Bartonella spp. Infections in Rodents of Cambodia,Lao PDR,and Thailand: Identifying Risky Habitats

Abstract

This study investigated the type of environmental habitat that may explain the infection of 1176 individuals from 17 rodent species by Bartonella species in seven sites in Cambodia, Lao PDR, and Thailand. No effects of host sex and host maturity on the level of individual infection by all Bartonella spp., but significant effects of locality, season, and host species were observed. The patterns differed when investigating the three more prevalent Bartonella species. For B. rattimassiliensis, season and habitat appeared to be significant factors explaining host infection, with higher levels of infection in wet season and lower levels of infection in rain-fed field, dry field, and human settlement habitats compared to forest habitat. The infection by B. queenslandensis was found to vary, although not significantly, with season and locality, and Bartonella n. sp. (a species mostly associated with Mus spp.) was found to be more prevalent in the wet season and dry field habitat compared to forest habitat. We discuss these results in relation to rodent habitat specificity.

Introduction

Members of the genus Bartonella are Gram-negative bacteria belonging to the class Alphaproteobacteria. Several species have been implicated as causing human diseases, ranging with short-term fever to severe endocarditis. Reports of Bartonella infections in humans have dramatically increased in Southeast Asian countries (Bhengsri et al. 2010, Kosoy et al. 2010, Pachirat et al. 2011, Bai et al. 2012). In Thailand, studies have reported human infections with B. elizabethae, B. henselae, B. tamiae, B. rattimassiliensis, B. tribocorum, and B. vinsonii subsp. arupensis (Kosoy et al. 2008, Paitoonpong et al. 2008, Kosoy et al. 2010). However, the identification of rodent reservoirs and particularly the role played by habitats in their infection are investigated far less (Castle et al. 2004, Bai et al. 2009, Jiyipong et al. 2012).

Southeast Asia is a recognized hotspot of both biodiversity at threat (Wilcove et al. 2013) and emerging infectious diseases (Coker et al. 2011), with biodiversity loss identified as a likely explanatory factor for the increase in zoonotic disease outbreaks in the region (Morand et al. 2014). Ongoing land use changes that characterize Southeast Asian countries may affect the transmission of rodent-borne diseases, and among others of Bartonella species, and then the risk of transmission to humans. The investigation of rodent infection in different habitats at different locations in Southeast Asia makes it possible to evaluate the particular effect of habitat, taking into account the influence of season and species.

The aim of this study was to determine the type of environmental habitat that may explain the infection of rodents by Bartonella species. For this we used a subset of the screening data of Jiyipong et al. (2012), who investigated the diversity of Bartonella infections from rodents and shrews that were trapped from seven localities (and four major habitats) in Cambodia, Lao PDR, and Thailand. Three zoonotic species, B. elizabethae, B. rattimassiliensis, and B. tribocorum, were found in the study of Jiyipong et al. (2012).

Material and Methods

Trapping protocol

Seven different localities were chosen in three countries (Thailand, Cambodia, and Lao PDR), as offering a representative overview of the various ecosystems that are affected by land use changes (see map in Jiyipong et al. 2012). These sampling sites were part of the CERoPath project (www.ceropath.org). Four main habitats were investigated in each locality. At each site, 30 lines of 10 traps were installed over four nights targeting three different habitats, i.e., forests, nonflooded lands (agricultural lands such as orchards, dry rice field, cassava field, or noncultivated land such as bush), and flooded-irrigated agricultural lands (i.e., paddy rice fields), for a total of 1200 night-traps and for each season (wet and dry). Villages and isolated houses, which corresponded to a fourth habitat category, the human settlement, were also sampled using cage-traps distributed to residents (with around 300 hundred traps, corresponding to similar trapping pressure as the other habitats).

Pictures, a habitat description, and coordinates of the trap lines are available in the “research/study” areas and “research/protocols” sections of the CERoPath project web site (www.ceropath.org). Two trapping sessions were realized per locality at the wet and at the dry season characterized according to the average rainfall recorded during the month of trapping and the former month, and provided by the Global Precipitation Climatology Centre (GPCC, http://gpcc.dwd.de). The trapping sessions were conducted during the years 2008–2009. For logistical reasons, some localities were investigated in wet season one year and the dry season the other year.

