A trait-based understanding of wood decomposition by fungi.

As the primary decomposers of organic material in terrestrial ecosystems, fungi are critical agents of the global carbon cycle. Yet our ability to link fungal community composition to ecosystem functioning is constrained by a limited understanding of the factors accounting for different wood decomposition rates among fungi. Here we examine which traits best explain fungal decomposition ability by combining detailed trait-based assays on 34 saprotrophic fungi from across North America in the laboratory with a 5-y field study comprising 1,582 fungi isolated from 74 decomposing logs. Fungal growth rate (hyphal extension rate) was the strongest single predictor of fungal-mediated wood decomposition rate under laboratory conditions, and accounted for up to 27% of the in situ variation in decomposition in the field. At the individual level, decomposition rate was negatively correlated with moisture niche width (an indicator of drought stress tolerance) and with the production of nutrient-mineralizing extracellular enzymes. Together, these results suggest that decomposition rates strongly align with a dominance-tolerance life-history trade-off that was previously identified in these isolates, forming a spectrum from slow-growing, stress-tolerant fungi that are poor decomposers to fast-growing, highly competitive fungi with fast decomposition rates. Our study illustrates how an understanding of fungal trait variation could improve our predictive ability of the early and midstages of wood decay, to which our findings are most applicable. By mapping our results onto the biogeographic distribution of the dominance-tolerance trade-off across North America, we approximate broad-scale patterns in intrinsic fungal-mediated wood decomposition rates.

Competitive network determines the direction of the diversity-function relationship.

The structure of the competitive network is an important driver of biodiversity and coexistence in natural communities. In addition to determining which species survive, the nature and intensity of competitive interactions within the network also affect the growth, productivity, and abundances of those individuals that persist. As such, the competitive network structure may likewise play an important role in determining community-level functioning by capturing the net costs of competition. Here, using an experimental system comprising 18 wood decay basidiomycete fungi, we test this possibility by quantifying the links among competitive network structure, species diversity, and community function. We show that species diversity alone has negligible impacts on community functioning, but that diversity interacts with two key properties of the competitive network-competitive intransitivity and average competitive ability-to ultimately shape biomass production, respiration, and carbon use efficiency. Most notably, highly intransitive communities comprising weak competitors exhibited a positive diversity-function relationship, whereas weakly intransitive communities comprising strong competitors exhibited a negative relationship. These findings demonstrate that competitive network structure can be an important determinant of community-level functioning, capturing a gradient from weakly to strongly competitive communities. Our research suggests that the competitive network may therefore act as a unifying link between diversity and function, providing key insight as to how and when losses in biodiversity will impact ecosystem function.

Reconciling empirical interactions and species coexistence.

Coexistence in ecological communities is governed largely by the nature and intensity of species interactions. Countless studies have proposed methods to infer these interactions from empirical data, yet models parameterised using such data often fail to recover observed coexistence patterns. Here, we propose a method to reconcile empirical parameterisations of community dynamics with species-abundance data, ensuring that the predicted equilibrium is consistent with the observed abundance distribution. To illustrate the approach, we explore two case studies: an experimental freshwater algal community and a long-term time series of displacement in an intertidal community. We demonstrate how our method helps recover observed coexistence patterns, capture the core dynamics of the system, and, in the latter case, predict the impacts of experimental extinctions. Collectively, these results demonstrate an intuitive approach for reconciling observed and empirical data, improving our ability to explore the links between species interactions and coexistence in natural systems.

Phenotypic variability promotes diversity and stability in competitive communities.

Intraspecific variation is at the core of evolutionary theory, and yet, from an ecological perspective, we have few robust expectations for how this variation should affect the dynamics of large communities. Here, by adapting an approach from evolutionary game theory, we show that the incorporation of phenotypic variability into competitive networks dramatically alters the dynamics across ecological timescales, stabilising the systems and buffering the communities against demographic perturbations. The beneficial effects of phenotypic variability are strongest when there are substantial differences among phenotypes and when phenotypes are inherited with moderately high fidelity; yet even low levels of variation lead to significant increases in diversity, stability, and robustness. By identifying a simple and ubiquitous stabilising force in competitive communities, this work contributes to our core understanding of how biological diversity is maintained in natural systems.

