John Harte, Professor

Closed (1) Advancing and testing ecological theory

Applications for Fall 2018 are now closed for this project.

Students will work with Prof. Harte to formulate and explore mathematical models describing ecosystems that are far from steady state as a consequence of human and/or natural disturbance. In Spring 2018, testing of model predictions with available data sets will also be carried.

Learning the essentials of several ecological theories;
Acquiring skill with stochastic modeling tools;
Numerical and analytical exploration of model predictions.

Qualifications: Strong quantitative skills required; Math through advanced calculus and differential equations required, Experience working with at least one mathematicall software package such as R or Python or Matlab. Interest in ecology, desired

Weekly Hours: 6-8 hrs

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Closed (2) Microclimate & conifer seedling establishment at multiple spatial scales

Closed. This professor is continuing with Spring 2018 apprentices on this project; no new apprentices needed for Fall 2018.

Vegetation and microclimate data collected from Sierran red fir forest will be used to assess the role of environmental parameters (e.g., temperature, soil moisture) in successful establishment of conifer tree seedlings. The project will advance niche theory by examining spatially-explicit relationships between the environmental parameters and measures of seedling survival, density, and annual growth. Results will better inform efforts to model species response to projected future climates.

Calibration of microclimate instrumentation will lead to a understanding of the fundamentals of microclimate measurement;
Modeling of below canopy radiation & analysis of terrestrial LiDAR data will enable acquisition of data processing and analysis techniques;
Digitization and organization of field data will develop data management skills;
Possible vegetation census field work in the spring will provide opportunity for field work.

Qualifications: Applicants need to enjoy working in a laboratory environment and be able to perform repetitive tasks with sustained accuracy. Familiarity with data processing software such as R or MatLab is desired but not essential. Enthusiasm and interest in environmental science and programming is a plus.

Weekly Hours: 6-8 hrs

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