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Making predictions about species, including how they respond to environmental change, is a central challenge for ecologists. Due to the huge number of species, ecologists seek generalizations based on species’ traits and phylogenetic relationships, but the predictive power of trait-based and phylogenetic models is often low. Species co-occurrence patterns may contain additional information about species’ ecological attributes not captured by traits or phylogenies.We propose using ordination to encode the information contained in species co-occurrence data in low-dimensional vectors that can be used to represent species in ecological prediction. We present an efficient method to derive species vectors from co-occurrence data using GloVe, an unsupervised learning algorithm originally designed for language modeling. To demonstrate the method, we used GloVe to generate vectors for nearly 40,000 plant species using co-occurrence statistics derived from a global vegetation dataset and tested their ability to predict elevational range shifts in European montane plant species.Co-occurrence-based species vectors were weakly correlated with traits or phylogeny, indicating that they encode unique information about species. Models including co-occurrence-based vectors explained twice as much variation in species range shifts as models including only traits or phylogenetic information.Given the widespread availability of species occurrence data, species vectors learned from co-occurrence patterns are a widely applicable and powerful tool for encoding ecological information about species, with many potential applications for describing and predicting the ecology of species, communities, and ecosystems.

A major goal in ecology is to make generalizable predictions of organism responses to environmental variation based on their traits. However, straightforward relationships between traits and fitness are rare and likely to vary with environmental context. Characterizing how traits mediate demographic responses to the environment may enhance the predictions of organism responses to global change. We synthesized 15 years of demographic data and species-level traits in a shortgrass steppe to determine whether the effects of leaf and root traits on growth and survival depended on seasonal water availability. We predicted that (1) species with drought-tolerant traits, such as lower leaf turgor loss point (TLP) and higher leaf and root dry matter content (LDMC and RDMC), would be more likely to survive and grow in drier years due to higher wilting resistance, (2) these traits would not predict fitness in wetter years, and (3) traits that more directly measure physiological mechanisms of water use such as TLP would best predict demographic responses. We found that graminoids with more negative TLP and higher LDMC and RDMC had higher survival rates in drier years. Forbs demonstrated similar yet more variable responses. Graminoids grew larger in wetter years, regardless of traits. However, in both wet and dry years, graminoids with more negative TLP and higher LDMC and RDMC grew larger than less negative TLP and low LDMC and RDMC species. Traits significantly mediated the impact of drought on survival, but not growth, suggesting that survival could be a stronger driver of species' drought response in this system. TLP predicted survival in drier years, but easier to measure LDMC and RDMC were equal or better predictors. These results advance our understanding of the mechanisms by which drought drives population dynamics, and show that abiotic context determines how traits drive fitness.

The Global Vegetation Project (http://gveg.wyobiodiversity.org) is a new initiative to host an online database of open-access, georeferenced vegetation photos. The mission of the Global Vegetation Project is ‘to inspire and empower people of all ages to learn about the diversity of vegetation on our planet and to provide educators with a resource for teaching ecology online’. The beta release includes two R-Shiny web applications that allow users to 1) submit photos of plant communities through a user-friendly online portal and 2) explore submissions made by others through an interactive global map. The spatial coordinates of each photo are used to extract information about the location including long-term and recent climate data to create Walter and Leith climate diagrams for each photo. User submitted photos can be filtered by biome, temperature, precipitation, and elevation on the map. The Global Vegetation Project will evolve to match the needs of vegetation scientists and ecology educators. We intend to enhance the educational value of the mapping application by incorporating additional search features, global data layers, and the publication of curricula geared towards primary, secondary, and post-secondary education. We encourage the global community of vegetation scientists to use this resource in their classrooms and to contribute photos of vegetation to grow this valuable resource for the world.

