Modeling and predicting the growth of the mussel, Mytilus edulis: Implications for planning of aquaculture and eutrophication mitigation

Ecology and Evolution

Volumn 5, Issue 24, 2015, Pages 5920-5933

  Bergström, Per ^a   Lindegarth, Susanne ^a   Lindegarth, Mats ^a  

^a UNIVERSITY OF GOTHENBURG   (Sweden)

Author keywords

Mytilus edulis; Bivalve; Ecosystem function; Growth; Mapping; Marine spatial planning; Predictive modeling

Indexed keywords

EID: 84956612664     PISSN: None     EISSN: 20457758     Source Type: Journal    
DOI: 10.1002/ece3.1823     Document Type: Article

References (75)

1. Araújo, M. B., and M. New . 2007. Ensemble forecasting of species distributions. Trends Ecol. Evol. 22:42-47.
2. Bates, D., M. Maechler, B. Bolker, and S. Walker . 2014. lme4 - linear mixed-effects models using eigen and S4. R package version 1.1-7.
3. Bergström, P., S. Lindegarth, and M. Lindegarth . 2013. Temporal consistency of spatial pattern in growth of the mussel, Mytilus edulis: implications for predictive modelling. Estuar. Coast. Shelf Sci. 131:93-102.
4. Bostock, J., B. McAndrew, R. Richards, K. Jauncey, T. C. Telfer, K. Lorenzen, et al. 2010. Aquaculture: global status and trends. Philos. Trans. R. Soc. Lond. B Biol. Sci. 365:2897-2912.
5. Breiman, L. 2001a. Random forests. Mach. Learn 45:5-32.
6. Breiman, L. 2001b. Statistical modelling: the two cultures. Stat. Sci. 16:199-231.
7. Brigolin, D., A. Davydov, and R. Pastres . 2006. Site selection criteria for off-shore mussel cultivation use: a modelling approach. Interim Report, IR-06-042. International institute for Applied Systems Analysis, Laxenburg.
8. Brown, G., and N. Fagerholm . 2015. Empirical PPGIS/PGIS mapping of ecosystem services: a review and evaluation. Ecosyst. Serv. 13:119-133.
9. Bučas, M., U. Bergström, A. L. Downie, G. Sundblad, M. Gullström, M. S. von Numers, et al. 2013. Empirical modelling of benthic species distribution, abundance, and diversity in the Baltic Sea: evaluating the scope for predictive mapping using different modelling approaches. ICES J. Mar. Sci. 70:1233-1243.
10. COM. 2008. Communication from the commission: Roadmap for Maritime Spatial Planning: achieving Common Principles in the EU. Commission of the European Communities, Brussels.
11. Crain, C. M., B. R. Silliman, S. L. Bertness, and M. D. Bertness . 2004. Physical and biotic drivers of plant distribution across estuarine salinity gradients. Ecology, 85:2539-2549.
12. Crawford, C. M., C. K. A. Macleod, and I. M. Mitchell . 2003. Effects of shellfish farming on the benthic environment. Aquaculture 224:117-140.
13. Crossman, N. D., B. Burkhard, S. Nedkov, L. Willemend, K. Petze, I. Palomo, et al. 2013. A blueprint for mapping and modelling ecosystem services. Ecosyst. Serv. 4:4-14.
14. Dame, R. F. 1996. Ecology of marine bivalves: an ecosystem approach. CRC, Boca Raton, Fl, London.
15. Douvere, F. 2008. The importance of marine spatial planning in advancing ecosystem-based sea use management. Mar. Policy 32:762-771.
16. Douvere, F., and C. N. Ehler . 2009. Neew perspectives on sea use management: initial findings from European experience with marine spatial planning. J. Environ. Manage. 90:77-88.
17. Elith, J., and J. R. Leathwick . 2009. Species distributions models: ecological explanation and prediction across space and time. Annu. Rev. Ecol. Evol. Syst. 40:677-697.
