Identification and Distribution of Tick Species in Cattle in Eastern Bhutan

Home , Acariformes, Amblyomma testudinarium, Haemaphysalis bispinosa, Haemaphysalis spinigera, Nuttalliella

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2020-11-04 Identification and Distribution of Tick Species in Cattle in Eastern Bhutan

Namgyal, Jamyang

Namgyal, J. (2020). Identification and Distribution of Tick Species in Cattle in Eastern Bhutan (Unpublished master's thesis). University of Calgary, Calgary, AB. http://hdl.handle.net/1880/112725 master thesis

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Identification and Distribution of Tick Species in Cattle in Eastern Bhutan

Jamyang Namgyal

A THESIS

SUBMITTED TO THE FACULTY OF GRADUATE STUDIES

IN PARTIAL FULFILMENT OF THE REQUIREMENTS FOR THE

DEGREE OF MASTER OF SCIENCE

GRADUATE PROGRAM IN VETERINARY MEDICAL SCIENCES

CALGARY, ALBERTA

NOVEMBER, 2020

Abstract

Tick infestation is the most reported parasitological problem in cattle in Bhutan. Its negative impacts on the health and production of cattle affect the livelihoods of resource-poor subsistence farmers. However, the current knowledge of tick species diversity, infestation prevalence, geographic distribution, and farmers’ perception on tick prevention and control practices is limited. Therefore, the objectives of this research were to 1) determine the presence, diversity and infestation prevalence of tick species in cattle in two districts of eastern Bhutan using a targeted field survey; 2) model the habitat suitability of selected tick species identified in these two districts using the MaxEnt modeling approach; and 3) assess the knowledge, attitude, and practices (KAP) of ticks and tick-borne diseases (TBDs) among cattle farmers in a selected area of eastern Bhutan. In May and June 2019, 3600 live adult ticks were collected from 240 cattle and morphologically identified to the species level. In June 2019, 246 cattle owners were interviewed using a structured questionnaire. Four genera and six species of ticks were found.

These were Rhipicephalus microplus (Canestrini) (70.2%), Rhipicephalus haemaphysaloides

Supino (18.8%), Haemaphysalis bispinosa Neumann (8.2%), Haemaphysalis spinigera

Neumann (2.5%), Amblyomma testudinarium Koch (n=7), and an unidentified species of Ixodes

(n=1). For all tick species except A. testudinarium and Ixodes sp., the high altitude northeastern part and the low altitude southernmost part of the study area were predicted as areas with a very low probability of tick(s) presence. The KAP study identified only 52% of the farmers with adequate knowledge about ticks as vectors of diseases and 36% with a favorable attitude toward tick control programs. The findings from this research are expected to guide the planning and implementation of effective tick prevention and control measures in Bhutan.

Keywords: ticks, cattle, habitat suitability modeling, MaxEnt, KAP, Bhutan

Acknowledgments

The past two years in the University of Calgary graduate program was a wonderful learning experience. Foremost, I would like to extend my gratitude to my supervisor, Dr. Susan

Catherine Cork, for the opportunity to pursue my post-graduate master’s research program.

Thank you for your exceptional guidance and support. I really do not know how I can justify the time and support you provided during this entire journey.

I acknowledge the members of my supervisory committee, Dr. Tim J. Lysyk, Dr. Carl

Ribble, and Dr. Sylvia Checkley, for their insights, commitment, guidance, and support during this entire program. This work would not have been possible without your guidance and feedback. I want to emphasize my deep gratitude to Dr. Susan Catherine Cork and Dr. Tim J.

Lysyk for visiting Bhutan to guide me through my fieldwork. Your guidance and advice in research and entomology were invaluable to me.

I thank Dr. Isabelle Couloigner for her passionate involvement, guidance, and support for my entire work. Your kindness and patience during our long sessions on modeling techniques were really amazing. And thank you for cheering me up when my learning curve was too steep. I would also like to thank Dr. Shaun Derguosoff, Research Scientist, Agriculture and Agri Food

Canada for his valuable insights. Thank you, Shaun! I look forward to working with you in the future.

I would like to express my gratitude to Dr. Sithar Dorjee, Director, Khesar Gyelpo

University of Medical Sciences, Bhutan, for agreeing to be my mentor in Bhutan and for his support during my fieldwork in Bhutan. I would like to thank Dr. Ratna B Gurung, Program

Director, National Centre for Animal Health (NCAH), Bhutan, and Dr. Tenzin, Veterinary

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Epidemiologist, NCAH, for their valuable insights, guidance and support while working on this project.

I would like to thank Dr. Tashi Samdrup, former Director General, Department of

Livestock (DoL), Ministry of Agriculture and Forests (MoAF), Dr. Karma Rinzin, former Chief

Veterinary Officer, DoL, Dr. Tshering Dorjee, former Regional Director, RLDC, Kanglung, Dr.

Kinzang Drukpa, former Program Director, NCAH, Dr. Kesang Wangchuk, Principal Research

Officer, DoL, Dr. Narapati Dahal, and Dr. Karma Wangdi, Animal Health Division, DoL, Dr.

Ugyen Namgyel, NCAH, Dr. Karma Phuntsho, Satellite Veterinary Laboratory, Nganglam,

Pema Gatshel, and Mr. N S Tamang, District Livestock Officer, Trashigang for their support during my fieldwork in Bhutan.

My special acknowledgment goes to my colleagues at Trashigang, Mr. Tashi Norbu, Mr.

Phurba Wangdi, Mr. Kinley Tenzin, Mr. Lhatru, Mr. Rinzin Namgay, Mr. Jigme Choeda, Ms.

Deki Jamtsho, Ms. Karma Wangmo and Mr. Kunzang Namgyal for their help in interviewing survey participants during my fieldwork. I also acknowledge the support of veterinary laboratory technicians, Mr. Lungten, Mr. Tenzinla, and Ms. Tshewang Dema during my fieldwork in

Bhutan.

Many thanks go to friends in Bhutan. Thank you, Ata Sangay Rinchen, for your guidance and support during my entire MSc journey. Ever since our BVSc & AH days in Pondicherry,

India, you have been shouldering the role of an elder brother to me. I am forever indebted to you.

Thank you, Ata Jigme Wangchuk, Thimphu, Aum Norbu Doelma, Chukha, and Ata Namgay

Dorji, Program Director, NCA, Gelephu, for motivating me to navigate through the various challenges of my MSc journey.

It was really wonderful time and experience, sharing time and office with graduate colleagues from different parts of the world (Mohammad Nazari, Karma Phuntsho, Fernando

Guardado, Mai Farghaly, Paul Burden, Abraham Munene, Phil Rasmussen, Lindsay Rogers,

Heather, Kelly, Alyssa, Kayla, Summer, Ana, Catalina, Shanas, and Sabrina). Thank you all for your warm friendship.

I would like to acknowledge the Department of Ecosystem and Public Health administrative staff, Robert Forsyth and Joy Punsalan, for their help and support in all the administration related works. Thank you, Rob and Joy.

I would like to acknowledge the journal clubs that have contributed to my learning.

Thank you, Dr. Sylvia Checkley (One Health Journal Club), Dr. Karin Orsel (Epidemiology

Journal Club), and Dr. David Hall (Livestock Policy Journal Club) for providing an interesting learning platform.

I would like to thank the Royal Government of Bhutan for allowing me to pursue this master’s program. I also appreciate the One Health Graduate Training Award and the University

International Grants that facilitated my research.

Finally, I would like to thank my wife, Mrs. Tenzin Dema, and son Jamyang R Namgyal for letting me take this incredible journey. And a very special thanks to my parents, siblings, relatives, and friends for their support and encouragement.

Dedication

This work is dedicated to my son Jamyang R Namgyal, the anchor of my life and the hero of my family.

Table of Contents

Abstract ...... ii Acknowledgments ...... iii Dedication ...... vi Table of Contents ...... vii List of Tables ...... x List of Figures ...... xii List of Abbreviations ...... xiv Chapter 1 Introduction and literature review...... 1 1.1 Introduction and study objectives ...... 1 1.2 Literature review ...... 5 1.2.1 Classification of ticks ...... 5 1.2.2 General morphology of ixodid ticks ...... 6 1.2.3 Life cycles of ixodid ticks ...... 8 1.2.4 Biology and ecology of ixodid ticks ...... 10 1.2.5 Collection of ticks ...... 15 1.2.6 Tick control ...... 17 1.2.7 Status of ticks in Bhutan and the neighboring regions ...... 22 1.2.8 Tick-borne diseases in cattle in Bhutan ...... 24 1.2.9 Species distribution modeling ...... 30 1.2.10 MaxEnt modeling...... 34 Chapter 2 Distribution of Ixodid Ticks in Cattle in Eastern Bhutan ...... 54 2.1 Background ...... 54 2.2 Materials and methods ...... 55 2.2.1 Ethics statement ...... 55 2.2.2 Study areas ...... 55 2.2.3 Sample size ...... 57 2.2.4 Sampling method ...... 57 2.2.5 Tick collection ...... 58 2.2.6 Specimen identification ...... 58 2.2.7 Statistical analyses ...... 59

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2.3 Results ...... 61 2.4 Discussion ...... 72 2.5 Conclusion ...... 81 Chapter 3 Modeling the habitat suitability of ticks identified in eastern Bhutan ...... 92 3.1 Background ...... 92 3.2 Materials and methods ...... 93 3.2.1 Ethics statement ...... 93 3.2.2 Study area ...... 93 3.2.3 Tick presence data ...... 95 3.2.4 Environmental data ...... 95 3.2.5 Statistical modeling ...... 97 3.3 Results ...... 99 3.4 Discussion ...... 111 3.5 Conclusion ...... 119 Chapter 4 A knowledge, attitude and practices survey on ticks and tick-borne diseases among cattle owners in a selected area of eastern Bhutan ...... 127 4.1 Background ...... 127 4.2 Materials and methods ...... 130 4.2.1 Study area ...... 130 4.2.2 Sample size ...... 131 4.2.3 Questionnaire survey ...... 132 4.2.4 Ethics statement ...... 133 4.2.5 Statistical analyses ...... 133 4.3 Results ...... 136 4.3.1 Sociodemographic characteristics ...... 136 4.3.2 Knowledge about ticks and TBDs ...... 136 4.3.3 Attitude toward tick prevention and control program ...... 140 4.3.4 Self-reported farm practices ...... 142 4.4 Discussion ...... 144 4. 5 Conclusion ...... 153 Chapter 5 Discussion ...... 163 5.1 Key findings ...... 165 5.2 Recommendations and further research ...... 174

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5.2.1 Enhancing active tick surveillance in domestic animals...... 174 5.2.2 Tick surveillance in wildlife and the environment...... 176 5.2.3 Enhancing tick surveillance at quarantine stations in Bhutan...... 176 5.2.4 Diagnosis of the potential pathogens of medical and veterinary importance in ticks in Bhutan...... 177 5.2.5 Enhancing modeling studies for tick species available in Bhutan...... 178 5.2.6 Enhancing awareness education about ticks and tick-borne diseases...... 179 5.2.7 Initiate acaricide monitoring ...... 180 5.2.8 Enhancing basic diagnostic tests for tick-borne diseases in cattle...... 181 Conclusion ...... 181 Appendix A: Tables ...... 193 Table A.1 Multiple logistic regression models developed to understand the association between geographic variables and the overall infestation prevalence (n=1004) ...... 193 Table A.2. Results of simple logistic regression analyses describing the relationships between infestation prevalence and coinfestation, and cattle and geographic parameters...... 194 Table A.3 MaxEnt models developed for predicting R. microplus occurrence in eastern Bhutan...... 196 Table A.4 MaxEnt models developed for predicting R. haemaphysaloides occurrence in eastern Bhutan...... 197 Table A.5 MaxEnt models developed for predicting H. bispinosa occurrence in eastern Bhutan...... 200 Table A.6 MaxEnt models developed for predicting H. spinigera occurrence in eastern Bhutan...... 202 Table A.7 Logistic regression analysis to investigate the association between the explanatory variables and the knowledge outcome (having adequate knowledge about ticks as potential vectors of diseases or not) ...... 206 Table A.8 Logistic regression analysis to investigate the association between the explanatory variables and the attitude outcome (having a favorable attitude toward tick prevention and control or not) ..... 207 Appendix B: Questionnaire ...... 208 B.1 The knowledge, attitudes, and practices (KAP) survey about ticks and tick-borne diseases among cattle owners in Samkhar subdistrict, Trashigang, Bhutan ...... 208

List of Tables

Table 1.1 Records of Haemaphysalis ticks from Assam, Arunachal Pradesh, West Bengal, and Sikkim. Compiled from Geevarghese & Mishra (2011)...... 23 Table 2.1 Geographic and climatic characteristics of the study area...... 56 Table 2.2 Characteristics of the explanatory variables (n=240) used for the logistic regression analyses...... 61 Table 2.3 Result of the multiple logistic regression model to understand the association between geographic variables and the overall infestation prevalence (Grouped data of cattle infested and cattle uninfested) ...... 63 Table 2.4 Tick species found in the study area...... 64 Table 2.5 Co-infestation (n=116) ...... 67 Table 2.6 Results of the multiple logistic regression of coinfestation and infestation prevalence of each tick species in relation to cattle and geographic variables...... 68 Table 3.1 Environmental variables used in building MaxEnt models ...... 96 Table 3.2 Variable contribution and permutation importance values obtained during the first MaxEnt run performed with all the variables for predicting tick species presence. Only variables that achieved values of more than 1% in both metrics are shown...... 100

Table 3.3 Correlation matrix showing Spearman correlation coefficient (rs) for variables that achieved more than 1% contribution and permutation importance in the first MaxEnt run for all tick species...... 101 Table 3.4 Best MaxEnt models developed for predicting tick species in eastern Bhutan: RM_B1 for R. microplus; RH_D1 for R. haemaphysaloides; HB_C2 for H. bispinosa; and HS_E1 for H. spinigera...... 102 Table 3.5 Study area (in km2 and percentage) classified by the probability of tick species occurrence in eastern Bhutan: RM_B1 for R. microplus; RH_D1 for R. haemaphysaloides; HB_C2 for H. bispinosa; and HS_E1 for H. spinigera...... 104 Table 4.1 Questions used for assessing participants’ knowledge about ticks as vectors of diseases...... 134 Table 4.2 Questions used for assessing participants’ attitudes toward prevention and control of ticks in cattle...... 135 Table 4.3 Sociodemographic characteristics of the respondents...... 137

Table 4.4 Final multiple logistic regression analysis to understand the association between the explanatory variables and the binary outcome variable (having adequate knowledge about ticks as vectors of diseases or not)...... 140 Table 4.5 Final multiple logistic regression analysis to understand the association between the explanatory variable and the binary outcome variable (having a favorable attitude toward tick control programs or not)...... 141

List of Figures

Figure 1.1 Male and female Rhipicephalus haemaphysaloides Supino with key characteristics labeled...... 7 Figure 2.1 Map of Bhutan showing the study area and tick sampling sites (black dots)...... 56 Figure 2.2 Representative images of tick species identified in the study area: dorsal and ventral views of male (A) and female (B) R. microplus, male (C) and female (D) R. haemaphysaloides; male (E) and female (F) H. bispinosa, male (G) and female (H) H. spinigera; male (I) and female (J) of A. testudinarium, and female (K) Ixodes sp...... 65 Figure 3.1 Elevation map of the study area showing tick sampling sites (black dots)...... 94 Figure 3.2 Habitat suitability maps for R. microplus developed by the two best models: full model RM_A using LULC, DEM_SRTM, and Bio 18; and model RM_B1 using LULC and DEM_SRTM...... 103 Figure 3.3 Variable contribution to the training gain of the final model for tick species occurrence in eastern Bhutan: (A) R. microplus; (B) R. haemaphysaloides; (C) H. bispinosa; and (D) H. spinigera...... 103 Figure 3.4 Response curve plotting the probability of R. microplus occurrence in eastern Bhutan against the values of the top environmental variables: (A) Elevation (DEM_SRTM) and (B) Land use and land cover 2016 (LULC)...... 104 Figure 3.5 Habitat suitability maps for R. haemaphysaloides developed by the two best models: model RH_C4 using LULC, Bio 16, Bio 10, and DEM_SRTM; and model RH_D1 using LULC, Bio 16, and Bio 10...... 105 Figure 3.6 Response curves plotting the probability of R. haemaphysaloides occurrence in eastern Bhutan against the values of the top environmental variables: (A) Temperature of the warmest quarter (Bio 10), (B) Land use and land cover 2016 (LULC), and (C) precipitation of the wettest quarter (Bio 16)...... 106 Figure 3.7 Habitat suitability maps for H. bispinosa developed by the two best models: model HB_B3 using LULC, Bio 18, Bio 16, and Bio 12; and model HB_C2 using LULC, Bio 18 and Bio 16...... 107 Figure 3.8 Response curves plotting the probability of H. bispinosa occurrence in eastern Bhutan against the values of the top environmental variables: (A) precipitation of the warmest quarter

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(Bio 18), (B) Land use and land cover 2016 (LULC), and (C) precipitation of the wettest quarter (Bio 16)...... 108 Figure 3.9 Habitat suitability maps for H. spinigera developed by the three best models: model HS_C2 using LULC, Bio 19, Bio 16, Bio 12 and Bio 8; model HS_D2 using LULC, Bio 19, Bio 16, and Bio 8; and model HS_E1 using LULC, Bio 19 and Bio 16...... 110 Figure 3.10 Response curves plotting the probability of H. spinigera occurrence in eastern Bhutan against the values of the top environmental variables: (A) precipitation of the coldest quarter (Bio 19), (B) Land use and land cover 2016 (LULC), and (C) precipitation of the wettest quarter (Bio 16)...... 111 Figure 4.1 Map of Bhutan showing the study area (pink shade)...... 131 Figure 4.2 Respondents who had “adequate vs. inadequate knowledge about ticks as vectors of diseases” categorized by husbandry practice (n=246)...... 139 Figure 4.3 Respondents who had a “favorable vs. unfavorable attitude towards tick control programs” categorized by gender (n=246)...... 141 Figure 4.4 Respondents who had a “favorable vs. unfavorable attitude towards tick control programs” categorized by husbandry practice (B) (n=246)...... 142

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List of Abbreviations

Symbol Definition AIC Akaike Information Criteria AUC Area Under the Curve Bhutan Agriculture and Food Regulatory BAFRA Authority CI Confidence Interval DoL Department of Livestock ELISA Enzyme-Linked Immunosorbent Assays FRMD Forests Resources Management Division GAM General Additive Model GIS Geographic Information System GLM General Linear Model GPS Global Positioning System ICT Immunochromatographic tests IFAT Indirect Fluorescent Antibody Test KAP Knowledge, Attitudes, and Practices KFD Kysanuar Forest Disease LULC 2016 Land Use and Land Cover 2016 MoAF Ministry of Agriculture and Forests NCAH National Centre for Animal Health National Center for Hydrology and NCHM Meteoreology NLC National Land Commission OIE World Organization for Animal Health OR Odds Ratio RLDC Regional Livestock Development Centre RNR Renewable Natural Resources ROC Receiver Operating Characteristics SDM Species Distribution Modeling TBDs Tick-Borne Diseases USGS United States Geological Survey VIF Variance Inflation Factor VIS Veterinary Information System WGS World Geodetic System WHO World Health Organization

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Chapter 1 Introduction and literature review

1.1 Introduction and study objectives

Bhutan is a small Himalayan Kingdom in South Asia located between China to the north, and the Indian states of Assam and West Bengal to the south, Arunachal Pradesh to the east, and

Sikkim to the west. It has a total land area of 38,394 km2, out of which 70.8% is covered with forest mainly dominated by broadleaf and mixed conifers (MoAF, 2019). It is divided administratively into 20 districts (dzongkhags) and 205 sub-districts (gewogs). The country’s population is 735,553 (as of 30 May 2017), of which 62.2% lives in rural areas, and their livelihoods depend on agriculture and livestock farming (National Statistics Bureau, 2018). The renewable natural resources (RNR) sector consisting of the agriculture, livestock, and forestry sectors, has been the third-largest contributor to Bhutan’s gross domestic product for the past several years (National Statistics Bureau, 2019) and accounts for 49.1% of employment in the country (National Statistics Bureau, 2018).

Geographically, Bhutan is characterized by high mountains, dense forests, and fast- flowing rivers that form narrow valleys before flowing out onto vast north Indian plains.

Elevation ranges from less than 100m in the south to 7,550m in the north (Dorji et al., 2016).

This elevation gradient and the north Indian monsoon have resulted in extremely diverse climatic conditions and ecosystems across Bhutan, resulting in six agro-ecological zones (wet subtropical, humid subtropical, dry subtropical, warm temperate, cool temperate, and alpine) (Banerjee &

Bandopadhyay, 2016). Vegetation coverage, cropping system, land use, and livestock farming are mainly determined by the agro-ecological zones.

Cattle, including yaks (Bos grunniens L.) and mithuns (Bos frontalis Lambert), are the predominant livestock farmed with the population of 0.35 million heads (DoL, 2018). The

2 crossbreeds of local indigenous Siri cattle (Bos taurus indicus L.) and mithun comprise 55% of the total cattle population while European breeds (Bos taurus taurus L.) such as Jersey, Brown

Swiss, and Holstein Friesian form 30%. The yaks and their crossbreeds comprise 14% of the country’s cattle population (DoL, 2018). In 1985, to intensify small-scale subsistence-oriented farms, the national cattle breeding policy introduced crossbreeding of local indigenous breeds with Brown Swiss cattle in high altitude areas and Jersey cattle in other regions (Samdup et al.,

2010). From 1998 onwards, the cattle breeding policy was changed to provide artificial insemination and breeding bulls of any breed to all districts based on farmers’ demand (Samdup et al., 2010). Currently, in response to the government’s subsidy schemes and growing economic opportunities, farmers also import high yielding European cattle such as Jersey and Holstein

Friesian from neighboring Indian states for achieving higher milk productivity.

Livestock farming plays an important role in the rural economy of Bhutan. It supports the livelihood of rural communities and contributes toward meeting the increasing demands for dairy products in the country. However, our farmers face a unique set of challenges in ensuring efficient livestock production. With just 2.75% of Bhutan’s land available for cultivation

(MoAF, 2019), the individual landholdings are limited in their ability to achieve any significant productivity. This is further challenged by other biophysical conditions such as steep terrain, erratic rainfall, poor soil fertility, predation of livestock by wild carnivores, and diseases such as foot and mouth disease. Bhutan has also been bearing the brunt of global climate change impacts such as erosion of fertile land by glacial lake outbursts floods and extreme weather patterns

(Wang et al., 2017). In recent years, the government, as a part of livestock intensification program, has been widely promoting stall-fed system of cattle rearing for achieving better productivity and management practices. Many farmers across the country are gradually adopting

3 the stall-fed system largely because of the government’s subsidy programs. Despite all these efforts, livestock farming in Bhutan is still dominated by small-scale subsistence-oriented farms

(i.e., with a herd size of 5-6 cattle) with low productivity due to multiple factors discussed above.

Livestock farming, particularly cattle rearing, in Bhutan is constrained by infectious diseases such as foot and mouth disease, hemorrhagic septicemia, black quarter, anthrax, rabies, brucellosis, and parasitic diseases (NCAH, 2019). Tick infestation is the most reported parasitological problem in cattle in Bhutan (NCAH, 2019). In 2019, 42% of Bhutan’s cattle population were reported to have been treated for tick infestation (NCAH, 2019) at the cost of approximately 3.18 million Bhutanese Ngultrum (1CAD= Nu.54) for purchasing acaricides (Dr.

Ugyen Namgyal, NCAH, pers comm, 2020).

Ticks not only transmit infectious agents to livestock but also cause skin irritation during attachment, blood loss, bite wounds, and sometimes lead to self-trauma and secondary bacterial infections (Minjauw & McLeod, 2003). Heavy infestations can result in anemia and significant weight loss, thereby reducing productivity (de Castro, 1997). Some ticks can produce toxins leading to toxicosis and subsequent tick paralysis (Estrada-Pena & Mans, 2013). In domestic animals, ticks transmit a wide range of diseases, the most important of which are anaplasmosis, babesiosis, cowdriosis, and theileriosis (de Castro, 1997; Minjauw & McLeod, 2003). In Bhutan, three tick-borne diseases (TBDs), anaplasmosis, babesiosis, and theileriosis, are reported in cattle, especially in the southern subtropical areas (Phanchung et al., 2007) but it is difficult to estimate the actual number of cases due to the limited use of confirmatory diagnostic tests, poor surveillance, and discrepancies in recorded data.

Veterinary services and therapeutics, such as drugs and vaccines, are provided free of cost in Bhutan by the government through a network of 20 veterinary hospitals at the district

4 level and 205 livestock extension centers at the sub-district level. The government also implements tick control through the Department of Livestock (DoL). The National Centre for

Animal Health (NCAH) under DoL is responsible for the selection, procurement, and supply of acaricides in the country. Currently, the acaricides used for tick control in Bhutan are liquid formulations of pyrethroid compounds (i.e., cypermethrin, deltamethrin, and flumethrin) and amidines (i.e., amitraz) imported from India (NCAH, 2013). These chemicals are provided free of cost to farmers and are available from any livestock center for direct topical application to host animals. The livestock centers keep records of acaricides dispensed and the number of animals reported for tick infestation. However, the current practice of tick control in Bhutan is not based on any strategic approach due to limited information on prevalent tick species, their life cycles, and seasonal patterns.

Ticks are prevalent throughout the country, but there is currently limited data on diversity, infestation prevalence, ecology, and geographic distribution. Current knowledge consists of one unpublished study (Cork et al., 1996) conducted over a period of one year in cattle in eastern Bhutan and one published government report (RLDC Wangdue, 2019) from western Bhutan. The study conducted by Cork et al. (1996) may be the first to identify some tick species present on cattle in Bhutan. They reported Rhipicephalus microplus (Canestrini) as the most predominant tick species with Haemaphysalis spp. and Hyalomma spp. identified to the genus level. The other study conducted by the Regional Livestock Development Center (RLDC

Wangdue, 2019) in western Bhutan identified the genera Rhipicephalus (Boophilus) spp.,

Rhipicephalus spp., Haemaphysalis spp., Ixodes spp., and Amblyomma spp. but no information on species is available. The success of any tick control program largely depends on farmers’ knowledge of ticks and TBDs, their perceptions on the effectiveness of the control methods, and

5 their involvement in the design and implementation of such programs (Adehan et al., 2018;

Sungirai et al., 2016). However, there has not been any KAP study conducted in Bhutan about ticks and TBDs. Therefore, the objectives of this study were to:

1. Determine the presence, diversity and infestation prevalence of tick species in cattle in

two districts of eastern Bhutan using a targeted field survey.

2. Model the distribution of selected tick species identified in eastern Bhutan under current

environmental conditions using the MaxEnt modeling approach.

3. Assess the knowledge, attitude, and practices (KAP) about ticks and tick-borne diseses

(TBDs) among cattle owners in a selected cattle farming area in eastern Bhutan.

1.2 Literature review

1.2.1 Classification of ticks

Ticks are obligate hematophagous arthropods belonging to the phylum Arthropoda, class

Arachnida, subclass Acari, and order Ixodida (formerly referred to as the Metastigmata) (Barker

& Murrell, 2002). There are 896 species of ticks in the three families: the Argasidae, the

Ixodidae, and the Nutalliellidae (Guglielmone et al., 2010). The Argasidae (with 193 species) are known as soft ticks because of their leathery cuticle; and the Ixodidae (with 702 species within

14 genera) are known as hard ticks because of their hard sclerotized dorsal scutum (Guglielmone et al., 2010; Sonenshine & Roe, 2013b). The family Nutalliellidae (monotypic) is represented by one species Nutalliella namaqua Bedford, whose morphological characteristics are similar to both argasids and ixodids (Latif et al., 2012). The family Ixodidae comprises two major groups: the Prostriata, consist of a single genus, Ixodes, and the Metastriata, comprises the remaining 13 genera (Sonenshine & Roe, 2013b).

1.2.2 General morphology of ixodid ticks

Externally, the body of ticks consists of 2 parts: the anterior gnathasoma (capitulum) and the posterior idiosoma (body), which bears the 4 pairs of legs (Sonenshine & Roe, 2013a). The capitulum consists of the basis capitulum to which palpi and mouthparts (i.e., hypostome and chelicerae) are attached. Chelicerae are paired structure that consists of a long movable shaft and cutting digits used for ripping and tearing host tissues to facilitate attachment (Lindquist et al.,

2016). The hypostome is a ventral structure of the capitulum armed with recurved teeth or denticles and used as the piercing organ (Walker et al., 2003). The arrangement of these teeth is a useful feature for the identification of Rhipicephalus (Boophilus) species; for example, the hypostome dentition of R. microplus is 4/4, whereas it is 3/3 in R. decoloratus Koch (Matthysee

& Colbo, 1987). The palps are four segmented, paired, and movable structure of the mouthparts

(Lindquist et al., 2016), and they are important diagnostic attributes in Haemaphysalis ticks

(Geevarghese & Mishra, 2011). For example, H. spinigera Neumann is identified based on a very well-developed ventral spurs on palp segments two and three (Geevarghese & Mishra,

2011). The basis capitulum of female ixodids also bears a pair of depression with pitted floors called porose areas that produce antioxidants to inhibit degradation of waxy compound coated on the eggs (Sonenshine & Roe, 2013a). Generally, the basis capituli and palps are very important structures for the identification of tick genera (Walker et al., 2003).

The idiosoma (body) is the largest region in the body of ticks. Dorsally, the idiosoma bears a sclerotized shield called scutum, which covers the anterior region in larvae, nymph, and adult ixodid females, and the entire dorsal region in adult ixodid males (Lindquist et al., 2016).

Along the lateral margins on each side of the scutum are the eyes (Lindquist et al., 2016), although these are absent in Ixodes and Haemaphysalis and indistinct in Rhipicephalus

(Boophilus) (Walker et al., 2003). The scutum of some adult ixodid ticks has a pattern of enamel-like color, and hence, they are known as ornate ticks (Lindquist et al., 2016). The shape and ornamentation of the scutum are important diagnostic characteristics for some ticks, particularly the genus Dermacentor (Walker et al., 2003). Laterally and posteriorly to the scutum of immature and female ixodids, the body has a soft and pliable dorsal cuticle called alloscutum

(Lindquist et al., 2016). On the ventral side, there is a genital pore (absent in larvae and nymphs).

In adult males, the genital pore is covered by a movable plate that can be elevated during copulation (Sonenshine & Roe, 2013a).

Figure 1.1 Male and female Rhipicephalus haemaphysaloides Supino with key characteristics labeled. These ticks were collected from Trashigang district in eastern Bhutan, and photographs were taken using a macro lens.

The other important structures on the ventral side are anus, anal grooves, adanal plates, and respiratory spiracular plates (also called stigmatal plates) in the nymphs and adults of all ticks

(Lindquist et al., 2016). The anal groove surrounds the anus anteriorly in the genus Ixodes (hence they are called prostriate ticks) and posteriorly in the other genera (thus, they are called metastriate ticks) (Walker et al., 2003). The posterior ventral margin of the idiosoma has a number of festoons that are subrectangular areas separated by grooves, although these are absent in Boophilus and Ixodes (Lindquist et al., 2016).

All adults and nymphs have four pairs of legs, while larvae have only three pairs of legs.

Legs are numbered I through IV, starting from the anterior-most pair (Sonenshine & Roe,

2013a). All the legs of ticks have six segments named (distal to proximal segments) the tarsus, metatarsus, tibia, femur, trochanter, and coxa plus the pretarsus that bears a pair of claws, and a well-developed pulvillus (Sonenshine & Roe, 2013a). The coxae are joined to the idiosoma and may bear spurs either internal (closest to the body midline) and external (farthest from the body midline) (Lindquist et al., 2016). The presence or absence, shape, and size of the spurs are used in the diagnosis of tick species, particularly for Haemaphysalis ticks (Geevarghese & Mishra,

2011). For example, males of H. bispinosa Neumann have long spur on coxa I (the coxa of leg I), whereas H. spinigera has a long spur on coxa IV (Geevarghese & Mishra, 2011). The tarsus of leg I of all ticks bear a sensory organ called Haller’s organ, which is unique, and it is believed to be olfactory (Sonenshine & Roe, 2013a).

1.2.3 Life cycles of ixodid ticks

The life cycle of all ixodid ticks has 4 stages, eggs and motile larva, nymph, and adult

(male and female) (Apanaskevich & Oliver, 2013). Ixodid tick lifecycles are classified into 3-, 2-

, or 1-host ticks depending on whether the molting of larvae to nymphs and nymphs to adults occurrs off or on the host (Apanaskevich & Oliver, 2013).

The majority of ixodid ticks have a 3-host type life cycle that may span from six months to several years, depending on species (Apanaskevich & Oliver, 2013; Walker et al., 2003). In a

3-host life cycle, eggs hatch into larvae, the larvae quest in the vegetation and attach to the first host (usually small mammals) and feed. Later, the engorged larvae drop off from the first host and molt to nymphs. The nymphs quest for a second host (the same or different species), attach and feed, drop off, and molt into adults (males and females). The adults seek a third host (usually a larger species), and they feed and mate on the host. Engorged females detach from the host, lay eggs in a suitable environment, and die. Males may remain on the host, feed several times, mate, and then die. Most tick species of the genera Amblyomma, Anomalohimalaya, Bothriocroton,

Haemaphysalis, and Ixodes are 3-host ticks. The majority of Rhipicephalus and Dermacentor species are also 3-host ticks (Apanaskevich & Oliver, 2013).

In a 2-host life cycle, engorged larvae do not drop but molt into nymphs on the first host; after that, nymphs feed, engorge, detach from the host, and molt into adults in the environment.

Then, the adults seek out the second host to continue the cycle. Hyalomma species such as H. isaaci Sharif and H. marginatum Koch are the typical examples of 2-host ticks where large ungulates are the primary host for the adult stage and the small mammals and birds for the immature stages (Geevarghese & Dhanda, 1987). In a 1-host life cycle, engorged larvae and nymphs both remain on the same host; and only engorged females drop off the host to lay eggs and continue the cycle (Apanaskevich & Oliver, 2013). Rhipicephalus microplus (Spickler,

2007) and Dermacentor albipictus Packard (Baldridge et al., 2009) are the typical examples of 1-

10 host ticks where larvae, after attaching to the host, completes all subsequent life stages on that same host.

1.2.4 Biology and ecology of ixodid ticks

Understanding the biology and ecology of ticks will help in the development and adoption of effective tick management and control strategies.

1.2.4.1 Host-seeking

All ticks spend most of their life cycle in the environment, away from hosts (Barker &

Walker, 2014). Based on host-seeking behavior in the environment, ticks are broadly categorized into two groups, nidicolous or non-nidicolous (Sonenshine & Roe, 2013b). Nidicolous (also known as endophillic) ticks live in and around the shelters used by their host, for example, nests, burrows, crevices, caves, etc. (Gray et al., 2013). Non-nidicolous (also known as exophillic) ticks live in open spaces such as forests, grasslands, and meadows (Randolph, 2013). The majority of the soft ticks and many species of the genus Ixodes are nidicolous (Gray et al., 2013); however, in this section, we will discuss only non-nidicolous ticks.

Most metastriate ixodid ticks are non-nidicolous or exophillic ticks and seek host in open spaces such as forests, grasslands, and meadow (Randolph, 2013). They are highly responsive to host-produced substances (kairomones), chemical stimuli (such as carbon dioxide and ammonia), humidity, vibrations, and temperature changes of the warm-blooded hosts (Parola & Didier,

2001; Randolph, 2013). There are two host-seeking strategies that occur among the exophillic ticks. In the questing or ambush strategy, ixodid ticks seek host by climbing the vegetation, waiting until activated by host vibrations or other stimuli, then spreading their first pair of legs containing Haller’s organ, and waiting to attach to the host as it passes by (Estrada-Peña, 2015;

Randolph, 2013). In the hunter strategy, ticks emerge from their habitat and usually pursue

11 suitable hosts resting nearby. Adult ticks of the genera Hyalomma and Amblyomma are the typical examples of hunter ticks (Barker & Walker, 2014).

These host-seeking activities depend on environmental conditions (mainly the temperature) and the tick’s ability to maintain energy reserve and water balance (Randolph,

2013). While seeking the hosts, ticks normally lose water and become desiccated, especially during periods of high temperature. Periodically, they must reabsorb water from a humid atmosphere, such as moist leaf litter in the lower vegetation (Estrada-Peña, 2015). Therefore, moisture and temperature are the primary determinants influencing these host-seeking behaviors in the environment (Randolph, 2013). These climatic factors, along with the abundance and availability of hosts, determine the seasonality of tick abundance as the host-seeking behaviors are synchronized to coincide with the most favorable climatic conditions (Estrada-Peña, 2015;

Randolph, 2013).

1.2.4.2 Feeding

Ticks generally feed by cutting the skin of the host and inserting their mouthparts (Barker

& Walker, 2014). Most ticks follow a definite feeding rhythm (Oliver, 1989); for example, R. microplus shows a circadian feeding rhythm where the sucking activity is maximum at night and minimum during the day (Wharton & Utech, 1970). Because ticks feed for long periods, most ticks have a preference for particular locations on their host’s body for attachment, and that tends to be in the most inaccessible places for the host to groom and remove the tick (Apanaskevich &

Oliver, 2013). This preference differs among tick species and different active stages of the same species. For example, most adult Hyalomma anatolicum Koch ticks in India were found on the groin, udder, and axilla of cattle while the majority of larvae and nymphs were found on the ears of the same host (Geevarghese & Dhanda, 1987).

Most ticks have a drop-off rhythm that allows engorged ticks to drop-off into the environment when conditions are favorable for further development, such as molting and oviposition (Apanaskevich & Oliver, 2013). The drop-off rhythm is regulated by host activity and behavior; for example, generally, ixodid ticks drop-off when their hosts are active, whereas, in nidicolous ticks, drop-off occurs when the hosts are resting in their nests. Most ticks are host specific, feeding only on a specific group of vertebrates (e.g., Rhipicephalus sanguineus Latreille has high specificity on dogs), whereas some ticks are opportunistic, feeding readily on any available host (e.g., the black-legged tick Ixodes scapularis Say) (Parola & Didier, 2001;

Sonenshine & Roe, 2013b). This host specificity is generally determined by habitat distribution because ticks that are adapted to a certain habitat will encounter hosts that are adapted to the same habitat (Parola & Didier, 2001).

The active stages of all ixodid ticks feed only once in their life, and they take several days or longer to engorge with blood (Oliver, 1989). Engorgement is reduced in males as they feed only enough to mature their sperm (Oliver, 1982). The number of eggs laid by the females varies depending on the tick species and the quantity of blood meal (Apanaskevich & Oliver, 2013).

The duration of a feeding is generally the same for all ixodid ticks; the majority of the larvae feed for 3-6 days, nymphs for 3-10 days, and adult females for 6-12 days (Apanaskevich &

Oliver, 2013). Ixodid ticks concentrate their blood meals by regulating excess water through the salivary gland, and this strategy of taking the largest blood meals possible is to ensure maximum egg production and also to meet the energetic demands of the survival in the environment

(Estrada-Peña, 2015; Randolph, 2013).

1.2.4.3 Reproduction

All hard ticks mate on the host except Ixodes, which may also mate on the vegetation or nests (Walker et al., 2003). In Ixodes or prostriate ticks, gametogenesis begins when nymphs molt to adults, and the newly molted adults are sexually mature even before blood-feeding, and therefore, mating may occur off-host (Sonenshine & Roe, 2013b). This mating, which occurs before attaching and feeding, is called preprandial mating (Yuval & Spielman, 1990). However, in metastriate ticks, newly molted adults are sexually immature, and gametogenesis begins only during blood-feeding, and therefore, mating has to occur on the host. Male ticks may mate with many females by remaining on the host, but females mate only once, then engorge, drop off from the host, lay eggs in a suitable microenvironment and die (Oliver, 1989; Walker et al., 2003).

Thousands of eggs are laid before females die, and egg mass typically ranges from 2000-10000 eggs (Sonenshine & Roe, 2013b). All ticks lay eggs in the physical environment, never on their hosts (Barker & Walker, 2014). Mating in ticks is regulated by multiple pheromones females secrete to attract fed males (Sonenshine & Roe, 2013a).

1.2.4.4 Diapause

All ticks have a seasonal rhythm to ensure that their critical activities, such as development and reproduction, are synchronized with the most favorable environmental conditions (Oliver, 1989). This synchronization is mediated by processes such as diapause and quiescence (Belozerov, 2008). Diapause is defined as a “neurohormonally mediated dynamic state of low metabolic activity” (Randolph, 2013). Quiescence is an arrest of development and activity due to adverse environmental conditions, or lack of vitally essential external factors

(Belozerov, 2008). Diapause is classified into 2 types: behavioral diapause in which there is an absence of host-seeking activity in unfed ticks and delay of engorgement in feeding ticks

14 attached to the host (e.g., Hyalomma scupense Schulze); and morphogenetic diapause in which there is a delay of molting in engorged immatures, oviposition of engorged females, and embryogenesis in the egg (Apanaskevich & Oliver, 2013; Oliver, 1989). Behavioral diapause has been observed in the genera Ixodes, Haemaphysalis, Amblyomma, Hyalomma, Rhipicephalus, and Dermacentor; and morphogenetic diapause was observed in the genera Ixodes,

Haemaphysalis, Hyalomma, and Dermacentor (Oliver, 1989).

