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

doi: 10.1111/pce.15042
pmid: 39101679
AbstractFuture changes in climate, together with rising atmospheric , 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, 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.
- Max Planck Institute of Neurobiology Germany
- Max Planck Society Germany
- UNSW Sydney Australia
- University of Melbourne Australia
Soil, Climate Change, Water, Environment, Ecosystem
Soil, Climate Change, Water, Environment, Ecosystem
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).1 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Average influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Average impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Average
