Skip to main content
NCBI home page
NIHPA Author Manuscripts logoLink to NIHPA Author Manuscripts
. Author manuscript; available in PMC: 2008 Jun 1.
Published in final edited form as: Aging Cell. 2008 Mar 18;7(3):426–437. doi: 10.1111/j.1474-9726.2008.00390.x

Age structure changes and extraordinary lifespan in wild medfly populations

James R Carey 1,2, Nikos T Papadopoulos 3, Hans-Georg Müller 4, Byron I Katsoyannos 5, Nikos A Kouloussis 5, Jane-Ling Wang 4, Kenneth Wachter 6, Wei Yu 4, Pablo Liedo 7
PMCID: PMC2398686  NIHMSID: NIHMS47788  PMID: 18363903

Summary

The main purpose of this study was to test the hypotheses that major changes in age structure occur in wild populations of the Mediterranean fruit fly (medfly) and that a substantial fraction of individuals survive to middle age and beyond (> 3–4 weeks). We thus brought reference life tables and deconvolution models to bear on medfly mortality data gathered from a 3-year study of field-captured individuals that were monitored in the laboratory. The average time-to-death of captured females differed between sampling dates by 23.9, 22.7, and 37.0 days in the 2003, 2004, and 2005 field seasons, respectively. These shifts in average times-to-death provided evidence of changes in population age structure. Estimates indicated that middle-aged medflies (> 30 days) were common in the population. A surprise in the study was the extraordinary longevity observed in field-captured medflies. For example, 19 captured females but no reference females survived in the laboratory for 140 days or more, and 6 captured but no reference males survived in the laboratory for 170 days or more. This paper advances the study of aging in the wild by introducing a new method for estimating age structure in insect populations, demonstrating that major changes in age structure occur in field populations of insects, showing that middle-aged individuals are common in the wild, and revealing the extraordinary lifespans of wild-caught individuals due to their early life experience in the field.

Keywords: age structure, captive cohort, Ceratitis capitata, deconvolution model, field demography, life tables

Introduction

In the 50 years since the distinguished ecologist LaMont Cole (1957) noted that problems involving age distribution in the field are ‘… among the most important and least elementary matters with which demographers have to deal’, it is remarkable that still so little is known about the age dynamics and lifespan potential of individuals in wild populations. There are at least two reasons for this paucity of information on population age structure and lifespan in the wild. The first reason is that technologies for estimating the age of individuals are woefully inadequate, particularly when used to estimate the ages of older members in a population. For example, the confidence limits for age estimates that are based on follicular relics in females (Tyndale-Biscoe, 1984), the accumulation of pteridines in insect eye capsules (Mail et al., 1983; Lehane, 1985; Lehane et al., 1986), transcriptional profiling (Cook et al., 2006, 2007), or the progressive layering of hydrocarbons in insect cuticles (Desena et al., 1999; Gerade et al., 2004) are often quite large even for young and middle-aged insects.

The second reason involves the constraints on the traditional approaches for tracking individuals in the field including mark–recapture techniques (Amstrup et al., 2005) and biotelemetry methods (Cooke et al., 2004). For example, several avian ecologists (Anderson et al., 1981; Lakhani & Newton, 1983) noted that one of the main models used to estimate age-specific survival rates from recovery data of birds ringed as young is ‘… misleading, untrustworthy and gives a false sense of precision’ (Anderson et al., 1985, p. 97). These ecologists believe that the use of this model may account for the conclusions forwarded by earlier researchers that avian mortality is age independent (Botkin & Miller, 1974). These conclusions are now in doubt (Wooler et al., 1992; Ricklefs, 2000). Similarly, research on water voles (Arvicola terrestris) revealed a male-skewed recruitment sex ratio due to the attachment of radio-collars to females (Moorhouse & MacDonald, 2005).

Several important and widely cited investigations concerned with aging in wild populations do not attempt to estimate field survival or population age structure but instead make life table comparisons in the laboratory between biotypes in one-of-two ways. The first involves comparisons between two or more field-collected biotypes such as between guppy biotypes collected from streams that differ in predation levels (Reznick et al., 1996, 2001), between grasshopper biotypes collected from regions of the Sierra Nevada that differ in altitude (Tatar et al., 1997), and between rotifer species collected from the wild that differ in preferred habitats (Kirk, 1997). The second type of comparison is between biotypes that differ in degrees of laboratory selection such as between laboratory-adapted and wild-type lines of Drosophila melanogaster (Sgro & Partridge, 2000; Linnen et al., 2001) or between laboratory vs. wild-derived mice (Miller et al., 2000, 2002).

With several important exceptions including studies of a radio-collared marsupial (Austad, 1993), a dipteran species that exhibits extremely high fidelity to mating sites (Bonduriansky & Brassil, 2002, 2005), and fruit-feeding butterflies that restrict their movement to well-circumscribed territories (Molleman et al., 2006), the majority of investigations on aging rate in the wild are based on large vertebrates, modest numbers, and life table and lifespan data that are either derived from cross-sectional data (Deevey, 1947; Caughley, 1977; Promislow, 1991) or augmented with longevity information for captive animals (Carey & Judge, 2000; Kohler et al., 2006). For studies in which the sample sizes are large and the data collection in the field is longitudinal, the long lifespans of species studied necessitate the construction of costly databases assembled over several decades (Clutton-Brock et al., 1982; Charmantier et al., 2006; Nussey et al., 2007).

In light of the technological, logistical, and conceptual constraints on studying aging in field populations, the paucity of published research on longevity and aging in the wild, and the need for data used in hypothesis-testing that is gathered from evolutionarily relevant (field) environments, we initiated a field study on the medfly to answer four questions: What is the age distribution in medfly populations through a field season? Does this distribution include individuals at middle and advanced ages? Is there evidence for changes in population age structure? Does the early life experience of free-living flies extend their longevity in the laboratory relative to flies that only experience laboratory conditions when young?

The approach we use in this research is based on four concepts. First, that the frailty of an individual of unknown age that is captured in the wild will be reflected in its remaining longevity. Thus, the postcapture lifespan of individuals that are young and robust when captured will, on average, be greater than that for individuals that are older and more frail. Second, that the postcapture mortality rates of these captured individuals reflects the frailty distribution and, in turn, the physiological age distribution in the population from which they were obtained. Third, that either a relative or an absolute increase in the estimated physiological age of the captured individuals between two sampling periods is an indication of a change in population age structure (Preston et al., 2001; Uhlenberg, 2005) that occurs due to a change in: (i) the proportion of older individuals in a population as indicated either by an change in the mean (or median) age of a population; or (ii) the proportion of individuals over a fixed older age (e.g. 65 years in humans; 40 days in medflies). Fourth, that baseline life tables and deconvolution models (Müller et al., 2004, 2007) can be brought to bear on mortality data derived from field-trapped medfly adults of unknown age that were maintained in the laboratory to estimate population age structure and, in turn, of changes in population age structure in the wild (see Supplemental material for additional details).

