Search NASA⌕ Search

DOE OSTI · 1784475

Warming induces divergent stomatal dynamics in co-occurring boreal trees

Abstract

Abstract Climate warming will alter photosynthesis and respiration not only via direct temperature effects on leaf biochemistry but also by increasing atmospheric dryness, thereby reducing stomatal conductance and suppressing photosynthesis. Our knowledge on how climate warming affects these processes is mainly derived from seedlings grown under highly controlled conditions. However, little is known regarding temperature responses of trees growing under field settings. We exposed mature tamarack and black spruce trees growing in a peatland ecosystem to whole-ecosystem warming of up to +9°C above ambient air temperatures in an ongoing long-term experiment (SPRUCE: Spruce and Peatland Responses Under Changing Environments). Here, we report the responses of leaf gas exchange after the first two years of warming. We show that the two species exhibit divergent stomatal responses to warming and vapor pressure deficit. Warming of up to 9°C increased leaf N in both spruce and tamarack. However, higher leaf N in the warmer plots translate into higher photosynthesis in tamarack but not in spruce, with photosynthesis being more constrained by stomatal limitations in spruce than in tamarack under warm conditions. Surprisingly, dark respiration did not acclimate to warming in spruce, and thermal acclimation of respiration was only seen in tamarack once changes in leaf N were considered. Our results highlight how warming can lead to differing stomatal responses to warming in co-occurring species, with consequent effects on both vegetation carbon and water dynamics.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Dusenge, Mirindi E., Ward, Eric J., Warren, Jeffrey M., Stinziano, Joseph R., Wullschleger, Stan D., Hanson, Paul J., Way, Danielle A.. 2021-04-23. Warming induces divergent stomatal dynamics in co-occurring boreal trees. https://doi.org/10.1111/gcb.15620

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related reports

Water4Energy Step-1 Band-M Ready-to-Train Samples for TVA Weeks-to-Years Prediction, Version 0

AI-ready Band-M (monthly) labelled training pack for the Water4Energy Genesis Task-1 project on weeks-to-years prediction of Tennessee Valley temperature and precipitation. The deposit includes leakage-aware issue-time samples (samples_M_v0.nc; N=486), train-only scalers, issue-time split table, supporting monthly panels, and Python generation scripts to recreate the pack from the companion Tier-1 raw observation collection (https://doi.org/10.13139/ORNLNCCS/3398576). Each sample pairs a 12-month lookback of teleconnection indices and SST box anomalies with TVA-mean ERA5 anomaly targets (t2m, tp, msl) at leads 1–3 months.

54 ENVIRONMENTAL SCIENCES↗

Multi-Angle Snowflake Camera, particle analysis

The c1 level data product for the Mutli-Angle Snowflake Camera contains snowflake fall speeds and particle size, among other analysis for images associated with each hydrometeor.

54 ENVIRONMENTAL SCIENCES↗

Multi-Angle Snowflake Camera, time bins

The c1 level data product for the Mutli-Angle Snowflake Camera contains snowflake fall speeds and particle size, among other analysis for images associated with each hydrometeor.

54 ENVIRONMENTAL SCIENCES↗