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'q -r u'é ~,-. 4 ‘a .‘_A . -. »- - .‘h 'J' «3&4. , "fl.- f ‘ .- \‘1. 1-5. vu THE“ This is to certify that the thesis entitled Pre-European Settlement Forests of Northern Lower Michigan: The Role of Landform in Determining Composition Across The Landscape presented by Jill Huckins Fisher has been accepted towards fulfillment of the requirements for M-S. degree in Forestry Major prgessor Date April 8, 1994 0-7639 MS U is an Affirmative Action/Equal Opportunity Institution A _.__._qu——_..—...._...._ « Illlllllllllllllll LIBRARY Mlchlgen State Unlveretty PLACE IN RETURN BOX to remove this checkout from your record. OID FINES return on or betore date due. DATE DUE DATE DUE DATE DUE .4 "J I , x. 4...; 5" " t (w. -1 a ( \ . , f I l (A 1" -‘fi l MSU Is An Aflinnetwe Action/Equal Opportunity Institution catchments-9.1 PRE-EUROPEAN SETTLEMENT FORESTS OP NORTHERN LOWER MICHIGAN: THE ROLE OF LANDFORM IN DETERMINING COMPOSITION ACROSS THE LANDSCAPE BY Jill Huckins Fisher A THESIS Submitted to Michigan State University in partial fulfillment of the requirements for the degree of MASTER OF SCIENCE Department of Forestry 1994 ABSTRACT PRE-EUROPEAN SETTLEMENT FORESTS OF NORTHERN LOWER MICHIGAN: THE ROLE OF LANDFORM IN DETERMINING COMPOSITION ACROSS THE LANDSCAPE BY Jill Huckins Fisher This study describes pre-European settlement forest composition at 101 sites across northern lower Michigan and assesses the role of landform in determining composition on eight landform types using data from the General Land Office Survey records. Mapped profile plots of the 101 samples revealed distinct regionalization of forest composition with composition in each region determined by a different combination of climate, soils, and landform. Composition in the northwest corner of the study area appeared to be controlled primarily by climate. Composition in the southeast corner appeared to be controlled by a lacustrine soils laid down over landforms. Landform appeared most influential in the interior of the state were climate is not lake-modified and the soils are distinguished based on the mode in which the glacier deposited them. Casey J, to you, me and Gheodie ACKNOWLEDGEMENTS The busy schedules of my committee members testify that their advice and wisdom is highly sought after. And so, Dr. Don Dickmann, Dr. Kay Gross and Dr. Patrick Webber, I thank you very much for your time, enthusiasm and comments on the text. I especially thank my advisor, Dr. Kurt S. Pregitzer for'the accumulated.consultation, prodding and words.of advice and encouragement. I am looking forward to seeing in press his photographic history of the deforestation of Michigan and wish him well in this endeavor. I extend my appreciation to Dr. Denny Albert and Dr. Patrick Comer of the Michigan Natural Features Inventory for their comments and suggestions. I thank Dr. Carl Ramm for his wise statistical advice and most famous comment "just.what.are you trying to get at here?". As the many before and after myself, I am indebted to my fellow graduate students who have greatly enhanced my scholastic and cosmic education. A hearty thanks goes to Dr. Leroy Barnett and his staff at the State of Michigan's Archives who were my assistants in the "field". They didn't have to smile when they brought out all those volumes of surveyor's notes to me, but they did. Thanks to my other field assistant, Dave Rygell, who ii. patiently transcribed for hours. These acknowledgements would not be complete without proper thanking of my family members, since I wouldn't be the me I am now without them. Thank you to my parents, Pat, Jeff and Sarah, and my brother Fred. The largest thanks goes to my best friend, Casey. Jessie and Jericho deserve their place here also, even if they can’t read English. iii TABLE OF CONTENTS Page I. Introduction......................................... Background.................................... Importance of Landforms....................... Objectives.................................... ptehap II. Methods............................................ Background.................................... Study Area Location and Climate.......... Glacial History.......... ..... ........... Sample Data.... ...... .................... Use of G.L.O. Records in Assessing Pre-European Settlement Vegetation.. 13 Line Descriptions........................ 14 Analyses...................................... 16 Species/Landform Associations............ 16 Variation of Composition within a Landform Type................................ 18 Forest Types.......... .......... ......... 18 Disturbance.............................. 19 \lO‘UIU'IU'I III. Results and Discussion............................. 21 Format of Species Counts and Citation Frequencies in Appendices....................... 21 Description of Pre-European Settlement Forests and Species Trends................................ 22 Community Variation Among Landforms Type...... 24 Profile Plots................................. 26 Region 1...................................... 34 Region 2...................................... 39 Region 3........................ ..... ......... 40 Region 4...................................... 40 Major Forest Types............................ 44 Comparison with Veatch and Albert et al....... 49 Disturbance................................... 50 Iv. conCIuSionSOOOOOOOOOIOO0.0.0.0...OOOOOOOOOOOOOOOOOO 59 V. Use of Pre-European Settlement Records for Current Land ManagementOOOOOOOOOOOOOOO0.0000000000000000000 62 iv table of contents (cont’d) Appendix A Literature Cited for Figure 1................. 65 Appendix B SAS Output for Correspondence Analysis........ 68 Appendix C Quarter Township Codes for Appendices C-E..... 72 Legend for Appendices C-E: Codes Given to Farrand's Landforms in Quaternary Geology of Michigan, Department of Geological Science, University of Michigan, Ann Arbor (1982)..... 73 Species Counts on Non-swampland............... 74 Appendix D Citation Frequency for Non-swampland.......... 82 Appendix E Cumulative Count Data for Swamps .......... .... 90 Literature Cited .................................. 97 LIST OF TABLES Table Page 1. List of townships sampled for intended use in this study, but found to have no species differentiation between the pines in the surveyor’s notes. The asterisks indicate townships that mention only pine in the subdivision notes but list more than one species of pine in the exterior township notes or in examinations of the surveyed work. This is not an inclusive list.’0.0.000000000000000000000000000000000000000000 11 List of species designated by the surveyors with scientific names and modern common names in parentheses (mOdified from Bourdo (1954))OOOOOOOOOOOOOOOOOOOOOO. 12 Results from Delcourt and Delcourt’s (1974) ANOVA tests for surveyor bias for species selection of witness trees. P values reflect the significant level for differences in average distances to dominant species. The dominant species are as follows: B=beech, H=hemlock, JP=jack pine, RM=red maple, RP=red pine, W0=white oak, WP=white pine ......... ................ 14 Results from Kolmogorov-Smirnov tests for surveyor bias on line tree selection. Significant differences are marked with an asterisk............................. 16 Comparison of species frequencies for all samples across all upland landforms. These data represent corner and line witness trees for a total sample of 2448 linear mil-88.0.0000...OOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOO 23 Haberman's standardized residuals for presence data of species by landforms for all 101 quarter townships. The sign reflects a positive or negative association and the value reflects the strength of the association......................................... 24 Comparison of species percentages by landform for Region 1. Numbers in parentheses indicate the number of quarter township samples summarized for each landform in Region 1. Each quarter township represents 24 linear miles of corner and line witness tree data... 42 vi 10. 11. 12. 13. 14. 15. List of tables (cont’d) Comparison of species percentages by landform for Region 2. Numbers in parentheses indicate how many quarter township samples were summarized for each landform in Region 2. Each quarter township represents a sample of 24 linear miles of corner and line witness tree data........................................... ..... Comparison of species percentages by landform for Region 3. Numbers in parentheses indicate how many quarter township samples were summarized for each 42 landform in Region 3. Each quarter township represents 24 linear miles of corner and line witness tree dataOOOOOOOOOO0.000000000000000000000000000000000000 Comparison of species percentages by landform for Region 4. Numbers in parentheses indicate the number of quarter township samples summarized for each landform in Region 4. Quarter townships represent a sample of 24 linear miles of corner and line witness tree data........................................... Comparison of citation frequency among clusters...... Comparison of relative dominance Among clusters...... Disturbance regime summarized by landform type....... Size of individual disturbances. Values indicate the total number of linear miles a disturbance covered according to the surveyor’s notes............ ...... . Percentage of each type of disturbance by region. Distances are in total linear miles of survey line 43 43 46 46 53 54 affected. Entries in parentheses are sites of windfall that were burnt..... ......... .... ..... . ..... ........ SAS output for correspondence analysis.............. C1-8. Raw species count data for each site by landform.. D1-8. Citation frequency for each site by landform...... E1-8. Species count data for wetlands for each site by landform...00...DOOOOOOOOOOOOOOOOOOOOOOO0...... vii 57 68 74 82 90 LIST OF FIGURES Figure Page 1. List and locations of previous work on pre-European settlement vegetation in Michigan prior to 1993. Appendix A lists full citations..................... 2 2. Location of all 101 quarter townships sampled in the study area. The first number of each label refers to the landform type, see legend. The second number refers to the sample number for that landform....... 9 3. Correspondence analysis for all the upland samples on all landforms. The first two dimensions, axes 1 and 2, account for 67.8 % of the total variance for eight species; sugar maple, hemlock, beech, red maple, white pine, white oak, red pine and jack pine. The numbers refer to landform types in the legend. Refer to Figures 5 and 6 for clarity of numbers ............. ........ 28 4. Correspondence analysis for the upland species on all eight landforms. The first two dimensions, represented by axes 1 and 2, account for 67.8 % of the total variance. Species codes refer to; S=sugar maple, =hemlock, B=beech, RM=red maple, WP=white pine, WO= white oak, RP=red pine and JP=jack pine. The species appear to be arranged along a moisture, nutrient, shade intolerance gradient or a combined gradient based on knowledge of their present-day habitats......... 29 5. Correspondence analysis for quarter townships on individual landforms. The data used are the same as those plotted in Figure 3. The sites on these landforms appear to have been dominated by more mesic species than sites on the landforms in Figure 6.... 