Designing observational biologging studies to assess the causal effect of instrumentation
Article first published online: 19 JUL 2013
© 2013 The Authors. Methods in Ecology and Evolution © 2013 British Ecological Society
Methods in Ecology and Evolution
Volume 4, Issue 9, pages 802–810, September 2013
How to Cite
Authier, M., Péron, C., Mante, A., Vidal, P., Grémillet, D. (2013), Designing observational biologging studies to assess the causal effect of instrumentation. Methods in Ecology and Evolution, 4: 802–810. doi: 10.1111/2041-210X.12075
- Issue published online: 2 SEP 2013
- Article first published online: 19 JUL 2013
- Accepted manuscript online: 18 JUN 2013 03:18AM EST
- Manuscript Accepted: 22 MAY 2013
- Manuscript Received: 29 APR 2013
Table S1. Estimated regression coefficients for the propensity score model.
Table S2. Estimated transitions and detection probabilities from the multi-state capture-recapture models. (propensity scores estimated with shrinkage).
Table S3. Estimated transitions and detection probabilities from the multi-state capture-recapture models. (propensity scores estimated without shrinkage).
Fig S1. Violations of the Stable Unit-Treatment Value Assumption (SUTVA).
Fig S2. Cumulative Distribution Function (CDF) of a standard logistic distribution and a Student-t distribution with 7 degrees of freedom and scale set to 1.5484.
Fig S3. Graphical display of covariate balance after matching on propensity scores (estimated without shrinkage).
Fig S4. Graphical representation of the multi-state capture recapture model used to estimate counterfactual outcomes for equipped birds.
Fig S5. Graphical representation of the Student t priors of location 0, scale 10 and 7 df on a logit scale used for detection probabilities π.
Fig S6. Comparison between predicted and observed breeding performance in 2012 for birds equipped with tags in 2011.
Data S1. Data and R codes to reproduce the analysis.
|mee312075-sup-0002-Appendix.pdf||application/PDF||44K||Appendix S1. Simpson's and Lord's Paradoxes.|
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