The Demographic Buffering Hypothesis: Evidence and Challenges
Authors: Christoffer H. Hilde, Marlène Gamelon, Bernt-Erik Sæther, Jean-Michel Gaillard, Nigel G. Yoccoz, Christophe Pélabon
Journal: Trends in Ecology & Evolution (2020)

Climate change is expected to increase the environmental variation of ecosystems on Earth, highlighting the need to understand how populations will respond to these new environmental conditions.The demographic buffering hypothesis is derived from classical models of population dynamics. It predicts selection for a reduction in variance of the vital rates with the strongest influence on population growth and individual fitness.We review current knowledge about demographic buffering and critically assess the various methods and results published so far.A pattern of reduced variation in the most influential vital rates emerges from the review.Differences in methodology highlight the need for further studies with standardized methods to reveal whether the observed pattern is a direct result of selection for lower variation of the most influential vital rates.Modern statistical methods that allow decomposing variation in population growth rate into environmental variation, density dependence, and demographic stochasticity can stimulate the search for improved models to predict the effect of increasing environmental variation on population dynamics and life-history evolution. In (st)age-structured populations, the long-run population growth rate is negatively affected by temporal variation in vital rates. In most cases, natural selection should minimize temporal variation in the vital rates to which the long-run population growth is most sensitive, resulting in demographic buffering. By reviewing empirical studies on demographic buffering in wild populations, we found overall support for this hypothesis. However, we also identified issues when testing for demographic buffering. In particular, solving scaling problems for decomposing, measuring, and comparing stochastic variation in vital rates and accounting for density dependence are required in future tests of demographic buffering. In the current context of climate change, demographic buffering may mitigate the negative impact of environmental variation and help populations to persist in an increasingly variable environment. In (st)age-structured populations, the long-run population growth rate is negatively affected by temporal variation in vital rates. In most cases, natural selection should minimize temporal variation in the vital rates to which the long-run population growth is most sensitive, resulting in demographic buffering. By reviewing empirical studies on demographic buffering in wild populations, we found overall support for this hypothesis. However, we also identified issues when testing for demographic buffering. In particular, solving scaling problems for decomposing, measuring, and comparing stochastic variation in vital rates and accounting for density dependence are required in future tests of demographic buffering. In the current context of climate change, demographic buffering may mitigate the negative impact of environmental variation and help populations to persist in an increasingly variable environment. Climate change is expected to severely impact most ecosystems. Alongside the changes in mean values, temporal variation in temperature, precipitation, and wind speed also increases, leading to more frequent occurrences of extreme weather events [1.Fischer E.M. Knutti R. Anthropogenic contribution to global occurrence of heavy-precipitation and high-temperature extremes.Nat. Clim. Chang. 2015; 5: 560-564Crossref Scopus (741) Google Scholar,2.Pachauri R.K. et al.Climate Change 2014: Synthesis Report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. IPCC, 2014Google Scholar]. How species will respond to these changes depends on both the mean and the variance of these new environmental conditions [3.Jackson S.T. et al.Ecology and the ratchet of events: climate variability, niche dimensions, and species distributions.Proc. Natl. Acad. Sci. U. S. A. 2009; 106: 19685-19692Crossref PubMed Scopus (381) Google Scholar,4.Lawson C.R. et al.Environmental variation and population responses to global change.Ecol. Lett. 2015; 18: 