Seasonal and Spatial Dynamics of Tick Infestation in Domestic Animals of Wayanad Forest Region, Southern India: A Longitudinal Survey

Abstract

Aim and background:

Ticks are hematophagous ectoparasites of veterinary and public health relevance. This study examines the seasonal distribution, host-specific burden, and species composition of ticks infesting domestic animals in the Wayanad district of Kerala, India.

Methods:

A longitudinal field survey was conducted from 2022 to 2024 across 40 locations in 3 ecological zones of Wayanad. Ticks were manually collected from domestic animals; cattle, goats, dogs, and cats during 3 seasons: monsoon, winter, and summer. Collected ticks were morphologically identified to the species level. Descriptive statistics, Chi-square tests, and Kruskal–Wallis H tests (α = .05) were performed using Jamovi (v2.6.44). Additionally, a negative binomial regression model was fitted using R (v4.2.2) to identify predictors of tick burden.

Results:

A total of 2562 ticks were collected. Tick abundance was highest during the monsoon (1231; 48.06%), followed by winter (965; 37.66%) and summer (366; 14.28%). Cattle accounted for the highest tick load (n = 1529; 59.7%), followed by goats (592; 23.1%), dogs (313; 12.2%), and cats (128; 5.0%). Mean tick burden per animal was highest in Zone 1 (12.79 ± 6.01), and lowest in Zone 3 (10.20 ± 5.37). Seasonally, winter showed the highest mean burden (4.91 ± 3.63). Seven tick species from 3 genera were identified, with Haemaphysalis bispinosa being the most dominant. Regression analysis identified season and host as significant predictors. Compared to monsoon, summer burden was lower (IRR = 0.36; 95% CI: 0.32-0.41; P < .001), while winter was moderately higher (IRR = 1.15; CI: 1.05-1.25; P = .0017). Cattle exhibited a 4.05-fold higher burden than cats (IRR = 4.05; CI: 3.39-4.87; P < .001).

Conclusion:

The presence of zoonotic tick vectors across hosts, seasons, and zones indicates high epidemiological risk the need for integrated ecological surveillance and region-specific tick control strategies.

Plain Language Summary

Tick Spread in Kerala’s Domestic Animals

Ticks are small, blood-sucking parasites that can spread dangerous diseases to animals and humans. In forest-rich areas like Wayanad district in Kerala, domestic animals often roam close to wildlife and may be at higher risk of tick infestations. However, little is known about which tick species are most common, which animals are most affected, and how tick numbers change with the seasons in this region. Between 2022 and 2024, we visited 40 locations across Wayanad and collected ticks from four types of domestic animals: cattle, goats, dogs, and cats. Tick collection was done during three seasons—monsoon, winter, and summer. We carefully identified the tick species and recorded how many ticks each animal had. We then analyzed the data using statistical methods to understand which factors (like season or animal type) influenced tick numbers. We collected a total of 2,562 ticks. Most ticks were found during the monsoon season, but individual animals had the highest tick burden during winter. Cattle had the most ticks, followed by goats, dogs, and cats. The most common tick was Haemaphysalis bispinosa, especially in cattle and goats. Statistical models confirmed that both season and host type significantly influenced tick burden. Cattle were over four times more likely to be heavily infested compared to cats, and summer had the lowest tick counts. Some of the ticks we found are known carriers of serious diseases like Kyasanur Forest Disease (KFD), babesiosis, and rickettsial infections. Since these animals live near forests and sometimes interact with wildlife, there’s a real risk of diseases spreading between animals and humans. Our findings highlight the need for regular tick monitoring and targeted control efforts in areas like Wayanad to protect both animal and human health.

