Journal article 39 views 4 downloads
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
,
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
-
PDF | Version of Record
© The Author(s) 2023. This article is licensed under a Creative Commons Attribution 4.0 International License.
Download (3.41MB)
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...
| 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 |
| 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 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. |
|---|---|
| Keywords: |
Fish migration; Hidden Markov modelling; Fine-scale acoustic telemetry; Behavioural states; Hydrodynamic modelling |
| College: |
Faculty of Science and Engineering |
| Funders: |
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. |
| Issue: |
1 |