Results

Composite survival

The average times-to-death for the 2653 females and 1435 males captured during the 3-year field study were 52.5 and 59.6 days, respectively. These values were less than their respective life expectancies at eclosion (x = 0) in the reference cohorts suggesting that the age of the medfly in the wild was greater than newly eclosed. The presence of younger flies of both sexes in the wild (Fig. 1) is evident from the 8% of field-caught females and 12% of males that survived for 100 days or more in the laboratory (i.e. assuming that only extremely young flies could survive to this age). The presence of older and/or more frail flies (both sexes) in the wild is indicated by the high mortality (10–12%) of captured flies during the first 20 days postcapture. Such high mortality is only found in the reference life table for flies that are already old. In the reference table, mortality during the first 20 days after eclosion was only around 1% per day.

Fig. 1.

Fig. 1

Open in a new tab

Sex-specific survival for two medfly life tables: (i) reference cohorts based on age-specific survival of 467 males and 505 females. The life expectancy and maximal age for males were 75.6 days and 168 days, respectively, and for females were 65.8 days and 139 days, respectively; and (ii) captive cohort based on the survival of 2653 female and 1435 field-captured male medflies, respectively, that were maintained in the laboratory until death. The average capture times to death were 59.6 and 52.5 days for males and females, respectively.

Population age changes

The differences between the mean ages of deaths in flies captured during the different sampling periods exhibited both within- and between-season trends (Figs 2, 3; Table 1) with average times-to-death of captured females at different dates differing by 23.9 days in 2003 (35.5 vs. 59.4 days), by 22.7 days in 2004 (38.8 vs. 61.5 days), and by 37.0 days in 2005 (30.5 vs. 67.5 days). Likewise, the differences in postcapture life expectancies for males captured at different dates in 2005 ranged from 20 to 30 days over the season. Assuming these shifts are proxies for relative shifts in the population age structure of the wild medflies, the results imply that the mean age in the wild medfly populations varied by a minimum of 3–4 weeks over the 3-year sampling period. Statistical tests revealed that differences in mean postcapture lifetimes due to both sex and month of capture were highly significant (p < 10−3; see Supplementary Table S2.1). These tests detect differences among amples rather than trends. The visual evidence for trends is largely confined to the year 2005 for which the sample sizes are more than three times as large as for the other years and correspondingly more persuasive. The significant differences among cohorts support the hypothesis that there were significant changes in age distribution in wild medfly populations over each of the three seasons, with a suggestion of younger populations later in the season.

Fig. 2.

Fig. 2

Open in a new tab

Postcapture lifespans for the 2653 medfly females and 1435 medfly males trapped during the 2003, 2004, and 2005 field seasons on Chios Island, Greece. The points (individual remaining lifespan) within each field season are connected by lines to depict both variation and seasonal trends. The running 30-day medians for each season are shown in red. The horizontal lines also in red showing 20-day (shorter-lived) and 100-day (longer-lived) postcapture ages are included for reference.

Fig. 3.

Fig. 3

Open in a new tab

Box-and-whisker plots showing the median (center band within box), first and third quartiles (upper and lower founds of box), and the minimum and maximum lifespans for individuals medflies captured in each of 29 sampling periods during 2003–2005 in Chios, Greece (see Table 1 for exact within-year sampling dates).

Table 1.

Capture dates, numbers captured, and summary statistics (mean, standard deviation, and maximum postcapture age) for trapped medflies in Chios, Greece, during the 2003–2005 field seasons. The number of medflies captured and monitored during the 2005 field season was much greater than the two previous seasons because individual-level reproductive data (unpublished) were not gathered during the last year. Data on male medflies were not gathered during the first half of the 2004 season because emphasis at that time was shifted to measuring individual reproduction for wild-caught females (unpublished)

Females
Males
Year/date n Mean (days) SD Maximum (days) n Mean (days) SD Maximum (days)
2003
 4 August 25 47.4 23.31 97 8 37.3 33.28 84
 26 August 84 35.5 16.91 120 30 65.5 39.08 150
 17 September 42 53.0 23.93 137 51 80.6 48.96 180
 8 October 99 59.4 32.08 147 64 64.0 51.24 180
 4 November 100 44.2 31.97 153 50 55.0 28.61 121
Season 350 47.7 28.86 130.8 203 60.5 40.2 143.0
2004
 14 June 15 45.9 28.35 115 – – – –
 29 June 4 61.5 18.81 81 – – – –
 14 July 87 55.8 27.74 127 – – – –
 30 July 26 38.8 23.67 92 – – – –
 17 August 11 42.8 26.73 93 – – – –
 31 August 52 40.3 15.52 70 – – – –
 14 September 100 47.8 20.71 118 60 74.3 38.68 156
 2 October 100 58.5 25.91 115 100 69.1 36.55 176
 11 November 100 40.8 18.63 73 18 61.1 36.85 120
Season 495 48.6 23.93 98.2 178 70.0 37.3 163.6
2005
 16 June 16 36.4 12.71 63 3 38.3 15.63 55
 25 June 8 38.4 15.34 57 5 41.6 10.36 57
 7 July 62 34.3 12.96 68 20 40.4 14.23 71
 14 July 54 30.5 15.17 74 26 41.5 21.36 98
 23 July 195 41.6 22.21 105 57 53.5 26.73 114
 7 August 126 66.4 35.43 169 79 62.1 30.08 157
 17 August 143 47.3 29.52 164 142 52.6 39.29 150
 26 August 183 67.5 35.45 138 169 62.8 24.61 138
2005
 6 September 223 48.7 28.53 152 137 49.8 28.57 129
 15 September 268 56.7 29.36 140 145 58.9 28.48 130
 24 September 189 62.5 34.91 148 126 50.3 28.45 138
 7 October 145 61.9 29.58 126 57 66.8 32.36 120
 16 October 103 49.9 26.09 125 45 59.3 33.62 125
 26 October 88 53.7 22.8 105 19 71.2 25.32 101
 12 November 5 44.4 12.82 57 – – – –
Season 1808 53.7 30.64 128.3 1030 53.5 25.6 113.1
Totals 2653 52.0 29.36 111.3 1435 54.4 30.0 117.4
Open in a new tab

High remaining lifespans

Remaining lifespans were extraordinarily high during selected periods, particularly during the fall when life expectancies were 52.9 and 62.1 days for the respective 1671 females and the 910 males captured each fall (i.e. September, October, and November). The frequency of captured female medflies that lived to 100 days or more in the laboratory ranged from less than 1-in-10 individuals trapped in June and July in the 2004 and 2005 field seasons to a remarkable 1-in-4 flies trapped in August and September during these same years. Nearly as surprising is the observation that 22% of all males captured in 2003 lived 100 days or more. A total of 19 captured females but no reference females survived in the laboratory for 140 days or more, and 6 captured but no reference males survived in the laboratory for 170 days or more.

The mean remaining lifetimes by sex for reference and captive cohorts at advanced ages are presented in Table 2. Note that differences between both sexes and cohort types were statistically significant for all ages despite the smaller reference sample size (Supplementary material). For example, female life expectancy at 80 days postcapture equaled 24.1 days for wild-caught flies, compared to 16.0 days for 80-day-old reference flies. We found that the differences are caused to a large extent by distributional differences in remaining lifetime after 80 days, where many more deaths occur in the reference than in the captures flies. The captured cohorts have a somewhat longer right tail (Supplementary material).

Table 2.

