Estimating Abundance From Bird Counts: Binomial Mixture Models Uncover Complex Covariate Relationships

By Marc Kéry. 2008

100 citations recorded by the source corpus.

Subfields: Ecological Modeling, Ecology, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/3099bc4f2bf916149791bc98c1d9b4b47c276840

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. Douglas H. Johnson
4. Beth Gardner
5. D. MacKenzie
6. J. Guélat
7. Holly H. Edwards
8. Julien Martin

In Ecology

1. Marc Kéry
2. J. Andrew Royle
3. Douglas H. Johnson
4. Beth Gardner
5. D. MacKenzie
6. G. Guillera‐Arroita
7. B. R. Schmidt
8. J. Guélat

In Nature and Landscape Conservation

1. Marc Kéry
2. Beth Gardner
3. D. MacKenzie
4. J. Andrew Royle
5. Douglas H. Johnson
6. G. Guillera‐Arroita
7. J. Guélat
8. Holly H. Edwards

Citations in the corpus

• In Defense of Indices: The Case of Bird Surveys (2008, methodology): “The model can be analyzed using conventional methods based on integrated likelihood or Markov chain Monte Carlo; Kéry (2008) provided some R and WinBUGS code for his examples.”
• Accounting for non‐independent detection when estimating abundance of organisms with a Bayesian approach (2011, methodology): “All data sets (simulated and real) were analysed with WinBUGS version 1Æ4 (Lunn et al. 2000) in batch mode using the R package R2WinBUGS (Sturtz, Ligges, & Gelman 2005; see also Kéry 2008, for similar approach).”
• Effects of spatial autocorrelation and imperfect detection on species distribution models (2018, background): “The consequences of ignoring imperfect detection will be more severe if a covariate has an effect on both distribution or abundance and on detection probability, especially if these effects have a different sign (Kéry, 2008).”