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Predicting habitat suitability for the endemic mountain nyala ( Tragelaphus buxtoni ) in Ethiopia


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The use of statistical models to predict species distributions and suitable habitat has become an essential tool for wildlife management and conservation planning. Models have been especially useful with rare and endangered wildlife species. One such species is the mountain nyala (Tragelaphus buxtoni), a spiral-horned antelope endemic to the Ethiopian highlands. The full range of the species has never been adequately defined and recent discoveries of new populations suggest that others may exist undetected. To identify potential mountain nyala occurrences, we used classification tree analysis to predict suitable habitat using 76 climatic, topographical and vegetative variables. Three model evaluation methods showed a strong performance of the final model with an overall accuracy of 90%, Cohen's maximised κ of 0.80 and area under the receiver operating characteristic curve (AUC) value of 0.89. Minimum temperature and maximum pre- cipitation generally had the greatest predictive contributions to suitable mountain nyala habitat. The predicted habitat co vered an area of 39378 km2, with the majority occurring in remote forests on the southern escarpment of the Bale Mountains. Other areas within the predicted range may be too impacted by human and livestock populations to support mountain nyala; however, the model will be useful in directing future surveys for new populations while offering clues to the species historical range.
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CSIRO PUBLISHING Wildlife Research, 2008, 35, 409–416
Predicting habitat suitability for the endemic mountain nyala
(Tragelaphus buxtoni ) in Ethiopia
Paul H. EvangelistaA,B,D, John Norman IIIA, Lakew BerhanuC, Sunil Kumar A and
Nathaniel AlleyB
ANatural Resource Ecology Laboratory, Colorado State University, Fort Collins, CO 80523-1499, USA.
BThe Murulle Foundation, PO Box 1442, Fort Collins, CO 80522, USA.
CEthiopian Wildlife Conservation Department, PO Box 386, Addis Ababa, Ethiopia.
DCorresponding author. Email:
Abstract. The use of statistical models to predict species distributions and suitable habitat has become an essential tool
for wildlife management and conservation planning. Models have been especially useful with rare and endangered wildlife
species. One such species is the mountain nyala (Tragelaphus buxtoni), a spiral-horned antelope endemic to the Ethiopian
highlands. The full range of the species has never been adequately defined and recent discoveries of new populations
suggest that others may exist undetected. To identify potential mountain nyala occurrences, we used classification tree
analysis to predict suitable habitat using 76 climatic, topographical and vegetative variables. Three model evaluation
methods showed a strong performance of the final model with an overall accuracy of 90%, Cohen’s maximised κ of 0.80
and area under the receiver operating characteristic curve (AUC) value of 0.89. Minimum temperature and maximum pre-
cipitation generally had the greatest predictive contributions to suitable mountain nyala habitat. The predicted habitat
covered an area of 39378 km2, with the majority occurring in remote forests on the southern escarpment of the Bale
Mountains. Other areas within the predicted range may be too impacted by human and livestock populations to support
mountain nyala; however, the model will be useful in directing future surveys for new populations while offering clues to
the species historical range.
Additional keywords: classification tree, ecological niche model, habitat suitability, WorldClim.
Tragelaphus buxtoni (mountain nyala) is a large spiral-horned open spaces, such as cultivated fields, grasslands, wet
antelope endemic to Ethiopia’s highlands. First identified by meadows, fens and the Afro-alpine habitats to feed and drink
the scientific community in 1908 (Lydekker 1910, 1911), very (Evangelista 2006b).
little is known about the biology, range and population of the Habitat loss and land degradation are the most significant
species. Mountain nyala are believed to inhabit only the south- threats to mountain nyala and the majority of Ethiopia’s
ern highlands east of the Rift Valley. Remnant populations exist wildlife. Ethiopia’s forests once covered 65% of the country
in the Chercher and Arussi mountains; however, most inhabit and 90% of the highlands. Today, Ethiopia’s forests cover only
the Bale Mountains (Fig. 1; Brown 1969a; Yalden and Largen 2.2% of the country and 5.6% of the highlands (FAO 2006). As
1992; Evangelista 2006b; Sillero-Zubiri 2007). Mountain nyala forest habitats are reduced by the growing human population
are generally found between 1800 and 4000 m, but have been and need for natural resources (e.g. agriculture, livestock
observed at elevations as low as 1500 m (K. Wakijira, pers. grazing, fuel-wood), mountain nyala populations are becoming
comm.). Generally, the species prefers dense highland forests fragmented and increasingly confined to small areas
for concealment, thermal regulation and the availability of sea- (Evangelista 2006b). Although it is evident that habitat loss is
sonal forage. In particular, mountain nyala prefer the upper and reducing the known range of mountain nyala, the full distri-
lower Afro-montane zones, which are dominated by Hagenia bution of the species has never been adequately determined. As
abyssincia, Hypericum revolutum, Juniperus procera and a result, total population estimates remain inconclusive, hinder-
Sinarundinaria alpina (Bekele-Tesemma et al. 1993; Miehe ing effective management strategies and skewing conservation
and Miehe 1994; Bussman 1997). Historic records suggest that policy (East 1999; Refera and Bekele 2004; Evangelista 2006b;
the ericaceous belt (sub-alpine zone dominated by Erica Evangelista et al. 2007; Sillero-Zubiri 2007). For the Ethiopian
trimera and E. arborea) was at one time prime mountain nyala Wildlife Conservation Department (EWCD), identifying the
habitat; however, much of this habitat type has been converted full range and total population of mountain nyala is a high
for agriculture and livestock grazing (Brown 1969a, 1969b; priority and a requirement for implementing effective manage-
Malcolm and Evangelista 2005; Evangelista et al. 2007). At ment plans, facilitating conservation strategies and formulating
night, mountain nyala have been observed congregating in wildlife policy (T. Hailu, pers. comm.).
© CSIRO 2008 10.1071/WR07173 1035-3712/08/050409
410 Wildlife Research
In recent years, several populations of mountain nyala have
been documented for the first time (Evangelista 2006a, 2006b).
In 2000 and 2001, the species was found to be widespread
throughout the eastern slopes of the Bale Mountains
(Evangelista 2006b; Evangelista et al. 2007). In response to
apparent land degradation and poaching activities, the EWCD
established three controlled hunting areas (CHA) in the Bale
Mountains to intensively manage the species through limited
trophy hunting. In 2005, the EWCD discovered two additional
populations: the first was located in the south-western region of
the Bale Mountains and the second on the north-eastern slope of
the Arussi Mountains (Fig. 1). Populations in these two areas
have been surveyed by the EWCD and are currently under
review for management through controlled hunting.
Collectively, the five new CHAs are estimated to have between
1750 and 2000 mountain nyala based on line-transect surveys
conducted by the EWCD (Evangelista 2006b). In 2006, moun-
tain nyala were found to be prevalent in the higher elevations of
the Harenna Forest, a cloud forest that covers the southern
escarpment of the Bale Mountains (P. Evangelista, pers. obs.
2006–2007; Evangelista et al. 2007). Population surveys have
not yet been conducted for the area, but the presence of moun-
tain nyala has been confirmed in four regions inside and outside
of the Bale Mountains National Park by EWCD and the authors.
These recent discoveries and other undocumented reports
suggest that additional populations of mountain nyala are likely
to exist and add to knowledge of the species’ habitat require-
ments. However, the Ethiopian highlands are vast and often
P. H. Evangelista et al.
inaccessible, creating many logistical challenges in the search
for new populations.
Fortunately, advancements in computer technology and soft-
ware are allowing ecologists to integrate field observations,
remotely sensed data, geographical information systems (GIS)
and digital image processing to define species distributions and
identify undiscovered populations. Specifically, probability
models are employed by ecologists to predict species occur-
rence (Corsi et al.1999; Pearson et al. 2007), migratory patterns
(Boone et al. 2006), critical habitats (Hatten and Paradzick
2003; Turner et al. 2004), risk of diseases (Broadfoot et al.