Mean remaining lifetimes (days) for male and female medflies from reference and captive cohorts and p-values for differences between males and females and between reference and captive cohorts

Male cohorts
Female cohorts
p-value*
Days Reference Captive Reference Captive Male–female comparisons Reference–captive comparisons
60 30.6 29.5 20.9 26.9 0.0000 0.0185
80 23.4 26.5 16.0 24.1 0.0011 0.0001
100 19.2 23.4 10.5 19.9 0.0012 0.0002
120 13.7 21.2 8.5 13.8 0.0004 0.0026
Open in a new tab
*

All values significant.

Wild age structure

Monthly changes in the actual age distributions of wild medflies were estimated using a deconvolution model (Müller et al., 2004, 2007) with three assumptions. First, that relative changes in the patterns of death between cohorts of medflies captured during two or more sampling periods reflects relative changes in their respective population age distributions (Ryder, 1975). Second, in conjunction with the use of age-specific mortality derived from reference life tables, that changes in these postcapture death patterns can be used to estimate the actual age structure in the field if both laboratory-reared and wild-caught adult medflies experience the same mortality risk in the laboratory (for perspective, see Carey et al., 1998; Mair et al., 2005). Third, if we further assume that wild medflies of all ages are captured in proportion to their abundance, then the estimated age distribution of wild-caught flies can also be used to infer the actual age structure in the field.

The overall age trends (Fig. 4) are consistent with the ecological argument that the availability of ovipositional hosts is the primary driver of medfly populations on Chios Island (Papadopoulos et al., 2001). We pool samples from the 3 years of data because of the small sample sizes for 2003 and 2004, which do not support separate analysis. The larger samples from 2005 drive the main patterns of older early populations and younger later populations seen in Fig. 4. Thus, spring medfly populations tend to be older because of the shortage of suitable hosts earlier in the season, and late summer and fall medfly populations tend to be younger because of the abundance of good hosts later in the season. Estimates of the medfly age compositions over the 6-month period from June through November indicate that: (i) during June and July nearly half the individuals were 30 days or older; (ii) during the remaining 4 months (i.e. August through November) from 70% to 90% of medflies were aged less than 30 days; and (iii) throughout the season individuals in all age classes up to 60 days were present in substantial numbers. Our finding that there are wild medfly populations that consist of individuals over 30 days old during a substantial part of the field season is consistent with the hypothesis that medflies survive long enough in their natural environment to mature. The percentage of the 505 medfly females in the reference cohort that were mature at 1, 2, and 3 weeks was 2.8%, 43.8%, and 77.2%, respectively, with an average maturation time of 16.3 days.

Fig. 4.

Fig. 4

Open in a new tab

Age distributions by month for male and female medflies trapped over three field seasons estimated using the deconvolution model. Too few total males were captured in June to estimate population age structure during this month.

Mortality crossovers

The logic of demographic selection (Vaupel et al., 1979; Vaupel & Carey, 1993) underlies the argument that the variance in the age composition of a group of mixed-aged individuals will progressively decrease as the individuals that were old at the beginning of the observation period die off and the individuals that were young at the beginning of the period continue to survive. It follows that the mortality trajectories for a group of individuals of mixed ages (i.e. wild-caught) and for a newborn cohort will converge if the individuals within each these cohorts are subject to the same age-specific rates. This is because the higher attrition of older individuals relative to the younger ones reduces age heterogeneity in the mixed-age (wild-caught) cohort with the passage of time. Thus, only the very youngest individuals present in the original captive cohort are also present (i.e. survive) at the most advanced ages postcapture. Because these individuals would be the demographic contemporaries of those constituting the radix of the reference cohort, the trajectories of the reference and captive cohort mortality rates will be different shortly after postcapture but will converge at advanced ages if age-specific mortality rates are identical in each cohort. Although the trajectories of the hazard rates (Müller et al., 1997) observed at young postcaptive ages for the group of wild-caught medflies are consistent with this logical prediction, they are inconsistent with this prediction at older postcaptive ages in that the trajectories cross rather than continue on similar paths (Fig. 5). And the crossing (Carey et al., 1995) is much more pronounced in female than in male medflies.

Fig. 5.

Fig. 5

Open in a new tab

Hazard rate functions of overall medfly female (top) and male (bottom) captured and reference cohorts. Chronological age (days) pertains to the reference medflies for which age is known and endurance pertains to the captured flies for which age is unknown but postcapture time is known.

By disaggregating the capture data by month, we discovered that the effect of lower old age hazard is tied to seasonal flies with strong effects (in decreasing order) for females in August, September, and October cohorts and higher old age mortality for June (highest of all relative to reference), July, and November cohorts (Fig. 6). Lower old age mortality for males occurred (in decreasing order) for October, September, and August cohorts and higher old age mortality for males occurred in July and November cohorts. Thus, longevity potential of medflies appears to have a strong seasonal component (Supplementary material).

Fig. 6.

Fig. 6

Open in a new tab

Hazard rate functions of seasonal medfly female (top) and male (bottom) captured and reference cohorts. Endurance refers to postcapture time.

Discussion

The results of this study involving the captive lifespans of over 4000 wild-caught medflies provide evidence in support of three important hypotheses: (i) that changes in population age structure occurs in wild medfly populations of both sexes; (ii) that middle-aged and moderately old medflies are common throughout much of the field season in central Greece; and (iii) that the lifespans of a fraction of once-wild flies would exceed the lifespans of any never-wild flies. Evidence in support of the first hypothesis was based primarily on the 20- to 40-day differences in postcapture survival times of field-caught medflies, while evidence in support of the second hypothesis was based on estimates from the deconvolution model derived by Müller et al. (2004, 2007). We elaborate in detail on evidence supporting the third hypothesis.

Because we assumed initially that the stress and wear-and-tear experienced by flies in the wild would reduce rather than extend their potential postcapture lifespans, the extraordinarily long lifespan of the medflies captured as adults in the wild was the most surprising findings of this study. The high survival rates for ‘fresh collections’ (field-collected) of D. melanogaster adults relative to the survival of laboratory-selected colonies was attributed in one study to genetic adaptation to laboratory conditions (Sgro & Partridge, 2000). However, our results raise the possibility that the higher survival of field-caught fruit flies is due to their experience (conditioning?) in the wild. This finding is important because it points to mechanisms that are either suppressing longevity in the captive environment or enhancing longevity in the wild. Genetic differences could not account for the exceptional longevity observed in captured medflies relative to reference medflies because the reference cohorts were derived from pupae collected from field-infested hosts over many different months during both the 2003 and 2005 seasons. Age bias of field captures cannot account for the increased frequency of survival to extreme ages since the reference cohorts were initiated entirely with newly eclosed medflies (i.e. the youngest flies captured from the wild could not be younger than newly eclosed).

The higher frequency of long-lived individuals in the group of captured medflies can be accounted for by invoking demographic selection arguments (Vaupel et al., 1979; Vaupel & Carey, 1993) – medflies in the wild that are more frail are probably less likely to be captured due to their reduced foraging ability or because they die sooner in the field than in the laboratory. Selection arguments might in principle account for these differences, but extended analysis (see Experimental procedures) indicates that selection is not likely to be strong enough to account on its own for the larger maximum lifespans found among the once-wild flies. For such an explanation to hold valid, two claims would have to be true. First, death at young ages in the wild would have to be substantially correlated with reduced late-life survival potential. Early death could not be largely random, nor could it be tied primarily to traits associated with early acting kinds of risks. Second, the raw amount of young mortality in the wild would have to be large enough to cull a substantial fraction of the weaker members of the population. Enough of the population would have to be removed to account for the special late survival of the remainder.

