Predicting population-level effects of landscape change depends on identifying factors that influence population connectivity in complex landscapes. However, most putative movement corridors and barriers have not been based on empirical data. In this study, we identify factors that influence connectivity by comparing patterns of genetic similarity among 146 black bears (Ursus americanus), sampled across a 3,000-km(2) study area in northern Idaho, with 110 landscape-resistance hypotheses. Genetic similarities were based on the pairwise percentage dissimilarity among all individuals based on nine microsatellite loci (average expected heterozygosity=0.79). Landscape-resistance hypotheses describe a range of potential relationships between movement cost and land cover, slope, elevation, roads, Euclidean distance, and a putative movement barrier. These hypotheses were divided into seven organizational models in which the influences of barriers, distance, and landscape features were statistically separated using partial Mantel tests. Only one of the competing organizational models was fully supported: patterns of genetic structure are primarily related to landscape gradients of land cover and elevation. The alternative landscape models, isolation by barriers and isolation by distance, are not supported. In this black bear population, gene flow is facilitated by contiguous forest cover at middle elevations.

Environmental DNA (eDNA) detection has emerged as a powerful tool for monitoring aquatic organisms, but much remains unknown about the dynamics of aquatic eDNA over a range of environmental conditions. DNA concentrations in streams and rivers will depend not only on the equilibrium between DNA entering the water and DNA leaving the system through degradation, but also on downstream transport. To improve understanding of the dynamics of eDNA concentration in lotic systems, we introduced caged trout into two fishless headwater streams and took eDNA samples at evenly spaced downstream intervals. This was repeated 18 times from mid-summer through autumn, over flows ranging from approximately 1-96 L/s. We used quantitative PCR to relate DNA copy number to distance from source. We found that regardless of flow, there were detectable levels of DNA at 239.5 m. The main effect of flow on eDNA counts was in opposite directions in the two streams. At the lowest flows, eDNA counts were highest close to the source and quickly trailed off over distance. At the highest flows, DNA counts were relatively low both near and far from the source. Biomass was positively related to eDNA copy number in both streams. A combination of cell settling, turbulence and dilution effects is probably responsible for our observations. Additionally, during high leaf deposition periods, the presence of inhibitors resulted in no amplification for high copy number samples in the absence of an inhibition-releasing strategy, demonstrating the necessity to carefully consider inhibition in eDNA analysis.

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Environmental DNA (eDNA) is being rapidly adopted as a tool to detect rare animals. Quantitative PCR (qPCR) using probe-based chemistries may represent a particularly powerful tool because of the method’s sensitivity, specificity, and potential to quantify target DNA. However, there has been little work understanding the performance of these assays in the presence of closely related, sympatric taxa. If related species cause any cross-amplification or interference, false positives and negatives may be generated. These errors can be disastrous if false positives lead to overestimate the abundance of an endangered species or if false negatives prevent detection of an invasive species. In this study we test factors that influence the specificity and sensitivity of TaqMan MGB assays using co-occurring, closely related brook trout (Salvelinus fontinalis) and bull trout (S. confluentus) as a case study. We found qPCR to be substantially more sensitive than traditional PCR, with a high probability of detection at concentrations as low as 0.5 target copies/µl. We also found that number and placement of base pair mismatches between the Taqman MGB assay and non-target templates was important to target specificity, and that specificity was most influenced by base pair mismatches in the primers, rather than in the probe. We found that insufficient specificity can result in both false positive and false negative results, particularly in the presence of abundant related species. Our results highlight the utility of qPCR as a highly sensitive eDNA tool, and underscore the importance of careful assay design.

Understanding how spatial genetic patterns respond to landscape change is crucial for advancing the emerging field of landscape genetics. We quantified the number of generations for new landscape barrier signatures to become detectable and for old signatures to disappear after barrier removal. We used spatially explicit, individual-based simulations to examine the ability of an individual-based statistic [Mantel's r using the proportion of shared alleles' statistic (Dps)] and population-based statistic (FST ) to detect barriers. We simulated a range of movement strategies including nearest neighbour dispersal, long-distance dispersal and panmixia. The lag time for the signal of a new barrier to become established is short using Mantel's r (1-15 generations). FST required approximately 200 generations to reach 50% of its equilibrium maximum, although G'ST performed much like Mantel's r. In strong contrast, FST and Mantel's r perform similarly following the removal of a barrier formerly dividing a population. Also, given neighbour mating and very short-distance dispersal strategies, historical discontinuities from more than 100 generations ago might still be detectable with either method. This suggests that historical events and landscapes could have long-term effects that confound inferences about the impacts of current landscape features on gene flow for species with very little long-distance dispersal. Nonetheless, populations of organisms with relatively large dispersal distances will lose the signal of a former barrier within less than 15 generations, suggesting that individual-based landscape genetic approaches can improve our ability to measure effects of existing landscape features on genetic structure and connectivity.

