Plant functional traits can predict community assembly and ecosystem functioning and are thus widely used in global models of vegetation dynamics and land–climate feedbacks. Still, we lack a global... Show morePlant functional traits can predict community assembly and ecosystem functioning and are thus widely used in global models of vegetation dynamics and land–climate feedbacks. Still, we lack a global understanding of how land and climate affect plant traits. A previous global analysis of six traits observed two main axes of variation: (1) size variation at the organ and plant level and (2) leaf economics balancing leaf persistence against plant growth potential. The orthogonality of these two axes suggests they are differently influenced by environmental drivers. We find that these axes persist in a global dataset of 17 traits across more than 20,000 species. We find a dominant joint effect of climate and soil on trait variation. Additional independent climate effects are also observed across most traits, whereas independent soil effects are almost exclusively observed for economics traits. Variation in size traits correlates well with a latitudinal gradient related to water or energy limitation. In contrast, varia- tion in economics traits is better explained by interactions of climate with soil fertility. These findings have the potential to improve our understanding of biodiversity patterns and our predictions of climate change impacts on biogeochemical cycles. Show less
The leaf economic spectrum is a widely studied axis of plant trait variability that defines a trade-off between leaf longevity and productivity. While this has been investigated at the global scale... Show moreThe leaf economic spectrum is a widely studied axis of plant trait variability that defines a trade-off between leaf longevity and productivity. While this has been investigated at the global scale, where it is robust, and at local scales, where deviations from it are common, it has received less attention at the intermediate scale of plant functional types (PFTs). We investigated whether global leaf economic relationships are also present within the scale of plant functional types (PFTs) commonly used by Earth System models, and the extent to which this global-PFT hierarchy can be used to constrain trait estimates. We developed a hierarchical multivariate Bayesian model that assumes separate means and covariance structures within and across PFTs and fit this model to seven leaf traits from the TRY database related to leaf longevity, morphology, biochemistry, and photosynthetic metabolism. Although patterns of trait covariation were generally consistent with the leaf economic spectrum, we found three approximate tiers to this consistency. Relationships among morphological and biochemical traits (specific leaf area [SLA], N, P) were the most robust within and across PFTs, suggesting that covariation in these traits is driven by universal leaf construction trade-offs and stoichiometry. Relationships among metabolic traits (dark respiration [R-d], maximum RuBisCo carboxylation rate [V-c,V-max], maximum electron transport rate [J(max)]) were slightly less consistent, reflecting in part their much sparser sampling (especially for high-latitude PFTs), but also pointing to more flexible plasticity in plant metabolistm. Finally, relationships involving leaf lifespan were the least consistent, indicating that leaf economic relationships related to leaf lifespan are dominated by across-PFT differences and that within-PFT variation in leaf lifespan is more complex and idiosyncratic. Across all traits, this covariance was an important source of information, as evidenced by the improved imputation accuracy and reduced predictive uncertainty in multivariate models compared to univariate models. Ultimately, our study reaffirms the value of studying not just individual traits but the multivariate trait space and the utility of hierarchical modeling for studying the scale dependence of trait relationships. Show less
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.... Show moreAim 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. Show less
A substantial body of evidence has demonstrated that biodiversity stabilizes ecosystem functioning over time in grassland ecosystems. However, the relative importance of different facets of... Show moreA substantial body of evidence has demonstrated that biodiversity stabilizes ecosystem functioning over time in grassland ecosystems. However, the relative importance of different facets of biodiversity underlying the diversity-stability relationship remains unclear. Here we use data from 39 grassland biodiversity experiments and structural equation modelling to investigate the roles of species richness, phylogenetic diversity and both the diversity and community-weighted mean of functional traits representing the 'fast-slow' leaf economics spectrum in driving the diversity-stability relationship. We found that high species richness and phylogenetic diversity stabilize biomass production via enhanced asynchrony in the performance of co-occurring species. Contrary to expectations, low phylogenetic diversity enhances ecosystem stability directly, albeit weakly. While the diversity of fast-slow functional traits has a weak effect on ecosystem stability, communities dominated by slow species enhance ecosystem stability by increasing mean biomass production relative to the standard deviation of biomass over time. Our in-depth, integrative assessment of factors influencing the diversity-stability relationship demonstrates a more multicausal relationship than has been previously acknowledged. Show less
