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

Nerea Lezama-Ochoa,Jon Lopez,Martin A Hall,Maria Grazia Pennino,Hilario Murua

doi:10.1038/s41598-020-73879-3

Abstract

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.

Highlights

The use of Species Distribution Models (SDMs) in conservation ecology has increased substantially in recent years
This study aims to describe the use of the Integrated-Nested Laplace Approximation (INLA)-Stochastic Partial Differential Equations (SPDE) Bayesian approach by using Generalized Additive Models to predict the occurrence of Mobula mobular taken incidentally in the tropical tuna purse-seine fishery of the eastern Pacific Ocean using Inter-American Tropical Tuna Commission (IATTC) observer bycatch data
The present study revealed that when either multiple factors or complex relationships are included in the INLA-SPDE model, the running process finished but the estimation was difficult to interpret

Summary

Introduction

The use of Species Distribution Models (SDMs) in conservation ecology has increased substantially in recent years. The need to account for spatial and temporal autocorrelations in data is common when modelling complex non-linear relationships between species and the environment and quantifying the various sources of uncertainty associated with input data, sampling processes, observer biases and analytical e rrors[9]. If these issues are ignored in SDMs the models could generate misleading estimations of species-environment relationships and misidentifications of predicted suitability areas. The Integrated-Nested Laplace Approximation (INLA) framework proposed by Rue, et al.[14] is a relatively novel, and much faster alternative to MCMC

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