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Climate-driven variation in the phenology of juvenile Ixodes pacificus on lizard hosts

Parasites & Vectors volume 18, Article number: 141 (2025) Cite this article

Abstract

Background

Ectothermic 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 examined Ixodes pacificus phenology on lizards, the primary juvenile tick host in California, and explored how climate factors influence phenological patterns.

Methods

Between 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.

Results

Mean 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.

Conclusions

Our study, which compiled 10 years of data, reveals significant regional variation in juvenile I. pacificus phenology 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

Background

Pathogen vectors, such as ticks, pose serious risks to the health of both humans and animals [1]. Ticks, as ectotherms, are highly sensitive to environmental conditions, and their population dynamics—including abundance, seasonality and host associations—are significantly affected by weather and climate conditions [2,3,4]. Notably, juvenile ticks, especially larval and nymphal Ixodes species, exhibit seasonal activity patterns that directly impact local pathogen prevalence of non-vertically transmitted pathogens such as Borrelia burgdorferi, the causative agent of Lyme disease [5,6,7]. For example, if uninfected larvae emerge prior to infected nymphs, the opportunities for acquiring infectious blood meals diminish [7]; conversely, the emergence of infected nymphs prior to larvae can enhance pathogen transmission by increasing the likelihood of larvae feeding on an infected blood meal and becoming infectious nymphs in subsequent seasons [7].

Lyme disease is a key example of how the seasonality of juvenile ticks can influence the risk to human health. The prevalence of Lyme disease is highly variable across the USA, even in areas where environmental conditions favor both competent Ixodes ticks and their vertebrate hosts [8,9,10]. Various ecological hypotheses have been proposed to explain these discrepancies, including differences in vertebrate community structures, habitat fragmentation that increases human-tick interactions and strain-specific variations in the Lyme disease bacterium [11,12,13]. However, many studies focus on small spatial scales when examining the relationship between these factors and disease risk, potentially overlooking the critical influence of larger scale climatic factors across the full range of Ixodes scapularis and Ixodes pacificus.

Linking climatic conditions to juvenile Ixodes seasonality is essential for explaining geographic patterns of Lyme disease risk and implications for how climate change may alter this risk. For example, juvenile seasonality has been cited as an explanation for heightened Lyme disease incidence in the cooler northeastern USA compared to the warmer southeastern regions [3, 14,15,16,17]. However, substantial time and resource constraints often hinder systematic longitudinal and latitudinal studies [16, 18]. Latitudinal studies that have successfully collected juvenile ticks in drier regions have typically relied on sampling juvenile tick hosts, such as lizards, rather than using traditional tick collection methods, such as dragging or flagging [14, 19, 20]. Sampling juvenile ticks in drier regions presents challenges due to lower overall tick abundances, fragmented distributions and the behavioral adaptations of ticks to desiccation pressures. These ticks are more likely to seek ground-level leaf litter where desiccation risk is reduced and hosts like lizards and rodents are more common rather than questing in aboveground vegetation [21,22,23,24]. Even in regions with less intense desiccation, sentinel hosts like deer and lizards can effectively detect ticks, even when regional tick densities are low [19, 25, 26]. Lizards are the preferred host for juvenile I. pacificus and, consequently, lizards typically have higher tick burdens than rodents captured within the same spatial and temporal context [27,28,29]. Although lizards do not directly provide infectious blood meals, lizards can serve as reliable proxies for broader juvenile tick activity patterns, offering insights into tick burden dynamics on rodents.

California’s western coastal region is particularly well-suited for investigating climate effects on juvenile phenology patterns for several reasons. First, Lyme disease risk is disproportionately high in northwestern California, with Mendocino County reporting an incidence rate of 3.9 cases per 100,000 people, compared to the state average of 0.2 [30] (see California Department of Public Health [CDPH] 2021 https://www.cdph.ca.gov/Programs/CID/DCDC/CDPH%20Document%20Library/LymeDiseaseEpiSummary2013-2019.pdf). Second, I. pacificus ticks and their preferred larval/nymphal host, the western fence lizard (Sceloporus occidentalis), share a broad latitudinal distribution [10]. Third, the region encompasses significant microclimate variability, with coastal areas exhibiting higher humidity levels and lower temperatures than nearby inland areas [19, 31, 32]. Finally, areas with significant desiccation stress have historically shown limited success in assessing juvenile tick activity using traditional sampling methods such as flagging or dragging [20, 22, 33]. Despite these factors, a systematic statewide analysis focusing on the preferred host of juvenile I. pacificus has not been conducted.

The aim of this study was to investigate juvenile phenology patterns across California’s latitudinal gradient in relation to climate variability, addressing key gaps in juvenile tick ecology and providing insight into potential ecological mechanisms driving variations in Lyme disease risk. We address the challenges encountered in collecting juvenile ticks in drier regions by incorporating data from lizard surveys and documenting attached juvenile I. pacificus across a latitudinal gradient, thereby creating a unique comprehensive dataset. By integrating existing tick research with high-resolution climate data and using generalized additive models (GAMs) [34], we have two main objectives: (i) to characterize seasonal patterns of juvenile tick abundance on their preferred host across latitudes; and (ii) to explore the relationship between juvenile abundances and climate conditions across California’s climatically diverse regions. Understanding the seasonality of juvenile ticks and their climate-driven variation will enhance Lyme disease risk predictions and inform public health strategies, as well as guide future empirical tick-related research.

Methods

The data for this study were collected from 2013 to 2022, primarily during the peak juvenile activity months of I. pacificus ticks, which are March through June. However, most of the northern sites were not sampled until April due to the on average cooler weather. This aggregated dataset includes 45 unique sampling locations and encompasses a total of 253 sampling days (Additional file 1: Table S1; Table S2). Of the 45 locations, 93% (n = 42) were sampled multiple times, with 84% (n = 38) of locations being sampled ≥ 3 times (Additional file 2: Figure S1). The data reflect the collective efforts of various laboratory groups engaged in ecological research, which included both lizard sampling and tick burden assessments (Additional file 1: Table S1; Additional file 2: Figure S2). The collectors (authors SS, AJM, AmS, AnS, HSY) led the original study design [19, 20, 35, 36] (Table 1).

Table 1 Juvenile phenology metrics for three climate regions of California collected from 2013 to 2022
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Study area

Sampling was conducted across various locations in California, primarily in the western coastal region. This region has a Mediterranean climate, characterized by relatively wet, cool winters that transition into warm, dry summers [37]. Sampling locations spanned a latitudinal gradient from Lake County in the north (39°05′24.00″ N, 122°45′36.00″ W) to Los Angeles County in the south (34°16′50.96″ N, 119°17′40.56″ W) (Fig. 1). These sites were situated in ecosystems known to harbor tick populations, typically including mixed oak woodlands, oak savannas or coastal chaparral habitats [10, 33]. Additional details about the study locations can be found in Additional file 2: Figure S1; Figure S2; Table S1) and in previously published studies [19, 20, 31, 36]. To establish climatically relevant groupings of field sites across our study area, we overlaid location coordinates onto Cal-Adapt’s climate region polygons (North Coast Region, San Francisco Bay Area Region, San Joaquin Valley Region, Central Coast Region, Los Angeles Region), as defined by California’s 4th Climate Change Assessment [38]. A large portion of sampling locations in the Los Angeles Region came from Santa Cruz Island and Santa Rosa Island.