Rodents were identified on the basis of their morphology or using species-specific primers and/or barcoding assignment (Chaval et al. 2010). Complete data for animals used as a reference for barcoding assignment can be consulted in the “Barcoding Tool/RodentSEA” section of the CERoPath project web site (www.ceropath.org).

Rodents were euthanized and dissected to collect blood and organs following the CERoPath protocols (Pagès et al. 2010, Herbreteau et al. 2011) (www.ceropath.org), which respect animal care, health security for field parasitologists, and quality data handling. Blood samples were collected by cardiocentesis, and stored at −80°C before shipment on dry ice to the URMITE CNRS-IRD UMR laboratory in Marseille, France.

Bartonella infection and identification

In the study of Jiyipong et al. (2012), DNA of blood samples was extracted from 1341 individuals, belonging to 19 rodent and shrew species, and screened using real-time PCR targeting the 16S–23S ribosomal RNA intergenic spacer region (internal transcribed spacer, ITS). Blood samples were also tested by culture method on Columbia agar supplemented with 5% sheep blood and incubated at 37°C in 5% CO₂ for up to 4 weeks. A single colony for each positive sample was picked, and its DNA was extracted and identified species using standard PCR amplification and a sequencing targeting two housekeeping genes (gltA and rpoB) and the ITS fragment (Roux and Raoult 1995, La Scola et al. 2003). Species were identified by comparing sequence data with the sequences of the Bartonella reference strains, which were retrieved from GenBank using ClustalW (see Jiyipong et al. 2012).

We limited our analyses to the 1176 individuals (of 1341) that were trapped according to the standardized protocol (excluding the individuals trapped by local hunters) and of 17 (19) rodent species that were identified to species level (and removing shrew species). Table 1 summarizes the species and number of rodents by locality and habitat where Bartonella species were found.

Table 1.

List and Number of Host Species Collected in Seven Localities of Cambodia, Lao PDR, and Thailand, with Number of Individuals from Rodent Species Trapped in Four Main Habitats (Forest, Rain-Fed Lands, Nonflooded or Dry Lands, Settlement) and Infected by Bartonella Species ^a

