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Journal : 3BIO: Journal of Biological Science, Technology and Management

Spatial Distributions and Model Selections of Commercial Estuarine Fish (Sciaenidae) Populations Related to Water Quality, Chl-a, and AML in Musi River mouth, South Sumatra Andriwibowo Andriwibowo; Adi Basukriadi; Erwin Nurdin
3BIO: Journal of Biological Science, Technology and Management Vol. 3 No. 2 (2021)
Publisher : School of Life Sciences and Technology, Institut Teknologi Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/3bio.2021.3.2.1

Abstract

Estuary and river mouth are essential habitats for many commercial estuarine fishes, including the Sciaenidae family. While recently, estuaries have been threatened by anthropogenic marine litter (AML) transported from nearby land and river. An important type of AML is plastic litter since it takes a long degradation time. In the South Sumatra Province, Indonesia, one of the vital estuaries is the Musi estuary. This paper aims to map the spatial distributions of two Sciaenids, including Panna microdon and Otolithoides pama, and Sciaenid’s environmental covariates, including water quality, chlorophyll a, and plastic litters in Musi estuary and model the correlations of Sciaenids with their covariates. The maps were developed using GIS, and the model was validated using AIC methods. The data were collected from 3 river mouths in the west, central, and east of the Musi estuary. The data showed that the populations of both Sciaenids were higher in the east river mouth rather than in the west. Sciaenid populations were positively correlated with high salinity, DO, chlorophyll a, moderate transparency, and low temperature. A high load of AML’s frequency (7.54 items/m2) and weights (36.8 gram/m2) has reduced both Sciaenid populations in the central river mouth of the estuary. In contrast, low AML loads in the east have correlated with high Sciaenid populations. Model selection based on AIC values shows the best model for P.microdon retained an effect of AML weight with AIC values of 22.591 and 28.321 for O. pama. This concludes that the weight of plastic litter in estuary water was the main limiting factor for Sciaenid populations in Musi.
Markov Chain and Cluster Model of Green Algae Phytoplankton (Chlorophyceae) Diversity and Spatial Distribution Pattern along Stream, Water Quality, and Land Use Gradients in Krukut River, Jakarta City Andriwibowo Andriwibowo; Adi Basukriadi; Erwin Nurdin; Amanda Zahra Djuanda; Elizabeth Adeline; Zeadora Abbya Trisya
3BIO: Journal of Biological Science, Technology and Management Vol. 4 No. 2 (2022)
Publisher : School of Life Sciences and Technology, Institut Teknologi Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/3bio.2022.4.2.2

Abstract

Green algae phytoplankton (Chlorophyceae) have a wide aquatic distribution, including saltwater and freshwater environments. Compared to the ones living in saltwater, green algae diversity in freshwater ecosystems in rivers is influenced by stream gradients, water quality, and land uses. Meanwhile, in Jakarta, 17 rivers have the potential to provide a habitat for green algae communities. Due to anthropogenic activities, river streams have been affected by influences that may affect the water quality and green algae community along stream gradients. One of the critical rivers in Jakarta is the Krukut river, which has the most extended stream spanning over 40 km and downstream in Jakarta bay. This study aims to model the diversity and distribution pattern of green algae in the Krukut river from its upstream segment in Jakarta city, surrounded by settlements, to the downstream segments in Jakarta bay. The distribution model uses the Cluster Analysis and Markov Chain Model to elaborate the probabilities of green algae phytoplankton distribution in downstream, midstream, and upstream segments of the Krukut river. The results show that 7 species of Chlorophyceae have been recorded in the Krukut river. All species had a high likelihood of being found downstream, particularly Cosmarium sp., Eudorina sp., Spyrogyra sp., and Volvox sp. Regarding distribution, all phytoplankton species have a high probability (4%–31%) and tendency to be distributed from upstream and midstream to downstream rather than from downstream to midstream and upstream, with probability ranges of 2%–27%. The probability and tendency of phytoplankton distribution towards downstream directions avoiding upstream were related to the deteriorating water quality in the upstream, characterized by high turbidity, low dissolved oxygen, and more acidic water.
Artificial Neural Networks (ANN) to Model Microplastic Contents in Commercial Fish Species at Jakarta Bay Andriwibowo, Andriwibowo; Basukriadi, Adi; Nurdin, Erwin; Meylani, Vita; Hasanah, Nenti Rofiah; Shiddiq, Zulfi Sam; Mulyanah, Sitiawati
3BIO: Journal of Biological Science, Technology and Management Vol. 6 No. 1 (2024)
Publisher : School of Life Sciences and Technology, Institut Teknologi Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/3bio.2024.6.1.3

Abstract

Jakarta Bay is known as one of the marine ecosystems that have been contaminated by microplastics. Despite massive loads of microplasticcontamination, Jakarta Bay is also habitat to potential commercial fish species, including anchovy Stolephorus commersonnii and mackerel Rastrelliger kanagurta. While information on the microplastic contents and their determining factors is still limited, the goal of this study was touse artificial neural networks (ANN) as a novel and useful tool to model the determinants of microplastic content in fish in Jakarta Bay, using fish weight and length as proxies. Inside the stomachs of S. commersonnii and R. kanagurta, the order of microplastics from the highest to thelowest was fiber > film > fragment > pellet. Based on the RMSE values of 3.199 for S. commersonnii and 2.738 for R. kanagurta, the ANNmodel of fish’s weight + length ~ pellet was found to be the best fitted model to explain the correlation of fish weight and length with microplastic content in the stomach. The results indicate that ANN is suitable for solving large, complex problems in determining and projecting microplastic contents and provides better estimates that can be used to manage R. kanagurta and S. commersonnii along with microplastic contamination threats.