The rule of thumb that fishing mortality to achieve maximum sustainable yield (FMSY) equals natural mortality (M) has been both criticised and supported by theoretical arguments. However, the relationship has been rarely investigated using empirical data. We carried out a meta-analysis on 245 fish species worldwide and linked three types of reference points (FBRP: FMSY, Fproxy, and F0.5r) to M and other life history parameters (LHP). We used Bayesian hierarchical errors-in-variables models to investigate the relationships and included the effect of taxonomic class and order. We compared various models and found that natural mortality is the most important LHP affecting FBRP. Other covariates, such as von Bertalanffy growth coefficient, asymptotic length, maximum age, and habitat types, add little to the relationship, partially because of correlation and large measurement and process errors. The best model results in FMSY = 0.87M (standard deviation (SD) = 0.05) for teleosts and FMSY = 0.41M (SD = 0.09) for chondrichthyans. Fproxy based on per-recruit analysis is about 15% smaller than FMSY. Results could be used to estimate FBRP from LHP in data-poor situations.

Accurate estimates of abundance are imperative for successful conservation and management. Classical, stratified abundance estimators provide unbiased estimates of abundance, but such estimators may be imprecise and impede assessment of population status and trend when the distribution of individuals is highly variable in space. Model-based procedures that account for important environmental covariates can improve overall precision, but frequently there is uncertainty about the contribution of particular environmental variables and a lack of information about variables that are important determinants of abundance. We develop a general semiparametric mixture model that incorporates measured habitat variables and a nonparametric smoothing term to account for unmeasured variables. We contrast this spatial habitat approach with two stratified abundance estimators and compare the three models using an intensively managed marine fish, darkblotched rockfish (Sebastes crameri). We show that the spatial habitat model yields more precise, biologically reasonable, and interpretable estimates of abundance than the classical methods. Our results suggest that while design-based estimators are unbiased, they may exaggerate temporal variability of populations and strongly influence inference about population trend. Furthermore, when such estimates are used in broader meta-analyses, such imprecision may affect the broader biological inference (e.g., the causes and consequences of the variability of populations).Résumé : Des estimations exactes de l'abondance sont essentielles au succès de la conservation et de la gestion. Si les estimateurs d'abondance stratifiés classiques fournissent des estimations non biaisées de l'abondance, ces estimateurs peuvent être imprécis ou entraver l'évaluation de l'état et de la tendance de la population si la répartition des individus est très variable dans l'espace. Si des procédures basées sur des modèles qui tiennent compte d'importantes covariables environnementales peuvent améliorer la précision globale, il y a souvent une incertitude associée à la contribution de différentes variables environnementales et un manque d'information sur les variables qui sont d'importants déterminants de l'abondance. Nous avons développé un modèle de mélange semi-paramétrique général qui incorpore des variables mesurées de l'habitat et un terme de lissage non paramé-trique pour tenir compte des variables non mesurées. Nous comparons cette approche d'habitat spatial à deux estimateurs d'abondance stratifiés à la lumière d'observations sur un poisson marin faisant l'objet d'une gestion intensive, le sébaste tacheté (Sebastes crameri). Nous démontrons que le modèle d'habitat spatial produit des estimations de l'abondance plus précises, interprétables et raisonnables du point de vue biologique que les méthodes classiques. Nos résultats donnent à penser que, si les estimateurs basés sur la conception de l'échantillonnage sont non biaisés, ils peuvent exagérer la variabilité temporelle des populations et influencer fo...

Research shows that population status can be predicted using catch data, but there is little justification for why these predictions work or how they account for changes in fisheries management. We demonstrate that biomass can be reconstructed from catch data whenever fishing mortality follows predictable dynamics over time (called “effort dynamics”), and we develop a state-space catch only model (SSCOM) for this purpose. We use theoretical arguments and simulation modeling to demonstrate that SSCOM can, in some cases, estimate population status from catch data. Next, we use meta-analysis to estimate effort dynamics for US West Coast groundfishes before and after fisheries management changes in the mid-1990s. We apply the SSCOM using meta-analytic results to data for eight assessed species and compare results with stock assessment and data-poor methods. Results indicate general agreement among all three methods. We conclude that effort dynamics provides a theoretical basis for using catch data to reconstruct biomass and has potential for conducting data-poor assessments. However, we still recommend that index and compositional data be collected to allow application of data-rich methods.