Country	Locality	Habitat	Host species	n	Bartonella n. sp.	B. rattimassiliensis	B. queenslandensis	B. tribocorum	B. coopersplainsensi	B. elizabethae	B. phoceensis
Thailand	Buriram	Forest	M. cookii	1
			R. tanezumi	5		3
		Rain-fed	B. indica	2
			Bandicota savilei	21
			M. caroli	13
			M. cervicolor	25
			R. argentiventer	2
			R. exulans	1
			R. sakaretensis	2
			R. tanezumi	9					1		1
		Dry land	B. indica	1
			R. tanezumi	6		2
		Settlement	R. exulans	67
			R. tanezumi	13
	Loie	Forest	B. berdmorei	1
			M. cookii	4	2
			N. fulvescens	5
		Rain-fed	M. caroli	3	1
			M. cervicolor	2	2
			R. sakaretensis	35
		Dry land	B. savilei	2
			B. berdmorei	2
			B. bowersi	1
			M. surifer	4
			M. caroli	2	1
			M. cervicolor	18	7
			M. cookii	6	2
			N. fulvescens	1
			R. tanezumi	1
		Settlement	R. exulans	18				1		1
	Nan	Forest	B. indica	6
			B. berdmorei	1
			B. bowersi	1
			M. cookii	2	1
			R. tanezumi	1
		Rain-fed	B. indica	32			2	1
			M. caroli	3
			M. cookii	3
			R. tanezumi	6
		Dry land	B. indica	3
			B. berdmorei	1
			M. caroli	1
			M. cervicolor	3
			M. cookii	15
			R. tanezumi	7			1
		Settlement	B. indica	3						1
			B. berdmorei	5			1
			B. bowersi	1
			R. exulans	50
			R. tanezumi	4		1	1
Cambodia	Mondolkiri	Forest	B. berdmorei	1
			Leopoldamys edwardsi	2
			M. surifer	31
			N. fulvescens	7			1
			R. tanezumi	12		1	1
		Rain-fed	B. indica	1
			B. savilei	32		4			2
			R. exulans	1
			R. tanezumi	1			1
		Dry land	B. indica	2
			B. savilei	37		1
			B. berdmorei	3
			M. surifer	2
			N. fulvescens	2
			R. tanezumi	7			1
		Settlement	R. exulans	45
			R. tanezumi	19		2
	Sihanouk	Forest	B. berdmorei	1
			M. surifer	23			1
			N. fulvescens	1
			R. argentiventer	1
			R. tanezumi	6		1
		Rain-fed	B. berdmorei	2
			R. argentiventer	14		1			1
			R. exulans	2
			R. norvegicus	3
			R. tanezumi	25
		Dry land	B. berdmorei	3			1
			M. surifer	33
			R. argentiventer	5
			R. exulans	4
			R. tanezumi	16		1	3
		Settlement	M. surifer	4
			R. argentiventer	19			1
			R. exulans	61			1	1		2
			R. norvegicus	18
			R. tanezumi	25		2	3	1
Lao PDR	Champasak	Forest	B. berdmorei	1
			M. surifer	1
			R. tanezumi	3
		Rain-fed	B. indica	1
			B. savilei	14			1
			B. berdmorei	1
			M. caroli	1
			R. exulans	16
			R. sakaretensis	5
			R. tanezumi	1
		Dry land	B. berdmorei	2
			R. sakaretensis	3
			R. tanezumi	1
		Settlement	M. surifer	2
			N. fulvescens	1
			R. exulans	81
			R. tanezumi	7
	Luang Prabang	Forest	M. cookii	3
			R. andamanesis	3		1
			R. nitidus	2
			R. tanezumi	35		5		3
		Rain-fed	M. caroli	2
			M. cookii	2	1
			R. nitidus	3
			R. tanezumi	3
		Dry land	B. indica	5
			B. berdmorei	1			1
			B. bowersi	2
			L. edwardsi	1
			M. caroli	10	1
			M. cookii	48	16
			R. nitidus	1
			R. tanezumi	5		4	1		1
		Settlement	M. caroli	5
			R. andamanesis	2
			R. tanezumi	10

These data are extracted from Jiyipong et al. (2012).

Statistical analysis

We performed generalized linear models (GLM), using a binomial distribution of individual host infection and logit function, to identify the likely variables that might explain the infection of rodents by Bartonella spp. in the R software. Selection of the best model was based on the Akaike information criterion (AIC) using host species, habitat, locality, season, maturity, and sex as independent variables. These localities represented a variety of habitats in relation to human pressures and land usage. Habitats were ranked as: (1) Forests and mature plantations, (2) nonflooded lands or fields (shrubby wasteland, young plantations, orchards), (3) rain-fed and irrigated lowland paddy rice fields (cultivated floodplain), and (4) settlement and households (in villages or city), which corresponded to an increasing gradient of human-dominated habitats.

We performed GLM analyses on all Bartonella spp., and on each of the three more prevalent species: Bartonella n. sp. (a new species mostly found associated with humans; see Jiyipong et al. 2012 ), B. rattimassiliensis, and B. queenslandensis. For the three prevalent species, only individuals from the host species reported infected were used in the analyses (see Table 1).

Results

Of the 1176 individuals, 112 were found infected by Bartonella spp. (8.7 %).

Factors of rodent infection by Bartonella species

We used the AIC to compare logistic regression models used to explain individual rodent infection. There were no effects of host sex and host maturity on the level of individual infection by all Bartonella spp., but a significant effect of locality, season, and host species (Table 2). Three localities showed higher host infection—Loei in Thailand, Luang Prabang in Lao PDR, and Sihanouk in Cambodia—with no effect of habitat. Rodents were more infected in the wet season (Table 3; p<0.0001), and two species, Maxomys surifer (a forest species) and Rattus exulans (a domestic species) were significantly less infected by Bartonella spp. (p<0.0001).

