Skip to main content

Advertisement

SpringerLink
Log in

Modelling of essential fish habitat based on remote sensing, spatial analysis and GIS

  • FISH HABITAT MAPPING
  • Published:
Hydrobiologia Aims and scope Submit manuscript

Abstract

We review the variety of existing modelling approaches applied to species habitat mapping and we discuss issues arising from the availability and nature of sampled biological data and corresponding ecological and environmental habitat descriptors, as well as the different spatial analysis approaches that are selected according to specific hypotheses. We focus on marine species habitat mapping, presenting an overview of work on modelling fish habitat carried out through a European Communities Policy-Support Action, EnviEFH ‘Environmental Approach to Essential Fish Habitat (EFH) Designation’ (2005–2008). The selection of the appropriate habitat model is dataset-specific and the resulting EFH maps are often similar in spite of using different models. Derived EFH maps are based on either environmental ranges (used as minimum and maximum environmental habitat descriptors) or probability of occurrence values. We apply model outputs to regions larger than sampled areas making use of the capacity of satellite data to cover wide areas.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  • Akaike, H., 1974. A new look at the statistical model identification. IEEE Transactions on Automatic Control 19: 716–723.

    Article  Google Scholar 

  • Araujo, M. B., R. J. Whittaker, R. J. Ladle & M. Erhard, 2005. Reducing uncertainty in projections of extinction risk from climate change. Global Ecology and Biogeography 14: 529–528.

    Article  Google Scholar 

  • Austin, M., 2007. Species distribution models and ecological theory: a critical assessment and some possible new approaches. Ecological Modelling 200: 1–19.

    Article  Google Scholar 

  • Austin, M. P. & P. C. Heyligers, 1989. Vegetation survey design for conservation: gradsect sampling of forests in north-east New South Wales. Biological Conservation 50: 13–32.

    Article  Google Scholar 

  • Austin, M. P. & P. C. Heyligers, 1991. New approach to vegetation survey design: gradsect sampling. In Margules, C. R. & M. P. Austin (eds), Nature Conservation: Cost Effective Biological Surveys and Data Analysis. CSIRO, Australia: 31–36.

    Google Scholar 

  • Behrouz, A.-N., A. St-Hilaire, M. Berube, E. Robichaud, N. Thiemonge & B. Bobee, 2006. A review of statistical methods for the evaluation of aquatic habitat suitability for instream flow assessment. River Research and Applications 22: 503–523.

    Article  Google Scholar 

  • Bishop, C. M., 1997. Neural Networks for Pattern Recognition. Oxford University Press, New York: 484.

    Google Scholar 

  • Blackburn, T. M., J. H. Lawton & J. Perry, 1992. A method for estimating the slope of upper bounds in plots of body size and abundance in natural animal assemblages. Oikos 65: 107–112.

    Article  Google Scholar 

  • Bourg, N. A., W. J. McShea & D. E. Gill, 2005. Putting a CART before the search: successful habitat prediction for a rare forest herb. Ecology 86: 2793–2804.

    Article  Google Scholar 

  • Boyce, M. S., P. R. Vernier, S. E. Nielsen & F. K. A. Schmiegelow, 2002. Evaluating resource selection functions. Ecological Modelling 157: 281–300.

    Article  Google Scholar 

  • Brosse, S., J. Guegan, J. Tourenq & S. Lek, 1999. The use of artificial neural networks to assess fish abundance and spatial occupancy in the littoral zone of a mesotrophic lake. Ecological Modelling 120: 299–311.

    Article  Google Scholar 

  • Brotons, L., W. Thuiller, M. B. Araujo & A. H. Hirzel, 2004. Presence–absence versus presence-only modelling methods for predicting bird habitat suitability. Ecography 27: 437–448.

    Article  Google Scholar 

  • Buckland, S. T. & D. A. Elston, 1993. Empirical models for the spatial distribution of wildlife. Journal of Applied Ecology 30: 478–495.

