Introduction-

Hunters and fisherman alike have been in pursuit of Northwoods monsters for decades.  Game animals, such as whitetail deer and muskellunge, have always been portrayed as being bigger and more elusive in this region known as the Northwoods.  The Northwoods attracts outdoor enthusiasts from around the country in search of finding that once in a lifetime trophy.

The objective of this project is to determine how the Northwoods has been portrayed by the popular media.  Are the Northwoods a distinct vernacular region of the United States, or is it just a loose term that has been applied to many areas of the country?  A vernacular region has been described as a spatial perception of average people (Zelinsky, 1980).  This project objective falls right under the class objective of trying to define the Northwoods as a distinct region.  Data will be collected from popular outdoor magazines and then analyzed.  Outdoor Life and Field & Stream are the two magazines that have been chosen for this project based on their popularity and range of article topics.  Only two magazines are being used to allow for more extensive research and data collection.  By the end of data collection and analysis, a delineated region of the Northwoods will be produced based on articles from Outdoor Life and Field & Stream.  However, it will be a rough delineation since no region is a discrete entity, having transitions and gradations (Wishart, 2004).

Another objective of this project will be trying to find areas of prime wildlife habitat based on soils data for Michigan, Minnesota, and Wisconsin.  Wildlife habitat will be broken down by type, such as woodland and wetland.  By the end of map production and analysis, areas of prime wildlife habitat will be identified and located.

Methods:

Steps to define “Northwoods” region using popular magazines:

1. Data Collection: Outdoor Life and Field & Stream were the two magazines used in this project.  Issues for both magazines are available online through the University of Wisconsin-Eau Claire McIntyre Library dating back to 2001.  By searching through all online issues, articles containing either “Northwoods” or “North Woods” were documented and recorded. 

Issues of Outdoor Life from 1966-2000 and issues of Field & Stream from 1970-2000 are available on microfilm in McIntyre Library.  In order to find articles in these issues, the plan was to search through the table of contents in each issue for “Northwoods” or “North Woods.”  However, two years of Field & Stream issues (table of contents) were searched and resulted in zero articles.  Thus, the issues on microfilm were abandoned due to time constraints and lack of results.  Using more issues would have given us even more data and a more delineated region.

2. Geospatial Techniques: Once all the data from the articles had been added to an Excel™ database all specific locations were looked up so that the county could be determined through the use of Google Earth™.  Once each location was specified to the county level a point was digitized into ArcMap™ inside each county and the newly created points FID code was entered into the Excel™ file for each of the corresponding entries to establish a way to display the data graphically.  After each point had a unique FID, the Excel™ file was joined to the freshly digitized points to give the Excel™ entries a spatial location.  Once each data entry had a spatial reference, they were ready to be analyzed.

3. Analysis Methods:  The analysis methods used in this part of the project were focused on finding a definable region known as the “Northwoods.” To do this, data was categorized by level of accuracy (Figure 4), the term used to label (Figure 6), and the activities that took place in the Northwoods (Figure 7).  Once these variables were displayed, a pattern was looked for to help delineate a distinct region with this available data.

Steps involving wildlife habitat soils:

1. Data Collection: State Soil Geographic (STATSGO) data from 2006 for each state was taken from the class folder.Geospatial Techniques: First, the STATSGO data was symbolized in ArcMap™ based on the wildlife habitat (coniferous, grain, grass, hardwood, herbaceous, open land, shallow water, wetland, wet plant, and woodland) field.  Each record was given a rating (good, fair, poor, very poor, or nothing/lakes) based on the soil of that record. 

2. Then each habitat type for Michigan, Minnesota, and Wisconsin was converted from vector to raster.  This was done to allow different habitat types to be combined more easily.  Then each habitat type was reclassified.  Figure 1 displays this step.

Once each habitat type was in raster, different habitat types could be added together using map algebra to determine where and how much different habitat types intersected.  The first habitat type kept its original classes and corresponding values (lakes-0, >fair-1, fair-2, and good-3).  Additional habitat types were reclassified and given different values (second habitat type: lakes-10, >fair-20, fair-30, and good-40; third habitat type: lakes-100, >fair-200, fair-300, fair-400) to ensure that unique outputs would be given.   Figure 2 displays this step.
Also, land cover data from another group was put over maps of wetland and woodland habitat.  The land cover data was displayed at 15% transparency to help show the wetland or woodland habitat underneath it. 

