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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Interactions Between Climate and Species Drive Future Forest Carbon and Water Balances

Global change is altering forest carbon and water balances; however, the extent to which tree species shape ecosystem‐scale responses to climate, particularly in biodiverse forests, remains unclear. To address this, we simulated the effects of an envelope of future climate conditions on watershed carbon and water balances and quantified the contributions of tree species based on their xylem anatomy. We accomplished this by incorporating species‐level transpiration calculations into a landscape‐scale ecosystem process model. Our revised model linked the effects of forest succession, species composition, and climate change on water and carbon. Calibration of forest water fluxes using sap flux measurements and catchment water balances captured variability in species transpiration and interannual ET in biodiverse, humid temperate forest catchments in the southern Blue Ridge Mountains, USA. Across wet and dry future climate projections, ET increased, and streamflow and net carbon uptake decreased, particularly under a scenario of increasing drought. Despite accounting for just 30% of current biomass, diffuse‐porous tree species were the main driver of carbon and water flux responses now and in the future, thus intensifying the increase in ET and decline in streamflow. As diffuse‐porous biomass continues to increase, these forests will be increasingly sensitive to drought, amplifying losses of carbon sequestration and freshwater delivery.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Emerging signals of declining forest resilience under climate change

Forest ecosystems depend on their capacity to withstand and recover from natural and anthropogenic perturbations (that is, their resilience). Experimental evidence of sudden increases in tree mortality is raising concerns about variation in forest resilience, yet little is known about how it is evolving in response to climate change. Here we integrate satellite-based vegetation indices with machine learning to show how forest resilience, quantified in terms of critical slowing down indicators, has changed during the period 2000–2020. We show that tropical, arid and temperate forests are experiencing a significant decline in resilience, probably related to increased water limitations and climate variability. By contrast, boreal forests show divergent local patterns with an average increasing trend in resilience, probably benefiting from warming and CO 2 fertilization, which may outweigh the adverse effects of climate change. These patterns emerge consistently in both managed and intact forests, corroborating the existence of common large-scale climate drivers. Reductions in resilience are statistically linked to abrupt declines in forest primary productivity, occurring in response to slow drifting towards a critical resilience threshold. Approximately 23% of intact undisturbed forests, corresponding to 3.32 Pg C of gross primary productivity, have already reached a critical threshold and are experiencing a further degradation in resilience. Together, these signals reveal a widespread decline in the capacity of forests to withstand perturbation that should be accounted for in the design of land-based mitigation and adaptation plans

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Soil organic matter, tree communities, and fungal communities across mycorrhizal gradients in the Eastern United States

We collected these data to investigate how mycorrhizal associations are related to soil C and N across four sites in the eastern U.S. broadleaf forest biome, which capture broad variability in climate and tree species. Our sites represented each of the four ecoregions in the eastern United States temperate forest—warm continental (New Hampshire), hot continental (Wisconsin), Prairie (Illinios), and subtropical (Georgia). This breadth naturally provided tree species diversity and allowed us to also investigate how soil C and N are related to canopy tree and EcM fungal community composition. Specifically, we analyzed the effects of mycorrhizal association, canopy tree family, and EcM fungal taxa on the proportion of C and N associated with soil minerals (MAOM) and bulk soil C:N. These are plot level data from forests in New Hampshire, Georgia, Wisconsin, and Illinois. The plots are 10 m in radius and vary across gradients of ectomycorrhizal tree basal area. Within each plot, data include the basal area of all trees >2 cm in diameter identified to species, the abundance of different fungal taxa based on ITS sequences, and the %C, %N, C:N of bulk soil and of soil density fractions (free light, occluded light, and particulate) as well as the proportion of soil C and N within each fraction. The soil data (both organic matter and fungal communities) are from the top 10 cm of the mineral soil. This also includes a meta-analysis of leaf litter k and C:N values for the tree species present in the forests.The "Tree_BA_ALL.csv" files include the basal area of all trees >2 cm in diameter at breast height within our 10 cm radii plots. The "Soil_Data_Final.csv" file contains the soil carbon and nitrogen in bulk and density fractions (concentrations as well as proportion for free light fraction (fLF), occluded light fraction (oLF), and heavy or dense fraction (HF), oxalate extractable iron and aluminum concentrations, as well as the geographic coordinates of each plot's location. The "FungiNonRelative.csv" file contains the abundances of fungal taxa identified via ITS sequencing for each plot (with the exception of 4 plots from GA where there was not enough quality DNA to extract).The "R_ReadyK.csv" and "R_Ready_CN.csv" files contain leaf litter decomposition constant (k) and C:N values from the literature for the tree species found at our sites.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

