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A comparison of artificial neural networks and random forests to predict native fish species richness in Mediterranean rivers

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A comparison of artificial neural networks and random forests to predict native fish species richness in Mediterranean rivers

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Olaya Marín, EJ.; Martinez-Capel, F.; Vezza, P. (2013). A comparison of artificial neural networks and random forests to predict native fish species richness in Mediterranean rivers. Knowledge and Management of Aquatic Ecosystems. 409(7):1-19. https://doi.org/10.1051/kmae/2013052

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Title: A comparison of artificial neural networks and random forests to predict native fish species richness in Mediterranean rivers
Author: Olaya Marín, Esther Julia Martinez-Capel, Francisco Vezza, Paolo
UPV Unit: Universitat Politècnica de València. Instituto de Investigación para la Gestión Integral de Zonas Costeras - Institut d'Investigació per a la Gestió Integral de Zones Costaneres
Universitat Politècnica de València. Departamento de Ingeniería Hidráulica y Medio Ambiente - Departament d'Enginyeria Hidràulica i Medi Ambient
Issued date:
[EN] Machine learning (ML) techniques have become important to support decision making in management and conservation of freshwater aquatic ecosystems. Given the large number of ML techniques and to improve the ...[+]

[FR] Les techniques d’apprentissage automatique (ML) sont devenues importantes pour aider à la décision dans la gestion et la conservation des écosystèmes aquatiques d’eau douce. Étant donné le grand nombre de techniques ...[+]
Subjects: Artificial neural networks , Random forests , Native fish , Species richness , Mediterranean rivers , Réseaux de neurones , Forêts aléatoires , Poissons indigènes , Richesse spécifique , Rivières méditerranéennes
Copyrigths: Reserva de todos los derechos
Knowledge and Management of Aquatic Ecosystems. (issn: 1961-9502 )
DOI: 10.1051/kmae/2013052
EDP Sciences
Publisher version: http://dx.doi.org/10.1051/kmae/2013052
Project ID:
Spanish Ministry of Economy and Competitiveness
info:eu-repo/grantAgreement/MICINN//CSD2009-00065/ES/Evaluación y predicción de los efectos del cambio global en la cantidad y la calidad del agua en ríos ibéricos/
Description: The original publication is available at www.kmaejournal.org
This study was partially funded by the Spanish Ministry of Economy and Competitiveness with the projects SCARCE (Consolider-Ingenio 2010 CSD2009-00065) and POTECOL "Evaluacion del Potencial Ecologico de R os Regulados por ...[+]
Type: Artículo


Abrahamsson C., Johansson J., Sparén A. and Lindgren F., 2003. Comparison of different variable selection methods conducted on NIR transmission measurements on intact tablets.Chemometrics Intell. Lab. Syst.,69, 3–12.

Aertsen W., Kint V., van Orshoven J., Özkan K. and Muys B., 2010. Comparison and ranking of different modelling techniques for prediction of site index in Mediterranean mountain forests.Ecol. Model.,221, 1119–1130.

Aertsen W., Kint V., Van Orshoven J. and Muys B., 2011. Evaluation of modelling techniques for forest site productivity prediction in contrasting ecoregions using stochastic multicriteria acceptability analysis (SMAA).Environ. Modell. Softw.,26, 929–937. [+]
Abrahamsson C., Johansson J., Sparén A. and Lindgren F., 2003. Comparison of different variable selection methods conducted on NIR transmission measurements on intact tablets.Chemometrics Intell. Lab. Syst.,69, 3–12.

Aertsen W., Kint V., van Orshoven J., Özkan K. and Muys B., 2010. Comparison and ranking of different modelling techniques for prediction of site index in Mediterranean mountain forests.Ecol. Model.,221, 1119–1130.

Aertsen W., Kint V., Van Orshoven J. and Muys B., 2011. Evaluation of modelling techniques for forest site productivity prediction in contrasting ecoregions using stochastic multicriteria acceptability analysis (SMAA).Environ. Modell. Softw.,26, 929–937.

Alba-Tercedor A., 1996. Macroinvertebrados acuaticos y calidad de las aguas de los ríos, IV Simposio del Agua en Andalucía (SIAGA), Almería, 203–213.