Table 2.

Comparison of Models Used to Test the Effect of Several Independent Variables (Locality, Habitat, Season, Sex, Age, and Species of Rodents) on Individual Rodent Infection (GLM with Logit Function) in Eight Sites in Thailand, Lao PDR, and Cambodia

Dependent variables	Model ranks	AIC
All Bartonella spp.	Province+season+species	609.9
	Province+season+habitat+species	611.3
	Province+season+habitat+sex+species	613.2
	Province+season+habitat+sex+maturity+species	617.0
B. rattimassiliensis	Season+habitat	229.7
	Season+habitat+sex	231.3
	Season+habitat+sex+maturity	233.4
	Province+season+habitat+sex+maturity	236.8
B. queenslandensis	Province+season	232.2
	Province	232.3
	Province+season+habitat	233.0
	Province+season+habitat+sex	233.3
	Province+season+habitat+sex+maturity	235.0
B. musii	Season+habitat	117.0
	Province+season+habitat	177.1
	Province+season+habitat+maturity	177.4
	Province+season+habitat+sex+maturity	178.2

Models are ranked from the least to the most supported according to corrected Akaike information criteria (AIC). For B. rattimassiliensis, B. queenslandensis, and B. musii only individuals from rodent species reported infected were used in the analyses (see Table 1).

GLM, generalized linear model.

Table 3.

Generalized Linear Model of Rodent Infection by Bartonella with Binomial Distribution and Logit Link Function (Log- Likelihood Type 1 Test) at Seven Sites in Thailand, Lao PDR, and Cambodia. Selection of the Best Model Using Akaike Information Criterion

Dependent variable	Variables	Category	Estimate (SD)	p Values	Log ratio chi squared (df)	p values (>chi squared)
All Bartonella spp.	Locality Buriram versus	Loei	2.35 (0.52)	<0.0001
		Luang Prabang	1.67 (0.49)	0.0007
		Sihanouk	1.57 (0.49)	0.001	39.8 (6)	<0.0001
	Season dry versus	Season wet	0.72 (0.28)	0.01	7.0 (1)	<0.0001
	Species B. indica versus	M. surifer	−2.59 (1.19)	0.03
		R. exulans	−1.87 (0.76)	0.01	92.9 (12)	<0.0001
B. rattimassiliensis	Season dry versus	Season wet	0.89 (0.44)	0.04	4.4 (1)	0.04
	Habitat forest versus	Rain fed field	−1.12 (0.51)	0.02
		Settlement	−1.66 (0.61)	0.006
		Dry field	−0.88 (0.52)	0.09	8.2 (3)	0.04
B. queenslandensis	Season dry versus	Season wet	−0.20 (0.10)	0.16	2.1 (1)	0.14
Bartonella n. sp.	Season dry versus	Season wet	2.58 (1.04)	0.01	12.8 (1)	0.0003
	Habitat forest versus	Dry fields	1.66 (0.63)	0.008	28.9 (3)	<0.0001

Significant categories (p values<0.05) among selected variables given in Table 2 are given with estimate and standard deviation.

SD, standard deviation; df, degrees of freedom.

The patterns differed when investing the three more prevalent Bartonella species. For B. rattimassiliensis, season and habitat appeared to be significant factors explaining host infection (Table 2), with higher levels of infection in the wet season and lower levels of infection in a rainfed field, dry field, and human settlement compared to forest habitat (Table 3; p=0.04). The infection by B. queenslandensis was found related to season and locality (Table 2) but not significantly (Table 3; p>0.05). Finally, Bartonella n. sp. was found more prevalent in wet season and in dry field habitat compared to forest habitat (Tables 2 and 3; p<0.001 and p<0.0001, respectively).

