Individual-Based Modelling As a Tool for Sustainable Red Deer Management

Abstract

Being able to predict how a population of red deer evolve over time for different hunting strategies, can be helpful for wildlife management in their decision-making as means for achieving a sustainable red deer population. This is an area where mathematical models turn out to be quite applicable. The goal of this thesis is to implement an individual-based model for simulating results of different hunting strategies. A thorough presentation of the model setup will be provided before different cases of hunting strategies are simulated. The influence from each strategy on the red deer population will be analyzed. In addition to these case studies, a study of the impact from the different parameters in the model will be presented. There are many possibilities of further studies with an individual-based model as the one implemented in this thesis. Examples such as the method of Monte Carlo Markov Chains and parameter estimation will be discussed at the end of the thesis. According to our results, hunting and the choice of hunting strategy turn out to be crucial for how the population of red deer evolve. Choosing the best fitted hunting strategy for every red deer habitat, could be vital for the quality of the red deer's lives. The simulations in this thesis indicate a higher probability of dying from environmental causes, such as starvation, illness or stress, if the hunting strategy fails or hunting is disregarded completely. Since we humans have such a large say in the matter of wildlife management, we need do what we can for preserving the nature and its wildlife. Hopefully, this thesis can be a positive addition to wildlife management and further research.