    Article  Google Scholar 

  • Burnham, K. P. & D. R. Anderson, 2004. Multimodel inference – understanding AIC and BIC in model selection. Sociological Methods & Research 33: 261–304.

    Article  Google Scholar 

  • Busby, J. R., 1991. BIOCLIM – A bioclimate analysis and prediction system. In Margules, C. R. & M. P. Austin (eds), Nature Conservation: Cost effective biological surveys and data analysis. CSIRO, Australia: 64–68.

    Google Scholar 

  • Cade, B. S. & B. R. Noon, 2003. A gentle introduction to quantile regression for ecologists. Frontiers in Ecology and the Environment 1: 412–420.

    Google Scholar 

  • Cade, B. S., B. R. Noon & C. H. Flather, 2005. Quantile regression reveals hidden bias and uncertaintly in habitat models. Ecology 86: 786–800.

    Article  Google Scholar 

  • Cade, B. S., J. W. Terrell & R. L. Schroeder, 1999. Estimating effects of limiting factors with regression quantiles. Ecology 80: 311–323.

    Article  Google Scholar 

  • Capen, D. E., J. W. Fenwick, D. B. Inkley & A. C. Boynton, 1986. On the measurement of error: multivariate models of songbird habitat in New England forests. In Verner, J. A., M. L. Morrison & C. J. Ralph (eds), Wildlife 2000: Modelling Habitat Relationships of Terrestrial Vertebrates. University of Wisconsin Press, Madison: 171–175.

    Google Scholar 

  • Carpenter, G., A. N. Gillison & J. Winter, 1993. DOMAIN – a flexible modelling procedure for mapping potential distributions of plants and animals. Biodiversity and Conservation 2: 667–680.

    Article  Google Scholar 

  • Clark, S. J., 2005. Why environmental scientists are becoming Bayesians. Ecology Letters 8: 2–14.

    Article  Google Scholar 

  • Decoursey, D. G., 1992. Developing models with more detail: do more algorithms give more truth? Weed Technology 6: 709–715.

    Google Scholar 

  • Dettki, H., R. Lofstrand & L. Edenius, 2003. Modelling habitat suitability for moose in coastal Northern Sweden: empirical vs. process-oriented approaches. Ambio 32: 549–556.

    Article  PubMed  Google Scholar 

  • Dormann, C. F., J. M. McPherson, M. B. Araujo, R. Bivand, J. Bolliger, G. Carl, R. G. Davies, A. Hirzel, W. Jetz, W. D. Kissling, I. Kuhn, R. Ohlemuller, P. R. Peres-Neto, B. Reineking, B. Schroder, F. M. Schurr & R. Wilson, 2007. Methods to account for spatial autocorrelation in the analysis of species distributional data: a review. Ecography 30: 609–628.

    Article  Google Scholar 

  • Eastwood, P. D., G. J. Meaden & A. Grioche, 2001. Modelling spatial variations in spawning habitat suitability for the sole Solea solea using regression quantiles and GIS procedures. Marine Ecology Progress Series 224: 251–266.

    Article  Google Scholar 

  • Efron, B. & R. Tibshirani, 1991. Statistical data analysis in the computer age. Science 253: 390–395.

    Article  PubMed  Google Scholar 

  • Elith, J. & M. A. Burgman, 2002. Predictions and their validation: rare plants in the Central Highlands, Victoria, Australia. In Scott, J. M. (ed.), Predicting Species occurrences: Issues of Accuracy and Scale. Island Press, Covelo CA: 303–314.

    Google Scholar 

  • Elith, J., C. H. Graham, R. P. Anderson, M. Dudik, S. Ferrier, A. Guisan, R. J. Hijmans, F. Huettmann, J. R. Leathwick, A. Lehmann, J. Li, L. G. Lohmann, B. A. Loiselle, G. Manion, C. Moritz, M. Nakamura, Y. Nakazawa, J. Mc C. Overton, A. T. Peterson, S. J. Phillips, K. S. Richardson, R. Scachetti-Pereira, R. E. Schapire, J. Soberon, S. Williams, M. S. Wisz & N. E. Zimmermann, 2006. Novel methods improve prediction of species’ distributions from occurrence data. Ecography 29: 129–151.

