Display options
Cite
Wilson AM, Brauning DW, Carey C, et al. Spatial models to account for variation in observer effort in bird atlases. Ecol Evol. 2017;7(16):6582-6594doi: 10.1002/ece3.3201.
Wilson, A. M., Brauning, D. W., Carey, C., & Mulvihill, R. S. (2017). Spatial models to account for variation in observer effort in bird atlases. Ecology and evolution, 7(16), 6582-6594. https://doi.org/10.1002/ece3.3201
Wilson, Andrew M, et al. "Spatial models to account for variation in observer effort in bird atlases." Ecology and evolution vol. 7,16 (2017): 6582-6594. doi: https://doi.org/10.1002/ece3.3201
Wilson AM, Brauning DW, Carey C, Mulvihill RS. Spatial models to account for variation in observer effort in bird atlases. Ecol Evol. 2017 Jul 18;7(16):6582-6594. doi: 10.1002/ece3.3201. eCollection 2017 Aug. PMID: 28861259; PMCID: PMC5574789.
Copy Download .nbib Format: NLM
Share it on

Ecol Evol. 2017 Jul 18;7(16):6582-6594. doi: 10.1002/ece3.3201. eCollection 2017 Aug.

Spatial models to account for variation in observer effort in bird atlases.

Ecology and evolution

Andrew M Wilson, Daniel W Brauning, Caitlin Carey, Robert S Mulvihill

Affiliations

  1. Environmental Studies Department Gettysburg College Gettysburg PA USA.
  2. Wildlife Management Bureau Pennsylvania Game Commission Harrisburg PA USA.
  3. Conservation Management Institute Virginia Tech Blacksburg VA USA.
  4. National Aviary Allegheny Commons West Pittsburgh PA USA.

PMID: 28861259 PMCID: PMC5574789 DOI: 10.1002/ece3.3201

Abstract

To assess the importance of variation in observer effort between and within bird atlas projects and demonstrate the use of relatively simple conditional autoregressive (CAR) models for analyzing grid-based atlas data with varying effort. Pennsylvania and West Virginia, United States of America. We used varying proportions of randomly selected training data to assess whether variations in observer effort can be accounted for using CAR models and whether such models would still be useful for atlases with incomplete data. We then evaluated whether the application of these models influenced our assessment of distribution change between two atlas projects separated by twenty years (Pennsylvania), and tested our modeling methodology on a state bird atlas with incomplete coverage (West Virginia). Conditional Autoregressive models which included observer effort and landscape covariates were able to make robust predictions of species distributions in cases of sparse data coverage. Further, we found that CAR models without landscape covariates performed favorably. These models also account for variation in observer effort between atlas projects and can have a profound effect on the overall assessment of distribution change. Accounting for variation in observer effort in atlas projects is critically important. CAR models provide a useful modeling framework for accounting for variation in observer effort in bird atlas data because they are relatively simple to apply, and quick to run.

Keywords: Pennsylvania; West Virginia; bird atlas; conditional autoregressive; observer effort; spatial model

References

  1. Ecol Lett. 2006 Oct;9(10):1136-45 - PubMed
  2. Conserv Biol. 2012 Feb;26(1):68-77 - PubMed
  3. Stat Methods Med Res. 2006 Dec;15(6):525-45 - PubMed
  4. Glob Chang Biol. 2015 Jun;21(6):2155-68 - PubMed
  5. PLoS Biol. 2010 Jun 01;8(6):e1000385 - PubMed
  6. Glob Chang Biol. 2014 Oct;20(10):2995-3003 - PubMed
  7. Ecol Appl. 2014 Mar;24(2):363-74 - PubMed
  8. Glob Chang Biol. 2013 Feb;19(2):420-30 - PubMed
  9. PLoS One. 2013;8(2):e55158 - PubMed
  10. Biol Lett. 2012 Jun 23;8(3):324-6 - PubMed
  11. BMC Bioinformatics. 2011 Mar 17;12:77 - PubMed
  12. Ecol Lett. 2013 Aug;16(8):1061-8 - PubMed
  13. Ecology. 2007 Jan;88(1):49-55 - PubMed
  14. Ecol Evol. 2017 Jul 18;7(16):6582-6594 - PubMed

Publication Types