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10 results for “outdoor recreation”
Replication data for Global variation in the preferred temperature for recreational outdoor activity
<p><strong>Description</strong></p> <p>This dataset contains the processed data used for the statistical analysis in Linsenmeier, M. (2024): <a href="https://doi.org/10.1016/j.jeem.2024.103032">Global variation in the preferred temperature for recreational outdoor activity</a>, published in the Journal of Environmental Economics and Management.</p> <p>The main data on temperature and rainfall are from ERA5 reanalysis (Hersbach et al. 2018). Data on mobile phone activity are from the Google Mobility Reports. Data on GDP per capita are from the World Bank.</p> <p><strong>Acknowledgements</strong></p> <p>The data contain modified Copernicus Climate Change Service information 2020. Neither the European Commission nor ECMWF is responsible for any use that may be made of the Copernicus information or data it contains.</p> <p><strong>Bibliography</strong></p> <ul> <li>Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., Thépaut, J-N. (2018): ERA5 hourly data on single levels from 1959 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). 10.24381/cds.adbb2d47</li> </ul>
Data from: Habitat selection and outdoor recreation explain human-carnivore conflict
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Data and R code for machine learning modelling of favourite places and routes of outdoor recreation
<p>This is a script showing the analysis used in a paper submitted for review in Landscape and Urban Planning, titled "Seeing through their eyes: Revealing recreationists’ landscape preferences through viewshed analysis and machine learning", by Carl Lehto, Marcus Hedblom, Anna Filyushkina and Thomas Ranius. </p> <p>The zip file contains an R script, data saved in .rds format and a R workspace. </p>
Data from: Low levels of outdoor recreation alter wildlife behavior
<p>Public interest in nature-based recreation is growing, including visitation to protected areas. However, the level of recreation in these areas that causes detectable changes in wildlife behavior remains unknown, and many studies that investigate wildlife responses to humans do so in high-visitation areas. </p> <p>We used camera traps to investigate the spatial and temporal responses of brown bears (<em>Ursus arctos</em>), black bears (<em>Ursus americanus</em>), moose (<em>Alces alces</em>), and wolves (<em>Canis lupis</em>) to experimentally manipulated levels of human activity in Glacier Bay National Park, Alaska during summers 2017 and 2018. Human activity was restricted at some sites and concentrated at others, and these human impact treatments were swapped mid-season. The park has very low on-land visitation (~40,000 on-land tourists per year), making it a unique study system to investigate wildlife responses to low levels of human activity. </p> <p>Detections did not exceed five per week for any species unless human activity was absent (zero photos of humans were taken). However, spatial and temporal patterns of wildlife activity in relation to human activity were nuanced and species-specific. Moose shifted their activity patterns to better align with when people were most active. Black bears were more likely to be detected in areas of high human activity but used high-use areas less intensely than low-use areas. Wolves used areas of high human impact more intensely, but shifted their activity to be more strongly nocturnal. </p> <p>Our results highlight the importance of considering both spatial and temporal responses of wildlife to human activity. Additionally, and arguably most importantly, we detected changes in wildlife behavior in response to humans in a national park with relatively low tourism. Although natural processes may dominate in protected areas, our results indicate that even low levels of human activity can alter wildlife behavior. </p> <p>Synthesis and applications: We demonstrated that nearly any level of human activity in a protected area may alter wildlife behavior. However, it is unreasonable to expect protected areas to be completely devoid of human activity. Thus, management of these areas will need to balance the desires of humans to view wildlife with the likely impacts. </p>
Hunting, but not outdoor recreation, modulates behavioural tolerance to human disturbance in Alpine marmots Marmota marmota
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Data from: Low levels of outdoor recreation alter wildlife behavior
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Data from: The role of human outdoor recreation in shaping patterns of grizzly bear-black bear co-occurrence
Species distributions are influenced by a combination of landscape variables and biotic interactions with other species, including people. Grizzly bears and black bears are sympatric, competing omnivores that also share habitats with human recreationists. By adapting models for multi-species occupancy analysis, we analyzed trail camera data from 192 trail camera locations in and around Jasper National Park, Canada to estimate grizzly bear and black bear occurrence and intensity of trail use. We documented (a) occurrence of grizzly bears and black bears relative to habitat variables (b) occurrence and intensity of use relative to competing bear species and motorised and non-motorised recreational activity, and (c) temporal overlap in activity patterns among the two bear species and recreationists. Grizzly bears were spatially separated from black bears, selecting higher elevations and locations farther from roads. Both species co-occurred with motorised and non-motorised recreation, however, grizzly bears reduced their intensity of use of sites with motorised recreation present. Black bears showed higher temporal activity overlap with recreational activity than grizzly bears, however differences in bear daily activity patterns between sites with and without motorised and non-motorised recreation were not significant. Reduced intensity of use by grizzly bears of sites where motorised recreation was present is a concern given off-road recreation is becoming increasingly popular in North America, and can negatively influence grizzly bear recovery by reducing foraging opportunities near or on trails. Camera traps and multi-species occurrence models offer non-invasive methods for identifying how habitat use by animals changes relative to sympatric species, including humans. These conclusions emphasise the need for integrated land-use planning, access management, and grizzly bear conservation efforts to consider the implications of continued access for motorised recreation in areas occupied by grizzly bears.
Data and R code for cluster analysis and machine learning modelling of favourite places for outdoor recreation
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Data from: The role of human outdoor recreation in shaping patterns of grizzly bear-black bear co-occurrence
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Data from: Measuring the human-dimension of outdoor recreation and its impacts on terrestrial wildlife
<p>Complete data set used to reproduce the analyses presented in Cardona et al. "Measuring the human-dimension and its impacts on terrestrial wildlife".</p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.