    Article  Google Scholar 

  • ESRI, 1994. ARC Macro Language. Environmental Systems Research Institute Inc. Redlands, CA, USA: 3–37.

    Google Scholar 

  • Ferguson, M. C., J. Barlowa, P. Fiedler, S. B. Reilly & T. Gerrodette, 2006. Spatial models of delphinid (family Delphinidae) encounter rate and group size in the eastern tropical Pacific Ocean. Ecological Modelling 193: 645–662.

    Article  Google Scholar 

  • Ferrier, S. & G. Watson, 1997. An evaluation of the effectiveness of environmental surrogates and modelling techniques in predicting the distribution of biological diversity. Environment, Australia Canberra.

    Google Scholar 

  • Fielding, A. H. & J. F. Bell, 1997. A review of methods for the assessment of prediction errors in conservation presence/absence models. Environmental Conservation 24: 38–49.

    Article  Google Scholar 

  • Fotheringham, A. S., C. Brunsdon & M. E. Charlton, 2002. Geographically Weighted Regression: The Analysis of Spatially Varying Relationships. Chichester, Wiley.

    Google Scholar 

  • Francis, M. P., M. A. Morrison, J. Leathwick, C. Walsh & C. Middleton, 2005. Predictive models of small fish presence and abundance in northern New Zealand harbours. Estuarine, Coastal and Shelf Science 64: 419–435.

    Article  Google Scholar 

  • Friedman, J. H., 1991. Multivariate adaptive regression splines. Annals of Statistics 19: 1–141.

    Article  Google Scholar 

  • Friedman, J. H. & J. J. Meulman, 2003. Multiple adaptive regression trees with application in epidemiology. Statistics in Medicine 22: 1365–1381.

    Article  PubMed  Google Scholar 

  • Giannoulaki, M., A. Machias, V. D. Valavanis, S. Somarakis, A. Palialexis & C. Papaconstantinou, 2006. Spatial modelling of the European anchovy habitat in the Eastern Mediterranean basin using GAMs and GIS technology. General Fisheries Commission for the Mediterranean Scientific Advisory Committee, Sub-Committee for Stock Assessment Working Group on Small Pelagic Species FAO, Rome, 11–14 September 2006.

  • Guisan, A., J. Edwards, C. Thomas & T. Hastie, 2002. Generalized linear and generalized additive models in studies of species distributions: setting the scene. Ecological Modelling 157: 89–100.

    Article  Google Scholar 

  • Guisan, A. & N. E. Zimmermann, 2000. Predictive habitat distribution models in ecology. Ecological Modelling 135: 147–186.

    Article  Google Scholar 

  • Harrell, F. E., 2001. Regression Modelling Strategies With Applications to Linear Models Logistic Regression and Survival Analysis. Springer-Verlag, New York.

    Google Scholar 

  • Hastie, T. & R. Tibshirani, 1990. Generalized additive models. Chapman and Hall, London.

    Google Scholar 

  • Hastie, T., R. Tibshirani & J. H. Friedman, 2001. The elements of statistical learning: data mining, inference and prediction. Springer-Verlag, New York.

    Google Scholar 

  • Heikkinen, R. K., M. Luoto, M. B. Araujo, R. Virkkala, W. Thuiller & M. T. Sykes, 2006. Methods and uncertainties in bioclimatic envelope modelling under climate change. Progress in Physical Geography 30: 1–27.

    Article  Google Scholar 

  • Hiddink, J. G., 2005. Implications of Liebig’s law of the minimum for the use of ecological indicators based on abundance. Ecography 28: 264–271.

    Article  Google Scholar 

  • Hirzel, A. H. & R. Arlettaz, 2003. Modelling habitat suitability for complex species distributions by the environmental-distance geometric mean. Environmental Management 32: 614–623.

