Cascaded neural networks improving fish species prediction accuracy: the role of the biotic information

[1]  T. K. Banerjee,et al.  Evaluation of pollution of Ganga River water using fish as bioindicator , 2016, Environmental Monitoring and Assessment.

[2]  J. Olden,et al.  A new R2-based metric to shed greater insight on variable importance in artificial neural networks , 2015 .

[3]  R. Muñoz‐Mas,et al.  Can multilayer perceptron ensembles model the ecological niche of freshwater fish species , 2015 .

[4]  G. Gourène,et al.  PREDICTING FACTORS THAT INFLUENCE FISH GUILD COMPOSITION IN FOUR COASTAL RIVERS (SOUTHEAST IVORY COAST) USING ARTIFICIAL NEURAL NETWORKS , 2015 .

[5]  E. J. Olaya-Marín,et al.  Modelling critical factors affecting the distribution of the vulnerable endemic Eastern Iberian barbel (Luciobarbus guiraonis) in Mediterranean rivers , 2015 .

[6]  Runsen Zhang,et al.  Landscape ecological security response to land use change in the tidal flat reclamation zone, China , 2015, Environmental Monitoring and Assessment.

[7]  A. M. Cunico,et al.  Comparison of fish and macroinvertebrates as bioindicators of Neotropical streams , 2015, Environmental Monitoring and Assessment.

[8]  Young-Seuk Park,et al.  Modelling Community Structure in Freshwater Ecosystems , 2014 .

[9]  N. R. Franssen,et al.  Prey and non-native fish predict the distribution of Colorado pikeminnow (Ptychocheilus lucius) in a south-western river in North America , 2014 .

[10]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[11]  C. Broder,et al.  Use of cross-reactive serological assays for detecting novel pathogens in wildlife: assessing an appropriate cutoff for henipavirus assays in African bats. , 2013, Journal of virological methods.

[12]  A. Eloranta,et al.  Interactions between invading benthivorous fish and native whitefish in subarctic lakes , 2013 .

[13]  Yintao Jia,et al.  River health assessment in a large river: Bioindicators of fish population , 2013 .

[14]  Sovan Lek,et al.  Predicting fish assemblages and diversity in shallow lakes in the Yangtze River basin , 2012 .

[15]  Agostino Di Ciaccio,et al.  Computational Statistics and Data Analysis Measuring the Prediction Error. a Comparison of Cross-validation, Bootstrap and Covariance Penalty Methods , 2022 .

[16]  David W. Armitage,et al.  A comparison of supervised learning techniques in the classification of bat echolocation calls , 2010, Ecol. Informatics.

[17]  Wisdom M. Dlamini,et al.  A Bayesian belief network analysis of factors influencing wildfire occurrence in Swaziland , 2010, Environ. Model. Softw..

[18]  M. Clavero,et al.  Biology and habitat use of three‐spined stickleback (Gasterosteus aculeatus) in intermittent Mediterranean streams , 2009 .

[19]  Michael J. Watts,et al.  Comparing ensemble and cascaded neural networks that combine biotic and abiotic variables to predict insect species distribution , 2008, Ecol. Informatics.

[20]  Lawrence M. Page,et al.  Handbook of European Freshwater Fishes , 2008, Copeia.

[21]  Julian D Olden,et al.  Machine Learning Methods Without Tears: A Primer for Ecologists , 2008, The Quarterly Review of Biology.

[22]  Michele Scardi,et al.  An expert system based on fish assemblages for evaluating the ecological quality of streams and rivers , 2008, Ecol. Informatics.

[23]  M. Araújo,et al.  The importance of biotic interactions for modelling species distributions under climate change , 2007 .

[24]  T. Hastie,et al.  Comparative performance of generalized additive models and multivariate adaptive regression splines for statistical modelling of species distributions , 2006 .

[25]  Julian D Olden,et al.  Rediscovering the species in community-wide predictive modeling. , 2006, Ecological applications : a publication of the Ecological Society of America.

[26]  Can Ozan Tan,et al.  Methodological issues in building, training, and testing artificial neural networks in ecological applications , 2005, q-bio/0510017.

[27]  W. Thuiller,et al.  Predicting species distribution: offering more than simple habitat models. , 2005, Ecology letters.

[28]  Michele Scardi,et al.  Optimisation of artificial neural networks for predicting fish assemblages in rivers , 2005 .

[29]  Russell G. Death,et al.  An accurate comparison of methods for quantifying variable importance in artificial neural networks using simulated data , 2004 .

[30]  R. Death,et al.  Predictive modelling and spatial mapping of freshwater fish and decapod assemblages using GIS and neural networks , 2004 .

[31]  J. Olden,et al.  ECOLOGICAL PROCESSES DRIVING BIOTIC HOMOGENIZATION: TESTING A MECHANISTIC MODEL USING FISH FAUNAS , 2004 .

[32]  Costas Papaconstantinou,et al.  Predicting demersal fish species distributions in the Mediterranean Sea using artificial neural networks , 2003 .

[33]  M. Gevrey,et al.  Review and comparison of methods to study the contribution of variables in artificial neural network models , 2003 .

[34]  Young-Seuk Park,et al.  Modelling the factors that influence fish guilds composition using a back-propagation network: Assessment of metrics for indices of biotic integrity , 2003 .

[35]  Christina Gloeckner,et al.  Modern Applied Statistics With S , 2003 .

[36]  Russell G. Death,et al.  Predictive modelling of freshwater fish as a biomonitoring tool in New Zealand , 2002 .

[37]  Julian D. Olden,et al.  Illuminating the “black box”: a randomization approach for understanding variable contributions in artificial neural networks , 2002 .

[38]  Brian D. Ripley,et al.  Modern Applied Statistics with S Fourth edition , 2002 .

[39]  Donald A. Jackson,et al.  Transactions of the American Fisheries Society 130:878–897, 2001 � Copyright by the American Fisheries Society 2001 Fish–Habitat Relationships in Lakes: Gaining Predictive and Explanatory Insight by Using Artificial Neural Networks , 2022 .

[40]  Samy Bengio,et al.  Taking on the curse of dimensionality in joint distributions using neural networks , 2000, IEEE Trans. Neural Networks Learn. Syst..

[41]  Sovan Lek,et al.  Artificial Neuronal Networks , 2000 .

[42]  Michele Scardi,et al.  Developing an empirical model of phytoplankton primary production: a neural network case study , 1999 .

[43]  Sovan Lek,et al.  Artificial neural networks as a tool in ecological modelling, an introduction , 1999 .

[44]  David J. Hand,et al.  Construction and Assessment of Classification Rules , 1997 .

[45]  I. Dimopoulos,et al.  Application of neural networks to modelling nonlinear relationships in ecology , 1996 .

[46]  I. Dimopoulos,et al.  Role of some environmental variables in trout abundance models using neural networks , 1996 .

[47]  J. R. Landis,et al.  The measurement of observer agreement for categorical data. , 1977, Biometrics.

[48]  Jacob Cohen A Coefficient of Agreement for Nominal Scales , 1960 .