Spatio-temporal dynamics before population collapse

Thesis: Ph. D., Massachusetts Institute of Technology, Department of Physics, 2014.

Bibliographic Details
Main Author: Dai, Lei, Ph. D. Massachusetts Institute of Technology
Other Authors: Jeff Gore.
Format: Thesis
Language:eng
Published: Massachusetts Institute of Technology 2015
Subjects:
Online Access:http://hdl.handle.net/1721.1/95869
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author Dai, Lei, Ph. D. Massachusetts Institute of Technology
author2 Jeff Gore.
author_facet Jeff Gore.
Dai, Lei, Ph. D. Massachusetts Institute of Technology
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description Thesis: Ph. D., Massachusetts Institute of Technology, Department of Physics, 2014.
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spelling mit-1721.1/958692019-04-12T13:54:14Z Spatio-temporal dynamics before population collapse Dai, Lei, Ph. D. Massachusetts Institute of Technology Jeff Gore. Massachusetts Institute of Technology. Department of Physics. Massachusetts Institute of Technology. Department of Physics. Physics. Thesis: Ph. D., Massachusetts Institute of Technology, Department of Physics, 2014. Cataloged from PDF version of thesis. Includes bibliographical references. Theory predicts that the approach of catastrophic thresholds in natural systems may result in an increasingly slow recovery from small perturbations, a phenomenon called critical slowing down. In this thesis, we used replicate laboratory populations of the budding yeast Saccharomyces cerevisiae for direct observation of critical slowing down in spatio-temporal dynamics before population collapse. In the first project, we mapped the bifurcation diagram experimentally and found that the populations became more vulnerable to disturbance closer to the tipping point. Fluctuations of population density increased in size and timescale near the tipping point, in agreement with the theory. In the second project, we used spatially extended yeast populations to evaluate early warning signals based on spatio-temporal fluctuations. We found that indicators based on fluctuations increased before collapse of connected populations; however, the magnitude of increase was smaller than that observed in isolated populations, as local variation is reduced by dispersal. Furthermore, we propose a generic indicator based on deterministic spatial patterns, recovery length. In our experiments, recovery length increased substantially before population collapse, suggesting that the spatial scale of recovery can provide a warning signal before tipping points in spatially extended systems. In the third project, we characterized how different environmental drivers influence the dynamics of yeast populations. We compared the performance of early warning signals across multiple deteriorating environments. We found that the varying performance is determined by how a system responds to changes in a specific driver, which can be captured by a relation between stability and resilience. Furthermore, we demonstrated that the positive correlation between stability and resilience, as the essential assumption of indicators based on critical slowing down, can break down when multiple environmental drivers are changed simultaneously. by Lei Dai. Ph. D. 2015-03-05T15:57:53Z 2015-03-05T15:57:53Z 2014 2014 Thesis http://hdl.handle.net/1721.1/95869 904052890 eng M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission. http://dspace.mit.edu/handle/1721.1/7582 144 pages application/pdf Massachusetts Institute of Technology
spellingShingle Physics.
Dai, Lei, Ph. D. Massachusetts Institute of Technology
Spatio-temporal dynamics before population collapse
title Spatio-temporal dynamics before population collapse
title_full Spatio-temporal dynamics before population collapse
title_fullStr Spatio-temporal dynamics before population collapse
title_full_unstemmed Spatio-temporal dynamics before population collapse
title_short Spatio-temporal dynamics before population collapse
title_sort spatio temporal dynamics before population collapse
topic Physics.
url http://hdl.handle.net/1721.1/95869
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