A Framework for Simulating Ecosystem Effects in Data-Poor Small-Scale Fisheries Using Science-Based and Local Ecological Knowledge-Based Models

Abstract

The constant demand for seafood products and the undeniable effects of fishing on marine ecosystems make it urgent to implement an ecosystem approach, even in data-poor scenarios such as small-scale fisheries. Understanding the impacts of fishing is essential for promoting management strategies that prevent irreversible damage to marine ecosystems. Thus, ecosystem quantitative science-based models have been frequently used to evaluate the effects of fishing, although fishers’ local ecological knowledge (LEK) can aid the implementation of qualitative models, particularly in data-poor conditions. Here, we present a framework for simulating and assessing the effects of fishing following two strategies: (1) for both types of models, we simulated species removal scenarios, and (2) for quantitative science-based models, we fitted time series to dynamically assessed impacts. The impacts were analyzed through ecological indicators commonly used for quantitative models, and because these indicators cannot be easily estimated for qualitative models, we propose the use of topological indicators in both types of models. The approach was applied to three case studies of small-scale finfish fisheries in northwestern Mexico. We found that the ecosystem response to species removal was different in each case study and that the target species can play an important role in ecosystems, but their removal does not generate abrupt changes in the ecosystem structure. The quantitative science-based models were able to reproduce the historical catch trends, which allowed us to reveal that changes in ecosystems are indeed influenced by fishing effort but also by underlying primary productivity. Furthermore, topological and ecological indicators showed similar trends in the quantitative models, which suggests that the former could be useful when data-poor conditions allow only qualitative models. This result confirms the relevance of the participation of fishers in generating qualitative models and their decisive role in the discussion of co-management strategies and risk scenarios in a better-informed manner.

Towards Sustainability: A Framework for Evaluating Portuguese Small-Scale Fisheries

“…The proposed framework includes indicators (Table 1) divided across the following four dimensions: economy [11], society [9], environment [14], and governance [8]. The framework provides a one-model-fits-all to address sustainability in SSFs across different contexts, highlighting the sector's sustainability performance and identifying potential data gaps [85,86].…”

“…Hence, a systematic literature review through Scopus may not provide enough information to characterize the Portuguese SSF sector. Furthermore, researchers frequently point out the difficulty in gathering SSF data, due to the variety inherent to the sector such as the wide range of gears employed and species targeted, covering large geographical and frequently remote areas [13,85,86].…”

“…This aspect limits the ability to perform comparative studies between fisheries and gears given that fishing fleets, gear, target species, locations, seasons, and ecosystems vary greatly, Portuguese fishery data are scattered throughout peer-reviewed publications (e.g., [18,52,53,56,57,[59][60][61][62][63][64][65]67]). These characteristic makes it more challenging to conduct research that compares fishing impacts among fisheries and gears [85,86]. An important step forward in data collection would be to promote research about gear selectivity (e.g., accidental catches and discards), which could be used to review fishing gear (e.g., mesh size, materials, number) and adjust them to reduce their impact on the marine environment [53].…”

Disaster impacts on co-management networks: longitudinal and comparative analysis of Chilean small-scale fisheries

“…The great reliance of co-management on the structure and dynamics of underlying social relationships has fostered vast research and policy opportunities for the use of network-based frameworks and tools (Carlsson and Berkes, 2005;Sandström et al, 2014;Cohen and Steenbergen, 2015;Fratsea and Papadopoulos, 2022;Gomez-Andujar et al, 2022;Zetina-Rejoń et al, 2022). Recent studies have investigated which kinds of social network structures better accommodate collaborative management to deal with uncertainty and change.…”