chapter 8: results - u-m seas · chapter 8: results 211 composite grid examining the drivers...

Chapter 8: Results__________________________________________________________________________

Chapter 8: Results 200

CHAPTER OVERVIEW

This chapter provides the results of the team’s analysis. The chapter consists of five mainsections, listed below:

� Conservation Drivers� Composite Grid� Conservation Focus Areas (CFAs) and 2nd and 3rd Tier Conservation Areas� Individual Land Parcels associated with top three CFAs in Kalkaska County� Threat Sources

CONSERVATION DRIVERS

The team analyzed the spatial extent of each weighted conservation driver across the entirestudy area. Seven out of the eight drivers used three different classifications to assignweights: 10 points, 5 points, and 0 points. The wetlands conservation driver, which the teamevaluated simply as present or absent, was the lone exception. It classified the study areainto only two categories – those lands receiving 10 points and those lands receiving 0 points.Table 8.1 summarizes the results of this analysis for each driver.

Table 8.1: Conservation driver resultsReceived 10 points Received 5 points Received 0 points

ConservationDriver

Acres % ofStudyArea

Acres % ofStudyArea

Acres % ofStudyArea

Groundwateraccumulation

26,542 7.8% 31,661 9.3% 282,820 82.9%

Wetlands 45,318 13.3% n/a n/a 295,705 86.7%Riparianecosystems

14,749 4.3% 10,473 3.1% 315,802 92.6%

Elementoccurrences

22,745 6.7% 50,624 14.8% 267,654 78.5%

Rare LandtypeAssociations

18,696 5.5% 35,191 10.3% 287,136 84.2%

Pre-settlementvegetation

24,715 7.2% 111,429 32.7% 204,882 60.1%

Large tracts ofunfragmentednatural areas

76,020 22.3% 70,910 20.8% 194,094 56.9%

Expansion andIntegrity ofProtectedLands

33,780 9.9% 51,426 15.1% 255,818 75.0%

*Note that study area totals 341,023 acres; some totals for drivers vary due to rounding.


As explained in Chapter Seven, the team designed the conservation drivers as a means toperform a cumulative prioritization analysis. In order to prioritize effectively, theconservation drivers have to be selective. In other words, if 95 percent of the study area hadreceived 10 points for the rare Landtype Association driver, this driver would have done littleto help the team prioritize lands for protection. The team would have either had to selectanother more appropriate driver or refine its weighting scheme so as to increase theselectivity of the driver.

On average, the eight drivers used in the analysis awarded 9.6 percent of the study area 10points and 13.7 percent of study area 5 points. Based on these results the team determinedthat the drivers were selective enough to prioritize parts of the study area for protection. TheLarge tracts of unfragmented natural areas driver was the least selective, assigning 43.1percent of the study either 10 or 5 points. The riparian ecosystems driver was the mostselective. Only 7.4 percent of the study area received points from this driver. Figure 8.1depicts the relative selectivity of the individual drivers.

Figure 8.1: Percentage of each conservation driver receiving points within study area

Each conservation driver produced a unique distribution of weighted grids cells across thestudy area. The basic spatial characteristics for each driver are described below and depictedgraphically in Figures 8.2 through 8.9.

� Groundwater accumulation – The majority of lands receiving points forgroundwater accumulation are located in linear bands on the western side of thestudy area. The largest concentration of 10-point lands lie around theconvergence of Ostego, Antrim, Kalkaska, and Crawford counties.

0%5%

10%15%20%25%30%35%40%45%50%

Ground

water

Wetlan

ds

Riparia

n

Elemen

ts

Rare LT

As

Pre-se

ttlemen

t

Unfrag

mented

Tracts

Protec

ted Area

s

Drivers

Perc

ent o

f Stu

dy

Area

Rec

eivi

ng P

oint

s

5 points10 points


� Wetlands – The study area’s wetlands are predominately located along the mainbranch and tributaries of the Manistee River. There is a large concentration ofwetlands around Lake Margrethe in Crawford County , and another sizablewetland complex around the confluence of the North Branch and the ManisteeRiver in Kalkaska County.

� Riparian ecosystems – Riparian ecosystems are located along and around thestudy area’s lakes, rivers, and streams. The study area’s two largest lakes areLake Margrethe in Crawford County and Manistee Lake in Kalkaska County.The largest concentration of riparian areas is found in south-central Kalkaska,where the North Branch and Little Cannon Creek converge with the main branchof the Manistee River from the north and south respectively.

� Element occurrences – Lands with higher densities of element occurrences arefound in a relatively diffuse pattern across the study area. There is a larger clusterof high priority lands around Lake Margrethe and its associated wetlands inCrawford County. Note that the scored areas appear as squares and triangles onthe driver map (Figure 8.5) because the team initially examined point densityusing larger grid cells before reconverting to the standard 30-meter cell size.

� Rare Landtype Associations – Lands receiving 10 points under this driver areclustered along the western edge of Kalkaska County. Lands receiving 5 pointsare also clustered, in this case primarily in a wide swath in north-central KalkaskaCounty and a smaller area in northern Missaukee County.

� Pre-settlement vegetation – Nearly 40 percent of the study area receives pointsunder this driver (Table 8.1), and this relatively large amount of scored lands isreadily apparent in this driver’s map (Figure 8.7). Lands receiving 5 points occurin a broad distribution across most of the study area. An especially large clusterof scored lands lies in the north-central portion of Kalkaska County and innorthern Missaukee County.

� Large tracts of unfragmented natural areas – As noted above, this driver is theleast selective of the eight used in this project. Over 40 percent of the study areareceived at least 5 points under this driver. The distribution of unfragmentedlands is itself, however, somewhat fragmented. In other words, while sizeabletracts of unfragmented lands still exist in the study area, there is only marginalcohesion between the tracts. The largest single complex of unfragmented lands isfound in the center of Kalkaska County, near the confluence of the North Branchwith the mainstem of the Manistee River.

� Expansion and Integrity of Existing Protected Lands – Over half of the study areais already protected in some fashion, mostly as state forest land managed by theMDNR. As the map of this driver (Figure 8.9) depicts, all the private in-holdingswithin the larger matrix of protected lands receive points under this driver. A thinbut lengthy strip of lands on the western exterior boundary of this matrix receives5 points under the expansion component of this driver.


Figure 8.2: Groundwater accumulation

ANTRIM

CRAWFORD

MISSAUKEE

KALKASKA

OTSEGO

N

0 10 20 30 Kilometers

Score*0 Points5 Points10 Points

County boundary

* Areas with accumulation levels greater than or equal to 2 SD: 10 points; Areas with accumulationlevels < 2 and > 1 SD: 5 points; All other areas: 0 points


Figure 8.3: Wetlands

ANTRIM

CRAWFORD

MISSAUKEE

KALKASKA

OTSEGO

N

Score*0 Points10 Points

County boundary


* Wetlands: 10 points; Non-wetlands: 0 points


Figure 8.4: Riparian ecosystems

ANTRIM

CRAWFORD

MISSAUKEE

KALKASKA

OTSEGO

N



County boundary

* Lands within 50m of all streams and lakes: 10 points; Lands between 50m and 300m of river'smainstem: 5 points; Lands outside those buffers: 0 points


Figure 8.5: Element occurrences

ANTRIM

CRAWFORD

MISSAUKEE

KALKASKA

OTSEGO

N



County boundary

* Areas with occurrence density > 2 SD: 10 points; Areas with occurrence density between < 2 and >0 SD: 5 points; Areas with occurrence density less than or equal to 0 SD: 0 points


Figure 8.6: Rare landtype associations

ANTRIM

CRAWFORD

MISSAUKEE

KALKASKA

OTSEGO

N



County boundary

* LTAs passing both rarity and study area representation thresholds: 10 points; LTAs passing onlyone threshold: 5 points; All other LTAs: 0 points


Figure 8.7: Pre-settlement vegetation

ANTRIM

CRAWFORD

MISSAUKEE

KALKASKA

OTSEGO

N



County boundary

* Highest priority areas of overlap: 10 points; High priority areas of overlap: 5 points; All other areas:0 points


Figure 8.8: Large tracts of unfragmented natural areas

ANTRIM

CRAWFORD

MISSAUKEE

KALKASKA

OTSEGO

N



County boundary

* Areas > or equal to 1,000 acres and centroid is > or equal to 500m from roads: 10 points; Areas >or equal to 1,000 acres and centroid is < 500m from roads: 5 points; All other areas: 0 points


Figure 8.9: Expansion and integrity of existing protected lands

KALKASKA

ANTRIM OTSEGO

CRAWFORD

MISSAUKEEN



Protected landsCounty boundary

* Lands within the integrity buffer and 880m or less from protected lands: 10 points; Lands withinexpansion buffer or within the integrity buffer but > 880m from protected areas: 5 points; All otherlands: 0 points


COMPOSITE GRID

Examining the drivers individually provides useful information regarding the spatial extentand distribution of each driver within the study area; combining the drivers and analyzing thecumulative results provides insight into the overall ecological values of different portions ofthe study area. As the first step in selecting and prioritizing areas for conservation, the teamadded the individual grids together to form a composite grid.

When the eight drivers were added together, they initially produced a composite grid thatreported the cumulative scores for each grid cell. Since the team used eight driver grids withpossible individual weights of 10, 5, or 0 points, possible total scores for the composite gridranged from 0 to 80 in multiples of five. In this analysis, however, the highest scoring gridcell received 65 total points. Figure 8.10 displays the results of the composite grid.

Figure 8.10: Composite grid - eight drivers combined scores for each grid cell

ANTRIM OTSEGO

KALKASKA

MISSAUKEE

CRAWFORD0 10 20 Kilometers

N

Scores051015202530

35404550556065

County boundaries

The relative dearth of high scoring grid cells demonstrates the convergence of two differentphenomena. First, as mentioned above, the drivers themselves are relatively selective andprioritize small areas for protection. Approximately 57 percent of the study area received 10points or fewer, and 86 percent received no more than 20 points. Less than one percent ofthe entire study area (~1,400 acres) received 45 points or more. Table 8.2 summarizes thescoring.


Table 8.2: Spatial extent of composite grid scoresComposite Grid

ScoreAcres % of Study Area

0 44,036 12.9%5 70,496 20.7%10 79,427 23.3%15 55,668 16.3%20 44,623 13.1%25 21,467 6.3%30 13,727 4.0%35 6,456 1.9%40 3,599 1.1%45 937 0.3%50 393 0.1%55 64 0.02%60 6 <0.001%65 >1 <0.001%

Second, and more importantly, there is not a strong spatial correlation between all eightindividual drivers, and this relative scarcity of spatial overlap magnifies the originalselectivity. For example, if the wetlands driver tended to be closely associated with the rareLTA driver, the riparian ecosystems driver, the element occurrence driver, and theunfragmented natural areas driver, a much larger percentage of the study area would receive50 points than actually does so in this analysis (<0.2%).

