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2023年美赛特等奖论文-B-2300136-解密.pdf
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大学生,数学建模,美国大学生数学建模竞赛,MCM/ICM,2023年美赛特等奖O奖论文
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Team # 2300136 Page 1 of 25
Problem Chosen
B
2023
MCM/ICM
Summary Sheet
Team Control Number
2300136
From Poachers to Conservationists: Community-Based Wildlife
Preservation in Maasai Mara
As the emerald in Kenya’s crown, the Maasai Mara is a haven for wildlife. We develop a community-
based approach to wildlife preservation in light of the need to better protect nature and achieve sustainable
development. By considering human interests as well as preservation goals, we determine the optimal policy
for each land type in the Mara and its corresponding long-term outcomes.
We construct a net-work based evaluation model to depict the conditions in the Maasai Mara preserve.
The model is composed of four sections, including wildlife protection, natural resources conservation, local
financial interests and animal tourism interactions. The sections are interdependent and linked through various
variables. We measure the wellbeing of each section using a score ranging from zero to one after normalization,
higher scores indicate better performances. To determine the weight of the four sections in different land types,
we employ the Analytical Hierarchy Process (AHP) to calculate specific parameters for each section. The
weighed scores are summed up to produce a final score as our objective. This final score takes into account all
sections in their determined weights to measures the wellbeing of the Maasai Mara preserve.
We divide the Maasai Mara preserve into three different land types: a core wildlife preservation zone, a
human settlement zone and a livestock grazing zone. Three policies are proposed for each land type, according
to their principal functions. In order to analyze the counteractions between different policies, we combine the
three policies in a given land type to produce twenty-four policy scenarios. The scenario with the highest final
score is chosen to be our optimal policy.
Based on previous data and researches, we use linear and exponential regression analysis to determine
future trends in our input variables. These trends can have an effect on our long-term outcome. The future
outcomes of policies are predicted over a timespan of twenty years. To minimize error through errant
parameters, we apply random residuals in our model to capture unobserved heterogeneity. Stochastic elements
that follow a normal distribution are randomly generated, increasing the precision and authenticity of our
future predictions.
Our results show that the optimal policy in the wildlife preservation zone is the combination of enforcing
hunting quotas, increasing grazing fines and restricting tourism. Focusing on the human settlement zone, the
best policy for most years is providing conservation job opportunities combined with establishing bridging
organizations. In the livestock grazing zone, the scenario that produces the highest score is the combination of
the three policies or the combination of restricting livestock grazing and controlling invasive species. These
policies can greatly enhance the welfare of the preserve, generating long-term benefits for wildlife and people
alike.
The results from our sensitivity analysis show that the model is stable and robust. We set varying
parameters to change the values of our input variables and assess the impact of these changes on our final
score. We also tested the universality of the model by putting forward a method to adjust our parameters in
order to befit the alternative preservation area. As a result, we validate our model’s conformance with reality,
determining that it can be of versatile use with certainty.
By incorporating known numerical values about wildlife conservation into net-work based modelling, we
shed light on the key factors that influence wildlife protection and propose optimal policies to balance the
interests of human and wildlife for sustainable development.
Key words: Wildlife conservation; Community-based management; Net-work based modelling.
Team # 2300136 Page 2 of 25
Report to the Kenyan Tourism and Wildlife Committee
Dear Committee representatives,
We, MCM team #2300136, were tasked with helping the Kenyan Tourism and Wildlife Committee
(KTWC) to develop better wildlife conservation policies in the Maasai Mara Preservation. By considering the
interests of local residents as well as wildlife protection, we built an evaluation model to offer suggestions on
the preserve’s management policies and propose the optimal strategy for sustainable development.
We take into consideration wildlife protection, natural resources conservation, local financial interests and
the negative interactions between wildlife and tourists. The prior two goals are set to preserve the grassland
ecosystem while the latter two examine human interests. We balance these objectives carefully according to
the land use type in the Mara, which we divide into a core wildlife preservation zone, a human settlement zone
and a livestock grazing zone. We propose three different policies for each land type, and test the interactions
between policies by combining them into eight scenarios. Nine policies are suggested in total, including
diverse perspectives such as hunting quotas, grazing fines, the adoption of bridging organizations and invasive
species control. We collect data from the Kenya Wildlife Service, the Kenya National Bureau of Statistics, the
Kenya State of Wildlife Conservancies, the official website of the Maasai Mara National Park and various
researches done on the area. Using data from the past, we simulate the effects of different policy scenarios
from 2002 to 2041, providing predictions about the long-term outcomes of policies on a twenty-year basis
from 2022-2041.
