A Model-Based Approach for Making Ecological Inference from Distance Sampling Data

• Activity Number: 572
• Type: Topic Contributed
• Date/Time: Wednesday, August 4, 2010 : 2:00 PM to 3:50 PM
• Sponsor: Section on Statistics and the Environment
• Author(s): Devin Johnson*+ and Jeff Laake and Jay Ver Hoef
• Companies: NOAA and NOAA and NOAA
• Keywords: Abundance ; Line transect ; Density ; Distance sampling ; Overdispersion ; Spatial point process

Abstract

We consider a fully model-based approach to 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. We demonstrate the model-based methodology with a small simulation study and analysis of the Dubbo weed data set. The simulation study showed that the model based approach compared favorably to conventional distance sampling methods. In addition, the overdispersion correction performed adequately when the number of transects was high.