DOI: 10.5061/dryad.9kd51c5v1
title: Data from: Climate differentially impacts ticks infected and uninfected with Borrelia burgdorferi

description: Climate change continues to alter the behavior and distribution of species
 worldwide, with major ramifications for the transmission and risk of
 infectious diseases, including those caused by zoonotic vector-borne
 pathogens. This study explores the potential implications of climate
 change for one such pathogen, Borrelia burgdorferi (the causative agent of
 human Lyme disease), in Ixodes pacificus ticks of the far-western United
 States. Nymphal tick infection prevalence and density are compared against
 several metrics for climate, while also accounting for habitat
 fragmentation, mammalian species richness, and rodent tick burden to
 eliminate confounding variables. Findings show that climate extremes, such
 as those forecast with climate change, correlate with a reduction of B.
 burgdorferi prevalence in nymphal ticks despite nominal impacts to
 uninfected tick density, contrasting traditional hypotheses that these
 changes will increase vector-borne pathogens.
description: <strong>Study system</strong> Data
 collection for this study took place between April and July from 2016 to
 2022. For primary purposes of this study, land patches were designated for
 field sampling within a four-hour radius of San Francisco State
 University, San Francisco, California and consisted primarily of oak and
 scrub vegetation in a variety of settings ranging from urban to protected
 national forest. Designated patches ranged from 2.5 to 151,123 hectares in
 size and consisted of 14 different sites - nine designated in 2016, an
 additional two in 2018, and three more in 2019. Once established,
 0.5-hectare sampling grids were marked on each plot with specific
 attention to canopy cover, slope, and edge proximity, with all sites being
 established under canopy cover a minimum distance of 20 m from the habitat
 edge and avoiding north-facing slopes.
 <strong>Tick collection</strong>
 Ticks were collected from each plot while questing using a
 standardized drag-sampling approach twice per season for a total sampled
 area of 995 m². Within each cohort, ticks were identified for life stage
 and species using microscopic identification of visual features according
 to previously established protocols. Once identified, only questing
 nymphal ticks were used for tick density in this study.
 <strong>Bacterial diagnostics</strong>
 The presence or absence of <em>B. burgdorferi</em> in
 each tick sample was identified using PCR analysis. Whole nymphs were
 stored at -80 °C until extraction for DNA, which was performed using the
 Qiagen DNA Easy Blood and Tissue Kit according to manufacturer
 instructions (Qiagen Redwood City Inc., Redwood City, California, USA; PN
 69504). Extracted DNA was then tested using a 5S-23S rDNA PCR protocol to
 identify the presence of <em>B. burgdorferi sensu lato</em>
 alongside gel electrophoresis and DNA sequencing, which was used to
 further distinguish species of pathogen down to <em>B. burgdorferi
 sensu stricto</em>. <strong>Mammalian
 species richness and tick burden</strong> Counts
 for mammalian species richness and rodent tick burden were supported
 through a combination of mark-recapture and trail camera techniques.
 Rodent captures were performed for three consecutive days a year at each
 field site between the months of April and July for all years except 2020
 and 2022. Rodents were captured in Sherman live traps (H.B. Sherman Traps,
 Tallahassee, FL, USA) set at 11.8 m intervals. In total, 49 traps were
 placed per site. Captured rodents were weighed, sexed, and identified to
 species using established protocols, which was confirmed
 with <em>Cytochrome-B</em> molecular analysis, if needed.
 Attached ticks were collected directly from trapped rodents for rodent
 tick burden counts. Additionally, two motion detector Bushnell cameras
 (Bushnell models #119736, #119836, #119836C, Bushnell, KS, USA) were
 anchored to trees approximately ~1 m above the ground for each field site.
 These cameras faced in opposite directions and recorded a total of 40 days
 and nights of wildlife activity for all years except 2020 and 2022.
 Recordings were used to visually identify all species present at each
 sample site using morphological characteristics. All mammal trapping was
 done in accordance with IACUC standards via San Francisco State University
 (AU16-05R1a and AU19-01R2) and was performed with California Department of
 Fish & Wildlife approval (collection permits SC-8407 and
 S-203370009). <strong>Land fragmentation and
 climate parameters</strong> All landscape
 measurements were performed manually using ArcGIS software (copyright
 ESRI) and data layers from the California Department of Forestry and Fire
 Protection. Climate parameters of interest were provided by
 VisualCrossing, which is a powerful weather API tool that interpolates
 between weather stations to fill in missing values and cleanses data for
 accuracy (Visual Crossing Corporation, Reston, Virginia, USA). As such, it
 has been validated for measurements at 1 km and 5-minute time point
 resolution. Captured data included averages for the parameters of interest
 for the month of April from all sites and years, as this month showed the
 most extremes for analysis and fell during the peak questing months for
 tick collection. <strong>Data
 analysis</strong> All response variables of
 interest were explored for normality, collinearity, Cook’s Distance, and
 leverage points using the plot function in the R “stats” package, as well
 as residual plots and histograms. As all data of interest were considered
 counts with non-normal distribution and overdispersion, models were run
 across all years using a Penalized Quasilikelihood GLMM with a Poisson
 distribution and log link. A random effect was included for site to
 account for non-independence among the explanatory variables. Stepwise
 model selection was performed using the “dredge” function in the MuMIn R
 packages. Year was initially explored as a random effect in all models,
 but was ultimately dropped due to poor fit. Final model output and all
 data was back-transformed, and confidence intervals established using the
 “exp” and “confint” functions in the R “stats” package. R squared values
 to determine goodness-of-fit were also obtained for all final models using
 the “R.squared” function in the “aod” package of R. A
 PLS-PM was used to assess relationships between climate, landscape, host,
 and tick parameters of interest for nymphal infection prevalence. This
 method was chosen due to its ability to explore potentially statistically
 significant relationships between observational data without strict
 criteria for data structure. For its construction, latent variables
 (theoretical outcomes) of interest were described, as well as the path
 (structural relationship) between each latent variable. Each latent
 variable was then defined by a second set of manifest variables (measured
 variables relating to the theoretical concept), and the data structure was
 explored for statistically significant correlations (i.e., regression
 coefficients) between manifest variables and their latent variable.
