EEMB 2 W25 1S (Pattern and Process) Introductory Biology II - PDF

Introductory Biology II - EEMB 2 (Introduction to Evolution & Ecology) Winter 2025  Introductory Biology II - EEMB 2 (Introduction to Evolution & Ecology) Instructors: Dr. Thomas Even Ecology Dr. John Latto Evolution Ac...

Introductory Biology II - EEMB 2 (Introduction to Evolution & Ecology) Winter 2025  Introductory Biology II - EEMB 2 (Introduction to Evolution & Ecology) Instructors: Dr. Thomas Even Ecology Dr. John Latto Evolution Academic coordinator: Dr. Alice Nguyen [email protected] Office hours: T,TH 2:00-3:00 PM and by appointment Via Zoom [email protected] Course home page URL: https://www.canvas.ucsb.edu Canvas will serve as the interactive hub for course materials (syllabus, recorded lectures and lecture slides, problem sets, study guides, practice questions, and office hour links)  EEMB 2 (Introduction to Ecology) Course Goals: Introduction to major concepts in population & community ecology, and evolution. (Detailed learning objectives can be viewed in the syllabus) Ecology Section: Distribution of populations and communities, population growth and regulation, species interactions, community structure and dynamics and species diversity. Evolution Section: microevolution, speciation and macroevolution. (adaptation, variation & natural selection, gene flow & genetic drift, reproductive isolation, & species formation). EEMB 2 Z students: (Transfer students) All Z students must take either the evolution or ecology exam. Any questions? See Ms. Nguyen.  EEMB 2 (Introduction to Ecology) Course Format: All materials on Canvas 1. In-person Lectures (synchronous): Then recorded & posted to Gauchocast (Asynchronous) 2. Problem Sets: 3 ecology problems sets & 3 evolution problem sets (Asynchronous with specific close dates for each set, see syllabus) 3. Office Hours: 2 per week via zoom (Synchronous), see syllabus for day/times 4. Examinations: Midterm and a final exam delivered online via Canvas quiz function during regularly scheduled lecture time (Synchronous)  EEMB 2 (Introduction to Ecology) Grading: 220 pts total - 1 midterm (100 pts) and a final examination (100 pts). Exams are non-cumulative. Midterm: multiple-choice, with some mathematical calculations (example based and problem solving) - 6 Computation Sets, 3 for ecology & 3 for evolution (3 pts each, 18 pts total). - Survey Questions (1pt each, 2 pts total) Make up policy: Missed exams. Must contact Dr. Nguyen within 24 hours, need verification of illness or emergency. Specific problems with exam dates, see Dr. Nguyen immediately. Academic conduct: Standard UCSB policy. If you cheat you fail, suspension, expulsion, etc. Reading Material: Campbell Biology 12th edition e-book or hardcopy, assigned readings listed on syllabus  Lecture Schedule Date Topic Jan 7 Ecology: Patterns and Processes Jan 9 Distribution of populations & communities Jan 14 Factors that limit distributions Jan 16 Patterns of population growth I Jan 21 Patterns of population growth II Jan 23 Species interactions: competition Jan 28 Species interactions: predation / herbivory Jan 30 The structure of ecological communities Feb 4 Patterns of species diversity Feb 6 MIDTERM EXAMINATION (100 pts.)  Patterns and Processes Ecology: The study of the distribution and abundance of organisms & the factors and interactions that determine distribution and abundance (Where are they, how many are there, and why?)  Patterns and Processes History of Ecology: The roots of modern ecology lie in natural history, human demography, biometry, and applied problems of agriculture and medicine Hunters and gatherers Aristotle 350 BC - Historia Animalium Herodotus and Plato - Providential Ecology Aristotle Herodotus Plato  Patterns and Processes History of Ecology: Graunt 1662, Leeuwenhoek 1687- Population growth Buffon 1756, Malthus 1798, Quetelet 1835, Verhulst 1838 - Population regulation Farr 1843 - Farr’s rule Density Mortality (relationship between the density of the population & the death rate) Buffon Malthus Quetelet Verhulst Farr  Patterns and Processes History of Ecology: Edward Forbes 1887, Henry Cowles 1899, - community regulation and succession Ronald Ross 1908 (systems analysis) Forbes Cowles - mathematical model of the spread of infectious disease A.G. Tansley 1904, F. E. Clements 1905, Charles Elton 1927 - some of the founders of modern ecology (experimental) Rachel Carson 1962 - Until 1960 ecology was not considered an important science by the general population Ross Elton - In the last 50 years, Ecology has become an increasingly rigorous science based on conceptual & mathematical theory, & experimentation - & increasingly important as a guide to sound environmental science  Patterns and Processes Environment: Abiotic components - non-living chemical & physical factors (temperature, light, nutrients, water) Biotic components - living (biological) factors (other organisms, competition, predation) Interaction - Abiotic and Biotic components interact (Organisms are affected by the environment but their presence / activities also change the environment)  Patterns and Processes Biological Scales: Levels of Biological Organization Biosphere Ecosystem Community Population Spatial Scale & Organism Temporal Scale Organ Tissue Cell Organelle Molecule  Patterns and Processes Ecological Scales: Levels of Biological Organization Biosphere Ecosystem Community Ecology Population Organism Organ Tissue Ecology: Cell - Typically operates at the highest scales of biological organization Organelle - Each level operates at different biological, temporal, Molecule and spatial scales  Patterns and Processes Ecological Scales: Definitions Organism - A single individual of a single species Population - Individuals of the same species living in the same geographical area Community - 2 or more populations living in the same geographical area Ecosystem - Comprising the community together with its physical environment Biosphere - Regions of atmosphere, hydrosphere & lithosphere occupied by living organisms  Patterns and Processes Bio. organization Levels of explanation (mechanism) Biosphere Ecosystem Energy flow and cycling of nutrients among abiotic and biotic components Community Interactions among organisms Population Factors that affect population size and composition Organism Behaviors, environmental physiology, morphology Tissue Cell Cell Physiology, Biochemistry Organelle Molecule Ecological levels of explanation operate at longer time scales and larger spatial scales than physiological mechanisms  Patterns and Processes Ecological evidence: a variety of sources 1. Observation and monitoring in the natural environment 2. Manipulative field experiments 3. Controlled, laboratory experiments 4. Mathematical models (The criterion of comparison is the distribution and abundance of organisms in natural environments) The goal of ecology: To observe patterns, describe processes and use this information to predict, manage and control.  Statistical Approaches in Ecology Methods of Approach: Statistics and scientific rigor Statistics = Estimates of population parameters (numerical features of the population) Random sampling: Ecology relies on obtaining estimates from representative samples (Scientific rigor vs. sampling error) - Application of statistics attach a level of confidence to conclusions that are drawn from the results of investigations - Allows us to make conclusions at the population level using sample data  Statistical Approaches in Ecology Methods of Approach: Hypothesis Testing Null hypothesis: Assume that there is no association between measured variables P-values (probability level): Measures the strength of conclusions being drawn from results Significance testing: used to accept or reject the null hypothesis If P < than 0.05 (5%), then results are statistically significant (Assuming the null hypothesis there is less than 5% probability of getting a data set (results) like ours due to random chance) P-values are generated by comparing sampling data to a frequency distribution assumed by the null hypothesis (observed vs. predicted)  Statistical Approaches in Ecology Frequency Distributions: Used to determine probability, which aids ecologists in making predictions 0 75 Example: What is the probability of getting a value > 75? (Impossible to answer this question) - Need to know how often values of 75 or larger occur - Need to add a second axis to our number line - Y-axis = frequency, or how often values of 75 or greater occur  Statistical Approaches in Ecology Example frequency distributions: Frequency) Frequency) 0 75 0 75 Left Plot: values of 75 or greater (shaded) occur only about 5% of the time. So, the probability of getting a value of 75 or greater is about 0.05 Right Plot: values of 75 or greater (shaded) occur about 1/3 of the time. The probability