The tundra is warming more rapidly than any other biome on Earth, and the potential ramifications are far-reaching because of global feedback effects between vegetation and climate. A better... Show moreThe tundra is warming more rapidly than any other biome on Earth, and the potential ramifications are far-reaching because of global feedback effects between vegetation and climate. A better understanding of how environmental factors shape plant structure and function is crucial for predicting the consequences of environmental change for ecosystem functioning. Here we explore the biome-wide relationships between temperature, moisture and seven key plant functional traits both across space and over three decades of warming at 117 tundra locations. Spatial temperature-trait relationships were generally strong but soil moisture had a marked influence on the strength and direction of these relationships, highlighting the potentially important influence of changes in water availability on future trait shifts in tundra plant communities. Community height increased with warming across all sites over the past three decades, but other traits lagged far behind predicted rates of change. Our findings highlight the challenge of using space-for-time substitution to predict the functional consequences of future warming and suggest that functions that are tied closely to plant height will experience the most rapid change. They also reveal the strength with which environmental factors shape biotic communities at the coldest extremes of the planet and will help to improve projections of functional changes in tundra ecosystems with climate warming. Show less
This paper introduces a modular processing chain to derive global high-resolution maps of leaf traits. In particular, we present global maps at 500 m resolution of specific leaf area, leaf dry... Show moreThis paper introduces a modular processing chain to derive global high-resolution maps of leaf traits. In particular, we present global maps at 500 m resolution of specific leaf area, leaf dry matter content, leaf nitrogen and phosphorus content per dry mass, and leaf nitrogen/phosphorus ratio. The processing chain exploits machine learning techniques along with optical remote sensing data (MODIS/Landsat) and climate data for gap filling and up-scaling of in-situ measured leaf traits. The chain first uses random forests regression with surrogates to fill gaps in the database (> 45% of missing entries) and maximizes the global representativeness of the trait dataset. Plant species are then aggregated to Plant Functional Types (PFTs). Next, the spatial abundance of PFTs at MODIS resolution (500 m) is calculated using Landsat data (30 m). Based on these PFT abundances, representative trait values are calculated for MODIS pixels with nearby trait data. Finally, different regression algorithms are applied to globally predict trait estimates from these MODIS pixels using remote sensing and climate data. The methods were compared in terms of precision, robustness and efficiency. The best model (random forests regression) shows good precision (normalized RMSE≤ 20%) and goodness of fit (averaged Pearson's correlation R = 0.78) in any considered trait. Along with the estimated global maps of leaf traits, we provide associated uncertainty estimates derived from the regression models. The process chain is modular, and can easily accommodate new traits, data streams (traits databases and remote sensing data), and methods. The machine learning techniques applied allow attribution of information gain to data input and thus provide the opportunity to understand trait-environment relationships at the plant and ecosystem scales. The new data products – the gap-filled trait matrix, a global map of PFT abundance per MODIS gridcells and the high-resolution global leaf trait maps – are complementary to existing large-scale observations of the land surface and we therefore anticipate substantial contributions to advances in quantifying, understanding and prediction of the Earth system. Show less
