Determining the Environmental Risk Factors for Cutaneous Leishmaniasis (CL) in the Negev, and Possibilities for Disease Control

Thesis submitted in partial fulfillment of the requirements for the degree of “DOCTOR OF PHILOSOPHY”

Ruti Berger

Submitted to the Senate of Ben-Gurion University of the Negev

May, 2009

Beer-Sheva

Determining the Environmental Risk Factors for Cutaneous Leishmaniasis (CL) in the Negev, and Possibilities for Disease Control

Thesis submitted in partial fulfillment of the requirements for the degree of “DOCTOR OF PHILOSOPHY”

Ruti Berger

Submitted to the Senate of Ben-Gurion University of the Negev

Approved by the advisor ______

______

Approved by the Dean of the Kreitman School of Advanced Graduate Studies ______

May, 2009

Beer-Sheva

This work was carried out under the supervision of

Prof. Burt P. Kotler, Mitrani Dept. of Desert Ecology Jacob Blaustein Institute for Desert Research Ben-Gurion University of the Negev Sede Boqer Campus

Prof. Alon Warburg, Department of Parasitology, Kuvin Center for the Study of Tropical and Infectious Diseases, Hebrew University- Hadassah Medical School, Jerusalem

Acknowledgments I am tremendously grateful to my advisors, Prof. Burt Kotler and Prof. Alon Warburg for the time and effort that they put into my work. Beyond their vast knowledge, they had a major contribution in making me see the whole picture beyond the details. I would like to thank Burt not only for his support and good advice, but also for his tremendous patience, and in his thoughtful way, for enabling me to face challenges using a healthy scientific approach. I would like to thank Alon for providing all the help he could give from a distance and especially for opening me the window to the enthralling field of Parasitology and Medical Entomology, which is different from Ecology, but is as interesting and as complicated. I would like to thank Yaacov, my husband, for his tremendous support and patience, and for his time and effort looking after the kids, which enabled me to accomplish the PhD. I would like to thank my adorable kids, Maya and Rotem, for coming with me to field when it was necessary, for helping me looking after the lab sand rats and for accepting the fact that mother has to work on her PhD even on weekends and holidays. I would like to thank my parents, my late father, Mordechay, and my mother, Hella, for their endless love and support, throughout the years. I would like to thank all my current and former lab members for their support, comments and technical help, especially: Shomen Mukharjee, Keren Embar, Valeria Hochman-Adler, Cecilia Iribarren, Vijayan Sundararaj, Oded Berger. I would like to thank all the dear people for their help in technical and field work or comments regarding the research conducting or the drafts: Gideon Wasserberg, Laor Orshan, Boris Krasnov, Georgy Shenbrot, Bert Boeken, Pedro Berliner, Naftali Lazarovitz, Lee Schnur for their advice regarding theory and field measurements, Aviva Vonshck for her help in the lab analysis of the tubes experiment. Shirli Bar David for letting me use her molecular lab and for good advice. Yaki Morgenstern, Assa Florentin, Danit Teller, Tanja Jaekel, for their dedicated lab and field work. Michal and Moran Segoli for all their help in several aspects, Itamar Giladi, Ishay Hoffman, Tali Brunner and all the people I could not mention in one page. Lastly, I would like to thank my Kitaido practice group in Adama, and especially Shahar the leader, who helped me get through this intensive period, alerted and with a smile.

This study was partially funded by Grants-In-Aid of Research from the Israel Science Foundation (grant number 144/06), Deployed War-Fighter Protection (DWFP)Research Program, funded by the U.S. Department of Defense through the Armed Forces Pest Management Board (AFPMB), and Fellowship from the Mitrani Department for Desert Ecology, The Blaustein Institutes for Desert Studies, Ben Gurion University.

Ruti Berger, May 2009, Midreshet Ben Gurion. I Table of Contents Page number

Abstract

Chapter 1: General Introduction

1.1 Background 1

1.2 The study system (organisms) 2

1.3 Research goal and hypotheses 6

1.4 Plot characteristics 7

Chapter 2: Population Dynamics of Psammomys obesus

2.1 Introduction 9

2.2 Methods 13

2.2.1 Dynamics of burrow occupancy 13

2.2.2 Habitat selection dynamics 15

2.2.3 Sand rat population dynamics in time and space 16

2.3 Results 17

2.3.1 Dynamics of burrow occupancy 17

2.3.2 Habitat selection dynamics 19

2.3.3 Sand rat population dynamics in time and space 24

2.4 Discussion 29

2.4.1 Dynamics of burrow occupancy 29

2.4.2 Habitat selection dynamics 30

2.4.3 Sand rat population dynamics in time and space 32

Chapter 3: Sand fly Dynamics

3.1 Introduction 34

3.2 Methods 35

3.3 Results 37

3.3.1 2007 season 37

3.3.2 2008 season 40

3.4 Discussion 43

Chapter 4: Cutaneous Leishmaniasis infection rates

4.1 Introduction 46

4.2 Methods 48

4.3 Results 50

4.4 Discussion 55

Chapter 5: Lushness Experiment

5.1 Introduction 58

5.2 Methods 59

5.3 Results 62

5.4 Discussion 64

Chapter 6: Insecticide treated Tubes

6.1 Introduction 69

6.2 Methods 72

6.2.1 Apparatus and methodology development in the laboratory 72 6.2.1.1 Assessing the effect of insecticide powdering on

the behavior of Psymmomys obesus individuals 72 6.2.1.2 Training P. obesus to pass through tubes 72

6.2.1.3 Determining the most appropriate tube length,

diameter, and carpet texture 73 6.2.1.4 Quantifying the amount of powder (cornstarch) transferred

from carpets and retained on the fur of individual Psammomys 74 III 6.2.2 Methods in the field 76 6.2.2.1 Assessing the tube usage in P. obesus burrows 76

6.2.2.2 Statistical analysis 77

6.3 Results 77

6.3.1 Laboratory results 77

6.3.1.1 Assessing the effect of insecticide powdering on

the behavior of Psymmomys obesus individuals 77

6.3.1.2 Determining the most appropriate tube length,

diameter, and carpet texture 78

6.3.1.3 Quantifying the amount of powder (cornstarch) transferred

from carpets and retained on the fur of individual Psammomys 78

6.3.2 Field results 79

6.3.2.1 Duration of tube usage 79

6.4 Discussion 81

Chapter 7: General Discussion 85

Appendix 1: Plots maps with burrows 90

References 95

I תקציר

IV List of Figures

Fig 1.1 Fat sand rat 5

Figure 1.2a Sand fly male 5

Figure 1.2b Sand fly female 5

Figure 1.3a Closed Habitat 8

Figure 1.3b Open Habitat 8

Figure 1.4 Cutaneous and visceral leishmaniasis in Israel 8

Figure 1.5 Study sites map 8

Figure 2.1 Population regulation models 11

Figure 2.2 Habitat isodars 11

Figure 2.3 Proportion of active burrows (Without Haroa plot) in different

plots and habitats. 18

Figure 2.4 Colonization level in different plots 19

Figure 2.5 Psammomys captured per plot per season 20

Figure 2.6 Fat sand rat captured by body size class 20

Figure 2.7 Sand rat densities in different plots along time, calculated by

Lincoln index 21

Figure 2.8 The North plot isodar for P. obesus between two habitats,

open and closed winter spring 22

Figure 2.9 The Plateau isodar for P. obesus between two habitats,

open and closed winter spring 22

Figure 2.10 The Wadi isodar for P. obesus between two habitats,

open and closed winter spring 23

Figure 2.11 The Plateau isodar for P. obesus between two habitats,

open and closed, all year. 24 V Figure 2.12 Distribution of open and closed habitat among the plots 25

Figure 2.13 Distribution of plant type according to open and closed habitats 25

Figure 2.14 Active burrow density in different plots 27

Figure 3.1 SF 2007 gender distribution, with species identifications of the males 38

Figure 3.2 SF 2007 abundance over time 38

Figure 3.3 SF 2007 monthly SF abundance per plot 39

Figure 3.4 SF 2007 gender temporal distribution including male species

temporal distribution 39

Figure 3.5 SF 2008 gender distribution, with species identifications of the males 41

Figure 3.6 SF 2008 abundance in different plots 41

Figure 3.7 SF 2008 abundance over time 42

Figure 3.8 SF 2008 monthly abundance, Wadi and plateau 42

Figure 3.9 SF 2008 monthly abundance according to moonlight 43

Figure 3.10 SF 2008 gender temporal distribution 43

Figure 4.1 CL infection rate in Israel 1956-2004 47

Figure 4.2 CL infection rate in Israel 2004-2008 47

Figure 4.3 Leishmania-specific PCR 51

Figure 4.4 Sand rat DNA presence PCR 51

Figure 4.5 L. major Minimal Infection Rate and SF abundance 2008

total SF population 53

Figure 4.6 L. major Minimal Infection Rate and SF abundance 2008, plateau 53 VI Figure 4.7 Temporal distribution of CL cases in Midreshet Ben Gurion 2008 54

Figure 4.8 Regression of active burrows density and sand fly abundance in

July and October 2007-2008 54

Figure 5.1a Cut bush 59

Figure 5.1b Control bush 59

Figure 5.2 Emerge trap 62

Figure 5.3 Sunfleck Ceptometer 62

Figure 6.1 Double chamber apparatus comprised of 2 rodent cages

connected by a 7.5 cm diameter PVC pipe 73

Figure 6.2 PVC pipes lines with different thickness carpets 73

Figure 6.3 Calibration Curve for known amount of cornstarch read at 550 OD 75

Figure 6.4 P. obesus with powder near a tube opening (lab experiment) 75

Figure 6.5 Tube with food at burrow opening 76

Figure 6.6 Fat Sand Rat emerging from its burrow through a tube 76

Figure 6.7 Average amount of powder found on animal fur 78

Figure 6.8 Average amount of corn starch retained on animal fur on days 1-5 79

Figure 6.9 Proportion of tube usage over time 80

Figure 6.10 Average number of times the tubes were used per day 80

Figure 6.11 Number of times the tubes were used per day in different plots 81

Figure 6.12 Number of new exits dug over time in different plots 81

VII List of Tables

Table 1.1 Plot Characteristics 7

Table 2.1 The regression outcome of the four population regulation models 12

Table 2.2 Trapping sessions information 15

Table 2.3 Two-Way ANOVA w/o Haroa regarding the effect of plot and

habitat on the proportion of active burrows 18

Table 2.4 Poisson regression model testing the effect of climatic variables

on the number of sand rat trapped 28

Table 2.5 Poisson regression model testing the effect of external and within

burrow climatic variables on the number of sand rat trapped in 2007-2008 28

Table 2.6 Burrow activity level and the plant phenological state 29

Table 3.1 Multiple regression testing the relationship between sand fly

abundance and selected meteorological variables 40

Table 4.1 Regression model testing the effect of season and plot on the

Leishmania presence in Sand fly population 52

Table 4.2 Regression of P. obessus density and sand fly abundance 54

Table 4.3 Summary of the human CL cases in Sede Boqer area and infection

rate in hosts and vectors in Sede Boqer area 55

Table 5.1 Poisson regression model testing the effect of burrow activity level

and the cutting on SF abundance 63

Table 5.2 Ordinal multinomial logistic regression testing the effect of the

cutting manipulation on burrow continuously active level 63

Table 6.1 Calibration Curve Building table 75

Table 6.2 Paired t-test comparing the Psammomys behavior before and

after powdering 77 Abstract Introduction Cutaneous Leishmaniasis, (CL) is a zoonotic skin disease caused by kinoplastid zooflagellates of the genus Leishmania. In Israel, CL is caused by two species Leishmania major and L. tropica. CL is prevalent generally in arid and semi-arid zones in Israel, with L. major prevalent in the Negev, the Arava, and the Jordan Valley. L. major is a parasite of rodents and transmitted by the sand fly vector Phlebotomus papatasi. Sand flies are nectivorous, but females also feed on blood required for egg production. The most common reservoir host species of L. major in the Middle East and North Africa is the fat sand rat Psammomys obesus. P. obesus is a common diurnal rodent in the Israeli Desert where it consumes mostly plants from the family Chenopodiaceae. It typically digs its large, multi-opening burrow beneath or next to a chenopod bush. The accumulation of organic material in its burrow and the moderate burrow environment are favorable for the sand fly vectors, and the sand rat’s aggregated distribution promotes transmission of the parasites. The goal of this study was to examine processes affecting the CL transmission cycle and assess the environmental risk factors while implementing an ecologically founded approach to understanding and analyzing a zoonotic disease. Methodology 1. Host and vector population dynamics and related environmental factors – I followed the sand rat and sand fly populations in the Sede Boqer area using field censuses. I looked at seasonal changes in those populations, at density dependent habitat selection behavior of the fat sand rats, and at the relationship between habitat selection and peaks in the abundances and activities of sand flies. I surveyed sand rat burrows in both disturbed and undisturbed areas and in open and closed habitats. The following parameters were measured: levels of sand rat activity (proportion of active burrows), host plant species, general condition of host plants, relative humidity, and temperature range. 2. Disease prevalence- I studied the disease prevalence in both host and vector. In addition, I collected information regarding human cases of CL in the Sede Boqer area and compared them to the host and vector population dynamics and infection rates. 3. Plant lushness- The lushness of the perennial plants associated with sand rat burrows is a risk factor previously reported to correlate with disease prevalence. I tested whether experimental reduction of plant lushness of borrow-associated shrubs by pruning would decrease the activity of both host & vector and the likelihood of burrow occupancy. 4. Insecticide-treated tubes- I studied the feasibility of using rodent hosts as carriers of insecticide into the burrows as a means for reducing sand fly abundance thereby reducing disease transmission. To this end, I developed a tube apparatus that can be inserted into sand rat borrows and through which the rodents can pass. The apparatus is designed to passively apply insecticide powder to rodents as they pass through. Results 1. Host and vector population dynamics and related environmental factors - I found a trend towards higher percentage of active burrows in the open habitat, and that both plot and habitat affected the proportion of active burrows. In the isodar analysis, I showed density dependent habitat selection behavior in sand rats and habitat preference towards the open habitat in the plateau, but not the Wadi. This was true using data from all year around, but especially in the winter-spring seasons. In the sand rat population dynamics in time and space, I found a dominancy of Atriplex as the main bush associated with sand rat burrows in both closed and open habitats. Both year and plot had a marginally significant effect on the number of active burrows. I showed a positive correlation between plant condition and burrow activity level. I examined the temporal variation of the sand fly vector abundance in two consecutive years. In 2007, sand flies peaked in abundance in August, while in 2008, they peaked in June. I trapped more than three times more sand flies in the same plots in 2008 compared to 2007, with males being relatively more dominant in 2008. I detected large spatial and temporal differences at a relatively small scale (< 6 km) between the Wadi and the plateau. The sand fly abundance was far higher in the Wadi, a pattern that suggests a source-sink dynamics, with the Wadi being the source and the plateau being the sink population. In both years, the Wadi presented a unimodal pattern in numbers of sand flies, with a delayed abundance peak in 2007. On the plateau, a bimodal pattern was detected in 2008 with the first peak in June and the second one in September. I also found a positive correlation between sand rat and sand fly density (in July and October). 2. Disease prevalence - In 2007-2008, the local CL human infection rate in Sede Boqer area (Kibbutz Sede Boqer and Midreshet Ben-Gurion) ranged from 721 to 2,164 per 100,000 (4-24 cases per year per settlement). The overall infection rate among the host fat sand rat was 9.06%, with the Wadi being the highest (22%). Infection rate correlated with higher body mass. The number of individuals trapped (13-40) and the infection rate (0-26%) of the host was highly variable between years. In 2007, the Minimal Infection Rate (MIR) of the sand fly population was 2.3%, with only one infected batch from August detected from the Wadi. In 2008, the MIR for the total sand fly population was 23.92%, with MIR of 24.88% in the Wadi and 19.86% in the plateau. I found a lag several weeks long between the abundance peak and the infection peak. 3. Plant lushness - Lushness does not directly affect sand fly abundance. Instead, it positively affects sand rat activity, which in turn may affect sand fly abundance. However, the small number of sand flies captured makes this relationship difficult to establish with certainty 4. Insecticide-treated tubes – Sand rats can pass through carpet-lined tubes easily and are willing to use them in the field for several days. In so doing, rodents can passively pick up powder placed in the tubes that can contain insecticide. This method of passively applying insecticide powder has the potential of being an effective tool for leishmaniasis control. Disease control application 1. Sand rat population dynamics as demonstrated by burrow occupancy can serve as an important tool for leishmaniasis control as host activity indicators. Host and vector abundance are correlated, and field surveys are relatively easy and cost effective. Such information obtained from surveys can be used for determining when and where to apply control efforts. 2. Human infection rates are correlated to the vector infection rates, rather than the host values. There is a several week lag between sand fly peak abundance and infection peak, suggesting an important temporal window for disease control efforts. 3. Self-application by passing through treated tubes is feasible in sand rats, and insecticide treated tubes may serve as a control method in the future. Keywords: Cutaneous Leishmaniasis, Zoonosis, disease control, population dynamics, disease ecology, Psammomys obesus, Phlebotomine papatasi, Leishmania major, Negev desert, Israel.

Chapter 1: General Introduction 1.1 Background Parasitism is an ecological association between species in which individuals of one species, the parasite, lives on or in the bodies of individuals of the other species, the host. The parasite obtains its nutrients from one or very few host individuals, normally causing harm but not immediate death. In many cases, the parasite is dependant on the host for regulation of its environment (Begon, et al. 1990, Anderson and May 1978). Parasites and pathogens are important since they comprise the most abundant and diverse group of organisms. They include microparasites (bacteria, viruses, fungi, protozoa), and macroparasites (helminthes, arthropods, parasitic higher plants). They cause direct harm to their hosts, by morbidity and mortality, as well as indirect harm by affecting domestic animals and crops. Parasites can be transmitted directly, from host to host, or indirectly, via another species, the intermediate host or a vector. The parasite-host interactions are viewed as a dynamic process, to the level that the parasite regulates the host population dynamics (Begon, et al. 1990, May and Anderson 1979). Some 80% of parasitic infections of humans are zoonoses (Ashford and Crewe 2003). A zoonosis is a disease transmitted from vertebrate animals to humans, but not between humans (Watt et al. 1995). The establishment of a zoonosis requires spatial and temporal co- occurrence of its biological components: host, vector, and pathogen. This is determined by a unique combination of specific environmental factors, including climate, vegetation, and soil (Sousa and Grosholtz 1991). Climatic conditions, and especially climatic changes, can greatly affect infectious diseases, including vector born diseases (VBD) (Paaijmans et al. 2009, Soverow et al, 2009). Habitat loss and urbanization can influence infection patterns of VBD,through the host, the vector, or the parasite (Abad-Franch et al. 2009, Bradley et al. 2008, Brown et al. 2008). Emerging infectious diseases (EID) are dominated by zoonoses (60.3% of EIDs; the majority of these (71.8%) originate in wildlife and are increasing significantly over time. EID origins are significantly correlated with socio- economic, environmental and ecological factors, and provide a basis for identifying regions where new EIDs are most likely to originate ('hotspots') (Jones, et al. 2008). Ashford (1997) defines a reservoir (of infection) as an ecologic system in which an infectious agent survives indefinitely. Until this decade, very little information was available on the ecology of reservoir hosts of zoonotic diseases (Ashford 1996; Mills and Childs 1998). Reservoir host characteristics that are important for their function in zoonotic systems include: distribution (regional and local); demography; population

1 dynamics; dispersal; habitat selection. Improving our understanding of these important ecological factors constitutes an objective of this study. Such an understanding may elucidate the “weak link” in the transmission cycle at which control efforts should be aimed. Anderson and May (1978, 1979) emphasized the importance of combining ecology, parasitology, and epidemiology for modeling and better understanding of host-parasite interactions. They raise the important role of merging epidemiological parameters such as transmission rate, β, virulence, α, and recovery rate, υ, with ecological concepts such as the Evolutionary Stable Strategy (ESS, Smith & Price, 1973). Interdisciplinary approaches have important roles in our understanding of zoonoses. Aagaard-Hansen et al (2009) suggest cross- disciplinary approaches that especially stress the combination of epidemiological, environmental, and social determinants that can combine the advantages of forecasting upcoming disease 'hot spots' with provision of evidence for long-term planning. These may apply to the combination of public health problems taking into account the local context. Conservation medicine studies the interactions between pathogens and disease on one hand, and species and ecosystems on the other. It focuses on the study of the ecological context of health and the remediation of ecological health problems. Environmental degradation has growing health implications. Conservation medicine purview includes examining the linkages among a) changes in habitat structure and land use; b) emergence and re-emergence of infectious agents, parasites, and environmental contaminants; c) maintenance of biodiversity and ecosystem functions as they sustain the health of plant and animal communities, including humans. Aguirre (2009) stresses the role of wild canids in a conservation medicine perspective. Similarly, this current study regarding Leishmaniasis can be put in the context of conservation medicine. 1.2 The study system (organisms): Leishmaniases are diseases caused by kinoplastid zooflagellates of the genus Leishmania. The parasites live and multiply in macrophage cells of vertebrate tissues and are transmitted by sand fly vectors. Epidemiologically, Leishmaniasis is considered a zoonosis. Pathologically, Leishmaniases are divided into three groups: 1. Visceral Leishmaniasis. This disease is caused mainly by Leishmania donovani, multiplies in reticuloendothelial tissues in the spleen, liver, bone marrow, and lymphatic nodules, and often results in the death of the host. In India and South Eastern Asia, it is an anthroponosis, with humans being the reservoir host, while in other regions such as Central Africa and the Mediterranean, it is a zoonosis with canids serving as the reservoir hosts.