Introduction

Ticks are among the most significant ectoparasites affecting livestock worldwide, acting as both direct parasites and vectors for various zoonotic pathogens. Their role in the transmission of bacterial, viral, and protozoan diseases makes them a cause of concern in both veterinary and public health domains. ¹ Studies have shown that the abundance of ticks increases depending on the forest landscape and more abundance is seen in tropical forests, where warm and humid conditions prevail with continuous access to the host.^2,3

In the forested ecosystems where domestic animals, wildlife, and humans frequently interact, tick infestation poses a considerable risk for pathogen spillover. The risk of spill over in these areas is high due to seasonal migrations of wild animals, shared grazing grounds, and unrestricted livestock movement near forest areas. This creates an opportunity for tick species that primarily parasitize wildlife to establish themselves in domestic animals, thereby expanding their ecological range and increasing human exposure risks.^4,5

The hematophagous ectoparasitc ticks can be classified into 2 familes; Ixodidae (Hard Ticks) and Argasidae (Soft Ticks). In India, a diverse range of tick species, primarily belonging to the family Ixodidae (hard ticks) were reported more frequently in association with several tick-borne diseases (TBDs). India, particularly South India, harbors a wide diversity of ticks, with Haemaphysalis, Rhipicephalus, Hyalomma, and Amblyomma being the most significant genera. These ticks act as vectors for multiple diseases affecting livestock, wildlife, and humans. The presence of dense forests, high livestock density, and interactions with wildlife contribute to tick abundance in South India.^6,7 A serological investigation by Mathai et al, ⁸ confirmed that cases of pyrexia of unknown origin commonly reported from Southern regions of India were actually due to TBDs such as spotted fever, epidemic/endemic typhus, and scrub typhus. Thereafter this geographic area has since become a focal point for research interest into TBDs and surveyed through various cross-sectional and longitudinal studies and TBDs such as Kyasanur Forest Disease (KFD), bovine theileriosis, Babesiosis, anaplasmosis, and rickettsiosis have been reported so far.^9-12

The majority of the TBDs in south India are reported from the dense hilly forest ranges of Kerala, Tamil Nadu, and Karnataka. The Wayanad forest region in Kerala is one such hotspot for TBDs, as it is home to a wide variety of tick species, some of which are known vectors of TBDs such as KFD, Rickettsial infections, Theileriosis, and Babesiosis. Several studies have documented the presence of ticks in this region, along with sporadic outbreaks of KFD, which is transmitted by Haemaphysalis spinigera, a tick species commonly found in these forested environments.^12,13 This high prevalence of TBDs can be attributed to a combination of climatic, ecological, and anthropogenic factors. The region’s tropical climate, dense forests, livestock-wildlife interactions, and human activities create an ideal environment for tick survival, proliferation, and disease transmission.

Although several cross-sectional surveys have described tick species in the Western Ghats, there is a paucity of longitudinal, zone-wise data from the Wayanad district that quantify seasonal dynamics and per-animal tick burden across domestic host species. Such detailed temporal and spatial information are essential to identify peak infestation periods and high-risk zones for livestock and humans, prioritize targeted acaricidal interventions and guide One-Health surveillance for tick-borne pathogens in a landscape with active forest-livestock interfaces.

Our current study explores the diversity and spatial distribution of ticks in livestock from Wayanad Forest Regions. This longitudinal survey will allow for a better understanding of seasonal trends in tick prevalence, helping to identify peak infestation periods and high-risk zones.

Materials and Methods

Study Area

The study was conducted in the hilly Wayanad district of Kerala, India, located between latitudes 11° 27′ and 15° 58′ North and longitudes 75° 47′ and 70° 27′ East, with elevation ranging from 700 to 2100 m above sea level. It encloses an area of 2131 km² in the Western Ghats. The district can be divided into 3 zones named Mananthavady (Zone-1), SulthanBathery (Zone-2), and Vythiri Taluks (Zone-3) which contain approximately 885.92 km² of forested area and a human population of 817 420, of which a significant number are tribals (See Figure 1).

Figure 1.

