LiDAR-derived high resolution vegetation structure and selection patterns of the common nightingale Luscinia megarhynchos in riparian habitats

Abstract

Human-induced alterations in natural water flow have seriously impaired the integrity of riverine ecosystems. Nonetheless, even in human-altered riverine and adjacent terrestrial habitats, there is considerable potential for the protection of rare species if management practices prioritize biodiversity conservation. However, the management of such areas often presents complex challenges. On the one hand, efforts to mitigate natural hazards frequently overshadow biodiversity conservation objectives. On the other hand, high-resolution maps of forest structures are often lacking but could be very useful for spatial prioritization of conservation efforts, especially as vegetation structure can be directly managed through local restoration activities. Here, we used an airborne LiDAR-derived vegetation structure along an 80 km stretch of the Rhône River (Valais, Switzerland) to assess the habitat characteristics that best explain the presence of a flagship species, the common nightingale Luscinia megarhynchos, a species that historically thrived along this river system but has experienced a drastic population decline over the past decades. Nightingales showed a preference for dense vegetation in the lower strata above ground (3–6 m), as opposed to an open and sparsely vegetated ground level (0–1 m). The preferred habitats were predominantly located within forested regions, as indicated by a preference for taller canopies. These findings align surprisingly well with prior field research on the species, demonstrating the capability of high-resolution LiDAR to upscale locally derived habitat preferences across very large areas. Based on LiDAR outputs, we proposed management recommendations for the whole river. Such spatially detailed information furthers our understanding of local habitat preferences of endangered species, thus facilitating the formulation of conservation recommendations at the scale of entire populations.