Applying occupancy estimation and modelling to the analysis of atlas data
Article first published online: 16 JAN 2013
© 2013 John Wiley & Sons Ltd
Diversity and Distributions
Volume 19, Issue 7, pages 804–814, July 2013
How to Cite
Sadoti, G., Zuckerberg, B., Jarzyna, M. A., Porter, W. F. (2013), Applying occupancy estimation and modelling to the analysis of atlas data. Diversity and Distributions, 19: 804–814. doi: 10.1111/ddi.12041
- Issue published online: 13 JUN 2013
- Article first published online: 16 JAN 2013
- Citizen science;
- forest birds;
- New York;
- observer effort;
- occupancy modelling;
- spatial autocorrelation;
- species distribution
Biological atlases are a globally widespread and effective means for documenting the distribution of numerous taxa and have been used to study many macroecological relationships. A common assumption when analysing atlas data is that species are detected perfectly (p = 1). This assumption is likely incorrect, but the application of methods to account for heterogeneous detectability (p < 1) has been difficult to implement. We provide an application of current methods of occupancy estimation and modelling to account for imperfect detection in the analysis of atlas data.
New York, USA.
We employed multiseason occupancy models substituting spatial replicates for temporally repeated surveys to examine changes in distribution of the Canada Warbler (Cardellina canadensis) using breeding bird atlases from 1980–1985 and 2000–2005. We compared estimates from models accounting for p < 1 versus those assuming p = 1 in assessing statewide patterns of occupancy, colonization and extinction.
We found forest cover, observer effort, information on previous detections and the sampling year were important predictors of detection. Environmental predictors of statewide occupancy dynamics were similar among models accounting for p < 1 versus those assuming p = 1. Despite these similarities, site-level estimates of occupancy from the model accounting for imperfect detection indicated 14% and 19% higher site occupancy in the 1980–1985 and 2000–2005 Atlases, respectively. In addition, relative to the model accounting for p < 1, the model assuming perfect detectability underestimated persistence and overestimated extinction between atlases. The model accounting for p < 1 had 0.3% and 7.9% higher accuracy in predicting occupancy in the 1980–1985 and 2000–2005 Atlases, respectively, than the models assuming p = 1.
Occupancy modelling and estimation can be successfully applied to broad-scale surveys, such as atlases, that do not explicitly implement repeated visits to a survey block. Occupancy modelling allows for a more rigorous analysis of atlas data for exploring species–environmental relationships and modelling species distributions while accounting for imperfect detection.