Evan Weiher’s research while affiliated with University of Wisconsin–Eau Claire and other places

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Publications (16)


Scatterplot indicating the relation of standard deviation within species and sample size on the example of SLA data (1/LMA) derived from the TRY database version 1 (Kattge et al.¹⁰, Fig. S1).
Climatic and geographical coverage of the dataset. Green points, occurrences according to the Global Biodiversity Information Facility (GBIF) (http://www.gbif.org) of species with information on at least one core trait (upper panels) and all six core traits (lower panels). Right panels show distribution in the global map (Robinson projection); grey: land surface. Maps are based on the R package ‘maps’, accessed at The Comprehensive R Archive Network (https://cran.r-project.org/web/packages/maps/index.html). Left panels show distribution in major climatic regions of the world; grey: MAP and MAT as in Climate Research Unit (CRU) CL v.1.0 0.5 degree climatology (http://www.cru.uea.ac.uk/data, ref. ²⁴⁵); Biome classification according to Whittaker²⁴⁶. This figure is reproduced from ref. ⁹ with permission.
Frequency distributions of species for the six core traits. Grey: species with all six traits; white: species with at least one trait. (a) Plant height, (b) Seed mass, (c) SSD: stem dry mass per stem fresh volume (stem specific density), (d) Leaf area, (e) LMA: leaf dry mass per leaf area, (f) Leaf Nmass: leaf nitrogen content per leaf dry mass (leaf nitrogen concentration).
The coverage of species per trait with respect to woodiness (woody versus non-woody incl. semi-woody). The coverage in the GIFT database²⁴⁷,²⁴⁸ a comprehensive baseline of plant growth form, is included for external comparison (see ref. ¹¹ for more details). In parentheses: the number of species with data for the trait and the number of species for which woodiness could be determined.
The global spectrum of plant form and function: enhanced species-level trait dataset
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December 2022

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2,948 Reads

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48 Citations

Scientific Data

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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.

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Overview of the 18 functional traits
a The unique geographic locations (n = 8683) where tree functional traits were recorded. The size of the circles denotes the relative number of unique traits (out of 18 possible) that were measured at each location, regardless of species identity. b Summary statistics for the 18 traits considered here (see Supplementary Table 1–3, Supplementary Figs. 1, 2 for additional information). The analysis included 491,001 trait measurements, encompassing 13,189 unique tree species and 2313 unique genera.
The dominant trait axes and relationships
Shown are the first two principal component axes capturing trait relationships across the 18 functional traits. a All tree species (n = 30,146 observations), b angiosperms only (n = 24,658), and c gymnosperms only (n = 5498). In a the three variables that load most strongly on each axis are shown in dark black lines, with the remaining variables shown in light grey. These same six variables are highlighted in b and c illustrating how the same relationships extend to angiosperms and gymnosperms (see Supplementary Figs. 10–12 for the full PCAs with all traits visible, and Supplementary Table 5 for the PC loadings).
The relationship between environmental variables and trait axes
a, b The relative influence of the environmental variables on the two dominant PC axes. The ten variables are sorted by overall variable importance in the models (see Methods). Yellow points are observations which have high values of that environmental variable; blue values are the lowest. Points to the right of zero indicate a positive influence on the PC axis; points to the left indicate a negative influence (see also Supplementary Figs. 17, 18). c–h The relationships between environmental variables and PC axis values for the three variables in a with the strongest influence. Values above zero show a positive influence on PC axis values; values less than zero indicate a negative influence.
Trait correlations and functional clusters
a Trait clusters with high average intra-group correlation. The upper triangle gives the species-weighted correlations incorporating intraspecific variation. The lower triangle gives the corresponding correlations among phylogenetic independent contrasts, which adjusts for pseudo-replication due to the non-independence of closely related species. The size of the circle denotes the relative strength of the correlation, with solid circles denoting positive correlations and open circles denoting negative correlations (see Supplementary Fig. 19 for the numeric values). b PC loadings for each trait and each of the first two principal component axes, illustrating which functional trait clusters align most strongly with the dominant axes of trait variation (see Supplementary Table 5 for the full set of PC loadings). c The species-level phylogenetic signal of each trait (Pagel’s λ), calculated using only the raw trait values.
Global relationships in tree functional traits

June 2022

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2,914 Reads

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69 Citations

Due to massive energetic investments in woody support structures, trees are subject to unique physiological, mechanical, and ecological pressures not experienced by herbaceous plants. Despite a wealth of studies exploring trait relationships across the entire plant kingdom, the dominant traits underpinning these unique aspects of tree form and function remain unclear. Here, by considering 18 functional traits, encompassing leaf, seed, bark, wood, crown, and root characteristics, we quantify the multidimensional relationships in tree trait expression. We find that nearly half of trait variation is captured by two axes: one reflecting leaf economics, the other reflecting tree size and competition for light. Yet these orthogonal axes reveal strong environmental convergence, exhibiting correlated responses to temperature, moisture, and elevation. By subsequently exploring multidimensional trait relationships, we show that the full dimensionality of trait space is captured by eight distinct clusters, each reflecting a unique aspect of tree form and function. Collectively, this work identifies a core set of traits needed to quantify global patterns in functional biodiversity, and it contributes to our fundamental understanding of the functioning of forests worldwide.


