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Keywords:

  • climate;
  • demographic;
  • fitness;
  • mismatch;
  • selection gradient;
  • synchrony;
  • timing;
  • vital rates

Summary

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Strong Individual-level but weak population effects of mismatch
  8. Effects of mismatch on natural selection
  9. Acknowledgements
  10. References
  11. Supporting Information
  1. Populations are shifting their phenology in response to climate change, but these shifts are often asynchronous among interacting species. Resulting phenological mismatches can drive simultaneous changes in natural selection and population demography, but the links between these interacting processes are poorly understood.

  2. Here we analyse 37 years of data from an individual-based study of great tits (Parus major) in the Netherlands and use mixed-effects models to separate the within- and across-year effects of phenological mismatch between great tits and caterpillars (a key food source for developing nestlings) on components of fitness at the individual and population levels.

  3. Several components of individual fitness were affected by individual mismatch (i.e. late breeding relative to the caterpillar food peak date), including the probability of double-brooding, fledgling success, offspring recruitment probability and the number of recruits. Together these effects contributed to an overall negative relationship between relative fitness and laying dates, that is, selection for earlier laying on average.

  4. Directional selection for earlier laying was stronger in years where birds bred on average later than the food peak, but was weak or absent in years where the phenology of birds and caterpillars matched (i.e. no population mismatch).

  5. The mean number of fledglings per female was lower in years when population mismatch was high, in part because fewer second broods were produced. Population mismatch had a weak effect on the mean number of recruits per female, and no effect on mean adult survival, after controlling for the effects of breeding density and the quality of the autumnal beech (Fagus sylvatica) crop.

  6. These findings illustrate how climate change-induced mismatch can have strong effects on the relative fitness of phenotypes within years, but weak effects on mean demographic rates across years. We discuss various general mechanisms that influence the extent of coupling between breeding phenology, selection and population dynamics in open populations subject to strong density regulation and stochasticity.


Introduction

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Strong Individual-level but weak population effects of mismatch
  8. Effects of mismatch on natural selection
  9. Acknowledgements
  10. References
  11. Supporting Information

Natural selection is an ongoing phenomenon in dynamic environments (Endler 1986). Temporal variations in extrinsic factors (e.g. climate, habitat, interspecific competition) and intrinsic factors (e.g. intraspecific competition for food or nest sites) drive phenotypic selection, which typically fluctuates in magnitude, form (Kingsolver et al. 2001; Bell 2010) and sometimes sign (Siepielski, DiBattista & Carlson 2009; but see Morrisey & Hadfield 2012). Stochastic environmental variation also directly influences age-/stage-specific average reproduction and survival and hence population demography (Coulson et al. 2001; Lande, Engen & Saether 2003; Jenouvrier et al. 2012). Until relatively recently, however, factors influencing the nature and strength of connections between natural selection and population dynamics have received little empirical attention (Saccheri & Hanski 2006; Kokko & Lopez-Sepulcre 2007).

Natural selection and population demography are both affected by individual variation in survival and reproductive success (Clutton-Brock 1998; Metcalf & Pavard 2007). The crucial difference is that selection is driven by differences in the relative fitness of individuals with different trait values, whereas population demography is shaped by variation in the absolute performance of individuals. It follows, therefore, that selection may influence population demography in situations where selection among alternative phenotypes alters mean survival or fecundity at the population level (Saccheri & Hanski 2006; Coulson, Tuljapurkar & Childs 2010). Charlesworth (1971, 1994) showed how population dynamic responses can be critically sensitive to selection on some life-history traits, but not others, depending on where in the life cycle selection occurs relative to population regulation. For example, in species with extended parental care such as altricial birds and mammals, selection arising from variation in breeding success (number of young raised to independence) might not be expected to impact demography much if the survival of offspring postindependence is higher in years where average breeding success is lower, because of reduced intra-cohort competition. Similarly, variation in breeding success might have weak effects on population demography if annual recruitment is driven more by exogenous factors (e.g. climate) during the nonbreeding season (Saether, Sutherland & Engen 2004).

The need to understand links between individual fitness, natural selection and population demography has become an issue of applied importance in the face of widespread, human-induced alterations to natural environments (Kinnison & Hairston 2007). Climate change, for example, is thought to represent perhaps the biggest threat to global biodiversity (Thomas et al. 2004; Malcolm et al. 2006), yet we know surprisingly little about how changes in climate translate into changes in local selective pressures and how these, in turn, influence the demographic responses of populations (Reed, Schindler & Waples 2010). One critical pathway via which changes in climate potentially influence fitness is phenology (Jenouvrier & Visser 2011), that is, the timing of life cycles in relation to key environmental factors. In seasonal environments, life-history events such as annual reproduction or migration are typically scheduled to coincide with favourable periods, for example benign weather conditions or seasonal peaks in food abundance. In many regions, these favourable periods are shifting as the climate changes, and species are adjusting their phenology (Parmesan & Yohe 2003; Root et al. 2003). Rates of phenological change have typically been observed to be unequal across functional groups (Thackeray et al. 2010), however, leading to mismatches between interacting species such as predators and prey (Visser & Both 2005). Ostensibly, mismatch should entail negative fitness consequences for the consumer, yet relatively little is known about the evolutionary and demographic implications (Both 2010; Miller-Rushing et al. 2010; Heard, Riskin & Flight 2012).

