LIFE HISTORY DATA SOURCES
Quality data for quantifying life histories takes much effort to collect as life histories cannot be described simply by observing static phenotypes. Measuring life history traits can be challenging: some infrequent, brief life history events can be at best rarely observed. Examples include reproduction in semelparous perennial species, or mortality in long-lived species. Much effort has been devoted to the inference of unobserved or imperfectly detected life history events in population studies [35]. The demographic context of traits like generation time and life expectancy means that they must be measured using samples of individuals within populations or species. Hence, life history trait data vary greatly in their abundance, accessibility, and quality (Table 1) [12,36], even among charismatic species [12].
The gold standard in demographic data is detailed schedules of age- or stage-specific vital rates . These rates of survival, development, and reproduction are becoming increasingly available through open databases of demographic data (e.g., [10,11,16–18,31,37,38]). Despite the diversity of model dimensions and complexity, life history traits can be derived from these schedules, yielding a set of common traits across species. This derivation overcomes some of the issues with simple trait data outlined in Table 1. Demographic quantities such as stationary [39] and transient [40] population dynamics can also be calculated from life history schedules, as can selection pressures [41]. However, although these databases are accessible, models vary in quality and data remain taxonomically biased [36]. Moreover, the traits derived from these models are mathematical functions of the same underlying schedules of survival, development, and reproduction, hence correlations among them are a combination of the real and the artefactual [42].