






























 
 

 

*Corresponding author:  

Email: twesigyeck@yahoo.com; Phone: +256- 782353775 

 

 

 

Biodiversity conservation and threat reduction in Kibale and Queen Elizabeth 
conservation areas, Uganda 

Joseph Katswera, Norah M. Mutekanga and Charles K. Twesigye*  

 
Department of Biological Sciences, Faculty of Science, Kyambogo University, P.O. Box 1 Kyambogo, 
Kampala, Uganda 

 
 

KEYWORDS:  
Biodiversity;  

Conservation;  

Protected area;  

Threats;  

Wildlife 

 

 

 

 

 

 

 

 

 

 

 
 

 

 

 

INTRODUCTION 

Protected areas harbour a particularly rich and 
unique biodiversity (Gibson et al.,2011; 
Tranquilli et al., 2014). However, their existence 

is challenged by many interrelated 
anthropogenic activities that have intensified 
over recent decades (Laurance, 1999; Sodhi et 
al., 2007; Wittemyer et al., 2008; Tranquilli et 
al., 2014). Increased human population growth 

Journal homepage : https://journals.hu.edu.et/hu-journals/index.php/eajbcs 
 

 Hawassa University
College of Natural & Computational Sciences

Year 2021

Volume xx No xx

East Afr. J. Biophys. Comput. Sci., Vol. 1, Issue. 1 
 

1

East African Journal of Biophysical and Computational Sciences 

Research article

This paper examines threats affecting the wildlife conservation areas, threat reduction  and
adaptive management strategies that enhance biodiversity conservation. The research was
conducted  through  a  survey,  and  data  was  collected  from  August  2018  to  April  2019  in
Kibale and Queen Elizabeth Conservation Areas using literature review, threat reduction
assessment  technique,  Key  Informant  Interviews  and  Focused  Group  Discussions  and
semi-structured  questionnaires.  The  data  was  analysed  using  Geographical  Information
System  software  ESRI  ArcGIS  version  10.31,  threat  reduction  assessment  tool,  Pearson
Chi  square  test,  Spearman’s  rho  correlation  coefficient,  Paired  Samples  t-Test,  and  one-
way Analysis of Variables. The study established that staff education level and experience
in  conservation  work  influences  biodiversity  conservation.  The  conservation  areas  are
threatened  by  habitat  transition/changes,  wild  fires,  human-wildlife  conflicts,  armed
poaching and illegal wildlife trade/trafficking in game meat and game products, increasing
human population pressure, and boundary encroachment.  Despite these threats, there was
a general increase in large mammal population over the past decades, which  collaborates
well  with  the  threat  reduction  assessment  indices.  Both  conservation  areas  had  an
ecological integrity rating average score of “yellow”  indicating  significant “concern” and
therefore  “dissatisfactory”.  The  two  conservation  areas  are  majorly  threatened  by
anthropogenic threats, natural threats, and administrative constraints. The wildlife agency
should  integrate  ecosystem  health  into  the  conservation  agenda.  The  agency  should  also
strengthen  adaptive  management,  law  enforcement,  and  collaboration  with  local
communities  and  other  stakeholders  to  reduce  on  the  threats.  Finally,  further  research
should  focus  on  ecosystem  health,  and  also  the  impact  of  tourism  infrastructural
development on biodiversity conservation.

ABSTRACT

mailto:twesigyeck@yahoo.com


East Afr. J. Biophys. Comput. Sci., Vol. 1, No. 1 
 

 

has promoted the rapid expansion of threats to 
wildlife, habitat destruction through agriculture 
and unsustainable hunting of wildlife. 
Biodiversity is the wealth of all life forms found 
on earth and encompasses all species of plants, 
animals, microorganisms, the ecosystems and 
ecological processes. Moral justification and 
value to human existence are two major reasons 
for conserving biodiversity (Christ et al., 2003). 
However, renewable natural resources are being 
utilized by humans at a rate exceeding their 
natural abilities to renew themselves (Christ et 
al., 2003). Human encroachment into natural 
ecosystems is increasing drastically throughout 
the world. Forests are being exploited and 
cleared, farmlands have increased in extent, 
demand for grazing areas is on the rise and 
unregulated harvesting of the wild resources is 
becoming uncontrollable. As human activities 
exert pressure on the global environment, 
biological diversity declines, habitats are 
transformed and the population of some species 
dwindles to the point of extinction (Whitmore 
and Sayer, 1992). Since man is constantly at 
variance with nature, the ever increasing human 
population coupled with technological 
development place stress on the environment 
and the world’s natural resources hence the 
unprecedented rate of biodiversity 
disappearance.  

Threats to biodiversity in Africa have led to the 
creation of numerous protected areas (PAs), 
which are intended to conserve both fauna and 
flora, whilst benefitting neighbouring human 
communities. Nevertheless, human populations 
throughout Africa have increased the amount of 
pressure being exerted on PAs. Thus, despite 
their legal protected status, PAs face significant 
threats(Tranquilli et al., 2014). The most  
concerns are overexploitation of natural 

resources, habitat loss, fragmentation and 
isolation (Wittemyer, 2008; Laurance, 2012). 
These factors impact severely on key species 
and especially taxa with large body sizes, slow 
reproductive rates, and little behavioural 
adaptability (DiMarco, 2014). 

Protected areas (PAs) are a cornerstone of 
biodiversity conservation efforts, as they 
provide various species with safe havens 
(Radeloff et al., 2010). Protected areas now 
cover more than 14.7% of the terrestrial land 
surface (UNEP-WCMC, IUCN, 2016). Recent 
syntheses suggest that PAs are performing better 
than the broader landscape (Barnes et al., 2016; 
Gray et al., 2016), although numerous studies 
suggest that biodiversity continues to decline 
within many PAs (Craigie et al., 2010; Laurance 
et al., 2012; Geldmann et al., 2013). A principal 
objective of PAs is to conserve nature by 
eliminating or reducing human pressures and 
threats operating within their boundaries. In 
addition to preserving biodiversity, PAs should 
maintain natural processes and promote survival 
of species by excluding threats (Margules & 
Pressey, 2000). To achieve these goals, we must 
understand what the main threats are, where the 
potential threats occur, and where high-risk 
areas are distributed. Identifying these threats is 
therefore crucial for conservation managers to 
take effective measures to mitigate some of the 
proximate threats to PAs (Wilson et al., 2005).  

Knowledge of the occurrence and severity of 
threats to PAs has largely been informed by 
remote sensing data (Geldmann et al., 2014), 
modeling (Hole et al., 2009), as well as 
questionnaire surveys with an emphasis on 
tropical regions (Bruner et al., 2001; 
Leverington et al., 2010; Laurance et al., 2012). 
Freely available satellite data offer global and 

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East Afr. J. Biophys. Comput. Sci., Vol. 1, No. 1 
 

 

standardized metrics form measuring those 
threats to PAs that can be observed remotely, 
such as deforestation (Joppa and Pfaff, 2011) 
and fires (Nelson and Chomitz, 2011). 
However, many other threats, including some of 
the most frequently reported threats to species, 
according to the International Union for 
Conservation of Nature (IUCN) Red List (e.g., 
overexploitation of species, invasive alien 
species, pollution, climate change), cannot be 
measured from space (Joppa et al., 2016) and 
require field-collected data (Mwangi et al., 
2010).  