Represents the spatial distribution map displaying 40 sampling sites of Wayanad district of Kerala, India (QGIS Geographic Information System, Version 3.34.12; See Supplemental Table 1 for sample location coordinates). Some sampling points lie outside the administrative boundary because the forest/settlement interface (green shading) extends beyond that polygon sampling targeted the ecotonal (forest-adjacent) settlements rather than the administrative boundary itself.

Ethics Section

This study involved non-invasive collection of ticks from domestic animals (cattle, goats, dogs, and cats) with the consent of the animal owners. Tick removal was performed using standard manual methods that did not cause harm or distress to the animals. Ethical clearance was obtained from Institutional Ethics Committee of Malabar Institute of Medical Sciences. LTD. (MIMS) IEC Reg. No: EC/NEW/INST/2019/406/& ECR/301/Inst/KL/2013/RR-19, under approval number: MIMS/SRC/DNB/CER/2020.

Sampling Methodology

Field sampling was conducted seasonally across 3 major periods: monsoon (June-September), winter/post-monsoon (October-January) and summer (February-May). Kerala’s climate is characterized by heavy southwest monsoon rainfall (June-September), followed by a mild humid winter and a hot humid summer.^14,15 A seasonal sampling design was therefore used to assess temporal variation in tick abundance and species composition.

The survey was carried out at 40 predefined locations distributed across 3 administrative taluks of Wayanad: Mananthavady (Zone-1), Sulthan Bathery (Zone-2), and Vythiri (Zone-3); site coordinates are listed in Supplemental Table S1. Sites were chosen to represent the district’s forest-edge, mixed-agriculture and settlement gradients. Each site was visited once per season between 2022 and 2024. At each visit we aimed to examine all available domestic animals whose owners consented, with a target of sampling a minimum of 3 animals per predominant host species when present. When host diversity permitted, at least 1 individual of each common host type (cattle, goat, dog, cat) was sampled to improve host representation, no individual animal was sampled more than once within the same seasonal visit.

For logistical and animal-welfare reasons, all ticks removed from a single animal during 1 visit were pooled into a single vial and labeled with that animal’s ID, site, and date. Thus, the sampling unit for the present study is the animal-visit. Ticks were removed manually using fine-tipped forceps, taking care to minimize host discomfort and avoid damage to specimens. Removed ticks from each animal were placed into separate, clearly labeled vials containing 70% ethanol and transported to the laboratory for morphological identification and life-stage scoring (larva, nymph, adult) following standard keys.^12,13 All collections were non-invasive; owner permission was obtained before sampling and the study procedures followed institutional ethical guidelines for animal handling.

Because availability of hosts varied among sites and seasons, sampling effort was not perfectly balanced. To account for this variation, we (a) report the number of animals sampled per site and season in, (b) present species percentages both as raw counts and normalized per animal sampled (ticks per animal), and (c) conducted inferential analyses using a negative-binomial generalized linear mixed model (GLMM) with sampling site as a random effect and appropriate offsets for sampling effort.

The same animals were sampled across seasons, with occasional new animals added due to natural factors such as births, deaths, or new inclusions. Since the study was conducted in the same area over multiple seasons, the majority of the animals sampled were likely the same individuals, with any variations reflecting natural turnover. This design allows us to track longitudinal trends in tick burden and seasonality while accounting for the dynamics of livestock populations within the Wayanad region.

Tick Identification

Ticks were sorted based on the host, zone of collection, and the season. Tick identification was was performed microscopically based on dichotomous taxonomic identification keys. Identification was carried out to the species level, and ticks were grouped under the appropriate genera.^16,17

Data Analysis

All tick collection data were systematically entered and organized using Microsoft Excel and subsequently analyzed using Jamovi (v2.6.44) and R (v4.3.2). Tick counts were categorized based on host species, tick species, sampling zones, and seasons.

Descriptive statistics, including frequencies, means, and standard deviations (SD), were used to summarize the overall tick burden and infestation trends across spatial and temporal strata.

