Abstract

Functional EcologyVolume 22, Issue 3 p. 393-406 Free Access Measuring senescence in wild animal populations: towards a longitudinal approach D. H. Nussey, Corresponding Author D. H. Nussey Institute of Evolutionary Biology, University of Edinburgh, The Kings’ Buildings, West Mains Road, Edinburgh EH9 3JT, UK; *Correspondence author. E-mail: [email protected]Search for more papers by this authorT. Coulson, T. Coulson Department of Life Sciences, Imperial College London, Silwood Park, Ascot, Berkshire, SL5 7PY, UK;Search for more papers by this authorM. Festa-Bianchet, M. Festa-Bianchet Département de Biologie, Université de Sherbrooke, Sherbrooke, Québec J1K 2R1, Canada; andSearch for more papers by this authorJ.-M. Gaillard, J.-M. Gaillard Unité Mixte de Recherche 5558 ‘Biométrie et Biologie Évolutive’, Université Claude Bernard Lyon I, 43 boul. du 11 Novembre 1918, 69622 Villeurbanne Cedex, FranceSearch for more papers by this author D. H. Nussey, Corresponding Author D. H. Nussey Institute of Evolutionary Biology, University of Edinburgh, The Kings’ Buildings, West Mains Road, Edinburgh EH9 3JT, UK; *Correspondence author. E-mail: [email protected]Search for more papers by this authorT. Coulson, T. Coulson Department of Life Sciences, Imperial College London, Silwood Park, Ascot, Berkshire, SL5 7PY, UK;Search for more papers by this authorM. Festa-Bianchet, M. Festa-Bianchet Département de Biologie, Université de Sherbrooke, Sherbrooke, Québec J1K 2R1, Canada; andSearch for more papers by this authorJ.-M. Gaillard, J.-M. Gaillard Unité Mixte de Recherche 5558 ‘Biométrie et Biologie Évolutive’, Université Claude Bernard Lyon I, 43 boul. du 11 Novembre 1918, 69622 Villeurbanne Cedex, FranceSearch for more papers by this author First published: 16 May 2008 https://doi.org/10.1111/j.1365-2435.2008.01408.xCitations: 302AboutSectionsPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Summary 1 A major current challenge in ageing research is to understand why senescence rates vary between individuals, populations and species in wild populations. 2 Recent studies clearly illustrate that senescent declines in key demographic and life-history traits can be observed in many wild animal systems. 3 Here, we summarize the key challenges facing researchers working to understand senescence in the wild. We concentrate on: (i) limited data availability, (ii) the substantial individual heterogeneity typical of wild populations, (iii) incomplete capture histories, and (iv) trade-offs across the life span. 4 We discuss analytical methods to overcome these challenges. We advocate the use of Capture–Mark–Recapture models to remove likely bias associated with re-sampling rates of less than one. We also illustrate that ageing trajectories may vary between different traits in wild populations. Wherever possible, researchers should examine ageing patterns in multiple traits. 5 Numerous models are available to describe the rate and shape of senescence in free-living populations, but there is currently little consensus regarding which is most appropriate in analyses of wild organisms. 6 We argue that only longitudinal studies of marked or recognizable individuals provide reliable sources of information in the study of senescence. Senescence is a within-individual process and only longitudinal studies allow researchers to separate within-individual ageing patterns from between-individual heterogeneity. 7 We examine two analytical approaches to measure ageing using longitudinal data from wild populations: a jack-knifing approach, well-suited to modelling survival probability, and a mixed-effects model approach. Both methods control for sources of between-individual heterogeneity to allow more accurate measurement of within-individual ageing patterns. Introduction The evolutionary ecology of senescence has been the focus of a recent surge in studies of free-living animal populations. These studies build upon the evolutionary literature on ageing: the well-established body of theory to explain the evolution of senescence (Medawar 1946; Williams 1957; Hamilton 1966), and the increasingly detailed understanding of the proximate physiological and molecular mechanisms underpinning senescence in several model laboratory study organisms (Partridge & Gems 2002, 2006, 2007). However, several assumptions of the classical evolutionary theory of ageing may rarely be met in free-living animal populations (Reznick et al. 2004; Williams et al. 2006). Recent work reveals that under such circumstances, classical theoretical predictions may not apply (Abrams 1993; Williams & Day 2003; Williams et al. 2006). Furthermore, the handful of intensely studied model laboratory systems cannot explain the remarkable variation in longevity and ageing rates observed both between and