724-736Crossref PubMed Scopus (128) Google Scholar] and their effects on the population growth rates [5.Boyce M.S. et al.Demography in an increasingly variable world.Trends Ecol. Evol. 2006; 21: 141-148Abstract Full Text Full Text PDF PubMed Scopus (323) Google Scholar]. The population growth rate is a direct function of the vital rates (see Glossary) that are influenced by individual attributes, such as sex or size, as well as by environmental fluctuations [6.Caswell H. Matrix Population Models. Wiley, 2001Google Scholar,7.Lande R. et al.Stochastic Population Dynamics in Ecology and Conservation. Oxford University Press, 2003Crossref Google Scholar]. A key result from stochastic modeling of population dynamics is that environmental fluctuations that increase the variance of vital rates usually decrease the stochastic long-run growth rate of populations [8.Boyce M.S. Population growth with stochastic fluctuations in the life table.Theor. Popul. Biol. 1977; 12: 366-373Crossref PubMed Scopus (67) Google Scholar,9.Tuljapurkar S.D. Orzack S.H. Population dynamics in variable environments I. Long-run growth rates and extinction.Theor. Popul. Biol. 1980; 18: 314-342Crossref Scopus (229) Google Scholar], increasing their extinction risk [10.Lande R. Risks of population extinction from demographic and environmental stochasticity and random catastrophes.Am. Nat. 1993; 142: 911-927Crossref PubMed Scopus (1465) Google Scholar,11.Lande R. Demographic stochasticity and Allee effect on a scale with isotropic noise.Oikos. 1998; 83: 353-358Crossref Scopus (161) Google Scholar]. This was first demonstrated 50 years ago by Lewontin and Cohen [12.Lewontin R.C. Cohen D. On population growth in a randomly varying environment.Proc. Natl. Acad. Sci. U. S. A. 1969; 62: 1056-1060Crossref PubMed Scopus (467) Google Scholar], who showed that, for unstructured populations, the logarithm of the geometric mean of annual population growth rates provides a more accurate estimate of the long-run growth rate than the logarithm of the arithmetic mean, because population growth is a multiplicative process. Using the logarithm of the arithmetic mean of annual population growth rates (ln λ1) would overestimate the long-run growth rate increasingly with increased variance in annual population growth rates (Λ). Thus, assuming that the annual population growth rates are identically and independently distributed, decreasing ln λ1 or increasing variance in the annual population growth rate (σΛ2) both decrease the long-run growth rate E(ln Λ) (Equation 1):ElnΛ≅lnλ1−σΛ22λ12.[1] Tuljapurkar [13.Tuljapurkar S.D. Population dynamics in variable environments. III. Evolutionary dynamics of r-selection.Theor. Popul. Biol. 1982; 21: 141-165Crossref Scopus (160) Google Scholar] showed that, for large populations neither subjected to density dependence nor influenced by demographic stochasticity and with small temporal variation in vital rates [i.e., coefficient of variation (CV) <<1] the stochastic long-run population growth rate, a, is negatively affected by the temporal variance of the vital rates as well as by the covariance among them (positively for negative covariance, Box 1). Ignoring for now covariances among vital rates and environmental autocorrelations that also affect the stochastic growth rate [14.Tuljapurkar S. Haridas C.V. Temporal autocorrelation and stochastic population growth.Ecol. Lett. 2006; 9: 327-337Crossref PubMed Scopus (85) Google Scholar], the negative effect of temporal variation in vital rates depends on their sensitivity (Box 1, Equations [I], [II]). Thus, the vital rates with the highest sensitivities are expected to be under strong selection for a reduction in temporal variation [15.Pfister C.A. Patterns of variance in stage-structured populations: evolutionary predictions and ecological implications.Proc. Natl. Acad. Sci. U. S. A. 1998; 95: 213-218Crossref PubMed Scopus (416) Google Scholar], while those with lower sensitivities can vary more freely or even be selected to vary (demographic lability) in response to changes in environmental conditions. Therefore, we expect a negative correlation between the sensitivity of vital rates and their temporal variation, a pattern termed 'demographic buffering' [16.Morris W.F. Doak D.F. Buffering of