of getting a value of 75 or greater is about 0.33  Statistical Approaches in Ecology Biological Data are often normally distributed: Ex: Height Most of us Symmetrical with a single central peak (mean) 50% of the distribution on either side of the mean Spread controlled by variability (↓spread, ↓ variability) - measured as standard deviation  Statistical Approaches in Ecology Biological data are often normally distributed: How do we determine if our sample group mean is or = to the population mean? ^ p = datum (sample mean) U^p = population mean σ ^p = standard error (standard deviation/√sample size) Z- test statistic: - How many standard deviations a datum is above or below the mean (Indicates how much higher or lower the value is from the mean)  Computational Set Practice Question 1: Z-test statistic Across the Congo, the average number of termites captured by chimpanzees during a 5-minute period is 100, with an SD of 10. - In a single troop, 25 chimpanzees collected an average of 96 termites. Does this troop capture fewer termites than other troops in the region? ^ = datum (sample mean) p U^p = population mean σ ^p = standard error (standard deviation/√sample size) Null hypothesis: No difference in termite capturing ability. What is the standard error ( σp^ )? a. 10 b. 2.5 c. 2 d. 25  Computational Set Practice Question 1: Z-test statistic Across the Congo, the average number of termites captured by chimpanzees during a 5-minute period is 100, with an SD of 10. - In a single troop, 25 chimpanzees collected an average of 96 termites. Does this troop capture fewer termites than other troops in the region? ^ = datum (sample mean) p U^p = population mean σ ^p = standard error (standard deviation/√sample size) Null hypothesis: No difference in termite capturing ability. What is the standard error (σp^ )? a. 10 b. 2.5 c. 2 σp^ = SD/√n = 10/√25 = 2 d. 25  Computational Set Practice Question 1: Z-test statistic Across the Congo, the average number of termites captured by chimpanzees during a 5-minute period is 100, with an SD of 10. - In a single troop, 25 chimpanzees collected an average of 96. Does this troop capture fewer termites than other troops in the region? ^ = datum (sample mean) p U^p = population mean σ ^p = standard error Null hypothesis: No difference in termite capturing ability. What is the Z-score? a. -100 b. 5 c. -2 d. 4  Computational Set Practice Question 1: Z-test statistic Across the Congo, the average number of termites captured by chimpanzees during a 5-minute period is 100, with an SD of 10. - In a single troop, 25 chimpanzees collected an average of 96 termites. Does this troop capture fewer termites than other troops in the region? ^ = datum p U^p = population mean σ ^p = standard error Null hypothesis: No difference in termite capturing ability. What is the Z-score? a. -100 b. 5 c. -2 Z = ^p – U^p = (96-100) = -2 σ ^p 2 d. 4  Computational Set Practice Question 1: Z-test statistic Across the Congo, the average number of termites captured by chimpanzees during a 5-minute period is 100, with an SD of 10. - In a single troop, 25 chimpanzees collected an average of 96. Does this troop capture fewer termites than other troops in the region? ^ = datum p U^p = population mean σ ^p = standard error Null hypothesis: No difference in termite capturing ability. The probability of observing a value (P-value) below or equal to -2 is 0.0068 according to the z-table, do you: a. accept the null hypothesis (Fail to reject the null)? b. reject the null hypothesis?  Computational Set Practice Question 1: Z-test statistic Across the Congo, the average number of termites captured by chimpanzees during a 5-minute period is 100, with an SD of 10. - In a single troop, 25 chimpanzees collected an average of 96. Does this troop capture fewer termites than other troops in the region? ^ = datum p U^p = population mean σ ^p = standard error Null hypothesis: No difference in termite capturing ability. If the probability of observing a value (P-value) below/equal to -2 is 0.0068 according to the z-table, do you: a. accept the null hypothesis (Fail to reject the null)? b. reject the null hypothesis These chimps capture fewer termites than other troops  Patterns and Processes Summary: Ecological phenomena - occurs at a variety of scales Ecological evidence - comes from a variety of different sources Ecology - relies on scientific evidence and the application of statistics