity of presumably dominant crab-eating foxes was high, which may facilitate their coexistence.

Estimates of abundance are extremely valuable for species conservation, yet determining abundance for elusive, wide-ranging, carnivores is difficult. We estimated density of pumas using remote cameras across study sites in Bolivia, Argentina, and Belize. We used obvious and subtle markings to identify individual pumas in photographs and conducted double-blind identifications to examine the degree of agreement among investigators. Average agreement on identification between pairs of investigators was nearly 80.0% and 3-way agreement was 72.9%. Identification of pumas as different individuals was uncommon (7.8% pairwise, 0.69% 3-way disagreement) with the remainder described as unidentifiable. Densities of pumas varied consistently from site to site regardless of investigator. Bolivian pumas moved the shortest distances between camera stations and Argentinean pumas the longest, but distances among cameras and area covered by surveys varied among sites. We applied a correction factor to the Bolivian data to account for the small area surveyed and found that, averaged across investigator, Bolivia had significantly more pumas per 100 km2 (mean ± SD; 6.80 ± 1.5) than Belize (3.42 ± 1.3) or Argentina (0.67 ± 0.2). Numbers of pumas in Argentina match those of low-density North American sites, and those for Belize are consistent with the Pantanal and high-density North American sites. Densities of pumas can be reliably estimated with remote cameras for these sites, and our work presents the 1st density estimates for Central America and for forested environments in South America.

Camera-trapping surveys, in combination with traditional capture–recapture or spatially explicit capture–recapture techniques, have become popular for estimating the density of individually identifiable carnivores. When only a portion of the population is uniquely identifiable, traditional and spatial mark–resight models provide a viable alternative. We reanalyzed a data set that used photographic capture–recapture methods to estimate the densities of pumas (Puma concolor) across 3 study sites in Belize, Argentina, and Bolivia using newer, more-advanced modeling including spatial and nonspatial mark–resight techniques. Additionally, we assessed how photo identification influenced density estimates by comparing estimates based on capture histories constructed by 3 independent investigators. We estimated the abundances of pumas using mark–resight models in program MARK and then estimated densities ad hoc. We also estimated densities directly using spatial mark–resight models implemented in a Bayesian framework. Puma densities did not vary substantially among observers but estimates generated from the 3 statistical techniques did differ. Density estimates (pumas/100 km2) from spatial mark–resight models were lower (0.22–7.92) and had increased precision compared to those from nonspatial capture–recapture (0.50–19.35) and mark–resight techniques (0.54–14.70). Our study is the 1st to estimate the density of a population of carnivores, where only a subset of the individuals are naturally marked, using camera-trapping surveys in combination with spatial mark–resight models. The development of spatial mark–resight and spatially explicit capture–recapture techniques creates the potential for using a single camera-trapping array to estimate the density of multiple, sympatric carnivores, including both partially marked and uniquely marked species. Los relevamientos con trampas-cámara en combinación con modelos tradicionales o espacialmente explícitos de captura–recaptura, se han convertido en metodologías muy utilizadas para estimar la densidad de carnívoros que pueden ser identificados individualmente. Cuando sólo una porción de la población puede ser identificada inequívocamente, los modelos de marcado–revisualización tradicionales y espacialmente explícitos proveen una alternativa viable. Reanalizamos un conjunto de datos, que se utilizó para estimar la densidad de pumas (Puma concolor) mediante el método fotográfico de captura–recaptura en 3 sitios de estudio en Belice, Argentina y Bolivia, utilizando modelos más novedosos y avanzados incluyendo técnicas de marcado–revisualización tradicionales y espacialmente explicitas. Adicionalmente, evaluamos cómo la identificación de fotografías influyó en las estimaciones de densidad, comparando estimaciones basadas en las historias de captura construidas por 3 investigadores independientes. Estimamos la abundancia de pumas usando modelos de marcado–revisualización en el programa MARK y luego estimamos las densidades ad hoc. También estimamos densidades usando modelos espaciales de marcado–revisualización espacialmente explícitos implementados en un marco Bayesiano. La densidad de pumas no varió sustancialmente entre observadores, pero las estimaciones generadas mediante los 3 modelos estadísticos fueron diferentes. Las densidades de pumas (pumas/100 km2) de modelos de marcado–revisualización espacialmente explícitos fueron más bajas (0.22–7.92) y aumentaron en precisión comparadas con aquellas de captura–recaptura (0.50–19.35) y técnicas de marcado–revisualización no espacialmente explícitos (0.54–14.70). Nuestro estudio es el primero en estimar la densidad mediante la utilización de datos de trampas-cámara en combinación con modelos marcado–revisualización espacialmente explícitos de una población de carnívoros donde sólo un subconjunto de individuos está marcado naturalmente. El desarrollo de técnicas de marcado–revisualización y captura–recaptura espacialmente explícitos ofrece la oportunidad de utilizar un mismo diseño de trampas-cámara para estimar la densidad de múltiples carnívoros simpátricos, incluyendo especies parcial o totalmente identificables individualmente.