3. Analysis Methods:  Most analysis was done by taking data from the attribute table of each map and putting it into Microsoft Excel™.  Once all the data was in Excel™, further analysis could be performed by summing and then dividing various totals.  Graphs were also created in Excel™ to help display the raw data.

Figure 1.

Figure 1.  Vector to raster conversion and reclassification flow chart.  Wildlife habitat using STATSGO data.  Blue circles represent data sets, yellow squares represent procedures performed on the data, and green circles represent the outputs. 

Figure 2.

Figure 2.  Map algebra using different habitat types from STATSGO data.  Additional habitats reclassified and given different values to ensure unique outputs.  Blue circles represent data sets, yellow squares represent procedures performed on the data, and green circles represent the outputs.

Results:

Results involving defining “Northwoods” region using popular magazines:

The results of the research show a “Northwoods” region that stretches across the northern halves of Minnesota and Wisconsin, the Upper Peninsula of Michigan and a large portion of Maine (Figure 3).  There also seems to be a difference in the term used to describe the area.  All across Maine and the Upper Peninsula of Michigan, the term “North Woods” is used while it isn’t until you get to the southwestern area of the perceived region that the term “Northwoods” becomes a descriptor for the region (Figure 6).  The results are partial skewed due to the fact that all data was generalized to the county level where possible and when not possible that data was omitted because an acceptable way to map data (such as the Northwoods region of New York to Main or the northern forests of Pennsylvania) was not found.  Also, the activities and animals sought after were looked at to see if a trend developed.  As one can see in Figures 7 and 8, whitetail deer are the animal sought after the most.  Trout or walleye are second depending on how you categorize the data.  Even though whitetail deer were the most sought after individual animal, fish jumped into the lead when the animals were broken down into generic categories like fish, big game, small game, and others.  This was mainly due to numerous articles talking about multiple types of fish at one time.  Articles about hunting a game animal rarely strayed from the main animal of the article to include other animals (Figure 10).  The activities of the “Northwoods” were also looked at and hunting stands out as the most common activity associated with the “Northwoods,” having nearly the same amount of mentions in our data as the other three categories combined (Figure 11).

Figure 3.

Figure 3.  This map shows the “Northwoods” as a region defined by popular media.  Through the use of 40 different articles from both Outdoor Life and Field & Stream resulting in 63 locations where the term “Northwoods” is applied.  All the locations more specific than the county level were generalized to the county level and any locations broader than the county level were omitted due to an acceptable way to display them.

Figure 4.

Figure 4.  This map shows the level accuracy in our data.  For our data we set up a level of accuracy scale to help map out our data.  The scale we used was broken down into 4 levels: specific location, specific city/town, and specific county.  Any data that we found that was beyond the county level we discard due to problems trying to map it out.

Figure 5.

Figure 5. This map displays the data points categorized by the magazine they were gathered from.  The majority of articles we found were in Outdoor Life with 30 points and only 7 articles from Field and Stream.

Figure 6.

Figure 6.  The term “Northwoods” was used to label only the 8 south westernmost counties while the term “North Woods” was used to label 32 counties.  The use of both terms for a particular county only happened twice in our data set.  Those two counties with both terms appear as a transition zone between the two terms.

Figure 7.

Figure 7.  This map shows what activities the articles for each county included.  All but 3 of the counties activities were either hunting, fishing or both.  Only 1 article mentioned camping as a Northwoods activity.  The split between just fishing and just hunting was almost equal with just hunting having just one more county than fishing.

Figure 8.

Figure 8.  This graph shows the specific animals sought atfer that were refered to in each of the 40 articles.  Whitetail deer mentioned nearly the same amount of times as the next 3 highest(Grouse, Lake Trout, and Walleye) combined.

Figure 9.

Figure 9.  This graph shows the same data as previous chart only that each specific animal has been generalized so that now lake trout, brown trout, and trout have combined into the generic category trout.  As one can see now the gap between Whitetail deer and the rest has been closed and salmon has moved up from having only one specific species (landlocked salmon) in the top 10 to being number 3 in animals sought after in the “Northwoods.”  This change affected primarily fish species because bears were the only non-fish species that was referred to by more than one name (black bear and bear).

Figure 10.

Figure 10. This graph shows the sought after animals generalized into broad categories (fish, big game, small game, and other animals).  Fish includes all fish species, big game is moose, cougar, bears and whitetail deer, Small Game is coyote, woodcock, grouse and snow shoe hare, all remaining animals are in the other category they include wolf, beaver, lynx and ducks.

