Using a Bayesian modelling approach (INLA-SPDE) to predict the occurrence of the Spinetail Devil Ray (Mobular mobular)
暂无分享,去创建一个
Maria Grazia Pennino | Nerea Lezama-Ochoa | Martin A. Hall | Jon Lopez | Hilario Murua | Jon López | H. Murua | M. Pennino | M. Hall | N. Lezama-Ochoa
[1] Scarla J. Weeks,et al. Biology, ecology and conservation of the Mobulidae. , 2012, Journal of fish biology.
[2] Helen Bailey,et al. A dynamic ocean management tool to reduce bycatch and support sustainable fisheries , 2018, Science Advances.
[3] L. Poggio,et al. Bayesian spatial modelling of soil properties and their uncertainty: The example of soil organic matter in Scotland using R-INLA , 2016 .
[4] R. C. Macridis. A review , 1963 .
[5] G. Moreno,et al. Seasonal Distribution of Tuna and Non-tuna Species Associated With Drifting Fish Aggregating Devices (DFADs) in the Western Indian Ocean Using Fishery-Independent Data , 2020, Frontiers in Marine Science.
[6] Trevor H. Booth,et al. bioclim: the first species distribution modelling package, its early applications and relevance to most current MaxEnt studies , 2014 .
[7] Heidi Dewar,et al. Vulnerabilities and fisheries impacts: The uncertain future of manta and devil rays , 2016 .
[9] John Bell,et al. A review of methods for the assessment of prediction errors in conservation presence/absence models , 1997, Environmental Conservation.
[10] Kate E. Jones,et al. Evaluating Bayesian spatial methods for modelling species distributions with clumped and restricted occurrence data , 2017, bioRxiv.
[11] D. Swofford,et al. Phylogeny of the manta and devilrays (Chondrichthyes: mobulidae), with an updated taxonomic arrangement for the family , 2018 .
[12] H. Rue,et al. Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations , 2009 .
[13] P. Diggle,et al. Model‐based geostatistics , 2007 .
[14] J. Elith,et al. Species Distribution Models: Ecological Explanation and Prediction Across Space and Time , 2009 .
[15] R. Tibshirani,et al. Generalized additive models for medical research , 1986, Statistical methods in medical research.
[16] Trevor Hastie,et al. Generalized linear and generalized additive models in studies of species distributions: setting the scene , 2002 .
[17] M. Cameletti,et al. Spatial and Spatio-temporal Bayesian Models with R - INLA , 2015 .
[18] Alan Y. Chiang,et al. Generalized Additive Models: An Introduction With R , 2007, Technometrics.
[19] P. Kinas,et al. Bayesian spatial predictive models for data-poor fisheries , 2017 .
[20] Andrew C. Parnell,et al. Disentangling spatio-temporal processes in a hierarchical system: a case study in fisheries discards , 2013 .
[21] P. Wade. Bayesian Methods in Conservation Biology , 2000 .
[22] Facundo Muñoz,et al. Fishery-dependent and -independent data lead to consistent estimations of essential habitats , 2016 .
[23] Antonio López-Quílez,et al. Spatio-Temporal model structures with shared components for semi-continuous species distribution modelling , 2017 .
[24] J. Monkman,et al. Setting the scene. , 2019, Nursing the elderly : in hospital, homes and the community.
[25] Jingyi Huang,et al. Evaluating a Bayesian modelling approach (INLA-SPDE) for environmental mapping. , 2017, The Science of the total environment.
[26] WenJun Zhang,et al. Recognizing spatial distribution patterns of grassland insects: neural network approaches , 2008 .
[27] Joshua D. Stewart,et al. Environmental characteristics associated with the presence of the Spinetail devil ray (Mobula mobular) in the eastern tropical Pacific , 2019, PloS one.
[28] R. Pearson. Species’ Distribution Modeling for Conservation Educators and Practitioners , 2010 .
[29] E. Hazen,et al. Oceanographic determinants of ocean sunfish (Mola mola) and bluefin tuna (Thunnus orientalis) bycatch patterns in the California large mesh drift gillnet fishery , 2017 .
[30] Facundo Muñoz,et al. Modeling sensitive elasmobranch habitats , 2013 .
[31] Håvard Rue,et al. Hierarchical analysis of spatially autocorrelated ecological data using integrated nested Laplace approximation , 2012 .
[32] L. Held,et al. Sensitivity analysis in Bayesian generalized linear mixed models for binary data , 2011 .