According to our results, in the core wildlife preservation zone, the best policy is to enforce hunting
quotas, increase grazing fines for residents and restrict tourism simultaneously. If there are budgeting
limits and the three policies cannot be applied at once, we suggest the duo-combination of establishing hunting
quotas while restricting tourism for short-term benefits and enforcing hunting quotas while increasing grazing
fines for long-term’s sake. If one policy were to be selected, we would recommend increasing grazing fines
because it produces the best outcomes in 2041. We expect that the establishment of hunting quotas can reduce
poaching and other forms of trading illegal animal products by 50%, causing wildlife numbers to increase 2%
annually. Grazing fines are a means to prevent local residents from grazing livestock in wildlife preservation
zones, which can increase grassland pasture resources for wildlife by 5%. By restricting the number of tourists
in the Mara, a 30% decrease in tourists’ populations can result in a 0.5% annual increase in wildlife numbers
and a 5% surplus in the amount of water resources. Because the welfare of wildlife outweighs all other
considerations, policies that will facilitate thriving of wildlife are all highly recommended.
As for the human settlement zone, the best policy for most years is providing conservation job
opportunities. However, the outcomes of providing conservation job opportunities combined with
establishing bridging organizations, solely establishing bridging organizations and not implementing any
policies come close behind to the results of our suggested best policy. It can be indicated that more possible
policies should be explored in the future to generate more distinctive results. Yet, the plan of constructing a
wildlife corridor through human settlement areas will backfire and reduce regional welfare. This is a very
noteworthy result as it excludes the probability of a seemingly possible policy. We believe that although the
construction of a wildlife corridor can increase wildlife numbers by 0.5% annually, it will reduce livestock
populations as a result of competition for grasslands. Additionally, this loss of opportunities will cause locals
to violate regulation rules more often to stive for their economic compensation. It can be expected that negative
Team # 2300136 Page 3 of 25
interactions such as poaching and grazing within the prohibited areas will increase significantly. Therefore,
we conclude that the policy of setting up a wildlife corridor within human settlement areas will actually
threaten wildlife development when considering all aspects.
In the livestock grazing zone, the optimal policy is to compensate livestock loss, restrict livestock
grazing and control invasive species at once. The scenario combining livestock grazing restrictions and
invasive species control follows the trio-combination hard on its heels. All policies and combination of policies
produce better results than our base-line scenario with no policies. This proves the effectiveness of our policy
selection. Of the three discussed policies, restricting livestock grazing is the most advantageous because it can
preserve grassland pastures and water resources in the long run by 10% and 8%, respectively. Invasive species
control has similar yet milder applications as it increases grassland pasture resources and water resources by
0.5% annually. In addition, we also expect a 0.7% increase in biodiversity as aboriginal species can obtain
more resources. The third policy we discuss is the compensation for livestock loss. Locals report that they lose
an average of USD$310 due to livestock killed by predators. Increased livestock predation can create increased
conflict between residents and the conservancies, and also between people and wildlife. However, our model
determines that compensations have a minimal effect on the preserve’s welfare because the amount of loss is
relatively insignificant on a regional level. Despite these results, we contend that the benefits of compensating
livestock losses go beyond measurable numbers and that it is vital in building the trust between locals and the
conservancies. All in all, we recommend all of the three policies to ensure the sustainable development of the
region.
Our model is built upon the principal foundation that wildlife preservation should include the harmonious
balance of human and nature. Most of the measures we adopted consider the interests of locals and tourists as
well as natural conservations in a community-based approach. Policies such as increasing grazing fines,
establishing bridging organizations and compensating for livestock loss highlight the complexities in
considering locals into wildlife conservation. We believe that wildlife preservation will require the combined
efforts of government authority and local people. In order to achieve this goal, conservancies should give
locals an economic and social incentive to become conservationists instead of poachers. However, presently,
institutional shortcomings constrain the capacity of the conservancies to deliver satisfactory services to a
varied cross-section of the locals. Therefore, it is crucial to take into consideration our proposed policies as
they aim to mitigate this gap in co-management preservation.
We sincerely hope that our policy recommendations can be of use in the development of an integrate and
effective policy system that will benefit the Maasai Mara and achieve a sustainable future.
