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Acoustic cameras are increasingly used for continuous, non-intrusive recording and counting of fish passage in natural environments and artificial structures such as fishways. However, analysing the large number of videos recorded is time consuming. Although automatic reading processes have been developed, the poor quality of acoustic images, including discontinuity of the signal for a single object, is challenging. We developed an innovative method for analysing acoustic videos. Unlike previous methods, it focuses on swimming locomotion instead of the morphological properties of fish. Each image of a fish is pre-processed to remove discontinuities and restore the entire fish body as a single cluster of pixels. The set of pixels is then tracked to retrieve movement, independent of the displacement of the fish, using a mesh and a solid deformable model. The deformation to which the mesh is subjected between each pair of frames (i.e., deformation of the fish body) is summarised in a deformation map for each fish passage. Testing the method using a dataset of four species strongly suggested that deformation maps are species-dependent. These results must be extended to other species to confirm the effectiveness of the method for automatic identification of fish species and characterisation of their behaviour using acoustic camera records.

IntroductionDiadromous fish populations have strongly declined over decades, and many species are protected through national and international regulations. They account for less than 1% of fish biodiversity worldwide, but they are among the most perceptible linkages between freshwater and marine ecosystems. During their migration back and forth, diadromous fish species are subjected to many anthropogenic threats, among which river damming can severely limit access to vital freshwater habitats and jeopardize population sustainability. Here, we developed a method based on a double-observer modeling approach for estimating the abundance of diadromous fish during their migration in rivers.MethodsThe method relies on two independent and synchronous records of fish counts that were analyzed jointly thanks to a hierarchical Bayesian model to estimate detection efficiencies and daily fish passage. We used simulated data to test model robustness and identify conditions under which the developed approach can be used. The approach was then applied to empirical data to estimate the annual silver eel run in the Touques River, France.ResultsThe analysis of simulated datasets and the study case gives evidence that the model can provide robust,accurate, and precise estimates of detection probabilities and total fish abundance in a set of conditions dependent on the information provided in the data (annual distribution of fish passage, annual number of observation, pairing period, etc.).DiscussionThen, the method can be applied to various species and counting systems, including nomad acoustic camera devices. We discuss its relevance for programs on river continuity restoration, notably to quantify population restoration associated with dam removals.

Acoustic cameras are increasingly used in monitoring studies of diadromous fish populations, even though analyzing them is time-consuming. In complex in situ contexts, anguilliform fish may be especially difficult to identify automatically using acoustic camera data because the undulation of their body frequently results in fragmented targets. Our study aimed to develop a method based on a succession of computer vision techniques, in order to automatically detect, identify and count anguilliform fish using data from multiple models of acoustic cameras. Indeed, several models of cameras, owning specific technical characteristics, are used to monitor fish populations, causing major differences in the recorded data shapes and resolutions. The method was applied to two large datasets recorded at two distinct monitoring sites with populations of European eels with different length distributions. The method yielded promising results for large eels, with more than 75% of eels automatically identified successfully using datasets from ARIS and BlueView cameras. However, only 42% of eels shorter than 60 cm were detected, with the best model performances observed for detection ranges of 4-9 m. Although improvements are required to compensate for fish-length limitations, our cross-camera method is promising for automatically detecting and counting large eels in long-term monitoring studies in complex environments.