There are reasons that argue against both these claims. With regard to correlated risks, previous experiments have been conducted to detect demographic selectivity and have found no effects. For example, experiments involving approximately 400 000 medflies subject to periodic starvation at younger ages had no detectable effect on either the level or the pattern of mortality at older ages (Carey et al., 1999). Similarly, dietary restriction studies on D. melanogaster (Mair et al., 2003) demonstrated that 2 days after the application of dietary restriction at any age, previously fully fed flies were no more likely to die than flies of the same age that had been subjected to long-term dietary restriction. While neither of these experiments fully mimic stresses in the wild, they suggest that high correlations of the relevant kind are not a run-of-the-mill phenomenon. Furthermore, death in the wild is generally regarded as having a strong random exogenous background component of luck and accident.

With respect to the required raw amounts of mortality for demographic selectivity, we are able to perform a mathematical calculation. This calculation leads to the conclusion that extra hazards experienced by flies in the wild before ages of capture would have to deplete the population by an extra factor of more than 99%. The wild and reference populations are separated at the larval stage. Conceivably, substantial mortality could occur in the soil for wild pupae as contrasted with the vermiculite for pupae in the reference population. But it would be a striking surprise if such mortality were strongly selective for late-life fly longevity. Instead, one would expect that the main extra mortality contributing to demographic selection would have to be occurring in the wild between eclosion and ages of capture. Based on the average age in medfly populations (shown in Fig. 4) that were estimated from the convolution model, we think that a plausible figure for young medfly mortality in the wild is on the order of 3–5% per day, or from 25% to 40% of all flies dead at 10 days. These are far below the required 99%. If one tried to argue for much higher daily mortality, say 90% spread over 2 days, one would be hard-pressed to offer mechanisms that would make such mortality not only severe but highly selective. In the Experimental procedures we describe the mathematical calculation which leads to our 99% figure and which suggests that demographic selection would not easily account for the observed differences in mean remaining lifetimes between wild-caught and reference cohorts.

We thus offer two hypotheses for the greater lifespan extremes observed in captured flies. The first is that the early life experience of free-ranging adults may reduce their reproductive costs by inhibiting their ability to habituate in the captive environment and therefore reduce their reproductive costs. This cost reduction is borne out in our study inasmuch as the total number of eggs laid by field-captured medfly females was 134.1 eggs/female compared with the 413.8 eggs/female in the reference cohort (Papadopoulos and Carey, unpublished data). The second hypothesis is that there is a window in the early adulthood of medflies when the presence of certain amounts or kinds of bacteria in their diet is important and that wild flies have access to these bacteria but that reference flies in the laboratory do not. This hypothesis is based on the recent finding that adult Drosophila maintained in axenic environments in early life experienced reduced longevity in later life even when switched to non-axenic environments relative to those that were given access to bacteria throughout their lives (Brummel et al., 2004). Further research is needed to explore this explanation.

This was not the first study to use mortality patterns derived from individuals of unknown age to gain insights into the actuarial properties of wild populations. For example, one Drosophila study reported survival patterns of wild-caught adults of unknown age between two different species (Begon, 1976) and another estimated age-at-capture by using the difference between the duration-to-death (captive age) and the life expectancy of laboratory-reared individuals (Boesiger, 1968). Unlike our study, however, no attempt was made in either of these investigations to interpret the data in broader demographic contexts, to develop models for estimating population age structure, or to compare systematically differences in the oldest ages of wild-caught flies vs. flies reared from eclosion in the laboratory. The recent modeling study by Anderson et al. (2008) provides a possible alterative to the method described here. In this framework, longevity is expressed through the rate of loss of vitality, an abstract stochastic measure of survival capacity. However, the approach does not describe age itself but rather the probability distribution of vitality as a function of age and stressors and characterizes extreme longevity in terms of vitality.

The approach we used to estimate age structure or to characterize changes in population age structure in this study have three potentially important sources of bias. The first source is from differences in age-specific mortality between wild-caught and laboratory-reared medflies. If same-aged mortality is higher in wild-caught relative to reference flies, the deconvolution model will overestimate the average age in the wild population. However, if mortality is lower in wild-caught vs. reference flies, the model will underestimate average age in the wild population. The magnitude of the errors in age estimates are conditional on the extent to which age-specific mortality differs between the once-wild and the reference cohorts (see the results of sensitivity analyses in Appendix S2 of the Supplementary material). The second source of potential bias is in the age and/or frailty composition of field captures. If trapping methods are biased towards capturing certain age classes over other age classes, the estimates of age structure will be valid only for the subgroups of flies captured but not for the wild population as a whole. However, there was no indication of major age bias in trapped medflies in this study (see Appendix S1 of the Supplementary material). A third source of potential bias applies to estimates of changes in population age structure. Whereas we attributed changes in the average ages of death for flies captured at different times in the season to shifts in population age structure, an alternative explanation is that the average ages of death were due to differences in adult fitness resulting from the nutritional characteristics of the larval hosts in which they developed (Carey, 1984). However, laboratory studies of host effects on adult medfly longevity (Krainacker, 1986; Krainacker et al., 1987) suggest that change in larval host quality would account for age shifts of 3–5 days in the neighborhood, with a maximum of 10 days under extreme conditions (i.e. single favorable or unfavorable host available). These potential shifts are far less than the 20–40-day differences in the average times-to-death that were observed in the captive medflies in the current study.

Although caveats certainly apply to the current results, they also apply to the results of virtually all other studies designed to estimate individual or population age in insects. For example, the transcriptional profiling method of aging applied to the mosquito Aedes aegypti (i.e. based on genes that display age-dependent expression) is not only costly and time-consuming, but generates age estimates that fall only within ±5 days of the actual age and only to around 3 weeks (Cook et al., 2006). Moreover, it is yet to be determined how robust the calibration models used in the transcriptional profiling method are across season or regions or whether the results can be replicated. Many similar issues arise with the use of cuticular hydrocarbons (Gerade et al., 2004), pteridine concentrations (Lehane & Mail, 1985), and follicular relics (Tyndale-Biscoe, 1984; Lehane et al., 1986) for estimating the age of insects in the field. Data obtained from insect mark–recapture studies used to estimate survival are often fraught with problems that violate many of the most basic assumptions of this method (e.g. independence of recapture probability, closed populations). These problems not only include the inability to distinguish between emigration and death, but also issues associated with large variation in capture probabilities due to changes in weather (e.g. persistent cold front, long rainy period), in insect behavior (e.g. dispersal tendencies when young, trap ‘happy’ or ‘shy’ individuals), in ambient food availability (i.e. competing attractants), or in trap densities (i.e. opportunities for capture). Thus, the sophistication of the analytical methods applied to mark–recapture data (e.g. White & Burnham, 1999) cannot completely offset errors introduced due to the violation of the basic assumptions of the models.

