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Abstract Ecosystem models offer a rigorous way to formalize scientific theories and are critical to evaluating complex interactions among ecological and biogeochemical processes. In addition to simulation and prediction, ecosystem models are a valuable tool for testing hypotheses about mechanisms and empirical findings because they reveal critical internal processes that are difficult to observe directly.However, many ecosystem models are difficult to manage and apply by scientists who lack advanced computing skills due to complex model structures, lack of consistent documentation, and low-level programming implementation, which facilitates computing but reduces accessibility.Here, we present the ‘pnetr’ R package, which is designed to provide an easy-to-manage ecosystem modeling framework and detailed documentation in both model structure and programming. The framework implements a family of widely used PnET (net photosynthesis, evapotranspiration) ecosystem models, which are relatively parsimonious but capture essential biogeochemical cycles of water, carbon, and nutrients. We chose the R programming language since it is familiar to many ecologists and has abundant statistical modeling resources. We showcase examples of model simulations and test the effects of phenology on carbon assimilation and wood production using data measured by the Environmental Measurement Station (EMS) eddy-covariance flux tower at Harvard Forest, MA.We hope ‘pnetr’ can facilitate further development of ecological theory and increase the accessibility of ecosystem modeling and ecological forecasting. 

Abstract Unlike trees, shrubs (i.e., multiple-stemmed woody plants) do not need evenly spaced large diameter structural roots and therefore should be more responsive to heterogeneous distributions of soil resources and spread further per unit belowground biomass. We therefore hypothesized that compared to trees, shrubs respond more to asymmetric distributions of nutrients, reach nutrient-rich patches of soil faster, and do so with less below-ground biomass. To test these three hypotheses, we planted individual seedlings of shrubs (Cornus racemosa, Rhus glabra, andViburnum dentatum) and trees (Acer rubrum, Betula populifolia, andFraxinus americana) in the centers of sand-filled rectangular boxes. In one direction we created a stepwise gradient of increasing nutrients with slow-release fertilizer; in the other direction, no fertilizer was added. Seedlings were harvested when their first root reached the plexiglass-covered fertilized end of their box; time taken, above-ground biomass, and below-ground biomass per nutrient segment were determined. Shrubs and trees did not consistently differ in precision of root foraging (i.e., the ratio of biomass in the fertilized and unfertilized soil) or in rates (g/day) and efficiencies (cm/day) of lateral root growth. Interspecific variation appeared more related to species’ habitats than to growth form. The fastest and most efficient roots were produced by the shrub (R. glabra) and the tree (B. populifolia), both characteristic of poor and heterogeneous soils. Root foraging byR. glabrawas also facilitated by rapid rhizomatous expansion. 

In ecosystems dominated by grasses and other small-statured plants, biodiversity and productivity have generally been found to be positively correlated. However, studies in forested ecosystems have found mixed results. Biodiversity in forests has typically been characterized using indices of species diversity, but recent studies have shown that other measures of biodiversity can also play an important role. Several indices of biodiversity were calculated using local-scale inventory measurements and aircraft Light Detecting and Ranging data from Bartlett (BEF) and Hubbard Brook Experimental Forests (HBEF) in New Hampshire, USA to look at relationships between different measures of biodiversity (species, functional, phylogenetic, and structural diversity) and site productivity. For this dataset, a total of 22 biodiversity indices were calculated using the 2001-2003 BEF and 1995-1998 HBEF inventory datasets. These biodiversity indices include three species diversity indices, five functional diversity indices, five phylogenetic diversity indices, and 12 structural diversity indices. In addition, measurements of wood growth and foliar nitrogen for a select number of plots previously collected in another study (Smith et al 2005) were used to represent plot productivity. Site characteristics and land use such as topography, management history, and forest type were also included. Hubbard Brook Experimental Forest and the Bartlett Experimental Forest are both operated and maintained by the USDA Forest Service, Northern Research Station. 

The basics of climate change science have been known for a long time, and the predicted impact of a doubling of atmospheric carbon dioxide on global temperature hasn’t changed much in 100 years. 

Leaf angle distribution (LAD) measurements were made during the growing season in 2021 at the Harvard Forest in Petersham, MA, USA, and in 2022 at the Thompson Farm Earth Systems Observatory in Durham, NH, USA. At both sites, a level-calibrated digital angle tool was used to measure LAD in upper canopy foliage of common northeastern temperate tree species accessed using a mobile canopy lift. Additionally, at Thompson Farm, measurements were made at multiple heights to characterize differences of LAD in high, middle, and low canopy positions. Here, we have published those measurements, including a summary table of species average leaf angles and calculated parameters for fitted beta distributions. Processing scripts can be made available upon request to the authors. Additionally, leaf chemical, physical, structure, optical and physiological traits have been measured at these site as well as canopy scale measures of structure and UAV-based spectral, thermal, and lidar imagery. 

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. 

LiDAR data were acquired over the footprint of the flux tower and established long-term study plots at Thompson Farm Observatory, Durham, NH during the growing season. Data were acquired using a LiVox Avia lidar sensor on a Green Valley International LiAirV70 payload. The LiVox Avia is a triple echo 905 nm lidar sensor with a non-repetitive circular scanning pattern that can retrieve ~700,000 returns per second. The sensor payload was flown on board a DJI M300 at an altitude of ~65 m above ground level in a double grid pattern with ~32 m flight line spacing, yielding a return density across the sampling area >500 points per square meter. Returns were georeferenced to WGS84 UTM Zone 19N coordinates with heights above ellipsoid using Green Valley International’s LiGeoreference software with automatic boresight calibration. Outliers were removed, then flight line point clouds were merged. Returns were classified as ground and non-ground returns using Green Valley International’s Lidar360 software and output as LAS (v 1.4) data sets. LAS files were subsequently tiled for publication. 

Orthorectified flight line hyperspectral cubes retiled for publication. Collectively, the tiled hyperspectral cubes cover the footprint of the flux tower and established long-term study plots at Thompson Farm Observatory, Durham, NH. Data were acquired using a Headwall Photonics, Inc. Nano VNIR hyperspectral line scanning imager with 273 bands from 400-1000 nm. The sensor was flown on board a DJI M600 hexacopter at an altitude of ~80 m above the forest canopy, yielding ~6 cm GSD. Flight lines were converted from raw sensor observations to upwelling radiance a using a vendor-supplied radiometric calibration file for the sensor, then converted to reflectance using a calibration tarp with known reflectance. Finally, cubes were orthorectified using a 1m DSM in Headwall’s SpectralView software, mosaicked to individual flight line cubes, then subsequently tiled for publication.