Photoperiodicity and temperature are extremely important factors that influence the seasonal rhythm of ticks (Estrada-Peña, 2015). Consequently, the patterns of diapause also vary among tick species and along latitudinal gradients (Madder et al., 1999). In the tropics, the increasing day length and temperature, along with the rainfall, stimulate tick activity and allows the life cycle to complete within the short season (Madder et al., 2002). In the temperate zones, both types of diapause occur with marked seasonality influenced by the temperature (Randolph,

2013).

1.2.4.5 Host and habitat

The host factors influencing the survival of ticks are availability, host resistance, the success of attachment by ticks, and the host behavioral responses such as grooming (Randolph,

2013). The abundance of ticks largely depends on the density, composition, and abundance of hosts (Estrada-Peña, 2015). Further, host movements are also responsible for tick dispersal and survival in each area. For example, if hosts consistently disperse ticks into unsuitable areas where eggs can not survive, the subsequent mortality can affect the tick population (Cumming,

1999).

The physical environment in which ticks live and their interaction with environmental variables such as climate, vegetation, and soil type determine their survival, development, and

15 activity (Barker & Walker, 2014). Climate is the most important component of the physical environment for ticks (Estrada-Peña, 2015). Generally, temperature and relative humidity are the most important climatic variables as the former affects the development rates of ticks such as molting and oviposition, and the latter regulates the water balance crucial for questing activity

(Estrada-Peña, 2015). The type of soil and its properties, such as water-retaining capabilities, can determine the survival and development of larvae since many tick species lay eggs in the soil

(Cumming, 1999). Predation, both on-host (e.g., by birds) and off-host (e.g., by ants), could substantially affect tick populations (Cumming, 1999). The frequency of habitat disturbances

(e.g., natural calamities like droughts, floods, forest fires, etc.) could have consequences on tick populations (Estrada-Peña, 2015). The biotic (e.g., vegetation cover and type, interspecies competition) and anthropogenic factors (e.g., use of acaricides, husbandry practices) will also have impacts on tick development and survival (Cumming, 1999).

1.2.5 Collection of ticks

Whether for disease surveillance or ecological studies, there are several standard methods that can be used for tick collection. These include dragging, flagging (Mays et al., 2016), tick walking (Chapman & Siegle, 2000), dry ice trapping (Ginsberg & Ewing, 1989) in suitable habitats, and manual collection from the bodies and premises of the nests and burrows of host animals (ECDC, 2018; Lydecker et al., 2019). The efficiency of tick collection by each of these methods varies according to tick species, life cycles, vegetation or habitat type, season, and host- seeking behavior (Dantas-Torres et al., 2013; Gherman et al., 2012).

Dragging is an off-host tick sampling technique that involves dragging a piece of flannel or white cotton cloth over the vegetation or plant litter on the ground to allow ticks to attach to the cloth as it passes (Mays et al., 2016). This is the most efficient technique for collecting free-

16 living ticks that exhibit questing behavior (Dantas-Torres et al., 2013; Mays et al., 2016) and then estimate the tick abundance (Tack et al., 2011). However, it is labor-intensive (Lindquist et al., 2016), collects ticks only at the surface of the vegetation (Ramos et al., 2014), and is impeded by dense vegetation (Mays et al., 2016). Flagging is a technique that involves using a piece of cloth attached to a handle resembling a flag to move back and forth, in and around multiple vegetation levels or leaf litter to allow ticks to attach to the cloth (Lindquist et al., 2016;

Mays et al., 2016). It is highly efficient for collecting free-living ticks questing for small mammals at the lower vegetation (Lindquist et al., 2016). Tick walking involves a person wearing a white, 100% cotton garment and walking in suitable habitat to allow ticks to attach on the clothing. This technique provides the best estimate of the human risk of encountering ticks

(Chapman & Siegle, 2000). Dragging and flagging are usually accompanied by tick walking because the person engaged is already walking (Lindquist et al., 2016). Dry ice trapping is a technique that exploits tick’s ability to sense carbon dioxide and move toward the source

(Ginsberg & Ewing, 1989). Many species of Ixodes and Dermacentor are collected by employing these methods (ECDC, 2018). However, the seasonality of ticks and the actual weather situation should be considered, especially when collecting ticks from the vegetation

(ECDC, 2018). For example, Ixodes ricinus L. in Europe are best collected by surveying the vegetation (when it is relatively dry) from April to November (ECDC, 2018).

On-host tick collection is generally done by manually feeling the hair coat and skin of the host, then grasping the ticks as close to the skin as possible and removing them using fine steel forceps (Barker & Walker, 2014). This method does not provide information on the exact location from where ticks originated (ECDC, 2018), and it can also underestimate the diversity and abundance of tick species in a given area, mainly because ticks spend most of their life cycle

17 in the environment (Dantas-Torres et al., 2013). Nonetheless, for ticks like Hyalomma, where collection from the ground using standard flagging and dragging is difficult, the on-host collection is considered the best method (ECDC, 2018). Tick collection by inspecting the shelters (e.g., nests, burrows, crevices) of the hosts is the best procedure for collecting the majority (if not all) of nidicolous ticks; for example, Rhipicephalus sanguineus can be best collected by surveying dog kennels (ECDC, 2018).

1.2.6 Tick control

Tick control is critical for the mitigation of the direct and indirect effects of ticks on livestock productivity (Jongejan & Uilenberg, 1994). There are many tick control approaches such as the use of chemical acaricides, anti-tick vaccines, and selection of genetically-resistant breeds but there is no single approach that could be considered as a stand-alone solution

(Willadsen, 2006). Direct topical application of chemical acaricides to host animals continues to be the most widely used method for controlling ticks on livestock (Minjauw & McLeod, 2003).

Chemical acaricides, when used properly, are efficient and cost-effective; however, their well- known disadvantages are the potential resistance of ticks to acaricides, environmental pollution, residues in food products such as meat and milk, and natural toxicity (de Castro, 1997;

Willadsen, 2006).

Many different acaricides have been used throughout the history of tick control in the world. Acaricides such as arsenic and organochlorine compounds were widely used but have now been replaced by organophosphates, pyrethroids, amidines, macrocyclic lactones, and benzoylphenylureas (Graf et al., 2004). Organophosphates (e.g., coumaphos, diazinon) and carbamates (e.g., carbaryl) are highly effective at low concentrations, but they get accumulated in tissues and milk (Minjauw & McLeod, 2003). Pyrethroids (e.g., deltamethrin, cypermethrin,

18 flumethrin) are safer than organophosphates in terms of toxicity to the hosts and have a prolonged residual activity (usually 7-10 days) (Minjauw & McLeod, 2003). Amidines (e.g., amitraz) have been widely used for controlling ticks that are resistant to organophosphates and pyrethroids (Li et al., 2005). Macrocyclic lactones (e.g., ivermectin and doramectin) are not exclusively acaricides but are effective against both endo and ectoparasites (Rodriguez-Vivas et al., 2018). Benzylphenylureas (e.g., fluazuron) do not kill ticks but inhibit their development, and the pour-on formulations were found to be very effective against R. microplus (Bull et al.,

1996).

As tick control relies heavily on chemical acaricides, the development of resistance to these chemicals has become a serious threat to the sustainability of this approach (Guerrero et al., 2013). The underlying process in the development of acaricide resistance is genetic selection, an evolutionary process (Kunz & Kemp, 1994), which is often associated with the use of lower than a recommended dose. Acaricide resistance is not equally apparent in all species of ticks, but it is most widespread and diverse in the single-host tick R. microplus (Kunz & Kemp, 1994).

This tick species has developed resistance to almost all currently used acaricides: organophosphates (Baxter & Barker, 1998); pyrethroids (Guerrero et al., 2012); amitraz (Li et al., 2004); and ivermectin (Klafke et al., 2006; Perez-Cogollo et al., 2010). The development of resistance to acaricides has been much slower in the two and three-host ticks (e.g.,

Rhipicephalus, Hyalomma, Amblyomma, etc.), where longer generation times, less exposure of the immature stages to acaricides, and the presence of a wide range of hosts may have resulted in less selection pressure (Kunz & Kemp, 1994; Mekonnen et al., 2002). On the contrary, the single-host tick like R. microplus has a short life cycle and shorter generation times, requiring

19 many acaricide treatments for control, and thus develops resistance at a faster rate due to high selection pressure (Peter et al., 2005).

The emergence of acaricide resistance has resulted in the development of new management strategies that largely focus on delaying the evolution of acaricide resistance and prolonging the effectiveness of the limited number of chemicals available now (Guerrero et al.,

2013). The main strategies are reducing the frequency of application, reformulation of acaricides, use of synergists, rotation among acaricide groups, strict biosecurity protocols to prevent the introduction of resistant tick population, and regular monitoring of resistance in the field (George et al., 2004). The frequency of acaricide application, at least theoretically, indicates the development of resistance in ticks (Guerrero et al., 2013). A higher frequency of acaricide application is associated with a higher prevalence of resistance (Jonsson et al., 2000). However, the timing of treatments based on the generation of ticks and their life cycles in a yearly cycle is more important than the frequency of application (Sutherst & Comins, 1979). This strategy of reducing the frequency of application should also be integrated with other control methods such as the use of resistant breeds and pasture management (e.g., rotational grazing and controlled burning) (Guerrero et al., 2013). Reformulated acaricides such as organophosphates and pyrethroids combination were widely used in Australia, but regulatory issues have currently limited their use (George et al., 2004). The use of a synergistic relationship between the acaricide classes is an effective resistance-management strategy; for example, the use of piperonyl butoxide with pyrethroids was found to be effective against pyrethroid-resistant ticks (Guerrero et al., 2013). Rotation of acaricides has shown some benefits in the management of acaricide resistance; for example, rotation between deltamethrin and coumaphos has shown to delay the emergence of deltamethrin resistance under laboratory conditions (Thullner et al., 2007). The

20 monitoring of acaricide resistance is also equally important. It is generally done by testing the efficacy of acaricides under field conditions (Guerrero et al., 2013) and laboratory conditions

(Drummond et al., 1973).

Genetic resistance of the host to ticks has been recognized as a possible biological control method for many years (Jongejan & Uilenberg, 1994). Host genetic resistance is life long and heritable, and it is immunologically mediated (de Castro, 1997). However, the immune response varies with the tick species and the cattle breed, between individuals depending on an individual’s ability to acquire immunity, and the external factors such as stress and nutrition

(Jongejan & Uilenberg, 1994). The variation in tick resistance between the cattle breeds is well known; for example, Asian or Zebu cattle (Bos taurus indicus) generally show much higher resistance than European or Taurine cattle (Bos taurus taurus) (Utech et al., 1978). The simplest form of exploiting this host genetic resistance is cross breeding resistant breeds of Zebu cattle or other resistant breeds (de Castro, 1997). This method has been successfully implemented in

Australia, where Australian Milking Zebu and Australian Friesian Sahiwal were selected by breeding as they were capable of acquiring effective resistance against R. microplus (Seifert,

1984). Other biological control methods using predators, parasitoids, and pathogens exist, but these, in general, have not been so effective and reliable in tick control (de Castro, 1997;

Ginsberg, 2013).

Immunological control of ticks using an anti-tick vaccine began with the release of the first commercial vaccine against R. microplus (Willadsen et al., 1995). However, existing vaccines have had a relatively small impact on tick control, mainly due to readily available acaricides that are effective, familiar, and actively promoted by manufacturers (Willadsen,

2006). Nevertheless, if greater vaccine efficacy could be achieved, it would lead to greater

21 adoption (Willadsen, 2006). Habitat manipulation through reduction of vegetation cover, controlled burning, the introduction of invasive plant species, and so on can make the environment less suitable for the survival of ticks, but these methods are often harmful to the environment and are no longer recommended (de Castro, 1997). Management practices like rotational grazing have proven to be successful in controlling ticks, especially for one host ticks like R. microplus, where the larva is the only questing stage seen in the grasses (Wilkinson,

1957); however, it has to be carefully timed to ensure that larvae die before hosts are made available, but that forage in the pasture is still used optimally. Zero grazing practice is often found successful in reducing tick challenge, especially in small-scale farms (de Castro, 1997).

Botanical acaricides, which are emerging, may have the potential to provide a safer and more environmentally friendly alternative for tick control (Nwanade et al., 2020).

Despite all these methods, chemical acaricides will continue to be the primary means of tick control for livestock because they are readily available and easy to use (Kunz & Kemp,

1994). However, increasing costs and the development of resistance will be major threats to the use of chemical acaricides in the future (Kunz & Kemp, 1994). Therefore, an integrated approach involving a systematic combination of two or more methods to control ticks is recommended (de Castro, 1997). The ultimate aim is to achieve tick control in a more sustainable, environmentally friendly, cost-effective manner than is achievable with a single method (Willadsen, 2006).

In Bhutan, chemical acaricide application is the primary method used for the control of ticks. But there are no dip tanks and spray race facilities for acaricide application. This is because most farms are small-scale farms scattered over a large area with 5-6 cattle per farm on an average (DoL, 2018; National Statistics Bureau, 2018), and the idea of community dip tanks and

22 spray race is practically not feasible in Bhutan. As a result, most of the farmers follow the hand dressing method, which involves applying diluted acaricides to the attachment sites of the host animals with a sponge or a cloth fabric. This method is considered to be an ideal method for small and zero grazed production systems (Minjauw & McLeod, 2003). However, little is known about what farmers in Bhutan know about ticks and TBDs, although the social context of farming and management is an important determinant in the development of effective control and eradication plans (Walker, 2011).

1.2.7 Status of ticks in Bhutan and the neighboring regions

Bhutan belongs to the Eastern Himalayan range and shares a similar geography and climatic conditions with Nepal, southeast Tibet in China, and the Indian states of Arunachal

Pradesh, Assam, West Bengal, and Sikkim (Banerjee & Bandopadhyay, 2016). As mentioned in section 1.1, the current knowledge of ticks in Bhutan is limited to two studies. The study by Cork et al. (1996) (unpublished) from eastern Bhutan reported R. microplus as the most predominant tick species with Haemaphysalis spp. and Hyalomma spp. identified to the genus level. The study by RLDC Wangdue (2019) from western Bhutan reported the genera Rhipicephalus

(Boophilus) spp., Rhipicephalus spp., Haemaphysalis spp., Ixodes spp., and Amblyomma spp. but no information on species is available. There are some reliable checklists of tick species reported from China (Chen et al., 2010; Zhang et al., 2019), Nepal (Clifford et al., 1975; Mitchell, 1979), and India (Ghosh et al., 2007). However, only ixodid tick species recorded in the neighboring

Indian states of Assam, Arunachal Pradesh, West Bengal, and Sikkim are listed below as they are the most relevant to Bhutan.

In the genus Rhipicephalus (Boophilus), only R. microplus was reported from all four states (Ghosh et al., 2007). Boophilus was previously classified as a genus, but after the

23 molecular and morphological studies revealed that it is more closely related to Rhipicephalus, it is now classified under the genus Rhipicephalus, but it is still retained as the subgenus (Murrell

& Barker, 2003). In the Haemaphysalis genus, 41 species are known to be present in India

(Geevarghese & Mishra, 2011). There are records of 12 species of Haemaphysalis from Assam,

9 from Arunachal Pradesh, 17 from West Bengal, and 5 from Sikkim (Table 1.1).

Table 1.1 Records of Haemaphysalis ticks from Assam, Arunachal Pradesh, West Bengal, and Sikkim. Compiled from Geevarghese & Mishra (2011).

Arunachal West Haemaphysalis Species Assam Sikkim Pradesh Bengal H. aborensis Warburton ✓ ✓ ✓ X H. anomala Warburton ✓ X X X H. aponommoides Warburton X ✓ ✓ ✓ H. birmaniae Supino ✓ ✓ ✓ X H. bispinosa Neumann ✓ ✓ ✓ ✓ H. canestrinii Supino ✓ X X X H. darjeeling Hoogstraal & Dhanda ✓ X ✓ X H. davisi Hoogstraal, Dhanda & Bhat ✓ ✓ ✓ ✓ H. himalaya Hoogstraal X X ✓ X H. hystricis Supino ✓ ✓ ✓ X H. intermedia Warburton & Nuttall ✓ X X X H. kashmirensis Hoogstraal & Verma X X ✓ X H. kinneari Warburton X X ✓ X H. montgomeryi Nuttall X X ✓ X H. nepalensis Hoogstraal X ✓ ✓ ✓ H. obesa Larrousse ✓ X ✓ X H. ramachandrai Dhanda, Hoogstraal & Bhat X X ✓ X H. shimoga Trapido & Hoogstraal ✓ ✓ ✓ ✓ H. spinigera Neumann X X ✓ X H. sulcata Canestrini & Fanzago X ✓ X X H. wellingtoni Nuttall & Warburton ✓ X ✓ X ✓denotes “reported” and X denotes “not reported”

Rhipicephalus haemaphysaloides and R. sanguineus were the only species in the genus

Rhipicephalus reported from all states but Sikkim (Ghosh et al., 2007). Similarly, Dermacentor auratus Supino was the only species of Dermacentor reported from all states but Sikkim (Ghosh et al., 2007). In the genus Amblyomma, 3 species (i.e., A. clypeolatum Neumann, A. testudinarium Koch, and A. varanense Supino) were reported from Assam, 1 species (i.e., A. testudinarium) from Arunachal Pradesh, 7 species (i.e., A. clypeolatum, A. gervaisi Lucas, A. testudinarium, A. helvolum Koch, A. integrum Karsch, A. javanense Supino, and A. pattoni

Neumann) from West Bengal, but not a single Amblyomma was reported from Sikkim (Ghosh et al., 2007).

In the genus Hyalomma, 2 species (i.e., Hy. anatolicum and Hy. kumari Sharif) were recorded from Assam, 1 species (i.e., Hy. issaci Sharif) from Arunachal Pradesh, 4 species (i.e.,

Hy. anatolicum, Hy. brevipunctata Sharif, Hy. detritum Schulze (now Hy. scupense Schulze), and H. issaci) from West Bengal, and 1 species (i.e., Hy. hussaini Sharif) from Sikkim (Ghosh et al., 2007). In the genus Ixodes, 2 species (i.e., I. acutitarsus Karsch and I. granulatus Supino) were recorded from Assam, 3 species (i.e., I. acutitarsus, I. granulatus, and I. ovatus Neumann) from Arunachal Pradesh, 4 species (i.e., I. acutitarsus, I. granulatus, I. holocyclus Neumann and

I. ovatus) from West Bengal, and 4 species (i.e., I. acutitarsus, I. granulatus, I. kashmiricus

Pomerantzev and I. ovatus from Sikkim (Ghosh et al., 2007).

1.2.8 Tick-borne diseases in cattle in Bhutan

In general, tick-borne diseases affect 80% of the world’s cattle population and are widely distributed in the tropics and subtropics (de Castro, 1997). An extensive review of all known tick-borne diseases (TBDs) in cattle is beyond the scope of this review; therefore, this section will discuss only three TBDs in cattle that have been recorded in Bhutan.

1.2.8.1 Bovine babesiosis

Bovine babesiosis is caused by an intraerythrocytic protozoan parasite of the phylum

Apicomplexa, order Piroplasmida, and genus Babesia (OIE, 2014). Three species of Babesia are responsible for most of the clinical cases in cattle: B. bovis Babes and B. bigemina Smith et

Kilborne in tropical and subtropical regions and B. divergens M’Fadyean & Stockman in some parts of Europe (Spickler, 2018). Ticks are the primary vectors of Babesia: R. microplus is the principal vector of B. bovis and B. bigemina; and Ixodes ricinus is the vector of B. divergens

(Bock et al., 2004). Cattle become infected when Babesia sporozoites enter the blood circulation through the bite of an infected tick (Bock et al., 2004). Sporozoites invade red blood cells

(RBCs), transform into trophozoites, and then grow and divide into two round, oval or pear- shaped merozoites (Homer et al., 2000). The merozoites rupture cells and subsequently infect new RBCs (Bock et al., 2004). The disease occurs when the rate of RBCs infection and loss is more than that of replacement (Homer et al., 2000). Transmission in ticks occurs both transovarially and transstadially, but the later does not occur at all stages (Bock et al., 2004;

Gray et al., 2019). Direct transmission between animals can also occur through fomites (e.g., reused needle) and mechanical vectors (e.g., biting flies), contaminated with infected blood

(Spickler, 2018).

Clinical signs usually occur 2-3 weeks after the bite from an infected tick, but the incubation period can be shorter, 4-5 days for B. bigemina, and 10-12 days for B. bovis (Spickler,

2018). The disease is characterized by high fever, varying degrees of hemolysis, and anemia

(Spickler, 2018). Hemoglobinuria is common in animals infected with B. bigemina and B. bovis

(Homer et al., 2000). The severity of the disease varies considerably among animals, and cattle younger than 9 months usually do not show clinical signs regardless of the Babesia species

(Bock et al., 2004; Spickler, 2018). Infected animals that survive develop life-long immunity against reinfection with the same species. There is also evidence of cross-protection to B. bovis infection in B. bigemina immune animals (OIE, 2014).

Diagnosis of Babesia is often done by examination of Giemsa stained blood smear under oil immersion (OIE, 2014). Blood smears are usually prepared from the capillary blood when B. bovis is suspected; however, B. bigemina and B. divergens can be found in normal venous blood samples too, as these parasites are uniformly distributed through the vasculature (Spickler,

2018). Serological tests such as enzyme-linked immunosorbent assays (ELISAs) and competitive

(C-ELISAs) to detect Babesia antibodies have replaced the indirect fluorescent antibody test

(IFAT) (OIE, 2014). Immunochromatographic tests (ICT) have been developed for B. bovis and

B. bigemina and are being used in epidemiological surveys (OIE, 2014). Clinical cases are treated with antiparasitic drugs such as imidocarb and diminazene aceturate (Bock et al., 2004).

Live Babesia vaccines are available, but for safety reasons, the recommendation is to limit to calves less than one year of age as they are likely to be naturally resistant to the disease (OIE,

2014). Protective immunity develops in 2-3 weeks, and once vaccinated, the animals are usually provided with lifelong immunity (OIE, 2014).

Bovine babesiosis is present in Bhutan, especially in the southern subtropical districts

(Phanchung et al., 2007). However, there have been a lot of discrepancies in the recorded data.

For example, the veterinary information system (VIS) of the NCAH has recorded 3506 cases in

2018 (NCAH, 2018) but only 5 cases in 2019 (NCAH, 2019). In Bhutan, this disease has often been confused with bovine enzootic haematuria associated with chronic ingestion of bracken fern

(Hidano et al., 2017). In most parts of Bhutan, suspected cases of babesiosis are rarely diagnosed based on laboratory examinations but clinical signs (i.e., fever and hemoglobinuria). Diminazene

27 aceturate (Berenil as a trade name) is used for treating suspected cases (NCAH, 2013). In recent years, babesiosis has been detected in the quarantine stations in cattle imported from the neighboring Indian states of Assam and West Bengal (Dr. Sherab Phuntsho, Quarantine Officer,

Bhutan, pers comm, 2020). Bovine babesiosis is prevalent in both Assam (Kakati et al., 2015) and West Bengal (Debbarma et al., 2018).

1.2.8.2 Theileriosis

Theileriosis is caused by an obligate intracellular apicomplexan parasite of the genus

Theileria, and it affects both wild and domestic animals, especially the bovines (OIE, 2018).

There are at least 15 species in the genus Theileria that infect domestic ruminants such as cattle, buffalo, sheep, and goats (Spickler, 2019). However, only two are the most pathogenic and economically important in cattle: T. annulata Dshunkowsky & Luhs that causes tropical theileriosis; and T. parva Theiler that causes East Coast fever (OIE, 2018). Theileria orientalis/T. buffeli are considered less pathogenic species (Spickler, 2019), but they have caused a number of outbreaks in cattle in New Zealand and Australia (Watts et al., 2016). Theileria is transmitted by ticks vectors: Rhipicephalus appendiculatus Neumann for T. parva (Fry et al., 2016), and

Hyalomma ticks for T. annulata (OIE, 2018). Other genera of ticks that can transmit Theileria are Haemaphysalis and Amblyomma (Spickler, 2019). Transmission in ticks occurs only transstadially (Watts et al., 2016). Mechanical transmission through fomites (e.g., reused needle) and mechanical vectors (e.g., biting flies and sucking lice) contaminated with infected blood can also occur (Spickler, 2019).

Infection in cattle occurs when theileria sporozoites enter the body through tick saliva during feeding (Fry et al., 2016). The sporozoites then invade leucocytes and develop into schizonts, which are usually found in peripheral blood (Watts et al., 2016). The schizonts

28 develop into merozoites and invade RBCs to develop into piroplasms (Watts et al., 2016).

Piroplasms infect ticks during feeding, and the cycle continues (Spickler, 2019).

The incubation period is usually 7-12 days for East Coast fever and 1-3 weeks for tropical theileriosis (Spickler, 2019). East Coast fever in cattle is characterized by fever, peripheral lymphadenopathy, anorexia, respiratory distress, and some animals, nasal discharge, and diarrhea

(Fry et al., 2016). Theileria parva can also cause a fatal condition known as “turning sickness” as a result of infected cells blocking brain capillaries (Spickler, 2019). Clinical signs in tropical theileriosis generally resemble East Coast fever, but T. annulata also destroys RBCs, causing anemia, sometimes jaundice, and hemoglobinuria (Spickler, 2019).

Diagnosis of Theileria in live animals is done by finding piroplasms or schizonts in

Giemsa stained thin blood or lymph node biopsy smears, respectively (OIE, 2018). Serological tests such as ELISAs and indirect IFAT are done to detect antibodies to T. parva and T. annulata. Sick animals are generally treated with buparvaquone, and reliable attenuated live vaccines are available for T. parva and T. annulata (OIE, 2018).

Theileriosis is present in Bhutan, especially in the southern subtropical districts

(Phanchung et al., 2007), but we do not know which species are present. However, T. orientalis is reported from Assam and T. annulata from West Bengal (Kakati et al., 2015), and in both the states, R. microplus is the vector. In Bhutan, diagnosis is rarely confirmed by laboratory tests but clinical signs (i.e., fever and lymph node swelling). Buparvaquone is used for treating suspected cases (NCAH, 2013). The veterinary information system has not recorded any case in 2018

(NCAH, 2018), and only 2 cases were recorded in 2019 (NCAH, 2019), indicating a significant gap in case reporting.

1.2.8.3 Bovine anaplasmosis

Bovine anaplasmosis is caused by intracellular rickettsial pathogen Anaplasma marginale

Theiler (Kocan et al., 2010). The second species Anaplasma centrale Theiler causes benign infections with some degree of anemia, but the clinical outbreak in the field has been extremely rare (OIE, 2015). The other Anaplasma species, A. phagocytophilum (Foggie 1949) Dumler et al.

2001 and A. bovis (ex Donatien and Lestoquard 1936) Dumler et al. 2001 have been reported in cattle, but they do not cause clinical disease (OIE, 2015). Anaplasma marginale mostly occurs in the tropical and subtropical regions of the world, and it has a significant economic impact on livestock productivity (Felsheim et al., 2010).

Bovine anaplasmosis is spread through tick bites and mechanical transfer of infected blood to susceptible cattle by biting flies or contaminated fomites (Aubry & Geale, 2011).

Nineteen different tick species are capable of transmitting A. marginale (OIE, 2015). These are from the genera Argas, Ornithodoros, Dermacentor, Hyalomma, Ixodes, and Rhipicephalus

(Kocan et al., 2010; OIE, 2015). Transmission in ticks occurs only transstadially (Kocan et al.,

2010). In cattle, RBCs are the only known site of infection by A. marginale, and the clinical presentation of the disease is marked by severe anemia and jaundice without hemoglobinemia or hemoglobinuria (Kocan et al., 2010). Cattle of all ages are susceptible to A. marginale infection, but older ones (more than 2 years) are severely affected (Aubry & Geale, 2011; Felsheim et al.,

2010). Cattle surviving infection becomes a lifelong carrier of the disease pathogen (Aubry &

Geale, 2011).

Diagnosis of Anaplasma spp. is done by examination of Giemsa stained thin smears prepared from blood in live animals and organ impressions from dead animals (OIE, 2015).

Serological tests such as C-ELISA and card agglutination tests are done to detect the antibodies

30 to Anaplasma (OIE, 2015). Sick animals are treated with antimicrobial drugs such as imidocarb and tetracyclines (Kocan et al., 2010). Live vaccines consisting of live A. centrale are widely used in many countries where A. centrale is endemic, and it gives partial protection against the virulent A. marginale (OIE, 2015).

Bovine anaplasmosis is present in Bhutan, especially in the southern subtropical districts

(Phanchung et al., 2007), but the current information on this disease is poor due to discrepancies in recorded data. The veterinary information system has not recorded any case in 2018 (NCAH,

2018), and only 3 cases were recorded in 2019 (NCAH, 2019). Bovine anaplasmosis is reported from Assam (Kakati et al., 2015) and West Bengal (Debbarma et al., 2018). The anaplasma species reported from Assam is A. marginale (Kakati et al., 2015). In Bhutan, diagnosis is rarely based on laboratory examination but clinical signs (i.e., fever and icterus). Oxytetracycline (long- acting) is used for treating suspected cases (NCAH, 2013).

1.2.9 Species distribution modeling

Species distribution modeling (SDM) is an empirical process that combines species occurrence data with environmental variables to predict species distribution across landscapes, sometimes involving extrapolation in space and time (Elith & Leathwick, 2009; Guisan &

Zimmermann, 2000). There are three components required to build an SDM: the ecological model, the data model, and the statistical model (Austin, 2002).

1.2.9.1 The ecological model

The ecological model considers the use of ecological knowledge and assumptions to be included in the species distribution models (Austin, 2002). This ecological concept formulates the purpose of the study, characteristics of species and, biotic and abiotic factors to be relevant for the species distribution, thereby determining important choices to be made with regard to

31 data collection and model selection. The ecological model also identifies the spatial and temporal scale of data to be used in SDMs because patterns of interaction between species and environment can change if different scales are used (Levin, 1992). The spatial scale includes grain (resolution) and the extent of the study area. The temporal scale defines the time frame in which species and environmental data are collected (Guisan & Thuiller, 2005).

1.2.9.2 The data model

The data model considers data collection and its measurement and estimation (Austin,

2002). It involves both species data and environmental data. Species data can be counts, abundance estimates, presence-absence, or presence-only records (Guisan & Zimmermann,

2000). In SDMs, species data form the response (dependent) variable. Species data can be collected using systematic, random, or stratified sampling techniques (Franklin, 2010). Another well-known sampling approach is “gradsect sampling”, a combination of random and stratified sampling (Austin & Heyligers, 1989), where sampling is conducted along selected transects that have the strongest environmental gradients. Sampling design should be environmentally representative to cover the entire ecological gradient in the study area in order to improve the accuracy of the models to be developed (Guisan & Zimmermann, 2000). Many times, only presence data are available; however, if extensive field surveys can be conducted, real absences can also be identified. Presence-absence data have the advantage of conveying information about the surveyed locations and prevalence (Elith & Leathwick, 2009). When there is no real absence data, the use of background or pseudo-absences points is common (Elith et al., 2019). Some argue that the use of absence points in SDMs confounds the information on habitats that are suitable but unoccupied due to reasons like inaccessibility (Elith & Leathwick, 2009).

Environmental data on the required spatial scale are mainly gathered from four sources: field surveys, digitized maps, remote sensing data, and maps from GIS-based modeling (Guisan

& Zimmermann, 2000). Environmental predictors (gradients) are classified by Austin (2002) into proximal and distal variables. Proximal variables are those that strongly determine the response of species occurrence, whereas distal variables are those that are related to proximal variables but do not have strong effects on species occurrence. Austin (2002) further classifies environmental predictors into direct, resource, and indirect predictors. Direct predictors (e.g., temperature) are those that have a direct physiological influence on species, whereas indirect predictors (e.g., elevation) are those that are correlated with direct predictors but do not have a direct physiological influence. Resource predictors (e.g., nutrients and water) are those that can be consumed by species. The assumptions of species response to climate and environmental variables and the selection of biologically relevant variables are an essential process in species distribution modeling.

1.2.9.3 The statistical model

The statistical model is a process that involves the choice of the statistical method to be developed for species distribution modeling (Austin, 2002). It generally follows three main steps: model formulation, model calibration, and model evaluation, and the details are described by

Guisan & Zimmermann, (2000).

Statistical model formulation involves the choice of a suitable algorithm for modeling a particular type of data (e.g., presence-absence or presence only) and its statistical distribution

(Guisan & Zimmermann, 2000). We will not discuss all the SDM approaches since it is beyond the scope of this review. We will only discuss the machine learning approach applied in SDM with reference to MaxEnt (Phillips et al., 2006) in the following section 1.2.10. The machine

33 learning approach to SDM is a method where, given the algorithm, rules are developed by the programs itself (self-learning) to correctly classify new areas for species distribution based on the existing relationship between observed areas of species and environmental data (Franklin,

2010).

Model calibration is defined as “the estimation and adjustment of model parameters and constants to improve the agreement between model output and a data set” (Rykiel, 1996). In

MaxEnt, the model calibration is done by calculating the gain, which is a measure of goodness of fit. The gain is the improvement in an average log-likelihood, minus a constant with uniform distribution of a null model with a zero gain (Elith et al., 2019). Another important aspect of model calibration is the variable selection process (Guisan & Zimmermann, 2000). Variables, as well as model selection, are mainly to identify the best set of predictors. The Akaike Information

Criterion, AIC (Akaike, 1998), is a well-established approach used for selecting models based on parsimony (i.e., few predictor variables) and performance (i.e., more deviance explained). The main goal of AIC is to quantify the amount of information lost when a model is used to describe reality. In practice, a model is selected as the best among competing models when it has the smallest AIC value indicating the minimum amount of information lost (Burnham & Anderson,

2004).

Model evaluation or validation is the accuracy assessment of the performance of the model based on the study aims and their applicability (Rykiel, 1996). There are two main approaches used for evaluating the performance of the model. The first method involves the use of a single dataset, while the second involves two separate datasets. A single dataset is commonly used for both calibration and evaluation when the number of observations is too small for further splitting the dataset. When the number of observations is large enough, a single

34 dataset can be separated into two sets: a training dataset for model calibration; and a testing dataset for model evaluation (Guisan & Zimmermann, 2000). In both cases, first, the stability of model prediction is assessed by validating model outputs through methods such as cross- validation, bootstrap, and jackknife. Then the predictive accuracy of the model is assessed by threshold-dependent and independent tests. Sensitivity (or true positive rates) and specificity (or true negative rates) are threshold-dependent tests, and they are conducted when the model has categorical response variables (e.g., presence/absence) that can be assessed for false positive

(type I errors) and false negative rates (type II errors). The area under the curve (AUC) of the receiver operating characteristics (ROC) plot (Hanley & McNeil, 1982) is a threshold- independent test that shows the probability, and it ranges from 0 (no fit) to 1 (perfect fit).

1.2.10 MaxEnt modeling

MaxEnt (Phillips et al., 2006) is a machine learning method that uses the maximum entropy concept for species distribution modeling. Entropy is described as “a measure of how much “choice” is involved in the selection of an event” (Shannon, 1948). Thus, the higher the entropy of the distribution, the more the choices. The maximum entropy distribution is defined as the ratio between the probability distribution of environmental variables at the species presence locations f1(X) over the probability distribution of environmental variables over the study area f(X) (Elith et al., 2019). The MaxEnt probability distribution is based on Gibbs distribution

(Phillips et al., 2006).

MaxEnt estimates the species probability distribution by finding the probability distribution of maximum entropy subject to environmental constraints, called features, which produce solutions that are close to reality. The space that defines the MaxEnt probability distribution is called the study area. The species presence records are latitude-longitude pairs of

35 the sampling locations, and the environmental variables are the features. These constraints require the expected value of each feature to match with that of empirical values over the presence locations. The MaxEnt software version 3.4.1 has six classes of feature: linear, which is equal to a continuous environmental variable; quadratic, which is square of the variable; a product which is the product of a pair of variables; and threshold and hinge features, which are step functions that describe different responses based on certain thresholds. Besides, MaxEnt also has a category indicator within its environmental layers to allow categorical variables

(Phillips et al., 2017), such as land covers.

MaxEnt gives four model outputs. 1) The primary one is the “raw” output that supplies the relative probability of presence at each site during the training of the model, which is summed up to 1 over the whole study area. 2) The cumulative format provides an output that defines omission rates (i.e., the fraction of positive sites that fall into sites that were considered negative). 3) The logistic output delivers an estimate of the probability of presence given the environmental condition of the study area. 4) The most recent model output available is cloglog, which estimates and predicts the probability of presence greater than the logistic one, especially at higher values (i.e., areas of moderately high output are more strongly predicted) (Phillips et al., 2017; Phillips & Dudík, 2008).

MaxEnt software is a powerful tool, and it has several advantages. It requires only presence data, and it performs well with limited presence points (Phillips et al., 2006). It can use both continuous and categorical variables and generate interactions (Phillips & Dudík, 2008). It has regularization parameters that can be adjusted to address the issue of overfitting and sampling bias (Phillips et al., 2017). It provides a clear and distinct output (Phillips et al., 2017), for example, habitat suitability probability values from 0 to 1. Despite these advantages, MaxEnt

36 predictions can be affected by background environmental data; therefore, caution should be taken while predicting it to different areas or climatic conditions (Phillips & Dudík, 2008). The more reliable predictions can be obtained when MaxEnt uses a few biologically meaningful variables as it reduces the model complexities (Elith et al., 2019).

MaxEnt has been increasingly used for species distribution modeling of ticks. Some examples are: modeling of Haemaphysalis longicornis Neumann in North America (Raghavan et al., 2019; Rochlin, 2018); Ixodes pacificus Cooley & Kohls and Ixodes scapularis in the United

States (Hahn et al., 2016); Dermacentor variabilis Say in the United States (James et al., 2015); and Rhipicephalus microplus in Benin, West Africa (De Clercq et al., 2015).

Thesis outline

With this introduction, objectives, and the literature review, the following thesis will present a chapter on each of the objectives mentioned under section 1.1. Chapter 2 presents the distribution of ixodid ticks in cattle in eastern Bhutan and Chapter 3 the habitat suitability modeling for the tick species identified in eastern Bhutan. Chapter 4 describes the knowledge, attitude, and practices with regard to ticks and tick-borne diseases among farmers in eastern

Bhutan. Chapter 5 (concluding chapter) discusses the main findings from each chapter and some specific recommendations for tick surveillance and control strategies in Bhutan.

References

Adehan, S. B., Adakal, H., Gbinwoua, D., Yokossi, D., Zoungrana, S., Toé, P., Ouedraogo, M.,

Gbaguidi, A. M., Adoligbé, C., Fandohan, A. B., Hounmanou, G., Glèlè Kakaï, R.,

Farougou, S., & De Clercq, E. M. (2018). West African Cattle Farmers’ Perception of

Tick-Borne Diseases. EcoHealth, 15(2), 437–449. https://doi.org/10.1007/s10393-018-

1323-8

Akaike, H. (1998). Information Theory and an Extension of the Maximum Likelihood Principle.

In E. Parzen, K. Tanabe, & G. Kitagawa (Eds.), Selected Papers of Hirotugu Akaike (pp.

199–213). Springer. https://doi.org/10.1007/978-1-4612-1694-0_15

Apanaskevich, D. A., & Oliver, J. H. (2013). Life Cycles and Natural History of Ticks. In

Biology of Ticks Volume 1 (pp. 59–73). OUP USA.

Aubry, P., & Geale, D. W. (2011). A Review of Bovine Anaplasmosis. Transboundary and

Emerging Diseases, 58(1), 1–30. https://doi.org/10.1111/j.1865-1682.2010.01173.x

Austin, M. P. (2002). Spatial prediction of species distribution: An interface between ecological

theory and statistical modelling. Ecological Modelling, 157(2), 101–118.

https://doi.org/10.1016/S0304-3800 (02)00205-3

Austin, M. P., & Heyligers, P. C. (1989). Vegetation survey design for conservation: Gradsect

sampling of forests in North-eastern New South Wales. Biological Conservation, 50(1),

13–32. https://doi.org/10.1016/0006-3207 (89)90003-7

Baldridge, G. D., Scoles, G. A., Burkhardt, N. Y., Schloeder, B., Kurtti, T. J., & Munderloh, U.

G. (2009). Transovarial Transmission of Francisella-Like Endosymbionts and

Anaplasma phagocytophilum Variants in Dermacentor albipictus (Acari: Ixodidae).

