The goal of this module is to provide a germane introduction to the concept and application of rarefaction for estimating species diversity, or other forms of diversity (e.g., functional), within the broader context of the approaches and tools used to develop and study ecological and evolutionary concepts, human-environment interactions, and the importance of bridging disciplinary boundaries to advance science. Although the generalized focus constrains some linkages to the 4DEE and CCT, more dimensions and themes can be included by combining this module with specific case studies, published or student-collected data sets, and topical areas in which the questions and hypotheses relate to exploring differences in biodiversity. For example, using data sets of species diversity and abundance along gradients in space (e.g., latitude/elevation/depth) or time (e.g., fossil record) can be used to discuss the core concept of biomes, the biosphere, and cross-cutting themes of evolution and space and time. Before class, journal articles readings introduce students to the historical context and controversy with measuring species diversity. These topics include levels of biological diversity (e.g., genetic, species, functional, trait, phylogenetic), quantitative measures (e.g., observed richness versus metrics such Shannon Index) and the effects of sampling effort due to differences in area, effort, and number of individuals and sample size. Students should submit questions about the readings onto a shared online platform that the instructor can review before the first face-to-face class or lab meeting. Instructors can provide specific prompts to guide the questions, leave the feedback open ended, or a combination. During the first class or lab meeting, the concept of species diversity, its measurement, controversy with its measurement, and rarefaction are introduced via lecture and discussion by the instructor and can be tailored to specific content depending on the course focal area. The second half of this class time should be allotted to discussion of the before-class readings. This discussion can be instructor or student led using the guide provided in the Procedure and general instructions below. The second class or lab meeting focuses on hands-on comparisons of the different methods for estimating species diversity (e.g., of beetles in forest of different age/stage) using the R shiny app version of iNEXT or, depending on the student familiarity with R, the command line version R and the iNEXT package. iNEXT (iNterpolation/EXTrapolation) is an R package and R Shiny app that provides simple functions to compute and graph rarefaction and extrapolation sampling curves for the three most widely used metrics of diversity (species richness, Shannon diversity and Simpson diversity). The webpage based R shiny app version of iNEXT will suffice and circumvents hurdles with teaching R coding as well as issues with student computer operating system compatibility with R. The goals of this session are as follows. First, introduce students to the state-of-the-art software tool used for estimating rarefied measures of biodiversity. Second, use the INEXT provided data sets or instructor/student data sets, to quantitatively compare observed measures of richness with size-based and coverage-based rarefaction estimates. Third, graph and discuss the sampling curves and extrapolation curves, and discuss the use and limitation of extrapolating estimates of biodiversity. Lastly, a problem set is given for learning and assessment outside of class. Pedagogical Use Description (150 words): This activity is designed as a stand-alone classroom-only but can be combined with a field exercise for advanced undergraduate environmental science or ecology students. Students should have taken introductory ecology, environmental science, or general biology, and some college math or statistics. Students learn to evaluate the scientific literature, apply this knowledge using software for quantitative data analyses, interpret the analytical and graphical output, evaluate the robustness and bias of different methods for estimating species diversity, and learn the current approaches for estimating species richness, a fundamental ecological measurement. Before class readings prepare students to engage during lecture and discussion. In class use of a web-based R shiny app requires students to apply concepts and evaluate the controversy with measuring species richness, using quantitative analyses and scientific-reasoning skills using real data sets. The activity highlights important concepts related to bias and probability stemming from numerical differences in relative abundance of species, and thus, the need for standardization size-based and coverage-based rarefaction, the application of mathematical theory, and the value of interdisciplinarity for solving a seemingly simple measure such as species richness. Learning objectives: 1) Explain the concept of species diversity, its different components, and issues with its measurement. 2) Identify the strengths and weaknesses of sample size-based versus coverage-based methods of rarefaction. 3) Analyze species diversity data using state-of-the-art methods for rarefaction that provide unbiased estimates. <sp
Brad Taylor (Mon,) studied this question.