Phenological mismatch drives selection on elevation, but not on slope, of breeding time plasticity in a wild songbird

Abstract

Phenotypic plasticity is an important mechanism for populations to respond to fluctuating environments, yet may be insufficient to adapt to a directionally changing environment. To study whether plasticity can evolve under current climate change, we quantified selection and genetic variation in both the elevation (RN E ) and slope (RN S ) of the breeding time reaction norm in a long‐term (1973–2016) study population of great tits ( Parus major ). The optimal RN E (the caterpillar biomass peak date regressed against the temperature used as cue by great tits) changed over time, whereas the optimal RN S did not. Concordantly, we found strong directional selection on RN E , but not RN S , of egg‐laying date in the second third of the study period; this selection subsequently waned, potentially due to increased between‐year variability in optimal laying dates. We found individual and additive genetic variation in RN E but, contrary to previous studies on our population, not in RN S . The predicted and observed evolutionary change in RN E was, however, marginal, due to low heritability and the sex limitation of laying date. We conclude that adaptation to climate change can only occur via micro‐evolution of RN E, but this will necessarily be slow and potentially hampered by increased variability in phenotypic optima.

Comparing two measures of phenological synchrony in a predator–prey interaction: Simpler works better

“…At the brood level, more food available during critical nestling stages increased survival probability of offspring, but this metric was statistically outperformed by a simple measure of the brood's match with the peak date in caterpillar availability. Similarly, at the population level, females’ reproductive timing (ELD) interacted more significantly with MD p than with MO p to predict the number of surviving offspring, indicating that selection was driven by a temporal mismatch with the food peak (see Ramakers, Gienapp, & Visser, ). In the latter analysis, the estimate of the main effect MD p (and that of MO p , for that matter) was small, with confidence intervals largely overlapping zero, confirming previous findings for this population that phenological mismatch does not in and of itself affect the mean fitness in the population (Reed et al, ).…”

Quantifying individual variation in reaction norms: Mind the residual

“…We used individual data of egg‐laying dates in two of our long‐term study populations of the great tit ( P. major ) at the Hoge Veluwe (HV; 52°01'57"N 5°52'05"E; ) and the island of Vlieland (VL; 53°18′N, 5°03′E; ; note that excluding birds with only one or two broods did not affect the results (not shown here)). For a full description of the data collection and methods, see Ramakers et al ().…”

“…The DOI for our data (Vlieland population) and R scripts is https://doi.org/10.5061/dryad.tqjq2bvts. Data for the Hoge Veluwe population were published previously (Ramakers, Gienapp, & Visser, ).…”

“…In contrast, Husby et al () let residual variances only differ between three decadal groups in a RRM estimating I × E in egg‐laying date in great tits. The rationale was that because phenotypic variance increased with temperature, and temperature increased over time due to climate change, fitting decade‐specific residual variances would capture the heteroscedasticity in the RRM, an assumption later found to be false (Ramakers, Gienapp, & Visser, ).…”

“…Although the biological importance of the residual variance is increasingly appreciated in the field of ecology and evolution (Nicolaus et al, ; Westneat et al, ), there appears to be no clear awareness among evolutionary ecologists about how heteroscedasticity may affect estimates of variation in plasticity (I × E) and how it should be dealt with within the context of RRMs (but see Cleasby & Nakagawa, for an application outside RRMs). If one is interested in the evolutionary potential of the reaction norm in wild populations (Gienapp & Brommer, ; Ramakers et al, ), the main goal is usually to get unbiased estimates of I × E and G × E. To achieve this, behavioural and evolutionary ecologists can make use of advocated mixed‐modelling tools (Dingemanse & Dochtermann, ; Nussey et al, ) and use RRMs in such a way that they effectively account for heterogeneity in residual variances.…”