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

By L. Soldaat, Hans Visser, M. Roomen, A. Strien. Journal of Ornithology, 2007

61 citations recorded by the source corpus.

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.

Subfields: Ecological Modeling, Ecology, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/39cd5c29bc2cce9f6db61936c95b5e468860f846

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Ecological Modeling

1. J. Andrew Royle
2. Marc Kéry
3. A. Strien
4. Josef Settele
5. P. Nowicki
6. C. V. van Swaay
7. I. Tulp
8. G. Guillera‐Arroita

In Ecology

1. Marc Kéry
2. J. Andrew Royle
3. G. Guillera‐Arroita
4. R. Dorazio
5. A. Strien
6. A. Zuiderwijk
7. L. Soldaat
8. Josef Settele

In Nature and Landscape Conservation

1. Marc Kéry
2. J. Andrew Royle
3. G. Guillera‐Arroita
4. R. Dorazio
5. A. Strien
6. I. Tulp
7. C. V. van Swaay
8. Josef Settele

Citations in the corpus

• Butterfly monitoring in Europe: methods, applications and perspectives (2008, methodology): “Trends and confidence intervals were assessed by structural time-series analysis and the Kalman Filter using the program Trendspotter (Soldaat et al. 2007) Biodivers Conserv (2008) 17:3455–3469 3465”
• Signals from the shallows: in search of common patterns in long-term trends in Dutch estuarine and coastal fish (2008, methodology): “A more detailed description of the method can be found in Visser (2004) and Soldaat et al. (2007).”
• Trend estimation in populations with imperfect detection (2009, background): “Soldaat et al. 2007 for an application) and formally partition the observed data into a stochastic state and a stochastic observation component, and may thus access features of the state, such as density-dependence, free from effects of the observation process (Lande et al. 2003; de Valpine 2003; D”