Network spandrels reflect ecological assembly.

Ecological networks that exhibit stable dynamics should theoretically persist longer than those that fluctuate wildly. Thus, network structures which are over-represented in natural systems are often hypothesised to be either a cause or consequence of ecological stability. Rarely considered, however, is that these network structures can also be by-products of the processes that determine how new species attempt to join the community. Using a simulation approach in tandem with key results from random matrix theory, we illustrate how historical assembly mechanisms alter the structure of ecological networks. We demonstrate that different community assembly scenarios can lead to the emergence of structures that are often interpreted as evidence of 'selection for stability'. However, by controlling for the underlying selection pressures, we show that these assembly artefacts-or spandrels-are completely unrelated to stability or selection, and are instead by-products of how new species are introduced into the system. We propose that these network-assembly spandrels are critically overlooked aspects of network theory and stability analysis, and we illustrate how a failure to adequately account for historical assembly can lead to incorrect inference about the causes and consequences of ecological stability.

Species associations overwhelm abiotic conditions to dictate the structure and function of wood-decay fungal communities.

Environmental conditions exert strong controls on the activity of saprotrophic microbes, yet abiotic factors often fail to adequately predict wood decomposition rates across broad spatial scales. Given that species interactions can have significant positive and negative effects on wood-decay fungal activity, one possibility is that biotic processes serve as the primary controls on community function, with abiotic controls emerging only after species associations are accounted for. Here we explore this hypothesis in a factorial field warming- and nitrogen-addition experiment by examining relationships among wood decomposition rates, fungal activity, and fungal community structure. We show that functional outcomes and community structure are largely unrelated to abiotic conditions, with microsite and plot-level abiotic variables explaining at most 19% of the total variability in decomposition and fungal activity, and 2% of the variability in richness and evenness. In contrast, taxonomic richness, evenness, and species associations (i.e., co-occurrence patterns) exhibited strong relationships with community function, accounting for 52% of the variation in decomposition rates and 73% in fungal activity. A greater proportion of positive vs. negative species associations in a community was linked to strong declines in decomposition rates and richness. Evenness emerged as a key mediator between richness and function, with highly even communities exhibiting a positive richness-function relationship and uneven communities exhibiting a negative or null response. These results suggest that community-assembly processes and species interactions are important controls on the function of wood-decay fungal communities, ultimately overwhelming substantial differences in abiotic conditions.

Biotic interactions mediate soil microbial feedbacks to climate change.

Decomposition of organic material by soil microbes generates an annual global release of 50-75 Pg carbon to the atmosphere, ∼7.5-9 times that of anthropogenic emissions worldwide. This process is sensitive to global change factors, which can drive carbon cycle-climate feedbacks with the potential to enhance atmospheric warming. Although the effects of interacting global change factors on soil microbial activity have been a widespread ecological focus, the regulatory effects of interspecific interactions are rarely considered in climate feedback studies. We explore the potential of soil animals to mediate microbial responses to warming and nitrogen enrichment within a long-term, field-based global change study. The combination of global change factors alleviated the bottom-up limitations on fungal growth, stimulating enzyme production and decomposition rates in the absence of soil animals. However, increased fungal biomass also stimulated consumption rates by soil invertebrates, restoring microbial process rates to levels observed under ambient conditions. Our results support the contemporary theory that top-down control in soil food webs is apparent only in the absence of bottom-up limitation. As such, when global change factors alleviate the bottom-up limitations on microbial activity, top-down control becomes an increasingly important regulatory force with the capacity to dampen the strength of positive carbon cycle-climate feedbacks.

Fungal interactions reduce carbon use efficiency.

The efficiency by which fungi decompose organic matter contributes to the amount of carbon that is retained in biomass vs. lost to the atmosphere as respiration. This carbon use efficiency (CUE) is affected by various abiotic conditions, including temperature and nutrient availability. Theoretically, the physiological costs of interspecific interactions should likewise alter CUE, yet the magnitude of these costs is untested. Here we conduct a microcosm experiment to quantify how interactions among wood-decay basidiomycete fungi alter growth, respiration and CUE across a temperature and nitrogen gradient. We show that species interactions induced consistent declines in CUE, regardless of abiotic conditions. Multispecies communities exhibited reductions in CUE of up to 25% relative to individual CUE, with this biotic effect being greater than the observed variation attributable to abiotic conditions. Our results suggest that the extent to which fungal-mediated carbon fluxes respond to environmental change may be influenced strongly by species interactions.