2001; Pfeiffer and Hugh-Jones 2002), invasion of non-native
species (Ficetola et al. 2007; Evangelista et al. in press) and
management priorities (Clevenger et al. 2002; Felix et al. 2004).
One of the most widely used model applications is classification
and regression tree (CART) analysis. CART analysis is a com-
monly used non-parametric modelling technique that predicts
the response of a dependent variable through a series of simple
regression analyses (Breiman et al. 1984; Hansen et al. 1996;
Lewis 2000). Unlike other regression approaches that conduct
simultaneous analyses, CART models statistically partition the
dependent data into two homogenous groups at a node, repeat-
ing the procedure for each group in a continuous process that
forms a hierarchal tree. Classification trees are used when the
dependent variables are categorical (i.e. presence, absence) and
regression trees are used when the dependent variables are con-
tinuous (e.g. percent basal cover, species richness). Several
characteristics of this modelling approach appeal to researchers
Fig. 1. Map of Ethiopia’s southern highlands and
known populations of mountain nyala.
Mountain nyala habitat Wildlife Research 411
and resource managers. First, the analyses explicitly allow for
non-linear relationships between the dependent and inde-
pendent variables. These methods make no a priori assumptions
about the distribution of the data, the relationships among inde-
pendent variables or relationships between the dependent and
independent variables. Second, they are well suited to handle
non-homogenous datasets (i.e. unbalanced sample sizes, high
variability). Finally, the results are easily interpreted and the
predictive strength of each independent variable is explicitly
reported in the results (Michaelsen et al. 1994; Andersen et al.
2000; Evangelista et al. 2004).
The aim of our study is to use classification tree analyses at
landscape and regional scales to identify areas suitable for
mountain nyala and to direct future survey efforts that may lead
to the discovery of undocumented populations. We rely on
known distribution information with a suite of environmental
variables to relate species occurrence and absence to geograph-
ical and topographical features. In addition, to model evalua-
tions that are built into classification tree analyses (i.e.
predictive strength of independent variables, number of parti-
tions and terminal nodes), we evaluate model performances
with three proven statistical tests.
Study area
Although mountain nyala are believed to inhabit only a small
portion of Ethiopia’s southern highlands, the extent of our initial
analyses included the entire county, which encompasses
1.12 million km2 (MoWR 2001). Ethiopia is located in East
Africa and often described as the ‘roof of Africa’ because of its
rugged mountain topography, which has the largest expanse of
Afro-alpine habitats on the continent (Gamachu 1988; Uhlig
and Uhlig 1991). Over 50% of Africa’s land mass above 2000 m
and over 80% of the land mass above 3000 m is found in
Ethiopia (McClanahan and Young 1996). The Ethiopian high-
lands are divided by the Rift Valley into two dominant massifs:
the Bale Mountains in the south-eastern block and the Simien
Mountains in the north-western block. Elevation in Ethiopia
ranges from 120 m below sea level to 4620 m above sea level.
The contrasting topography and Ethiopia’s situation within the
Intertropical Convergence Zone result in varying climates
across the country. Temperatures in the arid regions of the
country may be as high as 37°C, and temperatures at higher ele-
vations are known to dip as low as –15°C (MoWR 2001).
Annual precipitation also fluctuates across Ethiopia’s land-
scape, occurring in two distinct seasons: the Bega, a dry season
from October to May, and the Kiremt, a long rainy season from
June to September. A small rainy season called the Belg occurs
in April and May at the end of the Bega. Mean annual precipita-
tion ranges from 2000 mm in areas of the south-west to less than
200 mm over the Afar lowlands (MoWR 2001). Based on
Koppen’s climate classification system, 10 climate zones define
the Ethiopian landscape (Gonfa 1996). These climate types
harbour many unique and diverse ecosystems that include
desert, savanna grasslands, shrubland, savanna woodlands
(Acacia sp.), tropical forests, montane forests, bamboo
(Yushane alpine and Oxytenanthera abyssinica), heathlands
(Erica sp.) and Afro-alpine (Logan 1946; Von Breitenbach
1961; Egziabher 1988; Miehe and Miehe 1994; Bussman 1997;
Carr 1998; Embaye 2000).
Field data sources
Our first model relied on mountain nyala observations collected
at regional levels from: (1) line-transect surveys conducted by
the EWCD between 2002 and 2006 in nine CHAs (Evangelista
2006b); (2) population estimates from observations at Kuni-
Muktar Wildlife Reserve and Galama Mountains Forest Priority
Area (Malcolm and Evangelista 2005; F. Kebede, pers. comm.);
(3) direct counts in the northern portion of Bale Mountains
National Park (Refera and Bekele 2004); and (4) field observa-
tions using global positioning system (GPS) collected from the
Senetti Plateau and the Harenna Forest in Bale Mountains
National Park (P. Evangelista, pers. obs. 2002–2006). Most
presence data did not include specific coordinates of observa-
tions. For these, we created boundary layers of management
areas, CHAs and study sites in ArcGIS 9.2 (ESRI 2006) using
GPS surveys, paper maps of CHAs provided by the EWCD
(Evangelista 2006b), Refera and Bekele’s (2004) map of their
study site and Landsat7 TM satellite images (GLCF 2006).
Within each boundary layer, we randomly generated n presence
points based on the most current population estimate available
for each defined area. All presence points were integrated into a
single GIS shapefile and converted to a raster format with a
pixel size of 1 km2. When more than one presence point fell
within a single raster pixel, that pixel would only represent a
single occurrence for our analyses. This approach was necessary
to avoid habitat bias of mountain nyala in areas with unnaturally
high numbers (e.g. Kuni-Muktar; Evangelista et al. 2007) or
areas where animals are easily or regularly observed (e.g.
Gaysay; Refera and Bekele 2004). The total number of presence
points summed from regional population estimates was 4766
and the total number of presence cells used in our first analyses
was 1443. Additionally, we generated 4766 pseudo-absence
points throughout Ethiopia with the criteria that each point had
to be below 1500 m in elevation and it could not occur within
the known mountain nyala populations (shapefiles previously
described). We repeated the conversion from point data to raster
cells leaving 4756 absence cells for our analyses.
For our second model, we generated 3000 random points
within the predicted range of mountain nyala occurrence from
the first model. Based on previous mountain nyala population
field surveys and observations (Evangelista et al. 2007), we
defined each random point as a presence or absence and omitted
any points where occurrence was uncertain. Following this
process, 471 points (181 presence and 290 absence) remained
for the second analyses with 75% (144 presence and 233
absence points) used to train the model and 25% (37 presence
and 57 absence points) withheld to validate the model.
Spatial data sources
To predict suitable habitat for mountain nyala, we used 76 inde-
pendent spatial variables in our analyses that were derived from
various remotely sensed data and GIS analyses. Data at coarse
resolutions were re-sampled to 90 m and projected in the
Universal Transverse Mercator system (WGS 84, Zone 37N).
Continental and global datasets were reduced to the extent of
Ethiopia. Using a 90 m Digital Elevation Model (DEM) from
412 Wildlife Research
the National Aeronautics and Space Administration’s (NASA)
Shuttle Radar Topography Mission (SRTM; Jarvis et al. 2006;
Farr 2007), we generated slope in degrees, aspect, surface
roughness index, soil wetness index and solar insolation raster
layers. Slope in degrees and aspect were generated in ArcGIS
9.2 Spatial Analyst (ESRI 2006). Soil wetness index was calcu-
lated using the formula [ln(A/tan β)], where ln(.) is the natural
logarithm, A is the area drained per unit contour or specific area
and tan β is the slope (Moore et al. 1991; Wolock 1993). The
solar insolation grid was generated using the Shortwave
program developed by Kumar et al. (1997).
