Optimizing the size of the area surveyed for monitoring a Eurasian lynx (Lynx lynx) population in the Swiss Alps by means of photographic capture-recapture

We studied the influence of surveyed area size on density estimates by means of camera-trapping in a low-density felid population (1-2 individuals/100 km(2) ). We applied non-spatial capture-recapture (CR) and spatial CR (SCR) models for Eurasian lynx during winter 2005/2006 in the northwestern Swiss Alps by sampling an area divided into 5 nested plots ranging from 65 to 760 km(2) . CR model density estimates (95% CI) for models M0 and Mh decreased from 2.61 (1.55-3.68) and 3.6 (1.62-5.57) independent lynx/100 km(2) , respectively, in the smallest to 1.20 (1.04-1.35) and 1.26 (0.89-1.63) independent lynx/100 km(2) , respectively, in the largest area surveyed. SCR model density estimates also decreased with increasing sampling area but not significantly. High individual range overlaps in relatively small areas (the edge effect) is the most plausible reason for this positive bias in the CR models. Our results confirm that SCR models are much more robust to changes in trap array size than CR models, thus avoiding overestimation of density in smaller areas. However, when a study is concerned with monitoring population changes, large spatial efforts (area surveyed ≥760 km(2) ) are required to obtain reliable and precise density estimates with these population densities and recapture rates.

Bathymetry of Lake Constance - State-of-the-art in surveying a large lake

In 2014 the by far largest German lake has been newly surveyed. The transnational project is funded by the European Union and delivers a detailed 3D-model of the lake- floor. The German project name is »Tiefenschärfe – Hochauflösende Vermessung Bo- densee«, which in English roughly means: high-resolution survey of Lake Constance. The German term »Tiefenschärfe« (in optics and photography: depth of field) plays with the meanings of »Tiefe« (depth) and »Schärfe« (sharpness). The result of the sur- vey shall be a clear and sharp image of the deep and shallow lake- floor. At present the LiDAR and multibeam data are still processed, but first results are presented in this article.