An official website of the United States government
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
Regional and local variation in chemical, structural, and physical leaf traits for tree species in the northeastern United States
This dataset is a compilation of leaf trait measurements for tree species in the northeastern United States collected between 2017 and 2022 by the Terrestrial Ecosystems Analysis Lab at the University of New Hampshire. Currently, this dataset contains 1351 samples, including 18 chemical, physical and structural traits collected across 25 different tree species. Traits include stable isotopes for carbon (C) and nitrogen (N), percent C and N, C:N ratio, total chlorophyll (chl), chl a, chl b, chl a:b ratio, leaf mass per area, average leaf dry mass, average leaf area, length, and width, leaf water content, average petiole length and petiole dry mass, and petiole water content. Traits have been measured at plots spanning a wide range of latitude, longitude, elevation, and forest types. A simple table containing these plot descriptions have been included. Leaf physiological and optical traits have been measured concurrently on many of these samples and published separately.
more »
« less
- PAR ID:
- 10401350
- Publisher / Repository:
- Environmental Data Initiative
- Date Published:
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
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.more » « less
-
Abstract Community‐wide efforts to digitize herbarium specimens have facilitated novel uses of specimen data across scales. However, the need for destructive sampling has prevented large‐scale examination of foliar functional traits. We demonstrate that nondestructive reflectance spectra are an effective tool for estimating leaf traits from herbarium collections.Using partial least squares regression (PLSR), we developed and evaluated trait estimation models for leaf mass per area (LMA), nitrogen content (N) and carbon content (C) from nondestructive leaf‐level spectra and destructive trait measurements from herbarium specimens. Our modelling dataset consisted of temperate species collected in the Minneapolis‐St. Paul area (Minnesota, USA) between 1876 and 2022. We also trained biased models excluding sample subsets corresponding to specimen age, habit, growth form and species categories to assess the transferability of PLSR trait estimation models. Finally, we evaluated whether leaf traits estimated from spectra recover the temporal trends observed in destructively measured traits.Our PLSR models accurately estimate LMA and N, and reasonably estimate C. They are robust to sample greenness, specimen age and species, but sensitive to mismatches in functional group categories between the training and target data. Furthermore, spectrally estimated LMA and N reproduce temporal trends observed in measured traits over the 146‐year time series; estimated C reproduces most temporal trends despite poorer model performance than the other traits.Given the limitations of broad destructive sampling from herbarium collections, we show that PLSR models trained on younger specimens can reliably estimate leaf traits from older specimens. While we caution that trait estimation models are only as good as their training data, we are optimistic that spectroscopy can capture the extensive functional data preserved in herbarium collections in a largely nondestructive manner.more » « less
-
Climate change is stressing many forests around the globe, yet some tree species may be able to persist through acclimation and adaptation to new environmental conditions. The ability of a tree to acclimate during its lifetime through changes in physiology and functional traits, defined here as its acclimation potential, is not well known. We investigated the acclimation potential of trembling aspen (Populus tremuloides) and ponderosa pine (Pinus ponderosa) trees by examining within-species variation in drought response functional traits across both space and time, and how trait variation influences drought-induced tree mortality. We measured xylem tension, morphological traits, and physiological traits on mature trees in southwestern Colorado, USA across a climate gradient that spanned the distribution limits of each species and three years with large differences in climate. Trembling aspen functional traits showed high within-species variation, and osmotic adjustment and carbon isotope discrimination were key determinants for increased drought tolerance in dry sites and in dry years. However, trembling aspen trees at low elevation were pushed past their drought tolerance limit during the severe 2018 drought year, as elevated mortality occurred. Higher specific leaf area during drought was correlated with higher percentages of canopy dieback the following year. Ponderosa pine functional traits showed less within-species variation, though osmotic adjustment was also a key mechanism for increased drought tolerance. Remarkably, almost all traits varied more year-to-year than across elevation in both species. Our results shed light on the scope and limits of intraspecific trait variation for mediating drought responses in key southwestern US tree species and will help improve our ability to model and predict forest responses to climate change. Data were collected during the summers of 2014, 2018, and 2019. Many of the functional traits were measured on samples processed in a laboratory.more » « less
-
During the 2023 field season at a study site near Kangerlussuaq, Greenland, we collected multiple samples of three key caribou forage plants for chemical analysis. These included a graminoid (Poa pratensis), dwarf birch (Betula nana), and gray willow (Salix glauca). These samples were collected on multiple dates throughout the 2023 growing season for assessment of within season variation in plant leaf nitrogen content (%N by dry mass), carbon content (%C by dry mass), and carbon to nitrogen ratio (by dry mass, an index of forage quality). We also conducted a warming experiment using standardized protocols of the International Tundra Experiment to elevate near surface temperature on multiple plots from which Betula and Salix samples were collected at the end of the 2023 growing season. Our original plan included collecting graminoid (Poa) samples from experimentally-warmed plots as well, but unforeseen conditions at the field site, including unusual flooding of graminoid meadows, precluded such collections. Data on %N, %C, and C:N ratios of samples are provided here. All plant tissue samples were dried at 60C for at least 24 hours and submitted to the UC Davis Analytical Laboratory for ashing and determination of chemical content.more » « less