Alcaraz-Hernández J.D., Martínez-Capel F., Peredo-Parada M. and Hernández-Mascarell A.B., 2011. Mesohabitat heterogeneity in four mediterranean streams of the Jucar river basin (Eastern Spain).Limnetica,30, 363–378.

Allan J.D. and Castillo M.M., 2007. Stream ecology: structure and function of running waters, 2nd edn., Springer, Netherlands, 436 p.

Aparicio E., Vargas M.J., Olmo J.M. and de Sostoa A., 2000. Decline of native freshwater fishes in a Mediterranean watershed on the Iberian Peninsula: A quantitative assessment.Environ. Biol. Fishes,59, 11–19.

Aparicio E., Carmona-Catot G., Moyle P.B. and García-Berthou E., 2011. Development and evaluation of a fish-based index to assess biological integrity of Mediterranean streams.Aquat. Conserv.: Mar. Freshwat. Ecosyst.,21, 324–337.

Armitage D.W. and Ober H.K., 2010. A comparison of supervised learning techniques in the classification of bat echolocation calls.Ecol. Inform.,5, 465–473.

Beechie T.J., Sear D.A., Olden J.D., Pess G.R., Buffington J.M., Moir H., Roni P. and Pollock M.M., 2010. Process-based principles for restoring river ecosystems.Bioscience,60, 209–222.

Belmar O., Velasco J. and Martinez-Capel F., 2011. Hydrological classification of natural flow regimes to support environmental flow assessments in Intensively regulated Mediterranean Rivers, Segura River Basin (Spain).Environ. Manage.,47, 992–1004.

Bernardo J.M., Ilhéu M., Matono P. and Costa A.M., 2003. Interannual variation of fish assemblage structure in a Mediterranean river: implications of streamflow on the dominance of native or exotic species.River Res. Appl.,19, 521–532.

Breiman L., 2001a. Random Forests.Mach. Learn.,45, 5–32.

Breiman L., 2001b. Statistical modeling: the two cultures.Stat. Sci.,16, 199–231.

Breiman L., Friedman J., Olshen R. and Stone C., 1984. Classification and Regression Trees, Wadsworth International Group, Belmont, California, 368 p.

Caissie D., 2006. River discharge and channel width relationships for New Brunswick rivers. Canadian Technical Report of Fisheries and Aquatic Sciences, Rept. 2637, 26 p.

Carballo R., Cancela J., Iglesias G., Marín A., Neira X. and Cuesta T., 2009. WFD indicators and definition of the ecological status of rivers.Water Resour. Manag.,23, 2231–2247.

Cheng L., Lek S., Lek-Ang S. and Li Z., 2012. Predicting fish assemblages and diversity in shallow lakes in the Yangtze River basin.Limnologica,42, 127–136.

CHJ, 2007. Estudio general sobre la Demarcación Hidrográfica del Júcar, Confederación Hidrográfica del Júcar, Madrid, 206 p.

Corbacho C. and Sánchez J.M., 2001. Patterns of species richness and introduced species in native freshwater fish faunas of a Mediterranean-type basin: the Guadiana River (southwest Iberian Peninsula).Regul. River.,17, 699–707.

Costa R.M.S., Martínez-Capel F., Muñoz-Mas R., Alcaraz-Hernández J.D. and Garófano-Gómez V., 2012. Habitat suitability modelling at mesohabitat scale and effects of dam operation on the endangered Júcar nase,Parachondrostoma arrigonis(river Cabriel, Spain).River Res. Appl.,28, 740–752.

Cutler D.R., Edwards T.C., Beard K.H., Cutler A., Hess K.T., Gibson J. and Lawler J.J., 2007. Random Forests for classification in ecology.Ecology,88, 2783–2792.

Demuth H., Beale M. and Hagan M., 2010. Neural network toolbox user’s guide, The MathWorks Inc, Natick, Massachusetts, 901 p.

Dimopoulos Y., Bourret P. and Lek S., 1995. Use of some sensitivity criteria for choosing networks with good generalization ability.Neural Process. Lett.,2, 1–4.

Doadrio I., 2001. Atlas y libro rojo de los peces continentales de España, Ministerio de Medio Ambiente, Madrid, 358 p.

