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  1. Abstract

    Mixotrophs are ubiquitous and integral to microbial food webs, but their impacts on the dynamics and functioning of broader ecosystems are largely unresolved.

    Here, we show that mixotrophy produces a unique type of food web module that exhibits unusual ecological dynamics, with surprising consequences for carbon flux under warming. We develop a generalizable model of a mixotrophic food web module that incorporates dynamic switching between phototrophy and phagotrophy to assess ecological dynamics and total system CO2flux.

    We find that warming switches mixotrophic systems between alternative stable carbon states—including a phototrophy‐dominant carbon sink state, a phagotrophy‐dominant carbon source state and cycling between these two. Moreover, warming always shifts this mixotrophic system from a carbon sink state to a carbon source state, but a coordinated increase in nutrients can erase early warning signals of this transition and expand hysteresis.

    This suggests that mixotrophs can generate critical carbon tipping points under warming that will be more abrupt and less reversible when combined with increased nutrient levels, having widespread implications for ecosystem functioning in the face of rapid global change.

    Read the freePlain Language Summaryfor this article on the Journal blog.

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  2. Abstract

    Biotic specialization holds information about the assembly, evolution, and stability of biological communities. Partner availabilities can play an important role in enabling species interactions, where uneven partner availabilities can bias estimates of biotic specialization when using phylogenetic diversity indices. It is therefore important to account for partner availability when characterizing biotic specialization using phylogenies. We developed an index, phylogenetic structure of specialization (PSS), that avoids bias from uneven partner availabilities by uncoupling the null models for interaction frequency and phylogenetic distance. We incorporate the deviation between observed and random interaction frequencies as weights into the calculation of partner phylogenetic α‐diversity. To calculate the PSS index, we then compare observed partner phylogenetic α‐diversity to a null distribution generated by randomizing phylogenetic distances among the same number of partners. PSS quantifies the phylogenetic structure (i.e., clustered, overdispersed, or random) of the partners of a focal species. We show with simulations that the PSS index is not correlated with network properties, which allows comparisons across multiple systems. We also implemented PSS on empirical networks of host–parasite, avian seed‐dispersal, lichenized fungi–cyanobacteria, and hummingbird pollination interactions. Across these systems, a large proportion of taxa interact with phylogenetically random partners according to PSS, sometimes to a larger extent than detected with an existing method that does not account for partner availability. We also found that many taxa interact with phylogenetically clustered partners, while taxa with overdispersed partners were rare. We argue that species with phylogenetically overdispersed partners have often been misinterpreted as generalists when they should be considered specialists. Our results highlight the important role of randomness in shaping interaction networks, even in highly intimate symbioses, and provide a much‐needed quantitative framework to assess the role that evolutionary history and symbiotic specialization play in shaping patterns of biodiversity. PSS is available as an R package at

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  3. Abstract

    Heritable trait variation is a central and necessary ingredient of evolution. Trait variation also directly affects ecological processes, generating a clear link between evolutionary and ecological dynamics. Despite the changes in variation that occur through selection, drift, mutation, and recombination, current eco‐evolutionary models usually fail to track how variation changes through time. Moreover, eco‐evolutionary models assume fitness functions for each trait and each ecological context, which often do not have empirical validation. We introduce a new type of model, Gillespie eco‐evolutionary models (GEMs), that resolves these concerns by tracking distributions of traits through time as eco‐evolutionary dynamics progress. This is done by allowing change to be driven by the direct fitness consequences of model parameters within the context of the underlying ecological model, without having to assume a particular fitness function.GEMs work by adding a trait distribution component to the standard Gillespie algorithm – an approach that models stochastic systems in nature that are typically approximated through ordinary differential equations. We illustrateGEMs with the Rosenzweig–MacArthur consumer–resource model. We show not only how heritable trait variation fuels trait evolution and influences eco‐evolutionary dynamics, but also how the erosion of variation through time may hinder eco‐evolutionary dynamics in the long run.GEMs can be developed for any parameter in any ordinary differential equation model and, furthermore, can enable modeling of multiple interacting traits at the same time. We expectGEMs will open the door to a new direction in eco‐evolutionary and evolutionary modeling by removing long‐standing modeling barriers, simplifying the link between traits, fitness, and dynamics, and expanding eco‐evolutionary treatment of a greater diversity of ecological interactions. These factors makeGEMs much more than a modeling advance, but an important conceptual advance that bridges ecology and evolution through the central concept of heritable trait variation.

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  4. Summary

    Natural populations often show variation in traits that can affect the strength of interspecific interactions. Interaction strengths in turn influence the fate of pairwise interacting populations and the stability of food webs. Understanding the mechanisms relating individual phenotypic variation to interaction strengths is thus central to assess how trait variation affects population and community dynamics. We incorporated nonheritable variation in attack rates and handling times into a classical consumer–resource model to investigate how variation may alter interaction strengths, population dynamics, species persistence, and invasiveness. We found that individual variation influences species persistence through its effect on interaction strengths. In many scenarios, interaction strengths decrease with variation, which in turn affects species coexistence and stability. Because environmental change alters the direction and strength of selection acting upon phenotypic traits, our results have implications for species coexistence in a context of habitat fragmentation, climate change, and the arrival of exotic species to native ecosystems.

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  5. Abstract

    A pressing challenge in ecology is to understand the effects of changing global temperatures on food web structure and dynamics. The stability of these complex ecological networks largely depends on how predator–prey interactions may respond to temperature changes. Because predators and prey rely on their velocities to catch food or avoid being eaten, understanding how temperatures may affect animal movement is central to this quest. Despite our efforts, we still lack a mechanistic understanding of how the effect of temperature on metabolic processes scales up to animal movement and beyond. Here, we merge a biomechanical approach, the Metabolic Theory of Ecology and empirical data to show that animal movement displays multiple regimes of temperature dependence. We also show that crossing these regimes has important consequences for population dynamics and stability, which depend on the parameters controlling predator–prey interactions. We argue that this dependence upon interaction parameters may help explain why experimental work on the temperature dependence of interaction strengths has so far yielded conflicting results. More importantly, these changes in the temperature dependence of animal movement can have consequences that go well beyond ecological interactions and affect, for example, animal communication, mating, sensory detection, and any behavioral modality dependent on the movement of limbs. Finally, by not taking into account the changes in temperature dependence reported here we might not be able to properly forecast the impact of global warming on ecological processes and propose appropriate mitigation action when needed.

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