Fig. 1
figure 1

A Map of California, color-coded by climate regions. Black dots indicate unique sampling locations where lizard-attached ticks were collected. B Box plot showing log10 transformed CDD across the most frequently sampled climate regions. C–E Mean monthly maximum temperature (C), mean monthly specific humidity (D) and mean drought index (PDSI); shaded gray areas represent 95% confidence intervals. CC, Central Coast; CDD, cumulative degree days; LA, Los Angeles; PDSI, Palmer Drought Severity Index; SF, San Francisco Bay Area

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Field collection

Lizards were primarily captured using standard lassoing techniques, which involved a dental floss or fishing line lasso attached to the tip of a fishing pole, following the methodology of Swei et al. [39]. In some instances, lizards were captured by hand from under cover boards. Sampling typically occurred midday to target peak basking times. Ticks found on a lizard’s ears and nuchal pouches were carefully removed with fine-tipped forceps and placed in 70% ethanol for later identification in the laboratory. Detailed methods for each collector can be found in Additional file 3: Text S1.

Remotely sensed climate data

All climate data in this study were obtained from the gridded meteorological dataset, gridMET [40]. This dataset provides daily, high-resolution (approx. 4 km) meteorological data across the contiguous USA. In line with the tick-climate literature [41,42,43], the primary climate variables used in our analysis included near-surface specific humidity (sph; kg/kg), maximum temperature (tmmx; °C) and minimum temperature (tmmn; °C). Derived variables included the monthly Palmer Drought Severity Index (PDSI), a standardized index that estimates relative soil moisture conditions based on a simplified soil water balance, calculated every 5 days. For example, a PDSI value > 4 indicates very wet conditions, while a value < - 4 signifies extreme drought. For each site location coordinates (n = 45), data were downloaded for the continuous period from 2013 to 2022 to calculate various indices, such as mean maximum and minimum temperature in each season (spring, summer, fall, winter), and mean climate variables for all months. Using maximum and minimum temperatures, we calculated the cumulative degree days (CDD) for each year and location from 1 January to 31 March. This date range was selected to capture spring dynamics before the peak juvenile season, based on the assumption that a warmer spring (i.e. higher CDD) would result in earlier tick activity [44]. The base temperature was set at 10 °C, a known threshold for the thermal accumulation required by ectothermic invertebrates in California [45]. Daily climate variables were matched to the date of lizard-tick sampling. The gridMET data were accessed on 23 October 2024.

Statistical analyses

In our analysis, we focus on the abundance of ticks collected from individual lizards, which we aggregate to represent the mean tick abundance for each location or climate region per month. We further categorize the tick abundances by life stages—specifically larval and nymphal ticks. Since most sampling locations (n = 41) are concentrated in three regions, we focus our analysis on the San Francisco Bay Area, Central Coast and Los Angeles regions. However, in the Additional files we include summary statistics for two less frequently sampled but important areas of comparison: the North Coast and San Joaquin Valley regions (Additional file 1:  Table S1; Additional file 2: Figure S1; Additional file 3: Text S2; Figure S1).

Characterizing the seasonal patterns of juvenile ticks

Juvenile seasonal patterns are characterized by several metrics that capture various aspects of tick population dynamics, including phenology [46, 47]. We constrained our analysis to tick abundance data collected between 1 March and 30 June, which coincides with peak juvenile tick activity and corresponds to Julian dates 60 to 181 in non-leap years. During this period, we quantified the following juvenile seasonal patterns for each climate region and life stage: juvenile abundances on lizards, key phenological dates (e.g. date of peak abundance) and temporal overlap metrics. More details on these methods are found in the following text.

Juvenile abundance refers to the number of ticks removed from individual lizards, with the mean and standard deviation (SD) calculated per month and within each sampling location. Key phenological dates include the Julian date of peak abundance (i.e. the day of maximum mean abundance for each year) and the first and last appearance dates for each life stage. However, due to differences in sampling schedules, the first and last appearance dates serve as general indicators rather than definitive markers for a given region. Finally, we assess temporal overlap between larval and nymphal populations at each location using two metrics: kernel density estimation (KDE) and the Jaccard similarity coefficient (Jaccard index [JI]). KDE is a non-parametric method used to estimate population density, providing a smooth estimate of overlap between larval and nymphal distributions. The JI evaluates the degree of temporal overlap between the two life stages at each location. Together, these metrics provide insights into the temporal dynamics of juvenile tick populations. Higher values for both KDE and JI indicate greater overlap between larval and nymphal populations, implying more synchronized activity within a location. For further justification of these methods, see Additional file 3: Text S2.

Exploring the relationship between juvenile tick abundances and climate

We hypothesize that, as ectotherms, tick burdens are influenced by abiotic factors, particularly monthly temperatures, that trigger seasonal behaviors and sufficient humidity to mitigate daily desiccation pressures. Additionally, we aim to capture the long-term effects of local drought conditions on tick populations, if any. We fit GAMs to assess the relationship between tick burdens (i.e. tick count per lizard, including lizards with no ticks) and monthly abiotic variables of the sampling month, including tmmx, sph and the drought index (PDSI) [34]. These variables were standardized for interpretation and checked for multicollinearity (variance inflation factor [vif] < 3).

To capture potential nonlinear relationships between tick abundance and climate variables, we used cubic regression spline smoothing terms (bs = “cr”) for continuous predictors and included an interaction term between climate region and predictors to account for geographic variation in climate and tick burdens. To address spatial autocorrelation, we include Gaussian processing smoothing terms (bs = “gp”) for the latitude and longitude of each sampling location. The optimal smoothing parameter for each smooth term was determined using restricted maximum likelihood (REML) estimation [34]. The model was specified using a Tweedie distribution and a log link function, which is appropriate for modeling overdispersed, zero-inflated count data like tick burdens [48]. Due to limited observations in the North Coast and San Joaquin Valley regions and their relatively close geographic distances from other climate regions, we grouped those locations with the San Francisco Bay Area and Los Angeles regions, respectively. We ran this model first for juvenile tick abundances from individual lizards, followed by separate models for larval and nymphal burdens.

To evaluate model performance, we used the `mgcv` package to fit both GAMs and perform model diagnostics [34]. Within each model grouping, we compared fit using Akaike’s Information Criterion (AIC), selecting the model with the lowest AIC as the best-fitting model. The significance of each predictor was evaluated using likelihood ratio tests based on the estimated degrees of freedom (edf), and the model fit was compared to a null model with no smooth terms. We visualized the relationship of our outcomes to predictive values using the plot function to produce partial effects plots that show the predictor’s effect when other variables are at their average value. Additional model justifications are provided in Additional file 3: Text S2 and Additional file 2: Figure S1.