Journal of Medical Entomology, 46(3), 625–632. https://doi.org/10.1603/033.046.0330

Banerjee, A., & Bandopadhyay, R. (2016). Biodiversity hotspot of Bhutan and its sustainability.

Current Science, 110(4), 521–527. https://doi.org/10.18520/cs/v110/i4/521-528

Barker, S. C., & Murrell, A. (2002). Phylogeny, Evolution and Historical Zoogeography of

Ticks: A Review of Recent Progress. Experimental & Applied Acarology, 28(1), 55–68.

https://doi.org/10.1023/A:1025333830086

Barker, S. C., & Walker, A. R. (2014). Ticks of Australia. The species that infest domestic

animals and humans. Zootaxa, 3816(1), 1–144. https://doi.org/10.11646/zootaxa.3816.1.1

Baxter, G. D., & Barker, S. C. (1998). Acetylcholinesterase cDNA of the cattle tick, Boophilus

microplus: Characterisation and role in organophosphate resistance. Insect Biochemistry

and Molecular Biology, 28(8), 581–589. https://doi.org/10.1016/S0965-1748(98)00034-4

Belozerov, V. N. (2008). Diapause and quiescence as two main kinds of dormancy and their

significance in life cycles of mites and ticks (Chelicerata: Arachnida: Acari). Part 1.

Acariformes.

Bock, R., Jackson, L., Vos, A. D., & Jorgensen, W. (2004). Babesiosis of cattle. Parasitology,

129(S1), S247–S269. https://doi.org/10.1017/S0031182004005190

Bull, M. S., Swindale, S., Overend, D., & Hess, E. A. (1996). Suppression of Boophilus

microplus populations with fluazuron – an acarine growth regulator. Australian

Veterinary Journal, 74(6), 468–470. https://doi.org/10.1111/j.1751-0813.1996.tb07575.x

Burnham, K. P., & Anderson, D. R. (2004). Multimodel Inference: Understanding AIC and BIC

in Model Selection. Sociological Methods & Research, 33(2), 261–304.

https://doi.org/10.1177/0049124104268644

Chapman, E. S., & Siegle, T. E. (2000). A novel method for tick retrieval reveals northern

migration of Amblyomma americanum into Pike County, IL, USA. International Journal

of Acarology, 26(4), 391–393. https://doi.org/10.1080/01647950008684215

Chen, Z., Yang, X., Bu, F., Yang, X., Yang, X., & Liu, J. (2010). Ticks (Acari: Ixodoidea:

Argasidae, Ixodidae) of China. Experimental and Applied Acarology, 51(4), 393–404.

https://doi.org/10.1007/s10493-010-9335-2

Clifford, C. M., Hoogstraal, H., & Keirans, J. E. (1975). The Ixodes Ticks (Agarina: Ixodidae) of

Nepal. Journal of Medical Entomology, 12(1), 115–137.

https://doi.org/10.1093/jmedent/12.1.115

Cork, S., Dukpa, K., Gurung, J., & Charleston, W. A. G. (1996). A preliminary study of the ticks

and heamoparasites of cattle in Eastern Bhutan (Unpublished).

Cumming, G. (1999). The evolutionary ecology of African ticks [PhD Thesis]. University of

Oxford.

Dantas-Torres, F., Lia, R. P., Capelli, G., & Otranto, D. (2013). Efficiency of flagging and

dragging for tick collection. Experimental and Applied Acarology, 61(1), 119–127.

https://doi.org/10.1007/s10493-013-9671-0 de Castro, J. J. (1997). Sustainable tick and tickborne disease control in livestock improvement

in developing countries. Veterinary Parasitology, 71(2), 77–97.

https://doi.org/10.1016/S0304-4017 (97)00033-2

De Clercq, E. M., Leta, S., Estrada-Peña, A., Madder, M., Adehan, S., & Vanwambeke, S. O.

(2015). Species distribution modelling for Rhipicephalus microplus (Acari: Ixodidae) in

Benin, West Africa: Comparing datasets and modelling algorithms. Preventive

Veterinary Medicine, 118(1), 8–21. https://doi.org/10.1016/j.prevetmed.2014.10.015

Debbarma, A., Pandit, S., Jas, R., Baidya, S., Mandal, S. C., & Jana, P. S. (2018). Prevalence of

hard tick infestations in cattle of West Bengal, India. Biological Rhythm Research, 49(5),

655–662. https://doi.org/10.1080/09291016.2017.1395527

DoL. (2018). Livestock Statistics. Department of Livestock, Ministry of Agriculture and Forests,

Royal Government of Bhutan. http://www.dol.gov.bt/?page_id=394

Phanchung, Dorji, P., Sonam, T., & Pelden, K. (2007). Smallholder Dairy Farming in Bhutan:

Characteristics, Constraints, and Development Opportunities.

http://lib.icimod.org/record/21365/files/c_attachment_79_552.pdf

Dorji, U., Olesen, J. E., Bocher, P. K., & Seidenkrantz, M. S. (2016). Spatial Variation of

Temperature and Precipitation in Bhutan and Links to Vegetation and Land Cover.

Mountain Research and Development, 36(1), 66–79. https://doi.org/10.1659/MRD-

JOURNAL-D-15-00020.1

Drummond, R., Ernst, S., Trevino, J., Gladney, W., & Graham, O. (1973). Boophilus annulatus

and B. microplus—Laboratory Tests of Insecticides. Journal of Economic Entomology,

66(1), 130–133. https://doi.org/10.1093/jee/66.1.130

ECDC. (2018). Field sampling methods for mosquitoes, sandflies, biting midges and ticks.

https://www.ecdc.europa.eu/en/publications-data/field-sampling-methods-mosquitoes-

sandflies-biting-midges-and-ticks

Elith, J., & Leathwick, J. R. (2009). Species Distribution Models: Ecological Explanation and

Prediction across Space and Time. Annual Review of Ecology, Evolution, and

Systematics, 40(1), 677–697. https://doi.org/10.1146/annurev.ecolsys.110308.120159

Elith, J., Phillips, S. J., Hastie, T., Dudík, M., Chee, Y. E., & Yates, C. J. (2019). A statistical

explanation of MaxEnt for ecologists. Diversity and Distributions, 43–57.

https://doi.org/10.1111/[email protected]/ (ISSN)1472-4642.species-

distribution-models-in-conservation-biogeography

Estrada-Peña, A. (2015). Ticks as vectors: Taxonomy, biology and ecology: -EN- -FR- Les

tiques en tant que vecteurs : taxonomie, biologie et écologie -ES- Las garrapatas como

vectores: taxonomía, biología y ecología. Revue Scientifique et Technique de l’OIE,

34(1), 53–65. https://doi.org/10.20506/rst.34.1.2345

Estrada-Pena, A., & Mans, B. J. (2013). TICK-INDUCED PARALYSIS AND TOXICOSES. In

Page 313 Biology of Ticks Volume 2 (illustrated, revised, p. 496). OUP USA, 2013.

Felsheim, R. F., Chávez, A. S. O., Palmer, G. H., Crosby, L., Barbet, A. F., Kurtti, T. J., &

Munderloh, U. G. (2010). Transformation of Anaplasma marginale. Veterinary

Parasitology, 167(2), 167–174. https://doi.org/10.1016/j.vetpar.2009.09.018

Franklin, J. (2010). Mapping Species Distributions: Spatial Inference and Prediction. Cambridge

University Press.

Fry, L. M., Schneider, D. A., Frevert, C. W., Nelson, D. D., Morrison, W. I., & Knowles, D. P.

(2016). East Coast Fever Caused by Theileria parva Is Characterized by Macrophage

Activation Associated with Vasculitis and Respiratory Failure. PLOS ONE, 11(5),

e0156004. https://doi.org/10.1371/journal.pone.0156004

Geevarghese, G., & Dhanda, V. (1987). The Indian Hyalomma ticks (Ixodoidea: Ixodidae).

https://www.cabdirect.org/cabdirect/abstract/19880591406

Geevarghese, G., & Mishra, A. (2011). Haemaphysalis Ticks of India—1st Edition.

https://www.elsevier.com/books/haemaphysalis-ticks-of-india/geevarghese/978-0-12-

387811-3

George, J. E., Pound, J. M., & Davey, R. B. (2004). Chemical control of ticks on cattle and the

resistance of these parasites to acaricides. https://pubag.nal.usda.gov/catalog/3329

Gherman, C. M., Mihalca, A. D., Dumitrache, M. O., Györke, A., Oroian, I., Sandor, M., &

Cozma, V. (2012). CO2 flagging—An improved method for the collection of questing

ticks. Parasites & Vectors, 5(1), 125. https://doi.org/10.1186/1756-3305-5-125

Ghosh, S., Bansal, G. C., Gupta, S. C., Ray, D., Khan, M. Q., Irshad, H., Shahiduzzaman, Md.,

Seitzer, U., & Ahmed, J. S. (2007). Status of tick distribution in Bangladesh, India and

Pakistan. Parasitology Research, 101(2), 207–216. https://doi.org/10.1007/s00436-007-

0684-7

Ginsberg, H. S. (2013). Tick Control: Trapping, Biocontrol, Host Management, and Other

Althernative Strategies. In Biology of Ticks Volume 2 (pp. 409–443). OUP USA.

Ginsberg, H. S., & Ewing, C. P. (1989). Comparison of flagging, walking, trapping, and

collecting from hosts as sampling methods for northern deer ticks, Ixodes dammini, and

lone-star ticks, Amblyomma americanum (Acari: Ixodidae). Experimental & Applied

Acarology, 7(4), 313–322. https://doi.org/10.1007/BF01197925

Graf, J.-F., Gogolewski, R., Leach-Bing, N., Sabatini, G. A., Molento, M. B., Bordin, E. L., &

Arantes, G. J. (2004). Tick control: An industry point of view. Parasitology, 129(S1),

S427–S442. https://doi.org/10.1017/S0031182004006079

Gray, J. S., Estrada-Pena, A., & Vial, L. (2013). Ecology of Nidicolous Ticks. In Biology of

Ticks Volume 2 (pp. 39–60). OUP USA.

Gray, J. S., Estrada-Peña, A., & Zintl, A. (2019). Vectors of Babesiosis. Annual Review of

Entomology, 64(1), 149–165. https://doi.org/10.1146/annurev-ento-011118-111932

Guerrero, F. D., A, A., Leon, P. D., Rodriguez-Vivas, R. I., Jonsson, N., Miller, R. J., &

Andreotti, R. (2013). Acaricide Research and Development, Resistance, and Resistance

Monitoring. In Biology of Ticks Volume 2 (pp. 354–379). OUP USA.

Guerrero, F. D., Lovis, L., & Martins, J. R. (2012). Acaricide resistance mechanisms in

Rhipicephalus (Boophilus) microplus. Revista Brasileira de Parasitologia Veterinária,

21(1), 1–6. https://doi.org/10.1590/S1984-29612012000100002

Guglielmone, A. A., Robbins, R. G., Apanaskevich, D. A., Petney, T. N., Estrada-Pena, A.,

Horak, I. G., Shao, R., & Barker, S. C. (2010). The Argasidae, Ixodidae and

Nuttalliellidae (Acari: Ixodida) of the world: a list of valid species names. Zootaxa, 2528,

1–28. https://doi.org/10.11646/zootaxa.2528.1.1

Guisan, A., & Thuiller, W. (2005). Predicting species distribution: Offering more than simple

habitat models. Ecology Letters, 8(9), 993–1009. https://doi.org/10.1111/j.1461-

0248.2005.00792.x

Guisan, A., & Zimmermann, N. E. (2000). Predictive habitat distribution models in ecology.

Ecological Modelling, 135(2), 147–186. https://doi.org/10.1016/S0304-3800 (00)00354-9

Hahn, M. B., Jarnevich, C. S., Monaghan, A. J., & Eisen, R. J. (2016). Modeling the Geographic

Distribution of Ixodes scapularis and Ixodes pacificus (Acari: Ixodidae) in the

Contiguous United States. Journal of Medical Entomology, 53(5), 1176–1191.

https://doi.org/10.1093/jme/tjw076

Hanley, J. A., & McNeil, B. J. (1982). The meaning and use of the area under a receiver

operating characteristic (ROC) curve. Radiology, 143(1), 29–36.

https://doi.org/10.1148/radiology.143.1.7063747

Hidano, A., Sharma, B., Rinzin, K., Dahal, N., Dukpa, K., & Stevenson, M. A. (2017).

Revisiting an old disease? Risk factors for bovine enzootic haematuria in the Kingdom of

Bhutan. Preventive Veterinary Medicine, 140, 10–18.

https://doi.org/10.1016/j.prevetmed.2017.02.011

Homer, M. J., Aguilar-Delfin, I., Telford, S. R., Krause, P. J., & Persing, D. H. (2000).

Babesiosis. Clinical Microbiology Reviews, 13(3), 451–469.

James, A. M., Burdett, C., Mccool, M. J., Fox, A., & Riggs, P. (2015). The geographic

distribution and ecological preferences of the American dog tick, Dermacentor variabilis

(Say), in the U.S.A. Medical and Veterinary Entomology, 29(2), 178–188.

https://doi.org/10.1111/mve.12099

Jongejan, F., & Uilenberg, G. (1994). Ticks and control methods. 26.

Jonsson, N. N., Mayer, D. G., & Green, P. E. (2000). Possible risk factors on Queensland dairy

farms for acaricide resistance in cattle tick (Boophilus microplus). Veterinary

Parasitology, 88(1–2), 79–92. https://doi.org/10.1016/S0304-4017 (99)00189-2

Kakati, P., Sarmah, P. C., Ray, D., Bhattacharjee, K., Sharma, R. K., Barkalita, L. M., Sarma, D.

K., Baishya, B. C., Borah, P., & Stanley, B. (2015). Emergence of oriental theileriosis in

cattle and its transmission through Rhipicephalus (Boophilus) microplus in Assam, India.

Veterinary World, 8(9), 1099–1104. https://doi.org/10.14202/vetworld.2015.1099-1104

Klafke, G. M., Sabatini, G. A., de Albuquerque, T. A., Martins, J. R., Kemp, D. H., Miller, R. J.,

& Schumaker, T. T. S. (2006). Larval immersion tests with ivermectin in populations of

the cattle tick Rhipicephalus (Boophilus) microplus (Acari: Ixodidae) from State of Sao

Paulo, Brazil. Veterinary Parasitology, 142(3), 386–390.

https://doi.org/10.1016/j.vetpar.2006.07.001

Kocan, K. M., de la Fuente, J., Blouin, E. F., Coetzee, J. F., & Ewing, S. A. (2010). The natural

history of Anaplasma marginale. Veterinary Parasitology, 167(2), 95–107.

https://doi.org/10.1016/j.vetpar.2009.09.012

Kunz, S. E., & Kemp, D. H. (1994). Insecticides and acaricides: Resistance and environmental

impact: -EN- -FR- -ES-. Revue Scientifique et Technique de l’OIE, 13(4), 1249–1286.

https://doi.org/10.20506/rst.13.4.816

Latif, A. A., Putterill, J. F., de Klerk, D. G., Pienaar, R., & Mans, B. J. (2012). Nuttalliella

namaqua (Ixodoidea: Nuttalliellidae): First Description of the Male, Immature Stages and

Re-Description of the Female. Plos One, 7(7), e41651.

https://doi.org/10.1371/journal.pone.0041651

Levin, S. A. (1992). The Problem of Pattern and Scale in Ecology: The Robert H. MacArthur

Award Lecture. Ecology, 73(6), 1943–1967. https://doi.org/10.2307/1941447

Li, A. Y., Davey, R. B., Miller, R. J., & George, J. E. (2004). Detection and Characterization of

Amitraz Resistance in the Southern Cattle Tick, Boophilus microplus (Acari: Ixodidae).

Journal of Medical Entomology, 41(2), 193–200. https://doi.org/10.1603/0022-2585-

41.2.193

Li, A. Y., Davey, R. B., Miller, R. J., & George, J. E. (2005). Mode of Inheritance of Amitraz

Resistance in a Brazilian Strain of the Southern Cattle Tick, Boophilus microplus (Acari:

Ixodidae). Experimental & Applied Acarology, 37(3), 183.

https://doi.org/10.1007/s10493-005-3260-9

Lindquist, E. E., Galloway, T. D., Artsob, H., Lindsay, L. R., Drebot, M., Wood, H., & Robbins,

R. G. (2016). A Handbook to the Ticks of Canada (Ixodida: Ixodidae, Argasidae).

Biological Survey of Canada. https://biologicalsurvey.ca/monographs/read/18

Lydecker, H. W., Banks, P. B., & Hochuli, D. F. (2019). Counting Ticks (Acari: Ixodida) on

Hosts Is Complex: A Review and Comparison of Methods. Journal of Medical

Entomology, 56(6), 1527–1533. https://doi.org/10.1093/jme/tjz094

Madder, M., Speybroeck, N., Brandt, J., & Berkvens, D. (1999). Diapause induction in adults of

three Rhipicephalus appendiculatus stocks. Experimental & Applied Acarology, 23(12),

961–968. https://doi.org/10.1023/A:1006363316638

Madder, Maxime, Speybroeck, N., Brandt, J., Tirry, L., Hodek, I., & Berkvens, D. (2002).

Geographic variation in diapause response of adult Rhipicephalus appendiculatus ticks.

Experimental & Applied Acarology, 27(3), 209–221.

https://doi.org/10.1023/A:1021694207456

Matthysee, J. G., & Colbo, M. H. (1987). The Ixodid Ticks of Uganda: Together With Species

Pertinent to Unganda Because of Their Present Known Distribution. Entomological

Society of America.

Mays, S. E., Houston, A. E., & Fryxell, R. T. T. (2016). Comparison of novel and conventional

methods of trapping ixodid ticks in the southeastern U.S.A. Medical and Veterinary

Entomology, 30(2), 123–134. https://doi.org/10.1111/mve.12160

Mekonnen, S., Bryson, N. R., Fourie, L. J., Peter, R. J., Spickett, A. M., Taylor, R. J., Strydom,

T., & Horak, I. G. (2002). Acaricide resistance profiles of single- and multi-host ticks

from communal and commercial farming areas in the Eastern Cape and North-West

Provinces of South Africa. https://repository.up.ac.za/handle/2263/18296

Minjauw, B., & McLeod, A. (2003). Tick-borne diseases and poverty. The impact of ticks and

tickborne diseases on the livelihood of small-scale and marginal livestock owners in India

and eastern and southern Africa.

http://agris.fao.org/agrissearch/search.do?recordID=GB2012100456

Mitchell, R. M. (1979). A list of ectoparasites from Nepalese mammals, collected during the

Nepal Ectoparasite Program. Journal of Medical Entomology, 16(3), 227–233.

https://doi.org/10.1093/jmedent/16.3.227

MoAF. (2019). BHUTAN RNR STATISTICS – 2018 | Ministry of Agriculture and Forests.

http://www.moaf.gov.bt/bhutan-rnr-statistics-2018/

Murrell, A., & Barker, S. C. (2003). Synonymy of Boophilus Curtice, 1891 with Rhipicephalus

Koch, 1844 (Acari: Ixodidae). Systematic Parasitology, 56(3), 169–172.

https://doi.org/10.1023/B:SYPA.0000003802.36517.a0

National Statistics Bureau. (2019). National Accounts Statistics 2019.

http://www.nsb.gov.bt/publication/files/pub4dv5802vj.pdf

National Statistics Bureau. (2018). 2017 population & housing census of Bhutan: National report

= ʼBrug gi mi rlobs dang khyim gyi grangs rtsis 2017. National Statistics Bureau, Royal

Government of Bhutan.

NCAH. (2013). National Veterinary Drug Formulary (2nd Edition). Department of Livestock,

Royal Government of Bhutan. https://www.ncah.gov.bt/Downloads/File_3.pdf

NCAH. (2018). Veterinary information System 2018. http://vis.ncah.gov.bt/php/index.php

NCAH. (2019). Veterinary Information System. http://vis.ncah.gov.bt/php/index.php

Nwanade, C. F., Wang, M., Wang, T., Yu, Z., & Liu, J. (2020). Botanical acaricides and

repellents in tick control: Current status and future directions. Experimental and Applied

Acarology, 81(1), 1–35. https://doi.org/10.1007/s10493-020-00489-z

OIE. (2014). Manual of Diagnostic Tests and Vaccines for Terrestrial Animals 2019.

https://www.oie.int/en/standard-setting/terrestrial-manual/access-online/

OIE. (2015). Manual of Diagnostic Tests and Vaccines for Terrestrial Animals 2019.

https://www.oie.int/fr/normes/manuel-terrestre/acces-en-ligne/

OIE. (2018). Manual of Diagnostic Tests and Vaccines for Terrestrial Animals 2019.

https://www.oie.int/fr/normes/manuel-terrestre/acces-en-ligne/

Oliver, J. H. (1982). CHAPTER 8 - Tick Reproduction: Sperm Development and Cytogenetics.

In F. D. Obenchain & R. Galun (Eds.), Physiology of Ticks (pp. 245–275). Pergamon.

https://doi.org/10.1016/B978-0-08-024937-7.50013-5

Oliver, J. H. (1989). Biology and Systematics of Ticks (Acari: Ixodida). Annual Review of

Ecology and Systematics, 20, 397–430. JSTOR.

Parola, P., & Didier, R. (2001). Ticks and tickborne bacterial diseases in humans: An emerging

infectious threat. Clinical Infectious Diseases, 32(6), 897–928.

https://doi.org/10.1086/319347

Perez-Cogollo, L. C., Rodriguez-Vivas, R. I., Ramirez-Cruz, G. T., & Rosado-Aguilar, J. A.

(2010). Survey of Rhipicephalus microplus resistance to ivermectin at cattle farms with

history of macrocyclic lactones use in Yucatan, Mexico. Veterinary Parasitology, 172(1),

109–113. https://doi.org/10.1016/j.vetpar.2010.04.030

Peter, R. J., Van den Bossche, P., Penzhorn, B. L., & Sharp, B. (2005). Tick, fly, and mosquito

control—Lessons from the past, solutions for the future. Veterinary Parasitology, 132(3–

4), 205–215. https://doi.org/10.1016/j.vetpar.2005.07.004

Phillips, S. J., Anderson, R. P., Dudík, M., Schapire, R. E., & Blair, M. E. (2017). Opening the

black box: An open-source release of Maxent. Ecography, 40(7), 887–893.

https://doi.org/10.1111/ecog.03049

Phillips, S. J., Anderson, R. P., & Schapire, R. E. (2006). Maximum entropy modeling of species

geographic distributions. Ecological Modelling, 190(3), 231–259.

https://doi.org/10.1016/j.ecolmodel.2005.03.026

Phillips, S. J., & Dudík, M. (2008). Modeling of species distributions with Maxent: New

extensions and a comprehensive evaluation. Ecography, 31(2), 161–175.

https://doi.org/10.1111/j.0906-7590.2008.5203.x

Raghavan, R. K., Barker, S. C., Cobos, M. E., Barker, D., Teo, E. J. M., Foley, D. H., Nakao, R.,

Lawrence, K., Heath, A. C. G., & Peterson, A. T. (2019). Potential Spatial Distribution of

the Newly Introduced Long-horned Tick, Haemaphysalis longicornis in North America.

Scientific Reports, 9(1), 1–8. https://doi.org/10.1038/s41598-018-37205-2

Ramos, V. do N., Osava, C. F., Piovezan, U., Szabó, M. P. J., Ramos, V. do N., Osava, C. F.,

Piovezan, U., & Szabó, M. P. J. (2014). Complementary data on four methods for

sampling free-living ticks in the Brazilian Pantanal. Revista Brasileira de Parasitologia

Veterinária, 23(4), 516–521. https://doi.org/10.1590/s1984-29612014091

Randolph, S. E. (2013). Ecology of Non-nidicolous Ticks. In Biology of Ticks Volume 2 (pp. 3–

33). OUP USA.

RLDC Wangdue. (2019). 9th Annual Progress Report of RLDC Wangdue (2018-19). DoL,

MoAF, Wangdue, Bhutan. http://www.dol.gov.bt/?wpdmpro=rldc-wangdue-annual-

progress-report-fy-2018-19

Rochlin, I. (2018). Modeling the Asian Longhorned Tick (Acari: Ixodidae) Suitable Habitat in

North America. Journal of Medical Entomology, 56(2), 384–391.

https://doi.org/10.1093/jme/tjy210

Rodriguez-Vivas, R. I., Jonsson, N. N., & Bhushan, C. (2018). Strategies for the control of

Rhipicephalus microplus ticks in a world of conventional acaricide and macrocyclic

lactone resistance. Parasitology Research, 117(1), 3–29. https://doi.org/10.1007/s00436-

017-5677-6

Rykiel, E. J. (1996). Testing ecological models: The meaning of validation. Ecological

Modelling, 90(3), 229–244. https://doi.org/10.1016/0304-3800(95)00152-2

Samdup, T., Udo, H. M. J., Eilers, C. H. a. M., Ibrahim, M. N. M., & van der Zijpp, A. J. (2010).

Crossbreeding and intensification of smallholder crop-cattle farming systems in Bhutan.

Livestock Science, 132(1–3), 126–134. https://doi.org/10.1016/j.livsci.2010.05.014

Seifert, G. W. (1984). Selection of beef cattle in Northern Australia for resistance to the cattle

tick (Boophilus microplus): Research and application. Preventive Veterinary Medicine,

2(1), 553–558. https://doi.org/10.1016/0167-5877 (84)90101-6

Shannon, C. E. (1948). A Mathematical Theory of Communication. Bell System Technical

Journal, 27(3), 379–423. https://doi.org/10.1002/j.1538-7305.1948.tb01338.x

Sonenshine, D. E., & Roe, R. M. (2013a). External and Internal Anatomy of Ticks. In Biology of

Ticks Volume 1 (pp. 74–97). OUP USA.

Sonenshine, D. E., & Roe, R. M. (2013b). Overview: Ticks, People and Animals. In Biology of

Ticks Volume 1 (pp. 3–16). OUP USA.

Spickler, A. R. (2007). Rhipicephalus (Boophilus) microplus.

http://www.cfsph.iastate.edu/DiseaseInfo/factsheets.php

Spickler, A. R. (2018). Bovine Babesiosis.

http://www.cfsph.iastate.edu/DiseaseInfo/factsheets.php

Spickler, A. R. (2019). Theileriosis. http://www.cfsph.iastate.edu/DiseaseInfo/factsheets.php

Sungirai, M., Moyo, D. Z., De Clercq, P., & Madder, M. (2016). Communal farmers’

perceptions of tick-borne diseases affecting cattle and investigation of tick control

methods practiced in Zimbabwe. Ticks and Tick-Borne Diseases, 7(1), 1–9.

https://doi.org/10.1016/j.ttbdis.2015.07.015

Sutherst, R. W., & Comins, H. N. (1979). The management of acaricide resistance in the cattle

tick, Boophilus microplus (Canestrini) (Acari: Ixodidae), in Australia. Bulletin of

Entomological Research, 69(3), 519–537. https://doi.org/10.1017/S0007485300019015

Tack, W., Madder, M., De Frenne, P., Vanhellemont, M., Gruwez, R., & Verheyen, K. (2011).

The effects of sampling method and vegetation type on the estimated abundance of

Ixodes ricinus ticks in forests. Experimental and Applied Acarology, 54(3), 285–292.

https://doi.org/10.1007/s10493-011-9444-6

Thullner, F., Willadsen, P., & Kemp, D. (2007). Acaricide Rotation Strategy for Managing

Resistance in the Tick Rhipicephalus (Boophilus) microplus (Acarina: Ixodidae):

Laboratory Experiment with a Field Strain from Costa Rica. Journal of Medical

Entomology, 44(5), 817–821. https://doi.org/10.1093/jmedent/44.5.817

Utech, K. B. W., Wharton, R. H., & Kerr, J. D. (1978). Resistance to Boophilus microplus

(Canestrini) in different breeds of cattle. Australian Journal of Agricultural Research,

29(4), 885–895. https://doi.org/10.1071/ar9780885

Walker, A. R., Bouattour, A., Camicas, J.-L., Estrada-Pena, A., Horak, I. G., Latif, A. A.,

Pegram, R. G., & Preston, P. M. (2003). Ticks of domestic animals in Africa: A guide to

identification of species.

http://agris.fao.org/agrissearch/search.do?recordID=XF2015039701

Walker, Alan R. (2011). Eradication and control of livestock ticks: Biological, economic and

social perspectives. Parasitology, 138(8), 945–959.

https://doi.org/10.1017/S0031182011000709

Wang, S. W., Lee, W.-K., & Son, Y. (2017). An assessment of climate change impacts and

adaptation in South Asian agriculture. International Journal of Climate Change

Strategies and Management, 9(4), 517–534. https://doi.org/10.1108/IJCCSM-05-2016-

0069

Watts, J. G., Playford, M. C., & Hickey, K. L. (2016). Theileria orientalis: A review. New

Zealand Veterinary Journal, 64(1), 3–9. https://doi.org/10.1080/00480169.2015.1064792

Wharton, R. H., & Utech, K. B. W. (1970). The Relation Between Engorgement and Dropping of

Boophilus microplus (canestrini) (ixodidae) to the Assessment of Tick Numbers on

Cattle. Australian Journal of Entomology, 9(3), 171–182. https://doi.org/10.1111/j.1440-

6055.1970.tb00788.x

Wilkinson, P. R. (1957). The spelling of pasture in cattle tick control. Australian Journal of

Agricultural Research, 8(4), 414–423. https://doi.org/10.1071/ar9570414

Willadsen, P., Bird, P., Cobon, G. S., & Hungerford, J. (1995). Commercialisation of a

recombinant vaccine against Boophilus microplus. Parasitology, 110(S1), S43–S50.

https://doi.org/10.1017/S0031182000001487

Willadsen, Peter. (2006). Tick control: Thoughts on a research agenda. Veterinary Parasitology,

138(1), 161–168. https://doi.org/10.1016/j.vetpar.2006.01.050

Yuval, B., & Spielman, A. (1990). Sperm precedence in the deer tick Ixodes dammini.

Physiological Entomology, 15(1), 123–128. https://doi.org/10.1111/j.1365-

3032.1990.tb00498.x

Zhang, G., Zheng, D., Ian, Y. T., & Li, S. (2019). A dataset of distribution and diversity of ticks

in China. Scientific Data, 6, 105. https://doi.org/10.1038/s41597-019-0115-5

Chapter 2 Distribution of Ixodid Ticks in Cattle in Eastern Bhutan

2.1 Background

Bhutan is a small mountainous country in South Asia with the geographical area of

38,394 km2. It is one of the most affluent biodiversity areas in the world with varied agro- ecological and climatic conditions (Banerjee & Bandopadhyay, 2016). Livestock farming provides the major income for 49.1% of the population who are subsistence farmers (National

Statistics Bureau, 2018). Cattle are the most important livestock species with their diverse role in providing milk, draught power, transport, and organic manure (Hidano et al., 2016). However, tick infestation is considered to be one of the major economic burdens for the livestock sector in

Bhutan due to its negative impact on growth and production (Phanchung et al., 2007). Tick- borne diseases (TBDs) such as anaplasmosis, babesiosis, and theileriosis have been reported in cattle, especially from the southern tropical areas of the country (Phanchung et al., 2007).

In 2019, 42% of the cattle in Bhutan were reported to have been treated for tick infestation costing the government approximately 3.18 million Bhutanese Ngultrum (1CAD=

Nu.54) for purchasing synthetic acaricides alone (NCAH, 2019). Apart from using conventional methods of tick control, such as acaricides application, there is little understanding of the tick species and the associated tick-pathogens present. Although tick infestation is common in cattle, there is no comprehensive information on the presence, diversity, infestation prevalence, and the geographic distribution of tick species present in Bhutan. Current knowledge consists of a single unpublished study (Cork et al., 1996) and one government published report (RLDC Wangdue,

2019). Therefore, the objective of this study was to determine the presence, diversity, and infestation prevalence of tick species in cattle in two districts of eastern Bhutan using a targeted field survey.

2.2 Materials and methods

2.2.1 Ethics statement

The study protocol was approved by both the Animal Care Committee (ACC), University of Calgary, Canada (AC 19-0035) and the Research and Extension Division, Department of

Livestock, Ministry of Agriculture and Forests, Royal Government of Bhutan (Animal Research

Application Form-15/05/2019).

2.2.2 Study areas

Bhutan is divided administratively into 20 districts (dzongkhags) and 205 sub-districts

(gewogs). Trashigang and Pema Gatshel districts (Figure 2.1) in eastern Bhutan were selected for the study as these two districts cover the entire range of agro-ecological zones and the wide elevation represented in Bhutan. Moreover, these two districts also contain diverse breeds of cattle ranging from the indigenous breed Jaba (Bos taurus indicus) at the lower subtropical plains to yaks (Bos grunniens) at the higher alpine areas. Trashigang is predominantly a temperate district with 15 sub-districts. It shares a border with the Indian state of Arunachal Pradesh in the east. Pema Gatshel, located at the warmer south, is mainly a subtropical district with 11 sub- districts. It shares a border with the Indian state of Assam in the south. The geographic and climatic features, along with the cattle populations of the two districts, are given in Table 2.1.

Two sub-districts of the study area in Trashigang are highland (alpine) areas dominated by yaks and sheep (Ovis aries L.) reared in a free-range grazing system. However, tick specimens could not be obtained from these areas during the field survey conducted in May and June 2019; therefore, these two sub-districts were excluded from this study.

Figure 2.1 Map of Bhutan showing the study area and tick sampling sites (black dots). The maps were generated using QGIS software (QGIS Development Team, 2019). The shapefiles for the political boundary of Bhutan, including district and sub-district boundaries, were obtained from the National Land Commission of Bhutan.

Table 2.1 Geographic and climatic characteristics of the study area.

Characteristic Trashigang Pema Gatshel Total

Total land (km2) 2204 1023 3227 Agro-ecological zones (in % of land area) Wet Subtropical (100-600)* 0.1 20.9 6.7 Humid Subtropical (600-1200) 4.4 41.7 16.2 Dry Subtropical (1200-1800) 14.8 28.1 19.0 Warm Temperate (1800-2600) 29.2 9.3 22.9 Cool Temperate (2600-3600) 32.7 0 22.3 Alpine (>3600-7500) 18.8 0 12.8 Forest coverage (in %) 81.6 87.6 Vegetation type Temperate - broadleaf Broadleaf Climate Warm & temperate Hot & humid Annual rainfall (in mm) 1115.6 1649.6 Annual Temperature (min-max in oC) 10.3-24.2 13.1-22.1 Cattle population 40,685 8,252 *elevation range in meters

2.2.3 Sample size

The number of sites sampled was determined by the administrative units (i.e., sub- districts/gewogs). All sub-districts in the two districts were included for sampling except the two sub-districts in the alpine zone. As the primary objective of the study was the detection of tick species, the sample size for each sub-district was calculated using the formula 푛 =

1 푑 [1 − (1 − 푝 )푑] (푁 − ) + 1 (Thrusfield, 2018, p.238), where n=required sample size, 푙 2

N=number of cattle in sub-districts, d=minimum number of cattle expected to be infested with ticks in the population, p1=probability (0.95) of finding at least one cattle infested with ticks in the sample. The number of cattle (N) for each sub-district was obtained from the annual livestock census data (DoL, 2018). The expected proportion (d) was calculated as 0.3 for all sub-districts based on the tick infestation cases recorded in the veterinary information system (NCAH, 2018) and the livestock population data of each sub-district (DoL, 2018). The sample size calculated for each sub-district was 10 animals. Since the study area had 24 sub-districts to be sampled, the overall sample size was 240 animals.

2.2.4 Sampling method

For selecting the required number of animals, the list of households owning cattle in each sub-district was obtained from the livestock offices and used as the sampling frame. Ten households owning cattle were selected from each sub-district using simple random sampling in

MS Excel 2016 (Microsoft Excel 2016, Redmond, USA). Prior to tick collection, the objectives of the study were explained to the owners, and verbal consent was obtained to collect ticks from their cattle. All selected households agreed to the sampling. In each selected household, all the cattle present were visually examined for tick infestation, and one infested animal that could be properly restrained was selected for sampling (convenience sampling). Overall, this study

58 covered 240 households, visually examined 1,004 cattle for tick infestation, and collected ticks from 240 cattle.

2.2.5 Tick collection

Tick collection was conducted once per household during May to June 2019 in all 24 sub-districts. Since our study was intended to determine the presence and diversity of tick species, only live adult ticks were collected (Lorusso et al., 2013). Selected cattle were properly restrained, and 15 ticks were randomly collected from different predilection sites. The ears, dewlap, withers, knees, udder in females and testes in males, perineum region, and tail were visually checked as these sites are common predilection sites (Rehman et al., 2017; Theuret &

Trout Fryxell, 2018). High quality steel tweezers were used to grip the tick firmly over the mouthparts and pluck them from the skin of the animals. The ticks collected from each animal were placed separately in Eppendorf tubes containing 70% ethanol and labeled with a unique sample ID that included district and sub-district codes and the animal number. Other associated information such as age, sex and breed of the animal, GPS coordinates of the location, site altitude, owners’ names, and dates of the collection were recorded in an excel spreadsheet for data analyses and future reference. The samples were then transported to the national veterinary laboratory at the National Centre for Animal Health (NCAH), Thimphu, Bhutan, for identification.

2.2.6 Specimen identification

Ticks were identified under the stereomicroscope using a two-step process of species identification. Ticks were first identified to genus using the keys (Matthysee & Colbo, 1987) and

(Walker et al., 2003). Ticks were then identified to species using (Anastos, 1950) and (Robinson et al., 1926) for the members of the genus Amblyomma; (Matthysee & Colbo, 1987) and

(Estrada-Pena et al., 2012) for members of the genus Rhipicephalus (Boophilus); (Walker et al.,

2003) and (Anastos, 1950) for the remaining Rhipicephalus; (Geevarghese & Mishra, 2011) and

(Nutall et al., 1915) for the members of the genus Haemaphysalis; and (Guo et al., 2016) for the members of the genus Ixodes. The tick specimens, including the voucher specimens, are held at the national veterinary laboratory of the NCAH, Thimphu, Bhutan. The macro photography was done on some of the selected voucher specimens to get representative pictures of the identified ticks.

2.2.7 Statistical analyses

Statistical analyses were performed to 1) examine variations in proportions of infestation prevalence of tick species between Trashigang and Pema Gatshel districts, and 2) assess the relationship between geographic and cattle factors and infestation prevalence and co-infestation.

The raw data were collated in Excel spreadsheets (Microsoft Excel 2016, Redmond, USA) and imported into R computing software (R Core Team, 2018) for analyses. Descriptive analysis was performed with the entire dataset to calculate proportions and frequencies. Tick infestation prevalence was calculated as the number of cattle infested with ticks divided by the total number of cattle examined among the households sampled (Bush et al., 1997). Co-infestation (when two or more tick species occur together on the same animal) among the sampled cattle was calculated using the “dplyr” package (Wickham et al., 2019). The 95% binomial confidence interval for the infestation prevalence and co-infestation was calculated with the Clopper-Pearson exact method using the “PropCI” package (Scherer, 2018). Pearson’s chi-squared test using the native “stats” package (R Core Team, 2018) was performed to compare the difference in proportions of tick infestation prevalence and co-infestation between the districts.

The relationships between geographic and cattle factors and infestation prevalence and co-infestation were assessed using logistic regression analyses. The analyses were conducted using cattle age (categorized as young and adult), sex, breed (categorized as European and

Indigenous), site altitude (in 100 meters), latitude (in 1/10 decimal degrees), and longitude (in

1/10 decimal degrees) as the explanatory variables against the binary outcome variables of infestation prevalence of each tick species (categorized as infested or not) and co-infestation

(categorized as co-infested or not). The tick species considered for multiple logistic regression analyses were R. microplus, R. haemaphysaloides, H. bispinosa, and H. spinigera. The remaining two species, A. testudinarium, and Ixodes sp. were excluded from multiple logistic regression analyses as they were infrequently collected. Collinearity among the explanatory variables was assessed using the “Hmisc” package (Frank et al., 2020).

Simple logistic regression was conducted to examine the fit and direction of all parameters. The relationship between the infestation prevalence and co-infestation and all six explanatory variables was individually examined. For multiple logistic regression, Akaike

Information Criteria (AIC) based automated selection procedure was employed to generate a preliminary model with the parameter sets that gives the best fit. To describe this process, first, a null model with only the intercept was fit, and then a full model with all the explanatory variables was fit. Then, using the step(AIC) function in the “MASS” package (Venables &

Ripley, 2002) in R, the automated selection was run by specifying direction as “forward”. The use of all explanatory variables was mainly to ensure that all possible models were analyzed. The variables in the preliminary model that gave the best fit were then selected for manually building the final model. The final models were selected after examining the direction of the parameter estimates, Wald statistics, AIC, deviance, and variance inflation factor (VIF). Multicollinearity

61 of predictors in the models was assessed using the variance inflation factor (VIF) at the cut-off of

2.5 (Allison, 2012). The interaction was assessed by adding a two-way cross-product term (i.e.,

Latitude*Longitude). Only the variables at 0.05 level of significance were retained in the final model. The odds ratio (OR) and its 95% confidence interval (CI) of the variables associated with the outcome variables were calculated from the final models. The final models were evaluated using goodness-of-fit using the “LogisticDx” package in R (Dardis, 2015). The residual analysis of the final models was done using the “car” package (Fox & Weisberg, 2019). The fitted logistic regression curves of the final models were plotted using the “logihist” package

(Marcelino de la Cruz, 2017) to visualize associations between the significant explanatory variables and the outcome variables.

