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Competition among trees is an important driver of community structure and dynamics in tropical forests. Neighboring trees may impact an individual tree’s growth rate and probability of mortality, but large-scale geographic and environmental variation in these competitive effects has yet to be evaluated across the tropical forest biome. We quantified effects of competition on tree-level basal area growth and mortality for trees ≥10-cm diameter across 151 ~1-ha plots in mature tropical forests in Amazonia and tropical Africa by developing nonlinear models that accounted for wood density, tree size, and neighborhood crowding. Using these models, we assessed how water availability (i.e., climatic water deficit) and soil fertility influenced the predicted plot-level strength of competition (i.e., the extent to which growth is reduced, or mortality is increased, by competition across all individual trees). On both continents, tree basal area growth decreased with wood density and increased with tree size. Growth decreased with neighborhood crowding, which suggests that competition is important. Tree mortality decreased with wood density and generally increased with tree size, but was apparently unaffected by neighborhood crowding. Across plots, variation in the plot-level strength of competition was most strongly related to plot basal area (i.e., the sum of the basal area of all trees in a plot), with greater reductions in growth occurring in forests with high basal area, but in Amazonia, the strength of competition also varied with plot-level wood density. In Amazonia, the strength of competition increased with water availability because of the greater basal area of wetter forests, but was only weakly related to soil fertility. In Africa, competition was weakly related to soil fertility and invariant across the shorter water availability gradient. Overall, our results suggest that competition influences the structure and dynamics of tropical forests primarily through effects on individual tree growth rather than mortality and that the strength of competition largely depends on environment-mediated variation in basal area.

This article is a Commentary on Asner et al. (pp. 973–988), Bahar et al. (pp. 1002–1018), Chavana‐Bryant et al. (pp. 1049–1063), Goldsmith et al. (pp. 989–1001), Malhi et al. (pp. 1019–1032), Rowland et al. (pp. 1064–1077) and Wu et al. (pp. 1033–1048), all of which are published in this issue.

Climate change, increasing atmospheric CO2, and land use change have altered biogeochemical and hydrologic cycles world-wide, with grassland systems being particularly vulnerable to resulting vegetation shifts (Komatsu et al., 2019). Therefore, incorporating plant community dynamics into ecosystem models is critical for accurate forecasting of ecosystem responses to global change (Levine, 2016). Process-based ecosystem models, which simulate the biogeochemical transfers of mass and energy among biota, the subsurface, and atmosphere, require representation of dynamic composition of organisms within ecosystems. For example, these models simulate leaf and plant-level characteristics, such as electron transport rate and allometry of carbon (C) allocation, to predict how net primary productivity and other ecosystem processes respond to abiotic drivers. These models are particularly useful in scaling from organismal to ecosystem levels but are still underdeveloped in their ability to capture community change, especially in grassland ecosystems. To represent compositional changes, these models must simulate competition, mortality, establishment, and reproduction of plant populations within communities. Yet, current ecosystem modeling approaches to forecast plant community change have derived from studies of forested systems and are either too coarse to capture fine-scale community dynamics (e.g. dynamic global vegetation models (DGVMs)) or too complex to be used at large spatial scales (e.g. forest gap models).

Soil microbiomes play a key role in driving biogeochemical cycles of the Earth system. As drought frequency and intensity increase due to climate change, soil microbes and the processes they control will be impacted. Even after a drought ends, microbiomes and other systems take time to recover and may display a memory of previous climate conditions. Still, the mechanisms involved in these legacy effects remain unclear, making it difficult to predict climate and biogeochemical rates in the future. Here, we used a trait‐based microbiome model (DEMENTpy) to implement trade‐off‐mediated mechanisms that may lead to drought legacy effects on litter decomposition. Trade‐offs were assumed to follow the Y‐A‐S framework that defines three primary life‐history strategies of microorganisms: high growth Yield, resource Acquisition, and Stress tolerance. We represented cellular trade‐offs between osmolytes required for drought tolerance and investment in enzymes involved in litter decomposition. Simulations were run under varying levels of drought severity and dispersal. With high levels of dispersal, no legacy effects were predicted by DEMENTpy following drought. With limited dispersal, severe drought resulted in a persistent legacy of altered community‐level traits and reduced litter decomposition. Moderate drought resulted in a transient legacy that disappeared after two years, consistent with recent empirical observations in Southern California ecosystems. These results imply that greater movement along the trade‐off between enzyme investment and osmolyte production resulted in stronger legacy effects. More generally, factors that shift the position of a microbiome in YAS space may alter the legacy outcome following drought. Our trait‐based modeling study motivates additional empirical measurements to quantify YAS traits and trade‐offs that are needed to make accurate predictions of soil microbiome resilience and functioning. Also, our study illustrates an emerging approach for representing trait trade‐offs in microbiomes and vegetation that dictate ecosystem responses to drought and other environmental perturbations.