The team does not feel that the relative lack of large-scale spatial overlap in the project’sdrivers represents a weakness in the analysis. Instead, it simply highlights the selectivity ofthe prioritization scheme. For example, if certain lands in the study area contain the presenceof four or more drivers (as do all areas that receive 35 points or more), the team knows thatthese lands represent a rare and important convergence of a number of critical ecologicalfeatures on the landscape.

To ease the identification of priority lands within the study area, the team reclassified thecomposite grids and grouped the total cumulative scores into four different categories. Theresulting grid is seen in Figure 8.11.

Examining the reclassified composite grid reveals important information. First, a simplevisual inspection of the study area reveals that high and highest scoring areas are notdistributed at equal densities across the entire study area. Instead, there are numerousclusters of high and highest scoring lands, and these clusters provide the foundation for theidentification and delineation of Conservation Focus Areas. The majority of these clustersare found within Kalkaska County. The area around Lake Margrethe in western CrawfordCounty also constitutes a sizeable cluster of high and highest priority lands.

Second, a more quantitative examination of the reclassified data reveals that high and highestscoring lands account for nearly 47,000 acres, or 13.7 percent of the study area (Table 8.3).Thus, while only a small fraction of the study area received scores above 35 points, thisscoring distribution still selects an amount of land small enough to narrow GTRLC’s focusyet large enough to provide the organization with a meaningful slate of options.


Figure 8.11: Reclassified composite grid

ANTRIM OTSEGO

CRAWFORD

KALKASKA

MISSAUKEE

Conservation priority by scoreHighest (35-65 pts)High (21-34 pts)Medium (11-20 pts)Low (0-10 pts)

County boundaries


N


Table 8.3: Reclassified composite grid scoresClassification Score Range Acres % of Study Area

Low Priority 0-10 194,000 56.9%Medium Priority 11-20 100,300 29.4%High Priority 21-34 35,200 10.3%Highest Priority 35-65 11,460 3.4%

CONSERVATION FOCUS AREAS (CFAs) AND 2nd AND 3rd TIER CONSERVATION AREAS

Using the techniques and guidelines outlined in Chapter Seven, the team digitized a total of60 discrete polygons around these clusters of high and highest priority lands. Thesepolygons totaled 47,660 acres and fell into three different categories based on size:Conservation Focus Areas (largest), 2nd Tier Conservation Areas (next largest), and 3rd TierConservation Areas (smallest). Table 8.4 summarizes the results.

Table 8.4: Categories of prioritized lands within the study areaCategory Size Number of

Discrete UnitsAcreageTotals

% of StudyArea

Conservation Focus Areas(CFAs)

500 acres orlarger

18 39,771 11.7%

2nd Tier ConservationAreas

100 to 499 acres 27 7,125 2.1%

3rd Tier ConservationAreas

Less than 100acres

15 764 0.2%

RANKED CONSERVATION FOCUS AREAS

As the largest of the three categories of priority areas, the CFAs represent the top prioritylands within the study area and the clear category of emphasis for further analysis andeventual parcel prioritization efforts. To help reference each CFA individually, the teamnamed all 18 based on nearby streams, lakes, or other distinctive features. To compare eachCFA to its counterparts, the team ranked all 18 based on three different criteria – mean score,size, and shape (Table 8.5).

Assigning the final rank for CFAs based on the sum total of the individual ranks for Ssize,mean score, and shape allowed the team to analyze a broader range of important attributesthan would have been possible had it relied on a single criterion. The Deward CFA providesa good example. With a mean score of 29.9, Deward received the second highest mean scorerank of all the CFAs. Deward is also, however, the smallest of all the CFAs and its smallsize and irregular boundary gave it the second worst shape rank. Deward’s final rank of 14provides a more accurate measure of the CFA’s true conservation priority than wouldlooking at only its mean score or size. Figure 8.12 displays the study area’s ranked CFAs.


Table 8.5: CFA ranking scheme and resultsMean Score Size Shape

CFA MeanScore*

Rank Acres Rank Perimeter toArea Ratio

ShapeRank

SumRank

FinalRank**

LakeMargrethe

30.3 1 6,073 2 0.00129 1 4 1

North Branch 29.4 3 7,950 1 0.00266 5 9 2Goose Creek 28.8 4 3,733 3 0.00242 4 11 328 Lakes 27.5 8 2,896 5 0.00193 2 15 4DempseyCreek

28.2 6 1,972 8 0.00396 9 23 5

Little Cannon 26.3 13 3,021 4 0.00325 7 24 6Blue Hollow 23.9 18 2,864 6 0.00198 3 27 7Little Devil 27.0 11 931 12 0.00336 8 31 8Black Creek 26.7 12 1,691 9 0.00414 11 32 9Big Cannon 27.4 9 1,463 10 0.00452 14 33 10Filer Creek 27.3 10 1,007 11 0.00416 12 33 11Big Devil 27.7 7 743 14 0.00436 13 34 12Little GooseCreek

24.1 17 782 13 0.00320 6 36 13

Deward 29.9 2 556 18 0.00617 17 37 14Pierson Creek 25.1 15 2,181 7 0.00483 15 37 15Frenchman's 28.3 5 624 16 0.00737 18 39 16East Lakes 25.8 14 602 17 0.00412 10 41 17Headwaters 24.6 16 683 15 0.00567 16 47 18*For comparison, the mean score for the entire study area is 12.49**Mean score rank provides the tiebreaker for identical sum ranks.