For inferential statistics, Chi-square (χ²) tests were used to evaluate associations between categorical variables such as host species, season, and collection zone. The Kruskal–Wallis H test, a non-parametric equivalent to one-way ANOVA, was applied to assess differences in median tick burdens across hosts, seasons, and zones. Statistical significance was considered at P < .05. To account for overdispersion in tick count data and to control for unequal sampling effort, a Negative Binomial Regression model was fitted in R using the MASS package. The response variable was tick burden per animal, and the predictor variables included host species, season, and zone. Incidence Rate Ratios (IRRs) with 95% confidence intervals (CIs) were reported to interpret the relative change in expected tick counts across categories, using cats (host) and monsoon (season) as references.

Results

A total of 2562 ticks were collected from 581 domestic animals [cattle (n = 226), goats (n = 176), dogs (n = 107), and cats (n = 72)] across the 3 zones of Wayanad district during the 3 seasons (winter, summer, and monsoon). They were assigned to 3 genera (Haemaphysalis, Rhipicephalus, and Amblyomma) and 7 species (See Table 1).

Table 1.

Seasonal distribution of tick infestation across zones with normalized tick burden (ticks per animal). Values represent total tick counts and number of animals examined per zone and season. Tick burden is expressed as the mean number of ticks per animal to account for variation in sampling effort. Statistical significance was assessed using Chi-square test (χ² = 11.61, P = .0205) and Kruskal–Wallis test (H = 6.49, P = .0390).

Season	Zone	Total ticks (n)	Animals examined (n)	Ticks per animal (mean)
Monsoon	Zone 1	425	92	4.62
	Zone 2	446	82	5.44
	Zone 3	360	69	5.22
	Total	1231 (48.06%)	243	5.07
Summer	Zone 1	126	55	2.29
	Zone 2	123	66	1.86
	Zone 3	117	63	1.86
	Total	366 (14.28%)	184	1.99
Winter	Zone 1	387	62	6.24
	Zone 2	292	46	6.35
	Zone 3	286	46	6.22
	Total	965 (37.66%)	154	6.27
Overall	—	2562	581	4.41

The highest number of ticks were collected during monsoon season (n = 1231; 48.06%), followed by winter (n = 965; 37.67%) and summer (n = 366; 14.28%; Table 1). Zone-wise, the Mananthavady region (Zone 1) had the greatest tick burden (938 ticks; 36.6%), followed by SulthanBathery (Zone 2) with 861 ticks (33.6%), and Vythiri (Zone 3) with 763 ticks (29.8%).

Seasonal distribution of ticks also exhibited marked variation (See Table 1). The monsoon season recorded the highest tick abundance with 1231 ticks (48.06%), followed by winter (965 ticks; 37.66%), and summer (366 ticks; 14.28%; See Table 2).

Table 2.

Seasonal distribution of tick infestation across host species with normalized tick burden (ticks per animal). Values represent total tick counts and number of animals examined per host and season. Tick burden is expressed as the mean number of ticks per animal to allow comparison across host groups with unequal sampling effort. Chi-square analysis showed a significant association between host species and seasonal tick burden (χ² = 86.58, df = 6, P < .0001), whereas Kruskal–Wallis test indicated no significant difference in median tick counts across host groups (H = 2.19, P = .334).

Season	Host	Total ticks (n)	Animals examined (n)	Ticks per animal (mean)
Monsoon	Cattle	650	84	7.74
	Goat	311	73	4.26
	Dog	211	50	4.22
	Cat	59	36	1.64
	Total	1231	243	5.07
Summer	Cattle	212	77	2.75
	Goat	99	58	1.71
	Dog	37	31	1.19
	Cat	18	18	1.00
	Total	366	184	1.99
Winter	Cattle	667	65	10.26
	Goat	182	45	4.04
	Dog	65	26	2.50
	Cat	51	18	2.83
	Total	965	154	6.27
Overall	Cattle	1529 (59.7%)	226	6.77
	Goat	592 (23.1%)	176	3.36
	Dog	313 (12.2%)	107	2.93
	Cat	128 (5.0%)	72	1.78
	Grand Total	2562	581	4.41

