DOI: 10.5061/dryad.3ffbg79v3
title: Human-wildlife conflict is amplified during periods of drought

description: Climate change-induced alterations to human-wildlife interactions are
 recognized to pose a fundamental challenge for global conservation
 initiatives. However, the extent to which specific climatic disturbances
 influence the dynamics of human-wildlife conflict across different
 taxonomic groups remains poorly understood. Here, we leverage an extensive
 dataset of community-derived human-wildlife conflict incidents to examine
 the influence of drought, represented by the variation in summed
 precipitation over the prior 12 months, on conflict reporting. We show
 that prolonged decreases in precipitation are associated with increased
 overall conflict occurrences across taxa and are significantly associated
 with increased conflict with carnivore species in particular. A future
 with increasingly severe and frequent droughts could lead to resource
 scarcity that not only causes conflict between humans but also between
 humans and the natural world around them.
description: # Human-wildlife conflict is amplified during periods of drought This code
 examines the influence of drought on human-wildlife conflict across the
 state of California, USA. Drought and other climate change-driven
 disturbances are expected to amplify human-wildlife conflict in the
 future. However, limited empirical studies have explicitly tested whether
 this theorized relationship is consistent across a diverse group of
 species and land-use contexts. In this study, we leverage an extensive
 community science-derived dataset from the California Department of Fish
 and Wildlife (CDFW) of human-wildlife interaction incident reports to
 measure the influence of precipitation, human population density,
 seasonality, and tree cover on the frequency of reported incidents.
 Human-wildlife conflict remains one of the greatest challenges to wildlife
 conservation globally, and understanding the potential influences of
 climate change and climatic disturbances on conflict will be key to
 guiding future successful conservation strategies. ## Data Collection We
 used the CDFW's Wildlife Incident Reporting System (WIR) to examine
 the influence of changes in precipitation on the frequency of reported
 human-wildlife conflicts. Individual reports are added to the database
 through either self-reporting via the online Wildlife Incident Reporting
 (WIR) interface
 ([https://apps.wildlife.ca.gov/wir](https://apps.wildlife.ca.gov/wir)) or
 direct input by regional CDFW biologists and specialists on behalf of
 individuals between the years of 2017 and 2023. ## Analysis Methods We use
 two primary analysis methods to examine how changes in precipitation
 influence human-wildlife conflict reporting: 1. All Conflict Reports
 Negative-Binomial - Negative Binomial regression that models the number of
 aggregated incidents (Depredation and General Nuisance reports) for each
 annual month-grid cell as a function of precipitation, human population
 density, seasonality, and tree cover. In this model, all incidents were
 grouped together (i.e., not separated by species) in order to examine the
 influence of drought on the overall phenomenon of human-wildlife conflict.
 2. Diet-Group Multispecies Negative-Binomial Model - Bayesian hierarchical
 model that analyses the influence of drought and other environmental
 covariates on human wildlife conflict for diet-groups and individual
 species. Modeled species are split across 3 diet groups according to their
 life histories: herbivores, omnivores, and carnivores We use an additional
 4 analyses to compare whether precipitation impacts the type of report
 (Depredation, Sighting, Potential Human Conflict, and General Nuisance)
 differently. 1. Depredation Reports Negative-Binomial - Negative Binomial
 regression that models the number of aggregated depredation reports for
 each annual-month grid cell. 2. Nuisance Reports Negative-Binomial -
 Negative Binomial regression that models the number of aggregated general
 nuisance reports for each annual-month grid cell. 3. Potential Human
 Conflict Reports Negative-Binomial - Negative Binomial regression that
 models the number of aggregated potential human conflict reports for each
 annual-month grid cell. 4. Sighting Reports Negative-Binomial - Negative
 Binomial regression that models the number of aggregated sighting reports
 for each annual-month grid cell. ## Data CA-Fire-HWC.zip ### Data Files 1.
