Incorporating animal movement with distance sampling and spatial capture-recapture
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Date
06/12/2018Author
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Abstract
Distance sampling and spatial capture-recapture are statistical methods to estimate the
number of animals in a wild population based on encounters between these animals and
scientific detectors. Both methods estimate the probability an animal is detected during a
survey, but do not explicitly model animal movement.
The primary challenge is that animal movement in these surveys is unobserved; one must
average over all possible paths each animal could have travelled during the survey. In this
thesis, a general statistical model, with distance sampling and spatial capture-recapture
as special cases, is presented that explicitly incorporates animal movement. An efficient
algorithm to integrate over all possible movement paths, based on quadrature and hidden
Markov modelling, is given to overcome the computational obstacles.
For distance sampling, simulation studies and case studies show that incorporating animal
movement can reduce the bias in estimated abundance found in conventional models and
expand application of distance sampling to surveys that violate the assumption of no animal
movement. For spatial capture-recapture, continuous-time encounter records are used to
make detailed inference on where animals spend their time during the survey. In surveys
conducted in discrete occasions, maximum likelihood models that allow for mobile activity
centres are presented to account for transience, dispersal, and heterogeneous space use.
These methods provide an alternative when animal movement causes bias in standard methods and the opportunity to gain richer inference on how animals move, where they spend
their time, and how they interact.
Type
Thesis, PhD Doctor of Philosophy
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