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Teil C - Annex 
Identification and processing of bat calls 
Identification of bat species: 
Generally, the latest literature should be used for identification purposes (e. g. Skiba 2009, 
Barataud 2012, Russ 2012). The call characteristics of bats is highly dependent on the flight 
behaviour of the animals. The interpretation of bat call recordings and the identification of 
species should therefore be carried out by persons with long-standing experience in the bio 
acoustic analysis of bat calls. 
All recorded data must be manually assessed and identified. A large number of automatic sys 
tems for bat call identification is available. Automatic survey systems can feature an identifi 
cation error rate that is too high and therefore are not permissible for the survey required 
here. 
Calls that cannot be identified at the species level should be recorded as follows: 
• spec.; identification of genus (e. g. Myotis spec.) 
• Pnat/Ppip for Nathusius’s pipistrelle/Common pipistrelle 
• Ppip/Ppyg for Common pipistrelle/Soprano pipistrelle 
• Nyctaloid: identification possible only down to group comprising the species of the genera 
Nyctalus, Eptesicus and Vespertilio, the species of which cannot be safely identified under 
certain flight conditions. 
Identification and evaluation of activity: 
Similar to the night-time survey of migratory birds, the observation numbers for population 
surveys of bats must not be regarded as absolute abundance, since it is impossible to identify 
individuals. The data is rendered as “activity density”. All bat observations are therefore to be 
evaluated as a relative measure. 
The number of recorded call sequences constitutes the activity. If two different call sequences 
by one species are identified at the same time within one recording, this shall be registered as 
two activities: 
1 call sequence by one species = 1 activity 
2 call sequences by one species at the same time = 2 activities 
The data evaluation must contain: 
• Seasonal distribution of species-specific activity (cf. Fig. 13) 
• Blending of activity data with collected weather data (cf. Fig. 14).