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The need for reliable prediction of species distributions dependent upon traits has been hindered by a lack of model transferability testing. We tested the predictive capacity of trait-SDMs by fitting hierarchical generalised linear models with three trait and four environmental predictors for 20 eucalypt taxa in a reference region. We used these models to predict occurrence for a much larger set of taxa and target areas (82 taxa across 18 target regions) in south-eastern Australia. Median predictive performance for new species in target regions was 0.65 (area under receiver operating curve) and 1.24 times random (area under precision recall curve). Prediction in target regions did not worsen with increasing geographic, environmental or community compositional distance from the reference region, and was improved with reliable trait–environment relationships. Transfer testing also identified trait–environment relationships that did not transfer. These results give confidence that traits and transfer testing can assist in the hard problem of predicting environmental responses for new species, environmental conditions and regions.

Most future predictions of forest diversity and composition assume species will shift instantaneously. However, evidence suggests there will be considerable inertia between climate change and shifts in plant species distributions, for example due to resident communities resisting changes, the redistribution of resident species’ abundances, and dispersal limitation of species attempting to track preferred climates. To test how inertia in community responses to climate change may affect future primary productivity, and the capacity for communities to respond through local redistribution of abundances. We assessed relationships between community functional trait distributions, climate and environment with a remote-sensed measure of gross primary productivity (GPP) across 1032 woody plant communities and projected these spatially under current and future climates. Using a simulation approach, we also tested the capacity for communities to achieve future trait distributions by shifting the relative abundances of resident species. We found that climate change is likely to reduce GPP in southeast Queensland, and, contrary to expectations, there is little evidence to suggest distributional inertia will contribute substantially to this loss. We show that sites with high existing functional diversity are well-placed to achieve predicted future trait compositions in the absence of long-distance dispersal through redistribution of resident species abundances. Our results show that sites with high existing functional diversity are well placed to achieve predicted future trait compositions in the absence of long-distance dispersal through abundance redistribution of resident species, but it is unlikely this will mitigate strong climate change-driven losses of productivity.

We introduce the AusTraits database - a compilation of values of plant traits for taxa in the Australian flora (hereafter AusTraits). AusTraits synthesises data on 448 traits across 28,640 taxa from field campaigns, published literature, taxonomic monographs, and individual taxon descriptions. Traits vary in scope from physiological measures of performance (e.g. photosynthetic gas exchange, water-use efficiency) to morphological attributes (e.g. leaf area, seed mass, plant height) which link to aspects of ecological variation. AusTraits contains curated and harmonised individual- and species-level measurements coupled to, where available, contextual information on site properties and experimental conditions. This article provides information on version 3.0.2 of AusTraits which contains data for 997,808 trait-by-taxon combinations. We envision AusTraits as an ongoing collaborative initiative for easily archiving and sharing trait data, which also provides a template for other national or regional initiatives globally to fill persistent gaps in trait knowledge., Measurement(s)plant traitTechnology Type(s)digital curationSample Characteristic - OrganismViridiplantaeSample Characteristic - LocationAustralia Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.14545755

Given the magnitude and rate of ongoing climate change, the physiological capacity of species to tolerate extreme conditions will play a key role in influencing outcomes for biodiversity. It is also possible that species will respond to changes in climate by shifting their physiological tolerances, through genetic adaptation. How these processes influence biodiversity outcomes will be crucial in determining the most suitable management responses to retain diversity into the future. Here we assess how accounting for physiological tolerances, genetic adaptation and community assembly processes such as species replacement, influence projected climate change outcomes for the flora of Tasmania (all 2051 plant species). We incorporate these processes into the M‐SET metacommunity model and compare four different assumptions of species niches: realized niches, broader physiological tolerances and low or high capacity for genetic adaptation. Accounting for physiological tolerances rather than realized niches had the largest impact on projected outcomes, with 358 fewer species extinctions in the hottest climate scenario (mean = 30 extinctions). In contrast, adding the capacity for species physiological tolerances to shift through genetic adaptation resulted in little additional benefits for biodiversity outcomes, even under an optimistic level of adaptive capacity. We find that this is due largely to community assembly processes such as species replacement restricting the ability of species to persist and adapt in situ, as has been suggested from theoretical metacommunity models applied in simple artificial settings. Our results highlight the importance of accounting for species physiological tolerances and community‐level processes in biodiversity projections, while the potential role for genetic adaptation may be small, requiring further exploration in alternative contexts.

