Data-driven fuzzy habitat suitability models for brown trout in Spanish Mediterranean rivers

Mouton, Ans; Alcaraz‐Hernández, Juan Diego; Baets, Bernard De; Goethals, Peter; Martínez‐Capel, Francisco

doi:10.1016/j.envsoft.2010.12.001

Cited by 102 publications

(81 citation statements)

References 47 publications

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“…They found that the tree-based models were generally superior to predict species richness of reef fish. Furthermore, Mouton et al (2011) found similar predictive performance of RF and Fuzzy logic models to represent mesohabitat suitability for Salmo trutta in Spain, whereas Kampichler et al (2010) compared different ML techniques (including ANN and RF) for classification problems and recommend the use of RF in conservation biology. Given the large number of ML techniques, there are not established guidelines for defining the most appropriate method to address a particular ecological question or management action for freshwater ecosystems.…”

Section: Introductionmentioning

confidence: 94%

“…To cope with these issues, machine learning (ML) techniques have been widely used due to their ability to identify non-linear relationships and generate less uncertain predictive results (Olden et al, 2008). Several researchers have applied ML in ecological studies (Aertsen et al, 2010;Armitage and Ober, 2010;Leclere et al, 2011;Mouton et al, 2011). In particular, artificial neural networks (ANN) and random forests (RF) are two machine learning techniques which are currently valuable tools for ecological modelling, and are especially useful in analysing large datasets and identifying non-linear relationships (Drew et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

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

Olaya-Marín

Martínez‐Capel

Vezza

2013

Knowl. Managt. Aquatic Ecosyst.

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Key-words:Artificial neural networks, random forests, native fish, species richness, Mediterranean rivers 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 (R 2 ), the mean squared error (MSE) and the adjusted determination coefficient (R 2 adj ) and both models were developed using a k-fold crossvalidation procedure. According to the results, both techniques had similar validation performance (R 2 = 68% for RF and R 2 = 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. RÉSUMÉUne comparaison des réseaux de neurones et des forêts aléatoires pour prédire la richesse en espèces de poissons indigènes dans les rivières méditerranéennes 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 amélio-rer 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 artificiels (ANN) et les forêts aléatoires (RF) et (ii) d'évaluer la conformité en termes de sélection des variables importantes entre les deux approches de modélisa-tion. L'efficacité des modèles a été évaluée au moyen de trois indicateurs de Article published by EDP Sciences E.J. Olaya-Marín et al.: Knowl. Managt. Aquatic Ecosyst. (2013) 409, 07 performance : le coefficient de détermination (R 2 ), l'erreur quadratique moyenne (MSE) et le coefficient de détermination ajusté (R 2 adj ) et les deux modèles ont été développés en utilisant une procédure de validation croisée k-fold. Selon les résul-tats, les deux techniques ont des performances de validation similaires (R 2 = 68 % pour RF et R 2 = 66 % pour ANN). Bien que les deux...

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Section: Introductionmentioning

confidence: 94%

Section: Introductionmentioning

confidence: 99%

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

Olaya-Marín

Martínez‐Capel

Vezza

2013

Knowl. Managt. Aquatic Ecosyst.

Self Cite

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“…The total length of each sampling site was not constant, as the size of HMUs varied with flow (Costa et al, 2012). The four river stretches for model validation were shorter (ranging from 0.3 km to 0.6 km) and, due to the limited availability of access points to the river, V3 and V4 partially overlapped T3 and T4 respectively, but were Following previous research in Mediterranean rivers (Alcaraz-Hernández et al, 2011), five types of HMUs were considered: pool, glide, run, riffle and rapid. Pools were characterized by moderate to high water depth (> 0.5 m) generally associated with erosion phenomena, low flow velocity and a very low gradient.…”

