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Abstract Seeds provide the basis of genetic diversity in perennial grassland communities and their traits may influence ecosystem resistance to extreme drought. However, we know little about how drought effects the community functional composition of seed traits and the corresponding implications for ecosystem resistance to drought.We experimentally removed 66% of growing season precipitation for 4 years across five arid and semi‐arid grasslands in northern China and assessed how this multi‐year drought impacted community‐weighted means (CWMs) of seed traits, seed trait functional diversity and above‐ground net primary productivity (ANPP).Experimental drought had limited effects on CWM traits and the few effects that did occur varied by site and year. For three separate sites, and in different years, drought reduced seed length and phosphorus content but increased both seed and seed‐coat thickness. Additionally, drought led to increased seed functional evenness, divergence, dispersion and richness, but only in some sites, and mostly in later years following cumulative effects of water limitation. However, we observed a strong negative relationship between drought‐induced reductions in ANPP and CWMs of seed‐coat thickness, indicating that a high abundance of dominant species with thick seeds may increase ecosystem resistance to drought. Seed trait functional diversity was not significantly predictive of ANPP, providing little evidence for a diversity effect.Our results suggest that monitoring community composition with a focus on seed traits may provide a valuable indicator of ecosystem resistance to future droughts despite inconsistent responses of seed trait composition overall. This highlights the importance of developing a comprehensive seed and reproductive traits database for arid and semi‐arid grassland biomes. Read the freePlain Language Summaryfor this article on the Journal blog.
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Diagnosing evapotranspiration responses to water deficit across biomes using deep learning
Summary Accounting for water limitation is key to determining vegetation sensitivity to drought. Quantifying water limitation effects on evapotranspiration (ET) is challenged by the heterogeneity of vegetation types, climate zones and vertically along the rooting zone.Here, we train deep neural networks using flux measurements to study ET responses to progressing drought conditions. We determine a water stress factor (fET) that isolates ET reductions from effects of atmospheric aridity and other covarying drivers. We regress fET against the cumulative water deficit, which reveals the control of whole‐column moisture availability.We find a variety of ET responses to water stress. Responses range from rapid declines of fET to 10% of its water‐unlimited rate at several savannah and grassland sites, to mild fET reductions in most forests, despite substantial water deficits. Most sensitive responses are found at the most arid and warm sites.A combination of regulation of stomatal and hydraulic conductance and access to belowground water reservoirs, whether in groundwater or deep soil moisture, could explain the different behaviors observed across sites. This variety of responses is not captured by a standard land surface model, likely reflecting simplifications in its representation of belowground water storage.
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- Award ID(s):
- 1942133
- PAR ID:
- 10446812
- Publisher / Repository:
- Wiley-Blackwell
- Date Published:
- Journal Name:
- New Phytologist
- Volume:
- 240
- Issue:
- 3
- ISSN:
- 0028-646X
- Format(s):
- Medium: X Size: p. 968-983
- Size(s):
- p. 968-983
- Sponsoring Org:
- National Science Foundation
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