Identifikasi Scylla sp. Berdasarkan Karakter Morfometerik di Kawasan Hutan Mangrove Desa Lalombi Kec. Banawa Selatan Kab. Donggala dan Pemanfaatannya Sebagai Media Pembelajaran

This study aims to identify the morphometric characters of Scylla sp in the Mangrove forest area of Lalombi Village, South Banawa, Donggala and its use as a learning medium. This study uses a exploration method to find samples, with a sampling technique (Hand Collection) which is direct sampling. The samples obtained are identified morphologically and measure their morphometric characters. Morphometric data are analyzed using the Principal component analysis (PCA) method using the help of MVSP software. The results of this study indicate that (1) Scylla sp which has been carried out at two observation stations, overall Scylla was found consisting of 3 types of Scylla, namely Scylla paramamosia, Scylla olivaceae and Scylla serrata; (2) The morphometric characters of male Scylla paramamosian are at the highest PK 37.9 and female 30.9; male Scylla olivaceae are at the highest PK 56.5 and female 40.3; The male Scylla serrata had the highest PK of 60.3 and the female 47.5 so that in this observation the most dominant was Scylla serrata. (3) The most dominant character in the formation of the three groups of mangrove crabs in this study was the male Carapace Length (PK) and the female Carapace Length (PK).

Authors

Publication Details

Journal
Bioscientist Jurnal Ilmiah Biologi
Published
2025-06-12
DOI
https://doi.org/10.33394/bioscientist.v13i2.15446
Primary Topic
Plant Growth and Agriculture Techniques
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Identifikasi Scylla sp. Berdasarkan Karakter Morfometerik di Kawasan Hutan Mangrove Desa Lalombi Kec. Banawa Selatan Kab. Donggala dan Pemanfaatannya Sebagai Media Pembelajaran

Moh. Sabran, Syech Zainal, Bustamin Bustamin, Fatmah Dhafir et al.
Bioscientist Jurnal Ilmiah Biologi
Plant Growth and Agriculture Techniques
article

Identifikasi Scylla sp. Berdasarkan Karakter Morfometerik di Kawasan Hutan Mangrove Desa Lalombi Kec. Banawa Selatan Kab. Donggala dan Pemanfaatannya Sebagai Media Pembelajaran

Moh. Sabran, Syech Zainal, Bustamin Bustamin, Fatmah Dhafir, Abd Rauf, Rezky Annisa
article en

Abstract

This study aims to identify the morphometric characters of Scylla sp in the Mangrove forest area of Lalombi Village, South Banawa, Donggala and its use as a learning medium. This study uses a exploration method to find samples, with a sampling technique (Hand Collection) which is direct sampling. The samples obtained are identified morphologically and measure their morphometric characters. Morphometric data are analyzed using the Principal component analysis (PCA) method using the help of MVSP software. The results of this study indicate that (1) Scylla sp which has been carried out at two observation stations, overall Scylla was found consisting of 3 types of Scylla, namely Scylla paramamosia, Scylla olivaceae and Scylla serrata; (2) The morphometric characters of male Scylla paramamosian are at the highest PK 37.9 and female 30.9; male Scylla olivaceae are at the highest PK 56.5 and female 40.3; The male Scylla serrata had the highest PK of 60.3 and the female 47.5 so that in this observation the most dominant was Scylla serrata. (3) The most dominant character in the formation of the three groups of mangrove crabs in this study was the male Carapace Length (PK) and the female Carapace Length (PK).

Bioscientist Jurnal Ilmiah BiologiVol. 13(2)
Openalex Percentile: Top 69%
Plant Growth and Agriculture Techniques
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.