Journal of Agricultural, Biological, and Environmental Statistics

, Volume 16, Issue 3, pp 301–317

Species Occupancy Modeling for Detection Data Collected Along a Transect


    • School of Mathematics, Statistics and Actuarial ScienceUniversity of Kent
  • Byron J. T. Morgan
    • School of Mathematics, Statistics and Actuarial ScienceUniversity of Kent
  • Martin S. Ridout
    • School of Mathematics, Statistics and Actuarial ScienceUniversity of Kent
  • Matthew Linkie
    • Fauna & Flora International (FFI) in Aceh
    • Durrell Institute of Conservation and EcologyUniversity of Kent

DOI: 10.1007/s13253-010-0053-3

Cite this article as:
Guillera-Arroita, G., Morgan, B.J.T., Ridout, M.S. et al. JABES (2011) 16: 301. doi:10.1007/s13253-010-0053-3


The proportion of sampling sites occupied by a species is a concept of interest in ecology and biodiversity conservation. Occupancy surveys based on collecting detection data along transects have become increasingly popular to monitor some species. To date, the analysis of such data has been carried out by discretizing the data, dividing the transects into discrete segments. Here we propose alternative occupancy models which describe the detection process as a continuous point process. These models provide a more natural description of the data and eliminate the need to divide transects into segments, which can be arbitrary and may lead to increased bias in the estimator of occupancy or increased chances of obtaining estimates on the boundary of the parameter space. We present a model that assumes independence between detections and an alternative model that describes the detection process as a Markov modulated Poisson process to account for potential clustering in the detections. The utility of these models is illustrated with the analysis of data from a recent survey of the Sumatran tiger Panthera tigris sumatrae. The models can also be applied to surveys that collect continuous data in time, such as those based on the use of camera-trap devices. Supplementary materials for this article are available online.

Key Words

Clustered data Markov modulated Poisson process Wildlife monitoring Zero-inflated Poisson process

Supplementary material

Copyright information

© International Biometric Society 2011