Hostname: page-component-78c5997874-g7gxr Total loading time: 0 Render date: 2024-11-16T13:26:00.227Z Has data issue: false hasContentIssue false
  • English
  • Français

Patterning the distribution of threatened crayfish and their exotic analogues using self-organizing maps

Published online by Cambridge University Press:  03 June 2010

DOROTHÉE KOPP ,
FRÉDÉRIC SANTOUL ,
NICOLAS POULET ,
ARTHUR COMPIN  and
RÉGIS CÉRÉGHINO
DOROTHÉE KOPP
Affiliation:
EcoLab, UMR 5245, Université de Toulouse, bât 4R3, 118 route de Narbonne, 31062 Toulouse Cedex 9, France
FRÉDÉRIC SANTOUL
Affiliation:
EcoLab, UMR 5245, Université de Toulouse, bât 4R3, 118 route de Narbonne, 31062 Toulouse Cedex 9, France
NICOLAS POULET
Affiliation:
Office National de l'Eau et des Milieux Aquatiques, 16 avenue Louison Bobet, 94132 Fontenay-sous-Bois, France
ARTHUR COMPIN
Affiliation:
EcoLab, UMR 5245, Université de Toulouse, bât 4R3, 118 route de Narbonne, 31062 Toulouse Cedex 9, France
RÉGIS CÉRÉGHINO*
Affiliation:
EcoLab, UMR 5245, Université de Toulouse, bât 4R3, 118 route de Narbonne, 31062 Toulouse Cedex 9, France
*
*Correspondence: Professor Régis Céréghino Tel.: +33 561 558 436 e-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Summary

Ability to demonstrate statistical patterns of distribution by threatened species and by their potential competitors will determine success in forecasting locations at greatest risk, and ability to target management efforts. A self-organizing map algorithm (SOM) was used to derive probabilities of presence of native (Austropotamobius pallipes) and exotic (Orconectes limosus, Pacifastacus leniusculus and Procambarus clarkii) crayfish species with respect to physical and land-cover variables in a large stream system, using a simple presence-absence dataset of species. Crayfish were sampled at 128 sites representing 86 rivers. The probability of occurrence of the native species increased at higher elevations above sea level and lower temperatures; populations appeared to be mostly confined to headwater streams where exotic competitors were unable to withstand the colder conditions. The distribution of exotic species was correlated with anthropogenic factors, such as the degree of urbanization and agricultural land area. Complementary modelling tools, such as GIS and SOMs, can help to maximize the information extracted from available data in the context of biological conservation.

Keywords

freshwaterland useneural networksoccurrencespecies distribution
Type
Papers
Information
Environmental Conservation , Volume 37 , Issue 2 , June 2010 , pp. 147 - 154
Copyright
Copyright © Foundation for Environmental Conservation 2010