The fortedata R package: open-science datasets from a manipulative experiment testing forest resilience

The fortedata R package is an open data notebook from the Forest Resilience Threshold Experiment (FoRTE) – a modeling and manipulative field experiment that tests the effects of disturbance severity and disturbance type on carbon cycling dynamics in a temperate forest. Package data consist of measurements of carbon pools and fluxes and ancillary measurements to help analyze and interpret carbon cycling over time. Currently the package includes data and metadata from the first three FoRTE field seasons, serves as a central, updatable resource for the FoRTE project team, and is intended as a resource for external users over the course of the experiment and in perpetuity. Further, it supports all associated FoRTE publications, analyses, and modeling efforts. This increases efficiency, consistency, compatibility, and productivity while minimizing duplicated effort and error propagation that can arise as a function of a large, distributed and collaborative effort. More broadly, fortedata represents an innovative, collaborative way of approaching science that unites and expedites the delivery of complementary datasets to the broader scientific community, increasing transparency and reproducibility of taxpayer-funded science. The fortedata package is available via GitHub: https://github.com/FoRTExperiment/fortedata (last access: 19 February 2021), and detailed documentation on the access, used, and applications of fortedata are available at https://fortexperiment.github.io/fortedata/ (last access: 19 February 2021). The first public release, version 1.0.1 is also archived at https://doi.org/10.5281/zenodo.4399601 (Atkins et al., 2020b). All data products are also available outside of the package as .csv files: https://doi.org/10.6084/m9.figshare.13499148.v1 (Atkins et al., 2020c).

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Testing mechanisms of how mycorrhizal associations affect forest soil carbon and nitrogen cycling (Final Technical Report)

Trees are in a symbiotic partnership with mycorrhizal fungi in which they provide the fungi with carbon from photosynthesis and the fungi provide the trees with nutrients and water. In temperate forests, the vast majority of trees form symbioses with one of two types of mycorrhizal fungi—arbuscular mycorrhizal (AM) fungi or ectomycorrhizal (EcM) fungi. These fungi differ in their morphology, hyphal length, and nutrient acquisition strategies. Many studies have found systematic differences in soil organic matter and nitrogen availability between forest stands dominated by AM-associating trees versus EcM-associating trees. For instance, there is a larger proportion of organic matter that is mineral-associated, more available nitrogen, and lower soil carbon to nitrogen ratios in AM forest stands relative to EcM forests stands. However, the mechanisms driving these patterns are not known, which complicates our ability to model soil organic matter dynamics in forested ecosystems. The main objective of this research was to understand the degree to which the observed differences in soil C and N dynamics between AM and EcM dominated forests are driven by tree traits like litter decomposability and root exudation versus mycorrhizal fungal nutrient acquisition strategies. We investigated these mechanisms using observations and targeted experiments and incorporated this knowledge into a process-based soil organic matter model. The observational studies compared the importance of leaf litter decomposability versus fungal identity on soil organic matter processes. We found that often fungal identity and traits were more important drivers of soil organic matter patterns than leaf litter decomposability. We ran two novel experiments: 1) a growth chamber experiment across four EcM and four AM tree species using a 13 C-labeled atmosphere to trace seedling-derived C into hyphae, the rhizosphere, and soil; and 2) an in situ decomposition experiment of six different 13 C and 15 N labeled litters that ranged in decomposability incubated across a gradient of EcM dominance at three sites that capture important variation in climate, soils, and forest species composition. In the first experiment, we found no significant differences of seedling mycorrhizal association on soil carbon sequestration over a growing season, but we did find that mycorrhizal association affected rhizodeposition with EcM-associating seedlings depositing more carbon in response to increased nitrogen availability. The decomposition experiment is still ongoing, but thus far, we have found slower litter decomposition in only one of three EcM-dominated forests which suggests that differences between AM- and EcM-dominated forests depend on the environmental context and identity of the EcM fungi. Lastly, we explicitly incorporated mycorrhizal processes into the Carbon, Organisms, Rhizosphere, and Protection in the Soil Environment (CORPSE) model creating Myco-CORPSE. By including the different nutrient acquisition strategies of AM and EcM fungi, we explored the conditions under which EcM fungi can slow decomposition rates and lead to greater soil organic carbon accumulation compared to AM fungi. We found that the effect of EcM fungi was highly context dependent and that EcM fungi decreased decomposition in colder forests with recalcitrant litter inputs and when they produced oxidases and necromass-degrading enzymes. Our research highlights the importance of fungal nutrient acquisition in driving soil organic matter patterns and the need to move beyond the AM-EcM dichotomy to consider the identity and traits of the specific fungi participating in the symbiosis. Overall, this research has resulted in six, peer-reviewed published papers in journals such as Global Change Biology, Ecology (2), Soil Biology and Biochemistry, and Ecosystems (2). There are at least two more papers in progress on this research including one that was recently submitted to Global Change Biology.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Representation of Leaf-to-Canopy Radiative Transfer Processes Improves Simulation of Far-Red Solar-Induced Chlorophyll Fluorescence in the Community Land Model Version 5

