DOI: 10.5061/dryad.41ns1rnt4
title: Data from: Wildfire disturbance and ecological cascades: teasing apart the direct and indirect effects of fire on tick populations

description: Wildfires are a significant ecological force in the western United States,
 reshaping landscapes and ecological communities. However, assessing
 wildfires full impact is challenging due to the complexity of fire
 severity and its varied effects on ecological dynamics. Understanding
 species-specific responses to disturbances within their environmental
 context is essential for predicting cascading ecological impacts.
 Arthropods, including ticks, are particularly sensitive to both abiotic
 and biotic changes, making them especially vulnerable to the impacts of
 wildfire. In this study, we tease apart the complex direct and indirect
 effects of wildfire on tick populations through a combination of
 field-level measurements and remote sensing. We assessed tick densities
 across 88 plots within large, protected reserves in California following
 three wildfires in August 2020, using data on soil conditions, vegetation
 cover, tick densities, and landscape-level remotely sensed variables
 related to vegetation regeneration and vertebrate recolonization. To
 support a multi-scalar approach, we applied piecewise structural equation
 models (SEMs) to incorporate factors across distinct spatial scales and
 assess how fire severity affects tick populations, with vegetation and
 habitat structure as mediating variables, thereby evaluating the relative
 importance of local drivers within a broader landscape context. Our
 results indicate that tick densities were consistently lower in burned
 plots across all vegetation types, with higher fire severity associated
 with the greatest reductions. This direct effect of fire severity
 outweighed indirect influences such as the presence of remaining woody
 debris, which can support tick populations by offering microhabitat for
 vertebrate hosts following a fire event. Landscape-level characteristics –
 such as proximity to the fire perimeter and the percentage of the reserve
 burned – exerted stronger influences on tick densities than plot level
 fire severity. These broader spatial characteristics likely facilitate the
 movement of vertebrate hosts into unburned areas, promoting tick
 recolonization and recovery following wildfire disturbance. Our results
 suggest that simplified field assessments focusing on key habitat
 indicators may be effective for monitoring tick responses to wildfire.
 Synthesis and applications: This study highlights the importance of
 integrating multiple data sources and ecological scales to predict
 wildfire impacts on ecosystems and public health. By advancing our
 understanding of wildfire effect on ticks, the research offers valuable
 insights for ecosystem management and disease vector control. The use of
 advanced statistical tools, like piecewise SEMs, combined with remotely
 sensed data, can facilitate rapid assessments and targeted monitoring
 efforts.
description: # Data from: Wildfire disturbance and ecological cascades: teasing apart
 the direct and indirect effects of fire on tick populations Dataset DOI:
 [10.5061/dryad.41ns1rnt4](10.5061/dryad.41ns1rnt4) ## Description of the
 data and file structure In this study, we tease apart the complex direct
 and indirect effects of wildfire on tick populations through a combination
 of field-level measurements and remote sensing. We assessed tick densities
 across 88 plots within large, protected reserves in California following
 three wildfires in August 2020, using data on soil conditions, vegetation
 cover, tick densities, and landscape-level remotely sensed variables
 related to vegetation regeneration and vertebrate recolonization. To
 support a multi-scalar approach, we applied piecewise structural equation
 models (SEMs) to incorporate factors across distinct spatial scales and
 assess how fire severity affects tick populations, with vegetation and
 habitat structure as mediating variables, thereby evaluating the relative
 importance of local drivers within a broader landscape context. ### Files
 and variables #### File: 1_DataCleaning_v1.Rmd **Description:** Code to
 wrangle raw remotely sensed data and transform into data structures for
 analysis. #### File: 2_AnalysisTables_v1.Rmd **Description:** Code to
 calculate summary statistics, wilcoxon rank sum tests, and piecewise
 structural equation models. #### File: 3_Figures_v2.Rmd
 **Description:** Code to recreate figures used in manuscript. #### File:
 data.zip **Description:** Data in raw and cleaned up format to run all
 analyses and create figures.  * plotscoords_spatial_20250715.csv - used
 for spatial analysis and visualization * plot_name: name of individual
 plot (RESERVE - STATUS - VEG-REPLICATE) * latitude: latitude of plot
