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

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.

Geospatial modeling of pre-intervention prevalence of Onchocerca volvulus infection in Ethiopia as an aid to onchocerciasis elimination

“…Next, any variables with an absolute correlation coefficient less than 0.8 with other variables within the group were selected (46). For the set of covariates with a correlation coefficient greater than 0.8 and a VIF greater than 10, only one of the covariates was selected (33,47). The VIF measures how easily a given predictor can be predicted from a linear regression based on other predictors.…”

“…The regression coefficient of significant variables was at least one to two orders of magnitude greater than the non-significant ones. ߠ2 for spatial field -0.09636 (-0.17, -0.046) 95% BCI includes 0.025 quantiles and the 0.975 quantiles of the probability distribution of the coefficients Hyperparameters defining the SPDE mesh were used to calculate the spatial effect and project the spatial field (S6 Fig) . The spatial effect indicates the intrinsic spatial variability in the prevalence estimates, helping us understand the data's spatial structure (47). Further, the spatial field also represents the spatial effect that was not accounted for by the covariates included in the model (55).…”

Developing an Evidence-Based Coexistence Strategy to Promote Human and Wildlife Health in a Biodiverse Agroforest Landscape

“…These include the Master Plan for the Control of Neglected Tropical Diseases in Guinea Bissau, which takes (Lindgren et al, 2011;Rue et al, 2009) using the R-INLA package on R (Blangiardo et al, 2013;Blangiardo and Cameletti, 2015;R Core Team, 2020). Since its recent inception, this efficient statistical framework has become established across various scientific disciplines (Rue et al, 2017;Bakka et al, 2018) including wildlife ecology (e.g., Lezama-Ochoa et al, 2020). Its applicability to camera trap-based ecological research has also been recently demonstrated (Bersacola et al, 2021).…”

Quantifying status and trends from monitoring surveys: application to pygmy whitefish (Prosopium coulterii) in Lake Superior

“…Use of spatial INLA models is becoming common in marine fisheries analyses (e.g. Cosandey-Godin et al 2014;Quiroz et al 2015;Breivik et al 2017;Rufener et al 2017;Nikolioudakis et al 2019;Lezama-Ochoa et al 2020) but has been applied less frequently in freshwater (e.g. Gutowsky et al 2019;Enders et al 2021).…”