Partitioning drivers of spatial genetic variation for a continuously distributed population of boreal caribou: Implications for management unit delineation.

Isolation by distance (IBD) is a natural pattern not readily incorporated into theoretical models nor traditional metrics for differentiating populations, although clinal genetic differentiation can be characteristic of many wildlife species. Landscape features can also drive population structure additive to baseline IBD resulting in differentiation through isolation-by-resistance (IBR). We assessed the population genetic structure of boreal caribou across western Canada using nonspatial (STRUCTURE) and spatial (MEMGENE) clustering methods and investigated the relative contribution of IBD and IBR on genetic variation of 1,221 boreal caribou multilocus genotypes across western Canada.

Reduced abundance and earlier collection of bumble bee workers under intensive cultivation of a mass-flowering prairie crop.

One of the most commonly seeded crops in Canada is canola, a cultivar of oilseed rape (). As a mass-flowering crop grown intensively throughout the Canadian Prairies, canola has the potential to influence pollinator success across tens of thousands of square kilometers of cropland. Bumble bees ( sp.

Environmental and anthropogenic drivers of connectivity patterns: A basis for prioritizing conservation efforts for threatened populations.

Ecosystem fragmentation and habitat loss have been the focus of landscape management due to restrictions on contemporary connectivity and dispersal of populations. Here, we used an individual approach to determine the drivers of genetic differentiation in caribou of the Canadian Rockies. We modelled the effects of isolation by distance, landscape resistance and predation risk and evaluated the consequences of individual migratory behaviour (seasonally migratory vs.

CLIMATE CHANGE. Climate change impacts on bumblebees converge across continents.

For many species, geographical ranges are expanding toward the poles in response to climate change, while remaining stable along range edges nearest the equator. Using long-term observations across Europe and North America over 110 years, we tested for climate change-related range shifts in bumblebee species across the full extents of their latitudinal and thermal limits and movements along elevation gradients. We found cross-continentally consistent trends in failures to track warming through time at species' northern range limits, range losses from southern range limits, and shifts to higher elevations among southern species.

Grains of connectivity: analysis at multiple spatial scales in landscape genetics.

Landscape genetic analyses are typically conducted at one spatial scale. Considering multiple scales may be essential for identifying landscape features influencing gene flow. We examined landscape connectivity for woodland caribou (Rangifer tarandus caribou) at multiple spatial scales using a new approach based on landscape graphs that creates a Voronoi tessellation of the landscape.

Allelematch: an R package for identifying unique multilocus genotypes where genotyping error and missing data may be present.

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.

Automated measurement of Drosophila wings.

Background: Many studies in evolutionary biology and genetics are limited by the rate at which phenotypic information can be acquired. The wings of Drosophila species are a favorable target for automated analysis because of the many interesting questions in evolution and development that can be addressed with them, and because of their simple structure.

Results: We have developed an automated image analysis system (WINGMACHINE) that measures the positions of all the veins and the edges of the wing blade of Drosophilid flies.

Interpretation of the results of common principal components analyses.

Common principal components (CPC) analysis is a new tool for the comparison of phenotypic and genetic variance-covariance matrices. CPC was developed as a method of data summarization, but frequently biologists would like to use the method to detect analogous patterns of trait correlation in multiple populations or species. To investigate the properties of CPC, we simulated data that reflect a set of causal factors.