Jordi Landier
email: jordi.landier@gmail.com

Jean Gaudart

Kevin Carolan

Danny Lo Seen

Jean-Franc ¸ois Gue ´gan

Sara Eyangoh

Arnaud Fontanet

Gae ¨tan Texier

Keywords: Community disassemblage, metacommunity, stochastic and niche-based processes, zoonotic disease emergence, diversity loss, multi-host, Mycobacterium ulcerans, Buruli ulcer

come

Ecological niche modelling and its application to

environmentally acquired diseases, the case of Mycobacterium ulcerans and the Buruli ulcer

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& 4% * + " < ! " " ( < 4??E 4?83 * + " ! * + " ! % < " " 67G *76+ " ! % @ " = < < " < *& 8 +% @ " < 8??# " # 6 ( # % @ ! ! (1 =B F= , % @ = < J < = < = ) ) K ! = ) ) % @ 3 ! % < < ! ! ! " ! ! < " ! % " " " < ! " "" < < " ! % @ ! " ! " " 'F " ! " % ! < " < ! " " " < ! ! % @ ! !! " " < # = ! " % @ < !! & ' & , ) ' ) *, , + ' , -)! % 1 ! F % - " " K! % " " < " " ! % INTRODUCTION Currently,

Figure 1. Metacommunity framework used to investigate regiona

Mycobacterium ulcerans. In this example, each of three regions (blue, red encompasses two communities ( Regional prevalence i,j is influenced by regional diversity ( based processes driving dissimilarity between the t in two informative components: ordered loss of spec species between communities. Both, niche increase the extent of nestedness between communiti regional MU prevalence communities: probabilistic dispersal determined by (arrows) and the total abundance of animals in the based processes dominated the assembly of lentic communitie assembly of lotic communities. Hence, we analysed t communities as two separated metacommunities, and examined the results as a pseud to assess the effects of stochastic processes on the relationship between e found two main results. First, opposite to the prediction of the dilution generalized linear models (GLMs) revealed that MU prevalence was diversity in the lotic metacommunity, but not in the lentic processes may exert important effects on the relationship between Finally, as expected, we found that MU prevalence positively correlated with nestedness in both, the lentic metacommunity we accounted for stochastic processes represented b dispersal of species between communities.

Metacommunity framework used to investigate regiona . In this example, each of three regions (blue, red encompasses two communities (i and j) with their respective local diversities ( is influenced by regional diversity (γ i,j ), and stochastic and niche based processes driving dissimilarity between the two communities (β i,j ). in two informative components: ordered loss of species (nestedness) and turnover of species between communities. Both, niche-based and stochastic extinctions tends to increase the extent of nestedness between communities. Thus, we examine nestedness and regional MU prevalence i,j while accounting for stochastic pro communities: probabilistic dispersal determined by the connectivity between communities (arrows) and the total abundance of animals in the region, regardless the species.

ssembly of lentic communities whereas stochastic assembly of lotic communities. Hence, we analysed the lotic and lentic xamined the results as a pseudo-experiment the relationship between nestedness and MU opposite to the prediction of the dilution was positively correlated in the lentic one. This effects on the relationship between prevalence at regional level was metacommunity and in the lotic we accounted for stochastic processes represented by probabilistic Metacommunity framework used to investigate regional prevalence of . In this example, each of three regions (blue, red, and yellow) ) with their respective local diversities (α i and α j ).

), and stochastic and niche-). β i,j is decomposed tedness) and turnover of based and stochastic extinctions tends to es. Thus, we examine nestedness and while accounting for stochastic processes assembling the connectivity between communities region, regardless the species. METHODS MU is a relatively slow growing bacterium (~ 50h for replication) that synthetizes a lipophilic macrolide toxin (mycolactone), and evolved multiple DNA deletions and rearrangements associated with niche reduction [START_REF] Stinear | Reductive evolution and niche adaptation inferred from the genome of Mycobacterium ulcerans, the causative agent of Buruli ulcer[END_REF]). Some works support the existence of vector-borne transmission and amplification by Belostomatidae and Naucoridae water bugs (Marsollier et al. 2003), and previous studies suggest that MU is embedded within ecological networks (Roche et al.

Detection of M. ulcerans with qPCR

For each community and month sampled, the presence of MU was tested on 17 different taxonomic groups of animals [START_REF] Garchitorena | Mycobacterium ulcerans ecological dynamics and its association with freshwater ecosystems and aquatic communities: results from a 12-month environmental survey in Cameroon[END_REF]). However, not all of these groups were present across the study, and this may add variation in the presence of MU due to the differences across taxa in the affinity to harbour the bacterium (Portaels et al. 2001;Marsollier et al. 2004). To assure consistency in the sensitivity of detecting MU we focused on the orders of macro-invertebrates that were consistently present during the sampling year (Coleoptera, Diptera, Ephemeroptera, Hemiptera and Odonata, SM2). Quantitative PCR was used to detect two specific markers of MU in each sample: oligonucleotide primer and TaqMan probe sequences of IS2404 and the ketoreductase B domain of the mycolactone polyketide synthase (mls) gene from the plasmid pMUM001 (Garchitorena et al.

2014).

Probabilistic dispersal

Theory predicts that stochastic extinction of species in a community should decrease prevalence, as opposed to niche-based extinction of species that are expected to increase pathogen transmission ). In such instance, nearest communities are expected to be more similar than distant ones. Thus, we considered the connectivity between communities as surrogate of probabilistic dispersal, and accounted for its effects in our analyses in three ways: First we assessed whether our data (local MU prevalence and α-diversity) were structured by the connectivity between communities (SM3). Second, we included estimates of connectivity as predictors in the GLMs on regional MU prevalence (see below). Finally, because connectivity may interact synergistically with the other predictors of interest (i.e. nestedness and γdiversity) we tested for two-way interactions between connectivity and the other predictors included in the GLMs on regional MU prevalence.

Connectivity between communities was represented by two estimates: Euclidean connectivity and constrained connectivity (SM3). Euclidean connectivity assumed that host and non-host species can disperse freely between communities across the landscape. Constrained connectivity assumed that species disperse within the hydrological system in Akonolinga. Connectivity was measured as the inverse of distances between communities (Euclidean and constrained):

, from (0) no connectivity to (1) full connectivity. We present separate GLMs for each estimate of connectivity because including both estimates as predictors in the same GLM produced multicolinearity in the models.

Local analyses on MU prevalence

We performed GLMs to test the correlation between MU prevalence (the proportion of positive samples found during a year in each community) and α-diversity represented by the Shannon index (H). The Shannon index was estimated as:

; where was the proportional abundance of host species i and S was the total number of families in the community (Shannon with the MU prevalence. Finally, we ensured that the quality of our results was not sensitive to either the taxonomic variation of the samples in which MU was detected (SM2.1-2.2), and spatial autocorrelation (SM3).

Regional analyses on MU prevalence

We used stepwise model selection of GLMs based on the AIC, Akaike information criterion (Burnham & Anderson 2002), to test correlations between regional MU prevalence and γ-diversity, nestedness, connectivity and confounding factors: turnover, deforestation and total abundance of animals, henceforth abundance at regional level. Regional MU prevalence, γ-diversity (H), we tested each predictor in separate GLMs. Like in the analyses on local prevalence, we corroborated that our results were not sensitive to the taxonomic variation of the s rder of the host as random factor in the GLMs (SM2). Finally, we ensured that the results were not biased due to the contribution of a particular community by community from the data and repeating the analysis (SM7-8). This procedure was . However, MU prevalence in the lotic communities was positively correlated with diversity (b = 3.84 ± 1.65, Z = 2.33, p = 0.020, Fig. 3) but not with deforestation (b = 0.30, p = 0.768). The relationship between total abundance in separated GLMs, and we found a negative relationship in the lotic 0.84 ± 0.37, Z = -2.29, p = 0.022), and no significant relationship i 0.22 ± 0.30, Z = -0.72, p = 0.469).

Positive relationship between local prevalence of M. ulcerans

GLMs) and the Shannon index of α-diversity in lotic communities: mean values (solid line) and upper and lower 95% confidence intervals (grey shaded area) are represented.

was not significantly correlated with 0.19 ± 0.32, Z = -0.63, p lotic communities was positively correlated with g. 3) but not with deforestation (b = -0.08 ± he relationship between total abundance of animals and MU e relationship in the lotic 2.29, p = 0.022), and no significant relationship in the lentic M. ulcerans (predicted from diversity in lotic communities: mean values (solid (grey shaded area) are represented.

Regional analyses on MU prevalence

Stepwise model selection suggested that regional MU prevalence was positively correlated with γ-diversity in the lotic metacommunity whereas in the lentic metacommunity the correlation was not significant (Tables 1,2). Regional MU prevalence was positively correlated with nestedness in the lentic metacommunity (Table 1, Fig. 4). However, in the lotic metacommunity this correlation was either significant or not significant (Table 2A-D). The significance of this correlation relies upon the effects of connectivity between communities, and the surrogate of connectivity used as predictor in the models. While MU prevalence was not significantly correlated with the interaction between nestedness and constrained connectivity (Table 2A), it was positively correlated with the interaction between nestedness and Euclidean connectivity (Table 2B, 2D). This correlation supported our prediction suggesting that an increase in regional MU prevalence was associated with an increase in nestedness. Additionally, we found remarkable confounding effects in our GLMs. In both lentic and lotic metacommunities, an increase in regional abundance of animals was associated with a decrease in regional MU prevalence. In the lentic metacommunity, increments in regional deforestation and turnover were associated with an increase in MU prevalence (Table 1, Fig. 4); and in the lotic metacommunity MU prevalence decreased when turnover increased (Table 2B). ). On this controversy, our study adds evidence that the contribution of stochastic processes of community assembly, unrelated to inter-specific interactions, may also be key to understand the relationships between diversity and prevalence. Stochastic processes of community assemblage are more important in determining diversity in the lotic metacommunity than in the lentic metacommunity (SM1, Brown & Milner 2012); and correspondingly the positive relationship between diversity and MU prevalence is observed for the lotic metacommunity but not for the lentic one. Thus the possibility that MU prevalence and species diversity increase together in lotic communities, without encompassing key inter-specific interactions, should not be disregarded. The same stochastic processes that drive diversity may influence MU bacteria, hence increase of multi-host bacteria could be expected when diversity increases. Furthermore, two results of our regional analyses provide evidence supporting that stochastic processes may affect MU prevalence (Tables 1,2).