Software

All statistical analyses were conducted in RStudio version 4.4.1 [49] with a significance level set at P < 0.05. Climate data from gridMET were obtained through the ‘climateR` package [50]. Data cleaning and visualizations were conducted with ‘tidyverse’ and ‘ggplot2’ packages, respectively [51, 52]. To assess multicollinearity among climate variables, we used the vif function from the ‘car’ package [53]. The code for figures and analysis is available at: https://github.com/sbsambado/ca_lizardburden.

Results

Field collection summary

In total, 1527 individual lizards were sampled. The majority (62%) of the identified lizards were western fence lizards (Sceloporus occidentalis), while the remainder included alligator lizards (Elgaria spp; 37%) and common side-blotched lizards (Uta stansburiana; < 1%). Unidentified lizards were presumed to be S. occidentalis based on the objectives of each collecting group. Consequently, we assumed that the majority of attached ticks processed were I. pacificus, supported by historical data on tick attachment to S. occidentalis, Elgaria spp. and U. stansburiana [27, 28, 54]. Throughout the study period, a total of 9338 ticks were counted on lizards. However, not all ticks were identified to larval or nymphal stages due to damage sustained during the removal process from lizards. Among those identified, 4197 were larvae and 3239 were nymphs, representing 80% of the attached juvenile ticks. Of the ticks identified to life stage, 100% of ticks were I. pacificus.

Seasonal patterns of juvenile ticks vary

Tick abundances

Tick abundances on lizards exhibited considerable variability across our sampling locations, revealing several notable trends. The mean tick abundances on lizards ranged from 0.17 to 47.21 across locations and latitudinally declined, with the San Francisco Bay Area exhibiting the highest mean (± SD) tick burden (18.40 ± 23.07), followed by the Central Coast (3.84 ± 6.62) and Los Angeles region (1.64 ± 3.99). Key regional patterns included: (i) San Francisco Bay Area had the highest mean tick burden across all locations (18 ticks per lizard); (ii) the Los Angeles region had the greatest proportion of lizards with zero ticks (> 50% of sampled lizards); and (iii) peak mean nymphal densities occurred 25 days earlier than peak larval densities in the San Francisco Bay Area only (Fig. 2). Additional results for juvenile ticks by climate region can be found in Additional file 4: Figure S1; Additional file 4: Figure S2.

Fig. 2
figure 2

Mean tick abundances across lizards per month, broken down by life stage and climate region. Upper error bars represent the standard error of the mean

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Phenological dates

The Julian date of peak abundance—when the maximum mean burden per location was reached—for both larvae and nymphs on lizards was similar in the Central Coast and Los Angeles regions (Julian date approx. 100 = April 9th). In contrast, the San Francisco Bay Area experienced peak abundance later in the season. Notably, nymphs in the San Francisco Bay Area reached their peak abundance earlier (Julian date 119 = April 29th) than larvae, whose peak abundance occurred later (Julian date 144 = May 24). Other notable phenological dates include the first Julian dates of appearance, which varied by region (Additional file 4: Table S1). The earliest tick appearance occurred in the Central Coast (Julian date 63 = March 4), followed by Los Angeles region (Julian date 83 = March 24) and then by the San Francisco Bay Area (Julian date 94 = April 4). It should be noted that majority of San Francisco Bay Area sites were not sampled until April, although attempts to capture lizards in the region were made in March, yet no lizards were visually spotted.

Overlap metrics

Both KDE and the JI revealed regional variation in the seasonal overlap between larval and nymphal populations on lizards. The San Francisco Bay Area exhibited the highest overlap (KDE = 0.89, JI = 0.47), followed by the Central Coast (KDE = 0.32, JI = 0.38) and the Los Angeles region (KDE = 0.30, JI = 0.22) (Additional file 4: Table S1).

Climate-associated patterns of juvenile tick abundances

To investigate how juvenile tick abundances are influenced by climate, we fit GAMs to our juvenile abundances on individual lizards per sampling event (location-month-year). Our model for total tick abundances did not show significant linear relationships with climate regions, but it did reveal important interactions between climate regions and abiotic variables through the smoothing terms (Fig. 3). Notably, the interaction with monthly maximum temperature showed a significant negative relationship with tick abundances: in the San Francisco region, tick abundances decreased as temperature rose above the regional mean (P = 0.02), while in Los Angeles, tick abundances were negatively associated with temperature but followed a stronger, linear pattern (P = 0.004). For San Francisco, the relationship was slightly nonlinear (edf = 3.0), with a sharp increase in tick abundances below the mean temperature, followed by a gradual decline above the mean. In contrast, the Los Angeles region exhibited a strongly linear relationship (edf = 1.0) where higher maximum temperatures led to lower tick abundances.

Fig. 3
figure 3

Smooth effect of climate predictors on total tick abundances across California from 2013 to 2022 from the generalized additive model, stratified by climate region. A–C Estimated relationship between tick abundances and monthly climate predictors: A maximum temperature, B specific humidity, C PDSI (higher values, less drought). For each panel, the x-axis represents the standardized values of the predictor variables, and the y-axis shows the estimated effect on tick abundances (on the log10 scale). Asterisks (*) denote the significance level of the climate variable interaction switch climate region at *P < 0.1, **P < 0.01 and ***P < 0.001. CC, Central Coast; SF, San Francisco; LA, Los Angeles; PSDI, Palmer Drought Severity Index

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In addition, we found a significant positive effect of monthly specific humidity on tick abundances in the Los Angeles region (P < 0.001), whereas the Central Coast region showed an opposite trend, with tick abundances declining as humidity exceeded the regional mean (P = 0.003). Furthermore, the relationship between drought conditions (measured by the PDSI) and tick abundances was generally positive, with less drought (higher PDSI values) associated with greater tick abundances. However, this relationship was only statistically significant in Los Angeles (P = 0.002). Our total tick abundances model also accounted for spatial autocorrelation using a smoothing term for geographic location, which was highly significant (P < 0.001). Overall, the model explained 56.4% of the deviance, with an adjusted R2 of 47.2%. The model’s REML value was 3531.3, with a scale estimate of 3.38, and was based on 1,527 observations. Model diagnostics can be found in Additional file 5: Figure S1.

Separate models for larval and nymphal abundances failed to converge when the spatial autocorrelation term (s(lon, lat, bs = “gp”)) was included but were successfully fitted with the other smoothing interaction terms. Results from these models are shown in Additional file 5: Figure S2.

Discussion

Our study reveals significant variation in juvenile tick phenology across California, suggesting that climate-driven changes in juvenile tick host-seeking behavior may contribute to regional differences in Lyme disease prevalence in ticks. By using lizards—the preferred host for juvenile I. pacificus ticks [27, 28]—instead of traditional tick drags, we increased detection probability in areas with lower tick densities. This approach helps overcome previous challenges in detecting off-host juvenile ticks across the significant latitudinal gradient where this species is distributed. This study represents one of the most extensive spatial analyses of juvenile I. pacificus in California, providing valuable insights that could inform vector surveillance and improve our understanding of Lyme disease risk across a climatically diverse landscape.

