DOI: 10.5061/dryad.v6wwpzh67
title: Climate-driven variation in the phenology of juvenile Ixodes pacificus on lizard hosts

description: Background: Ectothermic arthropods, like ticks, are sensitive indicators
 of environmental changes, and their seasonality plays a critical role in
 tick-borne disease dynamics in a warming world. Juvenile tick phenology,
 which influences pathogen transmission, may vary across climates, with
 longer tick seasons in cooler climates potentially amplifying
 transmission. However, assessing juvenile tick phenology is challenging in
 arid climates because ticks spend less time seeking for blood meals (i.e.,
 questing) due to desiccation pressures. As a result, traditional
 collection methods like dragging or flagging are less effective. To
 improve our understanding of juvenile tick seasonality across a
 latitudinal gradient, we examine Ixodes pacificus phenology on lizards,
 the primary juvenile tick host in California, and explore how climate
 factors influence phenological patterns.   Methods: Between 2013
 and 2022, ticks were removed from 1,527 lizards at 45 locations during
 peak tick season (March-June). Tick counts were categorized by life stage
 (larvae and nymphs) and linked with remotely sensed climate data including
 monthly maximum temperature, specific humidity, and Palmer Drought
 Severity Index (PDSI). Juvenile phenology metrics, including tick
 abundances on lizards, Julian date of peak mean abundance, and temporal
 overlap between larval and nymphal populations, were analyzed along a
 latitudinal gradient. Generalized Additive Models (GAMs) were applied to
 assess climate-associated variation in juvenile abundance on
 lizards.    Results: Mean tick abundance per lizard
 ranged from 0.17 to 47.21 across locations, with the highest in the San
 Francisco Bay Area and lowest in Los Angeles, where more lizards had zero
 ticks attached. In the San Francisco Bay Area, peak nymphal abundance
 occurred 25 days earlier than peak larval abundance. Temporal overlap
 between larval and nymphal stages at a given location varied regionally,
 with northern areas showing higher overlap. We found that locations with
 higher temperatures and increased drought stress were linked to lower tick
 abundances, though the magnitude of these effects depended on regional
 location.   Conclusion: Our study, which compiled 10 years of
 data, reveals significant regional variation in juvenile I. pacificus
 phenology across California, including differences in the abundance, peak
 timing, and temporal overlap. These findings highlight the influence of
 local climate on tick seasonality, with implications for tick-borne
 disease dynamics in a changing climate. 
description: The data for this study was collected from 2013 to 2022,
 primarily during the peak juvenile activity months of <em>Ixodes
 pacificus</em> ticks, which are March through June. This dataset
 reflects the collective efforts of various lab groups engaged in
 ecological research, which included both lizard sampling and tick burden
 assessments in California, United States. This aggregated dataset includes
 45 unique sampling locations and encompasses a total of 253 sampling days.
 Of the 45 locations, 93% were sampled multiple times, with 84% being
 sampled three or more times. Various subsets of this data have been
 published previously by Swei et al 2011, MacDonald et al. 2018, Sambado et
 al. 2024, Copeland et al. 2025. 
description: # Climate-driven variation in the phenology of juvenile *Ixodes pacificus*
 on lizard hosts
 [https://doi.org/10.5061/dryad.v6wwpzh67](https://doi.org/10.5061/dryad.v6wwpzh67) ## Description of the data and file structure The data for this study was collected from 2013 to 2022, primarily during the peak juvenile activity months of Ixodes pacificus, which are March through June. This dataset reflects the collective efforts of various lab groups engaged in ecological research, which included both lizard sampling and tick burden assessments in California, United States. This aggregated dataset includes 45 unique sampling locations and encompasses a total of 253 sampling days. Of the 45 locations, 93% of locations were sampled multiple times, with 84% being sampled three or more times. Various subsets of this data have been published previously by Swei et al. 2011, MacDonald et al. 2018, Sambado et al. 2024, and Copeland et al. 2025.  ### Files and variables #### File: master\_lizardclimate\_20241023.csv **Description:**  Synthesized data collection of individual lizards with recorded tick burdens and associated climate data for each sample location. ##### Variables * collector: primary lead of the study that collected the data (e.g., Sambado, Copeland, MacDonald) * location: unique sampling location where data collection took place * date: collection date of individual lizards at unique locations * year: year of data collection * month: month of data collection * julian: Julian date of data collection * lat: latitude of unique sampling location * lon: longitude of unique sampling location * latitutdinal_region: assigned latitudinal region based on sampling location (i.e, northern, central, southern) * climate_region: assigned climate region based on climatic features associated with the sampling location designated by California's 4th Climate Change Assessments (e.g., San Francisco Bay Area climate region, San Joaquin Valley climate