Aim: Observed, realized niche space often underestimates species’ physiological tolerances due to interactions with other species, dispersal constraints, and because some combinations of influential environmental factors do not currently exist in the real world. Conversely, correlative ecological niche models rely on the assumption that the range of environmental conditions encompassed by a species’ geographic distribution accurately reflects their environmental tolerances, including community-level approaches like generalized dissimilarity modelling (GDM). We extend GDM to better understand what effect broader environmental tolerances could have on compositional turnover under climate change. Innovation: We show how GDM can be adjusted as a function of best-available estimates of the average ratio between realized and potential niche widths to modify projected temporal turnover. We demonstrate this approach by using the estimated niche ratios of Australian plant species (n = 7,184) relative to thermal extremes, and the rate at which this ratio varied with temperature. The modified GDMs showed existing thermal tolerance could reduce the turnover predicted by standard models under climate change by up to 11%. We further show how the reduction in expected turnover by 2090 will influence where a greater proportion of the current community will persist in a region. Main conclusions: We suggest that standard spatial GDMs and their modified versions represent the extremes of ecological niche perspectives (i.e., realized and potential) and the range of tolerance communities may have when responding to environmental change. GDM projections therefore identify the range of uncertainty associated with a critical model assumption, and as climate change continues, ongoing community monitoring could be used to validate the balance between the two possibilities.

AIM: Spatial modelling can provide new understanding of patterns in biodiversity and help inform policy, planning and management decisions. However, for many taxa, in many regions, issues with currently available biological data curtail the application of spatial biodiversity models. Here we develop techniques to model spatial patterns of diverse subterranean invertebrate fauna (troglofauna and stygofauna) in the Pilbara region, to better inform biodiversity planning and management for these groups. In this context, the available data are highly biased in their distribution, with specimens often identified only at coarse taxonomic resolution, and individual community surveys sampling a relatively small proportion of the total assemblage at a location. LOCATION: Pilbara bioregion, NW Australia. METHODS: To overcome the challenges presented by the subterranean fauna data sets, we developed a community‐level spatial biodiversity modelling framework, and applied this to model and project patterns in species richness and compositional turnover, for both troglofauna and stygofauna, using fine‐resolution (~30 m) environmental layers. RESULTS: While there was a large amount of inherent noise in the collated data, our spatial models detected potential environmental drivers of community diversity that align with existing knowledge. For troglofauna, species richness was associated with iron‐bearing geologies, while community compositional turnover was most strongly related to geographic distance. For stygofauna, groundwater depth was the primary predictor for richness and turnover, alongside other physicochemical groundwater attributes. We identified areas of high biodiversity significance by combining richness and turnover predictions. MAIN CONCLUSIONS: The spatial projections from our biodiversity models indicate the importance of the central Hamersley Range in harbouring unique and diverse subterranean assemblages. These models and projections provide an advanced platform for various biodiversity assessments, including identifying priorities for new surveys, and assessing the likely impacts of proposed development activities on the long‐term persistence of subterranean species in the Pilbara.

Understanding influences of environmental change on biodiversity requires consideration of more than just species richness. Here we present a novel framework for understanding possible changes in species' abundance structures within communities under climate change. We demonstrate this using comprehensive survey and environmental data from 1748 woody plant communities across southeast Queensland, Australia, to model rank-abundance distributions (RADs) under current and future climates. Under current conditions, the models predicted RADs consistent with the region's dominant vegetation types. We demonstrate that under a business as usual climate scenario, total abundance and richness may decline in subtropical rainforest and shrubby heath, and increase in dry sclerophyll forests. Despite these opposing trends, we predicted evenness in the distribution of abundances between species to increase in all vegetation types. By assessing the information rich, multidimensional RAD, we show that climate-driven changes to community abundance structures will likely vary depending on the current composition and environmental context.