In birds, mismatches have been shown or hypothesized to occur in a range of species for which synchronization of breeding with narrow seasonal food peaks is important (reviewed by Both 2010; Visser, te Marvelde & Lof 2011). Under the so-called mismatch hypothesis (Drever & Clark 2007; Dunn et al. 2011), fitness is lower for females breeding both earlier and later than the seasonal food peak, although fitness need not peak exactly when breeding coincides with the food peak given that other selective pressures can be involved (Visser, te Marvelde & Lof 2011; Lof et al. 2012). Climate change has led to an increase in positive mismatch years (late breeding relative to seasonal food peaks) for woodland birds in temperate regions, as spring/summer warming has tended to advance food peaks faster than avian phenology (Visser, Both & Lambrechts 2004; Jones & Cresswell 2010). While increasing mismatch has been linked to population declines in some species (e.g. long distance migrants, Both et al. 2006, 2010), evidence for negative fitness effects has been mixed in others (Eeva, Veistola & Lehikoinen 2000; Drever & Clark 2007; Shultz et al. 2009; Dunn et al. 2011; Vatka, Orell & Rytkönen 2011).

Here we explore relationships between phenological mismatch and components of fitness at the individual and population levels in great tits (Parus major L.), to better understand the various mechanisms by which climate effects on phenology simultaneously influence natural selection and population demography. Across Europe, populations of great tits have exhibited variable phenological responses to large-scale changes in spring temperature since 1980 (Visser et al. 2003). Great tits rely heavily on caterpillars during the breeding season to feed their chicks (van Balen 1973; Naef-Daenzer, Naef-Daenzer & Nager 2000; Mols, Noordwijk & Visser 2005; Wilkin, King & Sheldon 2009), and in some habitats (e.g. oak forests), caterpillar biomass typically shows a pronounced, narrow seasonal peak in late spring/early summer (Visser, Holleman & Gienapp 2006). Caterpillar development is strongly affected by temperature, and great tits at mid-latitudes use predictive cues such as early spring temperatures (Visser, Holleman & Caro 2009; Schaper et al. 2012) to adjust their egg-laying dates in line with fluctuations in the seasonal peak in caterpillar biomass. In our Hoge Veluwe study population in the Netherlands, advancements in laying dates in response to warmer springs have been insufficient to keep pace with stronger advancements in caterpillar phenology, and the population now breeds much later relative to the seasonal caterpillar peak (Visser 2008). While previous studies on this population have examined selection on laying dates (Visser et al. 1998; Gienapp, Postma & Visser 2006; Visser, Holleman & Gienapp 2006), the effects of mismatch on population demography have not been explored in detail, which requires separating within-year effects on individual fitness from between-year effects on average fitness.

The aims of this paper were therefore threefold: (1) To explore the impact of phenological mismatch on components of individual fitness, (2) to explore the effects of mean mismatch on population mean vital rates and (3) to link the individual and population impacts by estimating annual selection differentials and testing for an association with population mean mismatch.

Materials and methods

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Strong Individual-level but weak population effects of mismatch
  8. Effects of mismatch on natural selection
  9. Acknowledgements
  10. References
  11. Supporting Information

Study area and FIeld methods

The data analysed come from a long-term, individual-based demographic study of great tits (Parus major) at the Hoge Veluwe National Park in the Netherlands (52°02′07″ N 5°51′32″ E). The study area consists of mixed pine-deciduous woodland on poor sandy soils. A large block of pure pine plantation was included from 1955 to 1972, but this was damaged by a severe storm in the winter of 1972/1973. Here we focus on the years 1973–2011, when the study area included only mixed coniferous-deciduous woodland. The study area remained the same size across this period and the number of nest boxes was approximately constant, although some were replaced or moved as the study progressed. A surplus of nest boxes was provided to ensure that availability of artificial nest sites did not limit population size (the ratio of nest boxes to breeding females was approximately 3 : 1, on average). The study area is surrounded by a matrix of potentially suitable breeding habitat for great tits, and thus, the population is open to immigration and emigration.

During the breeding season (April to June/July), nest boxes were visited at least once per week. The number of eggs or nestlings present was counted at each visit. When the nestlings were 7–10 days old, the parents were caught on the nest using a spring trap. Parents already ringed were identified and unringed birds were given a metal ring with a unique number. Young were ringed on day 7. Female great tits are capable of producing a second brood each season (i.e. laying a second clutch and raising a new brood after successful fledging of the first brood), although the frequency of double-brooding in this population has declined in recent decades (Husby, Kruuk & Visser 2009). A small but variable proportion of breeding females each year were not caught, primarily those that desert their clutches early in the breeding attempt. Unknown females were not included in the survival analyses, as their survival to future breeding seasons could not be determined. Recapture probability was very high in females (average = 98·7%) and males (average = 95·5%). Female recapture probability did not exhibit any trends over time (P = 0·460, Fig. S2a) or any association with population mean mismatch (P = 0·425, Fig. S2b). Male recapture probability also did not exhibit any trends over time (P = 0·839, Fig. S2a) or association with population mean mismatch (P = 0·588, Fig. S2b). Therefore, we did not include recapture probability in our survival analyses.

In some years, brood size manipulation experiments were carried out that affected fledgling production or recruitment probability. Manipulated broods were excluded from all analyses. Data from the 1991 breeding season were also excluded, as this was an anomalous year where a late frost resulted in a very late caterpillar food peak (Visser et al. 1998). The analysed data set consisted of 3472 records of 2599 females breeding in 37 years. 560 of these records were of unknown females. The average number of breeding records per known female was 1·43.

Dates of the peak in caterpillar biomass were estimated for 1985–2010 from frass fall samples in the Hoge Veluwe. The most predominant species in our system are the winter moth (Operophtera brumata) and the oak leaf roller (Tortrix virirdana), although caterpillars of several other species are also present. The annual caterpillar peak is well predicted by mean temperatures from 8th March–17th May (r2 = 0·80), and this relationship was used to predict caterpillar peaks from 1973 to 1984. For full details see Visser et al. (1998) and Visser, Holleman & Gienapp (2006). The basic patterns presented in the results were similar when the analyses were restricted to the years where food peaks were measured directly, so we include all years in the final analysis.

Statistical analyses

Effects of mismatch on individual and population-level fitness components

Food demands of great tit nestlings are highest approximately 9–10 days after hatching (Royama 1966; Gebhardt-Henrich 1990; Keller & Noordwijk 1994; Mols, Noordwijk & Visser 2005) and females strive to match nestling energy requirements to the period when caterpillars are plentiful. The mismatch between a female's breeding time and the timing of the food peak was defined as the difference between the laying date of her first clutch and the food peak date, plus 30 days (i.e. individual mismatch = laying date + 30−food peak date). Laying dates are given as April-days (1 April is April-day 1, 24 May is April-day 54). This mismatch metric essentially measures laying dates relative to the food peak, but the constant value of 30 days was added to make the values more easily interpretable. Great tits in our study population typically lay nine eggs and incubate them for 12 days, and hence, nestling food requirements peak approximately 30 days (9 + 12 + 9) after laying of the first egg. Thus, according to this metric, a female laying too early relative to the food peak would have a negative value for individual mismatch (IM), a female laying too late would have a positive IM value, while a female who lays on the date such that her chicks are 9 days old at the food peak would have an IM value of 0 (see Fig. 1). We stress that this is purely an operational definition of mismatch; we do not assume that fitness is highest for females with an IM of 0.

image

Figure 1. Schematic illustration of population/individual-level mismatch. In both panels, solid black curves show the distribution of laying dates and dashed black curves show the distribution of chick food needs, which peak 30 days after egg-laying. Shaded portions represent female great tits that lay later than the annual average, open portions represent females that lay earlier than the population average. Solid grey curves show the seasonal distribution of caterpillar biomass. Top panel: example of a year where there is no population-level mismatch (PM) between the breeding phenology of great tits and the seasonal peak in caterpillar biomass. Late-laying females nonetheless produce broods after the caterpillar peak and thus exhibit positive values for individual mismatch (IM). Early females exhibit negative values for individual mismatch. Bottom panel: example of a year where caterpillar biomass peaks earlier, but there is no change in laying dates, which results in (a positive value for) population-level mismatch. Individual females breeding late relative to the food peak exhibit positive values for individual mismatch in this year, but so too do the earliest females, who are classified as breeding late relative to the food peak.

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Annual population mismatch (PM) was defined simply as the arithmetic average of IM values each year (Fig. 1). This difference between the mean phenology of birds and the food peak is only a proxy for true population-level mismatch, of course, but it does provide a straightforward, easily calculable metric comparable with previous studies on this (Nussey et al. 2005; Visser, Holleman & Gienapp 2006) and other species (Visser & Both 2005). See the aentary Material for more discussion of the pros and cons of our mismatch measure and potential alternatives.

Generalized linear mixed-effects models (GLMMs) were used to examine variation in fitness components in relation to individual- and population-level mismatch simultaneously. We separated IM from PM effects by standardizing IM within years (by subtracting year-specific PM values from IM values) and including both standardized IM and PM as fixed effects in the GLMMs. Thus, the fixed effect of PM measures the across-year effect of average mismatch, while the fixed effect of standardized IM effectively quantifies the within-year effect of individual breeding time relative to the mean breeding time that year. This is directly analogous to ‘within-subject centering’, a technique used in mixed-effects models to distinguish within-individual from between-individual effects (van de Pol & Wright 2009). Individual- and population-level effects of mismatch are illustrated graphically in separate figures (Figs.2 and 3), but the predicted effects themselves are estimated in the same GLMMs (see Table 1).

image

Figure 2. Individual-level plots of fitness components vs. individual mismatch. Data are binned into 10 equally spaced categories along the individual mismatch axis for ease of illustration (so each data point potentially consists of observations on the same or different females across years) but the statistical analyses are based on the full data set, with sample sizes given in Table 1. Curves show significant within-year effects of IM, predicted and back-transformed from the GLMMs which also accounted for between-year effects of PM (see Table 1). Error bars are standard errors.

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image

Figure 3. Population-level plots of average fitness components (demographic rates) vs. population mismatch. Data points are annual averages. Curves show the predicted, back-transformed fits for the effect of population mismatch from the minimum adequate GLMMs for each fitness component, summarized in Table 1. Error bars are standard errors.

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Table 1. Separating the effects of within-year variation in individual mismatch from between-year variation in average mismatch on components of great tit fitness in the Hoge Veluwe study population, Netherlands, from 1973–2010. Each sub-table represents the minimum adequate models for that fitness component. The levels for the factor ‘Mother age' are abbreviated as: EXB = experienced breeder, FTB = first-time breeder, U = unknown age. The levels for the factor BCI (beech crop index) are simply 1, 2 and 3. Intercepts correspond to the level EXB for mother age and 1 for BCI. Estimates are on the logit scale for models with binomial errors and on the log scale for models with Poisson errors
 EstimateSEz-valuePr(>|z|)
  1. PM = population mismatch. IM′ = standardized individual mismatch. ID VC = variance component for random effect of female identity. Year VC = variance component for random effect of year. no = number of total observations. nf = number of females. ny = number of years.

