Ecological models at fish community and species level to support effective river restoration

RESUMEN Los peces nativos son indicadores de la salud de los ecosistemas acuáticos, y se han convertido en un elemento de calidad clave para evaluar el estado ecológico de los ríos. La comprensión de los factores que afectan a las especies nativas de peces es importante para la gestión y conservación de los ecosistemas acuáticos. El objetivo general de esta tesis es analizar las relaciones entre variables biológicas y de hábitat (incluyendo la conectividad) a través de una variedad de escalas espaciales en los ríos Mediterráneos, con el desarrollo de herramientas de modelación para apoyar la toma de decisiones en la restauración de ríos. Esta tesis se compone de cuatro artículos. El primero tiene como objetivos modelar la relación entre un conjunto de variables ambientales y la riqueza de especies nativas (NFSR), y evaluar la eficacia de potenciales acciones de restauración para mejorar la NFSR en la cuenca del río Júcar. Para ello se aplicó un enfoque de modelación de red neuronal artificial (ANN), utilizando en la fase de entrenamiento el algoritmo Levenberg-Marquardt. Se aplicó el método de las derivadas parciales para determinar la importancia relativa de las variables ambientales. Según los resultados, el modelo de ANN combina variables que describen la calidad de ribera, la calidad del agua y el hábitat físico, y ayudó a identificar los principales factores que condicionan el patrón de distribución de la NFSR en los ríos Mediterráneos. En la segunda parte del estudio, el modelo fue utilizado para evaluar la eficacia de dos acciones de restauración en el río Júcar: la eliminación de dos azudes abandonados, con el consiguiente incremento de la proporción de corrientes. Estas simulaciones indican que la riqueza aumenta con el incremento de la longitud libre de barreras artificiales y la proporción del mesohabitat de corriente, y demostró la utilidad de las ANN como una poderosa herramienta para apoyar la toma de decisiones en el manejo y restauración ecológica de los ríos Mediterráneos. El segundo artículo tiene como objetivo determinar la importancia relativa de los dos principales factores que controlan la reducción de la riqueza de peces (NFSR), es decir, las interacciones entre las especies acuáticas, variables del hábitat (incluyendo la conectividad fluvial) y biológicas (incluidas las especies invasoras) en los ríos Júcar, Cabriel y Turia. Con este fin, tres modelos de ANN fueron analizados: el primero fue construido solamente con variables biológicas, el segundo se construyó únicamente con variables de hábitat y el tercero con la combinación de estos dos grupos de variables. Los resultados muestran que las variables de hábitat son los ¿drivers¿ más importantes para la distribución de NFSR, y demuestran la importancia ecológica de los modelos desarrollados. Los resultados de este estudio destacan la necesidad de proponer medidas de mitigación relacionadas con la mejora del hábitat (incluyendo la variabilidad de caudales en el río) como medida para conservar y restaurar los ríos Mediterráneos. El tercer artículo busca comparar la fiabilidad y relevancia ecológica de dos modelos predictivos de NFSR, basados en redes neuronales artificiales (ANN) y random forests (RF). La relevancia de las variables seleccionadas por cada modelo se evaluó a partir del conocimiento ecológico y apoyado por otras investigaciones. Los dos modelos fueron desarrollados utilizando validación cruzada k-fold y su desempeño fue evaluado a través de tres índices: el coeficiente de determinación (R2 ), el error cuadrático medio (MSE) y el coeficiente de determinación ajustado (R2 adj). Según los resultados, RF obtuvo el mejor desempeño en entrenamiento. Pero, el procedimiento de validación cruzada reveló que ambas técnicas generaron resultados similares (R2 = 68% para RF y R2 = 66% para ANN). La comparación de diferentes métodos de machine learning es muy útil para el análisis crítico de los resultados obtenidos a través de los modelos. El cuarto artículo tiene como objetivo evaluar la capacidad de las ANN para identificar los factores que afectan a la densidad y la presencia/ausencia de Luciobarbus guiraonis en la demarcación hidrográfica del Júcar. Se utilizó una red neuronal artificial multicapa de tipo feedforward (ANN) para representar relaciones no lineales entre descriptores de L. guiraonis con variables biológicas y de hábitat. El poder predictivo de los modelos se evaluó con base en el índice Kappa (k), la proporción de casos correctamente clasificados (CCI) y el área bajo la curva (AUC) característica operativa del receptor (ROC). La presencia/ausencia de L. guiraonis fue bien predicha por el modelo ANN (CCI = 87%, AUC = 0.85 y k = 0.66). La predicción de la densidad fue moderada (CCI = 62%, AUC = 0.71 y k = 0.43). Las variables más importantes que describen la presencia/ausencia fueron: radiación solar, área de drenaje y la proporción de especies exóticas de peces con un peso relativo del 27.8%, 24.53% y 13.60% respectivamente. En el modelo de densidad, las variables más importantes fueron el coeficiente de variación de los caudales medios anuales con una importancia relativa del 50.5% y la proporción de especies exóticas de peces con el 24.4%. Los modelos proporcionan información importante acerca de la relación de L. guiraonis con variables bióticas y de hábitat, este nuevo conocimiento podría utilizarse para apoyar futuros estudios y para contribuir en la toma de decisiones para la conservación y manejo de especies en los en los ríos Júcar, Cabriel y Turia.Olaya Marín, EJ. (2013). Ecological models at fish community and species level to support effective river restoration [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/28853TESI

