Learning from long time series of harvest and population data: Swedish lessons for European goose management. Issue 1 (15th March 2021)

• Record Type: Journal Article
• Authors: Liljebäck, Niklas; Bergqvist, Göran; Elmberg, Johan; Haas, Fredrik; Nilsson, Leif; Lindström, Åke; Månsson, Johan
• Is Part Of: Wildlife biology. Volume 2021:Issue 1(2021)
• Page Start: 1
• Page End: 10
• Publication Date: 2021-03-15
• Subjects: conservation -- goose populations -- harvest -- management -- monitoring programs -- population trajectories -- time series; Wildlife conservation; Wildlife management; Animal ecology; 590
• DOI: 10.2981/wlb.00733
• Languages: English
• ISSNs: 0909-6396

Abstract

Abstract : Goose management in Europe is faced by multiple challenges, as some species are declining and in need of conservation actions, while other populations have become very abundant, resulting in calls for increased harvest. Sweden has long‐term series of harvest data and counts of breeding and autumn‐staging geese. We used national data (indices) for greylag goose, bean goose and Canada goose to study shifts in temporal trends and correlative patterns, and to infer possible causal links between harvest and population trends. Our study provides an opportunity to guide management given the data collected within the present monitoring, as well as to suggest improvements for future data collection. The populations of greylag and Canada geese increased in Sweden 1979–2018, but this long‐term trend included a recent decrease in the latter species. Bean goose breeding index decreased, whilst staging numbers and harvest varied with no clear long‐term trend. For Canada goose, our analysis suggests that harvest may affect population growth negatively. For bean goose and greylag goose we could not detect any effect of harvest on autumn counts the following year. We find that the present data and analysis of coherence may suffice as basis for decisions for the current management situation in Sweden with its rather unspecific goals for greylag (very abundant) and Canada goose (invasive species) populations. However, for management of bean geese, with international concerns of over harvest, data lack crucial information. For future management challenges, with more explicit goals, for all goose species we advocate information that is more precise. Data such as hunting effort, age‐structure of goose populations and mark–recapture data to estimate survival and population size, is needed to feed predictive population models guiding future Swedish and European goose management.