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JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range of content in a trusted digital archive. We use information technology and tools to increase productivity and facilitate new forms of scholarship. For more information about JSTOR, please contact support@jstor.org.. British Ecological Society is collaborating with JSTOR to digitize, preserve and extend access to Journal of Ecology.Summary 1 Changes in demography and studies on physical condition of the Riviere George caribou Rangifer tarandus herd have suggested that its size may be primarily regulated by the amount of forage available on the summer range. 2 We therefore document the impact of grazing and trampling on composition and productivity of two plant communities, the shrub tundra and stands of dwarf birch, within this range. Ungrazed sites were rare, but four previously located small areas were used as control sites. 3 For the shrub tundra, the lichen mat was absent in grazed sites and ground previously occupied by lichens was either bare, covered by fragments of dead lichens and mosses or recolonized by early succession lichen species. Ground cover of shrubs not eaten by caribou was lower in grazed sites than in ungrazed sites, and coverage of graminoids, forage shrubs and forbs did not differ significantly between grazed and ungrazed sites. 4 In stands of dwarf birch grazed by caribou, ground cover and leaf biomass of Betula glandulosa was significantly lower than in ungrazed sites. 5 Productivity of forage plant species over the summer range was estimated at 22.5 g m-2 year-l in an ungrazed condition compared to 10.3 g m-2 year' when grazed. 6 At the landscape level, caribou have fragmented the distribution of their food resource by reducing biomass of shrub tundra and stands of dwarf birch to a very low level. 7 The serious negative impact of migratory ungulates on plant productivity of their summer range may be explained by characteristics of the vegetation and the high carrying capacity of winter compared to summer ranges. Significant factors related to the vegetation are its low resilience and productivity and the absence of a response of vascular plants following removal of lichens.

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We present allelematch, an R package, to automate the identification of unique multilocus genotypes in data sets where the number of individuals is unknown, and where genotyping error and missing data may be present. Such conditions commonly occur in noninvasive sampling protocols. Output from the software enables a comparison of unique genotypes and their matches, and facilitates the review of differences between profiles. The software has a variety of applications in molecular ecology, and may be valuable where a large number of samples must be processed, unique genotypes identified, and repeated observations made over space and time. We used simulations to assess the performance of allelematch and found that it can reliably and accurately determine the correct number of unique genotypes (± 3%) across a broad range of data set properties. We found that the software performs with highest accuracy when genotyping error is below 4%. The R package is available from the Comprehensive R Archive Network (http://cran.r-project.org/). Supplementary documentation and tutorials are provided.

Summary 1.Accurate resource selection functions (RSFs) are important for managing animal populations. Developing RSFs using data from GPS telemetry can be problematic due to serial autocorrelation, but modern analytical techniques can help to compensate for this correlation. 2. We used telemetry locations from 18 woodland caribou Rangifer tarandus caribou in Saskatchewan, Canada, to compare marginal (population-specific) generalized estimating equations (GEEs), and conditional (subject-specific) generalized linear mixed-effects models (GLMMs), for developing resource selection functions at two spatial scales. We evaluated the use of empirical standard errors, which are robust to misspecification of the correlation structure. We compared these approaches with destructive sampling. 3. Statistical significance was strongly influenced by the use of empirical vs. model-based standard errors, and marginal (GEE) and conditional (GLMM) results differed. Destructive sampling reduced apparent habitat selection. k -fold cross-validation results differed for GEE and GLMM, as it must be applied differently for each model. 4. Synthesis and applications . Due to their different interpretations, marginal models (e.g. generalized estimating equations, GEEs) may be better for landscape and population management, while conditional models (e.g. generalized linear mixed-effects models, GLMMs) may be better for management of endangered species and individuals. Destructive sampling may lead to inaccurate resource selection functions (RSFs), but GEEs and GLMMs can be used for developing RSFs when used with empirical standard errors.

Faecal material has increasingly become an important non-invasive source of DNA for wildlife population genetics. However, DNA from faecal sources can have issues associated with quantity (lowtemplate and/or low target-to-total DNA ratio) and quality (degradation and/or low DNA-to-inhibitor ratio). A number of studies utilizing faecal material assume and compensate for the above properties with minimal characterization of quantity or quality of target DNA, which can unnecessarily increase the risk of downstream technical problems. Here, we present a protocol which quantifies faecal DNA using a two step approach: (1) estimating total DNA concentration using a Picogreen TM fluorescence assay and (2) estimating target nuclear DNA concentration by comparing amplification products of field samples at suspected concentrations to those of control DNA at known concentrations. We applied this protocol to faecal material collected in the field from two species: woodland caribou (Rangifer tarandus) and swift fox (Vulpes velox). Total DNA estimates ranged from 6.5 ng/ll to 28.6 ng/ll (X = 16.2 ng/ll) for the caribou extracts and 1.0-26.1 ng/ll (X = 7.5 ng/ll) for the swift fox extracts. Our results showed high concordance between total and target DNA estimates from woodland caribou faecal extracts, with only 10% of the samples showing relatively lower target-to-total DNA ratios. In contrast, DNA extracts from swift fox scat exhibited low target DNA yields, with only 38% (19 of 50) of the samples showing comparative target DNA amplification of at least 0.1 ng. With this information, we were able to estimate the amount of target DNA entered into PCR amplifications, and identify samples having target DNA below a lower threshold of 0.2 ng and requiring modification to genotyping protocols such as multiple tube amplification. Our results here also show that this approach can easily be adapted to other species where faeces are the primary source of DNA template.

Summary1. Landscape genetics studies using neutral markers have focused on the relationship between gene flow and landscape features. Spatial patterns in the genetic distances among individuals may reflect spatially uneven patterns of gene flow caused by landscape features that influence movement and dispersal. 2. We present a method and software for identifying spatial neighbourhoods in genetic distance data that adopts a regression framework where the predictors are generated using Moran's eigenvectors maps (MEM), a multivariate technique developed for spatial ecological analyses and recommended for genetic applications. 3. Using simulated genetic data, we show that our MEMGENE method can recover patterns reflecting the landscape features that influenced gene flow. We also apply MEMGENE to genetic data from a highly vagile ungulate population and demonstrate spatial genetic neighbourhoods aligned with a river likely to reduce, but not eliminate, gene flow. 4. We developed the MEMGENE package for R in order to detect and visualize relatively weak or cryptic spatial genetic patterns and aid researchers in generating hypotheses about the ecological processes that may underlie these patterns. MEMGENE provides a flexible set of R functions that can be used to modify the analysis. Detailed supplementary documentation and tutorials are provided.