Using the CSR Theory when Selecting Woody Plants for Urban Forests: Evaluation of 342 Trees and Shrubs

Abstract Background The development of a framework for optimising plant selection, with the objective of integrating plant resilience for site and function, is crucial for urban forest managers and designers. The principal objective of this study was to evaluate the leaf economics spectrum of trees and shrubs and to categorise them using the CSR classification system, classifying plants according to 3 principal strategies (Competitors [C], Stress tolerators [S], Ruderal [R]), which represent a spectrum of plant forms and functions arising under conditions of competition, abiotic restriction to growth, or periodic disturbance, respectively. The second objective was to discuss how the CSR classification system applied plant ecological strategies to plant specification in urban environments. Methods The method for ordinating species in CSR space is based on leaf economic data including Leaf Area, Leaf Dry Matter Content, and Specific Leaf Area. Data were assembled at the Swedish University of Agricultural Sciences Campus Arboretum. Results A total of 342 taxa (170 tree and 172 shrubs) were examined in this study. The study taxa were distributed along the Competitor-Stress tolerator (CS) axis of the ternary plots. The data analysis indicated that shrubs exhibited a more expansive position in the model, displaying a greater prevalence of stress tolerators and species with a more comprehensive approach to disturbance, competition, and stress compared to trees. Conclusion The results provided an understanding and rationale for how species-specific selection for urban environments could be carried out. This was based on trait-oriented plant selection using the CSR classification, which was then adapted to different urban situations and functions. As a result, not only can quadratic equations be derived which describe the distribution of shrubs and trees of evolutionary variation, but also the uses of tree and shrub species in urban environments can be quantitatively described.