Two Graduate Student Opportunities in Aquatic Ecology

Two graduate student positions available at the MSc or PhD level:

1) Confluence symmetry and upstream similarity as indicators of downstream aquatic ecosystem types
2) Lake-stream and watershed-wide network measures of aquatic ecosystem types;

Primary location: Department of Biology, Trent University, Peterborough ON, Canada. This position will also include extended travel to Toronto and field work throughout Ontario, Canada and Ontario’s Far North.

Starting Date: Spring 2015. Actual start date depends on candidate availability. Funding is largely secured.

In support of the development of an aquatic ecosystem classification for the province of Ontario, we are looking for two graduate students for two related projects. Dividing stream networks into manageable size units for management and research is a fundamental practice. Although there are a plethora of terms used to describe streams at different scales, the stream valley segment is gaining popularity for management. Stream segments are defined as sections of streams with homogeneous characteristics, physical, chemical, thermal and flow regimes. Inflowing tributaries are assumed to impart changes in these characteristics leading to sudden ecological shifts and thus are classified differently than upstream neighbouring segments.

Project #1: Confluence symmetry and upstream influences. The student will examine stream network characteristics, longitudinal gradient patterns, and confluence symmetry ratios to guide the development of homogeneous stream valley segments.  More specifically, the student will determine which confluences should be used to differentiate stream segments for the classification.

Key tasks include:
* Review literature on confluence effects and the influence of upstream lake and stream characteristics
* Test and parametrise proposed implementation of confluence effects for software designed to identify unique stream segments in Ontario
* Design and undertake field-work to test expectations about the significance of confluences and upstream effects.
* GIS analysis and programming in R or Matlab

Project #2: Aquatic network measures. The student will develop an understanding of how lakes interact with streams on a network basis.  Focus will be on generating stream-lake network metrics that describe the size, number, arrangement, and order of lakes.  A subset of such metrics will be used to predict flow regime types based on landscape characteristics. The student will also investigate how lakes influence downstream reaches and how this influence attenuates with distance downstream, stream size and reach contributing characteristics.

Key tasks include:
* Review of literature on use of network measures in freshwater aquatic systems.
* Develop landscape pattern metrics (e.g., Fragstats) as descriptors of network structure that can be used to predict function.
* Develop and tailor network metrics (lake specific and catchment-wide) for use in aquatic stream-lake systems to predict flow regimes
* Design and undertake fieldwork to test network metric expectations.
* GIS analysis and programming in R or Matlab

This is classic stream ecology research and represents a terrific mix of applied and basic science. Both projects will require a thorough review of the literature and field work.  The students will be co-supervised by aquatic ecologist Nick Jones (Trent University) and landscape ecologist Dr. Stephanie Melles (Ryerson University).  Students will have the opportunity to interact with academia and government personnel and work as teaching assistants.

Application deadline: Feb 1 2015 or when filled.

The applicants should have a strong background in ecology.  Experience using statistical packages, GIS, MS Access is an asset but can be learned. Interested applicants should email a cover letter detailing experience, CV, and contact information for three references to nicholas.jones(AT)

Nicholas Jones (Nick) PhD
River and Stream Ecology Lab | Ontario Ministry of Natural Resources and Forestry
Adjunct Professor Trent University
Lab website

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