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Abstract In trees, large uncertainties remain in how nonstructural carbohydrates (NSCs) respond to variation in water availability in natural, intact ecosystems. Variation in NSC pools reflects temporal fluctuations in supply and demand, as well as physiological coordination across tree organs in ways that differ across species and NSC fractions (e.g., soluble sugars vs starch). Using landscape-scale crown (leaves and twigs) NSC concentration measurements in three foundation tree species (Populus tremuloides, Pinus edulis, Juniperus osteosperma), we evaluated in situ, seasonal variation in NSC responses to moisture stress on three timescales: short-term (via predawn water potential), seasonal (via leaf δ13C) and annual (via current year’s ring width index). Crown NSC responses to moisture stress appeared to depend on hydraulic strategy, where J. osteosperma appears to regulate osmotic potentials (via higher sugar concentrations), P. edulis NSC responses suggest respiratory depletion and P. tremuloides responses were consistent with direct sink limitations. We also show that overly simplistic models can mask seasonal and tissue variation in NSC responses, as well as strong interactions among moisture stress at different timescales. In general, our results suggest large seasonal variation in crown NSC concentrations reflecting the multiple cofunctions of NSCs in plant tissues, including storage, growth and osmotic regulation of hydraulically vulnerable leaves. We emphasize that crown NSC pool size cannot be viewed as a simple physiological metric of stress; in situ NSC dynamics are complex, varying temporally, across species, among NSC fractions and among tissue types. 

Multi‐year lags in tree drought recovery, termed ‘drought legacy effects’, are important for understanding the impacts of drought on forest ecosystems, including carbon (C) cycle feedbacks to climate change. Despite the ubiquity of lags in drought recovery, large uncertainties remain regarding the mechanistic basis of legacy effects and their importance for the C cycle. In this review, we identify the approaches used to study legacy effects, from tree rings to whole forests. We then discuss key knowledge gaps pertaining to the causes of legacy effects, and how the various mechanisms that may contribute these lags in drought recovery could have contrasting implications for the C cycle. Furthermore, we conduct a novel data synthesis and find that legacy effects differ drastically in both size and length across the US depending on if they are identified in tree rings versus gross primary productivity. Finally, we highlight promising approaches for future research to improve our capacity to model legacy effects and predict their impact on forest health. We emphasise that a holistic view of legacy effects – from tissues to whole forests – will advance our understanding of legacy effects and stimulate efforts to investigate drought recovery via experimental, observational and modelling approaches.

 

Abstract. Recent observations of near-surface soil temperatures over the circumpolarArctic show accelerated warming of permafrost-affected soils. Theavailability of a comprehensive near-surface permafrost and active layerdataset is critical to better understanding climate impacts and toconstraining permafrost thermal conditions and its spatial distribution inland system models. We compiled a soil temperature dataset from 72 monitoringstations in Alaska using data collected by the U.S. Geological Survey, theNational Park Service, and the University of Alaska Fairbanks permafrostmonitoring networks. The array of monitoring stations spans a large range oflatitudes from 60.9 to 71.3^∘N and elevations from near sea level to∼1300m, comprising tundra and boreal forest regions. This datasetconsists of monthly ground temperatures at depths up to 1m,volumetric soil water content, snow depth, and air temperature during1997–2016. These data have been quality controlled in collection andprocessing. Meanwhile, we implemented data harmonization evaluation for theprocessed dataset. The final product (PF-AK, v0.1) is available at the ArcticData Center (https://doi.org/10.18739/A2KG55).

 

How do antecedent (past) conditions influence land‐carbon dynamics after those conditions no longer persist? In particular, quantifying such memory effects associated with the influence of past environmental (exogenous) and biological (endogenous) conditions is crucial for understanding and predicting the carbon cycle. Here we show, using data from 42 eddy covariance sites across six major biomes, that ecological memory—decomposed into environmental and biological memory components—of daily net carbon exchange (NEE) is critical for understanding the land‐carbon metabolism, especially in drylands for which memory explains ~ 32% of the variation in NEE. The strong environmental memory in drylands was primarily driven by short‐ and long‐term moisture status. Moreover, the strength of environmental memory scales with increasing water stress. This universal scaling relationship, emerging within and among major biomes, suggests a potential adaptive response to water limitation. Our findings underscore the necessity of considering ecological memory in experiments, observations and modelling.