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Abstract BackgroundEctothermic arthropods, like ticks, are sensitive indicators of environmental changes, and their seasonality plays a critical role in the dynamics of tick-borne disease 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 examinedIxodes pacificusphenology on lizards, the primary juvenile tick host in California, and explored how climate factors influence phenological patterns. MethodsBetween 2013 and 2022, ticks were removed from 1527 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. ResultsMean tick abundance per lizard ranged from 0.17 to 47.21 across locations, with the highest abundance 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, possibly due to the bimodal seasonality of nymphs. We found that locations with higher temperatures and increased drought stress were linked to lower tick abundances, although the magnitude of these effects depended on regional location. ConclusionsOur study, which compiled 10 years of data, reveals significant regional variation in juvenileI. pacificusphenology across California, including differences in 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. Graphical Abstract 

Abstract Mosquito‐borne diseases contribute substantially to the global burden of disease, and are strongly influenced by environmental conditions. Ongoing and rapid environmental change necessitates improved understanding of the response of mosquito‐borne diseases to environmental factors like temperature, and novel approaches to mapping and monitoring risk. Recent development of trait‐based mechanistic models has improved understanding of the temperature dependence of transmission, but model predictions remain challenging to validate in the field. Using West Nile virus (WNV) as a case study, we illustrate the use of a novel remote sensing‐based approach to mapping temperature‐dependent mosquito and viral traits at high spatial resolution and across the diurnal cycle. We validate the approach using mosquito and WNV surveillance data controlling for other key factors in the ecology of WNV, finding strong agreement between temperature‐dependent traits and field‐based metrics of risk. Moreover, we find that WNV infection rate in mosquitos exhibits a unimodal relationship with temperature, peaking at ~24.6–25.2°C, in the middle of the 95% credible interval of optimal temperature for transmission of WNV predicted by trait‐based mechanistic models. This study represents one of the highest resolution validations of trait‐based model predictions, and illustrates the utility of a novel remote sensing approach to predicting mosquito‐borne disease risk. 

ABSTRACT Spatial processes, particularly scale‐dependent feedbacks, may play important and underappreciated roles in the dynamics of bistable ecosystems. For example, self‐organised spatial patterns can allow for stable coexistence of alternative states outside regions of bistability, a phenomenon known as a Busse balloon. We used partial differential equations to explore the potential for such dynamics in coral reefs, focusing on how herbivore behaviour and mobility affect the stability of coral‐ and macroalgal‐dominated states. Herbivore attraction to coral resulted in a Busse balloon that enhanced macroalgal resilience, with patterns persisting in regions of parameter space where nonspatial models predict uniform coral dominance. Thus, our work suggests herbivore association with coral (e.g., for shelter) can prevent reefs from reaching a fully coral‐dominated state. More broadly, this study illustrates how consumer space use can prevent ecosystems from undergoing wholesale state transitions, highlighting the importance of explicitly accounting for space when studying bistable systems. 

ABSTRACT Quantifying ecosystem services provided by mobile species like insectivorous bats remains a challenge, particularly in understanding where and how these services vary over space and time. Bats are known to offer valuable ecosystem services, such as mitigating insect pest damage to crops, reducing pesticide use, and reducing nuisance pest populations. However, determining where bats forage is difficult to monitor. In this study, we use a weather‐radar‐based bat‐monitoring algorithm to estimate bat foraging distributions during the peak season of 2019 in California's Northern Central Valley. This region is characterized by valuable agricultural crops and significant populations of both crop and nuisance pests, including midges, moths, mosquitos, and flies. Our results show that bat activity is high but unevenly distributed, with rice fields experiencing significantly elevated activity compared to other land cover types. Specifically, bat activity over rice fields is 1.5 times higher than over any other land cover class and nearly double that of any other agricultural land cover. While irrigated rice fields may provide abundant prey, wetland and water areas showed less than half the bat activity per hectare compared to rice fields. Controlling for land cover type, we found bat activity significantly associated with higher flying insect abundance, indicating that bats forage in areas where crop and nuisance pests are likely to be found. This study demonstrates the effectiveness of radar‐based bat monitoring in identifying where and when bats provide ecosystem services. 

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.  The data for this study was collected from 2013 to 2022, primarily during the peak juvenile activity months of Ixodes pacificus 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.  # 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. 

Abstract Anthropogenic land use change has led to considerable biodiversity loss, affecting ecosystem functions with unresolved consequences for zoonotic disease transmission. Functional diversity is understudied but potentially important for understanding the role of biodiversity because many zoonotic disease systems are maintained by species with different roles in disease transmission. Here, we explore how functional groups and pathogen genetic diversity influence transmission and human disease risk within the Lyme disease system. Our field and molecular ecology study examined ticks and vertebrates across a fragmented landscape and evaluated several metrics of disease risk. For predicting vector and infected vector density, rodent host richness had a positive effect and was most important, but vector infection prevalence was best predicted by rodent and predator richness together, reflecting how indirect effects may alter tick–host interactions and disease risk. These results indicate that examining species richness generally may obscure important interactions driven by richness within functional groups. Pathogen genotype richness was best predicted by overall vertebrate richness, providing support for the multiple niche polymorphism hypothesis. Our study offers an important perspective on the relationship between biodiversity and disease risk, suggesting that richness within functional groups may offer more nuanced insight into pathogen transmission dynamics than overall biodiversity. 

A vector's susceptibility and ability to transmit a pathogen—termed vector competency—determines disease outcomes, yet the ecological factors influencing tick vector competency remain largely unknown. Ixodes pacificus, the tick vector of Borrelia burgdorferi (Bb) in the western U.S., feeds on rodents, birds, and lizards. Rodents and birds are reservoirs for Bb and infect juvenile ticks, while lizards are refractory to Bb and cannot infect feeding ticks. Additionally, the lizard bloodmeal contains borreliacidal properties, clearing previously infected feeding ticks of their Bb infection. Despite I. pacificus feeding on a range of hosts, it is undetermined how the host identity of the larval bloodmeal affects future nymphal vector competency. We experimentally evaluate the influence of larval host bloodmeal on Bb acquisition by nymphal I. pacificus. Larval I. pacificus were fed on either lizards or mice and after molting, nymphs were fed on Bb-infected mice. We found that lizard-fed larvae were significantly more likely to become infected with Bb during their next bloodmeal than mouse-fed larvae. We also conducted the first RNA-seq analysis on whole-bodied I. pacificus and found significant upregulation of tick antioxidants and antimicrobial peptides in the lizard-fed group. Our results indicate that the lizard bloodmeal significantly alters vector competency and gene regulation in ticks, highlighting the importance of host bloodmeal identity in vector-borne disease transmission and upends prior notions about the role of lizards in Lyme disease community ecology.