Usually measured traits are poor predictors of individual tree growth

Teresa Rosas, Maurizio Mencuccini, Carles Batlles, Íngrid Regalado, Sandra Saura-Mas, Frank Sterck and Jordi Martínez-Vilalta

Sampling the canopy of one of the study beech (Fagus sylvatica) forests. Credit: Carles Batlles.
Sampling the canopy of one of the study beech (Fagus sylvatica) forests. Credit: Carles Batlles.

Ecology is more and more based on the notion that data on individual traits, increasingly available for large numbers of species, can be used to predict organismal responses to changes in the environment. This notion, however, has seldom been tested on mature trees in the field. In this study, we assess whether commonly used traits, describing the functioning of leaves and the water transport capacity of wood, can be used to predict tree growth rates along a regional water availability gradient, both within and among species. We carried out our study in a Mediterranean area (Catalonia, NE Spain), using data from six of the most common trees in the region: Scots pine (Pinus sylvestris), black pine (Pinus nigra), Aleppo pine (Pinus halepensis), beech (Fagus sylvatica), downy oak (Quercus humilis) and holm oak (Quercus ilex). Our results show that individual traits, including some of the most widely used functional traits, are poor predictors of tree growth. Even when trait-growth relationships exist, they do not necessarily conform to simple hypotheses based on our understanding of organ-level processes and how they affect growth. This is because traits covary with each other in complex ways (e.g., in our case more acquisitive leaves were associated with lower capacity of the wood to withstand drought, which is a key stress factor in the study area) and frequently show compensatory responses to changes in the environment. Overall, our study highlights the importance of accounting for trait coordination (i.e., how traits covary) to understand the variation of whole-tree performance along environmental gradients and, ultimately, to predict changes in forest demography in response to changing environmental conditions.

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