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ABSTRACT: Cyanobacterial blooms are a common issue in eutrophic freshwaters, and some cyanobacteria produce toxins, threatening the health of humans and livestock. Microcystin, a representative cyanobacterial hepatotoxin, is frequently detected in most Korean lakes and reservoirs. This study developed predictive models for cyanobacterial bloom using artificial neural networks (ANNs; self-organizing map [SOM] and multilayer perceptron [MLP]), including an evaluation of related environmental factors. Fourteen environmental factors, as independent variables for predicting the cyanobacteria density, were measured weekly in the Daechung Reservoir from spring to autumn over 5 years (2001, 2003–2006). Cyanobacterial density was highly associated with environmental factors measured 3 weeks earlier. The SOM model was efficient in visualizing the relationships between cyanobacteria and environmental factors, and also for tracing temporal change patterns in the environmental condition of the reservoir. And the MLP model exhibited a good predictive power for the cyanobacterial density, based on the environmental factors of 3 weeks earlier. The water temperature and total dissolved nitrogen were the major determinants for cyanobacteria. The water temperature had a stronger influence on cyanobacterial growth than the nutrient concentrations in eutrophic waters. Contrary to general expectations, the nitrogen compounds played a more important role in bloom formation than the phosphorus compounds.
Journal of Phycology 05/2011; 47(3):495 - 504. · 2.07 Impact Factor
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ABSTRACT: General and genetic statistical methods are commonly used to deal with microsatellite data (highly variable neutral genetic markers). In this paper, the self-organizing map (SOM) that belongs to the unsupervised artificial neural networks (ANNs) was applied to analyse the structure of 58 European and two Chinese pig populations (Sus scrofa) including commercial lines, local breeds and cosmopolitan breeds. Results were compared with other unsupervised classification or ordination methods such as factorial correspondence analysis, hierarchical clustering from an allele sharing distance and the Bayesian genetic model and with principal components analysis and neighbour joining from allelic frequencies and genetic distances between populations. Like other methods, SOMs were able to classify individuals according to their breed origin and to visualize similarities between breeds. They provided additional information on the within- and between-population diversity, allowed differences between similar populations to be highlighted and helped differentiate different groups of populations.
Genetics Research 05/2009; 91(2):121-32. · 1.71 Impact Factor
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ABSTRACT: This study focused on patterning waterbirds occurrences at the western costal area of the Korean Peninsula in winter and relating the occurrence patterns with their environmental factors. Waterbird communities were monitored at 10 different study areas, and the composition of land cover as environmental factors was estimated at each study area. Overall dabbling ducks were the most abundant with 84% of total individuals, followed by shorebird and diving ducks. Species Anas platyrhynchos was the first dominant species, and Anas formosa was the second one. Self-organizing map (SOM), an unsupervised artificial neural network, was applied for patterning wintering waterbird communities, and identified 6 groups according to the differences of com-munities compositions. Each group reflected the differences of indicator species as well as their habitats.
Korean Journal of Environmental Biology. 03/2007; 25(2):149-157.
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ABSTRACT: This study focused on characterizing fish assemblages in the Adour-Garonne basin and identifying the relative influences of landscape-scale features on observed patterns in stream fish assemblages. Two different artificial neural network algorithms were used: a self-organizing map (SOM) and a multilayer perceptron (MLP). A SOM was applied to determine fish assemblage types, and a MLP was used to predict the fish assemblage types defined by the SOM. Thirty four species were collected at 191 sampling sites in a major river-system, the Adour-Garonne basin, and topographical factors, namely altitude, distance from source and surface area of drainage basin were measured. Using GIS, land cover types (agricultural land, forests and urbanized artificial surface) were calculated for each site and expressed as percentage of the surface area of basin. These variables were introduced to the MLP and factorial discriminant analysis for the prediction of assemblage types. As a result, the SOM distinguished three fish assemblage types according to the differences of species composition, and the assemblage types were better predicted with landscape-scale features by MLP than discriminant analysis. The percentages of agricultural land and the surface area of a basin showed the greatest influence on assemblage types 1 and 2, and distance from source was the most important factor to determine assemblage type 3.