 centroid * longitude: longitude of plot centroid * reserve: name of UC
 reserve * treatment: burn status (Burn/Unburn) * veg_type: dominant
 vegetation type found on plot * reserve_dnbr_raster_20250620.csv - used
 for spatial analysis and visualization * reserve: name of UC reserve * x:
 longitude of plot centroid * y: latitude of plot centroid * dNBR: raster
 value of differenced normalize burn ratio * dNBR: classification of dNBR
 raster value (eg Unburned, Low Severity) * fieldmeasurements_20250715.csv
 - used for spatial analysis and visualization * plot_name: name of
 individual plot (RESERVE - STATUS - VEG-REPLICATE) * reserve: name of UC
 reserve * year: year of tick collection * month: month of tick collection
 * collection_date: date of tick collection * treatment: burn status
 (Burn/Unburn) * veg_category: categories of dominant vegetation found on
 plot into simpler classifications (ie Grassland, Scrub, Forest) *
 veg_type: dominant vegetation type found on plot * tick_density: density
 of ticks during a sample event (year-month-plotID) (100 m^2) * tick_count:
 count of ticks during a sample event (year-month-plotID) *
 soil_surface_severity_mean: field-assessed fire severity classification
 (0-4; unburned-high severity) per plot * bare_soil_char_mean:
 field-assessed average percentage of bare soil char per plot following
 fire event  * woody_debris_mean: field-assessed average diameter of coarse
 woody debris per plot following fire event (cm) * plotcoords_20250715.csv
 - used for spatial data extraction * reserve: name of UC reserve *
 plot_name: name of individual plot (RESERVE - STATUS - VEG-REPLICATE) *
 latitude: latitude of plot centroid * longitude: longitude of plot
 centroid * landscape_percentburned_20250715.csv - used for spatial
 analysis and visualization * reserve: name of UC reserve *
 reserve_percentburn: calculated percentage of reserve area that was burned
 within reserve boundary lines based on CALFIRE fire perimeter shapefiles *
 landscape_disttofire_20250715.csv - used for spatial analysis * reserve:
 name of UC reserve * plot_name: name of individual plot (RESERVE - STATUS
 - VEG-REPLICATE) * distance_to_perimeter: calculated distance from plot
 centroid to the edge of the fire perimeter based on CALFIRE fire perimeter
 shapefiles (m) * landscape_prism_20250715.csv - used for analysis and
 visualization * reserve: name of UC reserve * tmean_normal: PRISM derived
 value for mean normal temperature per plot (C) * vpd_mean_normal: PRISM
 derived value for mean normal vapor pressure deficit per plot (kPA) *
 vpd_min_normal: PRISM derived value for minimum normal vapor pressure
 deficit per plot (kPA) * ppt_meanpost-firewinter_1: PRISM derived value
 for the cumulative precipitation per plot in the first winter quarter post
 August 2020 (mm) * ppt_meanpost-firewinter_2: PRISM derived value for the
 cumulative precipitation per plot in the second winter quarter post August
 2020 (mm) * ppt_meanpre-firewinter_1: PRISM derived value for the
 cumulative precipitation per plot in the winter quarter prior to August
 2020 fires (mm) * sem_covariates_20250715.csv - used for analysis *
 plot_name: name of individual plot (RESERVE - STATUS - VEG-REPLICATE) *
 reserve: name of UC reserve * year: year of tick collection * month: month
 of tick collection * collection_date: date of tick collection * treatment:
 burn status (Burn/Unburn) * veg_category: categories of dominant
 vegetation found on plot into simpler classifications (ie Grassland,
 Scrub, Forest) * veg_type: dominant vegetation type found on plot *
 tick_density: density of ticks during a sample event (year-month-plotID)
 (100 m^2) * tick_count: count of ticks during a sample event
 (year-month-plotID) * soil_surface_severity_mean: field-assessed fire
 severity classification (0-4; unburned-high severity) per plot *
 bare_soil_char_mean: field-assessed average percentage of bare soil char
 per plot following fire event  * woody_debris_mean: field-assessed average
 diameter of coarse woody debris per plot following fire event (cm) *
 distance_to_perimeter: calculated distance from plot centroid to the edge
 of the fire perimeter based on CALFIRE fire perimeter shapefiles (m) *
 reserve_percentburn: calculated percentage of reserve area that was burned
 within reserve boundary lines based on CALFIRE fire perimeter shapefiles *
 tmean_normal: PRISM derived value for mean normal temperature per plot (C)
 * vpd_mean_normal: PRISM derived value for mean normal vapor pressure
 deficit per plot (kPA) * ppt_mean_overall: PRISM derived value for the
 mean cumulative precipitation per plot across the three winter quarters
 (mm) * ppt_meanpre-firewinter_1: PRISM derived value for the cumulative
 precipitation per plot in the winter quarter prior to August 2020