There has been a recent trend in genetic studies of wild populations where researchers have changed their sampling schemes from sampling pre-defined populations to sampling individuals uniformly across landscapes. This reflects the fact that many species under study are continuously distributed rather than clumped into obvious ''populations''. Once individual samples are collected, many landscape genetic studies use clustering algorithms and multilocus genetic data to group samples into subpopulations. After clusters are derived, landscape features that may be acting as barriers are examined and described. In theory, if populations were evenly sampled, this course of action should reliably identify population structure. However, genetic gradients and irregularly collected samples may impact the composition and location of clusters. We built genetic models where individual genotypes were either randomly distributed across a landscape or contained gradients created by neighbor mating for multiple generations. We investigated the influence of six different sampling protocols on population clustering using program STRUCTURE, the most commonly used model-based clustering method for multilocus genotype data. For models where individuals (and their alleles) were randomly distributed across a landscape, STRUCTURE correctly predicted that only one population was being sampled. However, when gradients created by neighbor mating existed, STRUCTURE detected multiple, but different numbers of clusters, depending on sampling protocols. We recommend testing for fine scale autocorrelation patterns prior to sample clustering, as the scale of the autocorrelation appears to influence the results. Further, we recommend that researchers pay attention to the impacts that sampling may have on subsequent population and landscape genetic results.

Population viability analysis (PVA) has become a commonly used tool in endangered species management. There is no single process that constitutes PVA, but all approaches have in common an assessment of a population's risk of extinction (or quasi extinction) or its projected population growth either under current conditions or expected from proposed management. As model sophistication increases, and software programs that facilitate PVA without the need for modeling expertise become more available, there is greater potential for the misuse of models and increased confusion over interpreting their results. Consequently, we discuss the practical use and limitations of PVA in conservation planning, and we discuss some emerging issues of PVA. We review extant issues that have become prominent in PVA, including spatially explicit modeling, sensitivity analysis, incorporating genetics into PVA, PVA in plants, and PVA software packages, but our coverage of emerging issues is not comprehensive. We conclude that PVA is a powerful tool in conservation biology for comparing alternative research plans and relative extinction risks among species, but we suggest caution in its use: (1) because PVA is a model, its validity depends on the appropriateness of the model's structure and data quality; (2) results should be presented with appropriate assessment of confidence; (3) model construction and results should be subject to external review, and (4) model structure, input, and results should be treated as hypotheses to be tested. We also suggest (5) restricting the definition of PVA to development of a formal quantitative model, (6) focusing more research on determining how pervasive densitydependence feedback is across species, and (7) not using PVA to determine minimum population size or (8) the specific probability of reaching extinction. The most appropriate use of PVA may be for comparing the relative effects of potential management actions on population growth or persistence. Uso y Temas Emergentes del Análisis de Viabilidad PoblacionalResumen: El análisis de viabilidad poblacional (AVP) es una herramienta de uso común en el manejo de especies en peligro. No hay un proceso único que constituya al AVP, pero todos los enfoques tienen en común la estimación del riesgo de extinción (o cuasi extinción) o la proyección del crecimiento poblacional, ya sea bajo las condiciones actuales o las esperadas del manejo propuesto. A medida que aumenta la sofisticación del modelo, y que se dispone de programas de cómputo que facilitan el AVP sin necesidad de experiencia en modelaje, hay una mayor posibilidad de desaprovechar el modelo y una mayor confusión en la interpretación de los resultados. En consecuencia, discutimos el uso práctico y las limitaciones del AVP en la planificación de conservación y discutimos algunos temas emergentes del AVP. Revisamos temas vigentes que son prominentes en el AVP, incluyendo el modelaje espacialmente explícito, el análisis de sensibilidad, la inclusión de la genética en el AVP, AVP en plantas y paquetes...

We propose a fundamental geographic distribution for the wolverine ( Gulo gulo (L., 1758)) based on the hypothesis that the occurrence of wolverines is constrained by their obligate association with persistent spring snow cover for successful reproductive denning and by an upper limit of thermoneutrality. To investigate this hypothesis, we developed a composite of MODIS classified satellite images representing persistent snow cover from 24 April to 15 May, which encompasses the end of the wolverine’s reproductive denning period. To investigate the wolverine’s spatial relationship with average maximum August temperatures, we used interpolated temperature maps. We then compared and correlated these climatic factors with spatially referenced data on wolverine den sites and telemetry locations from North America and Fennoscandia, and our contemporary understanding of the wolverine’s circumboreal range. All 562 reproductive dens from Fennoscandia and North America occurred at sites with persistent spring snow cover. Ninety-five percent of summer and 86% of winter telemetry locations were concordant with spring snow coverage. Average maximum August temperature was a less effective predictor of wolverine presence, although wolverines preferred summer temperatures lower than those available. Reductions in spring snow cover associated with climatic warming will likely reduce the extent of wolverine habitat, with an associated loss of connectivity.