2 2. Mucocutaneous Leishmanisis. This is a zoonosis, caused mainly by L. braziliensis, which attacks mucous tissues in the mouth, nose, and throat. It is common in tropical regions of Latin America, where the reservoir species are mostly rodents. 3. Cutaneous Leishmaniasis, (CL). This has two forms: zoonotic cutaneous leishmaniasis and anthroponotic cutaneous leishmaniasis (Killick-Kendrick and Ward 1981; Killick-Kendrick 1999; Sacks and Kamhawi 2001). In zoonotic CL, the parasites are deposited in the skin of the rodent host when an infected sand fly bites the rodent to feed on blood. There, the parasites are attacked and engulfed by skin-residing macrophages - the target cells for the parasite. Leishmania proliferates inside skin-residing macrophages as intracellular amastigotes. If the infected rodent is then bitten by another sand fly, the amastigotes can be taken up by susceptible sand flies, where they transform into extracellular flagellated promastigotes that proliferate in the gut of the sand fly (Sacks and Kamhawi 2001). The disease manifests itself as a 3-4 cm papular lesion, which heals by itself after several months and renders the person infected immune, but often ulcerates and leaves a disfiguring scar (Grevelink and Lerner 1996). CL is prevalent in arid and semi-arid zones including the Middle East (Ashford 1996). In Israel, CL is zoonotic and is caused by L. major and L. tropica (Greenblatt et al, 1985). Its distribution in Israel includes the Negev and Arava in the south (L. major) and several foci of L. tropica from the Galilee in the North to the Judean Desert in the South (Fig 1.4). The most common reservoir species of CL is Psammomys obesus (Fig 1.1) in the Middle East and North Africa and Rhombomys opimus in the Asian republics (Ashford 1996, 2000; Wasserberg et al., 2002; Strelkova 1996). P. obesus Cretzschmar 1828 (Gerbillinae: Cricetidae) is a diurnal rodent, widely distributed in the Arabian Desert as well as in the Negev and the Arava Deserts of Israel (Harrison & Bates 1991). It is herbivorous, consumes mostly halophytic plants belonging to the family Chenopodiaceae, does not drink water, and maintains constant body mass in summer and winter (Daly and Daly 1973, Degen et al., 1991, Harrison and Bates 1991, Qumsiyeh 1996, Mendelssohn and Yom-Tov 1999). This sand rat is a solitary, territorial animal that occupies a home range of approximately 10 m radius around its burrow, which is typically located beneath or next to a chenopod bush (Daly and Daly 1974, Ilan 1985). In the Negev Desert, its burrows are usually located under Atriplex halimus shrubs in dense habitat, and Anabasis articulata in more open habitat. In the central Negev Desert, P. obesus inhabits mainly deep valleys filled by loess and flanked by rocky hillsides (Krasnov et al., 1996). Sand rat burrow systems are complex, with several openings (Orr 1974). Individuals

3 often change their burrows (Shenbrot, 2004), and usually the number of available burrows is higher than the number of individuals in the population (Shenbrot, 2004). Sand rats reproduce mainly in the winter months (November-April), with the peak parturition occurring in January and February (Daly 1979, Ilan 1984, Fichet-calvet et al 1999). The sand rat has several attributes that make it a competent reservoir host. When infected, its relatively short, thick ear pinnae remain infectious for at least of 6 months usually, therefore, increasing the duration of infection persistence. In contrast, when rodents with long, thin ears (e.g. Gerbillus andersoni allenbyi) are infected, infected ear tissue ulcerates and falls off, decreasing infectivity period (Ashford & Bettini 1987). The accumulation of organic material in its burrow and the moderate burrow environment are favorable to the sand flies, and the sand rat’s aggregated distribution promotes transmission. However, features that limit its effectiveness as a reservoir host include breeding in the winter when sand flies are inactive. Thus, most sand rats are six months old before their first exposure to infection and over a year old before they can transmit it. Since its life expectancy is usually up to 18 months, most individuals are only prone to transmission towards the end of their life (Ashford 1996, Rioux et al 1992). A phlebotomine sand fly (Diptera: Psychodidae, subfamily Phlebotominae) is a nocturnal insect 2.5-3 mm long, with pointed wings. Sand flies (Fig 1.2a) are nectivorous, but females also feed on blood (Fig 1.2b), which is essential for egg production. (Killick-Kendrick 1981, Killick-Kendrick 1999). They are found mainly in the warm parts of the world including southern Europe, Asia, Africa, Central and South America, and Australia. Their distribution extends northwards above latitude 50°N in North America (Young 1984) and below this latitude in Europe and East Asia (Lewis 1982). Their altitudinal distribution is from below sea level (by the Dead Sea) up to over 3000 meters above sea level in Afghanistan (Lane 1993, Artemiev 1980). Phlebotomine sand fly is the proven vector of the leishmaniases (Desjeux 2001). There are six recognized genera, of which two have medical importance: Phlebotomus of the Old World, divided into 12 subgenera, and Lutzomyia of the New World, divided into 25 subgenera and species groups. All proven vectors of the leishmaniases are species of these two genera (Lane 1993). Leishmaniasis is transmitted by the bite of a phlebotomine sand fly that has previously fed on an infected mammal. Phlebotomus papatasi is the main vector of L. major (Killick- Kendrick 1999). In Israel, notification of the Ministry of Health (MOH) of Leishmaniasis is mandatory, but is believed to be under-reported (Anis 2001, Klaus 1994). However, the incidence of

4 Cutaneaus Leishmaniasis in Israel has increased over the last decades (Jaffe et al 2004, Wasserberg et al 2002). Reports regarding Cutaneus Leishmaniasis outbreaks have arrived from several foci in Israel including Tiberias in the Galilee (Jacobson et al 2003), Maale Edumim in the Judean Desert (Singer et al 2008, L. Orshan pers. comm), and the Negev (Giladi et al 1985, Biton et al 1997, Wasserberg et al 2002).

Richard Poche Fig 1.1 Fat sand rat, Psammomys obesus (young individual)

Fig 1.2a P. papatasi Sand fly male Fig 1.2b Sand fly female with blood meal

5 1.3 Research goal and objectives

The goal of this study was to examine processes affecting CL transmission cycle and assess its environmental risk factors while implementing an ecologically based approach to understanding and analyzing the zoonotic disease, Cutaneus Leishmaniasis. To achieve this goal the following features were examined: 1. Host and vector population dynamics – For this purpose, sand rat and sand fly populations in Sede Boqer area were followed, including field counts. Particular attention was paid to whether seasonal changes in habitat selection behavior of the fat sand rats coincide with peak abundance of sand flies. 2. Disease prevalence- the disease prevalence was studied in both host and vector and information was collected regarding cases of human infection in Sede Boqer area, which were subsequently compared to the host and vector population dynamics and infection rates. 3. The effect of plant lushness, a risk factor previously reported to be correlated with prevalence was examined – Plant lushness was reduced experimentally and the effect of this reduction on the activity of both host & vector and the likelihood of burrow occupancy was evaluated. 4. The feasibility of using rodent hosts as carriers of insecticide into the burrows as a means for reducing sand fly abundance thereby reducing disease transmission was evaluated.

6 1.4 Plot characteristics

In the first field season, I chose 5 plots of 0.9-3.2 hectares, with a minimum of 7 active burrows each (see Fig 1.5 study sites map and appendix 1 for detailed maps with burrows). One plot was located in the Zin Wadi, along the southern bank. The Wadi is characterized by floods, occurring up to a few times a year. The other four plots were located on the plain, two near kibbutz Sede Boqer, (Orchard and North) and two on the other side of the road (Mekorot and Haroa). I used the term “plateau” for referring to these 4 plots together, to distinguish them from the Wadi. I distinguished between natural plots (Wadi, Haroah) and disturbed plots (Orchard, Mekorot, North). Disturbance included various human influences, i.e. agriculture in the Orchard plot, ornament irrigation in the North plot, water and electricity service station, tourism (nearby picnic facilities), and pre-army training (Gadna) in the Mekorot plot. In each plot, I measured the area occupied by the closed and open habitats (Fig 1.3 a, b, see definition in section 2.2.1), soil moisture (measured in the dry season), and various plant characteristics as detailed below. I initially included two additional plots (Hut, near Ben Gurion’s hut and Cemetery near Kibbutz Sede Boqer cemetery) in the initial census, but dropped the Hut plot after it was ruined by construction work and the Cemetery plot after the first census trapping session revealed that it contained no Psammomys.

Table 1.1 Plot Characteristics Plot Size (ha) Altitude (m) Soil Proportion of Land use moisture open habitat Orchard 3.2 480 2 % 0.83 Agriculture* North 0.7 470 2.21% 0.85 Plantation edge Mekorot 2.1 470 2.31% 0.29 Service & Tourism Haroa 0.9 460 3.04% 0.81 Natural Wadi 3.2 370 6.94% 0.67 Natural & Tourism * At the beginning of the study the orchard was active agricultural land, Irrigation stopped on 2006, and the orchard was uprooted during 2008.

Fig 1.3a Closed Habitat Fig 1.3b Open Habitat

(Left) Figure 1.4 Cutaneous and visceral leishmaniasis in Israel and the Palestinian Authority Territories. Leishmania major and Leishmania tropica cause cutaneous disease, and Leishmania infantum cause visceral disease. The regions where L. major, L. tropica and L. infantum have been found are shown. Source: Jaffe et al 2004.

Fig 1.5 Study sites of the sand rat and sand fly census

8 Chapter 2. Population Dynamics of Psammomys obesus 2.1 Introduction Due to the emergence and reemergence of several human diseases associated with small mammal reservoir species in the recent years (e.g. hemorrhagic fever, leishmaniasis, Lyme disease), the interest in reservoir ecology has increased. Information is especially needed on the ecology of reservoir hosts of zoonotic diseases (Ashford 1996; Mills and Childs 1998). Reservoir host parameters that are important for understanding their function in zoonotic systems include distribution (regional and local), demography, population dynamics, dispersal, and habitat selection. Such an understanding may illuminate the “weak link” in the transmission cycle at which control efforts should be aimed. An integrative approach is essential in order to conduct comparisons between studies, and allow for the efficient use of resources and data (Ashford 1996, 1999; Mills and Childs 1998). Optimal habitat selection theory (see below), based on first principles of natural selection, illuminates optimal behavior of the reservoir host as it affects its distribution in time and space, and allows us to make predictions about its behavior. Density dependent habitat selection in turn may help to determine population dynamics of the vector as well, along with the species interaction between the disease and host. Such a system of host, vector, and disease offers opportunities for studying how optimal decisions of individuals cascade through a complex ecological system to determine population dynamics and species interactions. In zoonotic Cutanous Leishmaniasis (CL), the parasites are deposited in the skin of the rodent host when an infected sand fly bites the rodent to feed on blood. There, the parasites are attacked and engulfed by resident skin macrophages - the target cells for the parasite. The disease is transferred from one host to another by a bite of an infected sand fly female (see Chapter 1 for details). In this chapter I address the population dynamics of the fat sand rat. I had three main goals: 1. Study the dynamics of burrow occupancy (individual burrow level); 2. Study habitat selection dynamics (habitat level); 3. Study sand rat population dynamics in time and space (plot level). To address the first goal, I surveyed burrow occupancy (whether it is active or not) in a natural population of sand rats. I then analyzed the data using colonization and extinction estimations, following Clark and Rosenzweig (1994; see methods). For the second goal, I censused sand rat populations on 5 plots over 7 field seasons using mark and recapture live- trapping. I then characterized habitat selection by sand rats by plotting Isodars (Morris1987). I followed the same plots in winter and summer to detect long-term habitat preferences and

9 shifts. Finally, for the third goal, I compared the burrow activity level in different plots (see below) and population densities (census) along time including environmental factors that might affect it. Density –Dependent Habitat Selection Theory and the Isodar Density-dependent habitat selection theory (Rosenzweig 1981, 1991) is based on Fretwell and Lucas (1969) and assumes that there is a negative relationship between fitness and population density. Individuals should settle in the habitat that offers the highest fitness return. When we have more than one habitat, at low densities the most preferred habitat offering highest fitness returns will be occupied first. Then as the density increases, the fitness of each individual there decreases because resources are being shared among more and more individuals. As new individuals are added to the system, eventually it gets to a point where fitness returns have fallen so low that the return available in the next best habitat is equal or higher to that available in the first; the next individual is better off moving into the second habitat. When individuals distribute themselves across habitats such that fitness returns are equal across habitats and no individual can increase its fitness by unilaterally changing habitats, the population is in an Ideal Free Distribution (Fretwell and Lucas 1969). Habitat selection depends on population density, resource supply, habitat quality, and cost of foraging and dispersal (Rosenzweig 1981, Morris 1987). Predation risk is another factor affecting habitat selection (Kotler 1984, Gilliam and Fraser 1987, Lima and Dill 1990). In various taxa, density dependent habitat selection is the basic pattern of habitat use (Rosenzweig 1991), and therefore an important factor influencing population dynamics (Morris 1988, 1992). Morris (1987) developed a method to detect density-dependent habitat selection: the isodar. An isodar is a curve in a state space of population densities in two habitats where fitness is equal in two habitats. In practice, an isodar for a population of optimal habitat selectors can be found by regressing the density found in one habitat against the density found in the other. A significant positive regression is evidence for density dependent habitat selection. A positive intercept reveals quantitative differences between the habitats typically resulting from differences in productivity or resource availability; a slope that differs from 1 is evidence for qualitative differences resulting in differences in density-dependence. Morris further developed isodars (Morris 1988, 1989) to analyze models of habitat selection, community structure, and species interactions. He distinguished between different kinds of population regulation: parallel, divergent, crossover, and convergent. Four different population regulation models (Fig. 2.1) can be distinguished from

fitness-density graphs and their resulting isodars (Fig. 2.2). In Figure 2.1, K1 and K2 are the

10 carrying capacities of habitat 1 and 2, respectively, where at equilibrium (fitness = 1) population sizes are constant. A. Quantitative difference (parallel regulation), habitat 1 is quantitatively better than habitat 2. B. Qualitative difference (divergent regulation), habitat 1 is qualitatively better than habitat 2. C. Quantitative and qualitative difference (another divergent regulation), habitat 1 is quantitatively and qualitatively better than habitat 2. D. Habitat 2 is quantitatively better, but qualitatively poorer than habitat1 (crossover regulation). E. Habitat 2 is quantitatively better, but qualitatively poorer in a manner that K2 exceeds K1 (convergent regulation). Table 2.1 summarizes the regression outcome of the four population regulation models.

Fig 2.1 Population regulation models as fitness density graphs (from Morris 1988). See text for details.

Fig 2.2 Habitat isodars corresponding to the 4 population regulation models ordinate and the abscissa is the density in the habitat with higher maximum fitness, and second habitat, respectively (from Morris 1988).

11 Table 2.1.The regression outcome (slope and intercept) of the four population regulation models: Model Habitat difference Slope Intercept Parallel (A) Quantitative 1 >0 Divergent (B) Qualitative >1 0 Divergent (C) Quantitative + Qualitative >1 >0

Crossover (D) Quantitative + Qualitative <1 passes through N1- N2 >0 Convergent (E) Quantitative + Qualitative <1 >0

Psammomys obesus The fat sand rat, Psammomys obesus is the most common reservoir species of Leishmania major is in the Middle East and North Africa. Sand rats are diurnal, solitary, have small home range, burrows under shrubs, and eat chenopod shrubs, especially saltbush (Artiplex sp.). They are substrate opportunists that occupy sandy, clay, and loess soils, as well as stone terraces (Daly and Daly 1974, Harrison and Bates 1991, Qumsiyeh 1996, Mendelssohn and Yom-Tov 1999, Shenbrot et al. 1999, Fichet-Calvet et al. 2000). Habitat distribution is determined mostly by the relative abundance of its food plants (Daly and Daly 1974, Fichet-Calvet et al. 2000, Tchabovsky and Krasnov 2002). In most cases, the number of available burrows is greater than the number of individuals in the population (Fichet-Calvet et al. 2000), with sand rats frequently changing occupied burrows (Daly and Daly 1974). Thus, the cost of dispersal is considered negligible, due to no resistance to new animals, a necessary pre-condition for applying isodars to compare habitat differences. Sand rats display density dependent habitat selection (Shenbrot 2004, Cervantes, 2004) according to the Ideal Free Distribution” (Fretwell and Lucas 1969). There are two major habitat types occupied by the sand rats in the Negev Desert: an open habitat sometimes dominated by the perennial shrub Anabasis articulata, that grows in summer and autumn, and a more closed habitat dominated by the perennial shrub Atriplex halimus, that grows mainly in winter and spring (Tchabavsky et al 2001). Isodar analysis of sand rat population densities across habitats revealed that sand rats favor the closed habitat in winter and spring at all population densities. But the open habitat is favored at high population densities in the summer and autumn (Shenbrot 2004, Cervantes 2004). Consequently, the sand rats appear to shift from more sheltered to more open habitats seasonally and to shift burrow location frequently, potentially leaving behind the sand flies that may in turn more frequently come into contact with humans while trying to track shifting sand rat distributions. In disturbed areas close to human settlement, the closed habitat is both quantitatively and qualitatively superior all year round (Cervantes, 2004). Thus, the optimal decisions made by individual sand rats on habitat selection and burrow occupancy based on

12 density-dependent habitat selection may greatly affect sand fly population dynamics, species interactions among the disease, the vector, and the reservoir host, and even disease transmission to humans. These characteristics may provide opportunities for more effectively disrupting disease transmission.

2.2 Methods 2.2.1. Dynamics of burrow occupancy (individual burrow level) Burrow system definition Sand rat burrows have several openings. Based on Orr (1974) and Wasserberg et al. (2003b), a burrow system was defined according to a 10m nearest neighbor distance criterion: any burrow entrance that is located <10m from its nearest neighboring burrow entrance was defined as part of the same burrow system. Clusters of burrow entrances that are separated by more than 10 m were considered to be different burrow systems. I marked 15-38 burrow locations in each plot, depending on its size, using a hand-held GPS - Global Positioning System (Garmin GPS 12) devise.

Active burrow definition A burrow system was consider active if it contained at least one of the following signs of rodent activity near at least one of its entrances: loose soil kicked out from the entrance, distinct sand-rat tracks, fresh food debris, or fresh sand rat feces (see also Fichet-Calvet et al. 1999; Wasserberg et al. 2003b).

Plant type For each marked burrow, I recorded the species identity of the plant associated with it. I divided the plant type into 4 categories: Atriplex halimus, Anabasis articulata (and Hammada salicornica), other (i.e. Tamarix aphylla), and no bush.

Habitat definition per burrow (Open/Closed) An open habitat for a burrow system was defined as a burrow system when at least 50% of the 5m radius circle surrounding it was not covered with vegetation, since P. obessus occupies a home range of approximately 10 m radius around its burrow. A closed habitat was defined as a burrow system with at least 50% of the nearest 5m surrounding it covered with vegetation. The 50% criterion was chosen since it is relatively clear-cut especially compared to any other limit.

13 Colonization and extinction estimation Clark and Rosenzweig (1994) developed a method based on maximum likelihood to estimate the rates of population extinction and recolonization of a species. It applies to both regular and sporadic surveys. If a sufficiently long series of uninterrupted censuses are available, the extinction and colonization rate can be estimated. The method can be applied to measures such as burrow occupancy. To look at the burrow dynamics over time, I used Clark and Rosenzweig’s extinction and colonization parameter estimates from surveys. δ is the probability that a species present at time period t becomes absent in time period t+1. λ equals to the probability that a species absent at time t will be present at time t+1. λ and δ correspond directly to the observations. δ = Extinction estimates (after t +1 time units) λ = Colonization estimates Colonization estimate is the probability that the burrow is occupied by P. obesus, and extinction estimate is the probability that the burrow is not occupied. Data (a proportion) were square root and arcsine transformed. Consider a particular burrow that is checked periodically for whether it is occupied. For example, the sequence P,P,A,A,P,A,A… refers to periodic censuses that occur at successive periods t=1,2,…T in which signs of sand rats (see active burrow definition), are either absent (A) or present (P). The likelihood function to estimate the rates of extinction (the probability that the burrow is not occupied) and colonization (probability that the burrow is occupied) is: L = λk (1- λ)l δm(1- δ)n where k is the number of transitions from A to P, l is the transitions from A to A, m is the number of transitions from P to A, and n is the number of transitions from P to P. The extinction and colonization rate estimates are: λ = (k+ n)/ k+ l+ m+ n and δ =1- λ respectively. The extinction level is the complementary value of colonization level (δ =1- λ), with the same statistical values. Therefore, I chose to present only the colonization level.

Burrow activity estimation I quantified the activity level of the individual burrows - how many times each was colonized or abandoned (in this context, extinction is equivalent to abandoning a burrow). I

14 examined several environmental factors that may be affecting colonization or extinction rates. These data are shown under colonization level. In addition, I quantified at the proportion of active burrows and the associated factors from the plot level. These data are shown under proportion of active burrows. For each plot, I used the habitat definition per burrow (open/closed) described above.