In Uganda, however, little research has been 
done on the level of threats affecting 
biodiversity, their consequent impacts in 
protecting ecological integrity, and therefore, 
little documentation on the recommendations to 
mitigate biodiversity threats. Therefore, the 
main aim of this study was to document the 
threats affecting the protected areas and propose 
adaptive management strategies to biodiversity 
conservation. The specific objectives were to (i) 
identify threats to biodiversity in the case study 
conservation areas, (ii) generate the threat 
reduction assessment (TRA) index for each PA; 
and (iii) identify management measures 
employed by the parks that enhance 
effectiveness and efficiency in addressing 
threats. 

Overall, this study presents tools for PA 
managers to make effective conservation and 

restoration decisions and consequently an 
important influence on global biodiversity 
conservation.  

MATERIALS AND METHODS 

Selection of the conservation areas for the 
study 

This study was carried out in the national parks 
and wildlife reserves within Kibale and Queen 
Elizabeth Conservation Areas because their 
General Management Plans only highlight the 
different management challenges that affect the 
integrity of the protected areas without 
specifying the threat levels (UWA, 2012, 2015), 
and also for logistical reasons. Based on 
knowledge of national parks and wildlife 
reserves in Uganda, purposive sampling (Babbie 
and Benaquisto, 2002) was used to select the 
two conservation areas. Specifically, the 
national parks and wildlife reserves included in 
this study were: Kibale National Park (795 
km2), Semuliki National Park (220 km2), Toro-
Semliki Wildlife Reserve (542 km

2
) and 

Katonga Wildlife Reserve (207 km2) in Kibale 
Conservation Area; and Queen Elizabeth 

National Park (1978 km
2
), Rwenzori National 

Park (995 km
2.
), Kyambura Wildlife Reserve 

(157 km2) and Kigezi Wildlife Reserve (330 
km2) in Queen Elizabeth Conservation Area 
(Fig.1) 

 

3



Fig 1: Map showing Kibale and Queen Elizabeth Conservation Areas and location of study sites. 

The sample size and sampling technique 

The sample size of the respondents used during 
the study was determined using the method 
adopted from Krejcie and Morgan ( 1970) The 
study adopted stratified and purposive sampling 
technique to collect focused information. A total 
of 287 questionnaires were administered, and 
these were segregated as 208 individuals from 
households in the communities adjacent the case 
study wildlife protected areas, 53 to park staff, 4 

to ecological experts, 16 to representatives of 
local authorities, 2 to Uganda Wildlife 
Authority headquarters, 2 to Wildlife 
Conservation Society-Uganda, and 2 to the 
Ministry of Tourism, Wildlife and Antiquities. 

Data collection 

The data and information used to determine the 
threats to conservation and how they were being 
addressed was collected from August 2018 to 
April 2019. Permission to conduct the study was 

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East Afr. J. Biophys. Comput. Sci., Vol. 1, No. 1 

obtained from Uganda Wildlife Authority prior 
to the start of the survey. In-depth interviews 
with 53 randomly selected resident park staff in 
the four national parks and four wildlife 
reserves of Kibale and Queen Elizabeth 
Conservation Areas was carried out. Eight 
focused group discussions (FGDs), one per 
protected area, were made to collect data on the 
existing threats and identify management 
options to address them. Each FGD had 5 to 8 
park staff. Four ecological experts who had 
conducted and or supervised various studies in 
the case study protected areas were interviewed 
to get expert judgment on the ecological 
integrity indicators. Key informant interviews 
(KII) with two representatives (district political 
head and district environment/natural resources 
head) from each of the eight selected district 
local governments neighboring the parks and 
wildlife reserves; and six national level 
representatives mainly from Uganda Wildlife 
Authority, Wildlife Conservation Society-
Uganda, and Ministry of Tourism, Wildlife and 
Antiquities to gather more information. Semi-
structured questionnaire were administered to 
208 randomly selected community respondents 
living adjacent the parks and wildlife reserves. 
In addition, literature review to identify relevant 
existing information on the national parks and 
wildlife reserves pertaining wildlife 
conservation was also done. This data collection 
was guided by semi structured questionnaire 
that generated both qualitative and quantitative 
responses. Both primary and secondary data 
were collected. Specifically, data were obtained 
directly from the park staff, and existing 
literature including park reports, general 
management plans and annual operation plans, 
annual reports, field monitoring reports and 
routine reports. The threats to habitat integrity, 

quality and ecosystem functioning were 
identified.  

Data analysis 

Staff at each park and wildlife reserve 
headquarters were interviewed and GPS points 
were collected using Garmin eTrex GPS. These 
collected points in form of latitudes and 
longitudes were downloaded, entered in Ms-
excel, converted to decimal degrees and 
exported to Geographical Information System 
(GIS) software ESRI ArcGIS version 10.31 for 
map production. Responses from the park staff 
were analyzed using descriptive statistics, and 
inferential statistics as in the Statistical Package 
for the Social Sciences (SPSS) Version 22. The 
statistical tests used in analysis were Pearson 
Chi square test, Spearman’s rho correlation 
coefficient (r), Paired Samples t Test, Friedman 
test statistic, and Kruskal-Wallis Analysis of 
Variance (ANOVA). In addition, the TRA 
approach by Margoluis and Salafsky (1998) was 
adopted to assess the main types of threats 
affecting the PAs, their occurrence, their impact, 
and their reduction levels. Following Salafsky et 
al. (2008), threats are defined as any human 
activity or processes that caused destruction, 
degradation, and/or impairment of biodiversity 
targets. This approach based on three key 
assumptions: a) all biodiversity destructions are 
human-induced; b) all threats to biodiversity at a 
given site can be identified and c) changes in all 
threats can be measured or estimated (Margoluis 
and Salafsky, 1998). The TRA method identifies 
threats, ranks them based on the criteria and 
assesses the progress in reducing them (Rome, 
1999). The TRA approach followed the 
procedural approach developed by IUCN 
(1998), Margoluis and Salafsky (1999) that 
involved: 

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East Afr. J. Biophys. Comput. Sci., Vol. 1, No. 1 
 

 

a) Defining the protected area and listing 
all direct threats present at the site; 
b) Ranking each threat based on three 
criteria: area, intensity and urgency (area refers 
to the percentage of the habitats in the site; 
intensity refers to the impact or severity of 
destruction caused by the threat; and urgency 
refers to the immediacy of the threat. Out of the 
total threats, the highest ranked threat for each 
criterion receives the highest score, and lowest 
ranked score receives the lowest score; 
c) Adding up the scores across all the three 
criteria for total ranking; 
d) Determining the degree to which each 
threat has been met; 
e) Calculating the raw score for each threat 
and multiplying the total ranking by the 
percentage calculated to get the raw score for 
each threat; and 
f) Calculating the final threat reduction 
index score by adding up the raw scores for all 
threats, dividing by the sum of the total 
rankings, and multiplying by 100 to get the 
TRA index as a percentage. 