The distribution of functional traits in communities, and how trait distributions shift over time and space, is critical information for understanding community structure, the maintenance of diversity, and community effects on ecosystem function. It is often the case that traits tightly linked to ecological performance, such as physiological capacities, are laborious to measure and largely unknown for speciose communities; however, these traits are particularly important for unraveling the mechanistic basis of community structure. Here I develop a method combining sparse trait data with a statistical niche model to infer trait distributions for phytoplankton communities and how they vary over 10 yr in the western English Channel. I find that community-average nitrate affinity, light-limited growth rate, and maximum growth rate all show major seasonal patterns, reflecting alternate limitation by light vs. nitrogen. Trait diversity exhibits a variety of patterns distinct from community trait means, which suggests complex regulation of functional diversity. Patterns such as these are important for predicting how ocean ecosystems will respond to global change, and for developing trait-based models of emergent community structure. The statistical approach used here could be applied to any kind of organism, if it exhibits strong relationships between traits and statistical niche estimates.

Summary Plant interactions are fundamental processes for structuring plant communities and are an important mechanism governing the response of plant species and communities to environmental changes. Thus, understanding the role played by the interaction network in modulating the impact of environmental changes on plant community composition and diversity is crucial. Here, we aimed to develop a new analytical and conceptual framework to evaluate the responses of plant communities to environmental changes. This framework uses functional traits as sensitivity measures for simulated environmental changes and assesses the consequences of microhabitat loss. We show here its application to an alpine plant community where we recorded functional traits [specific leaf area (SLA) and leaf dry matter content (LDMC)] of all plants associated with three foundation species or the surrounding open areas. We then simulated primary species loss based on different scenarios of environmental change and explored community persistence to the loss of foundation species. Generally, plant community responses differed among environmental change scenarios. In a scenario of increasing drought alone (i.e. species with lower LDMC were lost first) or increasing drought with increasing temperature (i.e. species with lower LDMC and higher SLA were lost first), the plant community resisted because drought‐tolerant foundation species tolerated those deteriorating conditions. However, in scenarios with increasing nitrogen input (i.e. species having lower SLA were lost earlier), foundation species accelerated species loss due to their early primary extinctions and the corresponding secondary extinctions of species associated to their microhabitat. The resistance of a plant community depends on the driver of environmental change, meaning that the prediction of the fate of this system is depending on the knowledge of the main driver of environmental change. Our framework provides a mechanistic understanding of an ecosystem response to such environmental changes thanks to the integration of biology‐informed criteria of species sensitivities to environmental factors into a network of interacting species. A lay summary is available for this article. Lay Summary

1. Density dependence is of fundamental importance for population and range dynamics. Density-dependent reproduction of plants arises from competitive and facilitative plant-plant interactions that can be pollination independent or pollination mediated. In small and sparse populations, conspecific density dependence often turns from negative to positive and causes Allee effects. Reproduction may also increase with heterospecific density (community-level Allee effect), but the underlying mechanisms are poorly understood and the consequences for community dynamics can be complex. Allee effects have crucial consequences for the conservation of declining species, but also the dynamics of range edge populations. In invasive species, Allee effects may slow or stop range expansion. 2. Observational studies in natural plant communities cannot distinguish whether reproduction is limited by pollination-mediated interactions among plants or by other neighbourhood effects (e.g. competition for abiotic resources). Even experimental pollen supply cannot distinguish whether variation in reproduction is caused by direct density effects or by plant traits correlated with density. Finally, it is unknown over which spatial scales pollination-mediated interactions occur. 3. To circumvent these problems, we introduce a comprehensive experimental and analytical framework which simultaneously (1) manipulates pollen availability and quality by hand pollination and pollinator exclusion, (2) manipulates neighbourhoods by transplanting target plants, and (3) analyses the effects of con- and heterospecific neighbourhoods on reproduction with spatially explicit trait-based neighbourhood models. 4. Applying this framework to Senecio inaequidens, one of Europe's fastest plant invaders, we found that the seed set was strongly pollen limited. Reproduction had increased by pollinator-mediated facilitation by both con- and heterospecific neighbours which may lead to (community-level) Allee effects. Pollination-independent interactions, such as amelioration of abiotic conditions through neighbours, contributed to additional positive neighbour effects. However, these pollination-independent interactions were weaker than the pollination-mediated interactions and they occurred over smaller spatial scales. Finally, the strength and direction of neighbourhood effects depended on neighbour traits and thus changed with the trait composition of the neighbourhood. 5. Synthesis. By manipulating both pollen availability and target plant locations within neighbourhoods, we can comprehensively analyse spatially explicit density dependence of plant reproduction. This experimental approach enhances our ability to understand the dynamics of sparse populations and of species geographical ranges.

