Methodological artefacts cause counter-intuitive evolutionary conclusions in a simulation study.

In their simulation study, Garcia-Costoya et al. (2023) conclude that evolutionary constraints might aid populations facing climate change. However, we are concerned that this conclusion is largely a consequence of the simulated temperature variation being too small, and, most importantly, that uneven limitations to standing variation disadvantage unconstrained populations.

Seasonal specialization drives divergent population dynamics in two closely related butterflies.

Seasons impose different selection pressures on organisms through contrasting environmental conditions. How such seasonal evolutionary conflict is resolved in organisms whose lives span across seasons remains underexplored. Through field experiments, laboratory work, and citizen science data analyses, we investigate this question using two closely related butterflies (Pieris rapae and P. napi). Superficially, the two butterflies appear highly ecologically similar. Yet, the citizen science data reveal that their fitness is partitioned differently across seasons. Pieris rapae have higher population growth during the summer season but lower overwintering success than do P. napi. We show that these differences correspond to the physiology and behavior of the butterflies. Pieris rapae outperform P. napi at high temperatures in several growth season traits, reflected in microclimate choice by ovipositing wild females. Instead, P. rapae have higher winter mortality than do P. napi. We conclude that the difference in population dynamics between the two butterflies is driven by seasonal specialization, manifested as strategies that maximize gains during growth seasons and minimize harm during adverse seasons, respectively.

Thermal performance under constant temperatures can accurately predict insect development times across naturally variable microclimates.

External conditions can drive biological rates in ectotherms by directly influencing body temperatures. While estimating the temperature dependence of performance traits such as growth and development rate is feasible under controlled laboratory settings, predictions in nature are difficult. One major challenge lies in translating performance under constant conditions to fluctuating environments. Using the butterfly Pieris napi as model system, we show that development rate, an important fitness trait, can be accurately predicted in the field using models parameterized under constant laboratory temperatures. Additionally, using a factorial design, we show that accurate predictions can be made across microhabitats but critically hinge on adequate consideration of non-linearity in reaction norms, spatial heterogeneity in microclimate and temporal variation in temperature. Our empirical results are also supported by a comparison of published and simulated data. Conclusively, our combined results suggest that, discounting direct effects of temperature, insect development rates are generally unaffected by thermal fluctuations.