Lab 10

Part 1: Fearsome Frogs

I know that sometimes it seems like I’m being really glib, but I assure you I am not when I say this: I had no idea frogs could climb mesh fences. It’s one of those things that I guess should have been blatant, but when the gentleman from the video talks about the frogs climbing his fences, the mental picture nearly sent me rolling out of my chair giggling like … There’s really no appropriate way to finish that simile is there? Anyway, on to solutions: There really isn’t one to be honest. I know it sounds sad and really dismal, but once we screw something up like this, we can’t really fix it. The most direct fix would be bounty slayings to wipe them out (like we’ve done with larger predators on several occasions), but the lessons of that type of “fix,” have been hard learned, and held little variation: they always hurt the situation in small (or at times huge) ways. It turns out that if you cause an overpopulation, then reverse it to zero population or low population, something else winds up starving, dying, or combusting spontaneously. Okay, I’m kidding about the combustion thing, but it really can be startling when you realize that your actions have lead to some species of something that the animal you just got rid of interacted with has suffered and/or disappeared entirely from the region. To be completely honest, I think the best thing to do (even given my opposition to such behavior) would be a closely controlled bounty based culling to try to avoid throwing something else out of whack the other direction. I suppose option B would be convincing Arizonians that frog is tasty (which for the record it is) and rely on the sudden surge of interest in eating them (which couldn’t possibly result in something even worse). Look, the real lesson here is this: we really shouldn’t mess with this kind of thing simply because we fancy ourselves some kind of magical curator of nature; the fact is we rarely guess what our actions will result in correctly, and it seems like everything we alter (even when we’re trying to help) simply winds up in a worse way than when we found it due to some unforeseen variable or ripple effect started with intentions of pure gold.


Part 2: Sampling Lab

Random Sampling Data			Actual Data
Grid Segment (number and letter)	Number of Sunflowers		Total number of Sunflowers   228  (count by hand) Average number of Sunflowers (divide total by 100) Per grid 2.28 (about 2)
B4	2
E7	2
I8	1
G3	4
J9	1
H5	2
B1	3
G9	2
C6	1
B2	2
Total Number of Sunflowers	20
Average (divide total by 10)	2
Total number of plants in meadow (multiply average by 100)	200

 

1.      Compare the total number you got for sunflowers from the SAMPLING to the ACTUAL count.  How close are they?  The two averages are within .28 of one another (certainly close enough to be considered accurate). The total count (correlating with the average of course) is off by 28 flowers, again the sample count is definitely close enough to the actual count to be considered accurate.

2.      Why was the paper-slip method used to select the grid segments? The paper slip method was used in this lab to make sure the samples were legitimately random, and demonstrate the importance of randomizing your samples (rather they come out of a cup or are collected in person).

3.      A lazy ecologist collects data from the same field, but he stops just on the side of the road and just counts the ten segments near the road. These ten segments are located at J, 1-10. When she submits her report, how many sunflowers will she estimate are in the field?  She would report an average of .7 flowers per block (or 1 depending on her personal preference). Her estimate for total flowers would be around 70, which is (of course) quite off.

4.      Suggest a reason why her estimation differs from your estimation. Her estimation varies so greatly from mine because all her data was collected in one spot.  You (of course) can’t do this, because you have no idea if that one spot is in any way representative of the actual population over a broad area. It may not be perfect, but wander sampling can yield much more accurate results (and can alert the gatherer of said information of polar variations and their causes).

5.      Population sampling is usually more effective when the population has an even dispersion pattern. Clumped dispersion patterns are the least effective.  Explain why this would be the case. Any population that is evenly dispersed is easier to get a general count of. If you know that Wild Corn Vines (yes I just made that up) grow pretty evenly across a large area, it shrinks the area you must traverse to collect sample information; or at the very least makes your results tend to be more accurate. Now on the other hand, if the Wild Corn Vine (not giving it up) is known to clump together where it sprouts up (being a giant root and all) getting an accurate count can be a huge pain, because you may simply find a clump every other mile (where you happen to stop) then assert that there are thousands of Wild Corn Vines in that area, not realizing that your results have been tainted by their population being really clumped up, and in fact, the Wild Corn Vine is nearly extinct.

6.    Describe how you would use sampling to determine the population of dandelions in your yard. Unfortunately, I can answer this right now: there are zero :( However, if we were counting anything anywhere, the best ways are either mark out a map with a grid and make counts of each randomly selected square (or for fun, you and the team I’d hope you’d have for this type of work can each pick a square), or mark out the ground itself using chords or tape to create your grid. Suffice to say, the grid is the key.

7.    In an area that measures five miles by five miles, a sample was taken to count the number of desert willow trees. The number of trees counted in the grid is shown below. The grids where the survey was taken were chosen randomly. Determine how desert willow trees are in this forest using the random sampling technique. Show your calculations. Well, based on the information below, there were 25 areas 5 of which were counted for a total of 35 Desert Willow Trees in the sample group. So, to find the average, we take our 35 Desert Willow Trees and divide that number by the number of samples taken, in this case: 5 (35 / 5 = 7) resulting in an average count of 7. Since there are 25 areas total, we multiply the average count by the total number of areas (7*25=175) resulting in an estimated region population of Desert Willow Trees of 175.

	7
				3
			5
11		9

 

Reference

 

Biology Corner. Random Sampling. 2014 Apr 6. Web.