A comparison of modeling techniques to predict juvenile 0+ fish species occurrences in a large river system

Leclere, J.; Oberdorff, Thierry; Belliard, Jérôme; Leprieur, Fabien

doi:10.1016/j.ecoinf.2011.05.001

Cited by 36 publications

(25 citation statements)

References 51 publications

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“…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%

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: 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.

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“…In recent decades, CART has become a powerful yet simple technique for ecological data analysis (De'ath and Fabricius, 2000), and there have been numerous ecological applications of CART across a wide range of topics. CART has been used to develop habitat modes for threatened birds (O'Connor et al, 1996) and tortoise species (Andersen et al, 2000), and to predict occurrence and abundance of fish species (Francis et al, 2011;Leclere et al, 2011).…”

Section: Discussionmentioning

confidence: 99%

Effects of Environmental Factors on Natural Reproduction of the Four Major Chinese Carps in the Yangtze River, China

Gao

Yang

et al. 2013

Zoological Science

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“…1). Among these 80 sites, YOY fish assemblages were sampled and environmental conditions were described for a total of 523 habitats (i.e., specific habitat within sites, Leclere et al, 2011).…”

Section: Calibration Validation and Evaluation Datasetsmentioning

confidence: 99%

“…To establish the foundation of a future YOY fishbased index, we model here the occurrence of 16 YOY fish species in the river Seine (France) and its two main tributaries (i.e., Oise and Marne rivers), using boosted regression trees (BRT) (Leclere et al, 2011). We first use two independent data sets of reference (i.e., least disturbed) habitats: one to calibrate and one to validate the species models.…”

Section: Introductionmentioning

confidence: 99%

Young-of-the-year fish assemblages as indicators of anthropogenic disturbances in large tributaries of the Seine River Basin (France)

Leclere¹,

Belliard²,

Oberdorff³

2012

Hydrobiologia

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A variety of indices targeting a number of different biological assemblages have been developed to assess aquatic ecosystem condition, identify the drivers of alteration and provide information on possible restoration measures. Fish-based indices, commonly focused on adult assemblages, are capable of indicating declines in condition, but often provide limited information on the ultimate causes for the observed changes. Using young-of-the-year (YOY) fish assemblages has been suggested as a means of improving the sensitivity of fish-based indicators. To verify this assumption, we first model, using boosted regression trees, the occurrence of 16 YOY fish species as a function of twenty two environmental factors in 227 reference (i.e., least disturbed) habitats located in the river Seine (France) and its two main tributaries (i.e., Oise and Marne rivers). We then validate the species models using an independent dataset of 74 reference habitats. Finally, using three independent data sets reflecting different categories of disturbances (i.e., physico-chemical disturbances, physical disturbances induced by navigation and a mix of both disturbances), we measure the deviation between expected and observed species occurrences to evaluate our species models ability in detecting these disturbances. The models are ecologically meaningful and overall perform well in discriminating between reference and disturbed habitats, showing a stable response for all unimpaired habitats and highlighting the main habitat disturbances tested.

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A comparison of modeling techniques to predict juvenile 0+ fish species occurrences in a large river system

Cited by 36 publications

References 51 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

Effects of Environmental Factors on Natural Reproduction of the Four Major Chinese Carps in the Yangtze River, China

Young-of-the-year fish assemblages as indicators of anthropogenic disturbances in large tributaries of the Seine River Basin (France)

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