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Global maps of plant functional traits are essential for studying the dynamics of the terrestrial biosphere, yet the spatial distribution of trait measurements remains sparse. With the increasing popularity of species identification apps, citizen scientists contribute to growing vegetation data collections. The question emerges whether such opportunistic citizen science data can help map plant functional traits globally. Here we show that we can map global trait patterns by complementing vascular plant observations from the global citizen science project iNaturalist with measurements from the plant trait database TRY. We evaluate these maps using sPlotOpen, a global collection of vegetation plot data. Our results show high correlations between the iNaturalist- and sPlotOpen-based maps of up to 0.69 (r) and higher correlations than to previously published trait maps. As citizen science data collections continue to grow, we can expect them to play a significant role in further improving maps of plant functional traits.

Soil acidification induced by reactive nitrogen (N) inputs can alter the structure and function of terrestrial ecosystems. Because different N-transformation processes contribute to the production and consumption of H+, the magnitude of acidification likely depends on the relative amounts of organic N (ON) and inorganic N (IN) inputs. However, few studies have explicitly measured the effects of N composition on soil acidification. In this study, we first conducted a meta-analysis to test the effects of ON or IN inputs on soil acidification across 53 studies in grasslands. We then compared soil acidification across five different ON:IN ratios and two input rates based on long-term field N addition experiments. The meta-analysis showed that ON had weaker effects on soil acidification than IN when the N addition rate was above 20 g N m−2 year−1. The field experiment confirmed the findings from meta-analysis: N addition with proportions of ON ≥ 20% caused less soil acidification, especially at a high input rate (30 g N m−2 year−1). Structural equation model analysis showed that this result was largely due to a relatively low rate of H+ production from ON as NH3 volatilization and uptake of ON and NH4+ by the dominant grass species Leymus chinensis (which are both lower net contributors to H+ production) result in less NH4+ available for nitrification (which is a higher net contributor to H+ production). These results indicate that the evaluation of soil acidification induced by N inputs should consider N forms and manipulations of relative composition of N inputs may provide an effective approach to alleviate the N-induced soil acidification.

Plant traits - morphological, anatomical, biochemical, physiological or phenological features measurable at the level of individuals or their component organs or tissues - reflect the outcome of evolutionary and community assembly processes responding to abiotic and biotic environmental constraints. Therefore, measurements of plant traits and trait syndromes (consistent associations of plant traits) are valuable observations to evaluate models based on eco-evolutionary optimality (EEO) principles. In 2007 the TRY database project (https://www.try-db.org/) was initiated to improve the empirical basis for trait-based ecological studies, trying to bring together the different plant trait databases worldwide. As a result, the TRY Plant Trait Database has constantly been growing and has accomplished unprecedented coverage. Since 2019 the data are publicly available under a CC BY license. This presentation is supposed to provide an update on recent developments in the context of the TRY initiative, i.e. the recently released new version of the TRY database (version 6), the release of the 'Global Spectrum of Plant Form and Function Dataset', and the 'rtry' R package to support preprocessing of trait data retrieved from the TRY database.

As global change accelerates, the urgency for a solid understanding of biosphere-environment interactions grows. However, we need more data on plant functional traits to test such relationships reliably across ecosystems. The TRY database contains an impressive collection of plant trait measurements for thousands of species already, and there have been some approaches to spatially extrapolate them using geospatial predictors and remote sensing data; however, the original data is spatially sparse so that extrapolations come with substantial uncertainties. At the same time, citizen scientists have collected increasingly dense observations of species occurrences around the globe. Here, we test if we can link species occurrences from the citizen science project iNaturalist with trait observations from TRY to produce global trait maps without the need for spatial extrapolation. We generated spatial grids for 18 traits, calculating a mean for each grid cell by averaging trait values associated with observations within that cell. We compared mean trait values from iNaturalist observations to community-weighted mean traits from sPlotOpen, a globally sampled dataset of vegetation plot data. Our results show correlations between the two datasets of up to r = 0.69, especially in biomes with higher iNaturalist observation density and those not dominated by trees. Also, we show that iNaturalist-derived maps have higher correlations to sPlotOpen-derived maps than previously published trait maps. This strong correlation between two fundamentally different datasets is astounding and unexpected. iNaturalist is noisy and heterogenous, sampled by citizen scientists who share the species they encounter and find interesting; sPlotOpen is a data collection of vegetation plots that were measured and recorded within the framework of specific research questions. The fact that these two datasets exhibit such a strong resemblance opens up a promising avenue for using the data treasure trove that is crowd-sourced data to help fill the gaps in plant trait data and demonstrates that crowd-sourced data, such as the iNaturalist observations, can be used to complement professional data collection efforts.