This TRA approach is much simpler and cost 
effective, as it measures changes in the broader 
human activities that threaten the integrity of the 
resource, and then uses that information to draw 
inferences on the state of the resource itself.  

sampling method. By rule of thumb where there 
is no information for an area it is possible to 
take 50% of expected prevalence. Using 5% 
degree of absolute precision, 384 animals need 
to be sampled but, 400 animals were sampled.  

RESULTS  

The study documented the threats to 
biodiversity in the case study national parks and 

wildlife reserves. Both primary and secondary 
threats were identified (Table 2), and their threat 
reduction percentages and indices calculated 
(Table 3). The management strategies employed 
to reduce on the identified threats to enhance 
wildlife conservation were also documented 
(Table 4). 

Demographic characteristics of the 
respondents 

Park staff responses  

The park staff respondents were segregated as 
20.8% females and 79.2% males. The Chi 
square test result was statistically significant (χ2 
(1, N = 53) = 18.132, p =0.000, Cramer’s 
Value=0.445) and the high Cramer’s Value 
indicates a strong effect of gender in 
conservation. Further analysis using the paired 
samples t test revealed statistically significant 
results with t(52) = 8.616, p = 0.000, α = 0.05 
(Table 1) indicating that gender plays a vital 
role in conservation. The staff had varying 
education levels with majority (43.4%) having a 
diploma, and 35.8% a college degree in 
biological science and other related discipline. 
(Table 1) Analysis by the Pearson Chi-square 
test revealed statistically significant result that 
education level had an effect on biodiversity 
conservation (χ2 (21, N = 53) = 22.222, p 
=0.000, Cramer’s Value=0.374) and the high 
Cramer’s value indicates a very strong effect. 
Further analysis using a paired t test revealed 
statistically significant results t(52) = 5.654, p = 
0.000, α = 0.05 (Table 1) indicating that 
education had a strong correlation with 
biodiversity conservation. On work experience 
demographic, 30.2% had between 11 to 15 
years, and another 30.2 had over 16 years work 
experience in wildlife management and 

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East Afr. J. Biophys. Comput. Sci., Vol. 1, No. 1 
 

 

biodiversity conservation (Table 1), and the 
Pearson Chi-square test revealed statistically 
significant result that working experience had an 
effect on biodiversity conservation (χ2 (196, N = 
53) = 214.774, p = 0.000, Cramer’s Value = 
0.490). The high Cramer’s value indicates a 
very strong effect. Further analysis using the 
paired samples t test revealed statistically 
significant results t(52) = 3.817, p = 0.000, α = 

0.05. (Table 1) Further analysis on the 
relationship between length of work experience 
and level of understanding mandate of PAs 
using Spearman’s rho correlation coefficient (r) 
revealed statistically significance result 
(Spearman's rho, r= 0.780, p= 0.000, N=53). 

 

Table- 1: Socio-demographic characteristics of park staff 
Variable  Number 

(N) 
Percentage 
(%) 

Chi-square test (χ2) t test statistic 

Gender Male 42 79.2 χ2 (1, N = 53) = 18.132,  
p = 0.000, Cramer’s  
Value= 0.445 

t(52) = 8.616, p = 
0.000, α = 0.05  

Female 11 20.8 

Level of education Secondary 10 18.9 χ2 (21, N = 53) = 22.222,  
p = 0.000, Cramer’s  
Value= 0.374 

t(52) = 5.654, p = 
0.000, α = 0.05  

Certificate 1 1.9  
Diploma 23 43.4  
Degree 19 35.8 

Years in service  <5 years 8 15.1 χ2 (196, N = 53) = 214.774,  
p = 0.000, Cramer’s  
Value= 0.490 

t(52) = 3.817, p = 
0.000, α = 0.05 

 
[5-10) years 13 24.5  
[10-15) years 16 30.2  
[15-20] years 10 18.9  
>20 6 11.3 

 

Community responses  

Responses from the local communities adjacent 

the wildlife protected areas also revealed that 

that gender is an important factor in 

appreciating threats to the wildlife resources and 

participating in their reduction as revealed by 

significant χ2=137.263, d.f=3, p=0.000, N=205, 

Cramer’s Value=0.350 and t(145) = 0.311, p = 

0.265, α = 0.001. Other demographic factors 

mainly age [(χ2=137.263, d.f=3, p=0.000, 

N=205, Cramer’s Value=0.350; and t(145) =0 

.311, p = 0.265, α = 0.001)], education level 

[χ2=88.051, d.f=4, p=0.000, N=195, Cramer’s 

Value=0.159 and t(137) = 1.603, p = 0.001, α = 

0.001], length of residence [χ2=174.884, d.f=55, 

p=0.000, N=190, Cramer’s Value=0.617 and 

t(139) = 0.545, p = 0.019, α = 0.001] and land 

holding [χ2=79.258, d.f=2, p=0.000, N=194, and 

t(139) = 3.940, p = 0.000, α = 0.001] had an 

influence on community participation in wildlife 

conservation aimed to reduce threats to wildlife 

resources (Table 2).  

 

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East Afr. J. Biophys. Comput. Sci., Vol. 1, No. 1 
 

 

 

Table- 2: Socio-demographic characteristics of community respondents 
Variable  Numbe

r (N) 
Percentage 
(%) 

Chi-square test 
(χ2) 

t test statistic 

Gender Male 139 66.8 χ2=23.558, d.f=1, 
p=.000, N=208, 
Cramer’s 
Value=.231 

t(148) = 2.063, 
p = 0.000, α = 
0.001 

 
Female 69 33.2 

Age (in years) 18-31  56 27.3 χ2=137.263, d.f=3, 
p= 0.000, N=205, 
Cramer’s Value = 
0.350 

t(145) = 0.311, 
p = 0.265, α = 
0.001 

 
32-45  118 57.6  
46-60  15 7.3  
61+ years 16 7.8 

Level of education Primary 82 42.1 χ2=88.051, d.f=4, 
p=0.000, N=195, 
Cramer’s Value= 
0.159 

t(137) = 1.603, 
p = 0.001, α = 
0.001 

 
Secondary 57 29.2  
Certificate 21 10.8  
Diploma 20 10.3  
Degree 15 7.7 

Land holding Owns land 159 82.0 χ2=79.258, d.f=2, 
p=0.000, N=194 

t(139) = 3.940, 
p = 0.000, α = 
0.001 

 
Landless 35 18.0 

Owned Acreage (in hectare) <1 7 5.3 χ2=162.091, d.f=1, 
p=0.000, N=132, 
Cramer’s 
Value=0.249 

