terms.csv

TERM,CONTEXT
Natural Selection,"Course definition: Natural selection is the process by which individuals with heritable traits that enhance survival and reproduction are more likely to pass on their genes to the next generation. Over time, this leads to a change in the genetic makeup of a population, favoring advantageous traits.; Characteristics: Acts on phenotypes of individuals, but only heritable genetic changes (genotypes) are passed to offspring; Requires variation: Genetic diversity within a population is essential for natural selection to occur; Environmental influence: The environment determines which traits are advantageous; a trait beneficial in one setting may be detrimental in another; Adaptation: Over generations, natural selection can result in populations that are better adapted to their environments.; Misconceptions: While natural selection favors traits that increase fitness, it does not work towards a specific goal or perfect organism.; Related terms: Artificial selection, evolution, adaptation, fitness (direct and indirect), directional selection, stabilizing selection, disruptive selection, balancing selection, frequency-dependent selection, sexual selection, kin selection.; Key scientists: Charles Darwin, Alfred Russel Wallace, Hopi Hoekstra, Rosemary and Peter Grant, Rosemary Gillespie, and Paul Turner."
Sexual Selection,"Definition: A type of natural selection where traits that increase mating success are favored, even if they may decrease survival.; Course coverage: Associated lectures: Week 3 Monday; Associated assessments: Quiz 3, Midterm 1, Final Exam, Discussion Board Post Week 4; Resources: https://openstax.org/books/biology-2e/pages/19-3-adaptive-evolution; Key concepts: Intrasexual selection: Competition within one sex for access to mates; Intersexual selection: Choice of mates by one sex based on traits of the other sex; Sexual dimorphism: Physical differences between males and females resulting from sexual selection."
Mutation,"Definition: A change in the nucleotide sequence of DNA, which may alter gene function and provide the genetic variation necessary for evolution.; Examples discussed in class: Antibiotic resistance in bacteria; Sickle cell anemia; Huntington's disease; CRISPR/Cas9 gene editing technology.; Key concepts: Types: Point mutations, insertions, deletions, duplications, translocations; Effects: Neutral, beneficial, or harmful depending on context; Mutation rate: Varies across species and can be affected by environmental factors."
Genetic Drift,"Definition: Random changes in allele frequencies in a population due to chance events rather than selection.; Course learning objectives: Define genetic drift and explain its effects on allele frequencies and richness in small populations; Describe the founder effect and how it can lead to genetic drift; Understand the role of genetic drift in the development of new species; Discuss the impact of genetic drift on the genetic makeup of populations; Compare and contrast genetic drift with natural selection as drivers of evolution.; Key examples: Bottleneck effect: Population reduction leading to reduced genetic diversity; Founder effect: Establishment of a new population by a small number of individuals; Island populations: Isolated groups showing effects of genetic drift."
Gene Flow,"You may optionally leave this blank. The chatbot will then default to its own knowledge base."
Shannon Diversity Index,"Definition: A mathematical measure used to characterize species diversity in an ecological community.; Calculation method: 1. Goal: Calculate the Shannon Diversity Index (H) to measure species diversity in a sample; 2. Variables: pi = Proportion of each species in the total sample (e.g., Species A has 40 individuals in a sample of 100, so pi for Species A = 40/100 = 0.4); 3. Formula: H = -∑(pi × ln(pi)), where pi is the proportion of each species and ln(pi) is the natural logarithm of pi; 4. Example calculation: Suppose a sample has 3 species with the following proportions: Species A: pi = 0.6, Species B: pi = 0.3, Species C: pi = 0.1, H = -(0.6 × ln(0.6) + 0.3 × ln(0.3) + 0.1 × ln(0.1)) = 0.94; Application: Used to compare the biodiversity of different ecosystems or communities. Higher values indicate greater diversity."
Calculating a t-Test in Excel,"Purpose: Statistical test used to determine if there is a significant difference between the means of two groups.; Excel implementation: Function: The TTEST function returns the probability associated with a Student's t-test; Syntax: =TTEST(array1, array2, tails, type); array1: First data set range; array2: Second data set range; tails: 1 for a one-tailed test, 2 for a two-tailed test; type: 1 for paired t-test, 2 for two-sample equal variance (homoscedastic), 3 for two-sample unequal variance (heteroscedastic).; Example: Assuming your data sets are in cells A2:A11 and B2:B11: =T.TEST(A2:A11, B2:B11, 2, 2). This performs a two-tailed, two-sample equal variance t-test.; Interpreting results: P-value: The output from the t-test will include a p-value; If p-value < alpha (e.g., 0.05), reject the null hypothesis (significant difference); If p-value > alpha, fail to reject the null hypothesis (no significant difference)."
