Journal of Threatened Taxa |
www.threatenedtaxa.org | 26 April 2021 | 13(5): 18164–18176
ISSN 0974-7907 (Online) | ISSN 0974-7893
(Print)
https://doi.org/10.11609/jott.6715.13.5.18164-18176
#6715 | Received 15 September 2020 | Final
received 26 October 2020 | Finally accepted 09 April 2021
Ecological niche modelling predicts significant impacts of future
climate change on two endemic rodents in eastern Africa
Aditya Srinivasulu 1, Alembrhan
Assefa 2 &
Chelmala Srinivasulu 3
1 Wildlife Information
and Liaison Development (WILD) Society, No. 12, Thiruvannamalai
Nagar, Saravanampatti - Kalapatti
Road,
Saravanampatti, Coimbatore, Tamil
Nadu 641035, India.
2 Department of
Biology, College of Natural and Computational Science, Adigrat
University, P.O. Box: 50, Adigrat, Ethiopia.
2,3 Natural History
Museum & Wildlife Biology and Taxonomy Lab, Department of Zoology,
University College of Science,
Osmania University,
Hyderabad, Telangana 500007, India.
3 Centre for
Biodiversity and Conservation Studies, #F6 CFRD Building, Osmania University,
Hyderabad, Telangana 500007, India.
3 Systematics, Ecology
& Conservation Laboratory, Zoo Outreach Organization, 12 Thiruvannamalai Nagar, Saravanampatty,
Coimbatore, Tamil Nadu 641035, India.
1 a.chelmala1@gmail.com,
2 assefaw12@gmail.com, 3 chelmala.srinivasulu@osmania.ac.in
(corresponding author)
Editor: Anonymity requested. Date of publication: 26 April 2021
(online & print)
Citation: Srinivasulu,
A., A. Assefa & C. Srinivasulu
(2021). Ecological niche modelling predicts significant
impacts of future climate change on two endemic rodents in eastern Africa. Journal of Threatened
Taxa 13(5): 18164–18176. https://doi.org/10.11609/jott.6715.13.5.18164-18176
Copyright: © Srinivasulu et al. 2021. Creative Commons
Attribution 4.0 International License. JoTT allows unrestricted use, reproduction, and
distribution of this article in any medium by providing adequate credit to the
author(s) and the source of publication.
Funding: The major funding for carrying
out this research was granted
by the Government
of Ethiopia and Adigrat
University, Ethiopia.
Competing interests: The authors
declare no competing interests.
Author details: Aditya Srinivasulu is an independent
researcher associated with the Wildlife Information and Liaison Development
(WILD) Society, India, and is working on the application and integration of
modern techniques with systematics, biogeography, and conservation biology of
South Asian tetrapods. Alembrhan Assefa was PhD student at Department of Zoology,
Osmania University, India and worked on diversity and ecology of small mammals
in Kafta-Sheraro National Park, Tigray region,
Ethiopia. He is currently working in Department of Biology, Adigrat
University, Ethiopia. Chelmala Srinivasulu
is the head of Wildlife Biology and Taxonomy Laboratory, Department of Zoology,
Osmania University, India and is working on diversity and taxonomy of
vertebrates in South Asia.
Author contribution: All authors
contributed equally to the study, analysis, and writing the manuscript.
Acknowledgements: We thank the head,
Department of Zoology, Osmania University, India and head, Department of
Biology, Adigrat University, Ethiopia for the
facilities, and the Ethiopian Government for Research Fellowship to AA.
Abstract: The impact of climate
change on rodents is well studied, however, many of these studies are
restricted to the Americas. Small- to
medium-sized rodents, especially murids, are restricted in their home range and
microclimatic niche breadth, and are known to be more sensitive to changes in
bioclimatic conditions over time. We analyzed the effect of future climatic scenarios in the
near and distant future, using two global climate models (CanESM5 and
MIROC-ES2L) for two shared socio-economic pathways (SSP2-4.5 and SSP5-8.5), on
two eastern Africa endemic small-bodied mice: Stenocephalemys
albipes and Mastomys
awashensis. Our results indicate that while S.
albipes showed increases in area of climatic
suitability in the future, M. awashensis is
predicted to suffer severe decline in the area of its fundamental niche.
Keywords: Awash Multimammate Mouse, Ethiopian White-footed Mouse, decline,
microclimate, MaxEnt.
INTRODUCTION
Rodents are the most
diverse and abundant groups of mammals, accounting for approximately 2,200
species (Monadjem et al. 2015), distributed across
the world on every continent except Antarctica (Nowak 1999). They occur in a wide range of terrestrial
habitats, and serve the purpose of ecosystem engineers (Zhang et al. 2003) and
keystone species in an ecosystem. Africa
supports a large diversity of rodent species, with at least 463 known species (Monadjem et al. 2015), and new species described regularly;
however, the ranges and habitats of some rodent species in Africa are not
clearly known, due to many reasons ranging from inaccessible localities to
insufficient data or resources (Kingdon 1997; Habtamu & Bekele 2008; Takele
et al. 2011). This is of especial
concern as rodents are not only diverse, but are an integral part of the
ecosystem’s functioning and health, contributing essential services (Fischer
2017). They are also of biogeographic,
systematic, and conservation interest and priority (Happold
2013; Monadjem et al. 2015).
Rodents distributed
in xerothermic habitats have been known to benefit from climate change towards
a warmer, drier climate scenario, most likely due to their thermo-xerophilia being supported by the climatic conditions
(Cameron & Scheel 2001). Climate
change towards warmer and drier conditions has also resulted in an increase in
species diversity in rodents in warm regions (Szpunar
2008). It is also possible that due to
the effect of changing climate scenarios, migrations and emigrations take
place, resulting in new regional populations being seeded and established in
order to occupy the fundamental niche (Royer et al. 2016). As an extension of the conclusions drawn by Millien & Damuth (2004),
treating fragmented populations as islands, it may be inferred that there is a
possible slowing of the evolutionary rate of rodents as a result of climate
change.
