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Aligaz et al. BMC Ecology and Evolution _#####################_
https://doi.org/10.1186/s12862-024-02245-y BMC Ecology and Evolution
*Correspondence:
Bezawork Afework Bogale
bezawork.afework@aau.edu.et
Full list of author information is available at the end of the article
Abstract
Background Understanding the distribution pattern of species and their suitable habitat is key to focus conservation
eorts. Climate change has had notable impact on the distribution and extent of suitable habitats, and the long-
term survival of various species. We aim to determine the distribution and extent of suitable habitats for Tauraco
ruspolii and T. leucotis in Ethiopia and predict their range in the 2050s and 2070s using MaxEnt algorithm. We used
25 and 29 raried occurrence points for T. ruspolii and T. leucotis, respectively, and 13 environmental variables. Three
regularization multipliers and two cut-o thresholds were used to map the potential suitable habitats for each
species under current and future climates. Maps were assembled from these techniques to produce nal composite
tertiary maps and investigated the habitat suitability overlap between the two species using the UNION tool in the
geographical information system.
Result All model run performances were highly accurate for both species. Precipitation of the driest month and
vegetation cover are the most inuential variables for the habitat suitability of T. ruspolii. The habitat suitability of T.
leucotis is also mainly inuenced by mean temperature of the driest quarter and vegetation cover. Under the current
climate, the suitable habitat predicted for T. ruspolii covered about 24,639.19 km2, but its range size change shows
a gain and increase by 156.00% and 142.68% in 2050 and 2070, respectively. The T. leucotis‘s current suitable habitat
ranges about 204,397.62km², but this is reduced by 40.84% and 68.67% in 2050 and 2070, respectively. Our modeling
also showed that there was suitable habitat overlap between them at the margin of their respective habitat types in
time series.
Conclusion We concluded that there is a direct or indirect impact of climate change on the suitable habitat range
expansion for T. ruspolii and contraction for T. leucotis as well as overlapping of these turaco species in dierent regions
of Ethiopia. Therefore, understanding the distribution of current and future suitable habitats of the two turaco species
can provide valuable information to implement conservation practices for the species and the regions as well.
Distribution and extent of suitable habitats
of Ruspoli’s Turaco (Tauraco ruspolii)
and White-cheeked Turaco (Tauraco leucotis)
under a changing climate in Ethiopia
Mulatu AyenewAligaz1,5, Chala AdugnaKufa2,5 , Ahmed SeidAhmed3,5 , Hailu TilahunArgaw4,5 ,
MisganawTamrat5, MeseleYihune5, AnagawAtickem5, AfeworkBekele5 and Bezawork AfeworkBogale5*
Page 2 of 11Aligaz et al. BMC Ecology and Evolution _#####################_
Introduction
e distribution pattern of species and the availability
of their suitable habitats were mainly aected by climate
change which is driven by anthropogenic pressures at a
global scale in the current Anthropocene Epoch [1–4].
Recently, there has been an increasing interest in model-
ing and mapping the habitat suitability of species including
birds to prioritize conservation areas and predict the pos-
sible changes of their suitable habitats due to climate change
[5–8].
Bird species, like other species, either adapt to the accel-
erating climate change, shift out of their natural range (i.e.,
loss or gain), or become extinct [5, 6, 8]. Shifting is usually
altitudinal or latitudinal [9–11]. Restricted range birds are
more vulnerable to extinction when they experience climate
change due to loss of the suitable habitats [6, 12]. Globally,
an increase of temperature by 1ºC is projected to have a
non-linear increase in bird extinction by 100–500 species in
future climatic conditions [6]. is is more severe for forest-
specialist birds in the Afro-tropic biogeographic realm since
they require specic ecological conditions [13]. For instance,
several frugivores require tree holes and eshy-fruited trees
for reproduction and feeding, respectively [14, 15]. Turacos
are among the Afro-tropical montane forest specialist birds
and play critical ecological roles mainly as seed dispersers
[14]. Due to the rapid loss of forest cover and other fac-
tors in Africa, several turaco species are at risk of popula-
tion decline. Moreover, Tauraco ruspolii of the Ethiopian
endemic turaco and T. bannermani of West Africa are con-
sidered globally threatened [16]. Particularly, Tauraco rus-
polii is restricted in a narrow range in southern Ethiopia and
vulnerable to habitat degradation, illegal tree cutting, com-
petition, and hybridization with the least concern T. leucotis
[17, 18].
