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In northern hardwood forests, litter decomposition might be affected by nutrient availability, species composition, stand age, or access by decomposers. We investigated these factors at the Bartlett Experimental Forest in New Hampshire. Leaf litter of early and late successional species was collected from four stands that had full factorial nitrogen and phosphorus additions to the soil and were deployed in bags of two mesh sizes (63 µm and 2 mm) in two young and two mature stands. Litter bags were collected three times over the next 2 years, and mass loss was described as an exponential function of time represented by a thermal sum. Litter from young stands had higher initial N and P concentrations and decomposed more quickly than litter from mature stands (p = 0.005), regardless of where it was deployed. Litter decomposed more quickly in fine mesh bags that excluded mesofauna (p < 0.001), which might be explained by the greater rigidity of the large mesh material making poor contact with the soil. Neither nutrient addition (p = 0.94 for N, p = 0.26 for P) nor the age of the stand in which bags were deployed (p = 0.36) had a detectable effect on rates of litter decomposition. 

Abstract Few previous studies have described the patterns of leaf characteristics in response to nutrient availability and depth in the crown. Sugar maple has been studied for both sensitivity to light, as a shade-tolerant species, and sensitivity to soil nutrient availability, as a species in decline due to acid rain. To explore leaf characteristics from the top to bottom of the canopy, we collected leaves along a vertical gradient within mature sugar maple crowns in a full-factorial nitrogen (N) by phosphorus (P) addition experiment in three forest stands in central New Hampshire, USA. Thirty-two of the 44 leaf characteristics had significant relationships with depth in the crown, with the effect of depth in the crown strongest for leaf area, photosynthetic pigments and polyamines. Nitrogen addition had a strong impact on the concentration of foliar N, chlorophyll, carotenoids, alanine and glutamate. For several other elements and amino acids, N addition changed patterns with depth in the crown. Phosphorus addition increased foliar P and boron (B); it also caused a steeper increase of P and B with depth in the crown. Since most of these leaf characteristics play a direct or indirect role in photosynthesis, metabolic regulation or cell division, studies that ignore the vertical gradient may not accurately represent whole-canopy performance. 

Statistical confidence in estimates of timber volume, carbon storage, and other forest attributes depends, in part, on the uncertainty in field measurements. Surprisingly, measurement uncertainty is rarely reported, even though national forest inventories routinely repeat field measurements for quality assurance. We compared measurements made by field crews and quality assurance crews in the Forest Inventory and Analysis program of the U.S. Forest Service, using data from 2790 plots and 51 740 trees and saplings across the 24 states of the Northern Region. We characterized uncertainty in 12 national core tree-level variables; seven tree crown variables used in forest health monitoring; three variables describing seedlings; and 11 variables describing the site, such as elevation, slope, and distance from a road. Discrepancies in measurement were generally small but were higher for some variables requiring judgment, such as tree class, decay class, and cause of mortality. When scaled up to states, forest types, or the region, uncertainties in basal area, timber volume, and aboveground biomass were negligible. Understanding all sources of uncertainty is important to designing forest monitoring systems, managing the conduct of the inventory, and assessing the uncertainty of forest attributes required for making regional and national forest policy decisions. 

Some correlations between human traits can be explained by cross-trait assortative mating and not purely genetics. 

Wearable technologies for measuring digital and chemical physiology are pervading the consumer market and hold potential to reliably classify states of relevance to human performance including stress, sleep deprivation, and physical exertion. The ability to efficiently and accurately classify physiological states based on wearable devices is improving. However, the inherent variability of human behavior within and across individuals makes it challenging to predict how identified states influence human performance outcomes of relevance to military operations and other high-stakes domains. We describe a computational modeling approach to address this challenge, seeking to translate user states obtained from a variety of sources including wearable devices into relevant and actionable insights across the cognitive and physical domains. Three status predictors were considered: stress level, sleep status, and extent of physical exertion; these independent variables were used to predict three human performance outcomes: reaction time, executive function, and perceptuo-motor control. The approach provides a complete, conditional probabilistic model of the performance variables given the status predictors. Construction of the model leverages diverse raw data sources to estimate marginal probability density functions for each of six independent and dependent variables of interest using parametric modeling and maximum likelihood estimation. The joint distributions among variables were optimized using an adaptive LASSO approach based on the strength and directionality of conditional relationships (effect sizes) derived from meta-analyses of extant research. The model optimization process converged on solutions that maintain the integrity of the original marginal distributions and the directionality and robustness of conditional relationships. The modeling framework described provides a flexible and extensible solution for human performance prediction, affording efficient expansion with additional independent and dependent variables of interest, ingestion of new raw data, and extension to two- and three-way interactions among independent variables. Continuing work includes model expansion to multiple independent and dependent variables, real-time model stimulation by wearable devices, individualized and small-group prediction, and laboratory and field validation.