BIOEE 3610

Advanced Ecology

Course Description

Course information provided by the 2025-2026 Catalog.

This course provides an in depth survey of ecology emphasizing conceptual foundations and the integration of experimental and quantitative approaches, including population and community ecology, ecosystem biology, and ecological modeling. Current and classical ecological research is used to introduce major concepts and methods, derive major ecological principles, and critically discuss their applicability on multiple organizational levels, on multiple scales, and in a variety of ecosystems. Weekly discussion/lab sections focus on measurement techniques and computation (modeling, simulation and data analysis using the R language).

Prerequisites REF-FA25/Corequisites REF-FA25 one semester of calculus and BIOEE 1610 or equivalent, or permission of instructor. Corequisites: None.

Distribution Requirements (BSC-AG, DLG-AG, OPHLS-AG), (BIO-AS, SMR-AS)

Exploratory Studies (CU-SBY)

Last 4 terms offered (None)

Outcomes REF-FA25

• Read, synthesize, and critically discuss contemporary published research in ecology. Analyze ecological systems in terms of proximate and ultimate causation, and be able to work with multi-level systems interactions.
• Use basic conceptual and analytical tools for describing and quantifying ecological relationships.
• Understand and use fundamental analytical methods to describe structure and dynamics of populations and communities. Make predictions about population and community dynamics based on knowledge about biotic and abiotic factors influencing species interactions. Integrate knowledge about species interactions to explain higher level ecosystem processes.
• Evaluate differences across ecosystems in terms of differences in rates of metabolism and the relative open-ness of ecosystem cycles.
• Critically evaluate data from whole-ecosystem experiments and cross-ecosystem studies.
• Analyze some of the complex interactions between global change and ecological structure and function.
• Read a model, interpreting its equations as statements about underlying processes and assumptions about system structure and function.
• Modify existing models for applications to related systems or alternative scenarios.
• Use the R scripting language as an environment for implementing ecological models and studying their behavior.

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