Epomops franqueti
INTRODUCTION
Humans are affected by a large number of infectious diseases. Most of these infectious diseases are zoonoses. Most of zoonoses have mostly viral origin and are emerging and reemerging. The viral diseases of the Filoviridae family, such Ebola, causes particularly hemorrhagic fevers. Ebola hemorrhagic fever is indeed among the zoonotic viral disease with high mortality rate. The Ebola virus causes severe morbidity and high mortality in humans and wildlife (Bausch and Schwarz, 2014; Fiorilloet al. , 2018).
Number of Ebola virus disease outbreaks have occurred in humans last forty years, with mortality rates reaching values up to 90 % (Feldmann and Geisbert, 2011; Bausch and Schwarz, 2014). Most of Ebola hemorrhagic fever outbreaks have historically occurred in Central Africa. The first outbreak of Ebola virus in humans was registered in 1976 in Southern Sudan (Report of a WHO/International Study Team, 1978), but likely occurred as early as 1972 in Tandala, DRC. Until 2014, DR Congo had already recorded seven outbreaks of Ebola (Maganga et al. , 2014). There have been two other outbreaks, the latest of which is in Beni-Butembo, which is still at the beginning of 2019. Thus, to date, the DR Congo has already recorded nine Ebola outbreaks being hence one of the countries that have experienced the most Ebola outbreaks.
Up to now, it is not well understood the way the Ebola transmission between outbreaks is maintained (Peterson et al. 2004). However, significant progress has been made in identifying the potential reservoirs of Ebola viruses. Recent studies have identifiedHypsignathus monstrosus (H. Allen, 1861), Myonycteris torquata (Dobson, 1878) and Epomops franqueti (Tomes, 1860), three bat species, as the most likely to be the reservoirs of the Ebola viruses (Leroy et al. , 2005; Groseth et al. , 2007; Peterson et al. , 2007; Pourrut et al. , 2009; Haymanet al. , 2010; Olival and Hayman, 2014). Leroy et al. (2007) have shown that in Africa, there is evidence of Ebola outbreaks in humans due to exposure to bats. Based on this evidence, it can be assumed that the distribution of Ebola viruses is limited by certain factors, including the distribution of bats such H. monstrosus, M. torquata andE. franqueti (Nyakarahuka et al. , 2017).
It is believed that climate change will affect future distribution of bats reservoirs of Ebola virus. A number of studies have reported the impact of climate change on Ebola outbreaks in Africa and the migratory patterns of bats (Newson et al. , 2009). Indeed, migratory species are particularly likely to be affected by climate change at some point in their life cycles, and there is already compelling evidence for impacts on a wide range of birds, marine mammals, fish, sea turtles, squid, bats, terrestrial mammals and insects (Robinson et al. , 2009). Reduced precipitation, increasing temperatures and desertification have caused a large number of fruit bats to migrate from their ecological niches in the equatorial rain forest to other areas where environmental conditions are more favorable for survival (Omolekeet al. , 2016).
Actually, the world is facing climate change and this has been for decades. The effects that climate change is having on wildlife populations are of increasing interest to ecologists (Adams and Hayes, 2008). The survival of species and integrity of terrestrial ecosystems are threatened by the climate change. To adapt to climate change, a thorough knowledge of its impact on plant and animal species is of crucial importance (Fandohan et al. , 2013). Recent research increasingly shows that climate change will significantly affect biodiversity and species distribution. With the most recent research, one of the hypotheses put forward is that in Africa, 25 to 42 % of plant and animal species could be threatened and could thus lose up to 90 % of their geographical distribution areas by 2085 if global warming exceeds 1.5 to 2.5 degrees Celsius (Busby et al. , 2012). Elithet al. (2010) and Guisan and Thuiller (2005) have enumerated modeling tools among which species distribution models. The utilization of species distribution models is spreading out (Martínez-Meyer et al. , 2004; Papeş and Gaubert, 2007; Tsoar et al. , 2007; Jenningset al. , 2013). Species distribution models are numerical tools that provide potential distribution, aid in conservation planning and their project future behaviour in response to environment changes (Peterson, 2003, 2006; Thorn et al. , 2009; Barve et al. , 2011). The main objective of mapping spatial distribution of vectors and reservoirs of diseases is to manage diseases impacts by providing geographic information that enables decision-makers to make evidence-based decisions (Benedict et al. , 2007; Lindsay et al. , 2010) or the planning and targeting of surveillance and interventions (Dicko et al. , 2014). An additional motivation of the wide use of species distribution models techniques comes from the high impacts of diseases on humans, animal and plant and the fact that environmental drivers play a major role in their emergence (Chaveset al. , 2008; Jones et al. , 2008; Pautasso et al. , 2010). During last decade, modeling techniques have been developed to model ecological distribution. Maximum entropy (MaxEnt) approaches have recently been introduced and used by many research work and have shown to be very successful tool (Peterson et al. , 2007; Elith et al. , 2010). Several studies comparing species distribution modeling techniques indicated that MaxEnt modeling (Phillips et al. , 2006) performed as well or better than the other techniques (Elith et al. , 2006, 2011; Hernández et al. , 2006; Phillips et al. , 2006; Phillips and Dudík, 2008; Baldwin, 2009). As such, considering the new features integrated in the new release of MaxEnt program (Phillips, 2017), it should be a very useful and accurate tool for delineating species distributions.
