Abstract
BACKGROUND: 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 (Xiphopenaeus kroyeri, Heller, 1862). METHODS: 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. RESULTS: Analysis with bootstrapped ELEFAN (fishboot package) resulted in considerably lower estimates for asymptotic size (L (∞)), instantaneous growth rate (K), total mortalities (Z) and Z/K values compared to traditional methods. These parameters were highly influenced by L (∞) estimates, which exhibited median values far below maximum lengths for all samples. Contrastingly, traditional methods (PW method and L (max) approach) resulted in much larger L (∞) estimates, with average bias >70%. This caused multiplicative errors when estimating both Z and Z/K, 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 L (∞) > L (max) 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.