PC : Percent contribution ; PI : Permutation
importance
Response of variables to suitability
Response curves indicated the relationships between environmental
variables and the predicted probability of the presence of H.
monstrosus, M. torquata and E. franqueti . As stated above, the
spread of E. franqueti has showed to be mostly influenced by the
precipitation of the driest quarter and the temperature annual range.
For this species, the occurrence probability is high if the temperature
annual range is less than 8°C. The occurrence probability becomes almost
zero from an temperature annual range of 20°C while E. franquetihas shown to prefer area where the precipitation of driest quarter goes
from 100 mm to 300 mm. H. monstrosus has also shown to have
ecological preferences that are close to those of E. franquetibut has shown, furthermore, to be influenced by Precipitation of coldest
quarter. The occurrence probability has shown to be high in area where
precipitation of coldest quarter is greater than 200. M. torquatahas shown to be influenced the most by the Precipitation of Driest
Quarter and the Precipitation of Coldest Quarter. For this species, the
increase of the Precipitation of Driest quarter and the precipitation of
coldest quarter increase the probability of occurrence. The most
suitable area for M. torquata are the ones where Precipitation of
the driest quarter is greater than 140 mm and Precipitation of the
coldest quarter is greater than 900 mm (see Figure 4).
Predicting the current distribution of E. franqueti, H.
monstrosus and M. torquata in DR Congo
The result showed that the area from latitude -5° to 5° and from
longitude 17° to 30° are the primary potential suitable region ofE. franqueti, H. monstrosus and M. torquata in DR Congo
(see Figure 5). Current distribution models show that the most
favourable areas for E. franqueti are located in the territories
of Faradje, Aru, Mahagi, Djugu, Irumu, Beni, Lubero, Walikale, Rutshuru,
Masisi, Kalehe, Kabare, Idjwi and Walungu. The areas favourable toH. monstrosus are the same as those of E. franqueti , in
addition to which the areas located in the territories of Ango, Bambesa,
Polo, Niangara, Watsa, Mambasa, and Punia must be added. Unlike these
two species, M. torquata seems to find comfort in the territories
located in the far north of the DR Congo, particularly in the
territories of Zongo, Bosobolo, Gemena, Businga, Mobayi-Mbongo, Yakoma,
Bondo, Ango, Bambesa, Niangara, Dungu, Faradje, Aru and Djugu. A small
area favourable to M. torquata is also located in North Kivu
province, specifically in the territories of Walikale, Rutshuru and
Masisi. Overall, the ecological niche of these three species is the
Eastern and Northeastern regions of DR Congo.
Predicting the future distribution of E. franqueti,H. monstrosus and M. torquata in DR Congo
2050s distribution
Under future climate scenario RCP 4.5 and RCP 8.5 (Figure 6), the
suitable area of the three species will be decreasing showing that their
distribution will be strongly affected. These figures show that, even in
both 2050s climate scenarios the suitable of the three species will
decrease, the climate of RCP 8.5 will affect the most the distribution
of the three species. In this scenario, the suitable area will decrease
more than in the climate scenario of RCP 4.5. Overall, highly suitable
area will be concentrated in the Kivu provinces. Overall, the areas
favourable to these three bat species will be located in the territories
of Mahagi, Djugu, Rutshuru, Masisi, Punia, Shabunda, Kelehe, Kabare,
Idjwi, Walungu, Mwenga and Uvira.
2070s distribution
As stated above for the 2050s distribution of H. monstrosus ,M. torquata and E. franqueti , in all considered future
climate scenario, their suitable area will decrease severally. In 2070s,
the high suitable area of H. monstrosus , M. torquata andE. franqueti will be essentially located in the Kivu provinces in
DR Congo (see Figure 7). With the RCP 4.5 scenario, in general, the
favourable areas for these three species will be located mainly in the
territories of Mahagi, Djugu, Beni, Lubero, Rutshuru, Walikale, Masisi,
Kalehe, Idjwi, Kabare, Walungu, Mwenga and Uvira. However, these areas
will be greatly reduced with the RCP 8.5 scenario whereby areas
favourable to these species are mainly located in the territories of
Beni, Lubero, Walikale, Rutshuru, Kalehe, Idjwi, Kabare, Walungu, Mwenga
and Uvira.
