DOI: 10.5061/dryad.fqz612k4d
title: Long-term benefits of burns for large mammal habitat undermined by large, severe fires in the American West

description: Escalating wildfire frequency and severity are altering wildland habitats
 worldwide. Yet investigations into fire impacts on wildlife habitat rarely
 extend to the macroecological scales relevant to species conservation and
 global change processes. We evaluate the effects of wildfire on habitat
 quality and selection by large mammals spanning three trophic levels in
 the Western United States. We analyze 12 years of GPS telemetry data for
 2,966 mule deer (Odocoileus hemionus), 52 black bears (Ursus americanus),
 and 74 cougars (Puma concolor) across Utah and Nevada, USA. Over 800 areas
 burned between 1990-2022 overlapped with the home ranges of 1,892 animals,
 resulting in almost 23,000 km2 of burned habitat and representing 12.8% of
 the total home range area for animals in our sample. Habitat suitability
 models for 664 mule deer, 14 black bears, and 11 cougars indicated that
 burns improved summer home range quality for mule deer and black bears by
 7% and 14%, respectively, highlighting the benefits of fires for nutrient
 cycling, understory herbaceous growth, and resultant caloric value for
 animal nutrition. When making fine-scale movement decisions, however, mule
 deer avoided burned habitats, and all three species generally avoided
 high-severity burns for up to 30 years post-fire. Thus, the effects of
 burns on wildlife habitat selection appear to be dependent on spatial
 scale. Given projected increases in large, severe fires, our results
 suggest potential reductions in beneficial habitat for wildlife in the
 long term. However, our results also suggest that prescribed burns,
 because of their smaller spatial footprints and lower severity relative to
 wildfires, can benefit wildlife habitat quality through improvements in
 forage, cover, and other vegetation characteristics. Therefore, managing
 for low-severity burns and limiting large, severe wildfires, e.g., via
 prescribed burns or fire control policies, could positively impact the
 habitat quality of these three common species and, therefore, the economic
 and ecosystem services they provide.
description: # Long-term benefits of burns for large mammal habitat undermined by
 large, severe fires in the American West Dataset DOI:
 [10.5061/dryad.fqz612k4d](10.5061/dryad.fqz612k4d) ## Description of the
 data and file structure This data and code supports a study on wildlife
 responses to burned habitats at broad spatiotemporal scales. Data are
 available for cougar, black bear, and mule deer habitat selection models
 and other analyses.  ### Files and variables #### File: fire-SSF-models.R
 **Description:** Code for building individual step-selection functions,
 including model evaluation & coefficient bootstrapping #### File:
 rsf-predictions.R **Description:** Includes code (commented) to
 demonstrate the extraction process for RSF predictions in burned and
 simulated unburned habitats to calculate MQD (median quality difference).
 The output MQD data are available for the statistical analyses below the
 extraction procedures.  Also includes comparison of wildfires versus
 prescribed burns.  #### File: source_setup.R **Description:** Source file
 containing functions and package libraries. All R files have source
 command at beginning which should be run using this file.  #### File:
 fire-RSF-models.R **Description:** Code for building individual resource
 selection functions, including model evaluation & coefficient
 bootstrapping #### File: Data.zip **Description:** 4 data files: (1) data
 for SSF models, (2) data for RSF models, (3) data for MQD statistical
 analyses, and (4) data on burn characteristics to compare wildfires and
 prescribed burns. All latitude and longitude values in GPS datasets have
 been scaled and centered.  **Data File: final-RSF-model-data.rds**
 Description: Animal GPS data used for RSF models. Each row represents one
 animal ID-season, with a nested list-column "data" containing
 each animal's model dataset (R data frame with list-column). Data
 columns: "SAID" = animal ID; "species" = species of
 animal; "studyarea" = study area; "season" = season,
 summer or winter; "data" = list-column containing animal's
 GPS dataset; "pct_fire" = percent of animal's used GPS
 points within burn perimeters; "n_row" = number of rows in
 'data'. Columns within "data": "species" =
 species of animal; "studyarea" = study area;
 "animalID" = animal ID; "UID" = animal ID;
 "sex" = animal sex; "age" = animal age;
 "lat" = GPS location latitude; "long" = GPS location
 longitude; "ts_utc" = GPS location timestamp; "season"
 = season, summer or winter; "y_var" = real (1) or random
 location (0) for response variable in RSF model; "dem" =
 elevation; "tri" = terrain ruggedness index; "NDVI" =
 Normalized Difference Vegetation Index; "NDSI" = Normalized
 Difference Snow Index; "biomass" = satellite derived biomass;
 "ptid" = unique record ID; "FireDate" = date of most
 recent fire at location; "fire" = fire status, burned/unburned;
 "fire_dist" = distance to nearest fire perimeter;
 "fire_cbi" = composite burn index or most recent fire at
 location; "fire_yrs" = years since most recent fire at location;
 "fire_yrs_cat" = categorical years since fire;
 "fire_logdist"= log-distance to fire perimeter;
