Derived datasets used in analyses for spatiotemporal dynamics of Amblyomma americanum abundance and infection prevalence across a subcontinental scale
Data files
Jul 23, 2026 version files 125.06 KB
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Abundance_AIC.csv
32.50 KB
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Abundance_MRM_analysis.csv
40.40 KB
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Pathogen_AIC.csv
17.31 KB
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Pathogen_MRM_analysis.csv
27.33 KB
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README.md
7.52 KB
Abstract
Tick and tick pathogen dynamics are of particular concern in the United States, where tick outbreaks have led to higher incidences of tick-borne diseases. Tick and tick pathogen dynamics are well understood on a local- to regional scale, particularly in the Northeastern United States, where tick density is highest. Little is known, however, about tick and tick pathogen dynamics on a larger scale. National Ecological Observatory Network (NEON) data provides the opportunity to explore how tick and tick pathogen dynamics might look on a subcontinental scale, with standardized tick drags and pathogen sampling occurring at dozens of sites across North America. In the manuscript associated with this submission, we explored abundance and pathogen dynamics in the lone star tick, Amblyomma americanum, employed spatial multiple regression on distance matrix analysis to determine what variables influence synchrony in A. americanum abundance and pathogen status, and explored linear models to determine what variables influence A. americanum abundance and pathogen status. The accompanying data files are derived datasets from raw NEON tick drag sampling, NEON pathogen datasets, and ClimateNA climate data.
Dataset DOI: 10.5061/dryad.2z34tmq2d
Description of the data and file structure
Tick drag sampling data and tick pathogen data are publicly available and were downloaded from the National Ecological Observatory Network (NEON) database at data.neonscience.org, and transformed to create the derived datasets in this submission. Climate data from ClimateNA are publicly available and were downloaded from climatena.ca, and transformed and added to the derived datasets in this submission. Derived datasets were then used for multiple regression on distance matrix (MRM) analyses for both A. americanum nymph abundance and infection ("Abundance_MRM_analysis.csv"; "Infection_MRM_analysis.csv"), and for linear regression analyses and Akaike Information Criterion (AIC) model selection for A. americanum nymph abundance and infection ("Abundance_AIC.csv"; "Infection_AIC.csv").
Files and variables
File: Abundance_AIC.csv
Description: Dataframe used in linear model analysis and AIC model selection to determine what variables influence A. americanum nymph abundance.
Variables
- forestType: The National Landcover Database (NLCD) forest type that the specified NEON plot is located in (mixed forest, evergreen forest, or deciduous forest).
- lat: Latitude in decimal degrees of the geographic location of specified NEON plot.
- lon: Longitude in decimal degrees of the geographic location of specified NEON plot.
- elevation: Elevation in meters of specified NEON plot.
- plotID: Identifier for specified plot at a NEON site; contains four letter NEON site code followed by 3 number string of a specific plot.
- siteID: The four letter site code identifying NEON field sites.
- Genus: Genus of tick species sampled.
- year: Year of data collection.
- tick_abundance: Abundance of A. americanum nymphs (# nymphs/m2).
- meanJulytemp: Mean July temperature in degrees Celsius.
- meanJulyt1: Mean July temperature in degrees Celsius in year t−1.
- meanJulyt2: Mean July temperature in degrees Celsius in year t−2.
- Juneprecip: Total June precipitation in mm.
- Delta_t3: Mean July temperature in degrees Celsius in year t−3 minus mean July temperature in degrees Celsius in year t−4 (ΔT3).
- Delta_t4: Mean July temperature in degrees Celsius in year t−4 minus mean July temperature in degrees Celsius in year t−5 (ΔT4).
File: Abundance_MRM_analysis.csv
Description: Dataframe used in multiple regression on distance matrix (MRM) analysis to determine what variables influence synchrony in A. americanum nymph abundance.
Variables
- PLOT1: First of two NEON plots used in pairwise correlations between plots.
- PLOT2: Second of two NEON plots used in pairwise correlations between plots.
- JulyTcorr: Correlation in mean July temperature in degrees Celsius between two NEON plots.
