Accounting for uncertainty in dormant life stages in stochastic demographic models

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.

Pathways to global-change effects on biodiversity: new opportunities for dynamically forecasting demography and species interactions

In structured populations, persistence under environmental change may be particularly threatened when abiotic factors simultaneously negatively affect survival and reproduction of several life cycle stages, as opposed to a single stage. Such effects can then be exacerbated when species interactions generate reciprocal feedbacks between the demographic rates of the different species. Despite the importance of such demographic feedbacks, forecasts that account for them are limited as individual-based data on interacting species are perceived to be essential for such mechanistic forecasting-but are rarely available. Here, we first review the current shortcomings in assessing demographic feedbacks in population and community dynamics. We then present an overview of advances in statistical tools that provide an opportunity to leverage population-level data on abundances of multiple species to infer stage-specific demography. Lastly, we showcase a state-of-the-art Bayesian method to infer and project stage-specific survival and reproduction for several interacting species in a Mediterranean shrub community. This case study shows that climate change threatens populations most strongly by changing the interaction effects of conspecific and heterospecific neighbours on both juvenile and adult survival. Thus, the repurposing of multi-species abundance data for mechanistic forecasting can substantially improve our understanding of emerging threats on biodiversity.

Statistical inference for seed mortality and germination with seed bank experiments

Plant population ecologists regularly study soil seed banks with seed bag burial and seed addition experiments. These experiments contribute crucial data to demographic models, but we lack standard methods to analyze them. Here, we propose statistical models to estimate seed mortality and germination with observations from these experiments. We develop these models following the principles of event history analysis, and analyze their identifiability and statistical properties by algebraic methods and simulation. We demonstrate that seed bag burial, but not seed addition experiments, can be used to make inferences about age-dependent mortality and germination. When mortality and germination do not change with seed age, both experiments produce unbiased estimates but seed bag burial experiments are more precise. However, seed mortality and germination estimates may be inaccurate when the statistical model that is fit makes incorrect assumptions about the age dependence of mortality and germination. The statistical models and simulations that we present here can be adopted and modified by plant population ecologists to strengthen inferences about seed mortality and germination in the soil seed bank.

Assessing seasonal demographic covariation to understand environmental-change impacts on a hibernating mammal

Natural populations are exposed to seasonal variation in environmental factors that simultaneously affect several demographic rates (survival, development and reproduction). The resulting covariation in these rates determines population dynamics, but accounting for its numerous biotic and abiotic drivers is a significant challenge. Here, we use a factor-analytic approach to capture partially unobserved drivers of seasonal population dynamics. We use 40 years of individual-based demography from yellow-bellied marmots (Marmota flaviventer) to fit and project population models that account for seasonal demographic covariation using a latent variable. We show that this latent variable, by producing positive covariation among winter demographic rates, depicts a measure of environmental quality. Simultaneously, negative responses of winter survival and reproductive-status change to declining environmental quality result in a higher risk of population quasi-extinction, regardless of summer demography where recruitment takes place. We demonstrate how complex environmental processes can be summarized to understand population persistence in seasonal environments.

Life history responses of meerkats to seasonal changes in extreme environments

Species in extreme habitats increasingly face changes in seasonal climate, but the demographic mechanisms through which these changes affect population persistence remain unknown. We investigated how changes in seasonal rainfall and temperature influence vital rates and viability of an arid environment specialist, the Kalahari meerkat, through effects on body mass. We show that climate change-induced reduction in adult mass in the prebreeding season would decrease fecundity during the breeding season and increase extinction risk, particularly at low population densities. In contrast, a warmer nonbreeding season resulting in increased mass and survival would buffer negative effects of reduced rainfall during the breeding season, ensuring persistence. Because most ecosystems undergo seasonal climate variations, a full understanding of species vulnerability to global change relies on linking seasonal trait and population dynamics.

Forecasting species range dynamics with process-explicit models: matching methods to applications

Knowing where species occur is fundamental to many ecological and environmental applications. Species distribution models (SDMs) are typically based on correlations between species occurrence data and environmental predictors, with ecological processes captured only implicitly. However, there is a growing interest in approaches that explicitly model processes such as physiology, dispersal, demography and biotic interactions. These models are believed to offer more robust predictions, particularly when extrapolating to novel conditions. Many process-explicit approaches are now available, but it is not clear how we can best draw on this expanded modelling toolbox to address ecological problems and inform management decisions. Here, we review a range of process-explicit models to determine their strengths and limitations, as well as their current use. Focusing on four common applications of SDMs - regulatory planning, extinction risk, climate refugia and invasive species - we then explore which models best meet management needs. We identify barriers to more widespread and effective use of process-explicit models and outline how these might be overcome. As well as technical and data challenges, there is a pressing need for more thorough evaluation of model predictions to guide investment in method development and ensure the promise of these new approaches is fully realised.

Plant carnivory beyond bogs: reliance on prey feeding in Drosophyllum lusitanicum (Drosophyllaceae) in dry Mediterranean heathland habitats

Background and Aims

In a cost-benefit framework, plant carnivory is hypothesized to be an adaptation to nutrient-poor soils in sunny, wetland habitats. However, apparent exceptions to this cost-benefit model exist, although they have been rarely studied. One of these exceptions is the carnivorous subshrub Drosophyllum lusitanicum , which thrives in Mediterranean heathlands on dry sandstone soils and has relatively well-developed, xeromorphic roots. Here, the roles of leaf (carnivory) and root (soil) nutrient uptake in growth promotion of this particular species were assessed.

Methods

In a greenhouse experiment, plants were fed with laboratory-reared fruit flies ( Drosophila virilis ) and received two concentrations of soil nutrients in a factorial design. Above-ground plant growth and final above- and below-ground dry biomass after 13 weeks were recorded. Nutrient uptake via roots was also evaluated, using stable nitrogen isotope analysis.

Key Results

Insect feeding resulted in significantly higher growth and above- and below-ground biomass compared with soil fertilization. No additional benefits of fertilization were discernable when plants were insect-fed, indicating that roots were not efficient in nutrient absorption.

Conclusions

The first evidence of strong reliance on insect prey feeding in a dry-soil carnivorous plant with well-developed roots is provided, suggesting that carnivory per se does not preclude persistence in dry habitats. Instead, the combination of carnivory and xeromorphic root features allows Drosophyllum to thrive on non-waterlogged soils. New evidence is added to recent research emphasizing the role of root systems of carnivorous plants in explaining their distribution, partly challenging the cost-benefit hypothesis.