Search for: All records

Abstract Hyperspectral reflectance can potentially be used to non‐destructively estimate a diverse suite of plant physiochemical functional traits by applying chemometric approaches to leverage absorption features related to chemical compounds and physiological processes associated with these traits. This approach has considerable implications in advancing plant physiological and chemical ecology. For complex functional traits, however, there is a lack of well‐defined absorption features and features may be unevenly distributed across the reflectance spectrum, suggesting that the influence of wavelength ranges on the performance of chemometric models is potentially important for accurately estimating foliar functional traits.Here, we investigate the influence of spectral ranges on the performance of models estimating six tree functional traits: CO₂assimilation rate, specific leaf area, leaf water content and concentrations of foliar nitrogen, sugars and gallic acid. Using data collected from multiple different experiments, we quantified plant functional trait responses using standard reference measurements and paired them with proximal leaf‐level hyperspectral reflectance measurements spanning the wavelength range of 400–2400 nm. A total of 100 different wavelength range combinations were evaluated using partial least squares regression to determine the influence of wavelength range on model performance.We found that the influence of starting or ending wavelength on model performance was trait specific and better model outcomes were achieved when the starting and ending wavelengths encompassed absorption features associated with the specific leaf trait modelled. Interestingly, we found that including shortwave‐infrared wavelength ranges (1300–2500 nm) improved performance for all trait models.Collectively, our findings underscore the importance of optimal spectral range selection in enhancing the accuracy of chemometric models for specific foliar trait estimates. An emergent outcome of this work is that the approach can be used to (1) identify the important spectral features of traits that currently lack known absorption features or have multiple or weak absorption features, (2) expand the current suite of plant functional traits that can be estimated using spectroscopy and (3) ultimately advance the integration of a spectral biology approach in ecological research. 

Despite decades of research and practice restoring forests on surface coal mines, success is still variable, and failures often occur. To better understand how variation in site factors and management affects the success of restoration of coal mines, we conducted a survey of tree performance for mine sites in the Illinois Basin across Indiana, USA, reclaimed between 2008 and 2018. We explored the impacts of climate and soil conditions on the survival and growth of planted trees, as well as the cover of associated vegetation. Average overall survival (21%) was relatively low compared to typical unmined sites. Organic matter had one of the most consistent and largest effects on most oak species, with the highest performance between 3 and 5%. Soil phosphorus content was the most important factor determining whether a planting totally failed and affected the growth of White oak (Quercus alba) and Swamp white oak (Q. bicolor), though its effect on White oak was nonlinear. Rainfall in the year of planting had large positive effects on the survival and growth of Persimmon (Diospyros virginiana), and Black walnut (Juglans nigra) and temperature in the year of planting had large positive effects on the growth of Bur oak (Q. macrocarpa) and Persimmon. Planting a diverse mix of species is the best option both for approximating natural conditions, and for avoiding problems from the failure of individual species. While conditions vary, successful forest restoration on former coal mines can occur; this study reveals some of the most important factors affecting such success. 

Interactions between above- and below-ground monoculture forest plantation components are critical to tree growth and development. Within the Central Hardwoods Region (CHR), synergistic relationships between tree species and soil microbial community structure and function have received limited research attention. Soil microbes are integral to forest ecosystems as their activities intrinsically promote soil organic matter decomposition, nutrient cycling, and ecosystem functioning. Here, we examined soils from two perfectly aligned stands of black walnut (BW, Juglans nigra L.) and Northern red oak (RO, Quercus rubra L.) trees. Measurements of selected soil chemical properties, microbial community structure using ester-linked fatty acid methyl ester (EL-FAME), and soil enzyme activities (EAs) were used. Analysis of modifications within microbial communities showed a significant positive response to BW based upon soil EAs and microbial indicators, compared to RO. Seasonal comparisons predictably revealed higher microbial activities during summer. Fungi dominated the soil microbial community structure with a fungal/bacterial ratio of 2:1. Gram-positive rather than Gram-negative bacteria or actinomycetes dominated the bacterial community. The activity of the soil enzymes ß-glucosidase and arylsulfatase increased, but ß-glucosaminidase and acid phosphatase decreased. Additionally, acid phosphatase and arbuscular mycorrhizal fungi revealed strong correlations. The differences observed in biological properties, specifically microbial communities and EAs, highlight the varied responses to BW and RO soil biology and subsequent soil ecosystem functions. These results indicate that variations in microbial abundance and soil functions occur throughout the course of an entire year. 

Basal area is a key measure of forest stocking and an important proxy of forest productivity in the face of climate change. Black walnut ( Juglans nigra ) is one of the most valuable timber species in North America. However, little is known about how the stocking of black walnut would change with differed bioclimatic conditions under climate change. In this study, we projected the current and future basal area of black walnut. We trained different machine learning models using more than 1.4 million tree records from 10,162 Forest Inventory and Analysis (FIA) sample plots and 42 spatially explicit bioclimate and other environmental attributes. We selected random forests (RF) as the final model to estimate the basal area of black walnut under climate change because RF had a higher coefficient of determination ( R 2 ), lower root mean square error (RMSE), and lower mean absolute error (MAE) than the other two models (XGBoost and linear regression). The most important variables to predict basal area were the mean annual temperature and precipitation, potential evapotranspiration, topology, and human footprint. Under two emission scenarios (Representative Concentration Pathway 4.5 and 8.5), the RF model projected that black walnut stocking would increase in the northern part of the current range in the USA by 2080, with a potential shift of species distribution range although uncertainty still exists due to unpredictable events, including extreme abiotic (heat, drought) and biotic (pests, disease) occurrences. Our models can be adapted to other hardwood tree species to predict tree changes in basal area based on future climate scenarios. 

Intensively managed forest plantations often require fertilization to maintain site fertility and to improve growth and yield over successive rotations. We applied urea-based “enhanced-efficiency fertilizers” (EEF) containing 0.5 atom% 15N at a rate of 224 kg N ha−1 to soils under mid-rotation black walnut (Juglans nigra L.) plantations to track the fate of applied 15N within aboveground ecosystem components during the 12-month period after application. Treatments included Agrotain Ultra (urea coated with a urease inhibitor), Arborite EC (urea coated with water-soluble boron and phosphate), Agrium ESN (polymer-coated urea), uncoated urea, and an unfertilized control. Agrotain Ultra and Arborite EC increased N concentrations of competing vegetation within one month after fertilization, while neither Agrium ESN nor uncoated urea had any effect on competing vegetation N concentrations during the experiment. Agrotain Ultra and Arborite EC increased δ15N values in leaves of crop trees above those of controls at one and two months after fertilization, respectively. By contrast, Agrium ESN and uncoated urea had no effect on δ15N values in leaves of crop trees until three months after fertilization. Fertilizer N recovery (FNR) varied among ecosystem components, with competing vegetation acting as a sink for applied nutrients. There were no significant differences in FNR for all the urea-based EEF products compared to uncoated urea. Agrium ESN was the only EEF that exhibited controlled-release activity in this study, with other fertilizers behaving similarly to uncoated urea.