DISCUSSION
Current distribution of bat species
According to Peterson et al . (2004), african Ebola virus
reservoirs would be distributed principally in evergreen broadleaf
forest (rainforest) and the main focus of the geographic distribution of
the reservoirs would be in the Congo Basin. this is all the more true
since our results prove that the distribution areas of the three bat
species in DR Congo correspond roughly to the areas covered by Tropical
and subtropical moist broadleaf forests. With the results obtained in
this study it is clear that the current ecological niche of the three
bat species under study is the entire part from the centre to the north
of DR Congo. The most favourable environments for these species are
mainly the eastern and northeastern parts, border areas with southern
Sudan and Uganda, two countries that have already recorded cases of
Ebola hemorrhagic fever epidemics. Bats are hypothesized to be
reservoirs for filoviruses among which Ebola virus. They have been
identified as an important driver of outbreaks of filovirus diseases
(Fiorillo et al. , 2018). Nyakarahuka et al. (2017) found that
their distribution tends to correlate with that of filovirus predicted
niches. Clearly, with the results of this work, the populations that
appear to be most at risk are those in the areas listed above. This
would be all the more true since almost all cases of Ebola in the human
population in DR Congo have been recorded in these areas (from the
centre to the north of DR Congo). Pigott et al. (2014) found that the
vast majority of people living in suitable area of the three bat species
live in rural areas and populations of DR Congo are among those the most
at risk of experiencing Ebola outbreak. In addition, almost all
outbreaks of Ebola in the human population have been recorded in rural
areas.
Implications of the future distributions of bat species on the
Ebolavirus disease risk
In this study, we found that in the future, the areas favourable to the
distribution of these species will decrease considerably in terms of
available surface areas. The models of future distribution of these
three bat species show that the favourable environments for these
species will be mainly located in the former Kivu province and the
current Ituri province. These regions represent the part of the DR Congo
located in the Albertine Rift. Nyakarahuka et al. (2017) suggested the
Albertine Rift of East Africa to remain under heightened surveillance
especially now that oil exploration will be taking place bringing an
invasion of virgin lands by humans and interaction of wildlife and
humans. Based on our findings, we do agree with them because in DR Congo
bats will find in the future their more suitable habitat in area located
in the said rift. They noted also that in this region, several other
national parks as well as several forest reserves all of which harbor
various species of bats and other possible reservoirs of filoviruses.
The eating habits and socioeconomic status of the populations are
determinant in the emergence of Ebola viruses. Increasing human
encroachment and certain cultural practices sometimes linked with
poverty, such as bushmeat hunting, result in increasing exposure of
humans to animals which may harbour diseases including Ebola (Daszaket al. , 2000; Wolfe et al. , 2005, 2007). Indeed, bushmeat
represents a natural reserve that is exploited in the absence of
financial means for purchasing animal proteins. Bushmeat is considered a
daily food source in rural areas. The major challenge for accessing
protein sources is of economic nature. The inaccessibility of domestic
animal flesh also renders the bushmeat consumption particularly
important to households because it is free and they can have all the
parts of the animal (Dindé et al. , 2017). Because previous
outbreaks in Central Africa have been linked to reports of bushmeat
consumptions and deaths of wildlife (Leroy et al. , 2004), many
hypotheses have been put forward to suggest wildlife such as bats,
primates, and antelopes as possible sources of infection (Osterholmet al. , 2015; Nyakarahuka et al. , 2017).
Insights on variables contributions
Variable contribution assessment showed that precipitation variables
played the most important role in the distribution models. Indeed, in
this study, the Precipitation of the driest quarter (43.1% - 64.3 %)
played major role in the spread of H. monstrosus, M. torquata andE. franqueti (Table1 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%) and H.
monstrosus (10.4 %). The importance of precipitation and moderate to
high temperature was highlighted by (Peterson et al. , 2004) when
they modeled filovirus distribution in Africa. Rainfall is important for
the obvious reason that it provides water which is very important for
bats survival (Adams and Hayes, 2008; Russo et al. , 2012).
