Biodemographic Research

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Honeybees provide multiple, practical advantages over other models in biodemographic research: worker cohorts are readily obtainable and maintainable in large numbers from either genetically homogeneous or heterogeneous sources (Laidlaw and Page, 1997). Honeybee workers return to their hive daily as long as they live. Thus, their activity and lifespan can be directly monitored in observation hives without disturbance and under seminatural conditions.

Due to its commercial importance and widespread public interest, the honeybee was also one of the earliest insects with a recorded lifespan (Langstroth, 1866). Early studies were based on small sample sizes or indirect estimations, but they provided the first data on the intraspecific variability and plasticity in aging that remain today one of the major reasons to study honeybee aging (Omholt and Amdam, 2004). Larger, demographic studies showed that honeybee workers have a type I (convex) survivorship curve, which is rare in insects (Sakagami and Fukuda, 1968) but common in social organisms with extended brood care, such as humans. The pattern of age-specific mortality in honeybee workers is largely due to the hive bee to forager transition, which is accompanied not only by major physiological changes, but also by a dramatic increase in external mortality risk (see above). More than 90% of all worker deaths are reported to occur outside of the colony under natural conditions (Lundie, 1925). Thus, it is difficult to identify the exact cause of death for the majority of workers (reviewed by Page and Peng, 2001), and biodemographic studies of the mortality dynamics are important for our understanding of aging and mortality in the honeybee.

The seasonality of worker lifespan is one of the most striking features of the honeybee aging model system (see Temporal Cessation of Behavioral Development). The few studies addressing seasonality from a comparative demographic perspective reveal that not only the average life expectancy, but also the variation in life expectancy (Sakagami and Fukuda, 1968) and life history trade-offs that determine life expectancy vary seasonally (Neukirch, 1982). In general, the diutinus phase over the winter period seems to slow aging effectively without any adverse, compensatory effects before or after this stage (Omholt and Amdam, 2004), but more work on the mechanistic and demographic aspects of this retardation of aging remains to be done.

The age at the onset of foraging activity is a major determinant of lifespan for honeybee workers, although some aging may also occur pre-foraging (honeybees that initiate flight later in life have a shorter foraging lifespan: Guzman-Novoa et al., 1994). Sharply increasing mortality dynamics indicates that foragers senesce (Sakagami and Fukuda, 1968) either as a consequence of regulatory processes (see Endocrine Status), or wear-and-tear, or expenditure of internal resources (Neukirch, 1982).

However, there is also a report of a constant mortality rate, relative to the intensity of foraging (Visscher and Dukas, 1997). Comprehensive data on larger samples are needed to resolve this issue, as well as their late-life mortality dynamics, and the quantification of genetic versus environmental influences on lifespan.

One further interesting aspect, which is unique to the honeybee model, is the connection between social structure and individual life history and aging. Isolated studies have indicated that the caregiver-to-dependent (worker-to-brood) ratio is of crucial importance to the individual longevity of the progeny (Eischen et al., 1982), and possibly all other workers (Winston and Fergusson, 1985). The amount of brood and resources in a colony, and the age structure of the worker force, influence the age of foraging initiation of individual workers, and thus their lifespan (Winston, 1987). Furthermore, larger colony size confers a longevity advantage to the individuals presumably because individual workers in smaller colonies work harder, raising more offspring per adult bee (Harbo, 1986). These plastic reactions to social conditions are ultimately the result of dynamic colony-level resource allocation and optimization (Oster and Wilson, 1978). However, comprehensive quantitative models and experiments are rare because the combination of colony demography, resource flows, and individual physiology and behavior proves complex (Amdam and Omholt, 2002).

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