Anthropogenic Habitats Facilitate Dispersal of an Early Successional Obligate: Implications for Restoration of an Endangered Ecosystem

Landscape modification and habitat fragmentation disrupt the connectivity of natural landscapes, with major consequences for biodiversity. Species that require patchily distributed habitats, such as those that specialize on early successional ecosystems, must disperse through a landscape matrix with unsuitable habitat types. We evaluated landscape effects on dispersal of an early successional obligate, the New England cottontail (Sylvilagus transitionalis). Using a landscape genetics approach, we identified barriers and facilitators of gene flow and connectivity corridors for a population of cottontails in the northeastern United States. We modeled dispersal in relation to landscape structure and composition and tested hypotheses about the influence of habitat fragmentation on gene flow. Anthropogenic and natural shrubland habitats facilitated gene flow, while the remainder of the matrix, particularly development and forest, impeded gene flow. The relative influence of matrix habitats differed between study areas in relation to a fragmentation gradient. Barrier features had higher explanatory power in the more fragmented site, while facilitating features were important in the less fragmented site. Landscape models that included a simultaneous barrier and facilitating effect of roads had higher explanatory power than models that considered either effect separately, supporting the hypothesis that roads act as both barriers and facilitators at all spatial scales. The inclusion of LiDAR-identified shrubland habitat improved the fit of our facilitator models. Corridor analyses using circuit and least cost path approaches revealed the importance of anthropogenic, linear features for restoring connectivity between the study areas. In fragmented landscapes, human-modified habitats may enhance functional connectivity by providing suitable dispersal conduits for early successional specialists

Clinical Sequencing Exploratory Research Consortium: Accelerating Evidence-Based Practice of Genomic Medicine

Despite rapid technical progress and demonstrable effectiveness for some types of diagnosis and therapy, much remains to be learned about clinical genome and exome sequencing (CGES) and its role within the practice of medicine. The Clinical Sequencing Exploratory Research (CSER) consortium includes 18 extramural research projects, one National Human Genome Research Institute (NHGRI) intramural project, and a coordinating center funded by the NHGRI and National Cancer Institute. The consortium is exploring analytic and clinical validity and utility, as well as the ethical, legal, and social implications of sequencing via multidisciplinary approaches; it has thus far recruited 5,577 participants across a spectrum of symptomatic and healthy children and adults by utilizing both germline and cancer sequencing. The CSER consortium is analyzing data and creating publically available procedures and tools related to participant preferences and consent, variant classification, disclosure and management of primary and secondary findings, health outcomes, and integration with electronic health records. Future research directions will refine measures of clinical utility of CGES in both germline and somatic testing, evaluate the use of CGES for screening in healthy individuals, explore the penetrance of pathogenic variants through extensive phenotyping, reduce discordances in public databases of genes and variants, examine social and ethnic disparities in the provision of genomics services, explore regulatory issues, and estimate the value and downstream costs of sequencing. The CSER consortium has established a shared community of research sites by using diverse approaches to pursue the evidence-based development of best practices in genomic medicine

Study area in Maine/New Hampshire (USA).

<p>Top left two insets provide context for study area location in North America within the states of Maine and New Hampshire. Bottom left panel shows the full extent of the study area. I-95 is shown by solid black line partitioning east and west sides of the Kittery region. The Piscataqua River is visible in the southern portion of Kittery. Close ups of the two study study areas with landcover are shown in the top right for Cape Elizabeth and bottom right for Kittery. Locations of sampled New England cottontail individuals are shown by black points. Landcover key: gray = development, green = forest, orange = fields, yellow = scrub/shrub, dark blue = open water, and light blue = wetlands.</p

Multivariate landscape model results.

<p>Model selection results for multivariate linear mixed effects models of the relationship of landscape features on individual genetic distance, measured by Rousset’s <i>a</i>, for New England cottontails in the Kittery and Cape Elizabeth study areas. AICc is the second order or sample size corrected Akaike information criterion, delta AICc is the difference in AICc of each competing model relative to the best model, and AICcWt is the probability that the model is the best fit.</p

Landscape influences on cottontail gene flow.

<p>Landscape features evaluated in this study along with their hypothesized and empirically identified (from univariate least cost path models) influence on New England cottontail gene flow. Plus signs indicate postive relationship, minus signs indicate negative relationship.</p

Connectivity corridors for cottontails.

<p>Circuit analysis overlayed with least cost analysis (black lines) of New England cottontail gene flow across the Maine-New Hampshire study area. Areas in red indicate high current flow/high probability of movement while green/blue areas indicate low probability of movement.</p

Study area composition and configuration.

<p>Characteristics of occupied patches and proportion of each study area comprised by specific landcover types. Road and LiDAR percentages indicate the overall proportion of landscape that they cover and their coverage overlaps with that of other landcover types.</p

Univariate landscape model results.

<p>Model selection results for univariate linear mixed effects models of the relationship of landscape features on individual genetic distance, measured by Rousset’s <i>a</i>, for New England cottontails in the Kittery and Cape Elizabeth study areas. AICc is the second order or sample size corrected Akaike information criterion, delta AICc is the difference in AICc of each competing model relative to the best model, and AICcWt is the probability that the model is the best fit.</p