Gap Filling in the Plant Kingdom---Trait Prediction Using Hierarchical Probabilistic Matrix Factorization

Plant traits are a key to understanding and predicting the adaptation of ecosystems to environmental changes, which motivates the TRY project aiming at constructing a global database for plant traits and becoming a standard resource for the ecological community. Despite its unprecedented coverage, a large percentage of missing data substantially constrains joint trait analysis. Meanwhile, the trait data is characterized by the hierarchical phylogenetic structure of the plant kingdom. While factorization based matrix completion techniques have been widely used to address the missing data problem, traditional matrix factorization methods are unable to leverage the phylogenetic structure. We propose hierarchical probabilistic matrix factorization (HPMF), which effectively uses hierarchical phylogenetic information for trait prediction. We demonstrate HPMF's high accuracy, effectiveness of incorporating hierarchical structure and ability to capture trait correlation through experiments.Comment: Appears in Proceedings of the 29th International Conference on Machine Learning (ICML 2012

The Global Spectrum of Plant Form and Function: Enhanced Species-Level Trait Dataset

[Abstract] Here we provide the ‘Global Spectrum of Plant Form and Function Dataset’, containing species mean values for six vascular plant traits. Together, these traits –plant height, stem specific density, leaf area, leaf mass per area, leaf nitrogen content per dry mass, and diaspore (seed or spore) mass – define the primary axes of variation in plant form and function. The dataset is based on ca. 1 million trait records received via the TRY database (representing ca. 2,500 original publications) and additional unpublished data. It provides 92,159 species mean values for the six traits, covering 46,047 species. The data are complemented by higher-level taxonomic classification and six categorical traits (woodiness, growth form, succulence, adaptation to terrestrial or aquatic habitats, nutrition type and leaf type). Data quality management is based on a probabilistic approach combined with comprehensive validation against expert knowledge and external information. Intense data acquisition and thorough quality control produced the largest and, to our knowledge, most accurate compilation of empirically observed vascular plant species mean traits to date.The study has been supported by the TRY initiative on plant traits (https://www.try-db.org). TRY is an initiative of the Max Planck Institute for Biogeochemistry, bioDISCOVERY/Future Earth (ICSU), the German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig and Núcleo DiverSus (CONICET- Universidad Nacional de Córdoba, Argentina). The Global Spectrum of Plant Form and Function study has been supported by the European BACI project (Towards a Biosphere Atmosphere change Index, EU grant ID 640176), and grants to SD by FONCyT, CONICET, Universidad Nacional de Córdoba, the Inter-American Institute for Global Change Research, and The Newton Fund (NERC UK – CONICET ARG). VO thanks RSF (#19-14-00038p). Open Access funding enabled and organized by Projekt DEA

The TRY Database System

The TRY initiative (www.try-db.org) is a network of vegetation scientists providing curated plant trait data for the scientific community. The TRY Database currently contains about 7 million trait records for nearly 3000 different traits. The flexible database structure can hold any number of traits and a generic program can import any kind of data without requiring a template. About 10 million trait records for about 100 requests are released on a monthly basis. This is organized via the TRY Data Portal, which facilitates data contribution, exploration and customized requests. The Dataset Custodian Centre allows managing the status of contributed datasets and monitoring the use of these data from requests to scientific publications. The Request PI Centre allows managing and monitoring requests. Both centres facilitate direct contact of data contributors and users. In addition to the TRY Database we have established a file archive, which facilitates publication and DOIs for else unpublished plant trait datasets. The TRY Data Portal has evolved toward a long-term scientific data infrastructure, which combines the advantages of easy access to curated plant trait data almost ready for analyses, with direct contact of data providers and users, the opportunity for data providers to publish individual datasets and track the use of their data. This presentation will introduce details of the TRY database system

Towards global data products of Essential Biodiversity Variables on species traits

Essential Biodiversity Variables (EBVs) allow observation and reporting of global biodiversity change, but a detailed framework for the empirical derivation of specific EBVs has yet to be developed. Here, we re-examine and refine the previous candidate set of species traits EBVs and show how traits related to phenology, morphology, reproduction, physiology and movement can contribute to EBV operationalization. The selected EBVs express intra-specific trait variation and allow monitoring of how organisms respond to global change. We evaluate the societal relevance of species traits EBVs for policy targets and demonstrate how open, interoperable and machine-readable trait data enable the building of EBV data products. We outline collection methods, meta(data) standardization, reproducible workflows, semantic tools and licence requirements for producing species traits EBVs. An operationalization is critical for assessing progress towards biodiversity conservation and sustainable development goals and has wide implications for data-intensive science in ecology, biogeography, conservation and Earth observation

