Dissertation/Thesis Abstract

Mechanical Inference in Dynamic Ecosystems
by Langendorf, R. E., Ph.D., University of Colorado at Boulder, 2018, 138; 10792156
Abstract (Summary)

Empirical studies of graphs have contributed enormously to our understanding of complex systems, growing into a more scientific exploration of communities spanning the physical, biological, and social called network science. As the quantity and types of networks have grown so has their heterogeneity in quality and specificity resulting in a wealth of datasets that are not matched by existing theoretical methods. This is especially true in ecology where the majority of interactions are indirect and unobservable even in well-studied systems. As a result ecologists continue to grapple with three fundamental questions: Most basically, (i) `How do ecosystems function?' I answered this question by comparing networks to each other such that poorly-studied systems can be understood through their similarity to well-understood ones and theoretical models. To do this I created the alignment algorithm netcom which recasts ecosystem processes as statistical dynamics of diffusion kernels originating from a network's constituent nodes. Using netcom I constructed a supervised classifier which can distinguish processes in both synthetic and empirical network data. While this kind of inference works on currently available network data, I have shown how causality can serve as a more effective and unifying currency of ecological interaction. Measures of causality are even able to identify complex interactions across organizational scales of communities, answering the longstanding question (ii) `Can community structure causally determine dynamics of constituent species?' Moreover, causal inference can be readily combined with existing modeling frameworks to quantify dynamic interactions at the same scale as the underlying data. In this way we can answer the question (iii) `Which species in an ecosystem cause which other species?' These tools are part of a paradigm shift in ecology that offers the potential to make more reliable management decisions for dynamic ecosystems in real time using only observational data.

Indexing (document details)
Advisor: Doak, Daniel F.
Commitee: Clauset, Aaron, Collinge, Sharon K., Estes, James A., Novak, Mark
School: University of Colorado at Boulder
Department: Environmental Studies
School Location: United States -- Colorado
Source: DAI-B 79/10(E), Dissertation Abstracts International
Subjects: Ecology, Environmental science, Computer science
Keywords: Alignment algorithm, Causality, Ecosystem, Modeling, Network, Observational data
Publication Number: 10792156
ISBN: 9780355965940
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