Wednesday, September 16, 2015

Sept 2015: International Training course on Fishery Stock Assessment and Ecosystem Modeling

Owen Hamel, Aaron Berger and Eli Holmes will be teaching the International Training course on "Fishery Stock Assessment and Ecosystem Modeling" during September 16 - 22, 2015, in Hyderabad, India.  Organized by International Training Centre for Operational Oceanography (ITCOocean) and ESSO-INCOIS, Hyderabad, India.  This is part on an ongoing technical cooperation between Ministry of Earth Sciences (MoES), India and NOAA to enhance predictive capabilities for fisheries in India.

Saturday, January 10, 2015

Winter 2015 Online Course: Applied Time Series Analysis in Fisheries and Environmental Sciences

Fish 507: Applied Time Series Analysis in Fisheries and Environmental Sciences
Winter 2015
Fisheries Dept, University of Washington

Instructors: Eric Ward, Eli Holmes, Mark Scheuerell

Reviews current applications of univariate and multivariate time series models for biological and environmental data, emphasizing the estimation, inference, and forecasting aspects of time-series models. Explores effects of covariates and anthropogenic drivers for species that are exploited and/or of conservation concern. We taught a similar course 2 years ago. This time we are emphasizing how to fit these models in a Bayesian context with JAGS along with a MLE context with MARSS. We are recording the lectures and you can follow along with the course at:

Friday, January 24, 2014

New paper on MAR modeling of community dynamics

Quantifying effects of abiotic and biotic drivers on community dynamics with multivariate autoregressive (MAR) models

Stephanie E. Hampton, Elizabeth E. Holmes, Lindsay P. Scheef, Mark D. Scheuerell, Stephen L. Katz, Daniel E. Pendleton, and Eric J. Ward

Long-term ecological data sets present opportunities for identifying drivers of community dynamics and quantifying their effects through time series analysis. Multivariate autoregressive (MAR) models are well known in many other disciplines, such as econometrics, but widespread adoption of MAR methods in ecology and natural resource management has been much slower despite some widely cited ecological examples. Here we review previous ecological applications of MAR models and highlight their ability to identify abiotic and biotic drivers of population dynamics, as well as community-level stability metrics, from long-term empirical observations. Thus far, MAR models have been used mainly with data from freshwater plankton communities; we examine the obstacles that may be hindering adoption in other systems and suggest practical modifications that will improve MAR models for broader application. Many of these modifications are already well known in other fields in which MAR models are common, although they are frequently described under different names. In an effort to make MAR models more accessible to ecologists, we include a worked example using recently developed R packages (MAR1 and MARSS), freely available and open-access software.

Read More:

Thursday, January 16, 2014

Time Series (MARSS) Course offered in March in Stockholm

Mark and Eli are teaching a week-long multivariate time-series analysis course in Stockholm in March.  Course Announcement

Friday, November 29, 2013

MARSS 3.6 up on CRAN. Significant speed increases for large models

MARSS 3.6 has been uploaded to CRAN.  I fixed some inefficiencies that were causing DFA models with many time-series (n>100) and R="diagonal and unequal" to be very, very slow.  My tests show 10x faster fits for n=100 and R="diagonal and unequal" for DFA models.

Tuesday, October 8, 2013

New paper out by Jim and Eric on using Delta-GLMMs to analyze fisheries survey data

Thorson, J.T. and E.J. Ward. 2013. Accounting for space-time interactions in index
standardization models. Fisheries Research,147:426:433

Scientific survey data are used to estimate abundance trends for fish populations worldwide, and are frequently analyzed using delta-generalized linear mixed models (delta-GLMMs). Delta-GLMMs incorporate information about both the probability of catch being non-zero (catch probability) and the expected value for non-zero catches (catch rates). Delta-GLMMs generally incorporate year as a main effect, and frequently account for spatial strata and/or covariates. Many existing delta-GLMMs do not account for random or systematic differences in catch probability or rates in particular combinations of spatial strata and year (i.e., space–time interactions), and do not recognize potential correlation in random space–time interactions between catch probability and catch rates. We therefore develop a Bayesian delta-GLMM that estimates correlations between catch probability and rates, and compare it with either (a) ignoring year–strata interactions, (b) modeling year–strata interactions as fixed effects, or (c) estimating year–strata interactions in catch probability or rates as independent random effects. These four models are fitted to bottom trawl survey data for 28 species off the U.S. West Coast. The posterior median of the correlation is positive for the majority (18) of species, including all five for which the posterior distribution has little overlap with zero. However, estimating this correlation has little impact on resulting abundance indices or credible intervals. We therefore conclude that the correlated random model will have a little impact on index standardization of the West Coast bottom trawl dataset. However, we propose that the correlated model can quickly identify correlations between occupancy probability and density, and provide our code to allow researchers to quickly identify whether such a correlation is likely to be significantly different from zero for their chosen data set.

Friday, July 5, 2013

Building R packages with RStudio and embedding R in your documents and reports

Building R packages with RStudio plus Embedding R in documents short-course on-line:

  • how (and why) to make an R package using RStudio
  • installing packages from github, git or a url to your tar.gz file
  • Using Sweave and RStudio to do 'reproducible research/programming'.
  • Using OpenOffice + R to do the same, if you don't like LaTeX
  • Creating web-apps that run your R code (a few links to demos)