Challenges and solutions in regionalized stock assessment of the European eel (Anguilla anguilla)
An accurate assessment of fish stock status is pivotal in fisheries management, to guide decisions about sustainable exploitation levels or conservation measures. The abundance or biomass of a fish stock, however, is seldom directly observable, whereby population models have become the primary alternative to gauge fish stock status. Comprehensive and accurate assessments for widely distributed or highly migratory species are particularly challenging, involving a devolution of responsibilities for data collection, and sometimes also for the assessment itself, to the regional level among contracting countries. The critically endangered, facultative diadromous European eel (Anguilla anguilla) has a complex life-cycle, forming a single panmictic stock with a wide continental distribution, thereby epitomizing the challenges of regionalized stock assessment and management. This thesis reviews and examines the accuracy of currently used methods for the assessment of local eel stock status in terms of potential spawner (so-called “silver eel”) escapement, which is mandatory across EU countries. Particular focus was placed on the German Eel Model (GEM), which was validated through a comprehensive mark-recapture study on the Ems River as an exemplary system. The study found a substantial overestimation of the silver eel escapement by GEM compared to the monitored biomass, illustrative of the great uncertainties in local eel stock assessments. A comprehensive meta-analysis of similar validation studies for the heterogeneous regional eel assessment models across Europe underscored the tendency towards over-optimistic estimates for specific model types. Misestimations of stock size can have severe consequences for both the ecological and economic sustainability of a fish stock and its fishery, while over-optimistic assessments in particular increase the risk of insufficient conservation measures needed to enable species recovery.
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