Below are the questions from Lab #2.

Homework Questions for Lab #2

R Script Here:

Question 1:

Using your highlighted cut block, provide the perimeter, area, latitude, and longitude of your polygon. Describe the site characteristics of the cut block and the surrounding area in 1984. Slowly cycle through the satellite images in Google Earth Pro. Describe the temporal changes you can visualize in the cut block and surrounding habitat over time.

Perimeter: 1.91 miles

Area: 0.12 square miles

Latitude: 54° 4’12.85“N Longitude: 122°45’28.33”W

The shape of my polygon is roughly a rectangle. In 1984, the land appears to be mostly bare with the majority of the land lacking vegetation. Ten years later in 1994, the area is severely deforested and there are bright bare spots with little to no vegetation present. In 1996, there appears to be a small amount of vegetation covering the land so it is not bare anymore, but not as dark of green as the surrounding area. From that period of time and onward, the land had vegetation present with some areas darker than others. By 2004, the land was completely bare again with no vegetation present. The area remained bare until 2013 where there was some vegetation present, and defined areas that are still getting deforested. In the present moment, there is no vegetation and rarely any sign of vegetation.

Question 2:

Calculate the perimeter-area ratio for the five Great Lakes. Be sure to use the same units. Show your work to include the calculations. Compare each Great Lake to a perfect circle with the same area. You may need to old school math!! Compare these values. How does this translate into the shapes of the Great Lakes?

Lake Michigan

Perimeter: 844 miles

Area: 19,383 sq miles

Ratio: 0.0435

Circle Area: 56,685.9

Lake Superior

Perimeter: 1,096 miles

Area: 30,639 sq miles

Ratio: 0.0357

Circle Area: 95,589.73

Lake Erie

Perimeter: 549 miles

Area: 15,860 sq miles

Ratio: 0.0346

ircle Area: 23,984.73

Lake Huron

Perimeter: 708 miles

Area: 9,100 sq miles

Ratio: 0.0778

Circle Area: 39,889.32

Lake Ontario

Perimeter: 450 miles

Area: 6,664 sq miles

Ratio: 0.0675

Circle Area: 16,114.44

These values were extremely off, for the most part based on the different shapes, also because of the errors of me not being extremely accurate when drawing my polygon.

Question 3:

Cross reference your results with data posted on Wikipedia for the area and perimeter (a.k.a. shoreline) of each Great Lake. Calculate the perimeter–area ratio for Shoreline Length and Lake area. Compare your Google Earth Pro results to the measured Great Lake data.

Lake Erie

Perimeter 799 / Area 9,910 = 0.0806

Lake Michigan

Perimeter 1,400 / Area 22,404 = 0.0624

Lake Huron

Perimeter 1,850 / Area 23,007 = 0.0804

Lake Superior

Perimeter 1,729 / Area 31,700 = 0.0545

Lake Ontario

Perimeter 634 / Area 7,320 = 0.0866

My Google Earth Pro data was different than the measured Great Lake data.The differences were usually off by almost 1/4 of the measured data. I Had errors when gathering my Google Earth data because I was not perfectly outlining the lakes, so that can explain for the differences.

Question 4:

Find two imperiled areas that interest you. Choose one North American and one Non–North American area. In your own words, describe the ecoregion, the biome, major city centers, threats, and any conservation initiatives.

North American: The first imperiled area I chose was on the Eastern side of Lake Michigan. The ecoregion here is considered to be the Southern Great Lakes Forests. This ecoregion is spread thorughout most of southern Michigan, Ohio, Indiana, and southwestern Ontario as well as the western side of New York. This region is heavily populated and developed which is why the region is considered to be imperiled. The biome is temperate broadleaf and mixed forests.The current threats are habitat loss, fragmentation, a small degree of protection , and the remaining fragments of natural habitats are under pressure from agircultural expansion and conversion for corn, soybeans, and grains. The regions conservation partners are currently planning restoration projects for areas that have not been irreversibly altered yet.

