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Abstract BackgroundAs habitat fragmentation increases, ecological processes, including patterns of vector-borne pathogen prevalence, will likely be disrupted, but ongoing investigations are necessary to examine this relationship. Here, we report the differences in the prevalence of Lyme disease (Borrelia burgdorferisensu lato, s.l.) and haemoproteosis (Haemoproteusspp.) pathogens in avian populations of a fragmented habitat.B. burgdorferis.l. is a generalist pathogen that is transmitted byIxodes pacificusvectors in California, andHaemoproteusis an avian parasite transmitted byCulicoidesvectors. MethodsTo determine whether biotic (avian and mammalian abundance) or abiotic characteristics (patch size and water availability) correlated with infection prevalence change, we screened 176 birds sampled across seven sites in oak woodland habitat in northern California. ResultsWhile biotic factors correlated with an increase in both pathogens, infection prevalence ofHaemoproteusspp. was only associated with individual-level traits, specifically foraging substrate and diet, andB. burgdorferis.l. was associated with community-level characteristics, both total mammal and, specifically, rodent abundance. Proximity to water was the only abiotic factor found to be significant for both pathogens and reinforces the importance of water availability for transmission cycles. Larger patch sizes did not significantly affect infection prevalence ofHaemoproteus,but did increase the prevalence ofB. burgdorferi. ConclusionsThese results highlight that while environmental factors (specifically habitat fragmentation) have a limited role in vector-borne pathogen prevalence, the indirect impact to biotic factors (community composition) can have consequences for bothHaemoproteusandB. burgdorferiprevalence in birds. Given the pervasiveness of habitat fragmentation, our results are of broad significance. Graphical abstract 

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 

Synopsis The emergence of infectious diseases is largely driven by spillover events from animal communities into human populations, with zoonotic pathogens accounting for 75% of novel infectious agents. In recent years, the incidence and prevalence of these pathogens have been on the rise, and efforts to understand the underlying ecological principles responsible for the reported increases have highlighted the role of biodiversity loss as a major contributing factor. Despite its role in pathogen emergence, how biodiversity is measured can differ drastically and may underlie variability in study results, making the impacts of biodiversity on pathogen behavior difficult to untangle. Here, we first examine how landscape parameters affect disease transmission and then evaluate metrics used in various disease systems to discuss the ways that different aspects of biodiversity, such as functional, phylogenetic, and trophic diversity, can provide novel insight into the relationship between host communities and disease emergence and transmission. We focus on the tick-borne pathogen that causes Lyme disease in this review and discuss how functional, trophic, and phylogenetic diversity can improve our understanding of the relationship between host community structure and disease transmission. The growing public health burden of tick-borne diseases necessitates holistic thinking to inform actions to decrease the risk of disease to humans and protect natural communities. 

Abstract Large‐bodied wild ungulates are declining worldwide, while domestic livestock continue to increase in abundance. Such changes in large herbivore communities should have strong effects on the control of ticks and tick‐borne disease as they can indirectly modify habitat and directly serve as final hosts for ticks' lifecycles. Numerous studies have now linked changing ungulate communities to changes in tick populations and disease risk. However, the effects of changing large herbivore communities are variable across studies, and the effect of climate as a mediating factor of this variation remains poorly understood. Also, studies to date have largely focused on wildlife loss without considering the extent to which livestock additions may alter tick populations, even though livestock replacement of wildlife is the global norm. In this study, we used a large‐scale exclosure experiment replicated along a topo‐climatic gradient to examine the effects on tick populations of both large herbivore removal and livestock additions. We found that while questing ticks increased modestly, by 21%, when large herbivores were removed from a system they decreased more substantially, by 50%, when livestock (in the form of cattle) were added. Importantly, in addition to the direct effects of climate on tick populations, climate also mediates the effect of ungulates on questing tick density. Particularly, the addition of livestock under the most arid conditions decreased tick presence, likely due to changes in ground‐level microclimates away from those beneficial to ticks. Overall, the work contributes to our understanding of tick population responses to globally common human‐induced rangeland alterations under the concurrent effects of climate change. 