Doadrio I., 2002. Origen y Evolución de la Ictiofauna Continental Española.En: Atlas y libro rojo de los peces continentales de España. 2da ed, CSIC y Ministerio del Medio Ambiente, Madrid, 20–34.

Dolloff C.A., Hankin D.G. and Reeves G.H., 1993. Basinwide estimation of habitat and fish populations in streams, U.S. Department of Agriculture, Blacksburg, Virginia, 25 p.

Dormann C.F., 2011. Modelling species’ distributions.In: Jopp F., Reuter H. and Breckling B. (eds.), Modelling complex ecological dynamics: an Introduction into ecological modelling for students, teachers and scientists, Springer-Verlag, Berlin, 179–196.

Drew C.A., Wiersma Y. and Huettmann F., 2011. Predictive species and habitat modeling in landscape ecology: concepts and applications, Springer, New York, 328 p.

Estrela T., Fidalgo A., Fullana J., Maestu J., Pérez M.A. and Pujante A.M., 2004. Júcar Pilot River Basin, provisional article 5 report Pursuant to the Water Framework Directive, Confederación Hidrográfica del Júcar, Valencia, 200 p.

Evans J. and Cushman S., 2009. Gradient modeling of conifer species using random forests.Landsc. Ecol.,24, 673–683.

Evans J.S., Murphy M.A., Holden Z.A. and Cushman S.A., 2011. Modeling species distribution and change using Random Forest.In: Drew C.A., Wiersma Y.F. and Huettmann F. (eds.), Predictive Species and Habitat Modeling in Landscape Ecology, Springer New York, 139–159.

Fausch K., Torgersen C., Baxter C. and Li H., 2002. Landscapes to riverscapes: bridging the gap between research and conservation of stream fishes.Bioscience,52, 483–498.

Ferreira T., Oliveira J., Caiola N., De Sostoa A., Casals F., Cortes R., Economou A., Zogaris S., Garcia de Jalón D., Ilhéu M., Martinez-Capel F., Pont D., Rogers C. and Prenda J., 2007. Ecological traits of fish assemblages from Mediterranean Europe and their responses to human disturbance.Fisheries Manag. Ecol.,14, 473–481.

Filipe A.F., Magalhães M.F. and Collares-Pereira M.J., 2010. Native and introduced fish species richness in Mediterranean streams: the role of multiple landscape influences.Divers. Distrib.,16, 773–785.

Franklin J., 2010. Mapping species distributions: spatial inference and prediction, Cambridge University Press, New York, 338 p.

García-Berthou E., Alcaraz C., Pou-Rovira Q., Zamora L., Coenders G. and Feo C., 2005. Introduction pathways and establishment rates of invasive aquatic species in Europe.Can. J. Fish. Aquat. Sci.,62, 453–463.

Garófano-Gómez V., Martínez-Capel F., Peredo-Parada M., Olaya-Marín E.J., Muñoz-Mas R., Costa R. and Pinar-Arenas L., 2011. Assessing hydromorphological and floristic patterns along a regulated Mediterranean river: The Serpis River (Spain).Limnetica,30, 307–238.

Gevrey M., Dimopoulos I. and Lek S., 2003. Review and comparison of methods to study the contribution of variables in artificial neural network models.Ecol. Model.,160, 249–264.

Goethals P., Dedecker A., Gabriels W., Lek S. and De Pauw N., 2007. Applications of artificial neural networks predicting macroinvertebrates in freshwaters.Aquat. Ecol.,41, 491–508.

Granado-Lorencio C., 1996. Ecología de peces, Universidad de Sevilla, Sevilla, 353 p.

Granado-Lorencio C., 2000. Ecología de comunidades: el paradigma de los peces de agua dulce, Universidad de Sevilla, Sevilla, 284 p.

Guisan A. and Zimmermann N.E., 2000. Predictive habitat distribution models in ecology.Ecol. Model.,135, 147–186.

Gutiérrez-Estrada J.C. and Bilton D.T., 2010. A heuristic approach to predicting water beetle diversity in temporary and fluctuating waters.Ecol. Model.,221, 1451–1462.

Hastie T., Tibshirani R. and Friedman J., 2009. The Elements of Statistical Learning: data mining, Inference and prediction, Springer, 768 p.