Research Experience for Undergraduates (REU),"Definition: NSF-funded programs that provide undergraduate students with hands-on research experience in various STEM fields.; Key information: Programs typically run during summer (8-10 weeks); Students receive stipends, housing, and sometimes travel allowances; Applications usually due in January-February for summer programs; Open to U.S. citizens and permanent residents; Competitive application process typically requiring transcripts, essays, and letters of recommendation; Provides valuable experience for graduate school applications and research careers.; For comprehensive information and program listings, visit: https://www.nsf.gov/funding/initiatives/reu"
Making a figure in R using ggplot2,"Use the Palmer Penguins or iris dataset."
p-value,"Using Hardy-Weinberg equilibrium as an example: Definition: A p-value is the probability of obtaining results at least as extreme as those observed, assuming the null hypothesis is true.; Hardy-Weinberg example: 1. Null hypothesis (H₀): The population is in Hardy-Weinberg equilibrium for the locus (p² + 2pq + q² = 1); 2. Data collection: Count genotypes in the population (AA, Aa, aa); 3. Expected frequencies: Calculate expected genotype frequencies using H-W equation; 4. Statistical test: Use Chi-square test to compare observed vs. expected frequencies; 5. p-value interpretation: If p < 0.05: Reject H₀; population likely not in H-W equilibrium (significant deviation); If p ≥ 0.05: Fail to reject H₀; insufficient evidence that population deviates from H-W equilibrium.; Example calculation: Observed counts: 320 AA, 160 Aa, 20 aa (total n = 500); Allele frequencies: p = (2×320 + 160)/1000 = 0.8, q = 0.2; Expected counts: AA = 500×(0.8)² = 320, Aa = 500×2×0.8×0.2 = 160, aa = 500×(0.2)² = 20; Chi-square statistic = 0 (in this example, observed = expected); p-value = 1.0, indicating perfect alignment with H-W equilibrium."
tidy data,"Using the Palmer Penguins dataset as an example: Definition: Tidy data is a standardized way of organizing data where: 1. Each variable forms a column; 2. Each observation forms a row; 3. Each type of observational unit forms a table.; Example with Palmer Penguins: Untidy format (wide): species island year bill_length_Adelie bill_depth_Adelie bill_length_Gentoo bill_depth_Gentoo; Adelie Torgersen 2007 39.1 18.7 NA NA; Gentoo Biscoe 2007 NA NA 44.5 14.3.; Tidy format: species island year bill_length_mm bill_depth_mm; Adelie Torgersen 2007 39.1 18.7; Gentoo Biscoe 2007 44.5 14.3.; In R you can transform untidy data to tidy format using the tidyr package: library(tidyr); untidy_data %>% pivot_longer(cols = starts_with(\"bill_\") names_to = c(\".value\" \"species\") names_pattern = \"(.*?)_(.*)\" values_drop_na = TRUE); Benefits of tidy data: Consistent structure makes it easier to manipulate model and visualize; Works seamlessly with R's tidyverse packages; Facilitates data analysis and reproducibility."
Week 1 Learning Objectives,"1. Explain the mechanisms of evolution, with emphasis on understanding how natural selection, genetic drift, gene flow, and mutation contribute to changes in allele frequencies within populations.; 2. Apply quantitative methods to analyze biological diversity, including calculating and interpreting measures such as the Shannon Diversity Index and testing for Hardy-Weinberg equilibrium using statistical approaches.; 3. Differentiate between various types of selection (directional, stabilizing, disruptive, balancing, frequency-dependent, and sexual selection) and provide real-world examples of each.; 4. Evaluate common misconceptions about evolution, particularly understanding that natural selection is not goal-directed and does not necessarily lead to perfect organisms.; 5. Connect historical and contemporary research in evolutionary biology by examining the contributions of key scientists (such as Darwin and Wallace) alongside modern researchers (such as Hoekstra, the Grants, Gillespie, and Turner) and their findings."
Course Regrade Policy,"Any student can request a regrade for any assessment. The request must be made within one week of the assessment being returned. The request should include a detailed explanation of why you believe the assessment was graded incorrectly. All requests should be in writing and emailed directly to the instructor. The course staff will review the request and make a final determination. This determination is final and there will be no further appeals."
Effective study techniques,"Effective Study Strategies for College Biology: Active Recall: Regularly test yourself on key concepts and terms to enhance memory retention. Use flashcards, practice quizzes, or Schema Study to reinforce learning.; Spaced Repetition: Distribute your study sessions over time rather than cramming. This technique helps improve long-term retention of information.; Interleaved Practice: Mix different topics or types of problems in a single study session. This approach helps improve problem-solving skills and adaptability.; Elaborative Interrogation: Ask yourself 'why' and 'how' questions about the material to deepen understanding and create connections between concepts.; Self-Explanation: Teach the material to someone else or explain it out loud to yourself. This helps clarify your understanding and identify gaps in knowledge.; Metacognitive Strategies: Regularly assess your understanding and adjust your study methods as needed. Reflect on what study techniques work best for you.; Mind Mapping: Create visual representations of the material to organize and integrate information, making it easier to recall.; Healthy Study Habits: Ensure adequate sleep, nutrition, and exercise to support cognitive function and overall well-being."