Detecting genotyping errors and describing American black bear movement in northern Idaho

Leopard density and interspecific spatiotemporal interactions in a hyena-dominated landscape

Scavenging is widespread in the carnivore guild and can greatly impact food web structures and population dynamics by either facilitation or suppression of sympatric carnivores. Due to habitat loss and fragmentation, carnivores are increasingly forced into close sympatry, possibly resulting in more interactions such as kleptoparasitism and competition. In this paper, we investigate the potential for these interactions when carnivore densities are high. A camera trap survey was conducted in central Tuli, Botswana, to examine leopard Panthera pardus densities and spatiotemporal activity patterns of leopard and its most important competitors' brown hyena Parahyaena brunnea and spotted hyena Crocuta crocuta. Spatial capture-recapture models estimated leopard population density to be 12.7 ± 3.2 leopard/100 km², which is one of the highest leopard densities in Africa. Time-to-event analyses showed both brown hyena and spotted hyena were observed more frequently before and after a leopard observation than expected by chance. The high spatiotemporal overlap of both hyena species with leopard is possibly explained by leopard providing scavenging opportunities for brown hyena and spotted hyena. Our results suggest that central Tuli is a high-density leopard area, despite possible intense kleptoparasitism and competition.

An efficient method for simultaneous species, individual, and sex identification via in-solution single nucleotide polymorphism capture from low-quality scat samples

Understanding predator population dynamics is important for conservation management because of the critical roles predators play within ecosystems. Noninvasive genetic sampling methods are useful for the study of predators like canids that can be difficult to capture or directly observe. Here, we introduce the FAECES* method (Fast and Accurate Enrichment of Canid Excrement for Species* and other analyses) which expands the toolbox for canid researchers and conservationists by using in-solution hybridization sequence capture to produce single nucleotide polymorphism (SNP) genotypes for multiple canid species from scat-derived DNA using a single enrichment. We designed a set of hybridization probes to genotype both coyotes (Canis latrans) and kit foxes (Vulpes macrotis) at hundreds of polymorphic SNP loci and we tested the probes on both tissues and field-collected scat samples. We enriched and genotyped by sequencing 52 coyote and 70 kit fox scats collected in and around a conservation easement in the Nevada Mojave Desert. We demonstrate that the FAECES* method produces genotypes capable of differentiating coyotes and kit foxes, identifying individuals and their sex, and estimating genetic diversity and effective population sizes, even using highly degraded, low-quantity DNA extracted from scat. We found that the study area harbours a large and diverse population of kit foxes and a relatively smaller population of coyotes. By replicating our methods in the future, conservationists can assess the impacts of management decisions on canid populations. The method can also be adapted and applied more broadly to enrich and sequence multiple loci from any species of interest using scat or other noninvasive genetic samples.

Spatial proximity moderates genotype uncertainty in genetic tagging studies

Accelerating declines of an increasing number of animal populations worldwide necessitate methods to reliably and efficiently estimate demographic parameters such as population density and trajectory. Standard methods for estimating demographic parameters from noninvasive genetic samples are inefficient because lower-quality samples cannot be used, and they assume individuals are identified without error. We introduce the genotype spatial partial identity model (gSPIM), which integrates a genetic classification model with a spatial population model to combine both spatial and genetic information, thus reducing genotype uncertainty and increasing the precision of demographic parameter estimates. We apply this model to data from a study of fishers (Pekania pennanti) in which 37% of hair samples were originally discarded because of uncertainty in individual identity. The gSPIM density estimate using all collected samples was 25% more precise than the original density estimate, and the model identified and corrected three errors in the original individual identity assignments. A simulation study demonstrated that our model increased the accuracy and precision of density estimates 63 and 42%, respectively, using three replicated assignments (e.g., PCRs for microsatellites) per genetic sample. Further, the simulations showed that the gSPIM model parameters are identifiable with only one replicated assignment per sample and that accuracy and precision are relatively insensitive to the number of replicated assignments for high-quality samples. Current genotyping protocols devote the majority of resources to replicating and confirming high-quality samples, but when using the gSPIM, genotyping protocols could be more efficient by devoting more resources to low-quality samples.

Low wolverine (Gulo gulo) density in a national park complex of the Canadian Rocky Mountains

Large carnivores are sensitive to human-caused extirpation due to large home ranges, low population densities, and low reproductive rates. Protected areas help maintain populations by acting as sources, but human-caused mortality, habitat displacement, and edge effects occurring at protected area boundaries may reduce that function. The national parks Banff, Yoho, and Kootenay in the Canadian Rocky Mountains are refugia for large carnivores, including wolverines (Gulo gulo (Linnaeus, 1758)). Despite growing conservation concern, empirical baseline population data for wolverines remain scarce throughout their range, including most of Canada. We hypothesized (i) that in these national parks, wolverine density matched values expected for high-quality habitat, and (ii) that edge effects decreased density towards park boundaries. We conducted systematic non-invasive genetic sampling surveys covering >7000 km2 (2011 and 2013). Using spatial capture–recapture models, we estimated mean (±SE) female (1.5 ± 0.3 and 1.4 ± 0.3 wolverine/1000 km2), male (1.8 ± 0.4 and 1.5 ± 0.3 wolverine/1000 km2), and combined (3.3 ± 0.5 and 3.0 ± 0.4 wolverine/1000 km2) densities for 2011 and 2013, respectively. These estimates were lower than predictions based on density extrapolation from nearby high-quality habitat, and density decreased towards park boundaries. To benefit the population, we recommend creating buffer zones around parks that protect female habitat and prohibit harvest.

