Mycobacterium ulcerans is a slow-growing bacterium that causes a rare tropical disease in humans called Buruli ulcer. The infection affects the skin and underlying tissues, initially causing small painless lesions that can develop into large open sores or ulcers that are responsible for significant handicap in rural areas of sub-Saharan Africa.

Disease outbreaks generally occur in close association with stagnant and slow-flowing aquatic ecosystems, mostly after floods or other large environmental disturbances. It is believed that contact with water sources that contain the bacteria causes infection in humans, but the specific mode of transmission remains a mystery. By defining the factors that influence the bacteria's presence in the environment, public health officials could develop initiatives that would reduce an individual's risk of infection when conditions support a M. ulcerans outbreak.

To identify the environmental conditions that affect the prevalence of M. ulcerans in two regions of Cameroon where Buruli ulcer is present, Garchitorena et al. have now analyzed a large amount of ecological data about the bacteria using cutting-edge statistical techniques. This revealed that the amount of M. ulcerans varies following seasonal changes in climate, at least in the region dominated by tropical rainforest. In this region, the bacteria are also generally present in waters that are more alkaline and contain fewer animals, especially from certain species that could prevent the infection spreading to other aquatic hosts. In the other region, dominated by a savannah landscape, the bacteria are most abundant in stagnant or slowly moving waters that have optimal physical and chemical conditions and contain many diverse species of potential animal hosts. The discovery of contrasting results for the two regions suggests that there are at least two ways that M. ulcerans can persist in the environment and infect the aquatic animals. The prevailing method—through environmental transmission or through interactions between hosts—depends on the properties of the water.

Many other infectious diseases are caused by pathogens that, like M. ulcerans, infect many different hosts and persist in the environment for long periods. Future research following methods like those used by Garchitorena et al. would help to reveal whether these pathogens are affected by environmental factors in similar ways to M. ulcerans.

Despite increased understanding of infectious disease ecology and dynamics, recent decades have seen an upsurge in the emergence and re-emergence of multiple infectious diseases (Jones et al., 2008). Most emerging pathogens are zoonotic and have a very broad range of hosts (Woolhouse et al., 2001; Woolhouse and Gowtage-Sequeria, 2005), and as a result, host–parasite interactions are often embedded within complex host communities (Plowright et al., 2008; Roche et al., 2013b). Biotic interactions between hosts in the community may play an important role on disease transmission, either promoting or diluting the overall prevalence of the pathogen (LoGiudice et al., 2003; Keesing et al., 2010; Johnson et al., 2013; Roche et al., 2013a). In addition, the effect of seasonal variations in climate on abiotic conditions in water and soil can influence the composition of host communities and in turn have an impact on the ecological dynamics of the pathogens (Ostfeld et al., 2008). Because of the intertwined nature of biotic and abiotic drivers, disentangling their respective contribution to pathogen dynamics and transmission through complex and different-scale processes remains a challenge (Plowright et al., 2008). Nonetheless, identifying the underlying ecological mechanisms driving the emergence and persistence of diseases is essential to reduce disease risk in human populations (Roche and Guégan, 2011).

An illustrative example is the case of Mycobacterium ulcerans (MU), an environmentally persistent microorganism associated to freshwater ecosystems in tropical countries and present in a large variety of aquatic hosts (Benbow et al., 2008; Marion et al., 2010; Garchitorena et al., 2014). From a public health perspective, MU is the agent responsible for Buruli ulcer (BU), a devastating skin disease with great health and socio-economic consequences in tropical and subtropical countries (WHO, 2008). Emergence, distribution, and risk factors for BU in many parts of the world are associated with stagnant and slow-flowing ecosystems (Brou et al., 2008; Wagner et al., 2008; Jacobsen and Padgett, 2010; Marion et al., 2011). The environmental factors that favour MU persistence and transmission within these ecosystems are still poorly understood but the environmental and multi-host nature of the pathogen suggests that its environmental dynamics can be the result of a complex interplay between environmental factors and biotic interactions (Garchitorena et al., 2014; Morris et al., 2014).

