Predicting the effects of environmental variation on Atlantic salmon (Salmo salar) population dynamics
File(s)
Published version
Author(s)
Morris, Olivia
Type
Thesis
Abstract
Rapid environmental change is causing dramatic declines in marine and freshwater fish populations across the globe. Atlantic salmon populations, which have a life cycle that spans both marine and freshwater ecosystems, have declined by around 70% in the last 25 years. Understanding the factors that drive Atlantic salmon population dynamics are crucial to ultimately mitigate these declines. Within this thesis, we develop a novel mechanistic model by combining the Metabolic Theory of Ecology with Integral Projection Models, giving a Metabolic IPM (termed MIPM). In Chapter 2, we develop the MIPM and derive the thermal performance curve of Atlantic salmon population fitness, independently predicting the thermal niche across their geographical range. We find that under future global warming scenarios, the fitness of many populations will be negatively impacted, compounded by resource supply and thermal fluctuations. In Chapter 3, we evaluate the value of existing population data and compare different possible expansions to better inform the MIPM. We find that sampling in an additional month during the field season has greater power in uncovering model parameters, compared to deploying the same field effort to generate a longer time series, or more intensive sampling according to current practices. Finally, in Chapter, 4 we investigate the drivers of existing populations by comparing empirical juvenile Atlantic salmon data from populations that span their latitudinal distribution. We find that temperature is a governing factor, however, temperature-independent resource availability plays a significant role, having implications on the fitness of populations and the ability to adapt to climate change. Overall, our results provide novel insights from a model that can capture environmental constraints on Atlantic salmon populations, and we hope that our results can be used to better inform management programmes. Furthermore, our modelling framework is adaptable for predicting the impacts of environmental change on other focal species.
Version
Open Access
Date Issued
2024-01
Date Awarded
2024-05
Copyright Statement
Creative Commons Attribution NonCommercial Licence
License URL
Advisor
Woodward, Guy
Rosindell, James
Pawar, Samraat
Publisher Department
Department of Life Sciences
Publisher Institution
Imperial College London
Qualification Level
Doctoral
Qualification Name
Doctor of Philosophy (PhD)