31 6. Correspondence analysis for quarter townships on individual landforms. The data used are the same as those plotted in Figure 3. The sites on these landforms appear of have been more variable in composition and dominated by more xeric species than sites on the landforms in Figure 5................. 33 viii List of Figures (cont'd) 7. Profile plots for all 101 quarter townships on all upland landforms. The variables are percent of citation frequency for species from left to right: sugar maple, hemlock, beech, white pine, white oak, red maple, red pine and jack pine: refer to the example of a single profile plot for site 6-10. The first number of each label refers to the landform type, see legend. The second refers to the sample number for that landform. Profiles are used instead of histograms so that visual patterns are more apparent......................... 36 8. Profile plots for all 101 quarter townships with designated regions. Forest communities in each region appear to be controlled by a different combination of factors. The most influential factor for each region appears to be: landforms in Region 1, climate in region 2, fire disturbance in Region 3 and lacustrine soils in Region 4............. ........ ............ 37 9. Profile plots for outwash plains only showing geographic variation in pre-European settlement composition within one landform type ........... ....................... 38 10. Comparison of cluster analysis with regions from this study and with forest community designations from Veatch's 1959 map. The cluster analysis was done using Euclidean distance for the Ward minimum variance method............................................. 47 11. Comparison of regions from this study to Albert et al.’s districts from Regional Ecosystems of Michigan (1986)....00000000000000 .......... OOOOOOOOOOOOOOOOO so 12. Regional distribution of disturbances. 15:- fire C)-rwindthrow. Cited disturbances covered an estimated 17.9 t of the total area sampled. Region 1 contained the highest percent of both types of disturbance: 73% of all cited fires and 51% of cited windthrows occurred in Region 1.................... 56 ix INTRODUCTION Background As European immigrants settled this country, a majority of our forests were logged before they were described from an ecological perspective. Ecological historians have subsequently tried to accurately depict the condition of these forests prior to European settlement (for example: Abrams & Nowacki 1992, Braun 1950, Curtis 1959, Lorimer 1977). Michigan’s pre-European settlement vegetation history has been pieced together by many authors, however, these studies were conducted within limited geographic areas. Figure 1 lists authors and depicts the locations of areas that have been previously studied; the result is a patchwork of local studies across the state. Only two pre-European settlement vegetation maps have been produced for the entire state, but both lack critical information. Marschner’s 1946 map, based solely on the notes from the General Land Office Survey, is so generalized that many forest types are lumped together as "hardwoods" and "pinelands". Veatch (1959) produced a more detailed map, grouping forest communities by soil type, climate, and geomorphological features. However, his nu List of Authors and Locations: prior to 1993 ' " “" (1) £.A. Iourdo, 1956 10 (2) ILL. Loops, et. sl., 1991 (31 1.5. Frelich s Lorimer, 1991 9 “" ""'“ “- “'5‘- (4) I.J. P01"! 5 K.S. Pregitzer, 1991 (5) P.D. Kilburn, 1957 III-:- “a I... .- (6) .I. Kitteredoe l A.K. Chittenden, 1929 m m (7) 6.6. witney, 1966 t 1987 (3) Livingston 3.5., 1905 "" ""- (91 1.3. Mtsrd, 19a: 12 a (10) M. 11mm, 1986 .. "‘ n- (11) 11.0. Kepp, 1978 17 L__‘_ (12) 1.9. ttushen st sl., 1966 19 “I. (13) L.G. Brewer et al., 1964 (14) L.A. Kenoyer, 1930 13' (15) " 1' , 1934 1‘ _. 1 I I 1 l _. :13; .. a 1 1g: 4'31” ‘6 2° (18) T. tiedler et st., 1981 ’ (19) s.L. bodes, 1987 (20) it..l. Hertesveldt, 1951 (21) 4.9. Merit, 1951 (22) 9.8. Dick, 1937 General: 8.9. Psrselee, 1953 F.J. Nerschner, 1946 (up of state) J.0. Veatch, 1959 (esp of state) Figure 1. List and locations of previous work on pre-European settlement vegetation in Michigan prior to 1993. Appendix A lists full citations. 3 methodology for using these factors to characterize communities are not explicit and there are no data describing his communities (Dodge 1987, Veatch 1932). The accuracy of these maps has not been verified, and even if Veatch’s map is accurate, the environmental determinants of pre-European settlement composition across the landscape are not well understood. To gain a better understanding of pre-European settlement forests and their determinants, this study examined the role of glacial landforms in influencing the composition of pre-European settlement forests across the upper half of the lower peninsula of Michigan. Importance of Landforms The incorporation of geomorphology (equivalent to glacial landforms in Michigan) in classifying land is increasing. Rowe (1984) and Hills (1952) emphasize the need to consider landforms in the classification process because of the modifying effects of landforms on local climate and soil formation processes. Other authors have documented the importance of landforms in influencing patterns of vegetation for both pre-European settlement (Grimm 1984, Hushen et al. 1966, Kapp 1978, Whitney 1986, 1987) and present-day communities (Host et al. 1987, Host and Pregitzer 1992, Padley 1989). This study is unique in that it examines the role of landforms in determining pre-European settlement composition over an area large enough to encompass potential regional differences. Objectives The primary objectives of this study were to: 1) examine the composition of the pre-European settlement forests of northern lower Michigan to understand the role landform had in determining composition across the landscape: 2) describe the variation in forest composition within a landform type: 3) describe upland forest types associated with the different landforms. Forest type is defined in this study as an assemblage of communities with the same overstory dominants, although proportions of dominants and auxiliary species may differ: 4) verify Veatch's pre-European settlement forest composition map: 5) document disturbance in the study area to ascertain any regional patterns of disturbance or patterns associated with landform. METHODS Background Study Area Location and Climate The position of Michigan’s two peninsulas in the center of the Great Lakes subjects the state to a semi-marine climate even though it is located mid-continent (Eichenlaub et al. 1990). The average annual precipitation for the lower peninsula grades from 32" (81.3 cm) along the western shoreline to less than 28" (71.1 cm) along the eastern shoreline due to prevailing westerly winds. The average annual temperature varies from 46 degrees F (7.8 degrees C) along lake borders to less than 42 degrees F (5.5 degrees C) inland, .Also noteworthy is the decrease in the average number of annual thunderstorms from.south.to north (Eichenlaub»et a1. 1990). Braun described the present-day eastern forests of the United States in her 1950 book. The entire area for this study is included in Braun's Great Lake Section of the Hemlock-White Pine-Northern Hardwoods region. This region extends from northern Minnesota through northern Michigan towards the east across southern Canada and New England. The Great Lakes Section includes northern lower Michigan, eastern 5 6 upper'Michigan and.part.of Ontario between Lake Huron and Lake Ontario (Braun 1950). Braun describes the distinguishing forest community in this section as deciduous northern hardwoods dominated by sugar maple, beech and basswood with fewer yellow birch, white elm and red maple. Northern hardwoods occur on fine textured morainal ridges. Coniferous forests with white, red and jack pine occur on the dry, sandy outwash plains and mixed communities of hemlock and northern hardwoods dominate well drained morainal ridges and rolling ground moraines often with a fair amount of pine. Albert et al. ’s (1986) landscape ecosystem Region II, the northern half of the Lower Peninsula, was chosen as the study area. On Albert et al.’s map, regions and districts are delineated based on macroclimate and physiography, whereas the subdistricts are distinguished by finer differences in physiography and soils. The southern boundary represents the tension zone separating major temperature regimes (Kapp 1978, Hushen et al. 1966). Many differences in species distributions and soil types follow the tension zone, such as the southern limit of the range for jack and red pine and the shift of soil orders from southern Alfisols to northern Spodosols (Buol et al. 1973). Glacial History The last glacial lobes from the Laurentide Ice Sheet advanced over the entire state of Michigan and reached as far south as the middle of Indiana and the southern border of 7 Ohio. 'The first part of the study area to be uncovered by the retreating ice, approximately 14,000 years before present, was the central highlands region. The three ice lobes fully withdrew from lower Michigan 3,000 years later,and the area was left with a historical record of ice stagnations and retreats. Detailed descriptions of the glaciation of Michigan are given by Dorr and Eschman (1984) or Hough (1958). Landforms in this study refer to those mapped by Farrand and Bell (1982). A listing of landforms and codes used for this study are found in the legend of Figure 2. Farrand and Bell's map has not been field tested for detailed accuracy; however, it remains the most complete source covering large regions (GrahameaJ. Larson, 1993 pers. comm.). An.updated.map with subsurface inclusions may account for more variation in forest composition for each landform (Host and Pregitzer 1992). Sample Data The study area was stratified by the landform designations on Farrand’s map. Dunes, muck deposits and lacustrine silts and clays were not considered for this study due to the small area covered by these landforms. Quarter townships (nine square miles) were used as the sample unit instead of whole townships to gain more samples away from borders with other landforms and the shoreline of Lakes Michigan and Huron. All possible sample quarter townships were plotted and numbered on a mylar overlay. Fifteen quarter Legend Figure 2: Codes Given to Farrand's Landforms in Quaternary Geology of Michigan Map (Department of Geological Sciences, The University of Michigan, Ann Arbor. 