BatPopTrends

Methodological analyses for trend calculation and the estimation of population parameters for the mandatory reporting of ringing data from the bat marking centre Dresden for the Habitats Directive using the example of Greater mouse-eared bat (Myotis myotis) as well as other selected bat species

Project coordinator: Dr. Reinhard Klenke
Research: Dr. Reinhard Klenke
Funded by: Sächsisches Landesamt für Umwelt, Landwirtschaft und Geologie (LfULG)
Project management: Dr. Reinhard Klenke
Funded project duration: 01.02. 2017 - 30.10. 2017

Subquestions are further investigated in cooperation with LfULG within academic education.

Motivation & Description

In this project, ringing- and recapture data of bats are analysed, that have been collected by the bat marking centre of the federal states of Eastern Germany at LfULG. The aim is to measure population parameters like recapture rate, survival rate and population growth rate using state-of-the-art statistical methods for the interpretation of CMR (Capture–Mark-Recapture) data. Moreover, we intend to learn more about the exchange relationships between select bat populations using spatial analyses. The conclusions will be related to other results and used to calculate population trends. The project focuses on data of the species Greater mouse-eared bat (Myotis myotis), Common noctule (Nyctalus noctula), Lesser noctule (Nyctalus leisleri) and Brown long-eared bat (Plecotus auritus).

For the analysis, adequate monitoring, preparation and structure are required. This is solved by query tools in a relational SQL-data base. For further interpretations and spatial analyses, geographic information systems (GIS) and the statistical programming environment R are used. The work takes place in direct coordination with LfULG, aiming at transferring knowledge and technology, and continuing the cooperations. The findings of this project unveil possible synergies with other projects such as EE-Monitor, where data and information on trends are also needed and which in turn provide data and information on multiple drivers of land use change and environmental risks.