within species in nature, nor elucidate how environmental variation or life-history trade-offs really influence the rate of ageing and the force of selection on senescence (Partridge & Gems 2006, 2007). Senescence will reduce individual fitness (or age-specific vital rates) amongst individuals surviving into old age (Partridge & Barton 1996), but the evolutionary and ecological importance of such declines in free-living populations is largely unknown. The recent interest in measuring senescence in life-history traits and testing evolutionary theories of ageing in free-living non-model organisms has already shed light on these important issues (e.g. Bryant & Reznick 2004; Hendry et al. 2004; Charmantier et al. 2006b). Ultimately, our capacity to address these gaps in our knowledge relies on our ability to accurately and reliably measure senescence in wild populations. Senescence is a within-individual process caused by deterioration in molecular and physiological function. This deterioration increases an individual's probability of mortality and decreases its likelihood of successfully reproducing. A central challenge when studying free-living populations is to accurately measure either the ageing rates of specific individuals or the average ageing rate across individuals within a population. In this review, we argue that only longitudinal data sets, in which repeated measures are available for individuals across their lifetimes, are suitable for the estimation of ageing rates (Forslund & Part 1995; van de Pol & Verhulst 2006). Collecting such data from free-living populations can be logistically demanding and many potentially confounding factors can mask or emulate within-individual senescence patterns (Blarer, Doebeli & Stearns 1995; Gaillard et al. 2000b; van de Pol & Verhulst 2006). Here, we aim to review and assess the challenges associated with research into senescence in the wild; synthesize the approaches taken to measuring senescence to date; and describe analytical solutions to the central challenge of measuring within-individual ageing rates using longitudinal data. Throughout, we illustrate key points with unpublished examples using data from long-term individual-based studies of wild ungulates. We begin by describing four broad challenges to the accurate detection of senescence in wild populations. The challenges mortality rates and sample sizes In free-living animal populations, annual mortality rates are often high. Wild animals experience sources of mortality (e.g. predation, starvation) that few animals in the laboratory or captivity, or humans in the Western world, have to contend with. This has lead many eminent gerontologists and evolutionary biologists to speculate that senescence should rarely or never be observed in wild populations (e.g. Comfort 1979; Rose 1991; Hayflick 2000). As Rose (1991) puts the argument, ‘it is doubtful that many individuals [in free-living populations] would remain for study at the age at which laboratory populations exhibit aging.’ Whilst it is undoubtedly true that sample sizes for senescent individuals in wild populations are reduced by high pre-senescent mortality rates, a growing body of empirical research shows very palpably that declines in life-history traits in old age synonymous with senescence can be detected in wild animal populations. Table 1 shows a selection of studies of wild animals demonstrating age-related declines in a range of different traits, including probabilities of survival, fecundity or reproductive and physiological traits such as body mass, hormone levels and immune function. The list includes studies across a broad range of taxa showing marked variation in life history. Table 1 is not exhaustive and numerous studies have failed to detect senescence in wild populations (e.g. Slade 1995; Pistorius & Bester 2002). However, the list clearly exposes the suggestion that senescence is unlikely to be detected in natural populations as a fallacy. The question that remains largely unanswered is: what causes variation in the detectability and measured rate of senescence in different traits in wild populations? Table 1. Examples of studies of wild animal populations demonstrating declines in life-history traits in old age consistent with senescence. The table is not intended to be an exhaustive list, but rather to illustrate the accumulating evidence from a wide variety of different traits across a wide range of animal taxa for senescent declines in wild populations Trait Technique Function Species References Survival Mortality risk ML W Protopiophila litigate Bonduriansky & Brassil 2002 Mortality risk ML; PH G; W Oncorhynchys nerca Morbey et al. 2005 Mortality risk AFT; PH W Aphelocoma coerulescens Fox et al. 2006 Survival probability CMR Age Ovis aries Catchpole et al. 2000 Survival probability CMR Age Poecilia reticulata Bryant & Reznick 2004 Survival probability CMR Age Lacerta vivipara Ronce, Clobert & Massot 1998 Survival probability CMR Lt-L Cervus elaphus Moyes et al. 2006 Survival probability CMR G; W Capreolus capreolus Gaillard et al. 2004 Reproductive life-history traits Probability of reproducing TM Lt-Q Dama dama McElligott et al. 2002 Annual reproductive performance ML Q Accipiter nicus Newton & Rothery 1997 Annual fecundity GLMM Lt-Q Cervus elaphus Nussey et al. 2007a Offspring birth mass GLMM Q Halichoerus grypus Bowen et al. 2006 Breeding date GLMM Q Uria aalge Reed et al. 2008 Arrival time at breeding grounds anova Age Hirundo rustica Moller & De Lope 1999 Other traits Adult body mass ML Q Ovis canadensis Bérubéet al. 1999 Dispersal behaviour GLMM Age Sula nebouxii Kim et al. 2007 Foraging performance anova Age Thalassarche chrysostoma Catry et al. 2006 Ectoparasite burden anova Age Hirundo rustica Moller & De Lope 1999 Humoral immune response anova Age Ficedula albicollis Cichon et al. 2003 Corticosterone levels GLM Age Diomedea exulans Angelier et al. 2007 Abbreviations: Technique: ML, maximum likelihood-based linear or nonlinear curve fitting to age-specific trait averages; PH, proportional hazards model; AFT, accelerated failure time model; CMR, Capture–Mark–Recapture model; TM, multi-state transition model; anova, simple analysis of variance; GLM, generalized linear model; GLMM, generalized linear mixed-effects model. Function: Age, age class model; G, Gompertz; W, Weibull; L, linear; Q, quadtratic; Lt-L, logit-linear; Lt-Q, logit-quadratic. High rates of mortality in wild populations present problems of statistical power for studies of senescence. High rates of mortality inevitably mean that far lower numbers of individuals will be alive, and therefore available for sampling, in the eldest relative to the youngest age classes. The degree to which the magnitude of the mortality rate limits power to detect and model senescence will vary substantially between study systems. For example, a recent study of a Swedish population of collared flycatchers (Ficedula albicollis), a passerine bird experiencing relatively high annual mortality, revealed senescent declines in annual fitness from 5 years of age onwards in females (Brommer, Wilson & Gustafsson 2007). Because only around 7% of adult females survived to this age, an enormous sample (4992 females marked at birth) collected over 25 years was required to detect senescence and model causes of individual variation in senescence rates in this species (Brommer et al. 2007). On the other hand, many long-lived vertebrate species (such as ungulates, seabirds, turtles, bats and marine mammals) show relatively low adult mortality rates, with the onset of senescence in survival delayed until many years after maturity (e.g. Weimerskirch 1992; Gaillard et al. 1993). In a population of red deer (Cervus elaphus) in Scotland, the proportion of adult females surviving into senescent age classes was an order of magnitude higher than in the flycatcher study. Of 328 female deer that survived to maturity (3 years old), 70% survived to the age at which senescent declines in performance began (9 years old; Nussey et al. 2007a). Mortality rates have bearing not just on the statistical power of studies of senescence, but also on theoretical predictions regarding the force of selection in old age and hence future evolutionary trajectories of senescence (Williams 1957; Hamilton 1966). Williams (1957) predicted that populations experiencing increased ‘extrinsic’ mortality risk should evolve more rapid senescence rates. Laboratory and comparative studies have tested the prediction with mixed success (e.g. Promislow 1991; Ricklefs 1998; Stearns et al. 2000; Reznick et al. 2004). It has recently been argued that the assumptions underlying this prediction are overly simplistic and are almost certain to be violated in free-living populations (Williams et al. 2006). Changing these assumptions can markedly alter how theory predicts senescence should evolve (Abrams 1993; Williams & Day 2003). In practice, it is extremely difficult to separate (or even define) the extrinsic and intrinsic forces driving senescent declines in mortality or reproductive performance in wild populations (Williams & Day 2003; Williams et al. 2006). Most often the causes of mortality in such studies are unknown. Even when causes of death can be determined, it is likely that the intrinsic deterioration associated with senescence will interact with extrinsic mortality risks so as to make their separation unfeasible (Williams & Day 2003; Reznick et al. 2004; Vaupel et al. 2004). For example, older individuals may be at greater risk from predation (e.g. Festa-Bianchet et al. 2006; Wright et al. 2006). Similarly, extremely rapid declines in life-history traits just prior to death may reflect terminal illness rather than senescence (Coulson & Fairweather 2001), although the likelihood of contracting such an illness may increase with age. The distinction between ‘intrinsic’ and ‘extrinsic’ contributions to mortality may not be useful in most field studies, where decompositions of senescence into these components is, at best, challenging, and likely impossible (see Ricklefs 2008 for an alternative view). correcting for individual heterogeneity Individuals dying during a given period may not represent a random sub-section of a population (Vaupel, Monton & Stallard 1979; Vaupel & Yashin 1985). The importance of variation in any individual-level component of mortality risk was first noted by demographers, and this component has been termed ‘frailty’ and was assumed to be constant with age (Vaupel et al. 1979; Vaupel & Yashin 1985). If individuals with high frailty are more likely to die young, the average frailty of a cohort will decline with age (Vaupel et al. 1979). Consequently, even if within-individual mortality risk increases with age, the age-specific mortality estimated at the cohort or population level could actually plateau or decrease with age as low frailty individuals compose a larger and larger proportion of survivors (Vaupel et al. 1979). This individual heterogeneity in mortality risk can explain the puzzling decreases in mortality risk in extreme old age observed in large cohort experiments on fruit flies and nematode worms (Carey et al. 1992; Partridge & Mangel 1999). Ageing is a within-individual process and the accurate measurement of within-individual changes in phenotype across ages is therefore crucial to its study. However, disentangling the contributions of between-individual phenotypic heterogeneity from the within-individual ageing process in wild populations has proved challenging (Vaupel & Yashin 1985; van de Pol & Verhulst 2006). Heterogeneity in mortality risk is likely to be prevalent in wild populations for a variety of genetic and environmental reasons. It has the potential to mask patterns of senescence when measured among individuals at the cohort or population level. Since mortality can, by definition, only be observed once per individual, separating the within- and between-individual sources of variation in mortality is, strictly speaking, impossible. However, one can model frailty distributions, based on certain assumptions, and attempt to measure ageing in mortality risk at the within-individual level (see ‘Dealing with individual heterogeneity’ section). Between-individual variation is also frequently observed in traits other than survival and may have important ecological and evolutionary implications (van Noordwijk & de Jong 1986; Forslund & Part 1995). An individual's average phenotypic performance often covaries with longevity or age at maturity in wild populations (Bérubé, Festa-Bianchet & Jorgenson 1999; Reid et al. 2003; Weladji et al. 2006). This will generate heterogeneity in the phenotypic composition of different age classes and potentially mask the pattern of within-individual ageing observed at the population level (Curio 1983; Reid et al. 2003; van de Pol & Verhulst 2006). We can illustrate the issue using 581 annual body mass measurements from 205 female roe deer born between 1975 and 1996 in Trois Fontaines, France (Gaillard et al. 2000b). Females were separated into four age classes: 2, 3, 4–10 and > 10 year olds (following Gaillard et al. 2000a). Females measured more than once in an age class were assigned their median mass in that class. Females that were last measured at > 10 years were, on average, heavier than females that were last measured in middle age (4–10 years; t(df=489)= 3·43, P 10 year old class (t192 = 0·81, P = 0·42). However, the repeated measures available on females allow us to also directly assess within-individual changes in mass between age classes. Changes in mass assessed from females measured in consecutive age classes revealed that females actually lost an average of 1·56 kg (± 0·39 SE; t28 = 4·06; P < 0·001) between the middle and eldest age classes (Fig. 1b, open trianges). Individual heterogeneity can totally mask senescence when measured using cross-sectional age-specific means (Bérubéet al. 1999; Cam et al. 2002; van de Pol & Verhulst 2006; see also Wilson, Charmantier & Hadfield 2008 for further discussion of implications of heterogeneity for quantitative genetic studies). Figure 1Open in figure viewerPowerPoint Cross-sectional and longitudinal ageing patterns of body mass of females in a wild population of roe deer in Trois Fontaines, France. (a) Average individual body mass across lifetime for females of different ages at last weighing (with SEs). (b) Plots of body mass changes with age: cross-sectional average body mass for each age class with SE bars (filled squares, left y-axis) and estimates of within-individual change across age classes for females measured at consecutive ages with SE bars (open triangles, right y-axis). sampling error and detection probability Senescence in wild populations has been assessed using two very different types of information: cross-sectional and longitudinal data. Most early studies were based on cross-sectional (or transversal) life tables in which individuals are sampled only once, usually at death (Deevey 1947; Spinage 1972). Such analyses provided the first broad picture of age-specific mortality (e.g. Caughley 1966) and were subsequently used to assess senescence patterns in wild animal populations (Nesse 1988; Promislow 1991; Ricklefs 1998; Mysterud et al. 2001). This approach, however, relies on several assumptions that are unlikely to be met (Gaillard et al. 1994). Life-table models of ageing assume a stationary demography over the study period, which is rarely likely to be true in free-living populations (Festa-Bianchet, Gaillard & Côté 2003; Coulson et al. 2006). They also rely on an accurate means of estimating the age of recovered individuals (Vincent et al. 1994), and assume that age-specific carcass recovery (for mortality-based life-tables) or detection (for survival-based life-tables) probabilities are equal across individuals (Caughley 1966). Because most of these assumptions are typically violated, we contend that reliable conclusions about senescence patterns in free-living populations cannot be drawn from cross-sectional data. The second type of data collected on wild populations involves the longitudinal monitoring of recognizable individuals, ideally from birth to death. This approach avoids most of the assumptions implicit in transversal approaches. However, in most studies of wild animals, the life-history traits of all individuals are not measured at each age simply because not all individuals are caught or observed each year. In such conditions, the number of animals at risk of mortality at a given age (Ra) and the number alive at the next age (Aa+1) are not known exactly. To account for the ‘unseen’ proportion of the population, a probabilistic approach such as Capture–Mark–Recapture (CMR) is required (Lebreton et al. 1992). In the handful of studies of wild animals where detection probabilities are almost one, such considerations do not apply (McDonald, Fitzpatrick & Woolfenden 1996; Bonduriansky & Brassil 2002; Catchpole et al. 2004). Where detection probabilities are less than one, but do not vary with age or among years, the ratio Aa+1 : Ra still provides an unbiased estimate of age-specific survival. However, variation in capture rates or re-sighting probabilities between years, seasons, sex and age classes, and social or reproductive states are common in studies of wild animals. For instance, changes in sampling effort can result in widely varying capture and re-captures rates (Gaillard et al. 2000b), and annual variation in re-sighting probabilities have been reported for ibex (Capra ibex; Toïgo et al. 2007), isard (Rupicapra pyrenaica; Loison et al. 1999) and mouflon (Ovis gmelini; Cransac et al. 1997). Likewise, young and adults often differ markedly in capture rates, young being generally easier to catch than adults (Gaillard et al. 1997; Cohas et al. 2007). Both social status (Cohas et al. 2007) and reproductive status (Catchpole et al. 2004) are also known to influence detection probability. Ignoring variations in detection rates can lead to biased age-specific survival estimates, and thereby biased estimates of both the onset and rate of senescence (Lebreton et al. 1992; Gaillard et al. 2000b). life-history trade-offs Life-history theories of the evolution of ageing, including antagonistic pleiotropy and disposable soma theories, predict trade-offs between investment in reproduction in early life and survival, performance or rate of senescence in later life (Williams 1957; Kirkwood & Holliday 1979; Partridge & Barton 1996). Several studies of human and wild animal populations have provided empirical support for these predictions (Westendorp & Kirkwood 1998; Orrell & Belda 2002; Reid et al. 2003; Carranza et al. 2004; Bowen et al. 2006; Descamps et al. 2006; Nussey et al. 2006, 2008; Charmantier et al. 2006b; Reed et al. 2008). However, as with all life-history analyses of trade-offs, several factors could confound a possible functional relationship between early reproduction and longevity or any measure of senescence in survival or reproductive output. For example, individual heterogeneity in resource acquisition could mean that some individuals perform consistently better than others. This would lead