First Annual Report of the School Committee of the City of Westbrook 1891-2

Aims: Hemiparasitic plants often produce nutrient-rich litter with high decomposition rates, and thus can enhance nutrient availability. When plant species have differential affinities for this nutrient source, hemiparasitic litter might influence species composition in addition to the parasitic suppression of host species. We expected that species adapted to fertile habitats derive a higher proportion of nutrients from the hemiparasitic litter compared to other species. Methods: 15N-labeled litter of Rhinanthus angustifolius and Pedicularis sylvatica was added to experimental field plots and adjacent litter bags. We examined N release from the litter, N uptake by the vegetation 2, 4 and 12 months after litter addition and differences in the proportion of N taken up from the litter (NL) between co-occurring species. Results: The percentage of N in shoots of co-occurring plant species that is derived from the added hemiparasitic litter (NL) strongly differed between the species (0.1–6.2 %). After exclusion of species with an alternative N source (legumes as well as ectomycorrhizal and ericoid mycorrhizal species), NL was positively related (p < 0.001) with specific leaf area (SLA) and at Pedicularis sites with leaf N concentration (LNC) and leaf phosphorus concentration (LPC) (p < 0.05), i.e. leaf traits associated with a fast-growth strategy and adaptation to high-nutrient environments. Conclusions: Our results suggest that nutrient release from hemiparasitic litter favors plant species with a fast-growth strategy adapted to high-nutrient environments compared to species with a slow-growth strategy. Whether continued hemiparasitic litter inputs are able to change species composition in the long term requires further research.status: publishe

Taxonomic and functional diversity in Mediterranean pastures : insights on the biodiversity–productivity trade-off

1. Agricultural intensification is one of the main causes of biodiversity loss world-wide. The inclusion of semi-natural features in agricultural landscapes is suggested as a means of enhancing farm biodiversity, but this practice may have potential negative effects on yield production. Moreover, little evidence exists for effects of semi-natural features on other components of biodiversity, such as functional diversity. Yet this could provide a more comprehensive understanding of biodiversity–productivity trade-offs. 2. Here, we report the effects of semi-natural woody vegetation on taxonomic and functional diversity, and biomass production of herbaceous species at the field and farm scales by sampling 50 fields, ranging from 0 to 90% woody vegetation cover, on nine similarly managed farms in central-western Spain. 3. We found significant differences in herbaceous species richness among farms. Both taxonomic and functional b-diversity exhibited significant negative relationships with herbage production, highlighting the trade-off between biodiversity and productivity in these agroecosystems. 4. Woody vegetation cover had a significant negative relationship with biomass production and a unimodal relationship with species richness at the field scale. At high values of woody vegetation cover, species richness and functional diversity indices were decoupled, suggesting that at this extreme of the woody vegetation gradient, only herbaceous species with contrasting trait values were present. Our results showed both convergent and divergent patterns of trait values, suggesting that different assembly processes are acting concurrently along the gradient of woody vegetation. 5. Synthesis and applications. Our result indicates that management of woody vegetation may indeed increase both taxonomic and functional diversity, but this may come at the expense of key ecosystem services or other management goals, namely herbage production. Optimization of the trade-off between herbage diversity and productivity can be reached with a woody vegetation cover of c. 30% at the field scale.This study was funded by the European Union through the FP7 project BioBio (Indicators for biodiversity in organic and low-input farming systems; www.biobio-indicators.org). It was supported by the TRY initiative on plant traits (http://www.trydb.org). TRY has been supported by DIVERSITAS, IGBP, the Global Land Project, the UK Natural Environment Research Council (NERC) through its program QUEST (Quantifying and Understanding the Earth System), the French Foundation for Biodiversity Research (FRB), and GIS Climat, Environnement et Soci et e France. VR was supported by a postdoctoral grant from the National Research Foundation of South AfricaThe European Union through the FP7 project BioBio. It was supported by the TRY initiative on plant traits (http://www.trydb.org). TRY has been supported by DIVERSITAS, IGBP, the Global Land Project, the UK Natural Environment Research Council (NERC) through its program QUEST (Quantifying and Understanding the Earth System), the French Foundation for Biodiversity Research (FRB), and GIS Climat, Environnement et Societe France. VR was supported by a postdoctoral grant from the National Research Foundation of South Africa.http://onlinelibrary.wiley.com/journal/10.1111/(ISSN)1365-26642017-10-31hb2016Zoology and Entomolog