life histories against environmental stochasticity: Accounting for a spurious correlation between the variabilities of vital rates and their contributions to fitness.Am. Nat. 2004; 163: 579-590Crossref PubMed Scopus (138) Google Scholar].Box 1Population Growth Rate and Demographic BufferingAssuming small variation and no temporal autocorrelation in vital rates, Tuljapurkar [13.Tuljapurkar S.D. Population dynamics in variable environments. III. Evolutionary dynamics of r-selection.Theor. Popul. Biol. 1982; 21: 141-165Crossref Scopus (160) Google Scholar] showed that the long-term stochastic growth rate a of a large population not subjected to density dependence could be approximated using Equation I as:a≈lnλ1−12λ12∑i,jVarAi,jsi,j2−∑i,j≠k,lsi,jsk,lcovAi,jAk,l,[I] where λ1 is the dominant eigenvalue of the average vital rate matrix A, Var(Ai,j) is the variance of the different projection matrix elements (vital rates) in row i and column j, si,j is the sensitivity δλ1δAi,j of λ1 to a given change in the mean vital rate Ai,j and cov(Ai, j,Ak, l) is the covariance between the different matrix elements. Given that the difference between the terms between brackets in Equation I corresponds to the variance in annual population growth rates, the variance component is larger than that of the covariance ([86.Tuljapurkar S. Population Dynamics in Variable Environments. Springer, 1990Crossref Google Scholar] p. 93–94, [75.Eberhardt L.L. Assessing the dynamics of wild populations.J. Wildl. Manag. 1985; 49: 997-1012Crossref Scopus (147) Google Scholar] p. 397). Because sensitivities correspond to the absolute change in λ1 for an absolute change in a vital rate [87.Caswell H. A general formula for the sensitivity of population growth rate to changes in life history parameters.Theor. Popul. Biol. 1978; 14: 215-230Crossref PubMed Scopus (272) Google Scholar], it is difficult to interpret the relative contribution of the variation in each vital rate to changes in λ1 when the vital rates are on different scales. Elasticities(δlogλ1δlogAi,j [88.de Kroon H. et al.Elasticity: the relative contribution of demographic parameters to population growth rate.Ecology. 1986; 67: 1427-1431Crossref Google Scholar]) enable the comparison of the impact of variation in vital rates on population growth rate on a common (proportional) scale (but see Box 2 in the main text). Using elasticities, a becomes a function of the CV of the matrix elements and their corresponding elasticities [13.Tuljapurkar S.D. Population dynamics in variable environments. III. Evolutionary dynamics of r-selection.Theor. Popul. Biol. 1982; 21: 141-165Crossref Scopus (160) Google Scholar] (Equation II),a≈logλ1−12∑i,jei,j2CVi,j2+∑i,j≠k,lei,jek,lcovAi,jAk,lAi,j,Ak,l,[II] where CV2 is calculated as the variance divided by the mean square of the vital rate. Maximization of the long-term growth rate, a, can be achieved by increasing the mean growth rate λ1 or by reducing the variance in the growth rate. The demographic buffering hypothesis addresses the second option, that is, reducing ∑i,jei,j2CVi,j2 or ∑i,j var (Ai,j)si,j2. This represents an explicit prediction of the demographic buffering hypothesis that can be quantitatively tested by assessing whether the relationship between the squared sensitivity and the variance across vital rates (or the relationship between the squared elasticities and CV2) has a negative slope [20.Gaillard J.-M. Yoccoz N.G. Temporal variation in survival of mammals: a case of environmental canalization?.Ecology. 2003; 84: 3294-3306Crossref Scopus (424) Google Scholar]. Serial correlations of vital rates between years could have either a positive or negative effect on a [14.Tuljapurkar S. Haridas C.V. Temporal autocorrelation and stochastic population growth.Ecol. Lett. 2006; 9: 327-337Crossref PubMed Scopus (85) Google Scholar,67.Engen S. et al.Estimating the effect of temporally autocorrelated environments on the demography of density-independent age-structured populations.Methods Ecol. Evol. 2013; 4: 573-584Crossref Scopus (24) Google Scholar]. Assuming small variation and no temporal autocorrelation in vital rates, Tuljapurkar [13.Tuljapurkar S.D. Population dynamics in variable environments. III. Evolutionary dynamics of r-selection.Theor. Popul. Biol. 1982; 