(a) Probability of double-brooding (binomial errors, ID VC = 0·544, Year VC = 0·814, no = 3472, nf = 2599, ny = 37)
Intercept1·6720·7632·1920·028
PM−0·2250·029−7·743<0·001
IM′−0·0250·072−0·3470·728
IM′^2 −0·0060·002−2·9480·003
Mother age
FTB−0·3650·129−2·8360·005
U−5·6731·146−4·950<0·001
PM × IM′−0·0100·006−3·557<0·001
Density−0·0270·003−4·537<0·001
Density × IM′ −0·0010·0012·4480·014
(b) Clutch size (Poisson errors, ID VC <0·001, Year VC = 0·004, no = 3131, nf = 2263, ny = 37)
Intercept 2·4200·05147·44<0·001
IM′ −0·0090·001−7·61<0·001
Mother age
FTB−0·0210·013−1·590·112
U−0·0840·022−3·78<0·001
Density−0·002> 0·001−4·07<0·001
(c) Probability of producing zero chicks (binomial errors, Year VC = 0·188, no = 3469, nf = 2599, ny = 37)
Intercept −2·5630·133−19·284<0·001
PM0·0060·0150·3940·694
IM′0·0490·0124·061<0·001
Mother age
FTB0·1640·1421·1540·249
U4·5310·16627·375<0·001
PM × IM′0·0060·0023·519<0·001
(d) Number of fledglings produced (Poisson errors, ID VC = 0·007, Year VC = 0·016, no = 2680, nf = 1896, ny = 37)
Intercept 2·5550·09626·658<0·001
PM−0·0080·004−2·3550·019
IM′−0·0150·002−9·577<0·001
IM′^2−0·00060·0002−3·0110·003
Mother age
FTB−0·0790·015−5·170<0·001
U−0·1050·045−2·3170·023
Density−0·0040·0007−5·149<0·001
(e) Probability of recruitment (binomial errors, ID VC = 0·293, Year VC = 0·150, no = 2680, nf = 1896, ny = 37)
Intercept−1·7650·353−5·003<0·001
PM−0·0170·012−1·4360·151
IM′−0·0230·007−3·308<0·001
Density −0·0110·003−4·247<0·001
BCI
20·5780·1843·1400·002
30·7710·2173·558<0·001
PM × IM′−0·0030·001−2·634<0·001
(f) Number of recruits (Poisson errors, IDVC = 0·320, Year VC = 0·161, no = 3472, nf = 2599, ny = 37)
Intercept0·6500·361−5·0030·072
PM−0·0250·012−1·4360·038
IM′−0·0390·007−3·308<0·001
IM′^2−0·0020·001−4·2470·044
Mother age
FTB−0·138 0·0650·032
U−2·330 0·199<0·001
Density−0·014 0·003<0·001
BCI
20·4690·1840·1870·012
3 0·5130·2170·2210·020
PM × IM′−0·0040·0010·0010·001
(g) Female adult survival (binomial errors, ID VC <0·001, Year VC = 0·156, no = 2912, nf = 2039, ny = 37)
Intercept0·4090·3741·0950·273
Density−0·0080·003−2·9550·003
BCI
20·4110·2012·0490·041
3 0·5350·2312·3170·021
(h) Male adult survival (binomial errors, ID VC <0·001, Year VC = 0·151, no = 2912, nf = 2039, ny = 37)
Intercept 0·4240·3711·1460·252
Male age
FTB−0·0160·080−0·1950·846
U−1·0240·466−2·1960·028
Density−0·0080·003−2·9900·003
BCI
20·4170·1992·1010·036
30·5390·2282·3570·018

For each breeding record included in the GLMM analyses, mismatch was defined on the basis of first clutches (n = 3472 breeding records where the laying date of the first clutch was known), but fledglings and recruits produced from second clutches were included in the fitness calculations. The following fitness components were examined: (a) the probability of double-brooding, (b) clutch size of the first clutch (c) probability of producing zero fledglings that season (including those from second broods), (d) number of fledglings produced, given that one or more chicks were raised, (e) probability of recruitment (the total number of offspring per female surviving to breed themselves in subsequent years, divided by the total number of fledglings she produced that year), (f) total number of recruits, (g) female local survival (the probability that a female parent survives between year t and t + 1, that is, was observed as a breeder the following year) and (h) male local survival. Fitness components measured as probabilities (probability of double-brooding, probability of producing zero fledglings, offspring recruitment, adult survival) were analysed using GLMMs with logit-link functions and binomial errors. Fitness components measured as counts (clutch size, number of fledglings, number of recruits) were analysed using GLMMs with Poisson errors and log-link functions. The distribution of total number of fledglings per female is strongly zero-inflated, as many females fail to raise any chicks each year. Hence, the probability of producing zero fledglings was analysed separately to the number of fledglings produced given than one or more chicks were fledged. In the case of recruitment and adult survival, death cannot be distinguished from permanent emigration from the study area; thus, we effectively model apparent local recruitment and survival.