First, in both communities, lentic and lotic, regional MU prevalence decreases when the regional abundance of animals increases. These negative relationships contradict our expectations, and their underlying mechanisms deserve further research. Possible explanations may be the effects of water currents removing invertebrates, notably larvae or adults living at the bottom or in the middle of the water column, out of the communities and detaching MU from detritus; and/or that the slow growth rate of MU entails a slow infection rate, such that increasing abundance of animals reduces the probability of finding infected animals. Second, the surrogates of probabilistic dispersal (connectivity between communities) have substantial effects on regional MU prevalence in the lotic but not in the lentic metacommunities. In the lotic metacommunity, these surrogates interact with our estimates of β-diversity (turnover and nestedness) explaining substantial variation in regional MU prevalence (Table 2).

Our metacommunity approach (Figure 1) allows us to investigate evidence that nichebased processes influence MU prevalence. First, we find support to our hypothesis because regional MU prevalence positively correlates to nestedness between communities (Tables 1,2). Remarkably, in the lotic metacommunity this relationship is influenced by the interaction between nestedness and probabilistic dispersal, the Euclidean connectivity between communities (Table 2B Previous studies suggests the amplification of MU favoured by Hemiptera water bugs:

Belostomatidae and Naucoridae (Marsollier et al. 2003). Hence, it may be possible that indeed the hosts of MU are persistent across our study. Finally, regional MU prevalence in lentic communities is correlated with niche-based processes represented by species turnover (Tables 1-2A). Currently, the niche of MU is still poorly understood, making it difficult to disentangle how replacement of host species may affect regional prevalence. While replacing species may involve changes in ecosystem function, is not clear why turnover may boost or reduce MU prevalence. One plausible explanation is that some key species may act as amplifiers (e.g., Naucoridae and Belostomatidae water bugs) and boost prevalence while others may act as dilutors (e.g., Gastropods) and reduce prevalence (Marsollier et al. 2003;Marsollier et al. 2004). Likewise, other studies reveal that MU prevalence is associated with host species composition (Benbow et al. 2013), and the occurrence of some functional groups, such as filters and scavengers (Roche et al. 2013a). Our results provide evidence supporting these insightful studies, by suggesting that different taxa have different effects on MU prevalence. Indeed, investigating the symbiotic relationships between host species in the community and the bacteria will be a fundamental step towards mitigating Buruli ulcer. In this regard, intervention against deforestation may be key to reduce MU prevalence, as we find a positive relationship between these two variables in the lentic metacommunity (Table 1).

CONCLUSION

Taken together, our study provides firm evidence on the importance of differentiating niche-based processes and stochastic processes of community assembly to understand how local extinction of species may influence circulating multi-host pathogens. By using a metacommunity perspective we find empirical evidence supporting the theoretical study of Ostfeld and LoGuidice (2003). Nichebased extinction of species boosts prevalence of Mycobacterium ulcerans, however, the stochastic processes influencing diversity must be accounted for because they also influence the relationships between diversity and microbial prevalence. Furthermore, our study supports the insight that microbial prevalence is influenced by community composition, rather than biodiversity per se 3 )

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Introduction

Buruli ulcer (BU) disease is an extensively damaging skin infection caused by Mycobacterium ulcerans (MU), a pathogen distantly related to Mycobacterium tuberculosis and M. leprae [START_REF] Silva | Pathogenetic mechanisms of the intracellular parasite Mycobacterium ulcerans leading to Buruli ulcer[END_REF]2]. BU presents as a necrotising infection of the skin, causing debility and crippling deformity if left untreated. Initially described in Uganda and Australia [START_REF] Johnson | Buruli ulcer (M. ulcerans infection): new insights, new hope for disease control[END_REF], BU has been reported in 33 countries and mainly prevalent in tropical regions. In 2011, 4,009 cases were reported to the World Health Organization (WHO) by 14 countries [4]. The majority of BU cases (96%) originated from countries around the gulf of Guinea and Cameroon reported 256 cases.

Means of preventing the infection are still lacking as the mode of transmission of MU to humans remains unknown [START_REF] Johnson | Buruli ulcer (M. ulcerans infection): new insights, new hope for disease control[END_REF][START_REF] Merritt | Ecology and transmission of Buruli ulcer disease: a systematic review[END_REF]. It is unclear where the microbe resides in the environment: genomics data suggest recent adaptation to a new environmental niche and specialisation to a given host [START_REF] Stinear | Reductive evolution and niche adaptation inferred from the genome of Mycobacterium ulcerans, the causative agent of Buruli ulcer[END_REF]7] while environmental detection studies across a wide diversity of samples (insect and vertebrate fauna, water filtrates, vegetal debris) seem to indicate that the microbe could occupy a wide diversity of environments [START_REF] Merritt | Ecology and transmission of Buruli ulcer disease: a systematic review[END_REF][START_REF] Portaels | Insects in the transmission of Mycobacterium ulcerans infection[END_REF][START_REF] Johnson | Mycobacterium ulcerans in mosquitoes captured during outbreak of Buruli ulcer, southeastern Australia[END_REF][START_REF] Fyfe | A major role for mammals in the ecology of Mycobacterium ulcerans[END_REF]. How the microbe reaches human skin is also highly debated: a role of insects as vectors has been hypothesised [START_REF] Johnson | Mycobacterium ulcerans in mosquitoes captured during outbreak of Buruli ulcer, southeastern Australia[END_REF][START_REF] Marsollier | Aquatic insects as a vector for Mycobacterium ulcerans[END_REF], but remains controversial [START_REF] Merritt | Ecology and transmission of Buruli ulcer disease: a systematic review[END_REF].

In this context of unknown transmission mechanisms and unknown environmental location of the pathogen, the study of spatial and temporal variations of BU incidence could identify where and when MU transmission events are most likely to occur and provide insights towards understanding the elusive epidemiology of BU [START_REF] Lambin | Pathogenic landscapes: interactions between land, people, disease vectors, and their animal hosts[END_REF][START_REF] Faucher | Heterogeneity of environments associated with transmission of visceral leishmaniasis in South-Eastern France and implication for control strategies[END_REF]. BU incidence has been described as highly focal in countries such as Uganda, Benin, Co ˆte d'Ivoire and Ghana, where endemic regions are usually well defined. In Cameroon, three foci are identified, the Nyong valley in Ayos and Akonolinga (Centre region), Bankim (Adamaoua region) and Mbongue (South-West region), but sporadic cases originate from various places across the country, suggesting that the description of BU endemic regions is incomplete [START_REF] Boock | Preliminary national survey on Buruli ulcer in Cameroon[END_REF]. Descriptive maps of BU incidence or prevalence were established in Uganda as early as the 1970s [START_REF] Uganda | Buruli Group (1971) Epidemiology of Mycobacterium ulcerans infection (Buruli ulcer) at Kinyara, Uganda[END_REF][START_REF] Barker | The distribution of Buruli disease in Uganda[END_REF]. Since that period, several maps of prevalence or incidence rate have been established in endemic regions from Cameroon [START_REF] Noeske | Buruli ulcer disease in Cameroon rediscovered[END_REF][START_REF] Porten | Prevalence of Buruli ulcer in Akonolinga health district, Cameroon: results of a cross sectional survey[END_REF][START_REF] Marion | Geographic expansion of Buruli ulcer disease, Cameroon[END_REF], Democratic Republic of Congo [START_REF] Phanzu | Burden of Mycobacterium ulcerans disease (Buruli ulcer) and the underreporting ratio in the territory of Songololo, Democratic Republic of Congo[END_REF], Benin [START_REF] Wagner | Buruli ulcer disease prevalence in Benin, West Africa: associations with land use/cover and the identification of disease clusters[END_REF], Co ˆte d'Ivoire [START_REF] Marston | Emergence of Buruli ulcer disease in the Daloa region of Cote d'Ivoire[END_REF][START_REF] Brou | Landscape diversity related to Buruli ulcer disease in Co ˆte d'Ivoire[END_REF] and Ghana [START_REF] Amofah | Buruli ulcer in Ghana: results of a national case search[END_REF][START_REF] Williamson | Distribution of Mycobacterium ulcerans in buruli ulcer endemic and non-endemic aquatic sites in Ghana[END_REF]. These maps showed that the distribution of BU is highly focal at country scale, and also within endemic regions [START_REF] Barker | The distribution of Buruli disease in Uganda[END_REF][START_REF] Noeske | Buruli ulcer disease in Cameroon rediscovered[END_REF][START_REF] Marion | Geographic expansion of Buruli ulcer disease, Cameroon[END_REF].