 "log_dist_road" = log-distance to road; "dem_s" =
 scaled elevation; "tri_s" = scaled TRI; "NDVI_s" =
 scaled NDVI; "NDSI_s" = scaled NDSI; "biomass_s" =
 scaled biomass; "log_dist_road_s" = scaled log-distance to road;
  "Hab_type" = NLCD habitat type; "Hab_Shrub" = binary
 shrub habitat, yes (1) or no (0) **Data File: final-SSF-model-data.rds**
 Description: Animal GPS 'steps' used for RSF models. Each row
 represents one animal ID-season, with a nested list-column
 "data" containing each animal's model dataset (R data frame
 with list-column). Data columns: "species" = species of animal;
 "studyarea" = study area; "UID" = animal ID;
 "season" = season, summer or winter; UID_s = animal ID + season;
 "n_row" = number of rows in 'data'; "data" =
 list-column containing animal's GPS steps dataset. Columns within
 'data': "UID" = animal ID; "species" =
 species of animal; "studyarea" = study area;
 "animalID"  = animal ID; "sex" = animal sex;
 "age" = animal age; "burst_" = burst ID;
 "t1_" = timestamp at beginning of step; "t2_" =
 timestamp at end of step; "dt_" = time difference during step;
 "case_" = real step end point (TRUE) or randomly generated
 available endpoint (FALSE) used as response variable in SSF models;
 "step_id_" = Id of step for grouping available endpoints in
 model (strata); "sl_" = step length (m); "ta_" = turn
 angle; "log_sl" = log-step length; "cos_ta"= cosign of
 turning angle; "random_id" = unique point id; "season"
 = season; "lat" = latitude at step endpoint; "lon" =
 longitude at step endpo*;* "ts" = timestamp at end of step;
 "fire" = fire status, burned/unburned; "fire_dist" =
 distance to nearest fire perimeter; "fire_cbi" = composite burn
 index or most recent fire at location; "fire_yrs" = years since
 most recent fire at location; "fire_yrs_cat" = categorical years
 since fire; "fire_logdist"= log-distance to fire perimeter;
 "tri" = terrain ruggedness index;  "distance_to_road"
 = distance to road (m); "NDVI" = Normalized Difference
 Vegetation Index; "NDSI" = Normalized Difference Snow Index;
 "biomass" = satellite derived biomass;  "DEM" =
 elevation; "nlcd" = NLCD habitat type; "Hab_Shrub" =
 binary shrub habitat, yes (1) or no (0); "log_dist_road" =
 log-distance to road;  "biomass_s" = scaled biomass;
 "NDVI_s" = scaled NDVI; "NDSI_s" = scaled NDSI; 
 "log_dist_road_s" = scaled log-distance to road;
 "dem_s" = scaled elevation; "tri_s" = scaled TRI;
 **Data File: rsf-prediction-results-df.rds** Description: Records of
 fire-animal 95% home range overlap areas generated from RSF model
 predictions of MQD and MTBS fire perimeters (R data frame). Data columns:
 "Event_ID" = MTBS Fire ID; "Incid_Name" = MTBS fire
 name; "Incid_Type" = MTBS fire type; "Ig_Date" = fire
 date; "BurnBndLat" = fire latitude; "BurnBndLon" =
 fire longitude; "yrs_since_fire" = years between fire date and
 animal GPS data median date; "fire_yrs_cat" = cateogrical
 representation of years since fire; "UID" = animal ID;
 "season" = season, winter or summer; "id" = animal ID
 (minus study site); "hr_area" = animal 95% home range size;
 "yrs_min" = earliest GPS data year; "yrs_max" = latest
 GPS data year; "yrs_med"= median GPS data year;
 "date_min" = earliest GPS data date; "date_max" =
 latest GPS data date; "species" = animal species;
 "UID_s" = animal ID x season; "date_med" = median GPS
 data date; "nlcd_majority" = most common NLCD category in animal
 home range; "fire_area" = size of fire in sq-km;
 "mean_CBI" = mean Composite Burn Index value within fire
 perimeter; "pct_hr_fire" = percent of animal 95% home range
 overlapping with fire perimeter; "mean_rsf_diff" = average value
 of RSF difference between fire x HR overlap area; "med_rsf_diff"
 = median value of RSF difference between fire x HR overlap area;
 "fire_yrs_cat0-9" = RSF model coefficient for effect of recent
 fires on habitat selection; "fireburned:fire_logdist" = RSF
 model coefficient for effect of distance to fire perimeter, outside of
 fire perimeters, on habitat selection;
 "fireunburned:fire_logdist" = RSF model coefficient for effect
 of distance to fire perimeter, inside of fire perimeters, on habitat
 selection;  "fire_yrs_cat0-9:fire_cbi" = RSF model coefficient
 for effect of CBI of recent fires on habitat selection;
 "fire_yrs_cat10-30" = RSF model coefficient for effect of older
 fires on habitat selection; "fire_yrs_cat10-30:fire_cbi" = RSF
 model coefficient for effect of CBI of older fires on habitat selection 
 **Data File: rx-fire-plot-data.rds** Description: Records of all fires
 from MTBS overlapping with 95% animal home ranges included in our analyses
 (R data frame). Data columns:  [1] "Event_ID" = MTBS Fire ID;
 "Incid_Name" = MTBS fire name; "Incid_Type" = MTBS
 fire type; "Ig_Date" = fire date; "BurnBndLat" = fire
 latitude; "BurnBndLon" = fire longitude; "fire_area" =
 size of fire in sq-km; "median_cbi" = median Composite Burn
 Index value within fire perimeter; "mean_cbi" = mean Composite
 Burn Index value within fire perimeter; "nlcd" = most common
 NLCD category in fire perimeter; "log_area" = log-transformed
 area of fire ## Access information Data was derived from the following
 sources: * MTBS fire perimeter
 dataset: [https://www.mtbs.gov/](https://www.mtbs.gov/) * NLCD habitat
 type
 data: [https://www.usgs.gov/centers/eros/science/national-land-cover-database](https://www.usgs.gov/centers/eros/science/national-land-cover-database)