Combining habitat selection, behavioural states, and individual variation to predict fish spatial usage near a barrier

Bibtex

@misc{73557388-5eae-4bef-a0e5-c69e3c828681,
title = "Combining habitat selection, behavioural states, and individual variation to predict fish spatial usage near a barrier",
abstract = "Riverine barriers are threatening freshwater fish migration, with major impacts on fish populations. Effective management requires understanding of fish movement and behaviour as they approach a barrier and fish pass, which can inform optimal mitigation options and barrier management. Here, the movements of upstream migrating barbel Barbus barbus and grayling Thymallus thymallus near a barrier were analysed and results used to develop predictive models. Fish were tracked via 2D acoustic telemetry. Hidden Markov models were used to distinguish behavioural states and step selection functions were applied to determine habitat selection by the fish in each state. Model results were explored to assess the benefits of including behavioural state and understand state-specific habitat preferences, then cross-validated and used to develop an individual based model to predict fish spatial usage. Little difference existed in habitat selection between states and individual variation was high, limiting general trends that could be described. Overall, barbel preferred deeper or faster water while for grayling, few trends could be described. Under the tested flow conditions, high spatial usage was predicted in the area directly downstream of the barrier. In addition, barbel usage was high in the area by and downstream of the fish pass entrance but not for grayling, which may indicate a need to improve pass attractiveness for grayling. The predictive model produced directed upstream movements of fish similar to those expected for upstream migrating freshwater fish, highlighting model potential for fish passage applications in future iterations. The high individual variability in fish behaviour drives the need for individual-based approaches for predicting fish movement.",
author = "Rachel Mawer and Jelger Elings and Stijn Bruneel and Ine Pauwels and Eliezer Pickholtz and Renanel Pickholtz and Johan Coeck and Peter L.M. Goethals",
year = "2025",
month = mrt,
day = "01",
doi = "https://doi.org/10.1016/j.ecoinf.2024.102967",
language = "Nederlands",
publisher = "Elsevier",
address = "België,
type = "Other"
}

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