2.2.2. Habitat selection dynamics (habitat level) Rodent trapping I trapped rodents by placing out open-mesh live-traps (41 x 13 x 13 cm, Tomahawk M- 201) baited with fresh leaves from local plants (i.e. A. halimus) for four hours minimum, usually from 6 AM to 10 AM. Other baits including commercial rodents pellets did not increase trapping success. Prior to each trapping session, I marked the active burrows in each plot. Two traps were put near each active burrow system. Each trapping session in each plot was four to nine days long, terminated after obtaining 70% recapture rate (Brower and Zar 1998, Caughley 1977) or after nine days. See table 4.1 for trapping session details. I marked each captured rodent with an individually numbered metal ear tags (Salt Lake City Stamp Company), and recorded the following information: weight, sex, approximate age group (adult, juvenile), and existence of external infection marks. I also took small skin biopsies from its ear pinnae and blotted the biopsies onto filter paper for later parasite detection by kDNA PCR in the laboratory (see Chapter 4).

Table 2.2 Trapping sessions information Trapping session Dates Average number of Additional plots* days (per plot) Winter 2004-2005 13.10.04-14.1.05 7 Hut, Kibbutz cemetery Summer 2005 14.6.05-14.7.05 7.3 Hut Winter 2005-2006 28.12.05-5.2.06 7.66 Hut Winter 2006-2007** 24.1.07-6.3.07 9 Summer 2007 15.7.07-14.8.07 8.2 Winter 2007-2008 17.1.08-22.2.08 8 Summer 2008 137.08-31.7.08 6 * In addition to the 5 major plots described above. **Summer 2006-maternity leave.

2.2.3. Sand rat population dynamics in time and space (plot level) Plot area calculation and habitat definition at the plot level The size of the open and closed habitats in each plot was estimated using Google Earth. First, the total area of each site was calculated by sketching a polygon that enclosed the plot using the map’s gridlines, and then calculating the total area of the polygon in hectares. Second, each polygon was further divided into smaller polygons of open and closed habitats according to the different features in the map (a dark and dense area as a closed habitat and a clearer area as an open habitat), and the total areas of these smaller polygons were calculated.

Plant condition I recorded the condition for the plant associated with each burrow. I defined a 4-level scale of the plant condition, according to the lushness/ dryness of the leaves, with 1 being very good state, 2 good state, 3 mediocre state, and 4 poor state.

Percent plant coverage I estimated the percent plant coverage using the standard line intercept procedure modified from Krebs (1999) and followed by Wasserberg et al (2003a) and B. Boeken (pers comm). In each plot, I measured the percent plant coverage of chenopod bushes, other perennials, and annuals. To do so, I measured plant coverage along three 10m line transects radiating in random directions starting from 10 points randomly selected throughout each plot. Since the plots are heterogeneous, I divided each plot into four vegetation density categories: A-dense, B-less dense, C-spotty, D-sparse. The points were distributed in such a way that at least 2 points were selected in each of the four density categories. The data were then averaged according to the proportion of each vegetation density category in each plot.

Soil moisture measurement Six soil samples per plot were taken at a depth of approximately 30 cm using a soil corer, corresponding to the average depth of 75% of the P. obesus burrows in the area (Orr 1974). Two samples were taken near the root of a bush adjacent to a burrow (1 m) on the South and North axis. Four samples were taken from the margins of the plots (border plot). I collected soil samples in late October 2007 (the end of the dry season). To measure soil moisture content, soil samples were dried in an oven at 1050C for 48 hours. Soil moisture content was calculated as the ratio of the difference between pre-drying and post-drying soil

16 mass divided by the pre- drying soil mass all multiplied by 100: ((wet mass - dry mass)/dry mass) x 100 (Brower and Zar 1998).

Meteorological data Meteorological data was obtained from the Sede Boqer Meteorological Station, the Department of Solar Energy and Environmental Physics, Ben Gurion University. Variables included air temperature, soil temperature at –30cm, soil surface temperature, vapor pressure, relative humidity, and rainfall (daily and monthly) for each day of the trapping sessions (see table 2.2) average values from 6 AM to 10AM (the common time traps were open).

Burrow temperature and relative humidity For each plot, I measured burrow temperature and relative humidity by inserting a HOBO data logger inside a burrow for 48h, attached to a 1m metal wire. For each trapping session, I collected temperature and relative humidity data from 2-3 burrows per plot per trapping session. The average plot value was calculated for the minimum, maximum, mean values, and standard deviations.

Statistical analysis I used square root transformation of the sand rat density to normalize the data and to dissociate the mean-variance link of the Poisson distribution and make the variance independent of the mean. Similarly, for the colonization level, I used arcsine square root transformation. I used Poisson regression for the analysis regarding the number of individual sand rats. I used t-tests for detecting differences between continuous variables with two categories (e.g. open and closed habitat). ANOVA (one way and two way) were used for detecting differences between continuous variables with more than two categories (e.g. plots). Kruskal-Wallis was used for the same purposes when data was not normally distributed. I used chi square tests for detecting differences between categorical variables (e.g. plant type and habitat).

2.3 Results 2.3.1. Dynamics of burrow occupancy (individual burrow level) Proportion of active burrows The mean (+ SD) proportion of activity per single burrow over time was 0.47 (+ 0.28, range 0-1). There was a tendency for higher activity for burrows located in areas of higher

17 disturbance (t128 = 1.794, p=0.075). When all plots were entered the analysis, habitat type

(open or closed) was not significant (t128 = 1.534, p=0.127). There was a significant difference in proportion of active burrows among plots (F1,4 =3.077, p=0.019). In Two-Way ANOVA (Table 2.3, Fig 2.3), when Haroa was excluded (has only two burrows in the closed habitat), plot and habitat both affected the proportion of active burrows, but not the interaction between them. Table 2.3 Two-Way ANOVA w/o Haroa regarding the effect of plot and habitat on the proportion of active burrows. SS Degrees of MS F p Freedom Intercept 17.68 1 17.68 258 0.000 Plot 0.782 3 0.260 3.8 0.012 Habitat 0.496 1 0.496 7.2 0.008 Plot*Habitat 0.317 3 0.105 1.5 0.205 Error 7.310 107 0.068

Fig 2.3. Proportion of active burrows (Without Haroa plot) in different plots and habitats.

Colonization level

There was a significant difference in colonization level among plots (Fig 2.4, F1,4 =3.464, p=0.010). In post hoc analysis, the colonization level in the Orchard plot was significantly higher than that in the North plot (p=0.021) and the Wadi plot (p=0.020).

1.1 a

1.0

0.9

0.8

0.7 b b Colonization (asin)

0.6

0.5

0.4 Orchard North Haroah Mekorot Wadi Mean Plot Mean±SE Fig 2.4 Colonization level in different plots, ANOVA F1,4 =3.464, p=0.0102. Colonization level in the Orchard plot was significantly higher than the North plot (p=0.0211) and the Wadi (p=0.020).

There was a marginally significant positive effect of disturbance on colonization level

(t123 = 1.913, p=0.058). There was no significant difference in burrow colonization level in open and closed habitats. Plot size, soil moisture, percent active burrows, plant type, cover, and chenopod cover did not correlate with colonization level or proportion of active burrows.

2.3.2. Habitat selection dynamics (habitat level) Census data In total, 185 individuals (95 males) were captured in 7 field seasons between winter 2004-2005 and summer 2008. Fig 2.5 presents the total sand rat captures per plot per season. Fig 2.6 describes the body size category distribution in the population. The total number of individuals captured per plot was 75 in Mekorot, 46 in the Wadi, 28 in the Orchard, and 18 each in the North and Haroa plots.

19 30

Psammomys captured 10

Orchard North 0 Mekorot Win_04_05 Win_05-06 Sum_07 Sum_08 Sum_05 Win_06-07 Win_07_08 Haroah Wadi Season Fig 2.5 Psammomys captured per plot per season

Number of individuals captured N=185

120

100

Number of individuals of Number 20

0 0-100g 101-150g 151-200g >200g Body size class

Fig 2.6 Fat sand rat captured by body size class

In Poisson regression, plot, season, and their interaction affected the number of individual sand rats trapped (Log likelihood = -94.83, χ2 = 58.52 d.f =4, p<0.0001 for plot, Log likelihood = -84.54 χ2 = 20.56, d.f =6, p=0.002 for season and Log likelihood = -54.39, χ2 = 60.3, d.f =22, p<0.0001) for the interaction plot*season.

Population size I used mark-recapture technique (Lincoln index) (as described in Brower, Zar & von Ende) to calculate estimated population size. Fig 2.7 presents sand rat population densities from population size calculated by Lincoln index.

20 Population Density Haroa

20 Mekorot 18 Orchard 16 North 14 Wadi 12 10 8 6 4 Density (individual / ha) 2 0 Win_04_05 Sum_05 Win_05-06 Win_06-07 Sum_07 Win_07_08 Sum_08 Time

Fig 2.7 Sand rat densities in different plots along time, calculated by Lincoln index

Movement patterns 21 (11.35 %) individuals were recaptured in a different burrow than the initial one at which they were first captured; none of them moved from one plot to another. Average movement distance was 27.7 (±3.83) m, range 10.05-75.3 m. Movement was observed for both males (9) and females (12), but was more common in adults (16). I note the possibility that juveniles moved frequently, but were more likely to move beyond the plot boundaries and hence their movement not recorded. Movement between burrows was more frequent in the larger plots; Wadi, 9; Mekorot, 6; Orchard, 3. One infected female (see chapter 4) from the Wadi (captured in two different trapping session) was captured in a different burrow (pattern repeated in both years).

Isodar analysis In this study, isodars were based on habitat selection within plots. Winter-spring season—the plateau area: In the winter-spring season, plant growth occurred in A. halimus, the dominant shrub in the closed habitat and is the plant associated with two thirds of the burrows in the open habitat. The isodar regressions for active burrows (Fig 2.8 North, Fig 2.9 Plateau) showed that P. obesus displayed density dependent habitat selection (p<0.05) during this time.

21 Density regression for two-habitat comparison of P.obesus habitat selection - North Winter Spring

25 y = 0.2874x + 7.379 R2 = 0.6743 20 p=0.045

Open Habitat Habitat Open 5

0 0 5 10 15 20 25 30 35 40 45 50 Closed Habitat

Fig 2.8: The North plot isodar for P. obesus between two habitats, open and closed. The isodar slope (0.28) is significantly less than 1; the intercept (7.379) is significantly greater than 0.

Density regression for two-habitat comparison of P .obesus habitat selection - Plateau Winter Spring

25 y = 0.3466x + 5.1592 2 20 R = 0.4389 p<0.001 15

Open Habitat 5

0 0 5 10 15 20 25 30 35 40 45 50 Closed Habitat

Fig 2.9: The Plateau isodar for P. obesus between two habitats, open and closed. The isodar slope (0.34) is significantly less than 1; the intercept (5.159) is significantly greater than 0.

In this season, P. obesus preferred the open habitat. In the North plot separately and when two or four plateau plots were combined together, P. obesus selected the open habitat over the closed habitat in the winter and spring seasons. For this season, the regression equation that describes the arithmetic isodar solution for the North plot was: BDOH = 0.287 (BDCH) + 7.379 r2 = 0.674, p=0.045 where: BDOH = Burrow density in open habitat, BDCH = Burrow density in closed habitat. The slope was significantly less than one (95% confidence interval = 0.173 and 0.519),

22 and the intercept was significantly greater than zero (95% confidence interval =2.971 and 7.346). This model explained 67.4% of the variance. For this season, the regression equation that describes the arithmetic isodar solution for the plateau plots was: BDOH = 0.346 (BDCH) + 5.159 r2 = 0.438, p<0.001 The slope was significantly less than one (95% confidence interval = 0.0101 and 0.564), and the intercept was significantly greater than zero (95% confidence interval =1.376 and 13.381). The model explained 43.8% of the variance. In the winter and spring, I detected convergent population regulation, wherein the open habitat decreased in quality relative to the closed habitat (Fig 2.8 North, Fig 2.9, Plateau). When the first field season was removed, none of the isodars were significant. Winter spring season—The Wadi: In the Wadi, no significant effect was detected during the winter and spring season (Fig 2.10).

Density regression for two-habitat comparison of P .obesus habitat selection - Wadi Winter Spring

y = -0.1007x + 3.6481 6 R2 = 0.0538 5 N.S. 4 3 2 1 Open Habitat 0 0123456789 Closed Habitat (Wadi)

Fig 2.10: The Wadi isodar for P. obesus between two habitats, open and closed. The isodar slope (-0.1) is not significantly different from 1; the intercept (3.64) is significantly greater than 0.

Summer-autumn season: In the summer-autumn seasons, plant growth occurred in A. articulate, the plant associated with 22.6% of the burrows in the open habitat. During the summer and autumn seasons, no significant effect was detected in Plateau or the Wadi. All year—the plateau area: The area near two plots was irrigated most of the year and an additional plot received irregular additional water from leaks in pipes originating from a nearby water reservoir. Seasonality was reduced, and plant growth of A. halimus occurred throughout the year.

23 P. obesus preferred the open habitat. In the North plot separately and when two or four plateau plots were combined together, P. obesus selected the open habitat over the closed habitat.

Density regression for two-habitat comparison of P.obesus habitat selection - Plateau All year y = 0.2908x + 5.5873 25 R2 = 0.2636 p<0.001 20

Open HabitatOpen (Plateau) 0 0 1020304050 Closed Habitat (Plateau)

Fig 2.11: The Plateau isodar for P. obesus between two habitats, open and closed. The isodar slope (0.290) is significantly lower than 1; the intercept (5.587) is significantly greater than 0.

For the plateau, the regression equation that describes the arithmetic isodar solution for the plateau plots was: BDOH = 0.290 (BDCH) + 5.587 r2 = 0.263, p<0.001 The slope was significantly lower than one (95% confidence interval = 0.146 and 0.435), and the intercept was significantly greater than zero (95% confidence interval = 4.010 and 7.163). The model explained 26.3% of the variance. In the plateau area, throughout the year, I detected convergent population regulation, with the open habitat decreasing in quality relative to the closed habitat (Fig 2.11 Plateau).

2.3.3. Sand rat population dynamics in time and space (plot level) Fig 2.12 presents the number of burrows occurring in the open and closed habitats on the different plots. There was a significant difference among plots in the number of burrows in the open and closed habitats (χ2 = 20.028 d.f =4, p<0.001). In Orchard, North, and Haroah, there were many more burrows located in the open habitat, while in the Wadi, the burrows located in the open slightly outnumbered those in the closed habitat. Mekorot was the only plot with the majority of the burrows in the closed habitat.

24 Habitat distribution among plots 28

No of burrows 10

6 Orchard 4 North 2 Haroah Mekorot 0 Wadi Open closed

Habitat

Fig 2.12 Distribution of open and closed habitat among the plots

Altogether, sand rats located their burrows mostly under A. halimus shrubs (χ2 = 17.65 d.f =3 p=0.001). In the closed habitat, sand rats dug 96% (53/55) of their burrows under A. halimus, compared to 66% (50/75) in the open habitat (Fig 2.13). The number of burrows under A. halimus did not differ between the open and the closed habitat (χ2 = 1.584 d.f =1 N.S).

Plant type in both habitats 70

60 Closed 50 Open 40

20 Number of burrows 10

0 Atriplex Anabasis Other No Bush Plant type

Fig 2.13 Distribution of plant type according to open and closed habitats

25 Active burrow density Since I have one measurement of active burrow density per plot per season, I analyzed the data as randomized blocks. Plot, but not trapping session, had a significant effect on the density of active burrows (F1,4 =17.403 p<0.0001 for plot, Fig. 2.14). There are no repetitions in randomized block design, and therefore interactions cannot be evaluated.

26 1.1

1.0

0.9

0.8

0.7 Log Active burrow density

0.6

0.5 Orchard North Haroah Mekorot Wadi Mean Plot Mean±SE

Fig 2.14 Active burrow density in different plots F(1,4) = 17.403 p<0.0001, Orchard and Wadi are lower and significantly different from Mekorot, Haroa, and North p<0.05.

Log linear analysis of frequency tables To check for interactions of plot year and season I used log linear analysis of frequency tables. I evaluated the importance of each of the following parameters: plot (5 plots), year (1- 4), and season (winter, summer), and their interactions. To conduct log linear analysis of frequency tables, I created equal number of data points for each plot. I chose burrows for which there were data for 4 years for winter and summer seasons in each plot. I randomly chose 15 burrows for each plot, using plots for which more than 15 burrows were available. Both year and plot had a marginally significant effect on the number of active burrows (χ2 = 8.53 d.f =4, p=0.074 for plot and χ2 = 7.312 d.f =3, p=0.062 for year). The interactions were not significant.

27 Climatic variables In the Poisson regression model, the climatic variables that were positively correlated with number of sand rat trapped (in all seasons) were: air temperature, soil surface temperature, and vapor pressure. Monthly rainfall was negatively correlated with number of sand rat trapped (table 2.4). Table 2.4 Poisson regression model testing the effect of climatic variables on the number of sand rat trapped Degrees of Log Chi p-value Freedom Likelihood Square Intercept 1 -862.500 Air temp 1 -849.992 25.027 0.000001 Soil temp -30cm 1 -849.992 0.0008 0.977439 Soil surface temp 1 -833.758 32.4675 0.000000 Vapor pressure 1 -761.319 144.878 0.000000 Relative humidity 1 -761.253 0.1320 0.716324 Day Rainfall 1 -761.191 0.1231 0.725736 Monthly rainfall 1 -752.152 18.0782 0.000021

In 2007 and 2008, I also measured the temperature and relative humidity within burrows. In the Poisson regression model, the external climatic variables and within burrow variables that were positively correlated with the number of sand rat trapped were: maximal temperature within burrow and maximal relative humidity within burrow. Here, air temperature was negatively correlated with the number of sand rat trapped (table 2.5).

Table 2.5 Poisson regression model testing the effect of external and within burrow climatic variables on the number of sand rat trapped in 2007-2008 Degrees of Log Chi p-value Freedom Likelihood Square Intercept 1 -57.9426 Burrow T_Ave 1 -57.9416 0.00202 0.964185 Burrow T_Min 1 -57.8358 0.21147 0.645614 Burrow T_ Max 1 -48.5058 18.6599 0.000016 Burrow RH_Av 1 -47.0455 2.92062 0.087454 Burrow RH_Min 1 -46.9030 0.28509 0.593385 Burrow RH_Max 1 -42.0360 9.73399 0.001809 Air temp 1 -36.7109 10.6501 0.001101 Soil temp -30cm 1 -35.6017 2.21847 0.136368 Soil surface temp 1 -35.2324 0.73854 0.390129 Vapor pressure 1 -33.5535 3.35771 0.066891 Relative humidity 1 -33.5530 0.00105 0.974129 Monthly rainfall 1 -33.3240 0.45798 0.498572

28 Plant condition The condition of the plants associated with active burrows was better than those associated with non-active ones, in all but one season (table 2.6).

Table 2.6 Burrow activity level and the plant phenological state Season Chi square value Degrees of freedom p value Summer 2005 91.18 8 P<0.001 Winter 2006 77.77 8 P<0.001 Winter 2007 135.45 8 P<0.001 Summer 2007 55.30 8 P<0.001 Winter 2008 75.8 8 P<0.001 Summer 2008 8.48 8 N.S.

2.4 Discussion In this chapter I address the population dynamics of the fat sand rat. I had three main goals: 1. Study the dynamics of burrow occupancy (individual burrow level); 2. Study habitat selection dynamics (habitat level); 3. Study sand rat population dynamics in time and space (plot level). In the dynamics of burrow occupancy (individual burrow level) section, I showed that there was a trend towards higher percentage of active burrows in the open habitat, and that both plot and habitat affected the proportion of active burrows. In the habitat selection dynamics (habitat level) section, I presented the census data, including population size estimation calculated from the census data. In the isodar section, I showed habitat preference towards the open habitat in the plateau, but not the Wadi, all year around, but especially in the winter-spring seasons. In the sand rat population dynamics in time and space (plot level) section, I showed that the dominant plant associated with sand rat burrows is Atriplex, also in the open habitat. Both year and plot had a marginally significant effect on the number of active burrows. I found a positive correlation between plant condition and burrow activity level. I found a positive correlation between within burrow maximal temperature and relative humidity and number of trapped sand rats.

2.4.1. Dynamics of burrow occupancy (individual burrow level) The proportion of active burrows was higher in the open habitat, probably due to the Atriplex dominancy (see isodar discussion). Both plot and habitat affected the proportion of active burrows. Plots were distinguished by several characteristics such as soil moisture

29 distance from human dwelling, size, and proportion of closed and open habitat. Soil moisture is an outcome of disturbance and distance from human dwelling, and has previously been shown to affect the sand rat density (Wasserberg 2003b). There was a trend for higher colonization level in disturbed plots (see Chapter 1). These plateau plots received additional water supply from agriculture in the Orchard plot (in the first seasons), from nearby ornament irrigation in the North plot, and from leaks in pipes and anthropogenic activity (Picnic area, pre-army "Gadna" training) in Mekorot. My results support previous studies regarding anthropogenic disturbance. Wasserberg et al (2003b) showed in a correlation study that human disturbance and increased soil moisture in particular promote larger P. obesus populations by improving food quality. I found a marginally significant effect which supports this correlation (since it would be significant in one-tailed). In the current study, the more disturbed Orchard plot had the highest colonization level, and the least disturbed North plot (but still more disturbed than a natural one) had the lowest colonization level.