Three global vegetation indices (GVI) and 12 normalised
difference vegetation index (NDVI) scenes from moderate reso-
lution imaging spectroradiometer (MODIS) instruments aboard
NASAs Terra satellite were acquired from the Global Land
Cover Facility (GLCF. 2006). GVIs included woody tree cover,
herbaceous vegetation cover and bare ground cover at 500-m
resolution. Collectively, these indices total 100% of ground
cover and can be properly displayed in red, green and blue band
combinations (Hansen et al. 2003a, 2003b). NDVI scenes were
acquired for the 15th day of each month for 2003 at 250 m res-
olution (Kidwell 1990, 1991; Carrol et al. 2004). NDVI is cal-
culated by [(NIR red)/(NIR + red)], where NIR is band 2 (near
infrared) and red is band 1 (Sellers 1985; Myneni et al. 1995).
Nineteen bioclimatic parameters were acquired from the
WorldClim (Hijmans et al. 2005;
These fine resolution (~1 km) spatial data were interpolated
from weather stations across the globe, with averages calcu-
lated from at least 10 years of data (Hijmans et al. 2005, 2006).
The 19 BioClim variables are derived from mean monthly
and quarterly climate estimates (e.g. minimum, maximum,
mean temperature and precipitation) to approximate energy
and water balances at a given location (Nix 1986;
Statistical analyses and model evaluation
All statistical analyses were conducted using S-Plus 3 statistical
software (Insightful 2000). We generated two classification tree
models: the first to model suitable mountain nyala habitat for all
of Ethiopia and the second to refine the predicted suitable
habitat of the first. Classification trees were pruned using
10-fold cross-validation (Breiman et al. 1984). We used three
methods to evaluate the performance of each model: (1) a con-
fusion matrix that shows specificity and sensitivity and overall
accuracy; (2) Cohen’s maximised κ; and (3) area under the
receiver operating characteristic curve (AUC) (Fielding and
Bell 1997).
There are two possible errors that may occur in prediction
models: false negatives (under prediction or under-fit models)
and false positives (over predictions or over-fit models; Fielding
and Bell 1997). Using the validation data, we presented the rel-
ative proportions of these errors in a confusion matrix.
Specificity (the proportion of true-positive and false-positive
absences) and sensitivity (the proportion of true-positives and
false positive presences) are reported for the final model with
overall accuracy reported as a percentage (Fielding and Bell
1997). Threshold dependent evaluation measure Cohen’s max-
imised κ (Cohen 1960), was calculated using a cut-off threshold
determined by plotting sensitivity against specificity (Fielding
P. H. Evangelista et al.
and Bell 1997). The κ statistic measures the proportion of cor-
rectly classified points (i.e. presence, absence) after accounting
for the probability of chance agreement. κ statistic values range
from –1 to +1, where +1 would be perfect agreement and any
values less than 0 would indicate a performance no better than
random (Cohen 1960; Allouche et al. 2006). Landis and Koch
(1977) ranked analysis performances as poor when κ values are
<0.40; good when the κ values range from 0.40 to 0.75; and
excellent when κ values are >0.75
The AUC is calculated by generating a receiver operating
characteristic (ROC) curve to plot the sensitivity to 1 – speci-
ficity for all possible thresholds (Pearce and Ferrier 2000).
Commonly used in predictive models (Phillips et al. 2006;
Russell et al. 2007; Evangelista et al. in press), the AUC is a
measure of probability that a random positive point falls within
the predicted range of occurrence and a random negative point
falls outside (Pearce and Ferrier 2000). Strength of predictabil-
ity ranges from weak to strong or 0 to 1.0 respectively. An AUC
value of 0.5 represents complete random predictions, whereas a
value of 1.0 shows perfect discriminatory ability (Pearce and
Ferrier 2000).
Our first model had nine terminal nodes with a residual mean
deviance of 0.01 (Fig. 2). Of the 76 potential independent vari-
ables, seven predictors were selected in the model results with
precipitation of the warmest quarter (BIO18) occurring twice in
the tree. The probability of mountain nyala occurrence was
greatest when precipitation of the warmest quarter (BIO8) was
>270 mm (79% present, 21% absent), precipitation of the
wettest month (BIO13) was <241 mm (88% present, 12%
absent), minimum temperature of the coolest month (BIO6) was
<10.1 C° (100% present, 0% absent) and temperature seasonal-
ity (BIO4) was <1279.6 (100% present, 0% absent). Other pre-
dictor variables of mountain nyala occurrence include
maximum temperature of the warmest month (BIO5), precipita-
tion seasonality (BIO15) and NDVI for the month of September
(Fig. 2).
The final model, analysed within the boundaries of our first
model, had 10 terminal nodes with a residual mean deviance of
0.1008 (Fig. 3). Of the potential independent variables, nine
were significant in the model results with surface roughness
index occurring twice in the tree. The probability of mountain
nyala occurrence was greatest when minimum temperature of
the coolest month (BIO18) was <3.3 C° (92% present, 8%
absent) and precipitation of the warmest quarter (BIO18) was
>281 mm (98% presence, 2% absent). Other significant vari-
ables in the model include surface roughness, precipitation of
the wettest quarter (BIO16), minimum temperature for
December, mean temperature for March, precipitation of the
coldest quarter (BIO19) and herbaceous cover (Fig. 3). The pre-
dicted suitable habitat had an area of 39378 km2, with ~97% of
the predicted area in the Chercher, Arussi and Bale mountains
and nearly 2% of the predicted area in the Gurage Massif on the
west side of the Rift Valley (Fig. 4).
Evaluation methods showed that the final model’s perfor-
mance was strong for training and validation datasets. The train-
ing model had an AUC of 0.93 and κ value of 0.88. The
confusion matrix showed the training model to have an overall
Mountain nyala habitat Wildlife Research 413
Precip. of Warmest Quarter (BIO18)
< 270 mm, 0.01 > 270 mm, 0.79
Max. Temp. of Warmest Month (BIO5) Precip. of Wettest Month (BIO13)
< 12.9 C°, 0.88 > 12.9 C°, 0.01 < 241 mm, 0.88 > 241 mm, 0.01
Precip. Seasonality (BIO15) 0 Min. Temp. of Coolest Month (BIO6) 0
< 70.6, 0.90 > 70.6, < 0.01 < 10.1 C°, 1.00 > 10.1 C°, 0.28
Temp. Seasonality (BIO4) Precip. of Warmest Quarter (BIO18)
0 < 1279.6, 1.00 > 1279.6, 0.28 < 38 mm, < 0.01 > 38 mm, 0.06
NDVI (Sept)
1 0 0 < 3293.1, 0.99 > 3293.1, 0.06
accuracy of 94% with specificity at 99% and sensitivity at 87%.
The validation data had an AUC value of 0.89 and κ value of
0.80. Overall accuracy of the validation model was 90% with
specificity at 95% and sensitivity at 84%.
The results of our analyses support earlier assessments of the
habitat specialist characteristics of the mountain nyala and its
limited range within the Ethiopian highlands. At the landscape
scale, mountain nyala habitat is heavily reliant on precipitation
and temperature, which are known to have direct influences on
Min. Temp. of Coolest Month (BIO6)
< 3.3 C°, 0. 92 > 3.3 C°, 0.13
Precip. of Warmest Quarter (BIO18 ) Surface Roughness
< 281 mm, < 0.01 > 281 mm, 0.98 < 119.5; 0.02 > 119.5; 0.32
0 1 0 Precip. of Wettest Quarter
< 396 mm, 0.96 > 39 6 mm, 0.22
Fig. 2. Classification tree analysis of
suitable mountain nyala habitat for
Ethiopia. Bold numbers represent the
percentage of presence points that fell
within the given criteria of significant
independent variables.
1 0
vegetation structure and diversity (Bekele-Tesemma et al.
1993). These environmental variables also played significant
roles at a regional scale in addition to topographical and vegeta-
tion variables. Although the species’ range presented by the
final model is highly restricted, it is substantially larger than the
speculated range reported in the literature (Malcolm and
Evangelista 2005; Evangelista 2006b; Sillero-Zubiri 2007).
