Selectivity metrics for fisheries management and advice

Abstract

Fish and Fisheries. 2020;21:621-638. | 621 wileyonlinelibrary.com/journal/faf | INTRODUC TI ONAll fishing is fundamentally selective. Depending on what fishers want to catch, they choose gear type (e.g., longlines, set nets, trawls), gear specifications (e.g., mesh size, hook size), and the time and place to deploy these gears. These choices are influenced by the regulatory framework, that is gear regulations, limitations on the catch size and composition, and seasonal and temporal restrictions.Fisheries selectivity relates to desirable species (species selectivity) or sizes (size selectivity). There are three types of size selectivity: AbstractFisheries management typically aims at controlling exploitation rate (e.g., Fbar) to ensure sustainable levels of stock size in accordance with established reference points (e.g., F MSY , B MSY ). Population selectivity ("selectivity" hereafter), that is the distribution of fishing mortality over the different demographic components of an exploited fish stock, is also important because it affects both Maximum Sustainable Yield (MSY) and F MSY , as well as stock resilience to overfishing. The development of an appropriate metric could make selectivity operational as an additional lever for fisheries managers to achieve desirable outcomes. Additionally, such a selectivity metric could inform managers on the uptake by fleets and effects on stocks of various technical measures. Here, we introduce three criteria for selectivity metrics:(a) sensitivity to selectivity changes, (b) robustness to recruitment variability and (c) robustness to changes in Fbar. Subsequently, we test a range of different selectivity metrics against these three criteria to identify the optimal metric. First, we simulate changes in selectivity, recruitment and Fbar on a virtual fish stock to study the metrics under controlled conditions. We then apply two shortlisted selectivity metrics to six European fish stocks with a known history of technical measures to explore the metrics' response in real-world situations. This process identified the ratio of F of the first recruited age-class to Fbar (Frec/Fbar) as an informative selectivity metric for fisheries management and advice.

Drivers of the North Aegean Sea Ecosystem (Eastern Mediterranean) Through Time: Insights From Multidecadal Retrospective Analysis and Future Simulations

“…This partial recovery might be the result of reduction of fishing effort -which resulted in some fleets (e.g., bottom trawls) even after accounting for technology creep -and of some technical measures that have been applied in the past decade, such as the increase in cod-end mesh size and the partial reallocation of fishing effort from shallow to deeper waters for bottom trawls (Council Regulation (EC) No 1967/2006). These measures are not considered adequate to ensure the sustainability of the stocks (Vasilakopoulos et al, 2020;Lucchetti et al, 2021), but they may have contributed to the observed improvement, at least for some species (e.g. red mullets; Tserpes et al, 2019).…”

Use of the SmeltCam as an Efficient Fish Sampling Alternative Within the San Francisco Estuary

“…Detection efficiency can be further decomposed into components, which vary because of differing sources of sampling bias (Zhou et al 2014;Hostetter et al 2019). The common components of detection efficiency are the probability that a fish is present during sampling; the probability the fish, given it is present, is available to the gear; and the probability that a fish is retained once in contact with the gear, or retention probability (Hostetter et al 2019;Vasilakopoulos et al 2020). Various sampling designs have been developed to specifically target components of sampling efficiency (e.g., retention probability with covered cod-end trawling designs) or combinations of detection efficiency components (e.g., depletion surveys provide information specific to retention and availability probability, Hostetter et al 2019).…”

Reconciling the economic and biological fishery data gathered through the European Data Collection Framework: A new R-tool

“…A vessel can fish in different areas with different gears on different stocks over the year; hence, the overall sum of annual cost data only reflect an average across different fishing activities, not allowing to extrapolate the specificities of the different fishing activities that may relate not only to the impact on the marine resources (e.g., selectivity) [ 14 ] but also to differences in the operating costs’ structure.…”