Scientia Marina, Vol 74, No 4 (2010)

Forecasting the poleward range expansion of an intertidal species driven by climate alterations

Raquel Xavier
CIBIO, Centro de Investigação em Biodiversidade e Recursos Genéticos, Campus Agrário de Vairão - Departamento de Zoologia-Antropologia, Faculdade de Ciências da Universidade do Porto, Portugal

Fernando P. Lima
CIBIO, Centro de Investigação em Biodiversidade e Recursos Genéticos, Campus Agrário de Vairão - Department of Biological Sciences, University of South Carolina, Portugal

Antonio M. Santos
CIBIO, Centro de Investigação em Biodiversidade e Recursos Genéticos, Campus Agrário de Vairão - Departamento de Zoologia-Antropologia, Faculdade de Ciências da Universidade do Porto, Portugal


Accurate distributional models can be used to reliably predict the response of organisms to climatic changes. Though such models have been extensively applied to terrestrial organisms, they have hardly ever been applied to the marine environment. Recent changes in the distribution of the marine gastropod Patella rustica (L.) were previously modelled with Classification and Regression Tree (CART) and the results revealed that increases in temperature were the major driver of those changes. However, the accuracy scores during the validation of the model were unsatisfactory, preventing its use for forecasting purposes. To fulfil this objective, in the present study a more robust method, Artificial Neural Network (ANN), was employed to produce a model suited to forecasting changes in the distribution of P. rustica. Results confirmed that the ANN model behaved better than the CART, and that it could be used for forecasting future distributional scenarios. The model forecasts that by the 2020s P. rustica is likely to expand its range at least 1000 km northwards. These results should be interpreted with caution considering the dispersal limitations of this species, but if such an expansion took place, major changes in the colonized ecosystems are expected due to the key role of limpets in intertidal communities.


Patella rustica; climate change; range expansion; distributional model; intertidal; Artificial Neural Networks

Full Text:



Araújo, M.B., R.G. Pearson, W. Thuiller and M. Erhard. – 2005. Validation of species–climate impact models under climate change. Glob. Change Biol., 11(9): 1504-1513. doi:10.1111/j.1365-2486.2005.01000.x

Brosse, S., S. Lek and F. Dauba. – 1999. Predicting fish distribution in a mesotrophic lake by hydroacoustic survey and artificial neural networks. Limnol. Oceanogr., 44(5): 1293-1303. doi:10.4319/lo.1999.44.5.1293

Brown, J.H., T.J. Valone and C.G. Curtin. – 1997. Reorganization of an arid ecosystem in response to recent climate change. Proc. Natl. Acad. Sci. U.S.A., 94: 9729-9733. doi:10.1073/pnas.94.18.9729

Chapin, F.S., E.S. Zavaleta, V.T. Eviner, R.L. Naylor, P.M. Vitousek, H.L. Reynolds, D.U. Hooper, S. Lavorel, O.E. SalaI, S.E. Hobbie, M.C. Mack and S. Díaz. – 2000. Consequences of changing biodiversity. Nature, 405: 234-242. doi:10.1038/35012241 PMid:10821284

Crisp, D.J. and A.J. Southward. – 1958. The distribution of intertidal organisms along the coasts of English Channel. J. Mar. Biol. Ass. U. K., 37: 1031-1048. doi:10.1017/S0025315400014909

Ekebom, J., P. Laihonen and T. Suominen. – 2003. A GIS-based step-wise procedure for assencing physical exposure in fragmented archipelagos Estuar. Coast. Shelf Sci., 57(5): 887-898. doi:10.1016/S0272-7714(02)00419-5

Farrar, D.E. and R.R. Glauber. – 1967. Multicollinearity in regression analysis: the problem revisited. Rev. Econ. Statist., 49: 92-107. doi:10.2307/1937887

Firth, L.B. and T.P. Crowe. – 2008. Large-scale coexistence and small-scale segregation of key species on rocky shores. Hydrobiologia, 614: 233-241. doi:10.1007/s10750-008-9509-7

Fischer-Piètte, E. – 1955. Répartition, le long des cotes septentrionales de l’Espagne, des principales espèces peuplant les roches intercotidales. Ann. Inst. Ocèanogr. Paris, 31: 37-124.

Fischer-Piètte, E. – 1959. Repartition des principales espèces intercotidales de la côte atlantique Française en 1954-1955. Ann. Inst. Ocèanogr. Paris, 36: 275-388.

Fischer-Piètte, E. – 1963. La distribution des principaux organismes intercotidaux Nord-Ibériques en 1954-1955. Ann. Inst. Océanogr. Paris, 40: 165-312.

Fischer-Piètte, E. and J.-M. Gaillard. – 1959. Les patelles au long des côtes atlantiques Ibériques et Nord-Marocaines. J. Conchol., 99: 135-200.

Goodman, P.H. – 1996. NevProp software, version 3. In: URL: (ed.), University of Nevada, Reno, NV.