Figure 11 .

Figure 11.  This graph shows the totals for the activities of the “Northwoods” mentioned.  Hunting was the primary activity associated with the “Northwoods” having almost as many mentions as the other 3 categories combined.

Table 1.

Table 1.  Some of the raw data taken from the magazine articles.  For the hunting, fishing, and camping columns, 1 represents yes and 0 represents no.

Results involving wildlife habitat soils:

By symbolizing the STATSGO data using the wildlife coniferous field, it is evident that most of Michigan, Minnesota, and Wisconsin have fair or good soil conditions for coniferous habitat.  Parts of Northwest Minnesota, Central Wisconsin, and the Eastern Upper Peninsula of Michigan have poor soil conditions for coniferous habitat.  Refer to Figure 12 for a map of the coniferous habitat.

By symbolizing the STATSGO data using the wildlife grain field, it appears that most of the fair or good soil conditions for grain habitat are along the southern parts of each state.  Parts of Northwest Minnesota, Central and Northwest Wisconsin, and the Eastern U.P. and Northern Lower Peninsula (L.P.) of Michigan have poor soil conditions for grain habitat.  Refer to Figure 13 for a map of the grain habitat.

By symbolizing the STATSGO data using the wildlife grass field, it shows that most of Minnesota and Wisconsin, and the Southern L.P. of Michigan have fair or good soil conditions for grass habitat.  Parts of Northeast Minnesota, Central Wisconsin, and the Eastern U.P. and Northern L.P. of Michigan have poor soil conditions for grass habitat.  Refer to Figure 14 for a map of the grass habitat.

By symbolizing the STATSGO data using the wildlife hardwood and herbaceous fields, it is evident that most of each state has fair or good soil conditions for hardwood and herbaceous habitats.  Parts of Central and Northern Minnesota, Central and Northern Wisconsin, and the Eastern U.P. and Northern L.P. of Michigan have poor soil conditions for hardwood and herbaceous habitats.  Refer to Figure 15 for a map of the hardwood habitat and Figure 16 for a map of the herbaceous habitat. 

By symbolizing the STATSGO data using the wildlife open land field, it appears that most of Minnesota, Wisconsin, and the Southern L.P. of Michigan have fair or good soil conditions for open land habitat.  Parts of Central and Northern Minnesota, Central and Northern Wisconsin, and the Eastern U.P. and Northern L.P. of Michigan have poor soil conditions for open land habitat.  Refer to Figure 17 for a map of the open land habitat.

By symbolizing the STATSGO data using the wildlife shallow water, wetland, and wet plant fields, it shows that most of Northwestern and Southwestern Minnesota, Central Wisconsin, and the Eastern U.P. and Southeastern L.P. of Michigan have fair or good soil conditions for shallow water, wetland, and wet plant habitats.  Most of each state has poor soil conditions for shallow water, wetland, and wet plant habitats.  Refer to Figure 18 for a map of the shallow water habitat, Figure 19 for a map of the wetland habitat, and Figure 20 for a map of the wet plant habitats.
By symbolizing the STATSGO data using the wildlife woodland field, it is evident that most of each state has fair or good soil conditions for woodland habitat.  Parts of Central and Northern Minnesota, Central and Northern Wisconsin, and the Eastern U.P. and Northern L.P. of Michigan have poor soil conditions for woodland habitat.  Refer to Figure 21 for a map of the woodland habitat.

Next, each map of wildlife habitat was converted to raster and map algebra was performed to identify areas where different habitat types intersected.  First, the grain, grass, and herbaceous habitats were added together.  It is evident that Western and Southern Minnesota, Wisconsin, and the Southern L.P. of Michigan have fair or good soil conditions for grain, grass, and herbaceous habitat.  Parts of Central and Northern Minnesota, Central and Northern Wisconsin, and the Eastern U.P. and Northern L.P. of Michigan have poor soil conditions for grain, grass, and herbaceous habitats.  Refer to Figure 22 for a map of grain, grass, and herbaceous habitat.  According to Figure 33, most of each state has good soil conditions for grain, grass, and herbaceous habitat.  Table 8 displays the raw data.

