Multi-annual and seasonal patterns of waterbird assemblages in a Mediterranean coastal lagoon (El Mellah lagoon) of Northeastern Algeria

Recently, Mediterranean coastal lagoons have raised considerable environmental concerns. Long-term studies of seasonal changes in waterbird assemblages are therefore extremely important in terms of ecological relevance and conservation of these sensitive ecosystems. An ornithological survey of four years was carried out in a typical costal wetland (El Mellah lagoon) of Northeastern Algeria. Intra-seasonal comparison of waterbird assemblages (diversity indices) demonstrates clear changes between the wintering and the breeding periods. It seems that the first one was rich in term of species number than the second season (43 against 24). In contrast, the breeding seasons were more equilibrate (high values of Simpson, Shannon and evenness index). Additionally, curves in the diversity/dominance diagram revealed that both wintering and breeding assemblages share the same characteristics of community structure, few dominant species (with intermediate relative abundance) and many rare species with the relative abundance lower than 0.1. Invertebrates (25 species) and piscivorous (11 species) are the most abundant guilds over the four years of study (no significant differences among years have been calculated). The marked decline in bird species diversity recorded in this study (in comparison with previous studies) is mainly due to salinity oscillations (due to aquaculture activities) and may be of concern to wetland managers and it might be useful to provide some guidelines about the characteristics that coastal lagoons have to follow in the construction process to enhance the biodiversity

Modelling Microcystis Cell Density in a Mediterranean Shallow Lake of Northeast Algeria (Oubeira Lake), Using Evolutionary and Classic Programming

Caused by excess levels of nutrients and increased temperatures, freshwater cyanobacterial blooms have become a serious global issue. However, with the development of artificial intelligence and extreme learning machine methods, the forecasting of cyanobacteria blooms has become more feasible. We explored the use of multiple techniques, including both statistical [Multiple Regression Model (MLR) and Support Vector Machine (SVM)] and evolutionary [Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Bird Swarm Algorithm (BSA)], to approximate models for the prediction of Microcystis density. The data set was collected from Oubeira Lake, a natural shallow Mediterranean lake in the northeast of Algeria. From the correlation analysis of ten water variables monitored, six potential factors including temperature, ammonium, nitrate, and ortho-phosphate were selected. The performance indices showed; MLR and PSO provided the best results. PSO gave the best fitness but all techniques performed well. BSA had better fitness but was very slow across generations. PSO was faster than the other techniques and at generation 20 it passed BSA. GA passed BSA a little further, at generation 50. The major contributions of our work not only focus on the modelling process itself, but also take into consideration the main factors affecting Microcystis blooms, by incorporating them in all applied models