Abstract

Summary We compared the multivariate patterns of tree ring chronologies with those of floristic composition (and of its associated Ellenberg indicator values – EIVs) across the whole elevation gradient of Fagus sylvatica forests in Central Italy (from 300 to 1900 m a.s.l.). Both data sets were also compared with bioclimatic parameters obtained from reconstructed, site‐representative meteorological data. Procrustes analysis showed that the patterns of tree ring chronologies and floristic assemblages were significantly correlated. The two main bio‐climatic belts obtained from tree ring analysis showed significantly different floristic composition and significantly different distribution of EIVs. Constrained ordination with meteorological data as covariates showed that in both data sets, most of the variation was explained by mean summer temperature, while precipitation parameters had only minor explanatory power. Ellenberg indicator values for temperature showed a strong correlation with mean summer temperature obtained from the meteorological data. EIVs for continentality were weakly correlated with annual temperature range. No correlation emerged between the EIVs for moisture and the precipitation parameters. Constrained ordination of the tree ring data set with the floras' mean EIVs as covariates found that the most explanatory variables were the EIVs for temperature followed by the EIVs for light. This latter finding is probably a consequence of temperature control (via summer drought) on canopy Leaf Area Index. Synthesis. Ecological classification of beech forest stands through either tree ring chronologies or floristic composition yields very similar results. Both bio‐indication methods point to the predominant role of growing‐season temperatures in controlling patterns and processes of forest ecosystems across wide elevation gradients. Thus, similarities in tree ring chronologies of the past between sites could be used to infer analogies in past floristic assemblages. Moreover, temperature indicator values obtained from floras show excellent accordance with meteorological data, allowing reliable usage of diachronic floristic data for climate change monitoring at detailed spatial scale.

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