The maps of the ranked CFAs (Figure 8.12) and the protectable lands of the CFAs (Figure8.13) illustrate several interesting facts about the spatial extent and distribution of CFAswithin the study area. First, CFAs are not clustered in only one or two small portions of thestudy area. Rather, they are distributed fairly widely and occur in all five counties that thestudy area overlaps. There are, however, parts of the study area that support higherconcentrations of CFAs than do other parts. Nine of the 18 CFAs are found in a belt thatruns through central Kalkaska County to western Crawford County. Second, the CFAsthemselves are irregularly shaped; high scoring lands along stream corridors play a strongrole in the shape of many of the more linear CFAs. Third, CFAs varied widely in size: thelargest (North Branch) is nearly 8,000 acres. The smallest (Deward) is less than 600 acres.Finally, the protectable lands (private lands excluding large lakes) within individual CFAsoften are disconnected from one another, separated by protected lands or large lakes.

One interesting issue to consider is the relative contribution of each individual driver to thecumulative mean score for each CFA. The team gave each conservation driver equalimportance in its overall weighting scheme, but some drivers were clearly more responsiblefor the high scoring grid cells within certain CFAs than were other drivers. While the teamdid not run a multivariable regression analysis to determine the statistical correlation betweenthe drivers, simple review of the data reveals some of the more obvious connections. It isalso clear that more subtle connections between drivers help influence the location andrelative importance of resulting CFAs.


Figure 8.12: All Conservation Focus Areas (CFAs) with ecological ranks displayed

2 1

3

6

4

7

5

915

8

1011

13

12

18

16

17

14

ANTRIM

KALKASKA

CRAWFORD

OTSEGO

MISSAUKEE

N


Study areaCounty boundaries

CFAs (Listed in rank order) 1 - Lake Margrethe 2 - North Branch 3 - Goose Creek 4 - 28 Lakes 5 - Dempsey Creek 6 - Little Cannon 7 - Blue Hollow 8 - Little Devil 9 - Black Creek10 - Big Cannon11 - Filer Creek12 - Big Devil13 - Little Goose Creek14 - Deward15 - Pierson Creek16 - Frenchman's Creek17 - East Lakes18 - Headwaters


Figure 8.13: Protectable lands of all Conservation Focus Areas

ANTRIM

KALKASKA

CRAWFORD

OTSEGO

MISSAUKEE

Lake Margrethe

Twenty Eight Lakes

N


Study areaLakes

Protected landsCounty boundaries

Protectable lands of CFAs (Listed in rank order)

1 - Lake Margrethe2 - North Branch3 - Goose Creek4 - 28 Lakes5 - Dempsey Creek6 - Little Cannon7 - Blue Hollow8 - Little Devil9 - Black Creek10 - Big Cannon11 - Filer Creek12 - Big Devil13 - Little Goose Creek14 - Deward15 - Pierson Creek16 - Frenchman's Creek17 - East Lakes18 - Headwaters

* GTRLC will primarily focus its efforts on conserving “protectable” lands – the areas beyond large lakes (suchas Lake Margrethe) and currently protected lands.


To explore these issues, the team examined all 18 CFAs and calculated the mean score foreach driver within that CFA. The team then identified the top two drivers that contributedthe most points to the overall mean scores of each CFA. The team referred to these twodrivers as 1st and 2nd drivers.

The team found that in 14 of 18 CFAs, the 1st and 2nd drivers contributed over half of theCFA’s mean score. The 1st and 2nd drivers accounted for over 40 percent of the mean scoresin all CFAs. In addition, several drivers appeared as a 1st or 2nd driver much more frequentlythan other drivers. Tables 8.6 and 8.7 summarize the results of this analysis. See AppendixG for additional information on the relationship between drivers and CFAs.

One might conclude that the drivers that appeared as 1st and 2nd drivers the most frequentlywere simply those drivers that weighted a larger portion of the study area. The explanationfor the results is somewhat more complicated, however. The team observed the followingcharacteristics of the 1st and 2nd drivers:

1. 1st and 2nd drivers that appeared most frequently were usually, but not always, thosedrivers that weighted a larger portion of the study area. For example, the unfragmentednatural areas driver is a 1st or 2nd driver 10 times and weights a larger percentage of thestudy area (43.1 percent) than any other driver. As a counter example, the wetlandsdriver is also a 1st or 2nd driver 10 times, but it weighted a smaller portion of the studyarea (13.3%) than all but one other driver.

2. Drivers that generate more compact clusters of scored grid cells tend to be overlyrepresented as 1st and 2nd drivers. By definition, the unfragmented natural areas driverproduces sizable areas of high scoring lands. The wetlands, element occurrences, andexpansion and integrity of protected areas drivers also tended to produce clusters of highscoring lands of appreciable size and rated as 1st or 2nd Drivers 10, 5, and 6 timesrespectively. The rare LTA driver represents one clear exception to the influence oflarger clusters of high scoring lands. Since it prioritizes actual landtype associations, therare LTA produces clearly delineated and tightly clustered scored areas. Despite this, therare LTA rates as a 1st driver in only one CFA.

3. A lack of spatial correlation with other drivers may lead to a relatively small influence onCFA delineation. Again, the rare LTA driver provides a good example as it is rarelyspatially correlated with other drivers. In other words, those lands prioritized by the rareLTA driver were rarely prioritized by other drivers and vice versa. Indeed, 10 of 18CFAs receive 0 points from the rare LTA driver. The second most excluded driver iselement occurrences, which is completely absent from six CFAs. No other driver wascompletely absent from more than two CFAs.