2.4.2. Habitat selection dynamics (habitat level) Census data Census data and population densities calculated according to Lincoln index revealed fluctuations. These fluctuations are typical for many rodent populations (Ylonen et al 1991, Ylonen 1994). The highest number of trapped individuals in the study was in Mekorot plot, followed by the Wadi. The plot with the highest active burrow density was North, followed by Mekorot and Haroa. There is a difference in the activity seen in burrows and the number of animal actually captured. This can be caused from some of the animals being "trap shy" or burrow misclassification as separate ones or an active one. In addition, sand rats are known to be highly aggregated (Daly and Daly 1974, Ilan and Yom-Tov 1990, Fichet-Calvet et al. 2000), hence, the density per plot can be misleading, since the Wadi for example consists of areas with high density and others with low density that summarized together to a mediocre one, but the trapping was conducted only near active burrows. There is an apparent contradiction between the relatively low active burrow density and the actual trapping data. The sand rat population was biased towards larger body size classes; the majority of individuals entering the traps were adults. Two explanations are possible, the first is that there are more adults in the population, and the second one is that adults are more active, or less trap shy. If the first one is correct, it has an important consequence regarding disease transmission. Wasserberg et al (2003b) showed that infection prevalence among sand rats was positively

30 correlated with body size. Here, I found that the population of the trapped animals is skewed towards larger individuals, suggesting higher risk of transmission. Regarding the difference in population size and dynamics between the Wadi and the plateau, the Wadi is a good example for constant population size in a natural habitat with higher soil moisture. The plants are in good condition, and there are no major anthropogenic disturbances. In the plateau, the highest number of animals captured was in the highly disturbed area, the one with the maximal disturbances (water and electric stations, Picnic area, training area, camping site, and its location along the track to a small Bedouin settlement). In the other plots, the amount of human activity in a year is much lower. Other plots in the plateau are less disturbed and had lower numbers of trapped animals. Isodar analysis According to the Isodar analysis, sand rats show density dependent habitat selection in the plateau. This result is different from a previous Isodar analysis conducted on sand rats in this region. Cervantes (2004) found that in the plateau area, sand rats selected the closed habitat over the open habitat the whole year round. Here, I detected a preference for the open habitat. The difference from previously reported results can be attributed to several reasons. First, the two studies used different definitions of open and closed habitat that differed in their scales of resolution. Cervantes used an NDVI map that was superimposed on an aerial photograph in order to distinguish between different habitats across fairly large areas, while I used a definition based on small areas surrounding individual burrows. Second, Atriplex showed high dominancy in the plateau area, including in areas classified here as open habitat. Atriplex is in a better condition in winter and spring, and was the dominant shrub species in both the open and closed habitats in the current study. In contrast Anabasis is in better condition in the summer and autumn, and dominated in the open habitat in the previous studies (Shenbrot 2004, Cervantes 2004). Shenbrot (2004) reported seasonal habitat shift of sand rats from closed to open habitat similar to Cervantes in a natural area in Mitzpeh Ramon region with clear seasonal differences in plant condition. In this work, within the plateau area, most of the burrows were located in disturbed habitats, the Atriplex was in a good condition in both the summer and autumn, and the animals did not have to shift habitats as they usually do in natural areas where the plant conditions dictate it. In the Wadi, no significant effect was detected during the winter and spring season nor the summer and autumn. Possibly, frequent floods resulting from winter rains may cause natural disturbance and prevent obtaining equilibrium distributions in these seasons. Still, Cervantes (2004) found a significant preference for the closed habitat in the winter-spring and

31 a nearly significant preference for the open habitat in the summer-autumn. The several reasons outlined above for explaining the differences (i.e. different definition of habitat, different temporal scale) may also apply here.

2.4.3. Sand rat population dynamics in time and space (plot level) The dominant plant associated with sand rat burrows is Atriplex in both the open and the closed habitat. An increase in Atriplex dominancy has been reported before in disturbed habitats by Wasserberg et al (2003b), and it can explain part of the results reported here. Ecologists suspect that the vegetation diversity in the Negev Desert has changed in recent years (Bert Boeken pers comm.). The Atriplex dominancy can be an outcome of this. These changes might reflect processes such as decrease in average rain fall, global warming, and other unknown factors. Atriplex dominancy by itself is important regarding disease control, since Wasserberg (2003c) showed that A. halimus had a significant positive effect on infection probability. In most seasons, I found a positive correlation between phenologic condition of the plant and burrow activity level. It supports previous reported correlations (Mirzoyan 2006, Wasserberg 2003b). Results of the lushness chapter experimentally support this correlation found here (see chapter 4 for details). I also found a positive correlation between within burrow maximal temperature and relative humidity and number of animal trapped. The plot had a significant effect on the density of active burrows. This result is confirmed by the log linear analysis and is demonstrated strongly by the isodar analysis regardless of season. It is possibly related to the Atriplex dominancy and the disturbance level. Interestingly, the irrigation in the Orchard stopped after the second field season, and the plantation was uprooted before the last field season. Accordingly, there was a gradual decrease in active burrow density. However a similar decrease was observed in the other plots as well, suggesting that stopping the irrigation was not the only factor affecting the burrow density in this plot. The open habitat had the higher number of burrows. Although the proportion of active burrows was higher in the open habitat, we still need to take into account the dominancy of the Atriplex plant. In this chapter I presented the population dynamics of the fat sand rat. I showed that sand rats display a preference towards the open habitat, in parallel to the dominancy of A. halimus. This has an important impact on disease control since Wasserberg (2003 Thesis) showed that A. halimus had a significant positive effect on infection probability. Plot, but not

32 season, affected sand rat densities, and suggests the importance of local scale processes. It may also reflect vegetation change that favors conditions for Leishmania infection to proliferate.

33 Chapter 3. Sand fly dynamics

3.1 Introduction Phlebotomine sand flies are the proven vectors of the leishmaniases, parasitic diseases with a wide range of clinical symptoms that threaten some 350 million people in 88 countries around the world (Desjeux 2001). Leishmaniasis is transmitted by the bite of a phlebotomine sand fly that has previously fed on an infected mammal. The most prevalent form of leishmaniasis is cutaneous leishmaniasis (CL), caused in the Old World by 2 parasite species, Leishmania major and L. tropica. L. major is a parasite of rodents transmitted mainly by the sand fly Phlebotomus papatasi (Killick- Kendrick 1999). The most common reservoir species are Psammomys obesus in the Middle East and North Africa and Rhombomys opimus in the Asian republics of the former USSR (Wasserberg et al., 2002; Strelkova 1996). The breeding sites of most sand fly species can be only guessed. Based on observations on adult resting sites in nature and the behavior of laboratory-reared sand flies, researchers think it likely that eggs are laid in protected moist microhabitats such as rock crevices, animal burrows, termite mounds, cracks in the soil, domestic animal shelters, holes in trees and organic debris on the forest floor (Bettini 1989, Killick-Kendrick 1990, 1999, Alexander 2000, Feliciangeli 2004). Hence, the control of phlebotomine sand flies is notoriously problematic because the locations of their immature stages are usually unknown and inaccessible. Thus, source reduction is usually impractical in the case of Phlebotomines. Unlike mosquitoes, water is not essential for larval development, and therefore, sand flies may be found in large numbers in arid zones. In Israel and the Middle East, L. major is transmitted by the sand fly P. papatasi (Greenblatt et al. 1985, Wasserberg et al. 2003b), which tend to shelter in P. obesus burrows (Schlein et al., 1982; Schlein et al., 1984; Wasserberg et al., 2002). Being diurnal, and thus sheltering in the same burrows during the night that sand flies may occupy, P. obesus serve as a good source of blood meal. This may help make the cycle of transmission of L. major an exceptionally efficient one (Schlein et al., 1982; Schlein et al., 1984; Wasserberg et al., 2002). Phlebotomine sand flies are among the most prevalent blood-sucking insects affecting humans in desert and semi-desert regions of North Africa, the Middle East, and parts of Asia (Kamhawi et al 1993). Despite their small size, sand fly bites are painful, and people exposed to them tend to develop delayed-type hypersensitivity that may cause considerable discomfort for prolonged periods (Barral et al. 2000, Belkaid et al. 2000).

34 P. papatasi is highly anthropophilic (tends to bite humans), as well as endophilic (bites indoors). These behavioral patterns are largely responsible for high CL infection rates among the populations living in endemic foci. Sand fly presence in human dwellings can be a real burden. According to one recent report, for example, an average of 30-40 sand flies were collected in houses situated close to the periphery of a village in the Judean Desert East of Jerusalem (Schnur et al. 2004). According to another report from Tallil Air Force Base in Iraq, up to 1000 sand flies per trap per night were collected in Center of Disease Control (CDC) miniature light traps placed on the base, and about 10 flies per trap were collected inside tents (Burkett et al., 2007; Coleman et al., 2007; Dalton, 2008). Wasserberg et al (2002, 2003a, 2003b) demonstrated the differences between sand fly abundance and unique seasonal patterns at the regional scale in general, and in Nizzana at the local scale in particular, stressing the importance of environmental risk factors such as soil moisture and habitat. Therefore, identifying unique patterns in time and space within their environmental context has an implication on our understanding the disease dynamics in humans. The goal of this study was to detect seasonal (monthly) sand fly abundance patterns and local spatial pattern, in the Sede Boqer area, especially between the Wadi and the plateau (see chapter 1). The sand fly infection rate data is described in chapter 4.

3.2 Methods Collection of sand flies To quantify population dynamics of sand flies (SF) in the Sede Boqer region, I regularly monitored sand fly populations on 5 sampling grids over a 3-year period. In the first year (March through October 2005), I trapped nocturnally active sand flies using sticky traps (A4 papers smeared with castor oil) attached to a wooden peg and placed about 5 cm above the ground (Schlein et al. 1984). Each trapping unit contained three sticky traps, arranged in a triangle and spaced 1.5 m apart. Traps were deployed between ~5 PM and ~6 AM the following morning. Trapping stations were located near active sand rat burrow openings of the same burrow systems trapped during the rodent census (chapter 2), as well as at the four edges of each plot. Captured sand fly females were kept in 70% ethanol for parasite detection. Each monthly sand fly trapping session was two-days long. An average of 9.6 trapping units per plot was sampled, for a total of 24 nights per plot and 464 trap nights per season. Due to small number of flies trapped over the first year (17 SF, 4 females) the trapping method was changed for subsequent years. From the second year onwards (2007), sand flies

35 were trapped using miniature CDC light traps (Model 512, John W. Hock, Gainesville, FL, U.S.A.), powered by a 6-volt rechargeable battery (Model 3FM12, Amit Industries LTD. Ashdod, Israel) in place of the original 3-trap arrays. Traps were suspended either upside-down in the up-draft position, with trap entrance ~20 cm above the ground or in the downdraft position, with trap entrance 50 cm above the ground. To achieve higher sand fly specificity for the traps, the light bulbs were removed and traps were each baited with 1.5 kg dry ice placed in a tightly closed, insulated container. A rubber tube was affixed to the spout and its distal opening placed near the opening of the trap. The traps were deployed between ~5 PM and ~6 AM the following morning. Traps were located in the same plots used for the rodent censuses (see chapter 1 for study site details). Trapping usually took place during the waning moon phase, weather permitting. In 2007, I trapped for 14 nights (May through November) using 235 total trap nights. In 2008, I trapped for 14 nights (May through October) using 228 trap nights. Due to the small number of SF captured in the sticky traps I present only the CDC trap data.

Sand fly identification and Leishmania detection Trapped sand flies were transported on dry ice to the laboratory where they were counted, sexed, and preserved by freezing (-80oc). Sand flies were sent to Dr. Laor Orshan, the Entomology Lab, Ministry of Health, Jerusalem, Israel for taxonomical identification and Leishmania parasite detection. Leishmania parasite detection was conducted on the females while taxonomical identification was conducted on the males. Males were dissected and mounted in Hoyer’s medium on microscope slides for taxonomical identification. Species were identified based on the morphology of the pharynx and external male genital apparati using several keys (Perfil'ev et al. 1968, Artemiev 1978, Lewis 1982). The presence of Leishmania parasite DNA in pooled samples of sand fly females (5-20 specimens) was detected by PCR amplification of the internal transcribed spacer (ITS1). Leishmania species identification was carried out by restriction of the amplified fragment (Svobodova et al 2006).

Meteorological data Meteorological data was obtained from the Sede Boqer Meteorological Station, the Department of Solar Energy and Environmental Physics, Ben Gurion University. Variables included wind speed at 3.5m, wind speed at 10m, wind direction, air temperature, soil temperature at –10cm, soil temperature at –30cm, soil temperature at –1m, soil surface temperature, vapor pressure, air pressure, beam radiation, global radiation, diffused radiation,

36 and infra-red radiation. I collected and calculated the average hourly data for each trapping night from 5 PM until 7 AM (the timeframe in which the traps were set).

Statistical analysis I used square root transformation of the sand fly density, data were still not normally distributed, therefore, I conducted Kruskal-Wallis ANOVA. I checked the effect of plot and season on the SF abundance. I added moonlight (scaled from 0 -no moon to 8 -full moon) to the analysis as an adjustment to the confounding moonlight effect since not all trapping occurred during the waning moon.

3.3 Results 3.3.1 2007 Season In total, I collected 386 sand flies in 235 trap nights (mean 1.642 per trap, range 0-55), with 48.5% of traps containing sand flies. Sex ratio was significantly female biased (males: 45, females 341 χ2 = 226.9, p<0.0001), with males appearing in large numbers only later in the year. Species distribution (species identification conducted on males) was as follows (Fig 3.1): P. papatasi 24 (53.3%), P. sergenti 21 (47.7%). In 2007, there was no significant difference in sand fly abundance among the plots. There was a significant difference in SF abundance over time (Fig 3.2 Kruskal-Wallis test: H

(6,35) =14.215 p =0.0273). In post hoc analysis, the abundance of sand flies in June was significantly lower than the SF abundance at the beginning of August, p=0.020). The seasonal abundance in the Wadi was similar to the plateau plots, with one peak at the beginning of August, and a more gradual decline relative to the plateau plots (Fig 3.3). Moonlight was not correlated with SF abundance. Temporal distribution of both male species was similar (Fig 3.4, statistics was not conducted due to small sample size).

37 SF 2007 Gender distribution Males P. papatasi P. sergenti 24 21

Fe m a le s

341

Fig 3.1 SF 2007 gender distribution, with species identifications of the males

3.0

2.8

2.6

2.4

2.2

2.0

1.8

1.6

1.4

1.2 Average SF number / trap (Sqrt) 1.0

0.8

0.6

0.4 Mean May June July Aug_beg Aug_end oct Nov Mean±SE Time Fig 3.2 SF abundance 2007 over time, Kruskal-Wallis: H (6, 35) =14.215 p =0.0273, June SF abundance differs from SF abundance at the beginning of August p=0.0108.

Fig 3.3 SF 2007 monthly SF abundance per plot

200 Females 180 Males P. papatasi 160 Males P. sergenti 140 120 100 80 SF number 60 40 20 0 May July Aug_beg Aug_end October Time

Fig 3.4 Sand fly 2007 gender temporal distribution including male species temporal distribution

Meteorological data The following meteorological variables were independently positively correlated with sand fly abundance on simple regression: air temperature, soil temperature at –10 cm, soil temperature at –30 cm, soil temperature at –1 m, soil surface temperature, and vapor pressure.

39 None of them remained significant in a multiple regression analysis, but soil temperature at – 30 cm and –10 cm were marginally significant (Table 3.1).

Table 3.1: Multiple regression testing the relationship between sand fly abundance and selected meteorological variables. Coefficient SE t-value P-value Intercept 112.80 58.38 1.932 0.063 Air Temp 1.422 0.747 1.904 0.067 Soil Temp -10 21.90 10.895 2.011 0.054 Soil Temp -30 -30.48 15.11 -2.017 0.053 Soil Temp -100 -0.314 0.459 -0.685 0.499 Soil Surface Temp -4.415 2.315 -1.907 0.067 Vapor Pressure 4.876 2.467 1.976 0.058

3.3.2 2008 Season In 2008, I collected a total of 1239 sand flies during 228 trap nights (mean 5.657 per trap, range 0-107), with 57.5% of traps containing sand flies. Sex ratio was significantly female biased (males 414, females 825, χ2 = 136.3, p<0.0001), with males becoming common only at a later season. Species distribution (species identification conducted on males) was as follows (Fig 3.5, fig 3.10): P. papatasi 391 (94.4%), P. alexandri 20 (4.83%), P. sergenti 3 (0.72%). Thirty-three females (4.5%) contained visible blood. In 2008, the number of sand flies per trap differed among plots, (Fig. 3.6, Kruskal-

Wallis test: H (4,35) =12.566, p =0.0136). In post hoc analysis, number captured on the Wadi grid differed from that on Haroah, Mekorot and North (p=0.039, 0.039, 0.037, respectively.

Number of sand fly captures differed seasonally (Fig 3.7 Kruskal-Wallis test: H (6, 35) =16.108 p =0.0132). In post hoc analysis, the abundance of sand flies in May was significantly lower than the SF abundance at the beginning of June, p=0.020. The seasonal pattern of sand fly abundance in the Wadi was different from that of the plateau (see chapter 1 for plateau and Wadi description). In the Wadi, there was a peak in June with the highest abundance at the end of June, while in the plateau a bimodal pattern (2 peaks) was observed, with peaks, one at the beginning of June and a second at the end of September (Fig 3.8). Sand fly abundance was

negatively correlated with moonlight intensity, (Kruskal-Wallis test: H (2, 35) =7.774 p =0.0205. SF abundance was low on dark nights but high in the darkest nights p=0.0391(Fig 3.9).

40 Meteorological data None of the meteorological variables was significantly correlated with sand fly 2008 abundance.

SF 2008 Gender distribution Males 20 P. papatasi 391 3 P. alexandri P. sergenti

Fe m a le s

825

Fig 3.5 SF 2008 gender distribution, with species identifications of the males

5.0

4.5

4.0

3.5

3.0

2.5

2.0 Average SF number/ trap (Sqrt) number/ SF Average

1.5

1.0

0.5 Mean Haroah Mekorot North Orch Wadi Mean±SE Plot Fig 3.6 SF 2008 abundance in different plots, Kruskal-Wallis: H (4,35) =12.566 p =0.0136.

41 4.0

3.5

3.0

2.5

2.0

Average SF number / trap (Sqrt) 1.5

1.0

0.5 Mean May Jun_beg Jun_end July Sep_beg Sep_end Oct Mean±SE Time Fig 3.7 SF 2008 abundance over time, Kruskal-Wallis: H (6, 35) =16.108 p =0.0132

8 7 Wadi 6 Plateau 5 4 3 2 1 0 Average SF number May Jun_beg Jun_end July Sep_beg Sep_end October Time

Fig 3.8 SF 2008 monthly abundance (average SF number/ trap) Wadi and plateau

Fig 3.9 SF 2008 abundance according to moonlight, Kruskal-Wallis: H (2, 35) =7.774 p =0.0205

300

250 Females M P. papatasi 200 M P. alexandri 150 M P. sergenti

SF Number SF 100

0 1-2.5 2-3.6 26-27.6 29-30.7 2-3.9 23-24.9 23-24.10 Dates

Fig 3.10 Sand fly 2008 gender temporal distribution including male species temporal distribution

3.4 Discussion In this chapter, I presented the temporal variation of the sand fly vector abundance in two consecutive years. In 2007, sand flies peaked in abundance in August, while in 2008, they peaked in June, two months earlier. I trapped more than three times more total sand flies in the

43 same plots in 2008, compared to 2007, with males being relatively more dominant in 2008 (male: female ratio of 1:2 in 2008, compared to 1:7.5 in 2007). The climatic conditions differed in 2007 compared to 2008. The annual rainfall in winter 2006-2007 totaled 175 mm, with a big flood of 46.8 mm in mid April, and last rain event in May 2007. In contrast, the annual rainfall in the winter of 2007-2008 was lower, 70.4 mm, with the last rain event in February 2008. The spring started later in 2007 and probably contributed to the later peak abundance in sand fly that year. Spatial differences at a relatively small scale (< 6 km at the maximum) were detected between the Wadi and the plateau. The Wadi is lower, hotter, and characterized by higher level of soil moisture and more vegetation. All these create better reproductive conditions for the sand flies. In 2007, the sand fly abundance in the Wadi was nearly twice that of the plateau, while in 2008, it was nearly five times higher than the plateau abundance. This pattern perhaps suggests a source-sink pattern, with the Wadi being the source, and the plateau being the less stable sink population. In harsh conditions, like those following the dry 2007-2008 winter, the major contribution for the sand fly population came from the Wadi. Another possible explanation is that the Wadi has higher sand fly productivity than the plateau. Factors previously reported as positively correlated with higher sand fly abundance include soil moisture and disturbance (Schlein et al., 1984; Yuval 1991; Wassserberg et al 2003b). In the Wadi, soil moisture is higher (see chapter 1 plots characteristics) and disturbance level is lower than on the plateau since the Zin Valley (the Wadi) is a protected natural park. In this unique combination (elevated temperatures, higher soil moisture, and more vegetation) the Wadi has an advantage regarding sand fly abundance, compared to the disturbed, higher, colder plateau with lower soil moisture level. To examine the soil moisture as a single factor, I plan to manipulate the soil moisture near active P. obessus burrows in the plateau area and compare the sand fly abundance there to the abundance near control P. obessus burrows. Wasserberg et al (2003a) demonstrated differences in sand fly abundance at the regional as well as the local scale. They showed higher sand fly abundance in Nizzana ruins, compared with Mt. Keren Junction (~10 km apart). Nizzana ruins plots were disturbed and had higher soil moisture. Within Mt. Keren plots, sand fly abundance was higher in loess compared to sandy soil habitat. Here I show the importance of soil moisture in an undisturbed habitat. The sand fly abundance was higher in the undisturbed plot with higher soil moisture compared to the disturbed plots with lower soil moisture.

