Preview Software Manual - BioQUEST Curriculum Consortium
Contents
1. er errr 5 INVESTIGATE AN UNKINOW N 5 SAMPLE LABORATORY USING DEVELOPMENTAL 6 STATISTICAL ANALYSIS 7 USING EXCEL AND CRICKET GRAPH TO CALCULATE AND PLOT MEANS AND STANDARD ERRORS AND TO CONDUCT CORRELATION ANALYSIS cc cisssc casdaacened cos sndansanstneiesessbendeusvunsdeenphasetesbeearesscebeoeeseusees 9 Developmental Selection Seed Abortion in Perennial Legumes An Overview of the System A Pollen performance 15 influenced by the haploid genotype that they carry as illustrated by these pollen grains stained with 10dine to reveal their Waxy phenotype Pollen tubes carry sperm from the pistil s stigma the receptive surface of the maternal reproductive structure to the egg bearing ovules in the ovary The stigma and the ovary of a flower are separated by a long extension called a style which may serve as a race track in which pollen performance is tested There are often 7 20 pollen grains per ovule which means that pollen tube growth may take assume the nature of a race to the eggs pollen tube competition Pollen tube competition can influence the genetic quality of resulting seed progeny such as in the legume fruits above In many species the affects of pollen tube competition can be viewed in the pattern of seed abortion within fruits In fact the signature of pollen tube competition may be us2. dewEate 2 Repeat the same graph without the error bars Then select Curve fit Linear to conduct a correlation analysis Data fram dewrate means data CG w l iseser 0112594 RTS 0 797 1 0 El ri 0 6 E devEate 0 4 0 0 2 in 0 ri position The plot will be modified by a the addition of the best fit curve through these data a linear prediction equation and the squared correlation coefficient called the Coefficient of Determination or CD Take the square root of the CD to produce the correlation coefficient There are ten data points How many degrees of freedom do you have Is this a significant correlation Check a table of correlation coefficients K Note that clicking on an item in a Cricket Graph plot will usually call up an editing a m a a r ay Ta dialogue box Horizontal X Axis Format AXIS Maximum 12 000 of Minor Ticks i Linear 2 Log position Ticks Mark Grid Major Ej Minor Ej Inside Both sides Labels Bottom
3. Sample Laboratory Using Developmental Selection A Examine 10 fruits by clicking the Score Fruit button and then scoring each seed within a fruit This will clarify how data are collected from each fruit B Using the Score Fruits button test the following hypotheses with the specified samples Indicate whether the results seem unambiguous Be prepared to explain what these data might reflect about the causes of seed abortion in that hypothetical legume species 1 Set Low MRL and Low PTC What do you predict you will find N Pd base tip base tip base tip a Sample size 3 b Sample size 10 c Sample size 50 2 Set High MRL and Low PTC NN tip base tip base tip a Sample size 3 b Sample size 10 c Sample size 50 3 Set Low MRL and High PTC NN a base tip base tip base tip a Sample size 3 b Sample size 10 c Sample size 50 C Click on the unknown button Paste your data set into Excel to calculate means and standard errors Then paste your means and standard error data into Cricket graph and produce two plots 1 Means and two standard error bars Interpret this analysis 2 Acorrelation analysis Interpret this analysis D You will have an unknown next week You and your team will produce a poster or PowerPoint presentation in class to be presented at the end of the lab period Statistical Analysis Analysis may be facilitated by importing the data into a s
4. Ear Developmental Selection Seed Abortion and Pollen Tube Competition EES i Sir Library VH User s Manual Donald Buckley Quinnipiac University Martin Cohen University of Hartford A BioQUEST Library VII Online module published by the BioQUEST Curriculum Consortium The BioQUEST Curriculum Consortium 1986 actively supports educators interested in the reform of undergraduate biology and engages in the collaborative development of curricula We encourage the use of simulations databases and tools to construct learning environments where students are able to engage in activities like those of practicing scientists Email bioguest beloit edu Website http bioquest org Editorial Staff Editor Managing Editor Associate Editors John R Jungck Ethel D Stanley Sam Donovan Stephen Everse Marion Fass Margaret Waterman Ethel D Stanley Amanda Everse Sue Risseeuw Online Editor Editorial Assistant Beloit College Beloit College BIoQUEST Curriculum Consortium University of Pittsburgh University of Vermont Beloit College Southeast Missouri State University Beloit College BIoQUEST Curriculum Consortium Beloit College BIoQUEST Curriculum Consortium Beloit College BIoQUEST Curriculum Consortium Editorial Board Ken Brown University of Technology Sydney AU Joyce Cadwallader St Mary of the Woods College Eloise Carter Oxford College Angelo Collins Knowles Science Teaching Foundation Terry L Dertin