    Article  PubMed  Google Scholar 

  • Hirzel, A. H. & A. Guisan, 2002. Which is the optimal sampling strategy for habitat suitability modelling. Ecological Modelling 157: 331–341.

    Article  Google Scholar 

  • Hirzel, A. H., V. Helfer & F. Metral, 2001. Assessing habitat suitability models with a virtual species. Ecological Modelling 145: 111–121.

    Article  Google Scholar 

  • Huisman, J. & F. J. Weissing, 2002. Oscillations and chaos generated by competition for interactively essential resources. Ecological Research 17: 175–181.

    Article  Google Scholar 

  • Jaberg, C. & A. Guisan, 2001. Modelling the distribution of bats in relation to landscape structure in a temperate mountain environment. Journal of Applied Ecology 38: 1169–1181.

    Article  Google Scholar 

  • Johnson, J. B. & K. S. Omland, 2004. Model selection in ecology and evolution. Trends in Ecology and Evolution 19: 101–108.

    Article  PubMed  Google Scholar 

  • Kaiser, M. S., P. L. Speckman & J. R. Jones, 1994. Statistical models for limiting nutrient relations in inland waters. Journal of the American Statistical Association 89: 410–423.

    Article  Google Scholar 

  • Keitt, T. H., O. N. Bjornstad, P. M. Dixon & S. Citron-Pousty, 2002. Accounting for spatial pattern when modelling organism–environment interactions. Ecography 25: 616–625.

    Article  Google Scholar 

  • Kohonen, T., 1996. Self-organizing Maps. Springer-Verlag, New York: 428.

  • Korzukhin, M. D., M. T. Ter-Mikaelian & R. G. Wagner, 1996. Process versus empirical models: which approach for forest ecosystem management? Canadian Journal of Forest Research 26: 879–887.

    Article  Google Scholar 

  • Kuhn, T. S., 1996. The Structure of Scientific Revolutions. University of Chicago Press, USA.

    Google Scholar 

  • Lakatos, I., 1970. Falsification and the methodology of scientific research programmes. In Lakatos, I. & A. Musgrave (eds), Criticism and the Growth of Knowledge. Cambridge University Press, London: 91–195.

    Google Scholar 

  • Laurel, B. J., A. W. Stoner & T. P. Hurst, 2007. Density-dependent habitat selection in marine flatfish: the dynamic role of ontogeny and temperature. Marine Ecology Progress Series 338: 183–192.

    Article  Google Scholar 

  • Lawler, J. J., R. J. O’Connor, C. T. Hunsaker, K. B. Jones, T. R. Loveland & D. White, 2004. The effects of habitat resolution on models of avian diversity and distributions: a comparison of two land-cover classifications. Landscape Ecology 19: 515–530.

    Article  Google Scholar 

  • Leathwick, J. R., J. Elith, M. P. Francis, T. Hastie & P. Taylor, 2006a. Variation in demersal fish species richness in the oceans surrounding New Zealand: an analysis using boosted regression trees. Marine Ecology Progress Series 321: 267–281.

    Article  Google Scholar 

  • Leathwick, J. R., J. Elith & T. Hastie, 2006b. Comparative performance of generalized additive models and multivariate adaptive regression splines for statistical modelling of species distributions. Ecological Modelling 199: 188–196.

    Article  Google Scholar 

  • Leathwick, J. R., D. Rowe, J. Richardson, J. Elith & T. Hastie, 2005. Using multivariate adaptive regression splines to predict the distributions of New Zealand’s freshwater diadromous fish. Freshwater Biology 50: 2034–2052.

    Article  Google Scholar 

  • Legendre, P., 1993. Spatial autocorrelation: trouble or new paradigm? Ecology 74: 1659–1673.

    Article  Google Scholar 

  • Legendre, P. & L. Legendre, 1998. Numerical Ecology. Elsevier.

  • Lehmann, A., J. Mc, C. Overton & J. R. Leathwick, 2002. GRASP: generalized regression analysis and spatial prediction. Ecological Modelling 157: 189–207.

