Response of fish assemblages to water quality in boreal rivers

Sutela, Tapio; Vehanen, Teppo; Jounela, Pekka

doi:10.1007/s10750-009-0048-7

Cited by 19 publications

(21 citation statements)

References 32 publications

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“…Five metrics, previously shown to be the most suitable indicators to detect the human impact on fish in boreal rivers, were calculated (Sutela & Vehanen ; Sutela et al . ; Vehanen et al . ).…”

Section: Methodsmentioning

confidence: 99%

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Assessing electric fishing sampling effort to estimate stream fish assemblage attributes

Vehanen¹,

Sutela²,

Jounela³

et al. 2012

Fisheries Management Eco

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The EU Water Framework Directive (WFD) obligates authorities to assess the ecological status of rivers. The sampling of the fish assemblage must be based on a reliable and cost‐effective monitoring programme, and it should be optimised to measure the ecological quality cost‐effectively. The efficiency of single‐pass versus three‐pass backpack electric fishing was measured to assess ecological status. The data included 295 electric fishing samplings from 137 rivers in Finland and a 5‐year data series from six small forest streams in mid‐eastern Finland. Compared with three‐pass electric fishing, single‐pass electric fishing in the same area increased the risk of missing rare fish species and species with low catchability. The concordance of the calculated fish metrics (e.g. proportion of tolerant and intolerant species) between one‐ and three‐pass measurements was, however, high, suggesting that single‐pass electric fishing was a suitable sampling procedure to evaluate ecological status. Increasing the area sampled by single‐pass removals stabilised the variances in the fish metrics and similarity indexes. It was concluded that in small streams electric fishing an area 24–27 times the stream width and in general a minimum of 300 m2, but preferably more than 450 m2, was adequate to estimate fish assemblage attributes reliably in boreal streams.

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Section: Methodsmentioning

confidence: 99%

“…Previously developed fish metrics found to respond to anthropogenic pressures in the rivers of the boreal zone (mainly morphological and water quality pressures) were used as response variables (Sutela & Vehanen ; Sutela et al . ; Vehanen et al . ).…”

Section: Introductionmentioning

confidence: 99%

Assessing electric fishing sampling effort to estimate stream fish assemblage attributes

Vehanen¹,

Sutela²,

Jounela³

et al. 2012

Fisheries Management Eco

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“…Usually, σ is the sigmoid function, which is a smooth version of the step function. Alternatives to MLP, such as the generalized regression neural network, have been recently applied for stream health modelling (Mathon et al, 2013;Sutela, Vehanen, & Jounela, 2010). For ANN setting, more than one layer of hidden units can be used (Lek & Guégan, 1999).…”

Section: Artificial Neural Networkmentioning

confidence: 99%

“…Medium Maloney et al, 2012;Sauer et al, 2011Almeida et al, 2017Fukuda et al, 2013;Grenouillet et al, 2011;Maloney et al, 2012;Zhao et al, 2014 Shrinkage methods • Straightforward implementation and interpretation Grenouillet et al, 2011;He et al, 2010;Kwon et al, 2015; (Continues) Chon, 2011;Gazendam et al, 2016;Goethals et al, 2007;Mathon et al, 2013; Ans M. Mouton et al, 2010;Sauer et al, 2011Chon, 2011Fukuda et al, 2013;Grenouillet et al, 2011;Mathon et al, 2013;Olaya-Marín et al, 2012Olden et al, 2008;Sutela et al, 2010;…”

Section: Mediummentioning

confidence: 99%

“…Hence, key MLP parameters include the number of times that the training data is used to update the weights of the hidden units (i.e., epochs) and the number of hidden layers and units. Alternatives to MLP, such as the generalized regression neural network, have been recently applied for stream health modelling (Mathon et al, 2013;Sutela, Vehanen, & Jounela, 2010).…”

Section: Artificial Neural Networkmentioning

confidence: 99%

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A review of macroinvertebrate‐ and fish‐based stream health modelling techniques

Hernandez‐Suarez

Nejadhashemi

2018

Ecohydrology

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During the last three decades, explaining cause-effect relationships between natural and anthropogenic disturbances with measures of stream health have motivated the growing application of statistical, machine learning, and soft computing methods.The aim of this review is to provide insight into the most widely used methods for predicting biological variables based on macroinvertebrate and fish species in riverine ecosystems. Therefore, we describe several methods including multiple linear regression, generalized linear models, generalized additive models, boosted regression trees, random forests, artificial neural networks, fuzzy logic-based, and Bayesian belief networks along with recent applications of these. Moreover, issues regarding variable selection, model interpretability, ensemble modelling, and model evaluation and overfitting are discussed. Recent advances have suggested the need for integrated modelling systems to enhance the predictive ability and improve interpretability.However, trade-offs between model complexity and accuracy demand research efforts in uncertainty quantification/propagation in model ensembles. Additionally, models should be perceived as complementary tools that require further validation with field measurements. Therefore, a consensus regarding monitoring and modelling practices for stream health applications is recommended.

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Using a process‐based catchment‐scale model for enhancing field‐based stream assessments and predicting stream fish assemblages

Kautza

Sullivan

2012

Aquatic Conservation

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1. Catchment-modelling techniques, although not yet widely used in biological contexts, may be a valuable tool in the management and conservation of stream fishes. This study was undertaken to evaluate the potential of a catchment-scale hydrologic model, the Soil and Water Assessment Tool (SWAT), to explain characteristics of stream fish assemblages and to enhance current field-based stream assessments.2. Stream fish assemblages, instream habitat, geomorphology, and water quality were surveyed in 16 study catchments in Ohio, USA. Subsequently, SWAT was used to model stream discharge, sediment flux and concentration, and nitrogen and phosphorus yields for each catchment.3. Principal component analysis (PCA) and multiple linear regression were used to explore potential relationships between environmental factors (both reach-level measurements and SWAT-modelled catchment-scale processes) and fish assemblage descriptors assessed within the framework of an Ohio fish-based Index of Biotic Integrity (IBI).4. Reach-level factors contributed to three of the four significant regression models, explaining 12% to 14% of the variation in fish species richness, the number of darter (Etheostoma) species, and the number of sensitive/intolerant species. SWAT-modelled parameters including minimum and mean annual flow and sediment yields explained significant variation in total IBI scores (57%), species richness (63%), the number of darter species (60%), and the number of sensitive/intolerant species (61%).5. The results support the utility of SWAT as a complement to field-based surveys. It is proposed that process-based catchment-modelling techniques such as SWAT could improve stream fish management and conservation efforts when used as a screening tool to target streams and catchments for subsequent field surveys and as a method to model potential impacts to stream ecosystems and fish assemblages in highly sensitive areas and/or inaccessible catchments.

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Response of fish assemblages to water quality in boreal rivers

Cited by 19 publications

References 32 publications

Assessing electric fishing sampling effort to estimate stream fish assemblage attributes

Assessing electric fishing sampling effort to estimate stream fish assemblage attributes

A review of macroinvertebrate‐ and fish‐based stream health modelling techniques

Using a process‐based catchment‐scale model for enhancing field‐based stream assessments and predicting stream fish assemblages

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