21: 141-165Crossref Scopus (160) Google Scholar] showed that the long-term stochastic growth rate a of a large population not subjected to density dependence could be approximated using Equation I as:a≈lnλ1−12λ12∑i,jVarAi,jsi,j2−∑i,j≠k,lsi,jsk,lcovAi,jAk,l,[I] where λ1 is the dominant eigenvalue of the average vital rate matrix A, Var(Ai,j) is the variance of the different projection matrix elements (vital rates) in row i and column j, si,j is the sensitivity δλ1δAi,j of λ1 to a given change in the mean vital rate Ai,j and cov(Ai, j,Ak, l) is the covariance between the different matrix elements. Given that the difference between the terms between brackets in Equation I corresponds to the variance in annual population growth rates, the variance component is larger than that of the covariance ([86.Tuljapurkar S. Population Dynamics in Variable Environments. Springer, 1990Crossref Google Scholar] p. 93–94, [75.Eberhardt L.L. Assessing the dynamics of wild populations.J. Wildl. Manag. 1985; 49: 997-1012Crossref Scopus (147) Google Scholar] p. 397). Because sensitivities correspond to the absolute change in λ1 for an absolute change in a vital rate [87.Caswell H. A general formula for the sensitivity of population growth rate to changes in life history parameters.Theor. Popul. Biol. 1978; 14: 215-230Crossref PubMed Scopus (272) Google Scholar], it is difficult to interpret the relative contribution of the variation in each vital rate to changes in λ1 when the vital rates are on different scales. Elasticities(δlogλ1δlogAi,j [88.de Kroon H. et al.Elasticity: the relative contribution of demographic parameters to population growth rate.Ecology. 1986; 67: 1427-1431Crossref Google Scholar]) enable the comparison of the impact of variation in vital rates on population growth rate on a common (proportional) scale (but see Box 2 in the main text). Using elasticities, a becomes a function of the CV of the matrix elements and their corresponding elasticities [13.Tuljapurkar S.D. Population dynamics in variable environments. III. Evolutionary dynamics of r-selection.Theor. Popul. Biol. 1982; 21: 141-165Crossref Scopus (160) Google Scholar] (Equation II),a≈logλ1−12∑i,jei,j2CVi,j2+∑i,j≠k,lei,jek,lcovAi,jAk,lAi,j,Ak,l,[II] where CV2 is calculated as the variance divided by the mean square of the vital rate. Maximization of the long-term growth rate, a, can be achieved by increasing the mean growth rate λ1 or by reducing the variance in the growth rate. The demographic buffering hypothesis addresses the second option, that is, reducing ∑i,jei,j2CVi,j2 or ∑i,j var (Ai,j)si,j2. This represents an explicit prediction of the demographic buffering hypothesis that can be quantitatively tested by assessing whether the relationship between the squared sensitivity and the variance across vital rates (or the relationship between the squared elasticities and CV2) has a negative slope [20.Gaillard J.-M. Yoccoz N.G. Temporal variation in survival of mammals: a case of environmental canalization?.Ecology. 2003; 84: 3294-3306Crossref Scopus (424) Google Scholar]. Serial correlations of vital rates between years could have either a positive or negative effect on a [14.Tuljapurkar S. Haridas C.V. Temporal autocorrelation and stochastic population growth.Ecol. Lett. 2006; 9: 327-337Crossref PubMed Scopus (85) Google Scholar,67.Engen S. et al.Estimating the effect of temporally autocorrelated environments on the demography of density-independent age-structured populations.Methods Ecol. Evol. 2013; 4: 573-584Crossref Scopus (24) Google Scholar]. In the context of climate change, demographic buffering provides a clear framework based on life-history theory to link temporal variation in vital rates and Malthusian fitness (i.e., the long-run population growth rate [17.Fisher R.A. The Genetical Theory of Natural Selection. Clarendon Press, 1930Crossref Google Scholar,18.Sæther B.-E. Engen S. The concept of fitness in fluctuating environments.Trends Ecol. Evol. 2015; 30: 273-281Abstract Full Text Full Text PDF PubMed Scopus (114) Google Scholar]). If present, demographic buffering may allow populations to persist in a changing environment by reducing the effects of variation in environmental conditions on the long-run growth rate (Box 1). Several studies have investigated the demographic buffering hypothesis, but evidence supporting its existence in the wild remains ambiguous. The inconsistent results across studies could originate from the complex biological mechanisms involved in demographic buffering or from the use of different methods and metrics to assess it. Here, we provide a comprehensive review of the published studies that have examined the demographic buffering hypothesis. We present the different methods used for testing this hypothesis and identify scaling problems associated with these tests. In some cases, demographic buffering is not expected to occur and we discuss to what extent the species-specific life history, population size, and the functional relationship between vital rates and their environmental drivers enable the prediction of whether demographic buffering should occur. Testing for demographic buffering requires reliable estimates of how temporal variation in vital rates is affected by fluctuations in environmental variables. Thus, we also identify and discuss the methodological difficulties that can influence the estimation of the temporal variation in vital rates, and thereby the outcome of any test of demographic buffering. Finally, we suggest approaches to solve these difficulties and provide recommendations and guidelines on how to improve the test of the demographic buffering hypothesis in future studies. The demographic buffering hypothesis has been tested empirically in mammals [19.Forcada J. et al.Life history buffering in Antarctic mammals and birds against changing patterns of climate and environmental variation.Glob. Chang. Biol. 2008; 14: 2473-2488Google Scholar, 20.Gaillard J.-M. Yoccoz N.G. Temporal variation in survival of mammals: a case of environmental canalization?.Ecology. 2003; 84: 3294-3306Crossref Scopus (424) Google Scholar, 21.Reed A.W. Slade N.A. Buffering and plasticity in vital rates of oldfield rodents.J. Anim. Ecol. 2012; 81: 953-959Crossref PubMed Scopus (2) Google Scholar, 22.Rotella J.J. et al.Evaluating the demographic buffering hypothesis with vital rates estimated for Weddell seals from 30 years of mark–recapture data.J. Anim. Ecol. 2012; 81: 162-173Crossref PubMed Scopus (59) Google Scholar], birds [23.Sæther B.-E. Bakke Ø. Avian life history variation and contribution of demographic traits to the population growth rate.Ecology. 2000; 81: 642-653Crossref Google Scholar,24.Schmutz J.A. Stochastic variation in avian survival rates: life-history predictions, population consequences, and the potential responses to human perturbations and climate change.in: Cooch E.G. Modeling Demographic Processes In Marked Populations. Springer, 2008: 441-461Google Scholar], reptiles [25.Miller D.A. et al.Stochastic population dynamics in populations of western terrestrial garter snakes with divergent life histories.Ecology. 2011; 92: 1658-1671Crossref PubMed Scopus (50) Google Scholar], and plants [16.Morris W.F. Doak D.F. Buffering of life histories against environmental stochasticity: Accounting for a spurious correlation between the variabilities of vital rates and their contributions to fitness.Am. Nat. 2004; 163: 579-590Crossref PubMed Scopus (138) Google Scholar,26.Li S.L. Ramula S. Demographic strategies of plant invaders in temporally varying environments.Popul. Ecol. 2015; 57: 373-380Crossref Scopus (8) Google Scholar, 27.Jongejans E. et al.Plant populations track rather than buffer climate fluctuations.Ecol. Lett. 2010; 13: 736-743Crossref PubMed Scopus (63) Google Scholar, 28.McDonald J.L. et al.Divergent demographic strategies of plants in variable environments.Nat. Ecol. Evol. 2017; 1: 29Crossref PubMed Scopus (23) Google Scholar] (see Table 1 for explicit tests directly performed on temporal variation in vital rates and the supplemental information online for studies that support the buffering hypothesis without explicitly testing it), but the empirical evidence accumulated so far is equivocal. Even though there is general support of the demographic buffering hypothesis, several studies did not detect the expected negative relationship (e.g., [27.Jongejans E. et al.Plant populations track rather than buffer climate fluctuations.Ecol. Lett. 2010; 13: 736-743Crossref PubMed Scopus (63) Google Scholar,29.Jäkäläniemi