DOI: 10.5061/dryad.3bk3j9kjc
title: Learning from long time series of harvest and population data - Swedish insights for European goose management

description: 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.
description: We based this study on data from three independent long-term
 monitoring programs in Sweden, providing annual data of: 1) breeding
 season abundance (1998-2017), 2) autumn staging counts (1978-2017) and 3)
 national harvest estimates (1978-2017). These datasets are here used as
 indices for breeding and autumn staging population development and changes
 in harvest levels respectively, and represent the available nation-wide
 monitoring of goose populations in Sweden.
description: Temporal trends (finite
 growth rates) based on breeding season counts along the fixed routes, were
 analysed using TRIM (Trends & Indices for Monitoring data, v.3.53,
 Pannekoek and van Strien 2004www.ebcc.info/trim.html), taking into account
 that not all routes were done every year. The statistical model in TRIM
 builds on Poisson log-linear regression, estimating site and time (year)
 effects on species abundance (counts) as well as an overall linear trend
 (log-scale). The basic TRIM model is: expected count = year + site, where
 both year and site are fixed effects. Effects are estimated using maximum
 likelihood and generalized estimating equations, the latter to handle
 potential overdispersion and serial (auto) correlation. For autumn counts
 and harvest estimates, the finite rate of increase was calculated
 according to Caughley & Sinclair (1994). To identify possible changes in
 trends, we performed a breakpoint analysis using the package strucchange
 for all three species and all three indices (Kleiber et al. 2002).
 Breakpoint analysis is based on piecewise linear models and it identifies
 the time of significant shifts in trends (Zeileis et al. 2002). Coherence
 in timing of changes (breaks) between times series as well as differences
 in slopes of regression lines before and after breakpoints were used as
 basis to discuss plausible mechanisms and causality behind shifts in
 trends<i>. </i> To analyse the effect of harvest
 on the population growth rate, we need to relate harvest level to
 population size. By assuming that our indices provide relative numbers
 over time we calculated an annual (1977–2018) ‘relative harvest rate’ by
 dividing the harvest estimate (hunting year t) by the autumn count (year
 t). In a second step, relative harvest rate (year t) was related to the
 exponential rate of increase (Caughley and Sinclair 1994, Steidl et al.
 1997) based on the autumn count data from year t to year t+1, by using
 linear regression. If harvest affect the population growth rate, we expect
 a negative relationship between the relative harvest rate (year t) and the
 exponential rate of increase (year t+1).  All tests were performed using R
 3.3.3. (R core team 2013).