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

Abstract

AimPredictions 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.LocationGlobal.Time periodPresent.Major taxa studiedVascular plants.MethodsWe 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.ResultsEnsemble 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 conclusionsPlant 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.

Climatic and evolutionary contexts are required to infer plant life history strategies from functional traits at a global scale

“…High plant height and large seed size are adaptative in hot, moist, resource rich environments where biotic competition is high (Moles 2018; Boonman et al . 2020), height is important in competing for light, and larger seeds show higher establishment under biotic competition and shade (Westoby et al . 2002).…”

Global quantitative synthesis of effects of biotic and abiotic factors on stemflow production in woody ecosystems

“…We found that nonlinear responses of St to cross‐site biotic and abiotic factors were prevalent, suggesting the linear framework conventionally used in stemflow studies may not be adequate. The development of more advanced nonparametric and machine learning approaches in recent years has opened new ways of describing patterns as well as investigating nonlinear processes in ecological studies (Boonman et al, 2020; Wuest et al., 2020). This is well exemplified in our synthesis using BRT, which quantified the relative contribution of each individual predictor to global St and provided a mechanistic understanding of how they affect St (Figure 4).…”

Functional Diversity of Riparian Woody Vegetation Is Less Affected by River Regulation in the Mediterranean Than Boreal Region

“…The available dataset of species presence/absence permits the use of indices computed with binary data, namely FRic and FRed. Articles on functional diversity consisting of binary data sets have been published on riparian vegetation (Sonnier et al, 2014;Brice et al, 2017) and those datasets have been considered as reliable for predicting plant trait distributions globally (Boonman et al, 2020). In a conceptual study with an illustrated ecological hypothesis, Boersma et al (2016) stated that presence/absence data can serve to make the most straightforward interpretation of the results when disturbance acts as an environmental filter.…”