1982) Outwash plains Ground Moraines End Moraines of Ground Moraines End Moraines of Ground Moraines Lacustrine Silt End Moraines of meflmU'I-hwwlfl 0 of Fine Textured Till Lacustrine Sands and Gravels Ice Contact Features Coarse Textured Till of Coarse Textured Till Medium Textured Till of Medium Textured Till (not used) and Clay (not used) Fine Textured Till Note: Landforms that covered relatively little area were not used in this study. The numbering system was developed for a preliminary study and kept for continuity. L 15 fr re Ne- ari n01 of but Spe. Subd 10 townships were randomly selected for each landform type. Some samples were excluded from lacustrine sands and gravels and fine textured ground moraines because the area within those quarter townships contained more than fifty percent swamp or wetland. Medium textured till and end moraines of fine textured till were limited to 7 and 12 samples, respectively, due to limited area. The locations of all 101 final quarter township samples are shown in Figure 2. This scheme produced a 5.3% sampling of the entire study area. From examination of Figure 2, two areas appear to be under-sampled. The area in the northwestern corner of the Lower Peninsula, including Antrim, Charlevoix and Emmet counties, was under-sampled due to the choppy pattern of the landforms in that area permitting few quarter townships away from borders with other landforms. The second under- represented area covers the counties of Wexford, Lake, Newaygo, Mason and Oceana. A few of the townships in this area were resurveyed, but those that were not most often did not differentiate between the three species of pine. A list of townships that were examined for intended use in this study but were subsequently found to not differentiate between pine species is shown in Table 1. Surveyor’s common names and associated Latin names are listed in Table 2. Three surveyors repeatedly reported only "pine" in the subdivision. notes ‘where exterior ‘township lines or examinations run by other surveyors showed more than one pine species for the township: John Hodgson, Sye Sibley and John ll Mullet. The latter surveyor recorded several species of pine in surveys of other areas. The notes from the General Land Office survey’were then retrieved and recorded for each of the sample quarter townships to get count data and citation frequencies of trees. Table 1. List of townships sampled for intended use in this study, but found to have no species differentiation between the pines in the surveyor's notes. The asterisks indicate townships that mention only pine in the subdivision notes but list more than one species of pine in the exterior township notes or in examinations of the surveyed,work. This is not an inclusive list. Township Range 11 N 11 W 12 N 10 w, 11 w, 13 w 13 N 11 W, 13 W, 16 W* 14 N 9 w, 11 w 15 N 12 W 16 N 12 W 17 N 7 w, 9 w, 10 w, 12 w, 15 w*, 16 w 18 N 6 w, 7 w, 9 w*, 10 w, 11 w*, 12 w, 13 W, 16 W*, 18 W 19 N 9 w, 10 w, 11 w, 12 w, 13 w, 15 w, 16 w*, 18 w 20 N 11 W 12 W 13 W I I 12 Table 2. List of species designated by the surveyors with scientific ‘names and. modern common. names in. parentheses (modified from Bourdo (1954)). Fir (Balsam Fir) Abies balsamea (L.) Mill. Sugar (Sugar Maple) Ager saccharum Marsh. Maple (Red Maple) Acer rubrum L. Yellow birch Betula alleghaniensis Britt. White birch Betula papyrifera L. Birch @ White Ash # Eraxinus americana L. Black Ash # Eraxinus nigra Marsch. Tamarack (Eastern Larch) Larix laricina (Du Roi) K. Koch Ironwood Ostrya virginiana (Mill.)K. Koch Spruce (White and Picea glauca (Moench.) Voss Black) Eigea marigng (Mill.) B.S.P. Spruce Pine (Jack Pine) Pinus banksiana Lamb. Scrub Pine Yellow Pine (Red Pine) Pinus resinosa Ait. Norway Pine Pitch Pine @@ White Pine Pinus strobus L. Pine * Aspen (Quaking and Populus tremuloides Michx. Bigtooth) Populus grandidentata Michx. Black Cherry Prunus serotina Ehrh. White Oak Quercus alba Black Oak (Northern Red Oak) Quercus rubra L. Red Oak AA Willow Salix spp, Cedar (Northern White Cedar)**Thuja ocgidentalis L. Lynn (Basswood) Tilia americana L. Hemlock Tsuga canadensis (L.) Carr. Elm (American Elm) Ulmus americana L. (probably with Slippery Elm) Ulmus rubra Muhl. a Host of the townships used in this study listed birch separately from yellow birch, however; one case was found in the Upper Peninsula that listed white birch with birch mentioned in the low, wet spots, probably referring to yellow birch. # Often only ash was listed, the two have been combined for this study. 80 Cases have been found (Mason 00.) where pitch pine was used to designate jack pine. * Pine is cited as synonymous with white pine in Bourdo's list and kept separate in whitney's list since all 3 pines did occur in the area. Whether it designates white pine or only pine depends on the surveyor and the area of the survey record. “ in only a few instances was red oak specifically'mentioned. Following Whitney (1986), "black oak", "oak“ and "red oak" are listed here as an oak complex that probably refers to northern red oak but may include black oak, scarlet oak and northern pin oak. ** Eastern red cedar (Junipgrus virginiana L.) was not mentioned to be included with northern white cedar nor was it ever distinguished in the surveyor's notes examined for this study. Considering its present day range, cedar referred to northern white cedar. 13 Use of G.L.O. Records in Assessing Pro-European Settlement Vegetation Many studies of pre-European settlement vegetation have relied upon General Land Office surveys conducted in the state of'Michigan.from 1815 to 1855 (see methods in references cited in Figure 1). Bourdo (1954, 1956) remains the best source for reviewing the use of the GLO records in pre-European settlement vegetation reconstruction. Instructions to the surveyors were frequently updated, cases of fraudulent and fabricated work exist, and detail of written information varies with each surveyor (Dodds et al. 1943). However, as Bourdo concludes, the GLO records continue to be the most consistent method for reconstruction of pre-European settlement vegetation. A surveyor would walk a township or section line, set a post at each section corner and quarter mile mark, and record the species, diameter, direction and distance of the closest tree to the post in two of the four quadrants formed by the lines being laid. Bourdo (1954, 1956), following the instructions given to the surveyors in his area of study, resurveyed uncut tracts of forest in the Upper Peninsula of Michigan to determine if the surveyors showed bias in their witness tree selections. He concluded that no significant biases were evident for species selection, although bias for size was apparent. Many surveys in the Lower Peninsula were fraudulent, and a listing of resurveys was consulted in the final selection of townships (White 1984, Stewart 1935). 14 Delcourt and Delcourt’s (1974) method of ANOVA with completely randomized.design was used to evaluate a random 12% of the samples for bias of species selection (Table 3). This tests significant differences for the average distance to dominant tree species within a community type. Only one of the thirteen samples had a P value close to 0.05. This quarter townships had recorded evidence of recent disturbance which may explain biases in tree selection since few trees were left. Thus, I conclude that under normal conditions, there was no significant bias of species selection and.the GLO records represent a reasonable approximation of original forest composition. Line Descriptions Trees that intersected the survey line between corners were recorded in addition to the corner witness trees. Some surveyors would record only’ those line trees that. were approximately an.even distance between the corner post and the Table 3. Results from Delcourt and Delcourt's (1974) ANOVA tests for surveyor bias for species selection of witness trees. P values reflect the significant level for differences in average distances to dominant species. The dominant species are as follows: B=beech, "=hemlock, JP=jack pine, RM=red maple, RP=red pine, woawhite oak, HPswhite pine. County Sample Surveyor,0ate Dominant Species F Ratio P value Alcona 10-1,10-3 J.Mullet, 1846 U0, UP, RP 1.63 0.162 Clare 1-10 F.Coleman, 1856 JP, UP, RP 2.745 0.105 Clare 2-9 F.Coleman, 1856 8, RP, HP 0.017 0.896 Clare 5-5 G.Aclair, 1857 8, RP, HP 2.225 0.119 Gladwin 3-13,3-14 J.Mullet, 1846 H, HP 6.610 0.150 Kalkaska 1-5 G.Cannon, 1852 JP, HP, RP 0.924 0.402 Mason 1-4 J.Brink, 1839 JP, RP, HO, HP 2.244 0.056 Mason 1-12 J.Brink, 1839 B,JP,RM,RP,HO,HP 0.518 0.598 lewaygo 1-13 M.Nye, 1856 RP, HO, HP 0.683 0.509 Ogemaw 4-13,4-14 B.Hall, 1846 RP, HO, UP 1.822 0.169 15 quarter mile post. These data are difficult to use in quantitative expressions such as species density due to the variability with which they were recorded. Since forest composition, not structure, was addressed in this study, line trees were included.in count.data. Because surveyors had.more lee-way for selection of line trees, Kolmogorov-Smirnov tests were performed on the same samples tested above to assess surveyors bias of line trees. The K-S test measures the divergence between two sample cumulative distribution functions. Cumulative citation frequencies for dominant species recorded as corner trees were compared to cumulative citation frequencies for dominant species recorded as line trees. The test does not assume normal distributions, but does assume both samples came from the same distribution. 'The results in.Tab1e 4 show that no significant biases occurred in the selection of line trees for' most quarter ‘townships sampled. The one sample that did show a significant difference also had evidence of disturbance, which may explain the lack of a representative species in the line tree counts. 16 Table 4. Results from Kolmogorov-Smirnov tests for surveyor bias on line tree selection. Significant differences are marked with an asterisk. County Sample Maximum difference Alcona 10-1 0.25 10-3 0.33 Clare 2-9 0.25 1-10 0.5 5-5 0.17 Gladwin 3-13 0.33 3-14 0.67 * Kalkaska 1-5 0.33 Mason 1-4 0.14 1-12 0.2 Newaygo 1-13 0.2 Ogemaw 4-13 0.2 4-14 0.2 Analysis The Species/Landform Association Strahler (1975, 1977) showed that binary (presence or absence) data produced comparable results with quantitative data (such as percent cover) in relating woody species with topographic position and underlying rock type. Strahler used contingency tables for each combination of species and environmental variable to generate G-statistics to convert to standardized residuals. The sign of these residuals show positive and negative correlations of species to environmental variables and the values show the strength of these relationships. Following this method, contingency tables were constructed for each species by each landform and standardized 17 residuals were calculated to examine the first objective: are communities associated with landform type? To explore the variation of the species/landform association all 101 samples were plotted using ordination techniques. Correspondence analysis is the best method for ordination of binary data (Digby and Kempton 1987, Pielou 1984) and is used as a graphical tool for exploratory data analysis by reducing dimensionality in the data, but provides no tests of significance. The analysis is based on the singular value decomposition (SVD) of a n * p data matrix where n rows indicate individual sites and.p columns represent species counts at the sites. If most of the variation is captured in the first two dimensions, then the SVD values for the sites can be plotted and relationships examined in only two dimensions. The distances between plotted points reflect true distances of the counts for all species at one sample point compared to all the species at all other sample points. Infrequent species can skew the outcome of the analysis because the xi]. values are weighted by the column and row totals, xii/sqrt ricj. If a column sum for a species is small, the weighted value will overemphasize