Suitable habitat was predicted in areas that have not been
explored during previous population research efforts. These
areas are largely found in the south-western slopes of the Bale
Mountains and coincide with Brown’s surveys in the 1960s,
Fig. 3. Classification tree analysis of suit-
1 Surface Roughness
able mountain nyala habitat within the pre-
< 332, 0.14
Mean Temp. (March)
< 16.5 C°, 0.30 > 16.5 C°, 0.22
Precip. of Coldest Quarter (BIO19) 0
< 215 mm, 0.40 > 215 mm, < 0.01
Herbaceous Cover 0
< 55.4 %, 1.00 > 55.4 %, 0.25
> 332, 0.65
dicted range of the first model (Fig. 2). Bold
numbers represent the percentage of pres-
ence points that fell within the given criteria
Min. Temp. (Dec.)
of significant independent variables.
< 6.6 C°, 1.00 > 6.6 C°, 0.35
1 0
414 Wildlife Research P. H. Evangelista et al.
Addis Ababa
Gurage Massif
WabiShebele River
Weyb River
Awash River
Potential Mou ntain Nyala Habita t
Major Lakes
Major Rivers
Elevati on
4620 m
–120 m
0 25 50 75 100 km12.5
Rift Valley Lakes
Galama Mountains
Fig. 4. Map of suitable mountain nyala habitat based
on the second classification tree analysis.
occurrences reported by Yalden and Largen (1992) and several
recent discoveries we previously reported. In 2006, two of the
authors attempted to explore this region’s interior but found the
terrain difficult to navigate and traverse (Evangelista et al.
2007). The survey team was able to confirm the presence of
mountain nyala in much of the area, and local people that were
interviewed consistently reported that significant populations
were common throughout the large expanses of forest
(Evangelista 2006b; Evangelista et al. 2007).
The final model also predicts that several regions outside the
Bale Mountains may have suitable habitat for mountain nyala.
Of particular interest are the tributaries and headwaters of the
Wabe Shebele River located east of the Arussi Mountains
(Fig. 1). Although we have never adequately explored these
regions from the ground, several aerial surveys revealed pockets
of intact forests with steep topography that is prohibitive to
human settlement and livestock grazing. Additionally, we have
received reports from local people that mountain nyala are
found in these areas. The northern tributaries of the Wabe
Shebele River, which flow from the Arussi and Chercher moun-
tains, are currently managed for mountain nyala and include one
of the recently discovered populations in 2005 (Evangelista
2006b). A second area of interest is the Gurage Massif located
on the west side of the Rift Valley. Although mountain nyala
have never been documented beyond the southern highlands and
east side of the Rift Valley, it is possible that the species may
have historically occurred there. Aerial surveys of the Gurage
Massif reveal only remnant forested areas remain with large
expanses of heathlands densely populated with settlements and
livestock (P. Evangelista, pers. obs. 2007). Mountain nyala are
known to persist in areas heavily impacted by land-use activities
(e.g. Galama Mountains, Gaysay Valley); however, we find it
unlikely that the species can presently be found there. Future
field surveys and in-depth interviews with the local people are
warranted, and could reveal significant information on the
present and historical status of the mountain nyala throughout
the Gurage Massif.
Our models were fitted with environmental variables that
included climate, topography and vegetation indices, but lack
data on human and livestock populations, land-use and vegeta-
tion types. We believe that model performances could be
improved if such data were available and integrated into our
analyses. Similarly, we have compiled numerous reports from
local people regarding mountain nyala sightings, but we elected
to exclude this information from the analyses because it either
has not been adequately confirmed or the locations were some-
times vague. Although the results from the models suggest that
the distribution of mountain nyala and suitable habitat are sig-
nificantly greater than previously thought, it should be noted
that human population densities, agriculture, livestock grazing
and deforestation will greatly reduce the actual area of occu-
pancy (Evangelista et al. 2007; Sillero-Zubiri 2007).
Despite the caveats, the models have significant value in
identifying suitable habitat for mountain nyala and adding to the
Mountain nyala habitat Wildlife Research 415
scientific knowledge of this endemic species. Specifically, our
results provide the first map of probable range of the mountain
nyala based on field observations since Brown (1969a). Our
analyses also demonstrate the strong correlation between the
species’ range and specific climate and topographic conditions.
This information not only provides wildlife managers with
important geo-spatial data to support management and conser-
vation decisions, but can also guide future surveys to new areas
where the mountain nyala may likely persist. Finally, our results
offer clues to the of mountain nyala’s historic range, which may
have once included areas west of the Rift Valley. The current
known distribution of the mountain nyala and our model results
support the belief that the species’ range has always been iso-
lated to the Ethiopian highlands.
The authors would like to thank the Ethiopian Wildlife Conservation
Department and Oromia Regional Land and Natural Resource Department
for their support of this important work and for sharing their survey data. We
also would like to thank The Murulle Foundation for funding and logistical
support; the USA Geological Survey, Fort Collins Science Center and
Natural Resource Ecology Laboratory, Colorado State University, for tech-
nical support; ESRI® GIS and Mapping Software for software support; and
Ethiopian Rift Valley Safaris for use of their facilities and field personnel.
Additional gratitude is expressed to Banovich Wildscapes Foundation,
Conklin Foundation, Dallas Ecological Foundation, R. Baker, G. Bond,
A. Randell, P. Ripepi, J.C. and N.A. Roussos, A. Sackman, W. Stout, SCI
Pittsburg Chapter, Shikar Safari Club and P. Swartzinski.
Allouche, O., Tsoar, A., and Kadmon, R. (2006). Assessing the accuracy of
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... Landform and/or topography (e.g. slope) 3,5,9,10,13,25 Elevation 3,7,8,9,10,13,25 Vegetation cover (i.e. trees, shrubs, and herbs cover) 3,7,8,9,10,13,25 Towns and roads 6,9,25 Habitat suitability models are the simplest and perhaps the most frequently used form of ecological models. ...
... slope) 3,5,9,10,13,25 Elevation 3,7,8,9,10,13,25 Vegetation cover (i.e. trees, shrubs, and herbs cover) 3,7,8,9,10,13,25 Towns and roads 6,9,25 Habitat suitability models are the simplest and perhaps the most frequently used form of ecological models. 15 They are intended to be general indicators of habitat suitability that are easily and reputably applied under field conditions. ...
... slope) 3,5,9,10,13,25 Elevation 3,7,8,9,10,13,25 Vegetation cover (i.e. trees, shrubs, and herbs cover) 3,7,8,9,10,13,25 Towns and roads 6,9,25 Habitat suitability models are the simplest and perhaps the most frequently used form of ecological models. 15 They are intended to be general indicators of habitat suitability that are easily and reputably applied under field conditions. ...
... However, to maximize the usefulness of activity density as an indicator of habitat quality of a particular species, the field data should be collected over a range of seasons and environmental conditions 5,6,7 . Information on habitat selection and use can be summarized in various types of model that simulate the relation between an animal population and its habitats 3,8,9,10 . These include models of habitat suitability that identify features of the environment that correlate with individuals activity density, and models of habitat selection (i.e. ...
... The mountain nyala Tragelaphus buxtoni is an endemic species to Ethiopia 21 . Listed as endangered 26 , mountain nyala is only found in a few locations in the highlands of Ethiopia 10 . It is a sexually dimorphic antelope in which adult males are much larger than adult females. ...
... In conclusion, natural habitats of the mountain nyala are being destroyed or mostly converted to other unsuitable land use types throughout their ranges 10,22 . Understanding the adaptive habitat selection behaviors of the endangered mountain nyala enhances its conservation and management activities. ...
