climate change, anthropogenic pressure and …mit.edu.et/agshare/docs/2018_menigstu et...

1

Impacts on elephant distribution

JOURNAL OF THE DRYLANDS:

CLIMATE CHANGE, ANTHROPOGENIC PRESSURE AND HABITAT SUITABILITY FOR

AFRICAN ELEPHANT (LOXODONTA AFRICANA) IN KEFTA HUMERA, TIGRAY,

ETHIOPIA

Mengistu Tilahun1*

, Emiru Birhane

2, Amanuel Zenebe

3 and Abadi Mehri Abraha

1

Mengistu Tilahun, Emiru Birhane, Amanuel Zenebe and Abadi Mehri Abraha (2018): climate change, anthropogenic

pressure and habitat suitability for african elephant (loxodonta africana) in kefta humera, tigray, Ethiopia. Journal of the

Drlylands, 8(1): xxx-xxx

Climate change, habitat destruction and environmental degradation pose a threat to elephant (Loxodonta africana). Therefore, this study

aimed to: i) characterize future climate variables, ii) predict the distribution and habitat suitability for elephant and iii) identify

anthropogenic pressures that threaten elephant and its habitats. Climate data from 1980-2009 was used as a baseline for generating a

future climate scenario. The climate modeling RCPs (RCP4.5 and RCP 8.5) were used with three general circulation model (GCMs).

Anthropogenic pressure was assessed with questionnaires administered to 420 randomly selected households. The projection of

maximum and minimum temperature from 2010-2099 indicates an increase in future. Higher rainfall will be expected in End Term RCP

8.5. Maxent distribution modeling showed, habitat types and climatic variables had higher contribution for the distribution of

Loxodonta africana. Encroachment, over grazing, gold mining, cutting of tree and burning of charcoal were the major anthropogenic

pressures threaten elephant and its habitats.

Key Words: Climate variable, Downscale, Loxodonta Africana, Maxent and Projection

1Department of Wildlife and Ecotourism Management, Mekelle University, P.O.Box 231, Mekelle, Ethiopia

2Department of Land Resources Management and Environmental Protection, Mekelle University, P.O.Box 231,

Mekelle, Ethiopia

3Institute of climate change and society (ICS), Mekelle University, P.O.Box 231, Mekelle, Ethiopia

*Correspondence author: Mengistu Tilahun, Tel: +251925441375,

E-mail: [email protected]

INTRODUCTION

East Africa has diverse and high populations of

wildlife. A total of 288 species of mammals (29

endemics), 201 reptiles (10 endemics), 63

amphibians (25 endemics) and 188 fishes (37

endemics) were recorded in Ethiopia (Sewnet

2012).The African elephant (Loxodonta africana) is

being conserved in Ethiopia’s protected areas. But,

African elephants in Ethiopia are threatened by

extinction (Yirmed et al 2006). Due to threats,

Ethiopia has lost about 90% of African elephant

population (Yirmed et al 2006).

In the past, the number of elephants in Ethiopia was

decimated to an endangered level. As estimated by

Yirmed and Afework (2000), the total number of

elephants all over the country is approximate to

1,000. Now- a- days, nine isolated elephant

populations conservation areas are established in

Ethiopia (Yirmed 2004). Kefta Humera National Park

(KHNP) is one of the elephant’s conservation areas

of Ethiopia.

Habitat destruction and other sources of

environmental degradation including climate change,

poses a serious and increasing threat to African

elephant distribution (Walther et al 2002).The future

climate change is fundamentally important for

effective management and conservation of elephant

(Hannah et al 2002). Elephants are dependent on

habitat structure as well as climatic requirements for

their persistence. The future climate change and

change of habitat structure particularly in savannas

altered the distribution of elephants. In view of the

fact that, if dominated trees or grasses in savannas

destructed, the presence of elephant in this habitat is

faced in danger of extinction (Bond et al 2003).

Anthropogenic pressure on elephants increased

from time to time due to human interest of using

natural resources from National Parks which is

elephant’s habitats (Malcolm et al 2002). The

2


resettlement program around Kefta Humera National

Park, illegal hunting and exhaustive exploitation of

resources devastate the park and finally elephants

(Yirmed 2008). The anthropogenic pressure have

impact on the species ability to thrive by limiting

access to critical food and water sources made scarce

by seasonal climate changes (Lee and Graham

2006).

As a result, forest and wildlife managers are facing

with incorporating the conceptual connection

between future climate change, impact of

anthropogenic pressure and elephant distribution for

conservation and their decision making process. In

addition, there is no clear evidence of climate change

impacts on elephant distribution.