the importance of rare species. The original set of 17 species were reduced.to eight species after a few preliminary runs revealed that the combination of the eight remaining species explained the most variation. Correspondence analysis was run using SAS software (SAS 18 Institute Inc. 1985) and plotted with SYGRAPH software (Wilkinson 1988). Variation of Composition within a Landform Type SYGRAPH (Wilkinson 1988) software were used to generate profile plots with the citation frequency for the eight species used as the eight variables in the correspondence analysis. Based on current knowledge of species' habitats (Barnes and Wagner 1981) , species were arranged along the gradient produced from the correspondence analysis. This gradient could be interpreted to reflect shade tolerance, soil moisture, soil nutrient, or rate of disturbance and is probably a combination of all four. Profiles are simplified histograms that connect the center point of the top of each variable's bar; the purpose of using profiles instead of histograms is for easy pattern recognition. Plots for all 101 samples were placed on the site location map to examine regional patterns in forest composition. Forest Types Distinctive groups were observed after plotting the profile plots on a map. Cluster analysis was run to describe natural groupings of species and relate these groups to environmental and geographic factors. Like correspondence analysis and most other multivariate procedures, cluster analysis is for exploratory data analysis and hypothesis development. If an underlying structure to a data set exists, 19 then clustering by many different methods should consistently reveal real groups within the data. Both hierarchical and non-hierarchical methods were used to cluster citation frequency data. Hierarchical structuring imposes a nested structure on the data set. Merging is permanent and therefore early decisions affect later groupings. Average linkage and Ward's method were tried. Non-hierarchical structuring portions the data into "K" groups. Group membership may change as the grouping of data progresses. This method is more sensitive to outliers and also requires that the number of groups be provided, causing a problem if this is not known. The KMEANS method for non- hierarchical clustering was run using eight, five, and then four groups. All clustering was performed using Euclidean (root mean squared distance) as a metric for citation frequency data. Because citation frequencies were the data used, standardization was not necessary. Forest types from‘Veatch’s 1959 map where compared to the forest types suggested by the cluster analysis to assess the consistency of Veatch’s forest types. Disturbance Line descriptions and township sketch maps are commonly used to study natural disturbance in pre-European settlement forests (Whitney 1986, Loope 1991, Lorimer 1980, Canham and Loucks 1984). If past windthrow or fire were evident to the surveyors, they would mark the areas on the township maps 20 and/or record in their notes when they entered and left such disturbances. These line segments can be used to estimate the proportion of an area that was disturbed. Lorimer (1977) and Whitney (1986) used this method to determine disturbance frequencies in the hemlock-northern hardwood region from Wisconsin to Maine and for the pine communities of Crawford and Roscommon counties in northern Michigan, respectively. Evidence of past disturbance could remain visible to the surveyors for only a window of time which has been estimated _to vary from 15 to 30 years (Lorimer 1980, Canham and Loucks 1984, Whitney 1986). Therefore disturbance regimes for each landform type are represented by the possible range in return intervals. The return interval is defined as the time required for the total area of each landform type to be disturbed. RESULTS AND DISCUSSION Format of Species Counts and Citation Frequencies in Appendices There are three summary tables for each landform type; one for species counts on upland sections of survey line (Appendix C), one for citation frequency (Appendix D) and one for swamp or wetland data (Appendix E). .All tables are in the same format for comparison. The seventeen most common species appear as column headings. Species that occurred in samples but were infrequently cited appear at the bottom of the tables. Each table in Appendix C and D is in two halves. The upper half summarizes the species counts or citation frequencies for dominant species in the overstory, these were the species used in the correspondenCe analysis with the exception of black oak. The lower table represents species that were usually poorly represented in the overstory. Legal descriptions for the locations of each sample are given as well as the position of the quarter township sample within a township (see first page in Appendix C for the coding of quarter townships). The swamp data show the accumulated raw count.data for species on all wetlands within a sample and the percentage of the sample area cited as wetland. 21 22 Description of Pro-European Settlement Forests and Species Trends Although there was much variation in composition, it is clear when looking at the tables of citation frequencies in Appendix D that "typical" communities were associated with certain landforms. Table 5 summarizes the species percentages for all trees on each upland landform type from all 101 quarter townships. The "typical" community for outwash plains was a jack pine dominated forest with red pine as a principal associate. Beech and sugar maple reached peak dominance on end moraines of coarse and medium textured till. Hemlock, though not a common overstory species today, accounted for approximately 20% of the total trees on all landforms except the dry outwash and ice contact features. IHemlock and white pine reached.peak dominance on the ground. moraines of fine till and the lacustrine sands and gravels associated with glacial lake beds. White pine, the most famous and sought after species in the pre-European settlement forest, was prevalent.at.both.ends of the 'moisture spectrum and in addition to its mesic association with hemlock, it often dominated with the other pines on the dry sites. The relatively minor contribution of the oaks, aspen and birch in the pre-European settlement forests (Table 5) contrasts with present-day forests where these species now dominate forest cover over much of northern lower Michigan. 23 Table 5. Comparison of species frequencies for all samples across all upland landforms. These data represent corner and line witness trees for a total sample of 2448 linear miles. Outwash lce End Ground End End Ground Lacust- plains contact fine coarse coarse med fine rine Sugar maple 7.7 11.8 6.5 16.4 21.4 24.8 8.7 10.1 hemlock 6.9 11.7 20.8 17.9 17.1 17.7 27.1 23.3 Beech 11.0 17.8 11.7 22.6 31.6 37.1 16.7 18.8 White pine 17.1 14.0 16.7 11.9 8.9 4.9 24.3 22.0 Red maple 2.3 2.0 1.8 1.3 3.0 0.7 8.3 4.9 White oak 6.5 1.7 0.7 0.2 1.6 1.2 0.3 1.3 Black oak 1.9 2.4 2.3 1.7 1.3 0.5 0.4 0.1 Red pine 15.5 20.5 21.4 14.9 5.3 5.2 3.7 5.5 Jack pine 20.8 11.7 8.1 3.2 1.5 0.8 0.0 1.4 Fir 0.1 0.0 0.3 0.3 0.0 0.0 0.6 0.9 Ash 0.4 0.0 1.0 0.1 0.6 0.6 2.2 2.2 Cedar 0.6 0.0 1.3 1.2 0.2 0.4 1.4 3.1 Elm 0.4 0.4 0.6 1.8 3.3 1.8 1.9 1.2 Basswood 0.2 1.0 0.5 1.8 2.1 1.2 1.7 0.3 Aspen 1.4 3.0 3.6 2.3 0.8 0.7 1.3 1.8 Birch 0.8 2.0 2.1 1.7 1.0 0.5 0.9 2.5 Yellow birch 0.8 0.0 0.5 0.6 0.1 1.9 0.3 0.5 Black cherry 5.6 0.0 0.1 0.1 0.1 0.0 0.2 0.0 100 100 100 100 100 100 100 100 Aspen did not account for more than 15% of the species cited for any one site (see Appendix D) and was more common on the eastern side of the state. Apparently, birch, aspen and red maple grew on all landform types but were never dominant in pre-European settlement forests. White oak was most common on outwash plains and dominated only one sample, 1-14 in Newaygo County, with white pine. Sample 1-14 is also unique in that 21% of the lines in that quarter township occurred in a dry prairie with scattered white and black oaks among prickly pear cactus noted by the surveyors. The standardized residuals in Table 6 show the same relationships described above but lend statistical support to the relationships among species and landfonm. All species 24 were found to be significantly associated with landform at an alpha level of 0.001. Close examination of individual sites, however, reveals discrepancies in these trends (for example see sites 1-2, 1-11, and 1-15 in Table C1 in Appendix C) that required further examination. Table 6. Haberman's standardized residuals for presence data of species by landforms for all 101 quarter townships. The sign reflects a positive or negative association and the value reflects the strength of the association. Landforms Species G- Outwash Ice Fine end coarse gr med end lacust- fine gr Statistic contact moraine moraine moraine rine moraine Sugar maple 328.12** -6.9 -2.1 -7.7 4.0 10.0 -3.3 -2.1 Hemlock 292.61** -11.6 -6.4 3.8 1.1 0.7 5.9 9.6 Beech 132.62’* -8.3 -1.2 -6.9 4.4 14.1 0.0 -1.9 Red maple 62.84** -0.1 -1.3 -1.6 -3.5 -3.4 5.5 3.1 Uhite pine 283.07** 4.1 -1.1 1.8 -3.7 -8.5 6.9 9.4 White oak 224.34** 16.4 -1.0 -3.3 -S.S -1.6 -1.5 -4.2 Red pine 518.94** 5.4 11.1 10.1 3.3 -6.6 -7.2 -9.3 Jack pine 935.44** 26.4 8.3 2.3 -6.8 -7.3 -7.6 -9.7 for alpha 0.01, critical value > 18.48 * for alpha 0.001, critical value > 24.32 ** Community Variation Among Landforms Correspondence analysis was used to ordinate samples along an indirect environmental gradient. The first two dimensions accounted for 67.8 % of the total variance in the spectral decompositione Figures.3 and.4 show the plots of all samples and all species, respectively. The data resulted in 25 an arch pattern. The arch results from the gradation of one Gaussian distribution of a species into another along the ordination, and thus truly represents the nonlinear relationships of species distributed along environmental gradients. The arch makes any interpretation of the second axis or any axis other than the first nonsensical (Digby and Kempton 1987, Noy-Mier and Austin 1969, Wartenberg et al. 1987). In correspondence analysis, the proximity of variables (species) to observations (samples) represents an indirect measure of their association such that the samples to the extreme left of the ordination.had the highest counts of sugar maple, hemlock and beech (Figure 5). The right end of the ordination represents dry samples with high species counts of jack and red pine. The ordination of the sites with more mesic communities in Figure Siwere less variable than sites on the landforms shown in Figure 6, which spanned the gradient in composition. Lacustrine sands and gravels, fine textured ground moraines, and end moraines of medium textured till had the least variation. The sample to the far right for end moraines of coarse textured till had cited evidence of fire, perhaps explaining its placement (Figure 5) . Although samples from the dry end of the spectrum were predominantly associated with outwash plains or ice contact features, not all of the samples from these landforms were characterized by "dry" communities: rather these two particular landforms had samples across the gradient (Figure 6). 