For each fitness component, the full models contained the following fixed effects: intercept, standardized individual mismatch (hereafter IM′, with the prime symbol indicating the standardization relative to PM), a quadratic effect of IM′, PM, mother age class as a 2-level factor (first-time breeder or experienced breeder), breeding density (annual number of first clutches) and the interactions mother age × (IM′ + IM′2), PM × (IM′ + IM′2) and breeding density × (IM′ + IM′2). Quadratic effects of IM′ were included as we suspected that both breeding too early or too late relative to the food peak might negatively impact fitness. The interaction PM × (IM′ + IM′2) tested whether the potentially nonlinear effects of IM′ varied as a function of PM (e.g. fitness differences between early and late laying females might be larger in years where the population breeds too late on average). Mother age and the interactions with IM′ and IM′2 were included to examine potential differences in the relationships between fitness components and IM′ for inexperienced vs. experienced breeders. Demographic studies of great tits typically find that first-year females lay later, produce smaller clutches and recruit fewer offspring than older age classes (Perrins & Moss 1974; Harvey et al. 1979; Jarvinen 1991). Note that age information was not available for the 560 records of unknown females. Breeding density was included as a continuous covariate as previous studies have documented strong density dependence at various stages in the great tit life history (e.g. Dhondt, Kempenaers & Adriaensen 1992; Both, Visser & Verboven 1999) and on overall numbers (Saether et al. 1998; Grøtan et al. 2009). The interaction breeding density × (IM′ + IM′2) was included to test whether the (potentially nonlinear) effects of mismatch depended on breeding density. In GLMMs (e) to (h) we also included the explanatory variable beech crop index (BCI) as a factor with three levels, 3 being the highest. BCI quantifies the amount of beech nuts available in winter on a 3-point scale and also correlates with the crop size of other tree species in the region (see Perdeck, Visser & Van Balen 2000 for further details). Beech nuts are an important winter food source affecting the overwinter survival of juveniles and adults alike (Perrins 1965; Clobert et al. 1988; Grøtan et al. 2009). The interaction BCI × (IM′ + IM′2) was included in these models to test whether the effects of individual mismatch depended on the quality of the beech crop that year.

Random effects of female identity and year were included in all GLMMs. Models were fitted in R using the function glmer in the package lme4. We used a backwards stepwise model simplification procedure, sequentially removing nonsignificant fixed-effect terms (P > 0·05, where P values correspond to the z-values reported by glmer) starting with higher-order terms (first interactions involving quadratic terms, then linear terms), to yield minimum adequate models. We stress that the goal of these GLMMs was not to explain as much variation in each fitness component as possible using all possible candidate explanatory variables, but rather to characterize the relationships with phenological mismatch while correcting for key covariates known a priori to be important. Testing for significant interactions between individual mismatch and year-specific covariates (PM, density, BCI) also provides insights into the mechanisms underlying population-level relationships (or lack thereof) between mismatch and demographic rates. Overall raw relationships between demographic rates and year (i.e. not correcting for environmental variables) are presented in Fig. S1.

Selection analyses

Selection differentials, defined as the covariance between phenotype and relative fitness, quantify the strength of directional selection on a trait (Lande & Arnold 1983). We used the number of locally recruiting offspring per female as a measure of individual (annual) fitness. Fitness was converted to relative fitness by dividing by the mean number of recruits each year. Laying date, the phenological trait assumed to be under selection, was standardized within years to a mean of zero and a standard deviation (SD) of one by subtracting the annual mean and dividing by the annual SD. Each year t, a standardized estimate of annual directional selection (standardized linear selection differential, βt) can then be obtained as the slope of the regression of relative fitness on standardized laying dates. To explore which environmental factors best explained variation in annual directional selection, we regressed the βt estimates against PM, PM2, breeding density, BCI and age composition (the ratio of first-time breeding females to experienced breeders). Data points in this multiple regression were weighted by 1/[(standard error of βt)2], to account for the fact that βt estimates in some years were based on a small number of recruits (e.g. four recruits from the 1984 breeding season) and therefore much less certain than years with more recruits (e.g. 105 in 1976). We predicted that reproductive output might be lower, on average, in years where selection was stronger. To test this, we regressed the annual mean number of recruits per female against βt values and their square.

We also estimated standardized nonlinear selection differentials, given as twice the quadratic coefficient in a regression of relative fitness on standardized laying date + standardized laying date2. Note that quadratic regression coefficients and their standard errors must be doubled to obtain point estimates of annual nonlinear selection differentials (hereafter γt) and their uncertainty (Stinchcombe et al. 2008). We also tested for relationships between γt and PM, PM2, breeding density, BCI and age composition, weighting the annual data points by 1/[(standard error of γt)2].

Results

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Strong Individual-level but weak population effects of mismatch
  8. Effects of mismatch on natural selection
  9. Acknowledgements
  10. References
  11. Supporting Information

Effects of mismatch on individual and population-level fitness components

Within years, the probability that an individual female attempted a second brood was nonlinearly related to IM, with relatively early females (negative IM′ values) being more likely to attempt a second brood (Fig. 2A, linear effect: P = 0·728; quadratic effect: = 0·003; estimates ± SE and sample sizes are provided in Table 1). First-time breeders were less likely to attempt a second brood compared with experienced breeders (= 0·005). Across years, the mean probability of double-brooding brooding was negatively related to average mismatch (< 0·001, Fig. 3A) and to breeding density (< 0·001, Table 1a). The negative relationship between probability of double-brooding and IM′ was also stronger in years where PM was larger (IM′ × PM interaction term: < 0·001) and when breeding density was higher (IM′ × density interaction: = 0·014, Table 1a).

Females breeding late relative to the food peak laid significantly fewer eggs (i.e. a negative effect of IM′: < 0·001, Fig. 2B, Table 1b). There was no across-year relationship between mean clutch size and PM (Fig. 3B), but annual mean clutch size was negatively related to breeding density (< 0·001, Table 1b). Females that bred late relative to the food peak were more likely to fail to raise any fledglings (Fig. 2C; linear effect of IM′: < 0·001; quadratic effect of IM′: < 0·001; Table 1c). While there was no overall effect of PM on mean probability of producing zero fledglings (Fig. 3C), the effect of IM′ was stronger in years where PM was larger (Table 1c; IM′ × PM interaction: < 0·001). Among those females that did fledge chicks, there was a negative quadratic relationship between the number fledged and IM′ (linear effect: < 0·001; quadratic effect: P = 0·003; Table 1d, Fig. 1D). First-time breeders fledged fewer chicks than experienced breeders (< 0·001; Table 1d). Across years, the mean number of fledglings per female was negatively related to PM (= 0·019, Fig. 3D) and breeding density (< 0·001, Table 1d).