Several studies have shed light on spatial patterns of BU distribution and environments associated to BU at the national scale [START_REF] Wagner | Buruli ulcer disease prevalence in Benin, West Africa: associations with land use/cover and the identification of disease clusters[END_REF][START_REF] Brou | Landscape diversity related to Buruli ulcer disease in Co ˆte d'Ivoire[END_REF][START_REF] Wagner | A Landscape-based model for predicting Mycobacterium ulcerans infection (Buruli ulcer disease) presence in Benin, West Africa[END_REF] or at the regional scale [START_REF] Sopoh | Buruli ulcer prevalence and altitude, Benin[END_REF][START_REF] Van Ravensway | Climate and landscape factors associated with Buruli ulcer incidence in Victoria, Australia[END_REF]. The environmental factors associated to BU prevalence or incidence were: low elevation in Benin [START_REF] Wagner | Buruli ulcer disease prevalence in Benin, West Africa: associations with land use/cover and the identification of disease clusters[END_REF][START_REF] Sopoh | Buruli ulcer prevalence and altitude, Benin[END_REF] and South Australia [START_REF] Van Ravensway | Climate and landscape factors associated with Buruli ulcer incidence in Victoria, Australia[END_REF], high percentage of forest cover and low percentage of urban cover in Co ˆte d'Ivoire [START_REF] Brou | Landscape diversity related to Buruli ulcer disease in Co ˆte d'Ivoire[END_REF], Benin [START_REF] Wagner | Buruli ulcer disease prevalence in Benin, West Africa: associations with land use/cover and the identification of disease clusters[END_REF] and South Australia [START_REF] Van Ravensway | Climate and landscape factors associated with Buruli ulcer incidence in Victoria, Australia[END_REF]. In Benin, the standard deviation of wetness index, an indicator of areas with contrasted topographic features, was associated with a higher risk of BU [START_REF] Wagner | Buruli ulcer disease prevalence in Benin, West Africa: associations with land use/cover and the identification of disease clusters[END_REF]. In Co ˆte d'Ivoire, irrigated rice producing areas and the proximity of remnant rainforest patches were associated with a higher risk of BU [START_REF] Brou | Landscape diversity related to Buruli ulcer disease in Co ˆte d'Ivoire[END_REF]. These analyses contributed to identifying the characteristics of the regions at risk within countries, and a model from Benin could even be used to predict where these regions would be in neighbouring Ghana [START_REF] Wagner | A Landscape-based model for predicting Mycobacterium ulcerans infection (Buruli ulcer disease) presence in Benin, West Africa[END_REF]. However, they provided little insight on the local determinants of BU prevalence within endemic regions, where endemic and non-endemic villages can be very close. The spatial resolution was probably too low in these studies to distinguish local variations in prevalence or to provide sufficient contrasts in the descriptions of the environment [START_REF] Wagner | A Landscape-based model for predicting Mycobacterium ulcerans infection (Buruli ulcer disease) presence in Benin, West Africa[END_REF][START_REF] Van Ravensway | Climate and landscape factors associated with Buruli ulcer incidence in Victoria, Australia[END_REF].

In this article, we present the first analysis of BU incidence patterns at the village level in an African endemic region, the Akonolinga health district, Centre region, Cameroon. This analysis is based on one of the largest series of cases available to date and on a fine scale characterisation of the environment. The objectives of this study were: 1) to describe the local spatial patterns and spatio-temporal variations of BU incidence; 2) to characterise and quantify the environmental factors associated with high BU incidence in Akonolinga district.

Materials and Methods

Setting

This study was performed in Akonolinga health district, located 100 km east from Yaounde ´in the Centre Province of Cameroon. The predominant environment is tropical forest and the district is crossed by the Nyong River which flows from east to west. BU was first described in this area in 1977 [START_REF] Ravisse | Skin ulcer caused by Mycobacterium ulcerans in Cameroon. I. Clinical, epidemiological and histological study][END_REF]. BU prevalence in the district was described in 2001 [START_REF] Noeske | Buruli ulcer disease in Cameroon rediscovered[END_REF] and 2007 [START_REF] Porten | Prevalence of Buruli ulcer in Akonolinga health district, Cameroon: results of a cross sectional survey[END_REF]. Risk factors for BU have also been investigated in a case control study in 2006 and several individual risk factors related to the environment were identified, such as having activities in the Nyong River, or having forest or a cacao plantation close to the habitation [START_REF] Pouillot | Risk factors for buruli ulcer: a case control study in Cameroon[END_REF]. The present study was conducted based on BU cases recorded for Disease Surveillance activities from January 2002 (start of the treatment intervention) to May 2012.

BU case data

This study relied on the analysis of the registry [START_REF] Ravisse | Skin ulcer caused by Mycobacterium ulcerans in Cameroon. I. Clinical, epidemiological and histological study][END_REF] of BU patients included in the BU management intervention at Akonolinga District Hospital, for which the Centre Pasteur du Cameroun (CPC) performed biological confirmation of MU infection diagnosis as the National Reference Laboratory. All new patients treated for BU in Akonolinga after clinical diagnosis, and with a documented place of residence in Akonolinga district, were included in the analysis. Patients without a documented village of residence or with an unidentified village of residence were excluded. These data were collected routinely at Akonolinga District Hospital and at Centre Pasteur du Cameroun as part of the BU Disease Surveillance system of the National Control Program.

A clinical case was defined as a patient with a clinical diagnosis of BU, made at the Akonolinga District Hospital, by trained specialized health practitioners in charge of the BU treatment. A confirmed case was defined as a clinical case with a positive result for at least one of the two biological confirmation methods, microscopy [START_REF]Laboratory diagnosis of Buruli ulcer[END_REF] or PCR [START_REF] Fyfe | Development and application of two multiplex real-time PCR assays for the detection of Mycobacterium ulcerans in clinical and environmental samples[END_REF] which are performed routinely by the CPC as the National Reference Laboratory according to WHO recommendation [START_REF]Laboratory diagnosis of Buruli ulcer[END_REF]. Laboratory confirmation could not always be obtained, however clinical diagnosis was shown to be very reliable in endemic regions [START_REF] Mensah-Quainoo | Diagnosis of Mycobacterium ulcerans infection (Buruli ulcer) at a treatment centre in Ghana: a retrospective analysis of laboratory results of clinically diagnosed cases[END_REF].

In this study, no intervention was performed (either diagnostic or therapeutic) and we only relied on a retrospective collection of anonymous cases authorized by the Cameroonian Ministry of Health.

Population data

Population data for Akonolinga health district villages were obtained from the national population census bureau (BUCREP). These population data were a 2010 projection based on the detailed results of the 2005 census. Population settlements in the Centre region are typically hamlets relatively close to each other which form a village (''chefferie'') under the administrative authority of a traditional chief [START_REF] Laburthe-Tolra | Les seigneurs de la fore ˆt. Les seigneurs de la fore ˆt[END_REF][START_REF] Guyer | Family and farm in Southern Cameroon[END_REF]. Since cases were reported at the village level, we aggregated the population of hamlets at village level.

For the towns of Akonolinga and Endom, the urban neighbourhoods were aggregated.

Administrative data

In accordance with Cameroonian laws (decree 77/245), a village was defined as the collection of all hamlets under the jurisdiction of the same traditional chief and was represented on the maps as the surface encompassing all hamlets. Hamlets had been either geolocated using a GPS during previous fieldwork [START_REF] Pouillot | Risk factors for buruli ulcer: a case control study in Cameroon[END_REF][START_REF] Marion | Seasonal and regional dynamics of M. ulcerans transmission in environmental context: deciphering the role of water bugs as hosts and vectors[END_REF] or identified on a 1/2,00 000 scale map (Institut National de Cartographie, Yaounde ´, sheets of Yaounde ´, Nanga Eboko and Akonolinga).

Topographical and environmental data were extracted using a circular 5 km-radius buffer around the village centroid. This value of 5 km was chosen based on a socio-anthropologic evaluation done in the region (described in [START_REF] Giles-Vernick | The puzzle of Buruli ulcer transmission, ethno-ecological history and the end of ''love'' in the Akonolinga district[END_REF]) and it approximated the distance that could easily be walked by inhabitants for their daily activities: fishing, farming, going to school. Furthermore, each hamlet was located within the 5-km-radius buffer of its village.

Topographical data

Topography has been shown to be a major driver in most studies [START_REF] Wagner | Buruli ulcer disease prevalence in Benin, West Africa: associations with land use/cover and the identification of disease clusters[END_REF][START_REF] Brou | Landscape diversity related to Buruli ulcer disease in Co ˆte d'Ivoire[END_REF][START_REF] Sopoh | Buruli ulcer prevalence and altitude, Benin[END_REF]. A digital elevation model was used to obtain elevation data (Shuttle Radar Topography Mission, available from the U.S. Geological Survey). A map of 90 m-topographic wetness index (TWI), an indicator of zones where water tends to accumulate due to abundant runoff from the surrounding area and a low slope, was obtained from Africa Soil Information Service (http://www.africasoils.net). TWI was categorised around the value 18, since following fieldwork, TWI.18 corresponded best to the bottoms of valleys which were most likely to represent wetlands. The percentage of each buffer area within this class was used as an indicator of the abundance of wetlands. Data on the distribution of rivers and roads were obtained from IFORA project and Institut National de Cartographie du Cameroun.

