title: On observation distributions for state space models of population survey data.
abstract: 1. State space models are starting to replace more simple time series models in analyses of temporal dynamics of populations that are not perfectly censused. By simultaneously modelling both the dynamics and the observations, consistent estimates of population dynamical parameters may be obtained. For many data sets, the distribution of observation errors is unknown and error models typically chosen in an ad-hoc manner. 2. To investigate the influence of the choice of observation error on inferences, we analyse the dynamics of a replicated time series of red kangaroo surveys using a state space model with linear state dynamics. Surveys were performed through aerial counts and Poisson, overdispersed Poisson, normal and log-normal distributions may all be adequate for modelling observation errors for the data. We fit each of these to the data and compare them using AIC. 3. The state space models were fitted with maximum likelihood methods using a recent importance sampling technique that relies on the Kalman filter. The method relaxes the assumption of Gaussian observation errors required by the basic Kalman filter. Matlab code for fitting linear state space models with Poisson observations is provided. 4. The ability of AIC to identify the correct observation model was investigated in a small simulation study. For the parameter values used in the study, without replicated observations, the correct observation distribution could sometimes be identified but model selection was prone to misclassification. On the other hand, when observations were replicated, the correct distribution could typically be identified. 5. Our results illustrate that inferences may differ markedly depending on the observation distributions used, suggesting that choosing an adequate observation model can be critical. Model selection and simulations show that for the models and parameter values in this study, a suitable observation model can typically be identified if observations are replicated. Model selection and replication of observations, therefore, provide a potential solution when the observation distribution is unknown.
authors.affiliation: Department of Biology, Ecology Building, Lund University, SE-22362 Lund, Sweden. jknape@berkeley.edu
authors.forename: Jonas
authors.initials: J
authors.lastname: Knape
authors.forename: Niclas
authors.initials: N
authors.lastname: Jonzén
authors.forename: Martin
authors.initials: M
authors.lastname: Sköld
mesh_terms: D000818:Animals; D001499:Bayes Theorem; D016013:Likelihood Functions; D007614:Macropodidae; D008954:Models, Biological; D009517:New South Wales; D016011:Normal Distribution; D011157:Population Dynamics