Host-wise tick infestation data showed that cattle harbored majority of the ticks (1529; 59.7%), followed by goats (592; 23.1%), dogs (313; 12.2%), and cats (128; 5.0%). Tick burden varied geographically (Table 2). Zone 1 (Mananthavady) recorded the highest number of ticks (n = 938; 36.61%), followed by Zone 2 (SulthanBathery; n = 861; 33.6%), and Zone 3 (Vythiri; n = 763; 29.77%). Cattle were consistently the most infested host across all 3 zones, contributing over half of the ticks in each zone. The tick burden in goats, dogs, and cats followed a similar pattern, with Zone 1 being the hotspot (See Table 3).

Table 3.

Host-wise distribution of tick infestation across zones with zone-level normalized tick burden. Values represent total tick counts and number of animals examined per zone. Tick burden is expressed as mean ticks per animal to account for variation in sampling effort across zones. Host-wise values are presented as absolute counts due to the absence of host-stratified animal counts within each zone. Chi-square analysis indicated no significant association in host-wise tick distribution across zones (χ² = 2.33, df = 6, P = .887), whereas Kruskal–Wallis test showed a significant difference in tick burden across zones (H = 10.38, P = .0156).

Zone	Total ticks (n)	Animals examined (n)	Ticks per animal (mean)	Cattle (ticks)	Goat (ticks)	Dog (ticks)	Cat (ticks)
Zone 1 (Mananthavady)	938	209	4.49	551	219	119	49
Zone 2 (Sulthan Bathery)	861	194	4.44	515	195	111	40
Zone 3 (Vythiri)	763	178	4.29	463	178	83	39
Total	2562	581	4.41	1529	592	313	128

The zone-wise mean tick burden per animal showed that the highest burden was recorded in Zone 1 (12.79 ± 6.01), followed by Zone 2 (11.61 ± 5.91) and Zone 3 (10.20 ± 5.37; See Figure 2). Similarly, the season-wise mean tick burden per animal was highest during winter (4.91 ± 3.63), followed closely by the monsoon season (4.46 ± 2.50). The summer season recorded the lowest tick burden (1.66 ± 0.79; See Figure 3).

Figure 2.

Zone-wise comparison of mean tick burden per animal (mean ± SD) across the 3 zones of Wayanad district. Sample sizes were Zone 1 (n = 209), Zone 2 (n = 194), and Zone 3 (n = 178). Tick burden represents the number of ticks collected per animal during each sampling visit. Bars represent mean values and error bars represent ± SD. Differences across zones were not statistically significant (Kruskal–Wallis test: H = 3.97, P = .137).

Figure 3.

Seasonal variation in tick burden per animal across winter, monsoon, and summer sampling periods. Boxes represent median and interquartile range (IQR), with whiskers indicating data spread. Mean ± SD values are provided above each group (Winter: 4.91 ± 3.63; Monsoon: 4.46 ± 2.50; Summer: 1.66 ± 0.79). Sample sizes were Winter (n = 154), Monsoon (n = 243), and Summer (n = 184). Tick burden represents the number of ticks collected per animal per sampling visit. Differences across seasons were statistically significant (Kruskal–Wallis test: H = 35.90, P < .0001).

Negative Binomial Regression Analysis

To account for overdispersion and variation in sampling effort among host species and across ecological strata, we fitted a “Negative Binomial Regression” model with tick burden as the response variable and Season, Host species, and Zone as predictors. After confirming appropriate model fit (Likelihood Ratio χ² = 842.1, df = 5, P < .0001), the model also confirmed significant effects of both seasonality and host species on tick burden.

