Population mortality curves, otherwise known as lifetime distribution functions, can be indispensable in many areas of ecology and environmental management including population viability and stock management analyses, disaster-recovery monitoring, and fundamental evolutionary biology. Yet available modeling tools are often unable to estimate population mortality curves from commonly available datasets because these datasets fail to meet stringent experimental-design requirements. Here, we present a new method for estimating population mortality curves from records of marked individuals found dead. Such data are increasingly accessible in some of the largest biological datasets, such as continent- or nation-wide marking-and-recovery schemes for birds and other animals. The method accounts for known biases in availability by age class, variable monitoring effort through time, and mark loss. We show that our modeling approach generates accurate estimates of population mortality structure across a range of populations differing in marking histories, true mortality curves, monitoring regimes, and mark loss rates. Our approach can be applied to multiple species or groups at a time and can provide estimates of inter-annual adult, immature, and first-year survival rates, required by predictive modeling applications such as population viability analyses. Our approach is also capable of estimating apparent senescence rates for each population and facilitates evolutionary analyses of life-history traits. For example, our method is potentially useful for exploring the evolution of senescence or for inter-group comparisons of mortality rates where groups that differ by environment may be identified within mark-recovery records. To demonstrate the efficacy of this method, we present fitted population mortality curves for a suite of seabird species represented in a national mark–recapture database.
- mark recovery
- open data