[33] Ángel M. Fernández,et al. Bayesian spatio-temporal approach to identifying fish nurseries by validating persistence areas , 2015 .
[34] S. Griffiths,et al. Ecological Assessment of the Sustainable Impacts of Fisheries (EASI-Fish): a flexible vulnerability assessment approach to quantify the cumulative impacts of fishing in data-limited settings , 2019, Marine Ecology Progress Series.
[35] H. Murua,et al. Biodiversity and Habitat Characteristics of the Bycatch Assemblages in Fish Aggregating Devices (FADs) and School Sets in the Eastern Pacific Ocean , 2017, Front. Mar. Sci..
[36] Jeffrey C. Mangel,et al. Captures of manta and devil rays by small-scale gillnet fisheries in northern Peru , 2017 .
[37] L. Escalle,et al. Environmental factors and megafauna spatio‐temporal co‐occurrence with purse‐seine fisheries , 2016 .
[38] David Conesa,et al. Species distribution modeling: a statistical review with focus in spatio-temporal issues , 2018, Stochastic Environmental Research and Risk Assessment.
[39] A. Domingo,et al. Mobulid ray by-catch in longline fisheries in the south-western Atlantic Ocean , 2015 .
[40] David Conesa,et al. Identifying the best fishing-suitable areas under the new European discard ban , 2016 .
[41] K. Weng,et al. Movement and habitat use by the spine-tail devil ray in the Eastern Pacific Ocean , 2012 .
[42] W. Thuiller. BIOMOD – optimizing predictions of species distributions and projecting potential future shifts under global change , 2003 .
[43] Håvard Rue,et al. A Bayesian Approach to estimate the biomass of anchovies in the coast of Perú , 2014 .
[44] M. Francis,et al. Movement, depth distribution and survival of spinetail devilrays (Mobula japanica) tagged and released from purse‐seine catches in New Zealand , 2017 .
[45] N. Dulvy,et al. Sympathy for the devil: a conservation strategy for devil and manta rays , 2017, PeerJ.
[46] G. Moreno,et al. Comparing the distribution of tropical tuna associated with drifting fish aggregating devices (DFADs) resulting from catch dependent and independent data , 2020 .
[47] L. Beaumont,et al. Predicting species distributions: use of climatic parameters in BIOCLIM and its impact on predictions of species’ current and future distributions , 2005 .
[48] Jun Yu,et al. Deviance Information Criterion for Comparing Stochastic Volatility Models , 2002 .
[49] Antonio López-Quílez,et al. Bayesian spatio-temporal discard model in a demersal trawl fishery , 2014 .
[50] A. Richardson,et al. Mobulid rays feed on euphausiids in the Bohol Sea , 2017, Royal Society Open Science.
[51] H. Rue,et al. An explicit link between Gaussian fields and Gaussian Markov random fields: the stochastic partial differential equation approach , 2011 .
[52] D. Conesa,et al. Estimation and prediction of the spatial occurrence of fish species using Bayesian latent Gaussian models , 2013, Stochastic Environmental Research and Risk Assessment.
[53] D. Álvarez-Berastegui,et al. Using fisheries data to model the oceanic habitats of juvenile silky shark (Carcharhinus falciformis) in the tropical eastern Atlantic Ocean , 2020, Biodiversity and Conservation.
[54] Finn Lindgren,et al. Bayesian Spatial Modelling with R-INLA , 2015 .
[55] A. Gelfand,et al. Gaussian predictive process models for large spatial data sets , 2008, Journal of the Royal Statistical Society. Series B, Statistical methodology.
[56] Omri Allouche,et al. Assessing the accuracy of species distribution models: prevalence, kappa and the true skill statistic (TSS) , 2006 .
[57] Adam Nicholas Howard Smith. Bayesian Modelling of Direct and Indirect Effects of Marine Reserves on Fishes : A thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Statistics at Massey University, Albany, New Zealand. , 2016 .
[58] N. Lezama-Ochoa,et al. Spatial and temporal distribution of mobulid ray species in the eastern Pacific Ocean ascertained from observer data from the tropical tuna purse-seine fishery , 2019, Environmental Biology of Fishes.
[59] Robert P. Anderson,et al. Maximum entropy modeling of species geographic distributions , 2006 .
[60] Haavard Rue,et al. Bayesian Computing with INLA: A Review , 2016, 1604.00860.
[61] Boris Worm,et al. Applying Bayesian spatiotemporal models to fisheries bycatch in the Canadian Arctic , 2015 .
[62] S. Wood. Generalized Additive Models: An Introduction with R , 2006 .