region). * total_ticks: total ticks that were counted on an individual lizard * total_l: total larval ticks that were counted on an individual lizard * total_n: total nymphal ticks that were counted on an individual lizard * total_a: total adult ticks that were counted on an individual lizard * lizard_species: identified species of an individual lizard * notes: if the collector had any associated notes about an individual lizard collection * season: calendar season (e.g., Spring, Summer) of when the lizard collection occurred, based on date. * tmmn_daily: daily minimum temperature (C) from gridMET data source * tmmx_daily: daily maximum temperature (C) from gridMET data source * pet_daily: daily reference evapotranspiration (pet) from gridMET data source * pr_daily: daily precipitation (mm) from gridMET data source * sph_daily: daily specific humidity (sph) from gridMET data source * pdsi_monthly: monthly Palmer drought severity index from gridMET data source * tmmn_monthly: monthly minimum temperature (C) from gridMET data source * tmmx_monthly: monthly maximum temperature (C) from gridMET data source * pet_monthly: monthly reference evapotranspiration (pet) from gridMET data source * pr_monthly: total monthly precipitation (mm) from gridMET data source * sph_monthly: monthly specific humidity (sph) from gridMET data source * tmmn_fall: average minimum temperature (C) in the fall season from gridMET data source * tmmn_spring: average minimum temperature (C) in the spring season from gridMET data source * tmmn_summer: average minimum temperature (C) in the summer season from gridMET data source * tmmn_winter: average minimum temperature (C) in the winter season from gridMET data source * tmmx_fall: average maximum temperature (C) in the fall season from gridMET data source * tmmx_spring: average maximum temperature (C) in the spring season from gridMET data source * tmmx_summer: average maximum temperature (C) in the summer season from gridMET data source * tmmx_winter: average maximum temperature (C) in the winter season from gridMET data source #### File: climate\_full\_clean\_20241023.csv **Description:**  ##### Variables * location: unique sampling location where data collection took place * lon: longitude of unique sampling location * lat: latitude of unique sampling location * date: date of data collection * year: year of data collection * month: month of data collection * season: calendar season (e.g., Spring, Summer) of when the lizard collection occurred, based on date. * julian: Julian date of data collection * tmmn_daily: daily minimum temperature (C) from gridMET data source * tmmx_daily: daily maximum temperature (C) from gridMET data source * pet_daily: daily reference evapotranspiration (pet) from gridMET data source * pr_daily: daily precipitation (mm) from gridMET data source * sph_daily: daily specific humidity (sph) from gridMET data source * pdsi_monthly: monthly Palmer drought severity index from gridMET data source * tmmn_monthly: monthly minimum temperature (C) from gridMET data source * tmmx_monthly: monthly maximum temperature (C) from gridMET data source * pet_monthly: monthly reference evapotranspiration (pet) from gridMET data source * pr_monthly: total monthly precipitation (mm) from gridMET data source * sph_monthly: monthly specific humidity (sph) from gridMET data source * tmmn_fall: average minimum temperature (C) in the fall season from gridMET data source * tmmn_spring: average minimum temperature (C) in the spring season from gridMET data source * tmmn_summer: average minimum temperature (C) in the summer season from gridMET data source * tmmn_winter: average minimum temperature (C) in the winter season from gridMET data source * tmmx_fall: average maximum temperature (C) in the fall season from gridMET data source * tmmx_spring: average maximum temperature (C) in the spring season from gridMET data source * tmmx_summer: average maximum temperature (C) in the summer season from gridMET data source * tmmx_winter: average maximum temperature (C) in the winter season from gridMET data source ## Code/software The code to recreate the figures and analysis can be found on a public gitHub repository at [https://github.com/sbsambado/ca_lizardburden](https://github.com/sbsambado/ca_lizardburden). All of the software used for this manuscript is freely available online.  ## Access information Other publicly accessible locations of the data: * A public gitHub repository with the code to recreate figures and analysis can be found at [https://github.com/sbsambado/ca_lizardburden](https://github.com/sbsambado/ca_lizardburden). Data was derived from the following sources: * Climate data came from gridMET ([https://www.climatologylab.org/gridmet.html](https://www.climatologylab.org/gridmet.html)), an open data source of climate data across the continental United States (Abatzoglou 2013). The gridMET data was accessed on 2024-10-23. * The shapefiles for the climate regions from California's 4th Climate Change Assessments came from Cal-Adapt ([https://cal-adapt.org/](https://cal-adapt.org/)). Cal-Adapt website was developed by University of California at Berkeley’s Geospatial Innovation Facility under contract with the California Energy Commission. Retrieved [22 August 2023], from [https://ucanr-igis.github.io/caladaptr/articles/api-requests.html](https://ucanr-igis.github.io/caladaptr/articles/api-requests.html); 2023.