DOI: 10.5061/dryad.xksn02vvb
title: When the mean is meaningless: Drivers of spatial behavior in a generalist carnivore

description: Despite the ecological expression and conservation importance of diverse
 behavioral tactics in animals, there is often friction associated with
 conventional analytical approaches and inference concerning variation in
 spatial behavior. Implicitly or explicitly, population-level inferences
 are often the main objective of studies, but interpretations can be
 ambiguous in the presence of divergent behavioral tactics across
 individuals or cohorts, as with generalist species. We pursued a novel
 analytical approach and assessed the underlying mechanisms driving
 variation in spatial behaviors of generalist species using the American
 black bear (Ursus americanus) as our focal species. We quantified
 individual variation in habitat selection expressed by black bears using
 individual models for 35-collared bears across four study areas in
 Wyoming, USA. We modeled how state-dependent factors (age, sex,
 δ15Nitrogen and body fat) and resource availability influenced behavioral
 variation in resource selection. We observed vast variation among
 individuals, demonstrating patterns consistent with a generalist species.
 Black bear resource selection differed with changes in state-dependence
 and resource availability. Specifically, traits uniquely important to
 black bear success, body fat and carnivory, explained variation in
 selection for forage indexed by NDVI, forests, and riparian areas.
 Environmental heterogeneity via differences in resource availability
 magnified behavioral variation in resource selection by black bears.
 Selection trends for NDVI and deciduous shrubs were explained by resource
 availability, indicating black bears exhibited functional responses in
 habitat selection. These insights emerged from our analytical approach;
 had we implemented a more conventional, population-level assessment we
 would have simply concluded that black bears displayed behavioral
 neutrality with respect to forage resources. Acknowledgement of behavioral
 variation when considering spatial behavior of generalist species provides
 a more representative understanding of individuals within a population,
 and our analytical approach offers a solution to uncovering drivers of
 individual variation in spatial behavior.
description: # When the mean is meaningless: Drivers of spatial behavior in a
 generalist carnivore Dataset DOI:
 [10.5061/dryad.xksn02vvb](10.5061/dryad.xksn02vvb) ## Description of the
 data and file structure The data derive from American black bears (*Ursus
 americanus*) fitted with Iridium GPS collars across four study areas in
 Wyoming, USA. The goal of the study was to quantify individual variation
 in habitat selection and evaluate how state-dependent traits (age, sex,
 body fat, δ¹⁵N as an index of carnivory) and resource availability
 influenced spatial behaviors. We implemented a novel individual-level
 analytical approach to resource selection functions (RSFs), allowing us to
 identify drivers of behavioral variation that would be obscured by
 conventional, population-level analyses. ## **Data Products** Two primary
 data products are included: 1. **Input Data Frames** * Contain the data
 used to fit individual RSF models for each collared bear (n = 35). *
 Include both *scaled* and *unscaled* environmental covariates (e.g., NDVI,
 land cover, forest type, riparian habitat). * State-dependent variables
 (age, sex, δ¹⁵N, body fat) are included where applicable. 2. **Output Data
 Frames** * Contain the results from all individually fitted RSF models. *
 Variables include coefficient estimates, standard errors, and p-values. *
 Additional intrinsic and extrinsic variables are incorporated to
 facilitate subsequent models assessing how traits and resource
 availability shape individual variation in resource selection. ## Notes on
 Use * Input data are formatted as data frames suitable for RSF analysis. *
 Output data provide model summaries and can be used for meta-analyses,
 synthesis, or replication of the manuscript’s results. * All spatial
 covariates were derived from publicly available datasets (see Methods
 section of the manuscript for sources and processing details). ### Files
 and variables #### File: RSF_output.csv **Description:** Results from
 individually fitted resource selection function (RSF) models for collared
 black bears. Each row represents the parameter estimates for a covariate
 within an individual-month model, along with associated intrinsic and
 extrinsic variables. ##### Variables * **Estimate:** Coefficient estimate
 from the RSF model. * **Error:** Standard error of the estimate. * **z:**
 z-value (Estimate ÷ Standard Error). * **P:** p-value for the z-test of
 the parameter estimate. * **cov:** Covariate name corresponding to the
 estimate (e.g., NDVI, elevation, forest). * **aid:** Animal ID; unique
 identifier for each collared black bear. * **month:** Calendar month of
 the model (e.g., June, July). * **SA:** Study area code (e.g., LR =
 Laramie; other codes correspond to different Wyoming study areas). *
