Mikroplastik di perairan menjadi permasalahan yang cukup serius bagi organisme perairan. Organisme filter feeder seperti kerang memiliki resiko yang cukup besar untuk mengakumulasi mikroplastik ke dalam tubuhnya. Salah satu jenis organisme tersebut ialah kerang manila (Venerupis philippinarum) yang banyak terdapat di Perairan Maccini Baji, Kecamatan Labakkang, Kabupaten Pangkajene Kepulauan, Sulawesi Selatan. Penelitian ini bertujuan untuk mengetahui keberadaan dan konsentrasi mikroplastik yang terdapat pada daging kerang manila (Venerupis philippinarum). Pengambilan sampel kerang dilakukan dengan metode sampling acak berlapis (stratified random sampling), sehingga diperoleh sampel sebanyak 118 ekor. 118 sampel kerang manila tersebut kemudian dibagi menjadi tiga kelompok ukuran panjang cangkang kerang yaitu kelas A (3,11 – 3,86 cm), kelas B (3,87 – 4,82 cm), kelas C (4,83 – 6,01 cm). Pengamatan partikel mikroplastik dilakukan dengan menggunakan mikroskop stereo. Hasil pengamatan menunjukkan sebanyak 61 sampel (51,69%) kerang manila mengandung partikel mikroplastik. Mikroplastik yang ditemukan berbentuk fiber dan fragmen, dengan warna dominan biru, hitam, dan transparan. Ukuran mikroplastik yang ditemukan berkisar antara 0,090 – 4,919 mm. Nilai rata-rata konsentrasi mikroplastik pada masing-masing kelompok ukuran panjang cangkang kerang secara berurutan yaitu 0,6129 item/g, 0,6303 item/g, dan 0,2198 item/g. Kata kunci: Fiber, kerang manila, konsentrasi mikroplastik, Maccini Baji, Venerupis philippinarum.

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Bungo fish is one of the potential fishery resources in Sidenreng Regency, which has decreased due to continuous fishing. Therefore, information on fisheries biology is needed to support the management of Bungo fish resources to create sustainable fishing. This study aims to analyze the growth patterns and condition factors of Bungo fish based on gender, observation time, and gonad maturity level. The use of this research as a source of information on the management of Bungo fish, especially in Lake Sidenreng. This research was conducted for three months, from September to November 2020. Sampling was obtained from 2 Bungo fishers in Sidenreng Lake, Sidenreng Rappang Regency, South Sulawesi. The number of bungo fish samples obtained during the study was 235, consisting of 163 male Bungo fish and 72 female Bungo fish. Based on the results of research that has been done, the growth pattern of male and female Bungo fish is negative allometric; namely, the increase in body length is faster than the increase in body weight of the fish. The condition factors of male and female Bungo Fish are classified as fish in good condition. The condition factor of bungo fish. Based on the sampling time, male and female Bungo fish in November and September were classified as flat or plump fish, while in October, male and female Bungo fish were classified as flat or not fat. Gonad maturity of male fish has a relatively smaller average condition factor than female fish at the same gonad maturity. The condition factors of male and female Bungo Fish are classified as fish in good condition. The condition factor of bungo fish. Based on the sampling time, male and female Bungo fish in November and September were classified as flat or plump fish, while in October, male and female Bungo fish were classified as flat or not fat. Gonad maturity of male fish has a relatively smaller average condition factor than female fish at the same gonad maturity. The condition factors of male and female Bungo Fish are classified as fish in good condition. The condition factor of bungo fish. Based on the sampling time, male and female Bungo fish in November and September were classified as flat or plump fish, while in October, male and female Bungo fish were classified as flat or not fat. Gonad maturity of male fish has a relatively smaller average condition factor than female fish at the same gonad maturity.

Seagrass is the most effective ecosystem in absorbing carbon. The ability of seagrasses to absorb CO2 from the atmosphere is better than terrestrial ecosystems. Image processing methods and information regarding potential carbon stocks in seagrass beds can then be used as a basis for managing carbon stocks found in coastal areas and small islands. This study aims to estimate the carbon stock of seagrass beds in the waters of Kodingarenglompo Island using remote sensing technology. This research was conducted from March to August 2020. The stages of the field survey were to identify the percentage of seagrass cover in 62 plot points. Seagrass carbon stocks are known based on seagrass cover percentage data using the regression equation. The estimation of seagrass carbon stocks in the study area is divided into two, namely AGC and BGC. The image processing stage is by using the random forest regression algorithm in mapping seagrass carbon stocks. The results of this research survey revealed six species of seagrass, namely Cymodocea rotundata, Enhalus acoroides, Halodule uninervis, Halophila ovalis, Thalassia hemprichii and Syringodium isoetifolium and were dominated by 2 species of seagrass, namely Thalassia hemprichii and Enhalus acoroides. The results showed that remote sensing can be used to map seagrass carbon stocks. Seagrass carbon stocks can be mapped with a maximum accuracy of 67% (SE=1.96 KgC/Pixel), and 85% (SE=7.86 KgC/Pixel) for AGC and BGC. From this model, the total ecosystem carbon stock in seagrasses in the waters of Kodingarenglompo Island is estimated to be around 178.98 tons of organic carbon with an area of seagrass beds of 81.29 hectares. The availability of seagrass carbon stock maps is very important to provide a better understanding of the spatial and temporal distribution of carbon dynamics.  Lamun adalah ekosistem yang paling efektif dalam menyerap karbon. Kemampuan lamun untuk menyerap CO2 dari atmosfer lebih baik dari ekosistem darat. Metode pengolahan citra serta informasi mengenai potensi cadangan karbon pada padang lamun selanjutnya dapat dijadikan sebagai dasar pengelolaan stok karbon yang terdapat di pesisir dan pulau-puau kecil. Penelitian ini bertujuan untuk mengestimasi stok karbon padang lamun di perairan Pulau Kodingarenglompo menggunakan teknologi penginderaan jauh. Penelitian ini dilaksanakan dari bulan Maret sampai Agustus 2020. Tahapan survei lapangan yaitu mengidentifikasi persentase tutupan jenis padang lamun sebanyak 62 plot titik. Stok karbon lamun diketahui berdasarkan data persentase tutupan lamun menggunakan persamaan regresi. Estimasi stok karbon padang lamun pada daerah kajian dibedakan menjadi dua yaitu AGC dan BGC. Tahap pengolahan citra yaitu dengan menggunakan algoritma regresi random forest dalam memetakan stok karbon lamun. Hasil survei penelitian ini mendapatkan enam jenis lamun yaitu Cymodocea rotundata, Enhalus acoroides, Halodule uninervis, Halophila ovalis, Thalassia hemprichii dan Syringodium isoetifolium dan didominasi oleh 2 jenis lamun yaitu Thalassia hemprichii dan Enhalus acoroides. Hasil penelitian menunjukkan bahwa penginderaan jauh dapat digunakan untuk memetakan stok karbon lamun. Stok karbon lamun dapat dipetakan dengan akurasi maksimum 67% (SE=1,96 KgC/Piksel), 85% (SE=7,86 KgC/Piksel) untuk AGC dan BGC. Dari model tersebut, total stok karbon ekosistem pada lamun di perairan Pulau Kodingarenglompo diperkirakan sekitar 178,98 ton karbon organik dengan luas padang lamun yaitu 81,29 hektar. Ketersediaan peta stok karbon lamun sangat penting untuk memberikan pemahaman yang lebih baik tentang sebaran dinamika karbon spasial dan temporal.