Recent advances in satellite observations of solar-induced chlorophyll fluorescence (SIF) provide a new opportunity to evaluate and constrain the simulation of terrestrial gross primary productivity (GPP). Accurate representation of the processes driving SIF emission and the radiative transfer of SIF to remote sensing sensors is an essential prerequisite for the evaluation and data assimilation. Recently, SIF simulations have been incorporated into several land surface models, but the scaling of SIF from leaf-level to canopy level is usually not well-represented. In this work, we incorporate the simulation of far-red SIF observed at nadir into the Community Land Model version 5 (CLM5). An efficient and accurate method based on escape probability is developed to scale SIF from leaf-level to top-of-canopy while taking clumping and the radiative transfer processes into account. SIF simulated by CLM5 and a canopy-level model agreed well at sites except one in needle leaf forest (R2>0.91, root-mean-square error < 0.19W m -2 sr -1 um -1 ), and captured the day-to-day variation of tower-measured SIF at temperate forest sites (R 2 >0.68). At the global scale, simulated SIF generally captured the spatial and seasonal pat37 terns of satellite-observed SIF (R2 > 0.76 except for tropical forest). Factors including the fluorescence emission model, clumping, bidirectional effect, and canopy properties (leaf optical properties and leaf area index) had considerable impacts on SIF simulation, and the discrepancies between simulated and observed SIF varied with plant functional type. By improving the representation of radiative transfer for SIF simulation, our model allows better comparisons between simulated and observed SIF towards constraining and evaluating GPP simulations.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Soil Biogenic Volatile Organic Compound Flux in a Mixed Hardwood Forest: Net Uptake at Warmer Temperatures and the Importance of Mycorrhizal Associations

Abstract Biogenic volatile organic compounds (bVOCs) play important roles in ecological interactions and Earth system processes, yet the biological and physical processes that drive soil bVOC exchanges remain poorly understood. In temperate forests, nearly all tree species associate with arbuscular mycorrhizal (AM) or ectomycorrhizal (ECM) fungi. Given well‐established differences in soil biogeochemistry between AM‐dominated and ECM‐dominated stands, we hypothesized that bVOC exchanges with the atmosphere would differ between soils from the two stand types. We measured bVOC fluxes at the soil‐atmosphere interface in plots dominated by AM‐ and ECM‐associated trees in a deciduous forest in south‐central Indiana, USA during the early and late vegetative growing season. Soils in both AM‐ and ECM‐dominated plots were a net bVOC sink following leaf‐out and were a greater bVOC sink or smaller source at warmer soil temperatures ( T s ). The flux of different bVOCs from ECM plots was often related to soil water content in addition to T s . Methanol dominated total bVOC fluxes, and ECM soils demonstrated greater uptake relative to AM‐dominated plots, on the order of 170 nmol m −2 hr −1 during the early growing season. Our results demonstrate the importance of soil dynamics characterized by mycorrhizal associations to bVOC dynamics in forested ecosystems and emphasize the need to study bidirectional soil bVOC uptake and emission processes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Monitoring leaf phenology in moist tropical forests by applying a superpixel-based deep learning method to time-series images of tree canopies