26 The correspondence analysis demonstrates the need for explanation of the variability in composition within many of the landform types such as outwash plains and ground moraines of coarse textured till. Correspondence analysis coordinates for all samples (rows) and species (columns) produced by SYSTAT (Wilkinson 1989) are found in Appendix B. Profile Plots In landscape ecology, many facets must be considered when accounting for ‘the regional distribution. of communities. Albert et. al (1986) and Veatch (1932) list climate, geomorphology and soil as the three main factors controlling the broad distribution of current and pre-European settlement vegetation in Michigan. Although Veatch provided the list of factors used to determine his forest types on his 1959 pre- European settlement forest.map, he synthesized all factors and depicted forest composition on his map without providing information on the role each factor played in regulating forest composition across the state. As Dodge (1987) mentioned, Veatch provided no quantitative information to describe the communities appearing on his map. Albert et al. (1986) discussed the influence of different factors for their climatic ecosystem regions. My results agree to some extent with both those of Veatch and Albert et al. and this correspondence is discussed below. Based on my results, I conclude that in northern lower Michigan pre-European 27 Legend Figure 3: Codes Given to Farrand’s Landforms in Quaternary Geology of Michigan Map (Department of Geological Sciences, The University of Michigan, Ann Arbor. 1982) Outwash plains Ground Moraines End Moraines of Ground Moraines End Moraines of Ground Moraines Lacustrine Silt End Moraines of HmmflmUl-hUNI-J O of Fine Textured Till Lacustrine Sands and Gravels Ice Contact Features Coarse Textured Till of Coarse Textured Till Medium Textured Till of Medium Textured Till (not used) and Clay (not used) Fine Textured Till Note: Landforms that covered relatively little area were not used in this study. The numbering system was developed for a preliminary study and kept for continuity. 222 1 r i 1.1 - ‘ 9:] 0.0 - 46 ‘ 1 - C) ‘ 3 1 1 4 T 5 £5 14 4 14 -1.1 - ‘ - 1 1 _22 L 1 L -2.2 -1.1 0.0 1.1 22 CA1 Figure 3. Correspondence analysis for all the upland samples on all landforms. The first two dimensions, axes 1 and 2, account for 67.8 % of the total variance for eight species: sugar'maple, hemlock, beech, red maple, white pine, white oak, red pine and jack pine. The numbers refer to landform types in the legend. Refer to Figures 5 and 6 for clarity of numbers. 29 22 l l l .va 1.1 t- 7 RM. .Wp H .RP (\1 ES 00 ” B. S. ‘H ‘ .JP ~ -2.2 l l 1 ‘22 ‘11 0.0 1.1 2.2 CA1 Figure 4. Correspondence analysis for the upland species on all eight landforms. ‘The first twoldimensions, represented by axes 1 and 2, account for 67.8 % of the total variance. Species codes refer to: S=sugar maple, H-hemlock, B=beech, RM=red maple, WP =white pine, W0- white oak, RP=red pine and JP-jack pine. The species appear to be arranged along a moisture, nutrient, shade intolerance gradient or a combined gradient based on knowledge of their present-day habitats. 30 Legend Figure 5: Codes Given to Farrand’s Landforms in Quaternary Geology of Michigan Map (Department of Geological Sciences, The University of Michigan, Ann Arbor. 1982) Outwash plains Ground Moraines of Fine Textured Till Lacustrine Sands and Gravels Ice Contact Features End Moraines of Coarse Textured Till Ground Moraines of Coarse Textured Till End Moraines of Medium Textured Till Ground Moraines of Medium Textured Till (not used) Lacustrine Silt and Clay (not used) End Moraines of Fine Textured Till HmmflGmhuNl-J Note: Landforms that covered relatively little area were not used in this study. The numbering system was developed for a preliminary study and kept for continuity. CA2 CA2 31 22 r r f 22 1 1 r 11» ~ 11L 5 5 58 oo~ a « 3 oo- 7 7' , o i a 7 7 ~11» « -11— '22 ‘ ‘ 1 -2.2 . . . -22 -1.1 0.0 1.1 2.2 -2.2 -1.1 0.0 1.1 2.2 CA1 CA1 End Moraines of End Moraines of Coarse Till Medium Till Ground Moraines of Lacustrine Sand and Fine Till Gravel 22 f T I 22 T T r 1.1 - ~ 11 - as . 3 2? 33 3 3 oo» 1 «5? oo— 0 s 3 -11~ - -11- ~22 1 1 l -22 1 1 1 -22 ~11 _ 0.0 1.1 2.2 -2.2 -1.1 0.0 1.1 2.2 CA1 CA1 Figure 5. Correspondence analysis for quarter townships on individual landforms. plotted in Figure 3. The data used are the same as those The sites on these landforms appear to have been dominated by more mesic species than sites on the landforms in Figure 6. 32 Legend Figure 6: Codes Given to Farrand’s Landforms in Quaternary Geology of Michigan Map (Department of Geological Sciences, The University of Michigan, Ann Arbor. 1982) Outwash plains Ground Moraines of Fine Textured Till Lacustrine Sands and Gravels Ice Contact Features End Moraines of Coarse Textured Till Ground Moraines of Coarse Textured Till End Moraines of Medium Textured Till Ground Moraines of Medium Textured Till (not used) Lacustrine Silt and Clay (not used) End Moraines of Fine Textured Till HmmxlmU'l-wal-J Note: Landforms that covered relatively little area were not used in this study. The numbering system was developed for a preliminary study and kept for continuity. CA2 CA2 33 22 T Y 1 22 I I T 1.1 - < 11 _ 4 ‘4 4 o ‘ ‘ e e 66 oo- ‘4 ‘ « 3 OOL- ae « 4 t 0 O 6 f ‘ 0 a 4 4 -11 - 1 -11 - « -22 l 1 1 _22 1 4 l -242 -11 0.0 1.1 2.2 -2,2 -1.1 0.0 1.1 2.2 CA1 CA1 Ice Contact Features Ground Moraines of Coarse Till Outwash Plains End Moraines of Fine Textured Till 22 l I f 2-2 T I I 1 11 r- 4 11 L— -1 1 1 1 1 , 160 10 10 I N 19‘ 10 10 0.0 "' 1 . < 0.0 ~ 10 10 -« 11 O 10 1 1O 1 ‘11 '- ' 4 -11 '- -1 ‘ . 1 -22 1 1 1 -22 g 1 1 ~22 -1.1 0.0 - 1.1 2.2 -2.2 -1.1 0.0 1,1 22 CA1 CA1 Figure 6. Correspondence analysis for quarter townships on individual landforms. The data used are the same as those plotted in Figure 3. The sites on these landforms appear to have been more variable in composition and dominated by more xeric species than sites on the landforms in Figure 5. 34 settlement forest composition was controlled by a combination of factors and that the relative importance of each factor changed with geographic location. The profile plots in Figure 7 show pockets of similar forest composition. The study area was divided into four regions for examination. Similar profiles in the first region where found on similar landform types. The communities in the other three regions seemed to be responding more strongly to factors other than landform since profiles were similar across landforms within regions (Figure 8). Figure 9 distinctly shows the regionalization for only the outwash plain samples. Region 1 The profile plots in Region 1 and 3 were not all similar and appeared to be regulated by more "local" factors than in Regions 2 and 4. The major differences between Region 1 and 3 were the increase of oaks on dry landforms and the decrease of red pine on all landforms except ice contact features in Region 1. The importance of position in the landscape has been discussed by Kline and Cottam (1979) , Leitner et. a1 (1991), Palik and Pregitzer (1992) and Grimm (1984), and may be the most important factor in distinguishing between these two regions. For example, ground moraines of coarse till in Region 1 were adjacent and mostly surrounded by landforms with mesic forests. These samples contained very little red pine, whereas in Region 3 samples from this landform were adjacent to outwash.plains and ice contact features and.had.high counts 35 Legend Figure 7: Codes Given to Farrand’s Landforms in Quaternary Geology of Michigan Map (Department of Geological Sciences, The University of Michigan, Ann Arbor. 1982) Outwash plains Ground Moraines End Moraines of Ground Moraines End Moraines of Ground Moraines Lacustrine Silt End Moraines of Hmmqmmhwmw of Fine Textured Till Lacustrine Sands and Gravels Ice Contact Features Coarse Textured Till of Coarse Textured Till Medium Textured Till of Medium Textured Till (not used) and Clay (not used) Fine Textured Till Note: Landforms that covered relatively little area were not used in this study. The numbering system was developed for a preliminary study and kept for continuity. .f'» Fig} 96m Wit] mm ... Figure 9. Profile plots for outwash plains only showing geographic variation in pre-European settlement composition within one landform type. 39 for red pine. Fires starting on the outwash plains could have easily swept up onto the moraines in Region 3, thus explaining the higher counts of red pine due to increased fire disturbance. The pattern of fire disturbance provides support for this theory. Region 2 Samples from the northwestern third of the study area, (Region 2 in Figure 8) were consistently dominated by sugar maple and beech, often with a minor component of hemlock. Although.Veatch.did not include beech.as.a dominant species in his 1959 map, he listed it as a major species of the area in his book (Veatch, 1953). In this region, beech-maple forests dominated six of the eight landform types delineated by Farrand’s map, including landforms (e.g. outwash) supporting drier communities in other geographic regions. A peculiarity of this region is that it is composed almost exclusively of sugar maple, beech and hemlock with few other species. Weather would seem to have a great influence in this area since the predominant winds come off of Lake Michigan moisture-laden. and sweep eastward across the land. .An examination of weather conditions (Benton and Barnes 1988 and Eichenlaub 1979) shows that such a "lake-effect" has been well documented. However, most lake-effect designations are drawn closer to shore than the profiles in Figure 8 suggest. My results agree *with. the extent. of ‘Veatch's mesic forest community # 7 that extended further inland. 40 The area designated as Region 2 in Figure 8 does not contradict what Albert et al. (1986) described for moraines in each of the subdistricts within this area, but it does not support a need for divisions (districts and subdistricts) based on overstory composition alone. Region 3 The northeastern area, Region 3, was characterized by "drier" communities. Here, some moraines were dominated by mesic hardwoods with differing proportions of hemlock, white pine, white oak and red maple, while other moraines are dominated by the pines. Many fires were recorded by the surveyors in this region, likely contributing to pine dominance and suggesting that the soils on the moraines here may not be as mesic as they are in other regions. Samples from all moraines in this region had significantly more red pine than they did in Region 1. A general difference in soils and therefore communities across the state may be attributable to composition of till. The Michigan lobe of the Laurentide Ice Sheet scraped over different bedrock than the Huron lobe did (see bedrock.map in Hough 1958). Region 4 The area in the southeastern corner of the study area, Region 4, also exhibited a similar forest composition across landform types (Figure 8). The pre-European settlement 41 forests in this region were dominated primarily by white pine and hemlock, with a mixture of other mesic species, mostly red maple. The soils are dominantly clays and silts with overlain sand (see map of Whiteside et a1. 