Gea-Jae Joo

Prediction and elucidation of phytoplankton dynamics in the Nakdong River (Korea) by means of a recurrent artificial neural network

A recurrent artificial neural network was used for time series modelling of phytoplankton dynamics in the hypertrophic Nakdong River system. The model considered meteorological, hydrological and limnological parameters as input variables and chl. a concentration as output variable. It was trained and validated by means of a complex database measured from 1994 to 1998 at a study site 27 km upstream of the river mouth. The validation results for 1994 indicated that the recurrent training algorithm and a 3 days time lag of input data predict reasonably accurate the timing and magnitudes of chl. a. A comprehensive sensitivity analysis of the model revealed relationships between seasons, specific input variables and chl. a that correspond well with theoretical assumptions and literature findings.

Spatial and temporal dynamics of phytoplankton communities along a regulated river system, the Nakdong River, Korea

The longitudinal distribution and seasonal fluctuation of phytoplankton communities was studied along the middle to lower part of a regulated river system (Nakdong River, Korea). Phytoplankton biomass decreased sharply in the middle part of the river (182 km upward the estuary dam), and then increased downstream reaching a maximum at the last sampling station (27 km upward the estuary dam). In contrast, there was little downstream fluctuation in species composition, irrespective of pronounced differences in nutrient concentrations (TN, TP, NO3, NH4, PO4) as well as in algal biomass. In the main river channel, small centric diatoms (Stephanodiscus hantzschii, Cyclotella meneghiniana) and pennate diatoms (Synedra, Fragilaria, Nitzschia) were dominant from winter to early spring (November–April). A mixed community of cryptomonads, centric and pennate diatoms, and coenobial greens (Pediastrum, Scenedesmus) was dominant in late spring (May–June). Blue-green algae (Anabaena, Microcystis, Oscillatoria) were dominant in the summer (July–September). A mid-summer Microcystis bloom occurred at all study sites during the dry season, when discharge was low, though the nutrient concentration varied in each study site. Nutrients appeared everywhere to be in excess of algal requirement and apparently did not influence markedly the downstream and seasonal phytoplankton compositional differences in this river.

The phytoplankton succession in the lower part of hypertrophic Nakdong River (Mulgum), South Korea

Investigations were carried out to determine the mechanism of phytoplankton succession in the lower part of Nakdong River. Intensive monitoring was conducted from April 1993 to April 1995 at weekly or biweekly intervals. This river is the main source of drinking water for more than 8 million residents living in Pusan and the Southeastern region of Korea, and it is also important for industrial purposes. Due to the rapid urbanization and industrialization, this river has become hypertrophic and it is heavily regulated (mean chl. a, 79±232 µg l-1 n = 76; mean TN, 4.3±2.0 mg l-1 n=30; mean TP, 165 ± 108 µg l-1 n = 45). Bacillariophyceae were dominant year-round in 1993, with spring and late fall peaks. Dominant communities in 1994 were Bacillariophyceae in March and April, Chlorophyceae and Cryptophyceae in May, Cyanobacteria in July and August, Chlorophyceae and Cryptophyceae in October, and Bacillariophyceae in December. As drought persisted through the summer of 1994, elevated water temperature (over 30 °C) possibly triggered Cyanobacteria bloom (mainly Microcystis aeruginosa, maximum cell density, 1.6 × 107 cells ml-1). The most common diatom, Stephanodiscus hantzschii, repeatedly dominated from late fall to next spring (mean and maximum cell density, 7.5 × 104, 1.2 × 105 cells ml-1, respectively). This small centric diatom was favored by the low discharge and the cold water (water temperature, 3–10 °C). After the diatom bloom, this community shifted to peaks of colonial Chlorophyceae and motile Cryptophyceae, owing to the high rate of zooplankton grazing activity and increased water temperature. Overall, the phytoplankton periodicity was primarily governed by the hydrologic regime (discharge). Changes in silica concentration, water temperature and high zooplankton density might have played an important role in phytoplankton dynamics during the non-flooding periods. Compared to other large rivers, strong Microcystis bloom events in summer and Stephanodiscus bloom events in winter were noticeable in the dry year. The patterns of phytoplankton succession observed in this study may have a significance since most of the large rivers in Far Eastern Asian countries are subjected to eutrophication and regulation of discharge.