Modelling the multidimensional niche by linking functional traits to competitive performance.

Linking competitive outcomes to environmental conditions is necessary for understanding species' distributions and responses to environmental change. Despite this importance, generalizable approaches for predicting competitive outcomes across abiotic gradients are lacking, driven largely by the highly complex and context-dependent nature of biotic interactions. Here, we present and empirically test a novel niche model that uses functional traits to model the niche space of organisms and predict competitive outcomes of co-occurring populations across multiple resource gradients. The model makes no assumptions about the underlying mode of competition and instead applies to those settings where relative competitive ability across environments correlates with a quantifiable performance metric. To test the model, a series of controlled microcosm experiments were conducted using genetically related strains of a widespread microbe. The model identified trait microevolution and performance differences among strains, with the predicted competitive ability of each organism mapped across a two-dimensional carbon and nitrogen resource space. Areas of coexistence and competitive dominance between strains were identified,and the predicted competitive outcomes were validated in approximately 95% of the pairings. By linking trait variation to competitive ability, our work demonstrates a generalizable approach for predicting and modelling competitive outcomes across changing environmental contexts.

Predicting the responsiveness of soil biodiversity to deforestation: a cross-biome study.

The consequences of deforestation for aboveground biodiversity have been a scientific and political concern for decades. In contrast, despite being a dominant component of biodiversity that is essential to the functioning of ecosystems, the responses of belowground biodiversity to forest removal have received less attention. Single-site studies suggest that soil microbes can be highly responsive to forest removal, but responses are highly variable, with negligible effects in some regions. Using high throughput sequencing, we characterize the effects of deforestation on microbial communities across multiple biomes and explore what determines the vulnerability of microbial communities to this vegetative change. We reveal consistent directional trends in the microbial community response, yet the magnitude of this vegetation effect varied between sites, and was explained strongly by soil texture. In sandy sites, the difference in vegetation type caused shifts in a suite of edaphic characteristics, driving substantial differences in microbial community composition. In contrast, fine-textured soil buffered microbes against these effects and there were minimal differences between communities in forest and grassland soil. These microbial community changes were associated with distinct changes in the microbial catabolic profile, placing community changes in an ecosystem functioning context. The universal nature of these patterns allows us to predict where deforestation will have the strongest effects on soil biodiversity, and how these effects could be mitigated.

Treemendous: an R package for integrating taxonomic information across backbones.

Standardizing and translating species names from different databases is key to the successful integration of data sources in biodiversity research. There are numerous taxonomic name-resolution applications that implement increasingly powerful name-cleaning and matching approaches, allowing the user to resolve species relative to multiple backbones simultaneously. Yet there remains no principled approach for combining information across these underlying taxonomic backbones, complicating efforts to combine and merge species lists with inconsistent and conflicting taxonomic information. Here, we present Treemendous, an open-source software package for the R programming environment that integrates taxonomic relationships across four publicly available backbones to improve the name resolution of tree species. By mapping relationships across the backbones, this package can be used to resolve datasets with conflicting and inconsistent taxonomic origins, while ensuring the resulting species are accepted and consistent with a single reference backbone. The user can chain together different functionalities ranging from simple matching to a single backbone, to graph-based iterative matching using synonym-accepted relations across all backbones in the database. In addition, the package allows users to 'translate' one tree species list into another, streamlining the assimilation of new data into preexisting datasets or models. The package provides a flexible workflow depending on the use case, and can either be used as a stand-alone name-resolution package or in conjunction with existing packages as a final step in the name-resolution pipeline. The Treemendous package is fast and easy to use, allowing users to quickly merge different data sources by standardizing their species names according to the regularly updated database. By combining taxonomic information across multiple backbones, the package increases matching rates and minimizes data loss, allowing for more efficient translation of tree species datasets to aid research into forest biodiversity and tree ecology.

