One of the most common descriptors of the relationship between root and shoot biomass is the root : shoot ratio, which has become a core method for estimating root biomass from the more easily measured shoot biomass. Previous reviews have examined root : shoot ratio data, but have only considered particular vegetation types and have not always critically reviewed the data used. Reliable root : shoot ratios are needed for a wide range of vegetation types in order to improve the accuracy of root biomass estimates, including those required for estimating the effects of land management and land use change in National Greenhouse Gas Inventories.This study reviewed root : shoot ratios in terrestrial biomes. A key facet of our analysis was a critical methodological review, through which unreliable data were identified and omitted on the basis of specific criteria. Of the 786 root : shoot ratio observations collated, 62% were omitted because of inadequate or unverifiable root sampling methods. When only the reliable data were examined, root : shoot ratios were found to be negatively related to shoot biomass, mean annual precipitation, mean annual temperature, forest stand age, and forest stand height.Although a single allometric equation derived in this study reliably predicted root biomass from shoot biomass for forests and woodlands, in general, the use of vegetationspecific root : shoot ratios were found to be a more accurate method for predicting root biomass. When the root : shoot ratio data collated here were applied to an analysis of the global carbon budget, there was a 50% increase in estimated global root carbon stock, and a 12% increase in estimated total carbon stock of terrestrial vegetation. The use of the vegetation-specific root : shoot ratios presented in this study is likely to substantially improve the accuracy of root biomass estimates for purposes such as carbon accounting and for studies of ecosystem dynamics.

Plant traits-the morphological, anatomical, physiological, biochemical and phenological characteristics of plants-determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait-based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits-almost complete coverage for 'plant growth form'. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait-environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects.We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives. Geosphere-Biosphere Program (IGBP) and DIVERSITAS, the TRY database (TRY-not an acronym, rather a statement of sentiment; https ://www.try-db.org; Kattge et al., 2011) was proposed with the explicit assignment to improve the availability and accessibility of plant trait data for ecology and earth system sciences. The Max Planck Institute for Biogeochemistry (MPI-BGC) offered to host the database and the different groups joined forces for this community-driven program. Two factors were key to the success of TRY: the support and trust of leaders in the field of functional plant ecology submitting large databases and the long-term funding by the Max Planck Society, the MPI-BGC and the German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, which has enabled the continuous development of the TRY database.

Summary 1.Exper i mental studies have provided significant knowledge of how biodiversity can influence ecosystem processes. However, there is a growing need to relate these findings to natural communities. 2. Here we identify two major hypotheses for how communities may influence ecosystem processes: the 'diversity hypothesis' (the diversity of organisms in a community influences ecosystem processes through mechanisms such as complementary resource use), and the 'mass ratio hypothesis' (ecosystem processes are determined overwhelmingly by the functional traits of the dominant species). We then test which of these two hypotheses best explain variation in ecosystem properties and processes (biomass pools and fluxes, water use, light interception) in a temperate native grassland. We do this by applying various measures of diversity, functional diversity, and functional identity, whose significant relations with ecosystem processes would support either of the competing hypotheses. 3. Mean trait values best explained variation in five of the eight ecosystem processes examined, supporting Grime's mass ratio hypothesis, which proposes that the functional identities of the dominant species largely determine ecosystem processes. 4. Multi-trait functional diversity indices also explained large amounts of variation in ecosystem processes, while only weak relationships were observed between species richness and ecosystem processes. 5. To explore the mechanistic interactions between variables, we developed structural equation models (SEMs), which indicated that many of the community diversity and trait properties significantly influenced ecosystem processes, even after accounting for co-varying biotic/abiotic factors. 6. Synthesis . Our study is one of the first explicit comparisons of the 'diversity' and 'mass ratio' hypotheses, and our results most strongly support the mass ratio hypothesis, that is, the traits of the dominant species most influenced the ecosystem properties and processes examined. Our results suggest that the management of communities for the maintenance of ecosystem processes should focus on species dominance hierarchies.

Refugia – areas that may facilitate the persistence of species during large‐scale, long‐term climatic change –are increasingly important for conservation planning. There are many methods for identifying refugia, but the ability to quantify their potential for facilitating species persistence (ie their “capacity”) remains elusive. We propose a flexible framework for prioritizing future refugia, based on their capacity. This framework can be applied through various modeling approaches and consists of three steps: (1) definition of scope, scale, and resolution; (2) identification and quantification; and (3) prioritization for conservation. Capacity is quantified by multiple indicators, including environmental stability, microclimatic heterogeneity, size, and accessibility of the refugium. Using an integrated, semi‐mechanistic modeling technique, we illustrate how this approach can be implemented to identify refugia for the plant diversity of Tasmania, Australia. The highest‐capacity climate‐change refugia were found primarily in cool, wet, and topographically complex environments, several of which we identify as high priorities for biodiversity conservation and management.

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.

CABI:20153174020Understanding 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 259634 measurements collected in 176 different studies, from 21084 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

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.

Reliable projections of climate-change impacts on biodiversity are vital in formulating conservation and management strategies that best retain biodiversity into the future. While recent modelling has focussed largely on individual species, macroecology has the potential to add significant value to these efforts, by incorporating important community-level constraints and processes. Here we show how a new dynamic macroecological approach can project climate-change impacts collectively across all species in a diverse taxonomic group, overcoming shortfalls in our knowledge of biodiversity, while incorporating the key processes of dispersal and community assembly. Our approach applies a recently published technique (DynamicFOAM) to predict the present composition of every community, which form the initial conditions for a new metacommunity model (M-SET) that projects changes in composition over time, under specified climate and habitat scenarios. Applying this approach at fine resolution to plant biodiversity in Tasmania (2,051 species; 1,157,587 communities), we project high average turnover in community composition from 2010 to 2100 (mean Sorensen's dissimilarity = 0.71 (±7.0 × 10 )), with major reductions in species richness (32.9 (±0.02) species lost per community) and no plant species benefitting from climate change in the long term. We also demonstrate how our modelling approach can identify habitat likely to be of high value for retaining rare and poorly reserved species under climate change. Our analyses highlight the potential value of this dynamic macroecological approach, that incorporates key ecological processes in projecting climate change impacts for all species simultaneously and uses simple macroecological inputs that can be derived even for highly diverse and poorly studied taxa.