Large-bodied wild ungulates are declining worldwide, while domestic livestock continue to increase in abundance. Such changes in large herbivore communities should have strong effects on the control of ticks and tickborne disease as they can indirectly modify habitat and directly serve as final hosts for ticks’ lifecycles. Numerous studies have now linked changing ungulate communities to changes in tick populations and disease risk. However, the effects of changing large herbivore communities are variable across studies, and the effect of climate as a mediating factor of this variation remains poorly understood. Also, studies to date have largely focused on wildlife loss without considering the extent to which livestock additions may alter tick populations, even though livestock replacement of wildlife is the global norm. In this study, we used a large-scale exclosure experiment replicated along a topo-climatic gradient to examine the effects on tick populations of both large herbivore removal and livestock additions. We found that while questing ticks increased modestly, by 21%, when large herbivores were removed from a system they decreased more substantially, by 50%, when livestock (in the form of cattle) were added. Importantly, in addition to the direct effects of climate on tick populations, climate also mediates the effect of ungulates on questing tick density. Particularly the addition of livestock under the most arid conditions decreased tick presence, likely due to changes in ground-level microclimates away from those beneficial to ticks. Overall, the work contributes to our understanding of tick population responses to globally common human-induced rangeland alterations under the concurrent effects of climate change. ## Data Files **Tejon_MeanComparisons_Dataset.csv**: contains tick drag data aggregated by *year*, *month*, *site*, *block*, and *plot*. The tick drag data includes a *total* column of all tick species collected and additionally, four columns representing the three tick species collected via drags *deoc* (*Dermacentor occidentalis*), *ipac* (*Ixodes pacificus*), *deva* (*Dermacentor variabilis*), and *other* (for non-identified ticks). Finally, it contains a unique numerical ID for each of the 27 exclosure treatments of TREE, *plotID*. This data was used in the manuscript's analyses, which investigated the individual and interactive impacts of climate level and herbivore treatment on tick abundance. **Tejon_MixedModels_Dataset.csv**: in addition to tick drag data, it contains columns liz_estimate and mam_estimates created by the results located in the **tejon_mamliz_popestimates.csv** file detailed below. This data set analyzes climate and herbivore treatment impact on lizard and small mammal populations. These results and figures are included in the Appendix. NAs in the liz_estimate and mam_estimates columns indicate when model estimates were omitted because the resulting estimations were unrealistic. **Total_Ticks_2016_to_2018.csv**: raw data from field tick drags from 2016 - 2018. **Tick_19.csv**: raw data from field tick drags occurring in 2019. **lizardcounts_02172021.csv**: raw data file containing lizard surveys completed in the field. Lizard surveys were completed by mark and recapture surveys that took place at only the Arid and Intermediate climate level plots (N=18). The plots were sampled for three consecutive days, and when a lizard was spotted, they were sprayed with a scale-safe paint. Each day, there was a different color of paint. For example, in a row in the data set, a lizard spotted on day 1 will have a 1 in the column *day1*. If it were not spotted on day 2, there would be a zero in the *day2* column. And if it were spotted on day 3, there would be a 1 in the *day3* column. **mammal_counts_02182021.csv**: raw data file containing small mammal trapping data completed in the field. Small mammal surveys were completed using mark and recapture surveys. Each plot was surveyed for three consecutive days. When an animal was caught, they were given a metal ear tag (IACUC # 904.1). The column *tag* in the data file indicates the tag number assigned to the animal. Each row indicates how many times *day1*, *day2*, and *day3* that animal was caught over our sampling period. Those with an NA in the *tag* column were, for some reason (e.g., juvenile or deformity), unable to be tagged. **tejon_mamliz_popestimates.csv**: data file containing the abundance estimate results for small mammal and lizard populations using `Rcapture` to evaluate the raw data (two data files directly mentioned above) recorded in the field. **Dataset Clarifications** *site* = Climate Level, a factorial column denoting three climate levels of the study - Arid, Intermediate, and Mesic. *plot* = Herbivore treatment, a factorial column denoting three herbivory treatment types used in the study. Within the data set, they are referred to as Open, Partial, and Total. For data figures and discussion of results and analyses within the manuscript, these are changed to: * Open = *Cattle and Wildlife* (CW) * Partial = *Wildlife* (W) * Total = *Minus Cattle and Wildlife* (-CW) 

