Application of LiDAR to assess the habitat selection of an endangered small mammal in an estuarine wetland environment

Light detection and ranging (lidar) has emerged as a valuable tool for examining the fine-scale characteristics of vegetation. However, lidar is rarely used to examine coastal wetland vegetation or the habitat selection of small mammals. Extensive anthropogenic modification has threatened the endemic species in the estuarine wetlands of the California coast, such as the endangered salt marsh harvest mouse (Reithrodontomys raviventris; SMHM). A better understanding of SMHM habitat selection could help managers better protect this species. We assessed the ability of airborne topographic lidar imagery in measuring the vegetation structure of SMHM habitats in a coastal wetland with a narrow range of vegetation heights. We also aimed to better understand the role of vegetation structure in habitat selection at different spatial scales. Habitat selection was modeled from data compiled from 15 small mammal trapping grids collected in the highly urbanized San Francisco Estuary in California, USA. Analyses were conducted at three spatial scales: microhabitat (25 m2), mesohabitat (2,025 m2), and macrohabitat (10,000 m2). A suite of structural covariates was derived from raw lidar data to examine vegetation complexity. We found that adding structural covariates to conventional habitat selection variables significantly improved our models. At the microhabitat scale in managed wetlands, SMHM preferred areas with denser and shorter vegetation, and selected for proximity to levees and taller vegetation in tidal wetlands. At the mesohabitat scale, SMHM were associated with a lower percentage of bare ground and with pickleweed (Salicornia pacifica) presence. All covariates were insignificant at the macrohabitat scale. Our results suggest that SMHM preferentially selected microhabitats with access to tidal refugia and mesohabitats with consistent food sources. Our findings showed that lidar can contribute to improving our understanding of habitat selection of wildlife in coastal wetlands and help to guide future conservation of an endangered species.

激光雷达（Light Detection and Ranging, LiDAR）已成为探究植被精细尺度特征的重要工具。然而，激光雷达极少被用于研究滨海湿地植被以及小型哺乳动物的生境选择。大规模人为改造活动已对加州海岸河口湿地的特有物种构成威胁，其中包括濒危物种盐沼收获鼠（Reithrodontomys raviventris，简称SMHM）。深入了解盐沼收获鼠的生境选择，有助于管理者更好地保护该物种。本研究评估了机载地形激光雷达影像在植被高度区间较窄的滨海湿地中，测量盐沼收获鼠生境植被结构的能力。同时，本研究旨在进一步明确不同空间尺度下，植被结构在生境选择中所发挥的作用。研究基于美国加州高度城市化的旧金山河口区域内布设的15个小型哺乳动物诱捕网格所收集的数据，对生境选择进行建模分析。分析在微生境（microhabitat，25 m²）、中生生境（mesohabitat，2025 m²）与大生境（macrohabitat，10000 m²）三个空间尺度开展。研究从原始激光雷达数据中提取了一系列结构协变量，以探究植被复杂度。研究发现，在常规生境选择变量基础上加入结构协变量，可显著提升模型性能。在人工管理湿地的微生境尺度下，盐沼收获鼠偏好植被更茂密且更低矮的区域；而在潮汐湿地中，它们则倾向于选择靠近堤岸且植被更高的区域。在中生生境尺度下，盐沼收获鼠的分布与较低的裸地占比以及盐角草（Salicornia pacifica）的存在显著相关。在大生境尺度下，所有协变量均未表现出显著相关性。研究结果表明，盐沼收获鼠优先选择具备潮汐避难所的微生境，以及拥有稳定食物来源的中生生境。本研究证实，激光雷达可助力加深我们对滨海湿地野生动物生境选择的认知，并可为濒危物种的后续保护工作提供指导。

搜集汇总

背景与挑战

背景概述

该数据集应用激光雷达（LiDAR）技术研究濒危物种盐沼收获鼠在加州河口湿地中的栖息地选择。研究在多个空间尺度分析植被结构，发现微栖息地尺度上鼠类偏好密集矮植被，中栖息地尺度与食物来源相关，结果有助于指导濒危物种保护。