Résumé
Summary 1. Plant interactions play a central role in regulating plant communities and this role can be altered by abiotic stress. With increasing stress, ecological theory predicts that the role of competition decreases whilst that of facilitation increases. Such predictions have been tested with short-term plant removal experiments using two distinct indices evaluating the role of plant interactions: the intensity (absolute impact) and the importance (impact relative to that of other abiotic constraints) of plant interactions. 2. Using data on individual tree radial growth from more than 17 000 forest plots covering the habitat conditions of 16 species in the Alps and the Jura mountains of France, we show that non-manipulative estimates of plant interactions provide an alternative to this experimental approach. We developed a Bayesian neighbourhood growth competition model to test theoretical predictions about plant–plant interactions with a much larger spatio-temporal scope and set of study species than classically used in experimental studies of plant–plant interactions. 3. Our analyses revealed that competition – measured as neighbours effects on adult tree growth – varies in importance but not in intensity along two major bioclimatic gradients (degree-day sum and water availability). Observed patterns of competition importance differed between shade-tolerant and shade-intolerant tree species. First, the mean importance of competition was found to be much higher for shade-intolerant species. Second, for shade-intolerant species the importance of competition remained high even at low crowding indices (i.e. at a low competitor density), whereas for shade-tolerant species competition only became important at high crowding indices. 4. Synthesis. Our non-manipulative approach to the study of plant–plant interactions allows analysing interactions among many species over large climatic gradients. Our results clearly demonstrate that a quantitative estimation of density dependence effects is key to understanding how plant–plant interactions vary along abiotic gradients. Growth predictions derived from our model can easily be integrated with other results on tree regeneration and mortality in individual-based models to investigate how plant–plant interactions drive tree population and community dynamics under varying climatic conditions.