Population dynamics is the dominant branch of mathematical biology that deals with forces affecting changes in population densities or affecting the form of population growth. It is clear that predator population depends on their prey species for survival and affects the survival and fecundity rate of prey species. Therefore, predator population is affected by changes in prey population in a complex and cyclic predator-prey relationship [1] .

Many biological species have been driven to extinction and many others are at the verge of extinction due to several external forces such as over exploitation, predation, environmental pollution and mismanagement of the habitat [2] . Thus problems affecting wild animals and their habitats should be evaluated to ensure sustainable conservation of wildlife populations [3] .

Environmental pollution, catastrophes and mismanagement of the habitat may cause reduction of species population and probably lead to extinction due to perturbation of the system. Hazards such as fire and drought can also cause the decline of species in an ecosystem [4] . For example, the dry season drought of 1993 in the Serengeti ecosystem is a strong and well studied pertubation. Rainfall during the dry season was only 25% of the lowest previously recorded level, and directly led to the death of about 30% of the wildebeest and perhaps 40% to 50% of the total park population of the large mammals [5] . However, according to Holling, Serengeti is highly resilient system as it has historically absorbed a wide range of pertubations. Despite being resilient, the time interval for the system to return to its equilibrium seems to be long hence the need of control arises. For example, it took seven years for the wildebeest population to recover from 1993 severe drought and about six years for the lion population from 1994 Canine distemper virus [5] .

Kideghesho [6] , mentions poaching of wildelife to be among the main threats facing the Serengeti ecosystem. Poaching has become a threat to many migratory populations, particularly as human populations around protected areas increase [7] [8] . It has been reported that local consumption of bushmeat from the Serengeti National Park and surrounding areas is responsible for approximately 70,000 - 129,000 wildebeest deaths per year [9] and any further increase in the amount of poaching could lead to decline in the wildebeest population in the Serengeti-Mara ecosystem [10] . However, lion killing not only in the Serengeti ecosystem but for the entire East Africa is mainly due to Maasai retaliation as lions prey their livestock [11] . In his 19 months study, Kissui [12] , recorded 85 lions killings in 12 villages by Maasai retaliation. This happens because many parts of Maasai land have been preserved as wildelife protected areas (e.g. Serengeti, Tarangire and Amboseli) and Game reserves (e.g. Mkomazi and Loliondo) but none of these protected areas are fenced and lions are reported to frequently kill Maasai cattle in adjacent rangelands [13] .

Few studies on optimal control of Prey-Predator system such as those by Chakraborty et al., [1] , Kar and Ghosh [14] have determined various optimal control strategies. In particular, Kar and Ghosh [14] controlled alternative food to predator to an exploited prey-predator system. However, none of these studies have considered the aspect of drought, poaching and retaliatory killing as threats to be controlled for survival of prey-predator system particularly wildebeest and lions in the Serengeti ecosystem. Therefore, this study intends to apply optimal control theory to maximize Wildebeest-Lion prey-predator population in the Serengeti Ecosytem under threat of drought, poaching and retaliatory killings and ensuring the cost of applying these controls is minimum.

This paper is organized as follows: In Section 2, a threatened prey-predator model is developed. In Section 3, time-dependent controls are introduced and analysis of optimal control is carried out. In Section 4, scenarios for different control strategies are considered and the results are discussed. Lastly, Section 5 presents conclusions about the proposed control strategies suggested for this threatened system. The outcome tends to display a significant increase in the number of individuals due to control strategies employed.

Consider two populations of different species: a prey population, and, a predator population. The prey species is wildebeest and predator species is lion. Drought affect both species, while wildebeest is threatened by poaching lion is threatened by retaliatory killing. Prey species are assumed to grow logistically to the carrying capacity in the absence of the predator, poaching and drought, hence these are only causes of prey mortality. The rate of increase of the predator population depends on the amount of prey biomass it converts as food. Thus according to Hollying type II functional response [15] the two populations are modeled as follows:

where x is the prey density at time t, y is the predator density at time t, r is the intrinsic prey growth rate, k is the prey carrying capacity, w is the maximum per capita predation rate, is the predator biomass to the prey (conversion rate), p is the wildebeest poaching rate in Serengeti, c is lion death rate due to retaliatory killing, f is the percentage of wildebeest resilient to drought, hence is the wildebeest death rate due to drought, e is the percentage of lion resilient to drought, hence is the lion death rate due to drought, a is the predator half saturation and is the lion mortality rate.

We introduce into model (1), time dependent control efforts on anti-poaching patrols, construction of strong bomas, and construction of dams as controls to curtail the threats to the prey-predator system. The prey-predator model (1) thus becomes:

In this section we study numerically an optimal control of a threatened prey-predator system.

Let us take the system parameters [5] , [5] , [11] , [16] , [17] , [17] , [18] , [18] , [assumed], [calculated], together with the initial guess states and. Assume the weights for prey and predator at final time are being kept fixed as and. However the weights of controls, and are respectively assumed to be, and. The weights of state variables are usually assigned depending on their relative importance while those of controls are assigned relative to their cost implications.

Next, we investigate the effect of the following optimal control strategies on the threatened prey-predator population.

・ Strategy A: Application of anti-poaching patrols for controlling poaching.

・ Strategy B: Construction of strong bomas for control of retaliatory killings.

・ Strategy C: Construction of dams for drought control.

・ Strategy D: Combination of application of anti-poaching patrols and construction of strong bomas.

・ Strategy E: Combination of application of anti-poaching patrols and construction of dams.

・ Strategy F: Combination of construction of strong bomas and dams and.

・ Strategy G: Combination of application of anti-poaching patrols, construction of strong bomas and dams.

In this paper, we presented a threatened prey-predator model using a deterministic system of differential equations. The threats are poaching, retaliatory killings and drought. Controls are introduced to the system which are anti-poaching patrols, construction of strong bomas and dams for controlling poaching, retaliatory killings and drought, respectively. In investigating the effect of optimal control, we use one control at a time, a combination of two controls at a time while setting other(s) to zero to compare the effects of the control strategies on the eradication of threats to prey-predator system. Additionally, the case of all controls was also taken into consideration. Our numerical results suggest that the use of all three controls, anti-poaching patrols, construction of strong bomas and dams has highest impact on the control of the system threats. From these findings, we see that

when we have a system threatened by various threats, dealing with individual threat to the system does not often yield best results. Instead, combined approach of tackling all threats to yield better result is a good approach in the management of the wildlife populations. There is thus a booster effect when tackling many population decimating factors at a go than dealing with them individually. In case of single control strategy implementation, the priority would be construction of dams as it saves more individuals compared to others.