Inferring abundance trends of key species from a highly developed small-scale fishery off NE Atlantic

Abstract

Small-scale fisheries (SSFs) are large contributors to worldwide catches and local economies; however, SSFs have been historically under-attended in national and international policies. This work presents a standardization of catch-per-unit-of-effort data obtained from onboard observers for twenty commercial coastal species caught by the SSFs off the Galician coast from 2000 to 2016 using generalized linear mixed models. Operational, environmental, temporal and spatial data were included in the models as explanatory variables. The relative index of abundance for most species varied from year-to-year over the study period and between geographical areas (ICES 9.a and 8.c). Seasonal patterns, likely related to annual biological cycles, were found. Vessel size showed restricted significant effects in abundance estimates, and soak time of fishing operations revealed nonlinear responses of catch rates. Fishing at night or during daylight hours also have significant differences in catch rates that varied among species pointing to behavioural effects on catchability. Catch rates were often negatively related with depth as expected for species with a coastal distribution. Furthermore, seafloor type indicated species-specific habitat preferences, and sea surface temperature played a limited role in abundance estimations. The most common trend of species’ abundance indices showed opposite patterns among geographical areas, suggesting that local conditions may have a significant influence on fluctuations not only at the species level, but also at the community level. This study revealed a generalized lack of correlation between landings and abundance indices, thus landing data was considered, in general, a poor indicator of population status. The data presented increase the fishery knowledge of one of the most important European SSF fleets that ultimately would allow implementing future assessment strategies. It also underpins the fact that fine scale spatial, and temporal observer data, together with environmental and operational information, are critical to infer suitable and unbiased abundance indices within a complex SSF context.