 "all_monthly_data.csv" - all aggregated human-wildlife conflict
 reports by year-month-cells order: numerical order of rows month.year:
 unique ID representing each month-year (e.g., January 2017 = 1) gridID:
 unique ID of the grid cell (50x50km), conflicts were aggregated into year:
 year that the aggregated conflicts were reported in month: numerical month
 (1-12) in which the aggregated conflicts were reported in n.incidents:
 number of aggregated incidents within each year-month-cell 1.
 "data-rnD08.csv" - Historic precipitation data from California
 used for visualizations. Obtained from:
 [https://oehha.ca.gov/climate-change/epic-2022/changes-climate/precipitation](https://oehha.ca.gov/climate-change/epic-2022/changes-climate/precipitation) Year: year of recorded precipitation data Precipitation (inches): Annual statewide precipitation in inches 1. "depredation_monthly_data.csv" - all aggregated depredation conflict reports by year-month-cells order: numerical order of rows month.year: unique ID representing each month-year (e.g., January 2017 = 1) gridID: unique ID of the grid cell (50x50km), conflicts were aggregated into year: year that the aggregated conflicts were reported in month: numerical month (1-12) in which the aggregated conflicts were reported in n.incidents: number of aggregated incidents within each year-month-cell 1. "diet_group model_data.Rda" - Model output data of diet-group conflict multi-species analysis 2. "diet_multispecies_monthly_data.csv" - aggregated human-wildlife conflict reports aggregated by year-month-cells for the 15 species modeled by diet groups. order: numerical order of rows month.year: unique ID representing each month-year (e.g., January 2017 = 1) gridID: unique ID of the grid cell (50x50km), conflicts were aggregated into year: year that the aggregated conflicts were reported in month: numerical month (1-12) in which the aggregated conflicts were reported n.incidents: number of aggregated incidents within each year-month-cell species: species that the aggregated conflicts are associated with 1. "metadata_depredation.csv" - metadata for associated depredation conflict type analysis order: numerical order of rows month.year: unique ID representing each month-year (e.g., January 2017 = 1) gridID: unique ID of the grid cell (50x50km), conflicts were aggregated into year: year that the aggregated conflicts were reported in month: numerical month (1-12) in which the aggregated conflicts were reported in census: mean human population density on the log scale for each grid cell treecover: mean percent tree cover for each grid cell income: mean value of median household income for each grid cell ppt.lag.3: summed precipitation at a grid cell from the previous 3 months ppt.lag.6: summed precipitation at a grid cell from the previous 6 months ppt.lag.12: summed precipitation at a grid cell from the previous 12 months ppt.lag.3.scale: scaled and centered values of summed precipitation at each grid cell from the last 3 months ppt.lag.6.scale: scaled and centered values of summed precipitation at each grid cell from the last 6 months ppt.lag.12.scale: scaled and centered values of summed precipitation at each grid cell from the last 12 months census.scale: scaled and centered values of human population density at each grid cell treecover.scale: scaled and centered values of percent tree cover at each grid cell income.scale: scaled and centered values of median household income at each grid cell month_sin: sine term of circular transformation for month covariate month_cos: cosine term of circular transformation for the month covariate month.scale: scaled and centered values of numerical month data month.sin.scale: scaled and centered values of sine transformed month data month.cos.scale: scaled and centered values of cosine-transformed month data 1. "metadata_nuisance.csv" - metadata for associated general nuisance conflict type analysis order: numerical order of rows month.year: unique ID representing each month-year (e.g., January 2017 = 1) gridID: unique ID of the grid cell (50x50km), conflicts were aggregated into year: year that the aggregated conflicts were reported in month: numerical month (1-12) in which the aggregated conflicts were reported in census: mean human population density on the log scale for each grid cell treecover: mean percent tree cover for each grid cell income: mean value of median household income for each grid cell ppt.lag.3: summed precipitation at a grid cell from the previous 3 months ppt.lag.6: summed precipitation at a grid cell from the previous 6 months ppt.lag.12: summed precipitation at a grid cell from the previous 12 months