For this reason, this study aimed to: i)

characterize future climate variables (Maximum

temperature, minimum temperature and rainfall), ii)

predict the distribution and habitat suitability of

L.africana at the study area and iii) identify

anthropogenic pressure that threaten L.africana and

its habitats.

MATERIALS AND METHODS

Study area Description

Kefta Humera National Park is located in Kefta

Humera wereda, northwest of Ethiopia between 13o

04’ 5

”-14

o 01

’ 5

”N and 37

o 01

’ 5

”-38

o 04

’ 5”E. It is one

of the largest conservation areas in Ethiopia. It is

bordered by Eritrea in the north, Sheraro in the east,

Wolkait in the south and Humera in the west. While

the main river is the Tekeze, it is fed by a number of

rivers that originate in the Semen Mountains and

highlands of Wolkait. The elevation ranges from 550

m on the edge of Tekeze River to 1800 m on the

highlands of Kefta (Fig.1).

Figure 1: Map of study area

The agro-climatic zone is identified as low land with

an inclination to semi-arid. Vegetation communities

within the reserve include Acacia-commiphora,

combretum-terminalia, dry evergreen montane

woodlands and riparian types.

This National Park is important for the conservation

of elephants. Currently, the site seasonally holds

about 100-150 individual elephants; these elephants

spend the rest of the year in Eritrea. Besides

elephants, it conserves 42 mammals, 167 birds, and 9

reptile species (Wossenseged et al 2014, Atakilt et al

2016).

3


Climate data source and climatic variables projection approach

Climatic data is the major requirement for the analysis of current

and future climate change. Therefore, a30-years climate data of

Kefta Humera with daily weather dataset (maximum air temperature, minimum air temperature and rainfall) from 1980-

2009 was obtained from Ethiopian national metrology agency

(ENMA). This 30 years climate data was calibrated and used as baseline for future climate projection of Kefta Humera. The

agricultural modeling intercomparison and improvement project

(AgMIP) climate scenario generation script has been designed and generates future scenarios using climate modeling of Coupled

Model Intercomparison Project Phase 5 (CMIP5).

The latitude (14.104o), longitude (36.521o) and average

elevation (604 meters above sea level) of kefta Humera were used

to generate future climate scenarios. The script was created 120 delta adjusted data files (3 time scales (near term, midterm and end

term) x 2 RCPs (4.5 and 8.5) x 20 GCMs). The climate modeling

RCPs (RCP4.5 and RCP 8.5) mainly recommended by AgMIP (agricultural modeling intercomparison and improvement project)

were used. General Circulation Model (GCM) was used for

projection of climate change impacts (Robert 2007).

Three time slices 2010-2039 (for near-term), 2040-2069 (for

mid-century) and 2070-2099 (for end-of-century) were chosen and

general circulation models used were; CCSM4 (Community Climate System Model), of USA, which has Spatial Resolution

(Lon × Lat ~ Levels) ranges 288 × 192L26, HadGEM2-ES Met Office Hadley Centre (Hadley Centre Global Environment Model)

of UK, which has spatial resolution (Lon × Lat ~ Levels) ranges

192 × 145L40 and MIROC5 (Model for Interdisciplinary Research on Climate) which has spatial resolution (Lon × Lat ~ Levels)

ranges 256 × 128L40 (T85) (Sillmann and Roeckner 2008).

These GCMs were selected based on their consistency among

regions, their wide use in recent assessments, rigor of processes

and resolution, and performance in regions (Alexander Clarke Ruane, Ibid). Graphs showing projection of maximum temperature

(Tmax), minimum temperature (Tmin) and rainfall of study site

were designed.

The set of delta adjusted .AgMIP files were created for

temperature maximum (Tmax), temperature minimum (Tmin) and

rainfall. Four digit codes were used to identify the location of dataset (ETHUCEXA.AgMIP, ETHUEEXA.AgMIP,

ETHUGEXA.AgMIP, ETHUIEXA.AgMIP, ETHUKEXA.AgMIP

and ETHUMEXA.AgMIP) for GCM CCSM4, (ETHUCKXA.AgMIP, ETHUEKXA.AgMIP,

ETHUGKXA.AgMIP, ETHUIKXA.AgMIP,

ETHUKKXA.AgMIP and ETHUMKXA.AgMIP) for GCM HadGEM2-ES and (ETHUCOXA.AgMIP, ETHUEOXA.AgMIP,

ETHUGOXA.AgMIP, ETHUIOXA.AgMIP,

ETHUKOXA.AgMIP and ETHUMOXA.AgMIP) for GCM MIROC5.

