Modelling climate change impact on the spatial distribution of fresh water snails hosting trematodes in Zimbabwe
© Pedersen et al.; licensee BioMed Central. 2014
Received: 6 June 2014
Accepted: 11 November 2014
Published: 12 December 2014
Freshwater snails are intermediate hosts for a number of trematodes of which some are of medical and veterinary importance. The trematodes rely on specific species of snails to complete their life cycle; hence the ecology of the snails is a key element in transmission of the parasites. More than 200 million people are infected with schistosomes of which 95% live in sub-Saharan Africa and many more are living in areas where transmission is on-going. Human infection with the Fasciola parasite, usually considered more of veterinary concern, has recently been recognised as a human health problem. Many countries have implemented health programmes to reduce morbidity and prevalence of schistosomiasis, and control programmes to mitigate food-borne fascioliasis. As these programmes are resource demanding, baseline information on disease prevalence and distribution becomes of great importance. Such information can be made available and put into practice through maps depicting spatial distribution of the intermediate snail hosts.
A biology driven model for the freshwater snails Bulinus globosus, Biomphalaria pfeifferi and Lymnaea natalensis was used to make predictions of snail habitat suitability by including potential underlying environmental and climatic drivers. The snail observation data originated from a nationwide survey in Zimbabwe and the prediction model was parameterised with a high resolution Regional Climate Model. Georeferenced prevalence data on urinary and intestinal schistosomiasis and fascioliasis was used to calibrate the snail habitat suitability predictions to produce binary maps of snail presence and absence.
Predicted snail habitat suitability across Zimbabwe, as well as the spatial distribution of snails, is reported for three time slices representative for present (1980-1999) and future climate (2046-2065 and 2080-2099).
It is shown from the current study that snail habitat suitability is highly variable in Zimbabwe, with distinct high- and low- suitability areas and that temperature may be the main driving factor. It is concluded that future climate change in Zimbabwe may cause a reduced spatial distribution of suitable habitat of host snails with a probable exception of Bi. pfeifferi, the intermediate host for intestinal schistosomiasis that may increase around 2055 before declining towards 2100.
KeywordsSnail Species distribution modelling Climate change Regional climate models Schistosomiasis Fascioliasis
Schistosomiasis is a major health concern in many parts of the world where an estimated 207 million people are infected and 779 million are at risk of infection ,; and 85% of the people live in countries south of the Sahara. The high prevalence of schistosomiasis in Zimbabwe is well known from a recent national survey carried out in 2010 and 2011 . More than 2.2 million (18%) persons are estimated to be infected with Schistosoma haematobium, the cause of urinary schistosomiasis and close to 900,000 (7.2%) with S. mansoni causing intestinal schistosomiasis  (population numbers based on Tatem et al.). Fascioliasis, caused by Fasciola spp, primarily known to be of veterinary concern is increasingly recognised to be responsible for morbidity in humans with estimates of up to 17 million human infections ,-. As in most other parts of the world, the prevalence of human fascioliasis has not been intensively investigated in Zimbabwe, though prevalence of up to 5% has been reported ,. Fascioliasis affects ruminants and prevalence of 90% has been reported in cattle in some areas of Zimbabwe .
Parasites and the snail intermediate host are poikilotherms, and their intrinsic rate of development is dependent on temperature, which becomes an indirect predictor of transmission risk, however, other climatic and environmental factors contribute to the delimitation of their spatial distribution. Georeferenced collection-points for snail observations, in combination with environmental predictors, mainly climatic, were used to develop a model for prediction of spatial distribution for each of the three snail species in Zimbabwe, by the use of the Maxent modelling software . The prediction models were parameterised with climate projections using the regional climate model HIRHAM5 - for periods representative for present-day climate and two future periods.
A recent study from Zimbabwe substantiated how the snail distribution has changed in the 24 year period from 1988 to 2012 and that this may be the consequence of a change in climate . This earlier study focused on short term climatic changes, i.e. year-to-year variability rather than the decadal variability which is investigated in this paper. The question now remains how climate change may affect snail distribution and consequently the impact of schistosomiasis and fascioliasis in the future.
The distribution of the aforementioned parasitic infections is reliant on the presence of their respective intermediate snail host species. Distribution of the snails can thereby provide information on disease distribution though the presence of parasites and exposure are also the determining factors. A unique opportunity of having comprehensive data on schistosomiasis and fascioliasis prevalence from Zimbabwe enabled a translation of the Maxent model output of habitat suitability into a distribution, i.e. delimitation of area of occupation of the snails.
