Drivers of demography: past challenges and a promise for a changed future

There is an urgent need to understand how populations and metapopulations respond to shifts in the environment to mitigate the consequences of human actions and global change. Identifying environmental variables/factors affecting population dynamics and the nature of their impacts is fundamental to improve projections and predictions. This chapter examines how environmental drivers, both continuous (stress) and episodic (disturbance), are incorporated in demographic modelling across many types of organisms and environments, using both observational and experimental approaches to characterise drivers. It critically summarises examples of the main approaches and identifies major accomplishments, challenges, and limitations. The chapter points to promising approaches and possible future developments. In the initial sections, models in closed systems without migration among populations are considered. The chapter then focuses on metapopulation models, emphasising the importance of understanding drivers affecting migration and differential extinction among populations. Finally, it concludes with a discussion of some important and general problems associated with assessing how population dynamics may be affected by environmental drivers that are dynamic, nonlinear, and with indirect and/or interacting effects with other drivers..

Toward Consistent Observational Constraints in Climate Predictions and Projections

Observations facilitate model evaluation and provide constraints that are relevant to future predictions and projections. Constraints for uninitialized projections are generally based on model performance in simulating climatology and climate change. For initialized predictions, skill scores over the hindcast period provide insight into the relative performance of models, and the value of initialization as compared to projections. Predictions and projections combined can, in principle, provide seamless decadal to multi-decadal climate information. For that, though, the role of observations in skill estimates and constraints needs to be understood in order to use both consistently across the prediction and projection time horizons. This paper discusses the challenges in doing so, illustrated by examples of state-of-the-art methods for predicting and projecting changes in European climate. It discusses constraints across prediction and projection methods, their interpretation, and the metrics that drive them such as process accuracy, accurate trends or high signal-to-noise ratio. We also discuss the potential to combine constraints to arrive at more reliable climate prediction systems from years to decades. To illustrate constraints on projections, we discuss their use in the UK's climate prediction system UKCP18, the case of model performance weights obtained from the Climate model Weighting by Independence and Performance (ClimWIP) method, and the estimated magnitude of the forced signal in observations from detection and attribution. For initialized predictions, skill scores are used to evaluate which models perform well, what might contribute to this performance, and how skill may vary over time. Skill estimates also vary with different phases of climate variability and climatic conditions, and are influenced by the presence of external forcing. This complicates the systematic use of observational constraints. Furthermore, we illustrate that sub-selecting simulations from large ensembles based on reproduction of the observed evolution of climate variations is a good testbed for combining projections and predictions. Finally, the methods described in this paper potentially add value to projections and predictions for users, but must be used with caution.