Hutchinson (1957)
proposed the concept of the ‘ecological niche’ – an abstract representation of
the biotic and abiotic factors deciding and limiting the distribution and
abundance of a species. Identifying the
ideal environmental niche of a species by accounting for certain limiting factors
is one of the aims of ecological niche modelling (ENM) – this ideal niche is
referred to as the fundamental niche (Griesemer
1994). The fundamental niche does not
represent the real distribution of the species; in fact, it is usually larger
than the realised distribution of the species (Soberón
& Arroyo-Peña 2017). Ecological
niche modelling uses presence-only or presence-absence occurrence data of a
species and analyses it against a set of spatial covariates—most often,
bioclimatic variables are used as the covariates in a climate change ENM
study. Many diverse algorithms may be
used for ENM, including generalised linear models (GLM), multivariate adaptive
regression splines (MARS), and random forests (RF). MaxEnt (Phillips et
al. 2006), however, is by far the most widely used algorithm due to its use of
presence-only data, ease of access, customizability, and robustness
(Ortega-Huerta & Peterson 2008; Elith et al.
2011; Merow et al. 2013; Radosavljevic
& Anderson 2014).
The present study
analyses the effect of current and future climate scenarios on the predicted
fundamental niche of two Ethiopian-endemic rodents, the Awash Multimammate Mouse Mastomys
awashensis (Lavrenchenko
et al. 1998) and the Ethiopian White-footed Mouse Stenocephalemys
albipes (Rüppell, 1842)
(Image 1). It aims to predict the impact
of future climate change pathways (SSP2-4.5 and SSP5-8.5) on the niches of
these species using maximum entropy (MaxEnt)
modelling.
MATERIALS AND METHODS
Study area
This study is based
in Ethiopia and Eritrea, as both Mastomys awashensis and Stenocephalemys
albipes are endemic to this region (Image
2). M. awashenis
is distributed in the scrublands of the Awash River bank, which primarily
comprises small Acacia and Commiphora trees and
thorny scrubs, and is also found in agricultural fields and wild areas of the
northern highlands (Lavrenchenko et al. 1998; Meheretu et al. 2014).
S. albipes occur in moist montane
forests, scrublands at high altitudes, and agricultural fields (Yalden & Largen 1992; Tilaye
2005; Kassa & Bekele 2008) (Image 2). The study region varies widely in altitude,
geography, and climatic conditions, resulting in a high diversity of biological
resources and high levels of endemism.
The altitude of the region varies from 115m below sea level to 4,620m
above sea level, and it can be classified into three climatic zones – tropical,
subtropical, and cool. The mean annual
temperature ranges 16–27 OC, and the annual precipitation ranges
510–1,280 mm. While the study is
restricted to Ethiopia and Eritrea, the ecological niche modelling (ENM) was
conducted on the entirety of continental Africa to account for ecological niche
data outside the political borders of these countries; final models were then
cropped to Ethiopia and Eritrea’s national boundaries.
Data collection
Occurrence data of
the two study species were collected from Ethiopia and border regions in
Eritrea. A total of 101 presence records
were collected (34 for M. awashensis and 67
for S. albipes) from published literature (Lavrenchenko et al. 1998; Habtamu
& Bekele 2008; Colangelo et al. 2010; Assefa
& Srinivasulu 2019) and from GBIF (accessed
August 2020) (Image 2; Appendix 1).
Occurrence data of each species were spatially thinned using the package
spThin (Aiello-Lammens
et al. 2015) in R such that points within a 2km2 area of each other
were treated as duplicates and removed to account for spatial bias and
autocorrelation in sample collection.
Nineteen bioclimatic
environmental variables were acquired at a resolution of 2.5 arc-minutes from
the Worldclim 2 database for the current time period
(Fick & Hijmans 2017). For future scenarios, 2.5 arc-minute
resolution data from the Coupled Model Intercomparison
Project 6 (CMIP6) were acquired for two shared socioeconomic pathways - SSP2
representing a middle-of-the-road scenario (Fricko et
al. 2017) and SSP5 representing fossil-fuelled development in the future (Kriegler et al. 2017).
Two global climate models were used to account for inter-model
disparities in projection (Porfirio et al. 2014) - MIROC-ES2L (Tachiiri et al. 2019a,b) and CanESM5 (Swart
et al. 2019a,b). Data were acquired for
the 2041–2060 (near future) and 2061–2080 (distant future) time periods.
An analysis of
multicollinearity conducted using the package Virtualspecies
(Leroy et al. 2015) in R version 4.0.2 (R Core Team 2020) was used to select
relatively uncorrelated variables for the modelling. Variables with an absolute value of Pearson’s
r >0.75 were subjected to pairwise comparisons of perceived ecological
importance based on our understanding of the ecology and biology of the two
species. All climate data were initially
cropped to the extent of continental Africa; islands surrounding Africa
including Madagascar were included, but southern Europe, the Middle East, and
the Arabian Peninsula were not used.
Ecological niche
modelling
A presence-only
approach was used to model species distributions, using MaxEnt
version 3.4.1 (Phillips et al. 2006); however, careful consideration of biases
and selection of parameters is an essential step in order to maximise the
robustness and reliability of niche models generated using MaxEnt
(Derville et al. 2018). Hence, parameterisation was done according to
the processes outlined in Merow et al. (2013) and
Feng et al. (2019). To account
for spatial bias, a Gaussian kernel density bias file of bandwidth 0.5 was
created using the package SpatialEco (Evans
2020) in R, in order to weight the generation of background (pseudo absence)
points for the analysis.
