Utilisation of non-supervised neural networks and principal component analysis to study fish assemblages

Utilisation of non-supervised neural networks and principal component analysis to study fish... Kohonen self-organizing maps (SOM) belong to the non-supervised artificial neural network modelling methods. It typically displays a high dimensional data set in a lower dimensional space. In this way, that method can be considered as a non-linear surrogate to the principal component analysis (PCA). In order to test the efficiency of SOM on complex ecological data gathered in the natural environment, we made a comparison between PCA and SOM capabilities to analyse the spatial occupancy of several European freshwater fish species in the littoral zone of a large French lake. The same data matrix consisting of 710 samples and 15 species was analysed using PCA and SOM. Both methods provided insights on the major trends in fish spatial occupancy. However, a more detailed analysis showed that only SOM was able to reliably visualise the entire fish assemblage in a two dimensional space (i.e. both dominant and scarce species). On the contrary PCA provided irrelevant ecological information for some species. These drawbacks were afforded to data heterogeneity, scarce species being poorly represented on the PCA plane. These results led us to conclude that SOM constitute a more reliable data representation method than PCA when complex ecological data sets are used. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Ecological Modelling Elsevier

Utilisation of non-supervised neural networks and principal component analysis to study fish assemblages

Ecological Modelling, Volume 146 (1) – Dec 1, 2001

Loading next page...
 
/lp/elsevier/utilisation-of-non-supervised-neural-networks-and-principal-component-unrjSqyt08
Publisher
Elsevier
Copyright
Copyright © 2001 Elsevier Science B.V.
ISSN
0304-3800
eISSN
1872-7026
DOI
10.1016/S0304-3800(01)00303-9
Publisher site
See Article on Publisher Site

Abstract

Kohonen self-organizing maps (SOM) belong to the non-supervised artificial neural network modelling methods. It typically displays a high dimensional data set in a lower dimensional space. In this way, that method can be considered as a non-linear surrogate to the principal component analysis (PCA). In order to test the efficiency of SOM on complex ecological data gathered in the natural environment, we made a comparison between PCA and SOM capabilities to analyse the spatial occupancy of several European freshwater fish species in the littoral zone of a large French lake. The same data matrix consisting of 710 samples and 15 species was analysed using PCA and SOM. Both methods provided insights on the major trends in fish spatial occupancy. However, a more detailed analysis showed that only SOM was able to reliably visualise the entire fish assemblage in a two dimensional space (i.e. both dominant and scarce species). On the contrary PCA provided irrelevant ecological information for some species. These drawbacks were afforded to data heterogeneity, scarce species being poorly represented on the PCA plane. These results led us to conclude that SOM constitute a more reliable data representation method than PCA when complex ecological data sets are used.

Journal

Ecological ModellingElsevier

Published: Dec 1, 2001

References

You’re reading a free preview. Subscribe to read the entire article.


DeepDyve is your
personal research library

It’s your single place to instantly
discover and read the research
that matters to you.

Enjoy affordable access to
over 18 million articles from more than
15,000 peer-reviewed journals.

All for just $49/month

Explore the DeepDyve Library

Search

Query the DeepDyve database, plus search all of PubMed and Google Scholar seamlessly

Organize

Save any article or search result from DeepDyve, PubMed, and Google Scholar... all in one place.

Access

Get unlimited, online access to over 18 million full-text articles from more than 15,000 scientific journals.

Your journals are on DeepDyve

Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.

All the latest content is available, no embargo periods.

See the journals in your area

DeepDyve

Freelancer

DeepDyve

Pro

Price

FREE

$49/month
$360/year

Save searches from
Google Scholar,
PubMed

Create folders to
organize your research

Export folders, citations

Read DeepDyve articles

Abstract access only

Unlimited access to over
18 million full-text articles

Print

20 pages / month

PDF Discount

20% off