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Assessing Causes for Fitness Variability in Animal Populations Using Computer Emulations

Assessing Causes for Fitness Variability in Animal Populations Using Computer Emulations

Michael Baumann (ORCID: )
  • Grant DOI 10.55776/J1962
  • Funding program Erwin Schrödinger
  • Status ended
  • Start November 1, 2000
  • End December 31, 2000
  • Funding amount € 6,650
  • Project website

Disciplines

Biology (75%); Computer Sciences (25%)

Keywords

    ANIMAL SURVIVAL AND REPRODUCTION, BIOLOGICAL OPERATORS, COMPLEX ADAPTIVE SYSTEMS, COMPUTER EMULATION

Abstract

Erwin Schrödinger Fellowship J 1962 Causes for Variability in Animal Populations Michael BAUMANN 09.10.2000 Pacific salmon populations exhibit both large temporal and spatial variability in total cohort survival and reproduction rates. Total cohort survival rate is the product of the survival probabilities at various life history stages which are determined by interactions between individuals and their biotic and abiotic environment. These interactions will also determine the amount of internal resources available for reproduction. Assessing the magnitude, spatial and temporal variability, and causes of the survival probability and reproduction potential at a particular life history stage is an extremely difficult and data-intensive task. Consequently, lack and multiple interpretability of data has led to a plethora of untested as well as observationally untestable causal explanations for variability in animal fitness ranging from atmospheric forcings to anthropogenic influences. In Part 2 of my project towards predicting and understanding Pacific salmon survival, I will take a closer look at the various statistical and mechanistic models that have been proposed to explain variability in animal survival and reproduction, in general. I will assess the effects of various biological processes on animal fitness by incorporating genetical and phenotypic traits, as well as constant and variable biotic and abiotic environmental factors into evolutionary spatially-explicit individual-based emulations. My research in the last year has shown that biologically well-balanced rule-based models will be complex enough to plausibly represent nature but simple enough to perform completely controlled and reproducible computer experiments, and will thus advance understanding about mathematical models and of interacting levels of biological organization.

Research institution(s)
  • Universität Salzburg - 10%
  • University of British Columbia - 100%

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