DOI: 10.5061/dryad.4j0zpc8j7
title: Remotely sensed crown nutrient concentrations modulate forest reproduction across the contiguous United States

description: 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 crown nutrient
 concentrations from hyperspectral remote sensing at the National
 Ecological Observatory Network (NEON) across the 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) at the continental
 scale. With-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 study on hyperspectral imagery indicates promise for
 future monitoring of reproductive potential. The fact that both ISP and
 CSP decline at high foliar P levels has immediate applications in
 improving forest demographic and regeneration models by providing more
 realistic nutrient effects at multiple scales.
description: The fecundity data from the 13 NEON sites are part of the Masting
 Inference and Forecasting (MASTIF) network. MASTIF includes two types of
 raw data, seed traps (ST) and crop counts (CC). ST data were the number of
 seeds from seed traps associated with individual trees from the mapped
 stands at the 13 NEON sites. Data compilation, modeling, and computation
 are open-access in the R package MASTIF, with more details provided in
 Clark et al., 2019, 2021.  We generated wall-to-wall
 foliar nutrient maps following Wang et al., 2020 by applying the Partial
 Least Squares Regression (PLSR) coefficients to the hyperspectral surface
 reflectance data. The coefficients are available at
 https://ecosml.org/package/github/EnSpec/NEON-Trait-Models. Five
 nutrients, including Nitrogen (N), Phosphorus (P), Potassium (K),
 Magnesium (Mg), and Calcium (Ca), were analyzed based on their important
 roles in plant reproduction. The unit of the nutrient is mg per
 g.
description: # Data for: Remotely sensed crown nutrient concentrations affect forest
 reproduction across the United States --- The nutrient data are derived
 from hyperspectral remote sensing, where each column contains a specific
 nutrient concentration from the Partial Least Squares Regression (PLSR)
 model. Each row is a NEON plot. The fecundity data are derived from the
 Masting Inference and Forecasting (MASTIF) network, where each column
 contains a specific species and each row is a NEON site. The code provides
 instructions on how PLSR can be used and how a generalized Joint Attribute
 Model (GJAM) is fitted to the two data. The code can be executed on R
 studio.  ## Description of the data and file structure There are two CSV
 files, where nutrient variables (i.e., the predictors, unit mg/g) and
 fecundity estimation (i.e., the response, unit g per m^2) are saved in
 each file. The five nutrient predictors include Nitrogen (N), Phosphorus
 (P), Potassium (K), Magnesium (Mg), and Calcium (Ca). The response file
 lists the species in the first row. plot.ID is the plot identity
 designation. Data was derived from the following sources: * EcoSIS Library
 * Masting Inference and Forecasting Network The associated code is
 available on Zenodo at the link provided in the Related Works section of
 this dataset submission.