All organisms must simultaneously tolerate the environment and access limiting resources if they are to persist. Otherwise they go extinct. Approaches to understanding environmental tolerance and resource competition have generally been developed independently. Consequently, integrating the factors that determine abiotic tolerance with those that affect competitive interactions to model species abundances and community structure remains an unresolved challenge. This is likely the reason why current models of community assembly do not accurately predict species abundances and dynamics. Here, we introduce a new synthetic framework that models both abiotic tolerance and biotic competition by using functional traits, which are phenotypic attributes that influence organism fitness. First, our framework estimates species carrying capacities that vary along abiotic gradients based on whether the phenotype tolerates the local environment. Second, it estimates pairwise competitive interactions as a function of multidimensional trait differences between species and determines which trait combinations produce the most competitive phenotypes. We demonstrate that our combined approach more than doubles the explained variance of species covers in a wetland community compared to the model of abiotic tolerances alone. Trait-based integration of competitive interactions and abiotic filtering improves our ability to predict species abundances across space, bringing us closer to more accurate predictions of biodiversity structure in a changing world.

This data set consists of 101 permanent 1 m × 1 m (1-m

Functional traits affect the demographic performance of individuals in their environment, leading to fitness differences that scale up to drive population dynamics and community assembly. Understanding the links between traits and fitness is therefore critical for predicting how populations and communities respond to environmental change. However, the net effects of traits on species fitness are largely unknown because we have lacked a framework for estimating fitness across multiple species and environments.We present a modeling framework that integrates trait effects on demographic performance over the life cycles of individuals to estimate the net effect of traits on species fitness. This approach involves 1) modeling trait effects on individual demographic rates (growth, survival, and recruitment) as multidimensional performance surfaces that vary with individual size and environment and 2) integrating these effects into a population model to project population growth rates (i.e., fitness) as a function of traits and environment. We illustrate our approach by estimating performance surfaces and fitness landscapes for trees across a temperature gradient in the eastern United States.Functional traits (wood density, specific leaf area, and maximum height) interacted with individual size and temperature to influence tree growth, survival, and recruitment rates, generating demographic trade-offs and shaping the contours of fitness landscapes. Tall tree species had high survival, growth, and fitness across the temperature gradient. Wood density and specific leaf area had interactive effects on demographic performance, resulting in fitness landscapes with multiple peaks.With this approach it is now possible to empirically estimate the net effect of traits on fitness, leading to improved understanding of the selective forces that drive community assembly and permitting generalizable predictions of population and community dynamics in changing environments.

The popularity of trait-based approaches continues to rise despite challenges in identifying strong links between traits and organism performance. Here, we summarise evidence demonstrating that not all traits appear to be functional, and discuss how life history theory and demography can help elucidate which, how, where, and when traits gain functionality

This book addresses an important problem in ecology: how are communities assembled from species pools? This pressing question underlies a broad array of practical problems in ecology and environmental science, including restoration of damaged landscapes, management of protected areas, and protection of threatened species. This book presents a simple logical structure for ecological assembly and addresses key areas including species pools, traits, environmental filters, and functional groups. It demonstrates the use of two predictive models (CATS and Traitspace) and consists of many wide-ranging examples including plants in deserts, wetlands, and forests, and communities of fish, amphibians, birds, mammals, and fungi. Global in scope, this volume ranges from the arid lands of North Africa, to forests in the Himalayas, to Amazonian floodplains. There is a strong focus on applications, particularly the twin challenges of conserving biodiversity and understanding community responses to climate change.

International audience; Plant trait variation drives plant function, community composition, and ecosystem processes. However, our current understanding of trait variation disproportionately relies on aboveground observations. Here we integrate root traits into the global framework of plant form and function. We developed and tested an overarching conceptual framework that integrates two recently identified root trait gradients with a well-established aboveground plant trait framework. We confronted our novel framework with published relationships between above- and belowground trait analogues and with multivariate analyses of aboveground and belowground traits of 2510 species. Our traits represent the leaf- and root conservation gradients (specific leaf area, leaf and root nitrogen concentration and root tissue density), the root collaboration gradient (root diameter and specific root length), and the plant size gradient (plant height and rooting depth). We found that an integrated, whole-plant trait space required as much as four axes. The two main axes represented the fast-slow ‘conservation’ gradient on which leaf and fine-root traits were well aligned, and the ‘collaboration’ gradient in roots. The two additional axes were separate, orthogonal plant size axes for height and rooting depth. This perspective on the multi-dimensional nature of plant trait variation better encompasses plant function and influence on the surrounding environment.

