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Abstract Evergreen needleleaf forests (ENFs) play a sizable role in the global carbon cycle, but the biological and physical controls on ENF carbon cycle feedback loops are poorly understood and difficult to measure. To address this challenge, a growing appreciation for the stress physiology of photosynthesis has inspired emerging techniques designed to detect ENF photosynthetic activity with optical signals. This Overview summarizes how fundamental plant biological and biophysical processes control the fate of photons from leaf to globe, ultimately enabling remote estimates of ENF photosynthesis. We demonstrate this using data across four ENF sites spanning a broad range of environmental conditions and link leaf- and stand-scale observations of photosynthesis (i.e., needle biochemistry and flux towers) with tower- and satellite-based remote sensing. The multidisciplinary nature of this work can serve as a model for the coordination and integration of observations made at multiple scales. 

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. 

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. 

Premise of the StudyWe investigated the spatial and temporal patterns of vegetation phenology with phenometrics derived from PhenoCam imagery. Specifically, we evaluated the Bioclimatic Law proposed by Hopkins, which relates phenological transitions to latitude, longitude, and elevation. Methods“Green‐up” and “green‐down” dates—representing the start and end of the annual cycles of vegetation activity—were estimated from measures of canopy greenness calculated from digital repeat photography. We used data from 65 deciduous broadleaf (DB) forest sites, 18 evergreen needleleaf (EN) forest sites, and 21 grassland (GR) sites. ResultsDBgreen‐up dates were well correlated with mean annual temperature and varied along spatial gradients consistent with the Bioclimatic Law. Interannual variation inDBphenology was most strongly associated with temperature anomalies during a relatively narrow window of time.ENphenology was not well correlated with either climatic factors or spatial gradients, but similar toDBphenology, interannual variation was most closely associated with temperature anomalies. ForGRsites, mean annual precipitation explained most of the spatial variation in the duration of vegetation activity, whereas both temperature and precipitation anomalies explained interannual variation in phenology. DiscussionPhenoCam data provide an objective and consistent means by which spatial and temporal patterns in vegetation phenology can be investigated. 

Abstract Extensive ecological research has investigated extreme climate events or long‐term changes in average climate variables, but changes in year‐to‐year (interannual) variability may also cause important biological responses, even if the mean climate is stable. The environmental stochasticity that is a hallmark of climate variability can trigger unexpected biological responses that include tipping points and state transitions, and large differences in weather between consecutive years can also propagate antecedent effects, in which current biological responses depend on responsiveness to past perturbations. However, most studies to date cannot predict ecological responses to rising variance because the study of interannual variance requires empirical platforms that generate long time series. Furthermore, the ecological consequences of increases in climate variance could depend on the mean climate in complex ways; therefore, effective ecological predictions will require determining responses to both nonstationary components of climate distributions: the mean and the variance. We introduce a new design to resolve the relative importance of, and interactions between, a drier mean climate and greater climate variance, which are dual components of ongoing climate change in the southwestern United States. The Mean × Variance Experiment (MVE) adds two novel elements to prior field infrastructure methods: (1) factorial manipulation of variance together with the climate mean and (2) the creation of realistic, stochastic precipitation regimes. Here, we demonstrate the efficacy of the experimental design, including sensor networks and PhenoCams to automate monitoring. We replicated MVE across ecosystem types at the northern edge of the Chihuahuan Desert biome as a central component of the Sevilleta Long‐Term Ecological Research Program. Soil sensors detected significant treatment effects on both the mean and interannual variability in soil moisture, and PhenoCam imagery captured change in vegetation cover. Our design advances field methods to newly compare the sensitivities of populations, communities, and ecosystem processes to climate mean × variance interactions.