Seasonal Distribution of Tuna and Non-tuna Species Associated With Drifting Fish Aggregating Devices (DFADs) in the Western Indian Ocean Using Fishery-Independent Data

Abstract

Man-made floating objects in the surface of tropical oceans, also called drifting fish aggregating devices (DFADs), attract tens of marine species, including tunas and nontuna species. In the Indian Ocean, around 80% of the sets currently made by the EU purse-seine fleet are on DFADs. Due to the importance and value of this fishery, understanding the habitat characteristics and dynamics of pelagic species aggregated under DFADs is key to improve fishery management and fishing practices. This study implements Bayesian hierarchical spatial models to investigate tuna and non-tuna species seasonal distribution based on fisheries-independent data derived from fishers' echo-sounder buoys, environmental information (Sea Surface Temperature, Chlorophyll, Salinity, Eddie Kinetic Energy, Oxygen concentration, Sea Surface Height, Velocity and Heading) and DFAD variables (DFAD identification, days at sea). Results highlighted group-specific spatial distributions and habitat preferences, finding higher probability of tuna presence in warmer waters, with higher sea surface height and low eddy kinetic energy values. In contrast, highest probabilities of non-tuna species were found in colder and productive waters. Days at sea were relevant for both groups, with higher probabilities at objects with higher soak time. Our results also showed speciesspecific temporal distributions, suggesting that both tuna and non-tuna species may have different habitat preferences depending on the monsoon period. The new findings provided by this study will contribute to the understanding of the ecology and behavior of target and non-target species and their sustainable management.

Identifying Priority Conservation Areas of Largehead Hairtail (Trichiurus japonicus) Nursery Grounds in the East China Sea

“…Before running the models, the explanatory variables were standardized (difference from the mean divided by the corresponding standard deviation) to facilitate interpretation and compare relative weights between variables (Abada et al, 2020;Orúe et al, 2020). All environmental variables were tested for outliers, missing values, and correlation (Zuur et al, 2009a).…”

“…A variety of methodological approaches have been developed over the last decades to generate SDMs, such as Neural Networks (Özesmi and Özesmi, 1999), Boosted Regression Trees (Elith et al, 2008;Wege et al, 2021), Maximum Entropy (Phillips et al, 2006), Generalized Linear Regression Model (Guisan et al, 2002), and Additive Regression Models (Swartzman et al, 1992;Austin, 2007). However, the statistical challenges using SDMs have increased as datasets have become more complex over time (Orúe et al, 2020;Lloret-Lloret et al, 2021). Recently, Bayesian hierarchical spatiotemporal models with the Integrated Nested Laplace Approximation (INLA) methodology has acquired an important role, since it is ideally suited for fitting complex spatiotemporal covariance structures (Rue et al, 2009;Silva et al, 2017).…”

Studying the relationship of immersion duration and characteristics of natural materials FAD to fish aggregation in the sea

“…Studies conducted by (Lopez et al 2016;Orue et al 2019) showed that longer immersion of FADs could increase the diversity of fish species. However, it is highly dependent on environmental conditions (Maufroy et al 2015;Orue et al 2020). In this case, intratan and extranatant species usually need about 2-3 weeks to associate with FADs (Moreno et al 2007).…”

Modelling the impacts of climate change on skipjack tuna (Katsuwonus pelamis) in the Mozambique Channel

“…This migratory behaviour is related to seasonal variations (Campling, 2012; Kaplan et al, 2014) and linked to an environmental habitat suitability dependent on water temperature, feeding forage and oxygen concentration (Dueri et al, 2014; Lehodey et al, 2013). Variables, such as sea surface height, currents (speed, kinetic energy and direction) and mixed layer depth have also been considered to investigate tuna distribution and habitat preferences (e.g., Lopez et al, 2017; Mugo et al, 2010; Orúe, Lopez, et al, 2020; Orúe, Pennino, et al, 2020; Yen et al, 2016). However, studies analysing climate change impacts on the area are either scarce or non‐existent.…”

“…Although the exploitation of skipjack tuna stocks in the Indian Ocean is currently considered to be sustainable (IOTC, Database), skipjack tuna are highly sensitive to environmental conditions and changes (Loukos et al, 2003; Orúe, Pennino, et al, 2020; Yen et al, 2016). Given that climate change impacts will be particularly significant in marine ecosystems, any variation in environmental factors may lead to changes in fish distribution and catchability (Dueri et al, 2014).…”