The aim of the current study was to predict the nationwide spatial distribution of three trematode intermediate snail host species: Bulinus globosus (Morelet 1866), Biomphalaria pfeifferi (Kraus 1848) and Lymnaea natalensis (Kraus 1848) for present-day climate, and to forecast the distribution in a future climate, based on a climate change projection model. The overall present and future spatial distribution of potential suitable snail habitat is reported for Zimbabwe and the impact of climate change is discussed. Furthermore, the habitat suitability modelling results are translated into areas of occupation of the three snail species.
Study area and sampling method
Zimbabwe is a landlocked country situated in the southern tropical zone and comprises an area of 390,757 km2. Two bio-climatic zones exist, the highveld (1000-1500 m.a.s.l) and lowveld (500-1000 m.a.s.l.), primarily distinguished by high and low rainfall patterns, respectively. The highveld covers most of central Zimbabwe stretching in a southwest-northeast direction and the lowveld covers most of the northwest and southeast. There is a rainy- (Dec- Feb), post-rainy- (Mar -May), cold-dry- (Jun -Aug), and hot-dry (Sep -Nov) season -.
Model test statistics
0.00 – 0.81
0.00 – 0.88
0.00 – 0.90
AUC test statistics:
Sensitivity | specificity:
0.64 | 0.53
0.45 | 0.82
0.48 | 0.77
Variable contribution (%)
Precipitation of wettest month
Precipitation of warmest quarter
Precipitation of driest month
Precipitation of wettest quarter
Precipitation March, April-May
Temperature of driest quarter
Precipitation of driest quarter
pH – soil**
The modelling software, Maxent  was used to predict snail habitat suitability from snail presence data according to environment and climatic variables. The output from Maxent was considered as the probability of snail presence expressed as a map layer of habitat suitability on a scale of 0 - 1 for non-suitable and suitable habitat, respectively. The environmental data were loaded into Maxent, covering an area of 10 by 10 arc degrees, encompassing Zimbabwe and parts of neighbouring countries, and had a resolution of 0.1 by 0.1 arc degrees. Maxent was set to sample 10,000 background samples from the environmental variables, during fitting of the distribution model. Collection sites holding one or more specimen of a snail species were introduced by the ascribed coordinates of the collection sites. Sites, from where no specimens of the modelled species were found, were not used in the model. The average of 10 replicate model runs was reported and the model initialisation used random seeds and 10% of the observations were set aside for model testing.
All commonly accepted ecological zones in the modelling domain were present among the snail observation data to comply with Maxent’s constraint to not predict into novel eco-zones . Maxent provides a number of arithmetic products of the predictors denoted as “feature classes” which, in this study, were limited to “linear” and “quadratic”, omitting “product”, “threshold”, and “hinge”, due to the non-intuitive, function of these features, in terms of snail biology, and due to the non-linear response of the species to some of the environmental variables, following the recommendations of Merow . Area Under the receiver operator characteristic Curves (AUC) of the test data are reported as an expression of model performance as suggested by Liu , and is supported by measures of sensitivity and specificity following recommendations by Hu and Jiang . A build-in, Jack-knife procedure was used to quantify the explanatory power of each environmental variable.
The 1990 climate projection data were used to fit the snail habitat suitability prediction model and subsequently parameterised with climate projection data for 2055 and 2090, to produce a climate change impact prediction.
The prediction of Bi. pfeifferi is depicted in Figure 2d–f. The highveld and eastern highlands constituted the most suitable habitat in 1990 with a more distinct gradient between high- and lowveld compared to that of B. globosus. All parts of Zimbabwe are predicted to be highly suitable by 2055 (Figure 2e) forming the basis for increased transmission risk of intestinal schistosomiasis, but with a significant reduction toward the end of the century; however, areas with medium suitability are still present in the central highveld in 2090 (Figure 2f). Biomphalaria pfeifferi experience an increasingly favourable climate towards 2055 (Figure 3d) and, like B. globosus, a reduction towards the late century (Figure 3f). It is noteworthy that Bi. pfeifferi does not experience a linear habitat suitability reduction throughout the modelling period.
Suitable habitats for L. natalensis (Figure 2g) are predicted for the highveld and in the southern part of Zimbabwe for 1990. Furthermore, areas with low values for suitable habitats are present to the northwest and southeast. It is predicted that the distribution of suitable habitats are reduced in 2055 though relatively suitable habitats are present in large parts of the former core areas (Figure 2h). By 2090, most of the country is absent of suitable habitat, where only the very central highveld and the eastern highlands are predicted to be relatively suitable habitat (Figure 2i). The reduction follows a steady gradient throughout the modelling period (Figure 3g and Figure 3i).