sPlot – A new tool for global vegetation analyses

January 2020

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905 Reads

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82 Citations

Journal of Vegetation Science

Aims: Vegetation-plot records provide information on the presence and cover or abundance of plants co-occurring in the same community. Vegetation-plot data are spread across research groups, environmental agencies and biodiversity research centers and, thus, are rarely accessible at continental or global scales. Here we present the sPlot database, which collates vegetation plots worldwide to allow for the exploration of global patterns in taxonomic, functional and phylogenetic diversity at the plant community level. Results: sPlot version 2.1 contains records from 1,121,244 vegetation plots, which comprise 23,586,216 records of plant species and their elative cover or abundance in plots collected worldwide between 1885 аnd 2015. We complemented the information for each plot by retrieving climate and soil conditions and the biogeographic context (e.g., biomes) from external sources, and by calculating community-weighted means and variances of traits using gap-filled data from the global plant trait database TRY. Moreover, we created a phylogenetic tree for 50,167 out of the 54,519 species identified in the plots. We present the first maps of global patterns of community richness and community-weighted means of key traits. Conclusions: The availability of vegetation plot data in sPlot offers new avenues for vegetation analysis at the global scale.


TRY plant trait database – enhanced coverage and open access

January 2020

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10,276 Reads

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1,469 Citations

Global Change Biology

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– nvironmental 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.








Citations (6)


... Functional biogeography, though promising, is largely underrepresented here. A comprehensive theoretical and empirical framework linking functional ecology to predictive biogeography remains elusive (but see Violle et al. 2014, Díaz et al. 2022, Neyret et al. 2024. Similarly, ecological frameworks developed at small spatial and temporal scales must be scaled to larger extents to address global change scenarios. ...

Reference:

Emerging horizons in predictive biogeography
The global spectrum of plant form and function: enhanced species-level trait dataset

Scientific Data

... During their growth and development, plants develop a series of optimal combinations of functional traits to adapt to environmental changes (Westoby et al. 2002;Wright et al. 2007;Ahrens et al. 2020;Maynard et al. 2022). In the present study, we found that resource acquisition traits (SLA and leaf Mg and N concentrations) correlated significantly negatively with resource conservation traits (LDMC, LT, and leaf C and Ca concentrations). ...

Global relationships in tree functional traits

... Les traits fonctionnels peuvent être mesurés directement sur le terrain mais aussi peuvent être extraits dans des bases de données de traits. En effet, les données collectées par de nombreuses équipes ont été regroupées dans des bases de données collectives (Kattge et al. 2011, Kattge et al. 2020) Ces données de traits peuvent être extraites de ces bases de traits et combinées avec des relevés botaniques (liste d'espèces et abondances). Cette approche permet de réutiliser des bases de données et d'avoir des approches plus globales. ...

TRY plant trait database – enhanced coverage and open access

Global Change Biology

... ForestGEO (Anderson-Teixeira et al., 2015); International Long Term Ecological Research, ILTER). It is also key to have the mode of collaboration and data sharing well defined, with roles written and agreed by all members (sPlots (Bruelheide et al., 2019), ForestPlots.net (ForestPlots.net ...

sPlot – A new tool for global vegetation analyses

Journal of Vegetation Science

... Generally, in vegetation science, cover is the prevailing importance measure, as direct cover estimates in percent, in an ordinal cover scale or in a combined cover-abundance scale (like most variants of the Braun-Blanquet scale; see Dengler & Dembicz 2023). For example, in the global vegetation-plot database sPlot (Bruelheide et al. 2019), 96% of all plots used some form of cover information, 66% thereof different variants of the Braun-Blanquet cover-(abundance) scale and 15% an estimation in percent. The popularity of cover-based measures rests on their relatively high information content, combined with the low amount of time needed to record them with considerable reliability. ...

sPlot – A new tool for global vegetation analyses

Journal of Vegetation Science

... This is often seen in the form of shrubification which is a major mechanism for Arctic greening. Some browning events (e.g., fire, active layer detachments, etc.) may open up space for new establishment, which may then be colonized by plants that are more indicative of the current warmer climate (e.g., tall shrubs) [150,151]. Greater sensitivity to climatic events of evergreen shrubs compared to deciduous shrubs could lead to greater deciduousness. Beavers increase water area, but disturbances also increase exposure of mineral substrate that may facilitate shrub expansion [101]. ...

Plant functional trait change across a warming tundra biome

Nature