RESUMO
Utilizing the plankton 1 (505 Μm), 2 (160 Μm), 3 (77 Μm) nets to seasonally collect zooplankton samples at 10 stations and the corresponding abundance data was obtained. Based on individual zooplankton biovolume, size groups were classified to test the changes in spatiotemporal characteristics of both Sheldon and normalized biovolume size spectra in thermal discharge seawaters near the Guohua Power Plant, so as to explore the effects of temperature increase on zooplankton size spectra in the seawaters. The results showed that the individual biovolume of zooplankton ranged from 0.00012 to 127.0 mm3·ind-1, which could be divided into 21 size groups, and corresponding logarithmic ranges were from -13.06 to 6.99. According to Sheldon size spectra, the predominant species to form main peaks of the size spectrum in different months were Copepodite larvae, Centropages mcmurrichi, Calanus sinicus, fish larvae, Sagitta bedoti, Sagitta nagae and Pleurobrachia globosa, and minor peaks mostly consisted of individuals with smaller larvae, Cyclops and Paracalanus aculeatus. In different warming sections, Copepodite larvae, fish eggs and Cyclops were mostly unaffected by the temperature increase, while the macrozooplankton such as S. bedoti, S. nagae, P. globosa, C. sinicus and Beroe cucumis had an obvious tendency to avoid the outfall of the power plant. Based on the results of normalized size spectra, the intercepts from low to high occurred in November, February, May and August, respectively. At the same time, the minimum slope was found in February, and similarly bigger slopes were observed in May and August. These results indicated that the proportion of small zooplankton was highest in February, while the proportions of the meso- and macro-zooplankton were relatively high in May and August. Among different sections, the slope in the 0.2 km section was minimum, which increased with the increase of section distance to the outfall. The result obviously demonstrated that the closer the distance was from outfall of the power plant, the smaller the zooplankton became. On the whole, the average intercept of normalized size spectrum in Xiangshan Bay was 4.68, and the slope was -0.655.
Assuntos
Centrais Elétricas , Zooplâncton , Animais , Tamanho Corporal , China , Água do Mar , TemperaturaRESUMO
Zooplankton samples were seasonally collected at 10 stations in thermal discharge seawaters near Guohua Power Plant in Xiangshan Bay. The abundance data from these samples were pooled and further combined with field environmental factors, then generalised dissimilarity modelling (GDM) was used to explore the effects of environmental factors on ß diversity of zooplankton community. The results showed that altogether 95 species of zooplankton belonging to 14 taxa were found. In these taxa, small zooplankton with 62.6% of abundance was the main taxa, while copepods dominated in adult groups, which abundance accounted for 35.3%. According to Whittaker's definition and additive partition, a diversity accounted for 36.3% and ß diversity 63.7%. Environmental factors explained 43.8% of ß diversity, and geographical distance between sampling sites had no effect on ß diversity. However, there were still 19.9% of ß diversity remained to be explained. After GDM fitting, there were nine environmental variables affecting zooplankton ß diversity and explaining 68.8% of ß diversity. The variables contributing to ß diversity from high to low were seasonal water temperature, dissolved oxygen, seawater temperature increment, conductivity, suspended particulate matter, salinity, transparency, water depth and redox potential, respectively. Seasonal water temperature, dissolved oxygen and seawater temperature increment were the most important factors for driving ß diversity changes, and accounted for 23.9%, 13.7% and 9.7% of absolute contribution to the interpretable portion of the ß diversity, respectively. When seasonal water temperature, dissolved oxygen and seawater temperature increment were below 25 °C, greater than 5 mg · L(-1) and over 1 °C, respectively, ß diversity rapidly increased with the increasing variable gradients. Furthermore, other predictors had little effect on ß diversity.