Linking fine-scale behaviour to the hydraulic environment shows behavioural responses in riverine fish

Elings, J.; Mawer, Rachel; Bruneel, S.; Pauwels, I. S.; Pickholtz, E.; Pickholtz, R.; Coeck, J.; Schneider, M.; Goethals, P.

doi:10.1186/s40462-023-00413-1

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Linking fine-scale behaviour to the hydraulic environment shows behavioural responses in riverine fish

J. Elings, Rachel Mawer

, S. Bruneel, I. S. Pauwels, E. Pickholtz, R. Pickholtz, J. Coeck, M. Schneider, P. Goethals

Movement Ecology, Volume: 11, Issue: 1

Swansea University Author: Rachel Mawer

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DOI (Published version): 10.1186/s40462-023-00413-1

Abstract

Background: Fish migration has severely been impacted by dam construction. Through the disruption of fish migration routes, freshwater fish communities have seen an incredible decline. Fishways, which have been constructed to mitigate the problem, have been shown to underperform. This is in part due...

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Published in:	Movement Ecology
ISSN:	2051-3933
Published:	Springer Science and Business Media LLC 2023
Online Access:	Check full text
URI:	https://cronfa.swan.ac.uk/Record/cronfa71547

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last_indexed	2026-04-10T10:29:43Z
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Recent developments in tracking technology and modelling make it possible today to track (aquatic) animals at very fine spatial (down to one meter) and temporal (down to every second) scales. Hidden Markov models are appropriate models to analyse behavioural states at these fine scales. In this study we link fine-scale tracking data of barbel (Barbus barbus) and grayling (Thymallus thymallus) to a fine-scale hydrodynamic model. With a HMM we analyse the fish’s behavioural switches to understand their movement and navigation behaviour near a barrier and fishway outflow in the Iller river in Southern Germany. Methods: Fish were tracked with acoustic telemetry as they approached a hydropower facility and were presented with a fishway. Tracking resulted in fish tracks with variable intervals between subsequent fish positions. This variability stems from both a variable interval between tag emissions and missing detections within a track. After track regularisation hidden Markov models were fitted using different parameters. The tested parameters are step length, straightness index calculated over a 3-min moving window, and straightness index calculated over a 10-min window. The best performing model (based on a selection by AIC) was then expanded by allowing flow velocity and spatial velocity gradient to affect the transition matrix between behavioural states. Results: In this study it was found that using step length to identify behavioural states with hidden Markov models underperformed when compared to models constructed using straightness index. Of the two different straightness indices assessed, the index calculated over a 10-min moving window performed better. Linking behavioural states to the ecohydraulic environment showed an effect of the spatial velocity gradient on behavioural switches. On the contrary, flow velocity did not show an effect on the behavioural transition matrix. Conclusions: We found that behavioural switches were affected by the spatial velocity gradient caused by the attraction flow coming from the fishway. 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spelling	2026-04-09T16:24:13.6178817 v2 71547 2026-03-04 Linking fine-scale behaviour to the hydraulic environment shows behavioural responses in riverine fish b326ca8a689948f5f72cea5d46cf2194 0009-0003-0114-9691 Rachel Mawer Rachel Mawer true false 2026-03-04 BGPS Background: Fish migration has severely been impacted by dam construction. Through the disruption of fish migration routes, freshwater fish communities have seen an incredible decline. Fishways, which have been constructed to mitigate the problem, have been shown to underperform. This is in part due to fish navigation still being largely misunderstood. Recent developments in tracking technology and modelling make it possible today to track (aquatic) animals at very fine spatial (down to one meter) and temporal (down to every second) scales. Hidden Markov models are appropriate models to analyse behavioural states at these fine scales. In this study we link fine-scale tracking data of barbel (Barbus barbus) and grayling (Thymallus thymallus) to a fine-scale hydrodynamic model. With a HMM we analyse the fish’s behavioural switches to understand their movement and navigation behaviour near a barrier and fishway outflow in the Iller river in Southern Germany. Methods: Fish were tracked with acoustic telemetry as they approached a hydropower facility and were presented with a fishway. Tracking resulted in fish tracks with variable intervals between subsequent fish positions. This variability stems from both a variable interval between tag emissions and missing detections within a track. After track regularisation hidden Markov models were fitted using different parameters. The tested parameters are step length, straightness index calculated over a 3-min moving window, and straightness index calculated over a 10-min window. The best performing model (based on a selection by AIC) was then expanded by allowing flow velocity and spatial velocity gradient to affect the transition matrix between behavioural states. Results: In this study it was found that using step length to identify behavioural states with hidden Markov models underperformed when compared to models constructed using straightness index. Of the two different straightness indices assessed, the index calculated over a 10-min moving window performed better. Linking behavioural states to the ecohydraulic environment showed an effect of the spatial velocity gradient on behavioural switches. On the contrary, flow velocity did not show an effect on the behavioural transition matrix. Conclusions: We found that behavioural switches were affected by the spatial velocity gradient caused by the attraction flow coming from the