Centre for Ecology and Conservation, University of Exeter, Penryn, TR10 9FE, UK.
School of Life Sciences, University of Sussex, Brighton, BN1 9QG, UK.
Oecologia. 2023 Aug;202(4):641-653. doi: 10.1007/s00442-023-05429-3. Epub 2023 Aug 6.
The hazel dormouse is predominantly an arboreal species that moves down to the ground to hibernate in the autumn in temperate parts of its distributional ranges at locations not yet well understood. The main objective of this study is to test whether environmental characteristics surrounding hazel dormouse hibernacula can be identified using high-resolution remote sensing and data collected in situ. To achieve this, remotely sensed variables, including canopy height and cover, topographic slope, sky view, solar radiation and cold air drainage, were modelled around 83 dormouse hibernacula in England (n = 62) and the Netherlands (n = 21), and environmental characteristics that may be favoured by pre-hibernating dormice were identified. Data on leaf litter depth, temperature, canopy cover and distance to the nearest tree were collected in situ and analysed at hibernaculum locations in England. The findings indicated that remotely sensed data were effective in identifying attributes surrounding the locations of dormouse hibernacula and when compared to in situ information, provided more conclusive results. This study suggests that remotely sensed topographic slope, canopy height and sky view have an influence on hazel dormice choosing suitable locations to hibernate; whilst in situ data suggested that average daily mean temperature at the hibernaculum may also have an effect. Remote sensing proved capable of identifying localised environmental characteristics in the wider landscape that may be important for hibernating dormice. This study proposes that this method can provide a novel progression from habitat modelling to conservation management for the hazel dormouse, as well as other species using habitats where topography and vegetation structure influence fine-resolution favourability.
榛睡鼠主要是树栖物种，在其分布范围内的温带地区，到了秋天，为了冬眠，它们会下到地面。在这些地区，人们对榛睡鼠冬眠洞穴的位置还不太了解。本研究的主要目的是测试是否可以使用高分辨率遥感和现场收集的数据来识别榛睡鼠冬眠洞穴周围的环境特征。为了实现这一目标，对包括冠层高度和覆盖、地形坡度、天空视野、太阳辐射和冷空气排水在内的遥感变量进行了建模，这些变量围绕着英格兰（n=62）和荷兰（n=21）的 83 个榛睡鼠冬眠洞穴进行建模，并确定了可能有利于预冬眠榛睡鼠的环境特征。在英格兰的冬眠洞穴地点现场收集了落叶层深度、温度、冠层覆盖和与最近树木的距离等数据，并进行了分析。研究结果表明，遥感数据能够有效地识别榛睡鼠冬眠洞穴位置周围的属性，与现场信息相比，提供了更具结论性的结果。本研究表明，遥感地形坡度、冠层高度和天空视野对榛睡鼠选择合适的冬眠地点有影响；而现场数据表明，冬眠洞穴的平均日平均温度也可能有影响。遥感证明能够识别更广泛景观中的局部环境特征，这些特征可能对冬眠的榛睡鼠很重要。本研究提出，这种方法可以为榛睡鼠以及其他利用地形和植被结构影响精细分辨率有利性的物种的栖息地建模向保护管理提供新的进展。