4. Conversely, a positive spatial correlation with other drivers can boost a driver’s relativeimportance to CFAs. The wetlands and unfragmented natural areas drivers appear to becorrelated, for example. These two drivers serve as the top two drivers in seven of the 18CFAs. Given that wetlands frequently present legal or practical limitations todevelopment, it is not surprising that many large unfragmented areas contain wetlandsand that many large wetlands are themselves unfragmented.


Table 8.6: Occurrences of conservation drivers as CFAs’ 1st or 2nd driverConservation Driver # of Times as

1st Driver# of Times as

2nd Driver# of Times in

Top TwoUnfragmented natural areas 8 2 10Wetlands 4 6 10Expansion and integrity ofprotected areas

4 2 6

Element occurrences 1 4 5Groundwater accumulation 0 2 2Rare LTAs 1 0 1Pre-settlement vegetation 0 1 1Riparian ecosystems 0 1 1

Table 8.7: Importance of 1st and 2nd drivers to overall CFAs scores (CFAs listed in rank order)CFA 1st Driver 1st Driver

% of TotalScore

2nd Driver 2nd Driver% of Total

Score

Top 2 Driversas % of Total

Score

Total DriversRepresented

in CFALakeMargrethe

unfragmentedtracts

27.5% Wetlands 26.6% 54.1% 7

NorthBranch

unfragmentedtracts

31.7% Wetlands 22.9% 54.6% 8

GooseCreek

protectedareas

26.2% elementoccurrences

15.9% 42.1% 8

28 Lakes rare LTA 28.3% elementoccurrences

21.4% 49.7% 8

DempseyCreek

unfragmentedtracts

30.3% Wetlands 16.1% 46.5% 7

LittleCannon

wetlands 28.6% UnfragmentedTracts

23.8% 52.5% 7

Blue Hollow protectedareas


23.1% 53.3% 8

Little Devil elementoccurences

28.1% UnfragmentedTracts

21.9% 50.0% 7

Black Creek unfragmentedtracts

29.4% protectedareas

28.5% 57.8% 7

Big Cannon unfragmentedtracts

31.0% wetlands 20.4% 51.5% 7

Filer Creek wetlands 27.5% protectedareas

23.1% 50.6% 6

Big Devil unfragmentedTracts

35.7% wetlands 31.2% 66.9% 6

Little GooseCreek

unfragmentedTracts

40.6% Groundwater 36.0% 76.6% 4

Deward protectedareas

27.6% riparianecosystems

19.5% 47.1% 6

PiersonCreek

unfragmentedtracts

30.9% wetlands 27.1% 58.0% 7

Frenchman's

wetlands 26.6% Groundwater 25.3% 51.9% 6

East Lakes protectedareas


32.7% 68.7% 7

Headwaters wetlands 37.1% pre-settlement 24.6% 61.7% 7


RANKED CONSERVATION FOCUS AREAS WITHINKALKASKA COUNTY

As outlined in Chapter Seven, the team is only conducting the parcel analysis on the CFAsthat lie completely within Kalkaska County. For these 10 CFAs, the team conducted asecondary analysis by adding two criteria – conservation opportunity and conservationfeasibility – to the ranking scheme.

� Conservation opportunity measures how much land within a CFA is not alreadyprotected (as either public land or private land under easement) or is otherwiseunprotectable (large lakes). With this criterion, the team recognizes that GTRLC ismore interested in targeting CFAs that contain more private land in need of protection.

� Conservation feasibility measures how much of the land available for protection isfound in tracts 40 acres or larger. With this criterion, the team recognizes that, givenlimited time and resources, GTRLC can protect land more efficiently by targetinglarger parcels.

Table 8.8 on the following page displays the results of this analysis and Figure 8.13 showsthe ranked CFAs in Kalkaska County.

Incorporating the conservation opportunity and feasibility criteria into the ranking system forCFAs changes the final rank of several CFAs (Table 8.9). These adjustments in the rankingsare not surprising given that the initial ranking did not consider how the size, shape andextent of private land parcels associated with a given CFA affect its relative importance. Therefined ranking for CFAs within Kalkaska County incorporates such considerations directlyinto the overall analysis.

Table 8.9: Change in CFA ranks with inclusion of conservation opportunity and feasibility criteria

Conservation FocusArea

Relative Rank in Listof all 18 CFAs

Final Rank of 10 CFAsin Kalkaska County

Ranking Change

North Branch 1 1 none28 Lakes 2 2 noneDempsey Creek 3 7 -4Little Cannon Creek 4 5 -1Blue Hollow 5 4 +1Little Devil 6 8 -2Black Creek 7 3 +4Big Devil 8 10 -2Pierson Creek 9 6 +3East Lakes 10 9 +1


Big D

evil

East Lakes

Little D

evil

Dem

psey C

reek

Pierson C

reek

Little C

annon

Blue

Hollow

Black

Creek

28 Lakes

North

Branch

CFA

Nam

e

27.6

25.8

27.0

28.2

25.1

26.3

23.9

26.7

27.5

29.4

Mean

ParcelScore

3 8 5 2 9 7 10 6 4 1

Rank

Mean Score

743

602

931

1,972

2,181

3,021

2,864

1,691

2,896

7,950

Acres

9 10 8 6 5 2 4 7 3 1

Rank

Size

0.00436

0.00412

0.00336

0.00396

0.00483

0.00325

0.00198

0.00414

0.00193

0.00266

Perimeter

to Area

Ratio

9 7 5 6 10 4 2 8 1 3

Rank

Shape

316 857

575

707

4,310

563

2,307

4,055

3,940

6,576

Unprotected

Acres

*

10 6 8 7 2 9 5 3 4 1

Rank

Opportunity

278 400

69

118

3,483

389

1,107

4,055

2,871

5,655

Unprotected

Acres in 40+

Acre Parcels

8 6 10 9 3 7 5 2 4 1

Rank

Feasibility

39 37 36 30 29 29 26 26 16 7

SumR

ank

10 9 8 7 6 5 4 3 2 1

FinalR

ank**

*Unprotected acres w

ere calculated using parcels that overlap with C

FA boundaries. In some cases, the acreage for parcels exceeds that of the

corresponding CFA. Black C

reek CFA, for exam

ple, contains a very large parcel that is only partially within the C

FA. Thus, its unprotected acreageexceeds that w

ithin the boundaries of the official CFA.