The global distribution and drivers of wood density and their impact on forest carbon stocks.

The density of wood is a key indicator of the carbon investment strategies of trees, impacting productivity and carbon storage. Despite its importance, the global variation in wood density and its environmental controls remain poorly understood, preventing accurate predictions of global forest carbon stocks. Here we analyse information from 1.1 million forest inventory plots alongside wood density data from 10,703 tree species to create a spatially explicit understanding of the global wood density distribution and its drivers. Our findings reveal a pronounced latitudinal gradient, with wood in tropical forests being up to 30% denser than that in boreal forests. In both angiosperms and gymnosperms, hydrothermal conditions represented by annual mean temperature and soil moisture emerged as the primary factors influencing the variation in wood density globally. This indicates similar environmental filters and evolutionary adaptations among distinct plant groups, underscoring the essential role of abiotic factors in determining wood density in forest ecosystems. Additionally, our study highlights the prominent role of disturbance, such as human modification and fire risk, in influencing wood density at more local scales. Factoring in the spatial variation of wood density notably changes the estimates of forest carbon stocks, leading to differences of up to 21% within biomes. Therefore, our research contributes to a deeper understanding of terrestrial biomass distribution and how environmental changes and disturbances impact forest ecosystems.

The global biogeography of tree leaf form and habit.

Understanding what controls global leaf type variation in trees is crucial for comprehending their role in terrestrial ecosystems, including carbon, water and nutrient dynamics. Yet our understanding of the factors influencing forest leaf types remains incomplete, leaving us uncertain about the global proportions of needle-leaved, broadleaved, evergreen and deciduous trees. To address these gaps, we conducted a global, ground-sourced assessment of forest leaf-type variation by integrating forest inventory data with comprehensive leaf form (broadleaf vs needle-leaf) and habit (evergreen vs deciduous) records. We found that global variation in leaf habit is primarily driven by isothermality and soil characteristics, while leaf form is predominantly driven by temperature. Given these relationships, we estimate that 38% of global tree individuals are needle-leaved evergreen, 29% are broadleaved evergreen, 27% are broadleaved deciduous and 5% are needle-leaved deciduous. The aboveground biomass distribution among these tree types is approximately 21% (126.4 Gt), 54% (335.7 Gt), 22% (136.2 Gt) and 3% (18.7 Gt), respectively. We further project that, depending on future emissions pathways, 17-34% of forested areas will experience climate conditions by the end of the century that currently support a different forest type, highlighting the intensification of climatic stress on existing forests. By quantifying the distribution of tree leaf types and their corresponding biomass, and identifying regions where climate change will exert greatest pressure on current leaf types, our results can help improve predictions of future terrestrial ecosystem functioning and carbon cycling.

Alternative stable states of the forest mycobiome are maintained through positive feedbacks.

Most trees on Earth form a symbiosis with either arbuscular mycorrhizal or ectomycorrhizal fungi. By forming common mycorrhizal networks, actively modifying the soil environment and other ecological mechanisms, these contrasting symbioses may generate positive feedbacks that favour their own mycorrhizal strategy (that is, the con-mycorrhizal strategy) at the expense of the alternative strategy. Positive con-mycorrhizal feedbacks set the stage for alternative stable states of forests and their fungi, where the presence of different forest mycorrhizal strategies is determined not only by external environmental conditions but also mycorrhiza-mediated feedbacks embedded within the forest ecosystem. Here, we test this hypothesis using thousands of US forest inventory sites to show that arbuscular and ectomycorrhizal tree recruitment and survival exhibit positive con-mycorrhizal density dependence. Data-driven simulations show that these positive feedbacks are sufficient in magnitude to generate and maintain alternative stable states of the forest mycobiome. Given the links between forest mycorrhizal strategy and carbon sequestration potential, the presence of mycorrhizal-mediated alternative stable states affects how we forecast forest composition, carbon sequestration and terrestrial climate feedbacks.

Global relationships in tree functional traits.