Within years, recruitment probability was negatively related to IM (linear effect of IM′: < 0·001; Fig. 2E), with the relationship being stronger in years where average mismatch was larger (Table 1e; IM′ × PM interaction: < 0·001). Across years, there was no relationship between average recruitment probability and PM (= 0·151; Fig. 3E), a negative relationship with breeding density (< 0·001) and a positive relationship with BCI (Table 1e). A higher proportion of fledglings recruited in years where BCI was medium or high (two or three, on the 3-point scale) compared with years where BCI was low (one on the 3-point scale). The total number of recruits per female was negatively related to IM′ within years (Fig. 2F; linear effect of IM′: < 0·001; negative quadratic effect of IM′: = 0·044; Table 1f). Across years, there was a weak negative relationship between the mean number of recruits per female and PM (= 0·038, Fig. 3F), a negative relationship with breeding density (< 0·001) and a positive relationship with BCI (Table 1f). First-time breeders produced fewer recruits than experienced breeders (= 0·032; Table 1f). The negative relationship between the number of recruits per female and IM was stronger in years where PM was larger (Table 1f; IM′ × PM interaction: = 0·001).

Female adult survival was not related to mismatch within years, although there was a nonsignificant negative trend (= 0·068, Fig. 2G). There was no relationship between mean female survival and PM across years (Fig. 3G), while there was a negative effect of breeding density (= 0·003) and a positive effect of BCI (Table 1g). Similarly, there was no relationship between male adult survival and IM′ within years (Fig. 2F) or PM across years (Fig. 3F). Mean adult survival for males was negatively related to breeding density (= 0·003) and positively related to BCI (Table 1f).

Selection analyses

When data from all years were pooled, there was an overall negative relationship between relative fitness (the number of recruits relative to the annual mean) and standardized laying date, that is, directional selection for earlier egg-laying [overall standardized selection differential = −0·198 ± 0·035 (SE), t = −5·658, < 0·001, d.f. = 3470]. The annual point estimates for the strength of directional selection (i.e. βt values) varied considerably from year to year, but were negative in most years (Fig. S3a). There was a negative quadratic relationship between βt and the annual population mismatch (Fig. 4; βt  = −0·133 −0·007 × PM−0·002 × PM2; linear term: = 0·277; quadratic term: = 0·020; overall model: F(2,34) = 6·273, = 0·005). Directional selection was stronger in years where birds bred on average later than the food peak, but was weak or absent in years where the synchrony between birds and caterpillars was high or negative (Fig. 4). Density, BCI and age composition did not have significant effects on βt. There was no relationship between the annual mean number of recruits and βt (linear effect: = 0·445; quadratic effect: = 0·358).

image

Figure 4. Annual standardized linear selection differentials (βt) plotted against average population mismatch. Curve shows best-fit from a quadratic model, weighting each data point by 1/[(standard error of βt)2].

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Nonlinear selection was apparent in many years (Fig. S3c), but the form of this selection varied from concave (negative quadratic selection, reduced fitness for early as well as late breeders) to convex (positive quadratic selection, all but the very earliest birds fare poorly). There was no significant relationship between the strength of quadratic selection and PM, although there was a nonsignificant positive trend (= 0·107), that is, the relationship between relative fitness and laying date appeared to be more convex in years where most of the population bred too late relative to the food peak (Fig. S3d).

Discussion

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Strong Individual-level but weak population effects of mismatch
  8. Effects of mismatch on natural selection
  9. Acknowledgements
  10. References
  11. Supporting Information

In this study we explored relationships between climate, demography and natural selection in a great tit population that has experienced significant spring warming in recent decades. This warming has led to an increasing mismatch between the phenology of the birds and the seasonal peak in caterpillar abundance, the primary food source for nestlings. In the 1970s, typical breeding times closely matched the caterpillar biomass peak, but since then a mismatch of almost two weeks has developed (Fig. S3b) – many pairs now breed too late to profit fully from the short period in summer when caterpillars are plentiful (Visser et al. 1998; Nussey et al. 2005; Visser, Holleman & Gienapp 2006). This trophic asynchrony has imposed directional selection for earlier breeding (Fig. 4), and while laying dates have responded through phenotypic plasticity and possibly some microevolution (Gienapp, Postma & Visser 2006), the rate of advance has been much slower than that of caterpillar phenology. Similar mismatches are likely developing in many populations of temperate woodland bird species that are experiencing rapid spring warming (Leech & Crick 2007), yet very little is known about the demographic and evolutionary consequences (Both 2010; Heard, Riskin & Flight 2012).

Our primary goal in this study was to characterize relationships at both the individual and population levels between fitness components and mismatch. In doing so, we provide a comprehensive analysis of the various ways in which mismatch can affect individual performance and how these translate into signatures (or lack thereof) of climate change at the level of population demography. The results illustrate how phenological mismatch can be associated with strong phenotypic selection while having relatively weak or no apparent effects on key population vital rates (recruitment, adult survival) across years. This highlights the importance of distinguishing conceptually between the effects of mismatch on individual (relative) performance and those on mean productivity or other population-level parameters, and we show how this can be achieved statistically using generalized linear mixed models. Our results also suggest that caution is advisable when extrapolating individual-level relationships to the population level and vice versa, a general problem of statistical and logical inference in hierarchical systems known as ‘ecological fallacy’ (Robinson 1950; van de Pol & Wright 2009).

