Understanding how changes in the climate affect biological communities is essential in predicting the future size and composition of populations. However, accurate predictions pose a difficult challenge for researchers. For the majority of animal species it is not feasible or ethical to conduct experiments into how these populations will respond to a changing climate. To enable us to gain an insight into potential futures of a population under climatic change, we use a computational model. Specifically, we use an integral projection model to investigate how changes in the North Atlantic Oscillation will influence the body weight and population size of a population of Soay sheep. The North Atlantic Oscillation is a large scale weather pattern of temperature differences across the Atlantic Ocean, which alters the local weather patterns in the North Atlantic region. We used published predictions of the future values of the North Atlantic Oscillation for the 21^st Century. By doing this we are able to project the response of the study population to climate change based on our current best projections of the future climate.

Our model results, presented in the Early View paper “Analysis of phenotypic change in relation to climatic drivers in a population of Soay sheep”,  suggest that a continued positive trend in the North Atlantic Oscillation (positive pressure difference between Iceland and the Azores), as predicted by the majority of models, will be accompanied by a decrease in the population size of the Soay sheep and an increase in mean body weight. These changes are likely caused by a loss of smaller individuals from the population due to higher mortality in the adverse winters (mild but wet and windy) associated with the positive North Atlantic Oscillation.

Using an integral projection model as we have in this study gives us a glimpse into the potential future of populations where experimentation is difficult, and can improve our understanding of how populations will respond to changing climatic conditions. Using published climate predictions within our model also allows such studies to be placed in the realm of current climate research and (importantly) our projections can be updated as new climate predictions are released.