Research on the impact of climate change on avian spe-
cies in Africa particularly in Ethiopia is untouched except
[10] on the Ethiopian bush crow (Zavattariornis stresemani)
and white-tailed swallow (Hirundo megnensis) and [19] on
four highland birds. With this study, we employed one of the
Species Distribution Models (SDMs), Maximum Entropy
(MaxEnt, ), to predict the distribution and extent of suit-
able habitats of T. ruspolii and T. leucotis [20] under chang-
ing climates in Ethiopia. is model is the most popular and
robust even with small occurrence points [21]. us, we
aimed to determine the distribution and extent of suitable
habitats and their inuential predictors for the two turaco
species under the changing climatic conditions. Further-
more, the study was aimed to calculate the suitable habitats
overlap between the two turaco species.
Materials and methods
Species occurrence data
e present study utilized the occurrence data of target
species collected in Ethiopia (Fig.1). Ethiopia is home to
one of the richest and most unique assemblages of fauna
and ora on the African continent [22]. In the country,
about 863 bird species are believed to be recognized, of
which 19 are endemic to the country alone and addition-
ally 14 endemics shared with Eritrea [23]. e current
studied turaco species are distributed in almost in com-
mon altitudinal range from 450 to 3600m a. s. l [24].
We obtained 54 and 119 occurrence points for T. ruspolii
and T. leucotis, respectively, from the Global Biodiversity
Information Facility (GBIF, https://www.gbif.org) and previ-
ous published literature [25–28]. ese occurrence points
were spatially raried with 1 km² spatial resolution using
SDM toolbox V2.5 [29] to avoid spatial autocorrelations
[30]. us, the retained 25 and 29 occurrence points of T.
ruspolii and T. leucotis, respectively, were used for building
habitat suitability models of the target taxa (Fig.1; Table S1).
Environmental variables
Habitat suitability of species and their spatial distribution
depend on the cumulative interaction of various environ-
mental variables [31]. In this study, 19 bioclimatic variables,
topographic attributes, land use land covers as well as veg-
etation covers are considered.
e current bioclimatic data (i.e., an average of 1970 to
2000) were downloaded from WorldClim version 2.1 at a
spatial resolution of 30s arc (1 km2) [32]. By assuming all
current environmental variables will be unchanged, the
future bioclimatic variables were also downloaded from the
same source. e period of 2050s (2041–2060) and 2070s
(2061–2080) with two shared socioeconomic pathways (i.e.,
the intermediate emission pathway-SSP4.5 and the worst-
SSP8.5) developed by HadGEM2-Es global circulation mod-
els (GCMs) were used [32]. e topographic variables were
derived from the Shuttle Radar Topography Mission Digital
Elevation Model (SRTM-DEM) [33]. e Ethiopian vegeta-
tion types (http://landscapeportal.org/layers/geonode:veg.
ethiopia) whereas land use land cover map was obtained
from (https://cds.climate.copernicus.eu/) were also used.
All of the environmental variables were processed using
ArcGIS version 10.7 spatial analyst tools at 1 km2 resolution
to have the same extent, projection and resolution [20].
To avoid multi-collinearity among environmental vari-
ables and increas model accuracy, we employed Pearson’s
pair-wise correlation using DISMO package and then the
Variance Ination Factor (VIF) using USDM package [34]
using R v4.2.2. For this, we rst stacked 24 environmental
Keywords Climate change, Endemic species, Habitat suitability overlap, Maximum entropy, Species distribution
modeling, Turaco species
Page 3 of 11Aligaz et al. BMC Ecology and Evolution _#####################_
variables and extracted their values at each of the occur-
rence points and additionally at randomly generated 10, 000
pseudo-absence points [35]. As a threshold, we used a cor-
relation coecient |r| ≤ 0.70 [30)]and VIF ≤ 10, and nally,
we retained 13 of the same environmental variables for both
species’ distribution and habitat suitability modeling (Fig.
S1; Table1).