Full-text available
Knowledge of habitat quality and adaptive habitat selection behavior of endangered species such as the mountain nyala (Tragelaphus buxtoni) can be invaluable for conservation and management, but quantitative information is lacking. The objectives of this study were to: (1) investigate the environmental variables that determine the suitable habitats for the mountain nyala, and (2) apply the isodar technique to look for density-dependent habitat selection behavior in mountain nyala. Following transects aligned through three major habitat types, environmental variables and activity densities of mountain nyala were estimated. The fieldwork was carried out in the wet and dry season in Munessa, Ethiopia. In addition, with the help of a spotlight, night-time mountain nyala censusing was carried out during the dry season. The result revealed that mountain nyala didn't show density-dependent habitat selection behavior in the wet season. However, during the dry season, the natural forest was the most suitable habitat for the mountain nyala, when crown diameter of trees and abundance of shrubs affected the habitat suitability. Significant isodars were obtained only across season and dry season comparisons between natural forest versus plantation and natural forest versus cleared vegetation habitats. The regression analyses revealed that the natural forest was qualitatively, but not quantitatively, more suitable than both the plantation and the cleared vegetation habitats. The isodars suggested that the strength of density-dependence was lower in the natural forest than either in the plantation or the cleared vegetation habitat. Spotlight censusing revealed that mountain nyala selected the cleared vegetation habitat during the night-time. The study demonstrated that habitat suitability models are important tools to evaluate the habitat quality for mountain nyala. Isodar analyses support the habitat suitability models by increasing our understanding on the qualitative and quantitative differences in density-dependent habitat selection by mountain nyala and thereby to enhance their conservation and management.
... Mountain Nyala (Tragelaphus buxtoni) is one of the endemic flagship species in the southeastern highlands of Ethiopia (Hillman and Hillman, 1987). However, compared with its closest relatives such as the Greater Kudu (Tragelaphus strepsiceros) and the Nyala of southeastern Africa (Tragelaphus angasii), the Mountain Nyala is not well studied (Hillman, 1985;Shuker, 1993;Refera and Bekele, 2004;Evangelista et al., 2007;Mamo, 2007;Evangelista et al., 2008). For example, for many decades, field information on the habitat, population distribution, abundance, and dynamics of the elusive Mountain Nyala in Ethiopia was largely gathered and reported by trophy hunters (Evangelista et al., 2007). ...
... Habitat Suitability Index (HSI) models are crucial tools for helping managers and ecologists identify and evaluate habitat variables (Druce, 2005;Reid, 2005;Evangelista et al., 2008;Tadesse and Kotler, 2010), and thereby predict future conditions of the habitats for the survival and reproductive success of Mountain Nyala. The field information obtained through habitat sampling may help in designing future monitoring programs to track population size and status of Mountain Nyala. ...
... The habitats of Mountain Nyala are heavily affected by precipitation and temperature, which are known to have direct influences on vegetation structure and diversity (Brown, 1969b;Evangelista et al., 2008). Brown (1969b) suggested that the Mountain Nyala is a habitat specialist occurring mainly above 3400 m and being particularly common in the heath zone. ...
Full-text available
Understanding how the quality and the characteristics of the habitat influence habitat use and foraging behaviors of an animal is of paramount importance to ecology and species management. Theories of habitat selection and patch use can be applied in many creative ways to form a set of relatively simple behavioral assays that provide leading indicators of habitat quality and habitat use. The present study utilized the application of different behavioral approaches including habitat suitability models based on activity densities, isodars based on activity densities, behavioral models based on time budgets, and patch use models based on natural giving-up-densities to assess the habitat quality and foraging ecology of the endangered Mountain Nyala (Tragelaphus buxtoni). I worked in the biodiversity rich Munessa Forest and Bale Mountains National Park, Ethiopia, on populations of the endangered Mountain Nyala that live with considerable human and livestock pressures. The overall aim of this study was to examine the major environmental and anthropogenic factors affecting the habitat quality, habitat use, and foraging ecology of the charismatic Mountain Nyala. I conducted the fieldwork in the wet and the dry seasons in Munessa. I used different methods to acquire the field data. I conducted regular habitat inventory and population censusing along permanent transects stratified across major habitat types over the landscape. These included population censusing in both daylight and nighttime hours, with nighttime censusing being carried out only during the dry season and with the aid of a spotlight. Censusing yielded estimates of activity densities across habitats and age-sex classes. I measured important microhabitat variables in circular plots laid along each permanent transect and correlated these to local activity densities of Mountain Nyala to yield models of habitat suitability. I also carried out focal-animal observations in order to evaluate how the behavioral responses of Mountain Nyala vary with group size, sex-age categories, and habitat types across seasons in Munessa. I assessed and quantified the impacts of human and livestock encroachments on the habitats of Mountain Nyala in Munessa. Accordingly, along permanent transects, I estimated the activity densities of livestock. I also inspected and quantified the extent of human and livestock encroachments on the habitats of Mountain Nyala in circular plots laid along each permanent transect. In addition, I developed pretested, open- and closed-ended interview questionnaires and then administered them to local people living in the adjacent three peasant associations and one village in Munessa. I also held focal group discussions with key local community members. Finally, I observed Mountain Nyala in grasslands versus dense woodlands to quantify their time budgets, bite rates, and bite diameters while feeding on common natural forage plant species in the Bale Mountains National Park (BMNP). These yielded measures of foraging effort and natural giving-up-densities (GUDs: the amount or density of food resources left in a food patch when the most efficient forger leaves the resource patch), with greater bite diameters corresponding with lower costs and greater efficiencies. The browse species cropped by Mountain Nyala were identified and the diameters of all browsed twigs were measured. Focal observations were carried out and bite rates were recorded for each sex and age category of target animal. Time-budgets were also quantified for focal-animals according to sex-age categories, group size, and habitat type. Measurements of activity densities and environmental variables allowed me to construct models of habitat suitability and to estimate isodars describing density-dependent habitat selection. The habitat suitability model revealed that Mountain Nyala did not show significant habitat selection behavior during the wet season in Munessa. However, in the dry season, natural forest was the most selected habitat when only crown diameters of trees significantly affected the habitat suitability for Mountain Nyala. The slopes of the isodars also revealed that natural forest habitat is qualitatively, but not quantitatively, better than either the plantation or the clear cut habitat during the dry season. However, the result with spotlight censusing showed that Mountain Nyala selected the clear cut habitat during the nighttime when people and livestock are absent in Munessa. The behavioral study revealed that Mountain Nyala devoted much of their time to vigilance behavior during the wet season in Munessa; however, habitat type did not significantly affect vigilance. In addition, during the wet season, there was no significant difference in time spent in vigilance among the different sex-age classes. In the dry season, Mountain Nyala were significantly most vigilant in the clear cut habitat. Although more vulnerable animals, especially females with young, are expected to be more vigilant, adult males were more vigilant. This may reflect hunting pressure from humans that exclusively targets adult males. Livestock and human encroachments on the habitats of Mountain Nyala varied seasonally. The activity density of livestock was significantly highest in the natural forest habitat during the wet season. In contrast, during the dry season, livestock did not show significant difference in their relative habitat use. Overall, livestock activity densities in all habitat types were higher in the wet season than in the dry season. In both wet and dry seasons, the extent of stem and crown damage by humans was significantly highest in the plantation habitat. In both seasons, the evidence of wood use and the number of stumps cut were significantly highest in the natural forest. In both seasons, sign of habitat use by livestock did not differ among habitat types; rather it was dispersed throughout all habitats. However, in the wet season, the intensity of grazing / browsing by livestock was significantly heaviest in the natural forest. Generally, the results revealed that the impacts of human and