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Rachael’s paper on Malo curves

Read Rachael’s final paper, in which she investigates the extent to which flowering schedules differ between plants with only one head and plants with multiple heads.

initial aphid dataset for discussion about statistics

Here it is:
aphid1.csv

Field guide to insect visitors of Echinacea angustifolia

Steph wrote a field guide years ago to help us identify insects that visit Echinacea heads during the summer in Minnesota. It still serves us remarkably well, but hasn’t been posted until now. Here it is!

 

 

advice for presentations

Several web pages that provide helpful advice about
how to give a good scientific presentation:

http://tos.org/resources/publications/sci_speaking.html
http://disccrs.org/talking_tips

Specific for poster presentations:
http://colinpurrington.com/tips/academic/posterdesign

background reading on bees and neonicotinoids

Steve Ellis recommended some readings for us. Here they are:

http://libcloud.s3.amazonaws.com/93/3a/3/4738/GardenersBewareReport_2014.pdf

http://www.pnas.org/content/early/2013/10/18/1314923110.full.pdf+html

http://www.gmfreecymru.org/pivotal_papers/JEIT-D-12-00001_proofs.pdf

http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0103592

http://modernfarmer.com/2013/05/can-a-lawsuit-save-americas-bees/

Also, here are two mainstream media pieces on the topic of honeybees and pesticides:

http://video.msnbc.msn.com/nightly-news/47379683#47379683

http://www.mprnews.org/story/2013/05/26/environment/pesticides-suspected-in-minnesota-bee-deaths

When to harvest Echinacea

For many of our experiments we want to harvest Echinacea heads when they are as ripe as possible, but before any achenes have dropped.

The standard harvest indicators are as follows:

  1. Phyllaries (involucral bracts) are brown
  2. Bracts that subtend each disc floret are brown and sharp
  3. Flower stalk (peduncle) is brown (not purple)
  4. 1st (uppermost) cauline lf is brown (note: 1st lf may be close to hd!)

Once harvest indicators 1 – 4 are positive, or if a head has loose achenes or is in some way deformed and you think achenes may be lost before the next harvest, harvest the hd! Make sure to look for loose achenes at the top of every hd with brown bracts.

Harvest a head by cutting it off and placing it carefully into a labeled bag. When cutting the hd off, hold the head firmly in one hand and cut the peduncle with the pruners 3-5 cm under the hd. You don’t need to open the bag all the way and the hd doesn’t need to go all the way to the bottom of the bag.

That’s our standard harvest protocol! Everything’s flowering so late this year, we won’t be harvesting for a while, but I wanted to post this while I was thinking about it.

X-ray Radiation Dose

Hello everyone! This is Sebastian with another update on the x-ray machine. This post will discuss the various methods that can be used to determine the radiation dose of our x-ray machine. Below you will find my report on determining x-ray radiation doses.

Evaluating 3 methods for estimating radiation doses
23 March 2012
Sebastian Di Clemente

Introduction:
The population biology lab is trying to determine the dose of x-ray radiation that the x-ray tube emits per x-ray taken. Calculating the radiation dose is not an easy task because there is no straight forward way to do it. Each method used to determine the x-ray dose presents several differences in measure and calculation. Knowing the radiation dose of the x-rays can be used to determine what dose levels will hinder or harm a seed and what dose levels may even be beneficial to seeds; in short, knowing the radiation dose will allow researchers to quantify the point where seeds are affected by the radiation. With this experiment I will evaluate the sources that give the x-ray radiation dose and analyze the information given by each source.

Objectives:
1. To determine what method gives the most accurate information
2. To determine what method should be consulted to find the most appropriate radiation dose

Methods:
I gathered information based on web searches, contacting professionals, and contacting the x-ray machine manufacturer. I 1.) found a web page that calculates the x-ray radiation dose level and 2.) the manufacture provided the information that they have on dose levels that the Faxitron MX-20 machine produce at various settings. After receiving this information I test the web calculator by inputting the same settings that the manufacture provided and then compared the calculator reading to the value given by the manufacturer. I also further examined the information that the manufacturer provided and determined any differences in information or information format. The use of 3.) a dosimeter would give the most accurate measurement.

Results:
After comparing the web calculator result to the information given by the manufacturer using the same settings and criteria there is a significant difference in the dose level given. The web calculator had a dose level that was greater than the valued indicated by the manufacturer for lower level voltages (less than 20 kV), but the manufacturer indicated a greater dose level at anything above 20kV compared to the web calculator. The professionals offer the solution of a dosimeter. The comparison of the manufacturer data to the web calculator, and the three methods are provided in table below.