Aim Despite recognition that realized distributions inherently underestimate species' physiological tolerances, we are yet to identify the extent of these differences within diverse taxonomic groups. The degree to which species could tolerate environmental conditions outside their observed distributions may have a significant impact on the perceived extinction risk in ecological models. More information on this potential error is required to improve our confidence in management strategies. Location Australia. Time Period 1983–2012. Major Taxa Studied Plants. Methods To quantify the scale and spatial patterns of this disparity, we estimated the existing tolerance to thermal extremes of 7,124 Australian plants, more than one-third of the native continental flora, using data from cultivated records at 128 botanical gardens and nurseries. Hierarchical Bayesian beta regression was used to assess whether factors such as realized niches, traits or phylogeny could predict the incidence or magnitude of niche truncation (underestimation of thermal tolerances), while controlling for sources of collection bias. Results Approximately half of the cultivated species analysed could tolerate temperature extremes beyond those experienced in their native range. Niche truncation was predictable from the breadth and extremes of their realized niches and by traits such as plant growth form. Phylogenetic relationships with niche truncation were weak and appeared more suited to predicting thermal tolerances directly. Main conclusions This study highlights a widespread disparity between realized and potential thermal limits that may have significant implications for species' capacity to persist in situ with a changing climate. Identifying whether thermal niche truncation is the result of biotic interactions, dispersal constraints or other environmental factors could provide significant insight into community assembly at macroecological scales. Estimating niche truncation may help to explain why certain ecological communities are more resilient to change and may potentially improve the reliability of model projections under climate change.

Improving biodiversity predictions is essential if we are to meet the challenges posed by global change. As knowledge is key to feed models, we need to evaluate how debated theory can affect models. An important ongoing debate is whether environmental constraints limit the number of species that can coexist in a community (saturation), with recent findings suggesting that species richness in many communities might be unsaturated. Here, we propose that biodiversity models could address this issue by accounting for a duality: considering communities as unsaturated but where species composition is constrained by different scale-dependent biodiversity drivers. We identify a variety of promising advances for incorporating this duality into commonly applied biodiversity modelling approaches and improving their spatial predictions., Trends The majority of biodiversity modelling approaches do not explicitly address the question of saturation. Theoretical and methodological implications of saturation or unsaturation in biodiversity modelling. Addressing saturation or unsaturation is vital to produce more reliable conservation strategies. Integrative community modelling frameworks may be the way forward.

International audience; Aim: More than ever, ecologists seek to understand how species are distributed and have assembled into communities using the “filtering framework”. This framework is based on the hypothesis that local assemblages result from a series of abiotic and biotic filters applied to regional species pools and that these filters leave predictable signals in observed diversity patterns. In theory, statistical comparisons of expected and observed patterns enable data‐driven tests of assembly processes. However, so far this framework has fallen short in delivering generalizable conclusions, challenging whether (and how) diversity patterns can be used to characterize and understand underlying assembly processes better.Methods: By synthesizing the previously raised critiques and suggested solutions in a comprehensive way, we identify 10 pitfalls that can lead to flawed interpretations of α‐diversity patterns, summarize solutions developed to circumvent these pitfalls and provide general guidelines.Results: We find that most issues arise from an overly simplistic view of potential processes that influence diversity patterns, which is often motivated by practical constraints on study design, focal scale and methodology. We outline solutions for each pitfall, such as methods spanning over spatial, environmental or phylogenetic scales, and suggest guidelines for best scientific practices in community ecology. Among key future challenges are the integration of mechanistic modelling and multi‐trophic interactions.Main conclusions: Our conclusion is that the filtering framework still holds promise, but only if researchers successfully navigate major pitfalls, foster the integration of mechanistic modelling and multi‐trophic interactions and directly account for uncertainty in their conclusions.

