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Climate change is affecting species and ecosystems across the Northeast and Midwest U.S. Natural resource managers looking to maintain ecological function and species persistence have requested information to improve resource management in the face of climate change. Leveraging the research that has already been supported by the Northeast Climate Adaptation Science Center and its partners, this project used the latest modeling techniques combined with robust field data to examine the impact of specific climate variables, land use change, and species interactions on the future distribution and abundance of species of conservation concern. An interdisciplinary team worked to understand the mechanisms that are driving...
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Historical and projected climate data point toward significant changes in the future for the Northeastern and Midwestern U.S. These changes will include impacts to many species (like birds, fish, and mammals), ecosystems (like forests), and natural resources (like water) that humans appreciate and rely on. In order to prepare for these changes, land and resource managers need to be able to predict how species will respond, what specific mechanisms are driving these changes, and what thresholds wildlife species may soon be pushed across. Crossing these thresholds could lead to rapid change or decline in the health of a wildlife population. In response to this need, a team of researchers is working to identify the...
Abstract (from Diversity and Distributions): Aim Identifying the mechanisms influencing species' distributions is critical for accurate climate change forecasts. However, current approaches are limited by correlative models that cannot distinguish between direct and indirect effects. Location New Hampshire and Vermont, USA. Methods Using causal and correlational models and new theory on range limits, we compared current (2014–2019) and future (2080s) distributions of ecologically important mammalian carnivores and competitors along range limits in the northeastern US under two global climate models (GCMs) and a high-emission scenario (RCP8.5) of projected snow and forest biomass change. Results Our hypothesis that...
Categories: Publication; Types: Citation
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Remote cameras (“trail cameras”) are a popular tool for non-invasive, continuous wildlife monitoring, and as they become more prevalent in wildlife research, machine learning (ML) is increasingly used to automate or accelerate the labor-intensive process of labelling (i.e., tagging) photos. Human-machine hybrid tagging approaches have been shown to greatly increase tagging efficiency (i.e., time to tag a single image). However, those potential increases hinge on the extent to which an ML model makes correct vs. incorrect predictions. We performed an experiment using a ML model that produces bounding boxes around animals, people, and vehicles in remote camera imagery (MegaDetector), to consider the impact of a ML...
    map background search result map search result map Examining the Responses of Species to Climate Change: Will Wildlife Face Biological Thresholds? Evaluating a tandem human-machine approach to labelling of wildlife in remote camera monitoring Evaluating a tandem human-machine approach to labelling of wildlife in remote camera monitoring Examining the Responses of Species to Climate Change: Will Wildlife Face Biological Thresholds?