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For the study of long-term processes in forests, gap models generally sacrifice accuracy (i.e., simulating system behavior in a quantitatively accurate manner) for generality (i.e., representing a broad range of systems' behaviors with the same model). We selected the gap model ForClim to evaluate whether the local accuracy of forest succession models can be increased based on a parsimonious modeling approach that avoids the additional complexity of a 3D crown model, thus keeping parameter requirements low. We improved the representation of tree crowns by introducing feedbacks between (i) light availability and leaf area per tree and (ii) leaf area per tree and diameter growth rate to account for the self-pruning in real stands. The local accuracy of the new model, ForClim v2.9.5, was considerably improved in simulations at three long-term forest research sites in the Swiss Alps, while its generality was maintained as shown in simulations of potential natural vegetation along a broad environmental gradient in Central Europe. We conclude that the predictive ability of a model does not depend on its complexity, but on the reproduction of patterns. Most importantly, model complexity should be consistent with the objectives of the study and the level of system understanding. Pour l'etude des processus a long terme en foret, les modeles par trouee sacrifient generalement la precision (c.-a-d. la simulation du comportement du systeme d'une facon quantitativement precise) pour la generalite (c.-a-d. la representation d'un large spectre de comportements des systemes avec le meme modele). Nous avons choisi le modele par trouee ForClim pour evaluer si la precision locale des modeles de succession forestiere peut etre amelioree en utilisant une approche de modelisation parcimonieuse qui evite la complexite additionnelle de la modelisation des cimes en trois dimensions, ce qui minimise les besoins en parametres. Pour tenir compte de l'elagage naturel des peuplements sur pied, nous avons ameliore la representation de la cime des arbres en introduisant des boucles de retroaction entre (i) la disponibilite de la lumiere et la surface foliaire par arbre et (ii) la surface foliaire par arbre et le taux de croissance en diametre. La precision locale du nouveau modele, ForClim v2.9.5, a ete significativement amelioree dans le cas de simulations portant sur trois stations de recherche forestiere a long terme des Alpes suisses, alors que son caractere generale a ete maintenu comme le demontrent les simulations de la vegetation naturelle potentielle le long d'un vaste gradient environnemental en Europe centrale. Nous concluons que la capacite de prediction d'un modele ne depend pas de sa complexite, mais de la reproduction des patrons. Avant tout, la complexite d'un modele devrait etre compatible avec les objectifs de (etude et le niveau de comprehension du systeme. [Traduit par la Redaction], 1. Introduction Simulation models are commonly used to improve our understanding of the processes and patterns that drive forest dynamics (e.g., Shugart 1998; Didion et al. 2007; Wehrli et al. [...]