Michael Schaepman

Remote sensing of vegetation by spectroscopy is increasingly used to characterize trait distributions in plant communities. How leaves interact with electromagnetic radiation is determined by their structure and contents of pigments, water, and abundant dry matter constituents like lignins, phenolics, and proteins. High-resolution (“hyperspectral”) spectroscopy can characterize trait variation at finer scales, and may help to reveal underlying genetic variation—information important for assessing the potential of populations to adapt to global change. Here, we use a set of 360 inbred genotypes of the wild coyote tobacco Nicotiana attenuata: wild accessions, recombinant inbred lines (RILs), and transgenic lines (TLs) with targeted changes to gene expression, to dissect genetic versus non-genetic influences on variation in leaf spectra across three experiments. We calculated leaf reflectance from hand-held field spectroradiometer measurements covering visible to short-wave infrared wavelengths of electromagnetic radiation (400–2500 nm) using a standard radiation source and backgrounds, resulting in a small and quantifiable measurement uncertainty. Plants were grown in more controlled (glasshouse) or more natural (field) environments, and leaves were measured both on- and off-plant with the measurement set-up thus also in more to less controlled environmental conditions. Entire spectra varied across genotypes and environments. We found that the greatest variance in leaf reflectance was explained by between-experiment and non-genetic between-sample differences, with subtler and more specific variation distinguishing groups of genotypes. The visible spectral region was most variable, distinguishing experimental settings as well as groups of genotypes within experiments, whereas parts of the short-wave infrared may vary more specifically with genotype. Overall, more genetically variable plant populations also showed more varied leaf spectra. We highlight key considerations for the application of field spectroscopy to assess genetic variation in plant populations. © 2023, BioMed Central Ltd., part of Springer Nature.

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. © 2023 The Authors. Global Ecology and Biogeography published by John Wiley & Sons Ltd.

Genetic diversity influences the evolutionary potential of forest trees under changing environmental conditions, thus indirectly the ecosystem services that forests provide. European beech (Fagus sylvatica L.) is a dominant European forest tree species that increasingly suffers from climate change-related die-back. Here, we conducted a systematic literature review of neutral genetic diversity in European beech and created a meta-data set of expected heterozygosity (He) from all past studies providing nuclear microsatellite data. We propose a novel approach, based on population genetic theory and a min–max scaling to make past studies comparable. Using a new microsatellite data set with unprecedented geographic coverage and various re-sampling schemes to mimic common sampling biases, we show the potential and limitations of the scaling approach. The scaled meta-dataset reveals the expected trend of decreasing genetic diversity from glacial refugia across the species range and also supports the hypothesis that different lineages met and admixed north of the European mountain ranges. As a result, we present a map of genetic diversity across the range of European beech which could help to identify seed source populations harboring greater diversity and guide sampling strategies for future genome-wide and functional investigations of genetic variation. Our approach illustrates how to combine information from several nuclear microsatellite data sets to describe patterns of genetic diversity extending beyond the geographic scale or mean number of loci used in each individual study, and thus is a proof-of-concept for synthesizing knowledge from existing studies also in other species. © 2022, The Author(s).