    Article  Google Scholar 

  • Lek, S. & J. F. Guegan, 1999. Artificial neural networks as a tool in ecological modelling, an introduction. Ecological Modelling 120: 65–73.

    Article  Google Scholar 

  • Levins, R., 1966. The strategy of model building in population ecology. American Scientist 54: 421–431.

    Google Scholar 

  • Li, J. & D. Hilbert, in press. A new predictive model, LIVES, for the potential distributions and habitats of species using presence-only data. Ecological Modelling (in press).

  • Loehle, C. & D. LeBlanc, 1996. Model-based assessments of climate change effects on forests: a critical review. Ecological Modelling 90: 1–31.

    Article  CAS  Google Scholar 

  • Lutchman, I., 2003. New technical approaches in fisheries management: the precautionary approach and the ecosystem approach. In De Fontaubert, C. & I. Lutchman (eds), Achieving Sustainable Fisheries: Implementing the New International Legal Regime. IUCN, The World Conservation Union: 31–45.

    Google Scholar 

  • MacLeod, C. D., L. Mandleberg, C. Schweder, S. M. Bannon & G. J. Pierce, 2008. A comparison of approaches to modelling the occurrence of marine animals. Hydrobiologia. doi:10.1007/s10750-008-9491-0.

  • Maggini, R., A. Lehmann, N. E. Zimmermann & A. Guisan, 2006. Improving generalized regression analysis for the spatial prediction of forest communities. Journal of Biogeography 33: 1729–1749.

    Article  Google Scholar 

  • Maller, R. A., 1990. Some aspects of a mixture model for estimating the boundary of a set of data. Journal du Conseil International pour l’exploration de la Mer 46: 140–147.

    Google Scholar 

  • Manel, S., J. M. Dias, S. T. Buckton & S. J. Ormerod, 1999. Alternative methods for predicting species distribution: an illustration with Himalayan river birds. Journal of Applied Ecology 36: 734–747.

    Article  Google Scholar 

  • Maravelias, C. D., E. V. Tsitsika & C. Papaconstantinou, 2007. Evidence of Morocco dentex (Dentex maroccanus) distribution in the NE Mediterranean and relationships with environmental factors determined by Generalized Additive Modelling. Fisheries Oceanography 16: 294–302.

    Article  Google Scholar 

  • McCullagh, P. & J. A. Nelder, 1989. Generalized Linear Models. Chapman & Hall, London.

    Google Scholar 

  • Moisen, G. G. & T. S. Frescino, 2002. Comparing five modelling techniques for predicting forest characteristics. Ecological Modelling 157: 209–225.

    Article  Google Scholar 

  • Morrell, L. J. & R. James, 2008. Mechanisms for aggregation in animals: rule success depends on ecological variables. Behavioral Ecology 19: 193–201.

    Article  Google Scholar 

  • Munoz, J. & A. M. Felicisimo, 2004. A comparison between some statistical methods commonly used in predictive modeling. Journal of Vegetation Science 15: 285–292.

    Article  Google Scholar 

  • Nishida, T. & D. G. Chen, 2004. Incorporating spatial autocorrelation into the general linear model with an application to the yellowfin tuna (Thunnus albacares) longline CPUE data. Fisheries Research 70: 265–274.

    Article  Google Scholar 

  • Olden, J. D. & D. A. Jackson, 2002. A comparison of statistical approaches for modelling fish species distributions. Freshwater Biology 47: 1976–1995.

    Article  Google Scholar 

  • Olivier, F. & S. J. Wotherspoon, 2005. GIS-based application of resource selection functions to the prediction of snow petrel distribution and abundance in East Antarctica: comparing models at multiple scales. Ecological Modelling 189: 105–129.

    Article  Google Scholar 

  • Perry, A. L., P. J. Low, J. R. Ellis & J. D. Reynolds, 2005. Climate change and distribution shifts in marine fishes. Science 308: 1912–1915.

    Article  PubMed  CAS  Google Scholar 

  • Phillips, S. J., R. P. Anderson & R. E. Schapire, 2006. Maximum entropy modelling of species geographic distributions. Ecological Modelling 190: 231–259.

