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David M. Post

Researcher at Yale University

Publications -  105
Citations -  19779

David M. Post is an academic researcher from Yale University. The author has contributed to research in topics: Population & Alewife. The author has an hindex of 48, co-authored 102 publications receiving 17444 citations. Previous affiliations of David M. Post include Cornell University & University of Connecticut.

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Using stable isotopes to estimate trophic position: models, methods, and assumptions

TL;DR: In this article, the authors developed and discussed methods for generating an isotopic baseline and evaluate the assump- tions required to estimate the trophic position of consumers using stable isotopes in multiple ecosystem studies.
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Getting to the fat of the matter: models, methods and assumptions for dealing with lipids in stable isotope analyses

TL;DR: The results indicate that lipid extraction or normalization is most important when lipid content is variable among consumers of interest or between consumers and end members, and when differences in δ13C between end members is <10–12‰.
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Can stable isotope ratios provide for community-wide measures of trophic structure?

TL;DR: Building from extensive applications of stable isotope ratios by ecologists, the community-wide metrics may provide a new perspective on food web structure, function, and dynamics.
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Detritus, trophic dynamics and biodiversity

TL;DR: In this article, the authors developed an integrative framework for understanding the impact of detritus on food web dynamics, emphasizing the ontogeny and heterogeneity of detribus and the various ways that explicit inclusion of the detrital dynamics alters generalizations about the structure and functioning of food webs.
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Applying stable isotopes to examine food‐web structure: an overview of analytical tools

TL;DR: A comprehensive review of stable isotope analysis techniques, and a set of suggestions that transcend individual analytical approaches, are provided to help identify the most useful approaches to apply to a given data set.