Environmental data

First, we used aggregated measures to quantify vegetation cover in each buffer. We used a vegetation index calculated from remote sensing multispectral data measured by the MODIS satellite. The Enhanced Vegetation Index (EVI) is available from U.S. Geological Survey as a monthly image with 1 km 2 resolution averaging measurements performed with a 16-day period (30-Day L3 Global 1 km product -MOD13A3). EVI was used as a measurement of overall forest cover: it is directly related to photosynthetic activity and biomass and was developed specifically for high biomass areas such as tropical forests [START_REF] Gond | Vegetation structure and greenness in Central Africa from Modis multi-temporal data[END_REF]. Using images from December 2001 to December 2011, we calculated the mean EVI during the dry season for each village buffer to approximate vegetation cover. During the dry season, contrast was expected to be maximal between herbaceous or cultivated zones, where annual plants require rain for their growth, and forest where perennial vegetation relying on deep soil water, would still present a high photosynthetic activity [START_REF] Huete | Amazon rainforests green-up with sunlight in dry season[END_REF]. Deforestation was one of the major human-driven changes that we expected in this area. We calculated the mean EVI over months December to February at the beginning and end of the study period (dry seasons 2001-2002 and 2011-12). The difference between the two values was included as a crude proxy for quantitative vegetation change over the study period.

Second, we characterised the environment in more detail (forest type, cultivated areas…) using two distinct Land-use/Land-cover (LULC) datasets.

The first dataset was a classification constructed using two Landsat images from February and March 2001 which were selected for low cloud content. Initial exploratory maps were classified using multi-spectral decision trees in the software ENVI, version 4.8 (Exelis Visual Information Solutions, Boulder, Colorado). Following ground truthing of these initial maps in November 2012, they were refined using object orientated image analysis in the software eCognition (eCognition Developer version 8.9.1, Trimble Geospatial Imaging, Munich, Germany). This resulted in regions classified as Urban, Road, Forest, Crop, Flood plain or Swamp categories.

The second dataset was a map of forest types established in 2002 and obtained from the Forest Atlas of Cameroon [START_REF]Interactive Forest Atlas of Cameroon (version 3.0)[END_REF]. The study area presented 9 classes of vegetation: primary forest (dense humid evergreen or with raffia trees), secondary forest (young or adult, cultivated or not), forested wetlands/swamps, wetlands, and savannah. Secondary forest represents forest growing after being cleared (completely or partially). Two categories are distinguished according to the time elapsed since clearing. Young secondary forest corresponds to the first 5 to 20 years after clearing. It hosts mainly plant species that grow rapidly and in the light. With time, the number and variety of plant species increase, the canopy closes and the forest becomes adult secondary forest, characterised by a high biodiversity. In Akonolinga region, clearing resulted mainly from familial agriculture. The forest category indicated the intensity of human pressure on the environment.

The first dataset was used mainly for urban, agricultural, and wetland land-cover characterisation, which were combined with detailed forest data from the second dataset. New classes or new attributions were derived, such as ''cultivated wetlands'' corresponding to areas listed as cultivated in one dataset and swamp or swamp forest in the other.

Statistical analyses

All analyses were performed using R software version 3.0.2 (R Development Core Team, R Foundation for Statistical Computing, Vienna, Austria), including packages DCluster, SPODT, FactoMineR, bcp; and the software ArcGIS version 10.0 (ESRI Inc. Redlands, CA), including the extension Spatial analyst. Graphics were drawn using the ggplot2 R-package and maps were drawn using ArcGIS.

Incidence rate calculation and mapping. In order to analyse the distribution of cases in Akonolinga district, a map of the mean monthly incidence rate of BU per village was drawn for each phase and for the cumulative series. Mean incidence rate was expressed in cases per 1,000 person.years (cases/1,000py) and allowed comparisons between villages with different population sizes and different exposure times. To be represented on the maps, incidence rate were discretised using rounded values of the classes obtained by the Jenks method in ArcGIS, which enabled maximization of contrasts.

Analysis of spatial clustering. To address the question of whether cases occurred at random in the district or according to a given spatial pattern, we performed several statistical analyses. First, a general statistic of global aggregation, Moran's Index (I), was calculated to assess spatial autocorrelation [START_REF] Gaudart | Spatial cluster detection without point source specification: the use of five methods and comparison of their results[END_REF]. Statistical significance was calculated using bootstrap methods. Then, we evaluated the relative risk of BU over the Akonolinga region using the Spatial Oblique Decision Tree algorithm (R package SPODT). This method was used to identify homogeneous risk areas on the time-aggregated series and to quantify the risk associated with each zone. This method is adapted from classification and regression tree techniques and uses straight lines to split the study area in groups of villages as homogenous as possible regarding incidence rate [START_REF] Gaudart | Oblique decision trees for spatial pattern detection: optimal algorithm and application to malaria risk[END_REF]. It identifies clusters without any shape assumption, and is less biased by edge effects. It also provides risk estimates in all areas. Statistical significance was calculated using Monte-Carlo inference. We estimated the relative risk for each zone delimited by SPODT, by calculating an odds-ratio and its 95% significance interval.

Spatio-temporal analysis. Based on the spatial analysis, we analysed the incidence distribution over time and space using a ''heat-map'', displaying mean monthly incidence for each quarter and for each village after ordering them according to their distance to the Nyong River. We identified several ''phases'' in the timeseries defined as periods of time presenting heterogeneous spatial patterns of incidence. These phases were confirmed using Bayesian change-point detection methods [START_REF] Erdman | bcp: An R package for performing a Bayesian analysis of Change Point problems[END_REF] to have a high probability of representing a change in the time series. Maps of incidence were drawn.

Classification of villages into landscape groups. We performed a principal component analysis (PCA) on the environmental data extracted for each village on a 5 km-radius buffers (see Supplementary Figure S2 legend in Text S1 for details). This step allowed grouping variables from the different categories, removing colinearity and selecting the most relevant variables for describing the environment in the Akonolinga region. Homogeneous groups of villages with similar landscape environments were built by classifying the villages according to the PCA results using agglomerative hierarchical clustering with a Euclidean distance metric (unsupervised classification).

Estimation of landscape-associated risk. In order to estimate the risk associated to each landscape, a generalised linear model (GLM) was built. A binomial negative regression model was preferred, since it was more adapted to this series with count numbers, as in previous BU studies [START_REF] Wagner | Buruli ulcer disease prevalence in Benin, West Africa: associations with land use/cover and the identification of disease clusters[END_REF]. Categorical variables were included in the model: landscape profile as a single variable with one modality per group, and distance to the Nyong River in 4 categories, #5 km, .5-10 km, .10-20 km, .20 km, according to the activity range of populations. The model allowed estimation of an incidence ratio (IR) associated to each class. Interaction between landscape profile and distance to the Nyong River was investigated by splitting the landscape classes in groups of distance when all the villages of one landscape were not included in the same distance class. When it was found significant for one landscape, two subsets landscape were created for the final analysis, distance to Nyong #10 km and .10 km based on the distance where Nyong influence was significant.

Univariate and multivariate models were assessed for parsimony using Akaike information criterion (AIC). Fitting was assessed by the percentage of deviance explained.

To assess model performance at representing the spatial variations of BU incidence we mapped the model residuals and explored their distribution using Moran's I statistic. We expected that no autocorrelation would remain if the model accurately captured the spatial pattern of incidence resulting from the different landscapes.

Temporal evolution of BU incidence in each landscape. We drew the cumulative incidence graphs over time for each group of villages from the same landscape to examine the local temporal variations of BU. The cumulative incidence over time was fitted with a linear model. When a linear fit was appropriate and indicated constant incidence, average incidence in the landscape was calculated for the period 2002-2012. Exponential fit was also tested by fitting a curvilinear model to the logarithm of cumulative incidence.

Results

BU case data

From January 2002 to May 2012, 915 patients originating from Akonolinga health district were diagnosed and treated free of charge at the Akonolinga district hospital by the BU management intervention. Out of these patients, 853 were new cases and among those, 787 cases had a documented place of residence in one of the 154 villages of Akonolinga district included in the analysis. The total population of the study villages was 60,188 inhabitants and the study area had a surface of 3,685 km 2 . The north part of the district, 16 villages totalling 10 cases and 2,750 inhabitants, was excluded because the area was only documented in the forest LULC database.

Among the 787 clinical BU cases in the database, 513 (65%) had received a laboratory-confirmed MU infection diagnosis (396 by PCR and/or microscopy, 117 by microscopy only). All 787 clinical BU cases were included in the analysis.

BU incidence rate and spatial distribution in Akonolinga district

Global BU incidence rate in the study area was 1.25 cases/ 1,000py over a time period of 10 years and 5 months. Incidence per village ranged from 0, in 59 villages, to 10.4 cases/1,000py, and median incidence was 0.4 cases/1,000py (Interquartile Range = [0-1.1]). A map of cumulative incidence rate over the time-aggregated series is presented in Figure 1A. Cumulative incidence appeared to be highest in villages close to the Nyong River, east of Akonolinga town. There was a significant global aggregation of cases (Moran's I = 0.349, p,10 26 ). The SPODT algorithm identified that the highest risk zone was centered on the Nyong River upstream of Akonolinga (Figure 1B). A decreasing risk gradient with increasing distance to the Nyong was identified, and the highest risk zone had 67 times higher risk of BU than the lowest risk zone.