2.3 Results

2.3.1 Characteristics of the study animals and geographic variables.

(Table 2.2) shows the characteristics of the study animals and geographic variables.

Table 2.2 Characteristics of the explanatory variables (n=240) used for the logistic regression analyses. Pema Trashigang Total Percentage Variables Categories Gatshel 95%CI (n=130) (n=240) (%) (n=110) Cattle age* Adult 103 79 182 75.8 69.9-81.1 Young 27 31 58 24.2 18.9-30.1 Cattle sex Female 111 85 196 81.7 76.2-86.4 Male 19 25 44 18.3 13.6-23.8 Cattle breed European 73 80 153 63.8 57.3-69.8 Indigenous 57 30 87 36.2 30.2-42.7 Altitude** Mean 1687 1177

Range 897-2187 720-2060 Latitude*** Mean 27.28 26.99 Range 27.11-27.41 26.84-27.12 Longitude*** Mean 91.61 91.33 Range 91.44-91.76 91.09-91.46 *the cattle under calf and heifer were categorized as “young” while the rest were categorized as adults, **altitude in meters above sea level (masl), *** latitude and longitude in decimal degrees.

2.3.2 Tick infestation prevalence

This section reports the result of the initial survey on overall tick infestation in each household sampled. Overall, 1004 cattle were visually examined for tick infestation in 240 households. The data collected from this survey were grouped as infested cattle and uninfested cattle for each household sampled. Then it was used as an outcome variable for logistic regression analysis. The explanatory variables that applied to this grouped data were altitude, latitude, and longitude.

The overall tick infestation prevalence in the study was 91.2% (916/1004, 95% CI: 89.3-

92.9): the Trashigang district had the infestation prevalence of 94.13% (594/631, 95% CI: 92-

95.8) while the Pema Gatshel district had the infestation prevalence of 86.3% (322/373, 95% CI:

82.4-89.6). The infestation prevalence was greater (χ2=16.914, df =1, P<0.01) in Trashigang than that of Pema Gatshel. Simple logistic regression indicated that infestation prevalence increased with each of altitude, latitude, and longitude. Latitude and longitude were found to be correlated

(r = 0.84), but both were considered for analyses since they are important geographic variables.

In the subsequent multiple logistic regression analyses, all possible models were built as follows:

1) using reduced models that do not have collinearity issues; and 2) using an interaction term between latitude and longitude. The final model was selected after assessing the direction of the parameter estimates, Wald statistics, AIC, deviance, and VIF of all the models built (Appendix

A.1).

Multiple logistic regression analysis (Table 2.3) indicated that the infestation prevalence was influenced by altitude and longitude. The odds of the cattle being infested by ticks increase

1.13 times with every 100 meters increase in altitude [OR=1.13 (95%CI: 1.05-1.21)] when the other variable in the model was held constant. Similarly, the odds increase 1.21 times with every

1/10th degree increase in longitude [OR=1.21 (95%CI: 1.03-1.43)] when the other variable in the model was held constant.

Table 2.3 Result of the multiple logistic regression model to understand the association between geographic variables and the overall infestation prevalence (Grouped data of cattle infested and cattle uninfested)

Variables Estimate ± SE Z OR (95% CI) χ2 AIC

Intercept -178.26 ± 76.17 -2.34 51.18 374.61

Altitude/100 0.123 ± 0.37 3.308 1.13 (1.05-1.21)

Longitude*10 0.196 ± 0.084 2.341 1.21 (1.03-1.43)

All parameters significant at P < 0.05 AIC: Akaike Information Criteria

χ2 has df = 2

2.3.2 Tick diversity

A total of 3600 ticks were collected and identified to four genera and six species (Table

2.4 & Figure 2.2). The majority (70.2%, 95% CI: 68.7-71.7) was Rhipicephalus microplus

(Canestrini) (2530 specimens), of which 41.3% were males, and 58.7% were females.

Rhipicephalus haemaphysaloides Supino comprised 18.8% (95% CI: 17.5-20.1) of the collection

(677 specimens), of which 62.8% were males, and 37.2% were females. Haemaphysalis bispinosa Neumann comprised 8.2% (95% CI: 7.3-9.1) of the collection (295 specimens), of which 21.7% were males, and 78.3% were females. Haemaphysalis spinigera Neumann comprised 2.5% (95% CI: 2-3) of the collection (90 specimens), of which 53.3% were males, and 46.7% were females. The remainders were seven Amblyomma testudinarium Koch (4 males and 3 females) and one unidentified female species of Ixodes. Amblyomma testudinarium and

Ixodes sp. were found only in Pema Gatshel and Trashigang districts, respectively.

Table 2.4 Tick species found in the study area.

Males Females Total

Tick species Infested Ticks Infested Ticks Infested Ticks % Total

R. microplus 194 1046 202 1484 204 2530 70.28

R. haemaphysaloides 81 425 72 252 91 677 18.81

H. bispinosa 28 64 63 231 72 295 8.19

H. spinigera 23 48 19 42 28 90 2.50

A. testudinarium 4 4 3 3 7 7 0.19

Ixodes sp. 0 0 1 1 1 1 0.03

Infested = number of infested cattle Ticks = number of ticks collected

Figure 2.2 Representative images of tick species identified in the study area: dorsal and ventral views of male (A) and female (B) R. microplus, male (C) and female (D) R. haemaphysaloides; male (E) and female (F) H. bispinosa, male (G) and female (H) H. spinigera; male (I) and female (J) of A. testudinarium, and female (K) Ixodes sp.

2.3.3 Coinfestation

Coinfestation occurred in 116 animals (48.3%, 95% CI: 41.9-54.9) out of 240 cattle sampled (Table 2.5). Two different tick species co-infesting the same animal occurred in 78 animals (32.5%, 95%CI: 26.6-38.8); three different tick species co-infesting the same animal occurred in 31 animals (12.9%, 95%CI: 8.9-17.8); four different tick species co-infesting the same animal occurred in 5 animals (2.1%, 95%CI: 0.67-4.7); and five different tick species co- infesting the same animal occurred in 2 animals (0.83%, 95%CI: 0.10-2.9). Six different tick species were not found on the same host in this study. Co-infestation occurred on 72 animals

(55.38%, 95% CI: 46.4-64.1) out of 130 animals in Trashigang and 44 animals (40%, 95% CI:

30.8-49.8) out of 110 animals in Pema Gatshel. The co-infestation was greater (χ2=5.048, df =1,

P < 0.02) in the animals sampled in Trashigang compared with that of Pema Gatshel.

Simple logistic regression indicated that coinfestation varied significantly between cattle breeds, but not between sexes and age groups (Appendix A.2). Between the age groups, coinfestation occurred on 88 (48.3%, 95%CI: 40.9-55.9) out of 182 animals categorized as the adult and 28 (48.3%, 95%CI: 34.9-61.8) out of 58 animals categorized as the young. Between the sexes, it occurred on 19 (43.2%, 95%CI: 28.3-58.9) out of 44 male animals and 97 (49.5%,

95%CI: 42.3-56.7) out of 196 female animals. Between the breeds, it occurred on 63 (41.2%,

95%CI: 33.3-49.4) out of 153 animals in the European breed group and 53 (60.9%, 95%CI: 49.9-

71.2) out of 87 animals in the indigenous breed group. Simple logistic regression indicated that coinfestation increased with each of altitude, latitude, and longitude (Appendix A.2)

Multiple logistic regression analysis (Table 2.6) showed that the odds of cattle being coinfested were 2.13 times more likely in the indigenous breeds than the European breeds of cattle [OR=2.13 (95%CI: 1.24-3.7)], when the other variable in the model was held constant. The

67 odds increase by 1.1 times for every 100 meters increase in altitude, [OR=1.1 (95%CI: 1.02-

1.16)], when the other variable in the model was held constant.

Table 2.5 Co-infestation (n=116)

Occurrence Species combination No. of cattle Two species R. microplus + R. haemaphysaloides 32 R. microplus + H. bispinosa 32

R. haemaphysaloides + H. bispinosa 6

R. microplus + H. spinigera 5

R. microplus + A. testudinarium 2

H. bispinosa + H. spinigera 1

Sub-total 78

Three species R. microplus + R. haemaphysaloides + H. bispinosa 12 R. microplus + H. bispinosa + H. spinigera 6

R. haemaphysaloides + H. bispinosa + H. spinigera 6

R. microplus + R. haemaphysaloides + H. spinigera 5

R. microplus + R. haemaphysaloides + Ixodes sp. 1

R. microplus + R. haemaphysaloides + A. testudinarium 1

Sub-total 31

R. microplus + R. haemaphysaloides + H. bispinosa + Four species H.spinigera 3 R. microplus + R. haemaphysaloides + H. bispinosa + A. testudinarium 2

Sub-total 5

R. microplus + R. haemaphysaloides + H. bispinosa + Five species H.spinigera + A. testudinarium 2 Sub-total 2

Total 78 + 31 + 5 + 2 116

Table 2.6 Results of the multiple logistic regression of coinfestation and infestation prevalence of each tick species in relation to cattle and geographic variables.

Variable Estimate ± SE Z OR (95% CI) χ2 AIC Coinfestation Intercept -1.55 ± 0.50 -3.08 15.3 323.1 Breed (Indigenous)¥ 0.76 ± 0.28 2.72 2.13 (1.24 - 3.7) Altitude/100 0.08 ± 0.03 2.54 1.10 (1.02 - 1.16) R. microplus Intercept 506.59 ± 133.11 3.81 28.3 180.6 Age (Young)€ 1.72 ± 0.75 2.28 5.57 (1.5 - 35.5) Longitude*10 -0.55 ± 0.15 -3.80 0.57 (0.42 - 0.75) R. haemaphysaloides Intercept -191.81 ± 27.31 -7.02 63.1* 259.5 Latitude*10 0.70 ± 0.10 7.01 2.02 (1.67-2.48) H. bispinosa Intercept -2.12 ± 0.55 -3.82 9.3 290.0 Breed (Indigenous) 0.62 ± 0.29 2.11 1.85 (1.04 - 3.29) Altitude/100 0.07 ± 0.04 1.98 1.07 (1.002 - 1.14) H. spinigera Intercept 0.35 ± 0.74 0.47ns 19.8 159.2 Breed (Indigenous) 1.00 ± 0.43 2.35 2.72 (1.18 - 6.38) Altitude/100 -0.21 ± 0.06 -3.63 0.81 (0.72 - 0.90) All parameters significant at P < 0.05 except ns = non-significant at P > 0.05. χ2 has df = 2 except * has df=1. AIC: Akaike Information Criteria ¥ European breed as the referent category, € adult as the referent category

2.3.4 Rhipicephalus microplus

Overall, R. microplus infestation occurred in 204 cattle (85%, 95% CI: 79.8-89.3) out of

240 cattle sampled: 96 cattle (73.8%, 95% CI: 65.4-81.1) out of 130 cattle in Trashigang; and

108 cattle (98.2%, 95% CI: 93.6-99.8) out of 110 cattle in Pema Gatshel. The infestation

69 prevalence of R. microplus was greater (χ2=25.8, df =1, P = 0.000) in the animals sampled in

Pema Gatshel compared with that of Trashigang.

Simple logistic regression indicated that infestation by R. microplus varied between cattle age groups, but not between sexes or breeds (Appendix A.2). Between the age groups, R. microplus occurred on 148 (81.3%, 95%CI: 74.9-86.7) out of 182 animals categorized as the adult and 56 (96.5%, 95%CI: 88.1-99.6) out of 58 animals categorized as the young. Between the sexes, R. microplus occurred on 40 (90.9%, 95%CI: 78.3-97.5) out of 44 male animals and 164

(83.7%, 95%CI: 77.7-88.5) out of 196 female animals. Between the breeds, R. microplus occurred on 132 (86.3%, 95%CI: 79.8-91.3) out of 153 animals in the European breed group and

72 (82.7%, 95%CI: 73.2-90) out of 87 animals in the indigenous breed group. Simple logistic regression indicated that the prevalence of R. microplus declined with each of increasing altitude, latitude, and longitude (Appendix A.2).

Multiple logistic regression analysis (Table 2.6) indicated that longitude and cattle age together was the most closely associated with variation in infestation by R. microplus. The odds of cattle being infested with R. microplus were 5.57 times more likely in the young animals than the adult animals [OR=5.57 (95%CI: 1.5-35.5)], and there was a 43% [OR=0.57 (95%CI: 0.42-

0.75)] lower likelihood of R. microplus infestation with every 1/10th degree increase in longitude when the other variable in the model was held constant, respectively.

2.3.5 Rhipicephalus haemaphysaloides

Overall, R. haemaphysaloides infestation occurred in 91 cattle (37.9%, 95% CI: 31.7-

44.4) out of 240 cattle sampled: 74 cattle (56.9%, 95% CI: 47.9-65.6) out of 130 cattle in

Trashigang; and 17 cattle (15.4%, 95% CI: 9.3-23.6) out of 110 cattle in Pema Gatshel. The

70 infestation prevalence of R. haemaphysaloides was greater (χ2=41.78, df =1, P < 0.01) in animals sampled in Trashigang compared with that of Pema Gatshel.

Logistic regression indicated that infestation by R. haemaphysaloides varied among cattle age groups, but neither sex nor breed (Appendix A.2). Between the age groups, R. haemaphysaloides occurred on 78 (42.8%, 95%CI: 35.6-50.4) out of 182 animals categorized as the adult and 13 (22.4%, 95%CI: 12.5-35.3) out of 58 animals categorized as the young. Between the sexes, R. haemaphysaloides occurred on 14 (31.8%, 95%CI: 18.6-47.6) out of 44 male animals and 77 (39.3%, 95%CI: 32.4-46.5) out of 196 female animals. Between the breeds, R. haemaphysaloides occurred on 52 (33.9%, 95%CI: 26.5-42.1) out of 153 animals in the

European breed group and 39 (44.8%, 95%CI: 34.1-55.9) out of 87 animals in the indigenous breed group. Simple logistic regression indicated that infestation by R. haemphysaloides increased with each of altitude, latitude, and longitude (Appendix A.2). However, multiple logistic regression (Table 2.6) indicated that no other variable improved the fit of a model containing latitude. The odds of cattle being infested with R. haemaphysaloides increased 2.02 times with every 1/10th degree increase in latitude [OR=2.02 (95%CI: 1.67-2.48)].

2.3.6 Haemaphysalis bispinosa

Haemaphysalis bispinosa infestation occurred on 72 cattle (30%, 95% CI: 24.3-36.2) out of 240 cattle sampled: 39 cattle (30%, 95% CI: 22.3-38.6) out of 130 cattle in Trashigang; and 33 cattle (30%, 95% CI: 21.6-39.5) out of 110 cattle in Pema Gatshel. There was no significant

(χ2=0, df =1, P=1) difference in the infestation prevalence estimates of H. bispinosa between

Trashigang and Pema Gatshel districts.

Simple logistic regression indicates no significant effect of cattle age and sex on infestation by H. bispinosa, but a significant effect of the breed (Appendix A.2). Between the age

71 groups, H. bispinosa occurred on 54 (41.5%, 95%CI: 32.9-50.5) out of 182 animals categorized as the adult and 18 (16.4%, 95%CI: 9.9-24.6) out of 58 animals categorized as the young.

Between the sexes, H. bispinosa occurred on 11 (25%, 95%CI: 13.2-40.3) out of 44 male animals and 61 (31.1%, 95%CI: 24.7-38.1) out of 196 female animals. Between the breeds, H. bispinosa occurred on 38 (24.8%, 95%CI: 18.2-32.4) out of 153 animals in the European breed group and 34 (39.1%, 95%CI: 28.8-50.1) out of 87 animals in the indigenous breed group.

Simple logistic regression indicated a significant relationship between altitude and infestation by

H. bispinosa, but no relationship with latitude or longitude (Appendix A.2).

Multiple logistic regression analysis (Table 2.6) indicated infestation varied with both altitude and cattle breed. The odds of cattle being infested with H. bispinosa were 1.85 times more likely in the indigenous breeds than that of the European breeds of cattle [OR=1.85

(95%CI: 1.04-3.29)], and the infestation increased 1.07 times with every 100 meters increase in altitude [OR=1.07 (95%CI: 1.002- 1.14)] when the other variable in the model was held constant, respectively.

2.3.7 Haemaphysalis spinigera

Haemaphysalis spinigera infestation occurred in 28 cattle (11.7%, 95% CI: 7.9-16.4) out of 240 cattle sampled: 11 cattle (8.5%, 95% CI: 4.3-14.6) out of 130 cattle in Trashigang; and 17 cattle (15.4%, 95% CI: 9.3-23.6) out of 110 cattle in Pema Gatshel. There was no significant

(χ2=2.1895, df =1, P=0.139) difference in the estimates of infestation prevalence of H. spinigera between Trashigang and Pema Gatshel districts.

Simple logistic regression indicates no significant effect of cattle age and sex on infestation by H. spinigera, but a significant effect of the breed (Appendix A.2). Between the age groups, H. spinigera occurred on 23 (12.6%, 95%CI: 6.1-14) out of 182 animals categorized as

72 adult and 5 (8.6%, 95%CI: 2.8-18.9) out of 58 animals categorized as young. Between the sexes,

H. spinigera occurred on 2 (4.5%, 95%CI: 0.5-15.5) out of 44 male animals and 26 (13.3%,

95%CI: 8.8-18.8) out of 196 female animals. Between the breeds, H. spinigera occurred on 13

(8.5%, 95%CI: 4.6-14.1) out of 153 animals in the European breed group and 15 (17.2%,

95%CI: 9.9-26.8) out of 87 animals in the indigenous breed group. Simple logistic regression indicated a significant negative relationship between altitude and infestation by H. spinigera, but no relationship with latitude or longitude (Appendix A.2).

Multiple logistic regression analysis (Table 2.6) indicated infestation varied with both altitude and cattle breed. The odds of cattle being infested with H. spinigera were 2.72 times more in the indigenous breeds than that of the European breeds of cattle [OR=2.72 (95%CI:

1.18-6.38), and there was 19% [OR=0.81 (95%CI: 0.72- 0.90)] lower likelihood of H. spinigera infestation with every 100 meters increase in altitude when the other variable in the model was held constant, respectively.

2.4 Discussion

Host and geographic factors generally influence the variation in tick infestation in animals. The cattle (host) factors such as age, sex, and breed can influence the susceptibility of animals to tick infestation (Asmaa et al., 2014; Bianchi et al., 2003). Ticks are also generally dependent on the temperature and rainfall for their development and activity (Estrada-Peña,

2015). In Bhutan, temperature and rainfall are primarily determined by altitude and latitude

(Dorji et al., 2016). Thus, the relationships between cattle and geographic factors and infestation prevalence were assessed. In this study, R. microplus was found to have a wide geographic range in the surveyed area; it was collected from all sub-districts; and has high infestation prevalence.

These characteristics make R. microplus a primary tick species infesting cattle in eastern Bhutan.

Results agree with findings from the previous studies that have indicated R. microplus as the predominant tick species in cattle in the surveyed areas of eastern (Cork et al.,1996) and western

Bhutan (RLDC Wangdue, 2019).

Rhipicephalus microplus is a one-host tick distributed worldwide in tropical and subtropical regions (Spickler, 2007). It is considered to be the most important tick species of cattle in the world (Jongejan & Uilenberg, 2004). Rhipicephalus microplus is an important vector for babesiosis (caused by the protozoan parasite Babesia bigemina and Babesia bovis) and anaplasmosis (caused by Anaplasma marginale) (Spickler, 2007). It is one of the most predominant tick species infesting livestock in India (Ghosh et al., 2007). Recently, it was recognized that R. microplus is a complex species that is comprised of at least five taxa: R. australis, R. annulatus, R. microplus clade A, R. microplus clade B, and R. microplus clade C

(Burger et al., 2014; Low et al., 2015). However, the R. microplus we have collected was identified based on morphological keys (Estrada-Pena et al., 2012; Matthysee & Colbo, 1987), and further studies should be conducted to determine which clade of R. microplus it belongs to.

Rhipicephalus microplus has seven phases in its life cycle: pre-oviposition adult, ovipositing adult, incubating eggs, questing larva, attached larva, nymph, and feeding adult (Leal et al., 2018). Among these seven phases, questing larvae depend on temperature and humidity for questing behavior and survival (Leal et al., 2018; Sutherst & Bourne, 2006). In this study, R. microplus was found more frequently in animals sampled in Pema Gatshel (98.2%) compared with Trashigang (78.3%). Pema Gatshel, located at the warmer south, has the majority of its area dominated by typical subtropical climate with higher temperatures, abundant rainfall, and high relative humidity, while Trashigang has the majority of its area with a temperate climate dominated by temperature, rainfall, and relative humidity at a moderate level. This difference in

74 the climatic conditions could be one factor that could have led to the greater prevalence of R. microplus in Pema Gatshel compared with Trashigang. However, there are many other environmental, seasonal, and host factors that can influence tick distribution.

The infestation prevalence of R. microplus was found to decrease with increasing longitude [OR=0.57 (95%CI: 0.42-0.75)]. Geographically, the longitudes of our study area range from 91o to 92o east. The areas in our study area between the longitudes 91o to 91.5o east are at an elevation of below 2000m. Between the longitudes 91.5o to 92o east, the areas are at an elevation of more than 2000m. This variation in elevation toward the east with increasing longitude could be the reason for the decrease in R. microplus infestation. Rhipicephalus microplus is a tropical tick species that prefers warm and humid conditions (De Clercq et al.,

2012); therefore, the low temperatures and rainfalls at the higher elevations may have been limiting its infestation.

A greater prevalence of R. microplus was observed in younger cattle compared with that of adult cattle [OR=5.57 (95%CI: 1.5-35.5)], which is consistent with the similar studies conducted in the neighboring Indian state of West Bengal (Debbarma et al., 2018), Bangladesh

(Kabir et al., 2011), and Nigeria (Eyo et al., 2014). However, studies conducted in Pakistan

(Rehman et al., 2017), Ethiopia (Kemal et al., 2016), Egypt (Asmaa et al., 2014), and Nigeria

(Lorusso et al., 2013) indicated lower prevalence in young cattle due to factors such as frequent grooming by dams and innate immunity. In our study area, those calves that are reared in the stall-fed systems are deprived of grooming as the farmers tether them at a safe distance from their dam to avoid milk suckling. Furthermore, young calves, especially the males, are the least attended by farmers, unlike the milking adult females. In households that follow mixed practices of cattle rearing, calves and young heifers are mostly let out for grazing in nearby pastures and

75 forests while milking adults are kept tethered around the homestead. Lack of maternal grooming and free access to pastures and forests likely increase the risk of tick infestation, and this could be the reason for the greater prevalence of R. microplus in young animals in this study.

Rhipicephalus haemaphysaloides was the second most predominant tick species identified and was collected from 20 sub-districts. It is a typical three-host tick whereby the three motile stages (larva, nymph, and adult) feed on three different hosts, and after the blood meal, they always drop off the host to molt to the next stage (Diyes et al., 2017). This tick has a limited geographical distribution in the world (Diyes et al., 2017); however, it is widely distributed in

India (Ghosh et al., 2007) and China (Chen et al., 2010). In India, it has been incriminated as a potential vector for Kysanuar Forest Disease (KFD) virus (Bhat et al., 1978). In China, it is a vector of bovine babesiosis (Babesia orientalis) (Liu et al., 2007). In Sri Lanka, this tick is known to infest humans (Diyes et al., 2017).

Unlike R. microplus, the prevalence of R. haemaphysaloides was greater in animals sampled from Trashigang (56.9%) compared with that of Pema Gatshel (15.4%). It showed a positive association with latitude indicating northerly distribution in the study area [OR=2.02

(95%CI: 1.67-2.48)]. Trashigang, located toward the north in the study area, is at a higher elevation compared with Pema Gatshel. Temperatures and rainfalls are also comparatively low.

Generally, temperature is considered to be the key determinant of development progress and activity of ticks (Estrada-Peña, 2015). However, this finding from the study area suggests that R. haemaphysaloides might be tolerating cooler temperatures than other tick species at some point in their life cycle. Other factors like microclimate and host availability are also known to influence the development and activity of ticks (Estrada-Peña, 2015), but these factors were not

76 accounted for in this study. Further, we did not collect every tick present on the host, and this could have biased our findings of the prevalence of this tick.

Haemaphysalis bispinosa was the third most predominant tick species identified in this study and was collected from 22 sub-districts. In South Asia, this tick is distributed in India,

Nepal, Sri Lanka, Pakistan, Bangladesh, and Myanmar (Hoogstraal et al., 1969). In India, it is widely distributed throughout the country and has been reported from the Indian states of Assam,

Arunachal Pradesh, West Bengal, and Sikkim (Brahma et al., 2014; Geevarghese & Mishra,

2011; Ghosh et al., 2007) that border Bhutan. In China, this tick has been considered to exist in the southern parts of the country, but most of the H. bispinosa reported in the Chinese literature are considered to be, in fact, H. longicornis (Chen et al., 2015). This tick has been found carrying KFD virus, although its role in the transmission of the disease is not well known

(Geevarghese & Mishra, 2011). In China, H. bispinosa is considered to be a vector for Lyme disease (caused by Borrelia burgdorferi), theileriosis (caused by Theileria sergenti), and bovine babesiosis (Babesia bigemina) (Yu et al., 2015). Haemaphysalis bispinosa is a three-host tick with three developmental stages (larva, nymph, and adult), and the life cycle appears to complete in 102 days (Geevarghese & Mishra, 2011).

Haemaphysalis spinigera was collected from 14 sites/sub-districts in the study area. This tick was first described by Neumann in 1897 based on an adult tick collected in Sri Lanka

(Geevarghese & Mishra, 2011). It is found in the foothills of the central and eastern Himalayan region, through Nepal to West Bengal in India (Ghalsasi & Dhanda, 1974). This tick is widely distributed in India and has been reported from the Indian states of Assam and West Bengal

(Geevarghese & Mishra, 2011; Ghosh et al., 2007), which border Bhutan. Haemaphysalis spinigera is the predominant vector for KFD, a zoonotic disease localized in the southern Indian

77 state of Karnataka (Pattnaik, 2006). Kyasanur forest disease (KFD) is a major emerging tick- borne disease caused by KFD virus of the genus Flavivirus under the family Flaviviridae; and transmitted to humans and animals through the infective bite of H. spinigera nymphal ticks that prefer humans (Sadanandane et al., 2018). Haemaphysalis spinigera is a three-host tick, essentially considered a forest inhabiting species that prefers wet evergreen to moist deciduous vegetation and heavy to moderate rainfall (Ghalsasi & Dhanda, 1974). To date, there has been no outbreak of KFD in any neighboring Indian states that share a border with Bhutan. Nevertheless, future studies should look at the potential pathogens that this tick could transmit to humans and animals.

In this study, the prevalence of H. bispinosa [OR=1.85 (95%CI: 1.04-3.29)] and H. spinigera [OR=2.72 (95%CI: 1.18-6.38)] was greater in indigenous breeds compared to

European breeds. This could be attributed to the difference in preferred management systems used for European and indigenous breeds. The European breeds are mostly reared in a stall-fed system with no access to forests, whereas the indigenous breeds are mostly reared in a free- grazing system with easy access to forests. The risk of exposure to ticks become greater when cattle graze in the forests. Studies have observed a higher prevalence of tick infestation in grazing cattle (Kabir et al., 2011; Ogden et al., 2005; Tiki & Addis, 2011). Furthermore, indigenous breeds of cattle in Bhutan are generally considered resistant to many pests and diseases, and the management practices like grooming, brushing, and acaricide application, are rarely practiced by the farmers.

H. bispinosa infestation was found to increase with increasing altitude [OR=1.85

(95%CI: 1.04-3.29)] while H. spinigera infestation was found to decrease with increasing altitude [OR=0.81 (95%CI: 0.72- 0.90)]. This might indicate that H. bispinosa can tolerate a

78 cooler temperature at some point in its life cycle while H. spinigera prefers low altitude subtropical areas. However, altitude is not the only driving factor in tick distribution. Other environmental and climatic factors such as temperature, rainfall, relative humidity, vegetation, and host availability also influence tick distribution (Estrada-Peña & de la Fuente, 2014). In

Bhutan, the high mountains and broken terrains cause an extreme variation in climate.

Temperature is thought to be mainly affected by altitude and precipitation by latitude ( Dorji et al., 2016). Further, the types of vegetation in the country are thought to be determined by the terrain and local climatic conditions; therefore, in the absence of detailed data on these various climatic and environmental factors, it is difficult to interpret what factors largely drive tick distribution in Bhutan. Nevertheless, altitude could be one of the main driving factors determining the habitat suitability of tick species as it is believed to change the temperature by

0.50C with every 100m change in altitude (Dorji et al., 2016).

Seven specimens of A. testudinarium were collected from seven sites/sub-districts. This tick is considered to be a rare tick species reported only from Asian countries like Malaysia,

India, Japan, Korea, and China (Chao et al., 2017; Ghosh et al., 2007; Zheng et al., 2019). It is known to transmit Ehrlichia chaffensis (Cao et al., 2000) and Rickettsia tamurae (Imaoka et al.,

2011) in humans. In India, it has been reported from Assam, Arunachal Pradesh, and West

Bengal (Ghosh et al., 2007), which border Bhutan. In its native range in East Asia, A. testudinarium has been largely reported from reptilian hosts, wild animals, and there have been occasional reports of bites on humans (Chao et al., 2017). In the northeast Indian states of

Assam, Meghalaya, Arunachal Pradesh, and Mizoram, A. testudinarium was reported from cattle, mithuns, yaks, and wild animals such as the tiger, wild boar, barking deer, and elephant

(Chamuah et al., 2016). These reports suggest that this tick species is predominantly found on

79 the animals that live in and around the forests of the Himalayan foothills. In this study, A. testudinarium was found only in the southeastern district of Pema Gatshel. Since this study lacked year-round sampling and was conducted only in cattle, it is difficult to interpret why A. testudinarium was not collected from Trashigang. For better insights, future studies should conduct sampling throughout the year, targeting a wider range of hosts using various sampling methods such as flagging, dragging, and on host collections, especially from wild animals.

Despite these drawbacks, this preliminary finding presented here will help determine the geographical distribution of A. testudinarium.

One specimen of Ixodes was collected during this study; however, identification could not be done to the species level. Ixodes is the largest genus in the family Ixodidae (hard ticks), containing 243 species (Guglielmone et al., 2010). Ixodes are inornate ticks with long mouthparts, no eyes or festoons, and anal groove placed anteriorly to the anus, unlike in all other genera where the anal groove is placed posterior to the anus (Walker et al., 2003). There are 11 valid species of Ixodes reported from India (Ghosh et al., 2007), 24 valid species from China

(Guo et al., 2016), and 15 valid species from Nepal (Clifford et al., 1975). This tick can infest a wide range of hosts, including domestic animals and humans, small mammals, wildlife such as deer, birds, and reptiles (Clifford et al., 1975). However, in this study, only one specimen of

Ixodes was found on cattle. This could have been biased due to our sampling procedure as tick collection was done only from the cattle. For ticks like Ixodes, sampling from vegetation using flagging or dragging is considered as the most effective procedure (ECDC, 2018). Therefore, future studies should conduct flagging and dragging on vegetation and on host collection from a wider range of hosts. Generally, tick samplings are recommended in the months when ticks are usually active, but due to the lack of the previous study on seasonal pattern of ticks in Bhutan, it

80 is not known which months of the year to be targeted for sampling. Nevertheless, the warmer months of the spring and summer are likely to be the time where ticks will be active in Bhutan.

The overall tick infestation prevalence in the study area was 91.23%. However, the annual infestation prevalence was estimated to be 30% when calculated using tick infestation case records from the veterinary information system (NCAH, 2018) and the livestock census data

(DoL, 2018). This higher prevalence was due to the timing of our sampling, which was in the warmer months of May and June, where ticks are known to peak in their development and activity. Further, the June month of 2019 was Saga dawa-the auspicious month (Phuntsho, 2016) in the lunar calendar, where most of the Bhutanese people follow a cultural practice of avoiding non-virtuous and harmful activities. Killing ticks is also considered non-virtuous, and most of the farmers were found to have refrained from using acaricides on animals at this time.

In this study, both the overall infestation prevalence and coinfestation was greater in the animals sampled in Trashigang compared with Pema Gatshel. Both showed a positive association with altitude. Co-infestation was greater in the indigenous breeds compared to the exotic breeds [OR=2.13 (95%CI: 1.24-3.7)]. While there are many environmental and climatic factors that could influence these findings, one important factor could be host availability. The cattle population of Trashigang is approximately five times more than that of Pema Gatshel; the population of indigenous breeds is approximately eight times more in Trashigang compared with

Pema Gatshel (DoL, 2018). As the majority of the indigenous breeds are reared in a free-grazing system, they are more likely to be exposed to ticks while grazing in the pastures and forests.

Therefore, this could be one of the reasons why we found overall infestation prevalence and coinfestation greater in Trashigang compared with that of Pema Gatshel.

This study has some limitations. Since we focused on understanding the presence and species diversity of ticks infesting cattle, we collected only adult ticks. This could have biased our estimates on the prevalence of tick species. The sampling period from May to June 2019 was very short. More species might have been found if the sampling was conducted throughout the year, and for the same reason, the seasonal pattern of the tick species identified could not be studied. To reduce these knowledge gaps, future studies should focus on at least a year-long active surveillance targeting a wider range of hosts. Other sampling methods like flagging and dragging on vegetation should also be conducted to improve the knowledge of tick species diversity. There is also a need to look at what other environmental and climatic factors contribute to the prevalence of different tick species. And most important of all, future studies should look at determining the potential pathogens present in the ticks that have the potential to transmit infectious diseases to animals and humans.

2.5 Conclusion

This study reports the presence and distribution of tick species isolated from cattle in eastern Bhutan. Four genera and five species of hard ticks on cattle in eastern Bhutan were identified. The tick species diversity in the study area was found similar to the tick fauna of the

Eastern Himalayan range. This study’s high-quality photographs can be used as reference images in tick identification works in the future. All specimens, including the voucher specimens, which can be used for genetic and molecular studies, are preserved at the national veterinary laboratory,

NCAH, Thimphu, Bhutan. The molecular studies can provide more information regarding the phylogenetic status of the ticks identified and their potential in transmitting diseases to humans and animals.

References

Allison, P. (2012). When Can You Safely Ignore Multicollinearity? When Can You Safely Ignore

Multicollinearity? https://statisticalhorizons.com/multicollinearity

Anastos, G. (1950). Scutate ticks, or Ixodidae, of Indonesia. Brooklyn Entomological Society.

Asmaa, N. M., ElBably, M. A., & Shokier, K. A. (2014). Studies on prevalence, risk indicators

and control options for tick infestation in ruminants. Beni-Suef University Journal of

Basic and Applied Sciences, 3(1), 68–73. https://doi.org/10.1016/j.bjbas.2014.02.009

Banerjee, A., & Bandopadhyay, R. (2016). Biodiversity hotspot of Bhutan and its sustainability.

Current Science, 110(4), 521–527. https://doi.org/10.18520/cs/v110/i4/521-528

Bhat, H., Naik, S., Ilkal, M., & Banerjee, K. (1978). Transmission of Kyasanur Forest Disease

Virus by Rhipicephalus haemaphysaloides Ticks. Acta Virologica, 22(3), 241–244.

Bianchi, M. W., Barré, N., & Messad, S. (2003). Factors related to cattle infestation level and

resistance to acaricides in Boophilus microplus tick populations in New Caledonia.

Veterinary Parasitology, 112(1), 75–89. https://doi.org/10.1016/S0304-4017(02)00415-6

Brahma, R. K., Dixit, V., Sangwan, A. K., & Doley, R. (2014). Identification and

characterization of Rhipicephalus (Boophilus) microplus and Haemaphysalis bispinosa

ticks (Acari: Ixodidae) of North East India by ITS2 and 16S rDNA sequences and

morphological analysis. Experimental and Applied Acarology, 62(2), 253–265.

https://doi.org/10.1007/s10493-013-9732-4

Burger, T. D., Shao, R., & Barker, S. C. (2014). Phylogenetic analysis of mitochondrial genome

sequences indicates that the cattle tick, Rhipicephalus (Boophilus) microplus, contains a

cryptic species. Molecular Phylogenetics and Evolution, 76, 241–253.

https://doi.org/10.1016/j.ympev.2014.03.017

Bush, A. O., Lafferty, K. D., Lotz, J. M., & Shostak, A. W. (1997). Parasitology Meets Ecology

on Its Own Terms: Margolis et al. Revisited on JSTOR. https://www-jstor-

org.ezproxy.lib.ucalgary.ca/stable/3284227?origin=crossref&seq=1#metadata_info_tab_c

ontents

Cao, W.-C., Gao, Y.-M., Zhang, P.-H., Zhang, X.-T., Dai, Q.-H., Dumler, J. S., Fang, L.-Q., &

Yang, H. (2000). Identification of Ehrlichia chaffeensis by Nested PCR in Ticks from

Southern China. Journal of Clinical Microbiology, 38(7), 2778–2780.

Chamuah, J. K., Bhattacharjee, K., Sarmah, P. C., Raina, O. K., Mukherjee, S., & Rajkhowa, C.

(2016). Report of Amblyomma testudinarium in mithuns (Bos frontalis) from eastern

Mizoram (India). Journal of Parasitic Diseases: Official Organ of the Indian Society for

Parasitology, 40(4), 1217–1220. https://doi.org/10.1007/s12639-015-0654-4

Chao, L.-L., Lu, C.-W., Lin, Y.-F., & Shih, C.-M. (2017). Molecular and morphological

identification of a human biting tick, Amblyomma testudinarium (Acari: Ixodidae), in

Taiwan. Experimental and Applied Acarology, 71(4), 401–414.

https://doi.org/10.1007/s10493-017-0119-9

Chen, Z., Li, Y., Ren, Q., Liu, Z., Luo, J., Li, K., Guan, G., Yang, J., Han, X., Liu, G., Luo, J., &

Yin, H. (2015). Does Haemaphysalis bispinosa (Acari: Ixodidae) really occur in China?

Experimental and Applied Acarology, 65(2), 249–257. https://doi.org/10.1007/s10493-

014-9854-3

Chen, Z., Yang, X., Bu, F., Yang, X., Yang, X., & Liu, J. (2010). Ticks (Acari: Ixodoidea:

Argasidae, Ixodidae) of China. Experimental and Applied Acarology, 51(4), 393–404.

https://doi.org/10.1007/s10493-010-9335-2

Clifford, C. M., Hoogstraal, H., & Keirans, J. E. (1975). The Ixodes Ticks (Agarina: Ixodidae) of

Nepal. Journal of Medical Entomology, 12(1), 115–137.

https://doi.org/10.1093/jmedent/12.1.115

Dardis, C. (2015). LogisticDx: Diagnostic Tests for Models with a Binomial Response.

https://CRAN.R-project.org/package=LogisticDx

De Clercq, E. M., Vanwambeke, S. O., Sungirai, M., Adehan, S., Lokossou, R., & Madder, M.

(2012). Geographic distribution of the invasive cattle tick Rhipicephalus microplus, a

country-wide survey in Benin. Experimental and Applied Acarology, 58(4), 441–452.

https://doi.org/10.1007/s10493-012-9587-0

Debbarma, A., Pandit, S., Jas, R., Baidya, S., Mandal, S. C., & Jana, P. S. (2018). Prevalence of

hard tick infestations in cattle of West Bengal, India. Biological Rhythm Research, 49(5),

655–662. https://doi.org/10.1080/09291016.2017.1395527

Diyes, G. C. P., Apanaskevich, D. A., & Rajakaruna, R. S. (2017). Lifecycle of Rhipicephalus

haemaphysaloides under laboratory conditions. Medical and Veterinary Entomology,

31(3), 327–332. https://doi.org/10.1111/mve.12238

DoL. (2018). Livestock Statistics. Department of Livestock, Ministry of Agriculture and Forests,

Royal Government of Bhutan. http://www.dol.gov.bt/?page_id=394

Dorji, U., Olesen, J. E., Bocher, P. K., & Seidenkrantz, M. S. (2016). Spatial Variation of

Temperature and Precipitation in Bhutan and Links to Vegetation and Land Cover.

