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Generally, the effects of climate change on tree growth focus on changes in one dimension of a tree. However, diameter increment along the main stem reacts differently to climatic variables, which in turn influences tree form. These differences can thus have important implications on stem volume, which could induce biases in future forest biomass estimation. A stem taper model including climatic variables was fitted to stem analysis data of five different species (Abies balsamea, Betula papyrifera, Picea glauca, Picea mariana, Populus tremuloides) distributed along a gradient from the temperate to the boreal forest of Eastern Canada. The effects of shifts in stem form on tree volume between different climatic scenarios were then estimated and related to different functional traits. Changes in stem form with climatic variables were observed for four of the five species, with up to 5% differences in stem volume between different climatic situations. Changes in stem volume were found to decrease with increasing waterlogging and shade tolerance. The most important differences in stem volume can induce changes of 3–4% in the biomass of a single tree. Not taking into account shifts in stem form could have implications in forest biomass estimations.

In forestry, the variable of interest is not always directly available from forest inventories. Consequently, practitioners have to rely on models to obtain predictions of this variable of interest. This context leads to hybrid inference, which is based on both the probability design and the model. Unfortunately, the current analytical hybrid estimators for the variance of the point estimator are mainly based on linear or nonlinear models and their use is limited when the model reaches a high level of complexity. An alternative consists of using a variance estimator based on resampling methods (Rubin, D. B. (1987). Multiple imputation for nonresponse surveys. John Wiley & Sons, Hoboken, New Jersey, USA). However, it turns out that a parametric bootstrap (BS) estimator of the variance can be biased in contexts of hybrid inference. In this study, we designed and tested a corrected BS estimator for the variance of the point estimator, which can easily be implemented as long as all of the stochastic components of the model can be properly simulated. Like previous estimators, this corrected variance estimator also makes it possible to distinguish the contribution of the sampling and the model to the variance of the point estimator. The results of three simulation studies of increasing complexity showed no evidence of bias for this corrected variance estimator, which clearly outperformed the BS variance estimator used in previous studies. Since the implementation of this corrected variance estimator is not much more complicated, we recommend its use in contexts of hybrid inference based on complex models. -- Keywords : Estimator bias; Variance estimator; Estimate of the total; Horvitz-Thompson estimator; Log grade; Lumber recovery model.