LiDAR metrics predict suitable forest foraging areas of endangered Mouse-eared bats (Myotis myotis)

Habitat shift caused by human impact on vegetation structure poses a great threat to species which are special- ized on unique habitats. Single layered beech forests, the main foraging habitat of Greater Mouse-eared Bats (My- otis myotis), are threatened by recent changes in forest structure. After this species suffered considerable popula- tion losses until the 1970s, their roosts in buildings are strictly protected. However, some populations are still de- clining. Thus, the spatial identification of suitable foraging habitat would be essential to ensure conservation pol- icy. The aim of this study was (a) to verify the relevance of forest structural variables for the activity of M. myotis and (b) to evaluate the potential of LiDAR (Light Detection and Ranging) in predicting suitable foraging habitat of the species. We systematically sampled bat activity in forests close to 18 maternity roosts in Switzerland and applied a generalized linear mixed model (GLMM) to fit the activity data to forest structure variables recorded in the field and derived from LiDAR. We found that suitable forest foraging habitat is defined by single layered for- est, dense canopy, no shrub layer and a free flight space. Most importantly, this key foraging habitat can be well predicted by airborne LiDAR data. This allows for the first time to create nationwide prediction maps of potential foraging habitats of this species to inform conservation management. This method has a special significance for endangered species with large spatial use, whose key resources are hard to identify and widely distributed across the landscape.

Funding Information:

This work was supported by:
  • BAFU (link) (Grant/Award: 16.0100.PJ / R423-1 858)

Related Datasets

activity data, environment data, explanatory file, R code

Related Publications

Katja Rauchenstein, Klaus Ecker, Elias Bader, Christian Ginzler, Christoph Düggelin, Fabio Bontadina, Martin K. Obrist (2022) LiDAR metrics predict suitable forest foraging areas of endangered Mouse- eared bats (Myotis myotis). Forest Ecology and Management.


Rauchenstein, Katja; Ecker, Klaus; Bader, Elias; Ginzler, Christian; Düggelin, Christoph; Bontadina, Fabio; Obrist, Martin K. (2022). LiDAR metrics predict suitable forest foraging areas of endangered Mouse-eared bats (Myotis myotis). EnviDat. doi:10.16904/envidat.306.

DataCite ISO 19139 GCMD DIF README.txt BibTex RIS

Data and Resources


Field Values
DOI 10.16904/envidat.306
Publication State Published
  • Email: katja.rauchensteinfoo(at) ORCID: 0000-0002-5084-6763 Given Name: Katja Family Name: Rauchenstein Affiliation: SWILD DataCRediT: Collection, Validation, Curation, Software, Publication
  • Email: klaus.eckerfoo(at) ORCID: 0000-0002-6356-9590 Given Name: Klaus Family Name: Ecker Affiliation: WSL DataCRediT: Validation, Software, Publication, Supervision
  • Email: elias.baderfoo(at) Given Name: Elias Family Name: Bader Affiliation: KOF DataCRediT: Collection, Validation, Curation, Publication
  • Email: christian.ginzlerfoo(at) ORCID: 0000-0001-6365-2151 Given Name: Christian Family Name: Ginzler Affiliation: WSL DataCRediT: Software, Publication
  • Email: christoph.dueggelin Given Name: Christoph Family Name: Düggelin Affiliation: WSL DataCRediT: Software, Publication
  • Email: fabio.bontadinafoo(at) Given Name: Fabio Family Name: Bontadina Affiliation: SWILD Additional Affiliation : WSL DataCRediT: Publication, Supervision
  • Email: martin.obristfoo(at) ORCID: 0000-0001-6766-044X Given Name: Martin K. Family Name: Obrist Affiliation: WSL DataCRediT: Validation, Curation, Publication, Supervision
Contact Person Given Name: Martin K. Family Name: Obrist Email: martin.obristfoo(at) Affiliation: WSL ORCID: 0000-0001-6766-044X
Publication Publisher: EnviDat Year: 2022
  • Type: Collected Date: 2019-05-15 End Date: 2019-07-31
Version 1.0
Type dataset
General Type Dataset
Language English
Location Switzerland
Content License WSL Data Policy
Last Updated April 12, 2022, 12:26 (UTC)
Created April 8, 2022, 08:45 (UTC)