Rainfall also provides for the development of fruiting trees that
provide roosting areas for bats as well as food for fruit bats. Because
the risk of dehydration is the greatest physiological threat to life on
land, drinking water is a fundamental resource for all terrestrial
animals (Knut, 1997). Due to their peculiar morphology and physiology,
bats often face the risk of dehydration. Much water is lost through
their body surface, especially via the respiratory system and the
extensive surfaces of wing membranes (Chew and White, 1960; Thomas and
Cloutier, 1992). The importance of water availability has been
emphasized in studies addressing the impact of climate change on bats
(Adams and Hayes, 2008) as well as in those modelling bat distribution
patterns (Rainho et al. , 2010). In addition, DR Congo is endowed
with many water bodies and several rainforests; this could be why the
areas favourable to the distribution of bats cover a very large part of
the country.
Uncertainties related to the models
The models we obtained have shown to be accurate. The Area Under the
Curve (AUC) proportion was high, about 0.96. Therefore, the models are
considered to perform better than random. Accurate predictive models are
of particular importance for effective and adaptive management and
conservation, ecological research and prediction. Thus, predictive
accuracy is an important feature that is sought in species distribution
modeling (Jarnevich et al. , 2015). Although these models have
shown to be accurate, certain reasons may prevent complete reliance on
their predictions. Variation in the output of SDMs may arise due to
errors and uncertainties related to the SDMs themselves, characteristics
of species life histories and future climate models (Beaumont et
al. , 2008).
There is a debate criticizing the prediction results of these models.
What is really modeled, the fundamental niche or the realized niche?
There is ongoing debate in the ecological literature regarding exactly
what these distributions represent (Warren and Seifert, 2011; McInerny
and Etienne, 2013; Warren et al. , 2013). The lack of consensus on
how distribution modelling relates to niche concepts is probably caused
not only by inconsistency of niche definitions, but also the variability
in data, methods and scale across studies (Araújo and Guisan, 2006;
Soberón, 2007). Some studies have suggested that, if the realized niche
is the subset of abiotic environmental space to which a species is
restricted by biotic interactions (Hutchinson, 1957), then, by
definition, known occurrence points used to generate distribution models
represent the realized niche (Phillips et al. , 2006). Fundamental
or environmental niches are only considered to be approximated by
distribution models when occurrence data are drawn from a broad
geographical extent (relative to the total range of the species in
question) (Phillips et al. , 2006). Other studies caution against
such generalizations (Elith and Leathwick, 2009), arguing that the
different niches quantified using observed occurrences of species
reflect an unknown conjunction of the environmental niches of the
species, the biotic interactions they experience and the habitats
available to species and colonized by them (Soberón, 2007).
There are uncertainties that would result from biases in the data used
to develop the models. Indeed, the uncertainty associated with
ecological data is great challenge in species distribution modeling. It
must be accounted for if results are to be appropriately interpreted or
if they are the basis of a decision-making process (Elith et al. ,
2002; Barry and Elith, 2006). Several factors can affect the precision
of SDMs among which we can find factors such as spatial autocorrelation,
data sampling bias, varying detection probabilities (between species and
observers), non-representative data prevalence, mismatched scales data
misregistration or the failure to incorporate critical habitat variables
in the models that damage severally the quality of data to be used in
modeling (Pearce et al. , 2001; Boyce et al. , 2002; Kadmonet al. , 2003; Gu and Swihart, 2004; Barry and Elith, 2006;
Johnson and Gillingham, 2008; Osborne and Leitão, 2009). In addition,
the ecological characteristics of the species to be modeled can also
have an effect on its SDMs’ precision (McPherson and Jetz, 2007;
Franklin et al. , 2009). Imperfect detection can, for instance,
mislead inference about drivers and extent of species distributions,
quantifications of diversity and conclusions about environmental changes
(Guillera-Arroita, 2017). This is a major weakness of these models
because most surveys of natural populations, including opportunistic
surveys that produce presence only observations, are prone to imperfect
detection (Yoccoz et al. , 2001; Chen et al. , 2013).
Furthermore, Dorazio (2012) and Lahoz-Monfort et al. (2014) have shown
that failure to account for imperfect detectability in models of
Presence-only data induces bias in estimates of SDMs. Observed counts of
abundance are biased when individuals are imperfectly detected and this
lead to biased occurrence probabilities and stated occupancy (Royleet al. , 2005). In spite of this, some researchers have
recommended ignoring this challenge (Johnson and Gillingham, 2008;
Banks-Leite et al. , 2014; Stephens et al. , 2015). Hence,
there is no consensus about the importance of accounting for this sort
of measurement error in SDMs (Guélat and Kéry, 2018).