Mixotrophy, the combination of autotrophic and heterotrophic nutrition, is a common trophic strategy among unicellular eukaryotes in the ocean. There are a number of hypotheses about the conditions that select for mixotrophy, and field studies have documented the prevalence of mixotrophy in a range of environments. However, there is currently little evidence for how mixotrophy varies across environmental gradients, and whether empirical patterns support theoretical predictions. Here I synthesize experiments that have quantified the abundance of phototrophic, mixotrophic, and heterotrophic nanoflagellates, to ask whether there are broad patterns in the prevalence of mixotrophy (relative to pure autotrophy and heterotrophy), and to ask whether observed patterns are consistent with a trait-based model of trophic strategies. The data suggest that mixotrophs increase in abundance at lower latitudes, while autotrophs and heterotrophs do not, and that this may be driven by increased light availability. Both mixotrophs and autotrophs increase greatly in productive coastal environments, while heterotrophs increase only slightly. These patterns are consistent with a model of resource competition in which nutrients and carbon can both limit growth and mixotrophs experience a trade-off in allocating biomass to phagotrophy vs. autotrophic functions. Importantly, mixotrophy is selected for under a range of conditions even when mixotrophs experience a penalty for using a generalist trophic strategy, due to the synergy between photosynthetically derived carbon and prey-derived nutrients. For this reason mixotrophy is favored relative to specialist strategies by increased irradiance, while at the same time increased nutrient supply increases the competitive ability of mixotrophs against heterotrophs.

Dynamic global vegetation models (DGVMs) are powerful tools to project past, current and future vegetation patterns and associated biogeochemical cycles. However, most models are limited by how they define vegetation and by their simplistic representation of competition. We discuss how concepts from community assembly theory and coexistence theory can help to improve vegetation models. We further present a trait- and individual-based vegetation model (aDGVM2) that allows individual plants to adopt a unique combination of trait values. These traits define how individual plants grow and compete. A genetic optimization algorithm is used to simulate trait inheritance and reproductive isolation between individuals. These model properties allow the assembly of plant communities that are adapted to a site's biotic and abiotic conditions. The aDGVM2 simulates how environmental conditions influence the trait spectra of plant communities; that fire selects for traits that enhance fire protection and reduces trait diversity; and the emergence of life-history strategies that are suggestive of colonization–competition trade-offs. The aDGVM2 deals with functional diversity and competition fundamentally differently from current DGVMs. This approach may yield novel insights as to how vegetation may respond to climate change and we believe it could foster collaborations between functional plant biologists and vegetation modellers.

Oligosaccharides produced from the extracellular hydrolysis of biological materials can act as common goods that promote cooperative growth in microbial populations, whereby cell–cell aggregation increases both the per capita availability of resources and the per-cell growth rate. However, aggregation can also have detrimental consequences for growth, as gradients form within aggregates limiting the resource accessibility. We built a computational model, which predicts cooperation is restricted in dense cell aggregates larger than 10 µm because of the emergence of polymer and oligomer counter gradients. We compared these predictions to experiments performed with two well-studied alginate-degrading strains of Vibrio splendidus , which varied in their ability to secrete alginate lyase. We observed that both strains can form large aggregates (less than 50 µm), overcoming diffusion limitation by rearranging their internal structure. The stronger enzyme producer grew non-cooperatively and formed aggregates with internal channels that allowed exchange between the bulk environment and the aggregate, whereas the weak enzyme producer showed strongly cooperative growth and formed dense aggregates in which cells near the core mixed by active swimming. Our simulations suggest that the mixing and channelling reduce diffusion limitation and allow cells to uniformly grow in aggregates. Together, these data demonstrate that bacterial behaviour can help overcome competition imposed by resource gradients within cell aggregates. This article is part of a discussion meeting issue ‘Single cell ecology’.