Lec #1 EEMB 2 4S W19 (Pattern and Process).pdf

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Unformatted text preview: Introductory Biology II - EEMB 2 (Introduction to Evolution & Ecology) Introductory Biology II - EEMB 2 (Introduction to Evolution & Ecology) Instructors: Dr. Thomas J. Even Dr. John Latto Ecology Evolution Dr. Thomas Even (Introduction to Ecology) Office hours: TTH 1-2 PM and by appointment LSB RM. 4322 (805) 893-2904 [email protected] Winter 2019 EEMB 2 (Introduction to Ecology) Academic coordinator: Dr. Alice Nguyen (LSB 4326) [email protected] Reading Material: Campbell Biology 11th edition (Assigned readings listed on syllabus) Course home page URL: EEMB 2 (Introduction to Ecology) Grading and Exams: 1 midterm (100 pts) and a final examination (100 pts). Midterm: (Scantron) multiple-choice, matching, true and false, some simple mathematical calculations Course Goals: Introduction to major concepts in population & community ecology, and evolution Make up policy: Missed exams. Must contact Dr. Nguyen within 24 hours, need written verification of illness or emergency. Specific problems with exam dates, see Dr. Nguyen immediately. Ecology Section: Distribution of species and communities, population growth and regulation, species interactions, community structure and dynamics and species diversity. Academic conduct: Standard UCSB policy. If you cheat you fail or worse 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 Dr. Nguyen. 1 Patterns and Processes Lecture Schedule Ecology: Date Topic Jan 8 Jan 10 Jan 15 Jan 17 Jan 22 Jan 24 Jan 29 Jan 31 Feb 5 Ecology: Patterns and Processes Distribution of populations & communities Factors that limit distributions Patterns of population growth I Patterns of population growth II Species interactions: competition Species interactions: predation / herbivory The structure of ecological communities Patterns of species diversity Feb 7 MIDTERM EXAMINATION (100 pts.) 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 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 History of Ecology: Graunt 1662, Leeuwenhoek 1687- Population growth Hunters and gatherers Aristotle 350 BC - Historia Animalium Buffon 1756, Malthus 1798, Quetelet 1835, Verhulst 1838 - Population regulation Herodotus and Plato - Providential Ecology Farr 1843 - Farr’s rule Density Mortality (relationship between the density of the population & the death rate) Buffon Aristotle Herodotus Malthus Quetelet Verhulst Farr Plato 2 Patterns and Processes Patterns and Processes History of Ecology: Environment: Edward Forbes 1887, H.C. Cowles 1899, - community regulation and succession Ronald Ross 1908 (systems analysis) Forbes Cowles - mathematical model of the spread of infectious disease Abiotic components - non-living chemical & physical factors (temperature, light, nutrients, water) Biotic components - living (biological) factors 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 (other organisms, competition, predation) Interaction - Abiotic and Biotic components interact (Organisms are affected by the environment but their presence / activities also change the environment) important science by the general population Ross Elton - In the last 50 years, Ecology has become an increasingly rigorous science based on mathematical theory and experimentation - & increasingly important as a guide to sound environmental science Patterns and Processes Biological Scales: Levels of Biological Organization Patterns and Processes Ecological Scales: Levels of Biological Organization Biosphere Biosphere Ecosystem Ecosystem Community Community Population Organism Organ Tissue Cell Organelle Molecule Ecology Population Spatial Scale & Temporal Scale Organism Organ Tissue Ecology: Cell - Typically operates at the highest scales of biological organization Organelle Molecule - Each level operates at different biological, temporal, and spatial scales 3 Patterns and Processes 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 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 Behaviors, Environmental physiology, Morphology Organism Tissue Cell Organelle Molecule Patterns and Processes Ecological evidence: a variety of sources - Observation and monitoring in the natural environment - Manipulative field experiments - Controlled, laboratory experiments - 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. Cell Physiology, Biochemistry Ecological levels of explanation operate at longer time scales and larger spatial scales than physiological mechanisms 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 the results of investigations - Allows us to make conclusions at the population level using sample data 4 Statistical Approaches in Ecology Methods of Approach: Hypothesis Testing Null hypothesis: Assume that there is no association between variables Statistical Approaches in Ecology Frequency Distributions: Used to determine probability, which aids ecologists in making predictions P-values (probability level): Measures the strength of conclusions being drawn Significance testing: If P < than 0.05 (5%), then results are statistically significant (less than 5% probability that data are due to random chance) P-values are generated by comparing sampling data to a frequency distribution assumed by the null hypothesis (observed vs. predicted) 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: Statistical Approaches in Ecology Biological Data are often normally distributed: Ex: Height Most of us 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 Symmetrical with a single peak (mean) 50% of the distribution on each side Spread controlled by variability (↓spread, ↓ variability) 5 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? 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? ^ p = datum (sample mean) U^p = population mean σ ^p = standard error (standard deviation/√sample size) ^ = datum (sample mean) p 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) What is the standard error ( σp^ )? a. 10 Null hypothesis: No difference in termite capturing ability. b. 2.5 c. 2 d. 25 Computational Set Computational Set Practice Question 1: Z-test statistic 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. 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? Does this troop capture fewer termites than other troops in the region? ^ p = datum (sample mean) U^p = population mean σ ^p = standard error (standard deviation/√sample size) ^ p = datum (sample mean) U^p = population mean σ ^p = standard error Null hypothesis: No difference in termite capturing ability. Null hypothesis: No difference in termite capturing ability. What is the standard error ( σp^ )? a. 10 What is the Z-score? a. -100 b. 2.5 c. 2 σp^ = SD/√n = 10/√25 = 2 d. 25 b. 5 c. -2 d. 4 6 Computational Set Computational Set Practice Question 1: Z-test statistic 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. 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? Does this troop capture fewer termites than other troops in the region? ^ p = datum U^p = population mean σ ^p = standard error ^ p = datum U^p = population mean σ ^p = standard error Null hypothesis: No difference in termite capturing ability. Null hypothesis: No difference in termite capturing ability. What is the Z-score? a. -100 b. 5 c. -2 d. 4 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? ^ b. reject the null hypothesis? Z = p – U^p = (96-100) = -2 σ ^p 2 Computational Set Patterns and Processes Practice Question 1: Z-test statistic Summary: 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. Ecological phenomena - occurs at a variety of scales Does this troop capture fewer termites than other troops in the region? ^ p = datum U^p = population mean σ ^p = standard error Ecological evidence - comes from a variety of different sources 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 Ecology - relies on scientific evidence and the application of statistics b. reject the null hypothesis These chimps capture fewer termites than other troops 7 ...
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