We believe that the results of the current study are important for at least two reasons. First, the uncertainty of the causal mechanism(s) for the longevity-extension effect observed in wild-caught medflies points to a more general void in our understanding of longevity – not only are different factors that affect the aging rate in wild populations unknown, but the importance of factors that are known to affect aging (e.g. dietary restriction) in model systems are also poorly understood in virtually all wild populations (Austad & Kristan, 2003). Related to this point is that the interpretation of data that reflect fruit fly survival in the field rather than in the laboratory will require new thinking about the importance of aging in the larger context of ecology and evolution of the species (Kirkwood, 1992).

Second, the analytical and empirical techniques introduced here provide a new ecological tool for the demographic analysis of aging in wild populations by linking the theoretical, analytical, field, and laboratory components into a single fabric of demographic interconnectedness. This new approach may prove useful in a variety of contexts ranging from analysis of medically important insect populations (Styer et al., 2006) and ecological mark–recapture studies (Juanes & Smith, 1995; Hagler & Jackson, 2001; Amstrup et al., 2005) to field studies concerned with tests of evolutionary theory (Abrams, 1993), and development of new concepts in gerontology (Hayflick, 2002).

It is remarkable that a half century after Cole’s musings about demographic research in the field (1957), the evidence for both actuarial and changes in age structure in wild populations is still scarce. In light of the paucity of literature containing the results of aging studies conducted in evolutionarily relevant environments and of Medawar’s (1981) argument that the origin and evolution of aging is one of the most important unsolved problem in biology, we believe the methods presented in this paper have the potential to open up new areas of aging research (Partridge et al., 2005) through the use of field studies on insects. Our approach offers a logical framework, a new methodology, and testable hypotheses for revealing age patterns and lifespan potential in wild insect populations.

Experimental procedures

Life tables

Two different types of life tables were brought to bear on the analysis of age patterns in wild medfly populations. Reference life tables were constructed to obtain age- and sex-specific mortality rates for medfly adults of known age that emerged from field-infested hosts and maintained in the laboratory. Approximately one-third of medfly pupae were obtained from infested figs (Ficus carica) and two-thirds from infested mandarin oranges (Citrus aurantium) in Chios Island over three sampling periods: (i) September 2003 (135 females, 125 males); (ii) December 2004 (144 females, 133 males); and July/August 2005 (226 females, 209 males). Newly eclosed adults were placed individually in transparent plastic cages (0.4 L capacity), provided with adult food (4 : 1 ratio sugar + yeast hydrolysate), water and an oviposition device (females only). Stress-related mortality (e.g. transport, transfer) was extremely low (< 1% mortality during first 48 h postcapture). Survival of both sexes was monitored daily and the number of eggs laid by individual females was recorded for individuals captured during the 2003–2004 field seasons (although not reported in this paper). Laboratory conditions were maintained at 25 ± 2 °C, 65 ± 5% relative humidity, and 14 : 10 L: D photoperiod.

Captive life tables were constructed from the postcapture survival of free-ranging adults of unknown age that were trapped in the field and maintained in the laboratory through death. Thus, adult medflies of both sexes were trapped on the Greek island of Chios during the 2003–2005 field seasons using 30 McPhail traps (McPhail, 1939) baited with food attractants in 10 trapping locations (three traps per location) in two citrus orchards 2 km apart. The flies captured within a 1-day period were air-shipped for same-day arrival to the University of Thessaloniki, where they were transferred to the laboratory and maintained under the identical conditions as were individual medflies used to generate data for the reference life table.

Survival patterns of medflies in the captive life table were interpreted by comparing them with patterns observed in the reference life tables. Thus, captive life tables constructed using wild-caught medflies and that exhibited similar survival patterns and expectations of life to the reference life table in which life expectancy in females and males was 65.8 and 75.6 days, respectively, implied that the field population from which they were trapped was composed primarily of newly eclosed individuals. It follows that survival schedules of wild-caught flies that departed from this baseline survival schedule indicated flies that were older and/or more frail.

Age structure estimation

The general concept for estimating age structure in wild populations is that the age composition of captured flies can be coupled to their death rates in the laboratory. The convolution equation for this population age structure is:

fC(y)=∫0∞fR(x+y)fA(x)FR(x)dx (1)

where fA and fR are the probability density functions of age-at-death in the laboratory-raised reference cohort and of age-at-capture from the wild, respectively, and F̄R is the survival function of the wild-caught flies in the captive cohort. If each individual in the wild population has the same chance of being sampled, irrespective of its age, then fA also corresponds to the density of the age distribution in the wild. The convolution equation is a consequence of the assumption that the force of mortality acting on an individual depends solely on the individual’s age and on whether the individual is in the wild or in the laboratory. From life tables of the reference cohort and the captive cohort one can estimate fC, F̄R, and fR, and then obtais the target density fA by deconvolution. The details are described in the paper by Müller et al. (2007).

Sensitivity analysis of age structure estimates

The results of sensitivity analysis of a ±20% perturbation of the medfly female hazard function derived from the reference life table shown in Fig. 7 reveals differences between the actual age distribution of the population and the distribution estimated using the deconvolution model. When the captive population’s mortality in the laboratory is 20% lower than the mortality in the reference life table at each age, the estimates are skewed to younger ages relative to the actual because the lower mortality corresponds to younger ages. When the captive population’s mortality in the laboratory is 20% higher than the mortality in the reference life table at each age, the estimates are skewed to older ages relative to the actual because the lower mortality corresponds to older ages.

Fig. 7.

Fig. 7

Open in a new tab

Results of sensitivity analysis of a ±20% perturbation of the hazard function of the female medfly reference life table. The solid black line represents a hypothetical wild medfly age density function to be estimated and the dashed black line represents this estimate if the age-specific mortality of the captured flies is identical to that of the reference flies. The blue curve gives the age density for a captive cohort that experiences age-specific mortality that is 20% higher than the reference cohort, and the red line gives the age density for a captive cohort in which age-specific mortality is 20% lower than the reference cohort. The average lifetimes of true age density is 28.3 days, of the estimated age density is 28.3 days, and the estimated age density functions based on perturbed hazard rate functions of +20% is 27.1 days and of −20% is 31.5 days.

A summary of analyses across a range of perturbations from ±5% to ±30% (Fig. 8) reveals that the sign of the mortality rate differences between the captive and reference cohorts determines whether the average age in the field population is over- or underestimated. Thus, mean age is: (i) underestimated if age-specific mortality in the reference cohort is less than age-specific mortality in the captive cohort; and (ii) overestimated if mortality in the reference cohort is greater than mortality in the captive cohort. Inasmuch as the medflies captured in the field experienced lower mortality at older ages than the captive cohort, this finding suggests that the estimates of average ages in the wild were lower than the average age in wild medfly populations were slightly underestimated (i.e. the actual mean ages in wild populations were older than the estimated mean ages).

Fig. 8.

Fig. 8

Open in a new tab

Summary of perturbation analyses of mean age estimates in hypothetical medfly field population showing the estimation consequences of violation of the ‘memory-less mortality’ assumption in captured medflies: (i) the center line shows the actual mean age in the hypothetical field population; (ii) the upper curve depicts the estimates when the age-specific mortality of the reference flies is lower than the captured medflies; and (iii) the lower curve depict the estimates when the age-specific mortality of the reference flies is higher than in the captured flies.

