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.

Steller sea lion biological opinion released

The Steller Sea Lion Bering Sea and Aleutian Islands and Gulf of Alaska Groundfish Fisheries Section 7 Consultation was posted in August 2010 by the Alaska Regional Office. Eli's work on Steller sea lion fecundity figures prominently in that (papers and a reanalysis of rookery mark-resight data). http://alaskafisheries.noaa.gov/protectedresources/stellers/esa/biop/draft/0810.htm

New blog on Stable Isotope Analysis

Eric and Brice have a new blog on stable isotope analysis: Stable Isotope Models for Biologists and Environmental Scientists. The blog has links to their course material for the 2010 IsoEcol7 meeting in Fairbanks AK.

Paper on identifying groups using hierarchical Bayesian modeling

Ward, E.J., Semmens, B.X., Holmes, E.E., and K.C. Balcomb. 2010. Identifying links between population groupings and demography in at-risk species with multiple levels of social structure. In press,Conservation Biology

New paper on analysis of stable isotope data

Ward, E.J., Semmens, B.X., and D.E. Schindler. 2010. Including source uncertainty and prior information in the analysis of stable isotope mixing models. In press, Environmental Science & Technology. doi:10.1021/es100053v

MARSS package uploaded to CRAN

The official release of 1.0 has been uploaded to CRAN (Here's the link to the CRAN page). This is an R package to fit unconstrained and constrained multivariate autoregressive state-space models via maximum-likelihood (EM algorithm). It was developed by Eli Holmes, Eric Ward and Kellie Wills. It is fully documented with a user guide/manual: Analysis of multivariate time-series using the MARSS package, by E.E. Holmes and E.J. Ward. MARSS has bootstrap AICb and CIs and simulation features. MARSS 1.0 doesn't allow you to estimate the B matrix. MARSS 2.0 will allow that, and MARSS 3.0 will provide Bayesian fitting. If you are looking for the derivation of update equations for the Kalman-EM algorithm, take a look at Derivation of the EM algorithm for constrained and unconstrained multivariate autoregressive state-space (MARSS) models.

R short course

Taught by various statisticians at NWFSC and organized by Eric Ward. Lectures and exercises are online:
NWFSC R Short Course