Numerous methods have been developed to geolocate fish from data storage tags. Whereas demersal species have been tracked using tide-driven geolocation models, pelagic species which undertake extensive migrations have been mainly tracked using light-based models. Here, we present a new HMM-based model that infers pelagic fish positions from the sole use of high-resolution temperature and depth histories. A key contribution of our framework lies in model parameter inference (diffusion coefficient and noise parameters with respect to the reference geophysical fields—satellite SST and temperatures derived from the MARS3D hydrodynamic model), which improves model robustness. As a case study, we consider long time series of data storage tags (DSTs) deployed on European sea bass for which individual migration tracks are reconstructed for the first time. We performed a sensitivity analysis on synthetic and real data in order to assess the robustness of the reconstructed tracks with respect to model parameters, chosen reference geophysical fields and the knowledge of fish recapture position. Model assumptions and future directions are discussed. Finally, our model opens new avenues for the reconstruction and analysis of migratory patterns of many other pelagic species in relatively contrasted geophysical environments
A HMM-based model to geolocate pelagic fish from high-resolution individual temperature and depth histories: European sea bass as a case study
Large-scale electronic tagging is a very powerful tool to study how fish movements and migrations shape the internal dynamics of populations. This knowledge, crucial for improving fishery management, was still limited for the European seabass, whose stocks in the Northeast Atlantic have declined drastically over the last decade. To better understand the species ecology and the spatio-temporal structure of the population, we tagged seabass in the North Sea, the English Channel, and the Bay of Biscay, from 2014 to 2016. Out of 1220 deployed DSTs, 482 have been recovered by November 2022. Approximately half of them included a period of potential spawning migration. Reconstructed trajectories confirmed seabass to be a partial migratory species, as individuals exhibited either long-distance migrations or residence. Most migrants exhibited fidelity to summer feeding areas and winter spawning areas. Our dataset enriches the knowledge of seabass biological traits (e.g. temperature and depth ranges, vulnerability to predation and fishing). Our results suggest a spatial structure of the Atlantic population that differs from the stock structure currently considered for assessment and management. The consequences should be explored at both the European level and by regional managers involved in conservation outcomes.
Seasonal migration, site fidelity, and population structure of European seabass (Dicentrarchus labrax)
Geo-referenced data plays a crucial role in understanding and conserving natural resources, particularly in studying biolog- ical processes such as fish migration. Biologging, which in- volves attaching small devices to animals to track their behav- ior and gather environmental data, is a valuable tool in this regard. However, tracking fish directly underwater remains challenging. To address this, models have been developed to geolocate fish by utilizing temperature and pressure data from biologging devices and comparing them with ocean tempera- ture and bathymetry models as reference data. When applying these models, the accuracy and resolution of the reference data has a significant impact on the quality of reconstructed fish trajectories. With recent advancements in earth observation technology and modeling techniques like digital twins, huge amounts of earth science datasets are avail- able. However, the large size and limited accessibility of these datasets, as well as the computational requirements for anal- ysis, present technical obstacles. The Pangeo ecosystem was created by a community of engineers and geoscientists specifically to address these challenges. Making use of these new tools and to leverage the advancements in biodiversity research, a software called pangeo-fish has been developed. This paper focuses on its development for studying the movement of the European sea bass (Dicentrarchus labrax).
Pangeo For Studying Fish Movement Using Bio-logging And Earth Science Data
# Description **Duration**: 6 months **Start date**: September 2024. **Partners**: Jean-Marc Delouis (CNRS/IFREMER), Tina Odaka (IFREMER), Anne Fouilloux (Simula), Quentin Mazouni (Simula) ## Context Biologing seeks to determine the most likely fish trajectory using tagging data that records only temperature and pressure. Understanding the routes of fish is crucial for various ecological reasons to enhance the protection of the species under study. While biologing has demonstrated effectiveness [de Pontual et al, 2023], there remains scope for further improvement. Specifically, in calculating the likeliest path, the pangeo-fish algorithm models the fish's movement capability using a normal distribution, indicating that there is no favoured direction. We suggest incorporating external data to refine our understanding of the fish's most likely movement, including bathymetric data, identification of the fish through behavioural biology insights (such as depth data at high frequency) or detailed sea surface temperature measurements, among others. ## Detailed work - Month 1 - Construct the input data sets: During the first month, efforts will focus on assembling an appropriate data set for research. The initial task involves analysing the available biological dataset from the existing 450 sets and applying unsupervised classification techniques (e.g., Scattering Transform) to categorise fish behaviours. In parallel, satellite data (Sea Surface Temperature) and ocean modelling systems such as Copernicus Marine must be identified and collected. - Months 2 to 4 - Estimate a new probable trajectory for fish: This phase involves the employing cutting-edge algorithms that use scale coupling statistics methods. This approach aims to achieve highly resolved (both spatially and temporally) data to predict the direction of fish movement. The feasibility of validating this novel method is supported by multiple observations of some fish within a few tracks. Thus, disregarding this existing data, we can assess the deviation of the fish paths predicted by these algorithms from observed locations. - Months 5 to 6 - Apply the algorithm to the 450 tags and evaluate the performances: in the final two months, apply the developed algorithm to the 450 tags and assess its efficacy. If the results are satisfactory, a scientific publication can be expected at the end of this period. ## Computing & storage infrastructure Cloud resources (storage & computing) are available for the entire duration of the project. These include 1) GFTS Jupyterhub (http://gfts.minrk.net) to conduct computation tasks like applying algorithms and constructing datasets. All the data, inclusive of tagging and satellite data, will be securely stored and managed via S3-compatible object storage to facilitate access and reuse during the project. Access to GFTS JupyterHub will be granted at the start of the project. 2) Destination Earth Services (https://platform.destine.eu/) to access environmental data like Ocean data (Sea Surface Temperature, Sea Water Potential Temperature on model levels, Surface Temperature and bathymetric data) from the Climate Adaptation Digital twin; Access to the Destination Earth Services require user registration at https://platform.destine.eu/ and is subject to their availability during the project (services are still under development and not available yet).
Fouilloux, Anne, and Tina Odaka. "Fish Track mini-project." ROHub. Sep 04 ,2024. https://w3id.org/ro-id/4ba5a56b-e756-499f-8efa-1c32d24fce4f.
All the data, inclusive of tagging and satellite data, will be securely stored and managed via S3-compatible object storage to facilitate access and reuse during the project.
Science and technology/Natural science/Biology
The initial task involves analysing the available biological dataset from the existing 450 sets and applying unsupervised classification techniques (e.g., Scattering Transform) to categorise fish behaviours.
Economy, business and finance/Economic sector/Computing and information technology/Software
Science and technology/Technology and engineering/IT-computer sciences
Biologing seeks to determine the most likely fish trajectory using tagging data that records only temperature and pressure.