Environmental characteristics associated with the presence of the Spinetail devil ray (Mobula mobular) in the eastern tropical Pacific

Lezama-Ochoa, Nerea; Hall, Martín; Pennino, María Grazia; Stewart, Joshua; López, Jon; Murua, Hilário

doi:10.1371/journal.pone.0220854

Cited by 26 publications

(27 citation statements)

References 78 publications

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“…The Equatorial area was also predicted to be an important area of presence for the species. In that sense, the INLA-SPDE model confirms the results obtained by Lezama-Ochoa, et al 41 with the correct identification of the most important areas for the species.…”

Section: Discussionsupporting

confidence: 89%

“…Regarding evaluation of the model predictions, there are not many differences between frequentist and Bayesian approaches. For example, Lezama-Ochoa, et al 41 obtained similar accuracy indices with slightly better AUC values found in the frequentist GAM model (0.92) than in our INLA model (0.88).…”

Section: Discussionsupporting

confidence: 80%

“…The negative values estimated from the model in the case of the Fishing Aggregating devices show a weak preference of these species for areas where FAD fishery is operating. This is corroborated by the fact that mobulid rays seem to be found significantly more in Dolphin and School sets compared with floating object sets 41 . The reason are unknown, but probably is due to the distribution of FAD sets in open ocean far away from coastal areas; where the productivity is much lower and, hence, mobulid rays do not find high aggregations of food available as in coastal areas.…”

Section: Discussionmentioning

confidence: 61%

“…However, improvements are still needed. For instance, Lezama-Ochoa, et al 41 found that the frequentist GAM model is, from a computational point of view, a faster predictive technique than INLA. The model used by Lezama-Ochoa, et al 41 ran in a few minutes, whereas the INLA models took hours for each trial.…”

Section: Discussionmentioning

confidence: 99%

“…For instance, Lezama-Ochoa, et al 41 found that the frequentist GAM model is, from a computational point of view, a faster predictive technique than INLA. The model used by Lezama-Ochoa, et al 41 ran in a few minutes, whereas the INLA models took hours for each trial. INLA becomes quite slow when estimating non-linear posterior distributions of the covariates in a large datasets 12 , 46 , such as the IATTC database.…”

Section: Discussionmentioning

confidence: 99%

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Using a Bayesian modelling approach (INLA-SPDE) to predict the occurrence of the Spinetail Devil Ray (Mobular mobular)

Lezama-Ochoa

Pennino

Hall

et al. 2020

Sci Rep

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To protect the most vulnerable marine species it is essential to have an understanding of their spatiotemporal distributions. In recent decades, Bayesian statistics have been successfully used to quantify uncertainty surrounding identified areas of interest for bycatch species. However, conventional simulation-based approaches are often computationally intensive. To address this issue, in this study, an alternative Bayesian approach (Integrated Nested Laplace Approximation with Stochastic Partial Differential Equation, INLA-SPDE) is used to predict the occurrence of Mobula mobular species in the eastern Pacific Ocean (EPO). Specifically, a Generalized Additive Model is implemented to analyze data from the Inter-American Tropical Tuna Commission’s (IATTC) tropical tuna purse-seine fishery observer bycatch database (2005–2015). The INLA-SPDE approach had the potential to predict both the areas of importance in the EPO, that are already known for this species, and the more marginal hotspots, such as the Gulf of California and the Equatorial area which are not identified using other habitat models. Some drawbacks were identified with the INLA-SPDE database, including the difficulties of dealing with categorical variables and triangulating effectively to analyze spatial data. Despite these challenges, we conclude that INLA approach method is an useful complementary and/or alternative approach to traditional ones when modeling bycatch data to inform accurately management decisions.

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Section: Discussionsupporting

confidence: 89%

Section: Discussionsupporting

confidence: 80%

Section: Discussionmentioning

confidence: 61%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Using a Bayesian modelling approach (INLA-SPDE) to predict the occurrence of the Spinetail Devil Ray (Mobular mobular)

Lezama-Ochoa

Pennino

Hall

et al. 2020

Sci Rep

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A 40‐year chronology of the vulnerability of spinetail devil ray (Mobula mobular) to eastern Pacific tuna fisheries and options for future conservation and management

Griffiths

Lezama-Ochoa

2021

Aquatic Conservation

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Tuna fisheries are among the largest and most valuable fisheries in the world, but most interact with many non‐target species, including several of high conservation importance. The spinetail devil ray (Mobula mobular) – listed as ‘Endangered’ on the IUCN Red List of Threatened Species – is a commonly discarded bycatch species, particularly in the eastern Pacific Ocean, yet insufficient data exist to undertake a traditional population assessment. A new ecological risk assessment approach designed for data‐limited settings – Ecological Assessment of the Sustainable Impacts of Fisheries (EASI‐Fish) – was used to reconstruct the historical vulnerability status of M. mobular and to simulate potential changes in its status under 45 hypothetical conservation and management measures. These involved various temporal closures of the eastern Pacific Ocean tuna fishery, decreasing post‐capture mortality by improved handling and release practices, and combinations of the two. The species was classified as ‘Least Vulnerable’ between 1979 and 1993, but became ‘Most Vulnerable’ from 1994, which coincided with a rapid spatial expansion of the industrial purse‐seine fishery, and especially from 2011 following the rapid increase in the number of sets made on floating objects. Simulating the conservation and management measures in place in 2018 revealed that 31 of the 45 scenarios resulted in a change in classification of the species to ‘Least Vulnerable’, which primarily involved a reduction of post‐capture mortality by as little as 20%. It is fortuitous in that education of fishers to implement appropriate best handling and release practices is simpler, more rapid and more cost‐effective than the implementation of fishery closures or gear modifications, which can be expensive and complex to implement and monitor and will probably result in substantial reduction in the catches of target species.

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Population structure of endangered spinetail devil ray (Mobula mobular) in the Lesser Sunda Seascape, Indonesia, revealed using microsatellite and mitochondrial DNA

Al Malik,

Putra,

Topan

et al. 2023

Aquat Sci

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Environmental characteristics associated with the presence of the Spinetail devil ray (Mobula mobular) in the eastern tropical Pacific

Cited by 26 publications

References 78 publications

Using a Bayesian modelling approach (INLA-SPDE) to predict the occurrence of the Spinetail Devil Ray (Mobular mobular)

Using a Bayesian modelling approach (INLA-SPDE) to predict the occurrence of the Spinetail Devil Ray (Mobular mobular)

A 40‐year chronology of the vulnerability of spinetail devil ray (Mobula mobular) to eastern Pacific tuna fisheries and options for future conservation and management

Population structure of endangered spinetail devil ray (Mobula mobular) in the Lesser Sunda Seascape, Indonesia, revealed using microsatellite and mitochondrial DNA

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