Application of Métier-Based Approaches for Spatial Planning and Management: A Case Study on a Mixed Trawl Fishery in Taiwan

Abstract

Mixed fisheries refer to fishing activities that catch more than one species simultaneously, and a species may be fished using different gear. A trawl fishery shares these features to exploit multiple species simultaneously, with diverse fishing gear and strategies. The situation becomes more complex when interactions among fleet dynamics, fishing activities, and fishery resources are involved and influence each other. Information regarding the operational patterns may be hidden in a set of long-term big data. This study aims to investigate the fishery structure and fleet dynamics of trawl fisheries in Taiwan for spatial planning and management, based on a long-term dataset from a management system that collects information by using voyage data recorders (VDR) and dockside observers. We applied a two-step data mining process with a clustering algorithm to classify the main groups of fishery resources and then identified 18 catch métiers based on catch composition. The target species, operation pattern, and fishing season were determined for each métier, and associated with the relevant fishery resources and the fishing gear used. Additionally, fishing effects on target species were estimated using information on fishing grounds and trajectories from VDR. The métier-based approach was successfully applied to define the six major fishery resources targeted by trawlers. We examined the key features of fishing activity associated with catch composition and spatial-temporal fishing metrics, which could be used to provide suggestions for the spatial planning and management of the mixed trawl fishery in the offshore waters of Taiwan.

Environmental DNA (eDNA) Metabarcoding in the Fish Market and Nearby Seafood Restaurants in Taiwan Reveals the Underestimation of Fish Species Diversity in Seafood

“…The number of fish species were also found to vary with sampling dates and sites in the present study. The differences in the number of fish species between different sampling dates might be explained by the availability of fish species since fishing seasons varied with fish species in Taiwan [ 23 ]. As for the differences in the number of fish species between two sampling sites in the present study might be explained by the nature of eDNA samples sourced from fish market and seafood restaurants.…”

“…More than 3000 finfish species have been recorded in the water of Taiwan, accounting for 9% of the fish species in the world [ 22 ]. Coastal fisheries in Taiwan are highly active with more than 20 fishery sectors in this region [ 23 ]. Consequently, seafood has been one of major food resources in Taiwan.…”

Relevance of Data Analytics in Fisheries: Unveiling Insights for Sustainable Management - A Systematic Literature Review

“…For instance, onboard sensors have the ability to automate and simplify this time-consuming procedure [43] while also producing more thorough and trustworthy data that may be included into platforms known as electronic monitoring systems (EMSs) [30]. Additionally, tools like Vehicle Monitoring Systems (VMS) [17] and the Automatic Identi cation System (AIS) can gather data on a vessel's position [5], speed, and direction [38], supplement radar systems [1], and reduce the likelihood of marine collisions [4].…”

“…This data and methodology may be applied to data augmentation, re-application to data from other scienti c surveys, and haul contribution analysis-all of which could pro t from data analytics methods. Related to the above, a study conducted in the East and China Sea zone used clustering analysis using R to understand mixed trawl sheries characteristics and shing habits of targeted species [43]. For instance, in the area around Taiwan, The vast amount of data gathered from the mixed trawl sheries in Taiwan the data was analysed and interpreted using clustering analysis to classify catch métiers and group shing expeditions with comparable catch compositions.…”

Relevance of Data Analytics in Sustainable Fisheries Management: An Evidence-Based Study

“…According to the US National Oceanic and Atmospheric Administration (NOAA, 2023), traditional ocean monitoring mechanisms were mostly laborious, involving the use of observers and manual recording onboard vessels, whereas new devices have bridged these gaps. For instance onboard sensors have the ability to automate and simplify this time-consuming procedure (Lee et al, 2021) while also producing more thorough and trustworthy data that may be included in platforms known as electronic monitoring systems (EMSs) (Terracciano et al, 2020). Additionally, tools like Vehicle Monitoring Systems (VMS) (European Union, 2019) and the Automatic Identification System (AIS) can gather data on a vessel's position (D. Bradley et al, 2019), speed, and direction (Copernicus Marine Environment Monitoring Service, 2023), supplement radar systems (Fujita, 2021), and reduce the likelihood of marine collisions (Christiani et al, 2019).…”