Spatio-temporal variations of BU incidence in Akonolinga district, 2002-2012

The role of the Nyong River as a high risk area, and the decreasing risk gradient away from the river, led us to investigate the temporal variations of BU incidence per village according to their distance to the Nyong (Figure S1 in Supplementary Text S1). By change-point analysis process, we identified four phases corresponding to changes in the disease spatial distribution (Figure 2). In the first phase, corresponding to year 2002, the debuting BU treatment program only recruited cases from Akonolinga town and the neighbouring villages (Phase 1, Figure 2A). In the following phases, recruitment was on the entire district. From being centred on Akonolinga town from 2002 to 2006 (Phase2, Figure 2B), the high incidence area appeared to move, first eastward upstream the Nyong in the area of Abem (Phase 3, Figure 2C), then downstream along the Nyong, on the southern part of the river and on the Mfoumou, a tributary of the Nyong (Phase 4, Figure 2D).

Analysis of local environmental risk-factors for BU in Akonolinga district

Environment classification in landscape groups. The unsupervised classification of villages distinguished 7 landscape groups, organised on two main gradients (Table 1 and details in Supplementary Text S1, Figure S2). First, a clear separation was observed between villages with abundant forest cover compared to those where it was greatly reduced, as indicated by EVI values. This separation allowed the definition of a first gradient of increasing human alteration of landscape, based on abundant urban and agricultural land-use, and low proportion of forest cover. Landscape ''Urban Nyong'' and landscape ''Rural Nyong'' were characterised by a low forest cover and abundant areas dedicated to agriculture, as well as a high proportion of wetlands. A second gradient separated the villages according to the forest maturity (primary, secondary adult, secondary young) and the proportion of which was mosaicked with cultures. Landscapes ''Forest 1'' and ''Forest 2'' had the most abundant forest cover and were generally at a higher altitude with lower proportion of wetlands. ''Forest 1'' included remnants of dense humid evergreen primary forest, which marks the persistence of undisturbed ecosystems. ''Forest 2'' included a high proportion of secondary adult forest cover, a fraction of which was cultivated. Landscapes ''Cultivated forest'' and ''Young forest'' included intermediate features between these two groups, where young secondary forest, cultivated or not, dominated, indicating a more intense agricultural pressure. Both also presented abundant proportion of wetlands. Finally, landscape ''Savannah'' corresponded to 2 villages located in a specific area of savannah within the forest. Changes in forest cover, approximated by EVI difference between dry season 2001-02 and 2011-2012 were highest in landscapes ''Forest 2'' and ''Young forest''.

Estimation of landscape-associated risk. The use of a generalised linear model allowed estimation of BU incidence ratio (IR) from January 2002 to May 2012. Univariate analysis is presented in Table 2. In the landscape model, the highest risk zones corresponded to landscapes ''Urban Nyong'' and ''Rural Nyong'' compared to landscape ''Forest 1''. ''Young forest'' presented an intermediate risk and all other landscapes did not significantly differ from ''Forest 1''. In the Nyong River distance model, risk decreased with increasing distance to the river with a dose-response relationship.

For multivariate analysis, we combined Nyong distance and landscape and split landscape ''Cultivated forest'' according to the location of villages within or beyond the influence range of the Nyong, i.e. ''Cultivated forest, #10 km from Nyong'' and ''Cultivated forest, .10 km from Nyong''. All other landscapes were located within a single Nyong distance class or did not present significant differences in IR between the two distance classes. The resulting model (Table 3 andfigure ). Finally, risk for landscapes ''Forest 2'', ''Cultivated forest .10 km from Nyong'' and ''Savannah'' did not significantly differ from the ''Forest 1'' landscape. This model explained 41% of the variance between the villages. We performed a further analysis on the model residuals, and found that their spatial distribution presented no remaining spatial autocorrelation (Moran's I = 0.021, p = 0.65). This indicated that our model was able to capture most of the spatial pattern between the villages. Predicted incidence rate and actual cumulative incidence rate maps are presented in Figure 3B and 3C.

Temporal variations of BU incidence in each landscape. We studied the series of monthly incident cases for each landscape in order to characterise the temporal variations of BU incidence within the different landscapes (Supplementary Text S1, Figure S3). Landscape ''Forest 1'' presented only 4 cases during the study period (incidence of 0.2 cases/1,000py) and ''Savannah'' only 3 cases (0.5 cases/1,000py). Landscapes ''Urban Nyong'', ''Rural Nyong'', and ''Forest 2'' presented stable incidence rates over the study period, averaging respectively 2.1, 2.4 and 0.4 cases/1,000py. Finally, incidence was increasing in landscapes ''Cultivated forest'' and ''Young forest''. ''Cultivated forest #10 km from Nyong'' even presented an exponentially increasing incidence rate (R 2 = 0.97 for exponential fit compared to R 2 = 0.87 for linear fit).

Discussion

Our study relied on the analysis of 787 BU cases over 125 months of follow-up, which to our knowledge is amongst the highest reported incidences in an endemic region with more than 10 years of continuous follow-up [START_REF] Van Ravensway | Climate and landscape factors associated with Buruli ulcer incidence in Victoria, Australia[END_REF][START_REF] Morris | First detection of Mycobacterium ulcerans DNA in environmental samples from South America[END_REF]. We analysed BU spatio-temporal patterns and were able to reveal local-scale environmental determinants of BU incidence. We demonstrated that the Nyong River represented a major risk factor for BU, in conformity with previous studies of individual risk factors [START_REF] Pouillot | Risk factors for buruli ulcer: a case control study in Cameroon[END_REF] and environmental MU detection [START_REF] Marion | Seasonal and regional dynamics of M. ulcerans transmission in environmental context: deciphering the role of water bugs as hosts and vectors[END_REF][START_REF] Garchitorena | Mycobacterium ulcerans ecological dynamics and its association with freshwater ecosystems and aquatic communities: results from a 12-month environmental survey in Cameroon[END_REF]. We also identified different levels of risk along the river, which were associated to different environment profiles. We suggest that BU risk further increases with abundance of wetlands and with human modifications of landscape, such as cultivation and forest clearing. We also identified stable endemic areas and zones where incidence appears to be rising.

This work benefited from several methodological improvements compared to previous studies. By using the SPODT algorithm for identification of risk zones, we obtained a more accurate description than other studies [START_REF] Wagner | Buruli ulcer disease prevalence in Benin, West Africa: associations with land use/cover and the identification of disease clusters[END_REF], showing a decreasing risk gradient away from the Nyong River. By considering different categories of forest cover and management, cultivated and uncultivated wetlands, we accounted for local heterogeneities which would have been missed in broader analyses. Contrary to previous studies, which considered forest as a homogeneous category [START_REF] Wagner | Buruli ulcer disease prevalence in Benin, West Africa: associations with land use/cover and the identification of disease clusters[END_REF][START_REF] Brou | Landscape diversity related to Buruli ulcer disease in Co ˆte d'Ivoire[END_REF][START_REF] Van Ravensway | Climate and landscape factors associated with Buruli ulcer incidence in Victoria, Australia[END_REF] and found it a risk factor, we used a detailed LULC classification, ground-truthed, and a small buffer radius (5 km instead of 20 km). We demonstrated that the different forest categories presented different risk levels according to their status regarding human activities, and that BU risk followed a doseresponse relationship according to forest degradation [START_REF] Merritt | Ecology and transmission of Buruli ulcer disease: a systematic review[END_REF][START_REF] Hill | The environment and disease: association or causation?[END_REF]. Our study showed that BU incidence spatio-temporal patterns are complex, but might be explained for a large part by landscape characteristics and heterogeneities. We identified the Nyong River as a major driver of BU incidence in the Akonolinga region, and local scale environmental variations in the landscapes along the river were associated to significantly different risk levels.

These variations, distinguishing between landscapes at high and intermediate BU-risk were principally the proportion of wetlands, and the type and extent of forest cover. The proportion of wetlands was evaluated topographically (% surface with TWI.18) or in LULC descriptions, where cultivated wetlands occupied a larger surface in high-risk landscapes (''Rural Nyong'' and ''Urban Nyong'') and forested wetlands in intermediate-risk landscapes (''Young forest'' and ''Cultivated forest''). The type and extent of forest cover reflected the level of human modifications. In the highest-risk landscapes, forest cover was reduced and corresponded mainly to cultivated young secondary forest. These landscapes, located in the densely populated part of the district, are shaped by intense agricultural pressure, as indicated also by the proportion of cultivated lands, including wetlands. The intermediate-risk landscapes near the Nyong River, ''Cultivated forest ,10 km from the Nyong River'' and ''Young forest'', were less modified by human activities and retained important forest covers. The observed increase in incidence during the study period could result from recent environmental modifications: using only a crude measurement, we showed that ''Young forest'' is one of the landscapes with the largest decrease in EVI, indicating a decrease in forest cover. These areas of increasing incidence are located downstream from the floodplain of the town of Akonolinga. Speculatively, MU could have spread along the Nyong colonising new environments.