Model setting and prediction
For both studied species, we used a similar model setting in
Maximum Entropy (MaxEnt Version 3.4.4) [20]. e model
also iterated 5000 times with 10 replications and used a
default cross-validation run type. Regularization multiplier
was set in three complex levels (labeled as 1Reg, 5Reg, and
8Reg) [36, 37]. e remaining settings were left as default.
e predictive performance of the model was assessed
using the Area Under Curve (AUC) of the Receiving Opera-
tor Characteristics (ROC) curve which provides a thresh-
old-independent overall accuracy ranging between 0.5 and
1.0 [20]. us, models with AUC > 0.90 is considered to be
high accuracy, 0.70 < AUC < 0.90 is good, 0.50 < AUC < 0.70
low accuracy and AUC ≤ 0.50 no better than randomness
[38, 39].
We used two thresholds to classify the MaxEnt output
maps into binary suitable / unsuitable: (1)10 Percentile
Training Presence logistic threshold (10PTP) and (2) Maxi-
mum Test Sensitivity plus Specicity logistic threshold
(MTSS). 10TP is explained as the predicted probability at
Table 1 Selected variables and their contribution for model
prediction after testing Pearson’s paired-wise correlation and
Variance Ination Factor (VIF)
Variables Code T. ruspolii T. leucotis
VIF Contri-
bution
(mean)
VIF Contri-
bution
(mean)
Iso-thermality Biol3 7.19 2.57 7.29 4.20
Temperature seasonality Biol4 7.57 28.40 7.70 3.53
Temperature annual
range
Biol7 2.11 0.17 2.13 0.00
Mean temperature of
driest quarter
Biol9 3.42 0.00 3.36 39.77
Precipitation of wettest
month
Biol13 6.96 2.97 6.81 2.30
Precipitation of driest
month
Biol14 2.93 28.90 2.88 0.23
Precipitation seasonality Biol15 3.01 4.20 3.01 2.80
Precipitation of warmest
quarter
Biol18 2.34 10.30 2.31 0.37
Precipitation of coldest
quarter
Biol19 3.76 3.93 3.65 2.90
Vegetation cover
(categorical)
Vegetation 2.29 20.80 2.11 27.27
Land use land cover
(categorical)
Lulc 2.44 0.67 2.21 23.17
Slope (categorical) Slope 1.17 1.20 1.17 0.07
Aspect Aspect 1.01 0.43 1.01 0.40
Fig. 1 Occurrence points of the study species
Page 4 of 11Aligaz et al. BMC Ecology and Evolution _#####################_
10% omission rate of the training data while MTSS is the
probability of threshold at which the sum of fractions of
correctly predicted presence and pseudo-absence points
is the highest [40]. As a result, a total of six current binary
maps (three regularization multiplier times two thresh-
olds) and 24 binary maps for future projection (two future
periods times two scenario times three regularization mul-
tiplier times two thresholds) were produced per species.
en, we applied ensemble approach for these binary maps
and reclassied them into three habitat suitability classes
based on agreements among the pixels [41; 42]: (1), pixels
from less than 30% binary maps ( only one map for the cur-
rent and up to three maps for each future projection period
) were considered as unsuitable; (2) between 30% and 60%
binary maps (up to three for current and up to seven maps
for each future projection period) were assumed to be
uncertain and (3) above 60% (up to six for current and up to
twelve for each future projection period) were considered to
be suitable with high certainty.
Finally, species range size change between current pre-
diction and each future projection period was employed
to detect spatiotemporal change in habitat suitability
for each species using the ArcMap version 10.7. Spe-
cies range size change includes remain suitable, remain
unsuitable, loss, gain, current range size, future range size
and net species range size change. We also used UNION
tool of the ArcMap to detect suitable habitat overlap
between the two species [43]. e UNION tool provided
three types of polygons: (1) the area of the polygons
which has only T. ruspolii represent, (2) polygon which
represents only the areas where T. leucotis present, (3)
combined polygon which represents the areas of overlap
for the two species. en, we calculated the areas of these
polygons and the area percentage of T. ruspolii with the
suitable range of the T. leucotis and vice versa using the
formula:
Sp
ecies1(%)=
Areaofoverlap
Areaofspecies1
x
100
Results
Variable importance and model performance
e average percent contribution of variables indicated that
precipitation of the driest month (Biol14 = 28.9%), tempera-
ture seasonality (Biol4 = 28.4%), vegetation cover (20.8%),
and precipitation of the warmest quarter (Biol18 = 10.8%)
are the most determinant environmental variables for the
habitat suitability prediction of T. ruspolii (Table1). As the
response curves revealed, T. ruspolii preferred habitats with
Biol14 range from 10 to 20mm (Fig. S2a). Its high habitat
suitability (0.8) was also observed when the Biol4 (standard
deviation x100) ranged from 50-100oC (Fig. S2b). During
the warmest quarter, its habitat suitability increases until the
precipitation (Biol18) reaches 300mm then becomes stable
(Fig. S2c). Desert and semi-desert scrubland, dry evergreen
Afro-montane Forest and Combretum-Terminalia wood-
land are also the most preferable vegetation covers for this
species.