t(93) = 3.060, p 
= 0.000, α = 
0.001 

 
1-3  84 63.6  
3-5  13 9.8  
5-10  21 15.9  
>10  7 5.3 

Length of residence 1-3 years 8 4.2 χ2=174.884, d.f=55, 
p=0.000, N=190, 
Cramer’s 
Value=0.617 
  

t(139) = 0.545, 
p = 0.019, α = 
0.001 
 

 
4-6 years 13 6.8  
7-9 years 9 4.7  
≥10 years  160 84.2 

Occupation of respondents Formal employment 15 10.2  χ2=69.023, d.f=4, 
p=0.000, N=148, 
Cramer’s 
Value=0.598 

t(89) = 1.705, p 
= 0.092, α =0 
.05 
 

 
Business 23 15.5  
Religious leaders 2 1.4  
Peasant farmers 85 57.4  
Fisher folk 23 15.5 

Distance of household from 
the park boundary 

<5km 130 75.1 χ2=190.465, d.f=2, 
p=0.029, N=173, 
Cramer’s Value= 
0.201 

t(145) = 3.010, 
p = 0.003, α = 
0.001 

 
5-10km 32 18.5  
>10km 11 6.4 

 

Threats to biodiversity conservation 

Across the landscape, the study found that all 

the case study national parks and wildlife 

reserves experience nearly similar threats which 

affect conservation of biological diversity 

therein. Out of a total of 13 threats identified, 

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East Afr. J. Biophys. Comput. Sci., Vol. 1, No. 1 
 

 

six were primary and common threats to all and 

these were: i) increasing human population 

pressure leading to illegal activities/resource 

off-take, ii) poaching and illegal wildlife 

trade/trafficking in game meat and of recent in 

Ivory, iii) habitat transition/changes due to 

invasive alien species, iv) human-wildlife 

conflicts arising from wildlife attacks to humans 

and livestock, and destroying crops, v) wild 

fires, and vi) boundary encroachment through 

agricultural development and urbanization 

(Table 3).  

Table- 3: Prevailing Threats within the Conservation Areas 
  
  
  

  
  
Threats 

Conservation Area 
Kibale Queen Elizabeth 

K
ib

al
e 

Se
m

ul
ik

i  

To
ro

-
Se

m
lik

i 

K
at

on
ga

  

Q
ue

en
 

El
iz

ab
et

h 
 

R
w

en
zo

ri 
M

ou
nt

ai
ns

  

K
ya

m
bu

ra
  

K
ig

ez
i  

1 Human population pressure 1 1 1 1 1 1 1 1 
2 Poaching and illegal wildlife trade  1 1 1 1 1 1 1 1 
3 Habitat transition/changes  1 1 1 1 1 1 1 1 
4 Human-wildlife conflicts 1 1 1 1 1 1 1 1 
5 Wild fires 1 1 1 1 1 1 1 1 
6 Boundary encroachment  1 1 1 1 1 1 1 1 
7 Zoonotic and vector-borne diseases 1 1 0 1 1 0 1 1 
8 Infrastructure development  1 1 0 0 1 0 0 0 
9 Road kills 1 1 0 0 1 0 0 0 
10 Trans-boundary issues 0 1 0 0 1 0 0 1 
11 Poor waste management 1 0 0 0 0 1 0 0 
12 Variation in water quality and quantity 0 0 0 0 1 0 0 0 
13 Negative impacts of climate change 0 1 0 0 0 1 0 0 
1=present; 0=absent 

The rest of the threats were subjected to One-
Way ANOVA to compare their means. The 
results revealed sufficient evidence to conclude 
that the threats were different as the test statistic 
was significant for zoonotic diseases and vector-
borne diseases (F2, 50=21.708, p=0.001), 
infrastructural developments (F2, 50=10.201, 
p=0.001), transboundary issues (F2, 50=0.677, 
p=0.001), and negative impacts of climate 
change (F2, 50=10.362, p=0.001) and hence 
categorized as secondary threats. However, the 
test statistics results were not significant for 
road kills (F2, 50=2.722, p>0.001), and poor 

waste management (F2, 50=1.802, p>0.001) 
hence do not pose serious threats to wildlife 
protected areas.  

Further, administrative constraints pose a threat 
to biodiversity conservation. Analysis using the 
one way ANOVA test revealed that inadequate 
funding (F7, 45=5.095, p=0.000), insufficient 
incentives (F7, 45=0.35, p=0.000), and 
inadequate patrol equipment (F7, 45 =0.328, 
p=0.001) were statistically significant 
constraints to biodiversity conservation. Other 
constraints not statistically significant were poor 
staff housing (F7, 45 =0.35, p=0.926), and weak 

9



East Afr. J. Biophys. Comput. Sci., Vol. 1, No. 1 
 

 

support from neighboring communities (F7, 45 
=0.35, p=0.937).  

Threat Reduction Assessment Index and 
protected areas 

Threat Reduction  

Threat reduction analysis conducted showed 
that at all levels of area, intensity and urgency, 
the national parks and wildlife reserves in 
Kibale Conservation Area had a higher overall 
average threat reduction (57.6%) compared to 

those in Queen Elizabeth Conservation Area 
(48.25%) (Table 4). 

Specifically, habitat change/transition presented 
the highest threat with a paltry reduction of 
1.25% in Queen Elizabeth Conservation Area 
and 16.25% in Kibale Conservation Area 
whereas boundary encroachment was the lowest 
threat with the highest threat reduction in both 
conservation areas with 86.25% in Kibale 
Conservation Area and 83.8% in Queen 
Elizabeth Conservation Area (Table 4).  

Table -4: Threat Reduction in the Conservation Areas 

Conservation 
Area Protected Area 

Percentage Threat reduction (%TR) 

H
ab

ita
t 

ch
an

ge
s 

/ 
tra

ns
iti

on
 

 W
ild

 fi
re

s 

H
um

an
 

W
ild

lif
e 

C
on

fli
ct

  

Po
ac

hi
ng

 
an

d 
w

ild
lif

e 
tra

ff
ic

ki
ng

  

Po
pu

la
tio

n 
pr

es
su

re
 

/il
le

ga
l 

ac
tiv

iti
es

 
B

ou
nd

ar
y 

en
cr

oa
ch

m
en

t 

A
ve

ra
ge

 
pe

r 
PA

 

Kibale 
Conservation 
Area 

KNP 50 80 70 77.5 70 70 69.58 
SNP 5 40 60 50 50 90 49.20 
TSWR 5 50 50 65 80 90 56.67 
Katonga 5 50 60 50 70 95 55.00 
Overall average  in KCA 16.25 55 60 60.6 67.5 86.25 57.60 