Discussion

Species of Bartonella showed a great variability in their host specificity with B. queenslandensis presenting a low host specificity by infecting nine host species and five genera, followed by B. rattimassiliensis found in three species and two genera and Bartonella n. sp. in four species and two genera, although this last species seems to infect preferentially the three species of Mus investigated by Jiyipong et al. (2012).

The prevalence of infection reported here (8.7%; see also Jiyipong et al. 2012) is lower than those currently observed in natural populations of rodents with range from 50% to 70% according to the reviews of Kosoy et al. (2004a, b). Such a lower prevalence could be related to the high rodent species richness observed in these localities and/or to their low population densities (Blasdell et al. 2012), although accurate rodent population densities were lacking.

Although Bartonella infection may vary among localities, season appears to be a major factor for host infection, with an increase of infection during the wet season. We did not observe any influence of host sex as most other studies that investigated the epidemiology of Bartonella in rodent populations (Morway et al. 2008, Meheretu et al. 2013). Published studies showed that season may influence (Fichet-Calvet et al. 2000, Morway et al. 2008) or not (Morway et al. 2008) the prevalence of Bartonella. We found a higher prevalence in the wet season, which may be related to the arthropod vector populations that can be enhanced during the wet season. However, studies have shown that vector abundance did not appear important for the dynamics of Bartonella (Telfer et al. 2007, Meheretu et al. 2013), emphasizing that host densities are crucial for ectoparasite exchange between hosts and Bartonella infection (Telfer et al. 2007). Moreover, information on the abundance and diversity of ectoparasites on rodents is still missing. Moreover, depending on the Bartonella and host species, transmission may occur through intermediate hosts such as ticks, fleas, sand flies, and mosquitoes (Parola et al. 2003, Boulouis et al. 2005, Billeter et al. 2008, Chomel and Kasten 2010, Kabeya et al. 2010).

Rodent species living in close proximity with humans, such as R. exulans and Rattus tanezumi, host several Bartonella species. However, the levels of rodent infection in human settlement are significantly lower, particularly for the infection of the house rat R. exulans. Lower infection could be explained by a lower prevalence of ectoparasites in houses, although data are missing, as mentioned previously. Dry fields, which are preferential habitats of Mus species, showed higher level of infection by Bartonella n. sp. than forest habitats. Finally, the zoonotic B. rattimassiliensis appeared to preferentially infect the rodents from forests compared to all other habitats (i.e., rain-fed fields, dry fields, and human settlement), with R. tanezumi being the main infected host and reservoir for this Bartonella species.

Several rodent species investigated here show relatively strong habitat preferences: Rattus norvegicus and R. exulans in settlements; Rattus argentiventer, R. sakeratensis, Bandicota indica, and Mus caroli in rain-fed fields; Mus cookii and Berylmys berdmorei in nonflooded lands; and Maxomys surifer and Leopoldamys edwardsi in forests (Ivanova et al. 2012, Palmeirim et al. 2014). Some species show lower habitat preferences, including Niviventer fulvescens, which were found in forests or other non-flooded lands. Finally, R. tanezumi, demonstrates more generalist tendencies and was trapped in a variety of habitats, including households (Palmeirim et al. 2014). Our study confirms that if several rodent species can be reservoirs of zoonotic Bartonella, the generalist and synanthropic species such as R. tanezumi appear to be reservoirs of all detected Bartonella. R. tanezumi can be found in forest, which seems a likely habitat that favors the transmission of B. rattimassiliensis, particularly in the wet season. However, as this rodent species can be found in all habitats (Palmeirim et al. 2014), it may potentially enhance transmission among all habitats.

The ongoing land use changes in Southeast Asia, with increasing biodiversity loss, habitat fragmentation, and zoonotic outbreaks (Morand et al. 2014) may likely favor the population dynamics of synanthropic rodent species (Bordes et al. 2013), which in turn may affect the epidemiology of Bartonella species on the risk of transmission to humans (Herbreteau et al. 2012, Bordes et al. 2013).

