Environmental Control and Limnological Impacts of a Large Recurrent Spring Bloom in Lake Washington,USA |
| |
Authors: | ARHONDITSIS G BRETT M T FRODGE J |
| |
Institution: | (1) Department of Civil and Environmental Engineering, University of Washington, 313B More Hall, Box 352700, Seattle, Washington 98195, USA, US;(2) Department of Civil and Environmental Engineering, University of Washington, 301 More Hall, Box 352700, Seattle, Washington 98195, USA, US;(3) King County, Department of Natural Resources, 201 S. Jackson Street, Suite 700, Seattle, Washington 98104, USA, US |
| |
Abstract: | A series of statistical analyses were used to identify temporal and spatial patterns in the phytoplankton and nutrient dynamics
of Lake Washington, an mesotrophic lake in Washington State (USA). These analyses were based on fortnightly or monthly samples
of water temperature, Secchi transparency, ammonium (NH4), nitrate (NO3), inorganic phosphorus (IP), total nitrogen (TN), total phosphorus (TP), dissolved oxygen (DO), pH and chlorophyll a (chl a) collected during 1995–2000 from 12 stations. Lake Washington has a very consistent and pronounced annual spring diatom bloom
which occurs from March to May. During this bloom, epilimnetic chl a concentrations peak on average at 10 μg/L, which is 3 times higher than chl a concentrations typically seen during summer stratified conditions. The spring bloom on average comprised 62% diatoms, 21%
chlorophytes and 8% cyanobacteria. During summer stratification, diatoms comprised 26% of the phytoplankton community, chlorophytes
37% and cyanobacteria 25%. Cryptophytes comprised approximately 8% of the community throughout the year. Overall, 6 phytoplankton
genera (i.e., Aulacoseira, Fragilaria, Cryptomonas, Asterionella, Stephanodiscus, and Ankistrodesmus) cumulatively accounted for over 50% of the community. These analyses also suggest that the phytoplankton community strongly
influences the seasonality of NO3, IP, DO, pH and water clarity. According to a MANOVA, seasonal fluctuations explained 40% of the total variability for the
major parameters, spatial heterogeneity explained 10% of variability, and the seasonal-spatial interaction explained 10% of
variability. Distinctive patterns were identified between offshore and inshore sampling stations. The results of our analyses
also suggest that spatial variability was substantial, but much smaller than temporal variability. |
| |
Keywords: | : Lake ecosystems Heterogeneity Lake Washington Pattern Plankton dynamics Spring bloom |
本文献已被 PubMed SpringerLink 等数据库收录! |
|