44 Temporal patterns of abundances also differed between the Wadi and the plateau. In the Wadi during both years, a unimodal pattern was observed, with the peak densities shifting later in the season in 2007. On the plateau, in 2008 a bimodal pattern was detected with the first peak in June, and the second one in September. A bimodal pattern was detected in the Arava valley by Wasserberg et al (2003b), compared to a unimodal in Nizzana (regional scale). Janini reported a unimodal pattern in the Jordan Valley, with the peak in September. Variability in seasonal abundance of sand flies has been reported in the past and is often affected by temperature (Janini et al 1995; Killick-Kendrich 1999). Here, I present a difference in sand fly seasonal patterns of abundances within a short geographic distance (<6 km), with high sand fly abundance in the natural area. The seasonal activity of P. papatasi is highly influenced by climatic and environmental effects. Deeper understanding of seasonal patterns within their environmental contexts can help us determine peaks in sand fly abundance and by this, also suggest preferred time windows for Leishmaniasis control. Further research is still needed regarding early prediction of outbreaks, temporal increment of cases in known foci, or even more challenging, identifying a new or emerging focus using only our knowledge of the current environmental and climatic variables.

45 Chapter 4. Cutaneous Leishmaniasis infection rates 4.1 Introduction Infection rates in humans Leishmaniases is a group of diseases that currently threaten 350 million people in 88 countries (Desjeux, 2001). The World Health Organization (WHO) estimates that over 2.3 million new leishmaniasis cases occur each year and that at least 12 million people are presently infected worldwide (WHO, 2007). Cutaneous Leishmaniasis (CL) is caused by two parasite species Leishmania major and L. tropica. Leishmania major is prevalent in desert habitats (Schlein et al. 1984; Greenblatt et al, 1985; Wasserberg et al. 2002). In Israel, the incidence of cutaneous leishmaniasis has increased over the last decades (Jaffe et al 2004, Wasserberg et al 2002). The first large outbreak (~125 cases /year) occurred following the 1967 Israeli occupation of the West Bank (Anis 2001). A second large outbreak (~250 cases /year) was reported in the early 1980’s following the withdrawal of Israel from the Sinai Desert and people inhabiting previously undeveloped areas in the western part of the Negev desert (Giladi et al 1985). Another peak was observed in the mid 1990’s (Anis 2001). CL information collected by the Israel Defense Forces (IDF) shows a similar trend to that found by the Israel Ministry of Health (MOH; I. Grotto et al, unpublished) with incidence rates 25-60 times higher in soldiers (Anis 2001, Wasserberg 2002). Figure 4.1 presents the number of CL cases and the infection rate per 100,000 (L. major and L. tropica are combined) between the years 1956-2004 (MOH). Over the past 4 years, infection rate has been relatively steady, around 3 cases per 100,000 (Fig. 4.2). Although notification to the MOH of all leishmaniasis is mandatory by law in Israel, the number of cases is probably under-reported. One reason may be that CL is self-healing and patients do not always seek medical care. Lack of reporting by the medical team (physicians and diagnostic laboratories) may be another reason (Anis 2001, Klaus 1994). Since CL is a zoonosis, monitoring CL cases in humans is not sufficient, and the infection rate in the rodent host and the sand fly vector must be evaluated in parallel. The goal of this chapter was to study the disease prevalence in both host and vector and collect information regarding infection of human cases in Sede Boqer area and to compare them to the host and vector population dynamics and infection rates.

Figure 4.1: CL infection rate in Israel 1956-2004 Source: Notifiable infectious diseases in Israel 54 years of surveillance 1951-2004, MoH.

CL number of cases and rates per 100,000 2004-2008

Rate Cases 3.5 250

3 Number of cases 200 2.5

2 150

1.5 100 1 Rate per 100,000 50 0.5

0 0 2004 2005 2006 2007 2008 Year

Figure 4.2: CL infection rate in Israel 2004-2008 Source: Number of cases: MoH data base, Population number for rate calculation: Central Bureau for Statistics, Israel.

Infection rates in host The main reservoir host of L. major in Israeli deserts is the fat sand rat Psammomys obesus (Schlein 1984; Wasserberg et al., 2002). L. major has also been isolated from other hosts, mainly rodents (Ashford 1996). In the Jordan Valley, the Arava and the

47 Negev, L. major has been isolated from Meriones crassus and Gerbillus dasyurus (Schlein et al 1984, Wasserberg et al 2002). The infection rate of sand rat reported in the Israeli Desert was between 7.4 %-90.3% (Schlein et al 1982, Schlein et al 1984) in the Jordan Valley and up to 65% (Wasserberg et al 2002) in the loess habitat of the Nizzana area. Wasserberg (2002) showed that the main reservoir host of L. major in Nizzana area was P. obesus and not M. crassus as was previously reported (Schlein et al 1984). Hence, periodic sampling of potential reservoir host is important in understanding disease dynamics in time and space, since this plays a key role in planning disease control strategies.

Infection rates in vectors Results from previous studies show high variability in the infection rates of phlebotomine sand flies with Leishmania. There is high variability in detecting Leishmania parasite presence: 0–56% in the Jordan Valley (Schlein et al 1982, Schlein et al 1984, Yuval et al 1988), and 0.8-0.9 % infection rates in the first 2 years and no infection in the last year of a study conducted in Sinai on P. papatasi and L. major (Hanafi et al 2007). In addition, high variability in SF trapping in time and space has also been reported (Sawalha et al 2003, Wasserberg et al 2003a, Boussa et al 2005, Chelbi et al., 2007). P. papatasi is highly anthropophilic (tends to bite humans), as well as endophilic (bites indoors). Hence, identifying the SF population dynamics and infection rate are important for disease control. In this chapter I studied the disease prevalence in both host and vector and collected information regarding rates of human CL cases in the Sede Boqer area, with particular focus on the small settlements of Kibbutz Sede Boker and Midreshet Ben-Gurion. The disease incidence was studied in both host and vector and information was collected regarding cases of human infection in Sede Boqer area, which were subsequently compared to the host and vector population dynamics and infection rates. Quantifying disease incidence and host and vector dynamics in time and space, in parallel to the infection rate in humans, can help us obtain a better understanding of the disease pattern.

4.2 Methods Infection rates in humans Data regarding the number of cases from humans were taken from the environmental unit, Ramat Negev Regional Council. Estimates of population size were taken from the Central Bureau of Statistics and the settlements' secretaries.

48 Infection rates in sand rats Sand rat trapping method was described in chapter 2. Small skin biopsies were taken from the ear pinnae of captured rodents and blotted onto filter paper for parasite detection by kDNA PCR (see detailed protocol in this chapter). In the first seasons, skin biopsies were taken by scratching the skin with a scalpel. Later, I punched the ear with a paper puncher to take a small skin sample. Since L. major is the only Leishmania species known so far to be harboured in P. obesus in the Negev, I decided to use kDNA PCR which is the most sensitive technique but does not allow species identification. (Bensoussan et al 2006). Since several years have passed since the PCR was conducted, the first samples were relatively old , 70.8% of the individuals’ samples were checked for sand rat DNA presence, for increasing the infection rate accuracy estimation.

Laboratory methods- Sand rat DNA extraction and PCR analysis Filter papers with blotted sand rat tissues were cut with a disposable sterile scalpel and incubated in 250 µl cell lysis buffer as described by Schonian et al (2003). DNA was extracted from the lysates with phenolchloroform, and the pellets were air-dried. After being dissolved in 50 µl Tris-EDTA buffer (10 mM Tris and 1 mM EDTA, pH 8.0), the DNA was kept at 4°C until amplified by PCR. Clean filter paper was used as a negative control for DNA extraction. The sensitivity of each PCR was optimized on pure Leishmania DNA prior to use for diagnosis. The kDNA PCR using the primers 13A (5'-GTG GGG GAG GGG CGT TCT-3') and 13B (5'-ATT TTC CAC CAA CCC CCA GTT-3') was carried out essentially as described by Reale et al. (1999), except that the reaction was carried out in 25 µl, using 200 µM deoxynucleoside triphosphates (dNTP). Leishmanial DNA (20 ng) isolated from reference strains (see below) was used as a positive control. Reaction buffers without Leishmanial DNA were also included as negative controls in each PCR analysis. All PCRs were carried out in a 25 µl volume, using the optimal annealing temperatures, concentrations of primers, dNTP, Mg ions, Taq polymerase, and additives as necessary. Amplicons were stained with EZ-VitionTM (formerly known as En VISIONTM) and then analyzed on 1.5% agarose gel by electrophoresis at 100 V in 1 × Tris- acetate-EDTA buffer (0.04 M Tris-acetate and 1 mM EDTA, pH 8.0) and visualized by UV light. A PCR was considered positive when a band of correct size (kDNA~120 bp) was observed. Leishmanial species DNA markers were prepared from promastigotes of Leishmania

49 major (MHOM/PS/1998/ISL389), obtained from the WHO Reference Centre, Jerusalem, Israel, and their DNA was prepared as described by Schonian et al (2003). The PCR for sand rat DNA presence was carried out using the primers 5’-CCT AAC ATT TCA TCC TGA TGA-3’ (forward) and 5’- CTC AAA AAG ATA TCT GTC CTC-3’ (reverse) according to NCBI AY934540 (http://www.ncbi.nlm.nih.gov/nuccore/62866956). A PCR was considered positive when a band of correct size (~350 bp) was observed.

Infection rates in sand flies The method for trapping sand flies was described in chapter 3. Leishmania detection in sand flies was conducted by Dr. Laor Orshan, at the Entomology Laboratory, Ministry of Health, Jerusalem. The presence of Leishmania parasite DNA in pooled samples of sand fly females (5-20 specimens) was detected by PCR amplification of the internal transcribed spacer (ITS1). Leishmania species identification was carried out by restriction of the amplified fragment (Svobodova et al 2006). I calculated the minimal infection rate (MIR) in sand flies, assuming that only one sand fly was infected in each positive pool: Minimal Infection Rate = (Infected number of females / examined number of females) * 1000.

4.3 Results Infection rates in humans An increase in the number of CL cases was reported in Midreshet Ben Gurion and Kibbutz Sede Boqer between 2004 and 2008 (no cases in 2004-2005, 24 cases in Midreshet Ben Gurion in 2008, see table 4.3). In 2007-2008, the local CL infection rate was 721-2164 per 100,000 (table 4.3). In Midreshet Ben Gurion, in 2007, all reported cases were from October to December. In 2008 (except for few cases of late reporters from 2007 season), reports begin in July and the peak number (5 cases each) is in October and December (Fig 4.7). Most patients seek medical help about 2-3 months after the appearance of the initial papule, when the lesion is well developed.

Infection rates in sand rats I checked the samples (n=185) for Leishmania parasite presence using kDNA PCR (Fig 4.3). Eleven sand rat individuals were infected with Leishmania. Then, I checked a sample of 131 (70.8%) individuals for sand rat DNA presence (Fig 4.4). DNA was successfully extracted

50 from 86 individuals (extraction rate: 0.656). The Leishmania infection rate was calculated as: positive # of individuals divided by # individuals, divided by the DNA extraction rate = 11/ 185 /0.656 = 9.06%.

Fig 4.3 Leishmania-specific PCR using the primers 13A and 13B to amplify parasite kDNA from trapped rodents. Band size is ~120 bp.

Fig 4.4 Sand rat DNA presence PCR using the primer NCBI AY934540, band size is ~ 350bp

Demographic and spatial aspects Infection rates in female sand rats were equal to those in males. Infection rate correlated with higher body mass (0 in 0-100g, 4.35% in 101-150g, 12.35% in 151-200g and 13.3% in >200g, χ2 = 14.76, d.f=3, p=0.002). Fifty four percent (6/11) of the infected individuals came

51 from the Wadi plot. The infection rate for the Wadi individuals was 22.6% (n=6) compared to 6.2%-9.6% infection rate for the plateau individuals. Sand fly infection rates I conducted a logistic regression to check whether season or location (Wadi vs. plateau) correlated with presence of Leishmania parasites. Season, but not location, significantly affects Leishmania presence.

Table 4.1 Regression model testing the effect of season and plot on the Leishmania presence in Sand fly population Degrees Of Log- Chi- p Freedom Likelihood Square Intercept 1 -30.916 Season (categorical) 4 -23.654 14.524 0.0057 Location 1 -23.642 0.0240 0.8767 Season*Location 1 -22.838 1.6065 0.2049

I calculated the Minimal Infection Rate (MIR) of the total sand fly population (see methods, p. 48). In 2007, the MIR for the total trapped sand fly population was 2.3% with one infected batch from August from the Wadi plot out of 23 batches. In 2008, the MIR for the total trapped sand fly population was 23.92% (19 infected batches out of 47), with MIR of 24.88% (16 infected batches out of 39) in the Wadi and 19.86% (3 infected batches out of 7) in the plateau. Figure 4.5 presents the Leishmania minimal infection rate of the sand fly and the sand fly abundance along time. We can detect a lag between the sand fly peak abundance (June) and the MIR peak (July). In 2008, the total SF population abundance pattern is unimodal, and the MIR is bimodal. The SF 2008 abundance is unimodal in the Wadi, but bimodal in the plateau (chapter 3 fig 3.9), with the second peak in September. In accordance, the relationship between the MIR and the SF abundance in the Wadi is similar to that of the total population (data not shown), but in the plateau, there is a bimodal abundance pattern and two peaks in the MIR with a lag before the first one, and the second one overlaps with the SF peak in abundance (Fig 4.6).

52 L. major MIR and SF abundance 2008

SF # per trap MIR 20 60 Minimal Infection Rate (%) 50 16

40 12 30 8 10.36 20 10.97 6.34 4 10 SF number per trap (average) trap SF per number 3.68 0.49 0.30 3.38 0 0 May Jun beg June end July Sep beg Sep end October Month

Figure 4.5: L. major Minimal Infection Rate and SF abundance in 2008, total SF population

Plateau L. major MIR and SF abundance 2008

SF # per trap Plateau_MIR 4 140 Minimal Infection Rate (%) Rate Infection Minimal

120 3 100 3.18 2.95 80 2 60

40 1 1.04 0.19 20 0.06 0.54 0.67 SF number per trap (average) 0 0 May Jun beg June July Sep begSep endOctober end Month

Figure 4.6: L. major Minimal Infection Rate and SF abundance in 2008 in the plateau

53 Number of cases during 2008 N=24 6

Number ofcases 1

0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Time

Fig 4.7 Temporal distribution of CL cases in Midreshet Ben Gurion 2008

Sand fly abundance (active burrows density per plot) in July and October 2007 and 2008 (combined) was positively correlated to the sand rat density (average number per trap) in the five research plots (Table 4.2).

Table 4.2 Regression of P. obessus density and sand fly abundance (square root transformed) SS Degrees of MS F p Freedom Intercept 1.569 1 1.569 0.916 0.375 Sand rat density 10.27 1 10.27 5.996 0.049 Error 10.27 6 1.712

5.5

5.0

4.5

4.0

3.5

3.0

2.5

2.0 Sand Fly Sand abundance (Squareroot) 1.5

1.0

0.5 0.8 1.0 1.2 1.4 1.6 1.8 2.0 2.2 2.4 Active burrow density (Square root) Fig 4.8 Regression of active burrows density and sand fly abundance in July and October 2007-2008

54 Infection rate Integration

Table 4.3 summarizes the infection rate in humans, hosts (sand rat), and vector (Phlebotomine sand flies). Reported cases from humans increased over the last 5 years, especially in the last two to three years. The increase in human CL cases from 2007 to 2008 coincides with an increase in CL infection rate in the sand fly vector (low in 2007 and high in 2008). The sand rat infection rate fluctuated markedly over different years and did not match as closely with the human infection rate as did the sand fly data. Sand fly and sand rat abundance are correlated (Fig 4.8), but the human infection rate is more similar to the sand fly infection rate. The incidence of the human cases is temporally adjusted to the MIR peaks in the sand fly population (Fig 4.5 - 4.6 Vs. Fig 4.7).

Table 4.3 Summary of the human CL cases in Sede Boqer area and infection rate in hosts and vectors in Sede Boqer area. Year CL Cases (n) CL Cases (n) Rate per 105 Rate per 105 Sand rat Sand fly Midreshet Kibbutz Midreshet Kibbutz Infection Rate Minimal B.G.* S.B.** B.G. * S.B.** Infection Rate 2008 24 5 2164.11 1152 5.64% 23.92% 2007 8 4 721.37 883 0 2.93% 2006 8 0 778.96 0 26.13% No Data 2005 0 0 0 0 0 No Data 2004 0 0 0 0 17.42% No Data * Midreshet Ben Gurion ** Kibbutz Sede Boqer

4.4 Discussion In recent decades, we have been witnessing an increase in incidence and geographic range of various vector borne diseases such as Lyme disease, tick-borne encephalitis, yellow fever, plague, and dengue fever (Gratz 1999, Lindgren et al 2000). In Israel, the incidence of CL has increased over the last decades (Jaffe et al 2004, Wasserberg et al 2002). During the course of this study only, the incidence of CL increased from 2004 until 2008, from 0 to 5 cases and from 0 to 24 cases, in Kibbutz Sede Boqer and Midreshet Ben Gurion, respectively. Patients included not only children and newcomers, but also long-time adult inhabitants that were exposed to leishmaniasis over several years but only recently became infected. Environmental attributes such as soil moisture and temperature are well documented as factors affecting host and vector abundance and disease prevalence (Schlein et al 1984, Yuval 1991, Killick-Kendrick 1999, Wasserberg et al 2003a). Here, I put together the human, host, and vector infection rates in their environmental context to get a deeper understanding of the

55 transmission cycle. In addition to the important roles of soil moisture documented in literature, I also detected local scale differences in the abundance and infection rate of host and vectors. The overall infection rate amongst the host, the fat sand rat, in all plots was 9.06%, with the Wadi being the highest (22%). Infection correlated with higher body mass. The number of individuals trapped (13-40) as well as the infection rate (0-26%) of the host is highly variable between years and the host infection rate is relatively different from the human one. The PCR analysis revealed host DNA in about 65% of the individuals, therefore, the current infection rate is an estimation based on the samples that yield DNA, since we do not assume different infection rates in those individuals. The overall infection rate is within the range that was previously reported in other studies in Israel (Schlein 1982, Schlein 1984), and points to the importance of a long-term study. If I had looked at the 2006 data only, the reported infection rate would have been 26%. The overall infection rate is similar to the infection rate reported by Wasserberg et al (2003a) of 11 % infection rate in fat sand rat in Sede Boqer area in 1998. The Leishmania presence amongst the vector, Phlebotomine sand fly, mainly P. papatasi was affected by season. Different temporal patterns were observed between the two years regarding the sand fly abundance (Chapter 3) and the infection rate. The MIR in 2008 was higher than in the previous year. Also, the Wadi- plateau interaction is reminiscent of a source-sink relationship for both the sand fly population and the parasite population and disease. The Wadi can be perceived as a high-risk area for leishmaniasis not only for the local population, but also for the transit population of visitors coming to the Negev for tourism. Those people who get infected during their visit will develop the lesion later elsewhere, and will seldom be included in the statistics for Sede Boqer. This is similar to the soldiers in the Nizzana area discussed by Wasserberg et al (2003a), who get bitten in the Negev and then are frequently sent to other areas during their military service where they may develop the CL lesions. High variability in the prevalence of Leishmania parasites detected in phlebotomine sand flies is known in the literature. 9% in the Jordan Valley (28/311; Yuval et al 1988), and 0.8-0.9 % infection rates in the first 2 years and no infection in the last year of a study conducted in Sinai (Hanafi et al 2007). Therefore, frequent sampling can give more accurate estimates of current infection rates. The time between the peak sand fly abundance and the peak in the Leishmania infection rate in the sand fly should be considered as a critical time for concentrated and efficient control efforts. The control efforts should start just before the peak of sand fly abundance, but this is not always feasible. Hence even following the sand fly peak abundance the effort should still be considered beneficial since the flies mostly are not infectious yet.

56 Periods for the development stages of sand flies are highly affected by temperature, and the complete life cycle of the sand fly lasts several weeks (Killick-Kenrick 1999). This provides us an important time window to monitor vector abundance and to act using even relatively short time intervention methods (i.e. insecticide-spray strip, host targeted devices etc). What are the best indicators for CL? The vicinity of sand rats burrows to human dwellings. In this study, I sampled sand rat and sand fly populations located 0.5 km-2.5 km away from the human settlements. Sand flies tend to shelter in P. obesus burrows (Schlein et al., 1982; Schlein et al., 1984; Wasserberg et al., 2002). If burrows are close to settlements, we are likely to find high prevalence of CL in these settlements as a consequence. Sand fly and sand rat abundances are correlated (fig 4.7), so one may think either of them. In fact, the infection rate (MIR) of the sand flies reflects better the human infection rates including the seasonal fluctuation in disease incidence, and it is relatively easy to obtain this over the season. Still we need information from both host and vector. By combining both host and vector (relatively rapidly collected) field data, we can use the sand fly infection rate as an on-going temporal monitoring tool, and the burrow activity of the rodents as a spatial indicator for sand fly location and for targeting control efforts.

