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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 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, variation 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., Nature Ecology & Evolution, 6 (1), ISSN:2397-334X

Plant-pollinator interactions are often highly specialised, which may be a consequence of co-evolution. Yet when plants and pollinators co-evolve, it is not clear if this will also result in frequent cospeciation. Here, we investigate the mutual evolutionary history of South African oil-collecting Rediviva bees and their Diascia host plants, in which the elongated forelegs of female Rediviva have been suggested to coevolve with the oil-producing spurs of their Diascia hosts. After controlling for phylogenetic nonindependence, we found Rediviva foreleg length to be significantly correlated with Diascia spur length, suggestive of co-evolution. However, as trait correlation could also be due to pollinator shifts, we tested if cospeciation or pollinator shifts have dominated the evolution of Rediviva-Diascia interactions by analysing phylogenies in a cophylogenetic framework. Distance-based cophylogenetic analyses (PARAFIT, PACo) indicated significant congruence of the two phylogenies under most conditions. Yet, we found that phylogenetic relatedness was correlated with ecological similarity (the spectrum of partners that each taxon interacted with) only for Diascia but not for Rediviva, suggesting that phylogenetic congruence might be due to phylogenetic tracking by Diascia of Rediviva rather than strict (reciprocal) co-evolution. Furthermore, event-based reconciliation using a parsimony approach (CORE-PA) on average revealed only 11-13 cospeciation events but 58-80 pollinator shifts. Probabilistic cophylogenetic analyses (COALA) supported this trend (8-29 cospeciations vs. 40 pollinator shifts). Our study suggests that diversification of Diascia has been largely driven by Rediviva (phylogenetic tracking, pollinator shifts) but not vice versa. Moreover, our data suggest that, even in co-evolving mutualisms, cospeciation events might occur only infrequently.

Trees are of vital importance for ecosystem functioning and services at local to global scales, yet we still lack a detailed overview of the global patterns of tree diversity and the underlying drivers, particularly the imprint of paleoclimate. Here, we present the high-resolution (110 km) worldwide mapping of tree species richness, functional and phylogenetic diversities based on ∼7 million quality-assessed occurrences for 46,752 tree species (80.5% of the estimated total number of tree species), and subsequent assessments of the influence of paleo-climate legacies on these patterns. All three tree diversity dimensions exhibited the expected latitudinal decline. Contemporary climate emerged as the strongest driver of all diversity patterns, with Pleistocene and deeper-time (>107 years) paleoclimate as important co-determinants, and, notably, with past cold and drought stress being linked to reduced current diversity. These findings demonstrate that tree diversity is affected by paleoclimate millions of years back in time and highlight the potential for tree diversity losses from future climate change.

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

Although trees are key to ecosystem functioning, many forests and tree species across the globe face strong threats. Preserving areas of high biodiversity is a core priority for conservation; however, different dimensions of biodiversity and varied conservation targets make it difficult to respond effectively to this challenge. Here, we (i) identify priority areas for global tree conservation using comprehensive coverage of tree diversity based on taxonomy, phylogeny, and functional traits; and (ii) compare these findings to existing protected areas and global biodiversity conservation frameworks. We find that ca. 51% of the top-priority areas for tree biodiversity are located in current protected areas. The remaining half top-priority areas are subject to moderate to high human pressures, indicating conservation actions are needed to mitigate these human impacts. Our findings emphasize the effectiveness of using tree conservation priority areas for future global conservation planning.

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

Our ability to understand and predict the response of ecosystems to a changing environment depends on quantifying vegetation functional diversity. However, representing this diversity at the global scale is challenging. Typically, in Earth system models, characterization of plant diversity has been limited to grouping related species into plant functional types (PFTs), with all trait variation in a PFT collapsed into a single mean value that is applied globally. Using the largest global plant trait database and state of the art Bayesian modeling, we created fine-grained global maps of plant trait distributions that can be applied to Earth system models. Focusing on a set of plant traits closely coupled to photosynthesis and foliar respiration—specific leaf area (SLA) and dry mass-based concentrations of leaf nitrogen (Nm) and phosphorus (Pm), we characterize how traits vary within and among over 50,000 ∼50×50-km cells across the entire vegetated land surface. We do this in several ways—without defining the PFT of each grid cell and using 4 or 14 PFTs; each model’s predictions are evaluated against out-of-sample data. This endeavor advances prior trait mapping by generating global maps that preserve variability across scales by using modern Bayesian spatial statistical modeling in combination with a database over three times larger than that in previous analyses. Our maps reveal that the most diverse grid cells possess trait variability close to the range of global PFT means., This research was supported as part of the Energy Exascale Earth System Model (E3SM) project, funded by the US Department of Energy, Office of Science, Office of Biological and Environmental Research (Grant DE-SC0012677 to P.B.R. and A.B.). O.K.A. acknowledges the support of the Australian Research Council (CE140100008). This research was also funded by programs from the NSF Long-Term Ecological Research (Grant DEB-1234162) and Long-Term Research in Environmental Biology (Grant DEB-1242531). A.B., F.F., and P.B.R. acknowledge funding from NSF Grant IIS-1563950. P.B.R. also acknowledges support from two University of Minnesota Institute on the Environment discovery grants. This study has been supported by the TRY initiative on plant traits (www.try-db.org). The TRY database is hosted at the Max Planck Institute for Biogeochemistry (Jena, Germany) and supported by DIVERSITAS/Future Earth, the German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, and the EU H2020 project BACI (Grant 640176). B.B. acknowledges a Natural Environment Research Council (NERC) independent research fellowship NE/M019160/1. J.P. acknowledges the financial support from the European Research Council Synergy Grant ERC-SyG-2013-610028 IMBALANCE-P, the Spanish Government Grant CGL2013-48074-P, and the Catalan Government Grant SGR 2014-274. B.B.-L. was supported by the Earth System Modeling program of the US Department of Energy, Office of Science, Office of Biological and Environmental Research. K.K. acknowledges the contribution of the Wageningen University and Research Investment theme Resilience for the project Resilient Forest (KB-29-009-003). P.M. acknowledges support from ARC Grant FT110100457 and NERC Grant NE/F002149/1. W.H. acknowledges support from the National Natural Science Foundation of China (Grant 41473068) and the “Light of West China” Program of the Chinese Academy of Sciences.

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