The landscapes at lowest risk, ''Forest 1'', ''Forest 2'' and ''Cultivated forest .10 km from Nyong River'', were mainly composed of villages located far from the Nyong River and corresponding to the most preserved environments. In ''Forest 1'' landscape, BU incidence was about 100 times lower than in highest risk areas, while it was only about 50 times lower in ''Forest 2'' landscape. Even if not significant, we observed the same trend of BU risk increase with increasing forest degradation level. We can propose that spatial variations of BU incidence in Akonolinga Health District resulted from the superimposition of two main factors: a high or low baseline risk related to the Nyong River proximity, and additional risks related to wetland abundance and environmental modifications by human activities. The role of the wetlands was supported by analyses of MU presence in Akonolinga water bodies, which showed that wetlands acted as a permanent reservoir of MU over the year, while other water bodies presented season-specific peaks of MU colonisation [START_REF] Garchitorena | Mycobacterium ulcerans ecological dynamics and its association with freshwater ecosystems and aquatic communities: results from a 12-month environmental survey in Cameroon[END_REF]. The increase in BU risk associated with human modifications of the environment could result from contact with newly accessible but pre-existing high-risk environments, from an increase in the number of contacts with risk sources due to populations increasing their activity range, or from the transformation of natural environments into high-risk sources by human activities (such as clearing wetlands for cultivation) [START_REF] Kilpatrick | Drivers, dynamics, and control of emerging vector-borne zoonotic diseases[END_REF]. The contribution of each phenomenon could be evaluated using chronological descriptions of the environment evolution, as well as of human practices.

The main limitation of this work was that it relied on ''semiactive'' case detection. We analysed data from cases which were diagnosed and treated at Akonolinga district hospital. They may not represent all the cases that occurred in the Akonolinga health district over the study period, since patients tend to seek traditional treatments as a first option [START_REF] Peeters Grietens | What role do traditional beliefs play in treatment seeking and delay for Buruli ulcer disease?-insights from a mixed methods study in Cameroon[END_REF]. However, since BU is a slowly progressing disease and difficult to cure even in hospitals, cases are likely to seek medical care at some point, eventually after failure of traditional treatment [START_REF] Peeters Grietens | What role do traditional beliefs play in treatment seeking and delay for Buruli ulcer disease?-insights from a mixed methods study in Cameroon[END_REF]. We analysed the spatial incidence trends over large periods of time, which probably allowed us to capture a large proportion of the incident cases over the study period, even patients with long delay to diagnosis. In addition, we cannot be sure that cases were infected in a given location, but given the activity patterns and our study scale, we can be confident that our work focuses on the main environments frequented by the populations.

This series of cases originated from a single treatment centre with a defined population-catchment area. The BU program in Akonolinga district has established a dynamic network of community correspondents in the villages, who contribute to population information and awareness, as well as case detection. Regular investigations are also performed in the area by medical staff and social workers in order to examine suspect cases and advocate for hospital treatment in a context where traditional treatment is generally the first option, despite free treatments [START_REF] Hill | The environment and disease: association or causation?[END_REF]. This coverage ensured that a maximum number of incident cases were detected, diagnosed and treated, and therefore included in this analysis. Comparisons with previous data from cross-sectional surveys in Akonolinga region support our assumption that cases treated at Akonolinga hospital are representative of cases occurring in the Akonolinga district [START_REF] Noeske | Buruli ulcer disease in Cameroon rediscovered[END_REF][START_REF] Porten | Prevalence of Buruli ulcer in Akonolinga health district, Cameroon: results of a cross sectional survey[END_REF] and support our description of localised increases in incidence.

Our results are consistent with previous results regarding individual risk factors. In a case-control study from 2006, wading in the Nyong swamps and not wearing long protective clothing while farming were identified as risk factors [START_REF] Pouillot | Risk factors for buruli ulcer: a case control study in Cameroon[END_REF]. We showed that BU is associated to agricultural areas near the Nyong and suggested that the clearing and cultivation of swamps could have contributed to risk increase at the population level. This hypothesis was supported by interviews performed during an anthropologic study in Ekougou and Abem, two villages located near the Nyong flood plain in ''Rural Nyong'' landscape. The informants related BU to the practice of clearing swamps for vegetable cultivation, which started about 20-25 years ago in the flood plain near Akonolinga [START_REF] Giles-Vernick | The puzzle of Buruli ulcer transmission, ethno-ecological history and the end of ''love'' in the Akonolinga district[END_REF]. It would be interesting to document if these practices occur in landscapes with increasing incidence, and when they started. The informants also incriminated large bushfires in the 1980s, which deeply modified the ecosystem of the Nyong floodplain [START_REF] Giles-Vernick | The puzzle of Buruli ulcer transmission, ethno-ecological history and the end of ''love'' in the Akonolinga district[END_REF]. The increase in population could also explain a new need for land in more remote areas of the district, triggering deforestation and BU.

Conclusion

The present work provides a quantitative assessment of the link between BU, slow flowing rivers, like the Nyong River, landscape features and their modifications by human activities. We clarify the role of forest, previously considered as a risk factor, by distinguishing pristine from human-perturbed ecosystems. We also underline major heterogeneities within Akonolinga endemic area, which presents stable high and low endemic zones, and zones with a rising incidence rate. Further studies regarding environment sampling for MU detection in endemic areas, or identification of risk-factors should take into account that environments at risk are defined at a very local scale. Surveillance of BU and active case search programs in endemic regions should also include the fact that BU geography can be substantially modified on a short time span, endangering new populations.

  2013a). MU occurs on a wide diversity of substrata such as mud, organic detritus, biofilms on aquatic plants and in a large diversity of aquatic micro-and macro-invertebrates, fish, amphibians, reptiles and mammals (Portaels et al. 2001; Marsollier et al. 2004; Benbow et al. 2013; Willson et al. 2013; Garchitorena et al. 2014; Morris et al. 2014). Sampling and study sites Periodic sampling of aquatic communities was performed in Akonolinga, Cameroon between June 2012 and May 2013. Monthly samples were collected in 16 aquatic communities within a region of approximately 3,600 km 2 including a wide spectrum of streams, rivers, swamps and flooded areas. Sampling was performed during five consecutive days, and in each aquatic community four locations were chosen in areas of slow water flow and dominant aquatic vegetation. Classification of aquatic invertebrates was performed at the family level (see Garchitorena et al. 2014 for details).

(

  Ostfeld & LoGiudice 2003). Stochastic extinctions and colonisations of species in communities may occur due to probabilistic dispersal of species between neighbour communities (Connor & Simberloff 1979; Hubbell 2001; Chase & Myers 2011

  1948). Additionally, we considered two potential confounding effects that may influence MU prevalence: deforestation and total abundance of animals in the community, regardless the taxonomic group -henceforth abundance Deforestation is known to affect Buruli ulcer (Landier et al. 2014) and may thus influence MU prevalence as well. To account for this confounding effect in the analyses we considered deforestation as the area deforested within a circular perimeter of 1km radius around each community, between the years 2000-2011 (details in SM4). Abundance was used as surrogate of productivity and/or the accumulation of animals due to physical forces unrelated to ecological/taxonomic diversity. GLMs assumed a binomial distribution of the response variable, predictors were standardized (ln xln x mean ), and normality of residuals was assessed by graphical exploration and Shapiro-Wilk' tests (SM5). Because multicolinearity may be due to correlation between predictor variables, we ensured that the variance inflation factors (VIF) of each parameter included in the models were < 5 (Heiberger & Holland 2004). Abundance and Shannon index produced multicolinearity, hence we performed separate GLMs to analyse their correlations

Figure 2 .

 2 Figure 2. Decomposition of Bray represent turnover of species abundance ( unidirectional changes in abundance ( nestedness in which site 2 is a subset of site 1 ( from Baselga (2013).

Figure 3 .

 3 Figure 3. Positive relationship between local prevalence GLMs) and the Shannon index of line) and upper and lower 95% confidence intervals

Figure 4 .

 4 Figure4. Relationships between regional prevalence of M. ulcerans and turnover and nestedness (β gradient ) in the lentic metacocommunity. The percentage of individuals infected by MU, predicted by GLMs, is presented as a gradient from light gray (lowest probability) to dark gray (highest probability). The observed values are presented by a cross and dashed lines show isolines of prevalence values. In general, regional MU prevalence increases in correlation with both turnover and nestedness.

  3A) indicated that ''Urban Nyong'' and ''Rural Nyong'' had the highest risk, respectively IR = 15.7 (95%CI = [4.2-59.2]) and IR = 12.5 (95%CI = [3.7-42.8]) compared to landscape ''Forest 1''. Landscapes ''Young forest'' and ''Cultivated forest #10 km from Nyong'' had intermediate risk, respectively IR = 7.9 (95%CI = [2.2-28.8]) and IR = 4.9 (95%CI = [1.4-17.4]

Figure 1 .

 1 Figure 1. Identification of the Nyong as a major risk factor for BU incidence in Akonolinga 2002-2012 (spatial analysis on timeaggregated incidence rate of BU in Akonolinga). A: Incidence rate per village (cases/1,000py). B: Decreasing risk of BU with increasing distance to the Nyong River. Homogenous risk areas of Akonolinga district were identified using the SPODT algorithm. Associated odds-ratio and 95% CI are provided. doi:10.1371/journal.pntd.0003123.g001

Figure 2 .

 2 Figure 2. Maps of spatio-temporal variations of BU incidence in Akonolinga district. A-D: Incidence rate maps for the periods, phases 1 to 4, identified in the time-series (cases/1,000py). doi:10.1371/journal.pntd.0003123.g002

Figure 3 .

