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GENERAL55971
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Entry Properties
Last modified
8/24/2016 8:40:55 PM
Creation date
11/23/2007 10:58:47 PM
Metadata
Fields
Template:
DRMS Permit Index
Permit No
C1981010
IBM Index Class Name
General Documents
Doc Date
5/25/1983
Doc Name
Letter on criteria to determine revegetation success with Stip 1
From
TRAPPER MINING INC
To
MLRD
Permit Index Doc Type
VEGETATION
Media Type
D
Archive
No
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• • ~« <br />Department of Range Science <br />Reclamation Research Lab <br />303/491-6541 <br />Colorado State Unlverslty <br />Fort Collins, Colorado <br />80523 <br />~~ ~ q r: ~+r--C <br />\l . .. _ <br />20 September 1982 <br />f'+~~Y'~ ~ x:;83 <br />Mr. Wayne Sowards <br />Trapper Mine Inc. <br />P.O. Box 187 <br />Craig, Colorado 81626 <br />Colo. Dept. of n:~,w~~ rsesource~ <br />Dear Wayne: <br />I have studied the excerpt from your permit application which deals with <br />the use of linear regression to estblish a revegetation success standard. I <br />have also asked Mario to look it over; from our discussion I have the <br />following comments to offer, <br />First of all, a basic assumption can be made that the reference area and <br />the pre-mine area have a joint distribution (i.e., the changes over time in <br />one are not independent of the other). In the case of Trapper, this is just <br />common sense because you do not expect that the cover or biomass of the two <br />areas to be independent since the same environmental forces are influencing <br />both areas in the same way. If you were to try to develop a regression <br />between the two areas in a ig'ven year (that is regress data in one area <br />against data in the other area), then the procedure would be wrong because the <br />samples would not be taken as tuples (i.e., samples taken jointly or each <br />sample being a set of (X,Y) X = reference, Y = pre-mine) but they would be <br />taken at random within each of the areas and as such they are independent as a <br />consequence of the sampling rocedures. Now this is entirely different from <br />considering each year as a tuple (X,Y in which X and Y are the means for the <br />reference and pre-mine area because you are then looking at variations along a <br />gradient, represented in this case by time (years) and trying to correlate the <br />variation of Y as a function of X along this gradient. A similar example <br />would be to look at the correlation between growth of shrubs and grasses in a <br />given area for the purpose of determining how grass growth changes with shrub <br />growth. For this you need a gradient. You can build this gradient by looking <br />at areas with different shrub growth and regress them against each other or <br />select one area and use yearly changes in shrub growth as your gradient (by <br />the way this is the only way that you can set up a regression if you were <br />establishing a reclaimed area and you want to look at the competition effect <br />of shrubs over grasses). <br />However, if I were you, I would first look at the yearly data between <br />the two areas and run for each year an unpaired t-test to see if they are <br />significantly different in any year. Three situations could arise: <br />
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