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Multivariate state space modelling of bird migration count data
Lund University.
Lund University.
Örebro University, Swedish Business School at Örebro University.
2009 (English)In: Environmental and Ecological Statistics, ISSN 1352-8505, E-ISSN 1573-3009, Vol. 3, no 1, 59-79 p.Article in journal (Refereed) Published
Abstract [en]

We analyse 54 year long time series data on the numbers of common redstart (Phoenicurus phoenicurus), common whitethroat (Sylvia communis), garden warbler (Sylvia borin) and lesser whitethroat (Sylvia curruca) trapped in spring and autumn at Ottenby Bird Observatory, Sweden. The Ottenby time series could potentially serve as a reference on how much information on population change is available in count data on migrating birds. To investigate this, we combine spring and autumn data in a Bayesian state-space model trying to separate demographic signals and observation noise. The spring data are assumed to be a measure of the breeding population size, whereas the autumn data measure the population size after reproduction. At the demographic level we include seasonal density dependence and model winter dynamics as a function of precipitation in the Sahel region, south of the Sahara desert, where these species are known to spend the winter. Results show that the large fluctuations in the data restrict what conclusions can be drawn about the dynamics of the species. Annual catches are highly correlated between species and we show that a likely explanation for this is that trapping numbers are strongly dependent on local weather conditions. A comparative analysis of a related data set from the Courish Spit, Russia, gives rather different dynamics which may be caused by low information in the two data sets, but also by distinct populations passing Ottenby and the Courish Spit. This highlights the difficulty of validating results of the analyses when abundance indices derived by other methods or from other populations do not agree.

Place, publisher, year, edition, pages
Berlin: Springer , 2009. Vol. 3, no 1, 59-79 p.
National Category
Probability Theory and Statistics Social Sciences
Research subject
Statistics
Identifiers
URN: urn:nbn:se:oru:diva-5719DOI: 10.1007/978-0-387-78151-8_4OAI: oai:DiVA.org:oru-5719DiVA: diva2:173953
Available from: 2009-02-18 Created: 2009-02-18 Last updated: 2010-09-17Bibliographically approved

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Sköld, Martin
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Swedish Business School at Örebro University
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CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf