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Using Hidden Markov Models to Deal with Availability Bias on Line Transect Surveys
Author(s) -
Borchers D. L.,
Zucchini W.,
HeideJørgensen M. P.,
Cañadas A.,
Langrock R.
Publication year - 2013
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/biom.12049
Subject(s) - estimator , computer science , range (aeronautics) , independence (probability theory) , transect , parametric statistics , parametric model , markov chain , process (computing) , markov process , econometrics , statistics , mathematics , machine learning , engineering , geology , oceanography , aerospace engineering , operating system
Summary We develop estimators for line transect surveys of animals that are stochastically unavailable for detection while within detection range. The detection process is formulated as a hidden Markov model with a binary state‐dependent observation model that depends on both perpendicular and forward distances. This provides a parametric method of dealing with availability bias when estimates of availability process parameters are available even if series of availability events themselves are not. We apply the estimators to an aerial and a shipboard survey of whales, and investigate their properties by simulation. They are shown to be more general and more flexible than existing estimators based on parametric models of the availability process. We also find that methods using availability correction factors can be very biased when surveys are not close to being instantaneous, as can estimators that assume temporal independence in availability when there is temporal dependence.