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Allan, J.D. (2004) Landscapes and riverscapes: the influence of land-use on stream ecosystems. Annual Review of Ecology Evolution and Systematics 35: 257–284.CrossRefGoogle Scholar
Bessa-Gomes, C. & Petrucci-Fonseca, F. (2003) Using artificial neural networks to assess wolf distribution patterns in Portugal. Animal Conservation 6: 221–229.CrossRefGoogle Scholar
Bramard, M., Demers, A., Trouilhé, M-C., Bachelier, E., Dumas, J-C., Fournier, C., Broussard, E., Robin, O., Souty-Grosset, C. & Grandjean, F. (2006) Distribution of indigenous and non-indigenous crayfish populations in the Poitou-Charentes region (France): evolution over the past 25 years. Bulletin Français de la Pêche et de la Pisciculture 380–381: 857–866.CrossRefGoogle Scholar
Buric, M., Kozak, P. & Kouba, A. (2009) Movement patterns and ranging behavior of the invasive spiny-cheek crayfish in a small reservoir tributary. Fundamental and Applied Limnology 4: 329–337.CrossRefGoogle Scholar
Céréghino, R., Santoul, F., Compin, A. & Mastrorillo, S. (2005) Using self-organizing maps to investigate spatial patterns of non-native species. Biological Conservation 125: 459–465.CrossRefGoogle Scholar
Céréghino, R. & Park, Y.S. (2009) Review of the self-organizing map (SOM) approach in water resources: commentary. Environmental Modelling and Software 24: 945–947.CrossRefGoogle Scholar
Changeux, T. (2003) Evolution de la répartition des écrevisses en France métropolitaine selon les enquêtes nationales menées par le Conseil Supérieur de la Pêche de 1977 à 2001. Bulletin Français de la Pêche et de la Pisciculture 370–371: 15–41.CrossRefGoogle Scholar
Chon, T-S., Park, Y.S., Moon, K.H. & Cha, E.Y. (1996) Patternizing communities by using an artificial neural network. Ecological Modelling 90: 69–78.Google Scholar
Compin, A. & Céréghino, R. (2007) Spatial patterns of macroinvertebrate functional feeding groups in streams in relation to physical variables and land-cover in southwestern France. Landscape Ecology 22: 1215–1225.Google Scholar
Cruickshank, M.M. & Tomlison, R.W. (1996) Application of CORINE land cover methodology to the UK. Some issues raised from Northern Ireland. Global Ecology and Biogeography 4/5: 235–248.Google Scholar
Cruz, M.J. & Rebelo, R. (2007) Colonization of freshwater habitats by an introduced crayfish, Procambarus clarkii, in Southwest Iberian Peninsula. Hydrobiologia 575: 191–201.CrossRefGoogle Scholar
de la Hoz Franco, E.A. & Budy, P. (2005) Effects of biotic and abiotic factors on the distribution of trout and salmon along a longitudinal stream gradient. Environmental Biology of Fishes 72: 379–391.CrossRefGoogle Scholar
Dunn, J.C., McClymont, H.E., Christmas, M. & Dunn, A.M. (2009) Competition and parasitism in the native white-clawed crayfish Austropotamobius pallipes and the invasive signal crayfish Pacifastacus leniusculus in the UK. Biological Invasions 11: 315–324.CrossRefGoogle Scholar
Gevrey, M., Dimopoulos, L. & Lek, S. (2003) Review and comparison of methods to study the contribution of variables in artificial neural network models. Ecological Modelling 160: 249–264.CrossRefGoogle Scholar
Gevrey, M., Rimet, F., Park, Y.S., Giraudel, J.L., Ector, L. & Lek, S. (2004) Water quality assessment using diatom assemblages and advanced modelling techniques. Freshwater Biology 49: 208–220.Google Scholar
Gil-Sánchez, J.M. & Alba-Tercedor, J. (2002) Ecology of the native and introduced crayfishes Austropotamobius pallipes and Procambarus clarkii in southern Spain and implications for conservation of the native species. Biological Conservation 105: 75–80.Google Scholar
Gil-Sánchez, J.M. & Alba-Tercedor, J. (2006) The decline of the endangered populations of the native freshwater crayfish (Austropotamobius pallipes) in southern Spain: it is possible to avoid extinction? Hydrobiologia 559: 113–122.Google Scholar