Length weight relationships of two endemic fish species in the Júcar River Basin, Iberian Peninsula

This study provides length-weight relationship (LWRs) information for two fish species (family Cyprinidae) in two headwater streams of the Júcar River Basin (Eastern Iberian Peninsula). Both species are endemic to the Iberian Peninsula and have no previous LWR estimates.This study was partially funded by the Conselleria d'Infraestructures, Territori y Medi Ambient of the Generalitat Valenciana. The authors would like to thank the Spanish Ministry of Economy and Competitiveness for its financial support through the project SCARCE (Consolider-Ingenio 2010 CSD2009-00065).Alcaraz-Hernández, JD.; Martinez-Capel, F.; Olaya Marín, EJ. (2015). Length weight relationships of two endemic fish species in the Júcar River Basin, Iberian Peninsula. Journal of Applied Ichthyology. 31(1):246-247. https://doi.org/10.1111/jai.12625246247311Clavero, M., Blanco-Garrido, F., & Prenda, J. (2004). Fish fauna in Iberian Mediterranean river basins: biodiversity, introduced species and damming impacts. Aquatic Conservation: Marine and Freshwater Ecosystems, 14(6), 575-585. doi:10.1002/aqc.636Elvira, B., & Almodovar, A. (2001). Freshwater fish introductions in Spain: facts and figures at the beginning of the 21st century. Journal of Fish Biology, 59(sa), 323-331. doi:10.1111/j.1095-8649.2001.tb01393.xFroese, R. (2006). Cube law, condition factor and weight-length relationships: history, meta-analysis and recommendations. Journal of Applied Ichthyology, 22(4), 241-253. doi:10.1111/j.1439-0426.2006.00805.xGarcía-Berthou, E., Alcaraz, C., Pou-Rovira, Q., Zamora, L., Coenders, G., & Feo, C. (2005). Introduction pathways and establishment rates of invasive aquatic species in Europe. Canadian Journal of Fisheries and Aquatic Sciences, 62(2), 453-463. doi:10.1139/f05-01

