Article from the group on analysis of marine plankton community structure

Scheef, L.P., D.E. Pendleton, S.E. Hampton, S.L. Katz, E.E. Holmes, M.D. Scheuerell, and D.G. Johns. 2012. Assessing marine plankton community structure from long-term monitoring data with multivariate autoregressive (MAR) models: a comparison of fixed station vs. spatiallydistributed sampling data. Limnology & Oceanography: Methods 10: 54-64.

ABSTRACT: We examined how marine plankton interaction networks, as inferred by multivariate autoregressive (MAR) analysis of time-series, differ based on data collected at a fixed sampling location (L4 station in the Western English Channel) and four similar time-series prepared by averaging Continuous Plankton Recorder (CPR) datapoints in the region surrounding the fixed station. None of the plankton community structures suggested by the MAR models generated from the CPR datasets were well correlated with the MAR model for L4, but of the four CPR models, the one most closely resembling the L4 model was that for the CPR region nearest to L4. We infer that observation error and spatial variation in plankton community dynamics influenced the model performance for the CPR datasets. A modified MAR framework in which observation error and spatial variation are explicitly incorporated could allow the analysis to better handle the diverse time-series data collected in marine environments.

Time-series analysis workshop Sat Aug 5, Portland, OR

Eli, Eric and Mark will offer their 1-day workshop on multivariate time-series analysis using the MARSS package again at the annual ESA meeting (http://www.esa.org/portland).  Workshop is scheduled for the Sat before the meeting.  We are working on new case studies involving incorporation of covariates into analyses.  MARSS 3.0 will be done by then.  This is a major update that allows all parameters to incorporate time-varying covariates.

New article by Eric in Conservation Letters

Integrating diet and movement data to identify hot spots of predation risk and areas of conservation concern for endangered species,  Eric J. Ward, Phillip S. Levin, Monique M. Lance, Steven J. Jeffries, Alejandro Acevedo-Gutiérrez
Effective management of threatened and endangered species requires an understanding of how species of conservation concern are distributed spatially, as well as the spatial distribution of risks to the population, such as predation or human impacts (fishing, pollution, loss of habitat). Identifying high risk areas is particularly important when designing reserves or protected areas. Our novel approach incorporates data on distribution, movement, and diet of a generalist marine predator (harbor seals) to identify and map ‘hot-spots’ of predation risk for an endangered prey species (rockfish). Areas with high concentrations of seals (including some current marine reserves) are also estimated hot spots for rockfish predation. While marine reserve planning currently targets areas with good habitat and low human disturbance, our modeling suggests that future terrestrial and marine reserve design may be made more effective by incorporating other components of the food web that either directly or indirectly interact with target species.

http://onlinelibrary.wiley.com/doi/10.1111/j.1755-263X.2011.00210.x/abstract;jsessionid=7EB218B3908AAED4EC8A2E308F6C9046.d01t02

Post-doc opening in our research group

Time-series modeling of large-scale population and community processes
Northwest Fisheries Science Center, NOAA Fisheries, Seattle, WA

NOAA’s Northwest Fisheries Science Center (NWFSC) has a large research group using time-series modeling to study ecological dynamics.  Our research interests are diverse, including estimating species-interaction strengths, inferring environmental and anthropogenic drivers of population and community dynamics, estimating stability metrics, detecting change points and regime shifts.  We have numerous long-term and large-scale time-series data on fish, marine mammals, and plankton, which we use to study a wide array of basic and applied ecological and fishery questions.

We are seeking a post-doctoral scientist (recent or fairly recent grad) to join our research group.  Strong quantitative skills along with a background in aquatic or fisheries ecology are necessary for this position including a record of publication.  The ideal candidate would also have experience and interest in one or more of the following areas:
· statistical modeling, esp. hierarchical modeling
· time-series analysis
· population and/or community dynamics
· fisheries management
· salmon biology
Post-doctoral positions are initially supported for 1 year with extensions up to 3 years contingent on satisfactory progress and submitted publications.

Why come post-doc at the NWFSC?  You will join a supportive, collaborative and productive team of quantitative ecologists at NWFSC who are using time-series modeling to study ecological dynamics.  Our center of 300+ research scientists has a large number of post-doctoral fellows and provides a stimulating and productive environment for research.  Post-docs trained in our group obtain a strong grounding in modern ecological statistics and have high success obtaining positions at both federal research and academic institutions.  Close proximity to the University of Washington (a 10min walk) facilitates on-going  collaborations with faculty and post-docs in the UW School for Fishery and Aquatic Sciences and other departments across campus.

Interested? Contact one of the PIs below to discuss the position in more detail.  Please attach a CV, recent publications, , and a brief statement describing your background, including any programming and modeling expertise.  If you will be at the AFS meeting, we'd love to talk with you.  Review of candidates will start in earnest after AFS in mid-September.

PIs on this project are:

Eli Holmes   eli.holmes@noaa.gov    http://faculty.washington.edu/eeholmes/
Mark Scheuerell    mark.scheuerell@noaa.gov   http://faculty.washington.edu/scheuerl/
Eric Ward   eric.ward@noaa.gov       http://sites.google.com/site/ericward2/

Eric and Brice teach at an isotop mixing workshop in Ireland

Eric and Brice participated in a workshop on stable isotope analysis using their package MixSIR at a workshop sponsored by Trinty College in Dublin, Ireland.  http://www.tcd.ie/Zoology/research/research/theoretical/isotopeworkshop.php

Time-series analysis workshop at ESA on Sat Aug 6th

Eli, Eric and Mark will be teaching a 1-day workshop on ecological time-series analysis using state-space models at the ESA annual meeting:  http://eco.confex.com/eco/2011/webprogram/Session6999.html

MARSS 2.3 uploaded to CRAN

The MARSS package fits constrained and unconstrained linear multivariate autoregressive state-space (MARSS) models to multivariate time series data via priamarily an Expectation-Maximization (EM) algorithm, although fitting via the BFGS algorithm (using the optim function) is also provided. Functions are provided for parametric and innovations bootstrapping, Kalman filter and smoothing, bootstrap model selection criteria (AICb), confidences intervals via the hessian approximation and via bootstrapping and calculation of auxilliary residuals for detecting outliers and shocks. The user guide shows examples of using MARSS for parameter estimation for a variety of applications, model selection, dynamic factor analysis, outlier and shock detection, and addition of covariates.

http://cran.r-project.org/web/packages/MARSS/index.html

MARSS 2.3 is a big change over 1.1

• Allows B and Z estimation 
• Allows models with diagonal elements of R or Q set to 0. So you can have partially deterministic systems.
• Covariates are allowed (see user guide and look up 'covariates' in the index)
• Allows many types of Q and R matrices. Few constraints except it needs to be a valid variance-covariance matrix. See section in Derivations.pdf 
• Allows you to set the initial state at t=0 or t=1
• Allows diffuse priors if initial state at t=1 and method=BFGS
• case studies added to the user guide on dynamic factor analysis, B estimation, covariates, and detecting outliers and shocks