Hauser-Davis R.A., Oliveira T.F., Silveira A.M., Silva T.B. and Ziolli R.L., 2010. Case study: Comparing the use of nonlinear discriminating analysis and Artificial Neural Networks in the classification of three fish species: acaras (Geophagus brasiliensis), tilapias (Tilapia rendalli) and mullets (Mugil liza).Ecol. Inform.,5, 474–478.

He Y., Wang J., Lek-Ang S. and Lek S., 2010. Predicting assemblages and species richness of endemic fish in the upper Yangtze River.Sci. Total Environ.,408, 4211–4220.

Hermoso V. and Clavero M., 2011. Threatening processes and conservation management of endemic freshwater fish in the Mediterranean basin: a review.Mar. Freshwater Res.,62, 244–254.

Hooten M.B., 2011. The state of spatial and spatio-temporal statistical modeling.In: Drew C., Wiersma Y. and Huettmann F. (eds.), Predictive Species and Habitat Modeling in Landscape Ecology, Springer New York, 29–41.

Ibarra A.A., Gevrey M., Park Y.-S., Lim P. and Lek S., 2003. Modelling the factors that influence fish guilds composition using a back-propagation network: assessment of metrics for indices of biotic integrity.Ecol. Model.,160, 281–290.

Isa I.S., Omar S., Saad Z. and Osman M.K., 2010. Performance comparison of different multilayer perceptron network activation functions in automated weather classification. Proceedings of the 2010 Fourth Asia International Conference on Mathematical/Analytical Modelling and Computer Simulation, Kota Kinabalu, Malaysia, 71–75.

Jackson D.A., Peres-Neto P.R. and Olden J.D., 2001. What controls who is where in freshwater fish communities the roles of biotic, abiotic, and spatial factors.Can. J. Fish. Aquat. Sci.,58, 157–170.

Jorgensen S.E. and Fath B.D., 2011. Fundamentals of ecological modelling: applications in environmental management and research. 4th ed., Elsevier, Amsterdam, 432 p.

Kampichler C., Wieland R., Calmé S., Weissenberger H. and Arriaga-Weiss S., 2010. Classification in conservation biology: a comparison of five machine-learning methods.Ecol. Inform.,5, 441–450.

Karul C., Soyupak S., Çilesiz A.F., Akbay N. and Germen E., 2000. Case studies on the use of neural networks in eutrophication modeling.Ecol. Model.,134, 145–152.

Knudby A., LeDrew E. and Brenning A., 2010. Predictive mapping of reef fish species richness, diversity and biomass in Zanzibar using IKONOS imagery and machine-learning techniques.Remote Sens. Environ.,114, 1230–1241.

Kroes M.J., Gough P.P., Wanningen H., Schollema P., Ordeix M. and Vesely D., 2006. From sea to source. Practical guidance for the restoration of fish migration in European Rivers. Interreg IIIC Project “Community Rivers”, Groningen, The Netherlands, 119 p.

Kurková V., 1992. Kolmogorov’s theorem and multilayer neural networks.Neural Netw.,5, 501-506.

Leclere J., Oberdorff T., Belliard J. and Leprieur F., 2011. A comparison of modeling techniques to predict juvenile 0 + fish species occurrences in a large river system.Ecol. Inform.,6, 276–285.

Lek S., Scardi M., Verdonschot P., Descy J.P. and Park Y.S. (eds.), 2005. Modelling community structure in freshwater ecosystems, Springer-Verlag, Berlin.

Leopold L.B., Wolman M.G. and Miller J.P., 1964. Fluvial processes in geomorphology, W.H. Freeman, San Francisco, 544 p.

Leprieur F., Brosse S., García-Berthou E., Oberdorff T., Olden J.D. and Townsend C.R., 2009. Scientific uncertainty and the assessment of risks posed by non-native freshwater fishes.Fish. Fish.,10, 88–97.

Liaw A. and Wiener M., 2002. Classification and regression by Random Forest.R News,2, 18–22.

Magalhães M.F., Beja P., Schlosser I.J. and Collares-Pereira M.J., 2007. Effects of multi-year droughts on fish assemblages of seasonally drying Mediterranean streams.Freshw. Biol.,52, 1494–1510.

