Predicting the stability, equilibrium response, and nonequilibrium dynamics of ecological systems Public Deposited

http://ir.library.oregonstate.edu/concern/graduate_thesis_or_dissertations/qf85nf25z

Descriptions

Attribute NameValues
Creator
Abstract or Summary
  • In this dissertation, new theory and its applications are developed to predict three properties of complex ecological communities: stability, equilibrium response, and non-equilibrium dynamics. First, a graph-theoretic analysis identifies the interconnections in a complex ecosystem that promote or diminish stability (Chapter 2). The hierarchy of interactions that influences stability and feedback processes can guide resource allocation for environmental monitoring, investigate alternative management strategies, and help formulate novel research hypotheses. Second, a combined graph-theoretic and probabilistic approach evaluates the potential for long-term changes in equilibrium (Chapter 3). Conditional probabilities of long-term increase and decrease in variables are transferred from the graph-theoretic models into a Bayesian network. The Bayesian network allows researchers both to predict how an ecosystem might change given a perturbation and to diagnose which model structure best matches empirical observations. Third, a threshold index predicts whether or not largemagnitude short-term transitory changes in disease prevalence can occur (Chapter 4). The concept of reactivity is used to derive a threshold index for epidemicity, E0, which gives the maximum number of new infections produced by an infective individual at a disease free equilibrium. This index provides a threshold that determines whether or not major epidemics are possible. The relative importance of parameters differs between control strategies that seek to reduce endemicity and those that seek to reduce epidemicity. The index E0 therefore is an important measure of epidemic potential that may assist efforts to control epidemics. Together these approaches provide new theory that help bridge the gap between our need to understand complex ecological systems and the empirical data available for their characterization.
Resource Type
Date Available
Date Copyright
Date Issued
Degree Level
Degree Name
Degree Field
Degree Grantor
Commencement Year
Advisor
Committee Member
Academic Affiliation
Non-Academic Affiliation
Keyword
Subject
Rights Statement
Language
Replaces
Additional Information
  • description.provenance : Approved for entry into archive by Linda Kathman(linda.kathman@oregonstate.edu) on 2008-08-05T14:15:01Z (GMT) No. of bitstreams: 1 Hosack dissertation July.pdf: 937948 bytes, checksum: 97bcf1b82fafdf4edb4ea9596d73ea79 (MD5)
  • description.provenance : Approved for entry into archive by Julie Kurtz(julie.kurtz@oregonstate.edu) on 2008-08-01T15:32:55Z (GMT) No. of bitstreams: 1 Hosack dissertation July.pdf: 937948 bytes, checksum: 97bcf1b82fafdf4edb4ea9596d73ea79 (MD5)
  • description.provenance : Made available in DSpace on 2008-08-05T14:15:02Z (GMT). No. of bitstreams: 1 Hosack dissertation July.pdf: 937948 bytes, checksum: 97bcf1b82fafdf4edb4ea9596d73ea79 (MD5)
  • description.provenance : Submitted by Geoff Hosack (hosackg@onid.orst.edu) on 2008-07-21T22:03:20Z No. of bitstreams: 1 Hosack dissertation July.pdf: 937948 bytes, checksum: 97bcf1b82fafdf4edb4ea9596d73ea79 (MD5)

Relationships

In Administrative Set:
Last modified: 08/14/2017

Downloadable Content

Download PDF
Citations:

EndNote | Zotero | Mendeley

Items