Visualization of fish community distribution patterns using the self-organizing map: A case study of the Great Morava River system (Serbia)

Stojković, Milica; Simić, Vladica; Milošević, Djuradj; Mančev, Dejan; Penczak, T.

doi:10.1016/j.ecolmodel.2012.09.014

Cited by 28 publications

(21 citation statements)

References 53 publications

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“…Our results fitted very well with previous research that showed altitude considerably affects fish distribution in aquatic ecosystems (Amarasinghe & Welcomme 2002;Yoon et al 2011;Barradas et al 2012;Stojkovic et al 2013). However, altitude is a complex variable: it can cause direct and indirect effects on fish distribution.…”

Section: Potential Determinants Of Lake Fish Diversity and Assemblagessupporting

confidence: 81%

Large scale patterns in the diversity of lake fish assemblages in China and the effect of environmental factors

Guo¹,

Chen²,

Lek³

et al. 2016

fal

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Understanding variations in species diversity and assemblage related with environments at a large spatial scale has been a central aim of community ecology for decades. The current study contributes to building the global perspective on the lake fish diversity and assemblage in China, and identifying the potential determinants, based on the multi-species approach fitted by Multivariate Regression Trees. A total of 425 fish species and subspecies recorded in 135 lakes across the mainland China were modelled with 19 potential environmental factors. Five fish assemblages were then patterned according to the similarity of fish composition and determinants. Diversity partition (alpha diversity and gamma diversity) was employed to compare the differentiation of fish diversity in Chinese lakes in multiple spatial scales. A significant difference between each assemblage was found: alpha diversity was lower in plateau lakes than in plain lakes. However, gamma diversity on plateaus was higher than any other regions. Altitude, minimum temperature of the coldest month, annual temperature range and precipitation during the driest month were the most important determinants affecting fish assemblages and diversity. Consequently, our findings inform the potential implications of fish species conservation and natural resources management.

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Section: Potential Determinants Of Lake Fish Diversity and Assemblagessupporting

confidence: 81%

Large scale patterns in the diversity of lake fish assemblages in China and the effect of environmental factors

Guo¹,

Chen²,

Lek³

et al. 2016

fal

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“…Consequently, similar real chironomid samples are located nearby (in the same neuron or in adjoining neurons), and those that are considerably different are located in distant regions of the SOM (Bedoya et al, 2009;Penczak, 2011;Conti et al, 2012;Li et al, 2013). In the above-described way, the Kohonen algorithm recognises patterns in the chironomid samples and distinguishes their groups, which may then be the subject of data interpretation (Lek et al, 2005;Cheng et al, 2012;Stojković et al, 2013).…”

Section: Discussionmentioning

confidence: 99%

Kohonen Artificial Neural Networks and the IndVal Index as Supplementary Tools for the Quantitative Analysis of Palaeoecological Data

et al. 2015

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We applied two widely-used methods for data partitioning -constrained incremental sum-of-squares (CONISS) and Optimal Partitioning (OP) along with two supplementary methods, a Kohonen artificial neural network (self-organising map, SOM) and the indicator value (IndVal) index, for the quantitative analysis of subfossil chironomid assemblages from a palaeolake in Central Poland. The samples, taken from 79 core depths, were divided into 5-11 groups (five by SOM, seven by CONISS, 11 by OP), for which different numbers of indicator taxa were determined with the use of the IndVal index (18 for CONISS, 15 for SOM, 11 for OP). Only six indicator taxa were common to all three methods. The number of highly specific (p < 0.001) taxa was highest for SOM. Only the SOM analysis clearly reflected the rate of the changes in chironomid assemblages, which occurred rapidly in the Late Glacial (as a result of greater climate variability) and slowly in the Holocene (as a reflection of slow long-term changes in the local habitat, such as paludification). In summary, we recommend using SOM and the IndVal index in combination with CONISS and/or OP in order to detect different aspects of temporal variability in complex multivariate palaeoecological data.

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“…We are not aware of any application of an unsupervised nonlinear technique such as the Self-Organising Map (SOM) to larvae distribution, though a few examples of ecological modelling in other areas exist, such as in geology (Kosiba et al, 2010), water pollution (Shanmuganathan et al, 2003), vegetation (Foody, 1999), forest data (Giraudel and Lek, 2001) and riverine communities Stojkovic et al, 2013). Chon (2011) revised the applications of SOM to ecological modelling extensively.…”

Section: Introductionmentioning

confidence: 98%

“…Li et al (2012) applied SOM to abundance data of riverine macroinvertebrates to obtain community clusters, and then calculated mean values of environmental data for each cluster. Stojkovic et al (2013) compared an a priori clustering based on environmental data to a posteriori group of clusters based on SOM applied to riverine fish community abundance data. To our knowledge, no direct application of SOM to a joint dataset of community abundance and environmental data has been done.…”

Section: Introductionmentioning

confidence: 99%

Nonlinear ecological processes driving the distribution of marine decapod larvae

Peña¹,

Carbonell²,

Tor³

et al. 2015

Deep Sea Research Part I: Oceanographic Research Papers

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Visualization of fish community distribution patterns using the self-organizing map: A case study of the Great Morava River system (Serbia)

Cited by 28 publications

References 53 publications

Large scale patterns in the diversity of lake fish assemblages in China and the effect of environmental factors

Large scale patterns in the diversity of lake fish assemblages in China and the effect of environmental factors

Kohonen Artificial Neural Networks and the IndVal Index as Supplementary Tools for the Quantitative Analysis of Palaeoecological Data

Nonlinear ecological processes driving the distribution of marine decapod larvae

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