| Summary: | Erikstad, K.E., Reiertsen, T.K., Layton-Matthews, K., Ballesteros, M., Anker-Nilssen, T., Johansen, M., Lorentsen, S.-H., Sandvik, H., Strøm, H. & Systad, G.H.R. 2023. Population effects of acute oil spill events on seabirds – simulations using population models linked with colony-specific non-breeding distribution. NINA Report 2332. Norwegian Institute for Nature Research. Seabirds are particularly vulnerable to acute population reductions because of their slow pace of life, explaining their limited capacity for population growth and ability to recover from population losses. Acute events like disease, extreme weather or oil spills can cause acute mass mortality events with large impacts on breeding population sizes. In this report, we first propose and compare two modelling approaches to estimate the population impact of an acute population reduction. One approach is based on count data as input to a Brownian population model, the other is based on demographic data as input to a matrix-based population projection model. The comparison of the two approaches is limited to three species from four breeding populations. We then present a case study on the use of geolocation (GLS) data from four common guillemot populations, under two oil spill simulations and during two time periods of the non-breeding season. This case study illustrates how the season an oil spill occurs in can alter the overall impact on seabird populations. Finally, this report also discusses potential challenges and pitfalls regarding e.g., sample sizes of GLS loggers and how this can affect the representativeness of estimates of seabirds’ marine space use. The two modelling approaches differ in both the type of input data needed and the type of output data produced. While matrix-based models rely on more detailed demographic data of fecundity and age-specific survival, the Brownian modelling approach only requires population counts. Both models provide a measure of population recovery time or change in time to reach quasi-extinction. ...
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