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

The original publication is available at www.kmaejournal.org[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 understanding of ML utility in ecology, it is necessary to perform comparative studies of these techniques as a preparatory analysis for future model applications. The objectives of this study were (i) to compare the reliability and ecological relevance of two predictive models for fish richness, based on the techniques of artificial neural networks (ANN) and random forests (RF) and (ii) to evaluate the conformity in terms of selected important variables between the two modelling approaches. The effectiveness of the models were evaluated using three performance metrics: the determination coefficient (R2), the mean squared error (MSE) and the adjusted determination coefficient (R2adj) and both models were developed using a k-fold crossvalidation procedure. According to the results, both techniques had similar validation performance (R2 = 68% for RF and R2 = 66% for ANN). Although the two methods selected different subsets of input variables, both models demonstrated high ecological relevance for the conservation of native fish in the Mediterranean region. Moreover, this work shows how the use of different modelling methods can assist the critical analysis of predictions at a catchment scale.[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 ML pour améliorer la compréhension de l’utilité des ML en écologie, il est nécessaire de réaliser des études comparatives de ces techniques comme analyse préparatoire pour des applications de modèles futurs. Les objectifs de cette étude étaient : (i) de comparer la fiabilité et la pertinence écologique de deux modèles prédictifs pour la richesse de poisson, basé sur les techniques de réseaux de neurones artifi- ciels (ANN) et les forêts aléatoires (RF) et (ii) d’évaluer la conformité en termes de sélection des variables importantes entre les deux approches de modélisation. L’efficacité des modèles a été évaluée au moyen de trois indicateurs de performance : le coefficient de détermination (R2), l’erreur quadratique moyenne (MSE) et le coefficient de détermination ajusté (R2 adj) et les deux modèles ont été développés en utilisant une procédure de validation croisée k-fold. Selon les résultats, les deux techniques ont des performances de validation similaires (R2 = 68 % pour RF et R2 = 66 % pour ANN). Bien que les deux méthodes aient choisi différents sous-ensembles de variables d’entrée, les deux modèles ont démontré la pertinence écologique pour la conservation des poissons indigènes dans la région méditerranéenne. En outre, ce travail montre comment l’utilisation de différentes méthodes de modélisation peut aider à l’analyse critique des prévisions à l’échelle du bassin versant.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 Embalses y Desarrollo de Criterios para su mejora segun la Directiva Marco del Agua" (CGL2007-66412). In addition, the RF analysis was developed in the frame of the EU-funded HolRiverMed project (IEF, Marie Curie Actions). We thank the Confederacion Hidrografica del Jucar (Spanish Ministry of Agriculture, Food and Environment) for the data provided to develop this study and we also owe our gratitude to Sasa Plestenjak for the collaboration in building the first fish database for this research. We owe our gratitude to Chris Holmquist-Johnson and Leanne Hanson (USGS, Fort Collins Science Center) for the scientific review of the paper.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/2013052S1194097Abrahamsson 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 differen

Assessing hydromorphological and floristic patterns along a regulated Mediterranean river : The Serpis River (Spain)

