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

Leaf economics and plant hydraulics drive leaf : wood area ratios

This is the author accepted manuscript. The final version is available from Wiley via the DOI in this recordData accessibility: All data are archived and are available from the TRY plant trait data base: www.try-db.org (https://doi.org/10.1111/j.1365-2486.2011.02451.x).Biomass and area ratios between leaves, stems and roots regulate many physiological and ecological processes. The Huber value Hv (sapwood area/leaf area ratio) is central to plant water balance and drought responses. However, its coordination with key plant functional traits is poorly understood, which prevents developing trait-based prediction models. Based on theoretical arguments, we hypothesise that global patterns in Hv of terminal woody branches can be predicted from variables related to plant trait spectra, i.e., plant hydraulics and size and leaf economics. Using a global compilation of 1135 species-averaged Hv , we show that Hv varies over 3 orders of magnitude. Higher Hv are seen in short small-leaved low-SLA shrubs with low Ks in arid relative to tall large-leaved high-SLA trees with high Ks in moist environments. All traits depend on climate but climatic correlations are stronger for explanatory traits than Hv . Negative isometry is found between Hv and Ks , suggesting a compensation to maintain hydraulic supply to leaves across species. This work identifies the major global drivers of branch sapwood/leaf area ratios. Our approach based on widely available traits facilitates the development of accurate models of aboveground biomass allocation and helps predict vegetation responses to drought.Spanish Ministry of Economy and Competitiveness (MINECO)University of NottinghamSwedish Research Council Forma

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

Physical Performance Characteristics of Assisted Living Residents and Risk for Adverse Health Outcomes

Little is known about the physical performance ability of residential care/assisted living (RC/AL) residents and its relationship to adverse outcomes such as fracture, nursing home placement, functional decline, and death. The purposes of this paper are to: 1) describe the functional characteristics of RC/AL residents; 2) examine the relationships between resident- and facility-characteristics and physical performance; and 3) determine the predictive value of physical performance for adverse outcomes

Pressure-induced amorphization of YVO4:Eu3+ nanoboxes

This is an author-created, un-copyedited version of an article published in Nanotechnology. IOP Publishing Ltd is not responsible for any errors or omissions in this version of the manuscript or any version derived from it. The Version of Record is available online at http://dx.doi.org/10.1088/0957-4484/27/2/025701A structural transformation from the zircon-type structure to an amorphous phase has been found in YVO4:Eu3+ nanoboxes at high pressures above 12.7 GPa by means of x-ray diffraction measurements. However, the pair distribution function of the high-pressure phase shows that the local structure of the amorphous phase is similar to the scheelite-type YVO4. These results are confirmed both by Raman spectroscopy and Eu3+ photoluminescence which detect the phase transition to a scheelite-type structure at 10.1 and 9.1 GPa, respectively. The irreversibility of the phase transition is observed with the three techniques after a maximum pressure in the upstroke of around 20 GPa. The existence of two D-5(0)-> F-7(0) photoluminescence peaks confirms the existence of two local environments for Eu3+, at least for the low-pressure phase. One environment is the expected for substituting Y3+ and the other is likely a disordered environment possibly found at the surface of the nanoboxes.This work has been performed under financial support from Spanish MINECO under the National Program of Materials (MAT2013-46649-C4-1/2/3/4-P) and the Consolider-Ingenio 2010 Program (MALTA CSD2007-00045). Funding by the Fundacion Caja Canarias (ENER-01) and the EU-FEDER funds is also acknowledged. JR-F thanks the Alexander von Humboldt Foundation for a postdoctoral fellowship and NS thanks the German Research Foundation (DFG) for financial support (Project RA2585/1-1). We acknowledge Diamond Light Source for time on beamline I15 under proposals EE3652 and EE6517. 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On staying grounded and avoiding Quixotic dead ends

The 15 articles in this special issue on The Representation of Concepts illustrate the rich variety of theoretical positions and supporting research that characterize the area. Although much agreement exists among contributors, much disagreement exists as well, especially about the roles of grounding and abstraction in conceptual processing. I first review theoretical approaches raised in these articles that I believe are Quixotic dead ends, namely, approaches that are principled and inspired but likely to fail. In the process, I review various theories of amodal symbols, their distortions of grounded theories, and fallacies in the evidence used to support them. Incorporating further contributions across articles, I then sketch a theoretical approach that I believe is likely to be successful, which includes grounding, abstraction, flexibility, explaining classic conceptual phenomena, and making contact with real-world situations. This account further proposes that (1) a key element of grounding is neural reuse, (2) abstraction takes the forms of multimodal compression, distilled abstraction, and distributed linguistic representation (but not amodal symbols), and (3) flexible context-dependent representations are a hallmark of conceptual processing

TRY plant trait database - enhanced coverage and open access

Plant traits-the morphological, anatomical, physiological, biochemical and phenological characteristics of plants-determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait-based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits-almost complete coverage for 'plant growth form'. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait-environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives

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

Basin-wide variations in Amazon forest nitrogen-cycling characteristics as inferred from plant and soil ¹⁵N:¹⁴N measurements

Background: Patterns in tropical forest nitrogen cycling are poorly understood. In particular, the extent to which leguminous trees in these forests fix nitrogen is unclear. Aims: We aimed to determine factors that explain variation in foliar δ¹⁵N (δ¹⁵NF) for Amazon forest trees, and to evaluate the extent to which putatively N₂-fixing Fabaceae acquire nitrogen from the atmosphere. Methods: Upper-canopy δ¹⁵NF values were determined for 1255 trees sampled across 65 Amazon forest plots. Along with plot inventory data, differences in δ¹⁵NF between nodule-forming Fabaceae and other trees were used to estimate the extent of N² fixation. Results: δ¹⁵NF ranged from −12.1‰ to +9.3‰. Most of this variation was attributable to site-specific conditions, with extractable soil phosphorus and dry-season precipitation having strong influences, suggesting a restricted availability of nitrogen on both young and old soils and/or at low precipitation. Fabaceae constituted fewer than 10% of the sampled trees, and only 36% were expressed fixers. We estimated an average Amazon forest symbiotic fixation rate of 3 kg N ha¯¹ year‾¹. Conclusion: Plant δ¹⁵N indicate that low levels of nitrogen availability are only likely to influence Amazon forest function on immature or old weathered soils and/or where dry-season precipitation is low. Most Fabaceae species that are capable of nodulating do not fix nitrogen in Amazonia