 3 Figure 3. Landscape-associated risk of BU in Akonolinga district, 2002-2012. A: Classification of Akonolinga area villages according to landscape group and associated BU incidence ratio with 95% confidence interval. B: Predicted cumulative incidence for each village of the district according to the landscape model (cases/1,000py). C: Observed cumulative incidence rate for each village of the district (cases/1,000py). doi:10.1371/journal.pntd.0003123.g003

Belostomatidae dry season MESS values High : 100

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	E< # 5GB #%3=:#%< / ; !% G / , > , ; & ; , ; " , > 5 / & 5 + 5 + 3"3 ! "#M !6= 1 D ; 8 5 . ; B 8 ; * G & * G * 7 % * D : M 7 -7 7 7 : ) 0 7 ' 7 : ( 7 + ) + 3% & . Land cover Croplands Forest Savannah Swamp Urban Water bodies $ > > 0>1:;5) ) !=< B : %%=) * 0 $l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ational border Model testing sites (AG dataset) Model training sites (SME dataset) 0 350 700 175 Km "%% $ ( . $ 0 > 1 , %%! 0>1:; 5 . "( + "( 7 !#= > !!! , ( . %%#) 0>1:"D , , , 3 3 3( 5 ) 7 A : %% 2 ( ;( 0 : ( 9 %% 2 * ; 7 "( $ , && % 1 , && + 3 ) $ , + 3 G * ; * : , &&) B 3 < " % ( ' 74 J% %< 74 J% % * + 7 7 J% %6 74 J% %3 + ; $ ( ) * & A ( ; % %) , 0 350 700 175 Km 7 & A ( ) & ) 7 ( , B ) 7 Low : 0 ' B ) ;( 1 7 () %%<) National border > % <) 7 ( , % ) + 3 ; + 33 1 7 7 > ) 5 7 1 ;( + 0.30 0.35 0.40 % %) -; * ; ( ' # > * 0.000
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Correlation between Belostomatidae and Buruli ulcer

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	Belostomatidae habitat suitability

Correlation between Naucoridae and Buruli ulcer

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  a sixth mass extinction is occurring in the planet and species are declining locally at a high rate (Dirzo et al. 2014). Amid such events, preserving ecosystem health and human welfare requires understanding the consequences of local extinction of species on ecosystem function (Rafaelli 2004; Balvanera et al. 2006). Consequences of concern are the effects that eroding biodiversity produces on the emergence of zoonotic diseases in humans and wildlife (Harvell et al. 2002; Ostfeld et al. 2008). In this regard, the dilution effect hypothesis suggests that the percentage of individuals infected by a pathogen -henceforth prevalence-is influenced by inter-specific interactions that hinder infection of susceptible hosts and increase recovery of infected hosts

	SM1). Niche-based processes dominated the a
	epidemiological studies (Mihaljevic 2012). In principle, local (α) diversity of species in a processes dominated the assembly of lotic communities. Hence, we analysed t
	community would be determined by regional (γ) diversity in the metacommunity, and processes communities as two separated metacommunities, and e
	affecting β-diversity, i.e. the dissimilarity between communities (Whittaker 1972; Loreau 2000; to assess the effects of stochastic processes on
	Leibold et al. 2004; Chase & Myers 2011). β-diversity can be decomposed into two distinct prevalence. In general, we found two main results. First,
	informative components: species replacement (turnover) and loss of species in communities. Loss effect, generalized linear models (GLMs)
	of species in communities tends to generate nestedness of communities so that the poorest with α-diversity and γ-diversity
	community becomes a subset of the most diverse community (Ulrich & Almeida-Neto 2012; suggested that stochastic processes
	Baselga 2013). Furthermore, loss of species may be both random (stochastic) and niche-based, the prevalence and diversity. Finally,
	latter being ordered by species tolerance to abiotic conditions and symbiotic interactions (Tokeshi
	Communities are assembled and disassembled by both, (1) stochastic processes such as
	random extinction of species and probabilistic (passive) dispersal; and (2) niche-based processes
	including symbiotic interactions and species tolerance to abiotic conditions (Tokeshi 1999; Leibold
	et al. 2004; Helmus et al. 2007; Chase & Myers 2011; Moritz et al. 2013). In such instance, (lentic metacommunity), and nine communities mainly with water currents of the Nyong and
	predicting prevalence is daunting because host species may colonize and go locally extinct by the Mfoumou rivers (lotic metacommunity). A previous study suggested that the transformation of
	contribution of both, niche-based and stochastic processes, which are contingent on lentic systems into lotic systems, during 27 years, encompassed a shift in the processes assembling
	interconnectivity between local communities, ecosystem types, taxonomic groups, life history communities, from niche-based processes towards stochastic ones (Brown & Milner 2012). These
	observations were corroborated by metacommunity analyses on our data (Supplementary materials

(Keesing et al. 2006; Ostfeld et al. 2008). Hence, species diversity may reduce prevalence, and prevalence may boost if these inter-specific interactions are impaired by the local extinction of hosts and non-host species (Ezenwa et al. 2006; Suzán et al. 2009). Nevertheless, the underlying mechanisms of the dilution effect are controversial and elusive (Leibold et al. 2004; Randolph & Dobson 2012; Ostfeld 2013; Roche et al. 2013b). Further evidence suggests that the effects of biodiversity loss on prevalence are sensitive to disassembly rules determining the order at which host species go extinct in the community (Ostfeld & LoGiudice 2003; Lafferty 2012; Lacroix et al. 2013). For example, Ostfeld and LoGiudice (2003) found two contrasting relationships in a theoretical study. On the one hand, random extinction of host species reduced prevalence of nymphal ticks infected with Borrelia burgdorferi. On the other hand, extinction of species increased prevalence substantially when applying the disassembly rule that competent hosts (mice) were the last species to go extinct. These contrasting findings rest on the assumptions that local extinction of species is niche-based and competent hosts are persistent within local communities (Randolph & Dobson 2012; Johnson et al. 2013; Joseph et al. 2013). Therefore the generalization of these hypotheses must be investigated in the light of empirical evidence and integrative methods of community ecology (Leibold et al. 2004; Chase & Myers 2011). strategies, and inter-specific interactions (Diamond 1975; Connor & Simberloff 1979; Ricklefs 1987; Hubbell 2001; Bennett & Owens 2002). However, these processes can be analysed at the multiscale perspective of the metacommunity (Leibold et al. 2004), which is rarely used in 1999; Leibold et al. 2004; Helmus et al. 2007; Chase & Myers 2011; Moritz et al. 2013). Thus, if prevalence is boosted due to niche-based extinction, prevalence should positively correlate to the extent of nestedness once that stochastic effects are accounted for. We tested the above prediction by using a metacommunity framework (Fig. 1) to examine aquatic communities of tropical Africa and prevalence of the pathogenic bacterium Mycobacterium ulcerans (MU). MU produces a cutaneous necrotizing infection (Buruli ulcer) in humans inhabiting tropical rural areas in proximity to slow-flowing watercourses and stagnant water, and experiencing rapid environmental changes due to deforestation, agriculture and aquaculture (Wansbrough-Jones & Phillips 2006; Brou et al. 2008; Walsh et al. 2008; Merritt et al. 2010). Presence of MU is dependent on the composition of aquatic invertebrates and functional groups in the community (Benbow et al. 2013). However, it is unknown if these host species can amplify and transmit MU, or if they simply act as host carriers -henceforth hosts (Roche et al. 2013a). Here we investigated presence of MU on the five most represented and persistent orders of macro-invertebrates (Coleoptera, Diptera, Ephemeroptera, Hemiptera and Odonata), some of which are considered amplifiers of MU, e.g. Hemiptera (Portaels et al. 2001; Marsollier et al. 2003). To investigate diversity in the aquatic communities, we used a comprehensive and unique database for a Cameroon area near Akonolinga (Garchitorena et al. 2014). Within this region, two types of communities were recognized: seven communities with stagnant or very slow flowing water ponds positively correlated with nestedness in metacommunity, once that we accounted for stochastic processes represented b dispersal of species between communities.

Table 1 .

 1 Generalized

		starting model			final model
	predictors	b ± SE	Z (p)	VIF	b ± SE	Z (p)
	γ-diversity	-0.85 ± 0.74 -1.15 (0.252) 1.21
	abundance	-0.76 ± 0.28 -2.70 (0.007) 1.59 -0.57 ± 0.25 -2.29 (0.022)
	deforest	0.29 ± 0.14	2.06 (0.040) 1.60	0.25 ± 0.13	1.98 (0.047)
	connectivity constrained	0.50 ± 0.47	1.06 (0.291) 1.56
	turnover	1.68 ± 0.68	2.48 (0.013) 2.94	1.58 ± 0.60	2.66 (0.008)
	nestedness	1.19 ± 0.73	1.62 (0.105) 2.51	1.42 ± 0.69	2.04 (0.041)

linear models fitted on regional prevalence of Mycobacterium ulcerans (response variable) in 21 pairs of lentic communities. The final model (AIC = 130.5) was selected by stepwise (backward and forward) selection starting from the model with all principal effects (AIC = 131.8). All possible interactions between connectivity (constrained and Euclidean) and the other predictors were tested. Estimates (b), standard errors, Z tests supporting that b 0, and (VIF) variance inflation factor are presented. Results did not change in quality when using AICc penalization for small sample sizes during stepwise model selection. Significant parameters are presented in bold. Legend: γ-diversity is the Shannon index calculated for each pair of lentic communities, abundance is the total abundance of animals in each sample, regardless the species, turnover (β balanced ) is the replacement of species between pair of communities, and nestedness is the extent of nestedness (β gradient ) between each pair of communities.

Table 2 .

 2 Generalized linear models fitted on regional prevalence of M. ulcerans (response variable) in 36 pairs of lotic communities. Based on AIC, the final models were selected by stepwise selection of variables from the starting models containing only the principal terms (A and B models included γ-diversity, and C and D models included abundance as predictors in the starting model; seeSM 9). All two-way interactions between connectivity (constrained and Euclidean) and the other predictors were tested. All results presented here did not change in quality when using AICc penalization for small sample sizes during stepwise model selection. Abundance and γ-diversity were not included in the same GLM because they produced multicolinearity. Likewise, constrained connectivity and Euclidean connectivity were analysed in separate GLMs. Significant parameters are presented in bold. Estimates b, standard errors, and Z tests supporting that b 0, are presented. Legend: see Table1.