Gordon, C., C. Cooper, C.A. Senior, H.T. Banks, J.M. Gregory, T.C. Johns, J.F.B. Mitchell and R.A. Wood. – 2000. The simulation of SST, sea ice extents and ocean heat transports in a version of the Hadley Centre coupled model without flux adjustments. Clim. Dynam., 16(2/3): 147-168. doi:10.1007/s003820050010

Graham, M.H. – 2003. Confronting multicollinearity in ecological multiple regression. Ecology, 84(11): 2809-2815. doi:10.1890/02-3114

GRASS. – 2006. Geographic Resources Analysis Support System (GRASS).Trento, Italy.

Guisan, A. and W. Thuiller. – 2005. Predicting species distribution: offering more than simple habitat models. Ecol. Lett., 8(9): 993-1009. doi:10.1111/j.1461-0248.2005.00792.x

Guisan, A. and N.E. Zimmermann. – 2000. Predictive habitat distribution models in ecology. Ecol. Model., 135(2): 147-186. doi:10.1016/S0304-3800(00)00354-9

Heikkinen, R.K., M. Luoto, M.B. Araújo, R. Virkkala, W. Thuiller and M.T. Sykes. – 2006. Methods and uncertainties in bioclimatic envelope modelling under climate change. Prog. Phys. Geogr., 30: 751-777. doi:10.1177/0309133306071957

Hijmans, R.J. and C.H. Graham. – 2006. The ability of climate envelope models to predict the effect of climate change on species distributions. Glob. Change Biol., 12: 2272-2281. doi:10.1111/j.1365-2486.2006.01256.x

Johns, T., C. Durman, H. Banks, M. Roberts, A. McLaren, J. Ridley, C. Senior, K. Williams, A. Jones, A. Keen, G. Rickard, S. Cusack, M. Joshi, M. Ringer, B. Dong, H. Spencer, R. Hill, J. Gregory, A. Pardaen, J. Lowe, A. Bodas-Salcedo, S. Stark and Y. Searl. – 2004. HadGEM1 – Model description and analysis of preliminary experiments for the IPCC Fourth Assessment Report. Hadley Centre technical note 55.

Lek, S. and J.F. Guégan. – 1999. Artificial neural networks as a tool in ecological modelling, an introduction. Ecol. Model., 120(2/3): 65-73. Lima, F.P., N. doi:10.1016/S0304-3800(99)00092-7

Queiroz, P.A. Ribeiro, S.J. Hawkins and A.M. Santos.– 2006. Recent changes in the distribution of a marine gastropod, Patella rustica Linnaeus, 1758, and their relationship to unusual climatic events. J. Biogeogr., 33: 812-822. doi:10.1111/j.1365-2699.2006.01457.x

Lima, F.P., P.A. Ribeiro, N. Queiroz, S.J. Hawkins and A.M. Santos.– 2007a. Do distributional shifts of northern and southern species of algae match the warming pattern? Glob. Change Biol., 13: 2592–2604. doi:10.1111/j.1365-2486.2007.01451.x

Lima, F.P., P.A. Ribeiro, N. Queiroz, R. Xavier, P. Tarroso, S.J. Hawkins and A.M. Santos. – 2007b. Modelling past and present geographical distribution of the marine gastropod Patella rustica as a tool for exploring responses to environmental change Glob. Change Biol., 13: 2065-2077. doi:10.1111/j.1365-2486.2007.01424.x

MacLeod, C.D., L. Mandleberg, C. Schweder, S.M. Bannon and G.J. Pierce. – 2008. A comparison of approaches for modelling the occurrence of marine animals. Hydrobiologia, 612: 21-32. doi:10.1007/s10750-008-9491-0

Manel, S., J.M. Dias, S.T. Buckton and S.J. Ormerod. – 1999. Alternative methods for predicting species distribution: an illustration with Himalayan river birds. J. Appl. Ecol., 36(5): 734-747. doi:10.1046/j.1365-2664.1999.00440.x

McLaughlin, J.F., J.J. Hellmann, C.L. Boggs and P.R. Ehrlich. – 2002. Climate change hastens population extinctions. Proc. Natl. Acad. Sci. USA, 99(9): 6070-6074. doi:10.1073/pnas.052131199 PMid:11972020    PMCid:122903

McPherson, J.M., W. Jetz and D.J. Rogers. – 2004. The effects of species’ range sizes on the accuracy of distribution models: ecological phenomenon or statistical artefact? J. Appl. Ecol., 41(5): 811-823. doi:10.1111/j.0021-8901.2004.00943.x

Mitchell, T.D., T.R. Carter, P.D. Hulme and M. New. – 2004. A comprehensive set of high-resolution grids of monthly climate for Europe and the globe: the observed record (1901-2000) and 16 scenarios (2001-2100). Tyndal Working Paper, 55: 1-30.