57 Chapter 5: Lushness Experiment 5.1 Introduction Cutaneous Leishmaniasis (CL) has increased its prevalence and expanded its geographic range in Israel during the last two to three decades (Klaus et al 1994, Giladi et al 1985, Biton et al 1997, Wasserberg et al 2002). These changes were associated with anthropogenic influences including increasing human population size, together with agriculture and urban development within endemic CL foci (Giladi et al 1985, Greenblatt et al 1985, Biton et al 1997). Both epidemic processes--influx of susceptible human populations into zoonotic areas as well as human-induced ecological changes may be synergistic in causing the reported outbreaks. Examples of the effect of human immigration into zoonotic areas have been described in the past (Naggan et al. 1970, Giladi et al. 1985, Klaus et al. 1994, Morsy et al. 1995, Biton et al 1997, Ashford 1999, 2000, Oumeish 1999, Saliba and Oumeish 1999, Desjeux 2001). However, evidence for the ecological effect is scarce and circumstantial. Human-induced ecological changes may promote CL outbreaks (Greenblatt et al 1985, Ashford 1999, Oumeish 1999, Saliba and Oumeish 1999). Wasserberg et al. (2003b) found that the abundances of both the fat sand rat (Psammomys obesus) host and sand fly (Phlebotomus papatasi) vector, as well as Leishmania prevalence rate, are higher in disturbed habitats (Wasserberg et al 2003b). Also, vector abundance is related to additional environmental conditions. Sand flies breed better, gain shelter, and have superior feeding conditions under moist soil (Killick-Kendrick 1999), and Wasserberg and colleagues showed that soil moisture was positively associated with vector abundance (Wasserberg et al 2003b, Schlein et al 1984, Yuval 1991). In addition, soil organic matter content had a positive association with vector occurrence. However, organic matter content was not always related to higher disturbance level (see chapter 1 for disturbance details). When comparing five different CL areas, higher soil organic matter was found in two undisturbed habitats, while the opposite was found in the most xeric habitat (Wasserberg et al 2003b). Wasserberg (2003b) also found a role for plant lushness. Lushness, defined as percent coverage of a stick by green leafy matter, is an indicator for plant condition. Plant lushness was higher in disturbed plots as compared to undisturbed plots, and these plots were more dominated by Chenopodiaceae plants. Consequently, the fat sand rats that constitute the reservoir host for L. major and feed on chenopods occurred in higher abundance in areas of greater plant lushness (Wasserberg et al 2003b).

58 Lushness may affect vectors and hosts directly since it is an indicator for plant state, but lushness is also associated with other CL risk factors such as soil moisture which is often an outcome of disturbance, and so may have an indirect effect. Knowing whether or not the effect is direct can better direct control effort for disease management. To examine the direct effect of lushness, I manipulated lushness experimentally in order to isolate and test for its effects on sand fly and sand rat abundance in inhabited burrows.

5.2 Methods Bush cutting During spring 2008, I marked 4 plots containing active P. obesus burrows in the vicinity of Sede Boqer (see study area Chapter 1). On each plot, I chose pairs of target Atriplex bushes of similar size, located close to each other (but at least 10 m apart), and with active P. obesus burrows (see burrow definition Chapter 2). One bush from each pair, was trimmed sufficiently to remove all the leaves in winter/early spring, prior to SF activity season. First I marked two plots in which I chose six bushes each; later, to increase the sample size, I added two additional, but smaller plots, in each of which I chose one additional cut and one control bush. I trapped SF from sixteen bushes, altogether eight pairs of cut and control bushes. The larger plot sizes were 11,763 m2 and 2,706 m2, while the smaller plots were 1,111 m2 and 613 m2. I trimmed the bushes as necessary to remove the new growth every 7-10 days, for 12 weeks from March through May (Fig. 5.1a cut, Fig 5.1b control Atriplex bush).

Fig 5.1a. Cut bush Fig 5.1b. Control bush.

Sand fly trapping I monitored sand fly abundance by using emergence traps in all detectable burrow openings under each target bush during the active sand fly season. I used emergence traps due to the scale of the experiment; I needed to monitor SF only from the 16 experimental bushes,

59 and not from the surrounding area. In addition, due to the low capture success of emergence traps, I needed as large a sampling period as possible. Thus I ran the experiment for a time period that entirely included the active season for the sand flies. I first manipulated shrubs in March, prior to the active season of the sand flies. Because of this necessity, I was unable to monitor sand fly density prior to experimental manipulations. I placed traps for two consecutive nights every two weeks from July through October. I manufactured emergence traps by cutting the ends of standard 1 ½ liter plastic soft drink bottles and adding a cut 50 ml tube glued to one side, and a fine mesh screen glued to the other side, similar to an inverted funnel (Figure 5.2). These traps could then be inserted firmly into a burrow opening, with the 50ml tube side inserted into the burrow opening. Due to the small numbers of sand flies captured from just two nights of trapping, I increased the duration of the trapping sessions to 4, 5, and 7 days towards the end of the year, yielding a total trapping effort of 1528 trap-nights over 17 trapping sessions.

Sand fly identification and Leishmania detection Sand flies were sent to Dr. Laor Orshan, the entomology lab, Ministry of Health, Jerusalem, Israel for species identification, and for Leishmania parasite detection. The presence of Leishmania DNA in pooled samples of sand fly females (5-20 specimens) was detected by PCR amplification of the internal transcribed spacer (ITS1). Leishmania species identification was carried out by restriction of the amplified fragment (Svobodova et al 2006).

Burrow activity In parallel to the sand fly trapping, I monitored sand rat burrow activity around each target bush every two months from March through September 2008 (See Chapter 1 for active burrow definition), for a total of four activity checks. I created a parameter called “Burrow activity level” that incorporated all four activity checks. I summed the number of times the burrow was scored as active. In addition I created a dichotomous parameter called “continuously active” that I scored positively for the burrows that were active all four times, and negatively for the burrows that were not active for at least one sampling period.

Plant condition I quantified plant condition of target bushes using two different measures: branch density and Leaf Area Index. Leaf Area Index (LAI) was defined by Watson (1947) as the total one-sided area of leaf tissue per unit ground surface area of the bush. According to this definition, LAI is a dimensionless quantity characterizing the canopy of an ecosystem. Leaf

60 area index drives the within- and the below-canopy microclimate, determines and controls canopy water interception, water and carbon gas exchange, and is therefore a key component of biogeochemical cycles in ecosystems. Any change in canopy leaf area index (by frost, storm, defoliation, drought, management practice) is typically accompanied by modifications in productivity (Breda 2003).

Branch density Branch density was determined as the average number of intersections of shrub branches with a meter long ruler inserted into the shrub along two axes: a North-South axis and an East-West axis at 20cm height. For each axis, the count was standardized against the length of the ruler within the canopy, and then the two counts were averaged.

Leaf Area Index (LAI) The fraction of photosynthetically active radiation (PAR) that passes through the canopy of each shrub was measured using the PAR function of a Sunfleck ceptometer (Decagon Devices, Inc., Pullman, WA, USA). These data were used to determine Leaf Area Index (LAI) of the shrubs (Figure 5.3). In past studies, LAI has been related to both actual biomass area and the interception of PAR by a plant canopy. Chen et al. (1991) have proposed another view regarding LAI in which L, the actual biomass area, was related to a new term, Le, which represents the actual orientation of the canopy elements relating to the interception of PAR at a given angle. In situ measurements of LAI using hemispherical photography were equated with this new term, "effective plant area index" (Le), which was defined as: Le = ΩL where L represents the actual leaf area index (equal to a harvested leaf area measurement) and Ω refers to a clumping index resulting from the non-random distribution of canopy elements.

Fig 5.2 Emerge trap Fig 5.3 Sunfleck Ceptometer

Statistical analysis I used Poisson regression to analyze the sand fly abundance, since it is count data. I added moonlight (scaled from 0 -no moon to 8 -full moon) to the analysis as an adjustment to the confounding moonlight effect since trapping occurred during the several moon phases.

5.3 Results Plant condition As expected, cut bushes were in poorer condition than intact, control bushes. The mean (±SD) LAI was significantly lower in the cut bushes (0.43±0.20), compared to the control bushes (1.73±0.50) (t-test, separate variances, t9.28= -6.68, p<0.0001). The LAI was an effective method for estimating the foliage condition (defined as lushness in a previous study). The mean (±SD) branch density of the cut bush was significantly lower (0.060±0.027) than the control bushes (0.093±0.034), (t-test, t14= -2.18 p=0.0465). Since the LAI reflected more clearly the difference between the treatment and the control bushes, I used it as the dependent variable in the data analysis. Thus the experimental manipulation succeeded in reduction the lushness of targeted shrubs. Hence, we can go on to examine the consequences of lushness for activity of host and abundance of vector.

Sand fly abundance I collected a total of 85 sand flies during summer 2008 trapping. The mean (±SE) number of sand flies per trap was 0.055 (± 0.0062). Out of 16 burrows, 4 burrows yielded no sand flies during emergent trapping (2 burrows from each treatment group). The mean (±SE) number of sand flies per burrow for the entire 2008 season was 5.117 (± 0.646), and the

62 median was 3.5 (range 1 – 24) sand flies per burrow. Gender distribution was nearly equal (51% females), but males started to be active later (from mid August). Species identification was conducted on the males only: 72% of the males were P. papatasi, and 28 % were P.

alexandri. No specific temporal pattern was detected (Kruskal-Walis test: H (16, 17) =16, p =0.453). The mean (±SD) number of sand flies per burrow was not statistically different between treated and control shrubs, 2.079(±1.220) in the treatment group and 2.072(±1.396) in the

control group (t-test, t14= 0.010, p=0.992). Similarly, no correlation was found between SF number and LAI. To examine the effect of the cutting manipulation and the continuously active level of the burrows on the SF abundance I used Poisson regression (Table 5.1). SF abundance was significantly correlated with burrow activity level (Log likelihood = -61.236, χ2 = 29.63, p<0.0001).

Table 5.1 Poisson regression model testing the effect of burrow activity level and the cutting on SF abundance Degrees Of Log- Chi- p Freedom LikelihoodSquare Intercept 1 -76.528 Log LAI 1 -76.051 0.953 0.328 Burrow activity level 1 -61.236 29.63 <0.0001

Burrow Activity To evaluate the effect of the cutting manipulation on the continuous activity level of the burrows I used ordinal multinomial regression, since the dependent variable is ordinal with more than two categories (Table 5.2). LAI affected burrow continuously active level (Log likelihood = -17.37, χ2 = 5.08, p=0.024). This suggests that lushness positively affects P. obesus activity, with burrows under plant with higher LAI being more likely to be active.

Table 5.2 Ordinal multinomial logistic regression testing the effect of the cutting manipulation on burrow continuously active level. Degrees Of Log- Chi- p Freedom Likelihood Square Intercept 3 -19.92 Log LAI 1 -17.37 5.08 0.024

63 Leishmania Infection 12 SF females with blood were captured; Leishmania parasites were detected only once from sand flies collected in this experiment, from October (due to small number trapped all SF were combined, from the cutting and control burrows).

Moonlight effect To examine the effect of the moonlight on the SF abundance I used Poisson regression. SF abundance (average number per trap) was not correlated with burrow activity level (Log likelihood = -3.61, χ2 = 0.045, p=0.831).

5.4 Discussion This experiment provides evidence that lushness does not directly affect sand fly abundance. Rather, lushness positively affects sand rat activity, which in turn, may affect sand fly abundance. In addition, moonlight does not correlate with sand fly abundance. Previous work suggests that vegetation lushness is indirectly associated with CL risk factors, mainly through increased soil moisture, which affects sand rat abundance (Wesserberg et al 2003b). The lack of evidence for the direct effect of lushness on SF abundance appears to be the result of a real lack of association, although the small number of sand fly captures may make it difficult to measure any sort of a response. My experimental results suggest that the effect of lushness is indirect. Reduced plant lushness negatively affected sand rat abundance and activity, so plant condition affects host presence in the burrow. A close association between Psammomys obesus burrows and phlebotomine sand flies is well documented (Schlein et al., 1982; Schlein et al., 1984; Yuval & Schlein 1986; Wasserberg et al., 2002). The overall burrow activity level in turn affected sand fly abundance. All burrows chosen for this experiment were active prior to the experimental reduction of plant lushness. In response, rodents abandoned burrows that no longer provided the previous qualities when plant condition was better. This burrow abandonment should be distinguished from the P. obesus seasonal habitat shift reported before (Cervantes 2004) since the lower percentage of active burrows under cut bush (50% active) compared to control bushes (84.4% active) suggests that abandonment was higher in the same plots and during the same season. The positive correlation between plant lushness and P. obesus density is likely to be causal. Wasserberg et al. (2003b) previously reported such a relationship in an observational study. Here, this correlation emerged from an experiment in which I actively reduced plant

64 lushness. By manipulating lushness, I altered a number of related factors, including increasing direct radiation and evaporation level, decreasing soil moisture, and reducing feeding opportunities for the sand rats. These all may have contributed to the sand rat’s decline. The expected effect of all these environmental factors related to plant lushness should be reduced SF abundance. Although shrub lushness is not directly correlated to sand fly abundance, by affecting direct radiation, evaporation, and sand rat activity, it affects soil moisture and feeding opportunities for sand flies. Therefore, an important consequence of plant lushness is its effect on the SF abundance, in this case, through burrow activity level.

Host and vector Ecology Animal-plant interaction.--A study on the interaction between P. obessus burrows and A. halimus shrubs was conducted recently in Sede Boqer (Mirzoian 2006). A. halimus shrubs associated with P. obesus burrows were larger than those without a burrow. Since annual growth was not greater, this effect was likely due to the rodents’ choice of larger shrubs for burrowing. Foraging by P. obesus positively affected branch density, as well as other indicators of short-term compensatory responses to browsing (seed production, flower cluster). Burrowing and foraging activities together increased soil organic matter content, and by increasing soil porosity, also resulted in higher soil moisture in summer (Mirzoian 2006). Here I show the plant-animal interaction from the other side, when the lushness of shrubs associated with burrows was reduced, those burrows became less active burrows Is the lack of an effect of lushness reduction on sand fly density real? This question may still not be entirely resolved for several reasons. First, I captured a small number of sand flies in this study, and so in regards to sand fly, the power of resolution of the experimental manipulations may be small. Nonetheless, the means for sand flies captured under control and experimentally altered bushes are identical to 2 decimal places. In addition, other factors may have reduced the ability to show a correlation if one actually exists. The time scale under consideration is short between the cutting (March-May), and the census trapping of sand flies (July through October), may not have been sufficient for a population dynamic response. To address this question, I will continue to monitor these burrows in summer 2009. Disturbance is an important environmental CL risk factor affecting SF abundance (Wasserberg et al, 2003b, Frayauff et al 1993). The highest number of sand flies captured from a burrow in my current study (24) came from a burrow at a highly disturbed spot, across the road from an agricultural field. The control bush paired to this burrow was only 36.5m away from that burrow and yielded a total of only 6 SF per season. I carried out this entire

65 experiment in disturbed areas, but the burrow with the highest number had a particular high disturbance level.

Vector Ecology No specific temporal pattern was detected in the SF abundances, aside from population declines towards the end of the season. Not detecting specific seasonal trends may result from the small sample size. In CDC traps used in nearby areas (see Chapter 3 for details), the number of trapped SF was substantially higher, and a clear seasonal pattern was detected in which populations peaked in the beginning of August in 2007 and in June in 2008, followed by sharp declines towards the autumn in both years. Seasonal trends in abundances of SF are well documented (Brinson et al 1992, Comer at al 1994, Morrison et al 1995, Mchugh et al., 2001 Wasserberg et al 2003a), with low temperatures believed to restrict seasonal activity of P. papatasi (Janini et al., 1995; Killick-Kendrick, 1999). The sand fly sex ratio sampled in the emergence traps in this study was close to 1:1. A similar ratio was found in the Jordan Valley for sand flies trapped near burrow openings

(Yuval et al 1988). Interestingly, in miniature CDC traps (baited with CO2) sampling nearby during the same time period, sex ratio was females biased in both years. In other studies from the Middle East and the Mediterranean areas on P. papatasi the reported ratios were 1:0.27 in Morocco (Boussa et al, 2005), 1:3.4, in Sinai, Egypt (Hanafi 2007) and 1:0.904 in Saudi Arabia (El Badry 2008). In this study, P. alexandri composed about a third from the total sand fly population. P. papatasi was still the dominant species, but the proportion of P. alexandri is higher that reported in the past in this region (Wasserberg et al 2003b). This suggests that long term research and more frequent sampling are needed to detect a shift in species composition. Moonlight does not correlate with sand fly abundance, although this correlation has been reported in the literature. Morrison et al (1995) showed that sand fly activity increased in moonlight presence. Souza et al (2005) found that there is a significant positive correlation between moonlight intensity, and the numbers of two Lutzomyia species (L. intermedia and L. whitmani) females collected while blood-feeding, whereas the opposite was observed for concurrent trapping using CDC traps. These studies regarding moon phase were conducted on Lutzomia rather than Phlebotomus.

66 Infection Rate Sand flies in this study carried a high, but variable infection rate. The single SF female carrying Leishmania parasites trapped in the burrows during the lushness experiment yields an estimate of the Minimal Infection Rate (MIR; see Chapter 3) of 40%. In the miniature CDC traps, with a sample of nearly 800 females, I found a very high infection rate, MIR of 20.15% in the total SF collected in CDC traps throughout 2008 season, and MIR of 44.07% when restricted to the plateau for July-October only (see Chapter 3 for details). The high values reported in the plateau can be attributed to the relatively low SF abundance in the plateau compared to the Wadi. Hence, even one infected SF can yield a high MIR (See Chapter 4 for more details).

Disease Ecology Anderson and May (1978) emphasized the ecological context in our understanding of parasitic infections. Host-parasite interactions are parallel in many ways to predator-prey interactions. Therefore, understanding host ecology is an important tool in disease control. In this study, I showed that the overall burrow activity of sand rats in the context of plant lushness associated with the burrow correlated with SF abundance over the entire study. Understanding host ecology provides us with additional ammunition on the frontiers of disease control. An example for the control of Lyme disease is the use of Permethrin- impregnated cotton (Damminix©) to reduce Acari (Ixodes scapularis), previously named, Ixodes dammini, in white-footed mice in the USA (Deblinger & Rimmer 1991). Mice removed treated fiber from the tubes and transported it to their nests. This is an example of how understanding mouse ecology enabled the development of insecticide- impregnated nesting material. In CL control, Wesserberg et al (2002) found that 65% of the P. obessus in habitats with loessal soils had Leishmania infection, while the sand rats in the sandy habitat were not infected. Therefore, they recommended to Israel Defense Forces authorities that soldiers training in the area shift their nighttime camp during periods of outdoor training period from loess to sand in order to reduce the risk for CL.

Conclusions Efficient disease control is based on a profound understanding the components of a zoonotic disease. The biology and the ecology of the host and the vector and their spatial and temporal distribution are important for planning an intervenient for reducing CL infection rates. Here, I present evidence for direct evidence for the effect of shrub lushness on host

67 activity and its indirect effect on the SF abundance through host activity level. There is no direct evidence that lushness affects SF abundance, hence, it affects SF through its effect on sand rat activity. Host activity affects SF abundance. This provides us with additional tools to explain the epidemiology of the human cases (level of active burrows near human settlement). Disease ecology has a key role in understanding the components and its attributes of this complex zoonotic system, and it is crucial for effective control methods.

68 Chapter 6: Insecticide treated Tubes 6.1 Introduction The leishmaniases, a group of diseases that currently threaten 350 million people in 88 countries, are transmitted by phlebotomine sand flies (Desjeux, 2001). The World Health Organization (WHO) estimates that over 2.3 million new leishmaniasis cases occur each year and that at least 12 million people are presently infected worldwide (WHO, 2007). Increasing risk factors like urbanization and dam building are accompanied by habitat destruction and human development in previously uninhabited areas, are making leishmaniasis a growing public health concern for many countries around the world. Certain risk factors are new, while others previously known are becoming more significant (Desjeux, 2001). The control of phlebotomine sand flies (Diptera: Psychodidae) is notoriously problematic because the breeding sites of their immature stages are mostly unknown and usually inaccessible. Sand flies, unlike mosquitoes, do not breed in water and for that reason may be found in large numbers even in arid deserts. Thus larval source reduction, which is the main approach to mosquito control, is impractical in the case of Phlebotomines (Alexander and Maroli, 2003). Therefore, innovative control methods are constantly required and tested. Recent examples are insecticide-impregnated dog collars for visceral leishmaniasis control (Maroli et al., 2001; Gavgani et al. ,2002; Maroli & Khoury,2004), and feed through insecticides in bait for rodents (Mascari et al 2007, 2008). Depending on application techniques, timing, and target species, sand flies are known to be highly susceptible to insecticides, including pyrethroids in general and permethrin in particular (Alexander & Maroli, 2003; Wilamkowski & Pener, 2003; Orshan et al., 2006; Basimike & Mutinga 1995, Aboul Ela et al 1993). Residual formulations of DDT (Hertig & Fisher, 1945; Hertig & Fairchild, 1948; Hertig, 1949) and the synthetic pyrethroid deltamethrin are commonly used against sand flies (Le Pont et al., 1989; Bermudez et al., 1991; Marcondes & Nascimento, 1993). The burrows of the fat sand rat, Psammomys obesus, provide breeding and shelter sites for phlebotomine sand flies (predominantly Phlebotomus papatasi) in many parts of the Middle East and North Africa (Schlein et al., 1982; Schlein et al., 1984; Wasserberg et al., 2002). P. obesus is diurnal, and animals spend their nights resting in burrows when sand flies start to be active. For this reason, a very high percentage of sand flies feed on P. obesus, making the cycle of transmission of L. major an exceptionally efficient one (Schlein et al., 1982; Schlein et al., 1984; Wasserberg et al., 2002).