**Mean score rank provides the tiebreaker for identical sum

ranks.

Table 8.8: CFA

s within K

alkaska County - ranking schem

e and results


Figure 8.14: Ranked Conservation Focus Areas in Kalkaska County

KALKASKAANTRIM

MISSAUKEE

CR

AWFO

RD

Garfield

Kalkaska BearLake

OliverOrange

Excelsior

Blue Lake

Springfield

Cold Springs

Big Devil

Little Devil

East Lakes

Black Creek

Dempsey Creek

Little Cannon

North Branch

PiersonCreek

Blue Hollow

28 Lakes

N0 5 10 15 20 Kilometers

Ranked CFAs in Kalkaska County

1 - North Branch2 - 28 Lakes3 - Black Creek4 - Blue Hollow5 - Little Cannon6 - Dempsey Creek7 - Pierson Creek8 - Big Devil9 - Little Devil10 - East Lakes

CFAs out of Kalkaska

Study areaTownship boundariesCounty boundaries


As noted in Chapter 7, the team included the conservation opportunity and feasibility criteriato increase the utility of the results for GTRLC. One interesting side issue of effectivelyremoving the public or otherwise protected lands from the analysis is that the remainingprivate lands tend to produce higher mean scores. In other words, when measured against theproject’s eight conservation drivers, private lands within the CFAs tend to score higher onaverage than public or otherwise protected areas. Table 8.10 illustrates this phenomenon.

Table 8.10: CFA acreage and mean scores – with and without existing protected landsCFA Acres w/

ProtectedLands

Acresw/out

protectedlands

Difference(in acres)

Mean Scorew/ Protected

Lands

Mean Scorew/out

ProtectedLands

Change inMean Score

North Branch 7,950 4,138 -3,812 29.4 30.6 1.228 Lakes 2,896 2,534 -362 27.5 27.8 0.3Black Creek 1,691 1,366 -325 26.7 26.6 -0.1Blue Hollow 2,864 1,894 -970 23.9 26.2 2.3Little Cannon 3,021 539 -2,482 26.3 34.1 7.8Pierson Creek 2,181 2,078 -103 25.1 25.3 0.2DempseyCreek

1,972 560 -1,412 28.2 30.3 2.1

Little Devil 931 408 -523 27.0 28.5 1.5East Lakes 602 513 -89 25.8 26.9 1.1Big Devil 743 291 -452 27.6 32.7 5.1

2nd and 3rd TIER CONSERVATION AREAS

Because of their smaller size and more scattered distribution throughout the study area, theteam did not devote extensive attention to the 2nd and 3rd Tier Conservation Areas. Instead, itsimply wanted to identify and delineate these areas so that GTRLC was aware of theirexistence and could evaluate conservation options in these areas accordingly. As outlined inChapter Seven, the team ranked these areas based solely on their mean scores. See AppendixF for ranked lists of 2nd and 3rd Tier Conservation Areas. Figures 8.14 depicts the 2nd and 3rd

Tier Conservation Areas.


Figure 8.15: Second and Third Tier Conservation Areas

ANTRIM

KALKASKA

CRAWFORD

OTSEGO

MISSAUKEE

11

7

20

4 16

25

272

14

1

39

18

2617

15

21

8

1223

10

19

6

22

13 24

5

4

7101113

14

215 12

36

8

9

5

1

N


Study areaCounty boundariesConservation focus areas

Third tier areas (15 total)Second tier areas (27 total)

Ranked conservation areas


INDIVIDUAL LAND PARCELS WITHIN THETOP THREE CFAs IN KALKASKA COUNTY

The team identified 63 private land parcels of 40 acres or larger in the top three CFAs inKalkaska County – North Branch, 28 Lakes, and Black Creek. These parcels total over12,750 acres. After aerial photo inspection, the team determined that more than 11,000 ofthose acres are natural – having no residential, commercial, industrial, or agricultural landuses.

As outlined in Chapter 7, the team used a three-tiered hierarchical prioritization scheme forthe parcel analysis. When the team evaluated a given parcel’s overall importance, it firstconsidered the largest land unit – the Conservation Focus Area – within which the parcel waslocated. The higher the CFA rank, the higher the parcel’s overall value. The team thenexamined the second largest land unit – the landscape feature. Landscape features were usedto unify groups of parcels located in or around discrete ecological features within the CFA.The higher the landscape feature’s rank, the higher the parcel’s relative standing. Lastly, theteam considered the final score of the parcel itself.

Table 8.11 displays the results of this analysis. Results are grouped first by CFA, then bylandscape feature (if applicable – not all parcels were associated with a landscape feature),and lastly by parcel score. Table 8.11 lists both CFAs and landscape features within CFAsaccording to their rank (remember that landscape features are ranked solely by the totalnatural acres in their associated parcels). As such, parcels are listed roughly in order ofpriority, but the team concedes that on-the-ground implementation of these results mayrequire a more dynamic parcel selection strategy (see Chapter 9). See Appendix E for moreinformation on individual parcel scores and acreage figures for CFAs and identifiedlandscape features.