Muñoz, J. and Á.M. Felicísimo. – 2004. Comparison of statistical methods commonly used in predictive modelling. J. Veg. Sci., 15: 285-292. doi:10.1111/j.1654-1103.2004.tb02263.x

Nakicenovic, N., O. Davidson, G. Davis, A. Grübler, T. Kram, E.L.L. Rovere, Bert Metz, T. Moritz, W. Pepper, H. Pitcher, A. Sankovski, P. Shukla, R. Swart, R. Watson and Z. Dadi _ 2000. IPCC Special Report on Emission Scenarios. IPCC WGIII. Intergovernamental Panel on Climate Change.

Olden, J.D. and D.A. Jackson. – 2002. A comparison of statistical approaches for modelling fish species distributions. Freshwater Biol., 47(10): 1976-1995. doi:10.1046/j.1365-2427.2002.00945.x

Ozesmi, S.L., C.O. Tan and U. Ozesmi. – 2006. Methodological issues in building, training, and testing artificial neural networks in ecological applications. Ecol. Model., 195: 83-93. doi:10.1016/j.ecolmodel.2005.11.012

Park, Y.-S., R. Céréghino, A. Compin and S. Lek. – 2003. Applications of artificial neural networks for patterning and predicting aquatic insect species richness in running waters. Ecol. Model., 160(3): 265-280. doi:10.1016/S0304-3800(02)00258-2

Parmesan, C. – 2006. Ecological and Evolutionary Responses to Recent Climate Change. Annu. Rev. Ecol. Evol. Syst., 37: 637-669. doi:10.1146/annurev.ecolsys.37.091305.110100

Pearson, R.G. and T.P. Dawson. – 2003. Predicting the impacts of climate change on the distribution of species: are bioclimate envelope models useful? Global Ecol. Biogeogr., 12(5): 361-371. doi:10.1046/j.1466-822X.2003.00042.x

Pearson, R.G., Dawson, T.P. and C. Liu. _2004. Modelling species distributions in Britain: a hierarchical integration of climate and land-cover data. Ecography, 27: 285-298. doi:10.1111/j.0906-7590.2004.03740.x

Poloczanska, E.S., S.J. Hawkins, A.J. Southward and M.T. Burrows.– 2008. Modeling the response of populations of competing species to climate change. Ecology, 89(11): 3138-3149. doi:10.1890/07-1169.1

Rayner, N.A., P.Brohan, D.E.Parker, C.K.Folland, J.J.Kennedy, M.Vanicek, T.Ansell and S.F.B.Tett. – 2006. Improved analyses of changes and uncertainties in sea surface temperature measured in situ since the mid-nineteenth century: the new HadSST2 data set. J. Clim., 19(3): 446-469. doi:10.1175/JCLI3637.1

Ribeiro, P.A., R. Xavier, A.M. Santos and S.J. Hawkins. – 2009. Reproductive cycles of four species of Patella (Mollusca: Gastropoda) on the northern and central Portuguese coast. J. Mar. Biol. Ass. U. K., 89(6): 1215-1221. doi:10.1017/S0025315409000320

Soberón, J. and A.T. Peterson. – 2005. Interpretation of models of fundamental ecological niches and species’ distributional areas. Biodivers. Inform., 2: 1-10.

Spitz, F. and S. Lek. – 1999. Environmental impact prediction using neural network modelling. An example in wildlife damage. J. Appl. Ecol., 36(2): 317-326. doi:10.1046/j.1365-2664.1999.00400.x

Thompson, R.C., T.P. Crowe and S.J. Hawkins. – 2002. Rocky intertidal communities: past environmental changes, present status and predictions for the next 25 years. Environ. Conserv., 29(2): 168-191. doi:10.1017/S0376892902000115

Tu, J.V. – 1996. Advantages and Disadvantages of Using Artificial Neural Networks versus Logistic Regression for Predicting Medical Outcomes. J. Clin. Epidem., 49(11): 1225-1231. doi:10.1016/S0895-4356(96)00002-9

Vaughan, I.P. and S.J. Ormerod. – 2003. Improving the quality of distribution models for conservation by addressing shortcomings in the field collection of training data. Conserv. Biol., 17(6): 1601-1611. doi:10.1111/j.1523-1739.2003.00359.x

Woddruff, S.D., H.F. Diaz, J.D. Elms and S.J. Worley. – 1988. ICOADS release 2 data and metadata enhancements for improvements of marine surface flux fields. Phys. Chem. Earth, 23: 517-526. doi:10.1016/S0079-1946(98)00064-0

Yuval, N. – 2001. Enhancement and Error Estimation of Neural Network Prediction of Niño-3.4 SST Anomalies. J. Clim., 14: 2150-2163. doi:10.1175/1520-0442(2001)014<2150:EAEEON>2.0.CO;2

Copyright (c) 2010 Consejo Superior de Investigaciones Científicas (CSIC)

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.

Contact us

Technical support