Tropical leaf phenology-particularly its variability at the tree-crown scale-dominates the seasonality of carbon and water fluxes. However, given enormous species diversity, accurate means of monitoring leaf phenology in tropical forests is still lacking. Time series of the Green Chromatic Coordinate (GCC) metric derived from tower-based red-green-blue (RGB) phenocams have been widely used to monitor leaf phenology in temperate forests, but its application in the tropics remains problematic. To improve monitoring of tropical phenology, we explored the use of a deep learning model (i.e. superpixel-based Residual Networks 50, SP-ResNet50) to automatically differentiate leaves from non-leaves in phenocam images and to derive leaf fraction at the tree-crown scale. To evaluate our model, we used a year of data from six phenocams in two contrasting forests in Panama. Here, we first built a comprehensive library of leaf and non-leaf pixels across various acquisition times, exposure conditions and specific phenocams. We then divided this library into training and testing components. We evaluated the model at three levels: 1) superpixel level with a testing set, 2) crown level by comparing the model-derived leaf fractions with those derived using image-specific supervised classification, and 3) temporally using all daily images to assess the diurnal stability of the model-derived leaf fraction. Finally, we compared the model-derived leaf fraction phenology with leaf phenology derived from GCC. Our results show that: 1) the SP-ResNet50 model accurately differentiates leaves from non-leaves (overall accuracy of 93%) and is robust across all three levels of evaluations; 2) the model accurately quantifies leaf fraction phenology across tree-crowns and forest ecosystems; and 3) the combined use of leaf fraction and GCC helps infer the timing of leaf emergence, maturation and senescence, critical information for modeling photosynthetic seasonality of tropical forests. Collectively, this study offers an improved means for automated tropical phenology monitoring using phenocams.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Peak radial growth of diffuse-porous species occurs during periods of lower water availability than for ring-porous and coniferous trees

Climate models project warmer summer temperatures will increase the frequency and heat severity of droughts in temperate forests of Eastern North America. Hotter droughts are increasingly documented to affect tree growth and forest dynamics, with critical impacts on tree mortality, carbon sequestration and timber provision. The growing acknowledgement of the dominant role of drought timing on tree vulnerability to water deficit raises the issue of our limited understanding of radial growth phenology for most temperate tree species. Here, we use well-replicated dendrometer band data sampled frequently during the growing season to assess the growth phenology of 610 trees from 15 temperate species over 6 years. Patterns of diameter growth follow a typical logistic shape, with growth rates reaching a maximum in June, and then decreasing until process termination. On average, we find that diffuse-porous species take 16–18 days less than other wood-structure types to put on 50% of their annual diameter growth. However, their peak growth rate occurs almost a full month later than ring-porous and conifer species (ca. 24 ± 4 days; mean ± 95% credible interval). Unlike other species, the growth phenology of diffuse-porous species in our dataset is highly correlated with their spring foliar phenology. We also find that the later window of growth in diffuse-porous species, coinciding with peak evapotranspiration and lower water availability, exposes them to a higher water deficit of 88 ± 19 mm (mean ± SE) during their peak growth than ring-porous and coniferous species (15 ± 35 mm and 30 ± 30 mm, respectively). Given the high climatic sensitivity of wood formation, our findings highlight the importance of wood porosity as one predictor of species climatic sensitivity to the projected intensification of the drought regime in the coming decades.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Comparison of linear regression, k-nearest neighbour and random forest methods in airborne laser-scanning-based prediction of growing stock

