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Folders: ROOT > ScienceBase Catalog > National and Regional Climate Adaptation Science Centers > Northeast CASC > FY 2016 Projects ( Show direct descendants )

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Montane regions support distinct animal and plant communities that are widely viewed as communities of high conservation concern due to their significant contribution to regional biodiversity. These communities are also thought to be particularly vulnerable to anthropogenically caused stressors such as climate change, which is generally expected to cause upward shifts and potential range restrictions in montane plant and animal distributions. In the northern Appalachian Mountains of North America, not only is it becoming warmer at mid-elevations but the ecotone between the northern hardwood and the montane coniferous forests is also shifting. Therefore, species that are limited by climate or habitat along the elevational...
Remote telemetry data loggers are commonly used for monitoring wildlife species. Although remote telemetry data loggers provide reliable microhabitat use data, few studies have used them to evaluate landscape-scale, temporal, and spatial habitat use. We installed 3 data loggers along a mountain ridgeline that was being developed for a commercial wind farm in northern New Hampshire, USA, to monitor use of a high-elevation forest by American martens (Martes americana). We tested 1) the efficacy of data loggers to record presence–absence and index space use in a 6.75-km2 area, validating marten locations using radiotelemetry and camera traps; and 2) whether there were diel and seasonal biases of data logger detections....
Landscape capability (LC) models are a spatial tool with potential applications in conservation planning. We used survey data to validate LC models as predictors of occurrence and abundance at broad and fine scales for American woodcock (Scolopax minor) and ruffed grouse (Bonasa umbellus). Landscape capability models were reliable predictors of occurrence but were less indicative of relative abundance at route (11.5–14.6 km) and point scales (0.5–1 km). As predictors of occurrence, LC models had high sensitivity (0.71–0.93) and were accurate (0.71–0.88) and precise (0.88 and 0.92 for woodcock and grouse, respectively). Models did not predict point-scale abundance independent of the ability to predict occurrence...