69 The sand rat is a solitary, territorial animal that occupies a home range of approximately 10 m radius around its burrow. This burrow is typically located beneath or next to a chenopod bush (Daly and Daly 1974). Animals consume mostly plants from the family Chenopodiaceae (Daly and Daly 1973). P. obesus is a selective forager and is not attracted to artificial bait (Ashford 1999, Wasserberg et al. 2002). Therefore, bait stations for applying insecticides to sand rats do not seem a feasible option for controlling the sand fly. Because cutaneous leishmaniases of the Old World are vector-borne zoonoses with known reservoirs, we can more easily predict their outbreaks, and with some baseline data, we can take action to prevent or control outbreaks inexpensively and efficiently (Kamhawi et al. 1993). An example of such control was in a focus of CL, which emerged in a new army base constructed in the Negev Desert following the peace treaty between Israel and Egypt (1980). CL cases began occurring the following year, and soared to over 80 cases/year for the 3 years following. The disease was caused by L. major, harbored in very abundant populations of gentle jirds, Meriones crassus, located around the base and most notably in and around a garbage dump located less than a kilometer away and allowing transmission by the dense P. papatasi vector population. Schlein & Warburg (pers comm) recommended moving the dump farther away from the base, ploughing the area to destroy burrows, spraying houses with insecticides, and maintaining an adequate level of supervision and sanitation within a kilometer of the base. The number of cases decreased markedly following the implementation of these recommendations and disappeared altogether the following year (Schlein et al., 1984; Warburg pers comm; Giladi et al. 1985). Environmental modification for leishmaniasis control has been applied in and around permanent settlements (Kamhawi et al 1993), but is often extremely deleterious to the environment and impractical in many ways. Habitat destruction has been suggested before as non-insecticidal control method (Eliseev, 1980, Vioukov 1987); however, leveling a several hectare site at Tallil Air Base, Iraq and subsequently covering it with a 18"-thick layer of gravel had no impact on sand fly numbers (Dr. R. Coleman, pers comm). Habitat alternation was part of an integrated preventive techniques study conducted recently in a German army base in Afghanistan. It showed a dramatic reduction in cutaneous leishmaniasis cases in humans, decrement in sand fly numbers and complete eradication of the local rodent reservoir from the camp area, following integrated preventive techniques. These included personal protection (using skin repellents, ”long sleeves”, insecticide-impregnated uniforms, bed nets and curtains), sand fly and rodent monitoring and control (poison bait boxes for rodents and residual heat fogging for sand flies), and extended habitat sanitation. Habitat

70 modification included building a 3m stonewall surrounding the camp, removal of ≥30cm of the upper earth layer throughout the camp, soil compaction and stone paving ≥30cm depth and compaction of the 100m outside the camp, as well as regular vegetation eradication (Faulde et al 2009). Instead, I propose an alternative approach for the control of sand flies. Eliminating or reducing sand fly populations that reside inside rodent burrows may reduce in the overall burden of sand fly bites and the transmission of CL. This can be accomplished by applying insecticides to rodent fur as they enter and exit their burrows. Specifically, we can place insecticide-treated, carpet-lined walk-through plastic tubes at burrow entrances to form artificial extensions of the burrow openings. When a sand rat passes through such a tube as it enters and exits its burrow, insecticide in powdered form will be passively applied to its fur. Such a set-up will ensure that sand flies feeding on the rodents or present in the burrow will absorb the insecticide, and those attempting to exit will come in direct contact with the insecticide-treated surfaces at the burrow exit. In the harsh desert environment, direct sunlight inactivates insecticides. In this method, the insecticide will be protected from direct sunlight prolonging its active life even in arid zones. Host-targeted techniques are especially attractive as an alternative to treating large areas with insecticides because they reduce the risks of pesticide exposure for non-target species and generally require smaller pesticide quantities to be as effective as area wide application techniques. Host-targeted techniques require an efficient means of applying pesticide to the host in order to be successful, therefore, they should be based on the host behavior (foraging, nest building, self-cleaning). The most common approach is using the foraging behavior by placement of bait stations containing food and pesticides (Barnes et al. 1974; Sonenshine and Haines 1985). An example for a host targeted control method based on nest building behavior is the use of permethrin-impregnated cotton to reduce the tick vectors of Lyme disease (Ixodes dammini) on white-footed mice in the USA (Damminix©) (Deblinger & Rimmer 1991). Self-application of liquid permethrin-treated bait tubes for controlling fleas and ticks on Mexican wood rats had long-term efficacy persisting for over 7 months (Gage et al., 1997). Tubes containing liquid deltamethrin provided effective control of fleas on ground squirrels and chipmunks for 7 weeks (Bronson and Smith, 2002). Here, we exploit the burrow dwelling behavior of the sand rat to implement a host targeted control method for insecticide application.

71 6.2 Methods 6.2.1 Apparatus and methodology development in the laboratory 6.2.1.1 Assessing the effect of insecticide powdering on the behavior of Psymmomys obesus individuals. I first conducted laboratory trials to assess whether the application of insecticide powder to fat sand rats caused observable changes in rodent behavior. To do so, I quantified behavior of 2 P. obesus individuals for 3 days each prior to and 3 days following applying a powdered insecticide commonly used on household pets and containing 10% carbaryl & 0.2% permethrin (brand name Opigal made by Abik / Israel). I built an ethogram (list of behaviors) composed of 4 daily routine behavior patterns of a sand rat and added apathy to check for adverse effects of the insecticide. I recorded the behavior in the following manner: a) Number of scratches per min (1 min every 10 min) b) Self-cleaning activity i.e. licking the fore-legs and grooming (1 min every 10 min) c) Rubbing against the ground. d) Apathy (crouching in the corner of the cage) e) Food consumption (weight of leaves consumed per day - the amount of given food provided was weighed at feeding time and again the following day. For control of water loss, the same amount of food was placed in an empty cage and weighed the following day). I compared animal behavior in the first and second 3-day sequences. To apply Opigal powder to animals, individual animals were placed in a sac and weighed. I transferred each animal individually into a metal mesh cylinder (in which the animal could not turn), powdered it liberally, and released it back to the holding cage. Animals were observed for 30 min on consecutive days, during the morning hours after feeding.To control for the effects of handling, animals were placed in a bag and transferred to a cylinder without powdering and observed as described above. Experiments were conducted at room temperature. The soil in the animal cages was changed 1-2 days prior to each experiment to let the animals get used to the “new environment”.

6.2.1.2 Training P. obesus to pass through tubes I tested whether sand rats could be trained to pass regularly through narrow tubes as follows. I placed 4 wild caught P. obesus individually into rodent holding cages each linked by a PVC tube to another cage (Fig 6.1: example of 2 cages). I provided food (Atriplex leaves) in each linked cage so that the animals had to pass through the tube in order to forage. I allowed rodents to live in these cages for a minimum of three days. Rodents routinely learned to pass through the tube that links the two cages within the first day.

Fig 6.1: Double chamber apparatus comprised of 2 rodent cages connected by a 7.5 cm diameter PVC pipe

6.2.1.3 Determining the most appropriate tube length, diameter, and carpet texture Using the double chamber cage apparatus described above, I altered tube length and carpet thickness (carpet pile depth) for the tube linking the two cages. The intent was to find an appropriate tube length through which rodents would routinely pass and a tube diameter that still allowed rodents to pass while forcing them into contact with walls sufficiently to achieve powder transfer. I used standard 3" (7.5 cm) diameter PVC plumbing pipes. Different lengths were tested; 20, 30, & 40 cm. I altered tube diameter using carpeting with pile depths of 7.5 mm, 10mm, or 20mm (Fig 6.2). I placed four animals individually in apparatuses for a minimum of 10 days and monitored the crossing through the tube from one cage to the other, by quantifying the amount of food taken from the cage containing the food and by direct observation during 10 min following the tube replacement.

Fig 6.2: PVC pipes lines with different thickness carpets (20mm, 10mm and 7.5mm)

73 6.2.1.4 Quantifying the amount of powder (cornstarch) transferred from carpets and retained on the fur of individual Psammomys I placed 7 individual sand rats into a two-compartment apparatus linked by a tube. The food was given in one compartment, and animals had to pass through the tube in order to reach it. I used tubes lengths of 30cm and carpet piles of 10 mm in these trials. To quantify the amount of powder retained in the fur of animals passing through the tubes, cornstarch was used instead of insecticide powder (Figure 6.4). The texture of cornstarch powder is similar to insecticide powder, repeated exposure of the animals to cornstarch would be harmless, and the starch can be easily quantified using a simple colorimetric reaction with iodine (lugol reaction). I impregnated the carpet with powder as follows. I placed 20 g of powder in the tube, closed both ends with a plastic bag, and shook it vigorously for 2 minutes to spread the powder thoroughly. Then to remove excess powder, I placed the tube on its end over a piece of standard baking paper (38x42 cm) and tapped strongly 5 times or until no noticeable amounts of powder fell from the tube. I then weighed the amount of excess powder that fell from the tube and subtracted from total. The remaining amount is a measure of the capacity of the carpet to hold powder. In a second trial, I also impregnated the carpet with 40g of powder and shook the tube for 2 minutes to distribute the powder as before, but this time did not tap off excess powder. In both cases, I then allowed animals to pass through the tube from one compartment to the other as they chose for 1,2,3,4 or 5 days. I used a standard paintbrush (#12) pre-wetted with distilled water to quantify the amount of powder remaining on the fur of the animals at the end of each time period (number of days). I anesthetized the animals using Isoflurane, I swabbed the animal’s fur either two times over its entire body (for the 20 g treatment) or over two 3*3 cm quadrates, located on either side of the caudal part of the animal (40 g treatment). I rinsed the brush in 10 ml distilled water inside a 50 ml tube. I calculated the body surface area according to the formula of Meeh (cm2 = k* body weight (g)2/3 with k being 10.4 for rats; Dubois 1936; Spector 1956). I quantified the amount of powder collected against a standardized spectrometer calibration curve created using known amount of cornstarch for a specific reaction with Lugol

solution I2KI (Iodine-Potassium Iodide). This solution is used as an indicator to test for the presence of starches with which it reacts by turning a dark-blue/black. Iodine solutions like Lugol will stain starches due to iodine's interaction with the coil structure of the polysacharide. The darker the reading, the higher the starch concentration. Samples were read at 550 OD (Table 6.1, Figure 6.3). I obtained a total of 70 and 112 samples from fur for the 20 g and 40 g trials, respectively.

Table 6.1 Calibration Curve Building table Starch Conc. µg/ml Starch Vol µl Water Vol (µl) Lugol (µl) OD (550 nm) 0 0 1000 50 very low 5 5 995 50 0.054 10 10 990 50 0.055 20 20 980 50 0.131 30 30 970 50 0.201 40 40 960 50 0.255 100 100 900 50 0.453 200 200 800 50 0.928 500 500 500 50 2.163

Figure 6.3 Calibration Curve for known amount of cornstarch read at 550 OD

Fig 6.4 P. obesus with powder near a tube opening (lab experiment)

75 6.2.2 Methods in the field 6.2.2.1 Assessing the tube usage in P. obesus burrows Two active P. obesus burrow systems were chosen for preliminary trials (indirect observation). For each burrow, I inserted 5 tubes with plain carpet (not powdered) into burrow openings. Some of the openings were not active. Activity of the burrow openings was tracked by placing food at the burrow entrance (Fig 6.5) and by checking for animal tracks entering and exiting the opening via the tube. Due to the natural curvature of the rodent burrows, I chose to insert 30 cm or 20 cm tubes into openings, according to the field conditions. Proportion used was calculated as number of tubes used out of the total laid in the field.

Fig. 6.5: Tube with food at burrow opening

I chose four P. obesus burrow systems from 3 different plots (Mekorot, Orchard, North) for more extensive observations (direct observations). Tubes with plain carpet (not powdered) were inserted into all of the burrow openings of each system. One burrow system was found to be not active. In others, new openings were dug, and therefore, additional tubes were inserted into the active burrow openings, according to need. Both 30 cm and 20 cm tubes were used. Verification of activity of the burrows was followed by direct observations (Fig 6.6), laying food, and checking for animal tracks.

Fig.6.6: Fat Sand Rat emerging from its burrow through a tube

76 6.2.2.2 Statistical analysis I used paired t-test for comparing the ethogram results, before and after the powdering. I used 3 behavior indicators (feeding, grooming and resting), and therefore I applied a Bonferroni correction to yield a corrected critical p value of 0.0166. I used log transformation for the number of times the tubes were used, data was still not normally distributed, therefore, I conducted Kruskal-Wallis ANOVA.

6.3 Results 6.3.1 Laboratory Results 6.3.1.1 Assessing the effect of insecticide powdering on the behavior of Psymmomys obesus individuals In laboratory conditions, P. obesus powdered liberally with the insecticide Opigal did not alter their behavior (t-test for feeding, grooming and resting is –2.23, -0.75 and 3.50 and the p value is 0.076, 0.485 and 0.017, respectively, compared to a Bonferroni corrected p_value of 0.016). Animals continued to feed normally and showed no weight loss. P. obesus passed through tubes regularly for several purposes, mainly foraging, and were not deterred from using the tubes. Table 6.2 Paired t-test comparing the Psammomys behavior before and after powdering

Mean Std. Dv Diff. Std. Dv t value DF p_value CI CI Feeding Before 10.17 5.67 Feeding After 15.00 4.05 -4.83 5.31 -2.23 5 0.076 -0.74 10.40 Grooming Before 1.17 1.83 Grooming After 2.00 2.28 -0.83 2.71 -0.75 5 0.485 -2.02 3.68 Resting Before 14.33 5.96 Resting After 9.83 5.98 4.5 3.15 3.50 5 0.017 -7.80 -1.20 Other Before 4.33 3.01 Other After 3.17 3.06 1.17 2.14

77 6.3.1.2 Determining the most appropriate tube length, diameter, and carpet texture Standard 3" (7.5 cm) diameter PVC plumbing pipe, lined with 10mm carpet pile (Fig 6.2 center) was most efficacious, allowing rodents to pass, but forcing them to come in close contact with the sides of the tube. Pipes lined with 20mm pile created passageways that were too narrow, inhibiting passage; those 7.5 mm thick created excessively wide passageways, allowing rodents to pass with little contact with the sides and in a manner that would transfer little powder. Animals found no difficulty in traversing any of the examined tube lengths of 20, 30, & 40 cm and readily used all of them. I chose 30 cm tubes as most practical for the field trials (see field experiments below).

6.3.1.3 Quantifying the amount of powder (corn starch) transferred from carpets and retaining on the fur of Psammomys individuals The more powder there was on the carpet, the more that was transferred to the rodent. The net amount of powder transferred to the fur of the rodents (for all days) was larger in the

40g treatment (35.11mg/animal) compared to the 20g treatment (19.24mg/animal), t-test, t 160.7 -4.042, p<0.0001, but the variance in both groups was large, SD of 37.6 in the 40g test compared to 15.13 in the 20g test (Figure 6.7).

Figure 6.7: Average amount of powder found on animal fur, cumulative data for all days, according to initial amount of powder on rug. The initial amount 40g resulted in a larger final amount on the animal fur compared to the 20g (t-test, t160.7 = -4.042, p<0.0001).

I examined the effect of exposing the animals to the tubes for 1 day to 5 days. A significant difference was found between different numbers of days (Kruskal-Walis: 20g, H

(4,70) = 19.34, p=0.0007, 4≠1, 4≠5, 40g H (4,105) =24.33, p=0.0001 1≠2, 1≠3, 4≠2, 4≠3), no specific trend was detected over time (Figure 6.8).

78 80

20 verage finalamount fur on (mg)

A 10

0 12345 12345 Mean Mean±SE Initial amount: 20g Initial amount: 40g Days Figure 6.8: Average amount of corn starch retained on animal fur on days 1-5 of using impregnated carpet-lined artificial tunnels (Kruskal-Walis: 20g, H(4,70) = 19.34, p=0.0007, 4≠1, 4≠5, 40g H(4,105) = 24.33, p=0.0001, 1≠2, 1≠3, 4≠2, 4≠3). A. Carpets adsorbed with 20g cornstarch. B. Carpets adsorbed with 40g cornstarch

I also looked at the final amount on the animal fur relative to initial amount. The median (range) for initial amount 20g and 40g were 14.34 (2.28-65) mg and 24.12 (0.01-197.3) mg, respectively.

6.3.2 Field Results 6.3.2.1 Duration of tube usage Sand rats kept using the tubes for around one week. The proportion of tube usage (number of tubes used out of the total number of tubes inserted) over time was not significantly

different among plots F(2,11) =0.78, N.S, (Fig 6.9). A significant difference between plots was

observed regarding the number of times the tubes were used (Kruskal-Walis: H(2,16) =8.22, p=0.016, Orchard ≠ North); the animal in the north plot was more active (Fig 6.10, 6.11). Frequently, animals dug new openings to bypass the tubes, with difference among plots

(Kruskal-Walis: H(2,16) =8.22, p=0.0163, Orchard ≠ North), more new exits were dug in the Orchard Plot (Fig 6.12). Most importantly, sand rats in the field passed in and out of their burrows through the tubes inserted into their burrow opening for several days. Plot had a significant effect on the rate at which the tubes were used (p=0.016), with usage in the “North” plot being the highest.

79 New diggings were observed at different days during the observation period, while the “Orchard” plot had the highest number of new exits dug (p=0.016).

Proportion of tube usage with time

0.9 Mekorot 0.8 Orchard 0.7 North 0.6 0.5 0.4 0.3 0.2 0.1 0 Proportion of tube usage of tube Proportion 1234567 Days

Fig.6.9: Proportion of tube usage (tubes used out of total inserted) over time, in different plots. No significant difference was observed (F(2,11)=0.78, N.S).

2.0

1.8

1.6

1.4

1.2

1.0

0.8

0.6 Log tube usage

0.4

0.2

0.0

-0.2 Mean Mekorot Orchard North Mean±SE Plot Fig.6.10: Average number of times the tubes were used per day in different plots (log transformed) Kruskal-Walis: H(2,16)=8.22, p=0.016, Orchard ≠ North

80 40

Mekorot 30 Orchard North

20 Tube usage Tube 10

0 1234567 Day

Fig.6.11: Number of times the tubes were used per day in different plots in standard time of 3 hours over time

4 Mekorot Orchard 3 North

1 Number of new exits Number

0 1234567 Day

Fig.6.12 Number of new exits dug over time in different plots (Kruskal- Walis: H(2,16) =8.22, p=0.016, Orchard ≠ North).

6.4 Discussion The results of these experiments indicate that P. obesus individuals can pass through carpet-lined tubes easily and are willing to use them in the field for several days. This method of passively applying insecticide powder has the potential of being an effective tool for leishmaniasis control. In laboratory trials, P. obesus individuals passed routinely through tubes connecting two compartments of a laboratory apparatus, and traversed the tubes even when not obviously

81 foraging. The tubes are dark and of an approximate diameter of their natural burrows. In addition, the animals may perceive the tube as a refuge. Field observations confirmed that the animals are willing to use the tubes even for periods of one week. Opigal appears to be a suitable candidate for testing the efficacy of self-dusting in the field. First, P. obesus do not show any obvious side effects of being powdered liberally with Opigal. Lack of insecticide toxicity of the target host is the most important pre- condition for using insecticide treated tubes in the field. The toxicity and efficacy of surface-sprayed insecticides have been tested before, and Permethrin, one of the active components of Opigal, is effective against sand flies (Alexander & Maroli, 2003; Pener & Wilamkowski 1987; Aboul Ela et al 1993; Basimike & Mutinga 1995). The efficacy of topically applied powders, like Opigal against sand flies has not been documented and needs to be addressed prior to field experiments. Other means of host-targeted sand-fly control strategies may also be applicable. Feed- through insecticides such as Ivermectin and Novaluron, excreted in rodent feces, have been shown to be effective to control immature sand flies (Mascari et al 2007, 2008). These agents might be spread as jell or powder inside the tubes and would be taken up by the rodent through its grooming behavior. The tubes developed here can allow a cost effective and low impact way to control sand flies. Allowing the use of small doses of an insecticide that is toxic only to the vector and can be targeted only to sand rat burrows means that control efforts can be less of a burden to the environment and to public health budgets. Treating wild rodent hosts with effective amounts of pesticide is usually a challenge. This has previously been achieved for other hosts mainly using bait tubes, but also using permethrin-treated cotton balls. Here I present a passive self- application tool for Leishmania control that is host specific and avoids problems arising from the food selectivity of the host, by taking advantage of burrowing habits. Increasing our ecological knowledge of the host is a key element for developing such novel control approaches. How should tubes such as those presented here be designed? The most appropriate tube diameter together with the proper carpet should allow uninhibited passage of sand rats through the tube on the one hand, but be sufficiently narrow to ensure that animals rub against the carpet and thereby dust themselves with insecticide (Fig 6.2 center) on the other. The more comfortable and suitable the tubes, the more likely the sand rats will be to use them in the field. This combination was achieved by using standard 3” PVC plumbing pipes (7.5 cm), 30cm in length, lined with 10mm deep carpet. My experience showed that the maximum tube length

82 that can be conveniently fitted to the burrow should not exceed 30 cm. Longer tubes were harder to conceal and tended to protrude upwards above the soil. In addition, many burrows were curved, making the insertion of long, straight tubes impossible. Efforts to affect sand fly control using these tubes should be limited to short periods. First, their use requires much effort since replacing the carpets or the whole tube frequently can be tedious; it should be aimed mainly at burrows in the vicinity of human dwelling, or in areas with high vector abundance. Second, temporal variation in the vector peak season has been demonstrated locally in this work (see chapter 3), as well as in the past in different arid zones in Israel (Wasserberg et al 2003, Janini at al 1995), and elsewhere (Brinston 1992 et al, Morrison et al 1995). Hence, using such a control method should take into account the specific SF temporal pattern of each region. A major hurdle yet to be overcome is the variability in the amount of powder retained by the animals’ fur. This variance is very high, regardless of the number of days that animals use the tube. Lack of consistency may reflect flawed methodology in quantifying powder transfer. If so, then the collection method as well as the reading methods should be improved due to large differences between repetitions (times 0.03-times 62.6), and problems with interference with the reading from dust on the fur of the sand rats. The colorimetric reading was accurate (as shown in Fig 3.6), but the other components still need to be improved. The next stage should be field trials to quantify the residual amount of insecticide transferred to rodents using tubes under field conditions (decay curve) to derive estimates for carpet/ tube replacement schedule. For example, in California, modified bait tubes containing liquid deltamethrin maintained effective control for ground squirrels and chipmunks against fleas, vector of plague; seven weeks following the deployment of the first tubes, there were 29% flea-infested rodents in the treated site compared to 50% in the untreated site (Bronson and Smith, 2002). If the few weeks lag between the peak in sand fly numbers and the acquisition of the peak Minimal Infection Rate found in Sede Boqer is representative (see chapter 4, 2008 data), several weeks of protection starting just before the peak sand fly season might be beneficial to leishmaniasis control, especially if it is targeted near human dwelling or in high vector abundance areas. Use of tubes in the field should be accompanied by monitoring of the tubes. Field observation of tubes placed in the field showed new digging appearing after several days. Therefore, field surveys should be performed weekly to assess the percentage of active burrow openings containing tubes, to insert new ones as needed, or to plug new burrow openings to force animals to use openings in which a tube has been placed.

