Bayesian spatial predictive models for data-poor fisheries

Rufener, Marie-Christine; Kinas, Paul Gerhard; Nóbrega, Marcelo Francisco de; Oliveira, Jorge Eduardo Lins

doi:10.1016/j.ecolmodel.2017.01.022

Cited by 26 publications

(15 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…For example, there is no effort data for the analyzed area. Therefore, to measure the state of fish stocks, it is essential to adapt or develop flexible and reliable alternative analytical tools that can perform well with limited available information (Fonseca, Pennino, de Nóbrega, Oliveira, & de Figueiredo Mendes, 2017;Rufener, Kinas, Nóbrega, & Lins Oliveira, 2017).…”

Section: Discussionmentioning

confidence: 99%

Assessing drivers of tropical and subtropical marine fish collapses of Brazilian Exclusive Economic Zone

Verba

Pennino

Coll

et al. 2020

Science of The Total Environment

View full text Add to dashboard Cite

This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

show abstract

Section: Discussionmentioning

confidence: 99%

Assessing drivers of tropical and subtropical marine fish collapses of Brazilian Exclusive Economic Zone

Verba

Pennino

Coll

et al. 2020

Science of The Total Environment

View full text Add to dashboard Cite

show abstract

“…competition, interaction, dispersal, and ontogenetic shift), whenever they are available, in a straightforward way. Basically, it would require the update of distribution maps to include, for instance, the ontogenetic movements of fisheries target species 19 .…”

Section: Discussionmentioning

confidence: 99%

“…g . Zonation and Marxan) are not applicable in “data-poor” fisheries and hence reliable statistical tools that perform well with limited information still need to be developed 18 , 19 .…”

Section: Introductionmentioning

confidence: 99%

“…The proposed framework could be a powerful approach, even in data-poor fisheries, for reconciling the globally available environmental data with Bayesian models capable of dealing with limited information while simultaneously accounting for different sources of uncertainty 19 . Unlike other spatial zoning tools, the proposed decision-supporting mechanism could be considered a flexible white-box approach and be easily adapted to both terrestrial and aquatic contexts.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Searching for a compromise between biological and economic demands to protect vulnerable habitats

Pennino

Rufener

Thomé-Souza

et al. 2018

Sci Rep

Self Cite

View full text Add to dashboard Cite

Identifying vulnerable habitats is necessary to designing and prioritizing efficient marine protected areas (MPAs) to sustain the renewal of living marine resources. However, vulnerable habitats rarely become MPAs due to conflicting interests such as fishing. We propose a spatial framework to help researchers and managers determine optimal conservation areas in a multi-species fishery, while also considering the economic relevance these species may have in a given society, even in data poor situations. We first set different ecological criteria (i.e. species resilience, vulnerability and trophic level) to identify optimal areas for conservation and restoration efforts, which was based on a traditional conservationist approach. We then identified the most economically relevant sites, where the bulk of fishery profits come from. We overlapped the ecologically and economically relevant areas using different thresholds. By ranking the level of overlap between the sites, representing different levels of conflicts between traditional conservation and fishing interests, we suggest alternatives that could increase fishers’ acceptance of protected areas. The introduction of some flexibility in the way conservation targets are established could contribute to reaching a middle ground where biological concerns are integrated with economic demands from the fishing sector.

show abstract

“…Estudos com a aplicação desta metodologia para o grupo de elasmobrânquios são recentes, escassos e dirigidos a espécies do Mar Mediterrâneo (Pennino et al, 2013;Lauria et al, 2015), Báltico (Cosandey-Godin et al, 2015) e Atlântico Norte (Dell'Apa et al, 2017). No Brasil, trabalhos científicos com este tema estão restritos a recursos pesqueiros capturados na região nordeste (Roos et al, 2015;Rufener et al, 2017;Fonseca et al, 2017).…”

Section: Introductionunclassified

Modelagem espacial bayesiana para riqueza de elasmobrânquios do extremo sul do Brasil

Ivanoff¹,

Pennino²,

Rufener³

et al. 2019

Revista CEPSUL

Self Cite

View full text Add to dashboard Cite

Compreender a distribuição espacial de espécies vulneráveis a exploração pesqueira pode subsidiar ações de manejo e conservação. O objetivo deste estudo foi mapear possíveis habitats essenciais para riqueza de elasmobrânquios no sul do Brasil. Os dados são oriundos de cruzeiro científico realizado em fevereiro de 2005 entre o arroio Chuí (33°45'S) e o Cabo de Santa Marta Grande (28°36'S). As informações disponíveis são posições georeferenciadas de 64 estações oceanográficas, a quantificação das capturas de elasmobrânquios e teleósteos com pesca de arrasto de fundo e o registro dos parâmetros ambientais de profundidade, temperatura e salinidade. Os dados foram ajustados a um Modelo Linear Generalizado (GLM) Bayesiano via INLA (Integrated Nested Laplace Approximations) e módulo SPDE (Stochastic Partial Differential Equations), implementados no software R. A qualidade de ajuste e o poder preditivo do modelo foram avaliados mediante DIC (Deviance Information Criterion), WAIC (Watanabe-Akaike Information Criterion) e LCPO (Logarithm Conditional Predictive Ordinate). Foram registradas 16 espécies de elasmobrânquios, cujo número variou de 1 à 9 por lance. No modelo proposto, o efeito espacial e as variáveis ambientais foram relevantes para explicar a variação da riqueza de elasmobrânquios na costa do Rio Grande do Sul. Esta abordagem estatística permite estimar a probabilidade de ocorrência das espécies em áreas não amostradas, levando em conta a autocorrelação espacial dos dados e o conhecimento dos valores das co-variávies ambientais.

show abstract

Bayesian spatial predictive models for data-poor fisheries

Cited by 26 publications

References 35 publications

Assessing drivers of tropical and subtropical marine fish collapses of Brazilian Exclusive Economic Zone

Assessing drivers of tropical and subtropical marine fish collapses of Brazilian Exclusive Economic Zone

Searching for a compromise between biological and economic demands to protect vulnerable habitats

Modelagem espacial bayesiana para riqueza de elasmobrânquios do extremo sul do Brasil

Contact Info

Product

Resources

About