The model was
parameterised for feature classes and regularisation multipliers using the
package ENMEval (Muscarella
et al. 2014). We tested a set of five
regularisation multipliers: 0.5, 1, 2, 3, and 5, and six feature classes:
Linear, Linear+Quadratic, Hinge, Hinge+Quadratic,
Linear+Quadratic+Product, and Hinge+Quadratic+Product. Five-fold cross-validation was used and model
performance was assessed using the area under the receiver operating
characteristic curve (AUC) and the true skill statistic (TSS).
The continuous models
for each scenario and each time period, as output by MaxEnt,
were reclassified according to the maximum test sensitivity+specificity
(MSS) threshold into binary models – the positive cells represented the
fundamental niche of the species for each scenario and time period according to
bioclimatic data. Finally, the binary
models were cropped to Ethiopia and Eritrea’s national boundaries. Area of climatic suitability was calculated
as a percentage based on the ratio of positive to zero cells in the final
binary models.
RESULTS
Ecological niche
modelling
For the modelling of
both Mastomys awashensis
and Stenocephalemys albipes,
12 bioclimatic layers were selected based on multicollinearity analysis
(Appendix 2): BIO1 (Annual mean temperature), BIO2 (Mean diurnal range), BIO4
(Temperature seasonality), BIO5 (Maximum temperature of warmest month), BIO6
(Minimum temperature of coldest month), BIO8 (Mean temperature of wettest
quarter), BIO9 (Mean temperature of driest quarter), BIO14 (Precipitation of
driest month), BIO15 (Precipitation seasonality), BIO16 (Precipitation of
wettest quarter), BIO18 (Precipitation of warmest quarter), and BIO19
(Precipitation of coldest quarter).
After data cleaning and spatial thinning, 10 occurrence points were used
for M. awashensis and 65 occurrence points
were used for S. albipes. Models with the lowest Δ AICc
values were selected as the final models for ENM analyses of each species – for
M. awashensis this was Linear features with
RM= 0.5 (Δ AICc= 0), and for S. albipes this was Linear+Quadratic
features with RM= 0.5 (Δ AICc= 0). The models for M. awashensis
and S. albipes returned AUC values of 0.974 ±
0.009 and 0.977 ± 0.011, respectively, and TSS values of 0.735 and 0.801,
indicating robust performance for both species.
Mean diurnal range and temperature seasonality had high contribution to
the models of both species (Table 1).
Stenocephalemys albipes ENM
The ecological niche
model for S. albipes (MSS threshold 0.525)
showed that 20.704% of the study area is climatically suitable in the current
time period (Image 3; Table 2). In both
future time periods, scenarios, and GCMs, there was significant increase, with
an average increase of 18.437% to 39.141 ± 3.695 % in 2041–2060, and a further
increase of 1.373% to 40.514 ± 5.035 % in 2061–2080. There was little difference in the percentage
area of future climatic suitability between SSP2-4.5 and SSP5-8.5 (Image 3;
Table 2), indicating that different future climate scenarios have little impact
on the overall effect of climate change on this species.
The variables with
the highest percentage contribution and permutation importance for this species
were temperature seasonality (BIO4; 28% contribution, 38.8% p. imp.) and mean
diurnal range (BIO2; 15.4% contribution, 12.8% p. imp.) (Table 1). Additionally, annual mean temperature (BIO1)
had the highest percentage contribution to the model (41.2%), but showed 0
permutation importance, and similarly, mean temperature of the wettest quarter
(BIO8) showed the highest permutation importance (44.2%), but had a very low
percentage contribution to the model (0.9%).
In the current
scenario, highest environmental suitability (>75%) according to climate was
seen at a mean diurnal range (BIO2) of 14.901 ± 1.556 OC, and a mean
temperature seasonality (BIO4) of 114.903 ± 28.698 OC. In SSP2-4.5, representing a
middle-of-the-road scenario, BIO2 underwent a slight decrease to a mean value
of 14.137 ± 1.139 OC in the 2041–2060 time period, and further to
14.065 ± 1.185 OC in 2061–2080; BIO4 also decreased to a mean value
of 109.902 ± 30.14 OC in 2041–2060, and increased to 111.027 ±
32.302 OC in 2061–2080. In
SSP5-8.5, representing a fossil-fuelled economy, BIO2 underwent a decrease to a
mean value of 14 ± 1.171 OC in the 2041–2060 time period, and
further to 13.572 ± 1.258 OC in 2061–2080; BIO4, however, increased
to a mean value of 116.249 ± 33.281 OC in 2041–2060, and further to
123.561 ± 39.416 OC in 2061–2080 (Table 3).
Mastomys awashensis
ENM
The ecological niche
model for M. awashensis (MSS threshold 0.777)
showed that 46.077% of the study area is climatically suitable in the current
time period (Image 4; Table 2). In both
future time periods, scenarios, and GCMs however, there was complete and total
decline, resulting in 0% of the study area being climatically suitable by
2041–2060 and into the future (Image 4).
This indicates that M. awashensis is
extremely sensitive to climate change scenarios, and due to the effect of
climate change alone, will lose all of its fundamental niche in the near
future.
For this species,
temperature seasonality (BIO4; 47.6% contribution, 74.2% p. imp.) and mean
diurnal range (BIO2; 27.7% contribution, 18.2% p. imp.) were the highest
contributors (Table 1). All the other
variables had significantly lower percentage contribution and permutation
importance.
In the current
scenario, highest environmental suitability (>75%) according to climate was
seen at a mean diurnal range (BIO2) of 15.986 ± 1.075 OC, and a mean
temperature seasonality (BIO4) of 136.481 ± 33.077 OC (Table 3).
