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Abstract Plant disease often increases with N, decreases with CO₂, and increases as biodiversity is lost (i.e., the dilution effect). Additionally, all these factors can indirectly alter disease by changing host biomass and hence density-dependent disease transmission. Yet over long periods of time as communities undergo compositional changes, these biomass-mediated pathways might fade, intensify, or even reverse in direction. Using a field experiment that has manipulated N, CO₂, and species richness for over 20 years, we compared severity of a specialist rust fungus (Puccinia andropogonis) on its grass host (Andropogon gerardii) shortly after the experiment began (1999) and twenty years later (2019). Between these two sampling periods, two decades apart, we found that disease severity consistently increased with N and decreased with CO₂. However, the relationship between diversity and disease reversed from a dilution effect in 1999 (more severe disease in monocultures) to an amplification effect in 2019 (more severe disease in mixtures). The best explanation for this reversal centered on host density (i.e., aboveground biomass), which was initially highest in monoculture, but became highest in mixtures two decades later. Thus, the diversity-disease pattern reversed, but disease consistently increased with host biomass. These results highlight the consistency of N and CO₂as drivers of plant disease in the Anthropocene and emphasize the critical role of host biomass—despite potentially variable effects of diversity—for relationships between biodiversity and disease. 

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. 

Abstract Next‐Generation Sequencing (NGS) is a powerful tool that has been rapidly adopted by many ecologists studying microbial communities. Despite the exciting demonstration of NGS technology as a tool for ecological research, cryptic pitfalls inherent to its use can obscure correct interpretation of NGS data. Here, we provide an accessible overview of a NGS process that uses marker gene amplicon sequences (MGAS) that will allow scientists, particularly community ecologists, to make appropriate methodological choices and understand limits on inference about community composition and diversity that can be drawn from MGAS data.We describe the MGAS pipeline, focusing specifically on cryptic sources of variation that have received less emphasis in the ecological literature, but which may substantially impact inference about microbial community diversity and composition. By simulating communities from published microbiome data, we demonstrate how these sources of variation can generate inaccurate or misleading patterns.We specifically highlight sample dilution without researcher awareness and lane‐to‐lane variability, two cryptic sources of variation arising during the MGAS pipeline. These sources of variation affect estimates of species presence and relative abundance, particularly for species with moderate to low abundances. Each of these sources of bias can lead to errors in the estimation of both absolute and relative abundance within, and turnover among, microbial communities.Awareness and understanding of what happens and, specifically, why it happens during MGAS generation is key to generating a strong dataset and building a robust community matrix. Requesting sample dilution information from the sequencing centre, including technical replicates across sequencing lanes, and understanding how sampling intensity and community taxa distribution patterns shape the measurement of community richness, evenness and diversity are critical for drawing correct ecological inferences using MGAS data.