We observed substantial variability in mean juvenile tick abundances on lizards, ranging from 0.17 to 47.21, with the highest mean abundances found in the San Francisco Bay Area and the lowest mean abundances in the Los Angeles region. Notably, in the San Francisco Bay Area, nymphs peaked 25 days earlier than larvae, a pattern not observed in the Central Coast and Los Angeles regions, where larvae and nymphs reached peak mean abundance on the same Julian date for each respective region. The earlier peak in nymphal abundances in the San Francisco Bay Area may increase the potential for Lyme disease transmission, as infectious nymphs could infect the rodent host population which will host the larvae that emerge later, amplifying infection rates [55]. Such a timing difference in tick phenology may explain the higher Lyme disease prevalence in ticks observed in the San Francisco Bay Area, highlighting the importance of temporal dynamics in pathogen transmission [5, 33, 52, 56,57,58]. A number of ecological hypotheses suggest that this pattern may emerge in the San Francisco Bay Area, rather than in the Central Coast or Los Angeles regions, due to the generally lower annual mean temperatures [22]. These cooler conditions may allow juvenile ticks to extend their seasonal activity, enabling larvae to reach peak abundances later in the year compared to those in drier areas, where increased desiccation may prevent later peaks [59]. Alternatively, juvenile abundances are higher in northern California than in southern California, which may enhance our ability to detect more pronounced differences in the seasonal activity of juvenile I. pacificus [22].

We also quantified the overlap between larval and nymphal populations and observed a clear latitudinal trend in this overlap. The highest overlap occurred in the San Francisco Bay Area, which was approximately twofold higher than that in the Central Coast region and approximately threefold higher than that in the Los Angeles region. This finding contradicted our initial hypothesis that we would observe the highest juvenile overlap in warmer regions, such as Los Angeles, where a shorter season might intensify overlap due to an accelerated tick life-cycle [59]. However, this unexpected pattern may be influenced by the timing of our sampling in relation to tick emergence (Additional file 1: Table S2) and perhaps a longer season in northern than southern California [59]. Yet prior studies in Mendocino, in northwestern California, suggest similar temporal patterns of juvenile overlap as our San Francisco Bay Area results [9, 60, 61]. There is also the possibility that nymphs in the San Francisco Bay Area exhibit bimodal seasonality, while larvae follow a unimodal pattern [63]. This could explain our findings that the San Francisco Bay Area shows the highest degree of overlap (when one portion of nymphs coincide with the larvae) as well as the greatest separation of life stages (when the other portion of nymphs emerge outside the unimodal larval peak) [63]. A more systematic sampling design, spanning pre- and post-season periods, would better capture these dynamics and provide a clearer picture of how overlap varies across regions. Additionally, including data from more northern regions of the state, such as Mendocino County, could have provided a clearer contrast to the southern areas, given its higher precipitation, larger tick populations, and elevated risk of Lyme disease. Finally, we found that in months with higher maximum temperature and with more severe drought conditions, tick abundances on lizards were lower (Fig. 3; Table 2). While these findings are consistent with previously documented relationships, we extend this understanding by demonstrating that both the shape and magnitude of these relationships vary across different climate regions [9, 19, 47, 62, 63].

Table 2 Generalized additive models predicting log10-transformed juvenile tick abundance on individual lizards with climate variables standardized
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Our results confirm that tick phenology patterns are latitudinally structured, with tick abundances highest in cooler northern regions compared to warmer southern regions [17, 59, 64]. However, this simple north-to-south gradient narrative overlooks the complexity of tick seasonality, especially in a state with a large coastal climate influence that overlaps many established tick populations [41, 44, 65]. Additional factors, such as humidity, which is influenced by proximity to the coast, can extend the tick season, as observed in the Central Coast region (Fig. 2) [19, 44]. When examining the effect of humidity on total tick abundances, we found a strong positive relationship in the Los Angeles region, which is typically drier than Central California, suggesting that tick abundances in this region may be limited by moisture in the environment. In contrast, humidity above the regional mean decreased tick abundances in the Central California region, possibly because it surpassed the physiological tolerance threshold for juvenile ticks [19, 59]. While we observed that tick activity starts earliest in the Central Coast region, followed by Los Angeles and finally San Francisco Bay Area, this temporal pattern may reflect sampling design rather than underlying ecological differences (Additional file 1: Table S2). Although habitat suitability factors like elevation and forest cover may account for additional variation in our climate models, we believe that these habitat variables serve as proxies for humidity and temperature, particularly in California’s far western region [17, 66, 67].

An intriguing finding, although potentially influenced by sampling methods, was the earlier peak of nymphs compared to larvae in the San Francisco Bay Area. In California, it is generally assumed that larvae emerge at the same time as, or before nymphs, which may limit the transmission potential of the Lyme disease agent, as larvae can only acquire the bacterium through an infectious blood meal [68]. In our study, however, nymphal abundances peaked 25 days prior to the larval peak. If this earlier peak in nymphs is accurate, it could indicate that more vertebrates are exposed to infectious blood meals before larvae emerge. An intriguing follow-up study could explore the minimum time period or proportion of nymphs that need to feed before larvae in order to significantly increase the presence of B. burgdorferi in rodents. We also recognize that lizards do not directly provide infectious blood meals, but they may serve as proxies for broader juvenile tick activity patterns, potentially reflecting rodent tick burden dynamics [27,28,29].

If lizard burdens correlate with general tick burdens on pathogen-amplifying rodents, our model results suggest that climate change could influence tick-borne pathogen transmission dynamics in California. Our climate models indicate that tick burdens are highest under cooler monthly temperatures, optimal humidity levels and lower drought stress. As northern regions of the state warm due to climate change, we may see a latitudinal shift in phenology and transmission patterns. However, relative humidity plays a crucial role, and ticks in humid coastal areas may find refuge in the more temperate southern coastal regions [19, 31]. Additionally, while drought conditions are widespread across California, their impact on tick populations will depend on the intensity and duration of the droughts. In warmer, drier areas, more severe droughts are likely to cause a decline in tick populations [59]. Our results suggest that in regions with optimal climate conditions that support longer within-season activity, larger tick populations may drive higher transmission rates. In contrast, in suboptimal areas with shorter activity seasons and reduced tick populations, opportunities for juvenile ticks to interact with pathogen-amplifying rodent hosts are limited. Nonetheless, even in these suboptimal conditions, ticks can seek refuge in microclimates, allowing them to persist and still contribute to pathogen amplification, although likely at a reduced rate.