Science of The Total Environment 08/2006; 365(1-3):140-53. · 3.29 Impact Factor
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Ecological Informatics. 01/2006; 1:213-217.
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ABSTRACT: The behavior of indicator specimens in response to sub-lethal doses of toxic substances has been used to detect contamination in aquatic ecosystems. Changes in the movement behaviors of medaka (Oryzias latipes) were analyzed after being treated with diazinon at a concentration of 0.1 mg/l. The movement tracks of medaka were continuously recorded in two-dimension by a digital image processing system both before and after the treatments. Subsequently, two computational methods--two-dimensional fast Fourier transform (2D FFT) and self-organizing map (SOM), were implemented to extract information from the movement data. The differences in the shapes of the movement tracks before and after the treatments were clearly manifested through 2D FFT. The short-distance, irregular turnings in the movement tracks observed after the treatments in the time domain were characteristically transformed to circular or ellipsoidal patterns in the frequency domain. The amplitudes of 2D FFT were efficiently classified by SOM, demonstrating the effects of the different treatments. To evaluate the feasibility of information extraction by 2D FFT, SOM was similarly carried out on the parameters (speed, meander, stop duration, etc.) conventionally used for characterizing the movement tracks. 2D FFT was more efficient in information extraction from the movement data than the parameters. The 2D FFT and SOM were useful as computational methods for automatically detecting response behaviors of indicator specimens exposed to toxic chemicals in aquatic ecosystems.
Aquatic Toxicology 03/2005; 71(3):215-28. · 3.76 Impact Factor
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ABSTRACT: Benthic macroinvertebrate communities in stream ecosystems were assessed hierarchically through two-level classification methods of unsupervised learning. Two artificial neural networks were implemented in combination. Firstly, the self-organizing map (SOM) was used to reduce the dimension of community data, and secondly, the adaptive resonance theory (ART) was subsequently applied to the SOM to further classify the groups in different scales. Hierarchical grouping in community data efficiently reflected the impact of the environmental factors such as topographic conditions, levels of pollution, and sampling location and time across different scales. New community data not included in the training process were used to test the trained network model. The input data were appropriately grouped at different hierarchical levels by the trained networks, and correspondingly revealed the impact of environmental disturbances and temporal dynamics of communities. The hierarchical clusters based on a two-level classification method could be useful for assessing ecosystem quality and community variations caused by environmental disturbances.
Science of The Total Environment 08/2004; 327(1-3):105-22. · 3.29 Impact Factor
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Applied Entomology and Zoology. 01/2004; 39:79-96.
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ABSTRACT: The largest damming project to date, the Three Gorges Dam has been built along the Yangtze River (China), the most species-rich river in the Palearctic region. Among 162 species of fish inhabiting the main channel of the upper Yangtze, 44 are endemic and are therefore under serious threat of global extinction from the dam. Accordingly, it is urgently necessary to develop strategies to minimize the impacts of the drastic environmental changes associated with the dam. We sought to identify potential reserves for the endemic species among the 17 tributaries in the upper Yangtze, based on presence/absence data for the 44 endemic species. Potential reserves for the endemic species were identified by characterizing the distribution patterns of endemic species with an adaptive learning algorithm called a “self-organizing map” (SOM). Using this method, we also predicted occurrence probabilities of species in potential reserves based on the distribution patterns of communities. Considering both SOM model results and actual knowledge of the biology of the considered species, our results suggested that 24 species may survive in the tributaries, 14 have an uncertain future, and 6 have a high probability of becoming extinct after dam filling.Resumen: El proyecto de represa más grande a la fecha, la Presa Three Gorges fue construida en el Río Yangtze (China), el río con mayor riqueza de especies en la región Paleártica. Entre las 162 especies de peces que habitan el canal principal del alto Yangtze, 44 son endémicas y por tanto están seriamente amenazadas de extinción global por la presa. Consecuentemente, es urgente desarrollar estrategias para minimizar los impactos de los cambios ambientales drásticos asociados con la presa. Tratamos de identificar las reservas potenciales para las especies endémicas entre los 17 afluentes en el alto Yangtze, en base a datos de presencia y ausencia de las 44 especies endémicas. Se identificaron las reservas potenciales para la especies endémicas caracterizando los patrones de distribución de especies endémicas con un algoritmo de aprendizaje adaptivo denominado “mapa auto-organizante” (MAO). Con este método, también predijimos las probabilidades de ocurrencia de especies en reservas potenciales en base a los patrones de distribución de las comunidades. Tomando en cuenta tanto los resultados del modelo MAO como el conocimiento actual de la biología de especies en consideración, nuestros resultados sugieren que 24 especies pueden sobrevivir en los afluentes, 14 tienen un futuro incierto y 6 tienen una alta probabilidad de extinguirse después del llenado de la presa.