Modelling Microcystis aeruginosa bloom dynamics in the Nakdong River by means of evolutionary computation and statistical approach

Dynamics of a bloom-forming cyanobacteria (Microcystis aeruginosa ) in a eutrophic river � /reservoir hybrid system were modelled using a genetic programming (GP) algorithm and multivariate linear regression (MLR). The lower Nakdong River has been influenced by cultural eutrophication since construction of an estuarine barrage in 1987. During 1994 � /1998, the average concentrations of nutrients and phytoplankton were: NO3 � /N, 2.7 mg l � 1 ;N H 4 � /N, 0.6 mg l � 1 ;P O 4� � /P, 34.7 m gl � 1 ; and chlorophyll a , 50.2 m gl � 1 . Blooms of M. aeruginosa occurred in summers when there were droughts. Using data from 1995 to 1998, GP and MLR were used to construct equation models for predicting the occurrence of M. aeruginosa . Validation of the model was done using data from 1994, a year when there were severe summer blooms. GP model was very successful in predicting the temporal dynamics and magnitude of blooms while MLR resulted rather insufficient predictability. The lower Nakdong River exhibits reservoir-like ecological dynamics rather than riverine, and for this reason a previous river mechanistic model failed to describe uncertainty and complexity. Results of this study suggest that an inductive-empirical approach is more suitable for modelling the dynamics of bloom-forming algal species in a river � /reservoir transitional system. # 2002 Elsevier Science B.V. All rights reserved.

Prediction and Elucidation of Population Dynamics of the Blue-green Algae Microcystis aeruginosa and the Diatom Stephanodiscus hantzschii in the Nakdong River-Reservoir System (South Korea) by a Recurrent Artificial Neural Network

Ecological modeling is an interdisciplinary branch in ecology. A model synthesized from adequate laboratory and field data can explain observed patterns and predict future ecosystem behaviors (Odum 1983; Krebs 1994). For accomplishing both objectives, it is necessary to use models that adequately address the uncertainty and complexity of ecosystems. Artificial Neural Networks (ANN) have been demonstrated to successfully model non-linear and complex phenomena. They have been used in aquatic ecology (e.g. Recknagel, 1997; Brosse et al., 1999), medicine, linguistics, and social sciences (Bullinaria 1997; Blom et al. 1999; Carson et al. 1999; Young et al. 2000).

The longitudinal distribution and community dynamics of zooplankton in a regulated large river: a case study of the Nakdong River (Korea)

The longitudinal distribution and seasonal dynamics of zooplankton were examined along a 200-km section of the middle to lower Nakdong River, Korea. Zooplankton was sampled twice a month from January 1995 to December 1997 at five sites in the main river channel. There was considerable longitudinal variation in total zooplankton abundance (ANOVA, p < 0.001). All major zooplankton groups (rotifers, cladocerans, copepodids and nauplii) increased significantly with distance downstream along the river. There also were statistically significant seasonal differences in zooplankton abundance at the sampling sites (ANOVA, p < 0.01). Zooplankton abundance was high in spring and fall and low in summer and winter. The seasonal pattern of rotifers was similar to that of total zooplankton. This reflected the fact that rotifers (Brachionus calyciflorus, B. rubens, Keratella cochlearis and Polyarthra spp.) strongly dominated the zooplankton community at all locations. Among the macrozooplankton, small-bodied cladocerans (e.g. Bosmina spp.) dominated; the abundance of large-bodied cladocerans (e.g. Daphnia) was negligible (0–5 ind. l−1). Among the environmental variables considered, partial residence time seemed to play the most important role in determining characteristics of the river zooplankton community.

Application of DNA barcoding for identification of freshwater carnivorous fish diets: Is number of prey items dependent on size class for Micropterus salmoides?

Understanding predator–prey interactions is a major challenge in ecological studies. In particular, the accurate identification of prey is a fundamental requirement in elucidating food-web structure. This study took a molecular approach in determining the species identity of consumed prey items of a freshwater carnivorous fish (largemouth bass, Micropterus salmoides), according to their size class. Thirty randomly selected gut samples were categorized into three size classes, based on the total length of the bass. Using the universal primer for the mtDNA cytochrome oxidase I (COI) region, polymerase chain reaction (PCR) amplification was performed on unidentified gut contents and then sequenced after cloning. Two gut samples were completely empty, and DNA materials from 27 of 28 gut samples were successfully amplified by PCR (success rate: 96.4%). Sequence database navigation yielded a total of 308 clones, containing DNA from 26 prey items. They comprised four phyla, including seven classes, 12 orders, and 12 families based on BLAST and BOLD database searches. The results indicate that largemouth bass show selective preferences in prey item consumption as they mature. These results corroborate a hypothesis, presence of ontogenetic diet shift, derived through other methodological approaches. Despite the practical limitations inherent in DNA barcoding analysis, high-resolution (i.e., species level) identification was possible, and the predation patterns of predators of different sizes were identifiable. The utilization of this method is strongly recommended for determining specific predator–prey relationships in complex freshwater ecosystems.

