Data from: Wildfire disturbance and ecological cascades: teasing apart the direct and indirect effects of fire on tick populations
Data files
Sep 30, 2025 version files 48.22 MB
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1_DataCleaning_v1.Rmd
34.21 KB
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2_AnalysisTables_v1.Rmd
11.36 KB
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3_Figures_v2.Rmd
37.69 KB
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data.zip
48.13 MB
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README.md
12.78 KB
Abstract
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.
Dataset DOI: 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.
1Sentinel-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.
2PRISM data came from PRISM Climate Group, Oregon State University, 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 |
| Reserve boundary | UCNRS GIS Files | Polygon | https://ucnrs.org/research/research-resources/gis-database/ |
| Differenced Normalized Burn Ratio (dNBR) | Sentinel-2 through the Copernicus Open Access Hub1 | 20 m | https://browser.dataspace.copernicus.eu |
| Normalized Difference Vegetation Index (NDVI) | Sentinel-2 through the Copernicus Open Access Hub1 | 20 m | https://browser.dataspace.copernicus.eu |
| 30-year normal temperature (°C) | PRISM2 | ~ 4 km | https://prism.oregonstate.edu/normals/ |
| 30-year normal vapor pressure deficit (kPA) | PRISM2 | ~ 4 km | https://prism.oregonstate.edu/normals/ |
| Daily precipitation (mm) | PRISM2 | ~ 4 km | https://prism.oregonstate.edu/recent/ |