Conservation Biology 11/2003; 17(6):1748 - 1758. · 4.69 Impact Factor
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ABSTRACT: Artificial neural networks were used to predict the species richness of 4 major orders of aquatic insects (Ephemeroptera, Plecoptera, Trichoptera, and Coleoptera, i.e., EPTC) at a site, using a limited number of environmental variables. EPTC richness was recorded in the Adour-Garonne stream system (France), at 155 unstressed sampling sites, which were characterized using 4 environmental variables: elevation, stream order, distance from the source, and maximum water temperature. The EPTC and environmental data were first computed with the Self-Organizing Map (SOM) algorithm. Then, using the k-means algorithm, clusters were detected on the map and the sampling sites were classified separately for each variable and for EPTC richness. Four clusters could be identified on the SOM map, according to the 4 environmental variables, and this classification was chiefly related to stream order and elevation (i.e., the longitudinal location of sampling sites within a stream system). Similarly, 4 subsets were derived from the SOM according to a gradient of EPTC richness. There was also a high coincidence between observed (field data) and calculated (predicted from the output neurons of the SOM) species richness in each taxonomic group. Species richness relationships between Ephemeroptera, Trichoptera, and Coleoptera for both observed and predicted data were highly significant. However, correlation coefficients among species richness of Plecoptera and the other groups were low. Last, a multilayer perceptron neural network, trained using the backpropagation algorithm, was used to predict EPTC richness (output) using the 4 above-mentioned environmental variables (input). The model showed high predictability (r = 0.91 and r = 0.61 for training and test data sets, respectively), and a sensitivity analysis revealed that elevation and stream order contributed the most among the 4 input variables. Prediction of species richness using a limited number of environmental variables is, thus, a valuable tool for the assessment of disturbance in a given area. The degree to which human activities have altered EPTC richness can be determined by knowing what the EPTC richness should be under undisturbed conditions in a given area.
Journal of the North American Benthological Society 09/2003; 22(3):442-456. · 2.80 Impact Factor
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ABSTRACT: A counterpropagation neural network (CPN) was applied to predict species richness (SR) and Shannon diversity index (SH) of benthic macroinvertebrate communities using 34 environmental variables. The data were collected at 664 sites at 23 different water types such as springs, streams, rivers, canals, ditches, lakes, and pools in The Netherlands. By training the CPN, the sampling sites were classified into five groups and the classification was mainly related to pollution status and habitat type of the sampling sites. By visualizing environmental variables and diversity indices on the map of the trained model, the relationships between variables were evaluated. The trained CPN serves as a 'look-up table' for finding the corresponding values between environmental variables and community indices. The output of the model fitted SH and SR well showing a high accuracy of the prediction (r>0.90 and 0.67 for learning and testing process, respectively) for both SH and SR. Finally, the results of this study, which uses the capability of the CPN for patterning and predicting ecological data, suggest that the CPN can be effectively used as a tool for assessing ecological status and predicting water quality of target ecosystems.
