Conservation of Forest Biomass and Forest--Dependent Wildlife Population: Uncertainty Quantification of the Model Parameters

33 Pages Posted: 10 Jan 2023

See all articles by Ibrahim Fanuel

Ibrahim Fanuel

Nelson Mandela African Institution of Science and Technology Arusha-Tanzania

Silas Mirau

The Nelson Mandela African Institution of Science and Technology

Damian Kajunguri

Kabale University

Francis Moyo

affiliation not provided to SSRN

Abstract

The ecosystem is facing a number of challenges due to the increase in the human population and its associated activities. One of these challenges is the degradation of forest biomass, which leads to a decrease in forest area and threatens the survival of wildlife species by increasing intraspecific competition. In this paper, a non--linear mathematical model to study the conservation of forest biomass and forest--dependent wildlife population in the presence of the human population and its associated activities is developed and analysed. The study assessed the impacts of economic measures in the form of incentives on reducing population pressure on forest resources as well as the application of technological efforts to hasten the rate of reforestation. The qualitative and quantitative analyses were carried out, and the results show that both economic and technological factors are potential measures for resource conservation; however, these efforts can only be used to a limited extent, and contrary to that, the system will be destabilised.

Keywords: Uncertainty quantification, Conservation, Forest biomass, Forest-dependent wildlife population, Hypercube Latin Sampling

Suggested Citation

Fanuel, Ibrahim and Mirau, Silas and Kajunguri, Damian and Moyo, Francis, Conservation of Forest Biomass and Forest--Dependent Wildlife Population: Uncertainty Quantification of the Model Parameters. Available at SSRN: https://ssrn.com/abstract=4321352 or http://dx.doi.org/10.2139/ssrn.4321352

Ibrahim Fanuel (Contact Author)

Nelson Mandela African Institution of Science and Technology Arusha-Tanzania ( email )

447
Nelson Mandela Road 1
Arusha, 255
Tanzania

Silas Mirau

The Nelson Mandela African Institution of Science and Technology ( email )

Damian Kajunguri

Kabale University ( email )

Uganda

Francis Moyo

affiliation not provided to SSRN ( email )

No Address Available

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