Abstract In this study, for five sites around the world, we look at the effects of different model types and variable selection approaches on forest yield modelling performances in an area-based approach (ABA). We compared ordinary least squares regression (OLS), k-nearest neighbours (kNN) and random forest (RF). Our objective was to test if there are systematic differences in accuracy between OLS, kNN and RF in ABA predictions of growing stock volume. The analyses are based on a 5-fold cross-validation at five study sites: an eucalyptus plantation, a temperate forest and three different boreal forests. Two completely independent validation datasets were also available for two of the boreal sites. For the kNN, we evaluated multiple measures of distance including Euclidean, Mahalanobis, most similar neighbour (MSN) and an RF-based distance metric. The variable selection approaches we examined included a heuristic approach (for OLS, kNN and RF), exhaustive search among all combinations (OLS only) and all variables together (RF only). Performances varied by model type and variable selection approaches among sites. OLS and RF had similar accuracies and were more efficient than any of the kNN variants. Variable selection did not affect RF performance. Heuristic and exhaustive variable selection performed similarly for OLS. kNN fared the poorest amongst model types, and kNN with RF distance was prone to overfitting when compared with a validation dataset. Additional caution is therefore required when building kNN models for volume prediction though ABA, being preferable instead to opt for models based on OLS with some variable selection, or RF with all variables together.

Cosenza, Diogo N.↗
DOE OSTI2022

Feasibility of enhancing carbon sequestration and stock capacity in temperate and boreal European forests via changes to management regimes

Forest management practices might act as nature-based methods to remove CO 2 from the atmosphere and slow anthropogenic climate change and thus support an EU forest-based climate change mitigation strategy. However, the extent to which diversified management actions could lead to quantitatively important changes in carbon sequestration and stocking capacity at the tree level remains to be thoroughly assessed. To that end, we used a state-of-the-science bio-geochemically based forest growth model to simulate effects of multiple forest management scenarios on net primary productivity (NPP) and potential carbon woody stocks (pCWS) under twenty scenarios of climate change in a suite of observed and virtual forest stands in temperate and boreal European forests. Previous modelling experiments indicated that the capacity of forests to assimilate and store atmospheric CO 2 in woody biomass is already being attained under business-as-usual forest management practices across a range of climate change scenarios. Nevertheless, we find that on the long-term, with increasing atmospheric CO 2 concentration and warming, managed forests show both higher productivity capacity and a larger potential pool size of stored carbon than unmanaged forests as long as thinning and tree harvesting are of moderate intensity.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Variation in forest root image annotation by experts, novices, and AI

Abstract Background The manual study of root dynamics using images requires huge investments of time and resources and is prone to previously poorly quantified annotator bias. Artificial intelligence (AI) image-processing tools have been successful in overcoming limitations of manual annotation in homogeneous soils, but their efficiency and accuracy is yet to be widely tested on less homogenous, non-agricultural soil profiles, e.g., that of forests, from which data on root dynamics are key to understanding the carbon cycle. Here, we quantify variance in root length measured by human annotators with varying experience levels. We evaluate the application of a convolutional neural network (CNN) model, trained on a software accessible to researchers without a machine learning background, on a heterogeneous minirhizotron image dataset taken in a multispecies, mature, deciduous temperate forest. Results Less experienced annotators consistently identified more root length than experienced annotators. Root length annotation also varied between experienced annotators. The CNN root length results were neither precise nor accurate, taking ~ 10% of the time but significantly overestimating root length compared to expert manual annotation ( p = 0.01). The CNN net root length change results were closer to manual ( p = 0.08) but there remained substantial variation. Conclusions Manual root length annotation is contingent on the individual annotator. The only accessible CNN model cannot yet produce root data of sufficient accuracy and precision for ecological applications when applied to a complex, heterogeneous forest image dataset. A continuing evaluation and development of accessible CNNs for natural ecosystems is required.