MCM #2300136
2023.2.20
Team # 2300136 Page 4 of 25
Content
I. Introduction ..................................................................................................................................................... 5
1.1 Background .......................................................................................................................................... 5
1.2 Literary review.......................................................................................................................................... 5
1.3 Restatement of the problem ...................................................................................................................... 5
II. Foundations of the Model .............................................................................................................................. 6
2.1 Assumptions and justifications ................................................................................................................. 6
2.2 Nomenclature ............................................................................................................................................ 7
III. Model Design ............................................................................................................................................... 8
3.1 The net-work based model........................................................................................................................ 8
3.1.1 Wildlife protection section ................................................................................................................. 8
3.1.2 Natural resources conservation section ........................................................................................... 10
3.1.3 Local financial interest section ........................................................................................................ 12
3.1.4 Animal tourism interactions section ................................................................................................ 14
3.1.5 Constructing the final objective ....................................................................................................... 15
3.2 Policy scenarios ...................................................................................................................................... 16
IV. Results and Discussion ............................................................................................................................... 18
4.1. Core wildlife preservation zone ............................................................................................................ 18
4.2 Human settlement zone........................................................................................................................... 19
4.3 Livestock grazing zone ........................................................................................................................... 20
V. Sensitivity Analysis ..................................................................................................................................... 21
VI. Testing the Model ...................................................................................................................................... 22
VII. Strengths and Weaknesses ........................................................................................................................ 23
7.1 Strengths ................................................................................................................................................. 23
7.2 Weaknesses ............................................................................................................................................. 23
VIII. Conclusions ............................................................................................................................................. 24
IX. References .................................................................................................................................................. 25
Team # 2300136 Page 5 of 25
I. Introduction
1.1 Background
With rolling hills and sprawling savannahs, the Maasai Mara National Reserve in Kenya is a haven for
African wildlife. The Maasai Mara is home to 95 species of mammals, reptiles and amphibians; as well as
over 400 species of birds [1]. Historically, Kenya had spent great efforts in wildlife protection and the
conservation of other resources. However, since the 1970s, it was found that establishing islands of isolated
protection areas were inadequate for maintaining spatially heterogeneous biodiversity. Therefore, the
involvement of local communities in co-management were incorporated into the protection of natural
resources [2]. In 2013, Kenya’s parliament established the Wildlife Conservation and Management Act,
stressing the need for more equitable share of resources and community-based management efforts [3].
Since the passing of this legislation, numerous efforts including policy amendments and stakeholder’s
regulations have been made to better preserve wildlife. Yet, the concept of community-based conservation
was established and put in use fairly recently, the impact of specific policies on wildlife preservation within
and outside the boundaries of the park remain inadequately discussed. Therefore, to allow more efficient
preservation, we should take into account the interactions between humans and animals as well as the
economic impacts of certain policies.
1.2 Literary review
Previous research demonstrated that the interests of local residents are of great importance in wildlife
conservation. The costs and benefits of local residents near protected areas had been estimated in a number of
studies conducted in Kenya, Uganda and Nepal [2,4,5]. These studies focused on local profits, suggesting that
locals can be given the economic incentive to participate in wildlife preservation. When taking a co-
management perspective, Ward et al. [6] studied the livelihood impacts of establishing a co-management
system in Madagascar. Brehony et al. [7] identified seven barriers from successfully implementing wildlife
conservation policies in Kenya. The efficacy of government agencies and bridging organizations were also
discussed in detail [8,9]. However, the aforementioned studies were conducted under a very general approach
without implementing any specific policies. Therefore, their results were mostly based upon surveys of local
residents instead of results of evaluation modelling. Here, we provide a solution to this problem by constructing
a net-work based evaluation model to assess and compare the outcomes of different management strategies.
1.3 Restatement of the problem
We are tasked with providing optimal policies and management strategies in wildlife preservation zones
for long-term trends. Our objective is to balance wildlife protection, the sustainable development of
natural resources, interests of local residents and the negative interactions between animals and tourists.
There are at least two definitions of what the “interests of the people who live in the area” should be:
(1) financial profit, which maximizes household income; or (2) social profit, which focus on social equity,
employment and education [2]. Here, we focus exclusively on the first notion because it is easy to quantify
into a mathematical model. Similarly, the “negative interactions between animals and the people attracted to
the preserve” have multiple meanings. We define “the people attracted to the preserve” as tourists, since
tourism accounts for most of the Mara’s non-native population.
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