Abstract

Distance sampling (DS) and territory mapping (TM) are globally applied bird survey techniques. However, specifically designed studies comparing results of both methods in different habitats in the framework of a scientific experiment have rarely been conducted. To provide a more generalized guidance for the field surveyor, here we evaluated estimates of bird abundances and number of bird species in four different habitats (broad-leaved forest, coniferous forest, open woodland and farmland) in central Germany. Abundances were estimated in parallel by TM and DS in 2006 and 2008, following standard protocols. Detection probability differed significantly among habitats and species. Density estimates by DS were in total 24% lower than those estimated by standardized TM. While the number of bird species detected with both methods was approximately the same, the estimated abundances of 15 bird species showed significant differences. Increasing the number from two to four and five registrations to count a territory by using TM decreased the density on average about 28 and 42%, respectively. Using standardized TM resulted in an overestimation of abundances of species showing a high detection probability. In contrast, DS estimated very high densities for species that had a very low detection probability. In fact, a highly negative correlation was found between the density estimated by DS and the detection probability. Using standardized TM and setting a fixed number of registrations before a location qualifies for a bird territory cannot compensate for the large differences in species detectability. Instead, the number of registrations required to count a territory should be adjusted to differences in detection probabilities and seasonal activity. From our results we can recommend a mean of four registrations if eight visits were conducted to count a territory. However, the lack of any statistically-based quality assessment reduces the serious usability of TM for estimating densities for science-based management application, whereas, the clear advantage of DS is that it provides error estimates and considers differences in species detectability.

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