Compared to the monsoon season, tick infestation was significantly lower during summer (IRR = 0.36, 95% CI: 0.32-0.41) and moderately higher in winter (IRR = 1.15, 95% CI: 1.05-1.25). Host-wise, cattle exhibited the highest tick burden, with an estimated 4.05-fold (IRR = 4.05, 95% CI: 3.39-4.87) increase compared to the reference group (cats). Goats and dogs also showed significantly elevated infestation rates with IRRs of 2.01 (95% CI: 1.66-2.44) and 1.70 (95% CI: 1.38-2.09), respectively (See Table 4).

Table 4.

Negative Binomial Regression Results Showing Incidence Rate Ratios (IRRs) for Tick Burden Across Seasons and Host Species. The model estimates the effect of season, host type, and zone on per-animal tick burden. Incidence Rate Ratios (IRRs) represent the multiplicative change in expected tick counts relative to the reference categories: Cat (host) and Monsoon (season). Statistically significant predictors include season and host, with cattle exhibiting over fourfold higher tick burden than cats. Confidence intervals are reported at the 95% level.

Predictor	IRR (exp(β))*	95% CI**	P-value
Season
– Monsoon	Reference	–	–
– Summer	.36	0.32-0.41	<.001
– Winter	1.15	1.05-1.25	.0017
Host
– Cat	Reference	–	–
– Cattle	4.05	3.39-4.87	<.001
– Goat	2.01	1.66-2.44	<.001
– Dog	1.70	1.38-2.09	<.001

Cat and Monsoon season were treated as references. *IRR = Incidence Rate Ratio; **CI = Confidence Interval.

To further illustrate the combined influence of host species and seasonal variation on tick burden, predicted values from the negative binomial regression model were generated (Figure 4). These model-derived estimates allow visualization of adjusted tick counts while accounting for overdispersion, unequal sampling effort, and the confounding effects of geographical zone. Unlike raw descriptive comparisons, these predictions represent adjusted expected tick burdens per animal across seasons and host categories, thereby providing a clearer interpretation of interaction patterns identified in the regression analysis.

Figure 4.

Predicted tick burden across host species and seasons based on negative binomial regression model. The plot visualizes the model-estimated tick counts for each host category across different seasons, controlling for sampling effort. Tick burden is shown on a logarithmic scale. Higher predicted tick infestations are observed in cattle, particularly during winter and monsoon seasons. Estimates are derived from a model adjusted for host, season, and geographical zone effects.

To characterize host-specific distribution patterns of individual tick species, a species-wise cross-tabulation was performed across the 4 domestic host categories. This comparative visualization enables identification of dominant species–host associations and highlights variations in infestation intensity across host types. By presenting absolute species counts across cattle, goats, dogs, and cats, the heat map facilitates rapid interpretation of species predominance and relative host preference patterns within the sampled population (Figure 5).

Figure 5.

Heat map representing the distribution of tick species collected from different host animals in Wayanad district.

Haemaphysalis bispinosa (Figure 6) was the most dominant species across all hosts, particularly in cattle (910 ticks) and goats (353 ticks). Rhipicephalus spp. (Figure 7) were mainly associated with cattle (225 ticks) and dogs (113 ticks), indicating a preference for larger and more accessible hosts. H. turturis (Figure 8) and H. spinigera (Figure 9) were also commonly found on dogs and goats. The rare species H. shimoga (Figure 10), H. kyasanurensis (Figure 11), and Amblyomma spp. (Figure 12) had limited host distributions.

Figure 6.

H. bispinosa.

Figure 7.

Rhipicephalus spp.

Figure 8.

H. turturis.

Figure 9.

H. spinigera.

Figure 10.

H. shimoga.

Figure 11.

H. kyasanurensis.

Figure 12.

Amblyomma.

