Paul J. Wilson

A RELIABLE MOLECULAR METHOD OF GENDER DETERMINATION FOR MAMMALS

Abstract A polymerase chain reaction–based method was developed for gender determination in a wide variety of marine and terrestrial mammals, including cetaceans, pinnipeds, ungulates, canids, and ursids. An intron within the zinc-finger x (Zfx) and zinc-finger y (Zfy) genes was amplified. Size variation between the Zfx and Zfy introns results in a Y-specific band in males. Size of the Zfy intron varies more across species than that of the Zfx intron. This technique is simple and rapid, requires small amounts of DNA, and can be applied to a variety of mammalian species. In contrast to previous methods, only 1 primer set is needed for both gender determination and internal verification of amplification.

Genetic nature of eastern wolves: Past, present and future

Eastern North American wolves have long been recognized as morphologically distinct from both coyotes and gray wolves. This has led to questions regarding their origins and taxonomic status. Eastern wolves are mainly viewed as: (1) a smaller subspecies of gray wolf (Canis lupus lycaon), potentially the result of historical hybridization between gray wolves (C. lupus) and red wolves (C. rufus), (2) a hybrid, the result of gray wolf (C. lupus) and coyote (C. latrans) interbreeding, or (3) a distinct species, C. lycaon, closely related to the red wolf (C. rufus). Although debate persists, recent molecular studies suggest that the eastern wolf is not a gray wolf subspecies, nor the result of gray wolf/coyote hybridization. Eastern wolves were more likely a distinct species, C. lycaon, prior to the eastward spread of coyotes in the late 1800s. However, contemporary interbreeding exits between C. lycaon to both C. lupus and C. latrans over much of its present range complicating its present taxonomic characterization. While hybridization may be reducing the taxonomic distinctiveness of C. lycaon, it should not necessarily be viewed as negative influence. Hybridization may be enhancing the adaptive potential of eastern wolves, allowing them to more effectively exploit available resources in rapidly changing environments.

Applications of graph theory to landscape genetics

We investigated the relationships among landscape quality, gene flow, and population genetic structure of fishers (Martes pennanti) in ON, Canada. We used graph theory as an analytical framework considering each landscape as a network node. The 34 nodes were connected by 93 edges. Network structure was characterized by a higher level of clustering than expected by chance, a short mean path length connecting all pairs of nodes, and a resiliency to the loss of highly connected nodes. This suggests that alleles can be efficiently spread through the system and that extirpations and conservative harvest are not likely to affect their spread. Two measures of node centrality were negatively related to both the proportion of immigrants in a node and node snow depth. This suggests that central nodes are producers of emigrants, contain high‐quality habitat (i.e., deep snow can make locomotion energetically costly) and that fishers were migrating from high to low quality habitat. A method of community detection on networks delineated five genetic clusters of nodes suggesting cryptic population structure. Our analyses showed that network models can provide system‐level insight into the process of gene flow with implications for understanding how landscape alterations might affect population fitness and evolutionary potential.

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. 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.

The Effect of Map Boundary on Estimates of Landscape Resistance to Animal Movement

Background Artificial boundaries on a map occur when the map extent does not cover the entire area of study; edges on the map do not exist on the ground. These artificial boundaries might bias the results of animal dispersal models by creating artificial barriers to movement for model organisms where there are no barriers for real organisms. Here, we characterize the effects of artificial boundaries on calculations of landscape resistance to movement using circuit theory. We then propose and test a solution to artificially inflated resistance values whereby we place a buffer around the artificial boundary as a substitute for the true, but unknown, habitat. Methodology/Principal Findings We randomly assigned landscape resistance values to map cells in the buffer in proportion to their occurrence in the known map area. We used circuit theory to estimate landscape resistance to organism movement and gene flow, and compared the output across several scenarios: a habitat-quality map with artificial boundaries and no buffer, a map with a buffer composed of randomized habitat quality data, and a map with a buffer composed of the true habitat quality data. We tested the sensitivity of the randomized buffer to the possibility that the composition of the real but unknown buffer is biased toward high or low quality. We found that artificial boundaries result in an overestimate of landscape resistance. Conclusions/Significance Artificial map boundaries overestimate resistance values. We recommend the use of a buffer composed of randomized habitat data as a solution to this problem. We found that resistance estimated using the randomized buffer did not differ from estimates using the real data, even when the composition of the real data was varied. Our results may be relevant to those interested in employing Circuitscape software in landscape connectivity and landscape genetics studies.

