Tuesday, October 19, 2010

MARSS 1.1 posted to CRAN

An updated version of MARSS which fixes a few bugs and adds some features has been posted to CRAN today: MARSS 1.1 Main issues fixed are:
  • * Allow NA and NaN as miss.value
  • * Fixed bug that prevented Monte Carlo initialization searches
  • * Changed convergence test to use log param vs log iter (a standard test for actual convergence). Before we used new.LL-old.LL <= tol, but that is not a test for convergence actually. Now the fitting takes longer, but stops at convergence (or maxit).
  • * Fixed bug that was preventing certain types of non-design Z matrices
  • * Fixed summary(marssm). The labeling was off.
  • * Added function for likelihood profiling and changing defaults

Monday, October 18, 2010

Brice talking this week on using multivariate time-series models to combine multiple data sources

Integrating time-series of community monitoring data Semmens, BX, EE Holmes, EJ Ward, CV Pattengill-Semmens Linking Science to Management, Oct 19-22, 2010, Duck Key, FL http://conference.ifas.ufl.edu/floridakeys/index.html

Assessing population trends, evaluating management actions, and identifying community responses to anthropogenic impacts all require an accurate time-series of populations. In practice, such data are often scarce or of varying quality due to the limited resources of managing agencies. In such situations, analyses that integrating multiple data sources (e.g. agency monitoring programs, citizen science observations, fisheries catch records) can yield dramatic improvements in the estimation of population trajectories. To do so effectively, however, such integrative models must account for differences in observation errors across data sources. We used multivariate state space models (MSSMs) to assess the population trajectories of reef fish species from the Florida Keys National Marine Sanctuary based on data from 1) point count surveys conducted through academic institutions and 2) citizen-science monitoring surveys conducted by volunteer Scuba divers. By developing competing models and applying information theory, we demonstrate how MSSMs can be used to compare and integrate multiple monitoring time series, and ultimately improve estimates of the true states of populations though time. Additionally, we demonstrate that by combining multiple time series, it is possible to recover method-specific observation error estimates even for very short time series of data.