    Article  Google Scholar 

  • Pickett, S. T. A., G. Kolasa & C. G. Jones, 1994. Ecological Understanding: the Nature of Theory and the Theory of Nature. Academic Press, New York.

    Google Scholar 

  • Pierce, G. J., V. D. Valavanis, A. Guerra, P. Jereb, L. Orsi-Relini, J. M. Bellido, I. Katara, U. Piatkowski, J. Pereira, E. Balguerias, I. Sobrino, E. Lefkaditou, J. Wang, M. Santurtun, P. R. Boyle, L. C. Hastie, C. D. MacLeod, J. M. Smith, M. Viana, A. F. González & A. F. Zuur, 2008. A review of cephalopod-environment interactions in European Seas and other world areas. Hydrobiologia.

  • Pierce, G. J., J. Wang & V. D. Valavanis, 2002. Application of GIS to cephalopod fisheries: workshop report. Bulletin of Marine Science 71: 35–46.

    Google Scholar 

  • Pierce, G. J., J. Wang, X. Zheng, J. M. Bellido, P. R. Boyle, V. Denis & J.-P. Robin, 2001. A cephalopod fishery GIS for the Northeast Atlantic: development and application. International Journal of Geographical Information Science 15: 763–784.

    Article  Google Scholar 

  • Pollock, J. F., 2006. Detecting population declines over large areas with presence–absence, time-to-encounter, and count survey methods. Conservation Biology 20: 882–892.

    Article  PubMed  Google Scholar 

  • Popper, K. R., 1963. The Growth of Scientific Knowledge. Routledge, London.

    Google Scholar 

  • Prentice, I. C., 1986. Some concepts and objectives of forest dynamics research. In Fanta, J. (ed.), Forest Dynamics Research in Western and Central Europe. PUDOC, Wageningen: 32–41.

    Google Scholar 

  • Redfern, J. V., M. C. Ferguson, E. A. Becker, K. D. Hyrenbach, C. Good, J. Barlow, K. Kaschner, M. F. Baumgartner, K. A. Forney, L. T. Ballance, P. Fauchald, P. Halpin, T. Hamazaki, A. J. Pershing, S. S. Qian, A. Read, S. B. Reilly, L. Torres & F. Werner, 2006. Techniques for cetacean-habitat modelling. Marine Ecology Progress Series 310: 271–295.

    Article  Google Scholar 

  • Ripley, B. D., 1996. Pattern Recognition and Neural Networks. Cambridge University Press, London: 416.

    Google Scholar 

  • Rubio, G., J. Zhu & J. P. Lynch, 2003. A critical test of the two prevailing theories of plant response to nutrient availability. American Journal of Botany 90: 143–152.

    Article  CAS  Google Scholar 

  • Rushton, S. P., S. J. Ormerod & G. Kerby, 2004. New paradigms for modelling species distributions? Journal of Applied Ecology 41: 193–200.

    Article  Google Scholar 

  • Schwarz, G., 1978. Estimating the dimension of a model. Annals of Statistics 6: 461–464.

    Article  Google Scholar 

  • Segurado, P. & M. B. Araujo, 2004. An evaluation of methods for modelling species distributions. Journal of Biogeography 31: 1555–1568.

    Article  Google Scholar 

  • Shipley, B., 1999. Testing causal explanations in organismal biology: causation, correlation and structural equation modelling. Oikos 86: 374–382.

    Article  Google Scholar 

  • Stockwell, D. R. B., 1992. Machine learning and the problem of prediction and explanation in ecological modelling. Ph.D. Thesis, Australian National University.

  • Stockwell, D. & D. Peters, 1999. The GARP modelling system: problems and solutions to automated spatial prediction. International Journal of Geographical Information Science 13: 143–158.

    Article  Google Scholar 

  • Thompson, J. D., G. Weiblen, B. A. Thompson, S. Alfaro & P. Legendre, 1996. Untangling multiple factors in spatial distributions: lilies, gophers and rocks. Ecology 77: 1698–1715.

