DOI: 10.6078/d12h83
title: Movement behavior in a dominant ungulate underlies successful adjustment to a rapidly changing landscape following megafire

description: Climate and land use change have accelerated the frequency of extreme
 disturbances such as megafires. These megafires dramatically alter
 ecosystems and challenge the capacity of several species to adjust to a
 rapidly changing landscape. Previous work has shown behavioral plasticity
 is an important mechanism underlying whether large ungulates are able to
 adjust to recent changes in their environments effectively. Ungulates may
 respond to the immediate effects of megafire by adjusting their movement
 and behavior, but how these responses persist or change over time after
 disturbance is poorly understood. We examined how an ecologically dominant
 ungulate with strong site fidelity, Columbian black-tailed deer
 (Odocoileus hemionus columbianus), adjusted its movement and behavior in
 response to an altered landscape following a megafire. To do so, we
 collected GPS data from 24 individual female deer over the course of a
 year and used resource selection functions (RSFs) and hidden Markov
 movement models (HMMs). We found compelling evidence of adaptive capacity
 across individual deer in response to megafire. Deer avoided exposed and
 severely burned areas that could be riskier for predation immediately
 following the fire, but they later altered these behaviors to select for
 areas that burned at higher severities, potentially to take advantage of
 enhanced forage. These results suggest that despite their high site
 fidelity, deer can navigate altered landscapes to track rapid shifts in
 predation risk and resource availability. The successful adjustment of
 ungulate species to extreme disturbances could help facilitate resilience
 at broader ecological scales.
description: # Movement behavior in a dominant ungulate underlies successful adjustment
 to a rapidly changing landscape following megafire This dataset explores
 the behavioral responses of Black-tailed deer to a megafire in Mendocino
 County, CA, USA. Megafire dramatically changes landscapes, potentially
 placing pressure on native wildlife species. Recent work has found that
 Black-tailed deer alter their movement behaviors and home ranges following
 fire to compensate for the dramatic changes that follow these wildfire
 events. These analyses serve as a direct follow-up to that research and
 address how long these movement adjustments last on the landscape. ## Data
 Collection Collars were placed on deer from 2017-2020. We use recorded GPS
 locations to assess deer home range size (2017-2019), deer habitat
 selection (2018-2019), and movement behavior (2018-2019). ## Analysis
 Methods We use three primary analyses to assess how these changes in deer
 movement may change over time: 1. Comparison of Deer Home Range Sizes -
 Comparing deer home range size before and after the fire (as well as
 across seasons) using the 95% KUD method of home range size estimation. 2.
 Resource Selection Function (RSF) - How does habitat selection change over
 time across collared black-tailed deer following megafire? 3. Hidden
 Markov Movement Models (HMM) - How does movement behavior (behavioral
 states: resting vs. travel) change over time following a megafire? ## Data
 Note: NAs indicate the corresponding data is not available or applicable
 (e.g., The column 'Sex' in "Deer RSF Input Data.csv"
 is listed as either NA or False because all deer in the study are
 female/doe) * "deer gps data.csv" - all collected GPS points
 (without error points) used to estimate LoCoH home ranges ID: ID of the
 collared animal Timestamp: Date and time of the GPS fix Deployed: Date
 that the GPS collar was deployed onto the animal Dropped: Date that the
 GPS collar was dropped from the animal Period: Time period that the GPS
 data was collected within the study. The five study periods are:
 "fire/Recently Burned", "spring/First Spring",
 "fall/1 Year Post Fire", "prespring", and
 "prefire". * "Deer RSF Input Data.csv" - input GPS
 data and environmental covariates extracted from each GPS point to run the
 resource selection function model ID: ID of the collared animal Sex: NA,
 all deer for analyses are Does Timestamp: Date and time of the GPS fix
 Used: binary (0/1) indicates whether the GPS point is a true recorded GPS
 collar fix (1), or a randomly generated non-use point (0). TimePeriod:
 Time of day (categorical) that the GPS point was taken (Day, Night, or
 Crepuscular) Ruggedness: The extracted value of ruggedness (a variation on
 demography) at the GPS location Severity: Extracted value of fire severity
 at GPS location Severity_scaled: Scaled Severity value at GPS point
 Elevation: Extracted value of elevation at GPS location (meters)
 Elevation_scaled: the scaled value of Elevation predrisk: The extracted
 value of the probability of Mountain Lion presence at the GPS location
 predrisk_scaled: the scaled value of predrisk veg: Categorical value of