Strong Individual-level but weak population effects of mismatch

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Strong Individual-level but weak population effects of mismatch
  8. Effects of mismatch on natural selection
  9. Acknowledgements
  10. References
  11. Supporting Information

At the individual level, strong negative effects of mismatch, sometimes curvilinear, were detected for all fitness components examined except adult survival. In any given year, females breeding late relative to the seasonal peak in caterpillar biomass (i.e. females with positive values of individual mismatch) were less likely to produce a second brood, laid smaller clutches and were more likely to fledge no offspring (Fig. 2A–C). Among those females that did manage to raise some chicks to fledging, those breeding late relative to the food peak fledged fewer chicks (Fig. 2D), and these chicks in turn were less likely to recruit (Fig. 2E). The net result was that females laying relatively early produced more recruits (Fig. 2F), and hence, their relative fitness was on average higher than that of late-laying females.

Despite these pronounced individual-level effects, across-year relationships between mean demographic rates (i.e. annual averages for each fitness component) and population-level mismatch were either much weaker or entirely absent (Fig. 3). For example, annual variation in the mean number of recruits per female – the demographic rate that most strongly influences population fluctuations in this species (van Balen 1980) – was large and driven mostly by density effects and stochastic fluctuations in beech crop (Table 1f). Hence, the mismatch signal was not obvious at the population level for this demographic rate (Fig. 3F) and only statistically significant once breeding density, beech crop and additional stochastic variation owing to unknown environmental factors (captured by the ‘year’ random effect) were formally accounted for in the GLMM. A similarly weak negative relationship between the annual mean number of recruits and phenological asynchrony with caterpillars was found for a UK population of great tits (Charmantier et al. 2008).

Similar patterns were found for the number of fledglings: a strong negative curvilinear relationship with mismatch at the individual-level (Fig. 2D), but a much weaker negative linear relationship at the population level, with lots of scatter (Fig. 3D). Some of this interannual variation in fledgling production was accounted for by negative density dependence and fluctuations in age composition (Table 1d). The remaining unexplained variation could be due to many factors, for example direct climatic influences on chick mortality; our goal was not to explain as much variation in demographic rates as possible, but rather to understand the mechanisms and extent to which mismatch affects demographic performance. This level of understanding facilitates the development and parameterization of ecologically realistic population models, which can then be used to predict possible effects of climate change on population dynamics.

Several processes could explain why effects of breeding season mismatch on mean demographic rates were weak, despite strong within-year, among-individual effects. First, reductions in the reproductive output of individuals breeding late relative to the food peak might be offset by increases in early birds, for example if young fledged early in the season experience less-intense competition for food in years of high population mismatch because of the higher mortality of late broods. While we do not have direct evidence for this, we did find a significant interaction between PM and IM in the model of recruitment probability (Table 1e): the negative effect of IM was stronger in years of large PM, which is consistent with a scenario of frequency-dependent benefits of early fledging. Inspection of the annual relationships between relative reproductive success and standardized laying dates also revealed that the relative success of the earliest females has increased more over the study period than that of the latest females has decreased, which again suggests a role for frequency or density dependence. However, there were no significant interactions between IM and density in the GLMMs for the number of fledglings (Table 1d), probability of recruitment (Table 1e), or number of recruits (Table 1f), nor was there was any overall relationship between annual linear selection differentials and mean breeding density (e.g. stronger selection for earlier breeding in high-density years). The annual number of first clutches in the whole study area might be too coarse a measure of density to capture the relevant competition effects, although relative fledging mass might be more important than relative fledging date per se in this regard (Both, Visser & Verboven 1999).

Second, negative fitness effects of mismatch during the breeding season might be counterbalanced by improved survival at other times of the year, for example if winters become less severe because of global warming (Saether et al. 2000; Jenouvrier et al. 2006). We find no evidence in our study population for increases over time in juvenile or adult survival (Fig. S1); if anything, there was a marginally nonsignificant negative trend (P = 0·081) in adult female survival across the study period (Fig. S1G), which might be related to increased competition associated with a higher influx of immigrants (T.E. Reed & M.E. Visser, unpublished). Reductions in the total number of fledglings produced in years of large population mismatch could also be followed by improved average postfledgling survival, via density-dependent feedbacks, dampening the effects of mismatch on mean recruitment success. If this were true, however, we would also expect to find a significant statistical interaction between breeding density and individual-level mismatch on recruitment probability, but this was not observed (Table 1e).

The third, and in our opinion most likely, explanation for the weaker-than-expected effects of population mismatch on the mean number of fledglings and recruits, is that mismatch signals are simply difficult to detect at the population level because of high environmental stochasticity in these demographic rates. Year-to-year fluctuations in the survival of juvenile and adult great tits are strongly affected by the quality of the autumnal beech crop (Perdeck, Visser & Van Balen 2000; Grøtan et al. 2009) and by winter severity (Kluijver 1951; van Balen 1980), which adds considerable ‘environmental noise’ to any underlying influence of mismatch. Detecting mismatch effects on demographic rates thus becomes an issue of statistical power, which can easily be confirmed by simulations based on the observed individual-level relationships and between-year stochastic variance in fitness components (results not shown). This conclusion is itself biologically interesting: we have almost four decades of data on great tit demography, a period across which substantial spring warming occurred, yet we find very weak effects of mismatch on mean recruitment rates and no effects on adult survival. This suggests that very long time series, very strong climatic change, or both will be required to observe significant effects of phenological mismatch on population demography, although this of course will depend on the life history and ecology of the species being considered.

