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Spatio-Seasonal Habitat Suitability Model of Anisakis spp. in Chub Mackerel (Scomber Japonicus) as a Scientific-Based Approach to Provide Safety Assessment Policy of Fisheries Product in Indonesia
- Source :
- Squalen, Vol 18, Iss 3 (2023)
- Publication Year :
- 2023
- Publisher :
- Kementerian Kelautan dan Perikanan, 2023.
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Abstract
- The infestation of Anisakis spp. in Chub mackerel (Scomber japonicus) may have significant health and socioeconomic implications. Understanding the spatio-seasonal occurrence of Anisakis spp. in its host, S. japonicus, is crucial ecologically and for effective management. This research aimed to develop a predictive map (spatial-seasonal patterns) for Anisakis’s infestation in S. japonicus using the maximum entropy (MaxEnt) algorithm. Anisakis spp. and S. japonicus occurrences were obtained through a comprehensive Bibliographic analysis of the Scopus database (2017-2022) and the Ocean Biodiversity Information System (OBIS) database to collect the necessary data. Environmental predictors were sourced from the Global Marine Environment Data. The resulting model demonstrated a reliable performance, as indicated by an Area Under Curve (AUC) value on the Receiver Operating Characteristic (ROC) chart exceeding 0.8. The findings of this study revealed that the infestation of Anisakis spp. in S. japonicus is projected to be more prevalent during the fourth quarter of each year. Furthermore, the environmental factors influencing the infestation were identified as diffuse attenuation, water depth, and distance from the coast. These research outcomes can be a foundational reference for developing an effective control system for inspecting fresh or frozen fish within the quarantine department. By utilizing the spatial-seasonal patterns and environmental predictors identified in this study, authorities can implement targeted measures to prevent and mitigate the infestation of Anisakis spp. in S. japonicus, safeguarding public health and maintaining the quality of fish products.
Details
- Language :
- English
- ISSN :
- 20895690 and 24069272
- Volume :
- 18
- Issue :
- 3
- Database :
- Directory of Open Access Journals
- Journal :
- Squalen
- Publication Type :
- Academic Journal
- Accession number :
- edsdoj.5f60365f039048c6a6270a0ebe9e4571
- Document Type :
- article
- Full Text :
- https://doi.org/10.15578/squalen.804