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Estimating maturity from size-at-age data: Are real-world fisheries datasets up to the task?
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Estimating maturity from size-at-age data: Are real-world fisheries datasets up to the task?
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Journal Article
Estimating maturity from size-at-age data: Are real-world fisheries datasets up to the task?
2020
Overview
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 population-level 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, datasets that did satisfy data requirements generally showed no correlation between empirical and model-derived 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 under-representation 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.
