The ability to navigate in the world is crucial to many species. One of the most fundamental unresolved issues in understanding animal navigation is how the brain represents spatial information. Although navigation has been studied extensively in many taxa, the key efforts to determine the neural basis of navigation have focused on mammals, usually in lab experiments, where the allocated space is typically very small; e.g., up to one order of magnitude the size of the animal, is limited by artificial walls, and contains only a few objects. This type of setting is vastly different from the habitat of animals in the wild, which is open in many cases and is virtually limitless in size compared to its inhabitants. Thus, a fundamental open question in animal navigation is whether small-scale, spatially confined, and artificially crafted lab experiments indeed reveal how navigation is enacted in the real world. This question is difficult to study given the technical problems associated with in vivo electrophysiology in natural settings. Here, we argue that these difficulties can be overcome by implementing state of the art technology when studying the rivulated rabbitfish, Siganus rivulatus as the model animal. As a first step toward this goal, using acoustic tracking of the reef, we demonstrate that individual S. rivulatus have a defined home range of about 200 m in length, from which they seldom venture. They repeatedly visit the same areas and return to the same sleeping grounds, thus providing evidence for their ability to navigate in the reef environment. Using a clustering algorithm to analyze segments of daily trajectories, we found evidence of specific repeating patterns in behavior within the home range of individual fish. Thus, S. rivulatus appears to have the ability to carry out its daily routines and revisit places of interest by employing sophisticated means of navigation while exploring its surroundings. In the future, using novel technologies for wireless recording from single cells of fish brains, S. rivulatus can emerge as an ideal system to study the neural basis of navigation in natural settings and lead to “electrophysiology in the wild.”
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.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.