Spatially anonymized data from: Novel step selection analyses on energy landscapes reveal how linear features alter migrations of soaring birds ...
This dataset consists of spatially anonymized movement data as well as environmental covariate data to estimate energy landscape step selection selections for migratory golden eagles that summer in Alaska. Human modification of landscapes includes extensive addition of linear features, such as roads...
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| Format: | Dataset |
| Language: | English |
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Dryad
2020
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| Online Access: | https://dx.doi.org/10.5061/dryad.brv15dv7p https://datadryad.org/stash/dataset/doi:10.5061/dryad.brv15dv7p |
| Summary: | This dataset consists of spatially anonymized movement data as well as environmental covariate data to estimate energy landscape step selection selections for migratory golden eagles that summer in Alaska. Human modification of landscapes includes extensive addition of linear features, such as roads and transmission lines. These can alter animal movement and space use and affect the intensity of interactions among species, including predation and competition. Effects of linear features on animal movement have seen relatively little research in avian systems, despite ample evidence of their effects in mammalian systems and that some types of linear features, including both roads and transmission lines, are substantial sources of mortality. Here, we used satellite telemetry combined with step selection functions designed to explicitly incorporate the energy landscape (el‐SSFs) to investigate the effects of linear features and habitat on movements and space use of a large soaring bird, the golden eagle Aquila ... : The variables X.1, X, and nothing are old indexes and should not be used for anything. The variables k and tod were indicators previously used for subsetting the data. The variable id is the individual identifier for each individual, and cluster identifies steps. Use is a binary indicator of whether or not the point was used or available. Gamma is the movement correlation parameter estimated from the CRW movement model. Season is an indicator of fall and spring. The majority of remaining columns are covariates used in the SSFs. Any variables with raw in the header are the raw values, and any without are the centered and scaled quantities. ... |
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