MU is broadly present across taxa in aquatic communities over space and time (Benbow et al., 2008; Marion et al., 2010; Garchitorena et al., 2014), suggesting that a multiplicity of hosts can play a role in MU persistence in the environment. Biotic interactions between hosts are thought to be a pathogen transmission route between organisms (Merritt et al., 2010), with MU being integrated in the aquatic community from the environment thanks to filter feeder, herbivorous, and scavenger organisms and then transmitted across the community through predation (Marsollier et al., 2002, 2007a, 2007b; Mosi et al., 2008). As a result, community-level factors such as biodiversity or abundance of aquatic organisms could drive the environmental load of MU through amplification or dilution effects, as demonstrated for other pathogens (Ezenwa et al., 2006; Suzán et al., 2009; Keesing et al., 2010; Johnson et al., 2013). Furthermore, some keystone species could play an overwhelming role in the transmission and overall MU prevalence in host communities (Roche et al., 2013a).

Some specific water conditions, physical or chemical, could also favour MU environmental persistence and dynamics in aquatic ecosystems. MU seems to grow better under laboratory conditions with low oxygen, high temperature, and mildly acidic pH (Portaels and Pattyn, 1982; Palomino and Portaels, 1998; Palomino et al., 1998; Dega et al., 2000; Marsollier et al., 2004). Genomic studies show that MU is sensitive to UV light (Stinear et al., 2007; Doig et al., 2012) so turbid or protected environments could promote MU persistence. Many of these conditions are generally met in swamps and other stagnant and slow-flowing ecosystems, and therefore, if optimal abiotic conditions are met, MU could grow and persist in these ecosystems as free-living stages in the water and infect aquatic organisms directly, without the need for a trophic transmission to take place. Besides, numerous abiotic factors within aquatic ecosystems can influence host community structures and assemblages (Eric Benbow et al., 2013; Garchitorena et al., 2014), since aquatic invertebrate and vertebrate taxa have different ranges of optimal water conditions (Dickens and Graham, 2002). This could represent an indirect influence of abiotic conditions on MU transmission within aquatic communities.

A direct transmission of MU driven mostly by abiotic factors and a trophic transmission driven by biological interactions are not two mutually exclusive routes but rather could complement each other to allow persistence of MU under a wide range of environments. Identifying the contribution of such transmission routes requires a deep understanding of the dynamics of MU within aquatic communities through space and time. A recent characterization of MU dynamics with unprecedented detail in two BU endemic areas with very distinct environmental conditions (Garchitorena et al., 2014) offers the variability needed to address this question. Indeed, in Bankim, a region located in a transition zone between forest and savannah, swamps had remarkably higher MU positivity, as initially expected, whereas in Akonolinga, where rainforest is the prevailing landscape, all ecosystems had similar MU positivity. These regional differences suggested that savannah swamps had unique favourable conditions for MU that were not found elsewhere, but MU was still able to persist in unfavourable environments. Furthermore, temporal fluctuations in MU presence in Akonolinga suggested a potential role of seasonal climatic events as drivers of MU dynamics.

Relying on this work, the aim of this paper is to study for the first time the contribution of ecological factors, both biotic and abiotic, to the dynamics of MU in the aquatic environment. More specifically, we attempt to identify a set of abiotic conditions that could be optimal for MU growth and allow direct transmission to aquatic organisms, likely in stagnant waters and, in the absence of these, explore which biotic factors could still allow MU to persist, potentially through trophic transmission. Insights into the ecological mechanisms allowing for MU growth and persistence over space and time, while accounting for the potential impact of seasonal climatic events, may have profound implications for understanding BU risk to human populations.

