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1. Plant trait and vegetation data along a 1314 m elevation gradient with fire history in Puna grasslands, Perú

   https://doi.org/10.1038/s41597-024-02980-3  

   Halbritter, Aud H; Vandvik, Vigdis; Cotner, Sehoya H; Farfan-Rios, William; Maitner, Brian S; Michaletz, Sean T; Oliveras_Menor, Imma; Telford, Richard J; Ccahuana, Adam; Cruz, Rudi; et al (December 2024, Scientific Data)

   Abstract Alpine grassland vegetation supports globally important biodiversity and ecosystems that are increasingly threatened by climate warming and other environmental changes. Trait-based approaches can support understanding of vegetation responses to global change drivers and consequences for ecosystem functioning. In six sites along a 1314 m elevational gradient in Puna grasslands in the Peruvian Andes, we collected datasets on vascular plant composition, plant functional traits, biomass, ecosystem fluxes, and climate data over three years. The data were collected in the wet and dry season and from plots with different fire histories. We selected traits associated with plant resource use, growth, and life history strategies (leaf area, leaf dry/wet mass, leaf thickness, specific leaf area, leaf dry matter content, leaf C, N, P content, C and N isotopes). The trait dataset contains 3,665 plant records from 145 taxa, 54,036 trait measurements (increasing the trait data coverage of the regional flora by 420%) covering 14 traits and 121 plant taxa (ca. 40% of which have no previous publicly available trait data) across 33 families. 

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2. Plant functional traits from an early successional grassland community at KBS LTER

   https://doi.org/10.5061/dryad.47d7wm3vk  

   Perez, Sierra; Lau, Jennifer (January 2026, Dryad)

   Identifying the factors mediating the resilience and invariability of plant communities and their associated ecosystem functions is critical to understand the ecological impacts of climate change. Prior work has explored how classic “community properties” (characterizing a community by species composition) regulate the resilience and invariability of ecosystem functioning. Mechanistically, community properties influence resilience and invariability via species’ traits, and as a result, “functional properties” (characterizing a community via functional traits) might better predict these qualities. For example, functional traits associated with conservative resource-use strategies (e.g., short stature, low specific leaf area (SLA), high leaf dry matter content (LDMC)) are expected to promote both resistance to periods of stress or resource limitation and long-term invariability. While there is a strong conceptual basis linking functional traits and functional diversity to resilience and invariability, empirical evidence is thus far mixed, and sourcing accurate functional trait data may be challenging. Therefore, it is important to know if community properties are sufficient for evaluating the resilience and invariability of ecosystem functioning, or if functional properties provide necessary insights. Capitalizing on a decades-long study, we tested the effects of plant functional and community properties on the resistance of ecosystem functioning (a component of resilience) to drought or long-term invariability. Including functional properties did not improve our ability to explain primary productivity resistance to droughts but considerably improved our explanatory power for productivity invariability. Our results supported expectations that conservative trait strategies promote ecosystem functioning resistance to perturbations and temporal invariability. These findings highlight that the utility of functional properties in explaining the resilience and invariability of ecosystem functioning may depend on the attribute under consideration and that functional properties may primarily be useful for identifying the traits promoting resilience and invariability. In July 2024, we sampled functional traits of 31 plant species in the KBS LTER Main Cropping System Experiment (MCSE) early successional (T7) plots in Hickory Corners, MI, USA. Traits were measured from a minimum of 3 randomly selected individuals per species, and if possible, we sampled one individual per species per T7 replicate to account for spatial environmental variation (i.e., n = 3–6 individuals/species). For each plant, we measured height (cm) as the distance from ground to top of foliage, excluding reproductive structures, and we collected leaf samples for specific leaf area (SLA; cm² g^-1) and leaf dry matter content (LDMC) (following Pérez-Harguindeguy et al. 2016). To minimize dehydration, collected leaves were sealed in plastic bags with damp paper towels, and stored in a cooler until processing within 1–2 hrs. Fresh leaf samples with petioles removed were then scanned on a LI-3000A leaf area meter (LI-COR Biosciences, Lincoln, NE), weighed, oven-dried at 60 °C to constant mass, and then reweighed. # Plant functional traits from an early successional grassland community at KBS LTER Dataset DOI: [10.5061/dryad.47d7wm3vk](https://doi.org/10.5061/dryad.47d7wm3vk) ## Description of the data and file structure In July 2024, we sampled functional traits of 31 plant species in the KBS LTER Main Cropping System Experiment (MCSE) early successional (T7) plots in Hickory Corners, MI, USA. Traits were measured from a minimum of 3 randomly selected individuals per species, and if possible, we sampled one individual per species per T7 replicate to account for spatial environmental variation (i.e., n = 3–6 individuals/species). For each plant, we measured height (cm) as the distance from ground to top of foliage, excluding reproductive structures, and we collected leaf samples for specific leaf area (SLA; cm^2^ g^-1^) and leaf dry matter content (LDMC) (following Pérez-Harguindeguy et al. 2016). To minimize dehydration, collected leaves were sealed in plastic bags with damp paper towels, and stored in a cooler until processing within 1–2 hrs. Fresh leaf samples with petioles removed were then scanned on a LI-3000A leaf area meter (LI-COR Biosciences, Lincoln, NE), weighed, oven-dried at 60 °C to constant mass, and then reweighed. ### Files and variables #### File: KBS_LTER_T7_plant_traits.xlsx **Description:**  ##### Variables * Treatment: Treatment in KBS LTER Main Cropping System Experiment (MCSE)  * Rep: Treatment replicate (R1-R6) * Species: Species name * Latin: Species' full Latin name * Height: plant height (cm) from ground to top of foliage, excluding reproductive structures * Leaf_area_1: leaf area (cm^2^), first measurement  * Leaf_area_2: leaf area (cm^2^), second measurement  * Mass_fresh: leaf fresh mass (g)  * Mass_dried: leaf oven-dried (60 °C to constant mass) mass (g) * SLA: specific leaf area (cm^2^ g^-1^); calculated as average leaf area (i.e., mean of Leaf_area_1 and Leaf_area_2) divided by leaf dried mass * LDMC: leaf dry matter content (g / g); calculated as leaf dried mass divided by leaf fresh mass ## Code/software We used R Studio Version 2024.12.0+467 and R version 4.4.2 (2024-10-31). We used the fundiversity package to calculate functional richness (fd_fric) and functional evenness (fd_feve) from these plant functional trait data.  ## Access information Other publicly accessible locations of the data: * NA Data was derived from the following sources: * NA 