Demographic selection model

Calculations suggest that frailty selection would not easily account for the observed differences in mean remaining lifetimes between wild-caught and reference cohorts. The usual approach (Vaupel et al., 1979) assumes a lifetime frailty factor multiplying an individual’s hazard rate at all ages, with a gamma distribution of factors across the population. High early mortality in the wild before capture would remove frail flies from the population, leaving less frail, intrinsically longer-lived survivors after capture. The frailty model predicts how much of an increment in initial mortality would have to added to the rates of the reference life table to extend further life expectancy at ages like 80 up to the levels observed in the once-wild samples. It turns out in our setting that there is a lower bound on the required increment which holds across choices for the unknown variance in initial frailty. For the female sample, the increment would be as much or more than 99%. Extremely long-lived flies must have been young when captured. Such a level for young-age mortality is far in excess of plausible rates in the wild, which are thought to be on the order of 3% per day or 24% over 10 days.

The increment required for the mechanism of frailty selection to explain away the difference in late-life survival is too great to be credible. The standard frailty model (Vaupel et al., 1979) sets the hazard rate applying to an individual at age a equal to the product of a baseline hazard function and an individual frailty multiplier Z, where Z is drawn from a gamma distribution with unit mean and shape parameter k, corresponding to a variance of 1/k. In our notation, M(a) is the cumulative hazard up to age a in the hypothetical baseline schedule for the mortality of always-captive flies. W is an increment to the cumulative hazard contributed by extra mortality in the wild over and above baseline, here assumed to be concentrated at or near age zero. la is the survival schedule for the reference life table, assumed to be produced by frailty selection operating on the baseline hazard function; Sa is the survival schedule produced by frailty selection operating in the presence of the precapture increment W along with subsequent mortality corresponding to M(a). Under the model, the survival schedules produced by frailty selection are given by Laplace transforms of the gamma frailty distribution:

la=(kk)(M(a)+k)−k (1)
Sa=(kk)(W+M(a)+k)−k=(kk)(W+k(la)−1/k)−k (2)

Further life expectancy at age 80 in the presence of the increment W is given by the integral over a > 80 of Sa/S80. The integrand approaches la/l80 for all a both as the gamma shape parameter k goes to zero and as k goes to infinity, so further life expectancy has some maximum value at some intermediate value of k for each fixed value of W. We evaluate la by setting it equal to the empirical survival schedule for the reference life table, extended into the open-ended interval beyond the last observed death with a hazard estimated from the last 10 observed waiting times at 0.200 per day. For each k we calculate the value of W required to raise further life expectancy at 80 from 16.669 to 24.163. These values are bounded below by W = 4.591. The required increment to mortality is therefore at least 1 − exp(−W) = 0.9899.

This conclusion is strengthened by the expectation that a substantial portion of precapture mortality in the wild is bound to be exogenous, imposed by accident without a close relationship to individual frailty. Adding exogenous mortality to the extra mortalities required for frailty selection would produce even more extreme levels. Furthermore, the effect of extra precapture mortality on mean frailty among survivors would tend to taper with age, whereas the differences in observed survival curves appear to grow with age at extreme ages. Thus, while frailty selection may well operate to some extent, it is not likely to account for the main finding of extraordinary survival among once-wild flies.

Supplementary Material

Online Materia

Supplementary material

The following supplementary material is available for this article:

Appendix S1 Empirical and statistical results of medfly trap capture bias study.

Appendix S2 Medfly remaining lifetimes.

Appendix S2 Sensitivity analysis of deconvolution model.

Acknowledgments

This research was supported by the National Institute on Aging/National Institutes of Health through grants P01 AG022500-01 and P01 AG08761-10. We thank N. Zoudas C. Ioannous, A. Protogerou, A. Diamandidis, and M. Giannakou for technical assistance in Greece, Roger Vargas, Boaz Yuval, Deborah Roach, Ronald Lee, Freerk Molleman, Carol Horvitz, Steven Orzack, James Anderson, and Shripad Tuljapurkar for comments on earlier drafts, and Leslie Sandberg for editorial assistance.

Footnotes

This material is available as part of the online article from: http://www.blackwell-synergy.com/doi/abs/10.1111/j.1474-9726.2008.00390.x (This link will take you to the article abstract).

Please note: Blackwell Publishing are not responsible for the content or functionality of any supplementary materials supplied by the authors. Any queries (other than missing material) should be directed to the corresponding author for the article.