5. TUB 7 1 B B rows 2 to 6 you may need 3 decimal places for this variable for data in column Insert one last row seed position number E In order to plot this data or to conduct a correlation analysis of developmental rate and seed position the means standard errors and seed position data will need to be transposed into columns Select and copy the hilighted range B D FE Hf I J4 fruitNumber seedPos gt seedPos 2 seedPos 3 seedPos 4 seedPos 5 seedPos b seedPos T seedPos 8 seedPos zeedPos 10 Create a new file and select Paste Special from the edit menu Click on values and the Paste Special Paste CI All Comments C3 Formulas C3 Yalidatbon ao aloes C2 All except borders C2 Formats Operation amp Hone C Multiply Add C3 Divide C3 Subtract Skip blanks Transpose gate Lini OK G The data will re arranged in column format needed for analysis or plotting Select and copy the data so that 1t can be pasted into Cricket Graph Save your Excel file and Quit Excel H Launch Cricket Graph and paste in your data I You will need to complete three steps SER EL 72 1 Cut the variable names out of the cells in row 1 and paste them into column header cells e g like Column 1 2 The empty first row must be deleted Click on 1 on t
6. and Windows NT are either registered trademarks or trademarks of Microsoft Corporation Helvetica Times and Palatino are registered trademarks of Linotype Hell The BioQUEST Library and BIOoQUEST Curriculum Consortium are trademarks of Beloit College Each BioQUEST module is a trademark of its respective institutions authors All other company and product names are trademarks or registered trademarks of their respective owners Portions of some modules software were created using Extender GrafPak by Invention Software Corporation Some modules software use the BIoQUEST Toolkit licensed from Project BIAoQUEST Table of Contents AN OVERVIEW OF THE 1 THE PROBLEM AND SEVERAL EXPLANATORY 2 REPRODUCTIVE EXCESS AND EARLY SEED ABORTION ceseeeeee III II I me me ee eee 2 EXPLANATORY HYPOTHESE Sreser boc HUM EUER M AEDEM 2 D P M 2 E E EE E A 2 DCO DICT SCG O ERR 2 Maternal Resource Limitation cc ccc ccc ccc ccc cece cee eee EEE EEE EEE EEE EE re aenea 2 A NATURAL EXPERIMEN 2 THE DEVELOPMENTAL SELECTION cce e ehh 3 SIMULA TION SCREEN 3 dua ur P
7. d be considered a significant correlation P 0 05 again depends on the number of degrees of freedom df which in correlation analysis is the sample size minus 2 One compares the observed correlation value with a critical value for P 0 05 and the given number of degrees of freedom If the observed value is at least as large as the critical value then a significant correlation has been demonstrated Using Excel and Cricket Graph to Calculate and Plot Means and Standard Errors and to Conduct Correlation Analysis A Terminology l A spreadsheet is a special file intended to facilitate the handling of tabular information Columns are labeled with letters at their top whereas rows are numbered at the left the intersection of a row and a column is called a cell and 1s used to hold one piece of information A rectangular area is called a range and is specified listing the cell address on the top left followed by a colon and the cell address on the bottom right such as A1 A4 cells Al A2 and A4 Cells can contain either text as does cell Al below numeric values as does cell B1 or formulae see f below Note that the active cell highlighted is identified just above the title bar of the spreadsheet and its contents are displayed to the right of an equal sign in the editing field A1 Fern UU Cells can contain a third element formula formulae start with an equal