Queen 
Elizabeth 
Conservation 
Area 

QENP 5 60 20 50 80 80 49.17 
RMNP 0 40 80 40 40 80 46.67 
Kigezi NP 0 10 40 30 40 70 31.67 
Kyambura NP 0 90 50 75 80 95 65.00 
Overall average in 
QECA 

1.25 52.5 53.8 54.7 63.8 83.8 48.25 

Key: KNP=Kibale National Park ; SNP=Semuliki National Park; TSWR=Toro-Semliki Wildlife Reserve; KCA=Kibale 
Conservation Area; QENP=Queen Elizabeth National Park; NP=National Park; QECA=Queen Elizabeth Conservation Area 
 

Further, wild fires had reduced by 55% in 
Kibale Conservation Area compared to 52.5% 
in Queen Elizabeth Conservation Area. Human 
wildlife conflict was still a great challenge in all 
the PAs with a reduction of 60% in Kibale 
Conservation Area compared to 53.8% in Queen 
Elizabeth Conservation Area. Human 

population pressure leading to illegal activities 
inside the protected areas had reduced by 63.8% 
in Queen Elizabeth Conservation Area 
compared to 67.5% in Kibale Conservation 
Area. Boundary encroachment by adjacent 
communities had reduced by 86.25% in Kibale 
Conservation Area compared to 83.8% in Queen 

10



East Afr. J. Biophys. Comput. Sci., Vol. 1, No. 1 
 

 

Elizabeth Conservation Area. Armed poaching 
and illegal wildlife trade in game meat and 
game products had reduced to 60.6% in Kibale 
Conservation Area compared to 53.8% in Queen 
Elizabeth Conservation Area (Table 4).  

The national parks and wildlife reserves that lie 
entirely within Uganda had their threat 
reduction above 50% compared to those that are 
shared with Democratic Republic of Congo 
which had less than 50% (Table 4). 

In addition, the focused group discussions 
revealed that other developments such as road 
construction, staff accommodation, setting up of 
tourist lodges and trails inside the national parks 
and wildlife reserves could have potential 
impact on the ecosystem. Further, 
transboundary issues, political interference, poor 
waste management (including wastes from oil 

palm processing in Semuliki National Park) and 
the impacts of climate change could also pose a 
threat in most protected areas. 

Threat Reduction Assessment Index 

Effectiveness of PA management in managing 
threats  

Results of the comparison of staffing (staff per 
PA) and threat reduction assessment index (per 
PA) using Paired Samples t Test revealed that 
Staffing and TRA Index scores were moderately 
and positively correlated (r = 0.590, p = 0.001) 
and t7 = 1.412, p = 0.001. On average, Staffing 
scores were 44.2275 points higher than TRA 
Index scores (95% CI [-29.83, 118.29]). From 
the box plot, the TRA Index values positively 
correlated with the staffing level in the PAs. 
Both variables appear to be symmetrically 
distributed (Fig 2). 

 
Fig. 2: Relationship between staffing and threat reduction assessment index 

11



East Afr. J. Biophys. Comput. Sci., Vol. 1, No. 1 
 

 

 

Threat Reduction Assessment Index and PAs 

The threat reduction analysis looked at three 
criteria: area, intensity and urgency. Kibale 
Conservation Area had a higher overall average 
TRA index of 49.46% compared to Queen 

Elizabeth Conservation Area with 45.1%. At 
protected area level, Kibale National Park had 
the highest TRA Index of 58.35% and Kigezi 
Wildlife Reserve with had the lowest with 
37.76%. However, threat reduction assessment 
indices were independent of the size of the 
protected areas (Fig. 3).  

 

Fig. 3: Threat Reduction Assessment Indices in National Parks and Wildlife Reserves 
 

Threat reduction and mammal population in 
Kibale Conservation Area  

Anlysis of data from Kibale Conservation Area 
shows a general mammal population increase 
over the past decades. Specifically, in Kibale 
National Park, the population of the Black and 

White colobus monkeys increased from 7,346 in 
2005 to 10,459 in 2010; the Baboon population 
increased from 11,603 in 2005 to 12,191 
individuals in 2010. However, other primate 
populations have had a slight increase. The 

Size of PA (x10km2), 
Kibale Conservation 

Area (KCA) KNP, 79.5Size of PA (x10km2), 
Kibale Conservation 
Area (KCA) SNP, 22

Size of PA (x10km2), 
Kibale Conservation 
Area (KCA) TSWR, 

54.2

Size of PA (x10km2), 
Kibale Conservation 
Area (KCA) KTWR, 

20.7

Size of PA (x10km2), 
Kibale Conservation 

Area (KCA) KCA 
average, 0

Size of PA (x10km2), 
Queen Elizabeth 

Conservation Area 
(QECA) QENP, 197.8

Size of PA (x10km2), 
Queen Elizabeth 

Conservation Area 
(QECA) RMNP, 99.5

Size of PA (x10km2), 
Queen Elizabeth 

Conservation Area 
(QECA) KGWR, 33

Size of PA (x10km2), 
Queen Elizabeth 

Conservation Area 
(QECA) KYWR, 15.7

Size of PA (x10km2), 
Queen Elizabeth 

Conservation Area 
(QECA) QECA 

average, 0

(KCA) KNP, 58.35

(KCA) SNP, 42.1

TRA Index (%), Kibale 
Conservation Area 
(KCA) TSWR, 48.5TRA Index (%), Kibale 

Conservation Area 
(KCA) KTWR, 48.9

TRA Index (%), Kibale 
Conservation Area 
(KCA) KCA average, 

49.46

TRA Index (%), 
Queen Elizabeth 

Conservation Area 
(QECA) QENP, 46.37

TRA Index (%), 
Queen Elizabeth 

Conservation Area 
(QECA) RMNP, 44.19

TRA Index (%), 
Queen Elizabeth 

Conservation Area 
(QECA) KGWR, 37.76

TRA Index (%), 
Queen Elizabeth 

Conservation Area 
(QECA) KYWR, 52.01

TRA Index (%), 
Queen Elizabeth 

Conservation Area 
(QECA) QECA 
average, 45.1

Size of PA (x10km2) TRA Index (%)

12

TRA Index (%), Kibale 
Conservation Area 

TRA Index (%), Kibale 
Consvation Area 



East Afr. J. Biophys. Comput. Sci., Vol. 1, No. 1 
 

 

Chimpanzee population has remained stable over the years (Fig. 4).  
 