The fish fauna of mountain streams in South Korean national parks and its significance to conservation of regional freshwater fish biodiversity

Abstract The fish fauna of mountain streams in the Jiri (440 km 2 ) and Seorak (373 km 2 ) National Park areas of South Korea was investigated from September 1998 to May 2001. A total of 5979 fish were collected and classified into 22 families and 60 species from both national parks. At Jiri, 30 species in 12 families of fishes were collected, while at Seorak there were 42 species in 17 families. Zacco temmincki [relative abundance (RA) 47.9%] was dominant at both parks. Subdominant species were Rhynchocypris kumkangensis (RA 10.8%), Zacco platypus (RA 9.4%), and Pungtungia herzi (RA 5.3%). A total of 20 species were found to be Korean endemic species, representing seven families. The relative abundance of Korean endemic species was higher at Seorak (30.5%) than Jiri (18.5%). One exotic species ( Oncorhynchus mykiss ) and two species translocated outside their native catchments ( Hypomesus nipponensis and Coreoperca herzi ) were collected. The proportion of Korean endemic freshwater fish species in both parks (33.3%) was higher than the overall proportion on the Korean peninsula (23.6%). Within the 14 South Korean mountain-area national parks as a whole, larger parks tend to have higher fish diversity. We conclude that South Korean national parks are important for conservation of the regional fish fauna, especially for endemic and endangered species. Current threats to conservation of fishes within South Korean national parks are identified and management solutions are suggested.

Investigation of bioaccumulation and biotransformation of polybrominated diphenyl ethers, hydroxylated and methoxylated derivatives in varying trophic level freshwater fishes

The concentrations and distributions of polybrominated diphenyl ethers (PBDEs) and their hydroxylated and methoxylated derivatives (OH- and MeO-BDEs) were determined in seven representative fish species from a river in the Republic of Korea. The PBDEs and their derivatives were found to be accumulated in the internal organs of the fish to different extents. PBDEs were preferentially accumulated in the internal organs rather than muscle tissue, and especially, showed increasing accumulation tendencies with increasing bromination level in liver. The OH-BDEs and MeO-BDEs were preferentially accumulated in the liver and gastrointestinal tract, respectively. MeO-BDE concentrations were found to increase according to relative trophic level, suggesting that the PBDE derivatives can be biomagnified to a greater extent than the parent PBDEs in freshwater food webs. In a comparison with the dissolved analyte concentrations in the water that were measured by using semi-permeable membrane devices, the greater uptake of non-ortho substituted MeO-BDEs by fish was observed.

Discovery of predictive rule sets for chlorophyll-a dynamics in the Nakdong River (Korea) by means of the hybrid evolutionary algorithm HEA

Abstract This paper presents a hybrid evolutionary algorithm (HEA) to discover complex rule sets predicting the concentration of chlorophyll-a (Chl.a) based on the measured meteorological, hydrological and limnological variables in the hypertrophic Nakdong River. The HEA is designed: (1) to evolve the structure of rule sets by using genetic programming and (2) to optimise the random parameters in the rule sets by means of a genetic algorithm. Time-series of input–output data from 1995 to 1998 without and with time lags up to 7 days were used for training HEA. Independent input–output data for 1994 were used for testing HEA. HEA successfully discovered rule sets for multiple nonlinear relationships between physical, chemical variables and Chl.a, which proved to be predictive for unseen data as well as explanatory. The comparison of results by HEA and previously applied recurrent artificial neural networks to the same data with input–output time lags of 3 days revealed similar good performances of both methods. The sensitivity analysis for the best performing predictive rule set unraveled relationships between seasons, specific input variables and Chl.a which to some degree correspond with known properties of the Nakdong River. The statistics of numerous random runs of the HEA also allowed determining most relevant input variables without a priori knowledge.