In the European context, several methodologies have been developed to assess the ecological status and, specifically, the hydromorphological status of running surface waters. Although these methodologies have been widely used, few studies have focused on hydrologically altered water bodies and the factors that may determine their status. In this study, the Serpis River was divided into 16 segments from the Beniarr'es dam (40 km from the river mouth) to the sea, all of which are affected by flow regulation, but with different severity. In each segment, an inventory of the flora was conducted, and hydromorphological indices (QBR, Riparian Habitat Quality Index; and IHF, River Habitat Index) were applied. The objectives of the study were (A) to identify relationships between floristic composition and QBR components and (B) between instream habitat characteristics and IHF components as well as (C) to determine the main factors controlling the floristic composition and riparian habitat quality (QBR) and the factors controlling instream habitat characteristics and heterogeneity (IHF). A cluster analysis allowed grouping sites according to their floristic composition and instream habitat characteristics, and non-metric multidimensional scaling (NMDS) was used to ordinate the sites, obtaining the biotic and instream habitat characteristics, as well as the QBR and IHF subindices, which better explained the spatial patterns. Finally, a canonical correspondence analysis (CCA) with predictor variables (geographical, hydrological, geomorphological and human pressures) indicated the main factors controlling the spatial patterns of the floristic composition, instreamhabitat characteristics, riparian habitat quality and instream habitat heterogeneity. A gradient of riparian and instream habitat quality was identified. Our results suggest that physical constraints (presence of a gorge) have protected sites from severe human impacts, resulting in good ecological quality, despite hydrological alteration. This area, where there is geomorphological control, could be potentially reclassified into a different ecotype because regular monitoring could be using incorrect references for index scores, and naturally high scores could be confused with recovery from hydrological alteration or other pressures. The sites with the worst quality were near the river mouth and were characterised by an artificial and highly variable flow regime (related to large autumnal floods and frequent human-induced periods of zero flow). This artificial flow variability as well as the presence of lateral structures in the river channel and geomorphological characteristics were identified as the main factors driving the hydromorphological and floristic pattern in this regulated river.Diversas metodologías han sido desarrolladas en el contexto europeo para evaluar el estado ecológico, y específicamente el estado hidromorfológico de las aguas superficiales. Aunque éstas han sido ampliamente utilizadas, pocos estudios se han centrado en masas de agua hidrológicamente alteradas y en los factores que condicionan su estado. En este estudio, el río Serpis fue dividido en 16 segmentos desde la presa de Beniarrés (a 40 km de la desembocadura) hasta el mar, todos ellos afectados por la regulación de caudales con distinta severidad. En cada segmento se realizó un inventario florístico y se aplicaron índices hidromorfológicos (QBR, Calidad del Bosque de Ribera, e IHF, Índice de Hábitat Fluvial). Los objetivos del estudio fueron (A) identificar relaciones entre la composición florística y los componentes del QBR, (B) entre las caracteríısticas del hábitat fluvial y los componentes del IHF, (C) determinar los principales factores que controlan la composición florística y la calidad del hábitat ripario (QBR), y las características del hábitat fluvial y su heterogeneidad (IHF). Un cluster permitió agrupar los puntos de muestreo según su composición florística y las características del hábitat fluvial, y un escalado multidimensional no-métrico (NMDS) fue usado para ordenar los puntos, obteniendo las variables bióticas y características del hábitat y los subindices del QBR e IHF, respectivamente, que explicaban mejor los patrones espaciales. Finalmente, un análisis de correspondencias canónicas (CCA) con variables predictoras (geográficas, hidrológicas, geomorfológicas y presiones humanas) indicó los principales factores que controlan los patrones espaciales de la composición florística, las características del hábitat fluvial, la calidad del hábitat ripario y la heterogeneidad del hábitat fluvial. Se identificó un gradiente de calidad del hábitat ripario y fluvial. Los resultados sugieren que las limitaciones físicas (presencia de un cañón) han protegido a los tramos de impactos humanos severos, resultando en una buena calidad ecológica a pesar de la alteración hidrológica. Esta zona podría potencialmente ser reclasificada en un ecotipo diferente, ya que un monitoreo regular podría estar usando referencias incorrectas para los índices y sus altas puntuaciones naturales se podrían estar confundiendo con una recuperación de la alteración hidrológica o de otras presiones. Los puntos de muestreo con peor calidad estuvieron cerca de la desembocadura y tuvieron un régimen de caudales alterado y muy variable. Esta variabilidad artificial del caudal, junto con la presencia de estructuras laterales en el cauce y las características geomorfológicas fueron identificadas como los principales factores determinantes del patrón hidromorfológico y florístico en este río regulado

Modelling critical factors affecting the distribution of the vulnerable endemic Eastern Iberian barbel (Luciobarbus guiraonis) in Mediterranean rivers