Second, the grass and open land habitats were added together.  It appears that Minnesota, Wisconsin, and the Southern L.P. of Michigan have fair or good soil conditions for grass and open land habitat.  Parts of Central and Northern Minnesota, Central and Northern Wisconsin, and the Eastern U.P. and Northern L.P. of Michigan have poor soil conditions for grass and open land habitat.  Refer to Figure 23 for a map of grass and open land habitat.  According to Figure 32, most of each state has good soil conditions for grass and open land habitats.  Table 7 displays the raw data.

Third, the hardwood and woodland habitats were added together.  It shows that most of each state has fair or good soil conditions for hardwood and woodland habitat.  Parts of Central and Northern Minnesota, Central and Northern Wisconsin, and the Eastern U.P. and Northern L.P. of Michigan have poor soil conditions for hardwood and woodland habitat.  Refer to Figure 24 for a map of hardwood and woodland habitat.  According to Figure 30, most of each state has good soil conditions for hardwood and woodland habitat.  Table 5 displays the raw data.

Fourth, the shallow water, wetland, and wet plant habitats were added together.  It is evident parts of Northwestern and Southwestern Minnesota, Central Wisconsin, and the Eastern U.P. and Southeastern L.P. of Michigan have fair or good soil conditions for shallow water, wetland, and wet plant habitat.  Most of each state has poor soil conditions for shallow water, wetland, and wet plant habitat.  Refer to Figure 25 for a map of shallow water, wetland, and wet plant habitat.  According to Figure 31, most of each state has less than fair soil conditions for shallow water, wetland, and wet plant habitat.  Table 6 displays the raw data.

Fifth, the wetland and woodland habitats were added together.  It shows that parts of Northern Minnesota, Central Wisconsin, and the Southeastern L.P. of Michigan have fair or good soil conditions for wetland and woodland habitat.  Most of each state has poor soil conditions for wetland and woodland habitat.  Refer to Figure 26 for a map of wetland and woodland habitat.  According to Figure 34, most of each state has good soil conditions for woodland habitat and less than fair soil conditions for wetland habitat for wetland and woodland habitat.  Table 9 displays the raw data.

Next, forest land cover data was placed over the soils data symbolized using the wildlife woodland field.  The forest land cover appears to match up with the woodland habitat in Northeastern Minnesota, Northern Wisconsin, and the Western U.P. and Southern L.P. of Michigan.  The forest land cover and woodland habitat don’t agree in other parts of each state.  Refer to Figure27 for a map of forest land cover over woodland habitat.

Also, wetland land cover data was placed over the soils data symbolized using the wildlife wetland field.  The wetland land cover appears to match up with the wetland habitat in Northwestern Minnesota and the Central and Eastern U.P. of Michigan.  The wetland land cover and wetland habitat don’t agree in any part of Wisconsin and other parts of Minnesota and Michigan.  Refer to Figure 28 for a map of wetland land cover over wetland habitat.

Overall, each state has fair or good soil conditions for each habitat type.  Over 50 percent of each state has fair or good soil conditions for coniferous, grain, grass, hardwood, herbaceous, open land, and woodland habitats.  Less than 40 percent of each state has fair or good soil conditions for shallow water, wetland, and wet plant habitats.  Refer to Figure 29 for a graph of fair or good soil conditions for each habitat type.  Tables 2-4 display the raw data.

Figure 12.

Figure 12.  STATSGO data from 2006 symbolized using the wildlife coniferous field.  Dark green areas depict the soil most suited for coniferous habitat.

Figure 13.

Figure 13.  STATSGO data from 2006 symbolized using the wildlife grain field.  Dark green areas depict the soil most suited for grain habitat.

Figure 14.

Figure 14.  STATSGO data from 2006 symbolized using the wildlife grass field.  Dark green areas depict the soil most suited for grass habitat.

Figure 15.

Figure 15.  STATSGO data from 2006 symbolized using the wildlife hardwood field.  Dark green areas depict the soil most suited for hardwood habitat.

Figure 16.

Figure 16.  STATSGO data from 2006 symbolized using the wildlife herbaceous field.  Dark green areas depict the soil most suited for herbaceous habitat.

Figure 17.

Figure 17.  STATSGO data from 2006 symbolized using the wildlife open land field.  Dark green areas depict the soil most suited for open land habitat.

Figure 18.

Figure 18.  STATSGO data from 2006 symbolized using the wildlife shallow water field.  Dark green areas depict the soil most suited for shallow water habitat.

Figure 19.

Figure 19.  STATSGO data from 2006 symbolized using the wildlife wetland field.  Dark green areas depict the soil most suited for wetland habitat.