Abstract Changing climate has driven shifts in species phenology, influencing a range of ecological interactions from plant–pollinator to consumer–resource. Phenological changes in host–parasite systems have implications for pathogen transmission dynamics. The seasonal timing, or phenology, of peak larval and nymphal tick abundance is an important driver of tick‐borne pathogen prevalence through its effect on cohort‐to‐cohort transmission. Tick phenology is tightly linked to climatic factors such as temperature and humidity. Thus, variation in climate within and across regions could lead to differences in phenological patterns. These differences may explain regional variation in tick‐borne pathogen prevalence of the Lyme disease‐causingBorreliabacteria in vector populations in the United States. For example, one factor thought to contribute to high Lyme disease prevalence in ticks in the eastern United States is the asynchronous phenology of ticks there, where potentially infected nymphal ticks emerge earlier in the season than uninfected larval ticks. This allows the infected nymphal ticks to transmit the pathogen to hosts that are subsequently fed upon by the next generation of larval ticks. In contrast, in the western United States where Lyme disease prevalence is generally much lower, tick phenology is thought to be more synchronous with uninfected larvae emerging slightly before, or at the same time as, potentially infected nymphs, reducing horizontal transmission potential. Sampling larval and nymphal ticks, and their host‐feeding phenology, both across large spatial gradients and through time, is challenging, which hampers attempts to conduct detailed studies of phenology to link it with pathogen prevalence. In this study, we demonstrate through intensive within‐season sampling that the relative abundance and seasonality of larval and nymphal ticks are highly variable along a latitudinal gradient and likely reflect the variable climate in the far western United States with potential consequences for pathogen transmission. We find that feeding patterns were variable and synchronous feeding of juvenile ticks on key blood meal hosts was associated with mean temperature. By characterizing within‐season phenological patterns of the Lyme disease vector throughout a climatically heterogeneous region, we can begin to identify areas with high potential for tick‐borne disease risk and underlying mechanisms at a finer scale. 

Characterizing niche space is critical for predicting species interactions and responses to environmental change. To enhance our understanding of dietary niche breadth, we used DNA metabarcoding to examine how diets of a widespread, model herbivore (woodrats, genusNeotoma) respond to changing resources and the extent to which dietary specialization is conserved across space and time. We used diet data from 13 species, 57 populations, and over 500 individuals to examine predictors of niche breadth and interindividual diet variation at a landscape scale. Then, to test whether these patterns are conserved across scales, we explored the same questions using a single population sampled over 5 y and mark-recapture data from individuals sampled at least three times. We found that woodrats exhibited a continuum of dietary specialization that included species-level dietary generalists and specialists. Specialist species maintained narrow population-level niche breadths with little evidence of interindividual diet variation. In contrast, generalist species consisted of populations with varying degrees of dietary specialization and interindividual diet variation. Across sampling scales, increased population-level niche breadth was explained by both increased interindividual diet variation and increased diet richness. These results are consistent with the Niche Variation Hypothesis and suggest that diet breadth is constrained by costs of both specialization and generalization. 

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 Biodiversity has been linked to reduced disease transmission through the dilution effect process. Traditional ecological measures of biological diversity, such as species richness, are most commonly used to test for the dilution effect. However, such metrics of species diversity do not consider the evolutionary relationship between species, which has important implications for host immune processes and disease transmission. Phylogenetic diversity incorporates the evolutionary relationships of a wildlife community. Host reservoir competency is partly determined by their capacity to mount effective immune responses, which may be phylogenetically determined. As a result, phylogenetic diversity may be a better metric to evaluate the relationship between host diversity and disease transmission, given that closely related species may have more similar pathogen competencies than distantly related ones. Few studies have examined the relationship between phylogenetic diversity and disease transmission, particularly in vector‐borne transmission systems. This study seeks to quantify phylogenetic diversity in the western United States Lyme disease system, where the causal agentBorrelia burgdorferiis vectored by the western black‐legged tick,Ixodes pacificus.We empirically measured mammalian diversity and tick data over seven years. We collected data on ticks, host community, and infection prevalence withBorrelia burgdorferiand constructed generalized linear mixed‐effect models to evaluate the utility of phylogenetic diversity in predicting the prevalence of a tick‐borne pathogen. We found that phylogenetic diversity metrics improved our disease prediction models. Predictions of the overall density and infection prevalence of ticks were improved by the addition of phylogenetic metrics, whereas the density of infected nymphs was solely predicted by a phylogenetic metric over traditional species diversity or richness. Our study found that phylogenetic diversity improves statistical predictions of the Lyme disease pathogen and entomological risk in the western United States and may be informative in other contexts and systems as well.