Length-weight relationships of two endemic fish species in the Júcar River Basin, Iberian Peninsula

Alcaraz‐Hernández, Juan Diego; Martínez‐Capel, Francisco; Olaya-Marín, E. J.

doi:10.1111/jai.12625

Cited by 1 publication

(1 citation statement)

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“…However, this is most probably caused by differences in the available microhabitats, which, in these studies, were dominated by shallow and moderate-to-fast flowing riffles and runs (Martelo et al, 2014;Santos and Ferreira, 2008). Taking into account that Eastern Iberian chub rarely exceed 20 cm (Alcaraz-Hernández et al, 2015;Doadrio and Carmona, 2006) such a discrepancy can be caused by the relatively small size of the species, which may lead to inferior natatorial capacity, as has been demonstrated for other Iberian species (i.e., S. carolitertii) (Romão et al, 2012).…”

Section: Habitat Suitability For the Eastern Iberian Chub (Squalius Vmentioning

confidence: 70%

Revisiting probabilistic neural networks: a comparative study with support vector machines and the microhabitat suitability for the Eastern Iberian chub (Squalius valentinus)

Muñoz‐Mas

Fukuda

Pórtoles³

et al. 2018

Ecological Informatics

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Probabilistic Neural Networks (PNNs) and Support Vector Machines (SVMs) are flexible classification techniques suited to render trustworthy species distribution and habitat suitability models. Although several alternatives to improve PNNs' reliability and performance and/or to reduce computational costs exist, PNNs are currently not well recognised as SVMs because the SVMs were compared with standard PNNs. To rule out this idea, the microhabitat suitability for the Eastern Iberian chub (Squalius valentinus Doadrio & Carmona, 2006) was modelled with SVMs and four types of PNNs (homoscedastic, heteroscedastic, cluster and enhanced PNNs); all of them optimised with differential evolution. The fitness function and several performance criteria (correctly classified instances, true skill statistic, specificity and sensitivity) and partial dependence plots were used to assess respectively the performance and reliability of each habitat suitability model. Heteroscedastic and enhanced PNNs achieved the highest performance in every index but specificity. However, these two PNNs rendered ecologically unreliable partial dependence plots. Conversely, homoscedastic and cluster PNNs rendered ecologically reliable partial dependence plots. Thus, Eastern Iberian chub proved to be a eurytopic species, presenting the highest suitability in microhabitats with cover present, low flow velocity (approx. 0.3 m/s), intermediate depth (approx. 0.6 m) and fine gravel (64-256 mm). PNNs outperformed SVMs; thus, based on the results of the cluster PNN, which also showed high values of the performance criteria, we would advocate a combination of approaches (e.g., cluster & heteroscedastic or cluster & enhanced PNNs) to balance the trade-off between accuracy and reliability of habitat suitability models. 1 Introduction Humans have facilitated species extinctions, invasions, increased soil erosion, altered fire frequency and hydrology, and incited profound changes in primary productivity and other key biogeochemical and ecosystems processes (Ellis et al., 2010). Therefore, in the face of this global change, forecasting future ecosystem states, such as future species geographic distributions or land-use patterns, is currently a central priority in biogeographical and ecological sciences (Eberenz et al., 2016; Evans et al., 2016). As a consequence of this priority, scientists, conservationists and managers are repeatedly compelled to confront new problems requiring data analysis (LaDeau et al., 2016). Data analysis is largely classified into two broad categories: unsupervised and supervised (Olden et al., 2008). The former focus on revealing patterns and structures in data (e.g., finding groups of co-occurring species), such as the renowned Self-Organising Maps (SOM) (Kohonen, 1982) or the laureate t-Distributed Stochastic Neighbour Embedding (t-SNE) (Van Der Maaten and Hinton, 2008). Conversely, supervised approaches, such as decision trees (e.g., CART; Breiman et al., 1984) or the Generalised Additive Models (GAMs) (Hastie and Tibshirani, 1990),...

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Section: Habitat Suitability For the Eastern Iberian Chub (Squalius Vmentioning

confidence: 70%

Revisiting probabilistic neural networks: a comparative study with support vector machines and the microhabitat suitability for the Eastern Iberian chub (Squalius valentinus)

Muñoz‐Mas

Fukuda

Pórtoles³

et al. 2018

Ecological Informatics

View full text Add to dashboard Cite

show abstract

Length-weight relationships of two endemic fish species in the Júcar River Basin, Iberian Peninsula

Abstract: Summary This study provides length‐weight relationship (LWRs) information for two fish species (family Cyprinidae) in two headwater streams of the Júcar River Basin (Eastern Iberian Peninsula). Both species are endemic to the Iberian Peninsula and have no previous LWR estimates.

Cited by 1 publication

References 6 publications

Revisiting probabilistic neural networks: a comparative study with support vector machines and the microhabitat suitability for the Eastern Iberian chub (Squalius valentinus)

Revisiting probabilistic neural networks: a comparative study with support vector machines and the microhabitat suitability for the Eastern Iberian chub (Squalius valentinus)

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