Please use this identifier to cite or link to this item: http://hdl.handle.net/10266/6405
Title: Application of ecosystem models to preserve endangered species
Authors: Jain, Sanjoli
Supervisor: Roy, Parimita
Keywords: Reaction-diffusion model;Sensitivity Analysis;Stochastic differential equation;Extinction analysis, Conservation measure
Issue Date: 12-Nov-2022
Abstract: This thesis entitled "Application of ecosystem models to preserve endangered species" is a study carried out with the objective of understanding the importance of environmental fluctuation and the movement of species while predicting the dynamics of species in endanger. Recently, there has been a growing awareness of the importance of including a spatial aspect when developing realistic models of biological systems, with the consequent development of both approximate and rigorous mathematical methods of analysis. Besides, there have been studies of pattern formation in spatial epidemic models introduced by the pioneering work of A.M. Turing. It is well known that the spatial component of ecological interactions has been identified as an important factor in how ecological communities are functioning and shaped yet; understanding space's role is theoretically and empirically challenging. Spatial epidemiology with self-diffusion has become a principal scientific discipline aiming at understanding the causes and consequences of spatial heterogeneity in a prey-predator relationship. Moreover, significant environmental change can push a population near extinction, but in some cases, natural selection can intervene and save the population, allowing it to survive. Population densities of predators, prey, and competitors change significantly from season to season or year to year. Also, unusually rapid rates of infectious illnesses are spreading throughout wildlife populations. It is important to look into the assertion that infectious diseases frequently contribute to species extinction in light of the increased interest in both global species loss and newly developing infectious diseases. So, in this work, we tried and predict the dynamics for the species that are in danger of extinction due to disease pathogens. To accomplish the objective choosing a suitable model, fitting the model to the data, and using the fitted model to estimate the risk of extinction are the processes involved in conducting a population viability analysis.
URI: http://hdl.handle.net/10266/6405
Appears in Collections:Doctoral Theses@SOM

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