Mountain Research and Development, 36(1), 66–79. https://doi.org/10.1659/MRD-

JOURNAL-D-15-00020.1

ECDC. (2018). Field sampling methods for mosquitoes, sandflies, biting midges and ticks.

https://www.ecdc.europa.eu/en/publications-data/field-sampling-methods-mosquitoes-

sandflies-biting-midges-and-ticks

Estrada-Peña, A. (2015). Ticks as vectors: Taxonomy, biology and ecology: -EN- -FR- Les

tiques en tant que vecteurs : taxonomie, biologie et écologie -ES- Las garrapatas como

vectores: taxonomía, biología y ecología. Revue Scientifique et Technique de l’OIE,

34(1), 53–65. https://doi.org/10.20506/rst.34.1.2345

Estrada-Pena, A., Venzal, J. M., Nava, S., Mangold, A., Guglielmone, A. A., Labruna, M. B., &

de la Fuente, J. (2012). Reinstatement of Rhipicephalus (Boophilus) australis (Acari:

Ixodidae) With Redescription of the Adult and Larval Stages. Journal of Medical

Entomology, 49(4), 794–802. https://doi.org/10.1603/ME11223

Estrada-Peña, Agustín, & de la Fuente, J. (2014). The ecology of ticks and epidemiology of tick-

borne viral diseases. Antiviral Research, 108, 104–128.

https://doi.org/10.1016/j.antiviral.2014.05.016

Eyo, J. E., Ekeh, F. N., Ivoke, N., Atama, C. I., Onah, I. E., Ezenwaji, N. E., & Ikele, C. B.

(2014). Survey of tick infestation of cattle at four selected grazing sites in the tropics.

Global Veterinaria, 12(4), 479–486.

Fox, J., & Weisberg, S. (2019). {An {R} Companion to Applied Regression}, (Third). Sage.

{https://socialsciences.mcmaster.ca/jfox/Books/Companion/}, }

Frank, E., Harrell, J., & and with contributions from Charles Dupont and many others. (2020).

{Hmisc: Harrell Miscellaneous} ({R package version 4.4-0}) [Computer software].

{https://CRAN.R-project.org/package=Hmisc}

Geevarghese, G., & Mishra, A. (2011). Haemaphysalis Ticks of India—1st Edition.

https://www.elsevier.com/books/haemaphysalis-ticks-of-india/geevarghese/978-0-12-

387811-3

Ghalsasi, G. R., & Dhanda, V. (1974). Taxonomy and Biology of Haemaphysalis (Kaiseriana)

spinigera (Acarina: Ixodidae). Oriental Insects, 8(4), 505–520.

https://doi.org/10.1080/00305316.1974.10434886

Ghosh, S., Bansal, G. C., Gupta, S. C., Ray, D., Khan, M. Q., Irshad, H., Shahiduzzaman, Md.,

Seitzer, U., & Ahmed, J. S. (2007). Status of tick distribution in Bangladesh, India and

Pakistan. Parasitology Research, 101(2), 207–216. https://doi.org/10.1007/s00436-007-

0684-7

Guglielmone, A. A., Robbins, R. G., Apanaskevich, D. A., Petney, T. N., Estrada-Pena, A.,

Horak, I. G., Shao, R., & Barker, S. C. (2010). The Argasidae, Ixodidae and

Nuttalliellidae (Acari: Ixodida) of the world: a list of valid species names. Zootaxa, 2528,

1–28. https://doi.org/10.11646/zootaxa.2528.1.1

Guo, Y., Sun, Y., & Xu, R. (2016). The genus Ixodes (Acari: Ixodidae) in China with three new

record species. Acta Parasitologica, 61(4), 729–742. https://doi.org/10.1515/ap-2016-

0102

Hidano, A., Dukpa, K., Rinzin, K., Sharma, B., Dahal, N., & Stevenson, M. A. (2016). A cross-

sectional survey of population demographics, the prevalence of major disease conditions

and reason-specific proportional mortality of domestic cattle in the Kingdom of Bhutan.

Preventive Veterinary Medicine, 130, 1–9.

https://doi.org/10.1016/j.prevetmed.2016.05.012

Hoogstraal, H., Lim, B.-L., & Anastos, G. (1969). Haemaphysalis (Kaiseriana) bispinosa

Neumann (Ixodoidea: Ixodidae): Evidence for Consideration as an Introduced Species in

the Malay Peninsula and Borneo. The Journal of Parasitology, 55(5), 1075–1077.

JSTOR. https://doi.org/10.2307/3277178

Imaoka, K., Kaneko, S., Tabara, K., Kusatake, K., & Morita, E. (2011). The First Human Case of

Rickettsia tamurae Infection in Japan. Case Reports in Dermatology, 3(1), 68–73.

https://doi.org/10.1159/000326941

Jongejan, F., & Uilenberg, G. (2004). The global importance of ticks. Parasitology, 129(S1), S3–

S14. https://doi.org/10.1017/S0031182004005967

Kabir, M. H. B., Mondal, M. M. H., Eliyas, M., Mannan, M. A., Hashem, M. A., Debnath, N. C.,

Miazi, O. F., Mohiuddin, Kashem, M. A., Islam, M. R., & Elahi, M. F. (2011). An

epidemiological survey on investigation of tick infestation in cattle at Chittagong District,

Bangladesh. African Journal of Microbiology Research, 5(4), 346–352.

https://doi.org/10.5897/AJMR10.706

Kemal, J., Tamerat, N., & Tuluka, T. (2016). Infestation and Identification of Ixodid Tick in

Cattle: The Case of Arbegona District, Southern Ethiopia. Journal of Veterinary

Medicine, 2016. https://doi.org/10.1155/2016/9618291

Leal, B., Thomas, D. B., & Dearth, R. K. (2018). Population Dynamics of Off-Host

Rhipicephalus (Boophilus) microplus (Acari: Ixodidae) Larvae in Response to Habitat

and Seasonality in South Texas. Veterinary Sciences, 5(2).

https://doi.org/10.3390/vetsci5020033

Liu, Q., Zhou, Y. Q., Zhou, D. N., Liu, E. Y., Du, K., Chen, S. G., Yao, B. A., & Zhao, J. L.

(2007). Semi-nested PCR detection of Babesia orientalis in its natural hosts

Rhipicephalus haemaphysaloides and buffalo. Veterinary Parasitology, 143(3), 260–266.

https://doi.org/10.1016/j.vetpar.2006.08.016

Lorusso, V., Picozzi, K., de Bronsvoort, B. M., Majekodunmi, A., Dongkum, C., Balak, G.,

Igweh, A., & Welburn, S. C. (2013). Ixodid ticks of traditionally managed cattle in

central Nigeria: Where Rhipicephalus (Boophilus) microplus does not dare (yet?).

Parasites & Vectors, 6(1), 171. https://doi.org/10.1186/1756-3305-6-171

Low, V. L., Tay, S. T., Kho, K. L., Koh, F. X., Tan, T. K., Lim, Y. A. L., Ong, B. L.,

Panchadcharam, C., Norma-Rashid, Y., & Sofian-Azirun, M. (2015). Molecular

characterisation of the tick Rhipicephalus microplus in Malaysia: New insights into the

cryptic diversity and distinct genetic assemblages throughout the world. Parasites &

Vectors, 8(1), 341. https://doi.org/10.1186/s13071-015-0956-5

Marcelino de la Cruz. (2017). {logihist: Combined Graphs for Logistic Regression}.

{https://CRAN.R-project.org/package=logihist}

Matthysee, J. G., & Colbo, M. H. (1987). The Ixodid Ticks of Uganda: Together With Species

Pertinent to Uganda Because of Their Present Known Distribution. Entomological

Society of America.

National Statistics Bureau, N. (2018). 2017 population & housing census of Bhutan: National

report = ʼBrug gi mi rlobs dang khyim gyi grangs rtsis 2017. National Statistics Bureau,

Royal Government of Bhutan.

NCAH. (2018). Veterinary information System 2018. http://vis.ncah.gov.bt/php/index.php

NCAH. (2019). Veterinary Information System. http://vis.ncah.gov.bt/php/index.php

Nutall, G. H., Warburton, C., Cooper, W., & Robinson, L. (1915). Ticks. A Monograph of the

Ixodoidea. (Part III). Cambridge: 349–550.

Ogden, N. H., Swai, E., Beauchamp, G., Karimuribo, E., Fitzpatrick, J. L., Bryant, M. J.,

Kambarage, D., & French, N. P. (2005). Risk factors for tick attachment to smallholder

dairy cattle in Tanzania. Preventive Veterinary Medicine, 67(2), 157–170.

https://doi.org/10.1016/j.prevetmed.2004.10.011

Pattnaik, P. (2006). Kyasanur forest disease: An epidemiological view in India. Reviews in

Medical Virology, 16(3), 151–165. https://doi.org/10.1002/rmv.495

Phanchung, Dorji, P., Sonam, T., & Pelden, K. (2007). Smallholder Dairy Farming in Bhutan:

Characteristics, Constraints, and Development Opportunities.

http://lib.icimod.org/record/21365/files/c_attachment_79_552.pdf

Phuntsho. (2016). Saga Dawa, the auspicious month – KuenselOnline.

https://kuenselonline.com/saga-dawa-the-auspicious-month/

QGIS Development Team. (2019). QGIS Geographic Information System: Open Source

Geospatial Foundation Project. http://qgis.osgeo.org

R Core Team. (2018). R: A Language and Environment for Statistical Computing. R Foundation

for Statistical Computing. https://www.R-project.org/

Rehman, A., Nijhof, A. M., Sauter-Louis, C., Schauer, B., Staubach, C., & Conraths, F. J.

(2017). Distribution of ticks infesting ruminants and risk factors associated with high tick

prevalence in livestock farms in the semi-arid and arid agro-ecological zones of Pakistan.

Parasites & Vectors, 10(1), 190. https://doi.org/10.1186/s13071-017-2138-0

RLDC Wangdue. (2019). 9th Annual Progress Report of RLDC Wangdue (2018-19). DoL,

MoAF, Wangdue, Bhutan. http://www.dol.gov.bt/?wpdmpro=rldc-wangdue-annual-

progress-report-fy-2018-19

Robinson, L. E., Nuttall, G. H. F., & Warburton, C. (1926). The genus Amblyomma. University

Press.

Sadanandane, C., Gokhale, M. D., Elango, A., Yadav, P., Mourya, D. T., & Jambulingam, P.

(2018). Prevalence and spatial distribution of Ixodid tick populations in the forest fringes

of Western Ghats reported with human cases of Kyasanur forest disease and monkey

deaths in South India. Experimental and Applied Acarology, 75(1), 135–142.

https://doi.org/10.1007/s10493-018-0223-5

Scherer, R. (2018). PropCIs: Various Confidence Interval Methods for Proportions [R package

version 0.3-0]. https://CRAN.R-project.org/package=PropCIs

Spickler, A. R. (2007). Rhipicephalus (Boophilus) microplus. 3.

Sutherst, R. W., & Bourne, A. S. (2006). The effect of desiccation and low temperature on the

viability of eggs and emerging larvae of the tick, Rhipicephalus (Boophilus) microplus

(Canestrini) (Ixodidae). International Journal for Parasitology, 36(2), 193–200.

https://doi.org/10.1016/j.ijpara.2005.09.007

Thrusfield, M. (2018). Veterinary Epidemiology (Third). John Wiley & Sons.

Tiki, B., & Addis, M. (2011). Distribution of Ixodid Ticks on Cattle in and Around Holeta Town,

Ethiopia. 6. Global Veterinaria, 7(6), 527–531.

Venables, W. N., & Ripley, B. D. (2002). {Modern Applied Statistics with S} (Fourth).

{Springer}. http://www.stats.ox.ac.uk/pub/MASS4

Walker, A. R., Bouattour, A., Camicas, J.-L., Estrada-Pena, A., Horak, I. G., Latif, A. A.,

Pegram, R. G., & Preston, P. M. (2003). Ticks of domestic animals in Africa: A guide to

identification of species. http://agris.fao.org/agris-

search/search.do?recordID=XF2015039701

Wickham, H., Francois, R., Henry, L., & Muller, K. (2019). dplyr: A Grammar of Data

Manipulation. https://CRAN.R-project.org/package=dplyr

Yu, Z., Wang, H., Wang, T., Sun, W., Yang, X., & Liu, J. (2015). Tick-borne pathogens and the

vector potential of ticks in China. Parasites & Vectors, 8(1), 24.

https://doi.org/10.1186/s13071-014-0628-x

Zheng, W., Xuan, X., Fu, R., Tao, H., Xu, R., Liu, Y., Liu, X., Jiang, J., Wu, H., Ma, H., Sun, Y.,

& Chen, H. (2019). Preliminary investigation of ixodid ticks in Jiangxi Province of

Eastern China. Experimental and Applied Acarology, 77(1), 93–104.

https://doi.org/10.1007/s10493-018-0324-1

Chapter 3 Modeling the habitat suitability of ticks identified in eastern Bhutan

3.1 Background

Bhutan is a small Kingdom located in the eastern Himalayas between latitudes 26° 45' N and 28° 10' N, and longitudes 88° 45' E and 92° 10' E. It shares borders with China to the north, and the Indian states of Assam and West Bengal to the south, Arunachal Pradesh to the east, and

Sikkim to the west. It has a total land area of 38,394 km2, of which 70.77% is under forest cover

(MoAF, 2019). The country is divided into six agro-ecological zones (wet subtropical, humid subtropical, dry subtropical, warm temperate, cool temperate, and alpine) based on altitudinal ranges and climatic conditions (MoAF, 2019). There is a pronounced south-north elevation gradient (100-7500 meters above sea level) and an inverse north-south precipitation gradient

(500->2000mm) (MoAF, 2019; NCHM, 2017). The climate and vegetation coverage in the country are influenced by the extreme variation in altitude and the north Indian monsoon

(Banerjee & Bandopadhyay, 2016). The Himalayan region, including Bhutan, is considered to be particularly sensitive to the impacts of global climate change, with temperatures increasing at approximately three times the global average (Xu et al., 2009). This is expected to bring significant changes to the distribution and composition of ecosystems in the region.

Consequently, this may also affect the distribution of disease vectors, such as ticks in the region.

Despite ticks being prevalent throughout the country, there is currently limited knowledge of their ecology and distribution in Bhutan. There has been no comprehensive study conducted to understand the species composition, diversity, and the factors influencing their geographic distribution. Current knowledge consists of one unpublished study (Cork et al., 1996) conducted in eastern Bhutan and one government published report (RLDC Wangdue, 2019) from western Bhutan. The most recent information is from the study we conducted in eastern Bhutan

93 to determine the presence and diversity of tick species in cattle (Chapter 2). We identified four genera and six species of ticks based on morphological keys. These were R. microplus, R. haemaphysaloides, H. bispinosa, H. spinigera, A. testudinarium, and an unidentified species of

Ixodes.

In this study, a field survey information on tick species presence along with environmental data was used to model the relationship between tick species presence and the environmental variables, using the MaxEnt (Phillips et al., 2006) modeling approach. MaxEnt modeling was selected because it can build a reliable model of species distribution using presence-only data and environmental variables without assuming species absence in locations not sampled or surveyed (Elith et al., 2019). The objective of this study was to model the distribution of tick species identified in eastern Bhutan, under current environmental conditions, and to identify environmental factors associated with the geographical distribution of ticks. The findings from this study are expected to guide the planning and implementation of tick surveillance programs in the eastern region and other relevant areas in Bhutan.

3.2 Materials and methods

3.2.1 Ethics statement

The study protocol was approved by both the Animal Care Committee (ACC), University of Calgary, Canada (AC 19-0035) and the Research and Extension Division, Department of

Livestock, Ministry of Agriculture and Forests, Royal Government of Bhutan (Animal Research

Application Form-15/05/2019).

3.2.2 Study area

The study was conducted in the districts of Trashigang and Pema Gatshel in eastern

Bhutan (Figure 3.1), covering an area spanning from the latitudes 26° 75' to 27° 5' North, and the

94 longitudes 91° to 91° 85' East. The study area covers a geographical area of 2546 km2 and the entire range of agro-ecological zones represented in Bhutan. The elevation of the study area ranges from 46m to 4571m. Trashigang is predominantly a temperate district and shares a border with the Indian state of Arunachal Pradesh in the east. The average annual temperature is 16.90C, and the total annual rainfall is 855 mm (National Statistics Bureau, 2019b). The district is characterized by warm summers and cold winters. Pema Gatshel, located at the warmer south, is mainly a subtropical district and shares a border with the Indian state of Assam in the south. The average annual temperature is 17.30C, and the total annual rainfall is 1136 mm (National

Statistics Bureau, 2019b). The district is characterized by hot, humid summers and cool winters.

Most of the land in both districts are associated with high mountainous terrains separated by narrow valleys. Human settlements and farming activities are generally limited to these narrow valleys and the gentle slopes in the mountains.

Figure 3.1 Elevation map of the study area showing tick sampling sites (black dots).

3.2.3 Tick presence data

The details on tick sampling and tick identification procedures are available in Chapter 2.

Briefly, tick presence data were obtained by conducting a field survey of 240 randomly selected households owning cattle in eastern Bhutan. The sampling was conducted once per household in

May-June 2019. One animal that could be properly restrained was selected at each household, and 15 ticks were collected, providing a total of 3600 ticks to determine the presence and species diversity. All ticks except one specimen were identified to the species level using morphological keys. Geographic coordinates (i.e., latitude and longitude) in the World Geodetic System (WGS)

84 were recorded using smartphones equipped with Global Positioning System (GPS) application for the iOS software platform. The presence data for four tick species: R. microplus;

R. haemaphysaloides; H. bispinosa; and H. spinigera, were used in this study.

3.2.4 Environmental data

Bioclimatic variables were downloaded from the WorldClim database (Version 2)

(https://www.worldclim.org/) at 30 seconds (~1km2) spatial resolution (Hijmans et al., 2005).

The digital elevation - shuttle radar topography information (DEM_SRTM) at 1 arc-second global was downloaded from the United States Geological Survey (USGS) database

(https://www.usgs.gov/). The most recent land use and land cover data (LULC 2016) for Bhutan at 30m spatial resolution (classified from Landsat 8) was obtained from the National Land

Commission (NLC) of Bhutan. The LULC 2016 uses 17 classes: alpine scrubs (1); broadleaf (2); built-up (3); chuzhing (4); chirpine (5); fir (6); kamzhing (7); lake (8); landslides (9); meadows

(10); mixed conifer (11); non built-up (12); orchards (13); rivers (14); rocky outcrops (15); shrubs (16); and snow and glaciers (17). Chuzhing refers to irrigated and bench terraced agriculture land for paddy cultivation, and Kamzhing represents cultivated rain-fed dry land

96 primarily used for the cultivation of cereal crops (FRMD, 2017). The DEM_SRTM and LULC

2016 were organized as raster (grid) type files to the spatial extent and resolution of the

WorldClim layers. The environmental variables used in this study are listed in Table 3.1. Data preparation was done using raster (Hijmans, 2020), rgeos (Bivand & Rundel, 2019), and rgdal

(Bivand et al., 2019) packages in R statistical software (R Core Team, 2018).

Table 3.1 Environmental variables used in building MaxEnt models

Variable, unit Code Source Annual mean temperature, °C Bio 1 WorldClim Max temperature of warmest month, °C Bio 5 WorldClim Min temperature of coldest month, °C Bio 6 WorldClim Mean temperature of wettest quarter, °C Bio 8 WorldClim Mean temperature of driest quarter, °C Bio 9 WorldClim Mean temperature of warmest quarter, °C Bio 10 WorldClim Mean temperature of coldest quarter, °C Bio 11 WorldClim Annual precipitation, mm Bio 12 WorldClim Precipitation of wettest quarter, mm Bio 16 WorldClim Precipitation of driest quarter, mm Bio 17 WorldClim Precipitation of warmest quarter, mm Bio 18 WorldClim Precipitation of coldest quarter, mm Bio 19 WorldClim Elevation DEM_SRTM USGS Land Use & Land Cover 2016* LULC NLC, Bhutan *Categorical variable

Seven bioclimatic variables (i.e., Bio 2, Bio 3, Bio 4, Bio 7, Bio 13, Bio 14, and Bio 15) were excluded as they were not deemed ecologically relevant to the study area. Bio 2 (annual mean diurnal range) was excluded due to limited data range (2.30C) in the study area. Bio 3

(isothermality) was excluded as there is little day to night temperature oscillation in the study area. Bio 4 (temperature seasonality) and Bio 7 (annual temperature range) was excluded as their information is included in other temperature variables. Bio 13 (precipitation of the wettest month), Bio 14 (precipitation of the driest month), and Bio 15 (monthly precipitation variation) were excluded as the precipitation in Bhutan is seasonal depending on the North Indian

97 monsoon. The seasonal variations are more important than the monthly variations; therefore, the precipitation of the quarters were used.

3.2.5 Statistical modeling

Statistical modeling was conducted using MaxEnt v3.4.1 (Phillips et al., 2006) in R statistical software (R Core Team, 2018). The MaxEnt java executable file

(https://biodiversityinformatics.amnh.org/open_source/maxent/) was downloaded and made available to the dismo package.

Tick species occurrence datasets were transformed into the spatial data frame based on the associated coordinates using the coordinates function in the sp package (Pebesma & Bivand,

2005). All environmental layers were stacked as raster layers and restricted to the study area using crop and mask functions in the raster package (Hijmans, 2020). MaxEnt was run with iterations and background points set to default using the environmental variables that were transformed into the hinge feature that combines linear and step functions. This feature improves model performance when there are at least 15 presence points (Phillips & Dudík, 2008). The dismo package (Hijmans et al., 2017) and the prepPara function (Feng et al., 2017) were used for running MaxEnt. Of the four output formats (raw, cumulative, logistic, and cloglog) available in MaxEnt, logistic output was selected as it provides a clear and distinct output value from 0 to

1 for visualization purposes. It can be easily interpreted as an estimate of the probability of the presence of species at any given location ranging from 0 to 1. The low probability of species presence is represented by 0 and the very high relative probability of species presence by 1

(Phillips & Dudík, 2008). Cross-validation was selected as the replicate type to replicate our samples into folds, which in turn can be used for test data. The remaining parameters in MaxEnt were kept at their default settings.

The contribution and permutation importance of environmental variables were assessed from the MaxEnt output generated after the run. The variable contribution is calculated as the increasing training gain in each iteration of the model due to the variable (Phillips, 2006).

Permutation importance is the measure of accuracy difference when the final model is compared to the one in which values of the considered variables at presence and background points are permuted (Phillips, 2006). Variables contributing less than 1% to the increasing training gain or less than 1% permutation importance were considered non-significant and excluded from further analyses (Zuliani et al., 2015). Spearman’s correlation analysis was conducted among the selected variables using the corr.test function from the psych package (Revelle, 2019) to omit highly correlated variables. LULC 2016 was not included in the correlation analysis as it is a categorical variable.

Competing models were built using all selected variables and then subsequently reducing the model by removing the less important variables based on Akaike Information Criteria (AIC)

(Burnham & Anderson, 2004). The occurrence points for each tick species were separated into training (80%) and testing (20%) data using a k-fold methodology for evaluation in dismo package (Hijmans et al., 2017; Radosavljevic & Anderson, 2014). Model evaluations were done using the evaluate function from the dismo package (Hijmans et al., 2017), and the AIC was calculated using the cal.aicc function from the ENMeval package (Muscarella et al., 2014).

Competing models were then compared using threshold independent Receiver Operating

Characteristics (ROC) graphs (Fielding & Bell, 1997), correlation, and the AIC (Burnham &

Anderson, 2004; Warren & Seifert, 2011). The best models were then selected based on AIC values to avoid spatial sampling bias inherent to MaxEnt modeling. Further, the difference between AICci and AICcmin (Δi) was calculated to know the actual distance of each model from

99 the best model (i.e., the one with lowest AICc, AICcmin); and Akaike weights (ω) were also calculated for model averaging (Burnham & Anderson, 2004).

The spatial distribution of the best models was obtained by reclassifying and mapping the probability levels from 0 to 1 using rasterVis (Perpi & Hijmans, 2019) and RColorBrewer

(Neuwirth, 2014) packages. The probability levels of occurrence (p) obtained from the best

MaxEnt models generated were mapped following Zuliani et al. (2015) into 5 classes: class 1

(very low probability) for p between 0 and 0.2; class 2 ( low probability) for p between 0.2 and

0.4; class 3 (moderate probability) for p between 0.4 and 0.6; class 4 (moderate-high probability) for p between 0.6 and 0.8; and class 5 (high probability) for p between 0.8 and 1.0. The predicted maps generated by the best models for each tick species were compared for niche similarity using the nicheOverlap function in the dismo package (Hijmans et al., 2017; Warren et al.,

2008). For the best models, the response curves of the predictors were qualitatively assessed to understand the relationship between the variation in individual variables and the probability of tick species occurrence. Jackknife tests were also used to assess the most relevant predictors for the best models.

3.3 Results

Rhipicephalus microplus

All 204 locations where R. microplus was found were used as presence locations. In the first MaxEnt run, three variables (i.e., DEM_SRTM, LULC, and Bio 18) achieved more than 1% contribution and permutation importance (Table 3.2). There was a high correlation (rs = -0.93) between DEM_SRTM and Bio 18 (Table 3.3).

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Table 3.2 Variable contribution and permutation importance values obtained during the first MaxEnt run performed with all the variables for predicting tick species presence. Only variables that achieved values of more than 1% in both metrics are shown.

Variable % contribution Permutation importance R. microplus LULC 44.1 28.5 Bio 18 42.6 24.5 DEM_SRTM 9.6 28.2 R. haemaphysaloides LULC 47.8 8 Bio 18 31.6 24.5 Bio 16 7.2 31.1 Bio 10 2.1 28.9 DEM_SRTM 2 1.7 Bio 8 1.7 25.1 H. bispinosa LULC 63.9 39.9 Bio 18 25.1 24 Bio 12 4.2 9 Bio 11 3.9 3.1 Bio 16 1.2 9.4 H. spinigera Bio 19 29.7 55.7 LULC 22.4 11.7 Bio 12 10.1 6.6 Bio 17 3.5 2.3 Bio 8 2.8 5.2 Bio 11 2.5 5 Bio 16 2.2 13.2

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Variables R. microplus DEM_SRTM Bio 18 -0.93 R. haemaphysaloides Bio 16 Bio 10 Bio 8 DEM_SRTM Bio 18 1 0.91 0.91 -0.94 Bio 16 0.92 0.91 -0.94 Bio 10 1 -0.99 Bio 8 -0.98 H. bispinosa Bio 12 Bio 11 Bio 16 Bio 18 0.98 0.96 0.98 Bio 12 0.97 1 Bio 11 0.96 H. spinigera Bio 12 Bio 17 Bio 8 Bio 11 Bio 16 Bio 19 0.85 1 0.97 0.97 0.84 Bio 12 0.85 0.78 0.90 1 Bio 17 0.97 0.97 0.84 Bio 8 0.96 0.77 Bio 11 0.89

Four competing models were built with the selected variables (Appendix A.3). All models had high accuracy with AUC ranging from 0.77 to 0.80. Model RM_B1 had the lowest

AICc and was therefore selected as the best model for describing R. microplus distribution under current environmental conditions (Table 3.4). There was a 99.9% niche overlap between the two predicted maps (Figure 3.2) generated by the model RM_B1 and the full model RM_A, indicating little information was lost using the reduced, two-variable model. The Jackknife test from the best model RM_B1 indicated that DEM_SRTM had the most information that was not present in LULC to accurately predict the distribution of R. microplus (Figure 3.3A). The probability of R. microplus occurrence increased with an elevation between 500-1000m, then declined (Figure 3.4A). The probability of occurrence was most significant in land cover classes

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classified as kamzhing (0.84) and meadows (0.81) (Figure 3.4B). Shrubs were associated with a

0.55 probability of occurrence. The probability prediction by the remaining land cover classes

ranged from 0.40-0.42. The best model RM_B1 showed 49.7% of the study area as moderate to

high suitable areas for R. microplus (Table 3.5). The habitat suitability map (Figure 3.2 RM_B1)

showed that very low suitable areas for R. microplus included the northeastern part of the study

area with elevation >2000m and the southernmost part with elevation < 500m.

Table 3.4 Best MaxEnt models developed for predicting tick species in eastern Bhutan: RM_B1 for R. microplus; RH_D1 for R. haemaphysaloides; HB_C2 for H. bispinosa; and HS_E1 for H. spinigera. Variables, percent contribution (%), permutation importance (PI), AUC (training and testing), correlation (training and testing), corrected AIC, delta (Δ), Akaike weights (ω), and the number of parameters (#P) for each best model is given.

Train Train Test Test Model variable % PI AICc P AUC Cor AUC Cor RM_B1 DEM 56.1 76.4 0.803 0.5 0.809 0.39 3084.35 15

LULC 43.9 23.6

RH_D1 LULC 46 9.4 0.842 0.498 0.889 0.368 1360.47 13 Bio 16 43.1 29.6 Bio 10 10.9 61

HB_C2 LULC 63.2 39.3 0.857 0.497 0.895 0.329 1095.32 13 Bio 18 30.6 44.7 Bio 16 6.2 16.1

HS_E1 Bio 16 45.7 26.3 0.871 0.352 0.861 0.188 433.91 8 Bio 19 33.4 66.9 LULC 20.8 6.8

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Figure 3.2 Habitat suitability maps for R. microplus developed by the two best models: full model RM_A using LULC, DEM_SRTM, and Bio 18; and model RM_B1 using LULC and DEM_SRTM.

Figure 3.3 Variable contribution to the training gain of the final model for tick species occurrence in eastern Bhutan: (A) R. microplus; (B) R. haemaphysaloides; (C) H. bispinosa; and (D) H. spinigera. Dark blue bars depict the AUC values when variables are used in isolation. Light blue bars depict the AUC values when the model is run without the variables.

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Figure 3.4 Response curve plotting the probability of R. microplus occurrence in eastern Bhutan against the values of the top environmental variables: (A) Elevation (DEM_SRTM) and (B) Land use and land cover 2016 (LULC). The X-axis represents the variable value, and the Y-axis represents the probability of presence as predicted by the best MaxEnt model RM_B1.

Table 3.5 Study area (in km2 and percentage) classified by the probability of tick species occurrence in eastern Bhutan: RM_B1 for R. microplus; RH_D1 for R. haemaphysaloides; HB_C2 for H. bispinosa; and HS_E1 for H. spinigera.

Probability class

Model 1 (0-20) 2 (21-40) 3 (41-60) 4 (61-80) 5 (81-100)

km2 % km2 % km2 % km2 % km2 %

RM_B1 1028.5 40.4 251.9 9.9 1028.8 40.4 166.9 6.5 70 2.7 RH_D1 1119.9 43.9 728.2 28.6 467.8 18.4 134.4 5.3 95.9 3.8 HB_C2 955.4 37.5 957 37.6 502.2 19.7 19.8 0.8 111.7 4.4 HS_E1 1252.8 49.2 448.5 17.6 423.5 16.6 268.2 10.5 152.9 6

Rhipicephalus haemaphysaloides

All 91 locations where R. haemaphysaloides was found were used as presence locations.

Six variables, LULC, Bio 18, Bio 16, Bio 10, DEM_SRTM, and Bio 8, achieved more than 1%

contribution and permutation importance in the first MaxEnt run (Table 3.2). Correlation

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analyses indicated that all variables were highly correlated (rs > 0.90) (Table 3.3). Eighteen competing models were built using the selected variables (Appendix A.4). All models had high accuracy (AUC>0.80). The models RH_C4 and RH_D1 had similar AICc values (Δ=0.08). The model RH_C4 had the variable DEM contributing zero to the model (Appendix A.4); therefore, the model RH_D1 was selected as the best model for describing R. haemaphysaloides distribution under current environmental conditions (Table 3.4). There was more than 99.99% niche overlap between the two predicted maps (Figure 3.5) generated by models RH_C4 and

RH_D1.

Figure 3.5 Habitat suitability maps for R. haemaphysaloides developed by the two best models: model RH_C4 using LULC, Bio 16, Bio 10, and DEM_SRTM; and model RH_D1 using LULC, Bio 16, and Bio 10.

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Figure 3.6 Response curves plotting the probability of R. haemaphysaloides occurrence in eastern Bhutan against the values of the top environmental variables: (A) Temperature of the warmest quarter (Bio 10), (B) Land use and land cover 2016 (LULC), and (C) precipitation of the wettest quarter (Bio 16). The X-axis represents the variable value, and the Y-axis represents the probability of presence as predicted by the best MaxEnt model RH_D1.

The Jackknife test from the model RH_D1 indicated that Bio 16 had the highest gain when used in isolation, and LULC was the variable that decreased the gain the most when omitted, indicating that it had the most information that was not present in other variables to accurately predict the distribution of R. haemaphysaloides (Figure 3.3B). The probability of R. haemaphysaloides occurrence increased with the temperature of the warmest quarter (Bio 10) between 16 and 250C, beyond which there was a decline (Figure 3.6A). The probability of occurrence was most significant in kamzhing (0.82), followed by shrubs (0.6) and chuzhing

(0.59) (Figure 3.6B). The probability prediction by the remaining land cover classes was 0.46.

The probability of occurrence also increased with precipitation of the wettest quarter (Bio 16)

107 between 400 and 1100 mm, then declined when Bio 16 exceeded 1200 mm (Figure 3.6C). The best model, RH_D1, showed 27.4% of the study area as moderate to high suitable areas for R. haemaphysaloides (Table 3.5). The habitat suitability map (Figure 3.5 RH_D1) predicted the northeastern and the southernmost part of the study area as low suitable areas for R. haemaphysaloides.

Haemaphysalis bispinosa

All 72 locations where H. bispinosa was found were used as presence locations. Five variables, LULC, Bio 18, Bio 16, Bio 12, and Bio 11, achieved more than 1% contribution and permutation importance in the first MaxEnt run (Table 3.2). Correlation analyses indicated that all variables were highly correlated (rs > 0.90) (Table 3.3). Thirteen competing models were built using the selected variables (Appendix A.5).

Figure 3.7 Habitat suitability maps for H. bispinosa developed by the two best models: model HB_B3 using LULC, Bio 18, Bio 16, and Bio 12; and model HB_C2 using LULC, Bio 18 and Bio 16.

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All models had high AUC values ranging from 0.77 to 0.86. Model HB_C2 had the lowest AICc and was therefore selected as the best model for describing H. bispinosa distribution under current environmental conditions (Table 3.4). There was more than 97.8% niche overlap between the two predicted maps (Figure 3.7) generated by the best model HB_C2 and the second-best model HB_B3. The Jackknife test from the best model HB_C2 indicated

LULC had the highest gain when used in isolation and decreased the gain the most when omitted, indicating that it had the most information that was not present in other variables to accurately predict the distribution of H. bispinosa (Figure 3.3C).

Figure 3.8 Response curves plotting the probability of H. bispinosa occurrence in eastern Bhutan against the values of the top environmental variables: (A) precipitation of the warmest quarter (Bio 18), (B) Land use and land cover 2016 (LULC), and (C) precipitation of the wettest quarter (Bio 16). The X-axis represents the variable value, and the Y-axis represents the probability of presence as predicted by the best MaxEnt model HB_C2.

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The probability of H. bispinosa occurrence increased with precipitation of the warmest quarter (Bio 18) between 600 and 2000 mm, beyond which there was a decrease (Figure 3.8A).

The probability of occurrence also increased with precipitation of the wettest quarter (Bio 16) between 400 and 900 mm, then declined when Bio 16 exceeded 1600 mm (Figure 3.8C). The probability of occurrence was 0.83 in Kamzhing and 0.82 with meadows (Figure 3.8B). Shrubs and Chuzhing had 0.64 and 0.55 probabilities, respectively. The probability prediction by the remaining land cover classes ranged from 0.29-0.33. The best model HB_C2 showed 24.9% of the study area as moderate to high suitable areas for H. bispinosa (Table 3.5). The habitat suitability map (Figure 3.7 HB_C2) predicted the northeastern and the southernmost part of the study area as low suitable areas for H. bispinosa.

Haemaphysalis spinigera

All 28 locations where H. spinigera was found were used as presence locations. Seven variables, LULC, Bio 8, Bio 11, Bio 12, Bio 16, Bio 17, and Bio 19, achieved more than 1% contribution and permutation importance in the first MaxEnt run (Table 3.2). All variables were highly correlated (rs > 0.70) (Table 3.3). Twenty-six competing models were built using the selected variables (Appendix A.6). All models had high AUC values ranging from 0.82 to 0.88.

The models HS_C2, HS_D2, and HS_E1 had similar AUC, correlation, and AICc values (max

Δ=0.74). The niche similarity was also more than 99.9% among the three predicted maps (Figure

3.9) generated by these three best MaxEnt models. Therefore, using the parsimony principle, model HS_E1 was selected as the best model for describing H. spinigera distribution under current environmental conditions since it had the least number of parameters when compared with the two other best models, HS_C2 and HS_D2 (Table A.5).

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Figure 3.9 Habitat suitability maps for H. spinigera developed by the three best models: model HS_C2 using LULC, Bio 19, Bio 16, Bio 12 and Bio 8; model HS_D2 using LULC, Bio 19, Bio 16, and Bio 8; and model HS_E1 using LULC, Bio 19 and Bio 16.

The Jackknife test from the best model HS_E1 indicated that Bio 16 had the highest gain when used in isolation, and LULC was the variable that decreased the gain the most when omitted, indicating that it had the most information that was not present in other variables to accurately predict the distribution of H. spinigera (Figure 3.3D). The probability of H. spinigera occurrence increased with precipitation of the coldest quarter (Bio 19) between 15 and 40 mm, beyond which there was a decrease (Figure 3.10A). The probability of occurrence also increased with precipitation of the wettest quarter (Bio 16) at 400mm, then declined when Bio 16 exceeded

1600mm (Figure 3.10C). The probability of occurrence was 0.88 in kamzhing and 0.67 with shrubs (Figure 3.10B). The probability prediction by the remaining land cover classes was 0.57.

The best model HS_E1 showed 33.2% of the study area as moderate to high suitable areas for H. spinigera (Table 3.5). The habitat suitability map (Figure 3.9 HS_E1) predicted the northeastern and the southernmost part of the study area as low suitable areas for H. spinigera.

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Figure 3.10 Response curves plotting the probability of H. spinigera occurrence in eastern Bhutan against the values of the top environmental variables: (A) precipitation of the coldest quarter (Bio 19), (B) Land use and land cover 2016 (LULC), and (C) precipitation of the wettest quarter (Bio 16). The X-axis represents the variable value, and the Y-axis represents the probability of presence as predicted by the best MaxEnt model HS_E1.

3.4 Discussion

This is the first study to have attempted to model the habitat suitability for tick species identified in Bhutan. The habitat suitability of four tick species: R. microplus; R. haemaphysaloides; H. bispinosa; and H. spinigera, was modeled, and some climatic and environmental factors that could be influencing their spatial distribution were identified. Four

MaxEnt models were built to describe the spatial distribution of R. microplus under current environmental conditions. The best model RM_B1 (Table 3.4) predicted the current distribution using the relationship between R. microplus occurrence data and two environmental variables

(elevation and the land cover). Elevation was the most important environmental variable for the

112 distribution of R. microplus in this study. This corroborates the findings from a study in

Zimbabwe where the elevation was found the most influencing factor for the geographic distribution of R. microplus and R. decoloratus (Sungirai et al., 2018). The highest probability of

R. microplus occurrence corresponded to the low elevation range (500-1000m) and decreased after that threshold. This elevation range corresponds to the humid subtropical zone (600-1200m) in Bhutan, characterized by an annual mean temperature of 19.5oC and an annual rainfall of

1200-2500mm (MoAF, 2019).

Rhipicephalus microplus is a one-host tick widely distributed in tropical and subtropical regions of the world (Spickler, 2007). Temperature and rainfall are the most important climatic factors driving the geographic distribution of this tick species (Estrada-Peña et al., 2005; M.

Sungirai et al., 2018). Generally, this tick prefers warm and humid conditions (De Clercq et al.,

2012). It avoids higher altitudes such as mountains and plateaus, where low temperatures are prevalent (Estrada-Peña et al., 2006; Lynen et al., 2008). Low temperatures were also detrimental to the eggs of R. microplus under laboratory conditions (Sutherst & Bourne, 2006).

This tick species is also highly dependent on the presence of a bovine host (Estrada-Peña et al.,

2005). The other factors determining its distribution and abundance are host availability, host resistance, acaricide use, and grazing management (Randolph, 1997). In Bhutan, the areas with higher elevation are characterized by colder temperatures and low rainfalls, and that might be resulting in these areas being very low suitable habitats for R. microplus. The environmental variables such as temperature, rainfall, and humidity also change over short distances with a change in elevation in Bhutan (Hoy et al., 2016).

Land cover classes such as kamzhing and meadows were identified as a suitable habitat for R. microplus. This might be associated with the presence of the bovine host in these types of

113 land. Kamzhing is the most important land type for dry land agricultural farming in Bhutan.