8. ed as a test of whether seed abortion 15 selective The Problem and Several Explanatory Hypotheses Reproductive Excess and Early Seed Abortion Even in climax plant communities where the reproductive requirement of a parent 1s only to replace itself with one successful offspring parents may produce from hundreds to billions of offspring In these perennial plants species whose individuals live for more than one year many or most seeds and spores are aborted before they mature Explanatory Hypotheses Numerous hypotheses have been advanced to explain the evolutionary cause of this reproductive excess Dispersal Sites for the establishment of new offspring may be rare and or randomly dispersed in space or time Broadcasting a large number of offspring may be required to ensure dispersal into these recruitment sites but this hypothesis doesn t explain why numerous progeny abort before maturation Escaping Predators Masting plant species are the most extreme example of plants that reproduce much more intensively in some years than others Years of low reproductive output are usually more common and serve to maintain predators at low population sizes Infrequently these plants greatly increase their reproductive output to levels that exceed the harvesting capabilities of its predators which ensures the escape of some progeny from predators However this hypothesis does not contribute to our understanding of why many progeny abor
9. g Murray State University Roscoe Giles Boston University Louis Gross University of Tennessee Knoxville Yaffa Grossman Beloit College Raquel Holmes Boston University otacey Kiser Lane Community College Peter Lockhart Massey University NZ Ed Louis The University of Nottingham UK Claudia Neuhauser University of Minnesota Patti Soderberg Conserve School Daniel Udovic University of Oregon Rama Viswanathan Beloit College Linda Weinland Edison College Anton Weisstein Truman University Richard Wilson Emeritus Rockhurst College William Wimsatt University of Chicago Copyright 1993 2006 by Donald Buckley and Martin Cohen All rights reserved Copyright Trademark and License Acknowledgments Portions of the BioQUEST Library are copyrighted by Annenberg CPB Apple Computer Inc Beloit College Claris Corporation Microsoft Corporation and the authors of individually titled modules All rights reserved System 6 System 7 System 8 Mac OS 8 Finder and SimpleText are trademarks of Apple Computer Incorporated HyperCard and HyperTalk MultiFinder QuickTime Apple Mac Macintosh Power Macintosh LaserWriter ImageWriter and the Apple logo are registered trademarks of Apple Computer Incorporated Claris and HyperCard Player 2 1 are registered trademarks of Claris Corporation Extend is a trademark of Imagine That Incorporated Adobe Acrobat and PageMaker are trademarks of Adobe Systems Incorporated Microsoft Windows MS DOS
10. he left of the row and select delete from the Edit menu 3 Finally each column must be formatted Select a column and then choose Format Data from the Data menu Select Decimal and the number of decimal places that you will need File Edit Data Graph Curve Fit Goodies Formats Windows Help Untitled Data 1 a8 4 5 6 7 8 om rA eH A m Column Format deviate na TN D Format Justification ee D Alphabetic Left aligned Besdnca E E E MEE a Decimal C Centered ee 9 O Scientific Right aligned NOME 3 Dollars d6 ea a ETE E latu uU Dac epee teense ess C3 Percent Digits COCCO OK c wu Now you are ready to conduct your analysis Select Graph Scatter Choose position for your horizontal axis and devRate for your vertical axis Click OK You should see a graph like the one below There seems to be an increase in seed maturation rate toward the stylar end of the fruit but 15 it real Data fram deyrate means data CG m Ge a Ta position dewvEate 1 Plot Y Error Bars from the Goodies menu There is considerable overlap among these means and error bars so this kind of analysis will not be helpful in this case Data from devrate means data CG 0 8 E 0 4 n D n n n pozition
11. nvolving the fastest growing pollen tubes should be in proximity to the site from which pollen tubes emerge from their race through the style and into the ovary In most species that have been studied the pollen tubes enter the ovary near the stylar or distal end of the fruit which would result in a gradient of genetic quality within the fruit If seed abortions are influenced by the genetic quality of the seeds then seed maturation rates should be highest closest to the point of pollen tube entry into the ovary Conversely seed abortion may be non selective Perhaps proximity to the spigot the fruit s peduncle matters most Seeds at the base of the fruit may benefit from better nutrient access by being closer to the nutrient source in which case maturation rates should be highest at the base of the fruit not the stylar end The Developmental Selection Simulation Developmental Selection 15 a research simulation It 15 intended to provide practice in data collection data analysis and hypothesis testing Double click the Developmental Selection application to launch it Click on the title screen to advance into the simulation Simulation Screen The simulation screen 15 divided into three areas a b Ore Fr uil Fruits Reset All election PTE MRL Efhsets ina parent nem Lee sty fori Sliders Used to Control Biological Parameters On the left of