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

21 Pág. Instituto de Ciencias Forestales (ICIFOR), Ecological theory is built on trade-offs, where trait differences among species evolved as adaptations to different environments. Trade-offs are often assumed to be bidirectional, where opposite ends of a gradient in trait values confer advantages in different environments. However, unidirectional benefits could be widespread if extreme trait values confer advantages at one end of an environmental gradient, whereas a wide range of trait values are equally beneficial at the other end. Here, we show that root traits explain species occurrences along broad gradients of temperature and water availability, but model predictions only resembled trade-offs in two out of 24 models. Forest species with low specific root length and high root tissue density (RTD) were more likely to occur in warm climates but species with high specific root length and low RTD were more likely to occur in cold climates. Unidirectional benefits were more prevalent than trade-offs: for example, species with large-diameter roots and high RTD were more commonly associated with dry climates, but species with the opposite trait values were not associated with wet climates. Directional selection for traits consistently occurred in cold or dry climates, whereas a diversity of root trait values were equally viable in warm or wet climates. Explicit integration of unidirectional benefits into ecological theory is needed to advance our understanding of the consequences of trait variation on species responses to environmental change., sPlot was initiated by sDiv and funded by the German Research Foundation (FZT 118) and is now a platform of iDiv. The sRoot workshops and L.M. were also supported by NWO-Vidi grant 864.14.006. C.M.I. and the Fine-Root Ecology Database were supported by the Biological and Environmental Research program in the US Department of Energy’s Office of Science. J.B. was supported by Deutsche Forschungsgemeinschaft (DFG) project 432975993. N.R.G.-R. thanks the Dorothea Schlözer Postdoctoral Programme of the Georg-August-Universität.

This study examined long-term stability in water quality of Lake Okataina, North Island, New Zealand, inferred from sediments deposited from 1836 to 2009, during which time there was a pyroclastic volcanic eruption (Tarawera 1886) and successive introductions of invasive mammals. Multiple geochemical proxies were analysed stratigraphically for organic matter loading, primary productivity, redox potential and phosphorus retention capacity. Organic matter loading to the lake is mostly authochthonous (algae) although there was increased allochthonous (terrestrial) loading immediately after the Tarawera eruption. After 1920, there was a sustained increase in inorganic erosion flux. Primary productivity appeared to increase considerably about 1960, but has remained relatively unchanged more recently. Our results suggest that the lake could recover from the impact of the volcanic eruption within a few decades, but the introduction of mammals may affect its resilience and lead to more pervasive impacts on the biogeochemistry.

Abiotic and biotic factors control ecosystem biodiversity, but their relative contributions remain unclear. The ultraoligotrophic ecosystem of the Antarctic Dry Valleys, a simple yet highly heterogeneous ecosystem, is a natural laboratory well-suited for resolving the abiotic and biotic controls of community structure. We undertook a multidisciplinary investigation to capture ecologically relevant biotic and abiotic attributes of more than 500 sites in the Dry Valleys, encompassing observed landscape heterogeneities across more than 200 km2. Using richness of autotrophic and heterotrophic taxa as a proxy for functional complexity, we linked measured variables in a parsimonious yet comprehensive structural equation model that explained significant variations in biological complexity and identified landscape-scale and fine-scale abiotic factors as the primary drivers of diversity. However, the inclusion of linkages among functional groups was essential for constructing the best-fitting model. Our findings support the notion that biotic interactions make crucial contributions even in an extremely simple ecosystem., Charles Lee, Daniel Laughlin et al. use structural equation modeling to analyze ecological data from more than 500 sites in the Antarctic Dry Valleys. They find that although abiotic factors are the primary drivers of biodiversity variation, biotic interactions are needed to explain the data fully and may play previously underestimated roles.

It has long been anticipated that relating functional traits to species demography would be a cornerstone for achieving large-scale predictability of ecological systems. If such a relationship existed, species demography could be modeled only by measuring functional traits, transforming our ability to predict states and dynamics of species-rich communities with process-based community models. Here, we introduce a new method that links empirical functional traits with the demographic parameters of a process-based model by calibrating a transfer function through inverse modeling. As a case study, we parameterize a modified Lotka–Volterra model of a high-diversity mountain grassland with static plant community and functional trait data only. The calibrated trait–demography relationships are amenable to ecological interpretation, and lead to species abundances that fit well to the observed community structure. We conclude that our new method offers a general solution to bridge the divide between trait data and process-based models in species-rich ecosystems. Advances in process-based community ecology models are hindered by the challenge of linking functional traits to demography in species-rich systems, where a high number of parameters need to be estimated from limited data. Here the authors propose a new Bayesian framework to calibrate community models via functional traits, and validate it in a species-rich plant community.