Figure 20.

Figure 20.  STATSGO data from 2006 symbolized using the wildlife wet plant field.  Dark green areas depict the soil most suited for wet plant habitat.

Figure 21.

Figure 21.  STATSGO data from 2006 symbolized using the wildlife woodland field.  Dark green areas depict the soil most suited for woodland habitat.

Figure 22.

Figure 22.  STATSGO data from 2006 symbolized using the wildlife grain, grass, and herbaceous fields.  All three maps converted to raster and added together using map algebra.  Dark green areas depict the soil most suited for grain, grass, and herbaceous habitat.

Figure 23.

Figure 23.  STATSGO data from 2006 symbolized using the wildlife grass and open land fields.  Both maps converted to raster and added together using map algebra.  Dark green areas depict the soil most suited for grass and open land habitat.

Figure 24.

Figure 24.  STATSGO data from 2006 symbolized using the wildlife hardwood and woodland fields.  Both maps converted to raster and added together using map algebra.  Dark green areas depict the soil most suited for hardwood and woodland habitat.

Figure 25.

Figure 25.  STATSGO data from 2006 symbolized using the wildlife shallow water, wetland, and wet plant fields.  All three maps converted to raster and added together using map algebra.  Dark green areas depict the soil most suited for shallow water, wetland, and wet plant habitat.

Figure 26.

Figure 26.  STATSGO data from 2006 symbolized using the wildlife wetland and woodland fields.  Both maps converted to raster and added together using map algebra.  Dark green areas depict the soil most suited for wetland and woodland habitat.

Figure 27.

Figure 27.  STATSGO data from 2006 symbolized using the wildlife woodland field.  Dark green areas depict the soil most suited for woodland habitat.  Forest land cover data (brown) has been placed over the wildlife woodland data.

Figure 28.

Figure 28.  STATSGO data from 2006 symbolized using the wildlife wetland field.  Dark green areas depict the soil most suited for wetland habitat.  Wetland land cover data (dark blue) has been placed over the wildlife wetland data.

Figure 29.

Figure 29.  Bar graph displaying percent of each state covered by each habitat type.  Only records with fair or good ratings, based on soil, have been included in the data.  Refer to Tables 2-4 for raw data of each state.  STATSGO data from 2006.

Figure 30.

Figure 30.  Bar graph displaying hardwood and woodland layers' percent of each state.  Refer to Table 5 for ratings of each layer.  STATSGO data from 2006.

Figure 31.

Figure 31.  Bar graph displaying shallow water, wetland, and wet plant layers’ percent of each state.  Refer to Table 6 for ratings of each layer.  STATSGO data from 2006.

Figure 32.

Figure 32.  Bar graph displaying grass and open land layers’ percent of each state.  Refer to Table 7 for ratings of each layer.  STATSGO data from 2006.

Figure 33.

Figure 33.  Bar graph displaying grain, grass, and herbaceous layers’ percent of each state.  Refer to Table 8 for ratings of each layer.  STATSGO data from 2006.

Figure 34.

Figure 34.  Bar graph displaying wetland and woodland layers’ percent of each state.  Refer to Table 9 for ratings of each layer.  STATSGO data from 2006.

Table 2. Layer Lakes (km2) >Fair (km2) Fair (km2) Good (km2) Total (km2) Conifer 3,260 20,328 30,353 79,539 133,480 Grain 3,260 53,412 35,984 40,824 133,480 Grass 3,260 47,803 30,553 51,864 133,480 Hardwood 3,260 20,403 33,348 76,469 133,480 Herbaceous 3,260 16,393 48,808 65,019 133,480 Open Land 3,260 48,495 30,803 50,922 133,480 Shallow Water 3,260 89,867 8,872 31,481 133,480 Wetland 3,260 88,064 12,324 29,832 133,480 Wet Plant 3,260 93,251 8,468 28,501 133,480 Woodland 3,260 20,328 33,348 76,544 133,480

Table 2.  Michigan data for each wildlife habitat type in raster format.  STATSGO data from 2006.