ppt.lag.3.scale: scaled and centered values of summed precipitation at each grid cell from the last 3 months ppt.lag.6.scale: scaled and centered values of summed precipitation at each grid cell from the last 6 months ppt.lag.12.scale: scaled and centered values of summed precipitation at each grid cell from the last 12 months census.scale: scaled and centered values of human population density at each grid cell treecover.scale: scaled and centered values of percent tree cover at each grid cell income.scale: scaled and centered values of median household income at each grid cell month_sin: sine term of circular transformation for month covariate month_cos: cosine term of circular transformation for the month covariate month.scale: scaled and centered values of numerical month data month.sin.scale: scaled and centered values of sine transformed month data month.cos.scale: scaled and centered values of cosine-transformed month data 1. "metadata_potential.csv" - metadata for associated potential human conflict analysis order: numerical order of rows month.year: unique ID representing each month-year (e.g., January 2017 = 1) gridID: unique ID of the grid cell (50x50km), conflicts were aggregated into year: year that the aggregated conflicts were reported in month: numerical month (1-12) in which the aggregated conflicts were reported in census: mean human population density on the log scale for each grid cell treecover: mean percent tree cover for each grid cell income: mean value of median household income for each grid cell ppt.lag.3: summed precipitation at a grid cell from the previous 3 months ppt.lag.6: summed precipitation at a grid cell from the previous 6 months ppt.lag.12: summed precipitation at a grid cell from the previous 12 months ppt.lag.3.scale: scaled and centered values of summed precipitation at each grid cell from the last 3 months ppt.lag.6.scale: scaled and centered values of summed precipitation at each grid cell from the last 6 months ppt.lag.12.scale: scaled and centered values of summed precipitation at each grid cell from the last 12 months census.scale: scaled and centered values of human population density at each grid cell treecover.scale: scaled and centered values of percent tree cover at each grid cell income.scale: scaled and centered values of median household income at each grid cell month_sin: sine term of circular transformation for month covariate month_cos: cosine term of circular transformation for the month covariate month.scale: scaled and centered values of numerical month data month.sin.scale: scaled and centered values of sine transformed month data month.cos.scale: scaled and centered values of cosine-transformed month data 1. "metadata_sightings.csv" - metadata for associated sightings conflict type analysis order: numerical order of rows month.year: unique ID representing each month-year (e.g., January 2017 = 1) gridID: unique ID of the grid cell (50x50km), conflicts were aggregated into year: year that the aggregated conflicts were reported in month: numerical month (1-12) in which the aggregated conflicts were reported census: mean human population density on the log scale for each grid cell treecover: mean percent tree cover for each grid cell income: mean value of median household income for each grid cell ppt.lag.3: summed precipitation at a grid cell from the previous 3 months ppt.lag.6: summed precipitation at a grid cell from the previous 6 months ppt.lag.12: summed precipitation at a grid cell from the previous 12 months ppt.lag.3.scale: scaled and centered values of summed precipitation at each grid cell from the last 3 months ppt.lag.6.scale: scaled and centered values of summed precipitation at each grid cell from the last 6 months ppt.lag.12.scale: scaled and centered values of summed precipitation at each grid cell from the last 12 months census.scale: scaled and centered values of human population density at each grid cell treecover.scale: scaled and centered values of percent tree cover at each grid cell income.scale: scaled and centered values of median household income at each grid cell month_sin: sine term of circular transformation for month covariate month_cos: cosine term of circular transformation for the month covariate month.scale: scaled and centered values of numerical month data month.sin.scale: scaled and centered values of sine transformed month data month.cos.scale: scaled and centered values of cosine-transformed month data 1. "metadata.csv" order: numerical order of rows month.year: unique ID representing each month-year (e.g., January 2017 = 1) gridID: unique ID of the grid cell (50x50km), conflicts were aggregated into year: year that