The first 2 digits (ET) are an abbreviation for the country, Ethiopia; the second 2 digits (HU) refer to the specific site

location, which was assigned for Humera. The fifth digit is the

time period and emissions scenario C = RCP4.5 (2010-2039 for Near Term), E = RCP8.5 (2010-2039 for Near Term), G = RCP4.5

(2040-2069 for Mid Term), I = RCP8.5 (2040-2069 for Mid

Term), K = RCP4.5 (2070-2099 End Term), M = RCP8.5 (2070-2099 for End Term) they were selected for each three GCMs. The

sixth digit is the GCM of CMIP5 scenario for (E = CCSM4), (K =

HadGEM2-ES) and (O = MIROC5) were used.

Modeling elephant distribution using Maximum entropy

modeling (Maxent)

Bioclim current data (30 arc-seconds resolution) that are expected

to have an impact on the distribution of species and its main habitats were downscaled from the worldClim database according

to Hijimans et al (2005).

The Bioclim current data were clipped down on Arc MAP to the Kefta Humera wereda map extension. 550 ground point data

were collected in terms of coordinate pair as decimal latitude/longitude using a hand-held ground positioning system

(GPS).These points were collected from dropping spots of the

species at the study area. The habitat type samples were collected from forestland (110 Records), shrubland (110 records), grassland

(110), irrigation site (around irrigation land 110 Records) and

arable land (110 Records).

The maximum entropy modeling or Maxent model was mainly

used for predict the distribution of L. africana in the study area. Individual sampling location to current environmental geo-datasets

was used to estimate the probability of species (Philips et al 2006).

Habitat types (forest, shrubsland and grassland) data were organized to ASCII format for environmental layer and used as

inputs for the modeling approach. It was assumed that African

elephant distribution is not only determined by Bioclim variables. Then species occurrence and habitat type samples (Latitude and

Longitude) were arranged to CSV (comma delimited) format for

the samples file.

The modeling was used Area Under Curve (AUC) to verify

model performance. The curves plot sensitivity (a measure of the rate of correct prediction of known locations) and against 1–

specificity (a measure of the rate of correct prediction of absences)

were used to verify model performance. The area under curve (AUC) is then used to verify index of model accuracy. A value of

AUC was range between 0.5 and 1.0. With values above 0.9

indicating very well fit and model performance is good (Young et al 2011). The relative percent contribution value of each variable

(downscaled bioclim variables and Habitat type’s variables) was

done (Baldwin and Bender 2008). These values, in combination with the AUC values, were used to select the subset of variables

determining the distribution elephants.

From the output of Maxent, Map representing the model of species current and future distribution was done using HadGEM2-

ES GCM in near term (2010-2039), midterm (2040-2069) and end

term (2070-2099). Maps for distribution of elephants were prepared and used later to be reclassified into high distribution area

to low distribution area at the study area using Arc Map.

Model validation

Out of the total records (550 sample points), 75% were used for

model training and 25 % for testing to evaluate the model accuracy

(Philips et al 2006). AUC > 0.5 higher predictive power, AUC = 0.5 random chance, and AUC < 0.5 worse than random (Philips et

al 2006). In addition to jackknife results, environmental variables

response curves are displayed by maxent. Logistic output was selected because it is the easiest to conceptualize the

environmental variables (Pearson et al 2007)

Data collection from Local Community

The primary source of data was used to identify major

anthropogenic pressure on the elephant and its habitat at the study

area. At initial stage of the survey, informal meetings were undertaken with the staff of the park and the scouts in order to get

hold of the general socio-economic situation of the population of

the study area. Formal meetings also take place with key

4


informants (park manager, scouts, elder people, women, experts

and development agents) in order to gain in-depth knowledge about the area.

A random sampling technique was used to select 420

households out of 16,340 households living near the study area. Questionnaire was properly distributed and subsequently,

interviews were conducted by the researcher. Focus group

discussions were also used to reinforce questionnaire data (Mesele 2006). Focus group discussions in each Tabia and with the workers

of the study area were conducted. Local community was invited to

discuss issues according to their convenience. Finally, direct observation and field walks in the National Park also provided

good information.

Data analysis

R statistical software was used to generate climate scenarios from

CMIP5 using 30-year baseline climate data. Maximum entropy

modeling software (Maxent), AcrGIS and Arc MAP were used to

predict the distribution of the elephant in the study area. Statistical

Package for Social Science (SPSS) software was used to analyze

questionnaire data collected from community at the study area.

