Self-Organizing Maps in Revealing Variation in Non-obligatory Riverine Fish in Long-term Data

Abstract

Sensitiveness to the overall influence of the river channel regulation, impoundment and pollution was studied for 12 non-obligatory riverine (NOR) fish species in the Warta River, Poland, over the period of 1963-1998. Their total abundance has not considerably changed unlike the structure of their aggregation, which was revealed by a self-organizing map (SOM, Kohonen unsupervised artificial neural network) almost perfectly separating 1960s samples from 1990s ones. The greatest changes in proportion were recorded for the dominant roach Rutilus rutilus (from 28.6 to 60.0%), perch Perca fluviatilis (1.9 to 9.5%), pike Esox lucius (2.9 to 4.9%), and the former codominant bleak Alburbus alburnus (22.9 to 1.8%). They resulted from highly significant ( p < 0.001), reaching even one order of magnitude, differences in the abundance of these species. Such remarkable alternations in dominant NOR ichthyofauna obviously influenced the whole fish assemblage structure, which was best illustrated by a clear vertical gradient over SOM in Simpson and Shannon indices. Thus, NOR species, both studied together and separately, can supply valuable information on the fish assemblage dynamics and should not be neglected in temporal analyses, often focused only on vulnerable obligatory riverine, migratory and/or endemic species.

Long-term changes in fish assemblages of the Widawka and Grabia Rivers (Poland): pattern recognition with a Kohonen artificial neural network

“…In the degraded middle course of the Warta River, the increase in roach biomass was however realized not by higher body mass, but by an increased number of specimens (Kruk, unpublished data) attaining smaller body sizes (Przybylski 1996). In the Warta River, a long-term increase in dominance was also recorded for perch (Kruk 2006), whose strong increase can be favoured by channel engineering (Wolter & Vilcinskas 1997. This is clearly supported by this study, because the highest dominance (> 45%) of perch was recorded in neurons D2 and D4 containing samples collected in 2002-04 from the canalised section of the Widawka, while in 1963-66 the dominance of this species was several times lower (Fig.…”

“…Total dominance of (a) non-psammophilous rheophils (NPR) (see Appendix), (b) psammophilous rheophils (stone loach and gudgeon), and (c) roach and perch. The NPR are most vulnerable to worsened water quality, simplified channel structure and disrupted connectivity of riverine habitats, and this is why their declines and/or extinction are typical for unbalanced lotic ecosystems (Przybylski 1993, Kirchhofer & Hefti 1996, Oberdorff et al 2001, Kruk & Penczak 2003, Kruk 2004, 2006, 2007. Psammophils (stone loach and gudgeon) often predominate in degraded smaller streams (Bahlo 1991, Witkowski et al 1991, 1992, 2005, while a high dominance of roach and perch is typical for disturbed larger rivers (Przybylski 1993, Penczak et al 1999, Kruk 2006, 2007.…”

“…On the basis of those data and later fish sampling the temporal changes in the main channel of the Warta River have already been precisely described, including the extirpation of migratory and rheophilic fish, and an increase in tolerant roach and perch (Kruk et al 2001, Kruk 2004, 2006. Nevertheless, the main river may differ from smaller rivers not only in the composition of fish fauna but also in the extent of human-induced modifications of aquatic environments .…”

Applications of self‐organizing map for patterning macrofungal diversity of xerothermic swards

“…Macrofungal communities can be good indicators of the habitat diversity of these plant associations; they may also be used to identify modified, degraded and disturbed sites and thus to evaluate phytocoenoses. Using the SOM, both ecological assessments can be conducted easily (Kruk 2006) at various spatial scales. Additionally, the grouping of macrofungi samples by SOM has certain advantages: (1) it does not require a priori knowledge about species requirements; and (2) it takes into consideration all species.…”

“…The advantage of this method is that it can be used to effectively analyze complex data sets despite non-linear relationships and non-normal distributions, and it results in a two-dimensional map that is easy to interpret (Kohonen 1982). The SOM has proved to be an effective and powerful tool for exploring patterns in species distributions, and the structure of communities (Chon et al 1996;Giraudel and Lek 2001;Penczak et al 2005Penczak et al , 2009Kruk 2006;Lasne et al 2007;Kalteh et al 2008;Bedoya et al 2009). The SOM was simulated and cluster analysis was performed in Matlab (ver.…”