[Motivation]: Trait data are fundamental to the quantitative description of plant form and function. Although root traits capture key dimensions related to plant responses to changing environmental conditions and effects on ecosystem processes, they have rarely been included in large-scale comparative studies and global models. For instance, root traits remain absent from nearly all studies that define the global spectrum of plant form and function. Thus, to overcome conceptual and methodological roadblocks preventing a widespread integration of root trait data into large-scale analyses we created the Global Root Trait (GRooT) Database. GRooT provides ready to use data by combining the expertise of root ecologists with data mobilization and curation. Specifically, we (a) determined a set of core root traits relevant to the description of plant form and function based on an assessment by experts, (b) maximized species coverage through data standardization within and among traits, and (c) implemented data quality checks., [Main types of variables contained]: GRooT contains 114,222 trait records on 38 continuous root traits.Spatial location and grain: Global coverage with data from arid, continental, polar, temperate and tropical biomes. Data on root traits were derived from experimental studies and field studies., [Time period and grain]: Data were recorded between 1911 and 2019., [Major taxa and level of measurement]: GRooT includes root trait data for which taxonomic information is available. Trait records vary in their taxonomic resolution, with subspecies or varieties being the highest and genera the lowest taxonomic resolution available. It contains information for 184 subspecies or varieties, 6,214 species, 1,967 genera and 254 families. Owing to variation in data sources, trait records in the database include both individual observations and mean values., [Software format]: GRooT includes two csv files. A GitHub repository contains the csv files and a script in R to query the database., The study has been supported by the TRY initiative on plant traits (http://www.try-db.org). The TRY initiative and database are hosted, developed and maintained at the Max Planck Institute for Biogeochemistry, Jena, Germany. TRY is currently supported by DIVERSITAS/Future Earth and the German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig. Open access funding enabled and organized by Projekt DEAL.

Collaboration broadens the “root economics space” ranging from “do-it-yourself” to “outsourcing” to mycorrhizal partners., Plant economics run on carbon and nutrients instead of money. Leaf strategies aboveground span an economic spectrum from “live fast and die young” to “slow and steady,” but the economy defined by root strategies belowground remains unclear. Here, we take a holistic view of the belowground economy and show that root-mycorrhizal collaboration can short circuit a one-dimensional economic spectrum, providing an entire space of economic possibilities. Root trait data from 1810 species across the globe confirm a classical fast-slow “conservation” gradient but show that most variation is explained by an orthogonal “collaboration” gradient, ranging from “do-it-yourself” resource uptake to “outsourcing” of resource uptake to mycorrhizal fungi. This broadened “root economics space” provides a solid foundation for predictive understanding of belowground responses to changing environmental conditions.

Aim: Predictions of plant traits over space and time are increasingly used to improve our understanding of plant community responses to global environmental change. A necessary step forward is to assess the reliability of global trait predictions. In this study, we predict community mean plant traits at the global scale and present a systematic evaluation of their reliability in terms of the accuracy of the models, ecological realism and various sources of uncertainty. Location: Global. Time period: Present. Major taxa studied: Vascular plants. Methods: We predicted global distributions of community mean specific leaf area, leaf nitrogen concentration, plant height and wood density with an ensemble modelling approach based on georeferenced, locally measured trait data representative of the plant community. We assessed the predictive performance of the models, the plausibility of predicted trait combinations, the influence of data quality, and the uncertainty across geographical space attributed to spatial extrapolation and diverging model predictions. Results: Ensemble predictions of community mean plant height, specific leaf area and wood density resulted in ecologically plausible trait–environment relationships and trait–trait combinations. Leaf nitrogen concentration, however, could not be predicted reliably. The ensemble approach was better at predicting community trait means than any of the individual modelling techniques, which varied greatly in predictive performance and led to divergent predictions, mostly in African deserts and the Arctic, where predictions were also extrapolated. High data quality (i.e., including intraspecific variability and a representative species sample) increased model performance by 28%. Main conclusions: Plant community traits can be predicted reliably at the global scale when using an ensemble approach and high-quality data for traits that mostly respond to large-scale environmental factors. We recommend applying ensemble forecasting to account for model uncertainty, using representative trait data, and more routinely assessing the reliability of trait predictions. © 2020 The Authors. Global Ecology and Biogeography published by John Wiley and Sons Ltd

