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Advancements in monitoring: a comparison of traditional and application-based tools for measuring outdoor recreation. | LitMetric

AI Article Synopsis

  • Outdoor recreation is rapidly increasing, bringing benefits for well-being but also raising sustainability concerns due to ecological impacts and visitor experience pressures.
  • The study compares traditional data collection methods (like trail cameras) with app-based tools (like Strava) to analyze recreation patterns in the Columbia and Canadian Rocky Mountains, highlighting differences in accuracy and bias by activity type.
  • The research emphasizes the importance of combining both traditional and app-based tools to improve data quality for effective monitoring and management of outdoor recreation.

Article Abstract

Outdoor recreation has experienced a boom in recent years and continues to grow. While outdoor recreation provides wide-ranging benefits to human well-being, there are growing concerns about the sustainability of recreation with the increased pressures placed on ecological systems and visitor experiences. These concerns emphasize the need for managers to access accurate and timely recreation data at scales that match the growing extent of the recreation footprint. Here, we compare spatial and temporal patterns of winter and summer recreation using traditional (trail cameras, infrared counters, aerial surveys, participatory mapping) and application-based tools (Strava Metro, Strava Global Heatmap, Wikiloc) across the Columbia and Canadian Rocky Mountains of western Canada. We demonstrate how recreation use can be estimated using traditional and application-based tools, although their accuracy and utility varies across space, season and activity type. We found that trail cameras and infrared counters captured similar broad-scale patterns in count estimates of pedestrians and all recreation activities. Aerial surveys captured areas with low recreation intensity and participatory mapping captured coarser information on the intensity and extent of recreation across large spatial and temporal scales. Application-based data provided detailed spatiotemporal information on recreation use, but datasets were biased towards specific activities. Strava Metro data was more suited for capturing broad-scale spatial patterns in biking than pedestrian recreation. Application-based data should be supplemented with data from traditional tools to identify biases in data and fill in data gaps. We provide a comparison of each tool for measuring recreation use, highlight each tools' strengths and limitations and applications to address real-world monitoring and management scenarios. Our research contributes towards a better understanding of which tool, or combinations of tools, to use that can expand the rigor and scope of recreation research. These findings support decision-making to mitigate pressures on wildlife and their habitats while allowing for high-quality recreation experiences.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11397128PMC
http://dx.doi.org/10.7717/peerj.17744DOI Listing

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