livestock encroachments were high and persistent throughout Munessa. As a result, Mountain Nyala avoided both human and livestock impacts by becoming active during periods (e.g. in the nighttime) when people and livestock are absent. The social study revealed that attitudes of local people toward Mountain Nyala and its population increase were significantly affected by several socio-economic variables such as livelihood strategy, land ownership, livestock ownership, and knowledge. In addition, through focal group discussions, key community members shared their abundant indigenous knowledge about the different behaviors of Mountain Nyala and their habitats. The results revealed that bite diameters, bite rates, vigilance rates, and proportion of time feeding, all differed between habitats. In particular, Mountain Nyala had greater bite diameters, higher bite rates, and spent a greater proportion of their time feeding, and less in vigilance in the grassland habitat. In addition, adult females had the highest bite rates, and the browse species Solanum marginatum had the greatest bite diameter. The results show that grasslands are a higher quality habitat than woodlands, offering lower foraging costs, greater safety, and more time for foraging. The results further show how behavioral indicators and natural giving-up densities can reveal habitat quality for endangered wildlife through the use of non-invasive techniques. The present studies revealed that Mountain Nyala have faced several human and livestock induced challenges which likely threaten their fitness in Munessa. Mutually supportive relation between local people and the Munessa hunting block are crucial to the long-term success of Mountain Nyala population conservation. Thus, the study suggested several management recommendations that should be put in place to conserve and sustainably utilize the endangered Mountain Nyala. Most importantly, introducing and promoting community-based conservation that allows communities to derive economic benefits from ecotourism may promote conservation while at the same time providing a solution to resource use conflicts between the local people and the conservation of the Mountain Nyala population in Munessa. Introducing and advocating an economic benefit-sharing system with the full participation of the local community in the conservation and management processes is also equally important to plan and implement sustainable Mountain Nyala trophy sport-hunting in the Munessa hunting block. To conclude, the combined results obtained from the different approaches used in the present studies can help local policy makers and wildlife managers to understand the ecology and the habitats of Mountain Nyala. Behavioral indicators based on foraging theory have many advantages. They are often fast, inexpensive, and simple to implement. More importantly, they provide answers from the forager’s perspective rather than ours, and they have the potential to provide leading indicators of change. Because behavior is adaptive, the resulting measures are leading indicators of habitat change and can form the basis for a more proactive management approach. Changes in the behaviors reveal changes in fitness. The study thus improves our understanding of the adaptive habitat selection behaviors of Mountain Nyala. This is a basis for developing novel solutions to conserve and manage the endangered Mountain Nyala and its habitats in Ethiopia. The study also motivate local decision makers and wildlife managers to give due emphasis to the needs and the wants of the local people in the management processes. This will ultimately help establish ecologically sustainable, economically feasible, and socially acceptable conservation and management system for the endangered Mountain Nyala in Ethiopia. Keywords: Bale Mountains National Park; Behavioral indicators; Behavioral models; Bite diameter; Bite rates; Density-dependent habitat selection; Habitat quality; Habitat suitability models; Humans and livestock encroachments; Munessa; Tragelaphus buxtoni
... Landform and/or topography (e.g. slope) 3,5,9,10,13,25 Elevation 3,7,8,9,10,13,25 Vegetation cover (i.e. trees, shrubs, and herbs cover) 3,7,8,9,10,13,25 Towns and roads 6,9,25 Habitat suitability models are the simplest and perhaps the most frequently used form of ecological models. ...
... slope) 3,5,9,10,13,25 Elevation 3,7,8,9,10,13,25 Vegetation cover (i.e. trees, shrubs, and herbs cover) 3,7,8,9,10,13,25 Towns and roads 6,9,25 Habitat suitability models are the simplest and perhaps the most frequently used form of ecological models. 15 They are intended to be general indicators of habitat suitability that are easily and reputably applied under field conditions. ...
... slope) 3,5,9,10,13,25 Elevation 3,7,8,9,10,13,25 Vegetation cover (i.e. trees, shrubs, and herbs cover) 3,7,8,9,10,13,25 Towns and roads 6,9,25 Habitat suitability models are the simplest and perhaps the most frequently used form of ecological models. 15 They are intended to be general indicators of habitat suitability that are easily and reputably applied under field conditions. ...
Full-text available
Mountain nyala Tragelaphus buxtoni is one of the endangered and endemic flagship species in Ethiopia. The goal of this study was to identify and map the distribution of suitable habitats for the mountain nyala in the southeastern highlands of Ethiopia where the species can be found. The following two questions were addressed in this study1 what are the major abiotic, biotic, and anthropogenic factors that determine the suitable habitats for the mountain nyala in the southeastern highlands of Ethiopia? And,2 where are the areas of best habitat suitability for the mountain nyala? Environmental and anthropogenic variables, such as landform and/or topography (i.e., slope), elevation, vegetation cover (i.e., trees, shrubs, and herbs cover), towns, and roads were included to develop a GIS (Geographic Information System) habitat suitability model for the mountain nyala. The model predicted that a total of 2,436.98km2 of suitable habitats are currently available for the endangered mountain nyala in Ethiopia. Three environmental variables, landform and/or topography (e.g., slope), vegetation cover (i.e., trees, shrubs, and herbs cover), and elevation, were the most important predictors having the greatest contribution to the habitat suitability model. The model suggested that habitat fragmentation is a common problem for the survival of the mountain nyala throughout its ranges of distribution. Thus, future conservation and management action should gear towards solving this problem through designing appropriate corridors that help connect the fragmented suitable habitat patches for this endangered flagship species in Ethiopia.
... The mountain nyala is a species endemic to Ethiopia (Brown 1969). Listed as endangered, the mountain nyala is only found in a few locations in the Ethiopian highlands (Evangelista et al. 2008). It is a sexually dimorphic antelope in which adult males are much larger than adult females (Refera & Bekele 2004). ...
... In the wet season, the mountain nyala devoted a considerable proportion of their time to feeding where there is high per cent cover of grass and herbs. Furthermore, the mountain nyala allocated more time to feeding and less to vigilance in steep terrain, because slope provides the mountain nyala with safety (Evangelista et al. 2008), and hence they can allocate more time to feeding. Other studies also supported our findings. ...
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Activity patterns of animals are generally influenced by many factors. We hypothesized that the behavioural responses (i.e. activity time-budget allocated to vigilance, feeding and moving) of mountain nyala (Tragelaphus buxtoni) should vary with habitat type, season, group-size and sex-age class. We randomly established a total of 12 permanent walking transects with the aid of a GPS device across three major habitat types used by the mountain nyala (i.e. four transects in each habitat). Following each transect, we conducted focal-animal observations to quantify the time-budget allocated to vigilance, feeding and moving. A total of 119 and 116 focal-animals were assessed in the wet and dry season respectively. Moreover, along each transect, seven habitat variables were collected in systematically laid 109 circular plots each with a 5-m radius (i.e. 31, 41 and 37 plots in the cleared vegetation, plantation and natural forest respectively) in the wet and dry season. We developed behavioural models by correlating the time-budget (i.e. proportion of time vigilance, feeding and moving) of the focal-animals in accordance with habitat variables, group-size and sex-age class. In the wet season, mountain nyala devoted most of their time to vigilance, but they allocated the largest proportion of their time to moving in the dry season. Vigilance differed among the three habitats and was highest in the cleared vegetation during the dry season. Contrary to expectations, adult males were more vigilant than both adult females and sub-adults during the dry season. The behavioural models based on time-budget help to predict how the mountain nyala perceive their environment and trade-off between food acquisition and safety in the wet and dry season. The study also improves our understanding of the adaptive behavioural ecology of the endangered mountain nyala.
... Mountain nyala Tragelaphus buxtoni is an endemic flagship species to Ethiopia (Brown, 1969;Evangelista et al., 2007). Listed as endangered (IUCN, 2012), mountain nyala is only found in a few locations in the Ethiopian highlands (Evangelista et al., 2008). It is a sexually dimorphic antelope in which adult males are much larger than females (Refera and Bekele, 2004). ...
