REN R 469 - Biodiversity Analysis

3 units (fi 6)(SECOND, 3-0-0)

Introduction to the theory and application of biodiversity with an emphasis on quantitative analysis of biodiversity data. The course covers the concepts of biodiversity (genetic, species and ecosystem), dynamics of species populations, diversity measurements, estimation of species richness, diversity patterns (species-abundance, species-area, distribution-abundance, local-regional, beta diversity, richness-productivity, etc.), mechanisms of biodiversity maintenance, and methods and models for biodiversity conservation. Laboratory session involves using statistical software R for analyzing various real diversity data. Prerequisite: REN R 364.

REN R 569 - Biodiversity Analysis

3 units (fi 6)(SECOND, 3-0-2)

Introduction to the theory and application of biodiversity with emphasis on quantitative analysis of biodiversity data. The course covers the concepts of biodiversity (genetic, species and ecosystem), dynamics of species populations, diversity measurements, estimation of species richness, synthetic patterns of species diversity (species-abundance, species-area, distribution-abundance, local-regional, beta diversity, richness-productivity, etc.), theories of biodiversity maintenance, species distribution models, and methods and models of biodiversity conservation including estimating species extinction risk and viable population size. Laboratory session involves using statistical software R for analyzing various real diversity data. REN R 569 is built on REN R 469 with a focus on problem solving skills, individual projects and advanced R programming. Not to be taken if credit received for REN R 469.

REN R 586 - Analyzing Relationships in Data

1.5 units (fi 3)(SECOND, 1.5-0-1.5)

Focuses on analyzing complex biological or environmental data for the purpose of prediction and scientific hypothesis testing. Covers multiple regression for a continuous response, logistic regression for a binary response, and log-linear models for count data, non-linear regression and generalized additive models for non-linear relationships, path analysis using structural equation modeling. Prerequisite: knowledge equivalent to REN R 581 and REN R 582 is required.