Water Research 04/2003; 37(8):1749-58. · 4.86 Impact Factor
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ABSTRACT: Two artificial neural networks (ANNs), unsupervised and supervised learning algorithms, were applied to suggest practical approaches for the analysis of ecological data. Four major aquatic insect orders (Ephemeroptera, Plecoptera, Trichoptera, and Coleoptera, i.e. EPTC), and four environmental variables (elevation, stream order, distance from the source, and water temperature) were used to implement the models. The data were collected and measured at 155 sampling sites on streams of the Adour Á/Garonne drainage basin (South-western France). The modelling procedure was carried out following two steps. First, a self-organizing map (SOM), an unsupervised ANN, was applied to classify sampling sites using EPTC richness. Second, a backpropagation algorithm (BP), a supervised ANN, was applied to predict EPTC richness using a set of four environmental variables. The trained SOM classified sampling sites according to a gradient of EPTC richness, and the groups obtained corresponded to geographic regions of the drainage basin and characteristics of their environmental variables. The SOM showed its convenience to analyze relationships among sampling sites, biological attributes, and environmental variables. After accounting for the relationships in data sets, the BP used to predict the EPTC richness with a set of four environmental variables showed a high accuracy (r 0/0.91 and r 0/0.61 for training and test data sets respectively). The prediction of EPTC richness is thus a valuable tool to assess disturbances in given areas: by knowing what the EPTC richness should be, we can determine the degree to which disturbances have altered it. The results suggested that methodologies successively using two different neural networks are helpful to understand ecological data through ordination first, and then to predict target variables. # 2002 Elsevier Science B.V. All rights reserved.
Ecological Modelling 02/2003; 160(3):265-280. · 2.33 Impact Factor
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Véronique Gosselain,
Stéphane Campeau,
Muriel Gevrey,
Michel Coste,
Luc Ector,
Frédéric Rimet,
Juliette Tison,
François Delmas, Young-Seuk Park,
Sovan Lek,
Jean-Pierre Descy
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ABSTRACT: 1-URBO, Facultés Universitaires N-D de la Paix ; 61, rue de Bruxelles, B-5000 Namur (Belgium) 2-Section of Geography, Université du Québec à Trois-Rivières ; 3351, boulevard des Forges, C.P. 500, Trois-Rivières (Quebec, Canada) G9A 5H7 3-LADYBIO, UMR 5576, CNRS, Université Paul Sabatier ; 118, route de Narbonne, F-31062 Toulouse cedex (France) 4-Cemagref de Bordeaux, U.R Qualité des eaux ; 50, avenue de Verdun, F-33610 Cestas (France) 5-Centre de Recherche Publique-Gabriel Lippmann, Cellule de Recherche en Environnement et Biotechnologies (CREBS) ; 162a, avenue de la Faïencerie, L-1511 Luxembourg (Grand-Duchy of Luxembourg) *Corresponding author: tel. Abstract Benthic diatoms have been used for decades as indicators of stream water quality and environmental stress. While classification systems and monitoring methods have been developed mostly in Europe, the search for main factors determining assemblages at various scales has been mainly conducted on the American continent. We analysed a selection of 467 diatom records from stream with minimal human impact, from several countries and regions of Western Europe, using different multivariate techniques and artificial neural networks (ANN). The data matrix contained 123 diatom taxa X 23 environmental variables, and covered 35 major catchments. Data processing involved the use of PCA (Principal Component Analysis), DCA (Detrended Correspondence Analysis), CCA (Canonical Correspondence Analysis) and SOM (Self Organizing Maps). Multivariate analyses were useful for identifying the main environmental gradients, and combination of these analyses and SOM enabled to define 10 ecological groups, composed of key indicator taxa. Some of these groups could be identified as corresponding to near-natural conditions, allowing the definition of a biotypology of benthic diatom along a gradient of alkalinity, conductivity, pH – mainly determined by geological features – and a temperature/elevation gradient. Sensitivity analysis and box-plots of environmental variables helped identify the main factors determining stream conditions for these assemblages, and slightly altered conditions or particular situations were easily detected. Several possible bias were identified, either from imbalance among river types in the database, or from taxonomic and identification problems, or from collection of records from various sources. Taxa distribution maps, obtained from the SOM, have been used as a useful mean for representing auto-ecological properties of benthic diatoms and for identifying dual distributions resulting either from errors, from incorrect taxonomic status or from actual ecological differences within a same taxon. On the basis of available information, factors determining diatom assemblages are similar in different regions and even continents, which raises the question of the relevance of the eco-regional approach for this stream community.
