Exploring population responses to environmental change when there is never enough data: a factor analytic approach

Bet Hedging Is Not Sufficient to Explain Germination Patterns of a Winter Annual Plant

AbstractBet hedging consists of life history strategies that buffer against environmental variability by trading off immediate and long-term fitness. Delayed germination in annual plants is a classic example of bet hedging and is often invoked to explain low germination fractions. We examined whether bet hedging explains low and variable germination fractions among 20 populations of the winter annual plant Clarkia xantiana ssp. xantiana that experience substantial variation in reproductive success among years. Leveraging 15 years of demographic monitoring and 3 years of field germination experiments, we assessed the fitness consequences of seed banks and compared optimal germination fractions from a density-independent bet-hedging model to observed germination fractions. We did not find consistent evidence of bet hedging or the expected trade-off between arithmetic and geometric mean fitness, although delayed germination increased long-term fitness in 7 of 20 populations. Optimal germination fractions were two to five times higher than observed germination fractions, and among-population variation in germination fractions was not correlated with risks across the life cycle. Our comprehensive test suggests that bet hedging is not sufficient to explain the observed germination patterns. Understanding variation in germination strategies will likely require integrating bet hedging with complementary forces shaping the evolution of delayed germination.

Validating measurements of acclimation for climate change adaptation

Acclimation and other forms of plasticity that can increase stress resistance feature strongly in discussions surrounding climate change impacts or vulnerability projections of insects and other ectotherms. There is interest in compiling databases for assessing the adequacy of acclimation for dealing with climate change. Here, we argue that the nature of acclimation is context dependent and therefore that estimates summarised across studies, especially those that have assayed stress using diverse methods, are limited in their utility when applied as a standardized metric or to a single general context such as average climate warming. Moreover, the dynamic nature of tolerances and acclimation drives important variation that is quickly obscured through many summary statistics or even in effect size analyses; retaining a strong focus on the temporal-level, population-level and treatment-level variance in forecasting climate change impacts on insects is essential. We summarise recent developments within the context of climate change and propose how future studies might validate the role of acclimation by integration across field studies and mechanistic modelling. Despite arguments to the contrary, to date no studies have convincingly demonstrated an important role for acclimation in recent climate change adaptation of insects. Paramount to these discussions is i) developing a strong conceptual framework for acclimation in the focal trait(s), ii) obtaining novel empirical data dissecting the fitness benefits and consequences of acclimation across diverse contexts and timescales, with iii) better coverage of under-represented geographic regions and taxa.

Cumulative weather effects can impact across the whole life cycle

Predicting how species will be affected by future climatic change requires the underlying environmental drivers to be identified. As vital rates vary over the lifecycle, structured population models derived from statistical environment-demography relationships are often used to inform such predictions. Environmental drivers are typically identified independently for different vital rates and demographic classes. However, these rates often exhibit positive temporal covariance, suggesting that vital rates respond to common environmental drivers. Additionally, models often only incorporate average weather conditions during a single, a priori chosen time window (e.g. monthly means). Mismatches between these windows and the period when the vital rates are sensitive to variation in climate decrease the predictive performance of such approaches. We used a demographic structural equation model (SEM) to demonstrate that a single axis of environmental variation drives the majority of the (co)variation in survival, reproduction, and twinning across six age-sex classes in a Soay sheep population. This axis provides a simple target for the complex task of identifying the drivers of vital rate variation. We used functional linear models (FLMs) to determine the critical windows of three local climatic drivers, allowing the magnitude and direction of the climate effects to differ over time. Previously unidentified lagged climatic effects were detected in this well-studied population. The FLMs had a better predictive performance than selecting a critical window a priori, but not than a large-scale climate index. Positive covariance amongst vital rates and temporal variation in the effects of environmental drivers are common, suggesting our SEM-FLM approach is a widely applicable tool for exploring the joint responses of vital rates to environmental change.