[EN] Luciobarbus guiraonis (Eastern Iberian barbel) is an endemic fish species restricted to Spain, mainly distributed in the Júcar River Basin District. Its study is important because there is little knowledge about its biology and ecology. To improve the knowledge about the species distribution and habitat requirements, nonlinear modelling was carried out to predict the presence/absence and density of the Eastern Iberian barbel, based on 155 sampling sites distributed throughout the Júcar River Basin District (Eastern Iberian Peninsula). We used multilayer feed-forward artificial neural networks (ANN) to represent nonlinear relationships between L. guiraonis descriptors and variables regarding the physical habitat and biological components (macroinvertebrates, fish, riparian forest). The gradient descent algorithm was implemented to find the optimal model parameters; the importance of the ANN s input variables was determined by the partial derivatives method. The predictive power of the model was evaluated with the Cohen s kappa (k), the correctly classified instances (CCI), and the area under the curve (AUC) of the receiver operator characteristic (ROC) plots. The best model predicted presence/absence with a high performance (k= 0.66, CCI= 87% and AUC= 0.85); the prediction of density was moderate (CCI = 62%, AUC=0.71 and k= 0.43). The fundamental variables describing the presence/absence were; solar radiation (the highest contribution was observed between 2000 and 4200 WH/m2), drainage area (with the strongest influence between 3000 and 5.000 km2), and the proportion of exotic fish species (with relevant contribution between 50 and 100%). In the density model, the most important variables were the coefficient of variation of mean annual flows (relative importance of 50.5%) and the proportion of exotic fish species (24.4%). The models provide important information about the relation of L. guiraonis with biotic and abiotic variables, this new knowledge can help develop future studies and management plans for the conservation of this species in the Júcar River Basin District and, potentially, for the conservation of other endemic fish species of Barbus and Luciobarbus in Mediterranean rivers.This study was partially funded by the MINECO, Spanish Ministry of Economy and Competitiveness, with the projects SCARCE (Consolider-Ingenio 2010 CSD2009-00065), IMPADAPT (CGL2013-48424- C2-1-R) and with Feder funds. There was also funding from the Project POTECOL “Evaluación del Potencial Ecológico de Ríos Regulados por Embalses y Desarrollo de Criterios para su mejora según la Directiva Marco del Agua” (CGL2007-66412). In addition, the present research was developed in the frame of the EU-funded HolRiverMed project (Marie Curie Actions, IntraEuropean Fellowships for the mobility of European researchers). We thank the Confederación Hidrográfica del Júcar (Spanish Ministry of Agriculture, Food and Environment) for the data provided to develop this study and we also owe our gratitude to Juan Jiménez for the collaboration in building the first fish database for this research.Olaya Marín, EJ.; Martinez-Capel, F.; García Bartual, RL.; Vezza, P. (2016). Modelling critical factors affecting the distribution of the vulnerable endemic Eastern Iberian barbel (Luciobarbus guiraonis) in Mediterranean rivers. Mediterranean Marine Science. 17(1):264-279. https://doi.org/10.12681/mms.1351S26427917

Evaluacion de los patrones hidromorfol ´ ogicos y flor ´ ´ısticos a lo largo de un r´ıo mediterraneo regulado; el r ´ ´ıo Serpis (Espana)