  

 Legend 

 

 

 

Fig. 4: Population Estimate for Primates and other mammals in Kibale Conservation Area (Raw data 

adopted from UWA, 2018)  

 

Po
pu

la
tio

n

Period

Kibale National Park

Po
pu

la
tio

n

Period

Katonga Wildlife Reserve

Po
pu

la
tio

n

Period

Toro-Semliki Wildlife Reserve

13



East Afr. J. Biophys. Comput. Sci., Vol. 1, No. 1 
 

 

In addition, the Elephant and Buffalo 
populations have had an exponential increase. 
The Elephant population has increased from 262 
individuals in 2001 to 487 individuals in 2010; 
the buffalos from 124 individuals in 2001, to 
402 individuals in 2010; and the Bush pigs were 
only estimated at 400 individuals in 2001. (Fig. 
4) This general increase collaborates well with 
highest TRA Index of 58.35% recorded in KNP 
(Fig. 3). 

In Katonga Wildlife Reserve, there was a steady 
increase in wildlife population from 2004 to 
2013 (Fig. 4). The population of the Black and 
White colobus monkey increased from 1,342 in 
2004 to 3,335 in 2013. Duiker population rose 
from 295 in 2004 to 1,169 in 2008. The 
Reedbuck and Waterbuck had a more or less 
stable growth in population over the years.  

Similarly, the mammal population in Toro–
Semliki Wildlife Reserve had a general 
increase. Specifically, the Uganda Kob 
population increased from 3,460 individuals in 
1982 to 3,935 by 2015, the waterbuck 
population increased from 33 individuals in 
1982 to through 58 individuals in 2002 to 112 
by 2015, and the Buffalo population increased 
from 219 individuals in 2002 to 449 in 2015. 
(Fig. 4)  

Threat reduction and mammal population in 
Queen Elizabeth Conservation Area 

The elephant population in Queen Elizabeth 
Protected Area (QENP, KyamburaWR and 
Kigezi WR) reduced from 4,139 in 1969 to 
about 150 by 1980. It then started recovering 
until it reached 3018 individuals in 2012. Other 
wild animal populations have increased in 
QEPA (Fig. 5).  

 

Fig 5: Medium to Large Mammal Population in Queen Elizabeth Protected Area (Raw data adopted 

from UWA, 2018) 

Po
pu

la
tio

n

Period

Elephant

Buffalo

Hippo

Uganda Kob

Topi

Waterbuck

Lion

Warthog

14



East Afr. J. Biophys. Comput. Sci., Vol. 1, No. 1 
 

 

Threat reduction and ecological integrity rating 

The data indicators were analysed and various 
scores were assigned basing on the computed 
TRA Index in Table 4. Each indicator of 
ecological integrity was assigned a color score: 
dark green (TRA index 81-100%) for 
“acceptable” ecological integrity (very 
satisfactory), light green (TRA index 51-80%) 
for moderate ecological integrity (satisfactory), 
yellow (TRA index 21-50%) indicating a 
“concern,” (dissatisfactory) and red (TRA index 
0-20%) indicating “impaired” (very 
dissatisfactory) condition requiring immediate 
management action. (Table 5) Each national 
park and wildlife reserve had a score and each 
threat also had a score to show the level of 
threat reduction. On the whole, KNP and 
Kyambura WR had a “light green” score 
indicating moderate ecological integrity and 
hence satisfactory, while the rest of the PAs had 
each an average score of “yellow” indicating 
significant “concern” and therefore 
dissatisfactory (Table 5). 

In addition, using a scoring of 1 to 4 (where 1- 
very dissatisfactory, 2- dissatisfactory, 3- 
satisfactory, and 4- very satisfactory) on the 
state of conservation of wildlife resources in the 
national parks and wildlife reserves, the 
responses from ecological experts, selected 
district local governments, Uganda Wildlife 
Authority, Wildlife Conservation Society-
Uganda, and Ministry of Tourism, Wildlife and 
Antiquities indicated the overall performance of 
the national parks and wildlife reserves as 
dissatisfactory (t(25) = 14.148, p = 0.000, α = 
0.001). 

Measures by the parks that enhance 
effectiveness and efficiency in addressing 
threats 

In appreciation of the threats affecting wildlife 
conservation in the Conservation Areas, the 
study through the FGDs, identified management 
measures the parks have instituted to enhance 
effectiveness and efficiency in addressing 
threats and these include: 

a) Undertaking wildlife related disease 
surveillance in and around the national parks 
and wildlife reserves and conduct community 
sensitization programmes on wildlife related 
diseases 
b) Periodically monitoring wildlife and 
domestic animal movements to and from the 
national parks and wildlife reserves  
c) Carrying out massive conservation 
sensitization and educational programmes,  
d) Strengthening community conservation 
through the benefit sharing scheme, problem 
animal management, conservation education 
and awareness, and community participation in 
boundary management. 
e) Gathering, analysing and acting on 
intelligence information on illegal activities 
inside the national parks and wildlife reserves. 
f) Conducting cross border and or 
coordinated monitoring, control and 
surveillance patrols inside the national parks and 
wildlife reserves. This also involves cross 
border joint planning meetings, security 
operations. 
g) Put in place ecological maintenance and 
restoration programs especially in Kibale 
National Park and Queen Elizabeth National 
Park which are intended to counteract threats to 
ecological integrity. These include eliminating 
through uprooting hyperabundant species such 

15



East Afr. J. Biophys. Comput. Sci., Vol. 1, No. 1 
 

 

as Dichrostachys cinerea and Lantana camara 
that threaten the ecological integrity of the park 
ecosystems. 

 
 

Table- 5: Ecological Integrity Score Card using Data Indicators 
Protected 

Area (PA)  

Performance of Ecological Integrity using data indicators in the PAs 

La
rg

e 
m

am
m

al
 

po
pu

la
tio

n 
in

cr
ea

se
 

Fr
eq

ue
nc

y 
of

 
po

ac
hi

ng
 

in
ci

de
nc

es
 

H
um

an
 w

ild
lif

e 
co

nf
lic

t 

W
ild

 fi
re

s 

B
ou

nd
ar

y 
en

cr
oa

ch
m

en
t 

R
es

ou
rc

e 
ha

rv
es

tin
g 

Zo
on

ot
ic

 
di

se
as

es
 

H
ab

ita
t c

ha
ng

e 
 

R
oa

d 
ki

lls
 

A
ve

ra
ge

 ra
tin

g 
pe

r P
A

 

Kibale Conservation Area Y 

KNP 
Exponential  LG LG DG LG LG DG LG LG LG 

SNP N/A LG LG Y R 
 

DG 
  

Y 

TSWR General increase LG LG LG DG DG  N/A R R Y 

Katonga WR Exponential  LG LG LG DG LG LG R  N/A Y 

Queen Elizabeth Conservation Area Y 

QENP General increase LG DG LG DG DG DG R LG Y 

RMNP N/A Y DG Y DG Y  N/A DG  N/A Y 

Kigezi WR General increase Y Y R LG Y DG R  N/A Y 

Kyambura 

WR 

General increase LG LG DG DG DG  N/A R  N/A LG 

Overall rating 

per indicator 

LG LG LG DG LG LG DG R LG Y 

LG= light green; R =red; Y =Yellow; DG=Dark green; N/A= not applicable 

 

DISCUSSION 

The Kibale and Queen Elizabeth  conservation 
areas are key wildlife protected areas within the 
Albertine Graben which house a very rich 
mammal biodiversity. However, over time, they 
are faced with key threats that degrade their 
habitats and conservation in general. The threats 

identified in this study are similar to those 
reported by other researchers from a number of 
countries in Africa (Laurance et al., 2012; 
DiMarco et al., 2014; Tranquilli et al., 2014; 
Taylor, 2015; MacKenzie, 2017; Ryan, 2017; 
Chibueze, 2018; Benı´tez-Lo´pez et al., 2019; 
Kolinski and Milich, 2021). 

