Using big data to improve ecotype matching for Magnolias in urban forestry

Trees play major roles in many aspects of urban life, supporting ecosystems, regulating temperature and soil hydrology, and even affecting human health. At the scale of the urban forest, the qualities of these individual trees become powerful tools for mitigating the effects of, and adapting to climate change and for this reason attempts to select the right tree for the right place has been a long-term research field. To date, most urban forestry practitioners rely upon specialist horticultural texts (the heuristic literature) to inform species selection whilst the majority of research is grounded in trait-based investigations into plant physiology (the experimental literature). However, both of these literature types have shortcomings: the experimental literature only addresses a small proportion of the plants that practitioners might be interested in whilst the data in the heuristic (obtained through practice) literature tends to be either too general or inconsistent. To overcome these problems we used big datasets of species distribution and climate (which we term the observational literature) in a case study genus to examine the climatic niches that species occupy in their natural range. We found that contrary to reports in the heuristic literature, Magnolia species vary significantly in their climatic adaptations, occupying specific niches that are constrained by trade-offs between water availability and energy. The results show that not only is ecotype matching between naturally-distributed populations and urban environments possible but that it may be more powerful and faster than traditional research. We anticipate that our findings could be used to rapidly screen the world’s woody flora and rapidly communicate evidence to nurseries and plant specifiers. Furthermore this research improves the potential for urban forests to contribute to global environmental challenges such as species migration and ex-situ conservation.