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3. Leaf architecture and functional traits for 122 species at the University of California Botanical Garden at Berkeley

   https://doi.org/10.1002/ecy.4436  

   Matos, Ilaine Silveira; Boakye, Mickey; Antonio, Monica; Carlos, Sonoma; Chu, Ashley; Duarte, Miguel A; Echevarria, Andrea; Fontao, Adrian; Garcia, Lisa; Huang, LeeAnn; et al (December 2024, Ecology)

   Abstract The dataset contains leaf venation architecture and functional traits for a phylogenetically diverse set of 122 plant species (including ferns, basal angiosperms, monocots, basal eudicots, asterids, and rosids) collected from the living collections of the University of California Botanical Garden at Berkeley (37.87° N, 122.23° W; CA, USA) from February to September 2021. The sampled species originated from all continents, except Antarctica, and are distributed in different growth forms (aquatic, herb, climbing, tree, shrub). The functional dataset comprises 31 traits (mechanical, hydraulic, anatomical, physiological, economical, and chemical) and describes six main leaf functional axes (hydraulic conductance, resistance and resilience to damages caused by drought and herbivory, mechanical support, and construction cost). It also describes how architecture features vary across venation networks. Our trait dataset is suitable for (1) functional and architectural characterization of plant species; (2) identification of venation architecture‐function trade‐offs; (3) investigation of evolutionary trends in leaf venation networks; and (4) mechanistic modeling of leaf function. Data are made available under the Open Data Commons Attribution License. 

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4. Widespread variation in functional trait-vital rate relationships in tropical tree seedlings across a precipitation and soil phosphorus gradient

   https://doi.org/10.5061/dryad.mkkwh713s  

   Browne, Luke; Markesteijn, Lars; Manzané-Pinzón, Eric; Wright, Joe; Bagchi, Robert; Engelbrecht, Bettina; Jones, F. Andrew; Comita, Liza (January 2022, Dryad)

   A fundamental assumption of functional ecology is that functional traits are related to interspecific variation in performance. However, the relationship between functional traits and performance is often weak or uncertain, especially for plants. A potential explanation for this inconsistency is that the relationship between functional traits and vital rates (e.g., growth and mortality) is dependent on local environmental conditions, which would lead to variation in trait-rate relationships across environmental gradients. In this study, we examined trait-rate relationships for six functional traits (seed mass, wood density, maximum height, leaf mass per area, leaf area, and leaf dry matter content) using long-term data on seedling growth and survival of woody plant species from eight forest sites spanning a pronounced precipitation and soil phosphorus gradient in central Panama. For all traits considered except for leaf mass per area-mortality, leaf mass per area-growth, and leaf area-mortality relationships, we found widespread variation in the strength of trait-rate relationships across sites. For some traits, trait-rate relationships showed no overall trend but displayed wide site-to-site variation. In a small subset of cases, variation in trait-rate relationships was explained by soil phosphorus availability. Our results demonstrate that environmental gradients have the potential to influence how functional traits are related to growth and mortality rates, though much variation remains to be explained. Accounting for site-to-site variation may help resolve a fundamental issue in trait-based ecology – that traits are often weakly related to performance – and improve the utility of functional traits for explaining key ecological and evolutionary processes. 

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5. Predicting global intraspecific trait variation of grasses

   https://doi.org/10.1002/ecog.07627  

   Griffin‐Nolan, Robert J; Vorontsova, Maria S; Sandel, Brody (April 2025, Ecography)

   Plant traits are important for understanding community assembly and ecosystem processes, yet our understanding of intraspecific trait variation (ITV) is limited. This gap in our knowledge is partially because collecting trait data across a species' entire range is impractical, let alone across the ranges of multiple species within a plant family. Using machine learning techniques to predict spatial ITV is an attractive and cost‐effective alternative to sampling across a species range, although this has not been applied beyond regional scales. We compiled a trait database of over 1000 grass species (family: Poaceae), encompassing six key functional traits: specific leaf area (SLA), leaf dry matter content (LDMC), plant height, leaf area, leaf nitrogen (Nmass) and leaf phosphorus content (Pmass). Using a random forest machine learning approach, we predicted local trait values within species' ranges considering climate, soil type, phylogeny, lifespan, and photosynthetic pathway as influential factors. An iterative random forest modeling technique incorporated correlations between traits, resulting in improved model performance (observed versus predicted R range of 0.72–0.91). Our models also highlight the importance of climate in predicting trait variation. For a subset of species (n = 860), we projected trait predictions across their known distribution, informed by expert maps from Royal Botanic Gardens, Kew, to create global maps of ITV for grasses. Such maps have the potential to inform conservation efforts and predictions of grazing and fire dynamics in grasslands worldwide. Overall, our research demonstrates the value and ecological applications of predicting plant traits. 

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