[EN] In the European context, several methodologies have been developed to assess the ecological status and, specifically, the hydromorphological status of running surface waters. Although these methodologies have been widely used, few studies have focused on hydrologically altered water bodies and the factors that may determine their status. In this study, the Serpis River was divided into 16 segments from the Beniarrés dam (40 km from the river mouth) to the sea, all of which are affected by flow regulation, but with different severity. In each segment, an inventory of the flora was conducted, and hydromorphological indices (QBR, Riparian Habitat Quality Index; and IHF, River Habitat Index) were applied. The objectives of the study were (A) to identify relationships between floristic composition and QBR components and (B) between instream habitat characteristics and IHF components as well as (C) to determine the main factors controlling the floristic composition and riparian habitat quality (QBR) and the factors controlling instream habitat characteristics and heterogeneity (IHF). A cluster analysis allowed grouping sites according to their floristic composition and instream habitat characteristics, and non-metric multidimensional scaling (NMDS) was used to ordinate the sites, obtaining the biotic and instream habitat characteristics, as well as the QBR and IHF subindices, which better explained the spatial patterns. Finally, a canonical correspondence analysis (CCA) with predictor variables (geographical, hydrological, geomorphological and human pressures) indicated the main factors controlling the spatial patterns of the floristic composition, instreamhabitat characteristics, riparian habitat quality and instream habitat heterogeneity. A gradient of riparian and instream habitat quality was identified. Our results suggest that physical constraints (presence of a gorge) have protected sites from severe human impacts, resulting in good ecological quality, despite hydrological alteration. This area, where there is geomorphological control, could be potentially reclassified into a different ecotype because regular monitoring could be using incorrect references for index scores, and naturally high scores could be confused with recovery from hydrological alteration or other pressures. The sites with the worst quality were near the river mouth and were characterised by an artificial and highly variable flow regime (related to large autumnal floods and frequent human-induced periods of zero flow). This artificial flow variability as well as the presence of lateral structures in the river channel and geomorphological characteristics were identified as the main factors driving the hydromorphological and floristic pattern in this regulated river.[ES] Diversas metodolog´ıas han sido desarrolladas en el contexto europeo para evaluar el estado ecologico, y espec ´ ´ıficamente el estado hidromorfologico de las aguas superficiales. Aunque ´ estas han sido ampliamente utilizadas, pocos estudios se han centrado en masas de agua hidrologicamente alteradas y en los factores que condicionan su estado. En este ´ estudio, el r´ıo Serpis fue dividido en 16 segmentos desde la presa de Beniarres (a 40 km de la desembocadura) hasta el mar, todos ´ ellos afectados por la regulacion de caudales con distinta severidad. En cada segmento se realiz ´ o un inventario flor ´ ´ıstico y se aplicaron ´ındices hidromorfologicos (QBR, Calidad del Bosque de Ribera, e IHF, ´ ´Indice de Habitat Fluvial). Los ´ objetivos del estudio fueron (A) identificar relaciones entre la composicion flor ´ ´ıstica y los componentes del QBR, (B) entre las caracter´ısticas del habitat fluvial y los componentes del IHF, (C) determinar los principales fa ´ ctores que controlan la composicion flor ´ ´ıstica y la calidad del habitat ripario (QBR), y las caracter ´ ´ısticas del habitat fluvial y su heterogeneidad ´ (IHF). Un cluster permitio agrupar los puntos de muestreo seg ´ un su composici ´ on flor ´ ´ıstica y las caracter´ısticas del habitat fluvial, y un escalado multidimensional no-m ´ etrico (NMDS) fue usado para ordenar los puntos, obteniendo las ´ variables bioticas y caracter ´ ´ısticas del habitat y los subindices del QBR e IHF, respectivamente, que explicaban m ´ ejor los patrones espaciales. Finalmente, un analisis de correspondencias can ´ onicas (CCA) con variables predictoras (geogr ´ aficas, ´ hidrologicas, geomorfol ´ ogicas y presiones humanas) indic ´ o los principales factores que controlan los patrones espaciales de ´ la composicion flor ´ ´ıstica, las caracter´ısticas del habitat fluvial, la calidad del h ´ abitat ripario y la heterogeneidad del h ´ abitat ´ fluvial. Se identifico un gradiente de calidad del h ´ abitat ripario y fluvial. Los resultados sugieren que las limitaciones f ´ ´ısicas (presencia de un can˜on) han protegido a los tramos de impactos humanos severos, resultand ´ o en una buena calidad ecologica ´ a pesar de la alteracion hidrol ´ ogica. Esta zona podr ´ ´ıa potencialmente ser reclasificada en un ecotipo diferente, ya que un monitoreo regular podr´ıa estar usando referencias incorrectas para los ´ındices y sus altas puntuaciones naturales se podr´ıan estar confundiendo con una recuperacion de la alteraci ´ on hidrol ´ ogica o de otras presiones. Los puntos de muestreo con peor ´ calidad estuvieron cerca de la desembocadura y tuvieron un regimen de caudales alterado y muy variable. Esta variabilidad ´ artificial del caudal, junto con la presencia de estructuras laterales en el cauce y las caracter´ısticas geomorfologicas fueron ´ identificadas como los principales factores determinantes del patron hidromorfol ´ ogico y flor ´ ´ıstico en este r´ıo regulado.The authors would like to thank the reviewers for their comments and suggestions on an earlier version of this manuscript and the Jucar River Basin Authority (CHJ) for supplying public hydrological data. This study was supported by the Spanish Ministry of Environment and Rural and Marine Affairs (RIBERA Project) and the Ministry of Education and Science (projects TETIS-2, CGL2005-06219; POTECOL, CGL2007-66412; RIPFLOW, CGL2008-03076-E/BTE). We would also like to thank the CEIC Alfons El Vell (Gandia) for partially funding this research with a grant to Virginia Garofano G ´ omez. The translation of this paper was funded by the Universitat Politecnica de Val ` encia, Spain.Garófano-Gómez, V.; Martinez-Capel, F.; Peredo Parada, MM.; Olaya Marín, EJ.; Muñoz Mas, R.; Soares Costa, RM.; Pinar Arenas, JL. (2011). Assessing hydromorphological and floristic patterns along a regulated Mediterranean river: The Serpis River (Spain). Limnetica. 30(2):307-328. http://hdl.handle.net/10251/33344S30732830