 fires (mm) * ppt_meanpost-firewinter_1: PRISM derived value for the
 cumulative precipitation per plot in the first winter quarter post August
 2020 (mm) * ppt_meanpost-firewinter_2: PRISM derived value for the
 cumulative precipitation per plot in the second winter quarter post August
 2020 (mm) * landscape_dnbrraster_20250715.csv - used for spatial analysis
 and visualization * reserve: name of UC reserve * x: longitude of plot
 centroid * y: latitude of plot centroid * dNBR: raster value of
 differenced normalize burn ratio * dNBR: classification of dNBR raster
 value (eg Unburned, Low Severity) * landscape_nbr_20250715.csv - used for
 spatial analysis * reserve: name of UC reserve * plot_name: name of
 individual plot (RESERVE - STATUS - VEG-REPLICATE) * treatment: burn
 status (Burn/Unburn) * veg_category: categories of dominant vegetation
 found on plot into simpler classifications (ie Grassland, Scrub, Forest) *
 site: replication number of reserve-treatment-veg category * dNBR: raster
 value of differenced normalize burn ratio * pre_NBR: raster value
 of differenced normalize burn ratio before wildfire event * post_NBR:
 raster value of differenced normalize burn ratio after wildfire event *
 dNBR_class: classification of dNBR raster value (eg Unburned, Low
 Severity) * landscape_ndvi_20250715.csv - used for visualization *
 plot_name: name of individual plot (RESERVE - STATUS - VEG-REPLICATE) *
 NDVI: the mean raster value of normalized difference vegetation index from
 Sentinel-2 per plot * year: year of remotely sensed data collection *
 reserve: name of UC reserve * treatment: burn status (Burn/Unburn) *
 veg_category: categories of dominant vegetation found on plot into simpler
 classifications (ie Grassland, Scrub, Forest) * site: replication number
 of reserve-treatment-veg category * The folders 'shapefiles',
 'sentinel2', 'prism' contain publicly available data
 and their associated metadata can be found online using the 'Access
 Information' links. ## Code/software All of the analysis and figure
 components were made done using RStudio. Some figures were brought into
 BioRender to add illustrations (ie Graphical Abstract). All remotely
 sensed data used in this study was publicly available. **Software** R
 version 4.4.1 R packages * cowplot_1.1.3, ggrepel_0.9.5, ggspatial_1.1.9,
 gridExtra_2.3, patchwork_1.3.0, piecewiseSEM_2.3.0.1, purrr_1.0.4,
 raster_3.6-32, readr_2.1.5,  rnaturalearth_1.0.1, sf_1.0-20,
 stringr_1.5.1, tidyverse_2.0.0 ## Access information Summary of data
 sources used in the main and supplemental files with respective hyperlinks
 and references. For datasets with gridded spatial resolutions (i.e., 20 m
 or ~4 km), data was extracted at the centroid of each plot.  ^1^Sentinel-2
 imagery was accessed through Copernicus Open Access Hub. To define the
 area of interest, a .kmz file of boundary lines for each reserve was
 uploaded. The .kmz files were provided by the UCNRS GIS. For Sentinel-2
 L2A data, the wildfire feature – Normalized Burn Ratio (NBR) was selected.
 Up to 30% of cloud cover was allowed and selected the relevant date for
 download. The data was downloaded as 8-bit .tiff files, including
 near-infrared (Band 8) and short-wave infrared (Band 12) values.  ^2^PRISM
 data came from PRISM Climate Group, Oregon State University,
 [https://prism.oregonstate.edu](https://prism.oregonstate.edu/), data
 accessed 03 Jan 2025.  | **Data** | **Source** | **Spatial** |
 **Hyperlink** | | :-------------------------------------------- |
 :--------------------------------------------------- | :---------- |
 :------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | Fire perimeter boundary | CALFIRE FRAP | Polygon | [https://www.fire.ca.gov/what-we-do/fire-resource-assessment-program/fire-perimeters](https://www.fire.ca.gov/what-we-do/fire-resource-assessment-program/fire-perimeters) | | Reserve boundary | UCNRS GIS Files | Polygon | [https://ucnrs.org/research/research-resources/gis-database/](https://ucnrs.org/research/research-resources/gis-database/) | | Differenced Normalized Burn Ratio (dNBR) | Sentinel-2 through the Copernicus Open Access Hub^1^ | 20 m | [https://browser.dataspace.copernicus.eu](https://browser.dataspace.copernicus.eu/) | | Normalized Difference Vegetation Index (NDVI) | Sentinel-2 through the Copernicus Open Access Hub^1^ | 20 m | [https://browser.dataspace.copernicus.eu](https://browser.dataspace.copernicus.eu/) | | 30-year normal temperature (°C)  | PRISM^2^ | \~ 4 km | [https://prism.oregonstate.edu/normals/](https://prism.oregonstate.edu/normals/) | | 30-year normal vapor pressure deficit (kPA) | PRISM^2^ | \~ 4 km | [https://prism.oregonstate.edu/normals/](https://prism.oregonstate.edu/normals/) | | Daily precipitation (mm) | PRISM^2^ | \~ 4 km | [https://prism.oregonstate.edu/recent/ ](https://prism.oregonstate.edu/recent/ ) |