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Title: Competition and Coexistence in an Unpredictable World
Authors: Washburne, Alex
Advisors: Levin, Simon A
Contributors: Quantitative Computational Biology Department
Keywords: Competition
Subjects: Ecology
Applied mathematics
Evolution & development
Issue Date: 2015
Publisher: Princeton, NJ : Princeton University
Abstract: All living things “struggle for existence” as they compete with other organisms over limiting resources. Understanding how the diversity and dynamics of living systems are shaped by competition can help us better understand evolutionary problems of altruism, conservation management of competing species, and even economic policy making to promote productive competition in free markets. This thesis examines competition and its effects on diversity and dynamics in four systems: the slime mold Dictyostelium discoideum, predator-prey systems such as wolves in Yellowstone, the human microbiome and the S&P 500. Diversity in slime molds may be maintained despite competition for space in the spore capsules if the natural habitat of slime molds is variable in space and time; resource availability might mediate quorum sensing, and such molecular switches and bet-hedging can be advantageous over competitors without such plasticity. Competition between prey can be mediated by predators, but the ability of predators to stabilize prey communities depends on the size of the community relative to the attack rate of the predator, implying that some predators need especially large reserves to exhibit their full ecological effects. Snapshots of the human microbiome and the S&P 500 might suggest that they could arise from neutral competition, but time-series analysis reveals that many seemingly neutral communities may exhibit non-neutral dynamics. Understanding patterns of diversity and dynamics of adaptive systems requires understanding competition and coexistence in an unpredictable world.
Alternate format: The Mudd Manuscript Library retains one bound copy of each dissertation. Search for these copies in the library's main catalog
Type of Material: Academic dissertations (Ph.D.)
Language: en
Appears in Collections:Quantitative Computational Biology

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