Data from: An efficient method to exploit LiDAR data in animal ecology

1. Light detection and ranging (LiDAR) technology provides ecologists with high-resolution data on three-dimensional vegetation structure. Large LiDAR datasets challenge predictive ecologists, who commonly simplify point clouds into structural attributes (namely LiDAR-based metrics such as canopy height), which are used as predictors in ecological models, potentially with loss of relevant information. 2. We illustrate an efficient alternative approach to reduce the dimensionality of LiDAR data that aims at minimal data filtering with no a priori assumptions on the ecology of the target species. We first fit the ecological model exploiting the full variability of the LiDAR point cloud, then we explain the results using post-modelling LiDAR-data classification for ecological interpretation only. This is the classical logic of explorative, hypothesis generating and predictive statistics, rather than testing specific vegetation-structural hypotheses. 3. First, we reduce the dimensionality of the LiDAR point cloud by Principal Component Analysis (PCA) to fewer predictors. Secondly, we show that LiDAR-PCs are capable to outperforming commonly used environmental predictors in ecological modelling, including LiDAR-based metrics. We exemplify this by modelling red deer (Cervus elaphus) and roe deer (Capreolus capreolus) resource selection in the Bavarian Forest National Park, Germany. After fitting the ecological model, we provide an interpretation of the information included in LiDAR-PCs, which allows users to draw conclusions whenever using them as predictors. We make use of the PCA rotation matrix and post-modelling data classification, and document deer selection for understory vegetation at unprecedented fine scale. 4. Our approach is the first attempt in animal ecology to avoid the use of LiDAR-based metrics as model predictors, but rather generate principal components able to capture most of the LiDAR point cloud variability. Our study demonstrates that LiDAR-PCs can boost ecological models. We envision a potential use of LiDAR-PCs in several applications, particularly species distribution and habitat suitability models. We demonstrate an application of our approach by building suitability maps for both deer species, which can be used by practitioners to visualize model spatial predictions and understand the type of forest structures selected by deer.

1. 激光雷达（Light detection and ranging, LiDAR）技术可为生态学家提供高分辨率的三维植被结构数据。大规模激光雷达数据集给预测生态学家带来了挑战：这类研究者通常会将点云简化为结构属性（即基于激光雷达的指标，如冠层高度）并将其作为生态模型的预测变量，但该过程可能会丢失相关信息。
2. 我们提出了一种高效的替代方案以降低激光雷达数据的维度，该方案旨在尽可能减少数据过滤，且无需对目标物种的生态学特征做出先验假设。我们首先利用激光雷达点云的全部变异性拟合生态模型，随后仅针对生态学解释，通过建模后的激光雷达数据分类来阐释模型结果。该方法遵循探索性、假设生成与预测性统计的经典逻辑，而非针对特定植被结构假设开展检验。
3. 首先，我们通过主成分分析（Principal Component Analysis, PCA）对激光雷达点云进行降维，得到更少的预测变量。其次，我们证实，在生态建模中，激光雷达主成分（LiDAR-PCs）的表现优于常用的环境预测变量，其中包括基于激光雷达的指标。我们以德国巴伐利亚森林国家公园内马鹿（Cervus elaphus）和狍（Capreolus capreolus）的资源选择建模为例，验证了这一结论。在拟合生态模型后，我们对激光雷达主成分所包含的信息进行了解释，使得使用者在将其作为预测变量时能够得出相应结论。我们借助主成分分析的旋转矩阵与建模后的数据分类方法，以空前精细的尺度记录了鹿类对林下植被的选择偏好。
4. 本研究在动物生态学领域首次尝试摒弃将基于激光雷达的指标作为模型预测变量的做法，转而生成能够捕捉激光雷达点云绝大多数变异性的主成分。研究证实，激光雷达主成分能够提升生态模型的性能。我们设想激光雷达主成分可在多种场景中得到应用，尤其是在物种分布与栖息地适宜性模型领域。我们通过为两种鹿类绘制适宜性分布图，展示了本方法的应用实例，该分布图可供从业者直观呈现模型的空间预测结果，并理解鹿类所偏好的森林结构类型。