    Article  Google Scholar 

  • Tibshirani, R., 1996. Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society 58: 267–288.

    Google Scholar 

  • Ullman, D. S. & P. C. Cornillon, 2000. Evaluation of front detection methods for satellite-derived SST data using in situ observations. Journal of Atmopheric and Oceanic Techology 17: 1667–1675.

    Article  Google Scholar 

  • Valavanis, V. D., S. Georgakarakos, A. Kapantagakis, A. Palialexis & I. Katara, 2004. A GIS environmental modelling approach to Essential Fish Habitat Designation. Ecological Modelling 178: 417–427.

    Article  Google Scholar 

  • Valavanis, V. D., S. Georgakarakos, D. Koutsoubas, C. Arvanitidis & J. Haralabous, 2002. Development of a marine information system for cephalopod fisheries in the Greek seas (eastern Mediterranean). Bulletin of Marine Science 71: 867–882.

    Google Scholar 

  • Valavanis, V. D., I. Katara & A. Palialexis, 2005. Marine GIS: identification of mesoscale oceanic thermal fronts. International Journal of Geographical Information Science 19: 1131–1147.

    Article  Google Scholar 

  • Wagner, H. H. & M. J. Fortin, 2005. Spatial analysis of landscapes: concepts and statistics. Ecology 86: 1975–1987.

    Article  Google Scholar 

  • Wood, S. N., 2006. Generalized Additive Models: An Introduction with R. CRC Press, London.

    Google Scholar 

  • Wright, S., 1921. Correlation and causation. Journal of Agricultural Research 20: 557–585.

    Google Scholar 

  • Zaniewski, A. E., A. Lehman & J. Overton, 2002. Predicting species spatial distributions using presence-only data: a case study of native New Zealand ferns. Ecological Modelling 157: 261–280.

    Article  Google Scholar 

  • Zuur, A. F., E. N. Ieno & G. M. Smith, 2007. Analysing Ecological Data. Springer Series: Statistics for Biology and Health.

Download references

Acknowledgement

This review was supported by the EU-FP6-SSP Action EnviEFH ‘Environmental Approach to Essential Fish Habitat Designation’ (Project No: 022466).

Author information

Authors and Affiliations

  1. Marine GIS Laboratory, Institute of Marine Biological Resources, Hellenic Centre for Marine Research, Thalassocosmos, 71003, Heraklion Crete, Greece

    Vasilis D. Valavanis & Andreas Palialexis

  2. Centro Oceanográfico de Vigo, Instituto Español de Oceanografía, P.O. Box 1552, 36200, Vigo, Spain

    Graham J. Pierce

  3. Highland Statistics Ltd, 6 Laverock road, AB41 6FN, Newburgh, UK

    Alain F. Zuur

  4. Faculty of Geography and Ecology, Kazan State University, 8 Kremlyovskaya St., 420008, Kazan, Republic of Tatarstan, Russian Federation

    Anatoly Saveliev

  5. School of Biological Sciences (Zoology), University of Aberdeen, Tillydrone Avenue, AB24 2TX, Aberdeen, UK

    Graham J. Pierce, Isidora Katara & Jianjun Wang

Authors
  1. Vasilis D. Valavanis
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Graham J. Pierce
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Alain F. Zuur
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Andreas Palialexis
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Anatoly Saveliev
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Isidora Katara
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Jianjun Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Vasilis D. Valavanis.

Additional information

Guest Editor: V. D. Valavanis

Essential Fish Habitat Mapping in the Mediterranean

Rights and permissions

Reprints and permissions

About this article

Cite this article

Valavanis, V.D., Pierce, G.J., Zuur, A.F. et al. Modelling of essential fish habitat based on remote sensing, spatial analysis and GIS. Hydrobiologia 612, 5–20 (2008). https://doi.org/10.1007/s10750-008-9493-y

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10750-008-9493-y

Keywords

Search

Navigation