On the other hand, the habitat suitability of T. leucotis is
mainly inuenced by the mean temperature of driest quar-
ter (Biol9 = 39.77%), vegetation cover (27.27%) and land
use land cover (23.17%) (Table1). e non-linear response
curve in (Fig. S3a) depicted that the habitat suitability of T.
leucotis is negatively correlated with the mean temperature
of the driest quarter (Biol9). Its most preferred vegetation
cover is wide and ranges from Afro-alpine vegetation to
Combretum-Terminalia woodland.
e AUC values of training and test datasets of the stud-
ied species are almost similar in the corresponding regular-
ization multipliers. Since the AUC values of T. ruspolii are
> 0.90, its model performance is found in high accuracy in
all regularization multipliers and datasets (Table 2). e
model also showed high performance accuracy for T. leu-
cotis with the exception on the test dataset at 8Reg which
failed with good accuracy (Table2). e relation of regular-
izations and binary map thresholds indicated that the extent
of predicted suitable habitat is reduced as regularization
multipliers increase (i.e., model complexity decreases) in all
thresholds for both species (Table2). is implies that wide
suitable habitat resulted in higher model complexity (1Reg)
than at lower complexity (8Reg) regularization multiplier.
Table 2 Model performance and cut-o thresholds of binary maps for Tauraco ruspolii and Tauraco leucotis
Species Regularization AUC Cut-o threshold
Training Test Di 10PTP MTSS
T. ruspolii 1Reg 1.00 0.99 0.01 0.35 0.33
5Reg 0.99 0.99 0.00 0.58 0.57
8Reg 0.99 0.98 0.01 0.66 0.63
T. leucotis 1Reg 0.97 0.92 0.05 0.14 0.24
5Reg 0.90 0.93 -0.03 0.29 0.36
8Reg 0.90 0.87 0.02 0.40 0.41
Di: the di erence between tra ining and test AUC values
Page 5 of 11Aligaz et al. BMC Ecology and Evolution _#####################_
Current and future habitat suitability
e current predicted suitable habitat of T. ruspolii is mainly
found in southern Ethiopia (relatively large extent), south-
western and northwestern Ethiopia (Fig.2). Assembling of
the future projection of T. ruspolii showed suitable habitat
expansion with high certainty in both future periods (more
pronounced in 2050) (Table3). It is also more observed at
southern Ethiopia.
Intersect of current with future climatic conditions indi-
cated that T. ruspolii gain more than its current suitable
habitat range (more than 100%) in both projected future
periods (Table3). us, the net habitat suitability change
showed positive across the time series.
Our model prediction has shown that T. leucotis has
wide habitat suitability and distribution range relative to
T. ruspolii under current and future climate conditions.
e current predicted suitable habitat of T. leucotis is
extended mainly from central Ethiopia towards south,
southeastern, southwestern and northwestern Ethiopia
with localities dominated by Afro-montane vegetation,
dry evergreen forest, and Acacia-Commiphora woodland
of the Rift Valley and Combretum-Terminalia woodland
(Fig.3).
Unlike T. ruspolii, assembling the future projection of
T. leucotis showed a reduction of suitable habitat range
relative to the current climatic condition (Table3). How-
ever, it would be still higher than the projection of T.
ruspolii. e intersection of current with future climatic
conditions also revealed this reality by showing a negative
net change. T. leucotis lost more than 30.00% of its cur-
rent suitable habitat range but gained less than 1.00% in
both projection future periods (Table3). e majority of
its suitable habitats, particularly, in central Rift Valley, in
Fig. 2 Ensemble habitat suitability of T. ruspolii from three class maps (suitable, uncertain and unsuitable) for the current and future climate conditions
Page 6 of 11Aligaz et al. BMC Ecology and Evolution _#####################_
Hararge highlands, and around Lake Tana are expected
to be lost (Fig.4).