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ABSTRACT: e c o l o g i c a l m o d e l l i n g 2 0 3 (2 0 0 7) 109–118 a v a i l a b l e a t w w w . s c i e n c e d i r e c t . c o m j o u r n a l h o m e p a g e : w w w . e l s e v i e r . c o m / l o c a t e / e c o l m o d e l Evaluation of environmental factors Alert system for algal bloom a b s t r a c t The water quality and phytoplankton communities in the Daechung Reservoir, Korea, were monitored from summer to autumn in 1999, 2001, and 2003. The temporal patterns of cyanobacterial blooming caused by Microcystis were then elucidated using a combination of two artificial neural networks (ANNs): self-organizing map (SOM) and multilayer percep-tron (MLP). The SOM was initially used to cluster the phytoplankton communities, then the MLP was applied to identify the major environmental factors causing the abundance of phytoplankton in the clustered communities. The SOM divided the phytoplankton commu-nities into four clusters based on their algal composition (Cyanophyceae, Chlorophyceae, Bacillariophyceae, and others). In particular, cluster II was mostly composed of sampling times in August and September, and closely matched the period of severe cyanobacterial bloom dominated by Cyanophyceae. Meanwhile, cluster IV was mainly composed of the samples collected in the other periods, covering April, May, June, and October, and was mostly dominated by Bacillariophyceae. Cyanophyceae was the main component of the total algae, and its variation among the clusters showed a similar pattern to that of the changes in the chlorophyll-a concentration. Based on the MLP model, the water temper-ature, total particulate nitrogen, daily irradiance, and total nitrogen were highlighted as the four most important environmental variables predicting cyanobacterial abundance, yet quite different environmental variables were found to affect the chlorophyll-a concentra-tion. The usage of sampled data and analyses by ANNs are also discussed with reference to an early alert system for algal bloom.
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ABSTRACT: Ecological communities consist of a large number of species. Most species are rare or have low abundance, and only a few are abundant and/or frequent. In quantitative community analysis, abundant species are commonly used to interpret patterns of habitat disturbance or ecosystem degradation. Rare species cause many difficulties in quantitative analysis by introducing noises and bulking datasets, which is worsened by the fact that large datasets suffer from difficulties of data handling. In this study we propose a method to reduce the size of large datasets by selecting the most ecologically representative species using a self organizing map (SOM) and structuring index (SI). As an example, we used diatom community data sampled at 836 sites with 941 species throughout the French hydrosystem. Out of the 941 species, 353 were selected. The selected dataset was effectively classified according to the similarities of community assemblages in the SOM map. Compared to the SOM map generated with the original dataset, the community pattern gave a very similar representation of ecological conditions of the sampling sites, displaying clear gradients of environmental factors between different clusters. Our results showed that this computational technique can be applied to preprocessing data in multivariate analysis. It could be useful for ecosystem assessment and management, helping to reduce both the list of species for identification and the size of datasets to be processed for diagnosing the ecological status of water courses.
Ecological Informatics.
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ABSTRACT: Fish assemblages are reckoned as indicators of aquatic ecosystem health, which has become a key feature in water quality management. Under this context, guilds of fish are useful for both understanding aquatic community ecology and for giving sound advice to decision makers by means of metrics for indices of biotic integrity. Artificial neural networks have proved useful in modelling fish in rivers and lakes. Hence, this paper presents a back-propagation network (BPN) for modelling fish guilds composition, and to examine the contribution of five environmental descriptors in explaining this composition in the Garonne basin, south west France. We employed presence–absence data and five variables: altitude, distance from the river source, surface of catchment area, annual mean water temperature, and annual mean water flow. We found that BPN performed better for predicting species richness of guilds than multiple regression models. The standardised determination coefficient of observed values against estimated values was used to characterise model performance; it varied between 0.55 and 0.82. Some models showed high variability which was presumably due to spatial heterogeneity, temporal variability or sampling uncertainty. Surface of catchment area and annual mean water flow were the most important environmental descriptors of guilds composition. Both variables imply human influence (i.e. land-use and flow regulation) on certain species which are of interest to environmental managers. Thus, predicting guilds composition with a BPN from landscape variables may be a first step to assess metrics for water quality indices in the Garonne basin.
