Genomics meets applied ecology: Characterizing habitat quality for sloths in a tropical agroecosystem.

Understanding how habitat quality in heterogeneous landscapes governs the distribution and fitness of individuals is a fundamental aspect of ecology. While mean individual fitness is generally considered a key to assessing habitat quality, a comprehensive understanding of habitat quality in heterogeneous landscapes requires estimates of dispersal rates among habitat types. The increasing accessibility of genomic approaches, combined with field-based demographic methods, provides novel opportunities for incorporating dispersal estimation into assessments of habitat quality. In this study, we integrated genomic kinship approaches with field-based estimates of fitness components and approximate Bayesian computation (ABC) procedures to estimate habitat-specific dispersal rates and characterize habitat quality in two-toed sloths (Choloepus hoffmanni) occurring in a Costa Rican agricultural ecosystem. Field-based observations indicated that birth and survival rates were similar in a sparsely shaded cacao farm and adjacent cattle pasture-forest mosaic. Sloth density was threefold higher in pasture compared with cacao, whereas home range size and overlap were greater in cacao compared with pasture. Dispersal rates were similar between the two habitats, as estimated using ABC procedures applied to the spatial distribution of pairs of related individuals identified using 3,431 single nucleotide polymorphism and 11 microsatellite locus genotypes. Our results indicate that crops produced under a sparse overstorey can, in some cases, constitute lower-quality habitat than pasture-forest mosaics for sloths, perhaps because of differences in food resources or predator communities. Finally, our study demonstrates that integrating field-based demographic approaches with genomic methods can provide a powerful means for characterizing habitat quality for animal populations occurring in heterogeneous landscapes.

Effects of forest management on California Spotted Owls: implications for reducing wildfire risk in fire­prone forests.

Management of many North American forests is challenged by the need to balance the potentially competing objectives of reducing risks posed by high-severity wildfires and protecting threatened species. In the Sierra Nevada, California, concern about high-severity fires has increased in recent decades but uncertainty exists over the effects of fuel-reduction treatments on species associated with older forests, such as the California Spotted Owl (Strix occidentalis occidentalis). Here, we assessed the effects of forest conditions, fuel reductions, and wildfire on a declining population of Spotted Owls in the central Sierra Nevada using 20 years of demographic data collected at 74 Spotted Owl territories. Adult survival and territory colonization probabilities were relatively high, while territory extinction probability was relatively low, especially in territories that had relatively large amounts of high canopy cover (≥70%) forest. Reproduction was negatively associated with the area of medium-intensity timber harvests characteristic of proposed fuel treatments. Our results also suggested that the amount of edge between older forests and shrub/sapling vegetation and increased habitat heterogeneity may positively influence demographic rates of Spotted Owls. Finally, high-severity fire negatively influenced the probability of territory colonization. Despite correlations between owl demographic rates and several habitat variables, life stage simulation (sensitivity) analyses indicated that the amount of forest with high canopy cover was the primary driver of population growth and equilibrium occupancy at the scale of individual territories. Greater than 90% of medium-intensity harvests converted high-canopy-cover forests into lower-canopy-cover vegetation classes, suggesting that landscape-scale fuel treatments in such stands could have short-term negative impacts on populations of California Spotted Owls. Moreover, high-canopy-cover forests declined by an average of 7.4% across territories during our study, suggesting that habitat loss could have contributed to declines in abundance and territory occupancy. We recommend that managers consider the existing amount and spatial distribution of high-canopy forest before implementing fuel treatments within an owl territory, and that treatments be accompanied by a rigorous monitoring program.

Inferring recent historic abundance from current genetic diversity.

Recent historic abundance is an elusive parameter of great importance for conserving endangered species and understanding the pre-anthropogenic state of the biosphere. The number of studies that have used population genetic theory to estimate recent historic abundance from contemporary levels of genetic diversity has grown rapidly over the last two decades. Such assessments often yield unexpectedly large estimates of historic abundance. We review the underlying theory and common practices of estimating recent historic abundance from contemporary genetic diversity, and critically evaluate the potential issues at various estimation steps. A general issue of mismatched spatio-temporal scales between the estimation itself and the objective of the estimation emerged from our assessment; genetic diversity-based estimates of recent historic abundance represent long-term averages, whereas the objective typically is an estimate of recent abundance for a specific population. Currently, the most promising approach to estimate the difference between recent historic and contemporary abundance requires that genetic data be collected from samples of similar spatial and temporal duration. Novel genome-enabled inference methods may be able to utilize additional information of dense genome-wide distributions of markers, such as of identity-by-descent tracts, to infer recent historic abundance from contemporary samples only.

Detecting populations in the 'ambiguous' zone: kinship-based estimation of population structure at low genetic divergence.

Identifying population structure is one of the most common and important objectives of spatial analyses using population genetic data. Population structure is detected either by rejecting the null hypothesis of a homogenous distribution of genetic variation, or by estimating low migration rates. Issues arise with most current population genetic inference methods when the genetic divergence is low among putative populations. Low levels of genetic divergence may be as a result of either high ongoing migration or historic high migration but no current, ongoing migration. We direct attention to recent developments in the use of the tempo-spatial distribution of closely related individuals to detect population structure or estimate current migration rates. These 'kinship-based' approaches complement more traditional population-based genetic inference methods by providing a means to detect population structure and estimate current migration rates when genetic divergence is low. However, for kinship-based methods to become widely adopted, formal estimation procedures applicable to a range of species life histories are needed.