HABITAT SUITABILITY MODELLING AT MESOHABITAT SCALE AND EFFECTS OF DAM OPERATION ON THE ENDANGERED JúCAR NASE, <i>PARACHONDROSTOMA ARRIGONIS</i> (RIVER CABRIEL, SPAIN)

Costa, Rui; Martínez‐Capel, Francisco; Muñoz‐Mas, Rafael; Alcaraz‐Hernández, Juan Diego; Garófano‐Gómez, Virginia

doi:10.1002/rra.1598

Cited by 46 publications

(36 citation statements)

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“…Similarly, the RF model built with 20 variables also shows a notable difference in training and validation performances. Also for the RF case, the most important predictors in this model have been identified by previous studies as relevant to freshwater fish species (Bernardo et al, 2003;Costa et al, 2012). The high differences between training and validation performances confirm the hypothesis that a very complex model with many degrees of freedom is not robust (Maier and Dandy, 2000) Looking at the models built after variable selection, ANN and RF showed no significant differences of performance in the cross-validation procedure (R 2 = 68% for RF and R 2 = 66% for ANN, Table IV).…”

Section: Discussionmentioning

confidence: 99%

“…All these species perform small-scale migrations for reproduction within the river system and the only one migrating at a large scale is Anguilla Anguilla, a catadromous fish species with a complex life-history that includes migrations across the Atlantic Ocean. The number of individuals of these native fish species have decreased consistently in the last decades as a consequence of habitat modifications (including barriers) and pollution in the lower river reaches (Doadrio, 2001;Costa et al, 2012). 24 environmental variables (Table II) were used to construct the ANN and RF models, which were selected considering their ecological importance for fish life cycle (Granado-Lorencio, 1996;Jackson et al, 2001;Bernardo et al, 2003;Costa et al, 2012).…”

Section: Table Imentioning

confidence: 99%

“…The number of individuals of these native fish species have decreased consistently in the last decades as a consequence of habitat modifications (including barriers) and pollution in the lower river reaches (Doadrio, 2001;Costa et al, 2012). 24 environmental variables (Table II) were used to construct the ANN and RF models, which were selected considering their ecological importance for fish life cycle (Granado-Lorencio, 1996;Jackson et al, 2001;Bernardo et al, 2003;Costa et al, 2012). These variables belong to three categories: physicochemical water quality, hydro-morphology, and biologicallybased indicators of water and riparian habitat quality.…”

Section: Table Imentioning

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: Discussionmentioning

confidence: 99%

Section: Table Imentioning

confidence: 99%

Section: Table Imentioning

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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“…Ecological models can support decision making in environmental and conservation management [16] and have already been applied to assess the impact of dams on fish [17][18][19][20][21][22], macroinvertebrates [23][24][25], amphibians [26] and vegetation [27][28][29]. Models are being increasingly used in environmental assessment, planning and management [30].…”

Section: Introductionmentioning

confidence: 99%

Modelling Tools to Analyze and Assess the Ecological Impact of Hydropower Dams

Nguyen

Everaert

Boets

et al. 2018

Water

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Abstract:We critically analyzed a set of ecological models that are used to assess the impact of hydropower dams on water quality and habitat suitability for biological communities. After a literature search, we developed an integrated conceptual model that illustrates the linkages between the main input variables, model approaches, the output variables and biotic-abiotic interactions in the ecosystems related to hydropower dams. We found that variations in water flow and water depth coupled with increased nutrient availability are major variables that contribute to structural and functional ecosystem changes. We also found that ecological models are an important tool to assess the impact of hydropower dams. For instance, model simulation of different scenarios (e.g., with and without the dam, different operation methods) can analyze and predict the related ecosystem shifts. However, one of the remaining shortcomings of these models is the limited capacity to separate dam-related impacts from other anthropogenic influences (e.g., agriculture, urbanization). Moreover, collecting sufficient high-quality data to increase the statistical power remains a challenge. The severely altered conditions (e.g., generation of very deep lakes) also lead to difficulties for standardized data collection. We see future opportunities in the integration of models to improve the understanding of the different processes affected by hydropower dam development and operation, as well as the use of remote sensing methods for data collection.

