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Understanding the importance of spatio-temporal variation in oceanographic and climate conditions for biological traits

Project Description

The aim of this project is to understand the spatial and temporal scale at which the environment drives behaviour and breeding performance in Arctic seabirds.

The effect of environmental variation on the demography of marine vertebrates is well established. Studies have shown how survival and reproductive success can be linked to small-scale changes in oceanographic and atmospheric conditions, through to global climate phenomenon. However, current studies generally use predefined temporal and spatial scales to model environmental covariates despite evidence that they occur with long lags and at multiple scales. This could limit our understanding as these presupposed time windows and spatial scales have often not been robustly selected and ignore multi-level effects.

As wide-ranging top marine predators, seabirds are hugely affected by changes in the climate. Many species move across ocean basins, and environmental effects act throughout the annual cycle. We know that the oceanographic environment is very important at different spatial scales and time periods but studies have failed to fully allow models to detect the scale of effects, model multiple scales simultaneous or properly allow for lagged effects. Without such analyses, we cannot understand how individuals cope with changing environmental conditions to acquire resources and how this affects their survival and reproduction.

Mechanistically, the environment affects individuals through changes in food availability and prey type. It may directly affect energy budgets, or indirectly alter physiological state. Recent work has suggested that diet may have an important impact on an animal’s microbiome, known to underpin a huge range of ecological and evolutionary processes. To date, no study has linked behavioural responses to climate change, diet and the microbiome, nor included multi-scale environmental effects to address these questions.
This comprehensive project will use novel modelling techniques to quantify multiscale oceanographic and climate effects, in conjunction with biologging data and samples on Kittiwakes, breeding in Svalbard. The goals of this project are:
1) Use a combination of climate indices (e.g. well-known indices such as North Atlantic Oscillation, Northern Annual Mode, but also novel tailored indices) across time to examine the composite effects on behaviour in well-studied kittiwake populations. Temporal autocorrelation within and among indices will be decomposed, using wavelet analyses, to allow the identification of key time frames for climate drivers
2) Using satellite data and modelled products (e.g. SST, Chlorophyll-a, wind, Lyapunov exponents, Front metrics), multi-variate principal coordinate neighbour matrices will be used to identify important spatial scales for individual and composite oceanographic effects on seabird biology.
3) Using stable isotopes, identify differences in trophic niche among individuals and in response to multi-scale environmental effects.
4) Link microbiome measures to both diet and environment, to quantify how multiscale climate effects may alter the microbiomes of kittiwakes.
5) Combining results from objectives 1-4, examine how projected climate change scenarios may directly or indirectly affect seabird populations.

To apply for this opportunity, please visit: and click the ’Apply now’ button.

Funding Notes

Full funding (fees, stipend, research support budget) is provided by the University of Liverpool for 3.5 years for UK or EU citizens. Formal training is offered through partnership between the Universities of Liverpool and Manchester. Our training programme will provide all PhD students with an opportunity to collaborate with an academic or non-academic partner and participate in placements.


Trevail AM, Green JA, Sharples J, Polton JA, Miller PI, Daunt F, Owen E, Bolton M, Colhoun K, Newton S, Robertson G, Patrick SC. (2019) “Environmental heterogeneity decreases reproductive success via effects on foraging behaviour” Proc. R. Soc. B, 286
Cazelles B, Chavez M, Berteaux D, Menard F, Olav Vik J, Jenouvrier S and Stenseth N. (2008). Oecologia 156: 287.
Dray S, Legendre P, and Peres-Neto P. (2006) "Spatial modelling: a comprehensive framework for principal coordinate analysis of neighbour matrices (PCNM)." Ecological modelling 196.3-4: 483-493.
Videvall E, Strandh M, Engelbrecht A, Cloete S and Cornwallis C. (2018)"Measuring the gut microbiome in birds: comparison of faecal and cloacal sampling." Molecular ecology resources 18.3: 424-434.
Hewitt JE, Thrush SF, Dayton PK and Bonsdorff E. (2007).
The Effect of Spatial and Temporal Heterogeneity on the Design and Analysis of Empirical Studies of Scale‐Dependent Systems.
The American Naturalist 169:3, 398-408

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