Maier H.R. and Dandy G.C., 2000. Neural networks for the prediction and forecasting of water resources variables: A review of modelling issues and applications.Environ. Modell. Softw.,15, 101–124.

Mastrorillo S., Dauba F., Oberdorff T., Guégan J.-F. and Lek S., 1998. Predicting local fish species richness in the garonne river basin.C.R. Acad. Sci. - Ser. III - Sciences de la Vie,321, 423–428.

MMARM, 2008. Orden MARM/2656/2008 de 10 septiembre, por la que se aprueba la instrucción de planificación hidrológica. BOE núm. 229, de 22 de septiembre de 2008., Ministerio de Medio Ambiente, y Medio Rural y Marino (MMARM), Madrid.

Mouton A.M., Alcaraz-Hernández J.D., De Baets B., Goethals P.L.M. and Martínez-Capel F., 2011. Data-driven fuzzy habitat suitability models for brown trout in Spanish Mediterranean rivers.Environ. Modell. Softw.,26, 615–622.

Munné A., Prat N., Solà C., Bonada N. and Rieradevall M., 2003. A simple field method for assessing the ecological quality of riparian habitat in rivers and streams: QBR index.Aquat. Conserv.: Mar. Freshwat. Ecosyst.,13, 147–163.

Murphy M.A., Evans J.S. and Storfer A., 2010. QuantifyingBufo boreasconnectivity in Yellowstone National Park with landscape genetics.Ecology,91, 252–261.

Naiman R.J., Decamps H. and Pollock M., 1993. The role of riparian corridors in maintaining regional biodiversity.Ecol. Appl.,3, 209–212.

Oberdorff T., Guégan J.-F. and Hugueny B., 1995. Global scale patterns of fish species richness in rivers.Ecography,18, 345–352.

Olaya-Marín E.J., Martínez-Capel F., Soares Costa R.M. and Alcaraz-Hernández J.D., 2012. Modelling native fish richness to evaluate the effects of hydromorphological changes and river restoration (Júcar River Basin, Spain).Sci. Total Environ.,440, 95–105.

Olden J.D. and Jackson D.A., 2002. Illuminating the “black box”: a randomization approach for understanding variable contributions in artificial neural networks.Ecol. Model.,154, 135–150.

Olden J.D., Poff N.L. and Bledsoe B.P., 2006. Incorporating ecological knowledge into ecoinformatics: An example of modeling hierarchically structured aquatic communities with neural networks.Ecol. Inform.,1, 33–42.

Olden J.D., Lawler J.J. and Poff N.L., 2008. Machine learning methods without tears: A primer for ecologists.Q. Rev. Biol.,83, 171–193.

Ollero A., Ibisate A., Gonzalo L., Acín V., Ballarín D., Díaz E., Gimeno M., Domenech S., Granado D., García H., Mora D. and Sánchez M. 2011. The IHG index for hydromorphological quality assessment of rivers and streams: updated versionLimnetica,30, 255–262.

Özesmi S.L., Tan C.O. and Özesmi U., 2006. Methodological issues in building, training, and testing artificial neural networks in ecological applications.Ecol. Model.,195, 83–93.

Paredes-Arquiola J., Martinez-Capel F., Solera A. and Aguilella V., 2013. Implementing environmental flows in complex water resources systems–case study: the Duero river basin, Spain.River Res. Appl.,29, 451–468.

Paredes-Arquiola J., Solera-Solera A., Martínez-Capel F., Momblanch-Benavent A. and Andreu-Álvarez J. Integrating water management, habitat modelling and water quality at basin scale environmental flow assessment – Tormes River (Spain).Hydrol. Sci. J.-J. Sci. Hydrol., in press.

Poff N.L., Allan J.D., Bain M.B., Karr J.R., Prestegaard K.L., Richter B.D., Sparks R.E. and Stromberg J.C., 1997. The natural klow regime.Bioscience,47, 769–784.

Poff N.L., Richter B.D., Arthington A.H., Bunn S.E., Naiman R.J., Kendy E., Acreman M., Apse C., Bledsoe B.P., Freeman M.C., Henriksen J., Jacobson R.B., Kennen J.G., Merritt D.M., O’Keeffe J.H., Olden J.D., Rogers K., Tharme R.E. and Warner A., 2010. The ecological limits of hydrologic alteration (ELOHA): a new framework for developing regional environmental flow standards.Freshw. Biol.,55, 147–170.