Functional resilience against climate-driven extinctions: comparing the functional diversity of European and North Americantree floras

Future global change scenarios predict a dramatic loss of biodiversity for many regions in the world, potentially reducing the resistance and resilience of ecosystem functions. Once before, during Plio-Pleistocene glaciations, harsher climatic conditions in Europe as compared to North America led to a more depauperate tree flora. Here we hypothesize that this climate driven species loss has also reduced functional diversity in Europe as compared to North America. We used variation in 26 traits for 154 North American and 66 European tree species and grid-based co-occurrences derived from distribution maps to compare functional diversity patterns of the two continents. First, we identified similar regions with respect to contemporary climate in the temperate zone of North America and Europe. Second, we compared the functional diversity of both continents and for the climatically similar subregions using the functional dispersion-index (FDis) and the functional richness index (FRic). Third, we accounted in these comparisons for grid-scale differences in species richness, and, fourth, investigated the associated trait spaces using dimensionality reduction. For gymnosperms we find similar functional diversity on both continents, whereas for angiosperms functional diversity is significantly greater in Europe than in North America. These results are consistent across different scales, for climatically similar regions and considering species richness patterns. We decomposed these differences in trait space occupation into differences in functional diversity vs. differences in functional identity. We show that climate-driven species loss on a continental scale might be decoupled from or at least not linearly related to changes in functional diversity. This might be important when analyzing the effects of climate-driven biodiversity change on ecosystem functioning

Global Estimation of Biophysical Variables from Google Earth Engine Platform

This paper proposes a processing chain for the derivation of global Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Fraction Vegetation Cover (FVC), and Canopy water content (CWC) maps from 15-years of MODIS data exploiting the capabilities of the Google Earth Engine (GEE) cloud platform. The retrieval chain is based on a hybrid method inverting the PROSAIL radiative transfer model (RTM) with Random forests (RF) regression. A major feature of this work is the implementation of a retrieval chain exploiting the GEE capabilities using global and climate data records (CDR) of both MODIS surface reflectance and LAI/FAPAR datasets allowing the global estimation of biophysical variables at unprecedented timeliness. We combine a massive global compilation of leaf trait measurements (TRY), which is the baseline for more realistic leaf parametrization for the considered RTM, with large amounts of remote sensing data ingested by GEE. Moreover, the proposed retrieval chain includes the estimation of both FVC and CWC, which are not operationally produced for the MODIS sensor. The derived global estimates are validated over the BELMANIP2.1 sites network by means of an inter-comparison with the MODIS LAI/FAPAR product available in GEE. Overall, the retrieval chain exhibits great consistency with the reference MODIS product (R2 role= presentation \u3e2 = 0.87, RMSE = 0.54 m2 role= presentation \u3e2/m2 role= presentation \u3e2 and ME = 0.03 m2 role= presentation \u3e2/m2 role= presentation \u3e2 in the case of LAI, and R2 role= presentation \u3e2 = 0.92, RMSE = 0.09 and ME = 0.05 in the case of FAPAR). The analysis of the results by land cover type shows the lowest correlations between our retrievals and the MODIS reference estimates (R2 role= presentation \u3e2 = 0.42 and R2 role= presentation \u3e2 = 0.41 for LAI and FAPAR, respectively) for evergreen broadleaf forests. These discrepancies could be attributed mainly to different product definitions according to the literature. The provided results proof that GEE is a suitable high performance processing tool for global biophysical variable retrieval for a wide range of applications

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

Late Quaternary climate legacies in contemporary plant functional composition

The functional composition of plant communities is commonly thought to be determined by contemporary climate. However, if rates of climate‐driven immigration and/or exclusion of species are slow, then contemporary functional composition may be explained by paleoclimate as well as by contemporary climate. We tested this idea by coupling contemporary maps of plant functional trait composition across North and South America to paleoclimate means and temporal variation in temperature and precipitation from the Last Interglacial (120 ka) to the present. Paleoclimate predictors strongly improved prediction of contemporary functional composition compared to contemporary climate predictors, with a stronger influence of temperature in North America (especially during periods of ice melting) and of precipitation in South America (across all times). Thus, climate from tens of thousands of years ago influences contemporary functional composition via slow assemblage dynamics