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  • The Ecosystem as Super-organ/Ism, Revisited: Scaling Hydraulics to Forests under Climate Change. Integrative and comparative biology Wood, J. D., Detto, M., Browne, M., Kraft, N. J., Konings, A. G., Fisher, J. B., Quetin, G. R., Trugman, A. T., Magney, T. S., Medeiros, C. D., Vinod, N., Buckley, T. N., Sack, L. 2024


    Classic debates in community ecology focused on the complexities of considering an ecosystem as a super-organ or organism. New consideration of such perspectives could clarify mechanisms underlying the dynamics of forest carbon dioxide (CO2) uptake and water vapor loss, important for predicting and managing the future of Earth's ecosystems and climate system. Here, we provide a rubric for considering ecosystem traits as aggregated, systemic, or emergent, i.e., representing the ecosystem as an aggregate of its individuals, or as a metaphorical or literal super-organ or organism. We review recent approaches to scaling-up plant water relations (hydraulics) concepts developed for organs and organisms to enable and interpret measurements at ecosystem-level. We focus on three community scale versions of water relations traits that have potential to provide mechanistic insight into climate change responses of CO2 and H2O gas exchange and forest productivity: leaf water potential (╬Ęcanopy), pressure volume curves (eco-PV), and hydraulic conductance (Keco). These analyses can reveal additional ecosystem-scale parameters analogous to those typically quantified for leaves or plants (e.g., wilting point and hydraulic vulnerability) that may act as thresholds in forest responses to drought including growth cessation, mortality and flammability. We unite these concepts in a novel framework to predict ╬Ęcanopy and its approaching of critical thresholds during drought, using measurements of Keco and eco-PV curves. We thus delineate how extension of water relations concepts from organ- and organism-scales can reveal the hydraulic constraints on the interaction of vegetation and climate, and provide new mechanistic understanding and prediction of forest water use and productivity.

    View details for DOI 10.1093/icb/icae073

    View details for PubMedID 38886119