While tick abundances on lizards may provide a more accurate method for estimating juvenile I. pacificus population dynamics compared to traditional drag cloth sampling, there are opportunities to refine this approach. Future studies should collect additional data on population density of lizards as well as characteristics of individual lizards, such as snout-to-vent length and sex, to better standardize results across different individuals, as these factors may influence total tick abundances [21, 24, 69]. Our dataset included two primary species of lizards: alligator lizards and western fence lizards. While differences in size between these species could potentially introduce biases in tick averages, most alligator lizards were captured in southern California, where they exhibited, on average, lower tick burdens compared to their northern counterparts. Interestingly, these latitudinal trends in tick burdens closely mirrored those observed in western fence lizards. In the eastern USA, variation in host-seeking behavior of I. scapularis ticks has been hypothesized to be driven by evolutionary differences in thermal tolerance between southern and northern populations. A similar mechanism may be influencing the behavior of I. pacificus populations, which could be explored through future molecular studies [14, 23]. We acknowledge that repeated sampling at a single location, combined with data loggers (e.g., HOBO loggers), represents the gold standard for linking microclimatic conditions to tick seasonality. However, due to the time and logistical constraints associated with conducting such studies across multiple locations with diverse climate gradients, this approach may not be feasible for many research teams. We, therefore, recommend that future research incorporates unbiased data alongside the important metadata mentioned above, which will facilitate the synthesis of disparate ecological datasets to address broader ecological questions related to tick behavior and its implications for human health.

Conclusions

This study provides new insights into the regional variation in juvenile I. pacificus phenology across California, revealing climate-driven differences in tick abundances that may help explain regional Lyme disease prevalence. Our findings suggest that factors such as temperature, coastal humidity and drought severity influence tick seasonality, with notable differences in tick activity patterns across regions, particularly between the San Francisco Bay Area and Los Angeles regions. The earlier emergence of nymphs compared to larvae in the San Francisco Bay Area could increase the potential for Lyme disease transmission, which is consistent with variation in reported human incidence and epidemiological studies and warrants further investigation. By comparing data across a broad set latitudinal gradient, we offer valuable insights into juvenile tick dynamics and emphasize the need for continued longitudinal research to refine our understanding and inform vector control strategies.

Availability of data and materials

The datasets supporting the conclusions of this article as well as code for the figures and analysis are available in a Dryad digital repository (https://doiorg.publicaciones.saludcastillayleon.es/10.5061/dryad.v6wwpzh67) and a GitHub repository (https://github.com/sbsambado/ca_lizardburden).

Abbreviations

CDD:

Cumulative degree days

CC:

Central Coast region

GAMs:

Generalized additive models

JI:

Jaccard index or Jaccard similarity coefficient

KDE:

Kernel density estimation

LA:

Los Angeles region

SF:

San Francisco Bay Area region

References

  1. Rochlin I, Toledo A. Emerging tick-borne pathogens of public health importance: a mini-review. J Med Microbiol. 2020;69:781–91.

    Article  PubMed  PubMed Central  Google Scholar 

  2. Diuk-Wasser MA, Gatewood AG, Cortinas MR, Yaremych-Hamer S, Tsao J, Kitron U, et al. Spatiotemporal patterns of host-seeking Ixodes scapularis nymphs (Acari: Ixodidae) in the United States. J Med Entomol. 2006;43:166–76.

    Article  CAS  PubMed  Google Scholar 

  3. Ginsberg HS, Albert M, Acevedo L, Dyer MC, Arsnoe IM, Tsao JI, et al. Environmental factors affecting survival of immature Ixodes scapularis and implications for geographical distribution of lyme disease: the climate/behavior hypothesis. PLoS ONE. 2017;12:e0168723.

    Article  PubMed  PubMed Central  Google Scholar 

  4. Ostfeld RS, Brunner JL. Climate change and Ixodes tick-borne diseases of humans. Philos Trans R Soc B Biol Sci. 2015;370:20140051.

    Article  Google Scholar 

  5. Kurtenbach K, Hanincová K, Tsao JI, Margos G, Fish D, Ogden NH. Fundamental processes in the evolutionary ecology of Lyme borreliosis. Nat Rev Microbiol. 2006;4:660–9.

    Article  CAS  PubMed  Google Scholar 

  6. Ogden NH, Mechai S, Margos G. Changing geographic ranges of ticks and tick-borne pathogens: drivers, mechanisms and consequences for pathogen diversity. Front Cell Infect Microbiol. 2013. https://doiorg.publicaciones.saludcastillayleon.es/10.3389/fcimb.2013.00046/abstract.

    Article  PubMed  PubMed Central  Google Scholar 

  7. Hamer SA, Hickling GJ, Sidge JL, Walker ED, Tsao JI. Synchronous phenology of juvenile Ixodes scapularis, vertebrate host relationships, and associated patterns of Borrelia burgdorferi ribotypes in the midwestern United States. Ticks Tick-Borne Dis. 2012;3:65–74.

    Article  PubMed  Google Scholar 

  8. Eisen L, Eisen RJ. Changes in the geographic distribution of the blacklegged tick, Ixodes scapularis, in the United States. Ticks Tick-Borne Dis. 2023;14:102233.

    Article  PubMed  PubMed Central  Google Scholar 

  9. Eisen RJ, Eisen L, Ogden NH, Beard CB. Linkages of weather and climate with Ixodes scapularis and Ixodes pacificus (acari: Ixodidae), enzootic transmission of Borrelia burgdorferi, and Lyme disease in North America. J Med Entomol. 2016;53:250–61.

    Article  PubMed  Google Scholar 

  10. MacDonald AJ, McComb S, Sambado S. Linking Lyme disease ecology and epidemiology: reservoir host identity, not richness, determines tick infection and human disease in California. Environ Res Lett. 2022;17:114041.

    Article  Google Scholar 

  11. LoGiudice K, Ostfeld RS, Schmidt KA, Keesing F. The ecology of infectious disease: effects of host diversity and community composition on Lyme disease risk. Proc Natl Acad Sci USA. 2003;100:567–71.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Allan BF, Keesing F, Ostfeld RS. Effect of forest fragmentation on Lyme disease risk. Conserv Biol. 2003;17:267–72.

    Article  Google Scholar 

  13. Gatewood AG, Liebman KA, Vourch G, Bunikis J, Hamer SA, Cortinas R, et al. Climate and tick seasonality are predictors of Borrelia burgdorferi genotype distribution. Appl Environ Microbiol. 2009;75:2476–83.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Ginsberg HS, Hickling GJ, Burke RL, Ogden NH, Beati L, LeBrun RA, et al. Why Lyme disease is common in the northern US, but rare in the south: the roles of host choice, host-seeking behavior, and tick density. PLOS Biol. 2021;19:e3001066.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Ogden NH, Beard CB, Ginsberg HS, Tsao JI. Possible effects of climate change on Ixodid ticks and the pathogens they transmit: predictions and observations. J Med Entomol. 2021;58:1536–45.

    Article  PubMed  Google Scholar 

  16. Eisen L. Climate change and tick-borne diseases: a research field in need of long-term empirical field studies. Int J Med Microbiol. 2008;298:12–8.

