Above‐ and below‐ground trait coordination across 90 angiosperm and gymnosperm tree species in a common garden experiment

作者：Anvar Sanaei, Karl Andraczek, Lena Kretz, Florian Schnabel, Ronny Richter, Anja Kahl, Nicole Nabel, Lea von Sivers, Tom Künne, Julia Leonore van Braak, Ronja Felicitas Hofmann, C. Hensel, Karin Mora, Hannes Feilhauer, Christian Wirth, Alexandra Weigelt · 发表于：Journal of Ecology · 年份：2025 · DOI：10.1111/1365-2745.70220 · 被引用次数：5 · 研究领域：Ecology and Vegetation Dynamics Studies、Plant Water Relations and Carbon Dynamics、Plant and animal studies

Abstract Quantifying the variation in plant traits reveals the trade‐offs involved in plant ecological strategies, which is fundamental to understanding underlying plant fitness mechanisms. Thus, the ecological success of plant species in a certain habitat may depend on the coordinated performance of both leaves and roots. However, despite the growing interest in trait variation, it is still uncertain (i) to what extent the leaf economics spectrum (LES) and root economics space (RES) hold across locally planted tree species and (ii) whether analogous leaf and fine‐root traits are correlated. In a research arboretum, we simultaneously measured key traits in leaves (leaf nitrogen concentration and leaf mass per area) and fine roots (root diameter, specific root length, root nitrogen concentration and root tissue density) on 267 individuals belonging to 90 tree species, encompassing both angiosperm and gymnosperm species. We find varied plant resource strategies associated with leaves and fine roots for angiosperms and gymnosperms. Gymnosperms were characterized by higher leaf mass per area and root diameter but lower nitrogen tissue in leaves and fine roots, while the opposite was observed in angiosperms. Our results showed an expected trade‐off between the leaf traits underpinning the LES. We observe a clear RES for all species and angiosperms, highlighting two orthogonal conservation and collaboration gradients, but the RES was unclear for gymnosperms. Our multidimensional tr...