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Long-term ecological data are essential for detecting impacts of climate change and other global change factors, and for making informed predictions about future change. However, long-term measurements are rarely replicated at the site level, which raises questions about their representativeness. We used a multiscale approach to evaluate the agreement of parallel observations from AmeriFlux and NEON (National Ecological Observatory Network) towers at Bartlett Experimental Forest, New Hampshire, USA. The two towers are separated by a horizontal distance of 93 m. We focused our analysis on standard meteorological variables; fluxes of CO2, sensible heat, and latent heat measured by eddy covariance; and phenology derived from PhenoCam imagery. Results suggest excellent agreement between AmeriFlux and NEON in meteorology and phenology, and good agreement in fluxes at the half-hourly scale. However, large disagreements in CO2 and latent heat fluxes occurred at the annual scale, with implications especially for the forest carbon balance. The AmeriFlux tower measurements indicate a site that is close to carbon-neutral (-8 ± 65 g C m-2 y-1, mean ± 1 SD), whereas the NEON tower measurements indicate a forest that is a carbon sink (-137 ± 10 g C m-2 y-1). Causes of this disagreement may include measurement height (26 m vs. 35 m), which resulted in different flux footprints being measured by the two towers, and differences in the flux measurement systems. Our results suggest the need for caution when attempting to merge long-term flux data from two different measurement platforms, and when using measurements from any one measurement platform to inform decision-making on issues related to carbon accounting or natural climate solutions. 

Vegetation phenology, i.e., seasonal biological events such as leaf-out and leaf-fall, regulates local climate through biophysical processes like evapotranspiration (ET) and albedo. However, the net surface temperature impact of these processes—whether ET cooling or albedo-induced warming predominates—and how the dominance changes across phenological transitions and regions remains poorly understood. Here, we investigated the effects of vegetation foliage on daytime land surface temperature (LST) following six phenological transitions, spanning from the start of season to end of season, in deciduous and mixed forests across the mid- to high-latitude Northern Hemisphere during 2013–2021 using multiple satellite products and ground observations. We quantified vegetation effect as the difference between observed LST and LST estimates from the Annual Temperature Cycle (ATC) model, representing a no-foliage scenario. We found that vegetation-induced cooling consistently outweighs warming following all phenological transitions except for the end of the season. Cooling intensity increased with vegetation greenness, ranging from 1.0 ± 0.5 °C (mean ± 0.15 SD) in 59% of forests after the start of the season (SOS) to 6.1 ± 0.8 °C in 89% of forests following the onset of maturity, before declining toward the end of the season. Over half of the regions experiencing cooling showed intensification of surface cooling with climate warming, suggesting an amplified vegetation-mediated cooling under future climate change. The findings provide a more precise understanding of the role of vegetation in modulating climate at the intraseasonal scale, highlighting the importance of integrating phenological impacts into climate adaptation strategies and Earth system modeling. 

Eddy Covariance measurements are often subject to missing values, or gaps in the data record. Methods to fill short gaps are well-established, but robustly filling gaps longer than a few weeks remains a challenge. Marginal Distribution Sampling (MDS) is a standard gap-filling method, but its effectiveness for long gaps (> 30 days) is limited. We compared the performance of a machine learning algorithm, eXtreme Gradient Boosting (XGB) against MDS, using various artificial scenarios of gap lengths and locations. We gapfilled half hourly CO2 flux from a temperate deciduous forest, Bartlett Experimental Forest, from 2010 to 2022. Whereas the standard implementation of MDS uses a narrowly-prescribed set of predictor variables, with XGB we were able to include additional variables. The Green Chromatic Coordinate (GCC), derived from PhenoCam imagery, and diffuse photosynthetic photon flux density, emerged as two of the three most important predictor variables. Compared to MDS, the root mean square error (RMSE) of XGB decreased by 9.5 %, and the R2 increased by 2.7 % in a randomized 10-fold cross validation test. XGB outperformed MDS for both day and night times across different seasons. But annual NEE integrals varied across methods, with weaker annual net carbon uptake, by -110 ± 74 g C m-2 y-1 for XGB compared to MDS (214 ± 11 g C m-2 yr-1). In artificial gap experiments, when trained using the 13-year data record, XGB reliably filled gaps, showing little change in RMSE for gaps up to 240 days. In contrast, the performance of MDS steadily decreased as gap lengths increased. MDS was unable to fill gaps longer than 2 months. In summary, XGB demonstrates excellent performance as an alternative method to MDS, providing reliable predictions for temperate deciduous forest carbon fluxes under different gap lengths and location scenarios. Implementation of XGB is facilitated by easy-to-use packages. 

