Integrating multivariate conditionally autoregressive spatial priors into recursive bivariate models for analyzing environmental sensitivity of mussels

Spatial Statistics - Tập 22 - Trang 419-433 - 2017
Hauke Thaden1, María P. Pata2, Nadja Klein3, Carmen Cadarso-Suárez2, Thomas Kneib1
1Georg August University, Göttingen, Germany
2University of Santiago de Compostela, Spain
3Georg-August University Göttingen, Germany

Tài liệu tham khảo

Austin, 2007, Species distribution models and ecological theory: A critical assessment and some possible new approaches, Ecol. Modell., 200, 1, 10.1016/j.ecolmodel.2006.07.005

Blanchette, 2007, Distribution, abundance, size and recruitment of the mussel, Mytilus californianus, across a major oceanographic and biogeographic boundary at Point Conception, California, USA, J. Exp. Mar. Biol. Ecol., 340, 268, 10.1016/j.jembe.2006.09.014

Bollen, 1989

Broitman, 2005, Recruitment of intertidal invertebrates and oceanographic variability at Santa Cruz Island, California, Limnol. Oceanogr., 50, 1473, 10.4319/lo.2005.50.5.1473

Caballero, G., Garza, M.D., Varela, M., 2008. The governance of mussel production in Galicia: an institutional analysis. In: The European Association of Fisheries Economists Conference.

Caceres-Martinez, 1998, Long-term survey on wild and cultured mussels (Mytilus galloprovincialis) reproductive cycles in the Ria de Vigo (NW Spain), Aquaculture, 162, 141, 10.1016/S0044-8486(98)00210-5

Cesar Aldariz, J., 2000. Species associated with rocky-shore in Galicia (NW Spain), Santiago de Compostela, Ph.D. thesis.

Dame, 2011

Erlandsson, 2004, Spatial structure of recruitment in the mussel Perna perna at local scales: effects of adults, algae and recruit size, Mar. Ecol. Prog. Ser., 267, 173, 10.3354/meps267173

Erlandsson, 2005, Contrasting spatial heterogeneity of sessile organisms within mussel (Perna perna L.) beds in relation to topographic variability, J. Exp. Mar. Biol. Ecol., 314, 79, 10.1016/j.jembe.2004.09.010

Gaylord, 2000, Biological implications of surf-zone complexity, Limnol. Oceanogr., 45, 174, 10.4319/lo.2000.45.1.0174

Gelfand, 2003, Proper multivariate conditional autoregressive models for spatial data analysis, Biostatistics, 4, 11, 10.1093/biostatistics/4.1.11

Greene, 2003

Hoffmann, 2012, Spatio-temporal patterns of larval supply and settlement of intertidal invertebrates reflect a combination of passive transport and larval behavior, J. Exp. Mar. Biol. Ecol., 418, 83, 10.1016/j.jembe.2012.03.008

Klein, 2016, Simultaneous inference in structured additive conditional copula regression models: a unifying Bayesian approach, Stat. Comput., 26, 841, 10.1007/s11222-015-9573-6

Klein, 2015, Bayesian generalized additive models for location, scale, and shape for zero-inflated and overdispersed count data, J. Amer. Statist. Assoc., 110, 405, 10.1080/01621459.2014.912955

Labarta, 2005, Response of mussel recruits to pollution from the prestige oil spill along the Galician coast. A biochemical approach, Mar. Ecol. Prog. Ser., 302, 135, 10.3354/meps302135

Mardia, 1988, Multi-dimensional multivariate Gaussian Markov random elds with application to image processing, Journal of Multivariate Analysis, 24, 265, 10.1016/0047-259X(88)90040-1

McQuaid, 1998, Regionalism in marine biology: the convergence of ecology, economics and politics in South Africa, S. Afr. J. Sci., 94, 433

Menge, 1987, Community regulation: variation in disturbance, competition, and predation in relation to environmental stress and recruitment, Am. Nat., 130, 730, 10.1086/284741

Neelon, 2014, A spatial bivariate probit model for correlated binary data with application to adverse birth outcomes, Stat. Methods Med. Res., 23, 119, 10.1177/0962280212447149

Pata, M.P., 2015. Spatio-temporal modelling of mussel seed recruitment through Structured Additive Regression Models, Santiago de Compostela, Ph.D. thesis.

Pata, 2012, Categorical structured additive regression for assessing habitat suitability in the spatial distribution of mussel seed abundance, Environmetrics, 23, 75, 10.1002/env.1140

Peteiro, 2007, Settlement and recruitment patterns of Mytilus galloprovincialis L. in the Ria de Ares-Betanzos (NW Spain) in the years 2004/2005, Aquacult. Res., 38, 957, 10.1111/j.1365-2109.2007.01757.x

Pfaff, 2011, Upwelling intensity and wave action determine recruitment of intertidal mussels and barnacles in the Southern Benguela upwelling region, Mar. Ecol. Prog. Ser., 425, 141, 10.3354/meps09003

Phillips, 2006, Long-term survey on wild and cultured mussels (Natural variability in size and condition at settlement of three species of marine invertebrates), Integr. Comp. Biol., 64, 598, 10.1093/icb/icl008

Plummer, M., 2016. rjags: Bayesian Graphical Models using MCMC. https://CRAN.R-project.org/package=rjags R package version 4-6.

R Core Team, 2016

Reaugh-Flower, 2011, Scale-dependent patterns and processes of intertidal mussel recruitment around southern Africa, Mar. Ecol. Prog. Ser., 434, 101, 10.3354/meps09169

Rue, 2005, vol. 104

Simkanin, 2005, Using historical data to detect temporal changes in the abundances of intertidalspecies on Irish shores, Journal of Marine Biological Association UK, 85, 1329, 10.1017/S0025315405012506

Smith, 2009, Spatial patterns in recruitment and growth of the mussel Mytilus californianus (Conrad) in southern and northern California, USA, two regions with differing oceanographic conditions, J. Sea Res., 61, 165, 10.1016/j.seares.2008.10.009

Sutherland, 2006

Thaden, H., 2017. General Multivariate Effect Priors in Recursive Bivariate Gaussian Models, Zentrum Für Statistik, Universität Göttingen, Working Paper Series, https://Www.Uni-Goettingen.De/De/13_Thaden_02_2017/558175.html.

Underwood, 2000, Observations in ecology: you cant make progress on processes without understanding the patterns, J. Exp. Mar. Biol. Ecol., 250, 97, 10.1016/S0022-0981(00)00181-7

Wooldridge, 2002

Wright, 1918, On the nature of size factors, Genetics, 3, 367

Xavier, 2007, Abundance, growth and recruitment of Mytilus galloprovincialis on the west coast of South Africa in relation to upwelling, Mar. Ecol. Prog. Ser., 346, 189, 10.3354/meps07007