1968). These soils are the result of former glacial lake plains (Dorr and Eschman 1984). Topographical differences are not great until the high plains region which marks the boundary of the lake plain, this is shown as the western boundary for Region 4. Albert et al. (1986) stated that the climate changes slowly from the lake inland, and from south to north. The heavy clay soils deposited over the landforms and the equitable climate were apparently the dominant factors in determining forest composition. End and ground moraines of fine textured till and lacustrine sands and gravels showed little distinction in composition and are the only landforms in this region. Generalized maps of the pre-European settlement forests of Michigan classify Region 2, and the lake plain area in Region 4 as northern hardwoods (Marschner 1946, Kuchler 1964). Beech-maple forests occurred on only 2 of the 23 samples in Region 4. My results distinguish Regions 2 and 4: northern hardwoods with very little white pine to the northwest (Region 2) and white pine-hemlock to the southeast (Region 4). Tables similar to Table 6 but for each region are found in Tables 7 through 10. These tables support the previous descriptions.of thelcommunity'trends for'the regions, based on correspondence analysis and the profile plots. 42 Table 7. Comparison of species percentages by landform for Region 1. Numbers in parentheses indicate the timber of quarter township sauples summarized for each landform in Region 1. Each quarter township represents 24 linear miles of corner and line witness tree data. Outwash Ice Grd.mor. End mor. End mor. Lacust- plains contact coarse coarse medium rine (2) (3) (3) (3) (5) (4) Sugar maple 39.4 30.3 28.9 39.7 30.6 25.0 Hemlock 16.4 22.6 21.8 18.3 13.2 25.8 Beech 29.1 37.1 26.2 28.1 42.9 35.1 white pine 2.1 3.9 3.3 1.8 2.3 1.4 Red maple 4.5 0.7 0.6 0.4 1.0 0.3 white oak 0.0 0.0 0.0 0.0 0.3 0.0 Black oak 0.0 0.0 0.0 0.0 0.3 0.3 Red pine 0.0 0.2 0.8 0.2 1.9 0.3 Jack pine 0.0 0.0 0.0 0.0 0.2 0.0 Fir 0.0 0.0 0.8 0.0 0.0 0.6 Ash 0.0 0.0 0.0 0.2 0.5 0.3 Cedar 0.0 0.0 4.1 0.0 0.2 3.9 Elm 1.0 0.9 1.4 6.3 2.1 0.6 Basswood 0.0 0.9 2.2 3.4 1.6 0.8 Aspen 0.0 0.0 5.0 0.4 0.6 1.4 Birch 2.1 3.4 3.3 0.7 0.0 3.1 Yellow birch 5.1 0.0 1.7 0.4 2.3 1.1 Black cherry 0.3 0.0 0.0 0.2 0.0 0.0 100 100 100 100 100 100 Table 8. Comparison of species percentages by landform for Region 2. Numbers in parentheses indicate how many (porter township samples were sulmari zed for each landform in Region 2. Each quarter township represents a sample of 24 linear miles of corner and line witness tree data. lce Fine Fine Lacust- Contact End mor. Gr.mor. rine (2) (5) (7) (7) Sugar maple 24.0 8.2 6.3 2.4 Hemlock 19.0 34.1 22.1 22.8 Beech 26.9 17.2 12.9 6.1 white pine 9.5 16.9 32.1 34.7 Red maple 0.8 2.3 11.2 7.8 white oak 0.0 0.6 0.4 0.6 Black oak 5.0 0.6 0.7 0.0 Red pine 8.3 8.2 4.9 8.2 Jack pine 0.0 0.0 0.0 2.4 Fir 0.0 0.8 0.6 1.3 Ash 0.0 1.5 2.4 3.7 Cedar 0.0 2.7 1.5 3.1 Elm 0.8 1.3 2.2 1.8 Basswood 4.5 0.8 0.8 0.0 Aspen 0.8 1.5 0.7 2.3 Birch 0.4 2.9 1.0 2.6 Yellow birch 0.0 0.4 0.1 0.2 Black Cherry 0.0 0.0 0.0 0.0 100 100 100 100 43 Table 9. Comparison of species percentages by landform for Region 3. Numbers in parentheses indicate how many quarter township samples were summarized for each landform in Region 3. Each quarter township represents 24 linear miles of corner and line witness tree data. Outwash lce End mor. Crd.mor. End mor. End mor. Contact Fine Coarse Coarse Medium (1) (5) (7) (7) (1) (2) Sugar maple 0.0 2.3 5.5 10.7 6.0 8.2 Hemlock 0.0 6.7 12.7 11.8 6.0 30.5 Beech 0.0 8.5 8.4 11.4 10.0 20.9 white pine 15.2 15.6 16.6 12.8 5.0 12.3 Red maple 4.0 3.7 1.5 0.7 2.0 0.0 white oak 2.0 0.2 0.8 0.3 1.0 3.6 Black oak 1.0 0.4 3.3 3.6 2.0 0.9 Red pine 52.5 27.4 29.4 34.5 38.0 14.5 Jack pine 20.2 24.9 13.0 7.7 28.0 2.7 Fir 2.0 0.0 0.0 0.4 0.0 0.0 Ash 0.0 0.0 0.6 0.0 0.0 0.9 Cedar 0.0 0.0 0.4 0.7 0.0 0.9 Elm 0.0 0.2 0.3 0.6 0.0 0.9 Basswood 0.0 0.6 0.3 1.1 2.0 0.0 Aspen 1.0 6.7 4.3 2.4 0.0 0.9 Birch 2.0 2.9 2.3 1.0 0.0 1.8 Yellow birch 0.0 0.0 0.6 0.4 0.0 0.9 Black cherry 0.0 0.0 0.0 0.0 0.0 0.0 100 100 100 100 _a O O 100 Table 10. Comparison of species percentages by landform for Region 4. Numbers in parentheses indicate the nurber of quarter township sauples sunnarized for each landform in Region 4. Quarter townships represent a sample of 24 linear miles of corner and line witness tree data. Outwash Ice Grd.mor. End mor. Grd.mor. Lacust- plains contact coarse coarse fine rine (12) (4) (S) (9) (4) (1) Sugar maple 1.9 0.5 15.7 14.2 12.9 4.1 Hemlock 5.8 1.7 22 1 17.6 35.9 17.5 Beech 8.7 3.9 32.6 35.1 23.5 40.2 white pine 21.7 22.1 15.7 12.5 10.6 16.5 Red maple 1.9 1.0 2.2 4.4 3.2 4.1 white oak 8.7 6.3 0.3 2.4 0.0 10.3 Black oak 2.5 5.1 0.6 1.9 0.0 0.0 Red pine 17.3 39.1 1.8 4.9 1.5 7.2 Jack pine 26.9 18.0 0.3 0.0 0.0 0.0 Fir 0.0 0.0 0.0 0.0 0.7 0.0 Ash 0.6 0.0 0.1 0.8 2.0 0.0 Cedar 0.8 0.0 0.1 0.3 1.2 0.0 Elm 0.3 0.0 3.4 2.2 1.2 0.0 Basswood 0.2 0.0 2.2 1.5 3.2 0.0 Aspen 1.9 2.4 0.7 1.1 2.2 0.0 Birch 0.5 0.0 1.5 1.2 0.7 0.0 Yellow birch 0.0 0.0 0.1 0.0 0.5 0.0 Black cherry 0.2 0.0 0.3 0.0 0.5 0.0 100 100 100 _a O O 100 .a O O 44 Major Forest Types Some samples changed groups during the different cluster analyses, however, many members grouped together regardless of clustering method. The non-hierarchical method compared exactly to the groups of the Ward's method with the exception that a fifth group was made up of only sample 1-14, the only sample dominated by' white pine and. white oak. Slight alterations of Ward's method provided the most consistent groups. The first division using Ward's method split the sample locations into forests dominated by mesic species verses forests dominated by the conifers. These two groups were then further divided into two main groups each. The first group was composed primarily of sugar maple, hemlock and beech. The second group also was dominated by sugar maple, beech and/or hemlock but almost always included other species such.as white and red pine, white oak or red maple. The third group was predominately dominated by white pine and hemlock with much red pine, red maple and white oak. The fourth group was dominated by one or a combination of the pines. Figure 10 compares the results from the cluster analysis to Veatch’s forest communities and shows the consistency of forest types in Regions 2, 3 and 4. Table 11 compares the total citation frequency of each species for each cluster. Table 12 compares the total relative dominance of each species for each cluster. Although sugar maple and beech dominated the forest type in cluster 1 45 numerically, sugar maple and hemlock accounted for larger basal areas than beech. The hemlock in this region were larger on average than the beech, which although numerous, had small diameters on average. More and larger white pine occurred in the second forest type (cluster 2). White pine and hemlock dominated the basal area in this region with larger trees compared to the more frequent but smaller sugar maple and beech. White pine accounted for almost 50 percent of the basal area in the third forest type (cluster 3), even though it accounted for only a third of the citation frequency. This forest type, that occurred almost exclusively in the Saginaw Bay area in Region 3, contained the most and the largest white pine in the study area along with a fair amount of hemlock and red pine. The three pine species dominated the last forest type numerically (cluster'4), with.the highest.numbers for red pine and then jack pine and white pine. Red pine accounted for almost 50 percent of the basal area for this forest type, and white pine had the second largest basal area. Although many jack pine occurred in this forest type, they were usually small. Bourdo (1956) mentioned that the surveyors often selected large red pines over jack pine in areas of barrens due to their fire resistance. 46 Table 11 . Comparison of species citation frequency among clusters. Cluster Cluster Cluster Cluster #1 #2 #3 #4 Sugar maple 36.0 14.6 4.1 2.2 Hemlock 16.6 24.4 18.6 4.8 Beech 33.8 35.5 11.7 2.5 White pine 1.7 8.9 29.8 16.1 Red maple 0.6 2.7 6.1 1.2 White oak 0.0 1.0 4.4 1.7 Black oak 0.3 0.8 2.1 2.7 Red pine 0.5 3.3 13.0 34.2 Jack pine 0.0 0.1 2.8 27.7 Fir 0.0 0.3 0.4 0.2 Ash 0.1 1.1 1.5 0.0 Cedar 0.9 1.1 1.3 0.3 Elm 3.7 1.5 0.7 0.2 Basswood 2.3 1.7 0.5 0.3 Aspen 0.6 1.2 1.5 4.3 White birch 1.3 1.4 1.4 1.5 Yellow birch 1.4 0.3 0.2 9,2 100 100 100 100 Table 12. Comparison of species relative dominance among clusters. Cluster Cluster Cluster Cluster #1 #2 #3 #4 Sugar maple 39.0 13.1 3.3 2.0 Hemlock 23.3 26.3 15.2 6.3 Beech 20.7 18.8 5.4 1.4 White pine 3.5 23.2 49.5 27.4 Red maple 0.4 2.2 3.0 0.5 White oak 0.2 0.8 2.0 0.6 Black oak 0.0 1.8 2.1 2.6 Red pine 0.6 4.6 13.7 47.3 Jack pine 0.0 0.6 0.8 8.0 Fir 0.0 0.2 0.0 0.1 Ash 0.3 1.9 1.0 0.2 Cedar 0.6 1.1 0.6 0.6 Elm 5.7 2.1 1.2 0.5 Basswood 3.3 1.7 0.6 0.2 Aspen 0.0 0.0 0.6 1.4 White birch 1.3 1.4 0.6 0.6 Yellow birch 0.0 0.0 0.3 0,2 100 100 100 100 47 Region Veatch Semis 2 7 $1 2 7 14s 2 7 31 2 7 #1 g 7 s2 7 $3 3 353/73 3-3 c‘”“" “ 2 7 93 i 2 7 b3 2 1auu 33 2 7 r1 ' 2 7 31 i 2 7 v2 2 7 32 3 13 442 2 19- :33 P3 2 7 r3 2 7 r3 1 7 $3 2 7 r2 1 7 343 2 7 r7 1 7 54s 1 7 342 1 7 543 1 7 pm 1 3 541 2 7 b2 3 6MB 33 —‘ 3 13 1-3 3 m #7 1 mu7 340 : ‘g ? €10 Cluster '2 2 55 32 }' 1 11 * s-13 1 11 - 3-12 1 11 - 343 1' 1 23 240 1 “no. 243 1 7 so h 3 13 Thu 1 3 243 H 1 2311/3 1' 2-9 1‘ 2 7 b3 3 3 1&9 l 3 u 23 . Figure 10. Comparison of cluster analysis with regions from this study and with forest community designations from Veatch’s 1959 map. The cluster analysis was done using Euclidean distance for the Ward minimum variance method. Figure 10. (cont'd) Region Veatch wauwwuuuuuuuuwHuuwwuuu~wwwwwwwwwwwwwnwuuuweuHHeAHHHHH 2 3 17 3 16 24 H 0 (uuuuu \ (a p .Q aouceauueettetcacoucael \\ u an 1-14 1-9 1-4 1-8 1-13 10-12 3-7 5-5 5-10 4-6 4-15 1-12 10-7 6-7 7-6 10-13 6-11 3-11 3-13 2-6 3-12 2-5 3-14 2-7 3-8 3-15 10-11 2-4 2-8 2-12 6'15 6'. 6-10 6-9 4-13 4-4 4-4 1-1 4-14 1-7 10-1 10-3 10-4 10-5 4-5 5-. 10-4 4-4 4-9 1-5 4-10 1-10 1’6 1'3 “FEW “:1: I glgpter #3 fHI‘TE—‘Uté‘t‘ Cluster O4 49 Comparison with Veatch and Albert at al. Veatch’s map compared favorably with the results from the cluster analysis. The numbers for Veatch’s forest communities match those in the legend to his 1959 map. Veatch's map in many cases successfully provides a more detailed description of the pre-European settlement forests than Marschner's map (1946). Note that many of‘Veatch’s communities are consistent with the finer subdivisions in the cluster analysis and are more defined than the forest types of this study; The samples where Veatch's designation does not correspond with the cluster analysis are marked with an asterisk (Figure 10). Albert et al. (1986) note that boundaries on their map reflect hypotheses that need testing. This study agrees with most of the ecosystem districts delineated in Albert et al.’s Region II. Figure 11 compares the regions from this study based on pre-European settlement forest composition with the districts of Albert et al. The many subdivisions reflecting minor climatic and physiographic differences of Albert et. al's map are not distinguishable in Region 2 based on overstory composition of the pre-European settlement forest. Region 4 closely reflects the Albert et al. District 7 but appears to extend inland and cover more of Ogemaw and Clare Counties. The area covering Subdistrict 12.3 was avoided for this study because of the abundance of lowland wet areas, which supports this subdistrict designation. The evidence of much drier communities on moraines in Region 3 than on the same landform .Aoomnv cadence: no maoumamoom Hmcofioom sown mquuumdn m..