RESULTS AND DISCUSSION

Future trends of climate variables

Maximum temperature

The maximum temperature (Tmax) at the study area varied

according to scenarios and GCMs. The projection of maximum

temperature by HadGEM2-ES GCM (Fig. 2(a)), showed there will be an increase of maximum temperature by 1.2oC in near term

(2010-2039) for RCP 4.5, by 1.4oC in near term (2010-2039) for RCP 8.5, by 2.2oC in midterm (2040-2069) for RCP 4.5, by 3oC in

midterm (2040-2069) for RCP8.5, by 3oC in end term (2070-2099)

for RCP4.5 and by 5.3oC in end term (2070-2099) for RCP8.5 compared to the baseline. The Tmax of the study area will be

reached 41.82oC in end term RCP8.5 from the average Tmax of the

baseline which is 36.52oC.

Similar projection of maximum temperature indicated that,

according to MIROC5 GCM projection (Fig. 2 (b)), the maximum

temperature will be increased by 0.7oC in near term (2010-2039) for RCP 4.5, by 0.7oC in near term (2010-2039) for RCP 8.5, by

1.5oC in midterm (2040-2069) for RCP 4.5, by 1.7oC in midterm

(2040-2069) for RCP8.5, by 1.8oC in end term (2070-2099) for RCP4.5 and by 3oC in end term(2070-2099) for RCP8.5 compared

to the baseline. The Tmax of the study area will reaches 39.52oC in

end term RCP8.5 from the average Tmax of the baseline which is 36.52oC.

The other projection of maximum temperature by CCSM4

GCM (Fig. 2 (c)) confirmed that, the maximum temperature will be increased by 0.88oC in near term (2010-2039) for RCP 4.5, by

1oC in near term (2010-2039) for RCP 8.5, by 1.63oC in midterm

(2040-2069) for RCP 4.5, by 2.08oC in midterm (2040-2069) for RCP8.5, by 1.71oC in end term (2070-2099) for RCP4.5 and by

3.62oC in end term(2070-2099) for RCP8.5 compared to the

baseline. The Tmax of the study area will reach 40.14oC in end term RCP8.5 from the average Tmax of the baseline which is

36.52oC.

Therefore, the maximum temperature will be increased from the base line which is 36.52oC to 41.82 by HadGEM2-ES GCM for

RCP 8.5 in end term, 40.14oC by CCSM4 GCM for RCP8.5 in end term and 39.52oC by MIROC5 GCM for RCP8.5 in end term.

Generally, the projections of Tmax prove that, there will be an

increased of Tmax by 3.970C in future up to 2099.

1.2 1.4 2.2 3.0 3.0 5.3

0.05.0

10.015.020.025.030.035.040.045.0

Max

imu

m A

nn

ual

Tem

per

atu

re(o

c)

scenarios

Base line

TMAX

Trend

(a)HadGEM2-ES

0.7 0.7 1.5 1.7 1.8 3.0

0.05.0

10.015.020.025.030.035.040.045.0

Ma

xim

um

An

nu

al

Tem

per

atu

re(o

C)

Scenarios

Base

LineTMAX

(b)MIROC5

0.9 1.0 1.6 2.1 1.7 3.6

0.05.0

10.015.020.025.030.035.040.045.0

Max

imu

m a

nn

ual

Tem

per

atu

re(o

C)

Scenarios

Base line

TMAX

Trend

(c)CSSM4

5


Figure 2: Projected annual maximum temperature from Annual base line data of 1980-2009 and change trends using (a) HadGEM2-ES, (b)

MIROC5 and (c) CSSM4 GCMs

Minimum temperature

The projection of minimum temperature (Tmin) by HadGEM2-ES

GCM (Fig. 3 (a)), the minimum temperature will be increased by

1.4oC in near term (2010-2039) for RCP 4.5, by 1.7oC in near term (2010-2039) for RCP 8.5, by 3.5oC in midterm (2040-2069) for

RCP 4.5, by 4.5oC in midterm (2040-2069) for RCP8.5, by 4.4oC

in end term (2070-2099) for RCP4.5 and by 7.3oC in end term(2070-2099) for RCP8.5 compared to the baseline. The Tmin

of the study area will reach 29.89oC in end term RCP8.5 from the

average Tmin of the baseline which is 22.59oC.

Besides, projection of minimum temperature by MIROC5

GCM (Fig. 3 (b)), the minimum temperature will be increased by

0.8oC in near term (2010-2039) for RCP 4.5, by 0.9oC in near term (2010-2039) for RCP 8.5, by 1.6 oC in midterm (2040-2069) for

RCP 4.5, by 2.1oC in mid-term (2040-2069) for RCP8.5, by 2oC in

end term (2070-2099) for RCP4.5 and by 3.8oC in end-term(2070-2099) for RCP8.5 compared to the baseline. The Tmin of the study

area will reach 26.19oC in end term RCP8.5 from the average Tmin

of the baseline which is 22.59oC.