DISCUSSION
Ecological niche
models have often been used to model and project rodent distributions and
niches, but a large proportion of these studies are restricted to species found
in the Americas (Martínez-Salazar et al. 2012; Bean et al. 2014; Kubiak et al.
2017; Flores-Zamarripa & Fernández 2018;
Urbina-Cardona et al. 2019; Pardi et al. 2020). African rodents have also been studied using
ENM techniques; Taylor et al. (2015) showed that trends in the distribution of
Afromontane rodents reflect changes in biomes predicted by past, present, and
future climate scenarios. McDonough et
al. (2015) showed in a hindcasting-based study on the Bushveld Gerbil Gerbiscillus leucogaster
in Zambezi, that it is significantly impacted by changing climatic scenarios,
but this was not explored in terms of future climate change. A general ecological niche model fitted by Martinov et al. (2020) created an estimation of the current
predicted distribution of Mastomys species,
including M. awashensis, however this analysis
did not estimate the fundamental niche through binary modelling, and there was
no projection to future climate scenarios.
Our results are in
agreement with the findings of Martinov et al.
(2020), where the current distributions show high likelihood (>0.8) in areas
included under our predicted current fundamental niche. Our results also emphasise the importance of
ecological niche modelling and future projection of ENM analyses, due to the
severity of the impact of climate change on M. awashensis
(Ortega-Huerta & Peterson 2008).
The two species in
our study—Mastomys awashensis
and Stenocephalemys albipes—show
significant changes as a result of changing climate scenarios. The result of our study for S. albipes shows a percentage area of current climatic
suitability of 20.704%, with an increase of 18.437% in the near future
(2041–2060), and a further increase of 1.373% in the distant future (2061–2080)
in both climatic scenarios. Despite the
different perspectives SSP2-4.5 and SSP5-8.5 take in terms of socioeconomic
scenarios, emissions, and concentrations of greenhouse gases, there was
negligible difference between the two in the future predictions of the
fundamental niche of this species, suggesting that while climate change
positively impacts this species, there is little impact of specific climate
pathways. This result is in line with
conclusions drawn by McDonough et al. (2015), where it was shown that rodent
niches expanded from the last glacial maximum (approximately 200,000 years BP)
through the last interglacial period (approx. 130,000 to 118,000 years BP), to
the present day, most likely due to increasing temperatures across the
year. The decrease in predicted future
mean diurnal range most suitable for this species when compared to the current
time period shows that in both shared socioeconomic pathway scenarios, this
species will favour slightly colder climates.
This effect is very small, however,as the
largest change in mean diurnal range is from current to the 2061–2080 time
period, with a 1.329 ± 0.298 OC decrease.
In the case of M. awashensis, the current niche is relatively large, with
46.077% appearing to be climatically suitable for this species; however, it
appears to be incredibly sensitive to climate change events, as in all future
scenarios and time periods, none of the study area (and also the rest of
Africa) appeared to be climatically suitable.
This is a massive and drastic change, which reflects the high
sensitivity of this species to climate change.
Seasonal variation in temperature and mean diurnal range of temperature
are the most important predicting factors for this species, which leads to the
inference that this species is likely to be most affected by temperatures
getting generally warmer and less seasonally varied, which happens in both
scenarios.
According to the MaxEnt model, both species had relatively wide areas of
climatic suitability (Imgae 3, 4). For both species, the northern regions of
Ethiopia and parts of southern Eritrea were climatically suitable—this included
highland, some lowland regions of the Great Rift Valley, and some scattered
sites in southeastern Ethiopia. S. albipes
had climatically suitable regions in the highlands of northern, western, and
central Ethiopia, including Tigray, Amhara, northern Oromia, Southern Nations,
Nationalities, & Peoples’ (SNNP), Addis Ababa, and eastern Benishangul-Gumaz regions. There
are also some scattered suitable sites near Harari in Ethiopia, and Debub and
Gash-Barka regions in Eritrea. In all future scenarios and time periods,
this species’ fundamental niche was seen to expand and move westward in
Ethiopia and Eritrea, occupying the Tigray, Amhara, Benishangul-Gumaz, Oromia, Addis Ababa, Gambela,
and SNNP regions in Ethiopia & Gash-Barka and
Debub regions in Eritrea. Some scattered
areas of suitability were also seen in the Eritrean & Ethiopian highlands
and in the highlands south of Dire Dawa.
M. awashensis showed climatic suitability in
Tigray, Amhara, eastern Benishangul-Gumaz, Oromia,
SNNP, Addis Ababa, Harari, and some parts of northern Somali regions. In Eritrea, it showed high climatic
suitability in Gash-Barka and Debub. For both species, the Eritrean and Ethiopian
highlands formed a distinct geographical barrier, and no areas of climatic
suitability were present east of the hill range. Earlier studies of both species have shown
them to be restricted to highland habitats (Corti et
al. 2005; Mohammed et al. 2010; Meheretu et al.
2014), however, some later studies reported them to occur from lowlands as well
(Habtamu & Bekele 2008; Lavrenchenko
et al. 2010). Our study corroborates
these with our current predicted niche expanding to lowland regions as well as
highlands.
The results of the
present study show the efficacy of ecological niche modelling in offering
important insights into the potential geographic distributions of African
rodents. Although M. awashensis is present and has areas of climatic
suitability in protected areas, it is likely that there are no species-specific
conservation measures in place. The
eventual increase in anthropogenic impact on the natural areas will only
decrease the chances of the species’ survival in the future, as the impact of
climate change alone is very large. It
is important to plan ground-truthing of the sites shown as part of the
fundamental niche of both this study’s species in order to ascertain their true
distribution, range, and realised niche, as this will help create better
conservation strategies. It is
imperative that species-specific conservation measures are set in place based
on the results of said ground-truthing, including in situ conservation
management, captive breeding, and planned reintroductions.