Table 3. Layer Lakes (km2) >Fair (km2) Fair (km2) Good (km2) Total (km2) Conifer 13,037 29,105 66,348 86,105 194,595 Grain 8,294 49,288 60,948 76,065 194,595 Grass 8,294 22,483 54,132 109,686 194,595 Hardwood 12,425 19,264 71,926 90,980 194,595 Herbaceous 8,294 6,006 68,227 112,068 194,595 Open Land 8,294 30,989 59,456 95,856 194,595 Shallow Water 8,294 120,046 11,276 54,979 194,595 Wetland 8,294 123,066 13,011 50,224 194,595 Wet Plant 8,294 126,907 10,387 49,007 194,595 Woodland 13,122 17,245 69,959 94,269 194,595

Table 3.  Minnesota data for each wildlife habitat type in raster format.  STATSGO data from 2006.

Table 4. Layer Lakes (km2) >Fair (km2) Fair (km2) Good  (km2) Total  (km2) Conifer 0 0 0 0 0 Grain 2,270 27,011 28,800 70,713 128,794 Grass 2,270 14,985 19,283 92,256 128,794 Hardwood 2,270 25,927 3,988 96,609 128,794 Herbaceous 2,270 9,887 19,087 97,550 128,794 Open Land 2,270 17,126 17,860 91,538 128,794 Shallow Water 2,270 106,905 9,318 10,301 128,794 Wetland 2,270 107,183 9,040 10,301 128,794 Wet Plant 2,270 107,183 9,040 10,301 128,794 Woodland 2,270 23,768 5,975 96,781 128,794

Table 4.  Wisconsin data for each wildlife habitat type in raster format.  STATSGO from 2006.

Table 5. Layer Rating Michigan Minnesota Wisconsin Total Area (km2) % of State Total Area (km2) % of State Total Area (km2) % of State 10 Hardwood Lakes/Woodland Lakes 3,260 2.44 12,425 6.39 2,270 1.76 12 Fair Hardwood/Woodland Lakes 0 0.00 85 0.04 0 0.00 13 Good Hardwood/Woodland Lakes 0 0.00 612 0.31 0 0.00 21 >Fair Hardwood/>Fair Woodland 20,328 15.23 14,339 7.37 23,652 18.36 22 Fair Hardwood/>Fair Woodland 0 0.00 1,360 0.70 116 0.09 23 Good Hardwood/>Fair Woodland 0 0.00 1,546 0.79 0 0.00 31 >Fair Hardwood/Fair Woodland 0 0.00 4,925 2.53 2,275 1.77 32 Fair Hardwood/Fair Woodland 33,348 24.98 60,543 31.11 3,700 2.87 33 Good Hardwood/Fair Woodland 0 0.00 4,491 2.31 0 0.00 41 >Fair Hardwood/Good Woodland 75 0.06 0 0.00 0 0.00 42 Fair Hardwood/Good Woodland 0 0.00 9,938 5.11 172 0.13 43 Good Hardwood/Good Woodland 76,469 57.29 84,331 43.34 96,609 75.01 Total   133,480 100.00 194,595 100.00 128,794 100.00

Table 5.  Data for each layer after the hardwood and woodland habitats have been added together using map algebra.  STATSGO data from 2006.

Table 6. Layer Rating Michigan Minnesota Wisconsin Total Area (km2) % of State Total Area (km2) % of State Total Area (km2) % of State 110 Shallow Water Lakes/Wetland Lakes/Wet Plant Lakes 3,260 2.44 8,294 4.26 2,270 1.76 221 >Fair Shallow Water/>Fair Wetland/>Fair Wet Plant 87,996 65.92 119,862 61.60 106,905 83.00 222 Fair Shallow Water/>Fair Wetland/>Fair Wet Plant 0 0.00 3,020 1.55 278 0.22 233 Good Shallow Water/Fair Wetland/>Fair Wet Plant 3,452 2.59 4,025 2.07 0 0.00 241 >Fair Shallow Water/Good Wetland/>Fair Wet Plant 1,803 1.35 0 0.00 0 0.00 321 >Fair Shallow Water/>Fair Wetland/Fair Wet Plant 68 0.05 184 0.09 0 0.00 332 Fair Shallow Water/Fair Wetland/Fair Wet Plant 8,400 6.29 6,064 3.12 9,040 7.02 343 Good Shallow Water/Good Wetland/Fair Wet Plant 0 0.00 4,139 2.13 0 0.00 432 Fair Shallow Water/Fair Wetland/Good Wet Plant 472 0.35 2,192 1.13 0 0.00 433 Good Shallow Water/Fair Wetland/Good Wet Plant 0 0.00 730 0.38 0 0.00 443 Good Shallow Water/Good Wetland/Good Wet Plant 28,029 21.00 46,085 23.68 10,301 8.00 Total   133,480 100.00 194,595 100.00 128,794 100.00

Table 6.  Data for each layer after the shallow water, wetland, and wet plant habitats have been added together using map algebra.  STATSGO data from 2006.

