Identifying fish diversity hot-spots in data-poor situations

Fonseca, Vinícius Prado; Pennino, María Grazia; Nóbrega, Marcelo Francisco de; Oliveira, Jorge Eduardo Lins; Mendes, Liana F.

doi:10.1016/j.marenvres.2017.06.017

Cited by 38 publications

(25 citation statements)

References 55 publications

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“…A collection of some recent examples of spatial applications with the R‐INLA software, intended as a source of inspiration for the reader, follows; environmental risk factors to liver Fluke in cattle (Innocent et al, ) using a spatial random effect to account for regional residual effects; modeling fish populations that are recovering (Boudreau, Shackell, Carson, & den Heyer, ) with a separable space–time model; mapping gender‐disaggregated development indicators (Bosco et al, ) using a spatial model for the residual structure; environmental mapping of soil (Huang, Malone, Minasny, McBratney, & Triantafilis, ) comparing a spatial model in R‐INLA with “REML‐LMM”; changes in fish distributions (Thorson, Ianelli, & Kotwicki, ); febrile illness in children (Dalrymple et al, ); dengue disease in Malaysia (Naeeim & Rahman, ); modeling pancreatic cancer mortality in Spain using a spatial gender‐age‐period‐cohort model (Etxeberria, Goicoa, López‐Abente, Riebler, & Ugarte, ); soil properties in forest (Beguin, Fuglstad, Mansuy, & Paré, ) comparing spatial and nonspatial approaches; ethanol and gasoline pricing (Laurini, ) using a separable space–time model; fish diversity (Fonseca, Pennino, de Nóbrega, Oliveira, & de Figueiredo Mendes, ) using a spatial GRF to account for unmeasured covariates; a spatial model of unemployment (Pereira, Turkman, Correia, & Rue, ); distance sampling of blue whales (Yuan et al, ) using a likelihood for point processes; settlement patterns and reproductive success of prey (Morosinotto, Villers, Thomson, Varjonen, & Korpimäki, ); cortical surface fMRI data (Mejia, Yue, Bolin, Lindren, & Lindquist, ) computing probabilistic activation regions; distribution and drivers of bird species richness (Dyer et al, ) with a global model, and comparing several different likelihoods; socioenvironmental factors in influenza‐like illness (Lee, Arab, Goldlust, Viboud, & Bansal, ); global distributions of Lygodium microphyllum under projected climate warming (Humphreys, Elsner, Jagger, & Pau, ) using a spatial model on the globe; logging and hunting impacts on large animals (Roopsind, Caughlin, Sambhu, Fragoso, & Putz, ); sociodemographic and geographic impact of HPV vaccination (Rutten et al, ); a combined analysis of point‐ and area‐level data (Moraga, Cramb, Mengersen, & Pagano, ); probabilistic prediction of wind power (Lenzi, Pinson, Clemmensen, & Guillot, ); animal tuberculosis (Gortázar, Fernández‐Calle, Collazos‐Martínez, Mínguez‐González, & Acevedo, ); poliovirus eradication in Pakistan (Mercer et al, ) with a Poisson hurdle model; detecting local overfishing (Carson, Shackell, & Flemming, ) from the posterior spatial effect; joint modeling of presence–absence and abundance of hake Paradinas, Conesa, López‐Quílez, and Bellido (); topsoil metals and cancer mortality ...…”

Section: Introductionmentioning

confidence: 99%

Spatial modeling with R‐INLA: A review

Bakka

Rue

Fuglstad

et al. 2018

WIREs Computational Stats

266

222

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Coming up with Bayesian models for spatial data is easy, but performing inference with them can be challenging. Writing fast inference code for a complex spatial model with realistically‐sized datasets from scratch is time‐consuming, and if changes are made to the model, there is little guarantee that the code performs well. The key advantages of R‐INLA are the ease with which complex models can be created and modified, without the need to write complex code, and the speed at which inference can be done even for spatial problems with hundreds of thousands of observations. R‐INLA handles latent Gaussian models, where fixed effects, structured and unstructured Gaussian random effects are combined linearly in a linear predictor, and the elements of the linear predictor are observed through one or more likelihoods. The structured random effects can be both standard areal model such as the Besag and the BYM models, and geostatistical models from a subset of the Matérn Gaussian random fields. In this review, we discuss the large success of spatial modeling with R‐INLA and the types of spatial models that can be fitted, we give an overview of recent developments for areal models, and we give an overview of the stochastic partial differential equation (SPDE) approach and some of the ways it can be extended beyond the assumptions of isotropy and separability. In particular, we describe how slight changes to the SPDE approach leads to straight‐forward approaches for nonstationary spatial models and nonseparable space–time models. This article is categorized under: Statistical and Graphical Methods of Data Analysis > Bayesian Methods and Theory Statistical Models > Bayesian Models Data: Types and Structure > Massive Data

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Section: Introductionmentioning

confidence: 99%

Spatial modeling with R‐INLA: A review

Bakka

Rue

Fuglstad

et al. 2018

WIREs Computational Stats

266

222

View full text Add to dashboard Cite

show abstract

“…Rugosity is used as an index of benthic terrain complexity, accounting for variations in seafloor topography; values of rugosity range between 0 (no terrain variation) and 1 (complete terrain variation), and it is commonly used as a proxy for benthic diversity in the absence of more detailed information on sediment type and structure (Lauria et al 2015). Low rugosity values correspond to unconsolidated substrate, such as mud and sand, while high rugosity values are associated with rocky substrate (Fonseca et al 2017).…”

Section: Methodsmentioning

confidence: 99%

A Hierarchical Bayesian Modeling Approach for the Habitat Distribution of Smooth Dogfish by Sex and Season in Inshore Coastal Waters of the U.S. Northwest Atlantic

et al. 2018

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The Smooth Dogfish Mustelus canis is an abundant, small coastal shark occurring along the U.S. Atlantic coast. Despite being targeted by a directed fishery and having recently undergone a stock assessment that found the population neither overfished nor experiencing overfishing, little is known about the spatial and temporal distribution of this species. Here, we used catch data from the spring and fall Northeast Area Monitoring and Assessment Program's fishery‐independent trawl surveys conducted between 2007 and 2016 and various environmental factors to perform hierarchical Bayesian modeling as a first attempt to spatially predict adult Smooth Dogfish CPUE in U.S. northwest Atlantic Ocean waters by sex and season. Relevant environmental variables differed between both sexes and seasons. Male and female CPUEs were similarly associated with lower salinity and shallower depth in the spring. During fall, male CPUE was associated with sea surface temperature and bottom rugosity, and female CPUE was associated with chlorophyll‐a concentration, bottom rugosity, and year. Habitat modeling results predicted that areas of high male and female CPUEs would overlap during spring but strongly diverge during fall, when greater predicted CPUEs for males were distributed considerably farther north. These results suggest sexual segregation among Smooth Dogfish during fall, with the springtime overlap in distribution coinciding with the pupping and mating season in this population. This difference in distribution during fall may allow for a male‐only directed fishery for Smooth Dogfish in the northern extent of the species’ range in waters near southern New England and Georges Bank.

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“…A real challenge is understanding the importance of top-down cascades versus bottom-up forcing and associated production (Ban et al 2016, Stamoulis et al 2018. Elucidating the drivers that support the production and maintenance of fish biomass as a key feature of biodiversity on large spatial scales is critical for evaluating ecosystem function and performance (Fonseca et al 2017). Thus, contextualizing the processes and determining mechanisms that support fish biomass is critical as we continue to manipulate and manage marine ecosystems.…”

Section: Introductionmentioning

confidence: 99%