    Article  Google Scholar 

  17. Diuk-Wasser MA, Hoen AG, Cislo P, Brinkerhoff R, Hamer SA, Rowland M, et al. Human risk of infection with Borrelia burgdorferi, the Lyme disease agent, in eastern United States. Am J Trop Med Hyg. 2012;86:320–7.

    Article  PubMed  PubMed Central  Google Scholar 

  18. Estrada-Peña A, Cevidanes A, Sprong H, Millán J. Pitfalls in tick and tick-borne pathogens research, some recommendations and a call for data sharing. Pathogens. 2021;10:712.

    Article  PubMed  PubMed Central  Google Scholar 

  19. Sambado S, MacDonald AJ, Swei A, Briggs CJ. Climate-associated variation in the within-season dynamics of juvenile ticks in California. Ecosphere. 2024;15:e70064.

    Article  Google Scholar 

  20. Copeland S, Sambado S, Orr D, Bui A, Swei A, Young H. Variable effects of wildlife and livestock on questing tick abundance across a topographical-climatic gradient. Ecosphere. 2025;16:e70190.

    Article  Google Scholar 

  21. Schall J, Prendeville HR, Hanley KA. Prevalence of the tick, Ixodes pacificus, on western fence lizards, Sceloporus occidentalis: trends by gender, size, season, site, and mite infestation. J Herpetol. 2000;34:160–3.

    Article  Google Scholar 

  22. Lane RS, Fedorova N, Kleinjan JE, Maxwell M. Eco-epidemiological factors contributing to the low risk of human exposure to Ixodid tick-borne Borreliae in southern California, USA. Ticks Tick-Borne Dis. 2013;4:377–85.

    Article  PubMed  Google Scholar 

  23. Arsnoe I, Tsao JI, Hickling GJ. Nymphal Ixodes scapularis questing behavior explains geographic variation in Lyme borreliosis risk in the eastern United States. Ticks Tick-Borne Dis. 2019;10:553–63.

    Article  PubMed  Google Scholar 

  24. Ginsberg HS, Hickling GJ, Pang G, Tsao JI, Fitzgerald M, Ross B, et al. Selective host attachment by Ixodes scapularis (Acari: Ixodidae): tick–lizard associations in the southeastern United States. J Med Entomol. 2022;59:267–72.

    Article  PubMed  Google Scholar 

  25. Bouchard C, Leighton PA, Beauchamp G, Nguon S, Trudel L, Milord F, et al. Harvested white-tailed deer as sentinel hosts for early establishing Ixodes scapularis populations and risk from vector-borne zoonoses in southeastern Canada. J Med Entomol. 2013;50:384–93.

    Article  CAS  PubMed  Google Scholar 

  26. Raizman EA, Holland JD, Shukle JT. White-tailed deer (Odocoileus virginianus) as a potential sentinel for human Lyme disease in Indiana. Zoonoses Public Health. 2013;60:227–33.

    Article  CAS  PubMed  Google Scholar 

  27. Casher L, Lane R, Barrett R, Eisen L. Relative importance of lizards and mammals as hosts for Ixodid ticks in northern California. Exp Appl Acarol. 2002;26:127–43.

    Article  PubMed  Google Scholar 

  28. Slowik TJ, Lane RS. Feeding preferences of the immature stages of three western North American Ixodid ticks (Acari) for avian, reptilian, or rodent hosts. J Med Entomol. 2009;46:115–22.

    Article  PubMed  Google Scholar 

  29. Eisen L, Eisen RJ, Lane RS. The roles of birds, lizards, and rodents as hosts for the western black-legged tick Ixodes pacificus. J Vector Ecol. 2004;29:295–308.

    PubMed  Google Scholar 

  30. Salkeld DJ, Lagana DM, Wachara J, Porter WT, Nieto NC. Examining prevalence and diversity of tick-borne pathogens in questing Ixodes pacificus ticks in California. Appl Environ Microbiol. 2021;87:e0031921.

    Article  PubMed  Google Scholar 

  31. MacDonald AJ. Abiotic and habitat drivers of tick vector abundance, diversity, phenology and human encounter risk in southern California. PLoS ONE. 2018;13:e0201665.

    Article  PubMed  PubMed Central  Google Scholar 

  32. Salkeld DJ, Castro MB, Bonilla D, Kjemtrup A, Kramer VL, Lane RS, et al. Seasonal activity patterns of the western black-legged tick, Ixodes pacificus, in relation to onset of human Lyme disease in northwestern California. Ticks Tick-Borne Dis. 2014;5:790–6.

    Article  PubMed  Google Scholar 

  33. Padgett K, Bonilla D, Kjemtrup A, Vilcins I-M, Yoshimizu MH, Hui L, et al. Large scale spatial risk and comparative prevalence of Borrelia miyamotoi and Borrelia burgdorferi sensu lato in Ixodes pacificus. PLoS ONE. 2014;9:e110853.

    Article  PubMed  PubMed Central  Google Scholar 

  34. Wood SN. Generalized additive models: an introduction with R. 2nd ed. Boca Raton: Chapman and Hall/CRC; 2017. https://www.taylorfrancis.com/books/9781498728348. Accessed 18 Nov 2024.

  35. MacDonald AJ, Hyon DW, McDaniels A, O’Connor KE, Swei A, Briggs CJ. Risk of vector tick exposure initially increases, then declines through time in response to wildfire in California. Ecosphere. 2018;9:e02227.

    Article  Google Scholar 

  36. Lawrence A, O’Connor K, Haroutounian V, Swei A. Patterns of diversity along a habitat size gradient in a biodiversity hotspot. Ecosphere. 2018;9:e02183.

    Article  Google Scholar 

  37. Ackerly DD, Stock WD, Slingsby JA. Geography, climate, and biodiversity: the history and future of mediterranean-type ecosystems. In: Allsopp N, Colville JF, Verboom GA, editors. Fynbos. Oxford: Oxford University Press; 2014. p. 361–76. https://academic.oup.com/book/6673/chapter/150719233. Accessed 15 Nov 2022.

  38. Cal-Adapt. Climate regions for the 5th climate change assessment. 2023. Cal-Adapt website developed by University of California at Berkeley’s Geospatial Innovation Facility under contract with the California Energy Commission. https://ucanr-igis.github.io/caladaotr/articles/api-requests.html. Accessed 22 Aug 2023.

  39. Swei A, Ostfeld RS, Lane RS, Briggs CJ. Impact of the experimental removal of lizards on Lyme disease risk. Proc Biol Sci. 2011;278:2970–8.

    PubMed  PubMed Central  Google Scholar 

  40. Abatzoglou JT. Development of gridded surface meteorological data for ecological applications and modelling. Int J Climatol. 2013;33:121–31.

    Article  Google Scholar 

  41. MacDonald AJ, McComb S, O’Neill C, Padgett KA, Larsen AE. Projected climate and land use change alter western blacklegged tick phenology, seasonal host-seeking suitability and human encounter risk in California. Glob Change Biol. 2020;26:5459–74.