Table 1. Variable
contributions of each bioclimatic layer used in the analysis, for both species.
|
|
Percentage
contribution |
Permutation
importance |
|||
|
Variable |
Name |
Stenocephalemys albipes |
Mastomys awashensis |
Stenocephalemys albipes |
Mastomys awashensis |
|
BIO1 |
Annual mean
temperature |
41.2 |
0 |
0 |
0 |
|
BIO2 |
Mean diurnal range |
15.4 |
27.7 |
12.8 |
18.2 |
|
BIO4 |
Temperature
seasonality |
28 |
47.6 |
38.8 |
74.2 |
|
BIO5 |
Max temperature of
warmest month |
0.1 |
0 |
0 |
0 |
|
BIO6 |
Min temperature of
coldest month |
0.1 |
3.8 |
0.6 |
0.1 |
|
BIO8 |
Mean temperature of
wettest quarter |
0.9 |
12.1 |
44.2 |
3 |
|
BIO9 |
Mean temperature of
driest quarter |
2 |
0.2 |
0.4 |
0.5 |
|
BIO14 |
Precipitation of
driest month |
0.5 |
1.9 |
0.4 |
2 |
|
BIO15 |
Precipitation
seasonality |
0.6 |
1 |
1.4 |
0.9 |
|
BIO16 |
Precipitation of
wettest quarter |
0.5 |
0.6 |
0.6 |
0.6 |
|
BIO18 |
Precipitation of
warmest quarter |
0.8 |
2.6 |
0.8 |
0.3 |
|
BIO19 |
Precipitation of
coldest quarter |
9.9 |
2.4 |
0 |
0.1 |
Table 2. Changes in
climatically suitable areas of both species (in percentage values).
|
Mastomys awashensis |
Stenocephalemys albipes |
||||||
|
Scenario |
Time Period |
CanESM5 |
MIROC-ES2L |
Scenario |
Time Period |
CanESM5 |
MIROC-ES2L |
|
- |
Current |
46.077% |
- |
Current |
20.704% |
||
|
SSP2-4.5 |
2041–2060 |
0% |
0% |
SSP2-4.5 |
2041–2060 |
39.982 |
34.527 |
|
SSP2-4.5 |
2061–2080 |
0% |
0% |
SSP2-4.5 |
2061–2080 |
40.113 |
35.353 |
|
SSP5-8.5 |
2041–2060 |
0% |
0% |
SSP5-8.5 |
2041–2060 |
43.462 |
38.594 |
|
SSP5-8.5 |
2061–2080 |
0% |
0% |
SSP5-8.5 |
2061–2080 |
47.407 |
39.186 |
Table 3. Values for
BIO2 (Mean diurnal range) and BIO4 (Temperature seasonality), averaged across
both GCMs, for each time period and scenario for both species, at areas of high
climatic suitability. Future values for M. awashensis
are not given as it has 0 climatic suitability in all scenarios. Values are
given as Mean ± standard deviation.
|
Stenocephalemys albipes |
|||
|
Scenario |
Time Period |
BIO2 |
BIO4 |
|
- |
Current |
14.901 ± 1.556 |
114.903 ± 28.698 |
|
SSP2-4.5 |
2041–2060 |
14.137 ± 1.139 |
109.902 ± 30.14 |
|
SSP5-8.5 |
2041–2060 |
109.902 ± 30.14 |
14.065 ± 1.185 |
|
SSP2-4.5 |
2041–2060 |
14.065 ± 1.185 |
111.027 ± 32.302 |
|
SSP5-8.5 |
2041–2060 |
111.027 ± 32.302 |
14 ± 1.171 |
|
SSP2-4.5 |
2061–2080 |
14 ± 1.171 |
116.249 ± 33.281 |
|
SSP5-8.5 |
2061–2080 |
116.249 ± 33.281 |
13.572 ± 1.258 |
|
SSP2-4.5 |
2061–2080 |
13.572 ± 1.258 |
123.561 ± 39.416 |
|
SSP5-8.5 |
2061–2080 |
123.561 ± 39.416 |
14.935 ± 1.318 |
|
Mastomys awashensis |
|||
|
Scenario |
Time Period |
BIO2 |
BIO4 |
|
- |
Current |
15.986 ± 1.075 |
136.481 ± 33.077 |
For
figures & images - - click here
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Appendix 1. Localities used in
ENM analysis of Stenocephalemys albipes and Mastomys
awashensis.
