ALBERT — All Library Books, journals and Electronic Records Telegrafenberg

1

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Water quality assessment using diatom assemblages and advanced modelling techniques (2004)

Gevrey, Muriel ; Rimet, Frédéric ; Park, Young Seuk ; [et al.]

Oxford, UK : Blackwell Science Ltd

Freshwater biology 49 (2004), S. 0

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ISSN: 1365-2427

Source: Blackwell Publishing Journal Backfiles 1879-2005

Topics: Biology

Notes: 1. Two types of artificial neural networks procedures were used to define and predict diatom assemblage structures in Luxembourg streams using environmental data.2. Self-organising maps (SOM) were used to classify samples according to their diatom composition, and multilayer perceptron with a backpropagation learning algorithm (BPN) was used to predict these assemblages using environmental characteristics of each sample as input and spatial coordinates (X and Y) of the cell centres of the SOM map identified as diatom assemblages as output. Classical methods (correspondence analysis and clustering analysis) were then used to identify the relations between diatom assemblages and the SOM cell number. A canonical correspondence analysis was also used to define the relationship between these assemblages and the environmental conditions.3. The diatom-SOM training set resulted in 12 representative assemblages (12 clusters) having different species compositions. Comparison of observed and estimated sample positions on the SOM map were used to evaluate the performance of the BPN (correlation coefficients were 0.93 for X and 0.94 for Y). Mean square errors of 12 cells varied from 0.47 to 1.77 and the proportion of well predicted samples ranged from 37.5 to 92.9%. This study showed the high predictability of diatom assemblages using physical and chemical parameters for a small number of river types within a restricted geographical area.

Type of Medium: Electronic Resource

URL: http://dx.doi.org/10.1046/j.1365-2426.2003.01174.x

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Behaviour of roach (Rutilus rutilus L.) altered by Ligula intestinalis (Cestoda: Pseudophyllidea): a field demonstration (2001)

Loot, Géraldine ; Brosse, Sébastien ; Lek, Sovan ; [et al.]

Oxford UK : Blackwell Science Ltd

Freshwater biology 46 (2001), S. 0

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ISSN: 1365-2427

Source: Blackwell Publishing Journal Backfiles 1879-2005

Topics: Biology

Notes: 1. We studied the influence of a cestode parasite, the tapeworm Ligula intestinalis (L.) on roach (Rutilus rutilus L.) spatial occupancy in a French reservoir (Lake Pareloup, South-west of France).2. Fish host age, habitat use and parasite occurrence and abundance were determined during a 1 year cycle using monthly gill-net catches. Multivariate analysis [generalized linear models (GLIM)], revealed significant relationships (P 〈 0.05) between roach age, its spatial occupancy and parasite occurrence and abundance.3. Three-year-old roach were found to be heavily parasitized and their location toward the bank was significantly linked to parasite occurrence and abundance. Parasitized fish, considering both parasite occurrence and abundance, tended to occur close to the bank between July and December. On the contrary, between January and June no significant relationship was found.4. These behavioural changes induced by the parasite may increase piscivorous bird encounter rate and predation efficiency on parasitized roach and therefore facilitate completion of the parasite’s life cycle.

Type of Medium: Electronic Resource

URL: http://dx.doi.org/10.1046/j.1365-2427.2001.00733.x

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3

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The use of artificial neural networks to predict the presence of small-bodied fish in a river (1997)

MASTRORILLO, SYLVAIN ; LEK, SOVAN ; DAUBA, FRANCIS ; [et al.]

Oxford, UK : Blackwell Science Ltd

Freshwater biology 38 (1997), S. 0

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ISSN: 1365-2427

Source: Blackwell Publishing Journal Backfiles 1879-2005

Topics: Biology

Notes: 1. Discriminant factorial analysis (DFA) and artificial neural networks (ANN) were used to develop models of presence/absence for three species of small-bodied fish (minnow, Phoxinus phoxinus, gudgeon, Gobio gobio, and stone loach, Barbatula barbatula).2. Fish and ten environmental variables were sampled using point abundance sampling by electrofishing in the Ariège River (France) at 464 sampling points.3. Using DFA, the percentage of correct assignments, expressed as the percentage of individuals correctly classified over the total number of examined individuals, was 62.5% for stone loach, 66.6% for gudgeon and 78% for minnow. With back-propagation of ANN, the recognition performance obtained after 500 iterations was: 82.1% for stone loach, 87.7% for gudgeon and 90.1% for minnow.4. The better predictive performance of the artificial neural networks holds promise for other situations with non-linearly related variables.