Footnotes

Acknowledgments

This study was funded by the French ANR Biodiversity (grant ANR 07 BDIV 012), CERoPath project “Community Ecology of Rodents and their Pathogens in a changing environment” (www.ceropath.org), and the French ANR CP&ES (grant ANR 11 CPEL 002) BiodivHealthSEA (Local impacts and perceptions of global changes: Biodiversity, health and zoonoses in Southeast Asia) (www.biodivhealthsea.org). We especially thank all participants in the fieldwork for their great help. J.T. was supported by a fellowship from Infectiopôle Sud.

Author Disclosure Statement

No conflicting financial interests exist.

References

Bai

, Kosoy

, Lerdthusnee

, Peruski

, et al. Prevalence and genetic heterogeneity of Bartonella strains cultured from rodents from 17 provinces in Thailand. Am J Trop Med Hyg, 2009; 81:811–816.

Bai

, Kosoy

, Diaz

, Winchell

, et al. Bartonella vinsonii subsp. arupensis in humans, Thailand. Emerg Infect Dis, 2012; 18:989–991.

Bhengsri

, Baggett

, Peruski

Jr , Morway

, et al. Bartonella spp. infections, Thailand. Emerg Infect Dis, 2010; 16:743–745.

Billeter

, Levy

, Chomel

, Breitschwerdt

. Vector transmission of Bartonella species with emphasis on the potential for tick transmission. Med Vet Entomol, 2008; 22:1–15.

Blasdell

, Cosson

, Chaval

, Herbreteau

, et al. Rodent-borne hantaviruses in Cambodia, Laos PDR and Thailand. EcoHealth, 2012; 8:432–443.

Bordes

, Herbreteau

, Dupuy

, Chaval

, et al. The diversity of microparasites of rodents: A comparative analysis that helps in identifying rodent-borne rich habitats in Southeast Asia. Infect Ecol Epidemiol, 2013; 3:20178.

Boulouis

, Chang

, Henn

, Kasten

, et al. Factors associated with the rapid emergence of zoonotic Bartonella infections. Vet Res, 2005; 36:383–410.

Castle

, Kosoy

, Lerdthusnee

, Phelan

, et al. Prevalence and diversity of Bartonella in rodents of northern Thailand: A comparison with Bartonella in rodents from southern China. Am J Trop Med Hyg, 2004; 70:429–433.

Chaval

, Dobigny

, Michaux

, Pages

, et al. A multi-approach survey as the most reliable tool to accurately assess biodiversity: An example of Thai murine rodents. Kasetsart J Nat Sci, 2010; 44:590–603.

10.

Chomel

, Kasten

. Bartonellosis, an increasingly recognized zoonosis. J Appl Microbiol., 2010; 109:743–750.

11.

Coker

, Hunter

, Rudge

, Liverani

, et al. Emerging infectious diseases in Southeast Asia: Regional challenges to control. Lancet, 2011; 377:599–609.

12.

Fichet-Calvet

, Jomaa

, Ismail

, Ashford

. Patterns of infection of haemoparasites in the fat sand rat, Psammomys obesus, in Tunisia, and effect on the host. Ann Trop Med Parasit, 2000; 94:55–68.

13.

Herbreteau

, Jittapalapong

, Rerkamnuaychoke

, Chaval

, et al. Protocols for Field and Laboratory Rodent Studies. Bangkok: Kasetsart University Press, 2011.

14.

Herbreteau

, Bordes

, Jittapalapong

, Supputamongkol

, et al. Rodent-borne diseases in Thailand: Targeting rodent carriers and risky habitats. Infect Ecol Epidemiol, 2012; 2:18637.

15.

Ivanova

, Herbreteau

, Blasdell

, Chaval

, et al. Leptospira and rodents in Cambodia: Environmental determinants of infection. Am J Trop Med Hyg, 2012; 86:1032–1038.

16.

Jiyipong

, Jittapalapong

, Morand

, Raoult

, et al. Prevalence and genetic diversity of Bartonella spp. in small mammals from southeastern Asia. Appl Environ Microbiol, 2012; 78:8463–8466.