Figures 8.16, 8.17, and 8.18 illustrate the spatial characteristics of the results of the parcelanalysis. Figure 8.16 displays the relationship of all private parcels to the top three CFAs.8.17 displays parcels in colored-coded groups based on their associated landscape featurewithin the CFAs. Figure 8.18 displays parcels according to their total score.


Table 8.11: Results of parcel prioritization analysis

Sensitive Material – Removed fromPublic Copy


Table 8.11 (continued): Results of parcel prioritization analysis



Figure 8.16: Private parcels associated with the top three Conservation Focus Areas in Kalkaska County



Figure 8.17: Private parcels associated with top three Conservation Focus Areas in Kalkaska County, grouped by landscape feature



Figure 8.18: Final scores of private parcels associated with top three Conservation Focus Areas inKalkaska County



THREAT SOURCES

As Chapter 7 describes, the team divided the sources of threats into two categories – existingthreat sources and potential threat sources. This section outlines the results of the team’sanalysis of existing and potential threat sources and their relationships to the CFAs. Whileexisting sources such as oil and gas drilling sites and roads can rarely be removed, it isvaluable to understand their locations in the study area as many future threats will occur inareas of existing sources. For example, since existing oil and gas drilling pads are positionedover underground oil and gas reserves, it is likely that new drilling pads will be located in thevicinity of the existing sites. Similarly, newly paved or expanded roads can encourageresidential development in previously rural areas.

It is important to note that some mapped existing threat sources do not have a correspondingmap of potential sources because the team was unable to estimate the location of futuresources. For example, while the team knew the location of many existing off-road vehiclesand snowmobile trails, it could not accurately predict the location of future trails.Conversely, some mapped potential threat sources do not have an associated map of existingthreats because the team was unable to determine the location of existing threat sources. Forexample, the team assumed that most forested areas could be logged in the future, but couldnot ascertain the exact location of current logging areas.

While the team was not able to map some sources of threats, such as invasive species, firesuppression, and dams, GTRLC and others should consider these sources in makingdecisions about land protection within the CFAs. For example, invasive species are commonin the study area and are often associated with mapped sources of threats such as roaddevelopment and incompatible logging.

EXISTING SOURCES OF THREATS

Figure 8.19 – 8.23 display the locations of the existing threats in the study area that the teamspatially represented, including development, oil and gas drilling, roads, ORV andsnowmobile use, and incompatible agriculture and grazing. Table 8.12 explains therelationship between the CFAs and the existing sources of threats. Figure 8.24 spatiallyillustrates this relationship.


Figure 8.19: Existing threat source – development

KALKASKAANTRIM OTSEGO

CRAWFORD

MISSAUKEE

N

0 10 20 Kilometers


Residential, commercial, and industrial areas

Existing development

Figure 8.20: Existing threat source – roads


CRAWFORD

MISSAUKEE

%g72

%g66N

0 10 20 Kilometers

Existing roadsHighwayResidential/County



Figure 8.21: Existing threat source – off-road vehicle and snowmobile trails


CRAWFORD

MISSAUKEE

N

0 10 20 Kilometers


Existing trails (Antrim &Kalkaska Counties only)

Off-road vehicleSnowmobile

Source: Northwest Michigan Council of Governments

Figure 8.22: Existing threat source – oil and gas drilling sites

#

#

#

### ####

#

###

## ######

#

##

#

#

##

#

###

#

# #

##

#

##

#

# ##

##

#

#

#

###

#

###

#

#

#

#

##

####

#

#

#

# ##

#

##

#

#

#

#

#

#

#

#

#

#

# #

#

#

#

#

#

#

##

##

#

#

#

#

#

#

##

#

##

#

###

#

#

##

#

#

#

##

#

#

#

#

#

#

# ##

#

#

#

#

#

#

#

#

#

#

##

#

#

##

#

#

##

##

#

##

###

##

#

#

# ###

#

#

#

#

##

##

####

#

#

#

###

#

#

##

#

#

#

# #

##

##

#

#

# #

#

### #

#

#

#

###

#

#

##

###

###

###

#

#

#

#

#

#

#

#

#

## ##

#

##

##

#

##

#

##

###

#

#

#

#

##

##

###

#

#

#

#

#

#

#

#

##

#

#

##

###

#

#

#

#

#

#

#

#

#

#

##

###

###

###

##

#

#

##

#

##

#

#

#

#

#

#

#

##

#

#####

#

#

# ###### ###

#

#

#

#

##

##

##

#

#

##

#

## #

##

#

#

##

##

#

#

## ##

## ##

#

###

#

# ###

## ## ###

##

##

##

##

###

#

# ## #

##

#

## ####

#######

#

#

#

#

#

##

##

#

#######

#####

###

##

##

## ##

#

#########

#

#

#

#

###

#

#

############

#

#

###

#

###

##

#

#

#

#

#

#

#

#

#

####

#

##

#

#

#

##

#

#

#

#

##

#

#

#

###

#

## ###########

##

#

#

#

#

#

#

##

#

#

#

##

#

##

#

#

#

##

##

##

#

#

#

#

##

#

#

#

###

## ##

#

##

#

#

#

#

##

#

##

#

#

#

#

##

##

##

### #

#

#

#

##

## #

#

##############

#

###

#

#

##

#

#

#

##

##

####

#

#

#

#

#

#

#

#

#

#

#

##

####

#

###

###

#

## #

##

##

####

##

#

##

#

##

#####

## #

#

#

#

###


CRAWFORD

MISSAUKEE


CRAWFORD

MISSAUKEE

N

0 10 20 Kilometers

Source: Michigan Department of Natural Resources


# Bottom # Surface

Existing oil & gas wells


Figure 8.23: Existing threat source – areas of agriculture and grazing


CRAWFORD

MISSAUKEE

N

0 10 20 Kilometers

Source: Michigan Department of Natural Resources


Existing areas of agriculture and grazing

General locations

*Note that agriculture and grazing displayed on this map is not problematic per se – it is the areas ofincompatible agriculture and grazing that lie within the areas depicted that can most damage ecological health.

