Predicting potential fishing grounds of ribbonfish (Trichiurus lepturus) in the north-eastern Arabian Sea, using remote sensing data. Issue 1 (2nd January 2021)

• Record Type: Journal Article
• Authors: Abdul Azeez, P.; Raman, Mini; Rohit, Prathibha; Shenoy, Latha; Jaiswar, Ashok Kumar; Mohammed Koya, K.; Damodaran, Divu
• Is Part Of: International journal of remote sensing. Volume 42:Issue 1(2021)
• Page Start: 322
• Page End: 342
• Publication Date: 2021-01-02
• Subjects: Remote sensing -- Periodicals; Télédétection -- Périodiques; 621.3678
• DOI: 10.1080/01431161.2020.1809025
• Languages: English
• ISSNs: 0143-1161

Abstract

ABSTRACT: Ribbonfish ( Trichiurus lepturus ) is one of the major fishery resources of the north-eastern Arabian Sea having significance from commercial as well as ecological point of view. Information on habitat of the resource and its spatio-temporal variations is sparse limiting precise prediction of the grounds for efficient harvest and management of the resource. Habitat suitability modelling was applied to the ribbonfish presence/absence data from commercial trawlers using Generalized Additive Model (GAM) and Boosted Regression Tree (BRT) model along with environmental variables (euphotic depth ( Z eu ), Sea Surface Temperature (SST), bathymetry and Sea Surface Height anomaly (SSHa) to understand the influence of these on the spatio-temporal variation of ribbonfish in the north-eastern Arabian Sea. The predictive performances of the models compared with Area Under the Curve (AUC) and maximum kappa shows BRT model performed slightly better in predicting ability than GAM. Euphotic depth (28.5%) was observed to be the most significant contributor to the spatio-temporal distribution of ribbonfish followed by SST (24.3%), bathymetry (23.8%), and SSHa (23.5%) in the BRT model. Spatial variation of ribbonfish over the months modelled from BRT model indicated fish was strongly linked with bio-physical environment and the potential fishing grounds occurred along off Maharashtra coast during post-monsoon season. Field demonstration of the model was carried out by comparing the daily fish catch locations with weekly prediction maps. Analysis indicated the model to be in good agreement with the catch data and reliable for prediction of spatio-temporal variation in potential fishing grounds of ribbonfish in the north-eastern Arabian Sea.