A. et al.Variability of important vital rates challenges the demographic buffering hypothesis.Evol. Ecol. 2013; 27: 533-545Crossref Scopus (11) Google Scholar]), or they found results ranging from negative to positive relationships (demographic lability) [28.McDonald J.L. et al.Divergent demographic strategies of plants in variable environments.Nat. Ecol. Evol. 2017; 1: 29Crossref PubMed Scopus (23) Google Scholar], between the temporal variation in vital rates and their sensitivity or elasticity. Although these results may reflect a true absence of demographic buffering in some species, they may also stem from various difficulties associated with the test of this hypothesis.Table 1Review of Studies Published on Demographic BufferingaVital rates: growth, probability of transition to next age/stage class; shrinkage/reversion/retrogression, probability to revert to previous stage class; stasis, survival without growth; breeding propensity, probability of breeding after having bred the previous year; Pbreed = probability of breeding.Author (year)Taxonomic group(s)SpeciesNyears (min–max)Vital ratesScale correction (Y/N)bScale correction: Y1, 'amount of possible variance/relative variance', Var/Varmax or CV/CVmax. Y2, Arcsine scaling, q(μ) = sin–1(√μ) [91]. Y3, Logit scaling, q(μ) = logit(μ) = ln(μ/1–μ). Y4, Log scaling. Variance measure: VSS, Variance stabilized sensitivity.Variance measure and statistical testTest statisticSenescence correctioncSenescence correction: Y1, only prime-aged individuals considered.Corrected for σd or σsdCorrected variation: σd, demographic stochasticity, σs, sampling variance.ResultsPfister (1998)[15.Pfister C.A. Patterns of variance in stage-structured populations: evolutionary predictions and ecological implications.Proc. Natl. Acad. Sci. U. S. A. 1998; 95: 213-218Crossref PubMed Scopus (416) Google Scholar]Plants and animals20 species, 30 populations2–23Survival, fecundity, growth, shrinkageNS versus Var (χ260 = 145.73, P <0.001) (negative correlation); E versus CV (χ260 = 194.75, P <0.001) (negative correlation)Spearman correlation and partial correlation coefficientsNNo information'…stage of the life cycle with the highest variance generally has the lowest effect on population growth rate λ'Sæther and Bakke (2000)[23.Sæther B.-E. Bakke Ø. Avian life history variation and contribution of demographic traits to the population growth rate.Ecology. 2000; 81: 642-653Crossref Google Scholar]Birds15 species≥10Survival, fecundityNS versus SD (r = –0.72, n = 18, P <0.001)Linear regressionNNo'there was an inverse relationship between the.. (its elasticity) and its relative variability'Gaillard and Yoccoz (2003)[20.Gaillard J.-M. Yoccoz N.G. Temporal variation in survival of mammals: a case of environmental canalization?.Ecology. 2003; 84: 3294-3306Crossref Scopus (424) Google Scholar]Mammals14 ungulate species, 22 populationsNASurvivalY1logSadSjuvvs.logVaradVarjuv(r = –0.474, slope = –1.001 ± 0.372, P = 0.013)Linear regressionY1σs for some species'...a negative covariation occurs among mammals between the demographic sensitivity of fitness components and their sensitivity to environmental variation...'Pico et al. (2003)[55.Pico F.X. et al.Recruitment rates exhibit high elasticity and high temporal variation in populations of a short-lived perennial herb.Oikos. 2003; 103: 69-74Crossref Scopus (19) Google Scholar]PlantsHypericum cumulicola 13 populations6Seed survival, recruitment, and production; seedling production; plant survivalNE versus CV: statistically significant negative relationship in 8 of 13 populationsSpearman correlationNNA'The relationship between elasticity and temporal variability in vital rates was significant in 8 of 13 H. cumulicola populations'Morris and Doak (2004)[16.Morris W.F. Doak D.F. Buffering of life histories against environmental stochasticity: Accounting for a spurious correlation between the variabilities of vital rates and their contributions to fitness.Am. Nat. 2004; 163: 579-590Crossref PubMed Scopus (138) Google Scholar]PlantsSilene acaulis; 5 populations5Reproduction, stasis, growth, reversionY1S versus Var, negative slopes; E versus C, negative slopesSpearman correlation and quantile regressionNσs'...all 5 of the rank correlations between the relativized variances and the sensitivities of the 0-to-1 vital rates were significantly negative'Doherty et al. (2004)[90.Doherty P.F. et al.Testing life history predictions in a long-lived seabird: a population matrix approach with improved parameter estimation.Oikos. 