Original Article

Journal of Ornithology

, Volume 148, Supplement 2, pp 351-357

First online:

Open Access This content is freely available online to anyone, anywhere at any time.

Smoothing and trend detection in waterbird monitoring data using structural time-series analysis and the Kalman filter

  • Leo SoldaatAffiliated withStatistics Netherlands Email author 
  • , Hans VisserAffiliated withNetherlands Environmental Assessment Agency (MNP)
  • , Marc van RoomenAffiliated withSOVON Dutch Centre for Field Ornithology
  • , Arco van StrienAffiliated withStatistics Netherlands


Many wildlife-monitoring programmes have long time series of species abundance that cannot be summarized adequately by linear trend lines. To describe long time series better, generalized additive models may be used to obtain a smooth trend line through abundance data. We describe another approach to estimate a smoothed trend line through time series consisting of one observation per time point, such as year or month. This method is based on structural time-series models in combination with the Kalman filter and is computerized in the TrendSpotter software. One of its strengths is the possibility to test changes in smoothed abundances between years, taking into account serial correlation. The trend method is applied in the Dutch Waterbird Monitoring Scheme (DWMS), a monitoring scheme for migrating and overwintering waterbirds. Taking the numbers of overwintering Greater Scaup (Aythia marila) in the Netherlands as an example, we demonstrate three applications of the method: (1) trend calculation and classification for each year in the time series, (2) assessing alerts for alarming population declines and (3) testing yearly abundance against a population threshold. We discuss the situations where TrendSpotter is to be preferred over other methods.


Kalman filter Structural time-series analysis Population alert Trend analysis Generalized additive model (GAM) TRends and Indices for Monitoring data (TRIM) Aythia marila Greater scaup