We are facing a biodiversity crisis at the same time as we are acquiring an unprecedented view of the world's biodiversity. Vast new datasets (e.g., species distributions, traits, phylogenies, and interaction networks) hold knowledge to better comprehend the depths of biodiversity change, reliably anticipate these changes, and inform conservation actions. To harness this information for conservation, we need to integrate the largely independent fields of biodiversity modeling and conservation. We highlight new developments in each respective field, early examples of how they are being brought together, and ideas for a future synthesis such that conservation decisions can be made with fuller awareness of the biodiversity at stake.

Based on the sensitivity of species to ongoing climate change, and numerous challenges they face tracking suitable conditions, there is growing interest in species' capacity to adapt to climatic stress. Here, we develop and apply a new generic modelling approach (AdaptR) that incorporates adaptive capacity through physiological limits, phenotypic plasticity, evolutionary adaptation and dispersal into a species distribution modelling framework. Using AdaptR to predict change in the distribution of 17 species of Australian fruit flies (Drosophilidae), we show that accounting for adaptive capacity reduces projected range losses by up to 33% by 2105. We identify where local adaptation is likely to occur and apply sensitivity analyses to identify the critical factors of interest when parameters are uncertain. Our study suggests some species could be less vulnerable than previously thought, and indicates that spatiotemporal adaptive models could help improve management interventions that support increased species' resilience to climate change.

Aims Accurate field quantification of stem counts is time consuming and laborious, especially in ecosystems containing many small individuals where counts are immense. There are many large, long-term data sets worldwide that include various measures of woody plant abundance, such as crown cover, that are faster and easier to measure than true counts of individuals. Quantifying the number of individuals allows in-depth research into various topics including functional ecology, carbon accounting, conservation and metabolic theory. Here we test the accuracy of predicting the abundance of individual woody plants from cover and forest structure measures, and explore the implications of refined abundance measures for understanding the rarity of woody plant species across southeast Queensland. Location Southeast Queensland (SEQ), Australia. Methods To supplement an existing data set of 1251 sites with measured crown covers for woody plants in various strata, we sampled stem counts in an additional 30 sites, forming a calibration data set. We ensured that these 30 sites covered the environmental variation in the study area. Generalized additive mixed effects models were used to predict stem counts for all woody plants in the original data set from their crown cover and forest structure values and a site-level variable accounting for water balance. We used these refined abundance data to investigate relationships between local and regional abundance for different size classes. Results Stem densities increased with crown cover in a non-linear fashion; slowing once crown cover reached ca. 5% for shrubs and ca. 50% for trees, depending on their maximum potential height. Using the predicted stem count data, we found that shrubs were more locally abundant than trees, but less regionally abundant. Larger trees (>20 m maximum potential height) showed a negative relationship between local and regional abundance; there was no significant relationship between other size classes. Conclusions Our study demonstrates how crown cover can be converted to stem counts using a calibration data set and flexible mixed-effects models. This enables further ecological analysis at a fraction of the investment required to re-survey. Analyses of abundances revealed that tree species are less locally abundant than smaller woody species, likely due to space filling constraints, but more regionally abundant.

Aim Ecosystem functions such as productivity may be influenced not only by the biological diversity at each location (α-diversity) but also by the biological turnover between locations (β-diversity). We perform a continental-scale test of the strength and direction of relationships between gross primary productivity (GPP) and both α- and β-diversity. Location Continental Australia. Methods Species occurrence records were used to quantify the taxonomic α-diversity of vascular plants in approximately 11,000 1 km × 1 km grid cells across Australia, and to calculate the average β-diversity within a 10-km radius around each cell. The magnitude and variability of monthly, MODIS-derived remotely sensed GPP (2001–12) were summarized for continental Australia, as were rainfall and temperature over the same period. Generalized additive models were then used to test whether the magnitude or variability of GPP were distinctly influenced by either biodiversity measure, over and above the influence of environmental conditions. Results Precipitation and temperature explained large proportions of deviance in the magnitude (75.6%) and variability (38.3%) of GPP across the Australian continent. GPP was marginally more strongly related to species richness than it was to species turnover. However, neither diversity measure provided substantial increases in the explanatory power of GPP models over and above that of environment-only models (always