fishway. Insight into fish navigation and fish reactions to the ecohydraulic environment can aid in the construction of fishways and improve overall fishway efficiencies, thereby helping to mitigate the effects migration barriers have on the aquatic ecosystem. Journal Article Movement Ecology 11 1 Springer Science and Business Media LLC 2051-3933 Fish migration; Hidden Markov modelling; Fine-scale acoustic telemetry; Behavioural states; Hydrodynamic modelling 7 8 2023 2023-08-07 10.1186/s40462-023-00413-1 COLLEGE NANME Biosciences Geography and Physics School COLLEGE CODE BGPS Swansea University Another institution paid the OA fee This project has received funding from the European Union Horizon 2020 Research and Innovation Programme under the Marie Sklodowska-Curie Actions, Grant Agreement No. 860800. The study setup and data collection was funded by the European Union's Horizon 2020 (H2020) research and innovation program FITHydro (https://www.fithydro.wiki/index.php), under Grant Agreement No. 727830. 2026-04-09T16:24:13.6178817 2026-03-04T14:34:43.9932649 Faculty of Science and Engineering School of Biosciences, Geography and Physics - Biosciences J. Elings 1 Rachel Mawer 0009-0003-0114-9691 2 S. Bruneel 3 I. S. Pauwels 4 E. Pickholtz 5 R. Pickholtz 6 J. Coeck 7 M. Schneider 8 P. Goethals 9 71547__36488__b9e869a32f0a4eaf90c454c02f62a15c.pdf 71547.VoR.pdf 2026-04-09T16:22:58.9323879 Output 3579995 application/pdf Version of Record true © The Author(s) 2023. This article is licensed under a Creative Commons Attribution 4.0 International License. true eng http://creativecommons.org/licenses/by/4.0/
title	Linking fine-scale behaviour to the hydraulic environment shows behavioural responses in riverine fish
spellingShingle	Linking fine-scale behaviour to the hydraulic environment shows behavioural responses in riverine fish Rachel Mawer
title_short	Linking fine-scale behaviour to the hydraulic environment shows behavioural responses in riverine fish
title_full	Linking fine-scale behaviour to the hydraulic environment shows behavioural responses in riverine fish
title_fullStr	Linking fine-scale behaviour to the hydraulic environment shows behavioural responses in riverine fish
title_full_unstemmed	Linking fine-scale behaviour to the hydraulic environment shows behavioural responses in riverine fish
title_sort	Linking fine-scale behaviour to the hydraulic environment shows behavioural responses in riverine fish
author_id_str_mv	b326ca8a689948f5f72cea5d46cf2194
author_id_fullname_str_mv	b326ca8a689948f5f72cea5d46cf2194_***_Rachel Mawer
author	Rachel Mawer
author2	J. Elings Rachel Mawer S. Bruneel I. S. Pauwels E. Pickholtz R. Pickholtz J. Coeck M. Schneider P. Goethals
format	Journal article
container_title	Movement Ecology
container_volume	11
container_issue	1
publishDate	2023
institution	Swansea University
issn	2051-3933
doi_str_mv	10.1186/s40462-023-00413-1
publisher	Springer Science and Business Media LLC
college_str	Faculty of Science and Engineering
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department_str	School of Biosciences, Geography and Physics - Biosciences{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Biosciences, Geography and Physics - Biosciences
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description	Background: Fish migration has severely been impacted by dam construction. Through the disruption of fish migration routes, freshwater fish communities have seen an incredible decline. Fishways, which have been constructed to mitigate the problem, have been shown to underperform. This is in part due to fish navigation still being largely misunderstood. Recent developments in tracking technology and modelling make it possible today to track (aquatic) animals at very fine spatial (down to one meter) and temporal (down to every second) scales. Hidden Markov models are appropriate models to analyse behavioural states at these fine scales. In this study we link fine-scale tracking data of barbel (Barbus barbus) and grayling (Thymallus thymallus) to a fine-scale hydrodynamic model. With a HMM we analyse the fish’s behavioural switches to understand their movement and navigation behaviour near a barrier and fishway outflow in the Iller river in Southern Germany. Methods: Fish were tracked with acoustic telemetry as they approached a hydropower facility and were presented with a fishway. Tracking resulted in fish tracks with variable intervals between subsequent fish positions. This variability stems from both a variable interval between tag emissions and missing detections within a track. After track regularisation hidden Markov models were fitted using different parameters. The tested parameters are step length, straightness index calculated over a 3-min moving window, and straightness index calculated over a 10-min window. The best performing model (based on a selection by AIC) was then expanded by allowing flow velocity and spatial velocity gradient to affect the transition matrix between behavioural states. Results: In this study it was found that using step length to identify behavioural states with hidden Markov models underperformed when compared to models constructed using straightness index. Of the two different straightness indices assessed, the index calculated over a 10-min moving window performed better. Linking behavioural states to the ecohydraulic environment showed an effect of the spatial velocity gradient on behavioural switches. On the contrary, flow velocity did not show an effect on the behavioural transition matrix. Conclusions: We found that behavioural switches were affected by the spatial velocity gradient caused by the attraction flow coming from the fishway. Insight into fish navigation and fish reactions to the ecohydraulic environment can aid in the construction of fishways and improve overall fishway efficiencies, thereby helping to mitigate the effects migration barriers have on the aquatic ecosystem.
published_date	2023-08-07T05:51:50Z
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Linking fine-scale behaviour to the hydraulic environment shows behavioural responses in riverine fish

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