Assessing the performance of object‐oriented LiDARpredictors for forest bird habitat suitability modeling

Abstract

AbstractHabitat 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 LiDARvariables are often used as environmental variables inHSMs 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 ofOOvariables inHSMs will give similar performance asPCarea‐based models; and (2)OOvariables will improve model robustness to LiDARdatasets 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 usingOOvariables showed slightly lower discriminatory performance thanPCarea‐based models (average ΔAUC = −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 thanPCarea‐based models. In sum, LiDAR‐derivedPCarea‐based metrics andOOmetrics showed similar performance for modeling the distribution of the capercaillie. We encourage the further exploration ofOOmetrics for creating reliableHSMs, and in particular testing whether they might help improve the scientist–stakeholder interface through better interpretability.