Due to massive energetic investments in woody support structures, trees are subject to unique physiological, mechanical, and ecological pressures not experienced by herbaceous plants. Despite a wealth of studies exploring trait relationships across the entire plant kingdom, the dominant traits underpinning these unique aspects of tree form and function remain unclear. Here, by considering 18 functional traits, encompassing leaf, seed, bark, wood, crown, and root characteristics, we quantify the multidimensional relationships in tree trait expression. We find that nearly half of trait variation is captured by two axes: one reflecting leaf economics, the other reflecting tree size and competition for light. Yet these orthogonal axes reveal strong environmental convergence, exhibiting correlated responses to temperature, moisture, and elevation. By subsequently exploring multidimensional trait relationships, we show that the full dimensionality of trait space is captured by eight distinct clusters, each reflecting a unique aspect of tree form and function. Collectively, this work identifies a core set of traits needed to quantify global patterns in functional biodiversity, and it contributes to our fundamental understanding of the functioning of forests worldwide.

The global distribution and environmental drivers of aboveground versus belowground plant biomass.

A poor understanding of the fraction of global plant biomass occurring belowground as roots limits our understanding of present and future ecosystem function and carbon pools. Here we create a database of root-mass fractions (RMFs), an index of plant below- versus aboveground biomass distributions, and generate quantitative, spatially explicit global maps of RMFs in trees, shrubs and grasses. Our analyses reveal large gradients in RMFs both across and within vegetation types that can be attributed to resource availability. High RMFs occur in cold and dry ecosystems, while low RMFs dominate in warm and wet regions. Across all vegetation types, the directional effect of temperature on RMFs depends on water availability, suggesting feedbacks between heat, water and nutrient supply. By integrating our RMF maps with existing aboveground plant biomass information, we estimate that in forests, shrublands and grasslands, respectively, 22%, 47% and 67% of plant biomass exists belowground, with a total global belowground fraction of 24% (20-28%), that is, 113 (90-135) Gt carbon. By documenting the environmental correlates of root biomass allocation, our results can inform model projections of global vegetation dynamics under current and future climate scenarios.

Belowground community turnover accelerates the decomposition of standing dead wood.

Standing dead trees (snags) decompose more slowly than downed dead wood and provide critical habitat for many species. The rate at which snags fall therefore influences forest carbon dynamics and biodiversity. Fall rates correlate strongly with mean annual temperature, presumably because warmer climates facilitate faster wood decomposition and hence degradation of the structural stability of standing wood. These faster decomposition rates coincide with turnover from fungal-dominated wood decomposer communities in cooler forests to codomination by fungi and termites in warmer regions. A key question for projecting forest dynamics is therefore whether temperature effects on wood decomposition arise primarily because warmer conditions facilitate faster decomposer metabolism, or are also influenced indirectly by belowground community turnover (e.g., termites exert additional influence beyond fungal-plus-bacterial mediated decomposition). To test between these possibilities, we simulate standing dead trees with untreated wooden posts and follow them in the field across 5 yr at 12 sites, before measuring buried, soil-air interface and aerial post sections to quantify wood decomposition and organism activities. High termite activities at the warmer sites are associated with rates of postfall that are three times higher than at the cooler sites. Termites primarily consume buried wood, with decomposition rates greatest where termite activities are highest. However, where higher microbial and termite activities co-occur, they appear to compensate for one another first, and then to slow decomposition rates at their highest activities, suggestive of interference competition. If the range of microbial and termite codomination of wood decomposer communities expands under climate warming, our data suggest that expansion will accelerate snag fall with consequent effects on forest carbon cycling and biodiversity in forests previously dominated by microbial decomposers.

Predicting coexistence in experimental ecological communities.

The study of experimental communities is fundamental to the development of ecology. Yet, for most ecological systems, the number of experiments required to build, model or analyse the community vastly exceeds what is feasible using current methods. Here, we address this challenge by presenting a statistical approach that uses the results of a limited number of experiments to predict the outcomes (coexistence and species abundances) of all possible assemblages that can be formed from a given pool of species. Using three well-studied experimental systems-encompassing plants, protists, and algae with grazers-we show that this method predicts the results of unobserved experiments with high accuracy, while making no assumptions about the dynamics of the systems. These results demonstrate a fundamentally different way of building and quantifying experimental systems, requiring far fewer experiments than traditional study designs. By developing a scalable method for navigating large systems, this work provides an efficient approach to studying highly diverse experimental communities.