Similarly, the projection of minimum temperature by CCSM4

GCM (Fig. 3(c)), the minimum temperature will be increased by

0.81oC in near term (2010-2039) for RCP 4.5, by 1.01oC in near term (2010-2039) for RCP 8.5, by 1.59oC in midterm (2040-2069)

for RCP 4.5, by 2.12oC in Mid Term (2040-2069) for RCP8.5, by

1.64oC in end term (2070-2099) for RCP4.5 and by 3.62 oC in end term(2070-2099) for RCP8.5 compared to the baseline. The Tmin

of the study area will reach 26.21oC in end term RCP8.5 from the

average Tmin of the baseline which is 22.59oC.

Therefore, the minimum temperature will be increased from

the base line 22.59oC to 29.89oC by HadGEM2-ES GCM for RCP

8.5 in end term, 26.21oC by CCSM4 GCM for RCP8.5 in end term and 26.19oC by MIROC5 GCM for RCP8.5 in end term.

Generally, the projection of Tmin showed that, there will be an

increased of Tmin by 4.9 0C in future up to 2099.

Figure 3: Projection of annual minimum temperature from annual base line data of 1980-2009 and change trend using (a) HadGEM2- ES, (b)

MIROC5 and (c) CSSM4 GCMs

1.4 1.7 3.5 4.5 4.4

7.3

0.0

5.0

10.0

15.0

20.0

25.0

30.0

35.0

Min

imum

Annual

Tem

per

ature

(oC

)

Scenarios

Base line

TMIN

Trend

(a)HadGEM2-ES

0.8 0.9 1.6 2.1 2.0 3.8

0.0

5.0

10.0

15.0

20.0

25.0

30.0M

inim

um

Annual

Tem

per

ature

(oC

)

Scenarios

Base Line

TMIN

Trend

(b)MIROC5

0.8 1.0 1.6 2.1 1.6 3.6

0.0

5.0

10.0

15.0

20.0

25.0

30.0

Min

imum

anual

Tem

per

ature

(oC

)

Scenarios

Base line

TMIN

Trend

(c)CSSM

4

6


Rainfall

The HadGEM2-ES GCM projection Fig. 4(a) of rainfall indicated

that, there will be an increase of 6.2 mm in near term (2010-2039)

for RCP 4.5, 0.7 mm in near term (2010-2039) for RCP 8.5 of 8.5 mm in midterm (2040-2069) for RCP 4.5, 31.1 mm in midterm

(2040-2069) for RCP8.5, 15 mm in end term (2070-2099) for RCP

4.5 and 12.4 mm in end term (2070-2099) for RCP 8.5 compared to the baseline. The highest rainfall amount of the study area will

be reached 576.28 mm in midterm RCP 8.5 compared to baseline

which is 545.18 mm.

Alike projection by of rainfall by MIROC5 GCM (Fig. 4 (b)),

the rainfall will be increased by 76.5 mm in near term (2010-2039) for RCP 4.5, by 85 mm in near term (2010-2039) for RCP

8.5 by 60 mm in midterm (2040-2069) for RCP 4.5, by 156.9 mm

in midterm (2040-2069) for RCP 8.5, by 81.9 mm in end term (2070-2099) for RCP 4.5 and by 207.3 mm in end term (2070-

2099) for RCP 8.5 compared to the baseline. The study area will

receive higher rainfall amount (752.48 mm) in end term RCP 8.5

compare to the total rainfall of the baseline which is 545.18 mm.

In addition, the projection of the rainfall by CCSM4 GCM

(Fig. 4 (c)) be evidence that, the rainfall will be increased by 8.69

mm in near term (2010-2039) for RCP 4.5, by 5.78 mm in near term (2010-2039) for RCP 8.5, by 6.54 mm in end term (2070-

2099) for RCP 4.5 and the rainfall will be decreased by 20.48 mm

in midterm (2040-2069) for RCP 4.5, by 36.72 mm in midterm (2040-2069) for RCP 8.5, and by 8.18 mm in end term (2070-

2099) for RCP 8.5 compared to the baseline. The higher rainfall

amount of the study area will be reached 553.87 mm in near term RCP 4.5 compare to the baseline which is 545.18 mm.