The genetic network of greater sage-grouse: Range-wide identification of keystone hubs of connectivity

Genetic networks can characterize complex genetic relationships among groups of individuals, which can be used to rank nodes most important to the overall connectivity of the system. Ranking allows scarce resources to be guided toward nodes integral to connectivity. The greater sage-grouse (Centrocercus urophasianus) is a species of conservation concern that breeds on spatially discrete leks that must remain connected by genetic exchange for population persistence. We genotyped 5,950 individuals from 1,200 greater sage-grouse leks distributed across the entire species' geographic range. We found a small-world network composed of 458 nodes connected by 14,481 edges. This network was composed of hubs-that is, nodes facilitating gene flow across the network-and spokes-that is, nodes where connectivity is served by hubs. It is within these hubs that the greatest genetic diversity was housed. Using indices of network centrality, we identified hub nodes of greatest conservation importance. We also identified keystone nodes with elevated centrality despite low local population size. Hub and keystone nodes were found across the entire species' contiguous range, although nodes with elevated importance to network-wide connectivity were found more central: especially in northeastern, central, and southwestern Wyoming and eastern Idaho. Nodes among which genes are most readily exchanged were mostly located in Montana and northern Wyoming, as well as Utah and eastern Nevada. The loss of hub or keystone nodes could lead to the disintegration of the network into smaller, isolated subnetworks. Protecting both hub nodes and keystone nodes will conserve genetic diversity and should maintain network connections to ensure a resilient and viable population over time. Our analysis shows that network models can be used to model gene flow, offering insights into its pattern and process, with application to prioritizing landscapes for conservation.

wisepair: a computer program for individual matching in genetic tracking studies

Individual-based data sets tracking organisms over space and time are fundamental to answering broad questions in ecology and evolution. A 'permanent' genetic tag circumvents a need to invasively mark or tag animals, especially if there are little phenotypic differences among individuals. However, genetic tracking of individuals does not come without its limits; correctly matching genotypes and error rates associated with laboratory work can make it difficult to parse out matched individuals. In addition, defining a sampling design that effectively matches individuals in the wild can be a challenge for researchers. Here, we combine the two objectives of defining sampling design and reducing genotyping error through an efficient Python-based computer-modelling program, wisepair. We describe the methods used to develop the computer program and assess its effectiveness through three empirical data sets, with and without reference genotypes. Our results show that wisepair outperformed similar genotype matching programs using previously published from reference genotype data of diurnal poison frogs (Allobates femoralis) and without-reference (faecal) genotype sample data sets of harbour seals (Phoca vitulina) and Eurasian otters (Lutra lutra). In addition, due to limited sampling effort in the harbour seal data, we present optimal sampling designs for future projects. wisepair allows for minimal sacrifice in the available methods as it incorporates sample rerun error data, allelic pairwise comparisons and probabilistic simulations to determine matching thresholds. Our program is the lone tool available to researchers to define parameters a priori for genetic tracking studies.

When can noninvasive samples provide sufficient information in conservation genetics studies?

Noninvasive sampling, of faeces and hair for example, has enabled many genetic studies of wildlife populations. However, two prevailing problems common to these studies are small sample sizes and high genotyping errors. The first problem stems from the difficulty in collecting noninvasive samples, particularly from populations of rare or elusive species, and the second is caused by the low quantity and quality of DNA extracted from a noninvasive sample. A common question is therefore whether noninvasive sampling provides sufficient information for the analyses commonly conducted in conservation genetics studies. Here, we conducted a simulation study to investigate the effect of small sample sizes and genotyping errors on the precision and accuracy of the most commonly estimated genetic parameters. Our results indicate that small sample sizes cause little bias in measures of expected heterozygosity, pairwise FST and population structure, but a large downward bias in estimates of allelic diversity. Allelic dropouts and false alleles had a much smaller effect than missing data, which effectively reduces sample size further. Overall, reasonable estimates of genetic variation and population subdivision are obtainable from noninvasive samples as long as error rates are kept below a frequency of 0.2. Similarly, unbiased estimates of population clustering can be made with genotyping error rates below 0.5 when the populations are highly differentiated. These results provide a useful guide for researchers faced with studying the conservation genetics of small, endangered populations from noninvasive samples.

Spatial genetic analysis reveals high connectivity of tiger (Panthera tigris) populations in the Satpura-Maikal landscape of Central India

We investigated the spatial genetic structure of the tiger meta-population in the Satpura–Maikal landscape of central India using population- and individual-based genetic clustering methods on multilocus genotypic data from 273 individuals. The Satpura–Maikal landscape is classified as a global-priority Tiger Conservation Landscape (TCL) due to its potential for providing sufficient habitat that will allow the long-term persistence of tigers. We found that the tiger meta-population in the Satpura–Maikal landscape has high genetic variation and very low genetic subdivision. Individual-based Bayesian clustering algorithms reveal two highly admixed genetic populations. We attribute this to forest connectivity and high gene flow in this landscape. However, deforestation, road widening, and mining may sever this connectivity, impede gene exchange, and further exacerbate the genetic division of tigers in central India.