DOI: 10.5061/dryad.573n5tbfr
title: Dataset of how animal movement paths in a landscape vary due to wildfire

description: Animals navigate landscapes based on perceived risks vs. rewards, as
 inferred from features of the landscape. In the wild, knowing how strongly
 animal movement is directed by landscape features is difficult to
 ascertain, but widespread disturbances such as wildfires can serve as
 natural experiments. We tested the hypothesis that wildfires homogenize
 the risk/reward landscape, causing movement to become less directed, given
 that fires reduce landscape complexity as habitat structures (e.g., tree
 cover, dense brush) are burned. We used satellite imagery of a research
 reserve in Northern California to count and categorize paths made
 primarily by mule deer (Odocoileus hemionus) in grasslands. Specifically,
 we compared pre-wildfire (August 2014) and post-wildfire (September 2018)
 image history layers among locations that were or were not impacted by
 wildfire (i.e., a Before/After Control/Impact design). Wildfire
 significantly altered spatial patterns of deer movement: more new paths
 were gained and more old paths were lost in areas of the reserve that were
 impacted by wildfire; movement patterns became less directed in response
 to fire, suggesting that the risk/reward landscape became more homogenous,
 as hypothesized. We found evidence to suggest that wildfire affects deer
 populations at spatial scales beyond their scale of direct impact and
 raises the interesting possibility that deer perceive risks and rewards at
 different spatial scales. In conclusion, our study provides an example of
 how animals integrate spatial information from the environment to make
 movement decisions, setting the stage for future work on the broader
 ecological implications for populations, communities, and ecosystems, an
 emerging interest in ecology.
description: Animal paths were traced from satellite imagery before and after
 a wildfire, in areas impacted by fire and in unimpacted areas (i.e., a
 BACI design). Paths were then counted, categorized by size, and assessed
 to see whether paths were persistent or were formed or lost dynamically
 over time. This was a pandemic project.
description: # Dataset of how animal movement paths in a landscape vary due to wildfire
 This project has one script that performs all analyses and generates all
 figures. This project has two datasets. Dataset 1 (**fidelity data.csv**)
 is used for the majority of the analyses in the paper. Site and subplot
 are self explanatory - type indicates whether the data was collected on a
 plot that was impacted by wildfire between 2014 and 2018. The rest of the
 columns are counts of the number of deer paths of a certain thickness in
 2014 vs in 2018. For example, THIN_to_thick are paths that were thin in
 2014 and became thick in 2018. NONE_to_(thick/medium/thick) are paths that
 didn't exist in 2014, whereas (THICK/MEDIUM/THIN)_to_none are paths
 observed in 2014 that disappeared. Plots 10 and 20 do not have associated
 data because originally we were planning to have 10 replicates per burn
 treatment but could only find 9 that met our criteria for inclusion, and
 did not want to introduce errors by renumbering plots after the fact.
 Dataset 2 (**shrub cover.csv**) is used for a supplementary analysis of
 how much shrub cover changed over time and in response to wildfire.
 Img_Name is simply an ID used so the data collector could differentiate
 experimental units. It is not used in the script. Year indicates whether
 shrubs are being quantified in 2014 or 2018, whereas Plot is which plot
 shrubs were measured in. Shrub_cover is a measure of the percent cover of
 shrubs in a plot. Notes were notes made by the collector.