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Improving the analysis of movement data from marked individuals through explicit estimation of observer heterogeneity


  • Fränzi Korner-Nievergelt,

  • Annette Sauter,

  • Philip W. Atkinson,

  • Jérôme Guélat,

  • Wojciech Kania,

  • Marc Kéry,

  • Ulrich Köppen,

  • Robert A. Robinson,

  • Michael Schaub,

  • Kasper Thorup,

  • Henk Van Der Jeugd,

  • Arie J. Van Noordwijk

F. Korner-Nievergelt (correspondence), A. Sauter, J. Guélat, M. Kéry and M. Schaub, Swiss Ornithol. Inst., CH-6204 Sempach, Switzerland. E-mail:– P. W. Atkinson and R. A. Robinson, British Trust for Ornithol., The Nunnery, Thetford, IP24 2PU, UK. – W. Kania, Gdańsk Ornithol. Station, Mus. and Inst. of Zoology, Polish Academy of Sciences, Nadwiślańska 108, 80-680 Gdańsk 40, Poland. – U. Köppen, Hiddensee Bird Ring. Centr., State Office for Environ., Nature Conserv. and Geol. Mecklenburg-Western Pomerania, Badenstr 18, D-18439 Stralsund, Germany. – K. Thorup, Centr. for Macroecol., Evol. and Climate, Zool. Mus., Univ. of Copenhagen, Universitetsparken 15, DK-2100 Copenhagen, Denmark. – H. van der Jeugd, Vogeltrekstation Dutch Centr. for Avian Migr. and Demogr., NIOO-KNAW, Heteren, Netherlands. – A. J. van Noordwijk, Netherlands Inst. of Ecol., Boterhoeksestraat 48, NL6666GA, Heteren, Netherlands.


Ring re-encounter data, in particular ring recoveries, have made a large contribution to our understanding of bird movements. However, almost every study based on ring re-encounter data has struggled with the bias caused by unequal observer distribution. Re-encounter probabilities are strongly heterogeneous in space and over time. If this heterogeneity can be measured or at least controlled for, the enormous number of ring re-encounter data collected can be used effectively to answer many questions. Here, we review four different approaches to account for heterogeneity in observer distribution in spatial analyses of ring re-encounter data. The first approach is to measure re-encounter probability directly. We suggest that variation in ring re-encounter probability could be estimated by combining data whose re-encounter probabilities are close to one (radio or satellite telemetry) with data whose re-encounter probabilities are low (ring re-encounter data). The second approach is to measure the spatial variation in re-encounter probabilities using environmental covariates. It should be possible to identify powerful predictors for ring re-encounter probabilities. A third approach consists of the comparison of the actual observations with all possible observations using randomization techniques. We encourage combining such randomisations with ring re-encounter models that we discuss as a fourth approach. Ring re-encounter models are based on the comparison of groups with equal re-encounter probabilities. Together these four approaches could improve our understanding of bird movements considerably. We discuss their advantages and limitations and give directions for future research.