Habitat selection by the 1st reintroduced population of giant anteaters (Myrmecophaga tridactyla) was studied at Iberá Nature Reserve (northeastern Argentina), a subtropical region of wetlands, grasslands, and forests, with properties dedicated to nature conservation or livestock production. Eighteen animals were released and radiotracked between 2007 and 2012 for periods of 6–46 months, producing 1,181 locations. The effect of land use was assessed using chi-square tests. Resource selection functions were used to assess habitat selection at 2 spatial scales using as covariates habitat type (grassland, open savanna, closed savanna, and hygrophilous forest), distance to forest edge, and distance to a main road. Habitat selection was modeled for different ages (juveniles and adults), activity (resting or active), and seasons (summer, transition, and winter) and was validated with individuals previously excluded from our analysis. Anteaters strongly selected areas dedicated to nature conservation. Adults showed higher model fit. Hygrophilous forest was positively selected and open savanna was avoided. Anteaters rested almost exclusively in forests. While active they increased the use of other habitats, except for open savanna. Grasslands were most used during the winter season, probably because they provide a constant food source and better cover than savannas. Even though < 4% of the landscape was covered by forests, this habitat proved to be important for giant anteaters. Deforestation and traditional cattle management may have caused the local extinction of the species at Iberá. Establishment of strict conservation areas in nonflooded areas combined with proper management of forests and livestock may be essential for the long-term survival of reintroduced populations in Iberá and similar regions.

One of the benefits of modeling habitat selection for a given population is the ability to predict patterns in another population that inhabits an ecologically similar area. We studied habitat selection and home ranges of reintroduced and wild giant anteaters (Myrmecophaga tridactyla) in 2 South American wetlands (Iberá, Argentina, and Pantanal, Brazil). Nine reintroduced (Iberá) and 10 wild (Pantanal) adult animals were tracked via VHF and GPS between 2007 and 2015. We used resource selection functions to assess habitat selection for the wild anteaters from Pantanal. Generalized linear mixed models were constructed for resting and activity periods during both the wet and dry seasons. We then validated previous models built for reintroduced anteaters in Iberá using data from the wild animals from Pantanal. Habitat type (floodplain, grassland, open savanna, closed savanna, and forest) and distances to selected landscape traits were used as covariates. Locations near forests were positively selected in both populations. Selection of forests in Pantanal was less evident than in Iberá, probably due to the much higher availability of forests in the Brazilian site, with 38–53% of the landscape classified as good-to-high likelihood in Pantanal compared to only 4% in Iberá. Mean home-range size of males was larger in Iberá (32.50 ± 7.64 km2) than in Pantanal (14.07 ± 1.97 km2), whereas home-range sizes of females were similar in both areas (9.75 ± 1.74 km2 in Iberá; 9.62 ± 2.00 km2 in Pantanal). Results of this study suggest that model validation with geographically independent data is a useful tool to compare reintroduced and wild populations and to identify resources or landscape attributes that are important for a given species, even when these resources are abundant or highly available.

