A Model-Based Approach for Making Ecological Inference from Distance Sampling Data
Article first published online: 13 MAY 2009
© 2009, The International Biometric Society No claim to original US government works
Volume 66, Issue 1, pages 310–318, March 2010
How to Cite
Johnson, D. S., Laake, J. L. and Ver Hoef, J. M. (2010), A Model-Based Approach for Making Ecological Inference from Distance Sampling Data. Biometrics, 66: 310–318. doi: 10.1111/j.1541-0420.2009.01265.x
- Issue published online: 17 MAR 2010
- Article first published online: 13 MAY 2009
- Received July 2008. Revised January 2009. Accepted January 2009.
- Distance sampling;
- Line transect;
- Spatial point process
Summary We consider a fully model-based approach for the analysis of distance sampling data. Distance sampling has been widely used to estimate abundance (or density) of animals or plants in a spatially explicit study area. There is, however, no readily available method of making statistical inference on the relationships between abundance and environmental covariates. Spatial Poisson process likelihoods can be used to simultaneously estimate detection and intensity parameters by modeling distance sampling data as a thinned spatial point process. A model-based spatial approach to distance sampling data has three main benefits: it allows complex and opportunistic transect designs to be employed, it allows estimation of abundance in small subregions, and it provides a framework to assess the effects of habitat or experimental manipulation on density. We demonstrate the model-based methodology with a small simulation study and analysis of the Dubbo weed data set. In addition, a simple ad hoc method for handling overdispersion is also proposed. The simulation study showed that the model-based approach compared favorably to conventional distance sampling methods for abundance estimation. In addition, the overdispersion correction performed adequately when the number of transects was high. Analysis of the Dubbo data set indicated a transect effect on abundance via Akaike's information criterion model selection. Further goodness-of-fit analysis, however, indicated some potential confounding of intensity with the detection function.