Ecological Modelling.
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ABSTRACT: A self-organising map (SOM) was applied to extract long-term dynamic patterns of fish community in shallow eutrophic Lake Peipsi using the commercial fishery statistics recorded for 64 years from 1931 to 2002 (excluding 1940–1949 due to the World War II). In the dataset of fishery statistics, mainly 11 taxa were recognised according to their commercial importance. Therefore, we used the dataset consisting of 64 sample units (i.e., year) with 11 taxa for patterning the fish community by the SOM. Our results suggest that the fish community of Lake Peipsi was mostly gradually changed in long-term scale, but some abrupt changes were also noticeable. Despite the same species composition, the total annual catch has declined indicating changes of fish community. The fish community of Lake Peipsi has shifted in long-term scale from clean- and cold-water species like vendace Coregonus albula (L.), whitefish C. lavaretus L. and burbot Lota lota (L.) to more pikeperch Sander lucioperca (L.) preferring productive warm and turbid waters. Both, the deterioration of aquatic environment and predatory effect of pikeperch prevent recovery of vendace population. Gradual reduction of the stock of smelt Osmerus eperlanus L. is also a sign of ongoing eutrophication, while its disappearance from catches for 3 years (1973–1975) was the result of summer fish-kill. The effect of fishing is the most important human impact on the fish community in Lake Peipsi. Extensive use of fine-meshed towed fishing gear (e.g., trawls replaced later by bottom seines) affected mostly recruitment of pelagic predator, pikeperch, killing young specimens of this fish in large quantities. Even though the fishery methods have changed, the fish stocks of the lake have repeatedly suffered from the over-fishing.
Ecological Modelling.
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ABSTRACT: Analysis of patterns of temporal variation in community dynamics was conducted by combining two unsupervised artificial neural networks, the Adaptive Resonance Theory (ART) and the Kohonen network. The field data used as input for training represented monthly changes in density and species richness in selected taxa of benthic macroinvertebrates collected in the Suyong River in Korea from September 1993 to October 1994. The sampled data for each month was initially trained by ART, the weights of which preserved conformational characteristics among communities during the process of the training. Subsequently these weights were rearranged sequentially from 2 to 5 months, and were provided as input to the Kohonen network to reveal temporal variations in communities. The network was then able to extract the features of community dynamics in a reduced dimension covering the specified input period.
Ecological Modelling.
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ABSTRACT: The Kohonen network, an unsupervised learning algorithm in artificial neural networks, performs self-organizing mapping and reduces dimensions of a complex data set. In this study, the network was applied to clustering and patternizing community data in ecology. The input data were benthic macroinvertebrates collected at study sites in the Suyong river in Korea. The grouping resulting from learning by the Kohonen network was comparable to the classification by conventional clustering methods. Through patternizing, the network showed a possibility of producing easily comprehensible low-dimensional maps under the total configuration of community groups in a target ecosystem. Changes in spatio-temporal community patterns may also be traced through the recognition process.
Ecological Modelling.
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ABSTRACT: This study demonstrated prediction of breeding success of Black-tailed Gulls in relation to the selected environmental factors through evaluation of relative importance in determining breeding success. The data were obtained from the 258 selected and 120 non-selected sites for breeding of the gulls during the breeding periods in 2002–2003. Breeding success at the selected sites, and environmental factors such as vegetation cover, vegetation height, rock cover, nest-wall, nearest distance between neighbors and slope, were measured at each sampling site. For predicting breeding success of Black-tailed Gulls, we used two different artificial neural networks in this study: self-organizing map (SOM) and multilayer perceptron (MLP). SOM was used to classify the sampling sites based on the environmental factors, whereas MLP was implemented to prediction of breeding success of the gulls at the non-selected sites based on environmental conditions. In our results, SOM discriminated clearly the sampling sites and presented differences in environmental factors between the selected and non-selected sites. Subsequently, the breeding success was accordingly predicted by MLP. Nest-wall was considered the most important environmental factor in determining survival status of the gulls. An increase in nest-wall and vegetation cover was required to support breeding of the specimens for managing the habitats for Black-tailed Gulls.
Ecological Informatics.