Besides cultivating cereal crops, kamzhing is also used for growing fodder crops like oats and clover, especially in winters. Cattle are also tethered and allowed to graze on the small patches of grasses and weeds in the kamzhing areas. This system of mixed farming is still prevalent throughout the country. Such activities might explain why kamzhing is a suitable land type for ticks, especially for a one-host tick like R. microplus. However, the suitability would also depend on the presence of other favorable environmental conditions such as optimum temperature and humidity.

The high probability of occurrence in the meadows could also be due to host availability.

Most of the indigenous cattle breeds reared in a free grazing system are grazed in the meadows.

Frequently, wild cervids like deer also graze in the meadows. Shrublands were also identified as the suitable land type for R. microplus occurrence. Shrubs are perennial plants with a woody stem with a height of less than 5 meters (FRMD, 2017). Generally, shrublands in Bhutan are suitable for tick-host interactions because they are normal grazing areas for cattle and wild herbivores such as deer. Further, shrubs provide a conducive environment for small mammals, especially rodents, which can serve as reservoir hosts for the larvae and nymphs of multi-host tick species. The current habitat suitability map predicted by the MaxEnt for R. microplus

(Figure 3.2 RM_B1) has 50.28% (Table 3.5) of the study area predicted as low suitability (P <

0.4). Most of the low suitable areas are in the northeastern region that has a high elevation

(>2000m) with low temperatures and rainfall; and the southernmost part that has a low elevation

(<500m). The reasons for low suitability will be discussed later with consideration of other species of ticks.

114

Eighteen models were built with MaxEnt for R. haemaphysaloides to describe its distribution under current environmental conditions. The best model RH_D1 (Table 3.4) predicted the current distribution using three environmental variables, Bio 10 (temperature of the warmest quarter), Bio 16 (precipitation of the wettest quarter), and the land cover. This typical three-host tick is widely distributed in the neighboring Indian states bordering Bhutan (Ghosh et al., 2007). Temperature is considered to be the key climatic factor influencing the biological performance of this tick (Diyes et al., 2017). Under the laboratory conditions, the optimum temperature range for hatching eggs by engorged females was 27oC to 29oC (Diyes et al., 2017).

In this study, R. haemaphysaloides presence was related to Bio 10 (temperature of the warmest quarter) with the highest probability of occurrence at 25oC (Figure 3.6A). The presence of R. haemaphysaloides was also related to Bio 16 (precipitation of the wettest quarter) with the highest probability at 1100mm precipitation. Generally, the relative humidity plays a significant role in regulating the questing activity of ticks, and, further, the mortality of ticks also depends on the loss of water (Estrada-Peña, 2015). This tick species is an exophilic tick that may lose water while questing for a host, and thus its survival may be dependent on its ability to retain or gain water. Therefore, precipitation might play a critical role in the survival of this tick in the environment as the relative humidity is largely influenced by precipitation.

Kamzhing and shrublands were identified as suitable habitats for R. haemaphysaloides.

This finding may be explained as above. Chuzhing showed a 0.59 probability of R. haemaphysaloides occurrence. Theoretically, chuzhing should not be a suitable habitat as ticks would not be able to withstand flooding. However, the entire area categorized as chuzhing is not under rice cultivation. The insufficient irrigation supply, farm labor shortage, erratic rainfall, and human-wildlife conflicts, among many other factors, have pushed many farmers to leave their

115 chuzhing fallow (Kuensel, 2018). Consequently, these fallow lands have become a grazing area for domestic animals like cattle and wild cervids like deer. Occasionally, some farmers also grow fodder grasses on chuzhings that could not be utilized for rice cultivation. This changing pattern of land use might be the reason for chuzhing in our study area to feature as one of the suitable habitats for R. haemaphysaloides. The current habitat suitability map predicted by MaxEnt for R. haemaphysaloides (Figure 3.5 RH_D1) has 72.58% (Table 3.5) of the study area at low suitability (P < 0.4). Most of that area is located in the northeastern and the southernmost part of the study area.

For the two Haemaphysalis species (H. bispinosa & H. spinigera), thirteen and twenty- six models were built, respectively, with the MaxEnt to describe their distribution under current environmental conditions. The best model HB_C2 (Table 3.4) predicted the current distribution of H. bispinosa using three environmental variables: Bio 18 (precipitation of the warmest quarter), Bio 16 (precipitation of the wettest quarter), and the land cover. The best model HS_E1

(Table 3.4) predicted the current distribution of H. spinigera using three environmental variables:

Bio 18 (precipitation of the coldest quarter), Bio 16 (precipitation of the wettest quarter), and the land cover. Geographically, both species are established in the neighboring Indian states bordering Bhutan (Geevarghese & Mishra, 2011). In this study, their presence was related to precipitation variables along with the land cover. For both species, we found a similar pattern of influence by Bio 16 (precipitation of the wettest quarter). In both cases, the probability of occurrence increased at 400mm and then decreased when Bio 16 exceeded 1600mm (Figure

3.8C & 3.10C).

Other studies from the neighboring countries like Bangladesh and India had similar findings for these two Haemaphysalis species. Haemaphysalis bispinosa seems to thrive well in

116 areas with high summer rainfall in Bangladesh (Islam et al., 2006). In India, it was observed that the population of the engorged adults increased from July through August, then gradually decreased from September onwards, became absent from December to May, and reappeared in the next rainy season from June (Geevarghese & Mishra, 2011). July and August correspond to the monsoon season in India, where the entire subcontinent receives a lot of rain. These findings indicated that precipitation, especially during the warm and wet months of the monsoon, plays an important role in the survival of H. bispinosa at some point in their life cycle. This is true with H. longicornis, where precipitation, along with temperature and relative humidity, has been identified as one of the important environmental factors determining its geographic distribution

(Heath, 2016; Neilson, 1980).

Similarly, Haemaphysalis spinigera is also known to prefer wet and moist habitats with heavy to moderate rainfall (Geevarghese & Mishra, 2011). In South India, where Kysanuar forest disease (KFD) is endemic, the nymphal activity of H. spinigera (the principal vector of KFD) is known to peak in spring and summer seasons coinciding with the onset of monsoon (Ajesh et al.,

2017). Further, a MaxEnt modeling study (preprint) (Pramanik et al., 2020) from South India for

H. spinigera has identified precipitation variables such as annual precipitation and wettest month precipitation as important climatic variables influencing its geographical distribution. These findings indicated that precipitation is one of the important climatic factors for H. spinigera.

The highest probability of H. bispinosa occurrence was related to Kamzhing and meadows, followed by shrublands and chuzhing. For H. spinigera, the highest probability of occurrence was related to Kamzhing followed by shrublands. This can be explained as discussed above for R. microplus and R. haemaphysaloides. Shrublands were found to be the most adaptive habitat for other Haemaphysalis ticks, such as H. longicornis (Zheng et al., 2012). The current

117 habitat suitability maps predicted by the MaxEnt for these two Haemaphysalis species (Figure

3.7 HB_C2 & 3.9 HS_E1) showed 75.1% and 66.8% (Table 3.5) of the study area as low suitable areas (P < 0.4) for H. bispinosa and H. spinigera, respectively. They are the northeastern and the southernmost part of the study area for both Haemaphysalis species.

For all four tick species discussed above, the northeastern and the southernmost parts of the study area were predicted to be low suitable areas. The northeastern region is the area with higher elevations where temperatures and rainfall are low. In this region, the vegetation is predominantly mixed conifer forests. The low temperatures, scanty rainfall, and the occasional snow and frosts might be the limiting factors for tick survival. However, there might be some other species of ticks such as Hyalomma spp. which can tolerate high elevations. Cork et al.

(1996) collected Hyalomma spp. in eastern Bhutan in areas with high elevation (> 2700m).

Therefore, additional studies using other sampling methods such as flagging and dragging may have to be conducted for at least a year to ascertain whether these areas are unsuitable for ticks.

The southernmost part predicted as low suitable areas are the subtropical floodplains at a very low elevation (< 500m). The vegetation in these areas is predominantly broadleaf forests.

Climatically, these areas will be suitable for ticks, but the host availability (especially the bovines) might be the limiting factor. There are no human activities such as settlement and farming in these areas. The frequent flooding during the monsoon will also be one of the limiting factors. However, there are some wild animals inhabiting these areas, which might be potential hosts for some species of ticks. Therefore, future efforts must be targeted toward tick collection from wild animals and the environment in these areas to understand the habitat suitability better.

This study has some limitations. Since the primary aim of our study was to understand the presence and diversity of tick species in cattle, our sampling was biased toward areas

118 dominated by cattle rearing. The presence data used in this modeling study were based on tick collection from cattle, and it may not represent the exact location from where tick(s) originated.

Therefore, future studies should include tick sampling from vegetation to collect all life stages for obtaining better information on habitat and life cycle. The sampling for this study was conducted only once in May and June 2019 as this time period is thought to be the season for peak tick infestation in cattle in Bhutan; therefore, we could not use those locations where we did not find ticks as absence points. The three-host ticks like R. haemaphysaloides, H. bispinosa and

H. spinigera depend on multiple hosts to complete their life-cycle; however, ticks were collected only from the cattle, and the status of other potential hosts that these ticks might be feeding on is not known. Therefore, future studies should try to collect ticks from wild animals such as small mammals. In Bhutan, this could be done in collaboration with wildlife officials who often encounter wild animals and birds during rescue and relocation operations.

Generally, tick distribution is also influenced by other factors such as host availability, acaricide use, husbandry practices, and anthropogenic disturbances, but these factors were not incorporated in the models. The resampled bioclimatic variables may not represent the extreme climatic variations in the mountainous terrain of Bhutan, thereby increasing the uncertainty of the models. Further, the MaxEnt approach, as a correlative species distribution model, estimates the realized niche but not the fundamental niche of the species (Kearney et al., 2010); therefore, the actual suitable habitat is likely to be more than what is predicted by the habitat suitability maps.

Nevertheless, MaxEnt is a powerful software tool for habitat suitability modeling with several advantages that include the need for few presence points and the ability to use both continuous and categorical variables (Phillips et al., 2006). Despite these advantages, MaxEnt

119 predictions can be affected by overfitting and the background environmental data, especially when extrapolating to different areas or climate conditions (Phillips & Dudík, 2008). While

MaxEnt can be used for any future modeling study on ticks in Bhutan, other modeling approaches such as General Linear Model (GLM) and General Additive Model (GAM) should also be explored to compare the predictions among different modeling approaches.

3.5 Conclusion

This habitat suitability modeling study has identified some potential climatic and environmental factors to predict the current distribution of selected tick species in eastern

Bhutan. Land cover types such as kamzhing, meadows, chuzhing, and shrubland were identified as the preferred habitat of all four species of ticks modeled in eastern Bhutan. Elevation was found to be the most important environmental variable influencing the distribution of R. microplus. Bio 10 (temperature of the warmest quarter) and Bio 16 (precipitation of the wettest quarter) were identified as the most important climatic variables driving the distribution of R. haemaphysaloides. Precipitation variables (i.e., precipitation of the warmest, the coldest and the wettest quarters) were identified as the important climatic variables for two species of

Haemaphysalis (H. bispinosa and H. spinigera). Further, the habitat suitability maps for all tick species modeled are also presented. Therefore, the findings from this study are expected to inform the Bhutanese livestock officials and the future researchers on planning and implementation of tick surveillance programs.

120

References

Ajesh, K., Nagaraja, B. K., & Sreejith, K. (2017). Kyasanur forest disease virus breaking the

endemic barrier: An investigation into ecological effects on disease emergence and future

outlook. Zoonoses and Public Health, 64(7), e73–e80. https://doi.org/10.1111/zph.12349

Banerjee, A., & Bandopadhyay, R. (2016). Biodiversity hotspot of Bhutan and its sustainability.

Current Science, 110(4), 521–527. https://doi.org/10.18520/cs/v110/i4/521-528

Bivand, R., Keitt, T., & Rowlingson, B. (2019). {rgdal: Bindings for the “Geospatial” Data

Abstraction Library} ({R package version 1.4-8}) [Computer software].

{https://CRAN.R-project.org/package=rgdal}

Bivand, R., & Rundel, C. (2019). {rgeos: Interface to Geometry Engine—Open Source

('GEOS’)} ({R package version 0.5-2}) [Computer software]. {https://CRAN.R-

project.org/package=rgeos}

Burnham, K. P., & Anderson, D. R. (2004). Multimodel Inference: Understanding AIC and BIC

in Model Selection. Sociological Methods & Research, 33(2), 261–304.

https://doi.org/10.1177/0049124104268644

Cork, S., Dukpa, K., Gurung, J., & Charleston, W. A. G. (1996). A preliminary study of the ticks

and heamoparasites of cattle in Eastern Bhutan (Unpublished).

De Clercq, E. M., Vanwambeke, S. O., Sungirai, M., Adehan, S., Lokossou, R., & Madder, M.

(2012). Geographic distribution of the invasive cattle tick Rhipicephalus microplus, a

country-wide survey in Benin. Experimental and Applied Acarology, 58(4), 441–452.

https://doi.org/10.1007/s10493-012-9587-0

121

Diyes, G. C. P., Apanaskevich, D. A., & Rajakaruna, R. S. (2017). Lifecycle of Rhipicephalus

haemaphysaloides under laboratory conditions. Medical and Veterinary Entomology,

31(3), 327–332. https://doi.org/10.1111/mve.12238

Elith, J., Phillips, S. J., Hastie, T., Dudík, M., Chee, Y. E., & Yates, C. J. (2019). A statistical

explanation of MaxEnt for ecologists. Diversity and Distributions, 43–57.

https://doi.org/10.1111/[email protected]/(ISSN)1472-4642.species-

distribution-models-in-conservation-biogeography

Estrada-Peña, A. (2015). Ticks as vectors: Taxonomy, biology and ecology: -EN- -FR- Les

tiques en tant que vecteurs : taxonomie, biologie et écologie -ES- Las garrapatas como

vectores: taxonomía, biología y ecología. Revue Scientifique et Technique de l’OIE,

34(1), 53–65. https://doi.org/10.20506/rst.34.1.2345

Estrada-Peña, A., Acedo, C. S., Quílez, J., & Del Cacho, E. (2005). A Retrospective Study of

Climatic Suitability for the Tick Rhipicephalus (Boophilus) microplus in the Americas.

Global Ecology and Biogeography, 14(6), 565–573. JSTOR.

Estrada-Peña, A., García, Z., & Sánchez, H. F. (2006). The Distribution and Ecological

Preferences of Boophilus microplus (Acari: Ixodidae) in Mexico. Experimental and

Applied Acarology, 38(4), 307–316. https://doi.org/10.1007/s10493-006-7251-2

Feng, X., Gebresenbet, F., & Walker, C. (2017). Shifting from closed-source graphical-interface

to open-source programming environment: A brief tutorial on running Maxent in R

(e3346v1). PeerJ Inc. https://doi.org/10.7287/peerj.preprints.3346v1

Fielding, A. H., & Bell, J. F. (1997). A review of methods for the assessment of prediction errors

in conservation presence/absence models. Environmental Conservation, 24(1), 38–49.

https://doi.org/10.1017/S0376892997000088

122

FRMD. (2017). Land Use and Land Cover of Bhutan 2016, Maps and Statistics.

http://www.dofps.gov.bt/wp-content/uploads/2018/07/LULC2016_Maps-and-

Statistics.pdf

Geevarghese, G., & Mishra, A. (2011). Haemaphysalis Ticks of India—1st Edition.

https://www.elsevier.com/books/haemaphysalis-ticks-of-india/geevarghese/978-0-12-

387811-3

Ghosh, S., Bansal, G. C., Gupta, S. C., Ray, D., Khan, M. Q., Irshad, H., Shahiduzzaman, Md.,

Seitzer, U., & Ahmed, J. S. (2007). Status of tick distribution in Bangladesh, India and

Pakistan. Parasitology Research, 101(2), 207–216. https://doi.org/10.1007/s00436-007-

0684-7

Heath, A. C. G. (2016). Biology, ecology and distribution of the tick, Haemaphysalis longicornis

Neumann (Acari: Ixodidae) in New Zealand. New Zealand Veterinary Journal, 64(1),

10–20. https://doi.org/10.1080/00480169.2015.1035769

Hijmans, R. J. (2020). {raster: Geographic Data Analysis and Modeling} ({R package version

3.0-12}) [Computer software]. {https://CRAN.R-project.org/package=raster}

Hijmans, R. J., Cameron, S. E., Parra, J. L., Jones, P. G., & Jarvis, A. (2005). Very high

resolution interpolated climate surfaces for global land areas. International Journal of

Climatology, 25(15), 1965–1978. https://doi.org/10.1002/joc.1276

Hijmans, R. J., Phillips, S., Leathwick, J., & Elith, J. (2017). {dismo: Species Distribution

Modeling} ({R package version 1.1-4}) [Computer software]. {https://CRAN.R-

project.org/package=dismo}

Hoy, A., Katel, O., Thapa, P., Dendup, N., & Matschullat, J. (2016). Climatic changes and their

impact on socio-economic sectors in the Bhutan Himalayas: An implementation strategy.

123

Regional Environmental Change, 16(5), 1401–1415. https://doi.org/10.1007/s10113-015-

0868-0

Islam, M. K., Alim, M. A., Tsuji, N., & Mondal, M. M. H. (2006). An investigation into the

distribution, host-preference and population density of Ixodid ticks affecting domestic

animals in Bangladesh. Tropical Animal Health and Production, 38(6), 485–490.

https://doi.org/10.1007/s11250-006-4381-9

Kearney, M. R., Wintle, B. A., & Porter, W. P. (2010). Correlative and mechanistic models of

species distribution provide congruent forecasts under climate change. Conservation

Letters, 3(3), 203–213. https://doi.org/10.1111/j.1755-263X.2010.00097.x

Kuensel. (2018). Rice cultivation area shrinks – KuenselOnline. https://kuenselonline.com/rice-

cultivation-area-shrinks/

Lynen, G., Zeman, P., Bakuname, C., Di Giulio, G., Mtui, P., Sanka, P., & Jongejan, F. (2008).

Shifts in the distributional ranges of Boophilus ticks in Tanzania: Evidence that a

parapatric boundary between Boophilus microplus and B. decoloratus follows climate

gradients. Experimental and Applied Acarology, 44(2), 147–164.

https://doi.org/10.1007/s10493-008-9134-1

MoAF. (2019). BHUTAN RNR STATISTICS – 2018 | Ministry of Agriculture and Forests.

http://www.moaf.gov.bt/bhutan-rnr-statistics-2018/

Muscarella, R., Galante, P. J., Soley‐Guardia, M., Boria, R. A., Kass, J. M., Uriarte, M., &

Anderson, R. P. (2014). ENMeval: An R package for conducting spatially independent

evaluations and estimating optimal model complexity for Maxent ecological niche

models. Methods in Ecology and Evolution, 5(11), 1198–1205.

https://doi.org/10.1111/2041-210X.12261

124

National Statistics Bureau. (2019). Statistical Yearbook of Bhutan 2019. Royal Government of

Bhutan. http://www.nsb.gov.bt/publication/files/SYB_2019.pdf

NCHM. (2017). Bhutan State of the Climate 2017 (ISBN: 978-99936-721-8-0). National Center

for Hydrology and Meteorology, Royal Government of Bhutan.

http://www.nchm.gov.bt/attachment/ckfinder/userfiles/files/Bhutan%20State%20of%20t

he%20Climate%202017.pdf

Neilson, F. J. A. (1980). An investigation into the ecology, biology, distribution and control of

Haemaphysalis longicornis Neumann, 1901: A thesis presented in partial fulfilment of

the requirements for the degree of Master of Veterinary Science at Massey University

[Thesis, Massey University]. https://mro.massey.ac.nz/handle/10179/4944

Neuwirth, E. (2014). {RColorBrewer: ColorBrewer Palettes} ({R package version 1.1-2})

[Computer software]. {https://CRAN.R-project.org/package=RColorBrewer}

Perpi, O., & Hijmans, R. (2019). {{rasterVis}} ({R package version 0.47}) [Computer software].

{http://oscarperpinan.github.io/rastervis/}

Phillips, S. J. (2006). A Brief Tutorial on Maxent. 39.

Phillips, S. J., Anderson, R. P., & Schapire, R. E. (2006). Maximum entropy modeling of species

geographic distributions. Ecological Modelling, 190(3), 231–259.

https://doi.org/10.1016/j.ecolmodel.2005.03.026

Phillips, S. J., & Dudík, M. (2008). Modeling of species distributions with Maxent: New

extensions and a comprehensive evaluation. Ecography, 31(2), 161–175.

https://doi.org/10.1111/j.0906-7590.2008.5203.x

125

Pramanik, M., Singh, P., & Dhiman, R. (2020). Identification of Bio-climatic Determinants and

Potential Risk Areas for Kyasanur Forest Disease in Southern India using MaxEnt

Modelling Approach. https://doi.org/10.21203/rs.2.22417/v1

R Core Team. (2018). R: A Language and Environment for Statistical Computing. R Foundation

for Statistical Computing. https://www.R-project.org/

Radosavljevic, A., & Anderson, R. P. (2014). Making better Maxent models of species

distributions: Complexity, overfitting and evaluation. Journal of Biogeography, 41(4),

629–643. https://doi.org/10.1111/jbi.12227

Randolph, S. E. (1997). Abiotic and biotic determinants of the seasonal dynamics of the tick

Rhipicephalus appendiculatus in South Africa. Medical and Veterinary Entomology,

11(1), 25–37. https://doi.org/10.1111/j.1365-2915.1997.tb00286.x

Revelle, W. (2019). {psych: Procedures for Psychological, Psychometric, and Personality

Research} ({R package version 1.9.12}) [Computer software]. {Northwestern

University}. {https://CRAN.R-project.org/package=psych}

RLDC Wangdue. (2019). 9th Annual Progress Report of RLDC Wangdue (2018-19). DoL,

MoAF, Wangdue, Bhutan. http://www.dol.gov.bt/?wpdmpro=rldc-wangdue-annual-

progress-report-fy-2018-19

Spickler, A. R. (2007). Rhipicephalus (Boophilus) microplus. 3.

Sungirai, M., Moyo, D. Z., De Clercq, P., Madder, M., Vanwambeke, S. O., & De Clercq, E. M.

(2018). Modelling the distribution of Rhipicephalus microplus and R. decoloratus in

Zimbabwe. Veterinary Parasitology: Regional Studies and Reports, 14, 41–49.

https://doi.org/10.1016/j.vprsr.2018.08.006

126

Sutherst, R. W., & Bourne, A. S. (2006). The effect of desiccation and low temperature on the

viability of eggs and emerging larvae of the tick, Rhipicephalus (Boophilus) microplus

(Canestrini) (Ixodidae). International Journal for Parasitology, 36(2), 193–200.

https://doi.org/10.1016/j.ijpara.2005.09.007

Warren, D. L., Glor, R. E., & Turelli, M. (2008). Environmental Niche Equivalency Versus

Conservatism: Quantitative Approaches to Niche Evolution. Evolution, 62(11), 2868–

2883. https://doi.org/10.1111/j.1558-5646.2008.00482.x

Warren, D. L., & Seifert, S. N. (2011). Ecological niche modeling in Maxent: The importance of

model complexity and the performance of model selection criteria. Ecological

Applications, 21(2), 335–342. https://doi.org/10.1890/10-1171.1

Xu, J., Grumbine, R. E., Shrestha, A., Eriksson, M., Yang, X., Wang, Y., & Wilkes, A. (2009).

The Melting Himalayas: Cascading Effects of Climate Change on Water, Biodiversity,

and Livelihoods. Conservation Biology, 23(3), 520–530. https://doi.org/10.1111/j.1523-

1739.2009.01237.x

Zheng, H., Yu, Z., Zhou, L., Yang, X., & Liu, J. (2012). Seasonal abundance and activity of the

hard tick Haemaphysalis longicornis (Acari: Ixodidae) in North China. Experimental and

Applied Acarology, 56(2), 133–141. https://doi.org/10.1007/s10493-011-9505-x

Zuliani, A., Massolo, A., Lysyk, T., Johnson, G., Marshall, S., Berger, K., & Cork, S. C. (2015).

Modelling the Northward Expansion of Culicoides sonorensis (Diptera:

Ceratopogonidae) under Future Climate Scenarios. PLoS ONE, 10(8).

https://doi.org/10.1371/journal.pone.0130294

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Chapter 4 A knowledge, attitude and practices survey on ticks and tick-borne diseases

among cattle owners in a selected area of eastern Bhutan

4.1 Background

Bhutan is primarily an agrarian country with 62.2% of the population depending on agriculture and livestock farming for their livelihood (National Statistics Bureau, 2018b). Among all the livestock species, cattle (Bos taurus taurus and Bos taurus indicus) make the biggest contribution to income and food security in rural communities (Hidano et al., 2016).

Traditionally, the cattle rearing system in Bhutan was categorised into: a transhumant system of the high-altitude areas dominated by cattle migration; and a sedentary system in other areas characterized by crop-cattle integration (Phanchung et al., 2007). Currently, with the government’s effort targeted toward commercialization through the formation of farmers’ groups and primary dairy cooperatives, the cattle rearing system is gradually shifting from traditional subsistence production to modern market-based farming where the adoption of stall-feeding system is fast expanding. In this system, the European breeds of cattle such as Jersey and

Holstein Friesian are reared for higher milk yields, and they contribute primarily to financial capital in the form of cash income through the sale of milk and dairy products. They also have a secondary role in providing manure. The cattle reared in this system are housed in a hygienic dairy shed and fed with adequate quantities of commercial feed and fodder.

Livestock farming, particularly cattle rearing, in Bhutan is constrained by infectious diseases such as foot and mouth disease, hemorrhagic septicemia, black quarter, anthrax, rabies, brucellosis, and parasitic diseases (NCAH, 2019). However, tick infestation and its associated impact are considered to be one of the major parasitological problems faced by cattle farming communities (Phanchung et al., 2007). In 2019 alone, 42% of the cattle population in Bhutan

128 were reported to have been treated for tick infestation (NCAH, 2019), and this cost the government approximately 3.18 million Bhutanese Ngultrum (1CAD= Nu.54) for purchasing acaricides.

Veterinary services and therapeutics, such as drugs and vaccines, are provided free of cost in Bhutan by the government. Tick control is also a government-supported program, and it is implemented through the Department of Livestock (DoL). The National Centre for Animal

Health (NCAH) under DoL is responsible for the selection, procurement, and supply of acaricides in the country. The liquid formulation of pyrethroid compounds (i.e., cypermethrin, deltamethrin, and flumethrin) and amidines (i.e., amitraz) imported from India are supplied to farmers for direct topical application to host animals (NCAH, 2013). Livestock officials advise farmers to follow manufacturers’ instructions during on-farm dilution. These chemicals are preferred because of their broad spectrum of activity against ectoparasites and their mode of action (i.e., they act by contact), which makes their usage easy and convenient (NCAH, 2013).

Besides this conventional method of tick control using acaricides over the past many years, there has neither been any concerted effort to develop a more effective, sustainable and integrated control strategy nor an evaluation of the effectiveness of the current methods. The success of tick control programs largely relies on developing a good understanding of farmers’ knowledge about ticks and TBDs, their perceptions of the effectiveness of the proposed control methods, and the socio-cultural context in which such programs are to be implemented (Adehan et al., 2018; Sungirai et al., 2016). Such information is typically gathered through various types of cross-sectional studies, the most popular and widely used being the knowledge, attitude, practice (KAP) survey (Launiala, 2009).

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The KAP survey tradition started in the 1950s in the field of family planning and population studies (Schopper et al., 1993). Today, KAP surveys have become commonly used methodologies to investigate community perspectives and human behavior with regard to a wide range of topics (Launiala, 2009). KAP surveys are designed to collect information on what is known, what is believed, and what is commonly practiced in relation to a particular topic – in this case, ticks and TBDs (WHO, 2008). Some of the attractive characteristics contributing to

KAP surveys gaining popularity are that they are easy to design, cost effective, can obtain both qualitative and quantifiable data with concise results, can generalise small sample results to a wider population, and the ease with which enumerators can be trained (Bhattacharyya, 1997;

Stone & Campbell, 1984). Nevertheless, KAP surveys are also criticised because of the assumption that the data generated from such studies can be extrapolated to a wider population for planning purposes (Green, 2001; Launiala, 2009). Yet in the field of ticks and TBDs, KAP studies (Adehan et al., 2018; Butler et al., 2016; Gupta et al., 2018; Niesobecki et al., 2019;

Sungirai et al., 2016; Zoldi et al., 2017) have been conducted, and the data generated have resulted in the development of effective intervention strategies.

However, in Bhutan, there have not been any KAP studies conducted about ticks and

TBDs. Therefore, recognizing the importance of understanding farmers’ knowledge, attitude, and prevalent farm practices about ticks and TBDs, this KAP study was conducted in one of the sub- districts in eastern Bhutan with the primary objective to provide baseline data for subsequent planning and implementation of an effective tick prevention and control strategy. Findings from this study are expected to guide community-based awareness programs on ticks and TBDs in the study area and other relevant communities to improve the adoption of effective tick prevention and control measures.

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4.2 Materials and methods

4.2.1 Study area

Bhutan is divided administratively into 20 districts and 205 sub-districts. Bhutan's 20 districts are broadly grouped into the four developmental regions; eastern region, east central region, western region, and west central region. The KAP survey was conducted in the Samkhar sub-district/Gewog in Trashigang district, eastern Bhutan (Figure 4.1). The study area was selected based on convenience and purpose (Dohoo et al., 2003). The Regional Livestock

Development Centre (RLDC) of the eastern region is in Trashigang district from where it was logistically convenient to solicit support during the fieldwork. Further, the Samkhar Gewog is the most intensive dairy farming Gewog in the eastern region besides being one of the largest in

Trashigang. Commercial dairy farming is the main income-generating activity in the Gewog. The

Gewog has a population of 2109 persons living across 62 villages (National Statistics Bureau,

2018a), and the cattle population of 2022 in 632 households (DoL, 2018). There is one livestock extension center staffed by a para-veterinarian who provides basic veterinary services.

Geographically, the Gewog has the area of 90 km2, and 84% of the area is under forest cover.

The average altitude is 900 masl, and the climate is moderately warm in summer and cold in winter. It also receives continuous rainfall during monsoons.

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Figure 4.1 Map of Bhutan showing the study area (pink shade). The map was generated using ArcGIS software (ESRI, 2018). The shapefiles for the political boundary of Bhutan, including district and sub-district boundaries, were obtained from the National Land Commission of Bhutan. 4.2.2 Sample size

The sample size of 246 households was calculated using the formula n=Z2p(1-p)/e2

(Dohoo et al., 2003), where Z (95% level of confidence)=1.96, p= estimated baseline proportion of cattle owners who were presumed to have adequate knowledge on ticks and TBDs= 0.20, and e= margin of error =0.05. Since no previous study was conducted in this selected area, we assumed 20% of the cattle owners would have adequate knowledge about ticks and TBDs based on local knowledge of the government officials in the Samkhar Gewog. The official list of the households owning cattle in the Gewog was collected from the District Veterinary Hospital,

Trashigang, and used as the sampling frame. Households were selected using simple random sampling in MS Excel 2016 (Microsoft Excel 2016, Redmond, USA). However, during the field

132 survey, some selected households were unable to participate due to reasons such as not owning cattle any more or sociocultural incidents like the death of a family member in the household. In such cases, the nearest household fulfilling the study criteria was selected. Twelve households were replaced.

4.2.3 Questionnaire survey

A 44-item structured questionnaire consisting of four different sections was prepared in

English and used for the collection of data (Appendix B.1). Section one consisted of questions on sociodemographic and farming characteristics. Sections two, three, and four consisted of questions on knowledge, attitude, and practices regarding ticks and TBDs, respectively.

Six livestock personnel working in Trashigang were selected and trained as survey enumerators. Four local government officials assisted the enumerators in identifying the households during the survey. The questionnaire was pre-tested in 20 households of Rangshikhar village through mock interviews that were part of the survey enumerators’ training. Based on the pre-test, modifications were made to the questionnaire to suit the local context.

The two inclusion criteria were households owning cattle and respondents aged not less than 18 years. A typical Bhutanese household is characterized by a close knit family with an average household size (i.e., number of family members in a household) of 4-5 members

(National Statistics Bureau, 2017). Sixty-five percent of the total households in Bhutan are headed by males and 35% are headed by females (National Statistics Bureau, 2017). This study targeted household heads or any senior member of the family, who usually reside in a household throughout the year, to be the main respondents as they were usually directly responsible for management of cattle. Before starting the questionnaire survey, the enumerators explained the objective of the study to the selected respondents, and verbal consent was sought for the

133 interview. Respondents were informed that participation was voluntary and that they could quit anytime during the interview. All the selected respondents agreed to participate in the interview.

The face to face interviews were conducted in June 2019 in local dialects but recorded in

English. Data collection was conducted using an online mobile phone application, EpiCollect5

(https://five.epicollect.net/).

4.2.4 Ethics statement

The study protocol was approved by both the Conjoint Faculties Research Ethics Board

(CFREB), University of Calgary, Canada (REB19-0035), and the Research Ethics Board of

Health (REBH), Ministry of Health, Royal Government of Bhutan (ref no. REBH/PO/2019/029).

4.2.5 Statistical analyses

The data collected through EpiCollect were checked for completeness using data filtering options and downloaded as a comma-separated values (CSV) file. Then the CSV file was imported to R computing software (R Core Team, 2018) for analyses. The analyses were conducted using R packages, “dplyr”, “descr”, “lmtest”, “ggplot2”, “forcats” “splitstackshape”,

“stringr”, “LogisticDx” and “stargazer” within the R statistical software (Aquino, 2018; Dardis,

2015; Hlavac, 2018; Mahto, 2019; Wickham, 2016, 2019b, 2019a; Wickham et al., 2019; Zeileis

& Hothorn, 2002).

The method used by Rinchen et al. (2019) was adapted to categorize the respondents as either having “adequate knowledge” or “inadequate knowledge”. A score was assigned to two questions, and the knowledge was considered adequate when the respondents answered both the questions correctly (Table 4.1). Based on the score, the knowledge was converted into a binary outcome variable (i.e., 1 for respondents who had “adequate knowledge” and 0 for respondents

134 who had “inadequate knowledge”). The assumption was that the respondents with adequate knowledge would be aware that the ticks could transmit diseases to humans and cattle.

Table 4.1 Questions used for assessing participants’ knowledge about ticks as vectors of diseases.

Questions Score Criteria

Do you think cattle can get 1 A point was awarded if respondents answered diseases from ticks? * “Yes”; otherwise, no point was awarded.

Do you think humans can get 1 A point was awarded if respondents answered diseases from tick bites? * “Yes”; otherwise, no point was awarded.

*If respondents answered any of these questions incorrectly, they were categorized as not having adequate knowledge about ticks as vectors of diseases.

Similarly, the attitude was described using the method of Rinchen et al. (2019) to categorize respondents as either having a “favorable attitude” or “unfavorable attitude”. Three questions were scored to evaluate respondents’ attitudes to tick prevention and control programs

(Table 4.2). The respondents could choose an answer on a Likert scale of 5 (1: strongly disagree,

2: disagree, 3: no opinion, 4: agree, and 5: strongly agree). The attitude was considered favorable when the responses to all three questions were “strongly agree”. Based on the score, the attitude was converted into a binary outcome variable (i.e., 1 for respondents who had a “favorable attitude”, and 0 for respondents who had an “unfavorable attitude”).

The sociodemographic variables such as age, gender, education level, cattle holding per household, and husbandry practice were considered explanatory variables against the binary outcomes of knowledge and attitude variables. For analysis, the variable age was categorized

(based on the quartile distribution) as 18-35, 36-45, and >45 years, and the variable cattle holding per household as above 4 or below 4 based on the mean number of cattle owned by the households sampled. The variable education was categorized as “literate” or “illiterate”, and the

135 variable husbandry practice as “stall feeding” or “mixed practices”. A descriptive analysis was carried out for the entire dataset to calculate frequencies and proportions.

Table 4.2 Questions used for assessing participants’ attitudes toward prevention and control of ticks in cattle.

Questions Score Criteria

Do you agree? Proper use of synthetic 5 A point was awarded if the answer acaricides can reduce the cases of tick was “Strongly agree”; otherwise, no infestation in cattle.* point was awarded.

Do you agree? The risk of tick infestation 5 A point was awarded if the answer can be reduced by housing cattle always in was “Strongly agree”; otherwise, no the shed.* point was awarded.

Do you agree? Adopting good farm practices 5 A point was awarded if the answer can reduce the risk of tick infestation (e.g., was “Strongly agree”; otherwise, no regular washing of floor, regular checking of point was awarded. animals, avoiding bedding materials.* *If respondents answered any of these questions incorrectly, they were categorized as not having a favorable attitude toward prevention and control of ticks in cattle.

Logistic regression analyses were conducted using the sociodemographic variables as explanatory variables against each of the binary outcome variables of knowledge and attitude.

Correlation among the explanatory variables was assessed using the “Hmisc” package (Frank et al., 2020). The explanatory variables with P-values ≤ 0.25 in univariate analyses were selected for multiple logistic regression analysis (Dhimal et al., 2014). The final multiple logistic regression models were manually built using a forward stepwise approach. First, a variable with the smallest p-value in the univariate analysis was entered into the model. Subsequently, each of the remaining variables was individually added to the model to determine whether its addition improved the fit of the model significantly at P-value ≤ 0.05. A likelihood ratio test was conducted to select the variable that had the greatest improvement in the likelihood ratio statistic, and the process was repeated. Variables no longer associated with the outcome were removed,

136 and only the variables with p-values (P≤0.05) were retained in the final model. Confounding was assessed by adding the variables that were removed from the final model (Lindahl et al., 2015).

A variable was to be considered a confounder if it changed the coefficient of the significant variables by more than 25%. Multicollinearity of the predictors in the models was also assessed using the variance inflation factor (VIF) at the cut-off of 2.5 (Allison, 2012). Interactions were assessed by adding a cross-product term (i.e., cattle holding*husbandry practice). The odds ratio

(OR) and its 95% confidence interval (CI) of the variables associated with the outcome variables were calculated from the final multiple logistic regression model. The final models were evaluated using goodness-of-fit using the “LogisticDx” package in R (Dardis, 2015). The residual analysis of the final models was done using the “car” package (Fox & Weisberg, 2019).

4.3 Results

4.3.1 Sociodemographic characteristics

Two hundred forty-six respondents were interviewed, and the response rate was 100 percent. The mean age of the respondents was 46.19 years. The mean number of cattle owned was four. The details of the sociodemographic and farm characteristics in the study area are presented in (Table 4.3).

4.3.2 Knowledge about ticks and TBDs

All 246 respondents had seen ticks on the cattle: 106 (43.1%) had also seen them on vegetation in the forests; 11 (4.5%) had seen them on cattle and in pasturelands; and 8 (3.3%) on cattle and in agricultural fields too. The majority, 131 (53.3%) of the respondents, were not aware of how cattle became infested, while 103 (41.9%) identified the forest as the source, and

12 (4.8%) identified grazing land, fodder grasses, and bedding materials as the other sources of ticks. One-hundred sixty-eight respondents (68.3%) reported that the ticks were commonly seen

137 in summer, while 24 (9.8%) reported having seen them in winter. However, 54 (21.9%) reported that ticks were seen throughout the year. One-hundred twenty-two respondents (49.6%) reported that ticks were commonly found in warm places, while 29 (11.8%) reported finding them in cold places. However, 95 (38.6%) reported having found ticks in both warm and cold places.

Table 4.3 Sociodemographic characteristics of the respondents.

Total Variables Categories (n=246) Percentage Gender Male 100 40.5 Female 146 59.1 Age (years) 18-35 62 25.1 36-45 61 24.8 >45 123 50 Education level § Not attended any school 158 63.9 Attended/Attending NFE £ 54 21.9 Primary level 15 6.1 Secondary level 13 5.3 Buddhist studies € 6 2.4 Cattle holding per household Above or equal to 4 165 66.8 Below 4 81 32.8 Mix of stall feeding & Husbandry practice ¥ tethered grazing 110 44.5 Mix of stall feeding & free grazing 30 12.1 Stall feeding 102 41.3 All-time free grazing 4 1.6 § The participants under “Not attended any school” were considered “Illiterate”, while the rest were considered “Literate” in the analyses. £ NFE is a non-formal education program in Bhutan targeted toward building literacy in rural communities. € Buddhist studies refer to either formal or non-formal education imparted by Buddhist monasteries. ¥ The participants under “Stall feeding” were considered “Stall feeding”, while the rest were considered “Mixed practices”.