Table 7. Layer Rating Michigan Minnesota Wisconsin Total Area (km2) % of State Total Area (km2) % of State Total Area (km2) % of State 10 Grass Lakes/Open Land Lakes 3,260 2.44 8,294 4.26 2,270 1.76 21 >Fair Grass/>Fair Open Land 47,253 35.40 22,396 11.51 14,985 11.63 22 Fair Grass/>Fair Open Land 1,242 0.93 8,593 4.42 2,141 1.66 31 >Fair Grass/Fair Open Land 550 0.41 87 0.04 0 0.00 32 Fair Grass/Fair Open Land 29,311 21.96 44,948 23.10 17,142 13.31 33 Good Grass/Fair Open Land 942 0.71 14,421 7.41 718 0.56 42 Fair Grass/Good Open Land 0 0.00 591 0.30 0 0.00 43 Good Grass/Good Open Land 50,922 38.15 95,265 48.96 91,538 71.07 Total   133,480 100.00 194,595 100.00 128,794 100.00

Table 7.  Data for each layer after the grass and open land habitats have been added together using map algebra.  STATSGO data from 2006.

Table 8. Layer Rating Michigan Minnesota Wisconsin Total Area (km2) % of State Total Area (km2) % of State Total Area (km2) % of State 110 Grain Lakes/Grass Lakes/Herbaceous Lakes 3,260 2.44 8,294 4.26 2,270 1.76 221 >Fair Grain/>Fair Grass/>Fair Herbaceous 11,729 8.79 4,165 2.14 8,449 6.56 222 Fair Grain/>Fair Grass/>Fair Herbaceous 4,664 3.49 1,727 0.89 720 0.56 232 Fair Grain/Fair Grass/>Fair Herbaceous 0 0.00 114 0.06 0 0.00 242 Fair Grain/Good Grass/>Fair Herbaceous 0 0.00 0 0.00 718 0.56 321 >Fair Grain/>Fair Grass/Fair Herbaceous 29,827 22.35 16,504 8.48 5,408 4.20 331 >Fair Grain/Fair Grass/Fair Herbaceous 7,162 5.37 14,126 7.26 10,421 8.09 332 Fair Grain/Fair Grass/Fair Herbaceous 10,689 8.01 17,741 9.12 3,258 2.53 333 Good Grain/Fair Grass/Fair Herbaceous 1,130 0.85 0 0.00 0 0.00 342 Fair Grain/Good Grass/Fair Herbaceous 0 0.00 3,373 1.73 0 0.00 343 Good Grain/Good Grass/Fair Herbaceous 0 0.00 16,483 8.47 0 0.00 421 >Fair Grain/>Fair Grass/Good Herbaceous 1,583 1.19 87 0.04 408 0.32 431 >Fair Grain/Fair Grass/Good Herbaceous 2,356 1.77 14,330 7.36 2,153 1.67 432 Fair Grain/Fair Grass/Good Herbaceous 9,216 6.90 6,881 3.54 3,451 2.68 433 Good Grain/Fair Grass/Good Herbaceous 0 0.00 940 0.48 0 0.00 441 >Fair Grain/Good Grass/Good Herbaceous 755 0.57 76 0.04 172 0.13 442 Fair Grain/Good Grass/Good Herbaceous 11,415 8.55 31,112 15.99 20,653 16.04 443 Good Grain/Good Grass/Good Herbaceous 39,694 29.74 58,642 30.14 70,713 54.90 Total   133,480 100.00 194,595 100.00 128,794 100.00

Table 8.  Data of each layer after the grain, grass, and herbaceous habitats have been added together using map algebra.  STATSGO data from 2006.

Table 9. Layer Rating Michigan Minnesota Wisconsin Total Area (km2) % of State Total Area (km2) % of State Total Area (km2) % of State 10 Wetland Lakes/Woodland Lakes 3,260 2.44 8,294 4.26 2,270 1.76 11 >Fair Wetland/Woodland Lakes 0 0.00 1,113 0.57 0 0.00 12 Fair Wetland/Woodland Lakes 0 0.00 3,715 1.91 0 0.00 21 >Fair Wetland/>Fair Woodland 4,626 3.47 9,497 4.88 14,172 11.00 22 Fair Wetland/>Fair Woodland 3,452 2.59 1,553 0.80 0 0.00 23 Good Wetland/>Fair Woodland 12,250 9.18 6,195 3.18 9,596 7.45 31 >Fair Wetland/Fair Woodland 20,575 15.41 33,203 17.06 5,374 4.17 32 Fair Wetland/Fair Woodland 611 0.46 4,986 2.56 0 0.00 33 Good Wetland/Fair Woodland 12,162 9.11 31,770 16.33 601 0.47 41 >Fair Wetland/Good Woodland 62,863 47.10 79,253 40.73 87,637 68.04 42 Fair Wetland/Good Woodland 8,261 6.19 2,757 1.42 9,040 7.02 43 Good Wetland/Good Woodland 5,420 4.06 12,259 6.30 104 0.08 Total   133,480 100.00 194,595 100.00 128,794 100.00

Table 9.  Data of each layer after the wetland and woodland habitats have been added together using map algebra.  STATSGO data from 2006.

Summary:

In conclusion, a definable “Northwoods” region can be produced by using popular magazines like Outdoor Life and Field & Stream.  However, no discernable region could be produced with the wildlife habitat soils data.  Data showed that while the “Northwoods” is a unique region separate from the greater Midwest region, it is also not entirely inclusive to the Midwest due to the data showing that Maine is very much a part of the “Northwoods.”  Also, if more time had been available to delve further into the microfilm archived issues, more east coast states may enter the “Northwoods” like Northern Pennsylvania and upper New York.

References:

Michigan State Soil Geographic Data (STATSGO). 2006. U.S. Department of Agriculture, Natural Resources Conservation Service. Tabular digital data and vector digital data.  URL:http://SoilData.Mart.nrcs.usda.gov/.

Minnesota State Soil Geographic Data (STATSGO). 2006. U.S. Department of Agriculture, Natural Resources Conservation Service. Tabular digital data and vector digital data.  URL:http://SoilData.Mart.nrcs.usda.gov/.

USGS Seamless Server. Base map data.

Wisconsin State Soil Geographic Data (STATSGO). 2006. U.S. Department of Agriculture, Natural Resources Conservation Service. Tabular digital data and vector digital data.  URL:http://SoilData.Mart.nrcs.usda.gov/.

Wishart, David J. 2004. The Encyclopedia of the Great Plains. Lincoln: University of Nebraska Press.
Zelinksy, W. 1980. “North America’s Vernacular Regions.” Annals of the Association of American Geographers 70(1):1–16.

Magazine Article References:

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Bestul, Scott. "Good Deer Come to those who Wait." Field & Stream 106, no. 5 (September 2001): 58. Academic Search Complete, EBSCOhost (accessed November 23, 2009).

Bestul, Scott. "The Bois Brule." Field & Stream 105, no. 11 (March 2001): 60. Academic Search Complete, EBSCOhost (accessed November 23, 2009).

Bestul, Scott. "The Missing Lynx." Field & Stream 108, no. 2 (June 2003): 18. Academic Search Complete, EBSCOhost (accessed November 23, 2009).

Bestul, Scott. "We’re all a Fishing Family." Outdoor Life 211, no. 5 (June 2004): 126. Academic Search Complete, EBSCOhost (accessed November 23, 2009).

Bethge, Gerry. "Buzzer Beaters." Outdoor Life 216, no. 1 (December 2008): 40-45. Academic Search Complete, EBSCOhost (accessed November 23, 2009).

Bourjaily, Philip. "Early Birds." Field & Stream 108, no. 5 (September 2003): 96-101. Academic Search Complete, EBSCOhost (accessed November 23, 2009).

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McCombie, Brian. " Deer Forecast Midwest." Outdoor Life 211, no. 7 (September 2004): 106-109. Academic Search Complete, EBSCOhost (accessed November 23, 2009).

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Moore, Colin. "bad hare days." Outdoor Life 213, no. 1 (December 2005): 126-129. Academic Search Complete, EBSCOhost (accessed November 23, 2009).

Nickens, T. Edward. "Deer Camp." Field & Stream 114, no. 6 (October 2009): 47-52. Academic Search Complete, EBSCOhost (accessed November 23, 2009).

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Pyne, Lawrence. "Hunting the Rut for Bigger Bucks." Field & Stream 106, no. 7 (November 2001): 86. Academic Search Complete, EBSCOhost (accessed November 23, 2009).

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