Foliar traits such as specific leaf area (SLA), leaf nitrogen (N) and phosphorus (P) concentrations play an important role in plant economic strategies and ecosystem functioning. Various global maps of these foliar traits have been generated using statistical upscaling approaches based on in-situ trait observations.Here, we intercompare such global upscaled foliar trait maps at 0.5° spatial resolution (six maps for SLA, five for N, three for P), categorize the upscaling approaches used to generate them, and evaluate the maps with trait estimates from a global database of vegetation plots (sPlotOpen). We disentangled the contributions from different plant functional types (PFTs) to the upscaled maps and calculated a top-of-canopy-weighted mean (TWM) and the community-weighted mean (CWM) of sPlotOpen trait estimates.We found that the global foliar trait maps of SLA and N differ drastically and fall into two groups that are almost uncorrelated (for P only maps from one group were available). The primary factor explaining the differences between these groups is the use of PFT information combined with land cover products in one group while the other group relied only on environmental predictors. The impact of using TWM or CWM on spatial patterns is considerably smaller than that of including PFT and land cover information. The maps that used PFT and land cover information exhibit considerable similarities in spatial patterns that are strongly driven by land cover. The maps not using PFTs show a lower level of similarity and tend to be strongly driven by individual environmental variables.Overall, the maps using PFT and land cover information better reproduce the between-PFT trait differences and trait distributions of the plot-level sPlotOpen data, while the two groups performed similarly in capturing within-PFT trait variation. Upscaled maps of both groups were moderately correlated to grid-cell-level sPlotOpen data (R = 0.2-0.6), but only when accounting for the differences in processing in the upscaling approaches by applying similar scaling to the sPlotOpen data.Our findings indicate the importance of accounting for within-grid-cell trait variation, which has important implications for applications using existing maps and future upscaling efforts.

Aim: It is crucial to monitor how the productivity of grasslands varies with its temporal stability for management of these ecosystems. However, identifying the direction of the productivity-stability relationship remains challenging because ecological stability has multiple components that can display neutral, positive or negative covariations. Furthermore, evidence suggests that the direction of the productivity-stability relationship depends on the biotic interactions and abiotic conditions that underlie ecosystem productivity and stability. We decipher the relationships between grassland productivity and two components of its stability in four habitat types with contrasting environments and flora.