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The majority of the respondents (134, 54.5%) believed that the European breeds of cattle were more susceptible to tick infestation, 80 (32.5%) thought the indigenous breeds were more susceptible, and 32 (13%) responded “Don’t know”. Most of the respondents (152, 61.8%) thought old cattle to be the most affected by tick infestation, 35 (14.2%) thought young cattle,

31(12.6%) thought adult cattle, 13 (5.3%) thought heifers, and 16 (6.5%) responded “Don’t know”. With regard to predilection sites of ticks on the body of animals, neck and groin regions were considered to be the most common sites by 233 (94.7%) and 190 (77.2%) of the respondents, respectively. One-hundred sixteen (47.2%) thinks that ticks stay on the body of the animals unless removed, 111 (45.1%) reported that the ticks would drop off from the body of animals after the blood meal, and 19 (7.7%) responded “Don’t know”.

Weight loss due to tick infestation was reported by 239 (97.2%) of the respondents, blood-sucking by 191 (77.6%), production loss by 155 (63%), bite wound by 144 (58.5%), anorexia by 32 (13%), hide damage by 7 (2.8%), red/brown color urine by four (1.6%), and fever by just one (0.4%). Most respondents (156, 63.4%) considered ticks to be potential vectors of

TBDs in cattle; however, 242 (98.4%) indicated that they had never heard of any TBDs in cattle.

One-hundred forty-eight respondents (60.2%) experienced tick bites at one point in their lives.

However, 90 (36.5%) of the respondents incorrectly believed that the tick bites would not transmit diseases to humans. Pain and irritation as symptoms of tick bites were reported by 147

(99.3%) respondents out of 148 who experienced ticks bites, rash and swelling around the bite site by 111 (75%), fever and headache by 15 (10.1%), and “no symptom” by one respondent.

The analysis of the knowledge score showed that out of 246 respondents, 128 (52%) had adequate knowledge about ticks as vectors of diseases in humans and animals (Figure 4.2).

Univariate logistic regression analysis showed that two variables (i.e., gender and husbandry

139 practice) were associated with the knowledge outcome at P≤0.25 (Appendix A.7). The multiple logistic regression analysis showed that husbandry practice was the only variable significant in the final model. Individuals who practiced the stall-feeding system of cattle rearing were 2.8 times more likely to have adequate knowledge about ticks as vectors of diseases than that of others [OR=2.8 (95%CI: 1.66-4.78)] (Table 4.4). No confounding variable was found in this analysis. The interaction term (i.e., cattle holding*husbandry practice) was not significant in this analysis.

Figure 4.2 Respondents who had “adequate vs. inadequate knowledge about ticks as vectors of diseases” categorized by husbandry practice (n=246).

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Adequate Adjusted Variables Categories Total Estimate ± SE Z knowledge OR(95%CI) χ2 Yes No Husbandry Mixed 84 34 118 -0.336 ± 0.17 -1.99 reference practice practice* Stall- 60 68 128 1.0296 ± 0.27 3.82 2.8 (1.66-4.78) 15.16 feeding Parameter significant at P < 0.05 χ2 has df = 1 *the referent category

4.3.3 Attitude toward tick prevention and control program

Based on our scoring criteria, 90 (36.5%) of the respondents had a favorable attitude toward tick prevention and control programs (Figures 4.3 and 4.4). Univariate logistic regression analysis showed that three variables (i.e., gender, husbandry practice, and cattle number) were associated with the attitude outcome at P≤0.25 (Appendix A.8). The multiple logistic regression analysis showed that gender and husbandry practice were the significant variables in the final model. Men were 1.96 times more likely to have a favorable attitude toward tick prevention and control programs than women [OR=1.96 (95%CI: 1.15-3.38)]. The individuals who practiced stall-feeding were 2.13 times [OR=2.13 (95%CI: 1.25-3.67)] more likely to have a favorable attitude than that of others who followed mixed practices of cattle rearing, when the other variable in the model is held constant (Table 4.5). No confounding variable was found in this analysis. The interaction term (i.e., cattle holding*husbandry practice) was not significant in this analysis.

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Table 4.5 Final multiple logistic regression analysis to understand the association between the explanatory variable and the binary outcome variable (having a favorable attitude toward tick control programs or not).

Favorable Adjusted Variables Categories Total Estimate ± SE attitude Z OR(95%CI) χ2 Yes No Intercept -0.336 ± 0.17 -5.1 12.8 Husbandry Mixed 43 101 144 reference Practice practice* Stall- 47 55 102 0.76 ± 0.27 2.7 2.13 (1.24-3.66) feeding Gender Female* 45 101 146 reference Male 45 55 100 0.67 ± 0.28 2.4 1.96 (1.14-3.38) All parameters significant at P < 0.05 χ2 has df = 2 *the referent category

Figure 4.3 Respondents who had a “favorable vs. unfavorable attitude towards tick control programs” categorized by gender (n=246).

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Figure 4.4 Respondents who had a “favorable vs. unfavorable attitude towards tick control programs” categorized by husbandry practice (B) (n=246). 4.3.4 Self-reported farm practices

Of the 246 respondents: 147 (59.5%) reared cattle for generating income through the sale of milk and dairy products; 77 (31.2%) for family consumption of dairy products; 20 (8.1%) for manure; and 2 (0.8%) for draft purpose. One-hundred ninety-seven respondents (79.8%) had cattle shed with corrugated galvanized iron (CGI) sheet roofing and concrete flooring while 40

(16.2%) had a conventional type of shed built out of locally available materials. Only nine

(3.6%) practiced open-air tethering. Of the 197 (79.8%) respondents who had improved cattle sheds with CGI sheet roofing and concrete flooring, 189 (96.9%) reported washing floors on a daily basis. Overall, 95 (38.5%) reported using bedding materials in their cattle shed, out of which, 69 (72.6%) reported using leaf litter, 12 (12.6%) reported using bracken fern, 12 (12.6%) reported using corn straw, and 2 (2.1%) reported using paddy straw. Out of 95 respondents who

143 reported using bedding materials, 70 (73.7%) reported using in winter, 20 (21.1%) throughout the year, and 5 (5.3%) in summer.

The three most important animal health problems reported in this study were: milk fever that was reported by 159 (64.6%) of the respondents, mastitis by 157 (63.8%), and foot and mouth disease by 109 (44.3%). Only 95 (38.6%) of the respondents reported tick infestation as an important animal health problem. The three main purposes of visiting livestock centers were:

“to receive acaricide” that was reported by 244 (99.2%); “to receive medicine” by 239 (97.2%); and “to receive deworming drugs” by 175 (71.1%). All 246 respondents (100%) reported using acaricides as the primary method of controlling ticks. During the peak season: 147 (59.8%) of the respondents reported having used acaricides occasionally; 32 (13.8%), on a monthly basis; 43

(17.5%), on a fortnightly basis; and 24 (9%), on a weekly basis. While applying acaricides to host animals, 241 (98%) of the respondents reported having followed hand dressing method while 5 (2%) followed hand spraying.

To obtain basic information on the efficiency of the acaricides used, the respondents were asked about how long it took for the acaricides to cause ticks to drop off from the body of host animals. One-hundred five respondents (42.7%) reported that ticks dropped off within a day, 81

(32.9%), within a few hours, 58 (23.6%), within a few days, and 2 (0.8%), within a week.

Regarding the management of dropped off ticks: 133 (54.1%) of the respondents reported to have done nothing; 99 (40.2%) reported to have flushed them away with water; 11 (4.5%) reported to have collected and thrown them into the field, and 3 (1.2%) reported to have collected and burned.

Ninety (36.6%) of the respondents reported to have “always” checked their body for ticks after handling tick-infested cattle, 91(36%) reported to have checked “sometimes”; and 65

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(26.4%) never checked. Similarly, after visiting the forests, 85 (34.6%) reported to have

“always” checked their body, 101(41.1%) reported to have checked “sometimes”, and 60

(24.4%) never checked. When the veterinary centers had no acaricides, the respondents reported having followed the mixed practices of manual removal and indigenous medicine to control ticks on cattle. Of these methods: 136 (55.3%) followed manual removal; 66 (26.8%) applied

Zanthoxylum solution; 55 (22.4%) brushed animals, 38 (15.4%) applied salt solution; and 61

(24.8) reported to have done nothing. To determine the farmers’ awareness of acaricides’ properties, a question was asked if acaricides can be used for purposes other than treating tick infestation. One-hundred twenty-four respondents (50.4%) reported that they did not know about any other use; however, 122 (49.6%) reported that it could be used as either a pesticide (for crops) or an insecticide (at homes).

4.4 Discussion

To our knowledge, this is the first KAP study conducted to assess the farmers’ knowledge, attitude, and practices about ticks and TBDs in Bhutan. In this study, all respondents reported having seen ticks. This is not surprising given that the respondents were farmers whose lives are intimately linked with animals, pastures, and forests, where ticks are commonly found.

Despite all respondents having seen ticks, more than half of the respondents did not know how cattle become infested and where ticks are commonly found in the environment. Some respondents thought that ticks are more common in winter. Normally, low temperatures in winter are likely to slow down the developmental processes of ticks as the processes such as molting, oviposition, and questing are dependent on temperature (Estrada-Peña, 2015). In the neighboring

Indian state of West Bengal, ticks are more common in monsoon and summer than in winter

(Debbarma et al., 2018). However, winter in Bhutan is normally dry and cold, and the fodder

145 resources also become very scarce. Most cattle lose their body condition and immune capacity to build resistance to ticks, predisposing many to tick infestation. This could explain why some respondents thought ticks to be more common in winter.

Generally, indigenous breeds of cattle are considered to be highly resistant to ticks, and they are known to be reared with minimum tick control by exploiting their innate immunity

(Jonsson et al., 2014; Minjauw & McLeod, 2003; Phanchung et al., 2007). While more than half of the respondents recognized indigenous breeds of cattle as generally resistant to ticks, there were some respondents who reported a belief that indigenous breeds of cattle are the most affected. This inconsistency in the beliefs could be due to existing differences in the systems of rearing cattle between the European and indigenous breeds. The latter are mostly reared in a free grazing system where the animals spend most of their time in the forests and pasturelands. So, it is likely that they would be more frequently exposed to ticks compared to stall-fed European cattle. Unlike stall-fed European cattle, management practices like grooming and brushing are rarely practiced in indigenous breeds. Therefore, it is not uncommon to see them heavily infested with ticks. Although indigenous breeds are known to possess high innate resistance to ticks, their physical appearance (with a lot of ticks on their body) gives an impression that they are more susceptible. Among the age groups, old cattle and young calves are reported to be the most affected, which is in agreement with the findings from studies conducted in the Indian state of

West Bengal (Debbarma et al., 2018), Bangladesh (Kabir et al., 2011), Ethiopia (Kemal et al.,

2016), and Nigeria (Eyo et al., 2014). The reason for higher tick infestation in these two age groups is attributed to underdeveloped immunity in young and weak immunity in old cattle.

Moreover, unlike the productive adult cattle that are given the utmost managemental care, the

146 young calves and old cattle are the least attended. They can, therefore, act as a reservoir for ongoing environmental tick contamination.

Most of the respondents provided accurate and detailed clinical descriptions of tick infestation in cattle, but there were only four respondents who reported brown-colored urine

(hemoglobinuria), and one respondent who reported observing fever. Generally, fever and hemoglobinuria are typical signs of babesiosis in cattle (Spickler, 2018). The fact that only four and one respondents reported hemoglobinuria and fever, respectively, is an indication that the majority of the farmers could not relate clinical signs such as hemoglobinuria and fever to ticks and TBDs. Furthermore, hemoglobinuria is often confused with hematuria, which is commonly associated with bladder tumors linked to chronic bracken fern poisoning in Bhutan (Hidano et al., 2017).

Despite the presence of TBDs such as babesiosis and theileriosis in the study area, there has been no recent effort made from veterinary laboratories to diagnose and record cases in a systematic manner. This is also due to a shortage of manpower and resources in veterinary laboratories. The prevailing practice followed with regard to such TBDs is treating the animals based on clinical signs. Moreover, since there was no major outbreak resulting in mortality of animals due to such TBDs, the farmers, as well as the veterinary officials, had no reason to be concerned. Further, the immunity acquired through previous exposure(s) to ticks makes the cattle

“endemically stable” (Minjauw & McLeod, 2003). It was observed that a large proportion of the respondents had never heard of any particular TBDs in cattle. This could have been due to the lack of awareness programs on ticks and TBDs in recent years by livestock officials. This is also likely due to limited resources for conducting such awareness programs in the absence of any major outbreak.

147

According to our criteria, 52% of the respondents had adequate knowledge of ticks as potential vectors of diseases. The multiple logistic regression analysis showed that the farmers practicing the stall-feeding system of cattle rearing were more likely to have adequate knowledge about ticks as potential vectors of diseases than that of others following mixed practices of cattle rearing. This suggests the positive impact of the Royal Government of Bhutan’s livestock intensification program that promotes the stall-feeding system of cattle rearing. In this system, the primary focus is to enhance the health and productivity of cattle and subsequently improve rural livelihood through cash income generated from the sale of products. The government, through the provision of subsidized livestock inputs such as the purchase of high-yielding cows, shed construction materials, feed and fodder, and farm and marketing equipment, encourages as many farmers as possible to take up the modern market-based farming. Training and awareness programs on clean milk production, livestock health management, crossbreeding, fodder conservation, and so on are also provided regularly. As a result, farmers interact more with livestock officials and avail their technical support services. These interactions would have contributed to equipping farmers with some degree of knowledge about the role of ticks as vectors of diseases.

Overall, only 36% of the respondents had a favorable attitude toward tick prevention and control programs. The observation that men had a more favorable attitude than women could not be strongly associated with any social factor. However, the person’s knowledge, beliefs, emotions, and values, are closely interlinked with attitudes, which can either be positive or negative (Launiala, 2009). Therefore, a key factor could be that the men get more opportunities to attend government-initiated meetings and training programs leading to men having better knowledge about ticks and TBDs than women. However, this trend has been gradually changing,

148 and now women also attend such programs. The positive association between the farmers who were practicing the stall-fed system of cattle rearing and having a favorable attitude can likely be attributed to the adoption of the government’s livestock intensification and commercialization programs. These programs promote the stall-feeding system of rearing cattle, which most of the farmers in the study area practice. In this system, animals are normally kept inside sheds and are rarely let out for grazing. This not only protects the animals from accidental falls and fights with other animals but also reduces their exposure to ticks. Tick prevalence has been found to be lower in the farms where the stall-feeding system is practiced (Rehman et al., 2017).

Consequently, such positive outcomes largely influence the farmers’ attitude. Tick infestation is also known to be determined by the types of housing. The odds of acquiring tick infestation are higher in animals housed in poorly constructed sheds lacking proper ventilation (Rehman et al.,

2017). Plastering of floor surfaces and walls with smooth cement also helps avoid shed infestation by removing the potential hiding places of some tick species (such as Hyalomma) that hide in cracks and crevices (Minjauw & McLeod, 2003). Most of the cattle sheds in the study area have concrete flooring and CGI sheet roofing, and the floors are washed on a daily basis.

This practice could be mitigating the infestation of sheds, but we need to undertake additional studies to ascertain this.

Traditionally, farmers in Bhutan use leaf litter as bedding material in their cattle sheds, especially during the cold winter months. Leaf litter is not only the source of warmth for animals but also an important component of farm manure. Almost every household in the country was given a legal right to a small block of tree grove known as Sokshing for the collection of valuable leaf litter (Dorji et al., 2003). In our study area, some farmers reported using leaf litter as bedding material, especially in winter, but this practice is declining as the crop-cattle integration

149 system of farming is fast fading. Ticks are known to survive well in leaf litter as it provides consistent insulation from cold conditions of winter (Linske et al., 2019). The infestation of cattle in winter can also be associated with the use of leaf litter. Some farmers also reported using bracken fern as the bedding material, and many farmers (during informal conversations) in the study area consider it as one of the sources of ticks.

Milk fever, mastitis, and foot and mouth disease were perceived to be the three most important animal health problems. Meanwhile, tick infestation was rated as the sixth out of seven options. However, in the section where respondents were made to state the three main purposes of visiting livestock centers, “to receive acaricides” was the number one purpose. This inconsistency may be due to the free supply of synthetic acaricides from any livestock center in the country. In the similar KAP studies conducted in Tanzania (Laisser et al., 2015) and Benin

(Adehan et al., 2018), where farmers had to bear the cost of acaricides, tick infestation and TBDs were considered a major problem in livestock rearing. However, in Bhutan, farmers are provided free acaricides as and when required, and the subsequent application to the animals would remove ticks present on cattle. Therefore, it is possible that they might have never perceived tick infestation as an important animal health problem.

Throughout the world, acaricidal treatment is still one of the most widely used methods for controlling ticks in cattle (Minjauw & McLeod, 2003). In our study, too, all the respondents reported using acaricides on host animals to control ticks as well as considering it to be the primary method of controlling ticks. The majority of the farmers reported using acaricides occasionally (i.e., using them whenever the animals are noticed with high tick infestations).

These findings are indicative of lacking information on the optimal use of acaricides and effective control strategies. Although advice is being given to farmers to strictly adhere to a

150 specific dilution rate, there is no system to monitor the usage in the field. Effective tick control approaches such as seasonal treatments at the peak of tick activity and intensive treatments at the beginning of the tick season are not followed. This is also due to our livestock officials lacking information on local tick species diversity and life cycles.

Buddhist traditions and culture also influence tick control practices in Bhutan. Every year, the Bhutanese observe Saga dawa - the auspicious month, according to the Bhutanese lunar calendar (Phuntsho, 2016). This auspicious month normally falls sometime in the spring, coinciding with the peak tick season in Bhutan. In this month, most of the Bhutanese avoid non- virtuous and harmful activities, and killing ticks is also considered as a non-virtuous act.

Consequently, most of the farmers normally refrain from using acaricides during this month. Our survey coincided with the Saga dawa (which happened to have fallen in June that year).

Therefore, heavy tick infestation was observed in cattle, as most of the farmers thought that they would apply acaricides once the Saga dawa month was over. Such cultural practice should also be taken into consideration as we look ahead to improving tick control strategies.

The efficacy of acaricides on the susceptibility of ticks is assessed by conducting in vitro tick immersion assays using acaricide solutions prepared based on manufacturers’ instructions and then evaluating its impact on mortality and egg production by female ticks (Raynal et al.,

2013). To date, there has been no such assessment done to evaluate the efficacy of acaricides used in Bhutan. However, one crude way of assessing the efficacy at the farm level could be observing tick drop-off from the body of an animal after applying acaricides. In our study, the majority of the respondents reported tick drop-off occurring within a day, some reported within a few hours, and a few reported within a few days. While this finding does not indicate anything

151 substantial regarding the efficacy of the acaricides, it suggests that some of the farmers could be using an incorrect on-farm dilution of the acaricides.

Since the acaricide supply in Bhutan is regulated by the government through the

Department of Livestock (DoL), it is available only in the livestock centers in the country. When there is a shortage or an inconsistent supply, farmers either practice manual removal or use

Zanthoxylum armatum DC. seeds as traditional indigenous medicine against ticks. Zanthoxylum armatum, commonly known as “Thi-ngye” in Bhutan, is an important medicinal plant widely distributed in subtropical and temperate valleys of the Himalayas, including Bhutan, and it has several ethno-pharmacological uses (Phuyal et al., 2019). The use of Zanthoxylum spp. as an acaricide is documented in studies in Pakistan (Sindhu et al., 2010) and Brazil (Nogueira et al.,

2014). The latter determined the acaricidal properties of its essential oil through an adult immersion test using engorged female ticks. The essential oil (5% concentration) of Zanthoxylum caribaeum Lam. caused 65% mortality in day one, 85% in day two, and 100% in day five

(Nogueira et al., 2014). In Bhutan, when Zanthoxylum is used as an acaricide, the seeds are soaked in water overnight, and the solution is applied to the animals. In spite of such available options, 24.8% of the respondents in the study area reported doing nothing when there was no acaricide in livestock centers. This affirms how important it is for DoL to maintain a consistent supply of acaricides to farmers. Other non-chemical tick control methods such as predators (like backyard poultry), environmental clearing, and rotational grazing may be difficult to practice in

Bhutan. Backyard poultry destroy family vegetable crops, environmental clearing affects the environment as well as involves a considerable cost, and rotational grazing is not feasible as the individual landholdings are very small.

152

As Bhutan was slowly phasing out the supply of pesticides to farmers to make the country’s agriculture 100% organic, the cross-application of acaricides on the crops was a growing concern among livestock officials. To understand the situation at the farmers’ level, a question was asked if they knew any other use of acaricides besides controlling ticks. Half of the respondents reported not being aware of other purposes, while the other half reported that it could be used either as a pesticide (for crops) or as an insecticide (at homes). Some farmers in the study area also admitted to using leftover acaricides in their vegetable fields. Therefore, if left unregulated, there is a possibility that such incidents might increase over the years.

There are some inherent limitations of KAP studies. One of the main limitations is the difficulty in ensuring an accurate interpretation of data generated as this is often impacted by underlying contextual and cultural factors (Launiala, 2009). Further, developing questions to accurately determine attitudes is difficult (Bukachi et al., 2018). Social norms and pressures are also known to bias reporting as respondents often provide answers that are thought to be acceptable to the interviewer (Green, 2001). KAP surveys are also criticized because their findings can lead to prescriptive community based interventions rather than being targeted toward individuals (Launiala, 2009). Other major limitations of KAP surveys are that researchers use surveys to explain behavior under the assumption that knowledge and behavior are directly related (Launiala, 2009). Nevertheless, KAP surveys are very useful for obtaining general information about common practices, they are relatively cost effective for obtaining quick insights on certain topics to facilitate implementation of new programs, and they also facilitate community engagement (Bukachi et al., 2018; Launiala, 2009).

The main limitation of this KAP study is that it was designed for a specific location (i.e., targeted the most intensive dairy farming area), and the findings cannot be generalized to other

153 areas with different context and farming systems. However, the findings do provide useful information to assist in the development of education and extension activities that can be used even beyond the study area. Although the interview targeted household heads, during the survey, the enumerators interviewed those available at home. This could have led to some response bias in the answers to our questions. Considering the cultural background in Bhutan, household heads

(whether male or female), are closely involved in all farming activities, and so they are likely to provide reliable and accurate information about ticks and TBDs. The findings about “farm practices” could be biased as the responses were self-reported, and also the descriptive data about practices fails to explain why certain practices are chosen.

A bias could have occurred due to language and cultural context (e.g., questionnaire was prepared in English but the interview was conducted in a local dialect); however, we minimized this potential bias as the enumerators were livestock department officials familiar with the language, culture, and practices of the farmers interviewed. The interpretation bias was reduced as the author has previously worked in the study area and is familiar with the cultures and practices of the farmers in this region. Overall, the findings from this KAP study have contributed to the larger understanding of cattle farmers’ perspectives about ticks and TBDs in

Bhutan, and also provide useful baseline data that future researchers used to develop further studies. The study has also engaged community members directly and in doing so has already raised awareness about ticks and tick-borne diseases.

4. 5 Conclusion

In this study, only 52% the farmers had adequate knowledge about ticks as potential vectors of diseases, and only 36% of the farmers had a favorable attitude toward tick prevention and control programs. This could indicate the lack of awareness education conducted by

154 livestock officials in recent years. Therefore, awareness programs should focus on informing farmers on topics such as the role of ticks as potential vectors for diseases in animals and humans, the life cycle and seasonal pattern of locally present tick species, effective tick control strategies, and appropriate use of acaricides. This study also observed that the farmers in the study area did not perceive ticks and TBDs as significant problems for livestock health. This might be due to the free supply of acaricides by the government. However, in recent years, there has been a discussion at the policy level about supplying acaricides on a cost-sharing basis.

Should the government implement this cost-sharing system, farmers might need to design tick control strategies of their own and will likely want to reduce cost implications. The Department of Livestock (DoL) might have to provide technical support to strategize tick control in such a way that it suits a particular farming system. This is where the findings of this and other KAP studies would play an important role in designing and implementing tick control programs.

Therefore, we recommend similar KAP studies in other farming communities in Bhutan.

155

References

Adehan, S. B., Adakal, H., Gbinwoua, D., Yokossi, D., Zoungrana, S., Toé, P., Ouedraogo, M.,

Gbaguidi, A. M., Adoligbé, C., Fandohan, A. B., Hounmanou, G., Glèlè Kakaï, R.,

Farougou, S., & De Clercq, E. M. (2018). West African Cattle Farmers’ Perception of

Tick-Borne Diseases. EcoHealth, 15(2), 437–449. https://doi.org/10.1007/s10393-018-

1323-8

Allison, P. (2012). When Can You Safely Ignore Multicollinearity? When Can You Safely Ignore

Multicollinearity? https://statisticalhorizons.com/multicollinearity

Aquino, J. (2018). descr: Descriptive Statistics. https://CRAN.R-project.org/package=descr

Bhattacharyya, K. (1997). Key informants, pile sorts, or surveys? Comparing behavioral research

methods for the study of acute respiratory infections in West Bengal. The Anthropology

of Infectious Diseases: Theory and Practice on Medical Anthropology and International

Health, 211–238.

Bukachi, S. A., Mumbo, A. A., Alak, A. C. D., Sebit, W., Rumunu, J., Biéler, S., & Ndung’u, J.

M. (2018). Knowledge, attitudes and practices about human African trypanosomiasis and

their implications in designing intervention strategies for Yei county, South Sudan. PLOS

Neglected Tropical Diseases, 12(10), e0006826.

https://doi.org/10.1371/journal.pntd.0006826

Butler, A. D., Sedghi, T., Petrini, J. R., & Ahmadi, R. (2016). Tick-borne disease preventive

practices and perceptions in an endemic area. Ticks and Tick-Borne Diseases, 7(2), 331–

337. https://doi.org/10.1016/j.ttbdis.2015.12.003

Dardis, C. (2015). LogisticDx: Diagnostic Tests for Models with a Binomial Response.

https://CRAN.R-project.org/package=LogisticDx

156

Debbarma, A., Pandit, S., Jas, R., Baidya, S., Mandal, S. C., & Jana, P. S. (2018). Prevalence of

hard tick infestations in cattle of West Bengal, India. Biological Rhythm Research, 49(5),

655–662. https://doi.org/10.1080/09291016.2017.1395527

Dhimal, M., Aryal, K. K., Dhimal, M. L., Gautam, I., Singh, S. P., Bhusal, C. L., & Kuch, U.

(2014). Knowledge, Attitude and Practice Regarding Dengue Fever among the Healthy

Population of Highland and Lowland Communities in Central Nepal. PLOS ONE, 9(7),

e102028. https://doi.org/10.1371/journal.pone.0102028

Dohoo, I., Martin, W., & Stryhn, H. (2003). Veterinary Epidemiologic Research (2nd edition).

AVC Inc.

DoL. (2018). Livestock Statistics. Department of Livestock, Ministry of Agriculture and Forests,

Royal Government of Bhutan. http://www.dol.gov.bt/?page_id=394

Dorji, L., Webb, E., & Shivakoti, G. (2003). Can a nationalised forest management system

uphold local institutions? The case of leaf litter forest [sokshing] management in Bhutan.

Asian Studies Review, 27, 341–359. https://doi.org/10.1080/10357820308713382

ESRI. (2018). ArcGIS Desktop: Release 10. Redlands, CA: Environmental Systems Research

Institute. (Version 10.6.1) [Computer software].

Estrada-Peña, A. (2015). Ticks as vectors: Taxonomy, biology and ecology: -EN- -FR- Les

tiques en tant que vecteurs : taxonomie, biologie et écologie -ES- Las garrapatas como

vectores: taxonomía, biología y ecología. Revue Scientifique et Technique de l’OIE,

34(1), 53–65. https://doi.org/10.20506/rst.34.1.2345

Eyo, J. E., Ekeh, F. N., Ivoke, N., Atama, C. I., Onah, I. E., Ezenwaji, N. E., & Ikele, C. B.

(2014). Survey of tick infestation of cattle at four selected grazing sites in the tropics.

Global Veterinaria, 12(4), 479–486.

157

Fox, J., & Weisberg, S. (2019). {An {R} Companion to Applied Regression}, (Third). Sage.

{https://socialsciences.mcmaster.ca/jfox/Books/Companion/}, }

Frank, E., Harrell, J., & and with contributions from Charles Dupont and many others. (2020).

{Hmisc: Harrell Miscellaneous} ({R package version 4.4-0}) [Computer software].

{https://CRAN.R-project.org/package=Hmisc}

Green, E. C. (2001). Can qualitative research produce reliable quantitative findings? Field

Methods, 13(1), 3–19.

Gupta, S., Eggers, P., Arana, A., Kresse, B., Rios, K., Brown, L., Sampson, L., & Kploanyi, M.

(2018). Knowledge and preventive behaviors towards tick-borne diseases in Delaware.

Ticks and Tick-Borne Diseases, 9(3), 615–622.

https://doi.org/10.1016/j.ttbdis.2018.01.006

Hidano, A., Dukpa, K., Rinzin, K., Sharma, B., Dahal, N., & Stevenson, M. A. (2016). A cross-

sectional survey of population demographics, the prevalence of major disease conditions

and reason-specific proportional mortality of domestic cattle in the Kingdom of Bhutan.

Preventive Veterinary Medicine, 130, 1–9.

https://doi.org/10.1016/j.prevetmed.2016.05.012

Hidano, A., Sharma, B., Rinzin, K., Dahal, N., Dukpa, K., & Stevenson, M. A. (2017).

Revisiting an old disease? Risk factors for bovine enzootic haematuria in the Kingdom of

Bhutan. Preventive Veterinary Medicine, 140, 10–18.

https://doi.org/10.1016/j.prevetmed.2017.02.011

Hlavac, M. (2018). {stargazer: Well-Formatted Regression and Summary Statistics Tables}.

{Central European Labour Studies Institute (CELSI)}. {https://CRAN.R-

project.org/package=stargazer}

158

Jonsson, N. N., Piper, E. K., & Constantinoiu, C. C. (2014). Host resistance in cattle to

infestation with the cattle tick Rhipicephalus microplus. Parasite Immunology, 36(11),

553–559. https://doi.org/10.1111/pim.12140

Kabir, M. H. B., Mondal, M. M. H., Eliyas, M., Mannan, M. A., Hashem, M. A., Debnath, N. C.,

Miazi, O. F., Mohiuddin, Kashem, M. A., Islam, M. R., & Elahi, M. F. (2011). An

epidemiological survey on investigation of tick infestation in cattle at Chittagong District,

Bangladesh. African Journal of Microbiology Research, 5(4), 346–352.

https://doi.org/10.5897/AJMR10.706

Kemal, J., Tamerat, N., & Tuluka, T. (2016). Infestation and Identification of Ixodid Tick in

Cattle: The Case of Arbegona District, Southern Ethiopia. Journal of Veterinary

Medicine, 2016. https://doi.org/10.1155/2016/9618291

Laisser, E., Chenyambuga, S., Msalya, G., Kipanyula, M., Mdegela, Karimuribo, E., Mwilawa,

A., & Kusiluka, L. (2015). Knowledge and perception on ticks, tick-borne diseases and

indigenous cattle tolerance to East Coast fever in agro-pastoral communities of Lake

Zone in Tanzania. Livestock Research for Rural Development, 27.

Launiala, A. (2009). How much can a KAP survey tell us about people’s knowledge, attitudes

and practices? Some observations from medical anthropology research on malaria in

pregnancy in Malawi. Anthropology Matters, 11(1). https://doi.org/10.22582/am.v11i1.31

Lindahl, E., Sattorov, N., Boqvist, S., & Magnusson, U. (2015). A Study of Knowledge,

Attitudes and Practices Relating to Brucellosis among Small-Scale Dairy Farmers in an

Urban and Peri-Urban Area of Tajikistan. PLOS ONE, 10(2), e0117318.

https://doi.org/10.1371/journal.pone.0117318

159

Linske, M. A., Stafford, K. C., Williams, S. C., Lubelczyk, C. B., Welch, M., & Henderson, E. F.

(2019). Impacts of Deciduous Leaf Litter and Snow Presence on Nymphal Ixodes

scapularis (Acari: Ixodidae) Overwintering Survival in Coastal New England, USA.

Insects, 10(8). https://doi.org/10.3390/insects10080227

Mahto, A. (2019). splitstackshape: Stack and Reshape Datasets After Splitting Concatenated

Values. https://CRAN.R-project.org/package=splitstackshape

Minjauw, B., & McLeod, A. (2003). Tick-borne diseases and poverty. The impact of ticks and

tickborne diseases on the livelihood of small-scale and marginal livestock owners in

India and eastern and southern Africa. http://agris.fao.org/agris-

search/search.do?recordID=GB2012100456

National Statistics Bureau. (2017). Bhutan Living Standards Survey Report 2017.

http://www.nsb.gov.bt/publication/files/pub2yo10667rb.pdf

National Statistics Bureau. (2018a). PHCB Trashigang.

http://www.nsb.gov.bt/publication/publications.php?id=2

National Statistics Bureau, N. (2018b). 2017 population & housing census of Bhutan: National

report = ʼBrug gi mi rlobs dang khyim gyi grangs rtsis 2017. National Statistics Bureau,

Royal Government of Bhutan.

NCAH. (2013). National Veterinary Drug Formulary (2nd Edition). Department of Livestock,

Royal Government of Bhutan. https://www.ncah.gov.bt/Downloads/File_3.pdf

NCAH. (2019). Veterinary Information System. http://vis.ncah.gov.bt/php/index.php

Niesobecki, S., Hansen, A., Rutz, H., Mehta, S., Feldman, K., Meek, J., Niccolai, L., Hook, S., &

Hinckley, A. (2019). Knowledge, attitudes, and behaviors regarding tick-borne disease

160

prevention in endemic areas. Ticks and Tick-Borne Diseases, 10(6), 101264.

https://doi.org/10.1016/j.ttbdis.2019.07.008

Nogueira, J., Vinturelle, R., Mattos, C., Candido Tietbohl, L. A., Santos, M. G., Vaz Junior, I. D.

S., Mourao, S. C., Rocha, L., & Folly, E. (2014). Acaricidal Properties of the Essential

Oil From Zanthoxylum caribaeum Against Rhipicephalus microplus. Journal of Medical

Entomology, 51(5), 971–975. https://doi.org/10.1603/ME13236

Phanchung, Dorji, P., Sonam, T., & Pelden, K. (2007). Smallholder Dairy Farming in Bhutan:

Characteristics , Constraints , and Development Opportunities.

http://lib.icimod.org/record/21365/files/c_attachment_79_552.pdf

Phuntsho. (2016). Saga Dawa, the auspicious month – KuenselOnline.

https://kuenselonline.com/saga-dawa-the-auspicious-month/

Phuyal, N., Jha, P. K., Prasad Raturi, P., & Rajbhandary, S. (2019). Zanthoxylum armatum DC.:

Current knowledge, gaps and opportunities in Nepal. Journal of Ethnopharmacology,

229, 326–341. https://doi.org/10.1016/j.jep.2018.08.010

R Core Team. (2018). R: A Language and Environment for Statistical Computing. R Foundation

for Statistical Computing. https://www.R-project.org/

Raynal, J. T., Silva, A. A. B. da, Sousa, T. de J., Bahiense, T. C., Meyer, R., Portela, R. W.,

Raynal, J. T., Silva, A. A. B. da, Sousa, T. de J., Bahiense, T. C., Meyer, R., & Portela,

R. W. (2013). Acaricides efficiency on Rhipicephalus(Boophilus) microplus from Bahia

state North-Central region. Revista Brasileira de Parasitologia Veterinária, 22(1), 71–77.

https://doi.org/10.1590/S1984-29612013005000006

Rehman, A., Nijhof, A. M., Sauter-Louis, C., Schauer, B., Staubach, C., & Conraths, F. J.

(2017). Distribution of ticks infesting ruminants and risk factors associated with high tick

161

prevalence in livestock farms in the semi-arid and arid agro-ecological zones of Pakistan.

Parasites & Vectors, 10(1), 190. https://doi.org/10.1186/s13071-017-2138-0

Rinchen, S., Tenzin, T., Hall, D., Meer, F. van der, Sharma, B., Dukpa, K., & Cork, S. (2019). A

community-based knowledge, attitude, and practice survey on rabies among cattle owners

in selected areas of Bhutan. PLOS Neglected Tropical Diseases, 13(4), e0007305.

https://doi.org/10.1371/journal.pntd.0007305

Schopper, D., Doussantousse, S., & Orav, J. (1993). Sexual behaviors relevant to HIV

transmission in a rural African population: How much can a KAP survey tell us? Social

Science & Medicine, 37(3), 401–412. https://doi.org/10.1016/0277-9536(93)90270-E

Sindhu, Z.-D., Iqbal, Z., Khan, M. N., Jonsson, N. N., & Siddique, M. (2010). Documentation of

ethnoveterinary practices used for treatment of different ailments in a selected hilly area

of Pakistan. International Journal of Agriculture and Biology, 12(3), 353–358.

Spickler, A. R. (2018). Bovine Babesiosis.

http://www.cfsph.iastate.edu/DiseaseInfo/factsheets.php

Stone, L., & Campbell, J. (1984). The use and misuse of surveys in international development:

An experiment from Nepal. Human Organization, 43(1), 27–37.

Sungirai, M., Moyo, D. Z., De Clercq, P., & Madder, M. (2016). Communal farmers’

perceptions of tick-borne diseases affecting cattle and investigation of tick control

methods practiced in Zimbabwe. Ticks and Tick-Borne Diseases, 7(1), 1–9.

https://doi.org/10.1016/j.ttbdis.2015.07.015

WHO. (2008). A Guide to Developing Knowledge, Attitude and Practice Surveys. World Health

Organization.

162

https://apps.who.int/iris/bitstream/handle/10665/43790/9789241596176_eng.pdf?sequenc

e=1

Wickham, H. (2016). ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York.

https://ggplot2.tidyverse.org

Wickham, H. (2019a). {forcats: Tools for Working with Categorical Variables (Factors)} ({R

package version 0.4.0}) [Computer software]. {https://CRAN.R-

project.org/package=forcats}

Wickham, H. (2019b). stringr: Simple, Consistent Wrappers for Common String Operations.

https://CRAN.R-project.org/package=stringr

Wickham, H., Francois, R., Henry, L., & Muller, K. (2019). dplyr: A Grammar of Data

Manipulation. https://CRAN.R-project.org/package=dplyr

Zeileis, A., & Hothorn, T. (2002). Diagnostic Checking in Regression Relationships. 2(3), 7–10.

Zoldi, V., Turunen, T., Lyytikainen, O., & Sane, J. (2017). Knowledge, attitudes, and practices

regarding ticks and tick-borne diseases, Finland. Ticks and Tick-Borne Diseases, 8(6),

872–877. https://doi.org/10.1016/j.ttbdis.2017.07.004

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Chapter 5 Discussion

Ticks have been in existence for approximately 200 million years (Geevarghese &

Mishra, 2011). In Bhutan, the history of knowledge of ticks dates back to the 1990s. A study conducted by Cork et al. (1996) (unpublished) over a year in cattle in eastern Bhutan may be the first to have confirmed the identification (to genus level) of some tick species on cattle in

Bhutan. More than two decades later, in 2018, the Regional Livestock Development Center

(RLDC) of the western region conducted a similar study on cattle from September 2018 to June

2019 in western Bhutan (RLDC Wangdue, 2019). These two studies, which I have briefly described in Chapter 1, were the only source of information on ticks in Bhutan before this thesis.

Therefore, this study was conducted to address this gap in available information as well as for the reasons discussed below.

Tick infestation is the most commonly reported parasitological problem for cattle in

Bhutan (NCAH, 2019). In 2019, 89% of the reported parasite related cases in cattle were due to tick infestation (NCAH, 2019). Tick-borne diseases (TBDs) like anaplasmosis, babesiosis, and theileriosis are present in cattle in Bhutan, especially in the southern subtropical districts

(NCAH, 2019; Phanchung et al., 2007). However, due to the limited use of confirmatory diagnostic tests, poor surveillance, and discrepancies in recorded data, it is difficult to estimate the actual number of cases of these diseases. Ticks are primary vectors of many important TBDs in domestic animals; for example, R. microplus is the principal vector for B. bovis and B. bigemina that causes babesiosis in cattle (Bock et al., 2004). Therefore, to understand the likely distribution of TBDs, there was a need to assess the type of ticks infesting cattle and their geographical distribution in Bhutan. Such information is helpful for developing targeted surveillance programs for ticks and the TBDs that they might transmit. Thus, the study on

164 identification and distribution of tick species on cattle in eastern Bhutan was conducted

(Chapters 2 and 3).

Since the 1990s, acaricides have played an important role in controlling ticks in Bhutan, although tick control practices have not been based on any strategized plan or program. The

National Centre for Animal Health (NCAH) is responsible for the selection, procurement, and supply of acaricides to all districts in Bhutan. The most used acaricides in Bhutan are liquid formulations of pyrethroid compounds (i.e., cypermethrin, deltamethrin, and flumethrin) and amidines (i.e., amitraz) imported from India (NCAH, 2013). These chemicals are provided free of cost to the farmers by the government. As mentioned earlier, the NCAH (funded through the government) spends approximately 2-3 million Bhutanese Ngultrum annually to provide free acaricides to the farmers. However, acaricide alone is not a sustainable, environmentally friendly, and cost-effective approach (Kunz & Kemp, 1994). A good tick control practice involves a systematic combination of two or more methods (de Castro, 1997), for example, a combination of seasonal use of acaricides and zero-grazing management practice.