the aggregated conflicts were reported in month: numerical month (1-12) in which the aggregated conflicts were reported in census: mean human population density on the log scale for each grid cell treecover: mean percent tree cover for each grid cell income: mean value of median household income for each grid cell ppt.lag.3: summed precipitation at a grid cell from the previous 3 months ppt.lag.6: summed precipitation at a grid cell from the previous 6 months ppt.lag.12: summed precipitation at a grid cell from the previous 12 months ppt.lag.3.scale: scaled and centered values of summed precipitation at each grid cell from the last 3 months ppt.lag.6.scale: scaled and centered values of summed precipitation at each grid cell from the last 6 months ppt.lag.12.scale: scaled and centered values of summed precipitation at each grid cell from the last 12 months census.scale: scaled and centered values of human population density at each grid cell treecover.scale: scaled and centered values of percent tree cover at each grid cell income.scale: scaled and centered values of median household income at each grid cell month_sin: sine term of circular transformation for month covariate month_cos: cosine term of circular transformation for the month covariate month.scale: scaled and centered values of numerical month data month.sin.scale: scaled and centered values of sine transformed month data month.cos.scale: scaled and centered values of cosine-transformed month data 1. "monthly_precip_df.csv" - Average statewide precipitation from each month drawn from PRISM 30-year average data ([https://prism.oregonstate.edu/normals/](https://prism.oregonstate.edu/normals/)) Month: month of average precipitation Precipitation: 30-year average amount of precipitation across the state within each month month_num: numerical number of month (1-12) 1. "nuisance_monthly_data.csv" - all aggregated nuisance conflict reports by year-month-cells order: numerical order of rows month.year: unique ID representing each month-year (e.g., January 2017 = 1) gridID: unique ID of the grid cell (50x50km), conflicts were aggregated into year: year that the aggregated conflicts were reported in month: numerical month (1-12) in which the aggregated conflicts were reported in n.incidents: number of aggregated incidents within each year-month-cell 1. "potential_monthly_data.csv" - all aggregated potential human conflict reports by year-month-cells order: numerical order of rows month.year: unique ID representing each month-year (e.g., January 2017 = 1) gridID: unique ID of the grid cell (50x50km), conflicts were aggregated into year: year that the aggregated conflicts were reported in month: numerical month (1-12) in which the aggregated conflicts were reported in n.incidents: number of aggregated incidents within each year-month-cell 1. "sightings_monthly_data.csv" - all aggregated sighting reports by year-month-cells order: numerical order of rows month.year: unique ID representing each month-year (e.g., January 2017 = 1) gridID: unique ID of the grid cell (50x50km), conflicts were aggregated into year: year that the aggregated conflicts were reported in month: numerical month (1-12) in which the aggregated conflicts were reported in n.incidents: number of aggregated incidents within each year-month-cell ## Analysis ### R Scripts 1. "All_Species_Model_Setup.R" - Script to set up the input model data, constants, inits, and supporting functions for the All Species Negative Binomial Regression 2. "All_Species_Model.R" - Script to run the Bayesian negative binomial regression for the All Species Model 3. "Diet_Multispecies_Model_Setup.R" - Script to set up the input model data, constants, inits, and supporting functions for the Diet-Group Multispecies Negative Binomial Regression Model 4. "Diet_Multispecies_Model.R" - Script to run the Bayesian negative binomial regression for the Diet-Group Multispecies Model. 5. "Depredation_Model_Setup.R" - Script to set up the input model data, constants, inits, and supporting functions for the depredation reports Negative Binomial Regression 6. "Depredation_Conflict_Model.R" - Script to run the Bayesian negative binomial regression for the all depredations model 7. "Nuisance_Model_Setup.R" - Script to set up the input model data, constants, inits, and supporting functions for the nuisance reports Negative Binomial Regression 8. "Nuisance_Conflict_Model.R" - Script to run the Bayesian negative binomial regression for the all general nuisances model 9. "Potential_Model_Setup.R" - Script to set up the input model data, constants, inits, and supporting functions for the potential human conflict reports Negative