Knowing the factors that affect the abundance and activity patterns of pumas (Puma concolor) in South American forests may help in their conservation. Using camera traps, we conducted 4 surveys in 3 areas with different levels of protection against poaching and logging within the biggest continuous fragment of the Upper Parana Atlantic Forest. We used capture–mark–recapture population models to estimate the density of pumas for each area. The core area of Iguazú National Park, with low poaching pressure and no logging for >60 years, had the highest density of pumas (between 1.55 and 2.89 individuals/100 km2). Yabotí Biosphere Reserve, an area with the highest poaching and logging pressure, showed the lowest density (between 0.3 and 0.74 individuals/100 km2). Areas with intermediate levels of poaching and logging pressure had densities between 0.66 and 2.19 individuals/100 km2. Puma activity peaked during the 1st hours of morning in the most protected area, but became more crepuscular and nocturnal in areas with less protection. The lower abundance of pumas in the more degraded areas may be related to lower prey abundance. Differences in activity patterns of pumas among areas with different poaching pressures may be a direct response to poaching or to changes in the availability and activity patterns of primary prey. Conservation efforts should focus on decreasing poaching and logging pressures within protected areas to benefit pumas and other endangered species in the Atlantic Forest.

As top predators, wild cats play a key ecological role in tropical forests, but little is known about the factors that regulate their abundance. This study looked for correlates of ocelot (Leopardus pardalis) abundance at two spatial scales. First, camera-trap surveys conducted in the Atlantic Forest of Misiones, Argentina, were used to test the hypothesis that selective logging and poaching affect the local abundance of this cat. Second, published density estimates (N = 21) were used to test the hypothesis that rainfall and latitude are correlated with the abundance of ocelots across their continental range. In Misiones, ocelot densities ranged from 4.96 ± 1.33 individuals per 100 km2 in the intensely logged and hunted areas to 17.6 ± 2.25 individuals per 100 km2 in areas with low human impact. The frequency of records, number of individuals recorded per station, and density estimates were 2–3 times higher in areas with relatively low levels of logging and poaching. At a continental scale, ocelot densities decrease with latitude and increase with rainfall. Primary productivity seems to determine the abundance of wild cats across their range, but at a local scale their abundance may be affected by logging and poaching or by competition with other species.

The field of movement ecology has rapidly grown during the last decade, with important advancements in tracking devices and analytical tools that have provided unprecedented insights into where, when, and why species move across a landscape. Although there has been an increasing emphasis on making animal movement data publicly available, there has also been a conspicuous dearth in the availability of such data on large carnivores. Globally, large predators are of conservation concern. However, due to their secretive behavior and low densities, obtaining movement data on apex predators is expensive and logistically challenging. Consequently, the relatively small sample sizes typical of large carnivore movement studies may limit insights into the ecology and behavior of these elusive predators. The aim of this initiative is to make available to the conservation-scientific community a dataset of 134,690 locations of jaguars (Panthera onca) collected from 117 individuals (54 males and 63 females) tracked by GPS technology. Individual jaguars were monitored in five different range countries representing a large portion of the species’ distribution. This dataset may be used to answer a variety of ecological questions including but not limited to: improved models of connectivity from local to continental scales; the use of natural or human-modified landscapes by jaguars; movement behavior of jaguars in regions not represented in this dataset; intraspecific interactions; and predator-prey interactions. In making our dataset publicly available, we hope to motivate other research groups to do the same in the near future. Specifically, we aim to help inform a better understanding of jaguar movement ecology with applications towards effective decision making and maximizing long-term conservation efforts for this ecologically important species. There are no costs, copyright, or proprietary restrictions associated with this data set. When using this data set, please cite this article to recognize the effort involved in gathering and collating the data and the willingness of the authors to make it publicly available.

Fil: Perovic, Pablo Gastón. Administración de Parques Nacionales. Delegación Regional del Noroeste; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina