Bach, P., Gaertner, D., Menkes, C., Romanov, E., & Travassos, P. (2009). Effects of the gear deployment strategy and current shear on pelagic longline shoaling. *Fish Res.*, *95*, 55–64.

Historical longline catch per unit effort (CPUE) constitutes the major time series used in tuna stock assessment to followthe trend in abundance since the beginning of the large-scale tuna fisheries. The efficiency and species composition of a longline fishing operations essentially depends on the overlap in the vertical and spatial distribution between hooks and species habitat. Longline catchability depends on the vertical distribution of hooks and the aim of our paper was to analyse principal factors affecting the deviation of observed longline hook depths from predicted values. Since observed hook depth is usually shallower than predicted, this deviation is called longline shoaling.We evaluate the accuracy of hook depth distribution estimated from a theoretical catenary model commonly used in longline CPUE standardizations. Temperature-depth recorders (TDRs) were deployed on baskets of a monitored longline. Mainline shapes and maximum fishing depths were similar to gear configurations commonly used to target both yellowfin and bigeye tuna by commercial longliners in the central part of the South Pacific Ocean. Our working hypothesis assumes that the maximum fishing depth reached by the mainline depends on the gear configuration (sag ratio, mainline length per basket), the fishing tactics (bearing of the setting) and environmental variables characterizing water mass dynamics (wind stress, current velocity and shear). Based on generalized additive models (GAMs) simple transformations are proposed to account for the non-linearity between the shoaling and explanatory variables. Then, generalized linear models (GLMs) were fit to model the effects of explanatory variables on the longline shoaling. Results indicated that the shoaling (absolute aswell as relative) was significantly influenced by (1) the shape of the mainline (i.e., the tangential angle), which is the strongest predictor, and (2) the current shear and the direction of setting. Geometric forcing (i.e. transverse versus in-line) between the environment and the longline set is shown for the first time from in situ experimental fishing data. Results suggest that a catenary model that does not take these factors into consideration provides a biased estimate of the vertical distribution of hooks and must be used with caution in CPUEs standardization methods. Since catchability varies in time and space we discuss how suitable data could be routinely collected onboard commercial fishing vessels in order to estimate longline catchability for stock assessments.
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