    Article  Google Scholar 

  42. Bacon EA, Kopsco H, Gronemeyer P, Mateus-Pinilla N, Smith RL. Effects of climate on the variation in abundance of three tick species in Illinois. J Med Entomol. 2022;59:700–9.

    Article  CAS  PubMed  Google Scholar 

  43. Ogden NH, Lindsay LR, Beauchamp G, Charron D, Maarouf A, O’Callaghan CJ, et al. Investigation of relationships between temperature and developmental rates of tick Ixodes scapularis (Acari: Ixodidae) in the laboratory and field. J Med Entomol. 2004;41:622–33.

    Article  CAS  PubMed  Google Scholar 

  44. MacDonald AJ, O’Neill C, Yoshimizu MH, Padgett KA, Larsen AE. Tracking seasonal activity of the western blacklegged tick across California. J Appl Ecol. 2019;56:2562–73.

    Article  Google Scholar 

  45. Varela L, Cooper M, Bentley W, Smith R. Degree-day accumulations used to time insecticide treatments to control 1st generation European grapevine moths. University of California's Cooperative Extension. 2011. https://www.aphis.usda.gov/sites/default/files/DDfor1stgenerationEGVM.pdf. Accessed 7 Nov 2024.

  46. Levi T, Keesing F, Oggenfuss K, Ostfeld RS. Accelerated phenology of blacklegged ticks under climate warming. Philos Trans R Soc B Biol Sci. 2015;370:20130556.

    Article  Google Scholar 

  47. Eisen RJ, Clark RJ, Monaghan AJ, Eisen L, Delorey MJ, Beard CB. Host-seeking phenology of Ixodes pacificus (Acari: Ixodidae) nymphs in northwestern California in relation to calendar week, woodland type, and weather conditions. J Med Entomol. 2017;54:125–31.

    Article  PubMed  Google Scholar 

  48. Zuur A, Camphuysen C. Generalised additive models applied on northern gannets. In: Zuur A, editor. A beginner's guide to generalised additive models with R. Newburgh: Highland Statistics; 2012. p. 145–68.

  49. R Core Team. R: A language and environment for statistical computing. https://www.R-project.org/. Accessed 7 Oct 2024.

  50. Johnson M. climateR: climateR. 2024. https://github.com/mikejohnson51/climateR. Accessed 23 Oct 2024.

  51. Wickham H. ggplot2: Elegant graphics for data Analysis. 2nd ed. 2016. Cham: Springer International Publishing.

  52. Wickham H, Averick M, Bryan J, Chang W, McGowan L, François R, et al. Welcome to the Tidyverse. J Open Source Softw. 2019;4:1686.

    Article  Google Scholar 

  53. Fox J, Weisberg S. An R companion to applied regression. Third edition. Thousand Oaks,: Sage Publications; 2019. https://socialsciences.mcmaster.ca/jfox/Books/Companion/. Accessed 23 Oct 2024.

  54. Wright SA, Lane RS, Clover JR. Infestation of the southern Alligator Lizard (Squamata: Anguidae) by Ixodes pacificus (Acari: Ixodidae) and its susceptibility to Borrelia burgdorferi. J Med Entomol. 1998;35:1044–9.

    Article  CAS  PubMed  Google Scholar 

  55. MacDonald H, Akçay E, Brisson D. The role of host phenology for parasite transmission. Theor Ecol. 2021;14:123–43.

    Article  PubMed  Google Scholar 

  56. Salkeld DJ, Nieto NC, Carbajales-Dale P, Carbajales-Dale M, Cinkovich SS, Lambin EF. Disease risk & landscape attributes of tick-borne borrelia pathogens in the San Francisco Bay Area, California. PLoS ONE. 2015;10:e0134812.

    Article  PubMed  PubMed Central  Google Scholar 

  57. Fedorova N, Kleinjan JE, James D, Hui LT, Peeters H, Lane RS. Remarkable diversity of tick or mammalian-associated Borreliae in the metropolitan San Francisco Bay Area, California. Ticks Tick-Borne Dis. 2014;5:951–61.

    Article  PubMed  Google Scholar 

  58. Shaw G, Lilly M, Mai V, Clark J, Summers S, Slater K, et al. The roles of habitat isolation, landscape connectivity and host community in tick-borne pathogen ecology. R Soc Open Sci. 2024;11:240837.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. MacDonald AJ, Briggs CJ. Truncated seasonal activity patterns of the western blacklegged tick (Ixodes pacificus) in central and southern California. Ticks Tick-Borne Dis. 2016;7:234–42.

    Article  PubMed  Google Scholar 

  60. Eisen RJ, Eisen L, Lane RS. Predicting density of Ixodes pacificus nymphs in dense woodlands in Mendocino County, California, based on geographic information systems and remote sensing versus field-derived data. Am J Trop Med Hyg. 2006;74:632–40.

    Article  PubMed  Google Scholar 

  61. Eisen RJ, Eisen L, Castro MB, Lane RS. Environmentally related variability in risk of exposure to Lyme disease spirochetes in Northern California: effect of climatic conditions and habitat type. Environ Entomol. 2003;32:1010–8.

    Article  Google Scholar 

  62. McVicar M, Rivera I, Reyes JB, Gulia-Nuss M. Ecology of Ixodes pacificus ticks and associated pathogens in the western United States. Pathogens. 2022;11:89.

    Article  PubMed  PubMed Central  Google Scholar 

  63. Hacker GM, Jackson BT, Niemela M, Andrews ES, Danforth ME, Pakingan MJ, et al. A comparison of questing substrates and environmental factors that influence nymphal Ixodes pacificus (Acari: Ixodidae) abundance and seasonality in the Sierra Nevada foothills of California. J Med Entomol. 2021;58:1880–90.

    Article  PubMed  PubMed Central  Google Scholar 

  64. Ogden NH, Pang G, Ginsberg HS, Hickling GJ, Burke RL, Beati L, et al. Evidence for geographic variation in life-cycle processes affecting phenology of the Lyme disease vector Ixodes scapularis (Acari: Ixodidae) in the United States. J Med Entomol. 2018;55:1386–401.

    Article  PubMed  Google Scholar 

  65. Hahn MB, Feirer S, Monaghan AJ, Lane RS, Eisen RJ, Padgett KA, et al. Modeling future climate suitability for the western blacklegged tick, Ixodes pacificus, in California with an emphasis on land access and ownership. Ticks Tick-Borne Dis. 2021;12:101789.

    Article  PubMed  PubMed Central  Google Scholar 

  66. Hahn MB, Jarnevich CS, Monaghan AJ, Eisen RJ. Modeling the geographic distribution of Ixodes scapularis and Ixodes pacificus (Acari: Ixodidae) in the contiguous United States. J Med Entomol. 2016;53:1176–91.