AimDespite several recent efforts to map plant traits and to identify their climatic drivers, there are still major gaps. Global trait patterns for major functional groups, in particular, the... Show moreAimDespite several recent efforts to map plant traits and to identify their climatic drivers, there are still major gaps. Global trait patterns for major functional groups, in particular, the differences between woody and herbaceous plants, have yet to be identified. Here, we take advantage of big data efforts to compile plant species occurrence and trait data to analyse the spatial patterns of assemblage means and variances of key plant traits. We tested whether these patterns and their climatic drivers are similar for woody and herbaceous plants. LocationNew World (North and South America). MethodsUsing the largest currently available database of plant occurrences, we provide maps of 200 × 200 km grid‐cell trait means and variances for both woody and herbaceous species and identify environmental drivers related to these patterns. We focus on six plant traits: maximum plant height, specific leaf area, seed mass, wood density, leaf nitrogen concentration and leaf phosphorus concentration. ResultsFor woody assemblages, we found a strong climate signal for both means and variances of most of the studied traits, consistent with strong environmental filtering. In contrast, for herbaceous assemblages, spatial patterns of trait means and variances were more variable, the climate signal on trait means was often different and weaker. Main conclusionTrait variations for woody versus herbaceous assemblages appear to reflect alternative strategies and differing environmental constraints. Given that most large‐scale trait studies are based on woody species, the strikingly different biogeographic patterns of herbaceous traits suggest that a more synthetic framework is needed that addresses how suites of traits within and across broad functional groups respond to climate. Show less
Shipley, B; Belluau, M.; Kuehn, I.; Soudzilovskaia, N.A.; Bahn, M.; Penuelas, J.; ... ; Poschlod, P. 2017
Questions Heinz Ellenberg classically defined "indicator" scores for species representing their typical positions along gradients of key environmental variables, and these have proven very useful... Show moreQuestions Heinz Ellenberg classically defined "indicator" scores for species representing their typical positions along gradients of key environmental variables, and these have proven very useful for designating ecological distributions. We tested a key tenent of trait‐based ecology, i.e. the ability to predict ecological preferences from species’ traits. More specifically, can we predict Ellenberg indicator scores for soil nutrients, soil moisture and irradiance from four well‐studied traits: leaf area, leaf dry matter content, specific leaf area (SLA) and seed mass? Can we use such relationships to estimate Ellenberg scores for species never classified by Ellenberg?Location Global. MethodsCumulative link models were developed to predict Ellenberg nutrients, irradiance and moisture values from Ln‐transformed trait values using 922, 981 and 988 species, respectively. We then independently tested these prediction equations using the trait values of 423 and 421 new species that occurred elsewere in Europe, North America and Morocco, and whose habitat affinities we could classify from independent sources as three‐level ordinal ranks related to soil moisture and irradiance. The traits were SLA, leaf dry matter content, leaf area and seed mass. ResultsThe four functional traits predicted the Ellenberg indicator scores of site fertility, light and moisture with average error rates of <2 Ellenberg ranks out of nine. We then used the trait values of 423 and 421 species, respectively, that occurred (mostly) outside of Germany but whose habitat affinities we could classify as three‐level ordinal ranks related to soil moisture and irradiance. The predicted positions of the new species, given the equations derived from the Ellenberg indices, agreed well with their independent habitat classifications, although our equation for Ellenberg irrandiance levels performed poorly on the lower ranks.ConclusionsThese prediction equations, and their eventual extensions, could be used to provide approximate descriptions of habitat affinities of large numbers of species worldwide. Show less
Temperature is a primary driver of the distribution of biodiversity as well as of ecosystem boundaries1,2. Declining temperature with increasing elevation in montane systems has long been... Show moreTemperature is a primary driver of the distribution of biodiversity as well as of ecosystem boundaries1,2. Declining temperature with increasing elevation in montane systems has long been recognized as a major factor shaping plant community biodiversity, metabolic processes, and ecosystem dynamics3,4. Elevational gradients, as thermoclines, also enable prediction of long-term ecological responses to climate warming5,6,7. One of the most striking manifestations of increasing elevation is the abrupt transitions from forest to treeless alpine tundra8. However, whether there are globally consistent above- and belowground responses to these transitions remains an open question4. To disentangle the direct and indirect effects of temperature on ecosystem properties, here we evaluate replicate treeline ecotones in seven temperate regions of the