Dead or alive: carbon as a currency to integrate disease and ecosystem ecology theory

Author:

Seabloom Eric W.1ORCID,Peace Angela2ORCID,Asik Lale3ORCID,Everett Rebecca A.4ORCID,Frenken Thijs56ORCID,González Angélica L.7ORCID,Strauss Alexander T.18910ORCID,Van de Waal Dedmer B.5ORCID,White Lauren A.11ORCID,Borer Elizabeth T.1ORCID

Affiliation:

1. Dept of Ecology, Evolution, and Behavior, Univ. of Minnesota St. Paul MN USA

2. Dept of Mathematics and Statistics, Texas Tech Univ. Lubbock TX USA

3. Dept of Mathematics and Statistics, Univ. of the Incarnate Word TX USA

4. Dept of Mathematics and Statistics, Haverford College Haverford PA USA

5. Dept of Aquatic Ecology, Netherlands Inst. of Ecology Wageningen the Netherlands

6. Great Lakes Inst. for Environmental Research, Univ. of Windsor Windsor ON Canada

7. Dept of Biology and Center for Computational and Integrative Biology, Rutgers Univ. Camden NJ USA

8. Odum School of Ecology, Univ. of Georgia Athens GA USA

9. Center for the Ecology of Infectious Diseases, University of Georgia Athens GA USA

10. Natl Socio‐Environmental Synthesis Center, Univ. of Maryland Annapolis MD USA

11. River Basin Center, University of Georgia Athens GA USA

Abstract

Death is a common outcome of infection, but most disease models do not track hosts after death. Instead, these hosts disappear into a void. This assumption lacks critical realism, because dead hosts can alter host–pathogen dynamics. Here, we develop a theoretical framework of carbon‐based models combining disease and ecosystem perspectives to investigate the consequences of feedbacks between living and dead hosts on disease dynamics and carbon cycling. Because autotrophs (i.e. plants and phytoplankton) are critical regulators of carbon cycling, we developed general model structures and parameter combinations to broadly reflect disease of autotrophic hosts across ecosystems. Analytical model solutions highlight the importance of disease–ecosystem coupling. For example, decomposition rates of dead hosts mediate pathogen spread, and carbon flux between live and dead biomass pools are sensitive to pathogen effects on host growth and death rates. Variation in dynamics arising from biologically realistic parameter combinations largely fell along a single gradient from slow to fast carbon turnover rates, and models predicted higher disease impacts in fast turnover systems (e.g. lakes and oceans) than slow turnover systems (e.g. boreal forests). Our results demonstrate that a unified framework, including the effects of pathogens on carbon cycling, provides novel hypotheses and insights at the nexus of disease and ecosystem ecology.

Publisher

Wiley

Subject

Ecology, Evolution, Behavior and Systematics

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