|
Name |
Latitude |
Longitude |
Locality |
Reference |
|
Stenocephalemys albipes |
5.800 |
39.200 |
Kebre Mengist, Ethiopia |
GBIF |
|
Stenocephalemys albipes |
6.217 |
37.667 |
Dega Done, Gemu-Gofa, SNNP, Ethiopia |
Demeke et al. 2007 |
|
Stenocephalemys albipes |
6.233 |
37.567 |
Mt Dorse, Chenckia, Gemu-Gofa, SNNP,
Ethiopia |
GBIF |
|
Stenocephalemys albipes |
6.383 |
38.583 |
Kebre Mengist, Sidamo, Ethiopia |
GBIF |
|
Stenocephalemys albipes |
6.833 |
40.550 |
Jebo Samo, Bale, Ethiopia |
GBIF |
|
Stenocephalemys albipes |
6.917 |
39.167 |
Gedeb Mts., Ethiopia |
GBIF |
|
Stenocephalemys albipes |
6.983 |
40.020 |
7 km SE of Goba, Bale,
Ethiopia |
GBIF |
|
Stenocephalemys albipes |
7.050 |
39.167 |
Webi river, north of Dodola, Arsi, Ethiopia |
GBIF |
|
Stenocephalemys albipes |
7.100 |
39.767 |
Webi river, W of Dinshu, Bale, Ethiopia |
Zerihun et al. 2012 |
|
Stenocephalemys albipes |
7.117 |
39.733 |
5 km of W of Dinshu, Bale,
Ethiopia |
GBIF |
|
Stenocephalemys albipes |
7.133 |
39.717 |
Mount Gaysay, Bale,
Ethiopia |
GBIF |
|
Stenocephalemys albipes |
7.134 |
36.954 |
Gorka Bersa, Chebera-Churchura NP,
Ethiopia |
Demeke & Afework 2014 |
|
Stenocephalemys albipes |
7.433 |
35.000 |
Godare forest, Tepi, Ethiopia |
Lavrenchenko 2017 |
|
Stenocephalemys albipes |
7.580 |
36.800 |
Seka, 3 Km N Of, Horo,
Ethiopia |
GBIF |
|
Stenocephalemys albipes |
7.600 |
38.450 |
Alage, Ethiopia |
Agerie & Afework 2015 |
|
Stenocephalemys albipes |
7.620 |
36.770 |
Buyo Kechema, Ethiopia |
GBIF |
|
Stenocephalemys albipes |
7.650 |
36.800 |
Jiren Farm, Jimma, Ethiopia |
Tadesse & Afework 2012 |
|
Stenocephalemys albipes |
7.667 |
39.333 |
Albasso forest, Ethiopia |
GBIF |
|
Stenocephalemys albipes |
7.750 |
36.730 |
Atro, Agaro, Ethiopia |
GBIF |
|
Stenocephalemys albipes |
7.820 |
36.680 |
Agaro, 14 km by road SE
of Mejo, Ethiopia |
GBIF |
|
Stenocephalemys albipes |
7.833 |
39.333 |
Wodajo, Ethiopia |
GBIF |
|
Stenocephalemys albipes |
7.917 |
39.283 |
Jawi Chilalo, Galama mtn, Arsi, Ethiopia |
Mohammed et al. 2010 |
|
Stenocephalemys albipes |
7.917 |
39.450 |
Mt Albasso, Camp Wodajo, Ethiopia |
GBIF |
|
Stenocephalemys albipes |
8.155 |
35.525 |
Illubabor, W of Gore,
Ethiopia |
GBIF |
|
Stenocephalemys albipes |
8.183 |
35.367 |
Lemen, Ethiopia |
GBIF |
|
Stenocephalemys albipes |
8.250 |
36.167 |
Yemenigisit Den Yebaja Chaka, Ethiopia |
GBIF |
|
Stenocephalemys albipes |
8.280 |
36.900 |
Atenago, Ethiopia |
GBIF |
|
Stenocephalemys albipes |
8.367 |
35.817 |
Wabo, 5 km of W of Scecchi river, Ethiopia |
GBIF |
|
Stenocephalemys albipes |
8.500 |
34.775 |
Addo, 7km SW of Dembidolo,
Ethiopia |
GBIF |
|
Stenocephalemys albipes |
8.517 |
39.200 |
Wonji Sugarcane, Qoboluto Tumsa, Ethiopia |
Serekebirhan et al. 2011 |
|
Stenocephalemys albipes |
8.917 |
38.583 |
Dima Goranda, Ethiopia |
GBIF |
|
Stenocephalemys albipes |
9.017 |
35.250 |
Sido Were Wele, Ethiopia |
GBIF |
|
Stenocephalemys albipes |
9.050 |
38.520 |
Berifeta Lemefa, near Holetta, Ethiopia |
GBIF |
|
Stenocephalemys albipes |
9.067 |
38.650 |
Menagesha forest, Shoa, Ethiopia |
Afework 1996 |
|
Stenocephalemys albipes |
9.117 |
37.050 |
Bako, Shoa, Ethiopia |
GBIF |
|
Stenocephalemys albipes |
9.517 |
38.217 |
Subagajo, Ethiopia |
GBIF |
|
Stenocephalemys albipes |
10.333 |
37.833 |
Debra Markos, Gojjam,
Amhara, Ethiopia |
Ejigu & Afework 2013 |
|
Stenocephalemys albipes |
10.494 |
39.611 |
Yetere forest, Ethiopia |
Gezahegn et al. 2016 |
|
Stenocephalemys albipes |
10.667 |
38.167 |
Debre Werk, Ethiopia |
GBIF |
|
Stenocephalemys albipes |
10.667 |
37.917 |
Naziret M Alem, Ethiopia |
GBIF |
|
Stenocephalemys albipes |
10.739 |
36.800 |
Arditsy forest, Awi zone, Ethiopia |
Getachew & Afework 2015 |
|
Stenocephalemys albipes |
10.846 |
38.675 |
Borena-Sayint NP, Ethiopia |
Meseret & Solomon 2014 |
|
Stenocephalemys albipes |
11.117 |
37.317 |
Amedamit Mount, Amhara,
Ethiopia |
GBIF |
|
Stenocephalemys albipes |
11.167 |
36.250 |
Pawe area, B. Gumuz, Ethiopia |
Tilahun et al. 2012 |
|
Stenocephalemys albipes |
11.267 |
36.833 |
Dangila, Amhara, Ethiopia |
GBIF |
|
Stenocephalemys albipes |
11.417 |
37.967 |
Shime, Ethiopia |
GBIF |
|
Stenocephalemys albipes |
11.583 |
37.417 |
Bihar-Dar, Amhara, Ethiopia |
GBIF |
|
Stenocephalemys albipes |
11.717 |
37.917 |
Mahdere Marayam, Gondar, Amhara, Ethiopia |
GBIF |
|
Stenocephalemys albipes |
12.350 |
35.783 |
Alatish NP, Ethiopia |
Tadesse & Afework 2008 |
|
Stenocephalemys albipes |
12.617 |
37.483 |
Gondar, Amhara, Ethiopia |
GBIF |
|
Stenocephalemys albipes |
12.633 |
37.500 |
NE of Angereb Dam, Gondar,
Amhara, Ethiopia |
GBIF |
|
Stenocephalemys albipes |
12.750 |