12. preadsheet to calculate means and values two standard errors above and below the means Your final data set will contain one column for seed position and columns for maturation rate and a two standard error value for plotting error bars There will therefore be eleven rows with variable names in the first row and ten rows to describe the ten seed positions The data will be saved as a text file so that it can be imported into Cricket Graph to be plotted and or to conduct a correlation analysis of means against seed position A A statistic of location Mean maturation rate the number of mature seeds over the total number of aborted and mature seeds This value describes the most common outcome although sample means are influenced by chance Increasing the sample size increases the precision of the estimate The variation in sampling means is often quantified using Standard Errors Sample means will be found within one standard error of the true mean about 67 of the time and within two standard errors of the true mean about 95 of the time Therefore two standard error bars are often plotted above and below the sampled mean as a method of conducting a graphical estimate of whether two means are statistically different In the two examples below the means are different but with small sample sizes the neighboring means fall within the 2 standard error range and can t conclude that the samples are really different Increasing the sample size dec
13. reases the range of the error bars and we can recognize the difference between the two samples statistically a mean exceeding two standard errors approximates a P 0 05 criterion for rejection means dissimilar variability great FUB HONO a lt common results with En at small sample size questionable 3 Control Treatment Control Treatment means dissimilar little variability less overlap differences T oi these results only clear with large sample size Control Treatment Control Treatment For frequencies such as the seed maturation rate per seed position the value of one standard error is SE SORT pq N where p is the maturation rate q 1 and N is the sample size C Sometimes paired comparisons are not very useful such as when seeking evidence of a trend Correlation or regression analyses are often used in these circumstances Regression is used when comparing two variables one of which is known to cause the other unilaterally We will use correlation analysis which makes no claims about causation Demonstrating a correlation merely means that a pattern of concerted change has been demonstrated A positive correlation indicates that both variables increase or decrease together A negative correlation means that one variable decreases as the other increases Correlations are quantified by correlation coefficients r which range from 1 0 to 1 0 in value Whether a particular r value shoul
14. seed abortion patterns within fruits The points are mean maturation rates the number of mature seeds at the position divided by the number of fruits examined Two standard error bars are plotted also approximating 9596 confidence limits Neighboring mean maturation rates that fall outside the error bars are significantly different P 0 05 Note that as sample size increases the shape of the curves reflect the underlying biology more faithfully and the error bars decrease in size which allows real differences to be recognized more reliably Trends may apparent despite the fact that neighboring means fall within the error bars To resolve these trends regression or correlation analysis must be applied Export Data Click on the clipboard icon right to load the dataset into the clipboard for pasting into a spreadsheet or other data analysis package The data will be tab delimited Each record will represent one fruit Column 1 contains the fruit number and columns 2 11 contain scores for seed positions 1 10 respectively Seed development scores are 0 aborted and 1 mature Investigate an Unknown In this mode the sliders for the seed ovule ratio pollen tube competition and maternal resource limitation are hidden Click on the Unknown button at the bottom of the simulation window to hide the sliders To return to the standard simulation screen click on the black arrow on the bottom right of the screen