References

  1. Abrams PA. Does increased mortality favor the evolution of more rapid senescence? Evolution. 1993;47:877–887. doi: 10.1111/j.1558-5646.1993.tb01241.x. [DOI] [PubMed] [Google Scholar]
  2. Amstrup SC, McDonald TL, Manly BFJ. Handbook of Capture-Recapture Analysis. Princeton, NJ: Princeton University Press; 2005. [Google Scholar]
  3. Anderson DR, Burnham KP, White GC. Problems in estimating age-specific survival rates from recovery data of birds ringed as young. J Anim Ecol. 1985;54:89–98. [Google Scholar]
  4. Anderson JJ, Gildea MC, Williams DW, Li T. Linking growth, survival, and heterogeneity through vitality. Am Nat. 2008;171:E20–E43. doi: 10.1086/524199. [DOI] [PubMed] [Google Scholar]
  5. Anderson DR, Wywialowski AP, Burnham KP. Tests of the assumptions underlying life table methods for estimating parameters from cohort data. Ecology. 1981;62:1121–1124. [Google Scholar]
  6. Austad SN. Retarded senescence in an insular population of Virginia opossums (Didelphis virginiana) J Zool. 1993;229:695–708. [Google Scholar]
  7. Austad SN, Kristan DM. Are mice calorically restricted in nature? Aging Cell. 2003;2:201–207. doi: 10.1046/j.1474-9728.2003.00053.x. [DOI] [PubMed] [Google Scholar]
  8. Begon M. Temporal variations in the reproductive condition of Drosophila obscura Fallen and D. subobscura Collin. Oecologia. 1976;23:31–47. doi: 10.1007/BF00351213. [DOI] [PubMed] [Google Scholar]
  9. Boesiger E. Estimation globale de l’âge des femelles de Drosophila melanogaster capturées dans des populations naturelles (Rough estimation of age of Drosophila melanogaster females in natural populations) C R Seances Soc Biol Fil. 1968;162:358–361. [PubMed] [Google Scholar]
  10. Bonduriansky R, Brassil CE. Rapid and costly ageing in wild male flies. Nature. 2002;420:377. doi: 10.1038/420377a. [DOI] [PubMed] [Google Scholar]
  11. Bonduriansky R, Brassil CE. Reproductive ageing and sexual selection on male body size in a wild population of antler flies (Protopiophila litigata) J Evol Biol. 2005;18:1332–1340. doi: 10.1111/j.1420-9101.2005.00957.x. [DOI] [PubMed] [Google Scholar]
  12. Botkin DB, Miller RS. Mortality rates and survival of birds. Am Nat. 1974;108:181–192. [Google Scholar]
  13. Brummel T, Ching A, Seraude L, Simon AF, Benzer S. Drosophila lifespan enhancement by exogenous bacteria. Proc Natl Acad Sci USA. 2004;101:12974–12979. doi: 10.1073/pnas.0405207101. [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Carey JR. Host-specific demographic studies of the Mediterranean fruit fly, Ceratitis capitata. Ecol Entomol. 1984;9:261–270. [Google Scholar]
  15. Carey JR, Judge DS. Longevity Records: Life Spans of Mammals, Birds, Reptiles, Amphibians and Fishes. Odense, Denmark: Odense University Press; 2000. [Google Scholar]
  16. Carey JR, Liedo P, Muller H-G, Wang J-L, Chiou J-M. Relationship of age patterns of fecundity to mortality, longevity, and lifetime reproduction in a large cohort of Mediterranean fruit fly females. J Gerontol A Biol Sci Med Sci. 1998;53:B245–B251. doi: 10.1093/gerona/53a.4.b245. [DOI] [PubMed] [Google Scholar]
  17. Carey JR, Liedo P, Müller H-G, Wang J-L, Chiou J-M. Mortality oscillations induced by periodic starvation alter sex-mortality differentials in Mediterranean fruit flies. J Gerontol A Biol Sci Med Sci. 1999;54A:B424–B431. doi: 10.1093/gerona/54.10.b424. [DOI] [PubMed] [Google Scholar]
  18. Carey JR, Liedo P, Orozco D, Tatar M, Vaupel JW. A male-female longevity paradox in medfly cohorts. J Anim Ecol. 1995;64:107–116. [Google Scholar]
  19. Caughley G. Anal of Vertebrate Popul. Chichester, UK: John Wiley & Sons; 1977. [Google Scholar]
  20. Charmantier A, Perrins C, McCleery RH, Sheldon BC. Quantitative genetics of age of reproduction in wild swans: support for antagonistic pleiotropy models of senescence. Proc Natl Acad Sci USA. 2006;103:6587–6592. doi: 10.1073/pnas.0511123103. [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Clutton-Brock TH, Guinness FE, Albon SD. Red Deer: Behaviour and Ecology of Two Sexes. Chicago, IL: University of Chicago Press; 1982. [Google Scholar]
  22. Cole LC. Sketches of general and comparative demography. Cold Spring Harb Symp Quant Biol. 1957;22:1–15. doi: 10.1101/sqb.1957.022.01.004. [DOI] [PubMed] [Google Scholar]
  23. Cook PE, Hugo LE, Iturbe-Ormaetxe I, Williams CR, Chenoweth SF, Ritchie SA, Ryan PA, Kay BH, Blows MW, O’Neil SL. The use of transcriptional profiles to predict adult mosquito age under field conditions. Proc Natl Acad Sci USA. 2006;103:18060–18065. doi: 10.1073/pnas.0604875103. [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Cook PE, Hugo LE, Iturbe-Ormaetxe I, Williams CR, Chenoweth SF, Ritchie SA, Ryan PA, Kay BH, Blows MW, O’Neil SL. Predicting the age of mosquitoes using transcriptional profiles. Nat Protocols. 2007;2:2796–2806. doi: 10.1038/nprot.2007.396. [DOI] [PubMed] [Google Scholar]
  25. Cooke SJ, Hinch SG, Wikelski M, Andrews RD, Kuchel LJ, Wolcott TG, Butler PJ. Biotelemetry: a mechanistic approach to ecology. Trends Ecol Evol. 2004;19:334–343. doi: 10.1016/j.tree.2004.04.003. [DOI] [PubMed] [Google Scholar]
  26. Deevey ESJ. Life tables for natural populations of animals. Q Rev Biol. 1947;22:283–314. doi: 10.1086/395888. [DOI] [PubMed] [Google Scholar]
  27. Desena ML, Edman JD, Clark JM, Symington SB, Scott TW. Aedes aegypti (Diptera: Culicidae) age determination by cuticular hydrocarbon analysis of female legs. J Med Entomol. 1999;36:824–830. doi: 10.1093/jmedent/36.6.824. [DOI] [PubMed] [Google Scholar]
  28. Gerade BB, Lee SH, Scott TW, Edman JD, Harrington LC, Kitthawee S, Jones JW, Clark JM. Field validation of Aedes aegypti (Diptera: Culicidea) age estimation by analysis of cuticular hydrocarbons. J Med Entomol. 2004;41:231–238. doi: 10.1603/0022-2585-41.2.231. [DOI] [PubMed] [Google Scholar]
  29. Hagler JR, Jackson CG. Methods for marking insects: current techniques and future prospects. Annu Rev Entomol. 2001;46:511–543. doi: 10.1146/annurev.ento.46.1.511. [DOI] [PubMed] [Google Scholar]
  30. Hayflick L. The twenty-fifth anneversary of the handbooks of aging. Gerontologist. 2002;42:416–421. [Google Scholar]
  31. Juanes F, Smith LD. The ecological consequences of limb damage and loss in decapod crustaceans: a review and prospectus. J Exp Mar Biol Ecol. 1995;193:197–223. [Google Scholar]
  32. Kirk KL. Life-history responses to variable environments: Starvation and reproduction in planctonic rotifers. Ecology. 1997;78:434–441. [Google Scholar]
  33. Kirkwood TBL. Comparative life spans of species: why do species have the life spans they do? Am J Clin Nutr. 1992;55:1191S–1195S. doi: 10.1093/ajcn/55.6.1191Sa. [DOI] [PubMed] [Google Scholar]
  34. Kohler IV, Preston SH, Lackey LB. Comparative mortality levels among selected species of captive animals. Demographic Res. 2006;15:413–434. [Google Scholar]
  35. Krainacker DA. Demography of the Mediterranean Fruit Fly: Larval Host Effects. University of California; Davis: 1986. MSc Thesis. [Google Scholar]
  36. Krainacker DA, Carey JR, Vargas R. Effect of larval host on the life history parameters of the Mediterranean fruit fly, Ceratitis capitata. Oecologia. 1987;73:583–590. doi: 10.1007/BF00379420. [DOI] [PubMed] [Google Scholar]
