welcome

Welcome to the Systems Ecology and Ecoinformatics Laboratory at the UNCW. The overarching goals of our work are

  1. to understand the lawful processes that create, constrain, and sustain ecological systems, and
  2. to develop a formal science of environment that we can use to comprehend the causes and consequences of both local and global environmental changes.

We use a variety of quantitative, computational and informatic methods to address questions like:

  • What role do indirect effects play in ecological interactions? How rapidly do they develop?
  • What processes are required to explain phytoplankton dynamics in the Ross Sea?
  • What makes an ecosystem sustainable?
  • How do we quantitatively predict environmental impacts?
  • What is environment?

If you are interested in learning more, joining us, or collaborating with us, please contact Dr. Stuart Borrett at borretts _at_ uncw _dot_ edu.

news

Our new article titled "enaR: an R package for Ecosystem Network Analysis" is now available at Methods in Ecology and Evolution. Thanks UNCW and Harvard Forest for the shoutout!

Dr. Borrett is participating in the 2014-15 Program on Mathematical and Statistical Ecology at SAMSI.

Our paper "Rise of Network Ecology: Maps of the topic diversity and scientific collaboration" is now available in Ecological Modelling (doi: 10.1016/j.ecolmodel.2014.02.019).

Thanks to Pawandeep Singh for a successful summer internship in the lab!

Congratulations to Emily Oxe for successfully defending her MS thesis!

Emily Oxe, David Hines, and Dr. Borrett pesented new research at the 2014 Benthic Ecology Meeting in Jacksonville, FL.

Dr. Borrett presented new research at the International Conference on Environmental Biology and Ecological Modelling, Feb 24-26 held at Visva-Bharati University in India

We've released version 2.7 of our enaR package for Ecosystem Network Analysis. It is now available from CRAN and this vignette shows how to use it. This package now includes a library of 100 ecosystem models.

We've learned new tricks to visualize our network models in R. See here and click on software & data (models) tab to learn more.