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.

Tuesday, September 21, 2010

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.

Thursday, September 9, 2010

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

Thursday, June 24, 2010

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

Tuesday, June 22, 2010

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.

Wednesday, May 26, 2010

R short course

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

Wednesday, April 21, 2010

Two new papers: stable isotopes meet hierarchical Bayesian models

Semmens, B.X., E.J. Ward, J.W. Moore, and CT. Darimont. 2009. Quantifying inter- and intra-population niche variability using hierarchical Bayesian stable isotope mixing models. PLoS One. Open Access Paper

Semmens, B. X., J. W. Moore, and E. J. Ward. 2009. Improving Bayesian isotope mixing models: a response to Jackson et al. (2009). Ecology Letters 12:E6-E8.

Paper on killer whale menopause

Ward, E., Parsons, K., Holmes, E., K. Balcomb, and J. Ford. 2009. The role of menopause and reproductive senescence in a long-lived social mammal. Frontiers in Zoology 6:4 Open Access PDF

New paper on spatial population structure in California sea lions

Ward, E. J., Chirakkal, H., González-Suárez, M., Aurioles-Gamboa, D., Holmes, E. E. and Gerber, L. 2009. Inferring spatial structure from time-series data: using multivariate state-space models to detect metapopulation structure of California sea lions in the Gulf of California, Mexico. Journal of Applied Ecology 47:47-56. Abstract

"Red Flags" NCEAS Working Group proposal accepted

Eli is a participant on this NCEAS working group headed by Robin Waples and Jeff Hutchings

Red flags and species endangerment: Meta-analytical development of criteria for assessing extinction risk starting Spring 2010
The proposed project builds on previous work (some of it sponsored by NCEAS) to evaluate performance of criteria for identifying species at risk. Novel aspects of our approach include the following: 1) We begin with a conceptual definition of an endangered species (one that has entered a Red Zone where both extinction risk and uncertainty about biological processes increase non-linearly); 2) We will leverage large datasets that have become available over the last decade, including those for taxa (e.g., marine fishes) for which application of standard risk criteria has been very controversial; 3) We propose a rather broad interpretation of depensation and Allee effects that facilitates consideration of the importance of ecological and evolutionary processes; 4) We will explicitly consider how risks scale on the continuum populations/metapopulations/ESUs/species; 5) We will evaluate practical utility of candidate RedFlag criteria by applying them to case studies of species that have been formally considered for federal protection in the US and Canada.