	Selected	A		B		C	D
	model (AIC)	(174.1)		(173.5)		(176.8)	(177.1)
	Predictors	b ± SE Z (p)	b ± SE Z (p)	b ± SE Z (p)	b ± SE Z (p)
	γ-diversity	3.06 +	4.51 (<	2.53 +	2.99
		0.68	0.001)	0.85	(0.003)
	abundance					-0.46 +	-3.69 (<	-0.45 +	-3.01
						0.13	0.001)	0.15	(0.003)
	deforestation					-0.20 +	-1.53	-0.24 +	-1.78
						0.13	(0.125)	0.14	(0.076)
	connectivity	1.47 +	2.01
	constrained (c)	0.73	(0.044)
	connectivity			-0.64 +	-1.45	-0.25 +	-0.61
	euclidean (e)			0.44	(0.146)	0.41	(0.544)
	turnover (t)	1.53 +	1.43	-1.14 +	-2.36
		1.07	(0.152)	0.48	(0.018)
	nestedness (n)			-4.68 +	-2.51	-3.04 +	-2.02
				1.87	(0.012)	1.51	(0.044)
	c:t	-3.31 +	-1.97
		1.68	(0.048)
	e:n			5.28 +	2.36	3.51 +	1.76
				2.24	(0.018)	1.99	(0.078)

  , 2D). According toOstfeld and LoGiudice (2003), niche-based extinction with persistence of host species in communities produces a boost in prevalence of the pathogen causing Lyme disease. In our study we cannot ensure that the hosts are persistent because is unknown which host species can amplify and transmit MU and/or act simply as host carriers (Roche et al. 2013a). Nevertheless, MU was detected in the order Hemiptera and four other orders that were persistent across communities in our study.

  Randolph & Dobson 2012; Benbow et al. 2013; Roche et al. 2013b). Hence, results suggest that direct or indirect impacts exerted on host diversity and habitats might have strong influence on the dynamics of environmentally-persistent microbial agents, and that understanding and forecasting emerging infectious diseases requires fundamental research on niche-based and stochastic processes that influence host communities and microbial forms within a whole metacommunity.

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Table 1 .

 1 Selected environment characteristics of landscape groups defined in Akonolinga district.

			Mean EVI in	EVI decrease	Area with	Forested	Cultivated		Total	Main
	Landscape	Major features*	December*	2001-12*	WI.18*	Wetland*	Wetland*	N villages	Population	watershed
	Savannah	Savannah (81%)	0,367	0,049	6%	0	0	2	538	Nyong
	Urban Nyong	Young cultivated	0,381	0,047	37%	7%	8%	7	17813	Nyong
		secondary forest
		(29%), Urban (5%),
		and cultivated land (9%)
	Rural Nyong	Young cultivated	0,392	0,062	20%	5%	3%	20	6656	Nyong
		secondary forest (25%)
	Cultivated Forest	Young cultivated	0,417	0,039	6%	10%	3%	16	5015	Nyong
	,10 km Nyong	secondary forest (37%)
	Cultivated Forest	Young cultivated	0,416	0,026	7%	10%	2%	31	8529	Dja
	.10 km Nyong	secondary forest (39%)
	Young Forest	Young secondary	0,417	0,074	5%	9%	+	12	3069	Nyong
		forest (38%)
	Forest 2	Adult secondary	0,415	0,076	5%	0	+	58	16729	Nyong
		forest (50%)
	Forest 1	Adult secondary forest	0,420	0,068	4%	+	+	8	1839	Dja
		(48%) and Primary forest
		(18%)

*median value for each landscape group; + present, ,1%. doi:10.1371/journal.pntd.0003123.t001

Table 2 .

 2 Univariate analysis.Incidence rate ratios estimated for the landscape groups and the distance to the Nyong River in 154 villages of Akonolinga district, Cameroon, 2002-2012. :10.1371/journal.pntd.0003123.t002

	IRR 1	[95%CI] 2	p-value

1

IRR: Incidence Rate ratio. 2 [95%CI]: 95% confidence interval. doi

Table 3 .

 3 Incidence rate ratios estimated for the landscape groups combined with Nyong River distance in 154 villages of Akonolinga health district, Cameroon, 2002-2012.

	Landscape group	IRR 1	[95%CI] 2	p-value
	Urban Nyong	15.7	4.2-59.4	,0.001
	Rural Nyong	12.5	3.7-42.9	,0.001
	Savannah	5.4	0.8-34.2	0.077
	Cultivated forest; #10 km to Nyong	4.9	1.4-17.4	0.014
	Cultivated forest; .10 km to Nyong	1.6	0.4-5.5	0.499
	Young forest	7.9	2.2-28.9	0.002
	Forest 2	2.0	0.6-6.6	0.277
	Forest 1	1	reference
	Deviance explained: 41%; Akaike Information Criterion: 578.4.

1

IRR: Incidence Rate ratio. 2 [95%CI]: 95% confidence interval. doi:10.1371/journal.pntd.0003123.t003

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PLOS Neglected Tropical Diseases | www.plosntds.org

September 2014 | Volume 8 | Issue 9 | e3123

PLOS Neglected Tropical Diseases | www.plosntds.org

ACKNOWLEDGEMENTS

This work is supported by the Fondation pour la Recherche sur la Biodiversité (FRB) through its Centre de Synthèse et d'Analyse sur la Biodiversité (CESAB) and its funded working group programme "Disentangling the linkages between biodiversity and emerging infectious diseases" (BIODIS), and with additional support from the international research programme DIVERSITAS.

GEGP received a post-doctoral fellowship from FRB/CESAB. GEGP and GS and are members of Asociación Mexicana de Medicina de la Conservación (Kalaankab). BR, KC, EC and J-FG were funded by the Laboratoire d'Excellence Centre d'Etude de la Biodiversité Amazonienne (grant ANR-10-LABX-25-01). AG, KC, BR and JFG were supported by a grant from the French National Research Agency (ANR 11 CEPL 007 04 EXTRA-MU). We also thank the BIODIS working-group from CESAB/FRB for insightful discussions on this study.

Acknowledgments

The authors would like to thank Tamara Giles-Vernick for sharing her understanding of population practices in Akonolinga, Juliette Paireau for numerous tips using R and ArcGIS, Benjamin Roche and Romulus Breban for useful insights on data analysis, Joachim Etouna (Institut National de Cartographie du Cameroun), Minia Vale `re (BUCREP) who contributed population and administrative data, Herve ´Chevillotte (IRD Cameroon), for environmental data from the IFORA project (ANR-Biodiv grant IFORA), the partner teams of the EXTRA-MU project, the Mycobacteriology Service staff at Centre Pasteur du Cameroun who performed diagnosis confirmation, and Drs Gervais Folefack and Michae ¨l Tsoungui at Akonolinga district hospital and their staff.

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'" 3= 3= *:"3 "% 6 =# 6= % %%b %\"#) +0# '< " =<6 *:" " <%" ! < + 3 ; ;< ;" ;( ;( "( Laboratory for BU in Cameroon at the Centre Pasteur du Cameroun. These data belong to the Cameroonian ministry of Health of which the Centre Pasteur du Cameroun belongs. The point of contact is Ms Vale ´rie Flore Donfack Donkeng at LNR PNLUB, Centre Pasteur du Cameroun (+237 22 23 10 15).

Funding: This research was supported by a grant from the French National Research Agency ANR 11 CEPL 007 04 EXTRA-MU (http://www.agence-nationalerecherche.fr/). Additional funding was obtained from the Fondation Raoul Follereau (http://www.raoul-follereau.org/). JL is supported by fellowships from the French Ministry of Higher Education and Research and from Fondation pour la Recherche Me ´dicale (FDT 20130928241). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

The authors confirm that, for approved reasons, some access restrictions apply to the data underlying the findings. Data are available on demand at the National Reference

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Ethics statement

This study used anonymised case data, aggregated by village and by month, which were collected by the Service de Mycobacte ´riologie of the Centre Pasteur du Cameroun as part of the surveillance activity of the National Reference Laboratory for BU in Cameroon, within the National BU Control Program.

Author Summary

Buruli ulcer (BU) remains a mysterious disease without efficient prevention since the mode of transmission of its agent, Mycobacterium ulcerans, is still unknown. The disease is highly localised within countries and even at the village scale within endemic regions, but environmental factors explaining this focal distribution have not been described yet. In this article, we rely on a large series of cases originating from Akonolinga region, Centre region, Cameroon, and recorded at the BU treatment center of the hospital of Akonolinga. The series of 787 patients over 10 years allows us to describe the distribution of BU incidence in the region and its changes over time and space. We identify the Nyong River as a major risk factor, and identify environmental factors along the river that further increase the risk of BU, such as the high proportion of swamps, the degradation of forests and cultivation of lands by human populations. These results will help to locate where the transmission is most likely to happen, and provide useful elements for targeting case search, prevention actions and future research on M. ulcerans transmission.

Supporting Information

Checklist S1 STROBE checklist.

(DOC)

Text S1 Supporting figures and detailed legends. Includes: Figure S1: Space-time analysis of BU incidence in Akonolinga. Figure S2: Principal components analysis and selected results.