Discussion

This longitudinal survey across 3 ecological zones of Wayanad district provides a comprehensive assessment of spatiotemporal and host-specific variation in tick infestation dynamics. A total of 2562 ticks were collected from 581 domestic animals over 3 seasons, with all specimens belonging to the family Ixodidae and representing 3 genera and 7 species. The predominance of Haemaphysalis, Rhipicephalus, and Amblyomma highlights the ecological suitability of the Western Ghats for ixodid tick survival. Importantly, the study demonstrates clear seasonal and host-associated variation in tick burden, indicating that infestation dynamics are driven by a combination of environmental conditions, host behavior, and ecological interactions in forest-fringe ecosystems.

The observed seasonal variation in tick burden is strongly influenced by climatic and ecological factors characteristic of the Western Ghats. The monsoon season, marked by high rainfall and sustained humidity, provides optimal conditions for tick survival, oviposition, and larval questing activity. Elevated moisture levels reduce desiccation stress in off-host stages, thereby promoting tick persistence in vegetation and soil microhabitats. In contrast, the higher mean tick burden observed during winter may be attributed to increased host–tick contact under relatively stable microclimatic conditions, where livestock are often confined or graze within restricted areas, facilitating prolonged attachment and accumulation of ticks per host. The comparatively lower infestation during summer is likely due to higher ambient temperatures and reduced humidity, which adversely affect tick survival and questing efficiency.

The regression findings further reinforce these ecological observations and have important practical implications for tick control strategies. The significantly higher tick burden observed in cattle (IRR = 4.05) indicates that cattle act as primary amplification hosts for tick populations, making them critical targets for intervention. This suggests that prioritizing cattle-focused control measures, such as regular acaricide treatment, strategic grooming, and improved livestock management practices, could substantially reduce overall tick burden across the ecosystem. Similarly, the seasonal variation in tick burden, with significantly lower infestation during summer (IRR = 0.36) and moderately higher burden during winter (IRR = 1.15), highlights the importance of timing in control strategies. Preventive interventions implemented prior to peak infestation periods, particularly before the onset of winter and monsoon seasons, may be more effective in reducing tick population build-up.

Beyond overall infestation patterns, species-level distribution provides important insights into vector ecology and zoonotic risk in the region. Among the identified species, Haemaphysalis bispinosa emerged as the most predominant across all seasons and zones and showed a strong association with domestic livestock, particularly cattle and goats. This dominance may be attributed to its marked zoophilic preference and ecological adaptability to humid and forested environments. H. bispinosa is a well-recognized vector of pathogens such as Babesia, Theileria, and Anaplasma, contributing significantly to livestock morbidity and economic loss in endemic regions. Our findings are consistent with previous reports, ¹⁸ which have also documented its widespread occurrence in South India. In addition, studies by Balasubramanian et al ¹² have reported the prevalence of H. spinigera and H. turturis in Wayanad forests, emphasizing their role in the transmission of Kyasanur Forest Disease (KFD), which aligns with our observations.

The genus Rhipicephalus was the second most frequently encountered and demonstrated a broad host range, being identified in cattle, goats, dogs, and cats. These ticks are known vectors of several pathogens, including Ehrlichia, Anaplasma, and Rickettsia conorii, the causative agent of Mediterranean spotted fever. Their widespread distribution across hosts and ecological zones suggests a high potential for zoonotic transmission, particularly in settings with close animal–human interaction. Similar findings have been reported by Amrutha et al ¹⁹ and Hembram et al, ²⁰ supporting the epidemiological importance of this genus.

The detection of Haemaphysalis spinigera, a confirmed vector of Kyasanur Forest Disease virus (KFDV), is of particular public health significance. Its presence in domestic animals such as cattle and goats suggests a potential bridging role, facilitating pathogen transmission from wildlife reservoirs to human populations. This species is typically associated with forest-dwelling hosts such as monkeys; however, its recovery from livestock in the present study indicates probable spillover events in forest-fringe environments. Its peak occurrence during the monsoon season corresponds with known seasonal patterns of KFD outbreaks,^12,21 reinforcing its epidemiological relevance.