To address these questions, we model MU positivity in 32 aquatic communities over time with generalized linear mixed models (GLMMs), including all relevant seasonal, abiotic, and biotic factors as fixed effects. We use cutting-edge multi-model selection procedures and information theory to identify and quantify the most important predictors of MU dynamics, using a genetic algorithm to screen multiple models from all potential combinations of explanatory variables and making inference from a set of weighted best-performing models. In addition, we back the results of this novel approach, which deals with the uncertainty associated with model selection, by comparing them to those obtained by classical model selection procedures. We then discuss the implications of disentangling biotic and abiotic factors for host/parasite interactions and the importance of rigorously analysing the underlying drivers of pathogen dynamics mediated through complex and different-scale processes.

By simultaneously accounting for multiple seasonal, abiotic, and biotic factors, our results show the complex interplay that drives MU environmental dynamics. In a previous step to the statistical modelling of MU positivity, we performed principal component analysis (PCA) of the most relevant physico-chemical characteristics in the water, as a means to explore common patterns in the ecosystems and to include these PCs as alternative abiotic predictors in the model. The ecosystems sampled and followed up in both Akonolinga and Bankim regions consistently revealed common physico-chemical patterns depending on water flow (Table 1). The first PC, explaining about 50% of variation in aquatic ecosystems in both regions, showed a positive correlation between water flow, dissolved oxygen, and pH, while temperature was inversely correlated. Furthermore, the inverse correlation between temperature and water flow held in the second PC, and the positive correlation between water flow and oxygen was also present in the third PC. Using multi-model selection for our GLMMs, a combination of seasonal factors, water conditions (abiotic factors), and community-level characteristics (biotic factors) remained as important predictors of MU positivity in the final set of best models for Akonolinga (Table 2) and Bankim (Table 3). Among all possible models tested, 39 were selected for estimation of model average estimates in Akonolinga and 100 in Bankim, since these models were similarly performing according to their Akaike Information Criterion (AIC) scores (see the methodology section). Although all factors were included together as part of the full model, the results explained below are divided in groups of factors for clarity. Furthermore, comparison of multi-model inference results with those obtained by classical model selection procedures can be found in Appendix 1, section 1.

Understanding how environmental factors influence host–pathogen interactions in complex natural systems, where multiple feedbacks between biotic and abiotic factors take place, is especially important in the context of multi-host and environmentally persistent pathogens. In this study, we identify abiotic and biotic drivers that may promote or block MU transmission in aquatic communities in two climatically distinct regions of Cameroon through a comprehensive multi-model selection procedure. In Akonolinga, we show that MU follows seasonal dynamics and is mainly present in waters with higher pH and within low abundance communities, notably those with low abundance of Gastropoda and other orders such as Decapoda, Hemiptera, or Anura. In Bankim, we show that MU is most prevalent in stagnant ecosystems and those with low water flow, with highly diverse (and abundant) communities.

A seasonal effect for MU presence in Akonolinga remains in our final models after accounting for abiotic and biotic parameters in the water bodies, which also vary seasonally. This suggests that seasonal fluctuations in MU presence might be directly related to climatic pressures (Figure 1). Indeed, while the seasonal effect is not directly linked to rainfall dynamics (Pearson's correlation test, p = 0.45), it is highly correlated with the 3-month mean rainfall accumulation in the region (Current month, plus two previous months; Pearson's correlation test, p < 0.01). As a result, we propose that the cumulative effect of rainfall over several months, increasing water levels in the environment either boosts MU growth or washes it off from other environmental matrices (mud, soil, plants) to aquatic ecosystems, as previously suggested from epidemiological evidence (Morris et al., 2014). Furthermore, given the slow growth of MU (Palomino and Portaels, 1998; Stinear et al., 2007), the 2-month delay between the peaks in the dynamics of rainfall and those of the seasonal effect could represent the time that takes MU to grow and/or be transmitted through the aquatic community once the suitable habitats have been created. Unfortunately, the less frequent sampling in Bankim (only 4 months instead of 12) may have prevented to capture seasonal variations appropriately, explaining the lack of associations with MU in this region.

