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ABSTRACT Litter decomposition is an important ecosystem process and global carbon flux that has been shown to be controlled by climate, litter quality, and microbial communities. Process‐based ecosystem models are used to predict responses of litter decomposition to climate change. While these models represent climate and litter quality effects on litter decomposition, they have yet to integrate empirical microbial community data into their parameterizations for predicting litter decomposition. To fill this gap, our research used a comprehensive leaf litterbag decomposition experiment at 10 temperate forest U.S. National Ecological Observatory Network (NEON) sites to calibrate (7 sites) and validate (3 sites) the MIcrobial‐MIneral Carbon Stabilization (MIMICS) model. MIMICS was calibrated to empirical decomposition rates and to their empirical drivers, including the microbial community (represented as the copiotroph‐to‐oligotroph ratio). We calibrate to empirical drivers, rather than solely rates or pool sizes, to improve the underlying drivers of modeled leaf litter decomposition. We then validated the calibrated model and evaluated the effects of calibration under climate change using the SSP 3–7.0 climate change scenario. We find that incorporating empirical drivers of litter decomposition provides similar, and sometimes better (in terms of goodness‐of‐fit metrics), predictions of leaf litter decomposition but with different underlying ecological dynamics. For some sites, calibration also increased climate change‐induced leaf litter mass loss by up to 5%, with implications for carbon cycle‐climate feedbacks. Our work also provides an example for integrating data on the relative abundance of bacterial functional groups into an ecosystem model using a novel calibration method to bridge empiricism and process‐based modeling, answering a call for the use of empirical microbial community data in process‐based ecosystem models. We highlight that incorporating mechanistic information into models, as done in this study, is important for improving confidence in model projections of ecological processes like litter decomposition under climate change. 

Abstract Society increasingly demands accurate predictions of complex ecosystem processes under novel conditions to address environmental challenges. However, obtaining the process‐level knowledge required to do so does not necessarily align with the burgeoning use in ecology of correlative model selection criteria, such as Akaike information criterion. These criteria select models based on their ability to reproduce outcomes, not on their ability to accurately represent causal effects. Causal understanding does not require matching outcomes, but rather involves identifying model forms and parameter values that accurately describe processes. We contend that researchers can arrive at incorrect conclusions about cause‐and‐effect relationships by relying on information criteria. We illustrate via a specific example that inference extending beyond prediction into causality can be seriously misled by information‐theoretic evidence. Finally, we identify a solution space to bridge the gap between the correlative inference provided by model selection criteria and a process‐based understanding of ecological systems. 

Abstract To predict the behavior of the terrestrial carbon cycle, it is critical to understand the source, formation pathway, and chemical composition of soil organic matter (SOM). There is emerging consensus that slow‐cycling SOM generally consists of relatively low molecular weight organic carbon substrates that enter the mineral soil as dissolved organic matter and associate with mineral surfaces (referred to as “mineral‐associated OM,” or MAOM). However, much debate and contradictory evidence persist around: (a) whether the organic C substrates within the MAOM pool primarily originate from aboveground vs. belowground plant sources and (b) whether C substrates directly sorb to mineral surfaces or undergo microbial transformation prior to their incorporation into MAOM. Here, we attempt to reconcile disparate views on the formation of MAOM by proposing a spatially explicit set of processes that link plant C source with MAOM formation pathway. Specifically, because belowground vs. aboveground sources of plant C enter spatially distinct regions of the mineral soil, we propose that fine‐scale differences in microbial abundance should determine the probability of substrate–microbe vs. substrate–mineral interaction. Thus, formation of MAOM in areas of high microbial density (e.g., the rhizosphere and other microbial hotspots) should primarily occur through an in vivo microbial turnover pathway and favor C substrates that are first biosynthesized with high microbial carbon‐use efficiency prior to incorporation in the MAOM pool. In contrast, in areas of low microbial density (e.g., certain regions of the bulk soil), MAOM formation should primarily occur through the direct sorption of intact or partially oxidized plant compounds to uncolonized mineral surfaces, minimizing the importance of carbon‐use efficiency, and favoring C substrates with strong “sorptive affinity.” Through this framework, we thus describe how the primacy of biotic vs. abiotic controls on MAOM dynamics is not mutually exclusive, but rather spatially dictated. Such an understanding may be integral to more accurately modeling soil organic matter dynamics across different spatial scales. 

1. Some interactions previously described as mutualistic were revealed to be commensal or parasitic in subsequent investigations. Ant‐mediated seed dispersal has been described as a mutualism for more than a century; however, recent research suggests that it may be commensal or parasitic. Plants demonstrably benefit from ant‐mediated seed dispersal, although there is little evidence available to demonstrate that the interaction benefits long‐term ant fitness. 2. Field experiments were conducted in temperate North America focused on a key seed‐dispersing ant. All herbaceous plants were removed from a forest understorey for 13 years, and supplemented ant colonies with large elaiosome‐bearing seeds aiming to examine potential long‐ and short‐term myrmecochorous plant benefits for the ants. 3. If elaiosome‐bearing seeds benefit ants, suggesting a mutualistic relationship, it is expected that there would be greater worker and/or alate abundance and greater fat reserves (colony lipid content) with seed supplementation (short‐term) and in areas with high understorey herb abundance. 4. Short‐term seed supplementation of ant colonies did not result in an increase with respect to numbers or fat stores, although it did prompt the production of colony sexuals, which is a potential fitness benefit. In the long term, however, there was no positive effect on the ants and, instead, there were negative effects because the removal of elaiosome‐bearing plants corresponded with greater colony health. 5. The data obtained in the present study suggest that the ant–plant interaction ranged from occasionally beneficial to neutral to overall negative for the ant partner. Such results did not support considering the interaction as a mutualism. Collectively, the data suggest the need to reconsider the nature of the relationship between these ants and plants.