The stable isotopes of hydrogen and oxygen (δ 2 H and δ 18 O, respectively) have been widely used to investigate tree water source partitioning. These tracers have shed new light on patterns of tree water use in time and space. However, there are several limiting factors to this methodology (e.g., the difficult assessment of isotope fractionation in trees, and the labor-intensity associated with the collection of significant sample sizes) and the use of isotopes alone has not been enough to provide a mechanistic understanding of source water partitioning. Here, we combine isotope data in xylem and soil water with measurements of tree's physiological information including tree water deficit (TWD), fine root distribution, and soil matric potential, to investigate the mechanism driving tree water source partitioning. We used a 2 m 3 lysimeter with willow trees (Salix viminalis) planted within, to conduct a high spatial-temporal resolution experiment. TWD provided an integrated response of plant water status to water supply and demand. The combined isotopic and TWD measurement showed that short-term variation (within days) in source water partitioning is determined mainly by plant hydraulic response to changes in soil matric potential. We observed

The boreal forest is a major contributor to the global climate system, therefore, reducing uncertainties in how the forest will respond to a changing climate is critical. One source of uncertainty is the timing and drivers of the spring transition. Remote sensing can provide important information on this transition, but persistent foliage greenness, seasonal snow cover, and a high prevalence of mixed forest stands (both deciduous and evergreen species) complicate interpretation of these signals. We collected tower-based remotely sensed data (reflectance-based vegetation indices and Solar-Induced Chlorophyll Fluorescence [SIF]), stem radius measurements, gross primary productivity, and environmental conditions in a boreal mixed forest stand. Evaluation of this data set shows a two-phased spring transition. The first phase is the reactivation of photosynthesis and transpiration in evergreens, marked by an increase in relative SIF, and is triggered by thawed stems, warm air temperatures, and increased available soil moisture. The second phase is a reduction in bulk photoprotective pigments in evergreens, marked by an increase in the Chlorophyll-Carotenoid Index. Deciduous leaf-out occurs during this phase, marked by an increase in all remotely sensed metrics. The second phase is controlled by soil thaw. Our results demonstrate that remote sensing metrics can be used to detect specific physiological changes in boreal tree species during the spring transition. The two-phased transition explains inconsistencies in remote sensing estimates of the timing and drivers of spring recovery. Our results imply that satellite-based observations will improve by using a combination of vegetation indices and SIF, along with species distribution information. Plain Language SummaryThe boreal forest is one of the most sensitive regions on the planet to climate change, yet its sensitivity remains poorly understood. In particular, the timing and drivers of the spring transition, as the forest changes from a winter adapted state to a summer adapted state, carry significant uncertainties. Remote sensing metrics can be used to characterize the spring transition, but their interpretation is complicated by persistent greenness, frequent snow cover, and a high prevalence of forests containing both deciduous and evergreen species. We collected tower-based remotely sensed metrics, stem radius, and carbon uptake measurements and show that the spring transition occurs in two distinct phases. The first phase is a reactivation of photosynthesis in evergreens and is triggered by thawed stems, warm air temperature, and moist soil. The second phase is a change in evergreen photoprotective pigment levels and the leaf-out of deciduous species. It is triggered by soil thaw. Both phases were detected with different remote sensing metrics that depended on species type. Our results illustrate how satellite measurements could be improved to capture the spring transition over diverse landscapes and what environmental factors control the spring transition.

Rationale:The stable isotope compositions of hydrogen and oxygen in water (δ 2 H and δ 18 O values) have been widely used to investigate plant water sources, but traditional isotopic measurements of plant waters are expensive and labor intensive.Recent work with direct vapor equilibration (DVE) on laser spectroscopy has shown potential to side step limitations imposed by traditional methods. Here, we evaluate DVE analysis of plants with a focus on spectral contamination introduced by organic compounds. We present 17 O-excess as a way of quantifying organic compound interference in DVE. Methods: We performed isotopic analysis using the δ 2 H, δ 18 O and δ 17 O values of water on an Off-Axis Integrated Cavity Output Spectroscopy (IWA-45EP OA-ICOS) instrument in vapor mode. We used a set of methanol (MeOH) and ethanol (EtOH) solutions to assess errors in isotope measurements. We evaluated how organic compounds affect the 17 O-excess. DVE was used to measure the isotopic signatures in natural plant material from Pinus banksiana, Picea mariana, and Larix laricina, and soil from boreal forest for comparison with solutions. Results: The 17 O-excess was sensitive to the presence of organic compounds in water. 17 O-excess changed proportionally to the concentration of MeOH per volume of water, resulting in positive values, while EtOH solutions resulted in smaller changes in the 17 O-excess. Soil samples did not show any spectral contamination. Plant samples were spectrally contaminated on the narrow-band and were enriched in 1 H and 16 O compared with source water. L. laricina was the only species that did not show any evidence of spectral contamination. Xylem samples that were spectrally contaminated had positive 17 O-excess values. Conclusions: 17 O-excess can be a useful tool to identify spectral contamination and improve DVE plant and soil analysis in the laboratory and in situ. The 17 O-excess flagged the presence of MeOH and EtOH. Adding measurement of δ 17 O values to traditional measurement of δ 2 H and δ 18 O values may shed new light on plant water analysis for source mixing dynamics using DVE.