As Earth's climate has varied strongly through geological time, studying the impacts of past climate change on biodiversity helps to understand the risks from future climate change. However, it remains unclear how paleo-climate shapes spatial variation in biodiversity. Here, we assessed the influence of Quaternary climate change on spatial dissimilarity in taxonomic, phylogenetic, and functional composition among neighboring 200-kilometer cells (beta-diversity) for angiosperm trees worldwide. We found that larger glacial-interglacial temperature change was strongly associated with lower spatial turnover (species replacements) and higher nestedness (rich-ness changes) components of beta-diversity across all three biodiversity facets. Moreover, phylogenetic and functional turnover was lower and nestedness higher than random expectations based on taxonomic beta -di-versity in regions that experienced large temperature change, reflecting phylogenetically and functionally se-lective processes in species replacement, extinction, and colonization during glacial-interglacial oscillations. Our results suggest that future human-driven climate change could cause local homogenization and reduction in taxonomic, phylogenetic, and functional diversity of angiosperm trees worldwide. W.-B.X. and A.O. acknowledge support from the Aarhus University Research Foundation (AUFF) Starting Grant (AUFF-F-2018-7-8). J.-C.S. acknowledges support from the Danish Council for Independent Research|Natural Sciences (grant 6108-00078B) to the TREECHANGE Project. J.-C.S. also considers this work a contribution to the Center for Ecological Dynamics in a Novel Biosphere (ECONOVO) funded by Danish National Research Foundation (grant DNRF173) and the VILLUM Investigator project “Biodiversity Dynamics in a Changing World” funded by VILLUM FONDEN (grant 16549). W.L.E. was supported by the VILLUM FONDEN (grant 00025354). M.A. was supported by the Natural Sciences and Engineering Research Council of Canada (NSERC) Discovery grants program. C.B. was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (2022R1A2C1003504). A.G.G. was supported by the ANID PIA/BASAL (grant FB210006) and FONDECYT (grant 1200468). R.E.O. was supported by the German Research Foundation to German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig (DFG-FZT 118, 202548816). J.Pe. was supported by the Fundación Ramón Areces project (CIVP20A6621) and Spanish Government project (PID2019-110521GB-I00). J.Pi. was supported by the Estonian Research Council Grants (PUT1355, PRG 1405) and Mobilitas Pluss (MOBERC11). V.D.P. was supported by the National Council of Scientific and Technological Development (CNPq) of Brazil (grant 307689/2014-0).

Funding: Floristic identification in plots in the RAINFOR forest monitoring network have been supported by the Natural Environment Research Council (grants NE/B503384/1, NE/ D01025X/1, NE/I02982X/1, NE/F005806/1, NE/D005590/1 and NE/I028122/1) and the Gordon and Betty Moore Foundation. In a time of rapid global change, the question of what determines patterns in species abundance distribution remains a priority for understanding the complex dynamics of ecosystems. The constrained maximization of information entropy provides a framework for the understanding of such complex systems dynamics by a quantitative analysis of important constraints via predictions using least biased probability distributions. We apply it to over two thousand hectares of Amazonian tree inventories across seven forest types and thirteen functional traits, representing major global axes of plant strategies. Results show that constraints formed by regional relative abundances of genera explain eight times more of local relative abundances than constraints based on directional selection for specific functional traits, although the latter does show clear signals of environmental dependency. These results provide a quantitative insight by inference from large-scale data using cross-disciplinary methods, furthering our understanding of ecological dynamics. Publisher PDF

Safeguarding Earth?s tree diversity is a conservation priority due to the importance of trees for biodiversity and ecosystem functions and services such as carbon sequestration. Here, we improve the foundation for effective conservation of global tree diversity by analyzing a recently developed database of tree species covering 46,752 species. We quantify range protection and anthropogenic pressures for each species and develop conservation priorities across taxonomic, phylogenetic, and functional diversity dimensions. We also assess the effectiveness of several influential proposed conservation prioritization frameworks to protect the top 17% and top 50% of tree priority areas. We find that an average of 50.2% of a tree species? range occurs in 110-km grid cells without any protected areas (PAs), with 6,377 small-range tree species fully unprotected, and that 83% of tree species experience nonnegligible human pressure across their range on average. Protecting highpriority areas for the top 17% and 50% priority thresholds would increase the average protected proportion of each tree species? range to 65.5% and 82.6%, respectively, leaving many fewer species (2,151 and 2,010) completely unprotected. The priority areas identified for trees match well to the Global 200 Ecoregions framework, revealing that priority areas for trees would in large part also optimize protection for terrestrial biodiversity overall. Based on range estimates for >46,000 tree species, our findings show that a large proportion of tree species receive limited protection by current PAs and are under substantial human pressure. Improved protection of biodiversity overall would also strongly benefit global tree diversity. Made available in DSpace on 2022-06-21T15:23:55Z (GMT). No. of bitstreams: 1 Guo-et-al.-2022-PNAS-High-exposure-of-global-tree-diversity-to-human-pressure.pdf: 2523289 bytes, checksum: 245be3df3fb487ed152d25bf50f4e984 (MD5) Previous issue date: 2022 Proceedings of the National Academy of Sciences of the United States of America.