Another limitation of SDMs is that they do not correct for sampling
bias. Unrepresentative presence only locations of the region of interest
induce biased SDMs’ estimates (Phillips et al. , 2009; Yackulicet al. , 2013). Accounting for the effects of geographical
sampling bias in the acquisition of data can be critical to the accuracy
of SDMs made using presence only data (Phillips et al. , 2009).
Most of surveys of natural populations, including opportunistic surveys
that produce presence only data are prone to sampling bias. For
instance, if survey locations are selected based on their accessibility
or convenience induce bias in datasets. Most of time, presence only data
are collected based on accessibility of sampled locations. Thus, samples
are located near urban settlements, rivers and roads instead of being
collected systematically or randomly. Hence, their sample localities are
not representative of the real range of environmental conditions in
which the species of interest occurs (Reddy and Dávalos, 2003; Kadmonet al. , 2004). Such geographical sampling bias is a
characteristic of most specimen locality data available from open access
data portals (Hortal et al. , 2008). Failure to correct for
geographical sampling bias can result in a SDMs that reflects sampling
effort rather than the true distribution of a species (Phillips et
al. , 2009).
Species occurrence data suffer from the disadvantage of containing the
problem of spatial auto-correlation. We cannot afford to ignore this
bias in the modeling of species distribution. Species distribution
models implicitly assume that the geographical data points for species
records are independent and the environmental layers used as
hypothetical predictive variables and associated to the geographical
records of species also show problems of spatial auto-correlation
(Segurado et al. , 2006; Cruz-Cárdenas et al. , 2014). The
spatial auto-correlation caused by colonized sites tending to cluster
around initial invasion foci inflate not only model accuracy, but also
the estimated explanatory power of environmental predictors
(particularly when these are distally related to the requirements of the
focal species) and underestimate uncertainty in model parameters
(Dormann, 2007). Disregard and not avoid spatial auto-correlation has
consequences such as incurring in biased model and hence inflate type I
errors (Dormann, 2007; Cruz-Cárdenas et al. , 2014). Therefore,
where biological or population processes induce substantial
auto-correlation in the species distribution, and this is not modeled,
model predictions will be inaccurate.
Another source of uncertainty in the future model is related to the
climate model used. Climate models are currently the best tools we have
for simulating future climate scenarios. However, variation within and
among alternate climate models poses problems for end users trying to
identify optimal models from which to obtain simulations
(Martinez-Meyer, 2005; Perkins et al. , 2007). To date there is no
clear guidance on how to select the most appropriate simulations for a
given application (Beaumont et al. , 2008). Species distribution
models have been very criticized for their weaknesses in predicting
climate change impact on the geographic species dispersion. Among these
weaknesses, we can cite: uncertainties related to the models used,
difficulties in ecological interactions setting, individual
idiosyncratic responses of the species to climate change, limitations of
species-specific dissemination, plasticity of physiological limits and
disseminating agents responses (Fandohan et al. , 2013).
CONCLUSION
Although the models used and their predictions are highly criticized,
they nevertheless shed light on the management of current and future
risks of Ebola epidemics by mapping the areas where most attention and
prevention measures should be focused. Species distribution models are
useful tools for, among other things, informing the conservation
management of wildlife and their habitats under a rapidly changing
climate. They can provide decision makers with information about the
likely degree of change in a species climatic domain and geographic
distribution. The MaxEnt model is potentially useful for forecasting the
future adaptive distribution of the three bat species under climate
change, and it provides important guidance for comprehensive management
of the Ebolavirus risk. The results obtained in this study showed that
climate change will significantly reduce the areas favourable to the
distribution of these three species, reducing the risk they represent in
the emergence of the Ebola epidemic. However, since not all reservoirs
of the Ebolavirus are yet well identified and the other components of
the emergence of the Ebola epidemic have not been taken into account in
this study, it will not be possible to say that the effects of climate
change will reduce the risk associated with Ebola. But it is still
possible that this may be the case. It is therefore necessary for future
work to take into account these components to assess the effects of
climate change on the risk of Ebola occurrence in DR Congo.