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East Afr. J. Biophys. Comput. Sci., Vol. 1, No. 1 
 

 

Human-wildlife conflicts occur worldwide, and 
human injuries are the most severe 
manifestations of these human-wildlife conflicts 
(Packer et al., 2005; Kabuusu et al., 2018). But 
the killing of livestock and the crop raiding by 
wildlife are by far the most widespread source 
of such conflicts (Allendorf et al., 2012; 
Andrade and Rhodes, 2012; Kabuusu et al., 
2018). For instance, in Canada wolves are 
reported to have killed close to 3,000 domestic 
animals in 14 years, whilst elephants in India 
and China led to a reduction of approximately 
14% and 48%, respectively of annual crop 
production (Madhusudan, 2003; Zang and 
Wang, 2003). In Tanzania, 86% of the persons 
living in wildlife buffer zones reported crop 
damage, while 10% reported the killing of 
livestock and poultry Kabuusu (2018), and 
baboons have always caused significant crop 
destruction in Uganda. The incidence of 
wildlife-associated human injuries increased in 
QENP between 2006 and 2010, and was mostly 
caused by hippos (Kabuusu et al., 2018). 

Weather patterns define the corresponding 
farming activity and the level of threats. The dry 
season is characterized by limited pastures and 
water in QENP, consequently wild animals, 
particularly elephants, move out of the QENP 
into community areas in search of water and 
pasture, raiding crops in the process. It was also 
revealed that the dry season is also the harvest 
season, and farmers are injured because they 
stay outside at night for extended periods of 
time protecting their crops from elephants and 
thieves. This seasonal change, in both animal 
and human behavior, mirrors temporal 
differences in the incidence of wildlife-
associated human injuries (Kissui, 2008). This 
study aimed at determining the threats to 
conservation and how they were being 

addressed. Data analysis and interpretation 
revealed major findings. The study established 
that socio-demographic factors of park staff 
mainly gender, age, education level and length 
of work experience play significant role in 
addressing threats to wildlife conservation. 
Equally, the socio-demographic factors of local 
communities adjacent the national parks and 
wildlife reserves mainly gender, age, education 
level and length of residence in the community 
play significant role in participating in 
programmes that address threats to wildlife 
conservation. This agrees with Kabir (2013) that 
recognition of gender roles in biodiversity 
management is an important step in the 
achievement of conservation and sustainable use 
of biological resources. PA management 
considers education as a key factor in 
empowering their staff with knowledge, skills 
and enhancing capacity and competence to 
conserve biodiversity. The park employees had 
the required skills in wildlife management and 
biodiversity conservation. In addition, length of 
work experience influences the level of 
understanding and implementing the mandate of 
PAs. This implies that the park employees are 
knowledgeable and could provide the needed 
information on threats to biodiversity 
conservation in the Park over the years. The 
presence of professionals corroborates the 
suggestion of Green (1999) that some industries 
required specially trained personnel to actualize 
set goals, and biodiversity conservation is a 
peculiar example.  

The national parks and wildlife reserves in both 
KCA and QECA are faced with key primary 
threats that degrade their habitats and 
conservation in general. These include: i) 
habitat transition/changes due to invasive alien 
species, ii) wild fires, iii) human-wildlife 

17



East Afr. J. Biophys. Comput. Sci., Vol. 1, No. 1 
 

 

conflicts arising from wildlife attacks to humans 
and livestock, and destroying crops, iv) 
poaching and illegal wildlife trade/trafficking in 
game meat and of recent in Ivory, v) increasing 
human population pressure leading to illegal 
activities/resource off-take, and vi) boundary 
encroachment through agricultural development 
and urbanization. Also, secondary threats 
mainly zoonotic and vector-borne diseases, 
transboundary issues, negative impacts of 
climate change, and infrastructure developments 
within the PAs constrain conservation efforts. 
These threats could be attributed to 
anthropogenic factors (including settlement, 
infrastructural development, agricultural 
expansion, resource off-take for livelihoods), 
and natural disturbances such as climate change, 
biological invasion, etc. These factors affect the 
aesthetic value, scenery, and also release wastes 
to the ecosystem. Further, inadequate funding to 
the sector, insufficient incentives to park staff, 
and inadequate patrol equipment also constrain 
conservation efforts.  

The national parks and wildlife reserves in 
Kibale Conservation Area had a higher overall 
average threat reduction, and overall average 
TRA index compared to those in Queen 
Elizabeth Conservation Area. This higher 
overall average threat reduction compares well 
with the general increase in mammal population 
over the years in Kibale Conservation Area. 
This could be attributed to improved 
management of the park and law enforcement in 
particular combating poaching (UWA, 2018).  

Despite the prevailing threats, the population of 
large mammals has generally increased over the 
past decades. This collaborates well with the 
TRA indices for each national park and wildlife 
reserve in each conservation area. The increase 

in mammal population is probably due to 
creation of both institutional and legal 
framework, and strengthened implementation of 
existing policies, laws and regulations. 
Specifically, the recovering mammal population 
in Queen Elizabeth Protected Area could be 
attributed to better security within Uganda, 
immigrations and successful breeding. This 
agrees well with UWA (2018) that improved PA 
management, increased vigilance through 
intelligence and patrols, and most importantly 
the peace, security and stability in the country 
and the region as a whole, and increased 
community conservation programs which have 
contributed to threat reduction. However, the 
low threat reduction performance of national 
parks and wildlife reserves shared with 
Democratic Republic of Congo  is probably due 
to their transboundary nature, which presents 
with it varied management challenges (GVTC, 
2017). 

On the whole, the overall ecological integrity of 
Kibale and Queen Elizabeth Conservation Areas 
was rated “yellow indicating a “concern” and 
hence dissatisfactory, a condition that requires 
immediate management action. Specifically, 
both KCA and QECA had an average TRA 
index of less than 50%, an indication that both 
conservation areas only mitigate less of the PAs 
threats. This rating of the overall performance of 
the national parks and wildlife reserves as 
“dissatisfactory” by the ecological experts, park 
employees, district local governments, Uganda 
Wildlife Authority, Wildlife Conservation 
Society-Uganda, and Ministry of Tourism, 
Wildlife and Antiquities was a testimony that 
there was great “concern” to conserve wildlife 
resources. This level of performance could be 
attributed to the more emphasis the Wildlife 

18



East Afr. J. Biophys. Comput. Sci., Vol. 1, No. 1 
 

 

Agency puts on animal health issues and little 
on ecosystem health.  