Handy, Grace↗
DOE OSTI2026

Blodgett 13C–labeled litter incubation 2016-2019

The dataset is from 13C-labelled (stable isotope of carbon) root-litter in-situ field incubation experiment based on the whole-soil warming experiment at the Blodgett Forest Research Station, CA, USA. The files are in both ".csv" and ".xlsx" versions, and can be opened in "maCOS numbers", and "Microsoft Excel". The files includes several sheets with all the data published in the paper: Sun, B., Zosso, C., Wiesenberg, G. L. B., Pegoraro, E., Torn, M. S., and Schmidt, M. W. I.: Warming accelerates the decomposition of root-derived hydrolysable lipids in a temperate forest and is depth- and compound class-dependent, SOIL, 11, 1077–1093, https://doi.org/10.5194/soil-11-1077-2025, 2025. This dataset includes bulk soil carbon, nitrogen, delta 13C values, the normalized concentration (to organic carbon) of hydrolysable lipids identified, the absolute concentration (normalized to bulk soil) of hydrolysable lipids, hydrolysable lipids recovery, and weighted 13C-excess of bulk soil carbon, and weighted 13C-excess of each compound class in hydrolysable lipids. These data aim to answer two research questions: 1) How will warming affect the decomposition of 13C-labelled root-litter at different depth? 2) Will the decomposition of root-derived hydrolysable lipids under warming differ among different compound classes? The experiment sites located on the foothills of the Sierra Nevada near Georgetown, CA (120°3904000W; 38°5404300 N) at 1370m above see level. The Blodgett Forest is a mixed-coniferous forest. The site has a Mediterranean climate with a mean annual air temperature of 12.5 °C and a mean annual precipitation of 1774mm.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Studying drought-induced forest mortality using high spatiotemporal resolution evapotranspiration data from thermal satellite imaging

Drought can have pervasive and wide-spread impacts to forest health, as evidenced in several severe events occurring over the recent decades. Extensive forest die-off due to drought can impair the ecological functioning of forests, impacting habitat, water yield and quality from forested lands, and altering forest fire dynamics and intensity. Satellite remote sensing provides an effective means for detecting and monitoring spatial patterns of forest mortality over large areas, exploiting free and open long-term image archives available at a range in spatial and temporal resolutions. While remotely sensed surface reflectances and vegetation indices have been widely used to study optical response of forest canopies to drought events, retrievals of evapotranspiration (ET) derived from thermal satellite imagery – particularly at resolutions approaching crown scale - can provide insights into cumulative tree stresses that can incite disease and trigger mortality. In this study, we applied a multi-sensor satellite data fusion approach to estimate daily 30-m resolution ET and an associated Evaporative Stress Index (ESI) to study drought-induced mortality in a temperate forest at the Missouri Ozark AmeriFlux (MOFLUX) site, located in the central United States. The study covered the period from 2010 to 2014, including an exceptional drought year of 2012. Modeled ET agreed well with eddy flux measurements from the MOFLUX tower, with average monthly relative errors of 15%. Plot-scale ESI, describing temporal anomalies in the ratio of actual-to-reference ET, was used as an index of relative forest health to investigate relationships between forest mortality and drought severity. ESI showed good agreement with observed predawn leaf water potential, especially during the drought year. Furthermore, plot-scale ESI was also correlated with the subsequent year's tree mortality, suggesting the importance of considering the forest health condition prior to drought when studying drought-induced forest impacts. This study demonstrates the utility of multi-year ET remote sensing data at the stand or plot scale as an indicator of forest health and as a predictor of future mortality due to drought.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

A primer on forest structure measurement with lidar for ecologists

Light detection and ranging (lidar) technology has fundamentally advanced the way we measure forest structure, facilitating new insights into ecological processes. Lidar for forest ecology applications is deployed on multiple types of platforms that operate from the ground, air, or space, and each has associated strengths and limitations. Ideally, the choice of what kind of lidar to use in a particular study should be guided by the ecological question of interest; however, practical considerations of cost, data availability, and processing tools can be equally important. This synthesis is a practical introduction to how different lidar platforms characterize forest structure (e.g., tree size/location, wood volume, branching structure, aboveground biomass, leaf properties), designed for a general audience of ecologists (not remote sensing scientists) seeking an accessible introduction to the use of lidar. We also provide examples of novel ecological insights from recent lidar research and describe current limitations and areas of expected improvement. Last, we include an appendix of data collected from terrestrial, mobile, unoccupied aerial system, airplane, and satellite lidar platforms within a common temperate forest area, with associated code to allow new lidar users to visualize and manipulate data in R.

