LAUSR

Abstract Migratory species do not necessarily behave migratory continuously. An important aspect of studying migratory species is therefore to distinguish between movement and resident behavior. Telemetry is a rapidly evolving technique to study animal movement, but the number of data processing techniques to account for resident behavior remains limited. In this study we describe how models that were initially developed to predict human customer behavior, i.e. two-part and three-part models, provide new insights in the movement of migrating eel by accounting for resident behavior apparent from telemetry data sets. In econometrics, two-part models take into account that the decision of a customer to purchase an item and the decision of the customer on the purchase quantity of the concerning product, might be affected by different factors. Similarly, the factors that affect the decision of a fish to migrate or to stay resident might be different from the factors that affect the swimming speed of the fish. Telemetry data of eel movement in the Permanent Belgian Acoustic Receiver Network (PBARN) of the Scheldt Estuary was used. This network with high detection probabilities allowed residencies to be recognized, defined, and introduced as zero values in a movement-residency data set. Two-part models, which consider movement decision, i.e. residency or movement, and movement intensity, i.e. swimming speed, as two different processes or parts of one larger model, outperformed one-part models that do not make that distinction. This underlines the complex migration behavior eels exhibit. These two-part models in turn were outperformed by three-part models that also accounted for cryptic (i.e. unobserved) residencies. While the one-part model identified the tides and the distance from the most upstream gate as most important for movement, the three-part models identified the tides as most important for the movement decision and the distance from the most upstream gate as most important for the movement intensity. Considering movement decisions, cryptic residencies and movement intensity in modeling efforts increased model performance by 9.8%, underlining the importance of acknowledging the potentially complex behavior animals exhibit.