ES, (b) MIROC5 and (c) CSSM4 GCMs

Figure 4: Projection of annual rainfall from annual base line data of 1980-2009 and change trend using (a) HadGEM2-ES, (b) MIROC5 and (c)

CSSM4 GCMs

6.2 0.7 8.5 31.1 15.0 12.4

0.0

100.0

200.0

300.0

400.0

500.0

600.0

700.0

An

nu

al

Ra

infa

ll (

mm

)

Scenarios

Base line

Rainfall

Trend

(a)HadGEM2-

ES

76.5 85.2 60.0 156.9

81.9

207.3

0.0

100.0

200.0

300.0

400.0

500.0

600.0

700.0

800.0A

nn

ua

l R

ain

fall

(mm

)

Scenarios

Base Line

RAINFAL

L

(b)MIROC5

8.7 5.8

-20.5 -36.7 -8.1

6.5

-100.0

0.0

100.0

200.0

300.0

400.0

500.0

600.0

An

nu

al

Ra

infa

ll(m

m)

Scenarios

Base line

Rainfall

Trend

(c)CSSM4

7


Therefore, higher rainfall will be expected in end term RCP

8.5 by MIROC5 GCM which is 752.48 mm, in midterm RCP 8.5 by HadGEM2-ES GCM which is 576.28 mm and in near term RCP

4.5 by CCSM4 GCM which is 553.87 mm at the study area.

Associated with temperature change and rainfall, there was an intermittent draught and flood events at the study area especially in

2015/2016 due to El Niño. Therefore, there was observation of dry

up of water points in some parts of the Tabias with the general trend of the temperature and rainfall pattern of the Kefta Sheraro.

Poor people, specifically agriculture dependent communities and

Agro- pastoral at some parts of Kefta Humera were affected by the drought and flood events happened in 2015/2016 as observed from

focus group discussions held with selected community members

and direct observations during the study period.

Model performance and potential contribution of selected

variables for distribution of elephant

The training data Area under Curve (AUC) for the replicate was

0.98 and test data was 0.97 for elephant distribution at the study

area. Accordingly, these values indicate that, the Area under Curve

(AUC) values was closer to 1. Therefore, Sensitivity vs 1–

Specificity showed higher predictive power and the model

performed distribution of elephant in high-quality way.

The percent contribution and permutation importance of variables were used in the model for predicting distribution of

elephant at the study area. The higher the percent contribution, the

more impact that particular variable has on predicting the distribution elephant. Accordingly, Bio_12 (annual precipitation)

(0.72), Bio_18 (precipitation of warmest quarter) (0.55), and

Bio_13 (precipitation of wettest month) (0.35) were identified as the most important percent contribution of climatic variables

determining the distribution of elephant at the study area (table 1).

In comparable ways, the permutation importance of predicting model indicated, the distribution of elephant at the study area

heavily depends on variables with higher percentage. Bio_18

(precipitation of warmest quarter) (55.15), Bio_8 (mean temperature of wettest quarter) (1.16), and Bio_12 (annual

precipitation) (0.06) were identified as the most important climatic

variables contributing for distribution of elephant at the study area. In addition, the presence of forest (86.8), shrubland (6.6) and

grassland (4.38) were identified as the most essential habitats

variables contributing for distribution of elephant (table 1).

Table 1: Contribution and permutation importance of different variables for distribution of African elephant

Variables Percent Contribution 1Permutation importance

Forest 86.8006 40.20

Grassland 4.3832 0.64

Shrub land 6.6025 2.15

Bio_12 0.7276 0.06

Bio_13 0.3561 0

Bio-15 0.0433 0

Bio_18 0.5595 55.15

Bio_6 0.2095 0

Bio_8 0.2878 1.16

8


However, according to this study, habitat types (forest, shrubland and grassland) had higher contribution than climatic

variables (precipitation of warmest quarter, mean temperature of

wettest quarter and annual Precipitation) for distribution of

elephant. Similarly, jackknife test graphs of relative importance of each variables with respect to the AUC, test gain and regularized

training gain confirmed, habitat type (forest, shrubland and

grassland) and climatic variables (Bio_18, Bio_8 and Bio_12) were most indispensable variables contributing for distribution of

elephant at study area (Fig. 5).

9


Figure 5: Results of jackknife test of relative importance of each variables with respect to the AUC, test gain and regularized training gain for distribution of elephant

10


According to this study, forest (at threshold of 0.9), shrubsland (at

threshold of 0.66 to 0.84) and grassland (at threshold of 0.8 to 0.9) were identified as area of where high distribution of elephant. In

addition, the response curves also indicated the most important

variables for prediction of elephant distribution at the study area. Accordingly, the most suitable Bioclim variables for distribution of

elephant were annual precipitation (Bio_12) between 600mm to

745 mm.

The green indicates very high distribution, the yellow indicates

high distribution, lime indicates medium distribution, red indicates

very low distribution and the blue one indicates no distribution of

elephant for current (During study period 2015), near term (2015-2039), midterm (2040-2069) and end term (2070-2099) at the

study area (Fig. 6). Therefore, the Maxent current distribution of

elephant illustrated that, very high distribution of the species is mainly found at the shoreline of Tekeze River. This area is where

easily elephant obtains enough water for drink and bath. In

addition, elephant concentrated and depend on mainly forest for survival. But, there were anthropogenic threats which are restricted

elephant movements at riverside.

