Imputing missing data in plant traits: A guide to improve gap‐filling

Aim: Globally distributed plant trait data are increasingly used to understand relationships between biodiversity and ecosystem processes. However, global trait databases are sparse because they are compiled from many, mostly small databases. This sparsity in both trait space completeness and geographical distribution limits the potential for both multivariate and global analyses. Thus, ‘gap-filling’ approaches are often used to impute missing trait data. Recent methods, like Bayesian hierarchical probabilistic matrix factorization (BHPMF), can impute large and sparse data sets using side information. We investigate whether BHPMF imputation leads to biases in trait space and identify aspects influencing bias to provide guidance for its usage. Innovation: We use a fully observed trait data set from which entries are randomly removed, along with extensive but sparse additional data. We use BHPMF for imputation and evaluate bias by: (1) accuracy (residuals, RMSE, trait means), (2) correlations (bi-and multivariate) and (3) taxonomic and functional clustering (valuewise, uni-and multivariate). BHPMF preserves general patterns of trait distributions but induces taxonomic clustering. Data set–external trait data had little effect on induced taxonomic clustering and stabilized trait–trait correlations. Main Conclusions: Our study extends the criteria for the evaluation of gap-filling beyond RMSE, providing insight into statistical data structure and allowing better informed use of imputed trait data, with improved practice for imputation. We expect our findings to be valuable beyond applications in plant ecology, for any study using hierarchical side information for imputation

Imputing missing data in plant traits: A guide to improve gap‐filling

Aim: Globally distributed plant trait data are increasingly used to understand relationships between biodiversity and ecosystem processes. However, global trait databases are sparse because they are compiled from many, mostly small databases. This sparsity in both trait space completeness and geographical distribution limits the potential for both multivariate and global analyses. Thus, ‘gap‐filling’ approaches are often used to impute missing trait data. Recent methods, like Bayesian hierarchical probabilistic matrix factorization (BHPMF), can impute large and sparse data sets using side information. We investigate whether BHPMF imputation leads to biases in trait space and identify aspects influencing bias to provide guidance for its usage. Innovation: We use a fully observed trait data set from which entries are randomly removed, along with extensive but sparse additional data. We use BHPMF for imputation and evaluate bias by: (1) accuracy (residuals, RMSE, trait means), (2) correlations (bi‐ and multivariate) and (3) taxonomic and functional clustering (valuewise, uni‐ and multivariate). BHPMF preserves general patterns of trait distributions but induces taxonomic clustering. Data set–external trait data had little effect on induced taxonomic clustering and stabilized trait–trait correlations. Main Conclusions: Our study extends the criteria for the evaluation of gap‐filling beyond RMSE, providing insight into statistical data structure and allowing better informed use of imputed trait data, with improved practice for imputation. We expect our findings to be valuable beyond applications in plant ecology, for any study using hierarchical side information for imputation

A synthesis of tree functional traits related to drought-induced mortality in forests across climatic zones

1. Forest dieback caused by drought-induced tree mortality has been observed world-wide. Forecasting which trees in which locations are vulnerable to drought-induced mortality is important to predict the consequences of drought on forest structure, biodiversity and ecosystem function. 2. In this paper, our central aim was to compile a synthesis of tree traits and associated abiotic variables that can be used to predict drought-induced mortality. We reviewed the literature that specifically links drought mortality to functional traits and site conditions (i.e. edaphic variables and biotic conditions), targeting studies that show clear use of tree traits in drought analysis. We separated the review into five climatic zones to determine global vs. regionally restricted relationships between traits and mortality. 3. Our synthesis identifies a number of traits that have clear relationships with drought-induced mortality (e.g. wood density at the species level and tree size and growth at the individual level). However, the lack of direct relationships between most traits and drought-induced mortality highlights areas where future research should focus to broaden our understanding. 5. Synthesis and applications. Our synthesis highlights established relationships between traits and drought-induced mortality, presents knowledge gaps for future research focus and suggests monitoring and research avenues for improving our understanding of drought-induced mortality. It is intended to assist ecologists and natural resource managers choose appropriate and measurable parameters for predicting local and regional scale tree mortality risk in different climatic zones within constraints of time and funding availability

BHPMF – a hierarchical Bayesian approach to gap-filling and trait prediction for macroecology and functional biogeography

Aim: Functional traits of organisms are key to understanding and predicting biodiversity and ecological change, which motivates continuous collection of traits and their integration into global databases. Such trait matrices are inherently sparse, severely limiting their usefulness for further analyses. On the other hand, traits are characterized by the phylogenetic trait signal, trait–trait correlations and environmental constraints, all of which provide information that could be used to statistically fill gaps. We propose the application of probabilistic models which, for the first time, utilize all three characteristics to fill gaps in trait databases and predict trait values at larger spatial scales. Innovation: For this purpose we introduce BHPMF, a ierarchical Bayesian extension of probabilistic matrix actorization (PMF). PMF is a machine learning technique which exploits the correlation structure of sparse matrices to impute missing entries. BHPMF additionally utilizes the taxonomic hierarchy for trait prediction and provides uncertainty estimates for each imputation. In combination with multiple regression against environmental information, BHPMF allows for extrapolation frompoint measurements to larger spatial scales.We demonstrate the applicability of BHPMF in ecological contexts, using different plant functional trait datasets, also comparing results to taking the species mean and PMF. Main conclusions: Sensitivity analyses validate the robustness and accuracy of BHPMF: our method captures the correlation structure of the trait matrix as well as the phylogenetic trait signal – also for extremely sparse trait matrices – and provides a robust measure of confidence in prediction accuracy for each missing entry. The combination of BHPMF with environmental constraints provides a promising concept to extrapolate traits beyond sampled regions, accounting for intraspecific trait variability. We conclude that BHPMF and its derivatives have a high potential to support future trait-based research in macroecology and functional biogeography

Inferring plant functional diversity from space: the potential of Sentinel-2

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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