Inherent variation of functional traits in winter and summer leaves of Mediterranean seasonal dimorphic species: evidence of a ‘within leaf cohort’ spectrum

The covariation pattern among leaf functional traits involved in resource acquisition has been successfully provided by the leaf economic spectrum (LES). Nevertheless, some aspects such as how the leaf trait variation sources affect LES predictions are still little investigated. Accordingly, the aim of this paper was to test whether leaf trait variations within different leaf cohorts could alter LES. Improving this knowledge can extend the potential of trait-based approaches in simulating future climate effects on ecosystems. A database on leaf morphological and physiological traits from different leaf cohorts of Cistus spp. was built by collecting data from literature. These species are seasonal dimorphic shrubs with two well-defined leaf cohorts during a year: summer leaves (SL) and winter leaves (WL). Traits included: leaf mass area (LMA), leaf thickness (LT), leaf tissue density (LTD), net photosynthetic rate on area (A(a)) and mass (A(m)) base, nitrogen content on area (N(a)) and mass (N(m)) base. The obtained patterns were analysed by standardized major axis regression and then compared with the global spectrum of evergreens and deciduous species. Climatic variable effect on leaf traits was also tested. Winter leaves and SL showed a great inherent variability for all the considered traits. Nevertheless, some relationships differed in terms of slopes or intercepts between SL and WL and between leaf cohorts and the global spectrum of evergreens and deciduous. Moreover, climatic variables differently affected leaf traits in SL and WL. The results show the existence of a ‘within leaf cohort’ spectrum, providing the first evidence on the role of leaf cohorts as LES source of variation. In fact, WL showed a high return strategy as they tended to maximize, in a short time, resource acquisition with a lower dry mass investment, while SL were characterized by a low return strategy.