Spatio-temporal distribution of spinetail devil ray Mobula mobular in the eastern tropical Atlantic Ocean

Abstract

The distribution of the spinetail devil ray Mobula mobular in the eastern tropical Atlantic remains poorly known compared to the Pacific and Indian Oceans. We used fisherydependent data and generalized additive models to examine the environmental characteristics associated with the presence of M. mobular in the eastern Atlantic Ocean. Results revealed that the distribution of M. mobular is significantly associated with seasonal upwelling systems in coastal and pelagic areas. Our model predicted the presence of the species in areas where there is evidence of its occurrence, such as the Angolan upwelling system and the coast of Ghana. In addition, our model predicted new hotspot areas, including locations around the Mauritanian upwelling system, the Guinea coast, offshore Ghana and the south coast of Angola and Brazil, where sample sizes are limited. Those areas, as well as the environmental preferences depicted by the model, provide valuable information about the habitat and ecology of the spinetail devil ray. Future research lines derived from this study, as well as its limitations, are discussed. Furthermore, in light of our results we discuss the improvements that are needed to contribute to the conservation and management of this vulnerable species.

Manta and devil ray species occurrence and distribution in Venezuela, assessed through fishery landings and citizen science data

“…birostris were most abundant around the coral atoll systems of Los Roques Archipelago. The site preferences noted here match with what is known about habitat use for these species elsewhere throughout their range (Kashiwagi et al ., 2012, Hacohen‐Domené et al ., 2017, Stevens et al ., 2018, Lezama‐Ochoa et al ., 2020; Garzón et al ., 2021, Farmer et al ., 2022).…”

“…Despite the potential for manta ray-related eco-tourism activities to provide sustainable economic benefits to the area, the Venezuelan political and economic situation has hindered the growth of tourism in recent decades (Gutiérrez & Narváez, 2015). Consequently, citizen science data availability for mobulids and other megafauna in these species elsewhere throughout their range (Kashiwagi et al, 2012, Hacohen-Domené et al, 2017, Lezama-Ochoa et al, 2020Garz on et al, 2021, Farmer et al, 2022.…”

Spatio-Temporal Distribution of Juvenile Oceanic Whitetip Shark Incidental Catch in the Western Indian Ocean

“…Our species distribution model explained a small proportion of total deviance (9%). However, such low explained deviance is common in studies modeling the spatio-temporal distributions of data poor bycatch species (10.2-19%) (Lopez et al, 2017;Lezama-Ochoa et al, 2020). Studies modeling the spatial distributions of commercially important target species of tunas usually explain a higher percentage of total deviance in the models (33.7-62.4%) (Su et al, 2011;Erauskin-Extramiana et al, 2019), in part because these studies include data from fisheries that cover a large (or even whole) area of the species distribution.…”

“…Since 2012, the European Union (EU) and associated flags tropical tuna purse seine fishery have been voluntarily increasing its observer coverage over time, which is currently 100% in the Atlantic and Indian Oceans (Escalle et al, 2016;Ruiz et al, 2018). These fishery observer datasets were recently used to develop SDMs for vulnerable bycatch species, such as whale shark (Rhincodon typus) (Baéz et al, 2020), spinetail devil ray (Lezama-Ochoa et al, 2020), and silky shark (Carcharhinus falciformis) (Lopez et al, 2020) in the Atlantic Ocean.…”

Modelling the distribution of marine fishery resources: Where are we?

“…Compared with the mainland, wide zones of oceans remains unexplored. In fact, there are few studies based on fishery‐independent sampling (Pennino et al, 2019), which makes observations in many instances dependent on fisheries (e.g., Lezama‐Ochoa et al, 2020; Rezende et al, 2019; Rufener et al, 2017). In many countries, fishery data are the only source available to scientists, which requires their careful and validated use due to its commercially‐driven nature, resulting in possible distribution bias (i.e., preferential sampling) (Alglave et al, 2022; Rufener et al, 2021).…”