Estimating economic damage from climate change in the United States

Estimates of climate change damage are central to the design of climate policies. Here, we develop a flexible architecture for computing damages that integrates climate science, econometric analyses, and process models. We use this approach to construct spatially explicit, probabilistic, and empirically derived estimates of economic damage in the United States from climate change. The combined value of market and nonmarket damage across analyzed sectors—agriculture, crime, coastal storms, energy, human mortality, and labor—increases quadratically in global mean temperature, costing roughly 1.2% of gross domestic product per +1°C on average. Importantly, risk is distributed unequally across locations, generating a large transfer of value northward and westward that increases economic inequality. By the late 21st century, the poorest third of counties are projected to experience damages between 2 and 20% of county income (90% chance) under business-as-usual emissions (Representative Concentration Pathway 8.5).Costing out the effects of climate change Episodes of severe weather in the United States, such as the present abundance of rainfall in California, are brandished as tangible evidence of the future costs of current climate trends. Hsiang et al. collected national data documenting the responses in six economic sectors to short-term weather fluctuations. These data were integrated with probabilistic distributions from a set of global climate models and used to estimate future costs during the remainder of this century across a range of scenarios (see the Perspective by Pizer). In terms of overall effects on gross domestic product, the authors predict negative impacts in the southern United States and positive impacts in some parts of the Pacific Northwest and New England. Science, this issue p. 1362; see also p. 1330 One percent of gross domestic product per degree Celsius is a steep price to pay for climate change. Estimates of climate change damage are central to the design of climate policies. Here, we develop a flexible architecture for computing damages that integrates climate science, econometric analyses, and process models. We use this approach to construct spatially explicit, probabilistic, and empirically derived estimates of economic damage in the United States from climate change. The combined value of market and nonmarket damage across analyzed sectors—agriculture, crime, coastal storms, energy, human mortality, and labor—increases quadratically in global mean temperature, costing roughly 1.2% of gross domestic product per +1°C on average. Importantly, risk is distributed unequally across locations, generating a large transfer of value northward and westward that increases economic inequality. By the late 21st century, the poorest third of counties are projected to experience damages between 2 and 20% of county income (90% chance) under business-as-usual emissions (Representative Concentration Pathway 8.5).

Using a genetic network to parameterize a landscape resistance surface for fishers, Martes pennanti

Knowledge of dispersal‐related gene flow is important for addressing many basic and applied questions in ecology and evolution. We used landscape genetics to understand the recovery of a recently expanded population of fishers (Martes pennanti) in Ontario, Canada. An important focus of landscape genetics is modelling the effects of landscape features on gene flow. Most often resistance surfaces in landscape genetic studies are built a priori based upon nongenetic field data or expert opinion. The resistance surface that best fits genetic data is then selected and interpreted. Given inherent biases in using expert opinion or movement data to model gene flow, we sought an alternative approach. We used estimates of conditional genetic distance derived from a network of genetic connectivity to parameterize landscape resistance and build a final resistance surface based upon information‐theoretic model selection and multi‐model averaging. We sampled 657 fishers from 31 landscapes, genotyped them at 16 microsatellite loci, and modelled the effects of snow depth, road density, river density, and coniferous forest on gene flow. Our final model suggested that road density, river density, and snow depth impeded gene flow during the fisher population expansion demonstrating that both human impacts and seasonal habitat variation affect gene flow for fishers. Our approach to building landscape genetic resistance surfaces mitigates many of the problems and caveats associated with using either nongenetic field data or expert opinion to derive resistance surfaces.

A Genetic Assessment of the Eastern Wolf (Canis lycaon) in Algonquin Provincial Park

Abstract Recent genetic data indicate that the eastern wolf is not a subspecies of the gray wolf (Canis lupus), but is a North American wolf more similar to the red wolf (C. rufus) and closely related to the coyote (C. latrans). The eastern wolf has been proposed as a separate species, C. lycaon. The largest protected area containing this wolf is Algonquin Provincial Park in Ontario, Canada, which is bounded to the south by areas containing the Tweed wolf or eastern coyote, a hybrid of the western coyote and eastern wolf. We assessed the relationships of animals in the park by using DNA profiles that comprised the genotype from 17 autosomal and 4 Y-linked microsatellite loci and the mitochondrial DNA control region. These profiles were used to establish maternity, paternity, and kin relationships for 102 wolves that were studied from 24 packs over a 12-year period. Genetic data do not support the hypothesis that a pack comprises an unrelated breeding pair and their offspring. There is evidence of frequent pack splitting, pack fusion, and adoption. Some unrelated individuals in the packs were identified as immigrants into the park. We found high levels of genetic structuring between the Tweed wolves to the southeast and the Algonquin Park wolves (RST = 0.114). Lower levels of genetic differentiation with animals to the north and west (RST = 0.057 and RST = 0.036) and high genetic diversity suggest that park animals are not an island population but the southern part of a larger metapopulation of C. lycaon.

Forensic Application of Repetitive DNA Markers to the Species Identification of Animal Tissues

Highly repetitive DNA markers have been used for determining the species origin of animal tissues in cases of illegal commercialization and poaching of game animals. This approach has been used in cases involving white-tailed deer (Odocoileus virginianus), moose (Alces alces) and black bear (Ursus americanus). Digesting the DNA with various restriction enzymes, agarose electrophoresis and staining with ethidium bromide revealed unique banding patterns for each species. These patterns have been used to distinguish meat from game animal species from commercial sources of meat and organs. Data are presented from two Ontario court cases that demonstrate the application of the procedure.

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.