Suitable habitat overlaps between species
Habitat suitability overlap between T. ruspolii and T. leucotis
is observed in the projection periods with a slight shifting.
It is found at the habitat margin of the respective species
mainly in southern Ethiopia, northwestern Ethiopia (par-
ticularly, in Awi and East Gojjam), Western Ethiopia (East
Wollega) and southeastern Ethiopia (Bale and Arsi areas)
(Fig.5).
Minimum habitat suitability overlaps between T. ruspolii
and T. leucotis is observed in the 2070s whereas its maxi-
mum overlap is observed in 2050 (Table4). As a general
Table 3 Temporal change in the extent of potential suitable habitats across the time range for T. ruspolii and T. leucotis in Ethiopia
Species Intersection Area (km²) Change (%)
Remain Suitable Gain Loss Current range size Future projected Size Gain Loss Net Change
T.
ruspolii
Curr_2050 21903.20 41038.65 2735.99 24639.19 62941.85 167.00 11.00 156.00
Curr_2070 22342.93 37418.64 2296.26 24639.19 59761.57 152.00 9.32 142.68
T.
leucotis
Curr_2050 120510.92 413.72 83886.70 204397.62 120924.65 0.20 41.04 -40.84
Curr_2070 63861.70 194.00 140534.00 204396.04 64055.7 0.09 68.76 -68.67
Fig. 3 Ensemble habitat suitability of T. leucotis from three class maps (Suitable, Uncertain and Unsuitable) for the current and future climate conditions
Page 7 of 11Aligaz et al. BMC Ecology and Evolution _#####################_
trend, the area percentage of T. ruspolii that overlaps with
the range of T. leucotis is larger than the area percentage of
T. leucotis that overlaps with the range of T. ruspolii in all
periods (Table4). However, a larger dierence in area per-
centage overlap between these two species is observed in
the current climate condition (15.89%).
Discussion
Variable importance and model evaluation
Species Distribution Models are the most popular tech-
nique for assisting the management of specic species. e
models may be constructed using various algorithms, pre-
dictors, and numbers of response variables and the con-
dence of their results depend on the goal and accuracy of
the response variables. Among the bioclimatic variables,
the model predicted that precipitation of the driest month
(Biol14) and the warmest quarter (Biol18) aected the
habitat suitability of T. ruspolii. is is because precipi-
tation is linked to the richness of plant species and their
primary productivity, thereby providing habitat require-
ments for their survival and success of reproduction [44].
For instance, Gwitira et al. [45] revealed that plant spe-
cies richness increased as the precipitation of the warmest
quarter increased up to 450mm (which is not far from the
result of this study i.e., 300mm) in Southern Africa Savan-
nah. Low precipitation in lowland areas is also a factor for
the altitudinal shifting of lowland tropical birds to higher
altitudes where the availability of resources is highest [9,
46]. However, extreme precipitation leads to the decline of
Fig. 4 Intersect of the current tertiary maps with future climate conditions that showing projected habitat suitability change from current to future. The
rst row indicated the intersection of current with 2050s and 2070s of T. ruspolii whereas the second row indicated the intersection of current with 2050s
and 2070s of T. leucotis
Page 8 of 11Aligaz et al. BMC Ecology and Evolution _#####################_
reproduction due to ooding of nests, dying of broods and
limitation of food provision for brood [47].
Vegetation cover is the common and most important
factor for the habitat suitability of both T. ruspolii and T.
leucotis. Our model predicted that desert and semi-desert
scrubland (< 400m a.s.l.) and dry evergreen Afro-mon-
tane forest (1500–3500m a.s.l.) are suitable vegetation
cover for T. ruspolii [48] whereas T. leucotis have shown
wide vegetation cover range from Afro-alpine to
Combretum-Terminalia woodland (above 900 m a.s.l.).
e suitability of desert and semi-desert scrubland for
T. ruspolii is unexpected compared to the eldwork con-
ducted from 1995 to 2003 [17, 25, 49] because they did
not record the presence of both species in this vegetation
type. However, range expansion of T. ruspolii to this veg-
etation type might occur during the wet season because
this species is known to make localized seasonal move-
ments [17]. Since turacos are frugivores, plant species
that are found mainly in dry and moist evergreen Afro-
montane forests are the main sources of fruits for these
two turaco species [16, 50]. Borghesio [49] identied 10
plant species in dry evergreen Afro-montane forests as
food resources, of which, Ficus species and two conifer
species (Juniperus procera and Podocarpus gracilior) are
the most preferred.