Conserving different spatial and temporal dimensions of biological diversity is considered necessary for maintaining ecosystem functions under predicted global change scenarios. Recent work has shifted the focus from spatially local (α‐diversity) to macroecological scales (β‐ and γ‐diversity), emphasizing links between macroecological biodiversity and ecosystem functions (MB–EF relationships). However, before the outcomes of MB–EF analyses can be useful to real‐world decisions, empirical modeling needs to be developed for natural ecosystems, incorporating a broader range of data inputs, environmental change scenarios, underlying mechanisms, and predictions. We outline the key conceptual and technical challenges currently faced in developing such models and in testing and calibrating the relationships assumed in these models using data from real ecosystems. These challenges are explored in relation to two potential MB–EF mechanisms: “macroecological complementarity” and “spatiotemporal compensation.” Several regions have been sufficiently well studied over space and time to robustly test these mechanisms by combining cutting‐edge spatiotemporal methods with remotely sensed data, including plant community data sets in Australia, Europe, and North America. Assessing empirical MB–EF relationships at broad spatiotemporal scales will be crucial in ensuring these macroecological processes can be adequately considered in the management of biodiversity and ecosystem functions under global change.

Global biodiversity targets have far-reaching implications for nature conservation worldwide. Scenarios and models hold unfulfilled promise for ensuring such targets are well founded and implemented; here, we review how they can and should inform the Aichi Targets of the Strategic Plan for Biodiversity and their reformulation. They offer two clear benefits: providing a scientific basis for the wording and quantitative elements of targets; and identifying synergies and trade-offs by accounting for interactions between targets and the actions needed to achieve them. The capacity of scenarios and models to address complexity makes them invaluable for developing meaningful targets and policy, and improving conservation outcomes.

International audience; Aim: Modelling the response of β-diversity (i.e., the turnover in species composition among sites) to environmental variation has wide-ranging applications, including in- forming conservation planning, understanding community assembly and forecasting the impacts of climate change. However, modelling β-diversity is challenging, espe- cially for multiple diversity facets (i.e., taxonomic, functional and phylogenetic diver- sity), and current methods have important limitations. Here, we present a new approach for predicting the response of multifaceted β-diversity to the environment, called Multifaceted Biodiversity Modelling (MBM). We illustrate the approach using both a plant diversity dataset from the French Alps and a set of simulated data. We also provide an implementation via an R package.Location: French Alps.Methods: For both the French Alps and the simulated communities, we compute β- diversity indices (e.g., Sørensen dissimilarity, mean functional/phylogenetic pairwise distance) among site pairs. We then apply Gaussian process regression, a flexible nonlinear modelling technique, to predict β-diversity in response to environmental distance among site pairs. For comparison, we also perform similar analyses using Generalized Dissimilarity Modelling (GDM), a well-established method for modelling β-diversity in response to environmental distance.Results: In the Alps, we observed a general increase in taxonomic (TD) and functional (FD) β-diversity (i.e., site pairs were more different from each other) as the climatic distance between site pairs increased. GDM performed better for TD and FD when fitting to calibration data, whereas MBM performed better for both when predicting to a validation dataset. For phylogenetic β-diversity, MBM outperformed GDM in predicting the observed decrease in phylogenetic β-diversity with increasing climatic distance.Main conclusions: Multifaceted Biodiversity Modelling provides a flexible new ap- proach that expands our capacity to model multiple facets of β-diversity. Advantages of MBM over existing methods include simpler assumptions, more flexible modelling, potential to consider multiple facets of diversity across a range of diversity indices, and robust uncertainty estimation.