Type of Medium: Electronic Resource

URL: http://dx.doi.org/10.1046/j.1365-2427.1997.00209.x

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4

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Modelling roach (Rutilus rutilus) microhabitat using linear and nonlinear techniques (2000)

Brosse, Sebastien ; Lek, Sovan

Oxford, UK : Blackwell Science Ltd

Freshwater biology 44 (2000), S. 0

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ISSN: 1365-2427

Source: Blackwell Publishing Journal Backfiles 1879-2005

Topics: Biology

Notes: 〈list style="custom"〉1Multiple linear regression (MLR), generalised additive models (GAM) and artificial neural networks (ANN), were used to define young of the year (0+) roach (Rutilus rutilus) microhabitat and to predict its abundance.20+ Roach and nine environmental variables were sampled using point abundance sampling by electrofishing in the littoral area of Lake Pareloup (France) during summer 1997. Eight of these variables were used to set up the models after log10 (x+ 1) transformation of the dependent variable (0+ roach density). Model training and testing were performed on independent subsets of the whole data matrix containing 306 records.3The predictive quality of the models was estimated using the determination coefficient between observed and estimated values of roach densities. The best models were provided by ANN, with a correlation coefficient (r) of 0.83 in the training procedure and 0.62 in the testing procedure. GAM and MLR gave lower prediction in the training set (r = 0.53 for GAM and r = 0.32 for MLR) and in the testing set (r = 0.48 for GAM and r = 0.43 for MLR). In the same way, samples without fish were reliably predicted by ANN whereas GAM and MLR predicted absence unreliably.4ANN sensitivity analysis of the eight environmental variables in the models revealed that 0+ roach distribution was mainly influenced by five variables: depth, distance from the bank, local slope of the bottom and percentage of mud and flooded vegetation cover. The nonlinear influence of these variables on 0+ roach distribution was clearly shown using nonparametric lowess smoothing procedures.5Non-linear modelling methods, such as GAM and ANN, were able to define 0+ fish microhabitat precisely and to provide insight into 0+ roach distribution and abundance in the littoral zone of a large reservoir. The results showed that in lakes, 0+ roach microhabitat is influenced by a complex combination of several environmental variables acting mainly in a nonlinear way.

Type of Medium: Electronic Resource

URL: http://dx.doi.org/10.1046/j.1365-2427.2000.00580.x

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5

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Energy availability and habitat heterogeneity predict global riverine fish diversity (1998)

Lek, Sovan ; Oberdorff, Thierry ; Guégan, Jean-François

[s.l.] : Macmillan Magazines Ltd.

Nature 391 (1998), S. 382-384

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ISSN: 1476-4687

Source: Nature Archives 1869 - 2009

Topics: Biology , Chemistry and Pharmacology , Medicine , Natural Sciences in General , Physics

Notes: [Auszug] Processes governing patterns of richness of riverine fish species at the global level can be modelled using artificial neural network (ANN) procedures. These ANNs are the most recent development in computer-aided identification and are very different from conventional techniques,. Here we use ...

Type of Medium: Electronic Resource

URL: http://dx.doi.org/10.1038/34899

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6

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Use of some sensitivity criteria for choosing networks with good generalization ability (1995)

Dimopoulos, Yannis ; Bourret, Paul ; Lek, Sovan

Springer

Neural processing letters 2 (1995), S. 1-4

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ISSN: 1573-773X

Source: Springer Online Journal Archives 1860-2000

Topics: Computer Science

Notes: Abstract In most applications of the multilayer perceptron (MLP) the main objective is to maximize the generalization ability of the network. We show that this ability is related to the sensitivity of the output of the MLP to small input changes. Several criteria have been proposed for the evaluation of the sensitivity. We propose a new index and present a way for improving these sensitivity criteria. Some numerical experiments allow a first comparison of the efficiencies of these criteria.

Type of Medium: Electronic Resource

URL: http://dx.doi.org/10.1007/BF02309007

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7

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Stochastic models that predict trout population density or biomass on a mesohabitat scale (1996)

Baran, Philippe ; Lek, Sovan ; Delacoste, Marc ; [et al.]

Springer

Hydrobiologia 337 (1996), S. 1-9

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ISSN: 1573-5117

Keywords: trout ; habitat ; density and biomass ; modelling ; neural network ; multiple regression

Source: Springer Online Journal Archives 1860-2000

Topics: Biology

Notes: Abstract Neural networks and multiple linear regression models of the abundance of brown trout (Salmo trutta L.) on the mesohabitat scale were developed from combinations of physical habitat variables in 220 channel morphodynamic units (pools, riffles, runs, etc.) of 11 different streams in the central Pyrenean mountains. For all the 220 morphodynamic units, the determination coefficients obtained between the estimated and observed values of density or biomass were significantly higher for the neural network (r 2 adjusted= 0.93 and r 2 adjusted=0.92 (p〈0.01) for biomass and density respectively with the neural network, against r 2 adjusted=0.69 (p〈0.01) and r 2 adjusted = 0.54 (p〈0.01) with multiple linear regression). Validation of the multivariate models and learning of the neural network developed from 165 randomly chosen channel morphodynamic units, was tested on the 55 other channel morphodynamic units. This showed that the biomass and density estimated by both methods were significantly related to the observed biomass and density. Determination coefficients were significantly higher for the neural network (r 2 adjusted =0.72 (p〈0.01) and 0.81 (p〈0.01) for biomass and density respectively) than for the multiple regression model (r 2 adjusted=0.59 and r 2 adjusted=0.37 for biomass and density respectively). The present study shows the advantages of the backpropagation procedure with neural networks over multiple linear regression analysis, at least in the field of stochastic salmonid ecology.

Type of Medium: Electronic Resource

URL: http://dx.doi.org/10.1007/BF00028502

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