Summary Carbon reserves are distributed throughout plant cells allowing past photosynthesis to fuel current metabolism. In trees, comparing the radiocarbon (Δ¹⁴C) of reserves to the atmospheric bomb spike can trace reserve ages.We synthesized Δ¹⁴C observations of stem reserves in nine tree species, fitting a new process model of reserve building. We asked how the distribution, mixing, and turnover of reserves vary across trees and species. We also explored how stress (drought and aridity) and disturbance (fire and bark beetles) perturb reserves.Given sufficient sapwood, young (< 1 yr) and old (20–60+ yr) reserves were simultaneously present in single trees, including ‘prebomb’ reserves in two conifers. The process model suggested that most reserves are deeply mixed (30.2 ± 21.7 rings) and then respired (2.7 ± 3.5‐yr turnover time). Disturbance strongly increased Δ¹⁴C mean ages of reserves (+15–35 yr), while drought and aridity effects on mixing and turnover were species‐dependent. Fire recovery inSequoia sempervirensalso appears to involve previously unobserved outward mixing of old reserves.Deep mixing and rapid turnover indicate most photosynthate is rapidly metabolized. Yet ecological variation in reserve ages is enormous, perhaps driven by stress and disturbance. Across species, maximum reserve ages appear primarily constrained by sapwood longevity, and thus old reserves are probably widespread. 

Purpose of Review This review synthesizes recent advancements and identifies knowledge gaps in the tree growth phenology of both belowground and aboveground organs in extra-tropical forest ecosystems. Phenology, the study of periodic plant life cycle events, is crucial for understanding tree fitness, competition for resources, and the impacts of climate change on ecosystems. By examining the phenological processes of various tree organs, the review aims to provide a comprehensive understanding of how these processes are interconnected and how they influence overall tree growth and ecosystem dynamics. The review aims to provide a comprehensive overview of current knowledge, highlight recent technological advancements, and identify critical areas where further research is needed. Recent Findings The review highlights significant progress in monitoring leaf and canopy phenology, thanks to advancements in remote sensing and automated observation systems. These technologies have enhanced our ability to track seasonal changes in leaf development and canopy dynamics more accurately and over larger areas. There has also been a substantial increase in research on wood formation in stems, expanding beyond northern hemisphere conifers to include a broader range of functional groups. However, despite these efforts, identifying the precise drivers of wood formation remains challenging, necessitating further integration of molecular and eco-physiological insights. A critical area of focus is root phenology, encompassing both primary and secondary growth. Despite the fundamental role of roots in tree physiology and ecosystem dynamics, our understanding of root phenology remains limited, primarily due to the inherent difficulties in monitoring root growth. The review emphasizes the need for more detailed studies on root growth processes and the development of new methodologies and technologies to improve root phenology assessments. Summary The review highlights the importance of incorporating eco-physiological insights into phenological assessments. Leaf and canopy phenology would benefit from more studies focusing on autumnal events. Indeed, compared to the onset of the growing season, much less is known about its end, despite its critical importance for understanding processes such as carbon uptake and nutrient cycle. Advancing knowledge of wood growth phenology will require greater focus on angiosperms, as research on xylogenesis has historically been centered on gymnosperms. This will likely necessitate the development of new, tailored methodologies to address the characteristics of angiosperm wood formation. Similarly, further exploration of phloem phenology is essential to better understand the links between phenological processes across different organs. Finally, compared to other organs, root growth remains less well understood, underscoring the need for deepening the investigation on root phenology in the coming years. 

