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Further Information
The ELOIS website comprises data about the status of lynx in the Czech Republic, Austria and Germany ...
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Paper to read
The KORA project from Switzerland published "Guidelines for the Monitoring of Lynx" in December 2006, KORA Bericht Nr. 33 e.
It is very recommendable!



Questions about distribution, expansion, population trend, relative abundance, fragmentation and viability have to be answered to describe the population status of lynx.

The 'CELTIC lynx population' lives along country borders of several countries with several methods of data collection: sightings and signs , snow tracking, inquiries, radio telemetry and unspecific surveys.

The most basic information is about the distribution of lynx and shows where the lynx is present or where it is absent.

For a comparison across countries it is necessary to first harmonize the definition of presence/absence and of regular/irregular presence, and second to differentiate the quality and therefore reliability of the collected data. A common data classification is needed.

Reliability of data
Depending on the method used we get more or less reliable data.
A radio telemetry study helps to design the monitoring program and for the calibration of monitoring data. It delivers the most accurate data for an estimation of population density and size, proportion of reproducing animals and migration patterns.

Because financial resources are often restricted it is sometimes not possible to use relatively expensive methods like radio telemetry or systematic camera-trapping.

Then other methods have to be used. Data about population trend for example are therefore estimated on the frequency of direct or indirect signs (e.g. dead lynx, orphaned kittens, photos, tracks, killed livestock or wild prey, calls, scats etc.).

Process of data verification
Again for a comparison of lynx subpopulations the data should be treated the same way, e.g. controlled by a trained person after the same procedure. If that is not feasible those data should be marked as unverified or unverifiable.

The suggestions of the SCALP project how to treat lynx data are a good approach of classifying data.

The aim is to minimize bias. To do so you have (1) be aware of the bias, (2) to correct for the bias as well as possible, and third to consider the bias in the interpretation of the results.