Figure 6: Predicted potential distribution of elephant (a) Current, (b) Near Term (2010-2039), (c) Mid Term (2040-2069) and (d) End Term (2070-

2099)

11

Major Anthropogenic Pressure on Elephant and Its Habitats

Views about the anthropogenic pressure threat on elephant and its

habitats were collected from the respondents. Eight major anthropogenic pressures distressing elephant and its habitat at the

study area were identified. In view of respondents, (encroachment)

(89%), over grazing in the National Park (88%), gold mining (85%), fire (85%) and cutting of tree (for fire wood, burning of

charcoal) (76%) were the major anthropogenic pressures. In

addition, respondents mentioned that, settlement (36%), charcoaling (34%) and poaching (29%) were also imposing

pressure on elephant and its habitat.

Root Causes of the Problems

The root causes of those anthropogenic pressures were

unemployment (33.75%) followed by Poverty (economic weakness

of the community) and lack of awareness about conservation of elephant with natural resources (15%) as presented by pie chart. In

addition to these, exclusion against indigenous people during

decision making (11.25%), climate change (11.25%), population growth, lack of farmland (absence of land owners license) and

lack of Ethiopian Wildlife Conservation Authority (EWCA) policy

implementation were the revealed root causes for the anthropogenic pressure on elephant according to respondents at the

study area.

As a result, the park area was reduced from 5000km2 to 2176.43km2(from EWCA of the study area) which is by 56.47%

mainly due to encroachment (18.8%), habitat loss mainly due to

fire 23.8%), habitat fragmentation mainly due to irrigation inside the park (18.8%) and the extreme anxiety of thus three

consequences on the national park be a route to the mass migration

of the elephant (37.5%) out of the park crossing Tekeze river to

Eritrea.

DISCUSSION

The study of World Bank (2007) provides, under RCP 8.5 scenario, mean warming in Ethiopia is likely to be in the range

1.46 - 2oC by 2030s and 2.68 - 3.80oC by 2080s relative to the

historical period. But, According to this study, the Tmax will be an increased by 3.970C in future up to 2099 and the Tmin will be an

increased by 4.9 0C in future up to 2099. In addition, according to

World Bank (2007), most projections indicated that an increase in rainfall for eastern Africa. This is similar with this study, which

showed the higher rainfall will be expected in end term RCP 8.5 by

MIROC5 GCM which is 752.48 mm, in midterm RCP 8.5 by HadGEM2-ES GCM which is 576.28 mm and in near term RCP

4.5 by CCSM4 GCM which is 553.87 mm at the study area.

L.africana can travel long distance to search for favorable environment. But, according to this study, irrigation,

encroachment, gold mining and other anthropogenic pressures

restrict species in one area. They are mainly focused on selected habitats such as riverside which provide all necessary resources

particularly water, forages and shades (Shoshani et al 2004). Therefore, according to this study the maxent model prediction

showed high concentration of elephant dung spots were found

around Tekeze River. Lahm (1994) also suggested that human elephant conflict and continuous cultivation of elephant’s habitat

has impact on elephant which supported for this study.

In addition to this, Teshale (2007) found that, lack of

awareness about natural resource conservation as the root causes of anthropogenic pressure which is supported this study. According to

Nelson et al (2003), changes in land-use (i.e., fragmentation of

habitats, conversion of forest land to crop cultivation land,

settlement, and livestock grazing) have increased human impact on

elephant.

CONCLUSIONS

Temperature and rainfall will increase under future scenarios, but the best model to predict climate influence on L.africana was a

habitat types. Habitat types had higher contribution than climatic

variables for distribution of L. africana. Climate variables (variables from bioclim) and anthropogenic pressure (human

activities) have impacts on L.africana. The most suitable Bioclim

variables for distribution of L.africana were annual precipitation. Therefore, according to this study the maxent model prediction

showed high concentration of elephant found around Tekeze River.

On the other hand, Encroachment, over grazing, gold mining, cutting of tree and burning of charcoal were the major

anthropogenic pressures distressing elephant and its habitat at the

study area. The root causes for thus anthropogenic pressures were unemployment, economic weakness the community and lack of

awareness about conservation at the study area.

ACKNOWLEDGMENTS

The author acknowledges the Open Society Foundation (OSF)-African Climate Change Adaptation Initiative (ACCAI) for

financial support to study at ICS for two years. Mekelle University

College of Dryland Agriculture and Natural Resource(CDANR), Department of Animal, Range land and wild life science (ARWS)

and, Wildlife and Ecotourism management (WETM) for financial

support and supervision of field work.