Modelling H. monstrosus, M. torquata and E. franquetispecies distribution using MaxEnt modelling technique could essentially improve our understanding of the spatial distribution of current and future risk of increased or decreased distribution of these species.
In order to better understand the nature of Ebola viruses risk, this study aims to define areas of DR Congo where zoonotic transmission of Ebola viruses can occur, currently and in the future. Thus, by studying the spatial distribution of H. monstrosus , M. torquata andE. franqueti , potential reservoirs of the Ebolavirus, this study seeks to identify their current and future favorable and suitable areas in DR Congo. This study illustrates the climate change risk assessment of the spatial distribution of these species according to the climate scenario in 2050 (2041 - 2060) and 2070 (2061 - 2080), and to seek attention of their future favourable habitats in DR Congo.
MATERIALS AND METHODS
Occurrence records
In this study, due to the lack of sufficient occurrence data of DR Congo, we used occurrence data from the entire African continent to build the model and make the prediction only in DR Congo. The geographical coordinates (longitude and latitude) of H. monstrosus, M. torquata and E. franqueti were collected online from the GBIF database (http://www.gbif.org). All occurrence records were checked for accuracy in ArcGIS prior to use. The data were quality controlled in order to eliminate or remove suspicious or duplicate records (Lobo et al. , 2008; Warton and Shepherd, 2010; Stigall, 2012). A total of 123 observation points of H. monstrosus , 79 M. torquata and 201 E. franqueti in Africa continent were used for the modelling. In DR Congo, only 10 occurrence records of E. franqueti , 9 of H. monstrosus and 7 ofM. torquata were found. It was not possible to model the distribution of these species in DR Congo using only these data, since they were insufficient. This is why we came up with the idea of using data from all of Africa’s occurrence records to build the models and do prediction only on the extent of DR Congo. The map representing the points of occurrence is illustrated in Figure 1.
Environmental variables
In this study, we used elevation data together with bioclimatic data. Elevation data (Digital Elevation Model) was obtained from USGS database (https://earthexplorer.usgs.gov) and the current and future climate data were collected from WorldClim database (http://www.worldclim.org) and used to build the species distribution model in order to find the suitable areas for H. monstrosus, M. torquata and E. franqueti . Bioclimatic data collected from the WorldClim database are obtained from interpolations of monthly averages of precipitation and temperature taking into account climate data collected over long periods of time (Fick and Hijmans, 2017). The 19 bioclimatic variables (bio1; Mean Annual Temperature, bio2: Mean Diurnal Range, bio3: Isothermality, bio4: Temperature Seasonality, bio5: Maximum Temperature of Warmest Month, bio6: Min Temperature of Coldest Month, bio7: Annual Temperature Range, bio8: Mean Temperature of Wettest Quarter, bio9: Mean Temperature of Driest Quarter, bio10: Mean Temperature of Warmest Quarter, bio11: Mean Temperature of Coldest Quarter, bio12: Annual Precipitation, bio13: Precipitation of Wettest Month, bio14: Precipitation of Driest Month, bio15: Precipitation Seasonality, bio16: Precipitation of Wettest Quarter, bio17: Precipitation of Driest Quarter, bio18: Precipitation of Warmest Quarter and bio19: Precipitation of Coldest Quarter) have a high biological significance, are widely used to explain the adaptation of species to environmental factors and have been widely used in modelling species distribution. In the Worldclim database, the current period represents interpolations from monthly average precipitation and temperature data collected from 1950 to 2000. All environmental variables were in raster format with a 2.5-arc minute resolution (≈ 4.5 km2). The 20 environmental variables (19 bioclimatic variables and Elevation) were subject to the correlation test using the R software (R Development Core Team, 2018). Consequently, Pearson correlation coefficients belonging to the interval ]-0.8,0.8[ (|r| < 0.8) with only a subset of variable were included in order to eliminate the problem of collinearity in environmental predicators (Elith et al. , 2010).