Metabolic scaling theory (MST) is one of ecology's most high-profile general models and can be used to link size distributions and productivity in forest systems. Much of MST's foundation is based on size distributions following a power law function with a scaling exponent of −2, a property assumed to be consistent in steady-state ecosystems. We tested the theory's generality by comparing actual size distributions with those predicted using MST parameters assumed to be general. We then used environmental variables and functional traits to explain deviation from theoretical expectations. Finally, we compared values of relative productivity predicted using MST with a remote-sensed measure of productivity. We found that fire-prone heath communities deviated from MST-predicted size distributions, whereas fire-sensitive rainforests largely agreed with the theory. Scaling exponents ranged from −1.4 to −5.3. Deviation from the power law assumption was best explained by specific leaf area, which varies along fire frequency and moisture gradients. While MST may hold in low-disturbance systems, we show that it cannot be applied under many environmental contexts. The theory should remain general, but understanding the factors driving deviation from MST and subsequent refinements is required if it is to be applied robustly across larger scales.

Aim: Refugia under past climates have been important in structuring current patterns in diversity, while refugia under anthropogenic climate change will likely be important in retaining this diversity and shaping new patterns. However, few studies have examined the congruence of past, present and future refugia, or the spatiotemporal connectivity of these refugia. Our aim was to test the extent of overlap of refugia under Last Glacial Maximum (LGM), present (2015) and likely future climates (2100), for Tasmania's palaeoendemic flora. We then aimed to identify areas of high spatiotemporal refugia connectivity, as priority areas for conservation and management. Location: Tasmania, Australia. Methods: We developed and applied a new community-level approach to identifying refugia, based on generalized dissimilarity modelling of compositional turnover and a set of reference sites with known biodiversity value. Using these projections of palaeoendemic plant refugia for past, present and future climates, we developed and applied a second approach to quantify the level of connectivity of these refugia over space and time. Results: Although there was large overlap (85%) between current and future climates in the distribution of the highest value palaeoendemic refugia, the small congruence of these areas with refugia at the LGM resulted in only a small area ( c. km 2 ) of persistent high value refugia over all three time periods. Despite this, our spatiotemporal analysis identified several areas of high connectivity in refugial environments for Tasmania's palaeoendemic flora over time. Main conclusions: The community-level approaches we demonstrate here to quantify refugia and their spatiotemporal connectivity have the potential to advance our understanding of biodiversity dynamics, particularly for taxonomic groups that are species-rich, poorly studied or comprised of many rare species, where species-level approaches are less suitable.

Estimating extinction vulnerability for a large number of species presents significant challenges for researchers, but is of high importance considering the large number of species that are currently unassessed. We present a method using a type of artificial neural network (self organizing map; SOM), which utilizes the co-occurrence patterns of species to estimate each species' vulnerability to extinction. We use this method on Australian bird assemblages and compare the SOM-generated rankings for vulnerability with assessments from the IUCN Red List for those species in which populations have actually been estimated. For species that have had their populations estimated, the SOM performed well in distinguishing those species ranked of least concern by IUCN from those species in one of the other IUCN categories. Further, 19 species that were identified as highly vulnerable by the SOM analysis have never had their populations estimated and have been ranked by the IUCN of least concern. We show how the SOM can identify spatial variation in vulnerability for a species, and identify those regions in Australia in which the resident species have the greatest levels of vulnerability (central Australia). We conclude that the SOM provides a useful tool for researchers and agencies dealing with conservation strategies focused on large numbers of species and we urge a more detailed assessment of the 19 bird species identified by this analysis as vulnerable to extinction.

Aim Spatial turnover in plant species composition reflects the interplay between species’ environmental requirements and their dispersal dynamics. However, its origins are also historical, arising from speciation, extinction and past range dynamics. Here, we test the effects of current environmental gradients and geographical distance on regional species turnover. Using a new approach, we then interpret residual turnover patterns, unexplained by these factors. Location Native vegetation in the wheatbelt of the Southwest Australian Floristic Region (SWAFR). Methods We fitted a generalized dissimilarity model of species turnover across 650 floristic inventory plots as a function of current climate, topography, soils and inter-plot distances. We then decomposed the model errors into independent axes of residual variation, representing unexplained species turnover. We mapped the spatial patterns evident on the main residual axes, and interpreted these using ancillary data on the life form and geographical ranges of associated species, the distributions of some major habitat types, and anomalies between recent and Last Glacial Maximum (LGM) climate. Results Soil and climatic variables (especially phosphorus and wet quarter precipitation) made sizeable independent contributions to explaining deviance in species turnover, whereas deviance explained by inter-plot distances and climate co-varied strongly. Total explained deviance was 34%. Analyses of residual dissimilarity highlighted unexplained floristic similarity among many southern plots. A secondary residual axis highlighted unexplained variation in a broadly westerly to easterly direction. Consistent trends in the life form and geographical ranges of associated species suggest that these patterns are climate-related. LGM climatic anomalies were correlated with patterns on the second residual axis. Main conclusions Current soil and climatic gradients are important drivers of floristic turnover in the SWAFR. However, residual analyses revealed broad-scale gradients in floristic composition unexplained by these variables. These appear to reflect imperfect modelling of the effects of current and perhaps historical climate on species turnover.