18. Elith, J., C. H. Graham, R. P. Anderson, M. Dudik, S. Ferrier, A. Guisan, et al. 2006. Novel methods improve prediction of species' distributions from occurrence data. Ecography 29:129-151.
19. Ellis, J., V. Cummings, J. Hewitt, S. Thrush, and A. Norkko . 2002. Determining effects of suspended sediment on condition of a suspension feeding bivalve (Atrina zelandica): results of a survet, a laboratory experiment and a field transplant experiment. J. Exp. Mar. Biol. Ecol. 267:147-174.
20. ESRI. 2012. ArcGIS for desktop. ESRI, Redland, CA.
21. European Commission. 2008. Directive 2008/56/EC of the European parliament and of the Council of 17 June 2008 establishing a framework for community action in the field of marine environmental policy (Marine Strategy Framework directive). Official Journal of the European Union.
22. European Commission. 2014. Direcitive 2014/89/EU of the European Parliament and the Council of 23 July 2014 establishing a framework for maritime spatial planning.
23. Ferguson, R. L., and K. Korfmacher . 1997. Remote sensing and GIS analysis of seagrass meadows in North Carolina, USA. Aquat. Bot. 58:241-258.
24. Fielding, A. H., and J. F. Bell . 1997. A review of methods for the assessment of prediction error in conservation presence/absence models. Environ. Conserv. 24:38-49.
25. Filgueira, R., J. Grant, O. Strand, and J. Aure . 2010. A simulation model of carrying capacity for mussel aquaculture in a Norwegian Fjord: role of artificial-induced upwelling. Aquaculture 308:20-27.
26. Filgueira, R., J. Grant, R. Stuart, and M. S. Brown . 2013. Ecosystem modelling for ecosystem-based management of bivalve aquaculture sites in data-poor environments. Aquac. Environ. Interact. 4:117-133.
27. Filgueira, R., J. Grant, and Ø. Strand . 2014. Implementation of marine spatial planning in shellfish aquaculture management: modeling studies in a Norwegian fjord. Ecol. Appl. 24:832-843.
28. Filgueira, R., C. J. Byron, L. A. Comeau, B. Costa-Pierce, P. J. Cranford, J. G. Ferreira, et al. 2015. As we see it- An integrated ecosysstem approach for assessing the potential role of cultivated bivalve shells as part of the carbon trading system. Mar. Ecol. Prog. Ser. 518:281-287.
29. Frid, C. L. J., O. A. L. Paramor, S. Brockington, and J. Bremner . 2008. Incorporating ecological functioning into the designation and management of marine protected areas. Hydrobiologia 606:69-79.
30. Friedman, J. H. 1991. Multivariate adaptive regression splines. Ann. Stat. 19:1-67.
31. Frohn, R. C. 1998. Remote sensing for landscape ecology. Lewis Publishers, Washington, DC.
32. Gallardi, D. 2014. Effects of bivalve aquaculture on the environment and their possible mitigation: a review. Fish. Aquac. J., 5:1-8.
33. Gangnery, A., J.-M. Chabirand, F. Lagarde, P. Le Gall, J. Oheix, C. Bacher, et al. 2003. Growth model of Pacific oyster Crassostrea gigas, cultured in Thau Lagoon (Méditerranée, France). Aquaculture 215:267-290.
34. Grangeré, K., S. Lafebvre, C. Bacher, P. Cugier, and A. Ménesguen . 2010. Modelling the spatial heterogeneity of ecological processes in an intertidal estuarine bay: dynamic interactions between bivalves and phytoplankton. Mar. Ecol. Prog. Ser. 415:141-158.
35. Gren, I.-M., O. Lindahl, and M. Lindqvist . 2009. Values of mussel farming for combating eutrophication: an application to the Baltic Sea. Ecol. Eng. 35:935-945.
36. Guerry, A. D., M. H. Ruckelshaus, K. K. Arkema, J. R. Bernhardt, G. Guannel, C.-K. Kim, et al. 2012. Modeling benefits from nature: using ecosystem services to inform coastal and marine spatial planning. Int. J. Biodivers. Sci. Ecosyst. Serv. Manag., 8:1-15.