Table 8.12: Existing sources of threats in the CFAsRoads Development ORV/

Snowmobile UseOil and Gas

DrillingAgriculture/

Grazing28 Lakes X X XBig Cannon X XBig Devil XBlack Creek X X X XBlue Hollow X X X XDempsey Creek X X XDeward X X XEast Lakes X X XFiler Creek XFrenchman's X XGoose Creek X X XHeadwaters X XLake Margrethe X XLittle Cannon X X XLittle Devil X X XLittle Goose Creek XNorth Branch X X XPierson Creek X X


Figure 8.24: Existing sources of threats overlaid with CFAs

< MANUALLY INSERT MAP(S) OF EXISTING THREATS WITH CFAs>


Observations

� Roads are the dominant existing source of threat and intersect all CFAs.

� Development occurrs mainly around lakes and rivers, covering the most area inDeward, Blue Hollow, Lake Margrethe, Goose Creek, and Little Devil CFAs.

� ORV/snowmobile trails intersect the CFAs mainly in the central part of the study area.The abundance of trails on public land tends to concentrate ORV use in this area. Note,however, that the team was only able to acquire ORV/snowmobile data for twocounties – Antrim and Kalkaska.

� Existing oil and gas surface and bottom drilling sites occur in CFAs in the northern halfof the study area. However, both East Lakes CFA and Headwaters CFA are located inthis region but do not contain existing drilling sites based on the data collected.

� Agriculture and grazing activity are concentrated in the western part of the study areawith the highest concentration in Pierson Creek CFA.

� Most existing threats are concentrated in the northern third of the study area.

POTENTIAL SOURCES OF THREATS

Table 8.13 explains the relationship between the CFAs and the potential sources of threats.Logging, development, oil and gas drilling, and road development are the spatiallyrepresented sources that could potentially threaten the CFAs in the future (Figures 8.25 -8.28). Figure 8.29 illustrates the relationship between the location of the potential threatsources and the CFAs.

Table 8.13: Potential sources of threats and CFAsDevelopment Logging Oil and gas

drillingRoads

28 Lakes X XBig Cannon X XBig Devil X XBlack Creek X XBlue Hollow X X XDempsey Creek X XDeward X X XEast Lakes X X XFiler Creek X XFrenchman's X X XGoose Creek X X XHeadwaters X X XLake Margrethe X XLittle Cannon X XLittle Devil X XLittle Goose Creek X XNorth Branch X X XPierson Creek X X


Figure 8.25: Potential threat source – logging


CRAWFORD

MISSAUKEE

N

0 10 20 Kilometers

Low for all logging

Logging potential*

High for general logging & lower for hardwood logging

County boundariesStudy area

High for general logging &high for hardwood logging

* The team assessed logging potential based on two criteria – forest cover and landform. “General loggingpotential” utilizes the forest cover criteria by including any areas that the Michigan Department of NaturalResources designated as forest cover as of 1979. “Hardwood logging potential” utilizes both the forest coverand the terrain criteria. Forests located on moraines or ice contact terrain are considered to have high hardwoodlogging potential. Forests located on outwash plains are considered to have lower hardwood logging potential(Merriweather, 2002).

Figure 8.26: Potential threat source – development


CRAWFORD

MISSAUKEE

N

0 10 20 Kilometers

Moderate - other unprotected landsLeast - protected lands

Most - unprotected riparian lands


Potential development


Figure 8.27: Potential threat source – oil and gas drilling


CRAWFORD

MISSAUKEE


CRAWFORD

MISSAUKEE

N

0 10 20 Kilometers


Area most likely to support additional drilling, based on geology & locations of existing wells

Potential oil & gas drilling

Figure 8.28: Potential threat source – road development

%g66

%g72


CRAWFORD

MISSAUKEE

%g72

%g66

N

0 10 20 Kilometers

Study areaCounty boundariesHighways

Proposed location for new passing lanes

Potential road development


Figure 8.29: Location of potential threats to the CFAs

<MANUALLY INSERT MAP WITH CFAs OVERLAID WITH POTENTIAL THREATS>


Observations

� Development potential is highest in CFAs that contain the mainstem of the ManisteeRiver, tributaries, and lakes. This result is not surprising given that the team assumes thatdevelopment pressure on lands near water will be higher than any other areas.

� Potential logging operations threatens all CFAs since they all contain some forestedareas.

� All lands within CFAs that are close to current oil and gas development are potentiallease sites. Though no oil and gas drilling currently exists in East Lakes and HeadwatersCFAs, those CFAs are located above the Antrim Shale and Niagaran Reef formations andare vulnerable to oil and gas drilling activities.

� The road extension on M-72 will affect the top portion of the North Branch CFA andbisect part of the Goose Creek CFA.

� Potential threats are not clustered in any one portion of the study area but are insteadscattered throughout.

chapter 8: results - u-m seas · chapter 8: results 211 composite grid examining the drivers...

Documents