~s we uman~<.ou >psum mflsu Home aconou mo somwmnmaoo human mass lamb a:o«oo¢ ~41 .4. .na ouswwm Z a. Nd n6 .fi. nN. N;— N;- ~fl. manganese u..~o as saunas 51 types in Region 2, leads to a disagreement with District 12.1 as one regional ecosystem. Finally, Districts 10, 9, and 8 seem to be an accurate gradient.of diminishing lake affect whichuwas reflected.in the data used for this study. One very important conclusion is that the hierarchical regionalization used by Albert.et.al, is supported by the composition of the pre-European settlement forest. Their method is ecologically relevant. Climate is the most important, overriding factor affecting forest composition. Second, I believe major soil groups can have more of an affect on composition than landform and finally, all of the factors mentioned affect disturbance regimes. Disturbance Padley (1989) found that although two moraines from subsequent stages of the Huron ice lobe in Michigan differed in only a few soil properties, the present-day forests on the two moraines were very different. This was attributed to differences in disturbance histories, affecting the successional patterns of vegetation. The importance of the role of disturbance in controlling pre-European settlement forests has been documented for many areas: Roscommon and Crawford counties (Whitney 1986), a segment of shoreline on Lake Superior (Loope 1991), upper Wisconsin (Canham and Loucks 1984), and the "Big Woods" of Minnesota (Grimm 1984). The disturbance regime in a given area is strongly related to soil, vegetation and position in 52 the landscape. Grimm (1984) found that the pre-European settlement vegetation communities on the windward side of the Cannon River were subjected to fires more frequently than the communities on the east side. Grimm also stated that the mesic communities surrounding the dry communities acted as fire breaks because of the nonflammability of the vegetation. In this study, 22% of the total samples covering 17.9% of the total sample mileage had some kind of disturbance recorded by the surveyors. Table 13 summarizes these disturbances by type (windfall or fire) for each landform. More area was affected by fire than by windfall (14.9% and 3.1%, respectively). Fire was most frequent on outwash plains and ice contact features. The greatest disturbance by windfall occurred on ground moraines of fine-textured till. End moraines of medium and fine-textured till had the least disturbance. The return intervals in Table 13 can be compared to Whitney's disturbance regime table (1986). Whitney’s jack pine community showed a shorter return time (83-167 yr) than that determined for outwash samples (163-326) in this study. All of Whitney's return times for his forest types were shorter than my calculations. Although the total number of lines examined for each study are comparable (2448 for this study and 2270 for Whitney's) the area covered by Whitney's study was continuous and in only one region. Secondly, Whitney’s study area occurred in the middle of a band of disturbances that.covers the center of the study area (Figure 53 Table 13. Disturbance regime sumnarized by landform type. outwash ice fine end course gr course end med end lacust- fine gr total plains contact moraine moraine moraine moraine rine moraine Mileage sampled by landform type 317.2 329.7 250.0 216.6 316.1 156.6 221.1 208.8 2016.1 X of total (15.0) (16.3) (12.4) (10.7) (15.7) (7.8) (11.0) (10.3) (100) mileage Number of disturbances for each landform type fire 5 2 1 1 1 1 1 0 12 windfall 1 1 1 1 2 0 2 3 11 x tot. distance 9.0 2.4 0.8 1 0.3 1.3 0.9 0 14.89 affected by fire Est. return time (in years) based on interval surveyor could record * 15 yr 167 625 172 1428 5000 1149 16670 - 30 yr 333 1250 345 3030 10000 2325 37037 - 1 tot. distance 0.2 0.1 0.1 0.1 1.1 0 0 1.45 3.05 affected by windfall Est. return time (in years) based on interval surveyor could record 15 yr 7692 16667 16667 16667 1370 - - 1000 30 yr 14286 37037 37037 37037 2727 - - 2000 X tot. distance 9.2 2.5 0.9 1.1 1.4 1.3 0.9 1.45 17.94 affected by fire and/or windfall Est. return time (in years) based on interval surveyor could record 15 yr 163 600 1666 1370 1071 1154 1666 1034 326 1200 3333 2727 2143 2308 3333 2069 30 yr * Estimated return time is roughly the time required for disturbances to occur to cover an area equal total area sampled for each landform type. Note that the windfalls recorded by the surveyors would probably not include individual treefall gaps, but were larger wind disturbances such as those described by Lorimer (1977). 54 12). Roscommon and Crawford Counties are very dry (dominated by sandy soils) and not only have the greatest proportion of outwash.and ice contact features (Farrand’s map), but are also situated in the middle of dry areas with few'mesic communities for fire breaks. IKalkaska and.Crawford Counties contained the largest disturbances (Table 14) . The percentage of area effected by windfall was comparable in Whitney’s (0.1 to 1.2% for different forest types) and in this study (0.1 to 1.45% for different landforms). Table 14. Size of individual disturbances. Values indicate the total number of linear’ miles a disturbance covered according to the surveyors’ notes. FIRE WINDFALL Sample Size Sample Size 1-10 12.0 5-6 2.5 (burnt) 1-5 7.75, 0.5 5-1 1.25 3-3 7.3 2-9 0.8 4-9 6.25 1-9 0.8 1-3 5.9 10-3 0.6 1-12 2.8 3-12 0.55, 0.35 (burnt) 10-3 2.75 2-10 O.55,0.15,0.05,0.05 7-5 2.0 4-13 0.3 6-4 1.5 2-6 0.2 5-8 1.0 3-10 0.2 1-7 0.75 55 Canham and Loucks (1984) calculated an estimated return interval for the pre-European settlement hemlock-white-pine- northern hardwood forests of northern Wisconsin to be 1210 years. The average return time according to my calculations for the 15 year estimate was 1259 years for all ground and end moraines. Apparently windthrow in this area was a more consistent mode of disturbance, whereas fire was highly variable in extent. Figure 12 shows regionalization of disturbance across the study area. Percentages of fire and wind disturbances by region is listed in Table 15. Region 1 contained 73% of recorded occurrences of fire and 51% of recorded windfall. The northern hardwood forests in Region 1 had more moderately shade tolerant species such as white pine, red maple and oaks than the northern hardwoods in Region 2. The northern hardwood forests in Region 2 were distinctive in that they were dominated almost exclusively by sugar maple, beech and hemlock, usually in that order frequency. larger treefall gaps in Region 1 may have allowed for more shade intolerant species to exist compared to Region 2 (S.T.A. Pickett pers. com. 1993). Fire disturbances dominated in Region 3, accounting for 12% of all fire occurrences in the study area. The surveyors recorded only blowdowns for Region 4. The heavy clay soils may have promoted shallow root systems making trees more susceptible to blowdown. As Whitney (1986) stated, climate, disturbance, soils, Figure 12. Regional distribution of disturbances; ‘3 - fire, c>-windthrow. Cited disturbances covered an estimated 17.9 % of the total area sampled. Region 1 contained the highest percentage of both types of disturbance; 73% of all cited fires and 51% of cited windthrow occurred in Region 1. 57 Table 15. Percentage of each type of disturbance by region. Distances are in total linear miles of survey line affected. Entries in parentheses are sites of windfall that were burnt. FIRE Landform Region Region Region Region 1 2 3 4 outwash 29.28 ice contact 8.25 fine end moraine coarse grd moraine (2.15) 1.5 coarse end moraine 1.0 medium end moraine 2.0 fine grd moraine 2.1 lacustrine 4.5 (0.9) total miles 37.53 6.65 6.6 0.9 (grand total = 51.68 miles) % of fire ' disturbance 73% 13% 12% 2% WINDFALL Landform Region Region Region Region 1 2 3 4 outwash 0.75 ice contact 0.3 fine end moraine 1.48 0.2 coarse grd moraine 0.2 2.15 coarse end moraine 1.25 medium end moraine fine grd moraine 0.63 lacustrine 0.13 0.9 total miles 4.11 2.15 0.0 1.73 (grand total = 7.99 miles) % of windfall disturbance 51% 27% 0% 22% 58 and topography all influence one another and these factors cannot be addressed independently. This study clearly shows that the influence of disturbance also changes from region to region. CONCLUBIONB Whereas in some regions of northern lower Michigan local differences in pre-European settlement composition can be predicted from landform, landform alone cannot be used to predict the pre-European settlement composition anywhere in the state. Some generalizations can be made, such as mesic forests tended to occur on the moraines of all textural classes and did not usually occur on ice contact features or on outwash plains; however, regional differences in composition within a community-landform association must also be considered. Once a region is delineated, then predictions of community composition based on landform may be useful. This strong regional pattern has not been evident in previous pre-European settlement studies. This may be attributed to the small size or the mid-continental climate of the areas previously examined. Composition in the northwest corner of the study area appeared to be controlled primarily by climate. Composition in the southeast corner appeared to be controlled by a lacustrine soils laid down over landforms. Landform appeared most influential in distinguishing local ecosystems in the interior of the state where the climate is not lake-modified and the soils are distinguished based on the mode in which the 59 60 glacier deposited them. :Regional trends may not.be as evident in the southern half of Michigan’s Lower Peninsula since the lake effect is not as predominant (Albert et al. 1986). Patterns and rates of disturbance were different for the four regions. Slight differences in dominant species for a community type within regions may be due to the successional stage achieved since the last disturbance at the time that area was surveyed, assuming the areas covered by the regions were large enough to produce a quasi-equilibrium landscape where the proportion of the area in each successional stage remains constant over time (Frelich and Lorimer 1991). Of course other factors such as early competition, seed predation, differences in seed sources and attacks by pathogens must also be considered in influencing the successional pathways of the pre—European settlement forests (Abrams et al. 1985). .Although these factors certainly’played a part in succession of pre-European settlement forests, their role may be emphasized in present-day forests due to human influences such as exotic pest introductions (i.e. the gypsy moth), forest fragmentation and the resulting decrease in seed sources. The properties of landforms as well as their relative positions to each other did seem to influence disturbance regimes. Region 2, for example, had the least amount of disturbance and the most shade tolerant species while the mesic forests in Region 1 may have been disturbed on a more regular basis so that more shade intolerant species occurred 61 and a different steady-state with a shorter return time was attained. This study provided a fair testing of Veatch's 1959 map and found it to be mostly accurate though not fool proof. This study also provides quantitative data on the pre-European settlement communities in northern lower Michigan, information that was missing from Veatch's map. The results of this study compared well to the districts in Albert et al. 