WFD Indicators and Definition of the Ecological Status of Rivers

Carballo, R.; Cancela, J.J.; Iglesias, G.; Marín, Antonio Alberto; Neira, X. X.; García, Tomás S. Cuesta

doi:10.1007/s11269-008-9379-9

Cited by 22 publications

(12 citation statements)

References 24 publications

(24 reference statements)

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“…Xu and Zhang, 2001) In several studies (Oberdorff et al, 1995;Reyjol et al, 2007;Leprieur et al, 2009) DRA is considered an important environmental variable for fish species richness, integrating information related to habitat diversity, the variety of microclimates and flow regimes in the river basin (Allan and Castillo, 2007;Townsend et al, 2008) The negative relationship between DRA and richness may be due to the fact that the lower reaches of Júcar, Cabriel and Turia rivers are highly regulated and have higher levels of contamination than the upper reaches (Aparicio et al, 2011). Water quality and riparian forest play a key role in determining the richness of native fish Thus, QBR and IBMWP have been identified as relevant factors positively influencing fish species richness in Spain (Carballo et al, 2009;Sánchez-Montoya et al, 2010) and are widely used for the ecological monitoring of rivers. Indeed, the riparian forest provide shelter and food for aquatic organisms (Naiman et al, 1993) and can strongly influence the quality of aquatic habitats, particularly along a gradient of river regulation (Garófano-Gómez et al, 2011).…”

Section: Discussionmentioning

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: Discussionmentioning

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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“…the quantity and dynamics of flow), river continuity and morphological conditions affected by channel patterns (Annex V, Section 1.2.1). For a discussion on the indicators and definition of the 'ecological status' in the context of the WFD see Carballo et al (2009).…”

Section: Introductionmentioning

confidence: 99%

Environmental and Resource Costs Under Water Scarcity Conditions: An Estimation in the Context of the European Water Framework Directive

Martín-Ortega¹,

Giannoccaro

Berbel

2011

Water Resour Manage

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The European Water Framework Directive (WFD) requires the good ecological status of surface water bodies, which implies the improvement of both their physicochemical condition, as well as their flow and continuity. The WFD prescribes the assessment of environmental and resource costs and benefits associated with implementing these improvements. The recent literature focuses almost exclusively on the assessment of the economic values related to quality aspects. However, in much of southern Europe, fulfilling the WFD goals will greatly depend on maintaining sufficient water flow, as well. This study aims to fill this gap by assessing the non-market value of allocating enough water to the environment to ensure environmental services are sustained when water is scarce. The non-market value of guaranteeing water supply for secondary household uses is also estimated. Using the Guadalquivir River Basin in Spain as a case study, a choice experiment is applied with scenarios characterized by varying water flow levels and accompanying environmental impacts, and a different frequency of household water restrictions. The results show that the population derives significant benefits not only from the direct use of water, but that also holds non-use values related to the ecological status, although the latter has a considerably lower impact on consumer surplus. Additionally, we conclude that the costs of implementing the water saving measures currently included in the Program of Measures seem to be proportionate to its benefits in this case. © 2011 Springer Science+Business Media B.V

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“…In Europe, the Water Framework Directive has created the demand for assessment at a country and continent scale. 1 In Australia, the importance of water as an anthropogenic and environmental resource has resulted in mapping and monitoring of stream condition mainly at a state scale through field-based approaches.…”

Section: Introductionmentioning

confidence: 99%

Assessing stream bank condition using airborne LiDAR and high spatial resolution image data in temperate semirural areas in Victoria, Australia

Johansen

Grove

Denham

2013

J. Appl. Remote Sens

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Abstract. Stream bank condition is an important physical form indicator for streams related to the environmental condition of riparian corridors. This research developed and applied an approach for mapping bank condition from airborne light detection and ranging (LiDAR) and high-spatial resolution optical image data in a temperate forest/woodland/urban environment. Field observations of bank condition were related to LiDAR and optical image-derived variables, including bank slope, plant projective cover, bank-full width, valley confinement, bank height, bank top crenulation, and ground vegetation cover. Image-based variables, showing correlation with the field measurements of stream bank condition, were used as input to a cumulative logistic regression model to estimate and map bank condition. The highest correlation was achieved between field-assessed bank condition and image-derived average bank slope (R 2 ¼ 0.60, n ¼ 41), ground vegetation cover (R 2 ¼ 0.43, n ¼ 41), bank width/height ratio (R 2 ¼ 0.41, n ¼ 41), and valley confinement (producer's accuracy ¼ 100%, n ¼ 9). Crossvalidation showed an average misclassification error of 0.95 from an ordinal scale from 0 to 4 using the developed model. This approach was developed to support the remotely sensed mapping of stream bank condition for 26,000 km of streams in Victoria, Australia, from 2010 to 2012.

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WFD Indicators and Definition of the Ecological Status of Rivers

Cited by 22 publications

References 24 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

Environmental and Resource Costs Under Water Scarcity Conditions: An Estimation in the Context of the European Water Framework Directive

Assessing stream bank condition using airborne LiDAR and high spatial resolution image data in temperate semirural areas in Victoria, Australia

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