world. We find that declining temperatures with increasing elevation did not affect tree leaf nutrient concentrations, but did reduce ground-layer community-weighted plant nitrogen, leading to the strong stoichiometric convergence of ground-layer plant community nitrogen to phosphorus ratios across all regions. Further, elevation-driven changes in plant nutrients were associated with changes in soil organic matter content and quality (carbon to nitrogen ratios) and microbial properties. Combined, our identification of direct and indirect temperature controls over plant communities and soil properties in seven contrasting regions suggests that future warming may disrupt the functional properties of montane ecosystems, particularly where plant community reorganization outpaces treeline advance. Show less
Musavi, T.; Migliavacca, M.; Weg, M.J. van de; Kattge, J.; Wohlfahrt, G.; Bodegom, P.M. van; ... ; Mahecha, M.D. 2016
Terrestrial ecosystems strongly determine the exchange of carbon, water and energy between thebiosphere and atmosphere. These exchanges are influenced by environmental conditions (e.g.,... Show moreTerrestrial ecosystems strongly determine the exchange of carbon, water and energy between thebiosphere and atmosphere. These exchanges are influenced by environmental conditions (e.g., localmeteorology, soils), but generally mediated by organisms. Often, mathematical descriptions of theseprocesses are implemented in terrestrial biosphere models. Model implementations of this kind shouldbe evaluated by empirical analyses of relationships between observed patterns of ecosystem function-ing, vegetation structure, plant traits, and environmental conditions. However, the question of how todescribe the imprint of plants on ecosystem functioning based on observations has not yet been systemat-ically investigated. One approach might be to identify and quantify functional attributes or responsivenessof ecosystems (often very short-term in nature) that contribute to the long-term (i.e., annual but alsoseasonal or daily) metrics commonly in use. Here we define these patterns as “ecosystem functional prop-erties”, or EFPs. Such as the ecosystem capacity of carbon assimilation or the maximum light use efficiencyof an ecosystem. While EFPs should be directly derivable from flux measurements at the ecosystem level,we posit that these inherently include the influence of specific plant traits and their local heterogeneity.We present different options of upscaling in situ measured plant traits to the ecosystem level (ecosystemvegetation properties – EVPs) and provide examples of empirical analyses on plants’ imprint on ecosys-tem functioning by combining in situ measured plant traits and ecosystem flux measurements. Finally,we discuss how recent advances in remote sensing contribute to this framework. Show less
Plant traits – the morphological, anatomical, physiological, biochemical and phenological characteristics of plants and their organs – determine how primary producers respond to environmental... Show morePlant traits – the morphological, anatomical, physiological, biochemical and phenological characteristics of plants and their organs – determine how primary producers respond to environmental factors, affect other trophic levels, influence ecosystem processes and services and provide a link from species richness to ecosystem functional diversity. Trait data thus represent the raw material for a wide range of research from evolutionary biology, community and functional ecology to biogeography. Here we present the global database initiative named TRY, which has united a wide range of the plant trait research community worldwide and gained an unprecedented buy-in of trait data: so far 93 trait databases have been contributed. The data repository currently contains almost three million trait entries for 69 000 out of the world’s 300 000 plant species, with a focus on 52 groups of traits characterizing the vegetative and regeneration stages of the plant life cycle, including growth, dispersal, establishment and persistence. A first data analysis shows that most plant traits are approximately log-normally distributed, with widely differing ranges of variation across traits. Most trait variation is between species (interspecific), but significant intraspecific variation is also documented, up to 40% of the overall variation. Plant functional types (PFTs), as commonly used in vegetation models, capture a substantial fraction of the observed variation – but for several traits most variation occurs within PFTs, up to 75% of the overall variation. In the context of vegetation models these traits would better be represented by state variables rather than fixed parameter values. The improved availability of plant trait data in the unified global database is expected to support a paradigm shift from species to trait-based ecology, offer new opportunities for synthetic plant trait research and enable a more realistic and empirically grounded representation of terrestrial vegetation in Earth system models. Show less