37.700 |
Yerer mountain forest, Shoa, Ethiopia |
Yonas & Fikresilasie
2015 |
|
Stenocephalemys albipes |
13.133 |
37.917 |
Debark, NE Gondar, Amhara, Ethiopia |
GBIF |
|
Stenocephalemys albipes |
13.133 |
37.917 |
Semien Mts, Amhara, Ethiopia |
GBIF |
|
Stenocephalemys albipes |
13.192 |
37.893 |
Debir, Ethiopia |
GBIF |
|
Stenocephalemys albipes |
13.232 |
38.038 |
Semien NP, Ethiopia |
GBIF |
|
Stenocephalemys albipes |
13.650 |
39.172 |
Hagere-selam, Ethiopia |
Meheretu et al. 2012 |
|
Stenocephalemys albipes |
14.166 |
37.309 |
Habesha Adi Goshu, Ethiopia |
GBIF |
|
Stenocephalemys albipes |
14.183 |
37.305 |
Kunama Adi Goshe, Ethiopia |
GBIF |
|
Stenocephalemys albipes |
14.210 |
36.766 |
Adebayetown, Ethiopia |
GBIF |
|
Stenocephalemys albipes |
14.251 |
37.270 |
Kunama Adi Goshe, Ethiopia |
GBIF |
|
Stenocephalemys albipes |
14.284 |
36.688 |
Kafta-Sheraro NP, Tigray,
Ethiopia |
Alembrhan & Srinivasulu 2019 |
|
Stenocephalemys albipes |
14.291 |
36.677 |
Helet Coka, Ethiopia |
GBIF |
|
Stenocephalemys albipes |
14.950 |
38.270 |
Mt. Kullu, Shambiko, Eritrea |
GBIF |
|
Stenocephalemys albipes |
15.332 |
39.064 |
Nefasit, Eritrea |
GBIF |
|
Stenocephalemys albipes |
11.083 |
36.850 |
Aquatimo forest, Gojjam, Ethiopia |
Moges & Dessalegn 2015 |
|
Mastomys awashensis |
9.000 |
40.167 |
Awash, Ethiopia |
Lavrenchenko et al. 1998 |
|
Mastomys awashensis |
7.833 |
38.717 |
S of Ziway Lake, Ethiopia |
Corti et al. 2005 |
|
Mastomys awashensis |
8.383 |
39.150 |
E of Koka Lake, Bati Qelo, Ethiopia |
Lavrenchenko & Corti 2008 |
|
Mastomys awashensis |
9.065 |
42.275 |
Nigaya Bobasa, Babile Sanctuary,
Ethiopia |
Lavrenchenko et al. 2010 |
|
Mastomys awashensis |
13.668 |
39.175 |
Hagere-selam, Ethiopia |
Meheretu et al. 2014 |
|
Mastomys awashensis |
12.600 |
39.517 |
N of Lake Hashenge,
Ethiopia |
Mengistu et al. 2015 |
|
Mastomys awashensis |
14.210 |
36.766 |
Near Adebaye Town, Kafta Sheraro National Park,
Ethiopia |
Alembrhan & Srinivasulu 2019 |
|
Mastomys awashensis |
14.251 |
37.270 |
Kunama Adi Goshu, Kafta Sheraro National Park, Ethiopia |
Alembrhan & Srinivasulu 2019 |
|
Mastomys awashensis |
14.284 |
36.688 |
Helet Coka, Ethiopia |
GBIF |
|
Mastomys awashensis |
14.287 |
36.679 |
Adebaye Geter, E of Himora, Ethiopia |
GBIF |
|
Mastomys awashensis |
14.184 |
37.305 |
NW of Birkuta, Ethiopia |
GBIF |
|
Mastomys awashensis |
14.168 |
37.310 |
Habesha Adi Goshu, Ethiopia |
GBIF |
|
Mastomys awashensis |
7.2545 |
36.798 |
Gojeb River, Ethiopia |
Martynov et al. 2020 |
|
Mastomys awashensis |
7.4782 |
36.5334 |
Shebe, Ethiopia |
Martynov et al. 2020 |
|
Mastomys awashensis |
8.2331 |
37.5887 |
Gibe National Park, Ethiopia |
Martynov et al. 2020 |
|
Mastomys awashensis |
8.2338 |
37.5823 |
Gibe National Park, Ethiopia |
Martynov et al. 2020 |
|
Mastomys awashensis |
8.4651 |
39.1606 |
Lake Koka, Bati Qelo, Ethiopia |
Martynov et al. 2020 |
|
Mastomys awashensis |
8.6943 |
36.4149 |
Didessa River, Ethiopia |
Martynov et al. 2020 |
|
Mastomys awashensis |
8.8453 |
40.0119 |
Awash National Park, Ethiopia |
Martynov et al. 2020 |
|
Mastomys awashensis |
9.0586 |
42.2796 |
Babile Elephant
Sanctuary, Ethiopia |
Martynov et al. 2020 |
|
Mastomys awashensis |
9.1478 |
42.2624 |
Babile Elephant
Sanctuary, Ethiopia |
Martynov et al. 2020 |
|
Mastomys awashensis |
9.2249 |
34.8662 |
Dhati-Welel National Park,
Ethiopia |
Martynov et al. 2020 |
|
Mastomys awashensis |
9.2393 |
34.8653 |
Dhati-Welel National Park,
Ethiopia |
Martynov et al. 2020 |
|
Mastomys awashensis |
9.2449 |
34.8644 |
Dhati-Welel National Park,
Ethiopia |
Martynov et al. 2020 |
|
Mastomys awashensis |
9.5548 |
39.7818 |
Ankober, Ethiopia |
Martynov et al. 2020 |
|
Mastomys awashensis |
9.5554 |
39.7657 |
Ankober, Ethiopia |
Martynov et al. 2020 |
|
Mastomys awashensis |
11.0526 |
39.6481 |
Kombolcha, Ethiopia |
Martynov et al. 2020 |
|
Mastomys awashensis |
11.7525 |
37.9068 |
Gumara River, Ethiopia |
Martynov et al. 2020 |
|
Mastomys awashensis |
11.7797 |
37.7313 |
Gumara River, Ethiopia |
Martynov et al. 2020 |
|
Mastomys awashensis |
12.5492 |
39.6431 |
Adi Mancarre, Ethiopia |
Martynov et al. 2020 |
|
Mastomys awashensis |
12.6393 |
39.5383 |
Adi Aba Musa, Ethiopia |
Martynov et al. 2020 |
|
Mastomys awashensis |
12.6551 |
39.5816 |
Kube, Ethiopia |
Martynov et al. 2020 |
|
Mastomys awashensis |
13.1858 |
37.9671 |
Simien Mts National Park,
Ethiopia |
Martynov et al. 2020 |
|
Mastomys awashensis |
14.0945 |
37.4575 |
Mai-Temen, Ethiopia |
Martynov et al. 2020 |
Appendix 2. Correlation matrix
resulting from the spatial multicollinearity test of the 19 bioclimatic
variables used in the analysis.
