Sampling animal populations with camera traps has become increasingly popular over the past two decades, particularly for species that are cryptic, elusive, exist at low densities or range over large areas. The results have been widely used to estimate population size and density. We analyzed data from 13 camera trap surveys conducted at five sites across the Kaa-Iya landscape, Bolivian Chaco, for jaguar, puma, ocelot and lowland tapir. We compared two spatially explicit capture-recapture (SCR) software packages: secr, a likelihood-based approach, and SPACECAP, a Bayesian approach, both of which are implemented within the R environment and can be used to estimate animal density from photographic records of individual animals that simultaneously employ spatial information about the capture location relative to the sample location. As a non-spatial analysis, we used the program CAPTURE 2 to estimate abundance from the capturerecapture records of individuals identified through camera trap photos combined with an ad hoc estimation of the effective survey area to estimate density. SCR methods estimated jaguar population densities from 0.31 to 1.82 individuals per 100 km 2 across the Kaa-Iya sites; puma from 0.36 to 7.99; ocelot from 1.67 to 51.7; and tapir from 7.38 to 42.9. Density estimates using either secr or SPACECAP were generally lower than the estimates generated using the non-spatial method for all surveys and species; and density estimates using SPACECAP were generally lower than that using secr. We recommend using either secr or SPACECAP because the spatially explicit methods are not biased by an informal estimation of an effective survey area. Although SPACECAP and secr are less sensitive than non-spatial methods to the size of the grid used for sampling, we recommend grid sizes several times larger than the average home range (known or estimated) of the target species.

Over the past century, human activities and their side effects have significantly threatened both ecosystems and resident species. Nevertheless, the genetic patterns of large felids that depend heavily on large and well-conserved continuous habitat remain poorly studied. Using the largest-ever contemporary genetic survey of wild jaguars (Panthera onca), we evaluated their genetic diversity and population structure in natural (Brazilian Amazon) and highly modified habitats (e.g. Cerrado, Caatinga) including those close to the northern (Yucatan, Mexico) and southern (Pantanal) edge of the species' distribution range. Data from our set of microsatellites revealed a pronounced genetic structure, with four genetically differentiated geographic areas. Geographic distance was not the only factor influencing genetic differentiation through the jaguar range. Instead, we found evidence of the effects of habitat deterioration on genetic patterns: while the levels of genetic diversity in the Amazon forest, the largest continuum habitat for the species, are high and consistent with panmixia across large distances, genetic diversity near the edge of the species distribution has been reduced through population contractions. Mexican jaguar populations were highly differentiated from those in Brazil and genetically depauperated. An isolated population from the Caatinga showed the genetic effects of a recent demographic decline (within the last 20-30 years), which may reflect recent habitat degradation in the region. Our results demonstrate that the jaguar is highly sensitive to habitat fragmentation especially in human-dominated landscapes, and that in Brazil, the existing but limited genetic connectivity in the central protected areas should be maintained. These conclusions have important implications for the management of wide-ranging species with high dispersal and low population density. The restoration of ecological connectivity between populations over relatively large scales should be one of the main priorities for species conservation.

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