Life history strategies are fundamental to the ecology and evolution of organisms and are important for understanding extinction risk and responses to global change. Using global datasets and a multiple response modelling framework we show that trait-climate interactions are associated with life history strategies for a diverse range of plant species at the global scale. Our modelling framework informs our understanding of trade-offs and positive correlations between elements of life history after accounting for environmental context and evolutionary and trait-based constraints. Interactions between plant traits and climatic context were needed to explain variation in age at maturity, distribution of mortality across the lifespan and generation times of species. Mean age at maturity and the distribution of mortality across plants’ lifespan were under evolutionary constraints. These findings provide empirical support for the theoretical expectation that climatic context is key to understanding trait to life history relationships globally.

Aim: Here we examine the functional profile of regional tree species pools across the latitudinal distribution of Neotropical moist forests, and test trait–climate relationships among local communities. We expected opportunistic strategies (acquisitive traits, small seeds) to be overrepresented in species pools further from the equator, but also in terms of abundance in local communities in currently wetter, warmer and more seasonal climates. Location: Neotropics. Time period: Recent. Major taxa studied: Trees. Methods: We obtained abundance data from 471 plots across nine Neotropical regions, including c. 100,000 trees of 3,417 species, in addition to six functional traits. We compared occurrence-based trait distributions among regional species pools, and evaluated single trait–climate relationships across local communities using community abundance-weighted means (CWMs). Multivariate trait–climate relationships were assessed by a double-constrained correspondence analysis that tests both how CWMs relate to climate and how species distributions, parameterized by niche centroids in climate space, relate to their traits. Results: Regional species pools were undistinguished in functional terms, but opportunistic strategies dominated local communities further from the equator, particularly in the Northern Hemisphere. Climate explained up to 57% of the variation in CWM traits, with increasing prevalence of lower-statured, light-wooded and softer-leaved species bearing smaller seeds in more seasonal, wetter and warmer climates. Species distributions were significantly but weakly related to functional traits. Main conclusions: Neotropical moist forest regions share similar sets of functional strategies, from which local assembly processes, driven by current climatic conditions, select for species with different functional strategies. We can thus expect functional responses to climate change driven by changes in relative abundances of species already present regionally. Particularly, equatorial forests holding the most conservative traits and large seeds are likely to experience the most severe changes if climate change triggers the proliferation of opportunistic tree species.

From evolutionary biology, functional ecology, earth system modelling to landscape management, plant trait data are used to determine how the plants respond to the environmental factors and can act as indicators of ecosystem functions. In 2007, the TRY initiative was founded as an integrated database of trait data and all additional attributes relevant to understanding and interpreting a given trait value. Since then, the TRY database has integrated more than 400 datasets, including both original datasets and collective databases.Due to the unique long table structure, the relevant information (e.g. trait names, species names, ancillary data representing context information, units of trait data, and identifiers for duplicates and outliers) for trait data filtering is stored at different places of the released TRY data. This makes the process to find all relevant information to select or remove trait data not straightforward without knowledge of the inherent data structure.The ‘rtry’ package is an R package that provides a set of easily applicable functions for the basic steps of data preprocessing and is designed in particular to support the data exploration and removal of the plant trait data, taking advantage of the features of trait data released from the TRY database. This package is supposed to be applicable without advanced knowledge of the data structure released from TRY or the R software. Most importantly, despite the ‘rtry’ package being developed to support the application of plant trait data received via the TRY database, it is also applicable to other trait data.