Binomial Regression 10. "Potential_Conflict_Model.R" - Script to run the Bayesian negative binomial regression for the all potential human conflict model 11. "Sightings_Model_Setup.R" - Script to set up the input model data, constants, inits, and supporting functions for the sighting reports Negative Binomial Regression 12. "Sightings_Conflict_Model.R" - Script to run the Bayesian negative binomial regression for the all sightings model 13. "all_conflicts_samples_coda.Rda" - Posterior samples of the All Species Conflict Model ## Results ### Data Files 1. "all_conflicts_3monthlag_summary.csv" - Summarized model coefficients of the All Conflict Model using the summed 3 months prior to precipitation mean: Average value of parameter sd: Standard deviation of parameter 90%_HPDL: Lower bound value of 90% Confidence Interval of the parameter 90%_HPDU: Upper bound value of 90% Confidence Interval of the parameter Rhat: Rhat values for parameter calculated across 3 chains n.eff: Number of effective samplers for each parameter nonzero: Denotes whether the 90% Confidence Interval overlaps zero or not param: Code syntax name of parameter species: Animal species or species group for the parameter estimate layer: Denotes whether the parameter is a covariate or other model parameter parname: Name of each parameter 1. "all_conflicts_6monthlag_summary.csv" - Summarized model coefficients of the All Conflict Model using the summed 6 months prior to precipitation mean: Average value of parameter sd: Standard deviation of parameter 90%_HPDL: Lower bound value of 90% Confidence Interval of parameter 90%_HPDU: Upper bound value of 90% Confidence Interval of parameter Rhat: Rhat values for parameter calculated across 3 chains n.eff: Number of effective samplers for each parameter nonzero: Denotes whether the 90% Confidence Interval overlaps zero or not param: Code syntax name of parameter species: Animal species or species group for the parameter estimate layer: Denotes whether the parameter is a covariate or other model parameter parname: Name of each parameter 1. "all_conflicts_samples_coda.Rda" - Posterior samples of the all-species conflict model 2. "all_conflicts_summary.csv" - Summarized model coefficients of the All Species Conflict Model mean: Average value of parameter sd: Standard deviation of parameter 90%_HPDL: Lower bound value of 90% Confidence Interval of parameter 90%_HPDU: Upper bound value of 90% Confidence Interval of parameter Rhat: Rhat values for parameter calculated across 3 chains n.eff: Number of effective samplers for each parameter nonzero: Denotes whether the 90% Confidence Interval overlaps zero or not param: Code syntax name of parameter species: Animal species or species group for the parameter estimate layer: Denotes whether the parameter is a covariate or other model parameter parname: Name of each parameter 1. "all_depredations_summary.csv" - Summarized model coefficients of the Depredations Conflict Model mean: Average value of parameter sd: Standard deviation of parameter 90%_HPDL: Lower bound value of 90% Confidence Interval of parameter 90%_HPDU: Upper bound value of 90% Confidence Interval of parameter Rhat: Rhat values for parameter calculated across 3 chains n.eff: Number of effective samplers for each parameter nonzero: Denotes whether the 90% Confidence Interval overlaps zero or not param: Code syntax name of parameter species: Animal species or species group for the parameter estimate layer: Denotes whether the parameter is a covariate or other model parameter parname: Name of each parameter 1. "all_nuisance_summary.csv" - Summarized model coefficients of the Nuisance Conflict Model mean: Average value of parameter sd: Standard deviation of parameter 90%_HPDL: Lower bound value of 90% Confidence Interval of parameter 90%_HPDU: Upper bound value of 90% Confidence Interval of parameter Rhat: Rhat values for parameter calculated across 3 chains n.eff: Number of effective samplers for each parameter nonzero: Denotes whether the 90% Confidence Interval overlaps zero or not param: Code syntax name of parameter species: Animal species or species group for the parameter estimate layer: Denotes whether the parameter is a covariate or other model parameter parname: Name of each parameter 1. "all_potential_summary.csv" - Summarized model coefficients of the Potential Human Conflict Model mean: Average value of parameter sd: Standard deviation of parameter 90%_HPDL: Lower bound value of 90% Confidence Interval of parameter 90%_HPDU: Upper bound value of 90% Confidence Interval of parameter Rhat: Rhat values for parameter calculated across 3 chains n.eff: Number of effective samplers for each parameter