Model test statistics to establish model performance for predicting habitat suitability, and the accuracy of snail occurrence predictability are reported in Table 1 in the form of AUC, and measures of sensitivity and specificity. AUC values of 0.737, 0.771, and 0.765 (B. globosus, Bi. pfeifferi, and L. natalensis, respectively) indicate acceptable modelling performance whereas sensitivity scores of 0.45 and 0.48 for Bi. pfeifferi and L. natalensis, respectively, indicate poor ability to predict where snails are present. The model for B. globosus is to some extent better at predicting true presence with a sensitivity score of 0.64. The ability of the model to predict areas where snails are absent is fairly good for Bi. pfeifferi and L. natalensis with specificity scores of 0.82, and 0.77, respectively, whereas the score for B. globosus is low (0.53).
An entirely statistical approach to delimitate suitable versus unsuitable habitat can be used by making use of a statistical output from Maxent: the “maximum test sensitivity plus specificity logistic threshold”. These statistics state that the threshold for viable snail populations should be found at habitat suitability index classes of 0.45, 0.49, and 0.43 for B. globosus, Bi. pfeifferi, and L. natalensis, respectively (Table 1). Binary maps based on these thresholds are presented in Additional file 4.
In this study, it is shown by predictions of snail habitat suitability for B. globosus, Bi. pfeifferi and L. natalensis, that there is a distinct gradient of suitability across Zimbabwe. The three species share large areas with high suitability but also have unique “hot-spots”. Changes of spatial distribution in the future climate of 2055 and 2090 are apparent with a trend towards more locations with unsuitable habitats; though suitable habitats are still present (Figure 2). The predicted distribution of Bi. pfeifferi in 2055 indicates a substantial increase in habitat suitability. If this expansion is ascribed to the 3.1°C increase in the period averages, as it is observed in the temperature data for March, April, and May (Additional file 5), it can be concluded that the Bi. pfeifferi snail tolerates higher temperatures than the other two species. Even so, the temperature becomes above optimal at the end of the century. Biomphalaria pfeifferi is also the species that finds most suitable habitats in 2090 and in fact have a substantial area of distribution (Additional file 4f). The area of occupation, as opposed to habitat suitability, for the three snail species is estimated by evaluating site specific parasite transmission status in relation to habitat suitability index, and by test statistic. This information may suggest that snail populations are viable at approximately a suitability index of >0.4 with some variation depending on species, method, or purpose. It should be noted that defining a threshold can be controversial and may depend on the purpose. Conservationists, for example, may argue for a less conservative delamination (lower threshold) in questions regarding habitat protections. It may seem surprising that there are schools and dip tanks where transmission is on-going in almost all of the index classes, even classes with suitability index below that of the suggested 0.4. This may be explained by imported cases carrying transmission from elsewhere where habitat suitability is in fact high, a too coarse resolution of the model output or simply that snail populations are viable even at the lowest suitability index classes. The transmission-positive schools in low index classes along the shores of Lake Kariba may be an artefact of the model perhaps not “catching” the truly suitable habitats (the model may misclassify these areas and assign erroneously low suitability index). In addition, it is shown that many schools where transmission was not occurring are present in areas with highly suitable habitats which may be explained by possible on-going local treatment campaigns and/or by prioritisation of schistosomiasis survey efforts in areas otherwise known to have low incidence of schistosomiasis. Fasciola transmission-positive dip tanks in areas with a suitability index below 0.4 may be explained by dip tanks having a large catchment area i.e. cattle have been infected in adjacent high-index areas. In fact, the positive dip tanks at low suitability class locations are all found in areas close to the line of delimitation (Additional file 4g). Finally, the fact that the prevalence data of both schistosomiasis and fascioliasis, and snail observation data do not overlap in time, will inevitably lead to some deviations.
Variable-contribution reported in Table 1 informs about what factors might be driving the distribution model. The average temperature of March-May is by far the most contributing factor for the three snail species (49% to 70%) indicating that temperature may be the main driver for the distribution. All other variables have a contribution of 18% or less, and temperature (seasonality), again, has a higher degree of contribution together with two datasets on precipitation (seasonality and precipitation in the wettest month).
The AUC statistics indicate an “acceptable” model performance  but low sensitivity for Bi. pfeifferi and L. natalensis, implying that the model is less capable of predicting where these species are present, whereas more confidence can be put into the model’s ability to predict where snails are not likely to be found. For B. globosus the situation is the opposite with better performance at predicting true positives as opposed to true negatives.
