Assessing the performance of object‐oriented LiDAR predictors for forest bird habitat suitability modeling | Évaluation de la performance de métriques Lidar orienté-objet pour la modélisation de l'habitat d'oiseaux forestiers

[Departement_IRSTEA]Territoires [ADD1_IRSTEA]Dynamique et fonctionnement des écosystèmes

International audience

Inglés. Habitat suitability models (HSMs) are widely used to plan actions for species of conservation interest. Models that will be turned into conservation actions need predictors that are both ecologically pertinent and fit managers' conceptual view of ecosystems. Remote sensing technologies such as light detection and ranging (LiDAR) can describe landscapes at high resolution over large spatial areas and have already given promising results for modeling forest species distributions. The point‐cloud (PC) area‐based LiDAR variables are often used as environmental variables in HSMs and have more recently been complemented by object‐oriented (OO) metrics. However, the efficiency of each type of variable to capture structural information on forest bird habitat has not yet been compared. We tested two hypotheses: (1) the use of OO variables in HSMs will give similar performance as PC area‐based models; and (2) OO variables will improve model robustness to LiDAR datasets acquired at different times for the same area. Using the case of a locally endangered forest bird, the capercaillie (Tetrao urogallus), model performance and predictions were compared between the two variable types. Models using OO variables showed slightly lower discriminatory performance than PC area‐based models (average DAUC = -0.032 and -0.01 for females and males, respectively). OO-based models were as robust (absolute difference in Spearman rank correlation of predictions ≤ 0.21) or more robust than PC area-based models. In sum, LiDAR derived PC area-based metrics and OO metrics showed similar performance for modeling the distribution of the capercaillie. We encourage the further exploration of OO metrics for creating reliable HSMs, and in particular testing whether they might help improve the scientist-stakeholder interface through better interpretability.