The Dry Valleys of Antarctica are a unique ecosystem of simple trophic structure, where the abiotic factors that influence soil bacterial communities can be resolved in the absence of extensive biotic interactions. This study evaluated the degree to which aspects of topographic, physicochemical and spatial variation explain patterns of bacterial richness and community composition in 471 soil samples collected across a 220 square kilometer landscape in Southern Victoria Land. Richness was most strongly influenced by physicochemical soil properties, particularly soil conductivity, though significant trends with several topographic and spatial variables were also observed. Structural equation modeling (SEM) supported a final model in which variation in community composition was best explained by physicochemical variables, particularly soil water content, and where the effects of topographic variation were largely mediated through their influence on physicochemical variables. Community dissimilarity increased with distance between samples, and though most of this variation was explained by topographic and physicochemical variation, a small but significant relationship remained after controlling for this environmental variation. As the largest survey of terrestrial bacterial communities of Antarctica completed to date, this work provides fundamental knowledge of the Dry Valleys ecosystem, and has implications globally for understanding environmental factors that influence bacterial distributions.

Generalizing the effect of traits on performance across species may be achievable if traits explain variation in population fitness. However, testing relationships between traits and vital rates to infer effects on fitness can be misleading. Demographic trade-offs can generate variation in vital rates that yield equal population growth rates, thereby obscuring the net effect of traits on fitness. To address this problem, we describe a diversity of approaches to quantify intrinsic growth rates of plant populations, including experiments beyond range boundaries, density-dependent population models built from long-term demographic data, theoretical models, and methods that leverage widely available monitoring data. Linking plant traits directly to intrinsic growth rates is a fundamental step toward rigorous predictions of population dynamics and community assembly.

Functional traits affect species ranges, but it is unclear how above and below-ground traits determine species responses to flooding and herbivory in wetland communities. We analyzed community-weighted means (CWM) of nine functional traits along a hydrological gradient inside and outside of grazing exclosures in an ephemeral wetland in New Zealand. Root traits exhibited the strongest relationships with the flooding gradient. Root aerenchyma increased linearly as flooding increased but plateaued in the ungrazed treatment. Root dry matter content and root tissue density decreased non-linearly as flooding increased. Grazing reduced the strength of the trait-flooding relationships for all traits except root tissue density, specific root length, and aboveground dry matter content. Specific leaf area decreased non-linearly and height increased linearly with flooding in the ungrazed treatment. Our results indicate that fine root traits can provide more information about the functional adaptation of plants to variation in flooding duration than aboveground traits, but traits such as height and specific leaf area responded to the exclusion of herbivores. Altered hydrological dynamics driven by changes in precipitation regimes will induce shifts in species ranges. Fine root traits, especially aerenchyma, can be used to predict how wetland species will respond to changes in soil hydrology.

Aim: Plant trait databases often contain traits that are correlated, but for whom direct (undirected statistical dependency) and indirect (mediated by other traits) connections may be confounded. The confounding of correlation and connection hinders our understanding of plant strategies, and how these vary among growth forms and climate zones. We identified the direct and indirect connections across plant traits relevant to competition, resource acquisition and reproductive strategies using a global database and explored whether connections within and between traits from different tissue types vary across climates and growth forms. Location: Global. Major taxa studied: Plants. Time period: Present. Methods: We used probabilistic graphical models and a database of 10 plant traits (leaf area, specific leaf area, mass- and area-based leaf nitrogen and phosphorous content, leaf life span, plant height, stem specific density and seed mass) with 16,281 records to describe direct and indirect connections across woody and non-woody plants across tropical, temperate, arid, cold and polar regions. Results: Trait networks based on direct connections are sparser than those based on correlations. Land plants had high connectivity across traits within and between tissue types; leaf life span and stem specific density shared direct connections with all other traits. For both growth forms, two groups of traits form modules of more highly connected traits; one related to resource acquisition, the other to plant architecture and reproduction. Woody species had higher trait network modularity in polar compared to temperate and tropical climates, while non-woody species did not show significant differences in modularity across climate regions. Main conclusions: Plant traits are highly connected both within and across tissue types, yet traits segregate into persistent modules of traits. Variation in the modularity of trait networks suggests that trait connectivity is shaped by prevailing environmental conditions and demonstrates that plants of different growth forms use alternative strategies to cope with local conditions. © 2019 John Wiley and Sons Ltd

