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Quantifying uncertainty in forest assessments is challenging because of the number of sources of error and the many possible approaches to quantify and propagate them. The uncertainty in allometric equations has sometimes been represented by propagating uncertainty only in the prediction of individuals, but at large scales with large numbers of trees uncertainty in model fit is more important than uncertainty in individuals. We compared four different approaches to representing model uncertainty: a formula for the confidence interval, Monte Carlo sampling of the slope and intercept of the regression, bootstrap resampling of the allometric data, and a Bayesian approach. We applied these approaches to propagating model uncertainty at four different scales of tree inventory (10 to 10,000 trees) for four study sites with varying allometry and model fit statistics, ranging from a monocultural plantation to a multi-species shrubland with multi-stemmed trees. We found that the four approaches to quantifying uncertainty in model fit were in good agreement, except that bootstrapping resulted in higher uncertainty at the site with the fewest trees in the allometric data set (48), because outliers could be represented multiple times or not at all in each sample. The uncertainty in model fit did not vary with the number of trees in the inventory to which it was applied. In contrast, the uncertainty in predicting individuals was higher than model fit uncertainty when applied to small numbers of trees, but became negligible with 10,000 trees. The importance of this uncertainty source varied with the forest type, being largest for the shrubland, where the model fit was most poor. Low uncertainties were observed where model fit was high, as was the case in the monoculture plantation and in the subtropical jungle where hundreds of trees contributed to the allometric model. In all cases, propagating uncertainty only in the prediction of individuals would underestimate allometric uncertainty. It will always be most correct to include both uncertainty in predicting individuals and uncertainty in model fit, but when large numbers of individuals are involved, as in the case of national forest inventories, the contribution of uncertainty in predicting individuals can be ignored. When the number of trees is small, as may be the case in forest manipulation studies, both sources of allometric uncertainty are likely important and should be accounted for. 

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.

 

Trait-based analyses provide powerful tools for developing a generalizable, physiologically grounded understanding of how forest communities are responding to ongoing environmental changes. Key challenges lie in (1) selecting traits that best characterize the ecological performance of species in the community and (2) determining the degree and importance of intraspecific variability in those traits. Recent studies suggest that globally evident trait correlations (trait dimensions), such as the leaf economic spectrum, may be weak or absent at local scales. Moreover, trait-based analyses that utilize a mean value to represent a species may be misleading. Mean trait values are particularly problematic if species trait value rankings change along environmental gradients, resulting in species trait crossover. To assess how plant traits (1) covary at local spatial scales, (2) vary across the dominant environmental gradients, and (3) can be partitioned within and across taxa, we collected data on 9 traits for 13 tree species spanning the montane temperate—boreal forest ecotones of New York and northern New England. The primary dimension of the trait ordination was the leaf economic spectrum, with trait variability among species largely driven by differences between deciduous angiosperms and evergreen gymnosperms. A second dimension was related to variability in nitrogen to phosphorous levels and stem specific density. Levels of intraspecific trait variability differed considerably among traits, and was related to variation in light, climate, and tree developmental stage. However, trait rankings across species were generally conserved across these gradients and there was little evidence of species crossover. The persistence of the leaf economics spectrum in both temperate and high-elevation conifer forests suggests that ecological strategies of tree species are associated with trade-offs between resource acquisition and tolerance, and may be quantified with relatively few traits. Furthermore, the assumption that species may be represented with a single trait value may be warranted for some trait-based analyses provided traits were measured under similar light levels and climate conditions. 

