Data from: Different demographic drivers of recovery in two adjacent populations of a long-lived bird of prey

Understanding the demographic mechanisms underlying population decline or recovery is critical for conservation and management, yet similar population trends may arise from very different underlying processes. Comparing populations of vulnerable species groups like long-lived raptors in different environments can reveal how species respond to change. Here, we explore the demographic drivers of population recovery in two white-tailed eagle (Haliaeetus albicilla) populations, for which we have comprehensive data available: a mainly coastal population in the Baltic Sea region and an Arctic inland freshwater population primarily in Lapland. We analysed over 50 years of data, including breeding pair counts, productivity, ringing records, visual resightings, and genetic identification of individuals at natal nests and breeding sites. These data were combined in a Bayesian age-structured integrated population model to estimate key demographic parameters, population trends, and conduct prospective and retrospective analyses. Both populations experienced similar growth rates (Baltic Sea: 8.7% [CRI = -1.1% - 16.8%], Lapland: 8.8% [CRI = -7.0% - 28.0%]), primarily supported by high adult survival typical for long-lived species. Despite these similar growth rates, demographic pathways differed markedly between populations: subadult survival being higher in the Baltic Sea, whereas adult survival was higher in Lapland. Both populations showed evidence of negative density dependence, and in the Baltic Sea population this appeared to operate primarily through delayed recruitment. This was associated with an increase in the non-breeding floater cohorts, suggesting increasing constraints on recruitment into the breeding population. Our results demonstrate that comparable population growth can be sustained by contrasting demographic structures, implying that monitoring and management strategies should account for population-specific processes rather than relying on trends alone. More broadly, this study highlights the value of long-term, multi-source data and integrated demographic modelling for identifying density-dependent mechanisms and understanding recovery dynamics in long-lived species.
The dataset includes the raw population data and the simulation output from the Integrated Population Model. Model code can be found in the supplementary material of the respective publication.