 Manifest variables with poor relationship to their latent variable
 (regression coefficient &lt; 0.40; Dillon-Goldstein’s rho &lt;
 0.70) were dropped from the final model. The final PLS-PM model was run
 using the ‘plspm’ package in R and all outputs were explored for
 goodness-of-fit to determine their influence on the overall model outcome.
 Bootstrapping was performed to confirm model output and all findings were
 consistent.
description: # Data from: Climate differentially impacts ticks infected and uninfected
 with Borrelia burgdorferi
 [https://doi.org/10.5061/dryad.9kd51c5v1](https://doi.org/10.5061/dryad.9kd51c5v1) ## Description of the data and file structure The data contained in this repository was collected from 14 different sites throughout the San Francisco Bay Area, California, USA. Metrics collected include habitat fragmentation parameters (sourced from ArcGIS), climate parameters (obtained from VisualCrossing API), mammalian species richness (collected via trail camera), rodent tick-burden (collected via mark-recapture of rodents and manual tick remocal), *Ixodes pacificus* tick density (collected via tick-drags), and *I. pacificus* nymphal infection prevalence with *Borrelia burgdorferi sensu stricto* (obtained via PCR analysis of collected ticks). ### Files and variables #### File: PINCSET2RICH.xls **Description:**  ##### Variables * SITE: Location for sample collection. * NAME: Name of location. * YEAR: Year of data collection. * COUNTY: Californian county where site resides. * LATITUDE: Latitude of site location. * LONGITUDE: Longitude of site location. * GPS: GPS coordinates of site location. * AREAHA: Area in hectares. * DISTHUMANSM: Euclidean distance from site to nearest human neighborhood in meters. * CDSOURCE: Cost distance to source habitat in meters. * EUCSOURCE: Euclidean distance to source habitat in meters. * EUCWATER: Euclidean distance to freshwater in meters. * EUCSALT: Euclidean distance to saltwater in meters. * EUCFOREST: Euclidean distance to forest in meters. * DOT995: Total density of *Ixodes pacificus *ticks (all life stages) per 995 m2. * DOL: Density of larval *Ixodes pacificus* ticks per 995 m2. * DOA: Density of adult *Ixodes pacificus* ticks per 995 m2. * DON: Density of nymphal *Ixodes pacificus* ticks per 995 m2. * DINBBSS: Density of *Borrelia burgdorferi sensu stricto *infected nymphal *Ixodes pacificus* ticks per 995 m2. * NIPBBSS: Prevalence of *Borrelia burgdorferi sensu stricto *in nymphal *Ixodes pacificus* ticks per 995 m2 collected from each site. * MAXTEMPAPRIL: Maximum average daily temperature for the month of April at each site (F). * MINTEMPAPRIL: Minimum average daily temperature for the month of April at each site (F). * TEMPRANGE: Maximum average daily temperature for the month of April at each site - minimum average daily temperature for the month of April at each site (F). * HUMIDITYAPRIL: Average mass of water vapor per cubic meter of moist air (g) for the month of April. * PRECIPAPRIL: Moisture accumulation (cm) for the month of April. * WINDAPRIL: Average daily air movement from high to low pressure (kph) for the month of April. * CLOUDSAPRIL: Average daily total proportion of sky obscured by clouds (%) for the month of April. * UVAPRIL: Average daily total non-ionizing radiation emitted by the sun (mW/m2) for the month of April. * RODENTBURDEN: Total number of ticks removed from rodents captured at each site. * TOTALMAMMALRICHNESS: Total number of mammal species recorded at each site via mark-recapture (rodents only) and trail camera (megafauna). ## Code/software Data file can be viewed in .csv or .xls format in Excel or R Studio. ## Access information Other publicly accessible locations of the data: * None Data was derived from the following sources: * Visual Crossing ([https://www.visualcrossing.com/](https://www.visualcrossing.com/)) * ArcGIS ([https://www.arcgis.com/index.html](https://www.arcgis.com/index.html))