The quality of input data greatly influences the performance of any model. Snail occurrence data used in this study, are “plenty” for Maxent to characterise the environment at sampling sites . Sampling bias greatly influences reliability of model output. We do not have control of sampling procedure but know that many types of habitats have been sampled e.g. ponds, rivers etc. and in most parts of Zimbabwe. Additionally, we find many absence observations in the original dataset, suggesting that collection sites were not chosen after where specific species were expected to be present. In fact we see that sampling success rates are similar to that of the authors’ own study  where special attention was paid to sampling bias. Environmental data are of satisfactory resolution though more variables like e.g. NDVI and Growing Degree Days may have contributed to model reliability.
Compared to other combinations of GCMs and RCMs, the present study yields very dry future climate, even though the precipitation changes in the driving GCM (not shown) are much smaller. This somewhat counterintuitive behaviour can be explained with changes in soil moisture. In the HIRHAM RCM, soil moisture dries faster than in the GCM, thus leading to a further increase in temperature and less precipitation. There are some indications , for a decrease of precipitation during March-May, but the overall model spread is rather large and the mechanisms are not well understood .
The climate model data used in this study is downscaled from relatively low-resolution into high resolution regional fields, but the regional model can evolve freely apart from the forcing data moving into or out of the RCM domain from the driving model. Since the driving data is from a model rather than observations, individual events cannot be compared directly; however, in a statistical sense such a comparison is possible. Information on in-year weather extremes could therefore have been taken into account in this study but due to the data implementation, using 20 year averages, such weather events were not present in the data. Extreme events, such as floods, dry spells, and heat waves would most likely cause an even further reduction in the snail habitat suitability, as snails cannot exist in water at higher velocities than 0.3 m/s  and they can only survive dry-spells for a limited period of time ,.
The temperature is expressed as ambient temperature at 2 m above ground as opposed to temperature in the habitat water. The correlation between ambient- and water temperature may change between locations and the relationship may change in changed climate conditions .
Describing alkalinity (pH) of habitat water and its relation to snail biology has proved complicated. Diurnal variation, of photosynthesis in the water, faecal contamination, and upstream soil pH influence the snails in a non-straightforward manner ,, but the models still include the pH dataset as a predictor. Furthermore, the pH dataset used here  is based on pH in soil water and it is possible that geophysical characteristics are the underlying driver.
Some flaws in the data and modelling implementation can compromise conclusions on habitat suitability, distribution and impact of climate change. Global Positioning Systems (GPS) were not readily accessible in 1988 wherefore sampling locations were simply designated the arithmetic centre of a predefined grid of 26.5 km by 26.5 km. The consequence is that the collection sites and the environmental variables (10 km by 10 km resolution) are misaligned at some locations. There is a number of reasons why this is not considered to conflict with the conclusions of the modelling results: i) the variables most often have similar values in neighbouring cells, ii) variables are averages taken over a 20 year period, iii) and in some cases, averages over three months.
The presence of intermediate host snails is pivotal for disease transmission but at the same time it is not the only element in the parasite life cycle. Climatic variables and the geophysical environment also influence directly on the schistosome and Fasciola parasites’ free living life stages i.e. egg, miracidia, cercaria, and metacercaria (Fasciola). Thus, when discussing snail habitat suitability as predictor for schistosomiasis and fascioliasis, modelling of cercaria survival could be included to give an advantage such as exemplified by Stensgaard  and Valencia-Lopez , where development rate of the cercaria in relation to temperature was included.
In the present study the models based on snail presence data and climatic/environmental input data for two different time periods suggested that snail populations will experience less favourable conditions in Zimbabwe in the future, except for Bi. pfeifferi in mid-century. Some populations within Zimbabwe are already at the edge of their range of occupation, wherefore some populations are likely to disappear and consequently parts of Zimbabwe could become free of transmission of schistosomiasis and fascioliasis, though it may be speculated that a series of more favourable years in a generally unfavourable climate period can lead to re-establishment of snail population and subsequently transmission. An important factor would be the rate of reestablishment of snail populations, and parasite re-introduction. Snails are known to spread fast by eggs being transported by aquatic birds on feet or in plumage - and parasites can be introduced rapidly by infected human and animals. C Appleton and H Madsen  describe the re-emergence of schistosomiasis in a community in South Africa where it is indicated that the reintroduction correlated with climate fluctuations. In-depth studies on re-emergence of disease, including timelines and climate, based on the biological studies of snails and parasites and change in the environment can provide knowledge on the challenges in the future.
Finally, climate change may drive schistosomiasis and fascioliasis towards elimination in Zimbabwe in the far future of 2090, although other factors such as land-use changes, transmission awareness and interventions may play an important role on the distribution and may in fact overrule that of climate.
National Institute of Health Research, Ministry of Health and Child Care, Harare, Zimbabwe is acknowledged for kindly providing schistosomiasis prevalence data.
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