Modelling native fish richness to evaluate the effects of hydromorphological changes and river restoration (Júcar River Basin, Spain)

The richness of native fish is considered to be an indicator of aquatic ecosystem health, and improving richness is a key goal in the management of river ecosystems. An artificial neural network (ANN) model based on field data from 90 sample sites distributed throughout the Júcar River Basin District was developed to predict the native fish species richness (NFSR). The Levenberg-Marquardt learning algorithm was used for model training. When constructing the model, we tried different numbers of neurons (hidden layers), compared different transfer functions, and tried different k values (from 3 to 10) in the k-fold cross-validation method. This process and the final selection of key variables with relevant ecological meaning support the reliability and robustness of the final ANN model. The partial derivatives method was applied to determine the relative importance of input environmental variables. The final ANN model combined variables describing riparian quality, water quality, and physical habitat and helped identify the primary drivers of the NFSR patterns in Mediterranean rivers. In the second part of the study, the model was used to evaluate the effectiveness of two restoration actions in the Júcar River: the removal of two abandoned weirs and the progressive increase in the proportion of riffles. The model indicated that the combination of these actions produced a rise in NFSR, which ultimately reached the maximum values observed in the reference site of that river ecotype (sensu the European Water Framework Directive). The results demonstrate the importance of longitudinal connectivity and riffle proportion for improving NFSR and the power of ANNs to help decisions in the management and ecological restoration of Mediterranean rivers. Furthermore, this model at the basin scale is the first step for further research on the effects of water scarcity and global change on Mediterranean fish communities.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 Rios Regulados por Embalses y Desarrollo de Criterios para su mejora segun la Directiva Marco del Agua" (CGL2007-66412). We thank to Confederacion Hidrografica del Jucar (Spanish Ministry of Agriculture, Food and Environment) for the data provided to develop this study. We thank Sasa Plestenjak in the collaboration for building the first fish database elaborated in this research.Olaya Marín, EJ.; Martinez-Capel, F.; Soares Costa, RM.; Alcaraz-Hernández, JD. (2012). Modelling native fish richness to evaluate the effects of hydromorphological changes and river restoration (Júcar River Basin, Spain). Science of the Total Environment. 440:95-105. doi:10.1016/j.scitotenv.2012.07.093S9510544