In this study, multiple maps were produced by apply-
ing dierent regularization multipliers (complexity lev-
els). From these multiple maps, binary maps were also
Table 4 Area and percentage of suitable habitat overlap
between T. ruspolli and T. leucotis under current and future
climate conditions
Period Area overlap
(km²)
T. ruspolii
(%)
T. leucotis
(%)
Dif-
fer-
ence
(%)
Current 3405.99 17.56 1.67 15.89
2050 7674.78 18.31 6.36 11.95
2070 1837.19 9.16 2.89 6.27
Fig. 5 Suitable habitats overlap between T. ruspolii and T. leucotis across time series
Page 9 of 11Aligaz et al. BMC Ecology and Evolution _#####################_
produced using dierent cut-o thresholds through an
ensemble approach [41]. e use of dierent regulariza-
tion multipliers has the advantage of enhancing the reli-
ability of model performance and gives more condence
for taking conservation practice and management [42].
e AUC values of our model were greater than 0.90 for
T. ruspolii and greater than and equal to 0.87 for T. leu-
cotis (Table2). ese have shown that the model is found
as high predictive performance for both species [38, 39].
However, the predictive performance can be inuenced
by dierent factors (variables) such as the extent of the
study area and other species-related factors [51].
Current and future habitat suitability
e result of MaxEnt prediction depicted that the cur-
rent suitable habitat range of T. ruspolii is found in south-
ern Ethiopia (relatively large extent), southwestern and
northwestern Ethiopia with high fragmentation and cov-
ered about 24,639.19km². is predicted suitable habi-
tat range is less than the extent occurrence area of the
species (26,800km²) suggested by BirdLife International
[18]. Out of this predicted suitable habitat range, a sur-
vey was conducted only on the southern Ethiopia (par-
ticularly around Negele Borena, Genale, Kibre Mengist,
Shakiso and Arero) with the range of 8,000km² [17]. e
model indicated that northern Ethiopia (around Lake
Tana) is a potentially suitable habitat range for T. rus-
polii, but its presence has not been recorded yet. On the
other side, T. leucotis has wide current suitable habitat
ranges than T. ruspolii and can be extended from central
Ethiopia towards south, southwestern, southeastern and
northwestern Ethiopia. e model estimated the current
area coverage of 204,397.62km², which is less compara-
ble with 1.1million km² extent occurrence area of Bird-
Life International’s suggestion [52].
Assembling of the future projection of T. ruspolii indi-
cated the expansion of suitable habitats in both 2050 and
2070 relative to the current climate condition (Table3).
As model prediction and previous eld surveys [17]
conrmed, this turaco species preferred dry evergreen
Afro-montane forest which is its main food source. e
expansion of suitable habitat in the future for this spe-
cies might be due to two reasons: (1) dry evergreen Afro-
montane forest covers wide range compared to other
vegetation types of Ethiopia next to Acacia-Commiphora
woodland [48]; (2) even though there is anthropogenic
pressure in this vegetation type, there is also a potential
dry forest management and conservation practices in
dierent parts of the country including plantation devel-
opment to be buering for the natural forest which is
managing by the government, controlling of overgraz-
ing, traditional forest management practiced by dif-
ferent Ethnic groups like Gada system, a role model of
sacred grooves, especially Ethiopian Orthodox Tewahedo
Church [53–55].
In contrast to T. ruspolii, assembling of the future pro-
jection of T. leucotis in 2050 and 2070 indicated that loss
is higher than gain, and thus, the net change will be nega-
tive (Table3). e reason for this decline in the future
might be due to the rise of the mean temperature of the
driest quarter (Biol9) which in turn aects the availabil-
ity of food resources. T. leucotis is also preferred in moist
evergreen Afro-montane forests [17] where anthropo-
genic pressures (such as intensication of tea and coee
productivity, human settlement and dependency of the
local people on the forest products) are severe and rapid
[48, 56, 57]. Intensication of coee productivity leads to
the conversion of the natural coee forest into fully plan-
tation coee causing signicant plant diversity losses and
collapse of forest structure [56, 58].
