Optimising height‐growth predicts trait responses to water availability and other environmental drivers

Future changes in climate, together with rising atmospheric <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics> <mml:mrow> <mml:mrow><mml:msub><mml:mtext>CO</mml:mtext> <mml:mn>2</mml:mn></mml:msub> </mml:mrow> </mml:mrow> <mml:annotation><math altimg="urn:x-wiley:01407791:media:pce15042:pce15042-math-0001" wiley:location="equation/pce15042-math-0001.png" display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mrow><mrow><msub><mtext>CO</mtext><mn>2</mn></msub></mrow></mrow></math></mml:annotation></mml:semantics> </mml:math> , may reorganise the functional composition of ecosystems. Without long-term historical data, predicting how traits will respond to environmental conditions-in particular, water availability-remains a challenge. While eco-evolutionary optimality theory (EEO) can provide insight into how plants adapt to their environment, EEO approaches to date have been formulated on the assumption that plants maximise carbon gain, which omits the important role of tissue construction and size in determining growth rates and fitness. Here, we show how an expanded optimisation framework, focussed on individual growth rate, enables us to explain shifts in four key traits: leaf mass per area, sapwood area to leaf area ratio (Huber value), wood density and sapwood-specific conductivity in response to soil moisture, atmospheric aridity, <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics> <mml:mrow> <mml:mrow><mml:msub><mml:mtext>CO</mml:mtext> <mml:mn>2</mml:mn></mml:msub> </mml:mrow> </mml:mrow> <mml:annotation><math altimg="urn:x-wiley:01407791:media:pce15042:pce15042-math-0002" wiley:location="equation/pce15042-math-0002.png" display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mrow><mrow><msub><mtext>CO</mtext><mn>2</mn></msub></mrow></mrow></math></mml:annotation></mml:semantics> </mml:math> and light availability. In particular, we predict that as conditions become increasingly dry, height-growth optimising traits shift from resource-acquisitive strategies to resource-conservative strategies, consistent with empirical responses across current environmental gradients of rainfall. These findings can explain both the shift in traits and turnover of species along existing environmental gradients and changing future conditions and highlight the importance of both carbon assimilation and tissue construction in shaping the functional composition of vegetation across climates.

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Publication Details

Journal
Plant Cell & Environment
Published
2024-08-05
DOI
https://doi.org/10.1111/pce.15042
Citations
5
Primary Topic
Plant Water Relations and Carbon Dynamics
Type
article
Field-Weighted Citation Impact
1.41

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article

Optimising height‐growth predicts trait responses to water availability and other environmental drivers

Manon Sabot, Andrew O’Reilly‐Nugent, Peter A. Vesk, Isaac R. Towers et al.
5 citations
Plant Cell & Environment
Plant Water Relations and Carbon Dynamics
1.41
article

Optimising height‐growth predicts trait responses to water availability and other environmental drivers

Manon Sabot, Andrew O’Reilly‐Nugent, Peter A. Vesk, Isaac R. Towers, Daniel S. Falster
article en
5 citations

Abstract

Future changes in climate, together with rising atmospheric <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics> <mml:mrow> <mml:mrow><mml:msub><mml:mtext>CO</mml:mtext> <mml:mn>2</mml:mn></mml:msub> </mml:mrow> </mml:mrow> <mml:annotation><math altimg="urn:x-wiley:01407791:media:pce15042:pce15042-math-0001" wiley:location="equation/pce15042-math-0001.png" display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mrow><mrow><msub><mtext>CO</mtext><mn>2</mn></msub></mrow></mrow></math></mml:annotation></mml:semantics> </mml:math> , may reorganise the functional composition of ecosystems. Without long-term historical data, predicting how traits will respond to environmental conditions-in particular, water availability-remains a challenge. While eco-evolutionary optimality theory (EEO) can provide insight into how plants adapt to their environment, EEO approaches to date have been formulated on the assumption that plants maximise carbon gain, which omits the important role of tissue construction and size in determining growth rates and fitness. Here, we show how an expanded optimisation framework, focussed on individual growth rate, enables us to explain shifts in four key traits: leaf mass per area, sapwood area to leaf area ratio (Huber value), wood density and sapwood-specific conductivity in response to soil moisture, atmospheric aridity, <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics> <mml:mrow> <mml:mrow><mml:msub><mml:mtext>CO</mml:mtext> <mml:mn>2</mml:mn></mml:msub> </mml:mrow> </mml:mrow> <mml:annotation><math altimg="urn:x-wiley:01407791:media:pce15042:pce15042-math-0002" wiley:location="equation/pce15042-math-0002.png" display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mrow><mrow><msub><mtext>CO</mtext><mn>2</mn></msub></mrow></mrow></math></mml:annotation></mml:semantics> </mml:math> and light availability. In particular, we predict that as conditions become increasingly dry, height-growth optimising traits shift from resource-acquisitive strategies to resource-conservative strategies, consistent with empirical responses across current environmental gradients of rainfall. These findings can explain both the shift in traits and turnover of species along existing environmental gradients and changing future conditions and highlight the importance of both carbon assimilation and tissue construction in shaping the functional composition of vegetation across climates.

Plant Cell & EnvironmentVol. 47(12)
The University of Melbourne (AU), UNSW Sydney (AU), ARC Centre of Excellence for Climate System Science (AU), Max Planck Institute for Biogeochemistry (DE), Ecosystem Sciences (AU)
University of New South Wales, Climate Extremes, Australian Research Council
Openalex Percentile: Top 20%
Plant Water Relations and Carbon Dynamics
1.41
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