  37. Lakhani KH, Newton I. Estimating age-specific bird survival rates from ring recoveries – can it be done? J Anim Ecol. 1983;52:83–91. [Google Scholar]
  38. Lehane MJ. Determining the age of an insect. Parasitol Today. 1985;1:81–85. doi: 10.1016/0169-4758(85)90048-1. [DOI] [PubMed] [Google Scholar]
  39. Lehane MJ, Chadwick J, Howe MA, Mail TS. Improvements in the pteridine method for determining age in adult male and female Stomoxys calcitrans (Diptera: Muscidae) J Econ Entomol. 1986;79:1717–1719. [Google Scholar]
  40. Lehane MJ, Mail TS. Determining the age of adult male and female Glossina morsitans using a new technique. Ecol Entomol. 1985;10:219–224. [Google Scholar]
  41. Linnen C, Tatar M, Promislow D. Cultural artifacts: a comparison of senescence in natural, laboratory-adapted and artificially selected lines of Drosophila melanogaster. Evol Ecol Res. 2001;3:877–888. [Google Scholar]
  42. Mail TS, Chadwick J, Lehane MJ. Determining the age of adults of Stomoxys calcitrans (L.) (Diptera: Muscidae) Bull Entomol Res. 1983;73:501–525. [Google Scholar]
  43. Mair W, Goymer P, Pletcher SC, Partridge L. Demography of dietary restriction and death in Drosophila. Science. 2003;301:1731–1733. doi: 10.1126/science.1086016. [DOI] [PubMed] [Google Scholar]
  44. Mair W, Piper MDW, Partridge L. Calories do not explain extension of life span by dietary restriction in Drosophila. PLoS Biol. 2005;3:e223. doi: 10.1371/journal.pbio.0030223. [DOI] [PMC free article] [PubMed] [Google Scholar]
  45. McPhail M. Protein lures for fruitflies. J Econ Entomol. 1939;32:758–761. [Google Scholar]
  46. Medawar PB. The Uniqueness of the Individual. New York: Dover Publications, Inc; 1981. [Google Scholar]
  47. Miller RA, Dysko R, Chrisp C, Seguin R, Linsalata L, Buehner G, Harper JM, Austad S. Mouse (Mus musculus) stocks derived from tropical islands: new models for genetic analysis of life-history traits. J Zool. 2000;250:95–104. doi: 10.1111/j.1469-7998.2000.tb00580.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  48. Miller RA, Harper JM, Dysko RC, Durkee SJ, Austad SN. Longer life spans and delayed maturation in wild-derived mice. Exp Biol Med. 2002;227:500–508. doi: 10.1177/153537020222700715. [DOI] [PubMed] [Google Scholar]
  49. Molleman F, Zwaan BJ, Brakefield PM, Carey JR. Extraordinary long life spans in fruit-feeding butterflies can provide window on evolution of life span and aging. Exp Gerontol. 2006;42:472–482. doi: 10.1016/j.exger.2007.01.008. [DOI] [PMC free article] [PubMed] [Google Scholar]
  50. Moorhouse TP, MacDonald DW. Indirect negative impacts of radio-collaring: sex ratio variation in water voles. J Appl Ecol. 2005;42:91–98. [Google Scholar]
  51. Müller HG, Wang J-L, Capra WB. From lifetables to hazard rates: the transformation approach. Biometrika. 1997;84:881–892. [Google Scholar]
  52. Müller HG, Wang J-L, Carey JR, Caswell-Chen EP, Chen C, Papadopoulos N, Yao F. Demographic window to aging in the wild: constructing life tables and estimating survival functions from marked individuals of unknown age. Aging Cell. 2004;3:125–131. doi: 10.1111/j.1474-9728.2004.00096.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  53. Müller HG, Wang J-L, Yu W, Delaigle A, Carey JR. Survival in the wild via residual demography. Theor Popul Biol. 2007;72:513–522. doi: 10.1016/j.tpb.2007.07.003. [DOI] [PMC free article] [PubMed] [Google Scholar]
  54. Nussey DH, Metherell B, Moyes K, Donald A, Guinness FE, Clutton-Brock TH. The relationship between tooth wear, habitat quality and late-life reproduction in a wild red deer population. J Anim Ecol. 2007;76:402–412. doi: 10.1111/j.1365-2656.2007.01212.x. [DOI] [PubMed] [Google Scholar]
  55. Papadopoulos N, Katsoyannos BI, Carey JR, Kouloussis NA. Seasonal abundance of the Mediterranean fruit fly (Diptera: Tephritidae) in northern Greece. Ann Entomol Soc Am. 2001;94:41–50. doi: 10.1603/0022-0493-94.4.971. [DOI] [PubMed] [Google Scholar]
  56. Partridge L, Gems D, Withers DJ. Sex and death: what is the connection? Cell. 2005;120:461–472. doi: 10.1016/j.cell.2005.01.026. [DOI] [PubMed] [Google Scholar]
  57. Preston SH, Heuveline P, Guillot M. Demography: Measuring and Modeling Population Processes. Malden, MA: Blackwell Publishers; 2001. [Google Scholar]
  58. Promislow DEL. Senescence in natural populations of mammals: a comparative study. Evolution. 1991;45:1869–1887. doi: 10.1111/j.1558-5646.1991.tb02693.x. [DOI] [PubMed] [Google Scholar]
  59. Reznick D, Buckwalter G, Groff J, Elder D. The evolution of senescence in natural populations of guppies (Poecilia reticulata): a comparative approach. Exp Gerontol. 2001;36:791–812. doi: 10.1016/s0531-5565(00)00241-2. [DOI] [PubMed] [Google Scholar]
  60. Reznick D, Butler VIMJ, Rodd FH, Ross P. Life-history evolution in guppies (Poecilia reticulara) 6. Differential mortality as a mechanism for natural selection. Evolution. 1996;50:1651–1660. doi: 10.1111/j.1558-5646.1996.tb03937.x. [DOI] [PubMed] [Google Scholar]
  61. Ricklefs RE. Intrinsic aging-related mortality in birds. J Avian Biol. 2000;31:103–111. [Google Scholar]
  62. Ryder NB. Notes on stationary populations. Popul Index. 1975;41:3–28. [Google Scholar]
  63. Sgro CM, Partridge L. Evolutionary responses of the life history of wild-caught Drosophila melanogaster to two standard methods of laboratory culture. Am Nat. 2000;156:341–353. [Google Scholar]
  64. Styer LM, Carey JR, Harrington LC, Edman JD, Scott TW. Mosquitoes do senesce: departure from the paradigm of constant mortality. Am J Trop Med Hyg. 2006;76:111–117. [PMC free article] [PubMed] [Google Scholar]
  65. Tatar M, Grey DW, Carey JR. Altitudinal variation for senescence in Melanoplus grasshoppers. Oecologia. 1997;111:357–364. doi: 10.1007/s004420050246. [DOI] [PubMed] [Google Scholar]
  66. Tyndale-Biscoe M. Age-grading methods in adult insects: a review. Bull Entomol Res. 1984;74:341–377. [Google Scholar]
  67. Uhlenberg P. Demography of aging. In: Poston D, Micklin M, editors. Handbook of Population. New York: Kluwer Academic/Plenum Publishers; 2005. pp. 143–167. [Google Scholar]
  68. Vaupel JW, Carey JR. Compositional interpretations of medfly mortality. Science. 1993;260:1666–1667. doi: 10.1126/science.8503016. [DOI] [PubMed] [Google Scholar]
  69. Vaupel JW, Manton KG, Stallard E. The impact of heterogeneity in individual frailty on the dynamics of mortality. Demography. 1979;16:439–454. [PubMed] [Google Scholar]
  70. White GC, Burnham KP. Program MARK: survival estimation from populations of marked animals. Bird Study. 1999;46:S120–S139. [Google Scholar]
  71. Wooler RD, Bradley JS, Croxall JP. Long-term population studies of seabirds. Trends Ecol Evol. 1992;7:111–114. doi: 10.1016/0169-5347(92)90143-Y. [DOI] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Online Materia

Supplementary material

The following supplementary material is available for this article:

Appendix S1 Empirical and statistical results of medfly trap capture bias study.

Appendix S2 Medfly remaining lifetimes.

Appendix S2 Sensitivity analysis of deconvolution model.

NIHMS47788-supplement-Online_Materia.doc (88.5KB, doc)

RESOURCES