Section: Habitat Description and Fish Datamentioning

confidence: 99%

“…To model species distribution, Random Forests (RF, Breiman, 2001), a statistical method based on an automatic combination of decision trees, is currently considered a promising technique in ecology (Cutler et al, 2007;Franklin, 2010;Drew et al, 2011;Cheng et al, 2012). RF has been applied in freshwater fish studies (Buisson et al, 2010;Grenouillet et al, 2011;Markovic et al, 2012) and several authors have shown that, compared to other methodologies, RF often reach top performance in building predictive models of species distribution (Svetnik et al, 2003;Siroky, 2009;He et al, 2010;Mouton et al, 2011). Moreover, RF has been recently included in mesohabitat simulation tools, i.e., MesoHABSIM (Parasiewicz et al, 2013;Vezza et al, 2014a) to model fish ecological response to hydro-morphological alterations.…”

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confidence: 99%

Random forests to evaluate biotic interactions in fish distribution models

Vezza

Muñoz‐Mas

Martínez‐Capel

et al. 2015

Environmental Modelling & Software

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Previous research indicated that high predictive performance in species distribution modelling can be obtained by combining both biotic and abiotic habitat variables. However, models developed for fish often only address physical habitat characteristics, thus omitting potentially important biotic factors. Therefore, we assessed the impact of biotic variables on fish habitat preferences in four selected stretches of the upper Cabriel River (E Spain).The occurrence of Squalius pyrenaicus and Luciobarbus guiraonis was related to environmental variables describing interspecific interactions (inferred by relationships among fish abundances) and channel hydro-morphological characteristics. Random Forests (RF) models were trained and then validated using independent datasets. In both training and validation phases, RF showed high performance. Water depth, channel width, fine substrate and water-surface gradient were selected as most important habitat variables for both fish. Results showed clear habitat overlapping between fish species and suggest that interspecific competition is not a strong factor in the study area.

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“…for the four streams. The smallest watershed area is 55 km 2 in the highest segment of the Vallanca stream, and the largest is 268 km 2 in the lowest segment of the Villahermosa stream (Mouton et al, 2011). The average flow rate of the streams during the survey (July) showed a heterogeneous pattern ( Table 2).…”

Section: Study Areamentioning

confidence: 98%

Wireless CDN video streaming architecture for IPTV

Palau

Mares

Molina

et al. 2010

Multimed Tools Appl

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Mesohabitat heterogeneity in four mediterranean streams of the Jucar river basin (Eastern Spain)The hydromorphological units or mesohabitats of the headwaters of four Mediterranean streams were studied by classifying them according to their length, width, average and maximum depth, percentage of substrate and water volume. These characteristics of the streams were assessed over four consecutive years (2003)(2004)(2005)(2006). The main objective of the study was to analyse the spatial and temporal pattern of the physical attributes of the mesohabitats. Classification strength and dendrograms were examined for each stream, mesohabitat type, and combination of stream and mesohabitat type and for stream mesohabitat type and year, whereas Nonmetric Multidimensional Scaling ordination (NMDS) was used to analyse classification patterns. The Villahermosa and Ebrón streams were more homogeneous than the Palancia and Vallanca streams, whereas riffles and glides were more homogeneous than pools and rapids. The NMDS indicated that the slow water-velocity (pools, glides) and fast (riffles, rapids) mesohabitats tended to group together. Temporally, the Ebrón stream pools were homogeneous and very stable over the four years, whereas the Vallanca and Villahermosa pools showed a certain degree of variability. The Palancia pools were the most variable and heterogeneous during the study period. These results underscored the importance of separating different types of deep, slow-velocity habitats in mesohabitat inventories and the importance of evaluating habitat classifications in eco-hydromorphological and hydrobiological studies, especially in Mediterranean streams and rivers.Key words: Mediterranean streams, hydromorphological units, mesohabitat, habitat classification, similarity, Iberian Peninsula. (2003)(2004)(2005)(2006) RESUMEN Heterogeneidad del mesohábitat en cuatro ríos mediterráneos de la cuenca del río Jucar (Este de España) Las unidades hidromorfológicas o mesohábitats de cuatro ríos mediterráneos de cabecera fueron estudiadas en función de su longitud, anchura, profundidad media y máxima, porcentaje de sustrato y volumen de cada mesohábitat durante cuatro años consecutivos

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Data-driven fuzzy habitat suitability models for brown trout in Spanish Mediterranean rivers

Cited by 102 publications

References 47 publications

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

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

Random forests to evaluate biotic interactions in fish distribution models

Wireless CDN video streaming architecture for IPTV

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