37. Guisan, A., and N. E. Zimmermann . 2000. Predictive habitat distribution models in ecology. Ecol. Model. 135:147-186.
38. Hastie, T., and R. Tibshirani . 1986. Generalized additive models. Stat. Sci. 1:297-318.
39. Hauck, J., C. Görg, R. Varjopuro, O. Ratamäki, J. Maes, H. Wittmer, et al. 2013. "Maps have an air of authority". Potential benefits and challenges of ecosystem service maps at different levels of decision making. Ecosyst. Serv. 4:25-32.
40. Hawkins, A. J. S., P. Duarte, J. G. Fang, P. L. Pascoe, J. H. Zhang, X. L. Zhang, et al. 2004. A functional model of responsive suspension-feeding and growth in bivalve shellfish, configured and validated for the scallop Chlamys farreri during culture in China. J. Exp. Mar. Biol. Ecol. 281:13-40.
41. Honkoop, P. C. J., B. L. Bayne, A. J. Underwood, and S. Svensson . 2003. Appropriate experimental design for transplanting mussels (Mytilus sp.) in analyses of environmental stress: an example in Sydnet Harbour (Australia). J. Exp. Mar. Biol. Ecol. 297:253-268.
42. Hothorn, T., K. Hornik, and A. Zeileis . 2006. Unbiased recursive partitioning: a conditional inference framework. J. Comput. Graph. Stat. 15:651-674.
43. Hothorn, T., K. Hornik, C. Strobl, and A. Zeileis . 2013. Party: A Laboratory for Recursive Partytioning.
44. Johnston, C. A. 1998. Geographic Information Systems in Ecology. Blackwell Publishers, Oxford.
45. Joy, M. K., and R. G. Death . 2004. Predictive modelling and spatial mapping of freshwater fish and decapod assemblages using GIS and neural networks. Freshw. Biol. 49:1036-1052.
46. Liaw, A., and M. Wiener . 2012. randomForest - Breiman and Cutler's random forests for classification and regression.
47. Lindahl, O., R. Hart, B. Hernroth, S. Kollberg, L.-O. Loo, L. Olrog, et al. 2005. Improving marine water quality by mussel farming: a profitable solution for Swedish society. Ambio 34:131-138.
48. Lindegarth, M., U. Bergström, J. Mattila, S. Olenin, M. Ollikainen, A.-L. Downie, et al. 2014. Testing the potential for predictive modeling and mapping and extending its use as a tool for evaluating management scenarios and economic valuation in the Baltic Sea (PREHAB). Ambio 43:82-93.
49. Margules, C. R., and R. L. Pressey . 2000. Systematic conservation planning. Nature 405:243-253.
50. Martínez-Harms, M. J., and P. Balvanera . 2012. Methods for mapping ecosystem service supply: a review. Int. J. Biodivers. Sci. Ecosyst. Serv. Manag. 8:17-25.
51. Milborrow, S. 2014. earth: Multivariate Adaptive Regression Spline Models. R package version 3.2-7. http://CRAN.R-project.org/package=earth.
52. Moses, W., A. Gitelsan, S. Berdnikov, and V. Povazhnyy . 2009. Estimation of chlrorophyll-a concentration in case IIwaters using MODIS and MERIS data - Successes and challenges. Environ. Res. Lett. 4:045005.
53. Naturvårdsverket. 2006. Sammanställning och analys av kustnära undervattensmiljö. Report 5591.
54. Peters, R. H. 1991. A critique for ecology. Cambridge Univ. Press, Cambridge.
55. R Core Team. 2014. R: a language and environment for statistical computing. R foundation for statistical computing, Vienna, Austria.
56. Reiss, H., S. Cunze, K. König, H. Neumann, and I. Kröncke . 2011. Species distribution modelling of marine benthos: a North Sea case study. Mar. Ecol. Prog. Ser. 442:71-86.