15. sign and are used to make calculations and to perform other tasks Usually the cell shows the value returned by the formula and the field reveals the actual formula The formula on the left was keyed in by entering an equal sign then by clicking on cells B1 then B2 and finally B3 The formula on the right is called a function and utilizes a built in program to produce the same result The range of numbers to be added up was specified in the range B1 B3 Functions are available from the Insert menu 1 62 63 B Launch Microsoft Excel C Paste in the Developmental Selection data set It should look like this although the number of rows will depend on your sample size The base of the fruit 1s at the left seed position 1 and the stylar end of the fruit 1s on the right seed position 10 Seeds are aborted 0 or mature 1 EN E bD j E F amp H I 4 j K fruitNumber seedPos 1 seedPos 2 seedPos 3 seedPos 4 seedPos 5 seedPos 6 seedPos 7 seedPos 8 seedPos_9 seedPos_10 1 0 1 1 1 1 1 0 1 1 1 1 1 D Calculate means and two standard error values below LB fruitNumber seedPos 1 seedPos 2 seedPos 3 seedPos 4 seedPos 5 seedPos b seedPos 7 seedPos 8 seedPos 9 seedPos 10 0 00 Decimal places k SUMIBZ BBICOUNTIBZ BB Developmental rate 2 to 6 for data in column B rows I E epe Ed QR
16. t very young without being able to serve as a decoys for predators Developmental Selection In many species the over production of inexpensive propagules is mechanism employed to screen genetically variable offspring Many offspring are discarded by this kind of genetic testing but the progeny that result are a kind of genetic elite Pollen tube competition and seed abortion have been demonstrated to serve this function in numerous examples Maternal Resource Limitation Maternal resources may not be sufficient to mature the number of ovules fertilized earlier 1n the season In such cases seed abortion may be non selective The advantaged seeds may simply be the ones closest to maternal resources preempting resources from competing seeds A Natural Experiment In legume species the arrangement of seed within fruits lends itself to the testing of the Developmental Selection and Maternal Resource Limitation hypotheses Seeds are arranged in a linear array with the style at one end of the row of seeds and the peduncle the stalk through which the maternal parent provides resources to the seed within the fruit at the other A dissected legume fruit with mature M and aborted A seeds inside Note that the seeds are arrayed in a row with the two pattern producing structures located at opposite ends of the fruit If pollen tube competition has sorted the genetic quality of the seed within the fruit then the first fertilizations i
17. the screen you will see three sliders that let you modify seed ovule ratios the proportion of immature seeds to mature and the relative intensities of pollen tube competition and maternal resource limitation Click on the slider titles for more explanation especially about competing hypotheses and their predictions see figure below Click on the explanation to return to the simulation screen Data Collection The data collection and analysis tools are located on the right side of the screen Click on the Score Fruit button to create a fruit to study Below the fruit are buttons that activate drop down menus one for each seed position Clicking on the Score box displays a menu that allows you to score whether the ovule immature seed above it aborted small dot or matured into a seed large green circle If your choice is incorrect the computer will beep and record the correct score in red To add another fruit to your sample click Score Fruit again and a new fruit will be displayed As each fruit is completed all ovules are scored the plot below is updated You can direct the computer to create a complete sample by clicking the Score Fruits button A dialog box will prompt you to determine the sample size This technique is also useful to examine the effect of sample size on your analysis c Data Summary Seed maturation rate the proportion of seeds to mature is plotted against seed position in order to examine
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