    Article  PubMed  Google Scholar 

  67. Burtis JC, Foster E, Schwartz AM, Kugeler KJ, Maes SE, Fleshman AC, et al. Predicting distributions of blacklegged ticks (Ixodes scapularis), Lyme disease spirochetes (Borrelia burgdorferi sensu stricto) and human Lyme disease cases in the eastern United States. Ticks Tick-Borne Dis. 2022;13:102000.

    Article  PubMed  PubMed Central  Google Scholar 

  68. Lane RS. Lyme disease in California: ecology and epidemiology. Proc Vertebr Pest Conf. 2006; 22. https://escholarship.org/uc/item/2w83s251. Accessed 6 Oct 2024.

  69. Dudek K, Skórka P, Sajkowska ZA, Ekner-Grzyb A, Dudek M, Tryjanowski P. Distribution pattern and number of ticks on lizards. Ticks Tick-Borne Dis. 2016;7:172–9.

    Article  PubMed  Google Scholar 

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Acknowledgements

SS would like to acknowledge the University of California Natural Reserve System (https://doiorg.publicaciones.saludcastillayleon.es/https://doiorg.publicaciones.saludcastillayleon.es/10.21973/N3GW8N) staff for their support during the collection of samples. A special acknowledgment to Catherine Koehler (ML; https://doiorg.publicaciones.saludcastillayleon.es/https://doiorg.publicaciones.saludcastillayleon.es/10.21973/N3W08D), Shane Waddell (QR; https://doiorg.publicaciones.saludcastillayleon.es/https://doiorg.publicaciones.saludcastillayleon.es/10.21973/N30T0K), Joe Miller (FO; https://doiorg.publicaciones.saludcastillayleon.es/https://doiorg.publicaciones.saludcastillayleon.es/10.21973/N3GT0X), Jen Hunter (HT; https://doiorg.publicaciones.saludcastillayleon.es/https://doiorg.publicaciones.saludcastillayleon.es/10.21973/N33Q0G), Keith Sydel (RM; https://doiorg.publicaciones.saludcastillayleon.es/https://doiorg.publicaciones.saludcastillayleon.es/10.21973/N37943), Mark Readdie (BC; https://doiorg.publicaciones.saludcastillayleon.es/https://doiorg.publicaciones.saludcastillayleon.es/10.21873/N3NH24), Cris Sandoval (CO; https://doiorg.publicaciones.saludcastillayleon.es/https://doiorg.publicaciones.saludcastillayleon.es/10.21973/N3Z07N), Kate McCurdy and Nicole Evans (SD; https://doiorg.publicaciones.saludcastillayleon.es/https://doiorg.publicaciones.saludcastillayleon.es/10.21973/N3C08R), Lyndal Laughrin (SCI; https://doiorg.publicaciones.saludcastillayleon.es/https://doiorg.publicaciones.saludcastillayleon.es/10.21973/N3F08C) and Gary Bucciarelli (SR; https://doiorg.publicaciones.saludcastillayleon.es/https://doiorg.publicaciones.saludcastillayleon.es/10.21973/N3MH2T) who were instrumental in the success of this project. AMS thanks the numerous graduate and undergraduate students that assisted with field work. AS would like to acknowledge the following laboratory members: Laura A Hughes for identifying lizard-attached ticks, as well as Parker Kaye, Sukhman Sidhu, Rachel Grigich, Eric Seredian, Thor Olsen, Adrienne Almarinez, Samira Ghazi, Monika Koczela-Stillman, Judith Lindoro, John Navazo, and Ceili Peng for helping to process field samples. HSY and SC recognize the assistance of Dr. Devyn Orr, Ruby Harris-Gavin, Chelsea Steel, Maggie Klope, Benjamin Boyce, Johnson Lin, Jazmin Murphy, Trevor Ayers, Amanda Tokyama, Gabriella Najm, Isabella Mayorga, Carina Motta, Anna Burrows, Kelli Konicek and Dr. Michelle Lee for their help in lizard captures and sample processing. As well as the staff at the Tejon Ranch Conservancy, especially Mitchell Coleman. AJM acknowledges the assistance of David Hyon in lizard captures and sample processing.

Funding

SS acknowledges funding from the UCNRS Mildred E. Mathias Research Grant. SS, CB, AJM, KR acknowledge funding support from the Training Grant Program of the Pacific Southwest Regional Center of Excellence for Vector-Borne Diseases funded by the US Centers for Disease Control and Prevention (Cooperative Agreement U01CK000516 and U01CK000649). Its contents are solely the responsibility of the authors and do not necessarily represent the official views of the Centers for Disease Control and Prevention or the Department of Health and Human Services. AS acknowledges funding for data collection was provided by the National Science Foundation (#1745411, 175037). AJM was supported by the National Science Foundation and Fogarty International Center (DEB-2011147, DEB-2339209), and the USDA National Institute of Food and Agriculture (#2023-68016-40683). During the field collections, JS was supported by NIH MBRS-RISE: R25-GM059298. SC was supported by an NSF DEB-SG (#1900502) awarded to HSY and AS. Additional support was provided by a UC faculty Grant and a Hellman Foundation grant to HSY.

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Authors and Affiliations

  1. Ecology, Evolution and Marine Biology Department, University of California Santa Barbara, Santa Barbara, CA, USA

    Samantha Sambado, Hillary S. Young, Kacie Ring, Stephanie Copeland & Cheryl J. Briggs

  2. Biology Department, Westmont College, Santa Barbara, CA, USA

    Amanda Sparkman

  3. Biology Department, San Francisco State University, San Francisco, CA, USA

    Andrea Swei

  4. Bren School of Environmental Science and Management, University of California Santa Barbara, Santa Barbara, CA, USA

    Andrew J. MacDonald

  5. Ecology and Evolutionary Biology Program, Texas A&M University, College Station, TX, USA

    Jordan Salomon

  6. San Mateo County Mosquito and Vector Control District, Burlingame, CA, USA

    Arielle Crews

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  1. Samantha Sambado
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Contributions

SS, AJM, CJB designed the study. SS conducted the analysis and wrote the initial draft. SS, AmS, JS, AC, KR, SC, AJM collected field data. All authors reviewed and edited the manuscript.

Corresponding author

Correspondence to Samantha Sambado.

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Ethics approval and consent to participate

Research on vertebrate animals was conducted under multiple Institutional Animal Care and Use Committee protocols (Sambado—UCSB #952; Sparkman—Westmont #AY20-152; Swei—SFSU #AUC16-05; MacDonald—UCSB # 863; Young—UCSB #908.1) and the California Department of Fish and Wildlife Scientific Collecting Permits (Sambado—S-193220002-19357-001; Sparkman—S-200070002-20055-001; Swei—SC-8407; MacDonald—SC-12329; Young—SC-12998).

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Sambado, S., Sparkman, A., Swei, A. et al. Climate-driven variation in the phenology of juvenile Ixodes pacificus on lizard hosts. Parasites Vectors 18, 141 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s13071-025-06749-4

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Parasites & Vectors

ISSN: 1756-3305

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