To implement and achieve good tick control practices at the individual farm level, it requires farmers to understand about tick species present in their locality, their life cycles and seasonal patterns, and appropriate use of acaricides. But there has been no information on what farmers in Bhutan know about ticks and TBDs. Thus, a KAP study was conducted to assess the knowledge, attitude, and practices (KAP) about ticks and TBDs among farmers (Chapter 4). The information obtained from this KAP study conducted is expected to guide the planning and implementation of good tick control practices in Bhutan.

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5.1 Key findings

Chapter 2 primarily focused on gathering preliminary data to determine the presence and diversity of tick species on cattle in the Trashigang and Pema Gatshel districts of eastern Bhutan.

In May and June 2019, 3,600 ticks were collected from cattle in 240 randomly selected households that owned cattle. In Bhutan, these months coincide with the monsoon season characterized by warm and humid conditions, and tick infestation has been observed to be at its peak. Studies conducted in India and Bangladesh have also recorded a higher tick prevalence in the monsoon season (Debbarma et al., 2018; Kabir et al., 2011; Khajuria et al., 2015). Hence, the sampling for this study was targeted in these months.

All ticks (n=3,600) but one specimen were identified to species level using morphological keys. Four genera and six species of ticks were found. These were R. microplus

(70.2%), R. haemaphysaloides (18.8%), H. bispinosa (8.2%), H. spinigera (2.5%), A. testudinarium (n=7), and an unidentified species of Ixodes (n=1). The variation in the infestation prevalence of these tick species between the districts was compared using Pearson’s chi-squared test. The relationships between geographic and cattle (host) factors and infestation prevalence were also assessed using logistic regression. The geographic factors such as altitude and latitude are the primary determinants of climatic variables like temperature and rainfall in Bhutan (Dorji et al., 2016). Ticks are generally dependent on temperature and rainfall for their development and activity (Estrada-Peña, 2015). Thus, the relationships between geographic factors and infestation prevalence were assessed (Chapter 2).

The cattle (host) factors such as age, sex, and breed influence the susceptibility of animals to tick infestation (Asmaa et al., 2014; Bianchi et al., 2003). The breed is the major factor influencing the tick infestation variation in cattle (Utech et al., 1978a). European or

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Taurine cattle (Bos taurus taurus) are more susceptible to tick infestation compared to Asian or

Zebu cattle (Bos taurus indicus) (Utech et al., 1978a). In Bhutan, indigenous Siri cattle are considered more resistant to ticks than the exotic European cattle breeds (Phanchung et al.,

2007). Studies in cattle have observed a higher prevalence of ticks either in young or old animals

(Asmaa et al., 2014; Eyo et al., 2014; Kabir et al., 2011; Utech et al., 1978b). This variation is likely due to underdeveloped immunity in young animals and weak immunity in old animals

(Asmaa et al., 2014; Eyo et al., 2014). However, in young animals, factors like maternal grooming can also influence the variation in tick infestation (Bianchi et al., 2003). Tick infestation was also observed to differ between the sexes, but the exact cause is not well explained (Asmaa et al., 2014; Eyo et al., 2014; Kabir et al., 2011; Utech et al., 1978b).

However, events such as pregnancy and lactation are considered stressful to animals thereby decreasing resistance to ticks (Utech et al., 1978b). Further, the physical characteristics such as hair density, skin thickness, tongue papillae, self-grooming ability, and odor also can cause a variation in tick infestation (Tabor et al., 2017). The farm management practices, grazing behaviors, and nutritional status and body conditions also largely influence the tick infestation in cattle (Bianchi et al., 2003; Eyo et al., 2014). In the present study, cattle age and breed were found to be influencing the variation in tick(s) infestation (Chapter 2).

Rhipicephalus microplus was found to be the most predominant tick species in the surveyed areas of eastern Bhutan. The previous studies conducted by Cork et al. (1996) and

RLDC Wangdue (2019) had a similar finding in eastern and western regions of Bhutan, respectively. Further, in the neighboring Indian states of Assam and West Bengal, R. microplus is the most predominant tick species (Barman et al., 2018; Kakati et al., 2015). Considering the geographical proximity and other similarities in terms of host, climate, and the environment

167 between Bhutan and these Indian states, R. microplus is likely to be the most predominant tick species in cattle in Bhutan. Further, this can be supported by the fact that R. microplus is a one- host tick with an extremely short life cycle (as short as 8 weeks) and can produce multiple generations in a year depending on climatic conditions and host availability (Apanaskevich &

Oliver, 2013). Both the climate and host availability in most parts of Bhutan are likely to favor R. microplus development throughout the year but winter, where the low temperature in some parts of the country might be the limiting factor. Economically, R. microplus is considered the most important tick in the world as it is an important vector for major tick-borne diseases in cattle

(Jongejan & Uilenberg, 2004). Every year, in Brazil and Mexico, annual losses from infestation of R. microplus were estimated to be US$ 3.24 billion (Grisi et al., 2014) and US$ 573.61 million per annum (Rodríguez-Vivas et al., 2017), respectively.

In the veterinary setting, TBDs such as anaplasmosis, babesiosis, and theileriosis are reported in cattle in Bhutan (NCAH, 2019). Generally, R. microplus is the principal vector of B. bovis and B. bigemina that causes babesiosis in tropical and subtropical regions of the world

(Bock et al., 2004). Ticks of the genera, Rhipicephalus, Hyalomma, Haemaphysalis, and

Amblyomma transmit Theileria organisms that cause theileriosis (Fry et al., 2016; OIE, 2018;

Spickler, 2019). Nineteen different tick species from the genera Argas, Ornithodoros,

Dermacentor, Hyalomma, Ixodes, and Rhipicephalus are known to be capable of transmitting

Anaplasma marginale that causes bovine anaplasmosis (Kocan et al., 2010; OIE, 2015). In the neighboring Indian states of Assam and West Bengal, R. microplus is the main vector for bovine tick-borne diseases such as anaplasmosis, babesiosis, and theileriosis (Barman et al., 2018;

Kakati et al., 2015). However, in Bhutan, we do not know which species of ticks are associated with the transmission of the above-mentioned diseases.

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In the public health setting, there is high incidence of acute undifferentiated febrile illnesses (believed to be caused by rickettsial organisms) reported in Bhutan (Tshokey et al.,

2016). In 2015, the first serological study in Bhutan for rickettsial organisms was conducted in

864 persons and found the seroprevalence as follows: scrub typhus group (22.6%); spotted fever group (15.7%); Q fever (6.9%); and typhus group (3.5%) (Tshokey et al., 2017). Scrub typhus in

Bhutan is thought to be transmitted by “chiggers” mite that is endemic in the Himalayan region

(Rahi et al., 2015; Tshokey et al., 2016). Currently, there is no information on the role of ticks in the transmission of rickettsial diseases in Bhutan. Generally, many rickettsiae are maintained and transmitted by ticks (Brouqui et al., 2004). Considering the wide distribution of ticks throughout the country, it is likely that they can be potential vectors for rickettsial organisms such as

Coxiella burnetti (Derick 1938) Philip 1948 in Bhutan (Tshokey et al., 2017).

As described in Chapter 2, we have identified some species of ticks found in Bhutan, but this was based on a one-time sampling from cattle. We might have found more species of ticks in the country if surveillance was conducted in diverse species of hosts (i.e., diverse species of domestic animals and wildlife) and the environment (i.e., potential tick habitats) throughout the year. Cattle (including buffalo, yak, Zo/Zom), equines (Horse, Mule, Donkey), pigs, poultry, sheep, goats, cats, and dogs are the common domestic animals in Bhutan (DoL, 2018), and they are potential hosts for many species of ticks. Wild animals are also potential hosts for many species of ticks (Corn et al., 1994). Wild animals, especially small mammals such as rodents, often act as a reservoir of TBDs for domestic animals (Adinci et al., 2018). Migratory birds can transport ticks from one place to another, and ground-dwelling birds can host many species of ticks (White et al., 2020). Currently, there is no information on the species of ticks infesting wildlife in Bhutan.

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Environmental sampling can provide a more accurate prediction of the spatial distribution of ticks (ECDC, 2018) and better information on tick’s life cycle, density, and activity (Swart et al., 2014). Field sampling (e.g., dragging in vegetation) is an effective method of tick collection, given that many species of ticks spend most of their life cycle in the environment (Barker &

Walker, 2014). However, in Bhutan, there has not been any tick sampling conducted in the environment. In Chapter 1 (section 1.2.7), a list of ixodid tick species recorded in the neighboring Indian states of Assam, Arunachal Pradesh, West Bengal, and Sikkim was presented. Since these Indian states share borders as well as geographical and climatic similarities with Bhutan, it is likely that more tick species might be present in Bhutan. Therefore, there is a need to enhance active surveillance in domestic animals and initiate passive surveillance in wildlife. There is also a need to conduct environmental sampling in potential tick habitats to gather more information on tick species presence and diversity. For instance,

Hyalomma ticks were reported from all the Indian states mentioned above, and it was also collected from cattle in eastern Bhutan by Cork et al. (1996). In the present study, no Hyalomma ticks were collected. The short sampling period (i.e., May and June 2019) could have failed to coincide with the Hyalomma ticks’ peak season in Bhutan. In India, the peak incidences of

Hyalomma ticks were observed from July through October (Geevarghese & Dhanda, 1987).

In Chapter 3, a study on species distribution modeling of the tick species (except for A. testudinarium and Ixodes sp.) identified in eastern Bhutan under current environmental conditions was presented. Some environmental factors associated with the geographical distribution of these ticks were also presented and discussed. The MaxEnt (Phillips et al., 2006) was used for the modeling as the data gathered in the study was the presence-only data. The presence data for four tick species were used together with environmental variables (i.e., climatic

170 variables, elevation, and land cover) to develop MaxEnt models under current environmental conditions. Land cover was one of the key predictors for developing the best model for all four tick species. The other environmental variables that described the best habitat suitability models were: elevation for R. microplus; the warmest quarter temperature and the wettest quarter precipitation for R. haemaphysaloides; the warmest and wettest quarter precipitation for H. bispinosa; and the coldest and wettest quarter precipitation for H. spinigera. For all four tick species modeled, the northeastern part of the study area, which is at high elevation (> 2000m), and the southernmost part of the study area, which is at low elevation (<500m), were predicted as areas with a very low probability of presence.

One of the key findings from Chapter 3 was that the northeastern and the southernmost part of the study area was consistently predicted to have a very low probability of presence for all four tick species modeled. As discussed in Chapter 3, the northeastern region is the area with higher elevations where temperature and rainfall are low, which might be limiting factors. For example, elevation was an important limiting factor for ticks like Ixodes ricinus (Linnaeus) in

Scotland (Gilbert, 2010) and Ixodes scapularis (Say) in North America (Brownstein et al., 2005).

However, there might be some other species of ticks that can tolerate high elevations. For instance, Cork et al. (1996) collected Hyalomma spp. in some areas of eastern Bhutan with elevations as high as 2,700m. There are also records of finding ticks like Haemaphysalis himalaya (Hoogstraal) at elevations as high as 2,900m in India (Hoogstraal & El Kammah,

1970). Thus, there is a need to initiate surveillance in these areas to obtain more information on habitat suitability.

The subtropical floodplains, located in the southernmost part of the study area, were also predicted to have a very low probability of presence for all four tick species modeled. The

171 climate and vegetation in these areas can be suitable for ticks. However, there are no human activities such as settlement and farming in these areas. For ticks like R. microplus, which is highly dependent on bovine hosts, the lack of bovine hosts, especially cattle, can be one of the limiting factors. Further, frequent flooding during monsoons will also be one of the important limiting factors in these areas. However, some parts of these areas fall under the Royal Manas

National Park, which is home to 65 species of mammals and 489 species of birds (Namgay,

2020). These wild mammals and birds could be potential hosts for some species of ticks.

Therefore, it would be of interest to initiate wildlife and environmental surveillance in these areas to obtain more information on habitat suitability.

Another key finding from Chapter 3 was that despite using 17 classes of land cover identified by the National Land Commission of Bhutan, the only land types that had a high probability of presence for tick species modeled were Kamzhing, meadows, shrublands, and chuzhing. In Bhutan, forests form 70.8% of the country’s total land and are home to many species of wild animals and birds that can be potential hosts for ticks. But in this study, none of the areas classified as forests were predicted to have a high probability of presence for the tick species modeled. As discussed in Chapter 3, this could be due to the fact that our sampling was biased toward areas dominated by cattle rearing since the primary focus of our study was to understand the presence and diversity of tick species on cattle. Therefore, to better understand the habitat suitability of different land types for ticks, it would be of interest to initiate surveillance in wildlife and the environment (i.e., potential tick habitats).

In Chapter 4, the results of a knowledge, attitude, and practice (KAP) survey about ticks and TBDs among cattle owners in a selected area of eastern Bhutan were presented. A KAP survey is a study of a specific population to collect information on what is known, what is

172 believed, and what is commonly practiced about a particular topic – in this case, ticks and TBDs

(WHO, 2008). A KAP survey can measure the extent of a known situation, establish a baseline for future assessments, measures the effectiveness of health intervention activities on health- related behaviors of the people, and inform the planning of intervention strategies (du Monde,

2011). Regarding ticks and TBDs, understanding the KAP of the farmers plays an important role in designing effective and sustainable tick control strategies (Kerario et al., 2018). Before this study, there has not been any KAP study conducted to assess the farmers’ perceptions regarding ticks and TBDs in Bhutan. In June 2019, 246 cattle owners in the Samkhar subdistrict in

Trashigang, east Bhutan were interviewed using a structured questionnaire.

The analyses found that 52% of the respondents had adequate knowledge of ticks as vector of diseases, and the respondents practicing the stall-feeding system of cattle rearing were more likely to have adequate knowledge than that of others (i.e., respondents practicing other systems of cattle rearing, for example, the free-grazing system). Similarly, 36% of the respondents had a favorable attitude toward tick prevention and control programs. Men were more likely to have a favorable attitude than that of women, and the respondents practicing the stall-feeding system of cattle rearing were more likely to have a favorable attitude than that of others. Overall, only 38% of the respondents in our study reported tick infestation as an important animal health problem, but 100% reported using acaricides to control ticks on cattle. In a similar study conducted in Benin, West Africa, tick infestation was reported as the major problem in cattle (Adehan et al., 2018). The use of acaricides is the most commonly adopted method for the control of ticks in similar studies conducted in Tanzania (Kerario et al., 2018) and

Zimbabwe (Sungirai et al., 2016).

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The practice of stall feeding was positively associated with adequate knowledge and a favorable attitude. This most likely reflects the positive impacts of the Royal Government of

Bhutan’s livestock intensification program. This program promotes the use of improved breeds

(i.e., Jersey and Holstein Friesian) for better productivity, and advocates the adoption of the stall- feeding system of cattle rearing. The government promotes these programs in various ways, and the most notable ones are the provision of subsidized livestock inputs such as the purchase of high-yielding dairy cows and the free distribution of construction materials for constructing cattle sheds. Training and awareness programs on various topics on livestock health and production are also being provided regularly for those farmers who have adopted the stall- feeding system. This is because the farmers who practice the stall-feeding systems usually own cattle that are more expensive (i.e., imported cattle such as Jersey and Holstein Friesian from

India), and thus the training and awareness programs are prioritized to them to ensure better health and productivity of their animals. In every district, the livestock sector has an earmarked annual budget for conducting farmers’ training. Every year, 2-3 numbers of short-term training

(each training normally lasting for 2-3 days) is provided to these farmers (Karma Tenzin,

Livestock Production Officer, pers. comm, 2020). As a result, these farmers become better informed about animal health and the best practices of livestock farming. Further, the stall feeding system of cattle rearing was found to be associated with lower tick prevalence from studies in Pakistan (Iqbal et al., 2013; Rehman et al., 2017), Bangladesh (Kabir et al., 2011), and

Ethiopia (Dasgupta, 2015).

As mentioned above, only 38% of the respondents perceived tick infestation as an important animal health problem compared to other diseases such as milk fever, mastitis, and foot and mouth disease. This indicates that farmers in Bhutan do not understand a potential link

174 between tick infestation and TBDs in cattle, which could be linked to the fact that Bhutan has not recorded any outbreak of TBDs with substantial mortalities. Without having witnessed any major outbreak of TBDs in Bhutan, the farmers had no reason to attribute ticks as an important animal health problem. Further, farmers can get acaricides free of cost from livestock centers as and when required. On the contrary, a hundred percent of the respondents reported using acaricides to control ticks on cattle, suggesting that tick infestation is commonly observed in almost every cattle owning household at some point of time in a year. It is evident from these findings that tick infestation is an important animal health problem in the country, but there exists a perception gap among farmers, which could be due to the lack of awareness education about ticks and TBDs in recent years. Such perception gaps can hamper tick prevention and control efforts, and they might lead to incorrect use of acaricides on the farms. Incorrect use of acaricides will not only waste resources but also lead to the development of acaricide resistance. Therefore, there is a need to initiate awareness education about ticks and TBDs and tick prevention and control.

5.2 Recommendations and further research

Although there are some limitations to this research, as discussed more specifically in each chapter, the findings outlined in this thesis have led to the development of some recommendations presented in the following sections. Given the fact that there was a paucity of information on ticks in Bhutan, these recommendations can contribute to further enhancing the existing knowledge of ticks on cattle in Bhutan. They will also guide policymakers in designing targeted tick surveillance and control programs and to inform future research on ticks and TBDs in Bhutan.

5.2.1 Enhancing active tick surveillance in domestic animals.

The main aims and benefits of this recommendation are:

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 To understand the diversity of tick species infesting cattle on a nationwide scale. This

will provide information on the predominant tick species infesting cattle in Bhutan, and

subsequently, tick prevention and control efforts can be targeted toward that particular

tick species.

 To understand tick distribution and seasonality in cattle, and the variations among the

different regions in Bhutan. This information can be used to determine the timing of tick

treatments in different regions of the country. Subsequently, this strategy can reduce the

frequency of acaricide application, thereby leading to a reduction of acaricide demand in

the country.

 To detect tick species present on other domestic animal species such as dogs. This will

lead to a better understanding of tick-host associations and risk factors associated with

animal health and public health.

Currently, our information about ticks in cattle is limited to two regions (i.e., east and west) of the country. Rhipicephalus microplus is likely to be the most predominant tick species in cattle in Bhutan, but to support that, additional information is required from the remaining two regions (i.e., east central and west central). Therefore, we recommend initiating active tick surveillance in cattle in collaboration with the respective RLDCs of these two regions. To obtain more detailed information on tick distribution and seasonality, a year-round sampling in cattle throughout the country is needed. Therefore, we recommend NCAH to initiate year-round active tick surveillance in cattle by leveraging on a wide network of veterinarians and veterinary laboratory technicians at the regional and district level, and para-veterinarians at the sub-district level. The focus here is on cattle because cattle are economically the most important livestock species in Bhutan and the most affected domestic species by ticks. Tick surveillance on other

176 domestic species can, however, be done on an opportunistic basis (e.g., tick collection from dogs during animal birth control programs) or through passive surveillance.

5.2.2 Tick surveillance in wildlife and the environment.

The main aims and benefits of this recommendation are:

 To determine the tick species present on wildlife and the role that these wild animals and

birds play in the maintenance and spread of ticks in Bhutan. This will enhance the

existing knowledge about tick fauna in Bhutan and inform the risk of TBDs at the

interface of wild animals, domestic animals, and humans.

 To conduct environmental sampling to obtain more information on the tick’s life cycle,

density, and activity in Bhutan. This can also provide a more accurate prediction of the

spatial distribution of ticks. Consequently, more accurate species distribution models can

be built, which can be used for developing targeted tick surveillance programs in Bhutan.

Wildlife plays an important role in the maintenance and transmission of diseases, as 75% of the emerging zoonoses in humans are thought to originate in wildlife (Brown, 2004).

Currently, there is no information on ticks infesting wildlife in Bhutan. However, forestry officials in Bhutan often encounter wild animals and birds in various parts of the country for rescue, relocation, and so on. Ticks (if there is any) can be collected from wildlife species during such times. Therefore, we recommend initiating passive surveillance in wildlife in collaboration with the Department of Forests and Park Services. There has also not been any tick sampling conducted in the environment in Bhutan. Therefore, we suggest initiating environmental sampling in potential tick habitats using tick collection methods like dragging and flagging.

5.2.3 Enhancing tick surveillance at quarantine stations in Bhutan.

The main aims and benefits of this recommendation are:

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 To detect tick species and TBDs likely to be imported into Bhutan through imported

animals. This will help in the early detection of tick species and TDBs that are at risk of

importing in the country, and subsequently, prevention and control measures can be

placed.

Currently, livestock imported into Bhutan are required to undergo 14 days quarantine, but there is no mandatory examination for ticks and TBDs at the quarantine stations. While it is likely that animals will be treated with acaricides if they are infested with ticks, this does not ensure that imported livestock are free of TBDs. For instance, in 2015, Crimean-Congo hemorrhagic fever (CCHF) virus-specific IgG antibodies were detected in goats imported from

India (Wangchuk et al., 2016), and there is a growing public health concern in Bhutan. CCHF is a tick-borne, zoonotic viral disease, and human infections occur through bites of infected ticks, crushing infected ticks, and direct contact with infected blood and tissues of viremic livestock and humans (WHO, 2014). In recent years, babesiosis (diagnosed based on clinical signs) has been encountered in the quarantine stations in cattle imported from the neighboring Indian states of Assam and West Bengal (Dr. Sherab Phuntsho, Quarantine Officer, Bhutan, pers. comm,

2020). Therefore, we recommend surveillance for ticks and TBDs at quarantine stations in collaboration with the Bhutan Agriculture and Food Regulatory Authority (BAFRA).

5.2.4 Diagnosis of the potential pathogens of medical and veterinary importance in ticks in

Bhutan.

The main aims and benefits of this recommendation are:

 To determine the pathogen diversity across tick species identified in Bhutan to

understand the potential vector role these tick species might be playing in the

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transmission of TBDs. This will also help in informing the risk of potential TBDs that

can be transmitted to humans and animals in Bhutan.

We have already collected 3,600 tick specimens (from cattle) that were morphologically identified to six species representing four important genera of Ixodid ticks. The RLDC Wangdue has 2,165 tick specimens (from cattle) identified to four important genera of Ixodid ticks (RLDC

Wangdue, 2019). And the District Veterinary Hospital, Thimphu, has 425 tick specimens

(unidentified) collected from 206 dogs (Dr. Jigme Wangchuk, pers. comm, 2020). However, the pathogen diversity across these tick species is yet to be determined. Therefore, we propose determining pathogen diversity in the collected tick specimens using molecular techniques such as polymerase chain reaction assays and sequencing analysis. Some examples of such studies are

Wei et al., (2016) from China, Karim et al., (2017) from Pakistan, and Vayssier-Taussat et al.,

(2013) from Europe.

5.2.5 Enhancing modeling studies for tick species available in Bhutan.

 Enhancing modeling studies will identify more areas suitable for ticks in Bhutan, and this

will help in developing targeted tick surveillance programs, thereby reducing the cost of

surveillance.

Climate change is thought to influence tick distribution as well as the risk of TBDs transmission to humans (Dantas-Torres, 2015). Habitat suitability modeling of ticks is important to understand the changing scenarios of tick distribution and associated TBDs, as many components of ticks and TBDs are inextricably linked to climate and other environmental factors in the habitat. Currently, the habitat suitability modeling of ticks in Bhutan is limited to two districts in eastern Bhutan. The modeling study we conducted (Chapter 3) is the first to have attempted to model the habitat suitability for tick species identified in Bhutan. The western

179 region of the country has already some occurrence points recorded for tick species found in that region (Dr. B D Rai, pers.comm, 2020). Therefore, we suggest RLDC Wangdue to use the models (built using presence data from eastern Bhutan) from our study to be applied to the western region and subsequently evaluate them using their data (i.e., presence data from western

Bhutan). This will help in identifying suitable tick habitats in western Bhutan, and subsequently, findings can be used for developing targeted surveillance programs. As mentioned in Chapter 3, the resampled bioclimatic variables may not represent the extreme climatic variations in Bhutan, thereby increasing the uncertainty of the models. This is because the topography of Bhutan consists of high mountains and narrow valleys with extreme variations in climatic conditions over a short distance (Hoy et al., 2016). Therefore, we advise using more local climate or weather data for future modeling studies. Bhutan has 20 class A weather stations and 64 class C weather stations (Tenzin, 2017), and the data can be obtained from the National Centre for

Hydrology and Meteorology of Bhutan (https://www.nchm.gov.bt/).

5.2.6 Enhancing awareness education about ticks and tick-borne diseases.

 Enhancing awareness education to farmers about ticks and TBDs will lead to an adoption

of a better tick control practice.

Samkhar subdistrict (the KAP study area), by Bhutanese standard, is the most progressive dairy farming subdistrict. Generally, farmers in this subdistrict are thought to be better in terms of ideas and issues of livestock farming. However, as discussed in Chapter 3 and the earlier sections, there exists gaps in the knowledge, attitude, and practices regarding ticks and TBDs among these farmers. Therefore, we recommend the District Livestock Sector, Trashigang, to initiate awareness education about ticks and TBDs, and tick prevention and control in the

Samkhar subdistrict. The identified gaps in knowledge, attitude, and practices we presented can

180 be used as a baseline to conduct similar KAP studies in other parts of Bhutan, especially in areas where dairy farming is intensive. Further, we recommend all other districts to initiate this awareness education by tagging it along with other training programs such as livestock health management, clean milk production, zoonotic diseases, and so on to address resource constraints.

For such programs to be successful at farmers’ level, livestock officials might need to be provided short refresher courses on ticks and TBDs.

5.2.7 Initiate acaricide monitoring

 Acaricide monitoring will ensure that the chemicals are used properly in the field.

Further, it will also lead to an understanding of acaricide resistance.

As mentioned earlier, acaricides have played an important role in controlling ticks in

Bhutan, but monitoring their usage in the field has been limited. Although farmers are advised to strictly adhere to a specific dilution rate, there is no system to monitor the usage at the individual farm level. In recent years, there was also a growing concern among livestock officials on cross- application of acaricides on the crops. Therefore, there is a need to strengthen the acaricide monitoring system in the field to ensure that these chemicals are used properly (i.e., following specific dilution rates) for the purposes intended.

Further, in most parts of the world, ticks like R. microplus have developed resistance to almost all currently used acaricides (Rodriguez-Vivas et al., 2018). There are several reports of acaricidal resistance in R. microplus from various parts of India (Singh et al., 2015; Vatsya &

Yadav, 2011). However, in Bhutan, there has not been any study conducted to evaluate acaricide resistance. And paradoxically, R. microplus was the most predominant tick species from all three studies (including this thesis) conducted so far in Bhutan. As more and more European breeds of cattle are reared in Bhutan to improve the production of milk, the problems associated with ticks

181 and TBDs are also likely to increase as these breeds are more susceptible to ticks. As a consequence, the quantity and frequency of acaricide use in the country might even increase.

Therefore, there is a need to conduct studies on acaricide resistance in Bhutan.

5.2.8 Enhancing basic diagnostic tests for tick-borne diseases in cattle.

 Enhancing diagnostic tests will gather data on the prevalence of TBDs in cattle in

different regions of Bhutan, and at the farm level, it will help in providing accurate

clinical treatments to animals.

As described in Chapter 1, the major tick-borne diseases such as anaplasmosis, babesiosis, and theileriosis in cattle can be diagnosed by blood smear examination. Although all veterinary hospitals in the districts are equipped with basic diagnostic laboratory equipment, there are not many veterinary laboratories that conduct such blood smear examination for suspected TBDs. This could also be due to the shortage of veterinary laboratory technicians in some of the districts. As a result, the prevalence of these diseases in different regions of the country is not known. Therefore, we recommend enhancing blood smear examinations for all suspected TBDs, mainly as a part of TBDs surveillance in cattle in Bhutan.

Conclusion

Ticks are the most important parasites reported in Bhutan, but the knowledge about ticks and their distribution in Bhutan has been poor. The findings of this study will go a long way in contributing to tick studies in Bhutan. Our findings on the presence and diversity of tick species in two districts of eastern Bhutan can be used as a baseline for subsequent tick studies in Bhutan.

Our modeling study not only presents some interesting findings but also provides information on what has to be done for future modeling projects. The KAP study findings have identified the gaps in the knowledge, attitude, and practice in a subset of cattle farmers in Bhutan, leading to

182 some important recommendations like the need to initiate awareness education. Such awareness education will prepare our farmers for designing and adopting a more effective tick control strategies of their own should the government stop providing free acaricides. In summary, this study has generated some useful data on ticks in Bhutan and highlighted many potential areas of future research.

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References

Adehan, S. B., Adakal, H., Gbinwoua, D., Yokossi, D., Zoungrana, S., Toé, P., Ouedraogo, M.,

Gbaguidi, A. M., Adoligbé, C., Fandohan, A. B., Hounmanou, G., Glèlè Kakaï, R.,

Farougou, S., & De Clercq, E. M. (2018). West African Cattle Farmers’ Perception of

Tick-Borne Diseases. EcoHealth, 15(2), 437–449. https://doi.org/10.1007/s10393-018-

1323-8

Adinci, K. J., Akpo, Y., Adoligbe, C., Adehan, S. B., Yessinou, R. E., Sodé, A. I., Mensah, G.

A., Youssao, A. K. I., Sinsin, B., & Farougou, S. (2018). Preliminary study on the tick

population of Benin wildlife at the moment of its invasion by the Rhipicephalus

microplus tick (Canestrini, 1888). Veterinary World, 11(6), 845–851.

https://doi.org/10.14202/vetworld.2018.845-851

Apanaskevich, D. A., & Oliver, J. H. (2013). Life Cycles and Natural History of Ticks. In

Biology of Ticks Volume 1 (pp. 59–73). OUP USA.

Asmaa, N. M., ElBably, M. A., & Shokier, K. A. (2014). Studies on prevalence, risk indicators

and control options for tick infestation in ruminants. Beni-Suef University Journal of

Basic and Applied Sciences, 3(1), 68–73. https://doi.org/10.1016/j.bjbas.2014.02.009

Barker, S. C., & Walker, A. R. (2014). Ticks of Australia. The species that infest domestic

animals and humans. Zootaxa, 3816(1), 1–144. https://doi.org/10.11646/zootaxa.3816.1.1

Barman, U., Dutta, T. C., Baishya, B. C., Goswami, S., Islam, S., Phukan, A., Barman, D., &

Kakati, P. (2018). Study of prevalence and diagnosis of babesiosis in cattle. J. Entomol.

Zool. Stud, 6(3), 1613–1616.

184

Bianchi, M. W., Barré, N., & Messad, S. (2003). Factors related to cattle infestation level and

resistance to acaricides in Boophilus microplus tick populations in New Caledonia.

Veterinary Parasitology, 112(1), 75–89. https://doi.org/10.1016/S0304-4017(02)00415-6

Bock, R., Jackson, L., Vos, A. D., & Jorgensen, W. (2004). Babesiosis of cattle. Parasitology,

129(S1), S247–S269. https://doi.org/10.1017/S0031182004005190

Brouqui, P., Bacellar, F., Baranton, G., Birtles, R. J., Bjoërsdorff, A., Blanco, J. R., Caruso, G.,

Cinco, M., Fournier, P. E., Francavilla, E., Jensenius, M., Kazar, J., Laferl, H., Lakos, A.,

Lotric Furlan, S., Maurin, M., Oteo, J. A., Parola, P., Perez-Eid, C., … Wilske, B. (2004).

Guidelines for the diagnosis of tick-borne bacterial diseases in Europe. Clinical

Microbiology and Infection, 10(12), 1108–1132. https://doi.org/10.1111/j.1469-

0691.2004.01019.x

Brown, C. (2004). Emerging zoonoses and pathogens of public health significance–an overview.

Revue Scientifique et Technique-Office International Des Epizooties, 23(2), 435–442.

Brownstein, J. S., Holford, T. R., & Fish, D. (2005). Effect of Climate Change on Lyme Disease

Risk in North America. EcoHealth, 2(1), 38–46. https://doi.org/10.1007/s10393-004-

0139-x

Cork, S., Dukpa, K., Gurung, J., & Charleston, W. A. G. (1996). A preliminary study of the ticks

and heamoparasites of cattle in Eastern Bhutan (Unpublished).

Corn, J. L., Kavanaugh, D. M., Creekmore, T. E., & Robinson, J. L. (1994). Wildlife as hosts for

ticks (Acari) in Antigua, West Indies. Journal of Medical Entomology, 31(1), 57–61.

https://doi.org/10.1093/jmedent/31.1.57

185

Dantas-Torres, F. (2015). Climate change, biodiversity, ticks and tick-borne diseases: The

butterfly effect. International Journal for Parasitology: Parasites and Wildlife, 4(3),

452–461. https://doi.org/10.1016/j.ijppaw.2015.07.001

Dasgupta, R. (2015). Prevalence of major ixodid ticks of cattle in and around Bahir dar, Ethiopia.

JOURNAL OF NATURAL HISTORY, 11(1), 1–3. de Castro, J. J. (1997). Sustainable tick and tickborne disease control in livestock improvement

in developing countries. Veterinary Parasitology, 71(2), 77–97.

https://doi.org/10.1016/S0304-4017(97)00033-2

Debbarma, A., Pandit, S., Jas, R., Baidya, S., Mandal, S. C., & Jana, P. S. (2018). Prevalence of

hard tick infestations in cattle of West Bengal, India. Biological Rhythm Research, 49(5),

655–662. https://doi.org/10.1080/09291016.2017.1395527

DoL. (2018). Livestock Statistics. Department of Livestock, Ministry of Agriculture and Forests,

Royal Government of Bhutan. http://www.dol.gov.bt/?page_id=394

Dorji, U., Olesen, J. E., Bocher, P. K., & Seidenkrantz, M. S. (2016). Spatial Variation of

Temperature and Precipitation in Bhutan and Links to Vegetation and Land Cover.

Mountain Research and Development, 36(1), 66–79. https://doi.org/10.1659/MRD-

JOURNAL-D-15-00020.1 du Monde, M. (2011). The KAP Survey Model (Knowledge, Attitudes, and Practices).

https://www.spring-nutrition.org/publications/tool-summaries/kap-survey-model-

knowledge-attitudes-and-practices

ECDC. (2018). Field sampling methods for mosquitoes, sandflies, biting midges and ticks.

https://www.ecdc.europa.eu/en/publications-data/field-sampling-methods-mosquitoes-

sandflies-biting-midges-and-ticks

186

Estrada-Peña, A. (2015). Ticks as vectors: Taxonomy, biology and ecology: -EN- -FR- Les

tiques en tant que vecteurs : taxonomie, biologie et écologie -ES- Las garrapatas como

vectores: taxonomía, biología y ecología. Revue Scientifique et Technique de l’OIE,

34(1), 53–65. https://doi.org/10.20506/rst.34.1.2345

Eyo, J. E., Ekeh, F. N., Ivoke, N., Atama, C. I., Onah, I. E., Ezenwaji, N. E., & Ikele, C. B.

(2014). Survey of tick infestation of cattle at four selected grazing sites in the tropics.

Global Veterinaria, 12(4), 479–486.

Fry, L. M., Schneider, D. A., Frevert, C. W., Nelson, D. D., Morrison, W. I., & Knowles, D. P.

(2016). East Coast Fever Caused by Theileria parva Is Characterized by Macrophage

Activation Associated with Vasculitis and Respiratory Failure. PLOS ONE, 11(5),

e0156004. https://doi.org/10.1371/journal.pone.0156004

Geevarghese, G., & Dhanda, V. (1987). The Indian Hyalomma ticks (Ixodoidea: Ixodidae). The

Indian Hyalomma Ticks (Ixodoidea: Ixodidae).

https://www.cabdirect.org/cabdirect/abstract/19880591406

Geevarghese, G., & Mishra, A. (2011). Haemaphysalis Ticks of India—1st Edition.

https://www.elsevier.com/books/haemaphysalis-ticks-of-india/geevarghese/978-0-12-

387811-3

Gilbert, L. (2010). Altitudinal patterns of tick and host abundance: A potential role for climate

change in regulating tick-borne diseases? Oecologia, 162(1), 217–225.

https://doi.org/10.1007/s00442-009-1430-x

Grisi, L., Leite, R. C., Martins, J. R. de S., Barros, A. T. M. de, Andreotti, R., Cançado, P. H. D.,

León, A. A. P. de, Pereira, J. B., Villela, H. S., Grisi, L., Leite, R. C., Martins, J. R. de S.,

Barros, A. T. M. de, Andreotti, R., Cançado, P. H. D., León, A. A. P. de, Pereira, J. B., &

187

Villela, H. S. (2014). Reassessment of the potential economic impact of cattle parasites in

Brazil. Revista Brasileira de Parasitologia Veterinária, 23(2), 150–156.

https://doi.org/10.1590/S1984-29612014042

Hoogstraal, H., & El Kammah, K. M. (1970). Haemaphysalis (Herpetobia) himalaya Hoogstraal

(Ixodoidea: Ixodidae), Description of Immature Stages, Hosts, and Distribution. The

Journal of Parasitology, 56(5), 1023–1027. JSTOR. https://doi.org/10.2307/3277528

Hoy, A., Katel, O., Thapa, P., Dendup, N., & Matschullat, J. (2016). Climatic changes and their

impact on socio-economic sectors in the Bhutan Himalayas: An implementation strategy.

Regional Environmental Change, 16(5), 1401–1415. https://doi.org/10.1007/s10113-015-

0868-0

Iqbal, A., Sajid, M. S., Khan, M. N., & Khan, M. K. (2013). Frequency distribution of hard ticks

(Acari: Ixodidae) infesting bubaline population of district Toba Tek Singh, Punjab,

Pakistan. Parasitology Research, 112(2), 535–541. https://doi.org/10.1007/s00436-012-

3164-7

Jongejan, F., & Uilenberg, G. (2004). The global importance of ticks. Parasitology, 129(S1), S3–

S14. https://doi.org/10.1017/S0031182004005967

Kabir, M. H. B., Mondal, M. M. H., Eliyas, M., Mannan, M. A., Hashem, M. A., Debnath, N. C.,

Miazi, O. F., Mohiuddin, Kashem, M. A., Islam, M. R., & Elahi, M. F. (2011). An

epidemiological survey on investigation of tick infestation in cattle at Chittagong District,

Bangladesh. African Journal of Microbiology Research, 5(4), 346–352.

https://doi.org/10.5897/AJMR10.706

Kakati, P., Sarmah, P. C., Ray, D., Bhattacharjee, K., Sharma, R. K., Barkalita, L. M., Sarma, D.

K., Baishya, B. C., Borah, P., & Stanley, B. (2015). Emergence of oriental theileriosis in

188

cattle and its transmission through Rhipicephalus (Boophilus) microplus in Assam, India.

Veterinary World, 8(9), 1099–1104. https://doi.org/10.14202/vetworld.2015.1099-1104

Karim, S., Budachetri, K., Mukherjee, N., Williams, J., Kausar, A., Hassan, M. J., Adamson, S.,

Dowd, S. E., Apanskevich, D., Arijo, A., Sindhu, Z. U., Kakar, M. A., Khan, R. M. D.,

Ullah, S., Sajid, M. S., Ali, A., & Iqbal, Z. (2017). A study of ticks and tick-borne

livestock pathogens in Pakistan. PLOS Neglected Tropical Diseases, 11(6), e0005681.

https://doi.org/10.1371/journal.pntd.0005681

Kerario, I. I., Simuunza, M., Laisser, E. L. K., & Chenyambuga, S. (2018). Exploring knowledge

and management practices on ticks and tick-borne diseases among agro-pastoral

communities in Southern Highlands, Tanzania. Veterinary World, 11(1), 48–57.

https://doi.org/10.14202/vetworld.2018.48-57

Khajuria, V., Godara, R., Yadav, A., & Katoch, R. (2015). Prevalence of ixodid ticks in dairy

animals of Jammu region. Journal of Parasitic Diseases, 39(3), 418–421.

https://doi.org/10.1007/s12639-013-0354-x

Kocan, K. M., de la Fuente, J., Blouin, E. F., Coetzee, J. F., & Ewing, S. A. (2010). The natural

history of Anaplasma marginale. Veterinary Parasitology, 167(2), 95–107.

https://doi.org/10.1016/j.vetpar.2009.09.012

Kunz, S. E., & Kemp, D. H. (1994). Insecticides and acaricides: Resistance and environmental

impact: -EN- -FR- -ES-. Revue Scientifique et Technique de l’OIE, 13(4), 1249–1286.

https://doi.org/10.20506/rst.13.4.816

Namgay, P. (2020). Royal Manas National Park | Department of Forest & Park Services.

http://www.dofps.gov.bt/?page_id=296

189