Restoring resilient ecosystems in an era of rapid environmental change requires a flexible framework for selecting assemblages of species based on functional traits. However, current trait-based models have been limited to algorithms that select species assemblages that only converge on specified average trait values, and could not accommodate the common desire among restoration ecologists to generate functionally diverse assemblages. We have solved this problem by applying a nonlinear optimization algorithm to solve for the species relative abundances that maximize Rao's quadratic entropy (Q) subject to other linear constraints. Rao's Q is a closed-form algebraic expression of functional diversity that is maximized when the most abundant species are functionally dissimilar. Previous models have maximized species evenness subject to the linear constraints by maximizing the entropy function (H'). Maximizing Q alone produces an undesirable species abundance distribution because species that exhibit extreme trait values have the highest abundances. We demonstrate that the maximization of an objective function that additively combines Q and H' produces a more even relative abundance distribution across the trait dimension. Some ecological restoration projects aim to restore communities that converge on one set of traits while diverging across another. The selectSpecies r function can derive assemblages for any size species pool that maximizes the diversity of any set of traits, while simultaneously converging on average values of any other set of traits. We demonstrate how the function works through examples using uniformly spaced trait distributions and data with a known structure. We also demonstrate the utility of the function using real trait data collected on dozens of species from three separate ecosystems: serpentine grasslands, ponderosa pine forests, and subtropical rainforests. The quantitative selection of species based on their functional traits for ecological restoration and experimentation must be both rigorous and accessible to practitioners. The selectSpecies function provides ecologists with an easy-to-use open-source solution to objectively derive species assemblages based on their functional traits.

Resource availability and biotic interactions control opportunities for the establishment and expansion of invasive species. Studies on biotic resistance to plant invasions have typically focused on competition and occasionally on herbivory, while resource-oriented studies have focused on water or nutrient pulses. Through synthesizing these approaches, we identify conditions that create invasion opportunities. In a nested fully factorial experiment, we examined how chronic alterations in water availability and rodent density influenced the density of invasive species in both the Mojave Desert and the Great Basin Desert after fire. We used structural equation modeling to examine the direct and mediated effects controlling the density of invasives in both deserts. In the first 2 years after our controlled burn in the Great Basin, we observed that fire had a direct effect on increasing the invasive forb Halogeton glomeratus as well as a mediated effect through reducing rodent densities and herbivory. 4 years after the burn, the invasive annual grass Bromus tectorum was suppressing Halogeton glomeratus in mammal exclusion plots. There was a clear transition from years where invasives were controlled by disturbance and trophic interactions to years were resource availability and competition controlled invasive density. Similarly, in the Mojave Desert we observed a strong early influence of trophic processes on invasives, with Schismus arabicus benefitted by rodents and Bromus rubens negatively influenced by rodents. In the Mojave Desert, post-fire conditions became less important in controlling the abundance of invasives over time, while Bromus rubens was consistently benefitted by increases in fall rainfall.

Correlations between community-weighted mean (CWM) traits and environmental gradients are often assumed to quantify the adaptive value of traits. We tested this assumption by comparing these correlations with models of survival probability using 46 perennial species from long-term permanent plots in pine forests of Arizona. Survival was modelled as a function of trait × environment interactions, plant size, climatic variation and neighbourhood competition. The effect of traits on survival depended on the environmental conditions, but the two statistical approaches were inconsistent. For example, CWM-specific leaf area (SLA) and soil fertility were uncorrelated. However, survival was highest for species with low SLA in infertile soil, a result which agreed with expectations derived from the physiological trade-off underpinning leaf economic theory. CWM trait-environment relationships were unreliable estimates of how traits affected survival, and should only be used in predictive models when there is empirical support for an evolutionary trade-off that affects vital rates.

