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Summary Vegetative traits of plants can respond directly to changes in the environment, such as those occurring under climate change. That phenotypic plasticity could be adaptive, maladaptive, or neutral.We manipulated the timing of spring snowmelt and amount of summer precipitation in factorial combination and examined responses of specific leaf area (SLA), trichome density, leaf water content (LWC), photosynthetic rate, stomatal conductance and intrinsic water‐use efficiency (iWUE) in the subalpine herbIpomopsis aggregata. The experiment was repeated in three years differing in natural timing of snowmelt. To examine natural selection, we used survival, relative growth rate, and flowering as fitness indices.A 50% reduction in summer precipitation reduced stomatal conductance and increased iWUE, and doubled precipitation increased LWC. Combining natural and experimental variation, earlier snowmelt reduced soil moisture, photosynthetic rate and stomatal conductance, and increased trichome density and iWUE. Precipitation reduction reversed the mortality selection favoring high stomatal conductance under normal and doubled precipitation, and higher LWC improved growth.Earlier snowmelt is a strong signal of climate change and can change expression of leaf morphology and gas exchange traits, just as reduced precipitation can. Stomatal conductance and SLA showed adaptive plasticity under some conditions.
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Temporal constraints on leaf-level trait plasticity for next-generation land surface models
Abstract Background and AimsDynamic global vegetation models (DGVMs) are essential for quantifying the role of terrestrial ecosystems in the Earth’s climate system, but struggle with uncertainty and complexity. Eco-evolutionary optimality (EEO) theory provides a promising approach to improve DGVMs based on the premise that leaf carbon gain is optimized with resource costs. However, the timescales at which plant traits can adjust to environmental changes have not yet been systematically incorporated in EEO-based models. Our aims were to identify temporal constraints on key leaf photosynthetic and leaf functional traits, and develop a conceptual framework for incorporation of temporal leaf trait dynamics in EEO-based models. MethodsWe reviewed the scientific literature on temporal responses of leaf traits associated with stomata and hydraulics, photosynthetic biochemistry, and morphology and lifespan. Subsequent response times were categorized from fast to slow considering physiological, phenotypic (acclimation) and evolutionary (adaptation) mechanisms. We constructed a conceptual framework including several key leaf traits identified from the literature review. We considered temporal separation of dynamics in the leaf interior to atmospheric CO2 concentration (ci:ca) from the optimal ci:ca ratio [χ(optimal)] and dynamics in stomatal conductance within the constraint of the anatomical maximum stomatal conductance (gsmax). A proof-of-concept was provided by modelling temporally separated responses in these trait combinations to CO2 and humidity. Key ResultsWe identified 17 leaf traits crucial for EEO-based modelling and determined their response mechanisms and timescales. Physiological and phenotypic response mechanisms were considered most relevant for modelling EEO-based trait dynamics, while evolutionary constraints limit response ranges. Our conceptual framework demonstrated an approach to separate near-instantaneous physiological responses in ci:ca from week-scale phenotypic responses in χ(optimal), and to separate minute-scale physiological responses in stomatal conductance from annual-scale phenotypic responses in gsmax. ConclusionsWe highlight an opportunity to constrain leaf trait dynamics in EEO-based models based on physiological, phenotypic and evolutionary response mechanisms.
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- Award ID(s):
- 2045968
- PAR ID:
- 10636729
- Publisher / Repository:
- Oxford University Press
- Date Published:
- Journal Name:
- Annals of Botany
- Volume:
- 136
- Issue:
- 2
- ISSN:
- 0305-7364
- Format(s):
- Medium: X Size: p. 263-274
- Size(s):
- p. 263-274
- Sponsoring Org:
- National Science Foundation
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