57. Reiss, H., S. Birchenough, A. Borja, L. Buhl-Mortensen, J. Craeymeersch, J. Dannheim, et al. 2014. Benthos distribution modelling and its relevance for marine ecosystem management. ICES J. Mar. Sci. 72:297-315.
58. Remillard, M. M., and R. A. Welch . 1993. GIS technologies for aquatic macrophyte studies: modeling applications. Landscape Ecol. 8:163-175.
59. Sahlberg, J. 2009. The Coastal Zone Model. SMHI Repoerts Oceanography No.98.
60. Saraiva, S., J. van der Meer, S. A. L. M. Kooijman, and T. Sousa . 2011. Modelling feeding processes in bivalves: a mechanistic approach. Ecol. Model. 222:514-523.
61. Schmolke, A., P. Thorbek, D. L. DeAngelis, and V. Grimm . 2010. Ecological models supporting environmental desicion making: a strategy for the future. Trends Ecol. Evol. 25:479-486.
62. Scott, M. J., P. J. Heglund, M. L. Morrison, J. B. Haufler, M. G. Raphael, W. A. Wall, et al. 2002. Predicting species occurences: issus of accuracy and scale. Island Press, Washington.
63. Shumway, S. E., C. A. Davis, R. Downey, T. Karney, J. Kraeuter, J. Parsons, et al. 2003. Shellfish aquaculture - in phase of sustainable economics and environments. World Aquac. 34:15-17.
64. Silva, C., J. G. Ferreira, S. B. Bricker, T. A. DelValls, M. L. Martín-Diaz, and E. Yáñes . 2011. Site selection for shellfish aquaculture by means of GIS and farm-scale models, with an emphasis on data-poor environments. Aquaculture 318:444-457.
65. SMHI. 2014. Vattenweb. SMHI, http://vattenweb.smhi.se/
66. Stelzenmüller, V., J. Lee, A. South, J. Foden, and S. I. Rogers . 2013. Practical tools to support marine spatial planning: a review and some prototype tools. Mar. Policy 38:214-227.
67. Sutherland, W. J., R. P. Freckleton, H. C. J. Godfray, S. R. Bessinger, T. G. Benton, D. D. Cameron, et al. 2013. Identification of 100 fundamental ecological questions. J. Ecol., 101:58-67.
68. Svensson, J. R., L. Jonsson, and M. Lindegarth . 2013. Excessive spatial resolution decreases performance of quantitative models, contrary to expectationsfrom error analyses. Mar. Ecol. Prog. Ser. 485:57-73.
69. Thomas, Y., J. Mazurié, M. Alunno-Bruscia, C. Bacher, J.-F. Bouget, S. Pouvreau, et al. 2011. Modelling spatio-temporal variability of Mytilus edulis (L.) growth by forcing a dynamic energy budget model with satellite derived environmental data. J. Sea Res. 66:308-317.
70. Tobalske, C. 2002. Effects of spatial scale on the predictive ability of habitat models for the green woodpecker in Switzerland. Pp. 197-204 in J. Haufler, M. Morrison, M. Raphael and B. Wall, eds. Predicting species occurrences: issues of accuracy and scale. Island Press, Covelo, CA.
71. Toner, M., and P. Keddy . 1997. River hydrology and riparian wetlands: predictive model for ecological assembly. Ecol. Appl. 7:236-246.
72. Troy, A., and M. A. Wilson . 2006. Mapping ecosystem services: practical challenges and opportunities in linking GIS and value transfer. Ecol. Econ. 60:435-449.
73. Underwood, A. J. 1997. Experiments in ecology: their logical design and interpretation using analysis of variance. Cambridge Univ. Press, Cambridge.
74. Verbyla, D. L., and J. A. Litvaitis . 1989. Resampling methods for evaluating classification accuracy of wildlife habitat models. Environ. Manage. 13:783-787.
75. Wood, S. 2013. mgcv: Mixed GAM Computation Vehicle with GCV/AIC/REML smoothness estimation. pp. Routines for GAMs and other generalized ridge regression with multiple smoothing parameter selection by GCV, REML or UBRE/AIC. Also GAMMs.