We used eleven environmental variables in this model prediction. This was after assessing for collinearity in the model and removing all the collinear variables. The result from the correlation analysis identified eleven bioclimatic variables and elevation as contributing to the environmental variation across the study area: Precipitation of Driest Quarter (bio17), Annual Temperature Range (bio7), Elevation, Mean Diurnal Range (bio2), Precipitation Seasonality (bio15), Precipitation of Warmest Quarter (bio18), Precipitation of Wettest Quarter (bio16), Mean Temperature of Wettest Quarter (bio8), Mean Temperature of Warmest Quarter bio10), Min Temperature of Coldest Month (bio6), Precipitation of Coldest Quarter (bio19) and Mean Temperature of Driest Quarter (bio9).
To determine what the distribution of these species might be in the future and thus assess the potential impacts of climate change on their distribution, we used the model built from current data to make the prediction using bioclimatic future prediction data obtained using the future HadGEM-CC projection model. The impacts of climate change strategies on greenhouse gas emissions are considered more in the RCPs scenarios, and the projection of future climate change is more scientifically described. RCP4.5 (medium greenhouse gas emission scenario) and RCP8.5 (maximum greenhouse gas emission scenario) for the near future: 2050 (2041 - 2060) and the middle century: 2070 (2061 - 2080) were selected for the future model prediction of H. monstrosus , M. torquata and E. franqueti distribution in DR Congo.
Distribution modeling
We used MaxEnt software (Phillips et al. , 2006, 2017) to build model and predict suitable habitat distribution of H. monstrosus, M. torquata and E. franqueti in DR Congo. We used Presence-only data to model the suitable habitat of the three species. The MaxEnt model was built as a function of environmental variables, and it is consistently among the highest performing species distribution models (SDMs) (Bradie and Leung, 2017). Response curves indicate the relationships between climatic variables, and the predicted probability of the presence of each species was determined by MaxEnt. The percent contribution and permutation importance of environmental variables were calculated, and jackknife procedures were executed in MaxEnt. These analysis methods are all useful to measure the importance of the environmental variables. MaxEnt estimates the probability a species will be present based on presence records and randomly generates background points by finding the maximum entropy distribution. An estimate of habitat suitability for a species was exported from MaxEnt, and its range generally varied from 0 (lowest) to 1 (highest). For each species we ran 100 submodels each trained to a randomly selected bootstrap of the occurrence dataset. Prediction map form each submodel has been generated in order to calculate the mean prediction and standard deviation of each pixel for each species. Model predictions were imported into a geographic information system (GIS), and maps were generated using ArcMap 10.6. Five arbitrary categories of habitat suitability for the three species of bats were defined as not suitable habitat (0.00 - 0.05), slightly suitable habitat (0.05 - 0.25), moderately suitable habitat (0.25 - 0.50), suitable habitat (0.50 - 0.75) and highly suitable habitat (0.75 - 1.00) based on predicted habitat suitability.
In this study, the ROC curve method was utilized to assess the model’s explanatory power (Peterson et al. , 2007). The AUC (area under roc curve) is an effective threshold-independent index that can evaluate a model’s ability to discriminate presence from absence (or background). For reducing the bias of estimation, jackknife method has been used. This method can estimate parameters and adjust the deviation without assumptions of distribution probability. In SDM, the jackknife method was used to analyze the effects of environmental variables on model results to choose dominant factors. The specific process involves:
  1. Calculating the training gain for the model with only variable. Higher training gain indicates that the variable has high prediction power and contributes greatly to species distribution;
  2. Calculating the training gain for the model without a specific variable and analyzing the correlation between the removed variable and the omission error. If the removal of an environmental variable leads to a significant increase in the omission error, it indicates that the variable has a significant effect on the model’s prediction;
  3. Calculating the training gain for the model with all variables.
RESULTS
Model performance and contributions of variables
In this study, from the ROC curves, AUC values were used to evaluate the performance of the MaxEnt models. Many studies showed that an AUC of high values led to better results that significantly differed from the random predictions. The accuracy of prediction of H. monstrosus, M. torquata and E. franqueti during the current period was found to be excellent (Mean AUC ≈ 0.96, Figure 2) according to the identified evaluation criteria.
Among the environmental variables, the Precipitation of the driest quarter (43.1% - 64.3 %) played major role in the spread of H. monstrosus, M. torquata and E. franqueti (Table 1 and Figure 3). In addition, the Temperature Annual Range played also a major role in the spread of E. franqueti (12.7 %) and H. monstrosus(14.6 %) while the Precipitation of Coldest Quarter has also showed to play also a major role in the spread of M. torquata (36.4%) andH. monstrosus (10.4 %).
Tableau 1: Estimates of contribution and permutation importance of environmental variables in MaxEnt modeling