CONCLUSION 

The study established that the national parks and 
wildlife reserves are majorly threatened by 
habitat transition/changes, wild fires, human-
wildlife conflicts, armed poaching and illegal 
wildlife trade/trafficking, increasing human 
population pressure, and boundary 
encroachment through agricultural development 
and urbanization. Other  threats are zoonotic and 
vector-borne diseases, transboundary issues, 
negative impacts of climate change, and 
infrastructure developments within the PAs 
which constrain conservation efforts.  

Creation of both institutional and legal 
framework, and strengthening implementation 
of existing policies, laws and regulations is key 
to biodiversity conservation success. Protection 
of the integrity of wildlife conservation areas 
requires an integrative approach of interventions 
that focus on both animal health, and ecosystem 
health at national and transboundary level. 
Reduction in threats affecting wildlife 
conservation areas calls for a multidisciplinary 
approach involving local communities, park 
management, and security agencies. Therefore, 
improved park management, increased 
vigilance, and peace, security and stability in a 
country or region are prerequisites to achieve 
conservation of wildlife and other biodiversity. 
Various management approaches to enhance 
effectiveness and efficiency in addressing 
threats must include, but not limited to, 
undertaking wildlife related disease 
surveillance, periodically monitor wildlife in 
and outside the national parks and wildlife 
reserves, conduct conservation awareness and 

education, strengthen community conservation 
through the benefit sharing programme, 
strengthen intelligence information on illegal 
activities, strengthen adaptive management to 
address threats to ecological integrity.  

Recommendations 

The wildlife agency should strengthen adaptive 
management as a strategy to restore all degraded 
areas inside the national parks and wildlife 
reserves and carry out periodical environment 
audits..  

The wildlife agency should strengthen law 
enforcement and national-level coordination 
with the community and concerned stakeholders 
to combat poaching and other illegal activities 
in the park. 

Finally, more research should be conducted to 
investigate the impact of tourism infrastructure 
development on ecological integrity of the 
national parks and wildlife reserves. 

Acknowledgments 

We thank the people adjacent the national parks 
and wildlife reserves within the Kibale and 
Queen Elizabeth Conservation Areas for 
providing information. Thanks also go to 
Uganda Wildlife Authority for allowing us carry 
out the study, and their staff for providing us 
with useful information.  

Declarations of interest: None 

Authors’ contributions 

J.K. initiated and shared the research idea, 
drafted the concept, coordinated the data 
collection and analysis and wrote the first draft 

19



East Afr. J. Biophys. Comput. Sci., Vol. 1, No. 1 
 

 

of the manuscript. N.M.M and C.K.T assessed 
the draft concept of the manuscript, made 
conceptual guidance to collect right data and 
made critical intellectual adjustments on the first 
manuscript to make it a clear scientifically and 
logically drafted manuscript. All authors 
assessed and approved the final copy of the 
manuscript for submission. 

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	Cover V1.pdf (p.1)
	Slide Number 1

	blank page.pdf (p.2)
	Table of contents Vol1.pdf (p.3-5)
	Guideline to Authors_ EAJBCs.pdf (p.6-14)
	Back cover.pdf (p.15)
	Editorial Board members
	Table of contents
	Slide Number 4
	Slide Number 5
	Slide Number 6
	Volume 1.pdf
	1_Biodiversity conservation
	Joseph Katswera, Norah M. Mutekanga and Charles K. Twesigye*
	Fig 1: Map showing Kibale and Queen Elizabeth Conservation Areas and location of study sites.
	The study documented the threats to biodiversity in the case study national parks and wildlife reserves. Both primary and secondary threats were identified (Table 2), and their threat reduction percentages and indices calculated (Table 3). The managem...
	Demographic characteristics of the respondents
	Threats to biodiversity conservation
	Threat Reduction Assessment Index and protected areas
	Threat Reduction
	Threat Reduction Assessment Index
	Effectiveness of PA management in managing threats
	Fig. 2: Relationship between staffing and threat reduction assessment index
	Threat Reduction Assessment Index and PAs
	Threat reduction and mammal population in Kibale Conservation Area
	Threat reduction and mammal population in Queen Elizabeth Conservation Area
	Threat reduction and ecological integrity rating
	The data indicators were analysed and various scores were assigned basing on the computed TRA Index in Table 4. Each indicator of ecological integrity was assigned a color score: dark green (TRA index 81-100%) for “acceptable” ecological integrity (ve...
	Table- 5: Ecological Integrity Score Card using Data Indicators


	2_Prevalence of trypanosomiasis
	5_Climate change_Biju
	7_Public health implications
	Public health implications of bovine Cysticercosis from cattle slaughtered at Dilla municipal abattoir, Southern Ethiopia
	Fikadu Tesfaye, Jemere Bekele, Mesele Abera and Nebyou Moje*
	Hawassa University Faculty of Veterinary Medicine, P.O. Box- 05, Hawassa, Ethiopia
	Bovine cysticercosis is a cystic stage of Taenia saginata, zoonotic parasite with its significant impact on human health. The cystic stage usually affects the muscle of cattle where humans are susceptible from the contaminated raw meat (Taylor et al.,...
	Generally cysticercosis in animals is expected to have insignificant clinical effect. Nevertheless, it is economically important as it causes carcass condemnation arising from heavy infestation with the cysticerci of T. saginata. Additionally, there i...

	9_Fish Aeromonas bacteria

	cover 1-1.pdf
	Slide Number 1
	Editorial Board members

	back cover 1.pdf
	Slide Number 1

	INTRODUCTION
	EXTENSION OF THE MODIFIED MODEL INTO AN OPTIMAL CONTROL
	Optimal protection and hospitalization using modified model
	Existence of an optimal control
	The Hamiltonian and optimality system

	Numerical simulations of optimal control problem
	Optimal control comparisons and strategies

	CONCLUSION
	INTRODUCTION
	INTRODUCTION
	INTRODUCTION
	EXTENSION OF THE MODIFIED MODEL INTO AN OPTIMAL CONTROL
	Optimal protection and hospitalization using modified model
	Existence of an optimal control
	The Hamiltonian and optimality system

	Numerical simulations of optimal control problem
	Optimal control comparisons and strategies

	CONCLUSION
	INTRODUCTION
	INTRODUCTION
	INTRODUCTION
	EXTENSION OF THE MODIFIED MODEL INTO AN OPTIMAL CONTROL
	Optimal protection and hospitalization using modified model
	Existence of an optimal control
	The Hamiltonian and optimality system

	Numerical simulations of optimal control problem
	Optimal control comparisons and strategies

	CONCLUSION
	INTRODUCTION
	INTRODUCTION

