Evaluation of traditional and bootstrapped methods for assessing data-poor fisheries: a case study on tropical seabob shrimp ( Xiphopenaeus kroyeri ) with an improved length-based mortality estimation method

Unrealistic model assumptions or improper quantitative methods reduce the reliability of data-limited fisheries assessments. Here, we evaluate how traditional length-based methods perform in estimating growth and mortality parameters in comparison with unconstrained bootstrapped methods, based on a virtual population and a case study of seabob shrimp ( Heller, 1862). Size data were obtained for 5,725 seabob shrimp caught in four distinct fishing grounds in the Southwestern Atlantic. Also, a synthetic population with known parameter values was simulated. These datasets were analyzed using different length-based methods: the traditional Powell-Wetheral plot method and novel bootstrapped methods. Analysis with bootstrapped ELEFAN (fishboot package) resulted in considerably lower estimates for asymptotic size ( ), instantaneous growth rate ( ), total mortalities ( ) and values compared to traditional methods. These parameters were highly influenced by estimates, which exhibited median values far below maximum lengths for all samples. Contrastingly, traditional methods (PW method and approach) resulted in much larger estimates, with average bias >70%. This caused multiplicative errors when estimating both and , with an astonishing average bias of roughly 200%, with deleterious consequences for stock assessment and management. We also present an improved version of the length-converted catch-curve method (the iLCCC) that allows for populations with > and propagates the uncertainty in growth parameters into mortality estimates. Our results highlight the importance of unbiased growth estimates to robustly evaluate mortality rates, with significant implications for length-based assessments of data-poor stocks. Thus, we underscore the call for standardized, unconstrained use of fishboot routines.