Figure 1 with 1 supplement see all

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Our results for Bankim support the hypothesis that, under certain circumstances, conditions in stagnant and slow-flowing (lentic) bodies of water are favourable for MU presence. After controlling for all the other abiotic and biotic factors, sites with stagnant waters in this area have higher MU positivity than those with lentic waters (slow flow), and these have in turn higher positivity than sites with lotic waters (faster flow). PCA on physico-chemical parameters of these ecosystems provides some potential explanations (Figure 2). Sites with stagnant or lower water flows have higher temperatures (PC1 and PC2) and most have lower oxygen (PC1) and lower pH (PC1), all of which seem to promote MU growth in experimental studies (Portaels and Pattyn, 1982; Palomino and Portaels, 1998; Palomino et al., 1998; Dega et al., 2000; Marsollier et al., 2004). Indeed, a previous field study suggested that some water characteristics may be important for the presence of mycobacteria in water and biofilms throughout the year (Hennigan et al., 2013).

Figure 2

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While stagnant waters with lower pH contribute significantly to MU presence in Bankim, the results for Akonolinga show a positive association between pH and MU in this region. Significantly lower pH values in Akonolinga than in Bankim (t-test, p < 0.001) may be behind the disparity between the model results for each region (Figure 3). Indeed, pH range for slow-growing mycobacteria has been estimated between 5.8 and 6.5 (Portaels and Pattyn, 1982), which corresponds to the lower range of pH in Bankim, associated with stagnant waters. Because in Akonolinga, this optimal range corresponds to the upper range of values, stagnant waters with intolerably low pH might not meet all the optimal conditions for MU growth, which would explain the lack of association with these ecosystems (Garchitorena et al., 2014). The role of pH on MU growth in combination with other abiotic conditions needs to be urgently assessed, since it might be an important limiting factor in the environment.

Figure 3

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Biotic interactions seem to have an important effect on MU positivity in the local aquatic communities, especially in Akonolinga. Less abundant communities are associated with a reduction of MU in this region, and individual taxa have an independent effect on MU. Higher relative abundance of aquatic snails (Gastropoda), shrimps (Decapoda), water bugs (Hemiptera), and tadpoles (Anura) is associated with reduced MU prevalence in the aquatic community. The protective role of aquatic snails is supported by experimental infections, where MU has been unable to grow within these organisms (Marsollier and Sévérin, 2004), but this is not the case for Hemipteran water bugs, where MU can grow and even colonize their salivary glands after they have fed on infected prey (Marsollier et al., 2002, 2005; Mosi et al., 2008). Even though water bugs can host MU and allow its growth, they are voracious predators of aquatic organisms, and therefore, an increase in water bugs in the community may result in a decrease of infected prey available to other predators such as Coleoptera or Odonata; this could result in an overall reduction in MU positivity. This is an example of how considering the full breadth of factors taking place in real ecological systems can provide unexpected insights on this type of host–pathogen interactions. Furthermore, differences in community composition may partly explain the different effects of biotic factors in the two regions (Figure 3C–F). Total abundance in aquatic communities was significantly higher in Akonolinga (Mann–Whitney test, p < 0.001), and the relative abundance of more than half of the taxa included in our model was significantly different between Akonolinga and Bankim (Appendix 1, section 2).