to positive correlations between early and late life performance at the phenotypic level and mask underlying genetic trade-offs between early and late life reproduction (van Noordwijk & de Jong 1986). Environmental variation may also reduce the detectability of early- vs. late-life trade-offs. An analysis of historical data for humans in Germany showed that the predicted trade-off between longevity and lifetime number of children only occurred when economical status was accounted for, with the poorest social groups suffering more costs (Lycett, Dunbar & Voland 2000). The trade-offs predicted by life-history theories of senescence may be less detectable when resources are plentiful (Ricklefs & Cadena 2007). Life-history trade-offs may also confound studies of survival senescence (Blarer et al. 1995). For example, in age-structured populations life-history theory predicts that individuals should increase their reproductive effort as they age because reproductive value declines with increasing age after the first reproduction (Williams 1966; Schaffer 1974). Evidence for increasing allocation of resources to reproduction with age in wild populations is limited (Mysterud, Solberg & Yoccoz 2005; Velando, Drummond & Torres 2006; Descamps et al. 2007). However, a ‘terminal investment’ strategy could mask intrinsic senescent declines in reproductive performance if reproductive effect was increased at the expense of reduced survival probability. Similarly, where short-term trade-offs between reproductive investment and survival occur, increased investment in reproduction late in life would result in an increase in mortality risk driven by allocation strategy rather than senescent deterioration (Blarer et al. 1995; Bonduriansky & Brassil 2002). Life-history theory predicts age-dependent changes in resource allocation that may interact with or be independent of the process of senescence and the integration of life-history theory within studies of ageing in the wild remains an important challenge. Meeting the challenges: analysis of senescence patterns in wild animal populations Longitudinal studies clearly are the most powerful approach to studying senescence in the wild. However, even when all individuals in a population are monitored continuously from birth to death, detecting and describing within-individual ageing patterns in free-living systems is challenging. In this section, we discuss issues relating to statistical power, the selection of traits for analysis and the functional form used to describe ageing patterns in field studies. A key challenge for field studies is to harness the power of longitudinal data sets to generate reliable predictions of within-individual ageing rates, whilst controlling for sources of heterogeneity. We present and illustrate two analytical approaches: the first predicts an individual's age-specific survival probabilities based on its phenotype; the second uses a mixed-effects model framework to separate between- and within-individual components of age-dependent variation in life-history traits. statistical power The statistical power to detect and model senescence rates will depend on both study effort and the life history and mortality rates of the focal population. The number of individuals sampled and monitored from birth and the length of the study period are crucial. As discussed above, the adult mortality rate, dispersal rates, age at maturity and age at onset of senescence of the study population will also determine the sample size available for senescent age classes. It will be particularly difficult to detect senescence in small, short-lived, highly dispersive taxa, where it is difficult to follow marked individuals from birth to death or to estimate the age of individuals at first capture (Slade 1995; Bonduriansky & Brassil 2002; Muller et al. 2004). This has not stopped researchers from successfully undertaking longitudinal studies on amenable species in the wild (e.g. Bonduriansky & Brassil 2002, 2005), or from developing novel methods of estimating the age structure of animals captured in the wild (Muller et al. 2004). Longer-lived species which experience relatively low adult mortality, such as large ungulates and colonial seabirds, will yield more powerful data for investigating changes in life-history traits in old age. However, these species may show a late onset of senescence and require data collected over many decades. Ultimately, it is important that any study of senescence in the wild applies analytical techniques that account for the inevitably declining sample size with age (Slade 1995). Several researchers have advocated the use of weighted regression (e.g. Promislow 1991; Jones et al. in press), but the problem is typically accounted for within the statistical frameworks applied in stu