Haemaphysalis shimoga, another forest-associated species, was identified primarily in cattle and goats during the monsoon season. Although its vector role remains under investigation, its co-occurrence with other medically important tick species and its association with forest ecosystems suggest a potential role in enzootic transmission cycles. Similar observations have been reported in previous studies conducted in South India.^12,20,22

Amblyomma species were the least frequently encountered but are of notable medical importance. These ticks are typically associated with wildlife and possess long mouthparts capable of causing deep tissue attachment. Their detection in Zones 1 and 3, particularly during monsoon and winter, suggests ecological linkage with forested environments and wildlife reservoirs. Amblyomma spp. are known vectors of Rickettsia species and have been associated with severe dermatological and neurological conditions in animals, underscoring their potential role in zoonotic disease transmission.

The findings of the present study are consistent with several reports from the Western Ghats region, which have documented a high prevalence of Haemaphysalis species in forest-associated livestock populations. The study by Balasubramanian et al ¹² reported a predominance of H. spinigera and H. turturis in Wayanad, particularly during the monsoon season, emphasizing the influence of climatic factors on tick distribution. Similarly, Naren et al ¹¹ demonstrated that seasonal variation in tick infestation is closely linked to environmental conditions and host availability in the Western Ghats. However, most previous studies have been cross-sectional or focused on questing ticks and wildlife hosts, limiting their ability to capture temporal variation. In contrast, the longitudinal design of the present study allows a more comprehensive understanding of tick-host dynamics across seasons and ecological zones.

The higher tick burden observed in cattle in the present study contrasts with findings from certain other regions of India, where goats have been reported as the predominant host. ²³ This variation suggests that host preference and infestation intensity are influenced by localized ecological conditions, livestock management practices, and host movement patterns within forest-fringe ecosystems. These findings highlight the importance of region-specific surveillance strategies in ecologically diverse landscapes such as the Western Ghats.

The present findings have direct implications for tick control strategies in the region. Targeted interventions focusing on high-risk hosts, particularly cattle, combined with season-specific control measures such as pre-monsoon and pre-winter acaricide application, may significantly reduce tick burden. Integrated approaches incorporating both chemical and botanical acaricides, as demonstrated in previous studies,^24-27 may offer sustainable alternatives for livestock management in endemic areas.

Despite the strengths of this study, certain limitations should be acknowledged. The study focused exclusively on domestic animals and did not include wildlife reservoirs, which play a critical role in tick ecology and zoonotic transmission cycles. Only adult ticks were identified morphologically, while larvae and nymphs were not analyzed due to field limitations. Additionally, pooling of ticks at the animal level limited fine-scale assessment of species-specific prevalence per host. Although sampling effort was higher during the monsoon season, effort-adjusted analyses (ticks per animal and GLMMs with sampling offsets) were used to mitigate this imbalance.

Overall, the study highlights the significant veterinary and public health risks posed by ticks in the Wayanad region. The presence of multiple competent vector species capable of transmitting pathogens such as Kyasanur Forest Disease virus, Babesia, Theileria, Ehrlichia, and Rickettsia underscores the need for integrated One Health surveillance and targeted tick control strategies. Given the close ecological interaction between livestock, wildlife, and human populations, sustained monitoring and region-specific interventions are essential to mitigate the risk of tick-borne diseases in this biodiverse and high-risk landscape.

Conclusion

The study identified 7 tick species such as Haemaphysalis bispinosa, Rhipicephalus spp., Haemaphysalis turturis, Haemaphysalis spinigera, Haemaphysalis shimoga, Haemaphysalis kyasanurensis, and Amblyomma spp. Among the domestic animals sampled, cattle emerged as the primary host, harboring the majority of ticks across all seasons and zones. The monsoon and winter seasons exhibited elevated tick densities, while Zone 1 (Mananthavady) demonstrated the highest zone-wise tick burden. These findings underscore the need for targeted tick control measures, especially during peak seasons. Public health and veterinary authorities should implement region-specific surveillance and acaricidal interventions to reduce the risk of zoonotic diseases in high-burden areas.