83 This would increase the control efficiency, without much additional effort. To summarize, passive self-application achieved by allowing host individuals to pass through insecticide treated tubes may serve as a future control method of CL in the field. Using the host as a target for sand fly control is feasible in sand rats in part because, according to behavioral indicators used in the experiment, the sand rat does not show adverse behavioral changes in response to insecticide powder, but mostly because animals are willing to use the tubes in the field for several days.

84 Chapter 7: General Discussion "What are the relative contributions of ecology and evolution to the emergence of disease? ...It is clear that both can contribute to emergence (and re-emergence) of infectious diseases. However, despite the potential for rapid genetic changes... ecological change appears to be more general explanation for new epidemics" (Schrag and Wiener 1995). Our knowledge in ecology can be a useful tool for a deeper understanding of zoonotic systems. Mills and Childs (1998) emphasized the importance of reservoir studies as an essential component of any integrated public health response to established or emerging zoonotic diseases. They suggest several steps for merging appropriate field data into predictive models for disease control to assist health authorities in facing disease outbreaks. Ecological knowledge can point us to the most beneficial interventions in time and space for breaking the transmission cycle. This study focuses on Cutaneous Leishmaniasis (CL), which is highly endemic in the Jordan Valley, the Arava, and the Negev Desert in Israel. In the Negev, this zoonotic disease is caused by the intracellular parasite Leishmania major and transmitted by the vector sand fly Phlebotomus papatasi; the main reservoir host species is the fat sand rat Psammomys obesus. Risk factors for CL in this region include environmental and ecological aspects. Expanded civilian and military development in the Negev in the last decades that locally increased the soil moisture have likely caused a marked increase in CL incidence. Wasserberg et al (2003b) demonstrated that the addition of water to the soil improved vector-breeding conditions, increased host infection prevalence, and promoted larger host populations by improving the food quality. These effects amplify disease transmission risk to humans. Other findings were local differences in the sand rats’ CL prevalence rate and in the sand fly abundance between different habitats in the same region. Also, optimal density-dependent habitat selection behavior on the part of host individual sand rats lead to seasonal shifts in habitat selection and burrow occupancy (Shenbrot 2004) and may have an effect on the vector sand flies and on disease transmission. The goal of this study was to examine field processes affecting the CL transmission cycle and to assess their environmental risk factors while implementing an ecologically founded approach to understanding and analyzing the zoonotic disease, CL. I achieved this by examining several features: 1.Host and vector population dynamics- I followed the sand rat and sand fly populations in the Sede Boqer area, including field censuses. I looked at seasonal changes in habitat selection behavior of the fat sand rats and whether they coincide with peak

85 abundance of sand flies. 2. Disease prevalence- I studied disease prevalence in host, vector, and humans in the Sede Boqer area and compared them to the host and vector population dynamics and infection rates. 3. I tested whether experimental reduction of plant lushness would decrease the activity of both host and vector and the likelihood of burrow occupancy since plant lushness is a risk factor previously reported to be correlated with sand rat abundance. In addition, I tested the feasibility of using rodent hosts as carriers of insecticide into the burrows as a means for reducing sand fly abundance thereby reducing disease transmission. Pest management for small mammals often meant in the past the killing of as many individuals of the relevant pest species as possible, with the goal of total eradication of the entire population. This approach is unsustainable and in the long run, ineffective, in particular in outdoors areas like natural, agricultural, or urban environments where total population extermination may not be feasible (Monard et al 2006). Singleton (1997, 1999) suggested a different approach and termed the concept of Ecologically-Based Rodent Management (EBRM). This approach starts from understanding the pest rodents' biology (ecology, physiology, taxonomy) and the damage they cause, and then tries to identify related ecological factors. Usually, these factors are related to rodent abundance, and EBRM will investigate the best current effect on rodent abundance regarding damage, in this case the damage is disease transfer to humans. In the CL case, the population ecology of the fat sand rat is a relevant tool. In this study, I collected infection rates from the sand rat host, but these did not provide a good indicator of infection rates in human cases. Instead, sand rat activity pattern, which was positively correlated to the sand fly vector abundance, was a better tool for understanding the disease dynamics. Here, I emphasize the key role of sand rat population dynamics. This provides a simple and useful tool for monitoring the spatial and temporal dynamics of the disease – the field survey. A regular burrow survey in the vicinity of human settlements that also quantifies the state of the plants associated with burrows can reveal a snap shot of the host population and hence provide an important informative element in the disease dynamics. The survey can also be the basis for control efforts. Disease control methods should be applied on active burrows, taking into account the particular temporal and spatial dynamics. For example, the survey can provide the information necessary for deciding when best to insert insecticide treated tubes into which burrows. We can understand better the dynamics of both the host and the vector from density- dependent habitat selection and the Ideal Free Distribution (IFD). In this research, I used isodar theory, developed by Morris (see chapter 2 for details) and based on the IFD, for detecting

86 habitat selection by the reservoir species, the sand rats. IFD is well studied in rodents, including for some host species. But the IFD has rarely been applied to insect vectors. Kelly and Thompson (2000) incorporated the IFD theory into modeling of insect vectors, claiming that vectors must have evolved to choose the least defensive hosts in order to maximize their feeding success. They showed that the predicted distribution of insects over hosts may be heterogeneous within and between host species; and that the intensity of heterogeneity varies with host and vector density. In some cases this may lead to non-linear relationships between the host and the vector. Their model can shed light on disease transmission via predicting the effects of ecological parameters such as seasonality and vector density, or on disease control via vector control. Limitations of the study include small sample size like in the lushness experiment, and relatively small number of Anabasis bushes compared to Atriplex. Ideally, I would choose Anabasis to represent the open habitat and Atriplex to represent the closed one, but unfortunately, I did not find enough Anabasis plants in the area. Another limitation was finding undisturbed habitat in which I could conduct manipulations. The Wadi is a natural reserve in which I could not conduct manipulations. Also, most other areas were disturbed in one way or another or did not have enough active burrows to begin with. Regarding the lushness experiment, I had to face several limitations. One is the use of emergence traps that had low effectiveness and therefore led to a reduced the number of trapped SF. However, using emergence traps provided the only practical way to monitor a single burrow. The other one, was needing to cut the bushes for the lushness experiment during winter without being able to monitor them for several months prior to the manipulation. I had to cut them before the active season to increase sample size on one hand and to give the system time to adjust on the other hand, since I did not expect to see an immediate response. Another limitation is the long elapsed time (sometimes as much as 3 years) from when the skin sample was taken until it was analyzed. This may have contributed to some negative results in the PCR. Finally, in regards to the tubes experiment, the human component was dominant. One possible cause for the large variance in the amount of powder was the difficulty in standardizing the method, and possibly the difference between the individuals in preparing the tubes. Other study systems can provide insights applicable to CL. In Lyme disease, based on models and literature reviews, Keesing et al (2006) suggest that high host diversity is more likely to decrease than increase disease risk. This outcome is particularly likely when pathogen transmission is frequency-dependent, when it is greater within species than between species, and especially when the most competent hosts are also relatively abundant and widespread.

87 Kitron (1998) demonstrated the use of GIS, GPS, satellite imagery, spatial statistics, and the landscape ecology-epidemiology approach and their applications to vector-borne diseases including malaria, Lyme disease, and encephalitis in various countries. The reduction of Plague, caused by Yersinia pestis, by using host-targeted bait tubes for control of fleas on sciurid rodents (Bronson and Smith 2001), was the inspiration for the insecticide-tubes experiment for leishmaniasis control (Chapter 6). Successful combined efforts to achieve maximum protection against CL as reported by Foulde et al (2009) may contain several relevant components. These may include personal protection (topical repellents, insecticide-impregnated clothing), human dwelling protection using insecticide-impregnated bed nets and curtains, sand fly and rodent monitoring and control (such as in this research), extended habitat sanitation and alternation (earth removal up to 30cm depth, soil compaction, stone paving to a depth of ≥30cm, regular vegetation eradication), and health education. Some of these are less applicable in a civilian environment (i.e. insecticide-impregnated clothing). Also, radical habitat modifications are often not feasible. In the case of the Wadi in my study, it is not feasible because it is a nature reserve; in the case of the plateau, it may not be cost-effective, especially for a disease that is not life threatening. Lambrechts et al. 2009 advocate a shift in priorities to include simultaneous development of multiple, alternative control strategies. The knowledge across relevant diseases and disciplines should be better integrated, and disease prevention efforts should involve private industry, ministries of health, and local communities. To obtain information of more immediate significance to public health, the research focus must shift from laboratory models to natural pathogen-transmission systems. Identification and characterization of heterogeneities inherent to VBD systems should be prioritized to allow development of local, adaptive control strategies that efficiently use limited resources. There is a need for cooperation between different authorities, using an interdisciplinary approach. Such cooperation regarding the Leishmania monitoring and control has begun recently in Israel. The program includes the Ministry of Environmental Protection, the Ministry of Health, The Nature and National Parks Authority, and the Israel Defense Forces (Medical Corps). The program is being carried out in L. tropica and L. major foci all over the country. In addition to investigation of the human cases, two field inspectors collect samples from potential reservoir animals and sand flies. The samples are sent to the laboratory for Leishmania detection, sand flies are identified to the species level, and data regarding the source of blood meals are collected. By including surveillance of potential host reservoir

88 animals, sand fly vectors, and human cases, this program may help increase understanding of the epidemiology and employ environmental interventions to reduce disease transmission to people. Studying such a zoonotic system comprised of several taxa (rodent host, insect vector, and protozoan parasite) and including several levels of investigation and various temporal and spatial scales demands an interdisciplinary approach. I used ecological tools including long- term population dynamics in time and space and habitat selection, and collected environmental data. I also incorporated observational with experimental approaches and molecular and epidemiological tools. In addition, I studied an applied aspect of using the host as a carrier of insecticide into its burrow as a method for targeted disease control. We are making much progress in understanding the link between ecology and disease dynamics, but controlling the disease is still a challenge and a financial burden to the health authorities and a health burden on the populations that it affects. An interdisciplinary approach is the key for effective control and for understanding the transmission cycle as a whole rather than a narrower understanding regarding only a single component of the study system.

89 Appendix 1: Plots Maps with burrows

Orchard

90 Mekorot

91 Haroah

92 North

93 Wadi

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104 תקציר רקע: מחלת הליישמניאזיס העורית (ל"ע), הקרוייה גם "חבורת יריחו" הינה מחלת עור זואונוטית הנגרמת על ידי טפיל חד תאי מהסוג Leishmania. בישראל, ל"ע נגרמת על ידי שני מינים, Leishmania major ו- L. tropica. ל"ע נפוצה באזורים צחיחים וצחיחים למחצה, בארץ, L. major נפוצה באזור הנגב, הערבה ובקעת הירדן. זהו טפיל של מכרסמים המועבר על ידי זבוב החול Phlebotomus papatasi. זבובי החול ניזונים מצוף, אך הנקבות ניזונות גם מדם החיוני ליצור הביצים לרבייה. מאכסן המאגר העיקרי של L. major במזרח התיכון הוא פסמון המדבר Psammomys obesus. הפסמון הוא מכרסם פעיל יום, מין שכיח במדבריות ישראל, הניזון בעיקר משיחים ממשפחת הסלקיים, מחילתו רבת הפתחים נמצאת לרוב מתחת או בסמוך לשיח סלקי. הצטברות חומר אורגני במחילה, והתנאים הממוזגים בתוכה יוצרים תנאים המועדפים על ידי זבוב החול ודגם התפוצה המקובץ (aggregated distribution) של הפסמונים, מגביר את הפצת המחלה. מטרת עבודה זו הייתה בדיקת הקשר בין משתני סביבה לדגמי תפוצה ושפע של מיני המאגר, הטפיל והוקטור תוך יישום גישה המתבססת על כלים אקולוגיים להבנה וניתוח המחלה הזואונוטית, ל"ע. שיטות: 1. דינמיקת אוכלוסיות המאכסן והוקטור וגורמים סביבתיים רלוונטיים: עקבתי אחר אוכלוסיות הפסמונים וזבובי החול באזור שדה בוקר, באמצעות לכידות תקופתיות. בחנתי שינויים עונתיים באוכלוסיות אלה, התנהגות בחירת בית גידול בקרב פסמונים באמצעות איזודרים, והאם היא נמצאת במתאם עם שיא השפע של זבובי החול. המחקר נערך באזורים מופרעים וטבעיים ובבית גידול פתוח וסגור (עשיר בצמחיה). נמדדו הגורמים הבאים: רמת פעילות הפסמונים (רמת המחילות הפעילות), מין הצמח מעל המחילה, מצבו הפנולוגי של הצמח, הלחות היחסית ומשרע הטמפרטורות במחילה. 2. היארעות המחלה: חקרתי את שעורי הנגיעות במאכסן ובוקטור. אספתי מידע על דווחי מקרי ל"ע בבני אדם, באזור שדה בוקר והשוויתי אותם לדינמיקה של אוכלוסיות המאכסן והוקטור ולשעורי הנגיעות שלהם. 3. השפעת עסיסיות הצומח: גורם סיכון שדווח בעבר כקשור להמצאות המחלה. בניתי ניסוי הבודק האם הפחתה מכוונת של עסיסיות הצומח משפיעה על פעילות המאכסן והוקטור ועל אכלוס המחילה. 4. צינורות המטופלים בחומרי הדברה: בדקתי את ישימות השימוש במאכסני המאגר כנשאים של חומר הדברה לחרקים אל תוך המחילות כאמצעי להפחתת שפע זבובי החול, ובכך, לצמצום העברת המחלה. פיתחתי כלי בצורת צינור שניתן להחדרה לפתחי מחילות פסמונים, ומכרסמים מסוגלים לעבור דרכו. כלי זה תוכנן לצורך העברה פסיבית של חומר הדברה על ידי מעבר של מכרסמים. תוצאות: 1. דינמיקת אוכלוסיות המאכסן והוקטור וגורמים סביבתיים רלוונטיים: מצאתי מגמה המצביעה של שיעור גבוה יותר של מחילות פעילות בבית הגידול הפתוח, החלקה ובית הגידול השפיעו על שיעור המחילות הפעילות. בניתוח האיזודרים מצאתי כי הפסמונים מראים התנהגות בחירת בית גידול תלוית צפיפות (density dependent habitat selection), מעדיפים את בית הגידול הפתוח במישור צין, אך לא בואדי. ממצא זה היה נכון לכל אורך השנה, אך במיוחד בעונות החורף והאביב. בדינמיקת האוכלוסייה של הפסמונים בזמן ובמרחב, מצאתי דומיננטיות של שיחי מלוח בקרבת מחילת הפסמון, גם בבית הגידול הפתוח. מצאתי קשר חיובי בין מצב הצומח לרמת המחילות הפעילות. הצגתי את הפעילות העונתית של זבובי חול במשך שנתיים רצופות. שיא הפעילות ב- 2007 היה באוגוסט ואילו ב- 2008 ביוני. ב- 2008 לכדתי יותר מפי 3 זבובי חול, עם יותר זכרים באופן יחסי לעומת שנת 2007. מצאתי הבדלים מקומיים גדולים בין מישור צין לואדי. שפע זבובי החול היה רב יותר בואדי, עובדה המרמזת על דינמיקת מקור-מבלע (source-sink) כשהואדי משמש כמקור (source) ומישור צין כמבלע (sink). בשתי שנות המעקב, בואדי היה זמן שיא פעילות אחד, ובמישור צין היו שני שיאי פעילות ב- 2008, ביוני ובספטמבר. כמו כן, מצאתי מתאם חיובי בין צפיפות הפסמונים וזבובי החול (ביולי ואוקטובר). 2. היארעות המחלה: בשנים 2008-2007, שיעור היארעות ל"ע באזור שדה בוקר (קיבוץ שדה בוקר ומדרשת בן גוריון) עמד על 2164-721 ל- 100,000 (24-4 מקרים לישוב בשנה). שיעור הנגיעות הכולל בקרב הפסמונים היה ,9.06%, כשבואדי הוא היה הגבוה ביותר (22%). נמצא מתאם חיובי בין הנגיעות למשקל גוף גבוה. מספר הפרטים שנלכדו בעונה (40-13) ושיעורי הנגיעות (26%-0) השתנו באופן קיצוני בין השנים. ב- 2007, שיעור הנגיעות המזערי (MIR) בקרב זבובי החול היה 2.3% עם אצוה אחת חיובית מהואדי באוגוסט. ב- 2008, שיעור הנגיעות המזערי (MIR) של כל אוכלוסיית זבובי החול היה 23.92%, עם 24.88% בואדי ו- 19.86% במישור צין. מצאתי עיכוב של מספר שבועות בין שיא הפעילות לשיא הנגיעות. 3. עסיסיות הצומח: עסיסיות הצמח לא השפיעה באופן ישיר על שפע זבובי החול. הייתה לה השפעה חיובית על פעילות הפסמונים, וזו עשויה להשפיע על שפע זבובי החול. מספר זבובי החול שנלכדו היה קטן, ולכן יש להיות זהירים בניתוח תוצאות ניסוי זה. 4. צינורות המטופלים בחומרי הדברה: פסמונים עוברים בחופשיות בצינורות המרופדים בשטיחים ועברו דרכם גם בשדה במשך כשבוע. בדרך זו, מכרסמים יכולים להתאבק באופן פסיבי בחומר הדברה. שיטה זו של העברת חומר הדברה באופן סביל אל המחילות, עשויה לשמש בעתיד כאחת משיטות בקרת המחלה. המלצות לבקרה: 1. דינמיקות אוכלוסיית הפסמונים כפי שהוצגה על ידי נוכחות המכרסם במחילות יכולה לשמש ככלי לבקרת ל"ע, באמצעות סמנים לפעילות המאכסן. שפע המאכסן והוקטור קשורים זה בזה. כמו כן, סקרי מחילות בשדה הם קלים לבצוע ובעלי עלות נמוכה. 2. שיעור ההיארעות בבני אדם נמצאו כקשורים לשעורי הנגיעות בוקטור, ולא במאכסן. נמצא עיכוב של מספר שבועות בין שיא הפעילות של זבובי החול, לשיא הנגיעות שלהם. עיכוב זה מאפשר חלון זמן חשוב מבחינת מאמצי בקרת המחלה. 3. האבקה עצמית של פסמונים באמצעות צינורות המטופלים בחמרי הדברה היא יישומית. צינורות אלה יכולים לשמש בעתיד כאמצעי לבקרת המחלה. מילות מפתח: ליישמניאזיס עורית, זואונוזיס, בקרת מחלות, דינמיקה של אוכלוסיות, אקולוגיה של מחלות, פסמון המדבר, זבוב חול, ליישמניה מייג'ור, נגב, ישראל.

גורמי סיכון סביבתיים לתחלואה בלישמניה עורית בנגב, ובדיקת אפשרויות הדברת המחלה

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גורמי סיכון סביבתיים לתחלואה בלישמניה עורית בנגב, ובדיקת אפשרויות הדברת המחלה

מחקר לשם מילוי חלקי של הדרישות לקבלת תואר "דוקטור לפילוסופיה"

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אישור המנחים ______

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העבודה נעשתה בהדרכת

פרופ' ברט קוטלר, מרכז מיטרני לאקולוגיה מדברית המכונים לחקר המדבר ע"ש יעקב בלאושטיין, קמפוס שדה בוקר אוניברסיטת בן גוריון בנגב

פרופ' אלון ורבורג, המחלקה לפרזיטולוגיה מרכז קובין לחקר מחלות מידבקות האוניברסיטה העברית, בית הספר לרפואה הדסה ירושלים