These different distributions of biotic and abiotic factors can nevertheless yield a hypothesis to explain the contrasting results between the two regions. Indeed, two transmission routes, not competitively exclusive, may coexist for MU colonization of aquatic organisms, through a trophic transmission (Roche et al., 2013a) and/or a pathogen transmission through infection with free-living stages present in favourable aquatic environments (Merritt et al., 2010). In our study, community abundance has opposite effects in Akonolinga and Bankim, while water flow and pH suggest contrasted influence of stagnant waters in these regions. These results could suggest that the prevailing transmission modes could be different within these two environmentally distinct regions. Transmission could be mainly environmentally mediated in Bankim, since stagnant waters, through weak water flows and optimal physico-chemical conditions, are strongly associated with MU presence. Furthermore, a lack of association of MU abundance with specific taxa in addition to strong positive associations with host diversity and abundance under these favourable conditions, suggests that infection probability in these environments is density dependent, a characteristic feature of this type of transmission (Codeço, 2001). Conversely, in Akonolinga, trophic transmission may be expected since optimal abiotic conditions are not met in stagnant ecosystems, and host abundance has a negative impact on MU presence, suggesting that presence of some taxa, at least Gastropoda and Hemiptera, can limit transmission in aquatic communities. These alternative transmission routes proposed for MU to persist and thrive in aquatic ecosystems could partly explain why BU distribution in humans is greatly associated with stagnant ecosystems but expands over larger geographical regions (Brou et al., 2008; Wagner et al., 2008; Jacobsen and Padgett, 2010; Marion et al., 2011).

Our results demonstrate the complex interplay between abiotic and biotic factors driving the dynamics of multi-host/multi-environment diseases. By studying and comparing savannah- and rainforest-like regions, we provide a comprehensive ecological picture of MU, that is, a unified framework that reconciles the many contrasting findings observed during the last decade that could apply to a broader geographical area in the tropics and could help us understand the risk of BU for human populations. This study provides a new illustration of emerging infectious diseases for which further investigations looking for a ‘bigger picture’ are clearly needed in order to cope with the complexity of local and regional environmental situations, and different-scale processes. Judging by the number and importance of multi-host and environmentally persistent pathogens in the total number of emerging infectious diseases appeared in the last four decades such hypotheses deserve to be rigorously tested across multiple epidemiological systems and diverse local conditions. More comprehensive environmental studies in other contexts are needed to assess the generalizability of our findings.

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Author details

1. Andrés Garchitorena

   1. Maladies Infectieuses et Vecteurs: Ecologie, Génétique, Montpellier, France
   2. Ecole des Hautes Etudes en Santé Publique, Rennes, France

   Contribution

   AG, Conception and design, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article

   For correspondence

   andres.garchitorena@gmail.com

   Competing interests

   The authors declare that no competing interests exist.

2. Jean-François Guégan

   1. Maladies Infectieuses et Vecteurs: Ecologie, Génétique, Montpellier, France
   2. Ecole des Hautes Etudes en Santé Publique, Rennes, France
   3. International programme Future Earth, ecoHEALTH Initiative, Ottawa, Canada

   Contribution

   J-FG, Conception and design, Drafting or revising the article

   Competing interests

   The authors declare that no competing interests exist.

3. Lucas Léger

   Maladies Infectieuses et Vecteurs: Ecologie, Génétique, Montpellier, France

   Contribution

   LL, Analysis and interpretation of data, Drafting or revising the article

   Competing interests

   The authors declare that no competing interests exist.

4. Sara Eyangoh

   Laboratoire de Mycobactériologie, Centre Pasteur du Cameroun, Réseau International des Instituts Pasteur, Yaoundé, Cameroon

   Contribution

   SE, Conception and design, Drafting or revising the article

   Competing interests

   The authors declare that no competing interests exist.

5. Laurent Marsollier

   Institut National de la Recherche Médicale U892 (INSERM) et CNRS U6299, équipe 7, Angers, France

   Contribution

   LM, Drafting or revising the article, Contributed unpublished essential data or reagents

   Competing interests

   The authors declare that no competing interests exist.

6. Benjamin Roche

   International Center for Mathematical and Computational Modelling of Complex Systems (UMI IRD/UPMC UMMISCO), Bondy Cedex, France

   Contribution

   BR, Conception and design, Analysis and interpretation of data, Drafting or revising the article

   Competing interests

   The authors declare that no competing interests exist.

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