7 Life Histories

Northeast Pacific Shark Biology, Research, and Conservation

Life histories describe the speed of life and include traits related to survival, growth, and reproduction (Roff, 2002; Stearns, 1992). Life history theory suggests that due to constraints and trade-offs among life history traits, species fall along a continuum of slow to fast life histories (Charnov, 1993; Reynolds et al., 2001; Stearns, 1983, 1992). Slow life histories describe those species that have slower growth, lower reproductive output, long gestation times, later ages at maturity, higher longevities (and thus longer generation times), larger body sizes, and lower population growth rates. Fast life histories describe those species that are on the opposite end of the continuum with faster growth, higher reproductive output, shorter gestation times, earlier ages at maturity, shorter lifespans, shorter generation times, smaller body sizes, and higher population growth rates (Denney et al., 2002; Gunderson, 1980; Pianka, 1970). Although chondrichthyans generally are classified as a group with slower life histories than other vertebrates (e.g. Hoenig and Gruber, 1990; Hutchings et al., 2012), much variation exists among species (Table 4). For example, this variation is evident when contrasting the 18- to 24-month gestation length of the North Pacific Spiny Dogfish (Squalus suckleyi) with the 3-month gestation length of the Round Stingray (Urobatis halleri), both of which are present in the NEP (Ketchen, 1986; Mull et al., 2010; Tribuzio and Kruse, 2012).

Pinniped Life History

Optimal Life Histories: Modeling the Way Forward

Life history analysis in pinnipeds is fraught with difficulties. Longitudinal studies in which individuals are studied throughout their lifetimes can only be carried out on a narrow range of accessible populations and they are expensive and logistically complex to maintain over the time periods (usually decades) required to achieve useful results. Cross-sectional studies are extremely limited in what they can tell us about the dynamics of life histories, and commercial harvests, the usual source of these data, are a thing of the past. We have to find a new way forward.

To date, almost all studies of pinniped life histories have been empirically based and, as pointed out in this description, they have highlighted the interactive nature of parameters such as longevity and reproductive rate. A modeling framework is required in order to allow these interactions to be investigated, to make better use of the data sets that already exist, and to identify critical gaps in the empirical data.

If a pinniped is to maximize its lifetime fitness F, then it must choose the optimal allocation of resources to reproduction through its lifetime. Thus, F=fl+f2+f3…fn, where fa is the fitness contribution from year a in the life of the pinniped, which lasts n years. We know that there are certain functional relationships between maternal size or condition and the probability that mothers will reproduce or survive. If we assume that the relationship between offspring condition and its ultimate fitness is asymptotic, then, up to a certain level, the more energy that a female delivers to her offspring the greater will be her fitness. If the energy delivered to an offspring (ea) is a proportion p of the energy available to the mother, then from what we know of the growth patterns and the energetic efficiencies of pinnipeds, it is possible to estimate the energy available for reproduction throughout the life span of an average individual. By setting rules that an individual will only reproduce if it has a sufficient excess of energy above that required for maintenance, we may be able to investigate the life history patterns in different environments as well as the effects of stochastic variability in food availability on life histories.

Many of the dynamic relationships described here should become explicit in the results of such an energy-based life history model. Similarly, such a model could help the interpretation of some of the crosssectional population data in the context of dynamic life history processes. This type of approach seems to be essential if progress is to be made in pinniped life history analysis and for the full implications of life history analysis to be realized. Because the mechanism underlying population trajectories is the sum of individual life histories, understanding the environmental factors that affect life histories is fundamental to understanding population and species viabilities.