Gozlan, R.E., Peeler, E.J., Longshaw, M., St-Hilaire, S. & Feist, S.W. (2006) Effect of microbial pathogens on the diversity of aquatic populations, notably in Europe. Microbes and Infection 8: 1358–1364.Google Scholar
Guisan, A. & Zimmermann, N.E. (2000) Predictive habitat distribution models in ecology. Ecological Modelling 135: 147–186.CrossRefGoogle Scholar
Hastie, L.C., Boon, P.J. & Young, M.R. (2000) Physical microhabitat requirements of freshwater pearl mussels, Margaritifera margaritifera (L.). Hydrobiologia 429: 59–71.CrossRefGoogle Scholar
Hobbs, H.H., Jass, J.P. & Huner, J.V. (1989) A review of global crayfish introductions with emphasis on two North American species (Decapoda, Cambaridae). Crustaceana 56: 299–316.CrossRefGoogle Scholar
Holdich, D.M. (2002) Distribution of crayfish in Europe and some adjoining countries. Bulletin Français de la Pêche et de la Pisciculture 367: 611–650.Google Scholar
Jongman, R.H.G., ter Braak, C.J.F. & van Tongeren, O.F.R. (1995) Data Analysis in Community and Landscape Ecology, Fourth edition. Cambridge, UK: Cambridge University Press.Google Scholar
Joy, M.K. & Death, R.G. (2004) Predictive modeling and spatial mapping of freshwater fish and decapods assemblages using GIS and neural networks. Freshwater Biology 49: 1036–1052.Google Scholar
Kangur, K., Park, Y-S, Kangur, A., Kangur, P. & Lek, S. (2007) Patterning long-term changes of fish community in large shallow Lake Peipsi. Ecological Modelling 203: 34–44.CrossRefGoogle Scholar
Kohonen, T. (1982) Self-organized formation of topologically correct feature maps. Biological Cybernetics 43: 59–69.CrossRefGoogle Scholar
Kohonen, T. (2001) Self-Organizing Maps, Third edition. Berlin, Germany: Springer.CrossRefGoogle Scholar
Kohonen, T., Mäkisara, K. & Saramäki, T. (1984) Photonic maps: insightful representation of phonological features for speech recognition. In: Proceedings of 7th International Conference on Pattern Recognition, pp. 182–185. Los Alamitos, NM, USA: IEEE Computer Society Press.Google Scholar
Kozubiková, E., Petrusek, A., Ďuriš, Z., Martin, M.P., Diéguez-Uribeondo, J. & Oidtmann, B. (2008) The old menace is back: recent crayfish plague outbreaks in the Czech Republic. Aquaculture 274: 208–217.CrossRefGoogle Scholar
Legalle, M., Mastrorillo, S. & Céréghino, R. (2008) Spatial distribution patterns and causes of decline of three freshwater species with different biological traits (white-clawed crayfish, bullhead, freshwater pearl mussel): a review. Annales de Limnologie - International Journal of Limnology 44: 95–104.Google Scholar
Lek, S., Delacoste, M., Baran, P., Dimopoulos, I., Lauga, J. & Aulagnier, S. (1996) Application of neural networks to modelling nonlinear relationships in ecology. Ecological Modelling 90: 39–52.CrossRefGoogle Scholar
Lek, S. & Guégan, J.F. (2000) Artificial Neuronal Networks: Application to Ecology and Evolution. Berlin, Germany: Springer.Google Scholar
Light, T., Erman, D.C., Myrick, C. & Clarke, J. (1995) Decline of the Shasta crayfish (Pacifastacus fortis Faxon) of Northeastern California. Conservation Biology 9: 1567–1577.Google Scholar
Manel, S., Dias, J.M., Buckton, S.T. & Ormerod, S.J. (1999) Alternative methods for predicting species distributions: an illustration with Himalayan river birds. Journal of Applied Ecology 36: 734–747.Google Scholar
Margules, C.R. & Austin, M.P. (1994) Biological models for monitoring species decline: the construction and use of data bases. Philosophical Transactions of the Royal Society B 344: 69–75.Google Scholar
Marshall, J.C., Steward, A.L. & Harch, B.D. (2006) Taxonomic resolution and quantification of freshwater macroinvertebrate samples from an Australian dryland river: the benefits and costs of using species abundance data. Hydrobiologia 572: 171–194.Google Scholar