Non-North American: The second imperiled area I chose was Madagascar. The ecoregion here is the Madagascar succulent woodlands. This is located in southwestern and central western Madagascar. It has a tropical dry climate and a distinct dry season. The species here are special because they have water storage adaptations, stem photosynthesis and can survive without leaves. Its biome is deserts and xeric shrublands. There are numerous protected areas throughout the ecoregion, that are typically private reserves. However, some of the most important areas have no protection. The largest threat to this ecoregion is fire, whether it be unintentional wildfires, or intentional fires to expand agricultural lands. Hunting is also a threat to the lemurs that reside there, as well as overgrazing by cattle, and wood exploitation for charocal production.

Question 5:

Challenge your assumptions. What are your overall interpretations of this global data? Which areas do you think need the most attention? Does this change your perspective or confirm your suspicions regarding the state of biodiversity. Explain your answer.

I was impressed by how much information that this global data provides. I enjoyhow interactive themap is and how seamless the user can interact. The areas that I think need the most attention are practically all of them. I am imagining an international conservation project to protect at least 20% of each different type of ecoregion to ensure most of the biodiversity can be saved. I know how biodiversity is declining, but after actually being able to visually see this on the world map it makes me see how alrge and expansion of a problem this is.

Question 6:

Attach your graph / code when submitting. Yes, you can use R markdown!

YOUR ANSWER HERE

library(ggplot2)

#file.choose()

map <- read.csv("C:\\Users\\edward\\Downloads\\cardinal.csv")

summary(map)

##  cardinal     degree         distance  
##  E:1      Min.   :  0.0   Min.   :100  
##  N:1      1st Qu.: 67.5   1st Qu.:100  
##  S:1      Median :135.0   Median :100  
##  W:1      Mean   :135.0   Mean   :100  
##           3rd Qu.:202.5   3rd Qu.:100  
##           Max.   :270.0   Max.   :100

map$radians <- (map$degree) * (pi/180)

#xcoordinate conversion equation
map$cartX <- map$distance * sin(map$radians)

#y coordinate conversion equation
map$cartY <- map$distance * cos(map$radians)

CartMap <- ggplot(map, aes(x= cartX, y= cartY))

CartMap +
  geom_point() + 
  geom_text(aes(label=cardinal), hjust=-0.5, vjust=-0.5)

Question 7:

Turn–in your code / graph. Indicate with labels or figure caption text the North, East, South, and West.

##file.choose()
atrium <- read.csv("C:\\Users\\edward\\Downloads\\Seed Dispesal Lab Measurements - Sheet1.csv")

summary(atrium)

##        X                              Location Direction_degree
##  Min.   : 1.00   Brick Corner             :1   Min.   : 35.00  
##  1st Qu.: 3.25   Center of Sliding Doors  :1   1st Qu.: 81.75  
##  Median : 5.50   Corner by 110            :1   Median :136.00  
##  Mean   : 5.50   Doors to Center Stairwell:1   Mean   :145.50  
##  3rd Qu.: 7.75   East Corner of Bench     :1   3rd Qu.:184.25  
##  Max.   :10.00   Eastern Pillar           :1   Max.   :340.00  
##                  (Other)                  :4                   
##  Measurement_m  
##  Min.   :4.370  
##  1st Qu.:5.170  
##  Median :6.080  
##  Mean   :6.197  
##  3rd Qu.:6.975  
##  Max.   :8.700  
##

#convert degrees to radians
atrium$radians <- (atrium$Direction_degree) * (pi/180)

#xcoordinate conversion equation
atrium$cartX <- atrium$Direction_degree * sin(atrium$radians)

#y coordinate conversion equation
atrium$cartY <- atrium$Direction_degree * cos(atrium$radians)

atriumgraph <- ggplot(atrium, aes(x= cartX, y= cartY))

atriumgraph + 
  geom_point() +
  geom_text(aes(label=Location), hjust=-0.5, vjust=-0.5)

```

Question 8:

I am actually pretty surprised about how the points ended up on the graph. The outer points all represented the sides of the atrium, but the cluster of points on the right side weren’t accurately represented. When we were measuring we did alot of points on one sideof the atrium, so if I were to do this experiment again I would spread the points more evenly across the atrium. The amount of error for the points that framed the bench/plant area actually were too far off from eachother.