Testing the effect of quantitative genetic inheritance in structured models on projections of population dynamics

Abstract

Global climate change is altering the timing of life history events for species living in seasonal environments. These shifts in phenology can lead to the disruption of interspecific relationships with implications for individual fitness. Predicting phenological change and its population level consequences can provide insights into population persistence. Achieving this is challenging for labile traits as current structured population models do not explicitly distinguish between the roles of phenotypic plasticity and micro‐evolution, hindering realistic predictions of trait change. In this study we present the first empirical test of a new integral projection model (IPM) framework, which allows phenotypic plasticity and micro‐evolution to be teased apart by incorporating a quantitative genetic inheritance function. We parameterise this model for a population of wild great tits Parus major and test its predictive capabilities through K‐fold cross validation. We test the predictive accuracy of the quantitative genetic IPM in comparison to the standard IPM. We demonstrate that adding genetic inheritance rules maintains high accuracy of projections of phenological change, relative to the standard IPM. In addition, we find almost identical projections of population dynamics in this population for both IPMs, demonstrating that this model formulation allows researchers to investigate the contributions of phenotypic plasticity and micro‐evolution to trait change, without sacrificing predictive accuracy. Modelling in this way reveals that, under directional environmental change, both micro‐evolution and plasticity contribute to an advance of phenology, although the effect of plasticity is an order of magnitude higher than evolution. Despite this, synchrony between great tits and their caterpillar prey was reduced and population declines occurred. Our approach demonstrates that this model framework provides a promising avenue through which to explore the roles of phenotypic plasticity and evolution in trait changes and population dynamics.

Phenological asynchrony: a ticking time‐bomb for seemingly stable populations?

“…This model was previously cross validated (Simmonds et al ., 2019b) and was shown to capture hatch date dynamics and trends in population size, but under‐predict absolute population size, therefore we are confident that our model is robust to initial conditions and can capture the trait dynamics of this population.…”

“…The IPM was parameterised for the Wytham Woods population of great tits in Simmonds et al . (2019b). The data for parameterisation spanned 1961 to 2010 (50 years).…”

“…These identified cues were used within the development function and the function to predict caterpillar peak abundance. All other spring temperature measures in other functions remained the same as in Simmonds et al ., (2019b).…”

“…The survival and recruitment functions determined viability and fertility selection, the drivers of adaptive evolution. Each of these functions was parameterised using regression models (see, Simmonds et al ., 2019b for more details) that included the effects of spring and winter weather conditions, and food supply (beech mast quantity and the timing of the peak abundance of winter moth caterpillars). Beech mast is a key winter food source for the great tits and has a strong impact on annual survival (Perdeck et al ., 2000).…”

“…We explore the conditions under which asynchrony arises and its impact on population dynamics, using an evolutionarily explicit integral projection model (IPM) (Coulson et al ., 2017; Simmonds et al ., 2019b). Our stochastic, density‐dependent IPM combines demographic functions, phenotypic plasticity (change in phenotype for the same genotype in response to environmental change), and adaptive evolution to project population dynamics and phenological change to the end of this century.…”