Morgan, J.W. (1998) Patterns of invasion of an urban remnant of a species-rich grassland in southeastern Australia by non-native plant species. Journal of Vegetation Science 9: 181–190.Google Scholar
Moyle, P.B. & Light, T. (1996) Biological invasions of fresh water: empirical rules and assembly theory. Biological Conservation 78: 149–161.Google Scholar
Olden, J.A. & Jackson, D.A. (2001) Fish-habitat relationships in lakes: gaining predictive and explanatory insight by using artificial neural networks. Transactions of the American Fisheries Society 130: 878–897.Google Scholar
Olden, J.A. & Jackson, D.A. (2002) A comparison of statistical approaches for modelling fish species distributions. Freshwater Biology 47: 1976–1995.Google Scholar
Park, Y.-S., Céréghino, R., Compin, A. & Lek, S. (2003) Applications of artificial neural networks for patterning and predicting aquatic insect species richness in running waters. Ecological Modelling 160: 165–280.Google Scholar
Petrusek, A., Filipová, L., Ďuriš, Z., Horká, I., Kozák, P., Policar, T., Štambergová, M. & Kučera, Z. (2006) Distribution of the invasive spiny-cheek crayfish (Orconectes limosus) in the Czech Republic. Past and present. Bulletin Français de la Pêche et de la Pisciculture 380–381: 903–918.CrossRefGoogle Scholar
Renai, B., Bertocchi, S., Brusconi, S., Gherardi, F., Grandjean, F., Lebboroni, M., Parinet, B., Souty Grosset, C. & Trouilhé, M.C. (2006) Ecological characterisation of streams in Tuscany (Italy) for the management of the threatened crayfish Austropotamobius pallipes complex. Bulletin Français de la Pêche et de la Pisciculture 380–381: 1095–1114.CrossRefGoogle Scholar
Ricciardi, A. & Rasmussen, J.B. (1998) Predicting the identity and impact of future invaders: a priority for aquatic resource management. Canadian Journal of Fisheries and Aquatic Sciences 55: 1759–1765.CrossRefGoogle Scholar
Schulz, H.K., Smietana, P. & Schulz, R. (2002) Crayfish occurrence in relation to land-use properties: implementation of a geographic information system (GIS). Bulletin Français de la Pêche et de la Pisciculture 367: 861–872.Google Scholar
Sirola, M., Lampi, G. & Parviainen, J. (2004) Using self-organizing map in a computerized decision support system. In: Neural Information Processing, ed. Pal, N.R., Kasabov, N., Mudi, R.K., Pal, S. & Parui, S.K., pp. 136–141. Berlin, Germany: Springer-Verlag.Google Scholar
Tison, J., Giraudel, J.L., Coste, M., Park, Y.S. & Delmas, F. (2004) Use of unsupervised neural networks for ecoregional zoning of hydrosystems through diatom communities: case study of Adour-Garonne watershed (France). Archiv für Hydrobiologie 159: 409–422.CrossRefGoogle Scholar
Tockner, K., Robinson, C.T. & Uehlinger, U. (2009) Rivers of Europe, First edition. London, UK: Academic Press.Google Scholar
Trouilhé, M-C., Souty-Grosset, C., Grandjean, F., Parinet, B. (2007) Physical and chemical water requirements of white-clawed crayfish (Austropotamobius pallipes) in western France. Aquatic Conservation: Marine and Freshwater Ecosystems 17: 520–538.CrossRefGoogle Scholar
Vesanto, J., Himberg, J., Alhoniemi, E. & Parhankangas, J. (1999) Self-organizing map in Matlab: the SOM Toolbox. In: Proceedings Matlab DSP conference 1999, 16–17 November 1999, Espoo, Finland, pp. 35–40 [www document]. URL http://lib.tkk.fi/Diss/2002/isbn9512258978/article6.pdfGoogle Scholar
Vesanto, J., Himberg, J., Alhoniemi, E. & Parhankangas, J. (2000) SOM Toolbox for Matlab 5. Technical Report A57, Neural Networks Research Centre, Helsinki University of Technology, Helsinki, Finland: 60 pp.Google Scholar
Vorburger, C. & Ribi, G. (1999) Aggression and competition for shelter between a native and an introduced crayfish in Europe. Freshwater Biology 42: 111–119.Google Scholar