| dc.description.abstract | Population modeling is an area of interest in mathematics, statistics, population studies and ecological management among others disciplines. Population of wild animals especially those classifier as endangered species require to be managed in scientifically verifiable way. In this study emphasis has been put on the application of mathematical and statistical models to the African Elephant (Loxodonta
Africana) population dynamics. Data from published sources, independent conservation and research
organizations, and bodies mandated to conserve the elephant in different countries is used in our study
To be able to conserve African elephant population sustainably and make management decisions in , verifiable way, there is need for better understanding of; i) the current and expected future population trend, ii) the influence or each population vital rates to population dynamics, iii) the role of population demographic structure in population dynamics and management strategies, iv) how to incorporate processes uncertainty, observation error and model uncertainty in population models and forecasts, and
v) how scientific methods can be used to monitor population trends, study population regulation, and determine best management strategies. In the effort to study the elephant population it was noted that the biological, demographic, social processes and ecological characteristics governing entire elephant population process are not understood with certainty.
Integrated autoregressive moving average (AR1MA) models, log-linear, bootstrapping, structure population models were used in modeling the elephant population of different ecosystems and fOI different selected scenarios. The mathematical basis and analytical approach of these methods are given
The derived models were used to project, forecast future population and analyze age specific mortal it) required for zero population growth. A Bayesian state space modeling framework for unstructured and structured population was discussed. Most simulations and graphics have been done using Mi\TL/\I'3(j{ or R, a statistical environment free ware. ARIMA model were fitted the Amboscli National Park (i\NP total population abundance and the populations' growth rates time series. The log-linear fits indicate that the AENP population has a faster growth than the ANP elephant population. Population predictions and projection acquired from the Jog-linear models, time series models, and bootstrapping were compared
The populations considered showed increasing total population abundance.
Important properties and analysis techniques of a transition matrix in age structured population models were reviewed. Sensitivity analysis and elasticity analysis showed that population trend and growth rate was more sensitive to calving interval than age sexual maturity or age at reproductive menopause. The survival of animals less than 24 years and the fecundity of animals between ages of I ( to 30 years were found to be the most important ~o the elephant's population dynamics. The 1110S
sensitive transitions were for those classes with age less than 24 years. Different age dependent mortality scheme are necessary to course zero percent growth rate depending on the frequency of occurrence
These age dependent mortality schemes are higher for population with low average calving interval Populations with average caving interval above 5.5 year tend to stagnate or decline depending on the mortality levels. For population recovery purposes, it is recommended that management strategies the increase survival rates of classes with ages less than 24 years and fecundity of classes between 10 and 3( years would be the most effective. Removal or induced mortality of classes with age less than 30 year:
produces zero percent growth Cor lower percentages and shorter time periods. | en |
