rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    non sort: A
    title: Predator-prey System with Seasonal Reproduction
    sub title: Theoretical and Statistical Developments
  name:
    name part: Zhou, Can
    role:
      role term: creator
    role:
      role term: author.
  name:
    name part: Fujiwara, Masami
    name part: 1970-
    role:
      role term: thesis advisor.
  name:
    name part: Texas A & M University
    role:
      role term: degree granting institution.
  type of resource: text
  genre: bibliography
  origin info:
    place:
      place term: txu
    date issued: 2015
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: remote
    extent: 1 online resource.
  abstract: Predator-prey interaction (PPI) is important in both ecological theory and application. Various theoretical and empirical approaches have been proposed to develop mathematical and statistical tools for studying PPI. The aim of this dissertation is to expand our understanding along these two lines. Using mathematical models, I compare the dynamics of predator-prey models with one specific type of interaction, where both predator and prey exhibit seasonal reproduction and predation is continuous. I show that the use of a continuous-time predator-prey model with an instantaneous approximation of seasonal reproduction can produce unstable dynamics, whereas the use of a discrete-time model, which incorporates seasonal reproduction, always produces locally stable equilibria. Finally, with stage-structured predator-prey models, I demonstrate how demographic parameters affect asymptotic dynamics. I then explore statistical methods to infer the type of PPI from non-stationary time series. Traditional approaches assume that each population in a community is itself regulated, i.e., each time series is stationary. However, complex community structure and a lack of regulation in an individual population alone can result in inappropriate inferences based on traditional approaches. I introduce a statistical framework to analyze non-stationary time series that could be collectively regulated. I also demonstrate the method with time series of selected shrimp and fish populations in the Gulf of Mexico. Finally, I compare the performance of four methods to determine the type of PPI from short and noisy time series. The data-generating model includes nonlinear predation, compensatory density-dependence, and both process and observation errors. To represent our uncertainty about the true form of species interaction, none of the statistical models incorporate all these factors. I show that three methods that account for autocorrelation have better performance in terms of type-I error rat…
  note: by Can Zhou.
  note: "Major Subject: Wildlife and Fisheries Sciences"
  note: Includes vita.
  note: Doctor of Philosophy Texas A & M University 2015 http://hdl.handle.net/1969.1/155368
  note: Includes bibliographical references.
  note: Text (Dissertation)
  subject:
    topic: Major Wildlife and Fisheries Sciences
  subject:
    topic: Top down and bottom up
  subject:
    topic: Species interactions
  subject:
    topic: Co-integration
  subject:
    topic: Time series analysis
  subject:
    topic: Matrix population models
  identifier: http://hdl.handle.net/1969.1/155368
  record info:
    record content source: TXA
    record creation date: 160817
    record change date: 20170926141809.0
    record identifier: in00003746458
    language of cataloging:
      language term: eng