Assessing the reliability of predicted plant trait distributions at the global scale.

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.

Mapping local and global variability in plant trait distributions.

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 ([Formula: see text]) and phosphorus ([Formula: see text]), we characterize how traits vary within and among over 50,000 [Formula: see text]-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.

Should I stay or should I go? Pollinator shifts rather than cospeciation dominate the evolutionary history of South African Rediviva bees and their Diascia host plants.

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.

Climatic and soil factors explain the two-dimensional spectrum of global plant trait variation.

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.