Climate‐change‐induced shifts in the timing of leaf emergence during spring have been widely documented and have important ecological consequences. However, mechanistic knowledge regarding what controls the timing of spring leaf emergence is incomplete. Field‐based studies under natural conditions suggest that climate‐warming‐induced decreases in cold temperature accumulation (chilling) have expanded the dormancy duration or reduced the sensitivity of plants to warming temperatures (thermal forcing) during spring, thereby slowing the rate at which the timing of leaf emergence is shifting earlier in response to ongoing climate change. However, recent studies have argued that the apparent reductions in temperature sensitivity may arise from artefacts in the way that temperature sensitivity is calculated, while other studies based on statistical and mechanistic models specifically designed to quantify the role of chilling have shown conflicting results. We analysed four commonly used combinations of phenology and temperature datasets obtained from remote sensing and ground observations to elucidate whether current model‐based approaches robustly quantify how chilling, in concert with thermal forcing, controls the timing of leaf emergence during spring under current climate conditions. We show that widely used modeling approaches that are calibrated using field‐based observations misspecify the role of chilling under current climate conditions as a result of statistical artefacts inherent to the way that chilling is parameterised. Our results highlight the limitations of existing modelling approaches and observational data in quantifying how chilling affects the timing of spring leaf emergence and suggest that decreasing chilling arising from climate warming may not constrain near‐future shifts towards earlier leaf emergence in extra‐tropical ecosystems worldwide. 

AmeriFlux is a network of hundreds of sites across the contiguous United States providing tower-based ecosystem-scale carbon dioxide flux measurements at 30 min temporal resolution. While geographically wide-ranging, over its existence the network has suffered from multiple issues including towers regularly ceasing operation for extended periods and a lack of standardization of measurements between sites. In this study, we use machine learning algorithms to predict CO2 flux measurements at NEON sites (a subset of Ameriflux sites), creating a model to gap-fill measurements when sites are down or replace measurements when they are incorrect. Machine learning algorithms also have the ability to generalize to new sites, potentially even those without a flux tower. We compared the performance of seven machine learning algorithms using 35 environmental drivers and site-specific variables as predictors. We found that Extreme Gradient Boosting (XGBoost) consistently produced the most accurate predictions (Root Mean Squared Error of 1.81 μmolm−2s−1, R2 of 0.86). The model showed excellent performance testing on sites that are ecologically similar to other sites (the Mid Atlantic, New England, and the Rocky Mountains), but poorer performance at sites with fewer ecological similarities to other sites in the data (Pacific Northwest, Florida, and Puerto Rico). The results show strong potential for machine learning-based models to make more skillful predictions than state-of-the-art process-based models, being able to estimate the multi-year mean carbon balance to within an error ±50 gCm−2y−1 for 29 of our 44 test sites. These results have significant implications for being able to accurately predict the carbon flux or gap-fill an extended outage at any AmeriFlux site, and for being able to quantify carbon flux in support of natural climate solutions. 

Abstract Understanding tree transpiration variability is vital for assessing ecosystem water‐use efficiency and forest health amid climate change, yet most landscape‐level measurements do not differentiate individual trees. Using canopy temperature data from thermal cameras, we estimated the transpiration rates of individual trees at Harvard Forest and Niwot Ridge. PT‐JPL model was used to derive latent heat flux from thermal images at the canopy‐level, showing strong agreement with tower measurements (R² = 0.70–0.96 at Niwot, 0.59–0.78 at Harvard at half‐hourly to monthly scales) and daily RMSE of 33.5 W/m²(Niwot) and 52.8 W/m²(Harvard). Tree‐level analysis revealed species‐specific responses to drought, with lodgepole pine exhibiting greater tolerance than Engelmann spruce at Niwot and red oak showing heightened resistance than red maple at Harvard. These findings show how ecophysiological differences between species result in varying responses to drought and demonstrate that these responses can be characterized by deriving transpiration from crown temperature measurements.