International audience; The origins of agriculture were key events in human history, during which people came to depend for their food on small numbers of animal and plant species. However, the biological traits determining which species were domesticated for food provision, and which were not, are unclear. Here, we investigate the phylogenetic distribution of livestock and crops, and compare their phenotypic traits with those of wild species. Our results indicate that phylogenetic clustering is modest for crop species but more intense for livestock. Domesticated species explore a reduced portion of the phenotypic space occupied by their wild counterparts and have particular traits in common. For example, herbaceous crops are globally characterized by traits including high leaf nitrogen concentration and tall canopies, which make them fast-growing species and proficient competitors. Livestock species are relatively large mammals with low basal metabolic rates, which indicate moderate to slow life histories. Our study therefore reveals ecological differences in domestication potential between plants and mammals. Domesticated plants belong to clades with traits that are advantageous in intensively managed high-resource habitats, whereas domesticated mammals are from clades adapted to moderately productive environments. Combining comparative phylogenetic methods with ecologically relevant traits has proven useful to unravel the causes and consequences of domestication.

Restoring forest structure and composition is an important component of urban land management, but we lack clear understanding of the mechanisms driving restoration success. Here we studied two indicators of restoration success in temperate rainforests: native tree regeneration and epiphyte colonization. We hypothesized that ecosystem properties such as forest canopy openness, abundance of exotic herbaceous weeds, and the microclimate directly affect the density and diversity of native tree seedlings and epiphytes. Relationships between environmental conditions and the plant community were investigated in 27 restored urban forests spanning 3-70 years in age and in unrestored and remnant urban forests. We used structural equation modelling to determine the direct and indirect drivers of native tree regeneration and epiphyte colonization in the restored forests. Compared to remnant forest, unrestored forest had fewer native canopy tree species, significantly more light reaching the forest floor annually, and higher exotic weed cover. Additionally, epiphyte density was lower and native tree regeneration density was marginally lower in the unrestored forests. In restored forests, light availability was reduced to levels found in remnant forests within 20 years of restoration planting, followed shortly thereafter by declines in herbaceous exotic weeds and reduced fluctuation of relative humidity and soil temperatures. Contrary to expectations, canopy openness was only an indirect driver of tree regeneration and epiphyte colonization, but it directly regulated weed cover and microclimatic fluctuations, both of which directly drove the density and richness of regeneration and epiphyte colonization. Epiphyte density and diversity were also positively related to forest basal area, as large trees provide physical habitat for colonization. These results imply that ecosystem properties change predictably after initial restoration plantings, and that reaching critical thresholds in some ecosystem properties makes conditions suitable for the regeneration of late successional species, which is vital for restoration success and long-term ecosystem sustainability. Abiotic and biotic conditions that promote tree regeneration and epiphyte colonization will likely be present in forests with a basal area ≥27 m2 /ha. We recommend that urban forest restoration plantings be designed to promote rapid canopy closure to reduce light availability, suppress herbaceous weeds, and stabilize the microclimate.

Understanding the magnitude of phenotypic plasticity within a species is important, particularly when comparing species from diverse habitats or when using traits from global databases. Our objective was to quantify the magnitude of intraspecific variability of fine root, stem, and leaf traits in response to nutrient availability. We measured growth rates and traits from fine roots, stems, and leaves on replicate seedlings of four species grown in two treatments: low and high soil nutrient availability. We used ANOVA to test for effects of fertilizer, species, and their interaction on trait expression, and ranked intraspecific trait variation using the coefficient of variation. Root nutrient concentration, relative growth rate, and biomass allocation exhibited the most plastic responses to nutrient availability. Specific root length, specific leaf area, root diameter, and wood density were the least variable traits within species. Nutrient limitation only induced greater root branching in the non-mycorrhizal Proteaceae species that produced more cluster roots. Woody plants respond to nutrient limitation by increasing root mass fraction, rather than by adjusting root morphology or structure. We urge caution when using root tissue chemistry traits obtained from global databases in local studies, as this is one of the most plastic traits in response to nutrient availability.