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We studied the habitat use of mountain nyala Tragelaphus buxtoni in the northern edge of the Bale Mountains National Park, Ethiopia. The aims of this study were to: (1) measure and quantify habitat-specific stem bite diameters of mountain nyala foraging on common natural plant species in two major habitat types (i.e. grasslands versus woodlands), and (2) quantify the bite rates (number of bites per minute) and the activity time budgets of mountain nyala as functions of habitat type and sex-age category. We randomly laid out three transects in each habitat type. Following each transect, through focal animal observations , we assessed and quantified stem diameters at point of browse (dpb), bite rates, and time budgets of mountain nyala in grasslands versus woodlands. Stem dpb provide a measure of natural giving-up densities (GUDs) and can be used to assess foraging costs and efficiencies, with greater stem dpb corresponding to lower costs and greater efficiencies. The results showed that stem dpb, bite rates, induced vigilance, and proportion of time spent in feeding differed between habitats. In particular, mountain nyala had greater stem dpb, higher bite rates, and spent a greater proportion of their time in feeding and less in induced vigilance in the grasslands. In addition, adult females had the highest bite rates, and the browse species Solanum marginatum had the greatest stem dpb. Generally, grasslands provide the mountain nyala with several advantages over the woodlands, including offering lower foraging costs, greater safety, and more time for foraging. The study advocates how behavioural indicators and natural GUDs are used to examine the habitat use of the endangered mountain nyala through applying non-invasive techniques. We conclude that the resulting measures are helpful for guiding conservation and management efforts and could be applicable to a number of endangered wildlife species including the mountain nyala.
... The mountain nyala (Tragelaphus buxtoni) is an endemic antelope listed as Endangered on the International Union for Conservation of Nature (IUCN) Red List of Threatened Species (IUCN, 2016). The species is known to inhabit the Afroalpine and montane vegetations of the Bale, Arsi, and Ahmar Mountains in the southeast highlands of Ethiopia (Blower, 1968;Brown, 1969;Evangelista et al., 2008Evangelista et al., , 2012. However, livestock grazing, deforestation (Kubsa and Tadesse, 2002;Tadesse et al., 2014), agricultural expansion (Kindu et al., 2015), and rapid land conversion (Tadesse et al., 2014;Young et al., 2020) have resulted in significant habitat loss. ...
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Behavioral responses in wildlife due to human activities may often go unnoticed but have significant effects on population viability. This is a particular concern with endangered species characterized by small population sizes. From June 2016 to May 2017, we measured the effects of human activities on daily the activity budget and home range size of mountain nyala (Tragelaphus buxtoni), an endemic antelope of the Ethiopian highlands. We tracked two groups of mountain nyala from two study sites that differ in the level of human activities; Adaba-Dodola Community Conservation Area (Adaba-Dodola CCA) and Arsi Mountain National Park (Arsi Mountains NP). Our results showed that the time spent on vigilance and movement was dramatically higher in Adaba-Dodola Community CCA, where human presence is significant, than in Arsi Mountain NP, whereas the opposite was true for time spent foraging and resting. In addition, mean home range size (95% KDE) was significantly larger for the Adaba-Dodola CCA group (13 ± 7.4 km²) than for the Arsi Mountains NP group (6.3 ± 2.7 km²) covering larger areas during the dry season (18.7 ± 6.9 km²) than the wet season (4.9 ± 1.0 km²). The finding that increased investment in vigilance and movement trade-off against the restorative behaviors of foraging and resting in human-disturbed areas have implications for conservation managements; specifically, it underscores the need to (i) establish the fitness consequences of behavioral changes, and (ii) monitoring behavioral change in the disturbed population with the aim of bringing it closer to the undisturbed baseline. The study highlights the importance of protected areas, limiting human activities and monitoring the behavioral change of endangered species in human-disturbed areas.
... However, it was emphasised that ensemble prediction may be used in order to tap advantages of different algorithms used on one hand, and on the other to address the weakness of each individual algorithm Marmion et al., 2009). Ensemble modelling was thus used in several studies for single-and multi-species distribution analyses (Fitzpatrick et al., 2011), invasive species (Stohlgren et al., 2010;Poulos et al., 2012;Roura-Pascual et al., 2008), rare and endangered species prediction (Evangelista et al., 2008;Bombi et al., 2009), and forecasting distributions with climate change scenarios (Jarnevich & Stohlgren, 2009). ...
This book covers selected topics on research methods in modern ecology, focussing on animal ecology, landcover assessment and habitat change. This is plainly a vast area, due to the multiplicity of research foci and the great range of research techniques available. Two main clusters of research methods have emerged: laboratory studies and field techniques. Both are now totally reliant on computer hardware and software and associated technology. Recent developments in ecological research methods largely concern the adoption of enhanced computer software techniques and the syntheses of these into pre-existing research methods, the better to record, analyze and explain the behavioral patterns of the studied animals and/or plants, and their ecologies. Chemical analyses, including studies of animal dietary and foraging patterns are also important, given reductions in food resources, environmental degradation and animal intrusions into human life spaces. This volume focusses on specific examples that typify some of the recent developments in research methods. In this regard, the research methods are spread over several fields of research and the interests of several researchers. Animal and integrated ecology in these specialisms is also considered broadly, to include habitat analysis, animal behavior, landcover, habitat and plant ecological change and even human/animal relations, and international case studies along these lines. Chapters on plant ecology and genetics are included, to indicate that animal ecology is inherently linked to vegetation assessment and plant dynamics, as part of the animal species interactive environment and animal bodies and related genetic studies run through any studies of animal ecology. Technological and methodological advancements in the applied sciences may appear to be the main drivers in the development of animal ecology, but the latter discipline, based on people’s understanding of animals remains the main focus and guide for research method development. Remote sensing and geographical information systems are emerging as relevant, cutting-edge research methods, at small, medium, and large-scale levels. Animal behavioral studies at the more detailed, individual subject levels are also complementing this trend. To better explore animal behaviors and the relations between these and the environmental context, the key developments concern closer, less intrusive, and observable recording of animal life activities. This includes more critical human observation of animal behavior, and more detailed study of animal environments, including plant ecology. From a technological perspective, developments include more accurate positioning systems, more sensitive tracking systems, the removal of obstacles to clearer observation and species identification, such as darkness and poor lighting, dense vegetation and coarse image resolution and more comparative studies across different local contexts and global ecosystems. More detailed ecological studies and shared analyses of the research results may serve as guides for the development of more sophisticated research tools. The syntheses of these new developments with older research methods such as field foot surveys, coarse resolution satellite imaging and aerial photography, georeferencing with hand-held devices and eyeball observation of animals in the field may ensure the continuance of constructive research engagements
... During the field data collection, increased presence of livestock and human settlements was observed in the wet season that could initiate human-wildlife conflict. Various studies elsewhere have frequently reported that the level of disturbance in large mammals' habitat determines habitat use, and large mammals have been reported to avoid habitats with a high level of disturbance [7,[14][15][16][17][18][42][43][44][45][46][47]. Similar results were reported in the Alatish National Park, Ethiopia [28,43]. ...
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There is a lack of information on mammalian faunal resources of remote forests in Ethiopia; as a result, the findings of the research on large wild mammals at Nensebo forest is one of the steps in a continuing effort to document and describe the diversity and distribution of Ethiopian mammals in remote and less accessible forests. The survey was conducted to assess the species composition and relative abundance of large mammals. Two standardized survey techniques, direct (sighting/hearing) and indirect (scat/footprint), were employed using systematically established transect lines and field plots in two dominant habitat types (modified moist Afromontane forest and intact moist Afromontane natural forest) of the study area. A total of 16 species were recorded including two endemic mammals, namely, Tragelaphus buxtoni and Tragelaphus scriptus meneliki. Abundance of species among different habitat types was not significantly different (χ2 = 0.125, df = 1, p>0.05), and Colobus guereza was the most abundant species. In contrast, Felis serval, Panthera leo, and Tragelaphus buxtoni were the least abundant species. The highest diversity index was recorded in the natural forest habitat (H′ = 2.188), and the modified forest had the lowest diversity index (H′ = 1.373). There is an urgent need to minimize threats and mitigate impacts.