17.

Kabeya

, Colborn

, Bai

, Lerdthusnee

, et al. Detection of Bartonella tamiae DNA in ectoparasites from rodents in Thailand and their sequence similarity with bacterial cultures from Thai patients. Vector Borne Zoonotic Dis, 2010; 10:429–434.

18.

Kosoy

, Mandel

, Green

, Marston

, et al. Prospective studies of Bartonella of rodents. Part I. Demographic and temporal patterns in population dynamics. Vector Borne Zoonotic Dis, 2004a; 4:285–295.

19.

Kosoy

, Mandel

, Green

, Marston

, et al. Prospective studies of Bartonella of rodents. Part II. Diverse infections in a single rodent community. Vector Borne Zoonotic Dis, 2004b; 4:296–305.

20.

Kosoy

, Morway

, Sheff

, Bai

, et al. Bartonella tamiae sp. nov., a newly recognized pathogen isolated from three human patients from Thailand. J Clin Microbiol, 2008; 46:772–775.

21.

Kosoy

, Bai

, Sheff

, Morway

, et al. Identification of Bartonella infections in febrile human patients from Thailand and their potential animal reservoirs. Am J Trop Med Hyg, 2010; 82:1140–1145.

22.

La Scola

, Zeaiter

, Khamis

, Raoult

. Gene-sequence-based criteria for species definition in bacteriology: The Bartonella paradigm. Trends Microbiol, 2003; 11:318–321.

23.

Meheretu

, Leirs

, Welegerima

, Breno

, et al. Bartonella prevalence and genetic diversity in small from Ethiopia. Vector Borne Zoonotic Dis, 2013; 13:164–175.

24.

Morand

, Jittapalapong

, Supputamongkol

, Abdullah

, et al. Infectious diseases and their outbreaks in Asia-Pacific: Biodiversity and its regulation loss matter. PLoS One, 2014; 9:e90032.

25.

Morway

, Kosoy

, Eisen

, Montenieri

, et al. A longitudinal study of Bartonella infection in populations of woodrats and their fleas. J Vector Ecol, 2008; 33:353–364.

26.

Pachirat

, Kosoy

, Bai

, Prathani

, et al. The first reported case of Bartonella endocarditis in Thailand. Infect Dis Rep, 2011; 3:e9. doi: 10.4081/idr.2011.e9.

27.

Pagès

, Chaval

, Herbreteau

, Waengsothorn

, et al. Revisiting the taxonomy of the Rattini tribe: A phylogeny-based delimitation of species boundaries. BMC Evol Biol, 2010; 10:e184.

28.

Paitoonpong

, Chitsomkasem

, Chantrakooptungool

, Kanjanahareutai

, et al. Bartonella henselae: First reported isolate in a human in Thailand. Southeast Asian J Trop Med Public Health, 2008; 39:123–129.

29.

Palmeirim

, Bordes

, Chaisiri

, Siribat

, et al. Helminth parasite species richness in rodents from Southeast Asia: Role of host species and habitat. Parasitol Res, 2014; 113:3713–3726.

30.

Parola

, Sanogo

, Lerdthusnee

, Zeaiter

, et al. Identification of Rickettsia spp. and Bartonella spp. in from the Thai-Myanmar border. Ann NY Acad Sci, 2003; 990:173–181.

31.

R Development Core Team. R: A language and environment for statistical computing, 2008, http://cran.r-project.org

32.

Roux

, Raoult

. Inter- and intraspecies identification of Bartonella (Rochalimaea) species. J Clin Microbiol, 1995; 33:1573–1579.

33.

Telfer

, Begon

, Bennett

, Bown

, et al. Contrasting dynamics of Bartonella spp. in cyclic field vole populations: The impact of vector and host dynamics. Parasitology, 2007; 134:413–425.

34.

Wilcove

, Giam

, Edwards

, Fisher

, et al. Navjot's nightmare revisited: Logging, agriculture, and biodiversity in Southeast Asia. Trends Ecol Evol, 2013; 28:531–540.