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 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 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.

中文翻译：

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评估面向对象的LiDAR预测器在森林鸟类栖息地适应性建模中的性能

栖息地适应性模型（HSM）被广泛用于规划具有保护意义的物种的行动。要转化为保护行动的模型需要既具有生态意义又符合管理者对生态系统概念观点的预测因子。诸如光检测和测距（LiDAR）之类的遥感技术可以在较大的空间区域内以高分辨率描述景观，并且已经为森林物种分布建模提供了令人鼓舞的结果。基于点云（PC）区域的LiDAR变量通常在HSM中用作环境变量，并且最近已通过面向对象（OO）度量标准进行了补充。但是，尚未比较每种变量捕获森林鸟类栖息地结构信息的效率。我们检验了两个假设：（1）在HSM中使用OO变量将提供与基于PC区域的模型类似的性能；（2）OO变量将提高针对同一区域在不同时间获取的LiDAR数据集的模型鲁棒性。以当地濒临灭绝的森林鸟类为例，capercaillie（在两个变量类型之间比较了Tetrao urogallus），模型性能和预测。使用OO变量的模型比基于PC区域的模型表现出稍低的区分性能（女性和男性的平均ΔAUC= -0.032和-0.01）。与基于PC区域的模型相比，基于OO的模型具有更高的鲁棒性（预测的Spearman等级相关性的绝对差异≤0.21）或更强大。总之，基于LiDAR的基于PC区域的度量标准和OO度量标准在模拟Capercaillie的分布方面表现出相似的性能。我们鼓励进一步探索面向对象的度量标准以创建可靠的HSM，尤其是测试它们是否可以通过更好的可解释性来帮助改善科学家与利益相关者的接口。