There is increasing reliance on ecological models to improve our understanding of how ecological systems work, to project likely outcomes under alternative global change scenarios and to help develop robust management strategies. Two common types of spatiotemporally explicit ecological models are those focussed on biodiversity composition and those focussed on ecosystem function. These modelling disciplines are largely practiced separately, with separate literature, despite growing evidence that natural systems are shaped by the interaction of composition and function. Here we call for the development of new modelling approaches that integrate composition and function, accounting for the important interactions between these two dimensions, particularly under rapid global change. We examine existing modelling approaches that have begun to combine elements of composition and function, identifying their potential contribution to fully integrated modelling approaches. The development and application of integrated models of composition and function face a number of important challenges, including biological data limitations, system knowledge and computational constraints. We suggest a range of promising avenues that could help researchers overcome these challenges, including the use of virtual species, macroecological relationships and hybrid correlative-mechanistic modelling. Explicitly accounting for the interactions between composition and function within integrated modelling approaches has the potential to improve our understanding of ecological systems, provide more accurate predictions of their future states and transform their management.

Recently, there has been a surge in interest in how beta-diversity is defined and applied (e.g., [1]). Socolar et al. [2] have rightly recognised the fundamental importance of considering beta-diversity for conserving biodiversity, and clearly explain the various ways in which beta-diversity may respond to contrasting ecological and anthropogenic forces. However, there are many ways in which beta-diversity already underpins much of modern conservation science and practice, although differences in terminology means that this may not always be explicit.

Understanding how plants are constructed—i.e., how key size dimensions and the amount of mass invested in different tissues varies among individuals—is essential for modeling plant growth, carbon stocks, and energy fluxes in the terrestrial biosphere. Allocation patterns can differ through ontogeny, but also among coexisting species and among species adapted to different environments. While a variety of models dealing with biomass allocation exist, we lack a synthetic understanding of the underlying processes. This is partly due to the lack of suitable data sets for validating and parameterizing models. To that end, we present the Biomass And Allometry Database (BAAD) for woody plants. The BAAD contains 259 634 measurements collected in 176 different studies, from 21 084 individuals across 678 species. Most of these data come from existing publications. However, raw data were rarely made public at the time of publication. Thus, the BAAD contains data from different studies, transformed into standard units and variable names. The transformations were achieved using a common workflow for all raw data files. Other features that distinguish the BAAD are: (i) measurements were for individual plants rather than stand averages; (ii) individuals spanning a range of sizes were measured; (iii) plants from 0.01–100 m in height were included; and (iv) biomass was estimated directly, i.e., not indirectly via allometric equations (except in very large trees where biomass was estimated from detailed sub-sampling). We included both wild and artificially grown plants. The data set contains the following size metrics: total leaf area; area of stem cross-section including sapwood, heartwood, and bark; height of plant and crown base, crown area, and surface area; and the dry mass of leaf, stem, branches, sapwood, heartwood, bark, coarse roots, and fine root tissues. We also report other properties of individuals (age, leaf size, leaf mass per area, wood density, nitrogen content of leaves and wood), as well as information about the growing environment (location, light, experimental treatment, vegetation type) where available. It is our hope that making these data available will improve our ability to understand plant growth, ecosystem dynamics, and carbon cycling in the world's vegetation.