Cushman, KC [ORNL] (ORCID:0000000234641151)↗
DOE OSTI2022

Predicting spring green-up across diverse North American grasslands

Vegetation phenology influences many ecosystem and climate processes, such as carbon uptake and energy and water cycles. Thus, understanding drivers of vegetation phenology is crucial for predicting current and future impacts of climate change on ecological systems. Existing models can accurately predict the date of spring green-up in temperate forests but tend to perform poorly in grassland systems. We hypothesize this is because most do not incorporate water availability, a primary limiting factor for grassland plants. In this study, we used long-term datasets of digital imagery from the PhenoCam Network of 43 diverse North American grassland sites (195 site-years) to test existing spring phenology models, as well as develop several new models that incorporate precipitation or soil moisture (53 models). As a result, most of the new models performed substantially better, with the best model requiring sufficient accumulated precipitation followed by warm temperatures to trigger spring onset (root mean square error, RMSE, between predicted and observed dates = 16.0 days). Importantly, the best model performed well across all grassland types using a single set of parameters, from temperate to arid grasslands. Since plants are adapted to their local climates, model performance was further improved when parameters were independently optimized for four separate climate regions (RMSE = 10.4 days). Therefore, both sufficient precipitation and temperature are required for grassland green-up, but optimal thresholds vary by region. Running the top model with projected climate data (representative concentration pathway 8.5) suggests that, depending on the climate region, spring onset will occur up to 12 days earlier within 100 years in temperature-limited sites, but the trend is unclear for precipitation-limited sites (3.5 ± 8.0 days later). This new phenology model improves our ability to understand and predict grassland dynamics, with implications for both current and future ecosystem processes related to carbon and water cycling.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Does manganese influence grass litter decomposition on a Hawaiian rainfall gradient?

Plant litter is a well-defined pool of organic matter (OM) in which the influence of manganese (Mn) on decomposition (both decomposition rate and the mix of compounds ultimately transferred to soil OM) has been clearly demonstrated in temperate forests. However, no similar study exists on grasslands and the effect of foliar Mn versus soil-derived Mn on litter decomposition is poorly known. We used a 5-month and 12-month field, and 10-month laboratory experiments to evaluate litter decomposition on the Kohala rainfall gradient (Island of Hawai‘i) in areas with different foliar and soil Mn abundances, and on which a single plant species (Pennisetum clandestinum) dominates primary production and the litter pool. The chemical imaging analyses of decomposed litter revealed that Mn 2+ oxidized to Mn 3+ and Mn 4+ on grass litter during decompositions—hallmarks of Mn-driven litter oxidation. However, these transformations and Mn abundance did not predict greater litter mass loss through decomposition. These observations demonstrate that the importance of Mn to an ecosystem’s C cycle does not rely solely on the metal’s abundance and availability.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Root lateral interactions drive water uptake patterns under water limitation

The plasticity of root water uptake determines the maintenance of transpiration during periods of water limitation and drought. However, the mechanistic basis of plant water uptake, as well as the implications of water uptake strategies at the individual and ecosystem scale remain elusive. We model three-dimensional root water uptake under variably saturated conditions for a one-hectare temperate forest plot for a growing season with a pronounced mid-season dry period. Variations in root architecture, hydraulic properties, and degree of lateral interaction between root systems produce divergent local responses to water limitation and provide insights on individual and community response to meteorological conditions. Results demonstrate the plasticity of ecosystem-scale responses to surface drying, where interacting roots shift regions of active uptake to deeper soil layers with less abundant root biomass. These shifts, a product of both root system and soil hydraulic properties, illustrate intimate links between root and soil hydraulics in determining plant water sourcing. Furthermore, we demonstrate that root lateral interactions are beneficial at the ecosystem-scale, even when trees compete for water. Specifically, a more spatially extensive root system facilitates access to a larger soil water reservoir, often ameliorating water limitation and reducing sharp water potential gradients. While the reduction of water stress is a benefit, it can be offset by increased root construction and maintenance costs associated with the larger rooting system. A plausible “viability” region of root communal co-existence is therefore implied where competitive pressures and root production costs are balanced by a potential water benefit.

54 ENVIRONMENTAL SCIENCES↗