Inferring population connectivity in eastern massasauga rattlesnakes (Sistrurus catenatus) using landscape genetics.

Assessing the environmental factors that influence the ability of a threatened species to move through a landscape can be used to identify conservation actions that connect isolated populations. However, direct observations of species' movement are often limited, making the development of alternate approaches necessary. Here we use landscape genetic analyses to assess the impact of landscape features on the movement of individuals between local populations of a threatened snake, the eastern massasauga rattlesnake (Sistrurus catenatus). We linked connectivity data with habitat information from two landscapes of similar size: a large region of unfragmented habitat and a previously studied fragmented landscape consisting of isolated patches of habitat. We used this analysis to identify features of the landscape where modification or acquisition would enhance population connectivity in the fragmented region. We found evidence that current connectivity was impacted by both contemporary land-cover features, especially roads, and inherent landscape features such as elevation. Next, we derived estimates of expected movement ability using a recently developed pedigree-based approach and least-cost paths through the unfragmented landscape. We then used our pedigree and resistance map to estimate resistance polygons of the potential extent for S. catenatus movement in the fragmented landscape. These polygons identify possible sites for future corridors connecting currently isolated populations in this landscape by linking the impact of future habitat modification or land acquisition to dispersal ability in this species. Overall, our study shows how modeling landscape resistance across differently fragmented landscapes can identify habitat features that affect contemporary movement in threatened species in fragmented landscapes and how this information can be used to guide mitigation actions whose goal is to connect isolated populations.

Graphene Textile Strain Sensor with Negative Resistance Variation for Human Motion Detection.

Recently, wearable devices have been attracting significantly increased interest in human motion detection and human physiological signal monitoring. Currently, it is still a great challenge to fabricate strain sensors with high performance and good fit to the human body. In this work, we fabricated a close-fitting and wearable graphene textile strain sensor based on a graphene textile without polymer encapsulation. Graphene oxide acts as a colorant to dye the polyester fabric and is reduced at high temperature, which endows the graphene textile strain sensor with excellent performance. Compared with the previously reported strain sensors, our strain sensor exhibits a distinctive negative resistance variation with increasing strain. In addition, the sensor also demonstrates fascinating performance, including high sensitivity, long-term stability, and great comfort. Based on its superior performance, the graphene textile strain sensor can be knitted on clothing for detecting both subtle and large human motions, showing the tremendous potential for applications in wearable electronics.