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Camera-traps is a relatively new but already popular instrument in theestimation of abundance of non-identifiable animals. Although camera-traps areconvenient in application, there remain both theoretical complications such asspatial autocorrelation or false negative problem and practical difficulties,for example, laborious random sampling. In the article we propose analternative way to bypass the mentioned problems.  In the proposed approach, the raw video information collected from thecamera-traps situated at the spots of natural attraction is turned into thefrequency of visits, and the latter is transformed into the desired abundanceestimate. The key for such a transformation is the application of thecorrection coefficients, computed for each particular observation environmentusing the Bayesian approach and the massive database (DB) of observations undervarious conditions.  The main result of the article is a new method of census based on video-datafrom camera-traps at the spots of natural attraction and information from aspecial community-driven database.  The proposed method is based on automated video-capturing at a moderatenumber of easy to reach spots, so in the long term many laborious census worksmay be conducted easier, cheaper and cause less disturbance for the wild life.Information post-processing is strictly formalized, which leaves little chancefor subjective alterations. However, the method heavily relies on the volumeand quality of the DB, which in its turn heavily relies on the efforts of thecommunity. There is realistic hope that the community of zoologists andenvironment specialists could create and maintain a DB similar to the proposedone. Such a rich DB of visits might benefit not only censuses, but also manybehavioral studies.

Big-data approach in abundance estimation of non-identifiable animals with camera-traps at the spots of attraction