Spatio‐temporal variations in fish assemblages in a tropical regulated lower river course: an environmental guild

Abstract

Fish assemblage patterns were studied in the lower river reach of the Uthokawiphatprasit anti-salt dam, 6 km upstream from the Pak Panang River mouth, Southern Thailand, where the dam was opened occasionally depending on the upstream water level. Matrix data of the presence-absence of 71 fish species in 102 surveys was used in the analysis by applying a self-organizing map (SOM) model. The trained SOM (lattice 8 Â 7) showed that after 6 years of operation, five assemblage patterns were distinguished. These patterns described the probability of the occurrence of fish in each fish environmental guild, which according to changes in flow and water geomorphology. Clusters Ia and Ib were mostly the surveys in upstream stations and occupied by fish in potamonic guilds, whereas the fish in the estuarine guild and marine guilds showed a high probability of occurring in clusters IIa, IIb and IIc, which belonged to the surveys in downstream stations. The surveys of the stations near the dam (i.e. stations 5 and 6) during the opening phase were contained in cluster IIb. The Kruskal-Wallis test showed that there was no statistical difference in the probability of occurrence among assemblages of the diadromous, catadromous and semi-anadromous fishes but not the amphidromous fishes, which had a low probability of occurrence in clusters Ia and Ib. The fish assemblages were arrayed along a longitudinal gradient, where salinity and pH were the most important controlling variables and explained 94.0% of the total inertia.

Catch assemblages in the small-scale trap fishery with relation to hydrographic features of a tropical bay in the Gulf of Thailand

“…Among the species caught, only one taxon exhibits true diadromy, the giant prawn Macrobrachium soenbergii . The remaining taxa are amphidromous species or marine visitors, entering the bay mostly for feeding [ 12 , 37 , 38 ]. Soe et al [ 39 ] reported that the fishes of the inner bay community tend to be plankton feeders, meanwhile the community in the outer bay is dominated by piscivores.…”

“…This is likely because secondary freshwater fishes were not moving near the bottom and being caught by traps, whereas gillnets capture some secondary freshwater fishes when a high volume of freshwater is discharged into the Bay [ 14 ]. It is unfortunate that there were no samples from December, since it was expected to yield a different cluster showing high %O of secondary freshwater fishes, as experienced in the nearby Pak Panang Bay during the peak of the northeast monsoon (NEM) period, i.e., December to January [ 37 , 38 ]. Cluster C was composed of samples from late NEM to the first monsoon transition (i.e., from February to April), when water temperature was rising.…”

Disentangling the complexity of tropical small-scale fisheries dynamics using supervised Self-Organizing Maps

“…Because the xyf-SOM grid size is related to the power of prediction [ 58 ]; we tested several sizes, looking for the map size with the higher prediction rate and minimum distance (topographic error) between elements, but taking into account the relation between resolution and accuracy [ 23 , 26 , 59 ] ( Fig 2E ). The starting size of the xyf-SOM grid was based on the formula c = 5√n, where c is the number of nodes and n is the number of samples [ 23 , 60 , 61 ]. After we found the best size of the xyf-SOM ( Fig 2F ), the mean and the 90% confidence interval of correct classification percentages (prediction power) were estimated using 1000 bootstrap replicas from the new object sets [ 62 ] ( Fig 2G ).…”

“…A hierarchical cluster analysis based on Ward’s linkage method was performed on the weight vectors produced by the general xyf-SOM ( Fig 2H ), with the objective to analyze how the xyf-SOM identify relationships between the fish categories [ 28 , 60 ].…”

Does the use of various population parameters affect fish assemblage structures?

“…The method has been commonly used for data analysis in various fields, including ecology, agriculture, health, and climate and engineering (Chon, 2011). The SOM has been proven to be an effective and powerful tool for exploring patterns in species' distributions and the structure of plants and animal communities, and the presence of each of the samples in the same neuron states do not differ significantly with the relevant parameter values used for its creation (Chon et al, 1996;Giraudel and Lek, 2001;Penczak et al, , 2009Kruk, 2006Kruk, , 2007Kalteh et al, 2008;Bedoya et al, 2009;Jutagate et al, 2011;Stojkovic et al, 2013).…”