DOI: 10.5061/dryad.0cfxpnvzs
title: Data from: Responses of sympatric canids to human development revealed through citizen science

description: Measuring wildlife responses to anthropogenic activities often requires
 long-term, large-scale datasets that are difficult to collect. This is
 particularly true for rare or cryptic species, which includes many
 mammalian carnivores. Citizen science, in which members of the public
 participate in scientific work, can facilitate collection of large
 datasets while increasing public awareness of wildlife research and
 conservation. Hunters provide unique benefits for citizen science given
 their knowledge and interest in outdoor activities. We examined how
 anthropogenic changes to land cover impacted relative abundance of two
 sympatric canids, coyote (Canis latrans) and red fox (Vulpes vulpes) at a
 large spatial scale. In order to assess how land cover affected canids at
 this scale, we used citizen science data from bow hunter sighting logs
 collected throughout New York State, USA, during 2004–2017. We found that
 the two species had contrasting responses to development, with red foxes
 positively correlated and coyotes negatively correlated with the
 percentage of low-density development. Red foxes also responded positively
 to agriculture, but less so when agricultural habitat was fragmented.
 Agriculture provides food and denning resources for red foxes, whereas
 coyotes may select forested areas for denning. Though coyotes and red
 foxes compete in areas of sympatry, we did not find a relationship between
 species abundance, likely a consequence of the coarse spatial resolution
 used. Red foxes may be able to coexist with coyotes by altering their
 diets and habitat use, or by maintaining territories in small areas
 between coyote territories. Our study shows the value of citizen science,
 and particularly hunters, in collection of long-term data across large
 areas (i.e., the entire state of New York) that otherwise would unlikely
 be obtained.
description: This dataset "canid_data.csv" represents hunter
 observations of coyote (<em>Canis latrans</em>) and red fox
 (<em>Vulpes vulpes</em>) collected yearly from 2004-2017
 across the state of New York, USA as part of the New York Bowhunter
 Sighting Log. We calculated the sum of counts of each species by wildlife
 management unit (WMU) and year. These counts are in dataset columns
 "coyote" and "redfox". The total number of hunter
 observation hours (in units of 100 hours) in each WMU and year is in
 column "hours". Columns "ag", "urbanL", and
 "para_mn" represent percent agriculture, percent low-density
 urban, and agriculture perimeter-area ratio for each WMU and year, derived
 from the National Land Cover Database (NLCD). The values in these columns
 were scaled for analysis so they had a mean of 0 and a standard deviation
 of 1. The following three columns "ag_unscaled",
 "urbanL_unscaled", and "para_mn_unscaled" are the
 unscaled versions of these variables. Finally, the ID column is simply the
 observation number (unique for each row) used to index the
 observation-level random effect.
description: To replicate the analysis in the paper, put the dataset
 ("canid_data.csv") and the R analysis file
 ("canid_paper_analysis.R") in the same directory and run the R
 code. The R file contains inline comments with more details on each step
 of the analysis and how the resulting output matches tables in the
 paper.