nonzero: Denotes whether the 90% Confidence Interval overlaps zero or not param: Code syntax name of parameter species: Animal species or species group for the parameter estimate layer: Denotes whether the parameter is a covariate or other model parameter parname: Name of each parameter 1. "all_sightings_summary.csv" - Summarized model coefficients of the Sightings Conflict Model mean: Average value of parameter sd: Standard deviation of parameter 90%_HPDL: Lower bound value of 90% Confidence Interval of parameter 90%_HPDU: Upper bound value of 90% Confidence Interval of parameter Rhat: Rhat values for parameter calculated across 3 chains n.eff: Number of effective samplers for each parameter nonzero: Denotes whether the 90% Confidence Interval overlaps zero or not param: Code syntax name of parameter species: Animal species or species group for the parameter estimate layer: Denotes whether the parameter is a covariate or other model parameter parname: Name of each parameter 1. "derived_month_effect_plot_data.csv" - Predicted coefficients of the effect of seasonality (month) on reported conflict across species. Month: Month for which the predicted coefficient is estimated Pred.Low: Lower bound value of 90% Confidence Interval of predicted estimate Pred.High: Upper bound value of 90% Confidence Interval of predicted estimate Pred.Mean: Average value of predicted coefficient estimate Species: Species for which the predicted estimate is made 1. "derived_ppt_efect_plot_data.csv" - Predicted coefficients of the effect of precipitation on reported conflict across species. Precipitation: Precipitation for which the predicted coefficient is estimated Pred.Low: Lower bound value of 90% Confidence Interval of predicted estimate Pred.High: Upper bound value of 90% Confidence Interval of predicted estimate Pred.Mean: Average value of predicted coefficient estimate Species: Species for which the predicted estimate is made 1. "deviance_gof.csv" - Observed deviances of fit from posterior predictive check used for plotting goodness-of-fit type - categorical distinction if value is from the distribution of deviances from fit ("PPC") or the mean value of observations ("Observed") value - numerical value of deviation for model fit 2. "diet_multispecies_conflict_3monthlag_summary.csv" - Summarized model coefficients of the Diet Multispecies Model using the summed 3 months prior to precipitation mean: Average value of parameter sd: Standard deviation of parameter 90%_HPDL: Lower bound value of 90% Confidence Interval of parameter 90%_HPDU: Upper bound value of 90% Confidence Interval of parameter Rhat: Rhat values for parameter calculated across 3 chains n.eff: Number of effective samplers for each parameter nonzero: Denotes whether the 90% Confidence Interval overlaps zero or not param: Code syntax name of parameter species: Animal species or species group for the parameter estimate layer: Denotes whether the parameter is a covariate or other model parameter 1. "diet_multispecies_conflict_6monthlag_summary.csv" - Summarized model coefficients of the Diet Multispecies Model using the summed 6 months prior to precipitation mean: Average value of parameter sd: Standard deviation of parameter 90%_HPDL: Lower bound value of 90% Confidence Interval of parameter 90%_HPDU: Upper bound value of 90% Confidence Interval of parameter Rhat: Rhat values for parameter calculated across 3 chains n.eff: Number of effective samplers for each parameter nonzero: Denotes whether the 90% Confidence Interval overlaps zero or not param: Code syntax name of parameter species: Animal species or species group for the parameter estimate layer: Denotes whether the parameter is a covariate or other model parameter parname: Name of each parameter 1. "diet_multispecies_conflict_samples_coda.Rda" - Posterior samples of the multi-species diet conflict model 2. "diet_multispecies_conflict_summary.csv" - Summarized model coefficients of the Diet Multispecies Conflict Model mean: Average value of parameter sd: Standard deviation of parameter 90%_HPDL: Lower bound value of 90% Confidence Interval of parameter 90%_HPDU: Upper bound value of 90% Confidence Interval of parameter Rhat: Rhat values for parameter calculated across 3 chains n.eff: Number of effective samplers for each parameter nonzero: Denotes whether the 90% Confidence Interval overlaps zero or not param: Code syntax name of parameter species: Animal species or species group for the parameter estimate layer: Denotes whether the parameter is a covariate or other model parameter parname: Name of each parameter ## Figures ### R Scripts 1. "Visualizations.R" - Script to visualize results from analyses