Dynamic connectivity assessment for a terrestrial predator in a metropolitan region (data)
DOI10.5281/zenodo.10419385Zenodo10419385MaRDI QIDQ6723494
Dataset published at Zenodo repository.
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Publication date: 5 January 2024
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Data used in manuscript, Dynamic connectivity assessment for a terrestrial predator in a metropolitan region, containing coyote steps within the Greater Toronto Area, Canada with vegetation density (NDVI), impervious surface (building density), human population density, and distance to linear features extracted for each start and end step. Linear features labeled following traffic: low (LT), medium (MT), high (HT), and function hiking trails (NT) and public service (PS; (ie. railways and transmission lines). Code Repository:https://github.com/tgelmi-candusso/Dynamic-connectivity-assessment-for-a-terrestrial-predator-in-a-metropolitan-region Coyotes (n = 27; Figure 1) were monitored between 2012 and 2021 for 245 136 days (mean standard deviation). Coyotes were live-trapped with padded foothold traps, approved by the Ontario Ministry of Natural Resources Wildlife Animal Care Committee (protocols 75-12, 75-13, 75-14 ) or captured with nets by the Toronto Wildlife Centre, and fitted with self-releasing GPS-collars (Lotek Wildcell SG, Newmarket, Canada), recording location data, resampled following the median sampling frequency in order to maintain a constant sampling frequency for each individual (1-3 hours; Appendix S1: Table S1, http://doi.org/10.1002/fee.2633 ).The data were well balanced in terms of demographic traits (12 females/15 males, 19 adults/eight juveniles, 22 residents/15 transients). Residents and transients were distinguished based on movement characteristics. From consecutive GPS-collar locations, we calculated the turning angle and step length with the steps_by_burst() function from the R package amt (Signer et al. 2019). After fitting the distributions to observed step lengths and turning angles, we generated nine random available steps for each observed step using the random_steps() function from the R package amt (Signer et al. 2019). We standardized the fixed variables included in the model and extracted their values at the endpoint of each step. The fixed variables included four urban landscape covariates: vegetation density (normalized difference vegetation index or NDVI), human population density, impervious surface, and linear features. To measure the spatiotemporal dynamic responses of coyotes, we included the interaction of the fixed variables with three temporal scales (diel cycles, biological seasons, and climate seasons) and three demographic traits (coyote age, sex, and social status). More information on the data and how it was used available at http://doi.org/10.1002/fee.2633
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