 **weight:** Body weight of the individual (kg). * **sex:** Sex of the
 individual (M = male, F = female). * **est_age:** Estimated age at time of
 capture (not used in analysis). * **lab_age:** Laboratory-assigned age
 from tooth analysis (years). * **repro_status:** Reproductive status of
 the individual (e.g., with cubs, male; not used in analysis). *
 **bodyfat:** Percent body fat at capture (measured via bioelectrical
 impedance). * **weights:** Model weights used in meta-analysis across
 individuals. * **hr.avail:** Average value of available resources (e.g.,
 forest cover, NDVI, distance to berry patches) across the individual’s
 home range. (not used) * **sa.avail:** Average value of available
 resources across the broader study area. * **N:** Nitrogen isotope value,
 used as an index of carnivory. #### File: RSF_input.csv
 **Description:** Input dataset used to fit individual resource selection
 function (RSF) models for collared black bears. Includes both raw and
 standardized (z-scored) covariates. ##### Variables * **AID**: Animal ID;
 unique identifier for each collared black bear. * **TelemDate**: Date and
 time of GPS fix (YYYY-MM-DD HH:MM:SS). * **SA**: Study area code (e.g., LR
 = Laramies, others correspond to different Wyoming study areas). *
 **Month**: Calendar month of location fix (e.g., June, July). * **elev**:
 Elevation at GPS location (meters above sea level). * **hli**: Heat Load
 Index; terrain-derived measure of potential solar radiation. * **tpi33**:
 Topographic Position Index c * **forest**: Proportion or probability of
 forest cover at location (0–1). * **may_ndvi**: Normalized Difference
 Vegetation Index (NDVI) for May, representing vegetation
 greenness/productivity. * **june_ndvi**: NDVI for June. * **july_ndvi**:
 NDVI for July. * **aug_ndvi**: NDVI for August. * **sept_ndvi**: NDVI for
 September. * **dist2rip**: Distance to nearest riparian habitat (meters).
 * **dist2berry**: Distance to mapped berry-producing shrub patches
 (meters). * **used**: Resource selection indicator (1 = used bear GPS
 location; 0 = random available location). **Standardized (z-scored)
 variables:** * **TelemDate.z**: Standardized time of GPS fix. *
 **elev.z**: Standardized elevation. * **hli.z**: Standardized Heat Load
 Index. * **tpi33.z**: Standardized Topographic Position Index. *
 **forest.z**: Standardized forest cover. * **may_ndvi.z**: Standardized
 May NDVI. * **june_ndvi.z**: Standardized June NDVI. * **july_ndvi.z**:
 Standardized July NDVI. * **aug_ndvi.z**: Standardized August NDVI. *
 **sept_ndvi.z**: Standardized September NDVI. * **dist2rip.z**:
 Standardized distance to riparian habitat. * **dist2berry.z**:
 Standardized distance to berry patches. ## Code/software All data files
 can be viewed and analyzed using the free, open-source software **R**
 (version ≥ 4.2.0 recommended). The following R packages were used to
 process, analyze, and visualize the data: * `lme4` – for fitting linear
 and generalized linear mixed-effects models. * `afex` – for factorial
 analyses of mixed designs. * `performance` – for assessing model quality
 and diagnostics. * `tidyverse` – for data manipulation and visualization
 (`dplyr`, `ggplot2`, `tibble`, etc.). * `sf` – for handling spatial vector
 data. * `mapview` – for interactive visualization of spatial data. *
 `rgdal` – for reading and writing spatial data formats. * `sp` – for
 handling spatial data and coordinate systems. * `raster` – for working
 with raster data. * `rlist` – for flexible list operations in R.
 **Workflow:** 1. Raw GPS and environmental data were read into R using
 `read.csv()` or spatial data functions from `sf` and `raster`. 2.
 Covariates and availability measures were calculated for individual home
 ranges and study areas using `sf`, `raster`, and `dplyr`. 3.
 Individual-month resource selection function (RSF) models were fit using
 `lme4`. Model diagnostics were assessed with `performance`. ## Access
 information **Other publicly accessible locations of the data:**\ NA
 **Data was derived from the following sources:** * **LANDFIRE Existing
 Vegetation Type (EVT; LANDFIRE 2020):** Used to generate layers of
 forested habitat, deciduous shrubs (including berry-producing species),
 and riparian areas, as well as to calculate distances to these features.
 Available under the LANDFIRE open data license: https://www.landfire.gov/.
 * **Landsat 8 Surface Reflectance Composites (USGS/NASA):** Used to
 calculate monthly and winter Normalized Difference Vegetation Index (NDVI)
 for Wyoming from 2015–2021. NDVI layers were used as an index of
 photosynthetic activity and forage availability. Publicly available via
 Google Earth Engine. All derived layers (vegetative, topographic, and
 NDVI) were processed at 30 m resolution and summarized at 500 × 500 m
 pixel windows to align with black bear spatial ecology. Topographic
 indices including topographic position index (TPI) and heat load index
 (HLI) were calculated from elevation data following McCune & Keon
 (2002) and De Reu et al. (2013). Analyses including habitat selection
 functions (HSFs) and mixed-effects models were conducted in **R** (≥
 4.2.0) using publicly available packages (`lme4`, `afex`, `performance`,
 `tidyverse`, `sf`, `mapview`, `rgdal`, `sp`, `raster`, `rlist`).