Hidden process models for animal population dynamics.
Hidden process models are a conceptually useful and practical way to simultaneously account for process variation in animal population dynamics and measurement errors in observations and estimates made on the population. Process variation, which can be both demographic and environmental, is modeled by linking a series of stochastic and deterministic subprocesses that characterize processes such as birth, survival, maturation, and movement. Observations of the population can be modeled as functions of true abundance with realistic probability distributions to describe observation or estimation error. Computer-intensive procedures, such as sequential Monte Carlo methods or Markov chain Monte Carlo, condition on the observed data to yield estimates of both the underlying true population abundances and the unknown population dynamics parameters. Formulation and fitting of a hidden process model are demonstrated for Sacramento River winter-run chinook salmon (Oncorhynchus tshawytsha).
Newman
K B
KB
School of Mathematics and Statistics, University of St. Andrews, St. Andrews, Scotland. ken@mcs.st-and.ac.uk
Buckland
S T
ST
Lindley
S T
ST
Thomas
L
L
Fernández
C
C
D000818:Animals; D044822:Biodiversity; D003198:Computer Simulation; D003710:Demography; D016013:Likelihood Functions; D008390:Markov Chains; D008954:Models, Biological; D009010:Monte Carlo Method; D017683:Oncorhynchus / Q000254:growth & development*; D011157:Population Dynamics; D045483:Rivers / Q000737:chemistry