Upgrading from M version 0.2: An application-based method for practical estimation, evaluation and uncertainty characterization of natural mortality

Natural morality (M) is a notoriously difficult population parameter to estimate, yet it is also one of the most important measures of life history that sets, as Beverton and Holt called it, the course of events. Stock as-sessments that include this parameter often show great sensitivity to its value, reflecting the need to characetrize the uncertainty inherent in its estimation. Direct measurement of natural mortality is often limited to resource intensive tag-recapture studies. Indirect measures are more often used, and are built on life history theory, relating natural mortality to traits such as age, size, maturity and reproductive condition (or just assuming 0.2). The Natural Mortality Tool attempts to accumulate several empirical estimators of M into one application. Users simply input life history values to obtain estimates of natural mortality. These estimates can be taken individ-ually or can be combined into a weighted density function that can be used to develop an M prior that integrates uncertainty across several estimates. Comparing estimators can also reveal inconsistencies in life history values that may lead to further refining of basic biological understanding. Two examples are used to demonstrate tool functionality and highlight general recommendations on implementation. Making these estimators and the development of uncertainty in estimating natural mortality more widely available hopefully supports transparent and defensible decision-making on the treatment of this important population parameter.

Automated summary

Estimating natural mortality is a challenging task, but it is an important measure for understanding life history. Direct and indirect methods are used to estimate this parameter, and the Natural Mortality Tool aims to combine multiple empirical estimators into one application. Comparing these estimators can help identify inconsistencies in life history values and promote transparent decision-making.