R Development Core Team, 2009. R: A language and environment for statistical computing, R Foundation for Statistical Computing, Vienna, Austria, 409 p.

Reunanen J., 2003. Overfitting in making comparisons between variable selection methods.J. Mach. Learn. Res.,3, 1371–1382.

Reyjol Y., Hugueny B., Pont D., Bianco P.G., Beier U., Caiola N., Casals F., Cowx I., Economou A., Ferreira T., Haidvogl G., Noble R., De Sostoa A., Vigneron T. and Virbickas T., 2007. Patterns in species richness and endemism of European freshwater fish.Glob. Ecol. Biogeogr.,16, 65–75.

Sánchez-Montoya M.M., Vidal-Abarca M.R. and Suárez M.L., 2010. Comparing the sensitivity of diverse macroinvertebrate metrics to a multiple stressor gradient in Mediterranean streams and its influence on the assessment of ecological status.Ecol. Indic.,10, 896–904.

Singh K.P., Basant A., Malik A. and Jain G., 2009. Artificial neural network modeling of the river water quality–A case study.Ecol. Model.,220, 888–895.

Siroky D.S., 2009. Navigating Random Forests and related advances in algorithmic modeling.Statist. Surv.,3, 147–163.

Smith K.G. and Darwall W.R.T., 2006. The status and distribution of freshwater fish endemic to the mediterranean basin, IUCN – The World Conservation Union, Gland, Switzerland/Cambridge, UK., 41 p.

Strayer D.L. and Dudgeon D., 2010. Freshwater biodiversity conservation: recent progress and future challenges.J. N. Am. Benthol. Soc.,29, 344–358.

Tirelli T. and Pessani D., 2009. Use of decision tree and artificial neural network approaches to model presence/absence ofTelestes muticellusin piedmont (North-Western Italy).River Res. Appl.,25, 1001–1012.

Tirelli T. and Pessani D., 2011. Importance of feature selection in decision-tree and artificial-neural-network ecological applications.Alburnus alburnus alborella: A practical example.Ecol. Inform.,6, 309-315.

Tirelli T., Pozzi L. and Pessani D., 2009. Use of different approaches to model presence/absence ofSalmo marmoratusin Piedmont (Northwestern Italy).Ecol. Inform.,4, 234–242.

Townsend C., Begon M. and Harper J., 2008. Essentials of Ecology, 3rd edn, Wiley-Blackwell, Oxford.

van Jaarsveld A.S., Freitag S., Chown S.L., Muller C., Koch S., Hull H., Bellamy C., Kruger M., Endrody-Younga S., Mansell M.W. and Scholtz C.H., 1998. Biodiversity assessment and conservation strategies.Science,279, 2106–2108.

Vezza P., Comoglio C., Rosso M. and Viglione A., 2010. Low flows regionalization in North-Western Italy.Water Resour. Manag.,24, 4049–4074.

Vezza P., Parasiewicz P., Rosso M. and Comoglio C., 2012. Defining minimum environmental flows at regional scale: application of mesoscale habitat models and catchments classification.River Res. Appl.,28, 675–792.

Vila-Gispert A., Alcaraz C. and García-Berthou E., 2005. Life-history traits of invasive fish in small Mediterranean streams.Biol. Invasions,7, 107-116–116.

Vincenzi S., Zucchetta M., Franzoi P., Pellizzato M., Pranovi F., De Leo G.A. and Torricelli P., 2011. Application of a Random Forest algorithm to predict spatial distribution of the potential yield ofRuditapes philippinarumin the Venice lagoon, Italy.Ecol. Model.,222, 1471–1478.

Wells B., Yu C., Koroukian S. and Kattan M., 2011. Comparison of variable selection methods for the generation of parsimonious prediction models for use in clinical practice. In: Proceedings of the 33rd Annual Meeting of the Society for Medical Decision Making, Chicago, US.

Xu L. and Zhang W.-J., 2001. Comparison of different methods for variable selection.Anal. Chim. Acta,446, 475–481.




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