A large body of research shows that biodiversity loss can reduce ecosystem functioning. However, much of the evidence for this relationship is drawn from biodiversity-ecosystem functioning experiments in which biodiversity loss is simulated by randomly assembling communities of varying species diversity, and ecosystem functions are measured. This random assembly has led some ecologists to question the relevance of biodiversity experiments to real-world ecosystems, where community assembly or disassembly may be non-random and influenced by external drivers, such as climate, soil conditions or land use. Here, we compare data from real-world grassland plant communities with data from two of the largest and longest-running grassland biodiversity experiments (the Jena Experiment in Germany and BioDIV in the United States) in terms of their taxonomic, functional and phylogenetic diversity and functional-trait composition. We found that plant communities of biodiversity experiments cover almost all of the multivariate variation of the real-world communities, while also containing community types that are not currently observed in the real world. Moreover, they have greater variance in their compositional features than their real-world counterparts. We then re-analysed a subset of experimental data that included only ecologically realistic communities (that is, those comparable to real-world communities). For 10 out of 12 biodiversity-ecosystem functioning relationships, biodiversity effects did not differ significantly between the full dataset of biodiversity experiments and the ecologically realistic subset of experimental communities. Although we do not provide direct evidence for strong or consistent biodiversity-ecosystem functioning relationships in real-world communities, our results demonstrate that the results of biodiversity experiments are largely insensitive to the exclusion of unrealistic communities and that the conclusions drawn from biodiversity experiments are generally robust. By comparing data from real-world grassland communities with data from two of the longest-running grassland biodiversity-ecosystem functioning experiments, the authors show that conclusions derived from experimental systems are robust to the removal of unrealistic experimental communities., Nature Ecology & Evolution, 4 (11), ISSN:2397-334X

1. Plant functional traits impact the fitness and environmental niche of plants. Major plant functional types have been characterized by their trait spectrum, and the environmental and phylogenetic imprints on traits have advanced several ecological fields. Yet, very few trait data on epiphytes, which represent almost 10% of vascular plants, are available. 2. We collated 76,561 trait observations for 2,882 species of vascular epiphytes and compared these to non-epiphytic herbs and trees to test hypotheses related to how the epiphytic habit affects traits, and if epiphytes occupy a distinct region in the global trait space. We also compared variation in traits among major groups of epiphytes, and investigated the coordination of traits in epiphytes, ground-rooted herbs and trees. 3. Epiphytes differ from ground-rooted plants mainly in traits related to water relations. Unexpectedly, we did not find lower leaf nutrient concentrations, except for nitrogen. Mean photosynthetic rates are much lower than in ground-rooted plants and lower than expected from the nitrogen concentrations. Trait syndromes clearly distinguish epiphytes from trees and from most non-epiphytic herbs. 4. Among the three largest epiphytic taxa, orchids differ from bromeliads and ferns mainly by having smaller and more numerous stomata, while ferns differ from bromeliads by having thinner leaves, higher nutrient concentrations, and lower water content and water use efficiency. 5. Trait networks differ among epiphytes, herbs and trees. While all have central nodes represented by SLA and mass-based photosynthesis, in epiphytes, traits related to plant water relations have stronger connections, and nutrients other than potassium have weaker connections to the remainder of the trait network. Whereas stem-specific density reflects mechanical support related to plant size in herbs and trees, in epiphytes it mostly reflects water storage and scales with leaf water content. 6. Synthesis. Our findings advance our understanding of epiphyte ecology, but we note that currently mainly leaf traits are available. Important gaps are root, shoot and whole plant, demographic and gas exchange traits. We suggest how future research might use available data and fill data gaps. ISSN:0022-0477

Simulations of the land surface carbon cycle typically compress functional diversity into a small set of plant functional types (PFT), with parameters defined by the average value of measurements of functional traits. In most earth system models, all wild plant life is represented by between five and 14 PFTs and a typical grid cell (≈100 × 100 km) may contain a single PFT. Model logic applied to this coarse representation of ecological functional diversity provides a reasonable proxy for the carbon cycle, but does not capture the non-linear influence of functional traits on productivity. Here we show through simulations using the Energy Exascale Land Surface Model in 15 diverse terrestrial landscapes, that better accounting for functional diversity markedly alters predicted total carbon uptake. The shift in carbon uptake is as great as 30% and 10% in boreal and tropical regions, respectively, when compared to a single PFT parameterized with the trait means. The traits that best predict gross primary production vary based on vegetation phenology, which broadly determines where traits fall within the global distribution. Carbon uptake is more closely associated with specific leaf area for evergreen PFTs and the leaf carbon to nitrogen ratio in deciduous PFTs.

