PhD project: Understanding the drivers of population dynamics
How to apply: through the Oxford DTP, but please also get in touch if interested.
Project Summary:
Our understanding of the factors underpinning biodiversity loss is constrained by the low predictive power of trait-based ‘extinction models’, which is a great impediment to a genuinely predictive ecology. Modelling trends among populations is likely to deliver deeper insights, since external drivers impact primarily on population dynamic parameters (fecundity, mortality).
This project will address this gap using population time-series data that are replicated in space, including datasets held by CEH (UKBMS, ECN) and partner organisations (Rothamsted Insect Survey, Breeding Bird Survey, National Bat Monitoring Program). These datasets jointly comprise >20,000 time-series in excess of 15 years, spanning ~1000 species.
Population dynamics will be modelled in a hierarchical Bayesian framework to incorporate spatial autocorrelation among sites, phylogenetic non-independence among species, and density-dependence. Both linear and non-linear trends will be modelled in order to detect potential tipping points and the causes thereof. Population growth will be modelled as a function of species’ functional traits and measures of key extinction drivers such as climate change, land-use intensity (and change) and atmospheric deposition.
The results will provide novel insights into the relative importance of different extinction drivers and the role of species’ traits in mediating the response and resilience of populations.
Project Summary:
Our understanding of the factors underpinning biodiversity loss is constrained by the low predictive power of trait-based ‘extinction models’, which is a great impediment to a genuinely predictive ecology. Modelling trends among populations is likely to deliver deeper insights, since external drivers impact primarily on population dynamic parameters (fecundity, mortality).
This project will address this gap using population time-series data that are replicated in space, including datasets held by CEH (UKBMS, ECN) and partner organisations (Rothamsted Insect Survey, Breeding Bird Survey, National Bat Monitoring Program). These datasets jointly comprise >20,000 time-series in excess of 15 years, spanning ~1000 species.
Population dynamics will be modelled in a hierarchical Bayesian framework to incorporate spatial autocorrelation among sites, phylogenetic non-independence among species, and density-dependence. Both linear and non-linear trends will be modelled in order to detect potential tipping points and the causes thereof. Population growth will be modelled as a function of species’ functional traits and measures of key extinction drivers such as climate change, land-use intensity (and change) and atmospheric deposition.
The results will provide novel insights into the relative importance of different extinction drivers and the role of species’ traits in mediating the response and resilience of populations.