2004; 105: 606-618Crossref Scopus (48) Google Scholar]BirdsPhaeton rubricauda (red-tailed tropic bird)16Survival, reproduction, PbreedY2Sin–1-scaled Var versus sin–1 sensitivity (r = –0.31, P = 0.49); Log(s) versus var (r = 0.03, P = 0.95)Pearson correlationNσs'Results did not support our predictions that the variables to which λ was least sensitive…would be the variables most affected by El Niño events'Ezard et al. (2006)[109.Ezard T.H.G. et al.The contributions of age and sex to variation in common tern population growth rate.J. Anim. Ecol. 2006; 75: 1379-1386Crossref PubMed Scopus (58) Google Scholar]BirdsSterna hirundo (common tern)SurvivalNE versus SD: females: F1,6 = 18.75, P <0.01, R2 = 0.72; males: F1,6 = 15.95, P <0.01, R2 = 0.68ANOVAYN'There was a significant negative relationship between a demographic rate elasticity and the rate's variance'Delean (2007)J.S.C. Delean. PhD thesis. James Cook University, 2007MammalsPetrogale assimilis (rock wallaby)11Survival, fertility, transition rates, proportion birthsY1S versus Var (ρ = –0.65, P = 0.05); E versus CV (ρ = –0.34, P = 0.007)Spearman correlationYσs'there was a significant negative rank-order correlation between the sensitivities and variances of the vital rates…'Forcada et al. (2008)[19.Forcada J. et al.Life history buffering in Antarctic mammals and birds against changing patterns of climate and environmental variation.Glob. Chang. Biol. 2008; 14: 2473-2488Google Scholar]Mammals and birds2 seal and 3 bird species16–39Survival, breeding propensityY1E versus CV: slopes = (–0.4, –0.1, –0.5, –0.5, –0.3, –0.5); P-values (<0.01, 0.4, <0.02, <0.05, <0.1, <0.1)Quantile regressionNNo information'…Antarctic fur seals have lost life history buffering to increasing environmental variability'; '…support of life history buffering in the other species…'Schmutz (2008)[24.Schmutz J.A. Stochastic variation in avian survival rates: life-history predictions, population consequences, and the potential responses to human perturbations and climate change.in: Cooch E.G. Modeling Demographic Processes In Marked Populations. Springer, 2008: 441-461Google Scholar]Birds59 species, 62 populations5–42SurvivalY1,2E versus CV: arcsine: slope = –0.905 ± 0.164, r2 = 0.29; relative CV: slope = –1.439 ± 0.272, r2 = 0.318Linear regressionsNσs'...in an analysis of 62 populations, I confirm her (Pfister's) prediction by showing a negative relationship between the proportional sensitivity (elasticity) of adult survival and the proportional variance (CV) of adult survival'Burns et al. (2010)[77.Burns J.H. et al.Empirical tests of life-history evolution theory using phylogenetic analysis of plant demography.J. Ecol. 2010; 98: 334-344Crossref Scopus (52) Google Scholar]Plants185 iteroparous and 19 semelparous speciesNASurvival, fecundityY1No significant correlation between log(CV) and log(S) or log(E)Generalized least squaresNCV with bias correction for small N'…the direction of the trends for the correlation between the CV and the sensitivity for fecundity and survival were negative in some cases...these relationships were not statistically significant...'Jongejans et al. (2010)[27.Jongejans E. et al.Plant populations track rather than buffer climate fluctuations.Ecol. Lett. 2010; 13: 736-743Crossref PubMed Scopus (63) Google Scholar]Plants40 species3–21Survival, fecundity, growthN1No difference in slope of log(CV) versus log(E) between observed and simulated dataLinear regressionNNo information'…our results suggest that selection for reduced variability specifically in high-elasticity vital rates is undetectable over the entire range of life spans of the studied species'Miller et al. (2011)[25.Miller D.A. et al.Stochastic population dynamics in populations of western terrestrial garter snakes with divergent life histories.Ecology. 2011; 92: 1658-1671Crossref PubMed Scopus (50) Google Scholar]ReptilesThamnophis elegans (garter snakes); 2 populations7-13Survival, litter size, proportion gravidY1E versus CV (r = –0452, P = 0.01)Pearson correlationNσs'Our resu