More Like this

1. 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 

   more » « less
2. Empirical evidence and theoretical understanding of ecosystem carbon and nitrogen cycle interactions

   https://doi.org/10.1111/nph.20178  

   Stocker, Benjamin D; Dong, Ning; Perkowski, Evan A; Schneider, Pascal D; Xu, Huiying; de_Boer, Hugo J; Rebel, Karin T; Smith, Nicholas G; Van_Sundert, Kevin; Wang, Han; et al (October 2024, New Phytologist)

   Summary Interactions between carbon (C) and nitrogen (N) cycles in terrestrial ecosystems are simulated in advanced vegetation models, yet methodologies vary widely, leading to divergent simulations of past land C balance trends. This underscores the need to reassess our understanding of ecosystem processes, given recent theoretical advancements and empirical data. We review current knowledge, emphasising evidence from experiments and trait data compilations for vegetation responses to CO₂and N input, alongside theoretical and ecological principles for modelling. N fertilisation increases leaf N content but inconsistently enhances leaf‐level photosynthetic capacity. Whole‐plant responses include increased leaf area and biomass, with reduced root allocation and increased aboveground biomass. Elevated atmospheric CO₂also boosts leaf area and biomass but intensifies belowground allocation, depleting soil N and likely reducing N losses. Global leaf traits data confirm these findings, indicating that soil N availability influences leaf N content more than photosynthetic capacity. A demonstration model based on the functional balance hypothesis accurately predicts responses to N and CO₂fertilisation on tissue allocation, growth and biomass, offering a path to reduce uncertainty in global C cycle projections. 

   more » « less
3. 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. 

   more » « less
4. How Environment Affects Ontogenetic Differences in Leaf Functional Traits of Woody Plants

   https://doi.org/10.1111/geb.70133  

   Zhang, Ziyan; Hikosaka, Kouki; Niinemets, Ülo; Han, Qingmin; Cavender‐Bares, Jeannine; Zheng, Liting; He, Dong; Yan, Enrong; Han, Mengguang; Jin, Guangze; et al (October 2025, Global Ecology and Biogeography)

   ABSTRACT AimThe consistency of patterns in ontogenetic differences in plant traits across the globe has not been thoroughly studied. Environmental conditions affect leaf functional traits, and these effects can differ between adult trees and saplings due to varying environmental conditions in their aerial and soil environments. Our integrative analysis aims to reveal the global universality of woody plants' ontogeny and explores influencing factors. LocationGlobal. Time PeriodStudies published in 1989–2023. Major Taxa StudiedWoody plants. MethodsWe performed a global meta‐analysis of woody plants with different plant functional types at 64 sites around the world, assessed the ontogenetic differences in nine key leaf traits and explored the environmental factors that affected the ontogenetic differences. ResultsWe observed that (1) leaf traits differed significantly between adult trees and saplings, with environmental factors playing varying roles. Photosynthetic capacity per unit area (A_a) and nitrogen content per unit dry mass (N_m) were lower in saplings than in adults under low solar radiation, but this trend reversed with increased solar radiation. Differences in stomatal density (SD) and stable carbon isotope composition (δ¹³C) between adults and saplings were greatest under low solar radiation; (2) ontogenetic differences in leaf thickness (LT), leaf dry mass per area (LMA) and stomatal conductance (g_s) were greater at lower mean annual temperature (MAT); (3) at high mean annual precipitation (MAP), adults had higher nitrogen content per unit area (N_a), while saplings had higherN_mthan adults; (4) soil conditions were strongly correlated with ontogenetic differences in LT and SD, with soil pH as a key driver of variation inA_a, LT, SD,N_aandN_m. Main ConclusionsOur findings indicate that ontogeny strongly modifies leaf functional traits and that multiple environmental factors influence the magnitude of ontogenetic differences in leaf traits. This underscores the importance of considering ontogeny when predicting trait values across plant developmental stages, modelling vegetation composed of individuals of different ages and forecasting vegetation responses to environmental changes. 

   more » « less
5. Soil microenvironmental variation drives below‐ground trait variation and interacts with macroclimate to structure above‐ground trait variation of arctic shrubs

   https://doi.org/10.1111/1365-2745.14278  

   Fraterrigo, Jennifer M; Chen, Weile; Loyal, Joshua; Euskirchen, Eugénie S (April 2024, Journal of Ecology)

   Henn, J (Ed.)

   Abstract Intraspecific trait variation can influence plant performance in different environments and may thereby determine the ability of individual plants to respond to climate change. However, our understanding of its patterns and environmental drivers across different spatial scales is incomplete, especially in understudied regions like the Arctic.To fill this knowledge gap, we examined above‐ground and below‐ground traits from three shrub taxa expanding across the tundra biome and evaluated their relationships with multiple microenvironmental and macroclimatic factors. The traits reflected plant size and structure (plant height, leaf area and root to shoot ratio), leaf economics (specific leaf area, nitrogen content), and root economics and collaboration with mycorrhizal fungi (specific root length, root tissue density, nitrogen content, and ectomycorrhizal colonisation intensity). We also measured leaf and root δ¹⁵N and leaf δ¹³C to characterise nitrogen source and acquisition pathways and plant water stress. Traits were measured in replicated plots (N = 135) varying in soil microclimate, thaw depth and organic layer thickness established across five sites spanning a macroclimate gradient in northern Alaska. This hierarchical design allowed us to disentangle the independent and combined effects of fine‐scale and broad‐scale factors on intraspecific trait variation.We found substantial intraspecific variation at fine spatial scales for most traits and less variation along the macroclimate gradient and between shrub taxa. Consistent with these patterns, microenvironmental factors, mainly soil moisture and thaw depth, interacted with macroclimate, mainly climatic water deficit, to structure size‐structural and leaf trait variation. In contrast, most root traits responded additively to thaw depth and macroclimate.Synthesis. Our results demonstrate that above‐ground and below‐ground tundra shrub traits respond differently to microenvironmental and macroclimatic variation. These differing responses contribute to substantial trait variation at fine spatial scales and may decouple above‐ground and below‐ground trait responses to climate change. 

   more » « less