Suitable habitat overlaps between the species
In this study, habitat suitability overlaps between T.
ruspolii and T. leucotis were observed at the margin of
their respective suitable habitats. Such overlap was also
observed after 2001 during eld survey. Before that, T.
ruspolii preferred habitats of forest edge and Acacia
woodland whereas T. leucotis mainly occurred in the
moist (wetter) dense forests [17]. According to these
authors, habitat degradation due to anthropogenic pres-
sure is responsible for reducing the barriers between the
two species. Wide suitable habitat overlap was predicted
in the 2050s than the current climate condition and
2070s (Table4). As a general trend, the area percentage
of T. ruspolii that overlaps with the home range of T. leu-
cotis is larger than the area percentage of T. leucotis that
overlaps with the home range of T. ruspolli in all periods
(Table4). In other words, the model indicated that T. rus-
polii will be expanded into the range of T. leucotis. is
is in contrast to the eld survey of 2003 [17]. Whatever
the case, the overlapping of the two species’ habitat will
lead to resource competition. Furthermore, hybridization
between these two species was observed since 2001 [59]
which indicates the presence of habitat overlap. us,
the widespread hybridization at the overlap ranges leads
to the risk of genetic erosion of the Nearly reatened T.
ruspolii [60] and which in turn leads to extinction despite
the availability of suitable habitat in future climatic
conditions.
Conclusions
e study set out to determine the distribution and habi-
tat suitability of T. ruspolii and T. leucotis using biocli-
matic and non-bioclimatic factor. Model performance is
found in high accuracy for T. ruspolii while good perfor-
mance for T. leucotis. Precipitations of the driest month,
temperature seasonality, and vegetation cover are the
Page 10 of 11Aligaz et al. BMC Ecology and Evolution _#####################_
most contributor variables for the habitat suitability pre-
diction of T. ruspolii while mean temperature of the dri-
est quarter and vegetation cover for T. leucotis. e extent
of both the current and future suitable habitat of T. rus-
polii is less than that of T. leucotis. However, under future
climate conditions, the extent of its suitable habitat is
expected to be increased while this decreases for T. leuco-
tis. Suitable habitat overlapping between the two species
is also observed at the margin of their respective habitat
types in current and future climate conditions. erefore,
understanding the distribution of current and future suit-
able habitats of these turaco species can provide valuable
information to implement conservation practices for the
species and the regions as well. A comprehensive survey
for population assessment in highly suitable habitats is
also fundamental to understanding the current conserva-
tion status of both species. Future research may also con-
sider the application of numerous dierent models and
their ensemble approach.
Supplementary Information
The online version contains supplementary material available at https://doi.
org/10.1186/s12862-024-02245-y.
Supplementary Material 1
Acknowledgements
Addis Ababa University, Zoological Sciences provided facilities to carry out this
research.
Author contributions
M.A. involved in data compilation, modeling, analyzing the output and
drafting manuscript. C.A., A.S., and H.T. involved during title selection, data
analysis, and revising the manuscript. M.T., M.Y., A.A., A.B. and B.A. revised
the manuscript. We would like to clarify that the work presented here is
original research that has not previously been published and is not under
consideration for publication elsewhere, in whole or in part. All authors have
read and approved the nal manuscript.
Funding
Not available.
Data availability
No datasets were generated or analysed during the current study.
Declarations
Competing interests
The authors declare no competing interests.
Ethics approval and consent to participate
Not applicable.
Consent for publication
Not applicable.
Author details
1Department of Biology, Debre Markos University, P.O. Box, 269, Debre
Markos, Ethiopia
2Department of Biology, Natural and Computational Sciences, Woldia
University, P.O. Box, 400, Woldia, Ethiopia
3Department of Biology, Hawassa University, P. O. Box 05, Hawassa,
Ethiopia
4Department of Wildlife and Ecotourism Management, Wolkite University,
P.O. Box. 07, Wolkite, Ethiopia
5Department of Zoological Sciences, Addis Ababa University, P.O. Box.
1176, Addis Ababa, Ethiopia
Received: 2 January 2024 / Accepted: 21 April 2024
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