Predicting the structure and diversity of young‐of‐the‐year fish assemblages in large rivers
Identifieur interne : 001F36 ( Main/Exploration ); précédent : 001F35; suivant : 001F37Predicting the structure and diversity of young‐of‐the‐year fish assemblages in large rivers
Auteurs : R. E. Gozlan [France] ; S. Mastrorillo [France] ; G. H. Copp ; S. Lek [France]Source :
- Freshwater Biology [ 0046-5070 ] ; 1999-06.
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Abstract
1. Interactions between environmental variables and 0+ fish assemblages in the upper River Garonne (France) were quantified during late August 1995. 2. The abundance and diversity of the fish assemblages in floodplain channels were modelled using Artificial Neural Network (ANN) analysis and nine variables: the abundance of the six dominant species, fish specific richness, overall abundance of 0+ fish and the Shannon index of diversity. Multiple regression analysis was also used to assess ANN performance. 3. Using 596 samples, correlation coefficients (r adjusted) between observed and estimated values of the nine dependent parameters were all highly significant (P < 0.01). Expected values from the tested data were significantly related to the observed values. The correlation coefficient between observed and estimated values (r) varied from 0.70 to 0.85. 4. The ANN provided a high quality prediction, despite the complex nature of the relationship between microhabitat composition and fish abundance. 5. Garson’s algorithm was used to provide the explanatory power needed in ecology when using black‐box models. Parameters contained in the models (i.e. weighting) were used to determine the relative contributions of explanatory variables and thus to ascertain the structure of fish communities.
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DOI: 10.1046/j.1365-2427.1999.00423.x
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Interactions between environmental variables and 0+ fish assemblages in the upper River Garonne (France) were quantified during late August 1995. 2. The abundance and diversity of the fish assemblages in floodplain channels were modelled using Artificial Neural Network (ANN) analysis and nine variables: the abundance of the six dominant species, fish specific richness, overall abundance of 0+ fish and the Shannon index of diversity. Multiple regression analysis was also used to assess ANN performance. 3. Using 596 samples, correlation coefficients (r adjusted) between observed and estimated values of the nine dependent parameters were all highly significant (P < 0.01). Expected values from the tested data were significantly related to the observed values. The correlation coefficient between observed and estimated values (r) varied from 0.70 to 0.85. 4. The ANN provided a high quality prediction, despite the complex nature of the relationship between microhabitat composition and fish abundance. 5. Garson’s algorithm was used to provide the explanatory power needed in ecology when using black‐box models. Parameters contained in the models (i.e. weighting) were used to determine the relative contributions of explanatory variables and thus to ascertain the structure of fish communities.</div></front></TEI><affiliations><list><country><li>France</li></country><region><li>Languedoc-Roussillon-Midi-Pyrénées</li><li>Midi-Pyrénées</li></region><settlement><li>Toulouse</li></settlement></list><tree><noCountry><name sortKey="Copp, G H" sort="Copp, G H" uniqKey="Copp G" first="G. H." last="Copp">G. H. Copp</name></noCountry><country name="France"><region name="Languedoc-Roussillon-Midi-Pyrénées"><name sortKey="Gozlan, R E" sort="Gozlan, R E" uniqKey="Gozlan R" first="R. E." last="Gozlan">R. E. Gozlan</name></region><name sortKey="Lek, S" sort="Lek, S" uniqKey="Lek S" first="S." last="Lek">S. Lek</name><name sortKey="Mastrorillo, S" sort="Mastrorillo, S" uniqKey="Mastrorillo S" first="S." last="Mastrorillo">S. Mastrorillo</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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