Advances in remote sensing technologies are today making it essential for ecological modeling improvement. One of the most promising technique is Light Detection and Ranging (LiDAR) which provides accurate and highly precise data of the three dimensional structure of the environment. First, LiDAR was mostly used to better characterize forest stand (estimation of stem density, basal area) and was mainly used by forestry industries. However, nowadays LiDAR is increasingly used to improve habitat modeling for a wide variety of species including birds (Kathleen M. Bergen, Gilboy, and Brown 2007; K. M. Bergen et al. 2009). Especially it could be used to improve Species Distribution Models (SDMs) accuracy (He et al. 2015; Tattoni, Rizzolli, and Pedrini 2012). To obtain a good predicting model, it is acknowledged that we should use metrics that are meaningful from the species point of view and will accordingly explain best their distribution within the landscape (Johnson and Gillingham 2005). However, is obtaining a model that predicts well the habitat suitability of a species enough to impact deeply local conservation actions? In order to take appropriate and more efficient management decisions, we believe that the metrics explaining the species distribution must also be meaningful by for managers. Indeed, if some LiDAR metrics such as canopy cover (Graf et al. 2007), can be well understood , most of LiDAR extracted metrics used so far in SDMs such as the standard-deviation of penetration ratio between 0.5-10m (Bae et al. 2014) or the proportion of echo above five meters (Melin et al. 2016) are not easy to interpret once on the field. Therefore, the aim of this study is to improve forest management actions planning by using appropriate LiDAR predictors for both the species and managers. We are here focusing on the case of an avian species of conservation concern occurring in the French Jura Mountains: the Capercaillie (Tetrao urogallus). Capercaillies favor old mixed forest constituted of a mosaic of structurally different habitat (gap openings, moderate canopy cover area, isolated resting trees, presence of shelters) and is threatened mostly by habitat degradation and loss. Habitat restoration planning is then a fundamental point of the species conservation actions. To achieve our objectives we extracted numerous oriented-object metrics from LiDAR datasets, defined with the help of expert and forest managers. Habitat suitability models using maximum entropy methods (Phillips, Anderson, and Schapire 2006) will be compared using both commonly used “points clouds LiDAR metrics” and new “object-oriented LiDAR metrics”. Preliminary results show that both categories of metrics give quietly accurate predicting models of Capercaillie habitat suitability. Thus, we hope that the use of object-oriented variable over a large area will allow a wide diffusion of the different results and help future forest management planning in favor of Capercaillie conservation.