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Important course information 3 lectures; 2 pracs per week (see timetable) Evaluation –Class test (33%) + Prac work (67%)= course mark –Course mark (60%) + Exam mark (40%) = final mark –TESTS: (1) Friday 25 th April 1pm Z29 (2) OPTIONAL Saturday 3 rd May 8am Z29 –CONTINUOUS ASSESSMENT Report 1 (10%): Estimating population sizes for different organisms (essay OR presentation) –DUE DATE: April 14 th Report 2 (10%): Determining the age of individuals in a population (essay OR presentation) –DUE DATE: May 5 th Report 3 (20%): Practical report..mark-recapture –DUE DATE: May 12 th –Exam = open book

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Important course information PASS = Final mark 50% AND Exam mark 40% AND Practical mark 50% –Supplementary exam – conditional –If Prac mark 50% OR Course mark 40% …then not eligible to write the exam REPORTS 1 & 2 –Each student selects an organism ODD number –Report 1: Essay (April 14 th ) –Report 2: Presentation (May 5 th ) EVEN number –Report 1: Presentation (April 14 th ) –Report 2: Essay (May 5 th ) –Report 1: Review the literature and provide a summary of the methods used to estimate the population size of your organism –Report 2: Review the literature and provide a Summary of the methods used to estimate the age of individuals of your organism

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Important course information Reports must include: –Brief description of organism – biology, ecology, distribution and habitat –An overview of the methods used for estimating populations –A FULL bibliography One to be written as an essay, one to be delivered as PowerPoint presentation ESSAYS –750 1000 words (excl. references) –Must reference at least one journal article, maximum of 3 textbook articles and 3 internet articles –MUST attach copies of referenced text to your report (print/photocopy appropriate page and highlight cited text) –Reference any illustrations you use PRESENTATIONS –5 minute presentation to be given to the class –Max 5 slides –Must give slides to course co-ordinator 24 hours in advance (Report 1 - 11 April) –See rubric for presentation assessments NO MATHEMATICAL FORMULAE in ESSAY OR PRESENTATION. Focus on gathering information on all the types of field or simulation methods used to collect data

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Important course information See handout for evaluation criteria, author instructions and common mistakes PLAGIARISM –Offence 1: Zero for submitted work + written apology to department –Can resubmit, but will get maximum 50% for the work –Offence 2: Reported to University Proctor, possible disciplinary action –Sign course plagiarism declaration and submit now. –Assignment plagiarism declaration to be submitted with ALL assignments –Attach paper copies of all cited text to your assigments RECOMMENDED READINGS –Begon, M., Harper, J.L. and Townsend, C.R. (1990). Ecology: Individuals, Populations and Communities. Blackwell Scientific Publications, 945pp. –Begon, M. and Mortimer, M. (1986). Population Ecology: A Unified Study of Animals and Plants. Blackwell Scientific Publications, 220pp. –Ebert, T.A. (1999). Plant and Animal Populations: Methods in Demography. Academic Press, 312pp –Krebs, C.J. (1999). Ecological Methodology. Benjamin Cummings, 620pp. *** –Sutherland, W.J. (2000). Ecological Census Techniques: A Handbook. Cambridge University Press, 336pp –Zar, J.H. (1984) Biostatistical Analysis. Prentice-Hall *** Must make personal copies of chapters 2 and 4

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Important course information Students taking the course as an elective…if you decide to de-register from the course, you must do so by the end of THIS week. Online resources: http://www.bcb.uwc.ac.za –Click on resources –Click on Online resources –Follow links to BCB241 2008 –Lecture slides will be made available online at the end of each lecture block

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POPULATION DYNAMICS Required background knowledge: Data and variability concepts Measures of central tendency (Mean, median, mode, variance, Stdev) Normal distribution and SE Students t-test and 95% confidence intervals Chi-Square tests MS Excel

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THE SCIENTIFIC METHOD Hypothetico-deductive approach (Popper) based on principle of falsification: theories are disproved because proof is logically impossible. A theory is disproved if there exists a logically possible explanation that is inconsistent with it ModelExplanation or theory (maybe >1) HypothesisPrediction deduced from model Generate null hypothesis – H 0 : Falsification test TestExperiment IF H 0 rejected – model supported IF H 0 accepted – model wrong Pattern ObservationRigorously Describe * * * Statistics Can only really test hypotheses by experimentation

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Notiluca give off light when disturbed Pattern Observation Rigorously Describe ModelExplanation or theory (maybe >1) Give off light when attacked by copepods to attract fish (to eat the copepods) HypothesisPrediction deduced from model Generate null hypothesis – H 0 : Falsification test H 0 : Bioluminescence has no effect on predation of copepods by fish (or decreases predation) H 1 : Bioluminescence increases predation of copepods by fish Test Experiment IF H 0 rejected – model supported IF H 0 accepted – model wrong EXAMPLE OF THE SCIENTIFIC METHOD

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DESCRIBE DATA the raw material of Science DATAVARIABILITY

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DATA – the raw material of Science Data pl (datum, s) are observations, numerical facts Nominal data – gender, colour, species, genus, class, town, country, model etc Continuous data – concentration, depth, height, weight, temperature, rate etc Discrete data – numbers per unit space, numbers per entity etc Often referred to as VARIABLES because they vary Types of Data The type of data collected influences their analysis MaleFemaleBlueRedBlackWhite 100 g200 g 121.34 g162.18 g180.01 g 5 people

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DESCRIBE DATA the raw material of Science DATAVARIABILITY

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VARIABILITY – key feature of the natural world Genotypic/Phenotypic variation – differences between individuals of the same species (blood-type, colour, height etc) Variability in time/space – changes in numbers per unit space, time UniformRandom Clumped Measurement variability – experimental error (bias) Patterns of VARIABILITY

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VARIABILITY impossible to describe data exactly Uncertainty AccuracyPrecision

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ACCURACY – how close a measure is to the real value 20 cm + 20.63 cm 6 mm + 300 μm + 20.631506542 cm Accept a level of measurement error: be upfront

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VARIABILITY impossible to describe data exactly Uncertainty AccuracyPrecision

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PRECISION – how close repeat measures are to each other 20.632 19.986 21.102 20.493 20.578 20.710 22.356 20.623 20.755

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POPULATION DYNAMICS Required background knowledge: Data and variability concepts Data collection Measures of central tendency (Mean, median, mode, variance, Stdev) Normal distribution and SE Students t-test and 95% confidence intervals Chi-Square tests MS Excel

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Population the entire collection of measurements When taking samples it is vital that they are RANDOM and INDEPENDENT = Obtain ALL measurements SMALL POPULATION = LARGE POPULATION Obtain ALL measurements Take SAMPLES REPLICATES = AVERAGE measure REPRESENTATIVE of POPULATION e.g. mass of 19 yr old elephants the blood pressure of women between 16-18 yrs of age number of earthworms on UWC rugby field height of UWC BSc II students oxygen content of water DATA COLLECTION

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500 m e.g. How many earthworms in the field of 25 0000 m 2 ? 100 m A B C D How many earthworms in the field of 25 0000 m 2 ? SAMPLE REPLICATES Earthworms A – 1 (25 in the field) B – 17 (375 in the field) C – 10 (250 in the field) D – 4(100 in the field) DATA COLLECTION

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UWC Student POPULATIONUWC Student RANDOM SAMPLE Measure height of each student Student height values e.g. How tall are UWC students? DATA COLLECTION

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DESCRIBE DATA the raw material of Science DATA VARIABILITY Data Collection Statistics – summary, analysis and interpretation of data

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Measurements and Their Uncertainty 3.1

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