Estimating maturity from size-at-age data: Are real-world fisheries datasets up to the task?
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ORIGINAL RESEARCH
Estimating maturity from size-at-age data: Are real-world fisheries datasets up to the task? Henry F. Wootton
. John R. Morrongiello
. Asta Audzijonyte
Received: 23 November 2019 / Accepted: 7 August 2020 Ó Springer Nature Switzerland AG 2020
Abstract The size and age at which individuals mature is rapidly changing due to plastic and evolved responses to fisheries harvest and global warming. Understanding the nature of these changes is essential because maturity schedules are critical in determining population demography and ultimately, the economic value and viability of fisheries. Detecting maturity changes is, however, practically difficult and costly. A recently proposed biphasic growth modelling likelihood profiling method offers great potential as it can statistically estimate age-at-maturity from populationlevel size-at-age data, using the change-point in growth that occurs at maturity. Yet, the performance of the method on typical marine fisheries datasets remains untested. Here, we assessed the suitability of 12 North Sea and Australian species’ datasets for the likelihood profiling approach. The majority of the fisheries datasets were unsuitable as they had too small sample sizes or too large size-at-age variation. Further, Electronic supplementary material The online version of this article (https://doi.org/10.1007/s11160-020-09617-9) contains supplementary material, which is available to authorized users. H. F. Wootton (&) J. R. Morrongiello School of Biosciences, The University of Melbourne, Melbourne, VIC, Australia e-mail: [email protected] A. Audzijonyte IMAS, University of Tasmania, Hobart, TAS, Australia
datasets that did satisfy data requirements generally showed no correlation between empirical and modelderived maturity estimates. To understand why the biphasic approach had low performance we explored its sensitivity using simulated datasets. We found that method performance for marine fisheries datasets is likely to be low because of: (1) truncated age structures due to intensive fishing, (2) an underrepresentation of young individuals in datasets due to common fisheries-sampling protocols, and (3) large intrapopulation variability in growth curves. To improve our ability to detect maturation changes from population level size-at-age data we need to improve data collection protocols for fisheries monitoring. Keywords Biphasic growth model Lester model likelihood profiling Statistical maturity estimates Fisheries-induced evolution Maturity changes Simulations Life history
Introduction Phenotypic change is a prevalent response to human interference in wild populations (Hendry et al. 2008; Sih et al. 2011). In the sea, these changes are often attributed to plastic and evolutionary responses to harvest (Kuparinen and Festa-Bianchet 2017; Law 2007) and warming (Cheung et al. 2013; Crozier and
123
Rev Fish Biol Fisheries
Hutchings 2014). Rapid changes in age and size at maturation are particularly
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