Introduction: Technological innovation in fisheries can rapidly alter exploitation patterns, often outpacing the capacity of regulatory systems to respond and increasing the risk of stock depletion or collapse. To address this challenge, this study presents a Decision-Tree Framework (DTF) designed to support precautionary and adaptive management when new fishing gears or substantial changes in fishing practices emerge.Methods: The DTF combines an automated landing-based screening pathway, applied when at least six consecutive annual observations are available, with a structured expert-supported pathway for shorter time series or cases in which technological innovation is already recognised. The framework is implemented in R and as an interactive Shiny application. The performance of the automated landing-based screening component was evaluated through simulations representing abrupt and gradual increases in landings under alternative time-series lengths, effect magnitudes, observation noise levels and temporal autocorrelation conditions. The framework was applied to two contrasting Mediterranean case studies: the silver scabbardfish (Lepidopus caudatus) multi-gear fishery and the Mediterranean swordfish (Xiphias gladius) trap-line fishery.Results: Simulation and case-study results show that the DTF can provide early warning signals of potentially unsustainable exploitation and offer transparent guidance for precautionary management actions, even where conventional stock assessments are limited or unavailable. Retrospective application to the scabbardfish fishery indicates that the DTF would have identified an early diagnostic signal and supported the consideration of precautionary management measures, while application to the swordfish fishery demonstrates consistency with precautionary policy decisions adopted under data-limited conditions.Discussion: Overall, the DTF represents a proactive decision-support tool that operationalizes precautionary and adaptive management principles, enhances transparency in decision-making, and has the potential to support the management of emerging fisheries across diverse contexts, subject to further validation.
A decision-tree framework for the sustainable management of emerging fisheries and fishing innovations
Falsone, Fabio;Calabrò, Monica;Gancitano, Vita;Garofalo, Germana;Geraci, Michele Luca;Gjoni, Vojsava;Lauria, Valentina;Sardo, Giacomo;Scannella, Danilo;Vitale, Sergio;Fiorentino, Fabio
2026
Abstract
Introduction: Technological innovation in fisheries can rapidly alter exploitation patterns, often outpacing the capacity of regulatory systems to respond and increasing the risk of stock depletion or collapse. To address this challenge, this study presents a Decision-Tree Framework (DTF) designed to support precautionary and adaptive management when new fishing gears or substantial changes in fishing practices emerge.Methods: The DTF combines an automated landing-based screening pathway, applied when at least six consecutive annual observations are available, with a structured expert-supported pathway for shorter time series or cases in which technological innovation is already recognised. The framework is implemented in R and as an interactive Shiny application. The performance of the automated landing-based screening component was evaluated through simulations representing abrupt and gradual increases in landings under alternative time-series lengths, effect magnitudes, observation noise levels and temporal autocorrelation conditions. The framework was applied to two contrasting Mediterranean case studies: the silver scabbardfish (Lepidopus caudatus) multi-gear fishery and the Mediterranean swordfish (Xiphias gladius) trap-line fishery.Results: Simulation and case-study results show that the DTF can provide early warning signals of potentially unsustainable exploitation and offer transparent guidance for precautionary management actions, even where conventional stock assessments are limited or unavailable. Retrospective application to the scabbardfish fishery indicates that the DTF would have identified an early diagnostic signal and supported the consideration of precautionary management measures, while application to the swordfish fishery demonstrates consistency with precautionary policy decisions adopted under data-limited conditions.Discussion: Overall, the DTF represents a proactive decision-support tool that operationalizes precautionary and adaptive management principles, enhances transparency in decision-making, and has the potential to support the management of emerging fisheries across diverse contexts, subject to further validation.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