Effects of mismatch on natural selection

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Strong Individual-level but weak population effects of mismatch
  8. Effects of mismatch on natural selection
  9. Acknowledgements
  10. References
  11. Supporting Information

Estimating selection differentials provides further insight into links between individual-level and population-level processes. The individual-level analyses (Fig. 2) showed that timing of breeding relative to the seasonal peak in caterpillar biomass has a strong effect on individual relative fitness in our study population. If synchrony with the food peak was the only selective pressure and mean synchrony had not changed over time, then one would expect the fitness curves to more bell-shaped, with lower fitness for both relatively early and relatively late females (i.e. stabilizing selection). Indeed, fledging success and fledging mass in great tits tend to be lower both before and after the food peak, at least for first broods (Verboven, Tinbergen & Verhulst 2001; Visser, Holleman & Gienapp 2006). When negative and positive mismatch years are considered separately, the relationship between the number of recruits and IM is more obviously bell-shaped (Fig. S4). However, synchrony with the food peak is not the only selective factor, and average mismatch has increased significantly over time in our study population (Fig. S3). Considering all years together, the overall net effect is directional selection for earlier laying dates.

The current study is purely correlational and therefore we cannot exclude the possibility that factors other than timing relative to the food peak (e.g. phenotypic quality effects, seasonal changes in other factors) are responsible for the observed relationships. The relationship with clutch size (Fig. 2B), for example, is probably driven by the fact that early layers per se tend to produce larger clutches (Perrins 1970), rather than any causal effect of caterpillar availability given that eggs are laid well before the food peak. Alternatively, females might actively adjust their clutch size (and hence their reproductive effort) in response to environmental cues that predict subsequent caterpillar biomass (Verboven, Tinbergen & Verhulst 2001). The causal effects of caterpillar availability are better established for the relationships between fledgling success and mismatch (Verboven, Tinbergen & Verhulst 2001) and local recruitment and fledging date (Verboven & Visser 1998). Note that we do not account for individual variation in clutch size when calculating IM, which could introduce a potential bias into our estimation of the relationships between IM and fledging/recruitment success, given that late breeders tend to lay smaller clutches. However, the patterns remain largely unchanged when clutch size variation was taken into account (Fig. S5). Thus, we chose to account only for laying date variation when calculating IM, given that the primary timing decision for a female is when to initiate egg-laying, not how many eggs to lay (the latter being more related to parental investment decisions).

We found that directional selection was stronger in years where birds bred on average later than the food peak, but was weak or absent in years where there was little population mismatch (Fig. 4, see also van Noordwijk, McCleery & Perrins 1995; Charmantier et al. 2008). However, we stress that mismatch is not the only selective pressure affecting laying dates, and hence, perfect synchrony with the food peak is not necessarily optimal. For example, the interests of chicks and parents need not coincide exactly and females might be constrained, or unwilling, to breed at the optimal date in terms of chick survival prospects because of high costs of producing and incubating eggs early in the season when it is still cold and food is scarce (Perrins 1970; Visser & Lessells 2001). Being ‘adaptively mismatched’ by a few days might therefore be optimal from the perspective of parental fitness (Visser, te Marvelde & Lof 2011), particularly if day-to-day variation in temperature is high (Lof et al. 2012). Optimal laying dates may also depend on trade-offs between the fitness benefits of synchronizing the first brood with the food peak on the one hand, and reduced probability of producing a second brood (Fig. 2A), on the other (Verboven, Tinbergen & Verhulst 2001). In addition to these selective processes, females laying too early relative to the food peak may have higher-than-expected fitness simply because they are in better body condition, and thus, measured fitness curves need not be bell-shaped.

In conclusion, we show that in years of large population mismatch, in which a high proportion of females breed too late relative to the food peak, relative fitness differences among females breeding at different dates are large, but the average absolute fitness is similar to years where population mismatch is smaller or absent. Thus, phenological mismatch appears to have strong effects on selection pressures, but weak effects on key demographic rates. This result suggests that climatic influences on evolutionary and population dynamics might be uncoupled in this population, at least for the trait we considered and within the observed range of spring warming. However, it would be premature to conclude that future climate change does not pose a threat to this population, as reductions in vital rates could unfold rapidly if mismatch increases beyond a certain point.

Acknowledgements

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Strong Individual-level but weak population effects of mismatch
  8. Effects of mismatch on natural selection
  9. Acknowledgements
  10. References
  11. Supporting Information

We are very grateful to Bernt-Erik Saether, Vidar Grøtan and Luc te Marvelde for sharing their views on components of this paper, also to two reviewers for helpful comments on the manuscript. M.E.V. is supported by a NWO-VICI grant.

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  5. Results
  6. Discussion
  7. Strong Individual-level but weak population effects of mismatch
  8. Effects of mismatch on natural selection
  9. Acknowledgements
  10. References
  11. Supporting Information
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Supporting Information

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Strong Individual-level but weak population effects of mismatch
  8. Effects of mismatch on natural selection
  9. Acknowledgements
  10. References
  11. Supporting Information

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jane2020-sup-0001-AppendixS1-FigS1-S5.docxWord document164KAppendix S1. Potential limitations of the mismatch measure. Fig. S1. Population-level plots of mean demographic rates vs. year. Fig. S2. Annual recapture probability as a function of year and population-level mismatch. Fig. S3. Temporal trends in linear selection, nonlinear selection and population mismatch. Fig. S4. The number of recruits plotted as a function of IM, splitting the data in negative and positive mismatch years. Fig. S5. The sensitivity of patterns in Fig. 1 to how IM was defined.

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