|
Layer |
BIO1 |
BIO2 |
BIO3 |
BIO4 |
BIO5 |
BIO6 |
BIO7 |
BIO8 |
BIO9 |
BIO10 |
BIO11 |
BIO12 |
BIO13 |
BIO14 |
BIO15 |
BIO16 |
BIO17 |
BIO18 |
BIO19 |
|
BIO1 |
1.000 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
BIO2 |
-0.031 |
1.000 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
BIO3 |
0.141 |
-0.489 |
1.000 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
BIO4 |
-0.116 |
0.567 |
-0.951 |
1.000 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
BIO5 |
0.633 |
0.554 |
-0.604 |
0.666 |
1.000 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
BIO6 |
0.681 |
-0.599 |
0.745 |
-0.766 |
-0.123 |
1.000 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
BIO7 |
-0.120 |
0.769 |
-0.906 |
0.958 |
0.688 |
-0.805 |
1.000 |
|
|
|
|
|
|
|
|
|
|
|
|
|
BIO8 |
0.744 |
0.128 |
0.134 |
-0.128 |
0.471 |
0.486 |
-0.074 |
1.000 |
|
|
|
|
|
|
|
|
|
|
|
|
BIO9 |
0.516 |
-0.258 |
-0.074 |
0.116 |
0.405 |
0.330 |
0.001 |
-0.053 |
1.000 |
|
|
|
|
|
|
|
|
|
|
|
BIO10 |
0.754 |
0.312 |
-0.501 |
0.556 |
0.957 |
0.077 |
0.516 |
0.496 |
0.561 |
1.000 |
|
|
|
|
|
|
|
|
|
|
BIO11 |
0.775 |
-0.399 |
0.701 |
-0.713 |
0.022 |
0.969 |
-0.695 |
0.578 |
0.326 |
0.186 |
1.000 |
|
|
|
|
|
|
|
|
|
BIO12 |
-0.051 |
-0.627 |
0.767 |
-0.776 |
-0.660 |
0.555 |
-0.800 |
-0.076 |
-0.051 |
-0.534 |
0.464 |
1.000 |
|
|
|
|
|
|
|
|
BIO13 |
0.042 |
-0.513 |
0.718 |
-0.769 |
-0.565 |
0.574 |
-0.758 |
-0.002 |
-0.055 |
-0.452 |
0.527 |
0.920 |
1.000 |
|
|
|
|
|
|
|
BIO14 |
-0.079 |
-0.478 |
0.463 |
-0.377 |
-0.400 |
0.303 |
-0.461 |
-0.063 |
-0.002 |
-0.303 |
0.185 |
0.571 |
0.334 |
1.000 |
|
|
|
|
|
|
BIO15 |
0.425 |
0.282 |
0.092 |
-0.180 |
0.246 |
0.274 |
-0.053 |
0.490 |
-0.064 |
0.210 |
0.393 |
-0.154 |
0.111 |
-0.402 |
1.000 |
|
|
|
|
|
BIO16 |
0.004 |
-0.501 |
0.716 |
-0.762 |
-0.585 |
0.543 |
-0.747 |
-0.029 |
-0.075 |
-0.479 |
0.496 |
0.937 |
0.991 |
0.352 |
0.070 |
1.000 |
|
|
|
|
BIO17 |
-0.077 |
-0.530 |
0.507 |
-0.421 |
-0.436 |
0.342 |
-0.510 |
-0.072 |
0.017 |
-0.329 |
0.215 |
0.626 |
0.378 |
0.984 |
-0.428 |
0.394 |
1.000 |
|
|
|
BIO18 |
-0.170 |
-0.513 |
0.610 |
-0.637 |
-0.633 |
0.376 |
-0.653 |
-0.035 |
-0.244 |
-0.571 |
0.272 |
0.805 |
0.713 |
0.536 |
-0.150 |
0.728 |
0.577 |
1.000 |
|
|
BIO19 |
0.070 |
-0.461 |
0.456 |
-0.417 |
-0.318 |
0.404 |
-0.486 |
-0.074 |
0.185 |
-0.183 |
0.335 |
0.631 |
0.547 |
0.418 |
-0.208 |
0.562 |
0.454 |
0.279 |
1.000 |