- JulyT1corr: Correlation in mean July temperature in year t−1 in degrees Celsius between two NEON plots.
- JulyT2corr: Correlation in mean July temperature in year t−2 in degrees Celsius between two NEON plots.
- JunePcorr: Correlation in June precipitation in mm between two NEON plots.
- DT3corr: Correlation in ΔT3 between two NEON plots.
- DT4corr: Correlation in ΔT4 between two NEON plots.
- tickcount_corr: Correlation in abundance of A. americanum nymphs between two NEON plots.
- Distance: Geographic distance between two plots in km.
- Npairs: Number of years of overlap in data between two plots.
- proximity: Proximity between two plots, scaled between 0 and 1, with 1 being the most proximate (close together).
File: Pathogen_MRM_analysis.csv
Description: Dataframe used in MRM analysis to determine what variables influence synchrony in the proportion of A. americanum nymphs infected with a pathogen.
Variables
- Distance: Geographic distance between two plots in km.
- PLOT1: First of two NEON plots used in pairwise correlations between plots.
- PLOT2: Second of two NEON plots used in pairwise correlations between plots.
- tickpathcount_corr: Correlation in proportion of A. americanum nymphs infected with a pathogen between two NEON plots.
- proximity: Proximity between two plots, scaled between 0 and 1, with 1 being the most proximate (close together).
- JulyTcorr: Correlation in mean July temperature in degrees Celsius between two NEON plots.
- JulyT1corr: Correlation in mean July temperature in year t−1 in degrees Celsius between two NEON plots.
- JulyT2corr: Correlation in mean July temperature in year t−2 in degrees Celsius between two NEON plots.
- JunePcorr: Correlation in June precipitation in mm between two NEON plots.
- DT3corr: Correlation in ΔT3 between two NEON plots.
- DT4corr: Correlation in ΔT4 between two NEON plots.
- tickcount_corr: Correlation in abundance of A. americanum nymphs between two NEON plots.
File: Pathogen_AIC.csv
Description: Dataframe used in linear model analysis and AIC model selection to determine what variables influence the proportion of A. americanum nymphs infected with a pathogen.
Variables
- plotID: Identifier for specified plot at a NEON site; contains four letter NEON site code followed by 3 number string of a specific plot.
- siteID: The four letter site code identifying NEON field sites.
- forestType: The NLCD forest type that the specified NEON plot is located in (mixed forest, evergreen forest, or deciduous forest).
- lat: Latitude in decimal degrees of the geographic location of specified NEON plot.
- lon: Longitude in decimal degrees of the geographic location of specified NEON plot.
- elevation: Elevation in meters of specified NEON plot.
- Genus: Genus of tick species sampled.
- year: Year of data collection.
- tickabundance: Abundance of A. americanum nymphs (# nymphs/m2).
- prop_infected: Proportion of A. americanum nymphs infected with a pathogen.
- meanJulytemp: Mean July temperature in degrees Celsius.
- meanJulyt1: Mean July temperature in degrees Celsius in year t−1.
- meanJulyt2: Mean July temperature in degrees Celsius in year t−2.
- Juneprecip: Total June precipitation in mm.
- Deltat3: Mean July temperature in degrees Celsius in year t−3 minus mean July temperature in degrees Celsius in year t−4 (ΔT3).
- Deltat4: Mean July temperature in degrees Celsius in year t−4 minus mean July temperature in degrees Celsius in year t−5 (ΔT4).
Code/software
The programming software RStudio can be used to view all data files associated with this submission.
Access information
Data was derived from the following cited sources:
NEON (National Ecological Observatory Network). 2022a. Tick pathogen status (DP1.10092.001), RELEASE-2022. https://data.neonscience.org/data-products/DP1.10092.001
NEON (National Ecological Observatory Network). 2022b. Ticks sampled using drag cloth (DP1.10093.001), RELEASE-2022. https://data.neonscience.org/data-products/DP1.10093.001
Wang, T., A. Hamann, D. Spittlehouse, and C. Carroll. 2016. Locally Downscaled and Spatially Customizable Climate Data for Historical and Future Periods for North America. PLOS ONE 11:e0156720. https://climatena.ca