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. A video image is obtained with the aid of a simple suction device that immobilizes the wing of a live fly. Low-level processing is used to find the major intersections of the veins. High-level processing then optimizes the fit of an a priori B-spline model of wing shape. WINGMACHINE allows the measurement of 1 wing per minute, including handling, imaging, analysis, and data editing. The repeatabilities of 12 vein intersections averaged 86% in a sample of flies of the same species and sex. Comparison of 2400 wings of 25 Drosophilid species shows that wing shape is quite conservative within the group, but that almost all taxa are diagnosably different from one another. Wing shape retains some phylogenetic structure, although some species have shapes very different from closely related species. The WINGMACHINE system facilitates artificial selection experiments on complex aspects of wing shape. We selected on an index which is a function of 14 separate measurements of each wing. After 14 generations, we achieved a 15 S.D. difference between up and down-selected treatments. CONCLUSION: WINGMACHINE enables rapid, highly repeatable measurements of wings in the family Drosophilidae. Our approach to image analysis may be applicable to a variety of biological objects that can be represented as a framework of connected lines

Modelling biodiversity distribution in agricultural landscapes to support ecological network planning

Strategic approaches to biodiversity conservation increasingly emphasise the restoration of ecological connectivity at landscape scales. However, understanding where these connecting elements should be placed in the landscape is critical if they are to provide both value for money and for biodiversity. For such planning to be effective, it is necessary to have information of the distributions of multiple taxa, however, this is of poor quality for many taxa. We show that sparse, non-systematically collected biological records can be modelled using readily available environmental variables to meaningfully predict potential biodiversity richness, including rare and threatened species, across a landscape. Using a large database of ad-hoc biological records (50 501 records of 502 species) we modelled the richness of wetland biodiversity across the Fens, a formerly extensive wetland, now agricultural landscape in eastern England. We used these models to predict those parts of the agricultural ditch network of greatest potential conservation value and compared this to current strategic network planning. Odonata distribution differed to that of other groups, indicating that single taxon groups may not be effective proxies for other priority biodiversity. Our results challenged previous assumptions that river channels should comprise the main connecting elements in the Fens region. Rather, areas of high ditch density close to a main river are likely to be of greater value and should be targeted for enhancement. This approach can be adopted elsewhere in order to improve the evidence-base for strategic networks plans, increasing their value for money

The use of common principal component analysis in studies of phenotypic evolution, an example from the Drosophilidae

grantor: University of TorontoHave covariances among characters been relatively static throughout the diversification of a group of related organisms, or has there been evolution in the relationships among characters? Common principal component (CPC) analysis, an hypothesis testing analog of principal component analysis, has been used by others to test this question. First, the efficacy of CPC is evaluated using simulated data, and shown to have important flaws. Second, CPC is applied to pomace fly (Drosophilidae) wing morphometric data. Results indicate that there are differences in the phenotypic covariance matrices of the related species, which may suggest an evolution in the genetic relationship among traits. The demonstrated limitations of CPC, however, may be overestimating these observed differences. Factor analytical methods offer an alternative approach. Additional miscellaneous observations about pomace fly wing morphology are presented.M.Sc

Data from: 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. To illustrate the potential of the method, we generated five resistance surfaces to explain how landscape pattern may influence gene flow across the range of this population. We tested each resistance surface using a raster at the spatial grain of available landscape data (200 m grid squares). We then used our method to produce up to 127 additional grains for each resistance surface. We applied a causal modelling framework with partial Mantel tests, where evidence of landscape resistance is tested against an alternative hypothesis of isolation-by-distance, and found statistically significant support for landscape resistance to gene flow in 89 of the 507 spatial grains examined. We found evidence that major roads as well as the cumulative effects of natural and anthropogenic disturbance may be contributing to the genetic structure. Using only the original grid surface yielded no evidence for landscape resistance to gene flow. Our results show that using multiple spatial grains can reveal landscape influences on genetic structure that may be overlooked with a single grain, and suggest that coarsening the grain of landcover data may be appropriate for highly-mobile species. We discuss how grains of connectivity and related analyses have potential landscape genetic applications in a broad range of systems

Habitat raster

A raster in ArcASCII format giving the habitat feature classes in the study area (Smoothstone-Wapeweka caribou range, Saskatchewan, Canada

Smartphone GPS Locations of Students’ Movements to and from Campus

For many university students, commuting to and from campus constitutes a large proportion of their daily movement, and therefore it may influence their ability and willingness to spend time on campus or to participate in campus activities. To assess student engagement on campus, we collected smartphone GPS location histories from volunteers (n = 280) attending university in a major Canadian city. We investigated how campus visit length and frequency were related to characteristics of the commute using Bayesian regression models. Slower commutes and commutes over longer distances were associated with more time spent but less frequent visits to campus. Our results demonstrate that exposure to campus life, and therefore the potential for student engagement, may relate not just to whether a student lives on or near campus, but also to urban environmental factors that interact to influence the commuting experience.Forestry, Faculty ofNon UBCReviewedFacultyResearche

Priadka_etal_2018_genotypes

Genotypes for 1221 boreal caribou at 9 microsatellite loci. Missing data is indicated by "-99". Coordinates for sampling locations have not been included because boreal caribou are listed as Threatened under Canada's Species At Risk Act

Priadka_etal_2018_Landscape_rasters

Raster .asc files of landscape resistance models. Univariate models include resistance costs of 10, 50 and 100. Optimized models for Cluster 1 and Cluster 2 reflect the optimized resistance costs for roads and water bodies (land1), roads and forestry (land2), water bodies and forestry (land3) and roads, water bodies and forestry (land4). Raster cell size is 250m in planar projection