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“…According to Wootton (1998) heterogeneous systems supply a large variety of microhabitats with sites where fish species can shelter against predators and find a wide variety of feeding resources and places for reproduction. Recently, some studies using habitat simulation models have reinforced that physical habitat variables and cover availability determine patterns of habitat use in rivers and streams (Lee & Suen, 2012;Costa et al, 2012;Teresa & Casatti, 2013;Costa et al, 2013). However, the capability of prediction of these models depends on the understanding of how the species respond to environmental gradient conditions.…”

Section: Introductionmentioning

confidence: 99%

Application of the physical habitat simulation for fish species to assess environmental flows in an Atlantic Forest Stream in South-eastern Brazil

et al. 2015

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The physical habitat simulation sub-routine of the Instream Flow Incremental Methodology (IFIM) uses hydraulic modeling and suitability indices of target fish species to predict how differences in-stream flows affect the microhabitat occupation by fish species. This habitat modelling approach was adopted to assess the ecological effects of running flows on three neotropical fish species of different orders (Bryconamericus ornaticeps, Ancistrus multispinis and Geophagus brasiliensis).The study encompassed two reaches of an Atlantic Forest stream in Southeastern Brazil where topographic and hydraulic (depth, velocity and type of substrate) characteristics were measured to implement one-dimensional hydraulic simulation. Sub aquatic observation of fish was performed to collect data on microhabitat use and these data were used to develop habitat suitability curves that were used in the habitat simulation to obtain the habitat suitability index (HSI) and weighted usable area (WUA) versus flow curves. Upon these curves minimum and optimum environmental flows for the target fish species were proposed. Bryconamericus ornaticeps and A. multispinis selected microhabitats around 0.6 m depth, whereas G. brasiliensis showed a wider suitable range (0.35-0.9 m). All the three species were mainly observed in microhabitat with low flow velocity (0.1 m/s). Bryconamericus ornaticeps selected more frequently coarse substrate (e.g. boulders) but it appeared also over sandy substrate, whereas A. multispinis and G. brasiliensis selected preferably boulders. The range of 0.65-0.85 m 3 /s was found as the optimum to meet the needs of the three fish species. Our results agree with the necessary objective information to perform grounded management actions in the frame of a management program aiming at ecosystem conservation. Thereby it can be considered a successful pilot study in environmental flow assessment in an Atlantic Forest stream of Brazil.A sub-rotina simulação de habitat físico da metodologia incremental -IFIM usa variáveis hidráulicas e os índices de aptidão das espécies alvo para predizer como diferentes vazões afetam a ocupação de microhabitat por espécies de peixes. Esta abordagem de simulação de hábitat foi adotada para avaliar os efeitos ecológicos sobre três espécies de peixes tropicais de diferentes ordens (Bryconamericus ornaticeps, Ancistrus multispinis e Geophagus brasiliensis) em dois trechos de um riacho de Mata Atlântica do sudeste do Brasil, durante a estação chuvosa de 2013 e a estação seca de 2014. Características topográficas e hidráulicas (profundidade, velocidade e tipo de substrato) foram mensuradas para implementação da simulação de habitat em uma dimensão; observações subaquáticas dos peixes foram realizadas para avaliação do uso do microhabitat. As curvas de aptidão de habitat de cada variável hidráulica foram utilizadas na simulação de habitat para obtenção das curvas de aptidão composta (HSI) e a área útil ponderada (WUA) versus fluxo, e propor vazões ambientais mínimas e ideais para as espécies de peix...

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HABITAT SUITABILITY MODELLING AT MESOHABITAT SCALE AND EFFECTS OF DAM OPERATION ON THE ENDANGERED JúCAR NASE, PARACHONDROSTOMA ARRIGONIS (RIVER CABRIEL, SPAIN)

Cited by 46 publications

References 46 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

Modelling Tools to Analyze and Assess the Ecological Impact of Hydropower Dams

Application of the physical habitat simulation for fish species to assess environmental flows in an Atlantic Forest Stream in South-eastern Brazil

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