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Citizen Science Based Monitoring of Greylag goose (Anser anser) in Bavaria (Germany): Combining Count Data and Bag Data to Estimate Long-Term Trends between 1988/89 and 2010/11

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  • Andreas Grauer
  • Andreas König
  • Nils Bunnefeld

Abstract

Introduction, Material and Methods: Numbers of large grazing bird (geese, swans, cranes) have increased all over Europe, but monitoring these species, e.g. for management purposes, can be time consuming and costly. In Bavaria, sedentary Greylag geese (Anser anser) are monitored during the winter by two different citizen-based monitoring schemes: the International Waterbird Census [IWC] and hunting bag statistics. We compared the results of both schemes for the seasons 1988/89 to 2010/11 by analysing annual indices calculated using the software TRends and Indices for Monitoring Data—TRIM. Results and Discussion: We identified similar, highly significant rates of increase in both data sets for the entire region of Bavaria (IWC 14% [13–15%], bag 13% [12–14%]). Furthermore, in all of the seven Bavarian regions, trends in annual indices of both data sets correlated significantly. The quality of both datasets as indicators of abundances in Greylag geese populations in Bavaria was not undermined by either weaknesses typically associated with citizen based monitoring or problems generally assumed for IWC and bag data. We also show that bag data are, under the German system of collecting bag statistics, a reliable indicator of species’ distribution, especially for detecting newly colonized areas. Therefore, wildlife managers may want to consider bag data from citizen science led monitoring programmes as evidence supporting the decision making processes. We also discuss requirements for any bag monitoring schemes being established to monitor trends in species’ distribution and abundance.

Suggested Citation

  • Andreas Grauer & Andreas König & Nils Bunnefeld, 2015. "Citizen Science Based Monitoring of Greylag goose (Anser anser) in Bavaria (Germany): Combining Count Data and Bag Data to Estimate Long-Term Trends between 1988/89 and 2010/11," PLOS ONE, Public Library of Science, vol. 10(6), pages 1-16, June.
  • Handle: RePEc:plo:pone00:0130159
    DOI: 10.1371/journal.pone.0130159
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