Remotely sensed crown nutrient concentrations modulate forest reproduction across the contiguous United States.

Global forests are increasingly lost to climate change, disturbance, and human management. Evaluating forests' capacities to regenerate and colonize new habitats has to start with the seed production of individual trees and how it depends on nutrient access. Studies on the linkage between reproduction and foliar nutrients are limited to a few locations and few species, due to the large investment needed for field measurements on both variables. We synthesized tree fecundity estimates from the Masting Inference and Forecasting (MASTIF) network with foliar nutrient concentrations from hyperspectral remote sensing at the National Ecological Observatory Network (NEON) across the contiguous United States. We evaluated the relationships between seed production and foliar nutrients for 56,544 tree-years from 26 species at individual and community scales. We found a prevalent association between high foliar phosphorous (P) concentration and low individual seed production (ISP) across the continent. Within-species coefficients to nitrogen (N), potassium (K), calcium (Ca), and magnesium (Mg) are related to species differences in nutrient demand, with distinct biogeographic patterns. Community seed production (CSP) decreased four orders of magnitude from the lowest to the highest foliar P. This first continental-scale study sheds light on the relationship between seed production and foliar nutrients, highlighting the potential of using combined Light Detection And Ranging (LiDAR) and hyperspectral remote sensing to evaluate forest regeneration. The fact that both ISP and CSP decline in the presence of high foliar P levels has immediate application in improving forest demographic and regeneration models by providing more realistic nutrient effects at multiple scales.

'rtry': An R package to support plant trait data preprocessing.

Plant trait data are used to quantify how plants respond to environmental factors and can act as indicators of ecosystem function. Measured trait values are influenced by genetics, trade-offs, competition, environmental conditions, and phenology. These interacting effects on traits are poorly characterized across taxa, and for many traits, measurement protocols are not standardized. As a result, ancillary information about growth and measurement conditions can be highly variable, requiring a flexible data structure. In 2007, the TRY initiative was founded as an integrated database of plant trait data, including ancillary attributes relevant to understanding and interpreting the trait values. The TRY database now integrates around 700 original and collective datasets and has become a central resource of plant trait data. These data are provided in a generic long-table format, where a unique identifier links different trait records and ancillary data measured on the same entity. Due to the high number of trait records, plant taxa, and types of traits and ancillary data released from the TRY database, data preprocessing is necessary but not straightforward. Here, we present the 'rtry' R package, specifically designed to support plant trait data exploration and filtering. By integrating a subset of existing R functions essential for preprocessing, 'rtry' avoids the need for users to navigate the extensive R ecosystem and provides the functions under a consistent syntax. 'rtry' is therefore easy to use even for beginners in R. Notably, 'rtry' does not support data retrieval or analysis; rather, it focuses on the preprocessing tasks to optimize data quality. While 'rtry' primarily targets TRY data, its utility extends to data from other sources, such as the National Ecological Observatory Network (NEON). The 'rtry' package is available on the Comprehensive R Archive Network (CRAN; https://cran.r-project.org/package=rtry) and the GitHub Wiki (https://github.com/MPI-BGC-Functional-Biogeography/rtry/wiki) along with comprehensive documentation and vignettes describing detailed data preprocessing workflows.

Unveiling the transferability of PLSR models for leaf trait estimation: lessons from a comprehensive analysis with a novel global dataset.

Leaf traits are essential for understanding many physiological and ecological processes. Partial least squares regression (PLSR) models with leaf spectroscopy are widely applied for trait estimation, but their transferability across space, time, and plant functional types (PFTs) remains unclear. We compiled a novel dataset of paired leaf traits and spectra, with 47 393 records for > 700 species and eight PFTs at 101 globally distributed locations across multiple seasons. Using this dataset, we conducted an unprecedented comprehensive analysis to assess the transferability of PLSR models in estimating leaf traits. While PLSR models demonstrate commendable performance in predicting chlorophyll content, carotenoid, leaf water, and leaf mass per area prediction within their training data space, their efficacy diminishes when extrapolating to new contexts. Specifically, extrapolating to locations, seasons, and PFTs beyond the training data leads to reduced R2 (0.12-0.49, 0.15-0.42, and 0.25-0.56) and increased NRMSE (3.58-18.24%, 6.27-11.55%, and 7.0-33.12%) compared with nonspatial random cross-validation. The results underscore the importance of incorporating greater spectral diversity in model training to boost its transferability. These findings highlight potential errors in estimating leaf traits across large spatial domains, diverse PFTs, and time due to biased validation schemes, and provide guidance for future field sampling strategies and remote sensing applications.