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Details of Grant 

EPSRC Reference: EP/Y017838/1
Title: Modelling Phenotypic Plastic Responses to Environmental Change in Stage-Structured Populations
Principal Investigator: Cobbold, Professor C
Other Investigators:
Researcher Co-Investigators:
Project Partners:
Department: School of Mathematics & Statistics
Organisation: University of Glasgow
Scheme: Standard Research
Starts: 01 May 2024 Ends: 30 April 2027 Value (£): 426,494
EPSRC Research Topic Classifications:
Non-linear Systems Mathematics
EPSRC Industrial Sector Classifications:
Environment
Related Grants:
EP/Y017919/1
Panel History:
Panel DatePanel NameOutcome
16 Oct 2023 EPSRC Mathematical Sciences Prioritisation Panel October 2023 Announced
Summary on Grant Application Form
Climate change is having profound effects on species around the globe, and necessitates that species adapt, move, or face extinction. Phenotypic plasticity is a chief mechanism of species adaptation and is often regarded as a rapid-response mechanism that will enable organisms to adapt and survive when faced with environmental change. Phenotypic plasticity is the ability of an individual phenotype, defined by its observable traits, to express trait variation in response to changing biotic and abiotic environments. Despite its importance, phenotypic plasticity is not generally considered in mathematical models of species' responses to climate change. When it is considered, usually all individuals are treated as responding equally to environmental cues, or the focus is on a single trait, or omits the delays between the experience of an environmental cue and the ensuing individual response. We propose developing a suite of delay differential equation models that can remedy these issues, and develop methodology to track population level trait distributions that emerge from the interaction of delayed phenotypic plasticity and structured population dynamics. This will place us in a unique position to address key long-standing ecological questions and derive new mathematical theory.

Current ecological theory suggests that populations with high phenotypic plasticity are associated with increased population growth and productivity, decreased vulnerability to environmental change, and hence increased invasiveness and decreased extinction risk. However, empirical studies across taxa both support and dispute this view. An objective of the proposal is to resolve the debates surrounding the role of phenotypic plasticity in persistence and invasion under environmental change. Our recent work demonstrated that phenotypic plasticity can induce complex population dynamics, and that phenotype distribution may be an indicator of impending bifurcations synonymous with regime shifts or population collapse. However, the mechanisms leading to these shifts in phenotype distribution and dynamical transitions remain elusive, and thus obfuscate species' responses to environmental change. The project aims to provide the vital mathematical insight that is lacking.

We will develop a suite of life-cycle models that reflect biologically idealised descriptions of life-cycle dynamics, capturing key aspects of different types of delayed phenotypic plasticity observed in nature. Many traits such as development time, body size and fecundity are affected by changes in environmental factors such as nutrition, temperature and precipitation. Our approach accounts for the interdependency of such traits, via the spectrum of environmental cues they respond to, and via the complex feedback cycles and population dynamics they generate. These models are intended to be strategic and to illuminate general mechanisms providing a pragmatic way forward to generalisable ecological theory underpinned by rigorous mathematical models.

Our central objective is to develop a mathematical analysis and theoretical tools that resolve when, where and how phenotypic plasticity shapes species' responses to both long- and short-term environmental change. To achieve this, we will devise novel analysis building on linear analysis of the single-trait (all individuals share the same trait) solutions. Establishing connections between the single-trait solutions will help us to map out parameter space where more complex multi-trait solutions shape population dynamics. By introducing new population-trait metrics we will understand the bifurcation structure arising from the multi-trait-structured model framework. Ultimately, the proposed work will provide a mathematical foundation for mechanistic understanding of the population level effects of phenotypic plasticity and for research-based decision-making to mitigate the devastating effects of environmental change.

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