REFERENCES

Atakilt B, Gidey Y and Gebregizabher T (2016): Human-Wildlife

Conflict in Kafta-Sheraro National Park, Northern Ethiopia. World Journal of Zoology 11 (3): 154-159.

Baldwin RA and Bender LC (2008): Distribution, occupancy, and

habitat correlates of American martens (Martes Americana) in Rocky Mountain National Park, Colorado. J Mammal

89:419–427.

Bond WJ, Midgley GF and Woodward FI (2003): The importance of low atmospheric CO2 and fire in promoting the spread

of grasslands and savannas. Global Change Biology 9:

973–982. Hannah L, Midgley GF and Millar D (2002): Climate change-

integrated conservation strategies. Global Ecology and

Biogeography11:485-495. Hijimans R, Catherine J and Graham H (2005): The ability of

climate envelope models to predict the effect of climate

change on species distributions. Global Change Lahm S (1994): Impact of Elephants on Agriculture in Gabon.

African Elephant Conservation Programme Final Report,

AECCG, Oxford. Lee PC and Graham MD (2006): African elephants Loxodonta

africana and human-elephant interactions: implications for conservation. International Zoo Yearbook 40 (1): 9-19.

Malcolm JR, Canran L, Ronald PN, Lara H and Lee H (2002): Global

Warming and Extinctions of Endemic Species from Biodiversity Hotspots. Conservation Biology Volume 20, No.

2, 538–548.

Mesele Y (2006): Human-wildlife (The Ethiopian Wolf and Gelada baboon) conflict in and around the Simien

12

mountains National Park. Unpublished MSc Thesis, Addis

Ababa, Ethiopia.

Nelson A, Bidwell P and SilleroZubiri C (2003): A review of human-elephant conflict management strategies. People

and Wildlife Initiative. Wildlife Conservation Research

Unit, Oxford University. Pearson RG (2007): Species Distribution Modeling for

Conservation Educators and Practitioners. Report,

American Museum of Natural History, Available at http://ncep.amnh.org.

Phillips SJ, Anderson RP and Schapire RE (2006): Maximum

entropy modeling of species geographic distributions. Ecological Modelling 190: 231–259.

Robert LW and Christian WD (2007): A decision support tool for

the assessment of regional climate change impacts. Sewnet M (2012): Variation in the Ethiopian Common Mole Rat

(T.splindase) Based on Morphological, Chromosomal and

Molecular Characteristics. Phd Dissertation. Addis Ababa University, Addis Ababa.

Shoshani J, Hagos Y, Yacob Y, Ghebrehiwet M and Kebrom E

(2004): The elephants (Loxodonta africana) of Zoba Gash Barka, Eritrea: Part 2. Numbers and distribution, ecology

and behavior, and fauna and flora in their ecosystem.

Pachyderm 36:52-68. Sillmann J and Roeckner E (2008): Indices for extreme climate

events in projections of anthropogenic climate change.

Clim. Chang. 83-104.

Teshale Y (2007): Impact of Resettlement Program on Rangeland

Woody Vegetation and Large Wild Mammal of the Kafta-

Sheraro Wildlife Reserve in West Tigray. MSc Thesis in

Livestock Production and Pastoral Development, Mekelle

University, Mekelle, Ethiopia.

Walther GR, Post E, Convery P, Menzel A and Parmesan C (2002): Ecological responses to recent climate change. Nature

416:389-95.

World Bank (2007): Values are based on country surveys conducted between 1989 and 2005.

Wossenseged LT, Ibrahim A, Meshesha B, Teshome GM, Alon W and Asrat H (2014): Phlebotomus orientalis in extra-

domestic habitats of Kafta-Humera lowlands, Kala-azar

endemic, Northwest Ethiopia. Journal of Parasites and

Vectors 7:594.

Yirmed D (2004): Elephants in Ethiopia (Amharic Version). Berhanena Selam Printing Enterprise, Addis Ababa,

Ethiopia. Pp. 75.

Yirmed D and Afework B (2000): Study on the elephants of Mago National Park, Ethiopia. Pachyderm 28:32-43.

Yirmed D, Renfree MB, Short R and Barnes RW (2006): The

undisclosed facts about the relic elephant population in the horn of Africa. Proceeding: Biological Society of

Ethiopia, 16th annual conference and workshop. pp.13.

Yirmed D. (2008): The Ecology and Conservation of the Relice Elephant Population in the Horn of Africa. Four years

report. Addis Ababa, Ethiopia

http://ncep.amnh.org/

climate change, anthropogenic pressure and …mit.edu.et/agshare/docs/2018_menigstu et...

Documents