Fundamental knowledge about the processes that control the functioning of the biophysical workings of ecosystems has expanded exponentially since the late 1960s. Scientists, then, had only primitive knowledge about C, N, P, S, and H2O cycles; plant, animal, and soil microbial interactions and dynamics; and land, atmosphere, and water interactions. With the advent of systems ecology paradigm (SEP) and the explosion of technologies supporting field and laboratory research, scientists throughout the world were able to assemble the knowledge base known today as ecosystem science. This chapter describes, through the eyes of scientists associated with the Natural Resource Ecology Laboratory (NREL) at Colorado State University (CSU), the evolution of the SEP in discovering how biophysical systems at small scales (ecological sites, landscapes) function as systems. The NREL and CSU are epicenters of the development of ecosystem science. Later, that knowledge, including humans as components of ecosystems, has been applied to small regions, regions, and the globe. Many research results that have formed the foundation for ecosystem science and management of natural resources, terrestrial environments, and its waters are described in this chapter. Throughout are direct and implicit references to the vital collaborations with the global network of ecosystem scientists.
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We compared 3 naturally ignited burns with unburned sites in the Grand Staircase-Escalante National Monument. Each burn site was restored with native and nonnative seed mixes, restored with native seeds only, or regenerated naturally. In general, burned sites had significantly lower native species richness (1.8 vs. 2.9 species), native species cover (11% vs. 22.5%), and soil crust cover (4.1% vs. 15%) than unburned sites. Most burned plots, seeded or not, had significantly higher average nonnative species richness and cover and lower average native species richness and cover than unburned sites. Regression tree analyses suggest site variation was equally important to rehabilitation results as seeding treatments. Low native species richness and cover, high soil C, and low cover of biological soil crusts may facilitate increased nonnative species richness and cover. Our study also found that unburned sites in the region had equally high cover of nonnative species compared with the rest of the Monument. Cheatgrass (Bromus tectorum) dominated both burned and unburned sites. Despite the invasion of cheatgrass, unburned sites still maintain higher native species richness; however, the high cover of cheatgrass may increase fire frequency, further reduce native species richness and cover, and ultimately change vegetation composition in juniper woodlands.
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The southwestern willow flycatcher (SWFL; Empidonax traillii extimus) is an endangered songbird whose habitat has declined dramatically over the last century. Understanding habitat selection patterns and the ability to identify potential breeding areas for the SWFL is crucial to the management and conservation of this species. We developed a multiscaled model of SWTL breeding habitat with a Geographic Information System (GIS), survey data, GIS variables, and multiple logistic regressions. We obtained presence and absence survey data from a riverine ecosystem and a reservoir delta in south-central Arizona, USA, in 1999. We extracted the GIS variables from satellite imagery and digital elevation models to characterize vegetation and floodplain within the project area. We used multiple logistic regressions within a cell-based (30 X 30 m) modeling environment to (1) determine associations between GIS variables and breeding-site occurrence at different spatial scales (0.09-72 ha), and (2) construct a predictive model. Our best model explained 54% of the variability in breeding-site occurrence with the following variables: vegetation density at the site (0.09 ha), proportion of dense vegetation and variability in vegetation density within a 4.5-ha neighborhood, and amount of floodplain or flat terrain within a 41-ha neighborhood. The density of breeding sites was highest in areas that the model predicted to be most suitable within the project area and at an external test site 200 km away. Conservation efforts must focus on protecting not only occupied patches, but also surrounding riparian forests and floodplain to ensure long-term viability of SWTL. We will use the multiscaled model to map SWTL breeding habitat in Arizona, prioritize future survey effort, and examine changes in habitat abundance and quality over time.
Population data on raccoon (Procyon lotor) and striped skunk (Mephitis mephitis), collected between 1987 and 1996 in the city of Scarborough (Ontario, Canada), were used to develop spatially explicit population models for use in disease control planning. The objective of model development was to: (1) provide a standard analytical method to identify areas of high-density raccoon and skunk subpopulations within cities, and (2) to identify those subpopulations predicted to function as sites of high dispersal (either into or out of subpopulations). These areas could be targeted in disease control programs. The models combined landscape map data with a stochastic, age-structured population model, which incorporated habitat-specific demographic data and functions relating to animal dispersal. Using this approach, the assemblage of raccoons and skunks inhabiting Scarborough was modeled as occupying discrete subpopulations linked by dispersal (i.e., a metapopulation). The landscape data used in this study were derived from classified LANDSAT satellite imagery data. Population data were derived from the literature and from trapping data collected within the Scarborough study area. The resulting models depicted metapopulations containing 7432 ± 1529 raccoons (mean ± 1 sd) distributed throughout eight subpopulations, and 533 ± 125 skunks distributed throughout 10 subpopulations. Raccoon density within subpopulations ranged from 37 to 94 animals/km2. Skunk density within subpopulations ranged from 6.4 to 12.6 animals/km2. Five raccoon subpopulations and one skunk subpopulation were predicted to stabilize at high relative population densities (>125% carrying capacity), implying that these subpopulations were functioning as net importers of dispersing animals. As such, these subpopulations were at higher risk of being sites of rabies outbreaks than surrounding subpopulations, owing to their high population densities and greater likelihood of receiving infected individuals. In contrast, one raccoon subpopulation stabilized at low relative population density and therefore appeared to be functioning as a net exporter of dispersing animals. The disease control implications of these findings are discussed.
Two ecological belts of the Ethiopian mountains are described here - the Afro-alpine and the Afro-montane. The Afro- alpine belt consists of areas usually above 3,200 m. The rocks are volcanic, moisture is usually not a limiting factor, and soil temperatures are very low. Plants here are mostly adapted to drought and low temperature conditions; many are succulent, slow growing, and of low stature. Where there is no water, vegetation consists of meadow grasses, while aquatic species prevail under waterlogged conditions. Ericaceous scrubs grow at lower altitudes with shallow soils; on better soils are species of woody plants. Human population density is low and grazing is the most important activity. The Afro-montane belt extends from 900 to 3,200 m. Most of the plateau consists of volcanic rocks with deficient or toxic soils. Human activities here have had considerable impacts especially in the northeast, where devastation has been extreme. In the south- west some intact forest still remains, but on most of the plateau evergreen forest is now replaced by grasslands. Overgrazing is prevalent in most areas. Sedentary rain-fed agriculture is the usual practice, with mixed cereal agriculture in the north and east, and root crops in the west and south. Severe environmental deterioration is determined by socio-political rather than technical issues and calls
The 400–500 wolves currently living in the Apennine range of peninsular Italy are slowly recolonizing the Alps and are expected to move northward. A nationwide management plan for the Italian wolf population is being prepared, and a zoning system with connecting corridors has been suggested. We developed a large-scale probabilistic model of wolf distribution as a contribution to the planning process. Thirteen environmental variables related to wolf needs and human presence were analyzed in 12 well-studied wolf territories and in 100 areas where the species has been absent for the past 25 years. These two areas were used as a training set in a discriminant analysis to evaluate potential wolf presence throughout the entire country. We used the Mahalanobis distance statistic as an index of environmental quality, calculated as the distance from the average environmental conditions of the wolf territories. Based on the Mahalanobis distance statistics, we constructed an actual and potential spatial distribution of the wolf for all of peninsular Italy. The jackknife procedure was used to assess the stability of the distance model and showed good confidence in our model (coefficient of variation ≤ 13%). Distance from the wolf territories’ centroid as an index of environmental quality for the wolf was tested using 287 locations where wolves have been found dead in the past 25 years as a consequence of human action (poison, shotgun, car accidents). A useful contribution to conservation planning resulted from comparing the frequency distribution of the Mahalanobis distance of the dead wolf locations with the percentage of study area within each distance class. This showed how the number of wolf casualties would greatly decrease with protection of only a minor part of the study area and indicated the usefulness of our approach for evaluation of other conservation options, such as core areas and corridor identification.