Comparison between manufacturer data and web calculator:

View image

The web calculator:

http://www.radprocalculator.com/XRay.aspx

The information given by the manufacturer is given in the following documents:

Dosage MX 20.pdf

mx-20 EXPOSURE DATA.pdf

MX-20 mR Ouput versus time.pdf

The professionals offer the solution of a dosimeter.

Conclusion:
Considering all of the information that I gathered I would trust the manufacture data over the web calculator data. The web calculator is good for fast calculations and changing between what units the dose level will be expressed in. Although, after testing the web calculator and see such a significant difference between it’s calculation and the manufacturer data, I feel that the manufacture would be more likely to have more accurate information.

Since the manufacture data is most reliable it is the clear choice to use. The manufacturer data covers more information, such as time, voltage, as well as unit conversions for other factors. Considering that more information is provided more variations to experiments can be made and the radiation does would still be available after simple unit conversions.

The other option presented by professionals would be to use a dosimeter to directly measure the radiation dose. This option would be the easiest way out of the three options, and would cater more to a researcher’s specific setting. If a dosimeter is available to use I would make this device my choice for determining radiation dose.

Asteraceae Breeding Systems: Data Analysis!

Hi all!
I’ve been back at Grinnell for about a month now, and so far my semester’s off to a great start! Very busy, but not busy enough to prevent me from finishing up my data analysis.

It looks like Echinacea purpurea‘s breeding system is similar to Echinacea angustifolia‘s. Styles that receive compatible pollen shrivel up within a few days, whereas styles that don’t persist longer, often for more than a week. In addition, E. purpurea is self-incompatible. (This means that an individual plant can’t pollinate itself, it needs pollen from another plant of the same species.)

To complicate things a little:
Not all of the flower heads I was studying had finished flowering by the time I left Minnesota, so I do not have much data on the top several rows of styles on many of them. Since styles in higher-up rows persist somewhat shorter than in lower rows, I cannot be sure that the trends I saw in my data hold for the top rows of all flower heads.
Also, some of the statistical tests I ran showed that style persistence in the self-pollination treatment differs significantly from the control treatment, while others do not. I’m not sure why that would be.

Here are my csv file and my analysis in R:
epurpurea.csv
epurpureaAnalysis.R

If anyone has any suggestions for improvement or other things I could look at with this data, please let me know!

And I posted much of my data analysis on H. helianthoides already, but here are the “final” versions. (But, again, I’m open to suggestions for further improvement!)
hhelianthoides.csv
FinalAnalysisLeeRodman.R

Some C. palmata and H. helianthoides Results

H. helianthoides appears to be self-incompatible. Here’s the data I collected:
hhelianthoides.csv
I’ve also started my data analysis in R. It’s not done yet, but here’s what I have so far:
Data Analysis.txt
Basically, styles persist significantly longer when self-pollinated or not pollinated than when cross-pollinated. However, in the top rows of florets on each flower head, style persistence does not differ as much between the treatments (because all the styles in these rows shrivel rather quickly). Therefore, when using style persistence to study other aspects of this species’ breeding system (ex. pollinator efficiency, compatibility of specific individuals), one should use the bottom several rows of florets. In these rows, cross-pollinated styles always shrivel within three days of pollen application, whereas styles that do not receive compatible pollen never shrivel so quickly.

As I mentioned before, I was unable to collect much quantitative data about C. palmata style persistence. But I did notice some things that might be helpful to anyone interested in studying this species further. The following document gives a brief summary:
C. palmata Summary.doc

Dataset for Callin’s Compatibility Experiment

Here’s the final dataset for my compatibility experiment. The experiment is officially ended today (I collected the last bit of data). The dataset contains GPS data (column name distBetween). I missed one plant while GPS-ing, so I used the hand-measured data (for flag #6 at Nessman’s). I also corrected several errors in the datasheet.

Data for Analysis — cswitzer — 31 July 2011.csv

We spent some time GPS-ing the plants, so we could get the exact distances between them. Here is a csv file with the gps data.

I have been working on analyzing all my data. I looked at plots of each of my individual sties, as well as all the data combined. The data are almost exactly opposite of what I expected.
Here’s the script I’ve been exploring:
callinCompatRScript31july2011.R

Here’s a picture of Josh, Amber Z, and I out in the field (having a lot of fun).
IMG_0340.JPG