Drought‐induced groundwater decline and warming associated with climate change are primary threats to dryland riparian woodlands. We used the extreme 2012–2019 drought in southern California as a natural experiment to assess how differences in water‐use strategies and groundwater dependence may influence the drought susceptibility of dryland riparian tree species with overlapping distributions. We analyzed tree‐ring stable carbon and oxygen isotopes collected from two cottonwood species (Populus trichocarpaandP.fremontii) along the semi‐arid Santa Clara River. We also modeled tree source water δ¹⁸O composition to compare with observed source water δ¹⁸O within the floodplain to infer patterns of groundwater reliance. Our results suggest that both species functioned as facultative phreatophytes that used shallow soil moisture when available but ultimately relied on groundwater to maintain physiological function during drought. We also observed apparent species differences in water‐use strategies and groundwater dependence related to their regional distributions.P.fremontiiwas constrained to more arid river segments and ostensibly used a greater proportion of groundwater to satisfy higher evaporative demand.P.fremontiimaintained ∆¹³C at pre‐drought levels up until the peak of the drought, when trees experienced a precipitous decline in ∆¹³C. This response pattern suggests that trees prioritized maintaining photosynthetic processes over hydraulic safety, until a critical point. In contrast,P.trichocarpashowed a more gradual and sustained reduction in ∆¹³C, indicating that drought conditions induced stomatal closure and higher water use efficiency. This strategy may confer drought avoidance forP.trichocarpawhile increasing its susceptibility to anticipated climate warming.

 

Dryland riparian woodlands are considered to be locally buffered from droughts by shallow and stable groundwater levels. However, climate change is causing more frequent and severe drought events, accompanied by warmer temperatures, collectively threatening the persistence of these groundwater dependent ecosystems through a combination of increasing evaporative demand and decreasing groundwater supply. We conducted a dendro-isotopic analysis of radial growth and seasonal (semi-annual) carbon isotope discrimination (Δ13C) to investigate the response of riparian cottonwood stands to the unprecedented California-wide drought from 2012 to 2019, along the largest remaining free-flowing river in Southern California. Our goals were to identify principal drivers and indicators of drought stress for dryland riparian woodlands, determine their thresholds of tolerance to hydroclimatic stressors, and ultimately assess their vulnerability to climate change. Riparian trees were highly responsive to drought conditions along the river, exhibiting suppressed growth and strong stomatal closure (inferred from reduced Δ13C) during peak drought years. However, patterns of radial growth and Δ13C were quite variable among sites that differed in climatic conditions and rate of groundwater decline. We show that the rate of groundwater decline, as opposed to climate factors, was the primary driver of site differences in drought stress, and trees showed greater sensitivity to temperature at sites subjected to faster groundwater decline. Across sites, higher correlation between radial growth and Δ13C for individual trees, and higher inter-correlation of Δ13C among trees were indicative of greater drought stress. Trees showed a threshold of tolerance to groundwater decline at 0.5 m year−1 beyond which drought stress became increasingly evident and severe. For sites that exceeded this threshold, peak physiological stress occurred when total groundwater recession exceeded 3 m. These findings indicate that drought-induced groundwater decline associated with more extreme droughts is a primary threat to dryland riparian woodlands and increases their susceptibility to projected warmer temperatures. 

Autotrophic respiration is a major driver of the global C cycle and may contribute a positive climate warming feedback through increased atmospheric concentrations ofCO₂. The extent of this feedback depends on plants' ability to acclimate respiration to maintain a constant carbon use efficiency (CUE).

We quantified respiratory partitioning of gross primary production (GPP) andCUEof field‐grown trees in a long‐term warming experiment (+3°C). We delivered a¹³C–CO₂pulse to whole tree crowns and chased that pulse in the respiration of leaves, whole crowns, roots, and soil. We also measured the isotopic composition of soil microbial biomass and the respiration rates of leaves and whole crowns.

We documented homeostatic respiratory acclimation of foliar and whole‐crown respiration rates; the trees adjusted to experimental warming such that leaf‐level respiration rates were not increased. Experimental warming had no detectable impact on respiratory partitioning or mean residence times. Of the¹³C label acquired by the trees, aboveground respiration consumed 10%, belowground respiration consumed 40%, and the remaining 50% was retained.

Experimental warming of +3°C did not alter respiratory partitioning at the scale of entire trees, suggesting that complete acclimation of respiration to warming is likely to dampen a positive climate warming feedback.