 vegetation type at GPS location (1 = chaparral, 2 = grassland, 3 =
 woodland) distWater: Distance of GPS location from nearest stream/ravine
 (meters) distWater_scaled: the scaled value of distWater Severity_sq:
 Squared (non-linear) value of Severity at GPS location Period: Time period
 that the GPS data was collected within the study. The five study periods
 are: "fire/Recently Burned", "spring/First Spring",
 and "fall/1 Year Post Fire". Fire_Date: Date of 2018 Mendocino
 Complex Fire (July 27, 2018) TimeSince_Burn: Number of Days since
 "Fire_Date" (days) BurnLag: Scaled value of TimeSince_Burn *
 "Deer RSF output.csv" - Tidy model output of the deer resource
 selection function effect: Type of effect included in the model (fixed or
 random effect) group: Grouping value of random effects term: Covariate
 included in the RSF model estimate: Estimated beta-coefficient for
 covariates within the model std.error: Estimated standard error of
 estimate for each covariate statistic: the value of T-statistic for each
 covariate in the RSF model p.value: p-value associated with a statistic of
 the RSF model * "deer_LoCoH_hrs.csv" - Estimated home range size
 for deer across all time periods id: ID of the collared animal Area:
 Estimated area of home range for individual animals (meters) Period:
 Period: Time period that the GPS data was collected within the study. The
 five study periods are: "fire/Recently Burned",
 "spring/First Spring", "fall/1 Year Post Fire",
 "prespring", and "prefire". area_km: Estimated area of
 home range for individual animals (kilometers) * "evi severity
 comparison pts.csv" - randomly sampled points from the study area
 used to compare evi and severity values following the 2018 Mendocino
 complex fire Number: Number of sampled points Severity: Extracted fire
 severity value from the sampled point NDVI: Extracted NDVI value from the
 sampled point NDVI_scaled: Scaled NDVI value from the extracted point EVI:
 extracted EVI value from the sampled point EVI_scaled: Scaled EVI value
 from extracted point veg: Categorical value of vegetation type at sampled
 point (1 = chaparral, 2 = grassland, 3 = woodland) Vegetation Type:
 Categorical vegetation type at the sampled point long: Longitude of
 recorded GPS point lat: Latitide of recorded GPS point Period: Time period
 that the GPS data was collected within the study. The five study periods
 are: "fire/Recently Burned", "spring/First Spring",
 and "fall/1 Year Post Fire". * "HMM_input_data.csv" -
 Input GPS data and extracted covariate values for hidden Markov model
 (HMM) ID: ID of the collared animal and time period of recorded GPS point
 Timestamp: Date and time of the GPS fix Deployed: Date that the GPS collar
 was deployed onto the animal Dropped: Date that the GPS collar was dropped
 from the animal Severity: Extracted value of fire severity at GPS location
 Severity_scaled: Scaled Severity value at GPS point predrisk: The
 extracted value of the probability of Mountain Lion presence at the GPS
 location predrisk_scaled: scaled value of predrisk veg: Categorical value
 of vegetation type at GPS location (1 = chaparral, 2 = grassland, 3 =
 woodland) distWater: Distance of GPS location from nearest stream/ravine
 distWater_scaled: the scaled value of distWater Period: Time period that
 the GPS data was collected within the study. The five study periods are:
 "fire/Recently Burned", "spring/First Spring", and
 "fall/1 Year Post Fire". Fire_Date: Date of 2018 Mendocino
 Complex Fire (July 27, 2018) TimeSince_Burn: Number of Days since
 "Fire_Date" BurnLag: Scaled value of TimeSince_Burn chap: binary
 variable, yes (1) or no (0) if GPS location was within chaparral grass:
 binary variable, yes (1) or no (0) if GPS location was within grassland
 wood: binary variable, yes (1) or no (0) if GPS location was within
 woodland * state_data.csv Value: Number of estimated points (with error
 included) in each behavioral state State: The estimated behavioral State
 (1 = Resting, 2 = Traveling) Period: Time period that the GPS data was
 collected within the study. The five study periods are:
 "fire/Recently Burned", "spring/First Spring", and
 "fall/1 Year Post Fire". perc: Percent of total GPS points in
 the associated behavioral state during the indicated period. * "deer
 RSF model.RData" - Output model of deer resource selection function *
 "HMM_model.RData" - Output model of deer hidden Markov model ##
 R Scripts 1. "Home Range Analyis.R" - Script for estimating deer
 home ranges from recorded GPS locations. Uses paired t-tests to compare
 home range sizes across time periods 2. "Deer RSF Models.R" -
 Script to run resource selection function to estimate deer habitat
 selection by comparing true use points to available points. 3. "Deer
 HMM 2-State Model.R" - Script to run hidden Markov model to assign
 behavioral states ("resting" or "traveling") to deer
 GPS movement data. Also estimates the probability of a GPS point being in
 the behavioral state as a function of covariates. 4. Visualizations.R -
 Code to create figures and visuals from analyses.