Nowadays, more and more biodiversity datasets containing observational and experimental data are collected and produced by different projects. In order to answer the fundamental questions of biodiversity research, these data need to be integrated for joint analyses. However, to date, too often, these data remain isolated in silos. Both in academia and industry, Knowledge Graphs (KGs) are widely regarded as a promising approach to overcome issues of data silos and lack of common understanding of data (Fensel and Şimşek 2020). KGs are graph-structured knowledge bases that store factual information in the form of structured relationships between entities, like “tree_species has_trait average_SLA” or “nutans is_observed_in SCH_Location" (Hogan et al. 2021). In our context, entities could be, e.g., abstract concepts like a kingdom, a species, or a trait, or a concrete specimen of a species. Example relationships could be "co-occurs" or, "possesses-trait". KGs for biodiversity have been proposed by Page 2019 and have also been the topic at prior TDWG conferences *1 (Page 2021). However, to date, uptake of this concept in the community has been rather slow (Sachs et al. 2019). We argue that this is at least partially due to the high effort and expertise required in developing and managing such KGs. Therefore, in our ongoing project, iKNOW (Babalou et al. 2021), we aim to provide a toolbox for reproducible KG creation. While iKNOW is still in an early stage, we aim to make this platform open-source and freely available to the biodiversity community. Thus, it can significantly contribute to making biodiversity data widely available, easily discoverable, and integratable. For now, we focus on tabular datasets resulting from biodiversity observation or sampling events or experiments. Given such a dataset, iKNOW will support its transformation into (subject, predicate, object) triples in the RDF standard (Resource Description Framework). Every uploaded dataset will be considered as a subgraph of the main KG in iKNOW. If required, data can be cleaned. After that, the entities and relationships among them should be extracted. For that, a user will be able select one of the existing semi-automatic tools available on our platform (e.g., JenTab (Abdelmageed and Schindler 2020)). The entities in this step can be linked to respective global identifiers in Wikidata, GBIF, the Global Biodiversity Information Facility, or any other user-selected knowledge resource. In the next step, (subject, predicate, object) triples based on the extracted information from the previous steps will be created. After these processes, the generated sub-KG can be used directly. However, one can take further steps such as: Triple Augmentation (generate new triples and extra relations to ease KG completion), Schema Refinement (refine the schema, e.g., via logical reasoning for the KG completion and correctness), Quality Checking (check the quality of the generated sub-KG), and Query Building (create customized SPARQL queries for the generated KG). iKNOW will include a wide range of functionalities for creating, accessing, querying, visualizing, updating, reproducing, and tracking the provenance of KGs. The reproducibility of such a creation is essential to strengthening the establishment of open science practices in the biodiversity domain. Thus, all information regarding the user-selected tools with parameters and settings, along with the initial dataset and intermediate results, will be saved in every step of our platform. With the help of this, users can redo the previous steps. Moreover, this enables us to track the provenance of the created KG. The iKNOW project is a joint effort by computer scientists and domain experts from the German Centre for Integrative Biodiversity Research (iDiv). As a showcase, we aim to create a KG of plant-related data sources at iDiv. These include, among others: TRY (the plant trait database) (Kattge and DÍaz 2011), sPlot (the database about global patterns of taxonomic, functional, and phylogenetic diversity) (Bruelheide and Dengler 2019), and PhenObs (the dataset of the global network of botanical gardens monitoring the impacts of climate change on the phenology of herbaceous plant species) (Nordt and Hensen 2021), LCVP, the Leipzig Catalogue of Vascular Plants, (Freiberg and Winter 2020), and many others. The resulting KG will serve as a discovery tool for biodiversity data and provide a robust infrastructure for managing biodiversity knowledge. From the biodiversity research perspective, iKNOW will contribute to creating a dataset following the Linked Open Data principles by interlinking to cross-domain and specific-domain KGs. From the computer science perspective, iKNOW will contribute to developing tools for dynamic, low-effort creation of reproducible knowledge graphs.

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