's map. Overstory composition may be affected by factors on a different scale than those for the understory and.ground flora. .Areas onAlbert et al.'s regional ecosystem districtsiand subdistricts that conflict with the results from this study may reflect differences in factors important to vegetation in structural layers other than the overstory. The composition and extent of Michigan’s present-day forest types is vastly different than that of the pre-European settlement forests This study has provided information on the minor role species such as aspen, red maple and the oaks played in the pre-European settlement forests of northern lower Michigan. The explosion of these species since the turn of the century serves as a measure of how much humans have disturbed the landscape of the state. USE OF THE PRE-EUROPEAN SETTLEMENT RECORDS FOR CURRENT LAND MANAGEMENT Previously, knowledge of pre-European settlement forests was sought mostly by historians and scientists, but now natural resource managers are seeking this information. Organizations such as the Michigan Chapter of the Nature Conservancy' are using pre-European settlement information to provide target communities for management goals of natural areas (1992). The Michigan Natural Features Inventory is in the process of producing a pre-European settlement map for the entire state of ‘Michigan 'using the GLO records (1993). However, the survey records are only a snapshot in time. For this reason, some ecologists argue the validity of using such information to manage our current forests towards a historical model that may have existed under different circumstances. Have past logging, settlement, and resulting populations of species such as white-tailed deer, altered the soil, disturbance regimes, local climate and regeneration processes enough that pre-European settlement targets are now irrelevant? If an organization decrees a pre—European settlement.community'a management goal, a few issues should.be recognized. As mentioned above, time since last disturbance 62 63 significantly altered the species composition in an area. Use of the GLO surveys to predict the pre-European settlement vegetation that occurred on a particular piece of ground may be misleading if only the notes containing the local area are examined. I suggest an examination of a particular region will give a general picture of the disturbance regimes of the area, as well as an overall forest composition, and the patterning of forest types across the region. This information combined with the MNFI pre-European settlement map should provide adequate pre-European settlement information for managers of natural land. Return intervals should also provide a baseline for management of natural stands. These should be constructed for the region in question to gain better accuracy, and would be most useful if calculated for forest types, not landforms (Whitney 1986). Tree density was not considered in this study but would be useful to the forest manager (see methods in Cottam and Curtis 1956). Again, the scale with which the surveyor's recorded this data should be kept in mind and the densities calculated should.be used to compare areas or forest types and not taken as an absolute for a given area. Examination of pre-European settlement forests provides valuable information on the successional dynamics of our country's forests. Further studies to answer the following questions would be of value in understanding our pre-European settlement forests. How was the variability in rate of disturbance influenced by the patch size within and among 64 vegetation types? Today’s forests are highly fragmented, but the original forests covered. most of the land area of Michigan. Did the continuous forested landscape ameliorate differences between sites within a region? And finally, in addition to the factors controlling forest composition already described, can we predict the effect of the position in the landscape on the type of forest found there? Appendix A APPENDIX A Literature Cited for Figure 1 Bourdo, E.A.,Jr. 1956. A review of the General Land Office survey and its use in quantitative studies of former forests. Ecology 37:754-768. Brewer, L.G., T.W. Hodler, and H.A. Raup. 1984. Presettlement vegetation of southwestern Michigan. Mich. Bot. 23:153-156. Dick, W.B. 1937. A study of the original vegetation of Wayne County, Michigan. Pap. Mich. Acad. Sci. Arts Let. 22:329-334. Dodge, S.L. 1987. Presettlement forests of south-central Michigan. Michigan Bot. 26:139-152. Frelich, L.E. and C.G. Lorimer. 1991. Natural disturbance regimes in hemlock-hardwood forests of the upper Great Lakes region. Ecol. Monogr. 61(2):145-164. Hartesveldt, R.J. 1951. Forest tree distribution in Jackson County, Michigan, according to original land survey records. M.S. thesis, University of Michigan. 24pp. + maps. Hazlett, B.T. 1986. The vegetation and flora of the Nordhouse dunes, Manistee National Forest, Mason County, Michigan. Mich. Bot. 25:74-92. Hodler, T., R. Brewer, L. Brewer, and H. Raup. 1981. Presettlement vegetation of Kalamazoo County, Michigan. Department of Geography, Western Michigan University, Kalamazoo, MI. Map. Hushen, T.W., R.O. Kapp, R.D. Bogue, and J. Worthington. 1966. Presettlement forest patterns in Montcalm County, Michigan. Mich. Bot. 5:192-211. 65 66 Kapp, R.O. 1978. Presettlement forests of the Pine River watershed (central Michigan) based on original land survey records. Mich. Bot. 17:3-15. Kenoyer, L.A. 1930. Ecological notes on Kalamazoo County, Michigan, based on the original land survey. Pap. Mich. Acad. Sci. Arts Let. 11:211-217. Kenoyer, L.A. 1934. Forest distribution in southwestern Michigan as interpreted from the original land survey (1826-32). Pap. Mich. Acad. Sci. Arts Let. 19:107-111. Kenoyer, L.A. 1940. Plant associations in Barry, Calhoun, and Branch Counties, Michigan, as interpreted from the original survey. Pap. Mich. Acad. Sci. Arts Let. 25: 75-77. Kenoyer, L.A. 1943. Forest associations of Ottawa County, Michigan, at the time of the original survey. Pap. Mich. Acad. Sci. Arts Let. 28:47-50. Kilburn, P.D. 1957. Historical development and structure of the aspen, jack pine and oak vegetation types on sandy soils in northern lower Michigan. Ph.D. dissertation, University of Michigan. 267 pp. Kittredge, J. and A.K. Chittenden. 1929. Oak forests of northern Michigan. Mich. Agr. Exp. Sta. Special Bulletin 190. Livingston. B.E. 1905. The relation of soils to natural vegetation in Roscommon and Crawford Counties, Michigan. Bot. Gazette 39:22-41. Loope, W.L. 1991. Interrelationships of fire history, land use history and landscape pattern within Pictured Rocks National Lakeshore, Michigan. Canad. Field-Naturalist 105(1):18-28. Marschner, F.J. 1946. Original Forests of Michigan (redrawn by A.D. Perejda). Wayne State University Press. Detroit, Michigan. Map. Merk, J.W. 1951. Tree species distribution on the basis of the original land survey of Washtenaw County, Michigan. M.S. thesis, University of Michigan. 14pp. + maps. Mustard, T.S. 1983. The vegetation of the Manistee National Forest, Oceana and Mason Counties, Michigan I. Physical, Historical and Ecological Aspects. Mich. Bot. 22:111-122. 67 Palik, B.J. and K.S. Pregitzer. 1992. A comparison of presettlement and present-day forests on two bigtooth aspen-dominated landscapes in northern lower Michigan. Am. Midl. Nat. 127:327-338. Parmelee, G.W. 1953. The oak upland continuum in southern Michigan. Ph.D. dissertation. Department of Botany, Michigan State College, East Lansing, MI. 278 pp. Veatch, J.O. 1959. Presettlement Forest in Michigan. Department of Resource Development, Michigan State University, East Lansing, MI. Map. Whitney, G.G. 1986. Relation of Michigan's presettlement pine forests to substrate and disturbance history. Ecology 67:1548-1559. Whitney, G.G. 1987. An ecological history of the Great Lakes forest of Michigan. J. Ecology 75:667-684. Appendix B OQNO‘UIS‘uN-DM £55#5458-##535bUM“040404040401uuuNNNNNNNNNNN-a-o-a-e-n-bd—b—s—b-aa-s—n—ar- aba a baeau u u uuuu --~ -~aaaaaaaaaa oawguguwaoogwgmfiumaozmwouem~aoomflaueu~ao 643 Appendix B SAS Output for Correspondence Analysis 1 Raw Data s u 3 up no u 32 JP 0 o o 15 2 3 52 20 33 23 37 2 0 1o 0 o o o o 1 2 o 12 93 o o o 25 13 o 13 21 o o o 23 3 3 3s 35 o o o 13 o 1 17 71 o 3 2 20 2 2 22 7 o 5 5 55 2 3 22 22 o o o 37 17 1 33 3 2 3 1 23 1 1 13 73 23 23 72 11 o 2 o o o 3 20 20 3 3 21 s 1 3o 15 25 19 3 27 o o o 2 32 37 1 o o 37 20 33 3 o 3 o o s 31 11 13 o 3 o o 11 25 21 35 o 7 3 o 9 3 13 53 o 17 10 o 2 13 1 20 o 9 5 o 7 13 3 32 2 1s 3 o 3 23 13 31 1 9 3 o 12 33 25 11 o 3 2 o 12 31 31 13 o 1 o o 3 33 23 39 o 17 o o 13 33 21 3 o 3 3 o 12 20 13 3 o 3 o o 23 11 13 o o o o o 23 35 33 o o o o o 13 11 22 3 o o 1 o 23 25 39 2 o 1 o o 1 21 13 32 2 1 20 11 2 20 2 33 o 13 1 o 3 17 39 13 1o 3 7 o 2 32 3 23 2 3 3 o 3 11 2 33 o 3 2 o 3 27 5 23 o 3 3 o 1 11 3 33 o 7 13 o o 19 3 31 o 7 3 3 3o 21 33 2 o 1 o o 25 33 33 13 o 2 1 o 33 35 53 2 o o o o o 1 o 21 o 1 55 29 0 1o 1 10 o 1 23 33 3 1o 19 3o 0 1o 19 o 3 12 23 9 1 5 5 o o o 3 21 3 o 35 13 o o 2 29 3 o 32 3o 0 2 o 11 15 2 35 33 23 23 29 13 o 2 15 o 01 0 Raw Count Date (continued) 51 4 4 0 21 6 1 44 10 52 4 2 3 11 20 11 3 40 6 53 4 O 16 21 32 0 7 33 1 54 5 65 20 30 2 0 0 0 0 55 5 56 21 38 0 0 1 0 0 56 5 60 24 40 0 0 0 0 0 57 5 55 11 60 0 0 0 0 0 58 5 6 15 14 45 20 5 17 0 59 5 38 36 47 8 0 O 1 0 60 5 6 6 10 5 1 2 38 28 61 5 26 45 47 9 0 9 6 0 62 5 2 13 40 52 4 16 23 0 63 5 11 28 46 9 0 3 0 0 64 5 6 34 37 15 1 6 2 O 65 5 30 26 66 4 0 5 0 0 66 5 6 32 36 14 2 6 10 0 67 5 26 4 70 0 1 2 0 0 68 6 42 21 41 0 0 0 0 0 69 6 51 30 35 0 0 0 0 0 70 6 12 28 19 12 0 2 3 0 71 6 19 8 8 17 1 1 32 30 72 6 3 11 21 2 0 0 12 0 73 6 24 26 26 0 0 0 0 0 74 6 21 21 14 11 0 0 25 8 75 6 5 14 6 11 0 0 61 2 76 6 1 3 5 20 0 3 53 5 77 6 2 0 0 29 1 1 60 9 78 6 8 53 10 24 0 4 12 2 79 6 37 25 62 4 0 1 0 0 80 6 45 9 69 2 0 0 0 0 81 6 14 41 35 19 2 7 0 0 82 6 1 20 42 56 0 3 0 0 83 7 52 26 51 0 0 0 0 0 84 7 52 14 65 0 0 0 0 0 85 7 41 14 45 4 0 0 0 0 86 7 37 12 40 3 0 0 6 1 87 7 12 24 29 12 8 0 9 2 88 7 6 43 17 15 0 0 23 4 89 7 8 16 65 7 2 6 3 0 90 10 7 9 3 26 0 O 40 7 91 10 6 6 4 33 0 0 49 0 92 10 3 25 2 10 1 1 19 23 93 10 0 19 4 10 3 0 44 48 94 10 2 1 3 16 4 3 36 23 95 10 11 16 12 16 0 5 29 8 96 10 14 18 38 19 1 3 14 0 97 10 10 31 16 14 1 0 6 0 98 10 11 45 35 1 0 2 0 0 99 10 13 20 8 24 0 5 5 0 100 10 0 36 9 16 2 2 10 0 101 10 5 31 14 26 0 2 18 0 The Correspondence Analysis Procedure Inertia and Chi-Square Decomposition Singular Principal Chi- Velues Inertias Squares Percents 9 18 27 36 45 ----+----+----+----+----+--- o . 7773‘ o . 60426 6176 . 13 (.6 . 80x it*ttiiifltitittttiiifltttit 0.52015 0.27056 2765.36 20.95% ************ 0.40468 0.16377 1673.89 12.68% *'***** 0.35288 0.12453 1272.79 9.64% ***** 0.24581 0.06042 617.56 4.68% *** 0.21923 0.04806 491.22 3.72% ** 0.14000 0.01960 200.32 1.52% * 1.29119 13197.3 (Degrees of Freedom = 700) Landform UIUIUIU'IUIJ‘J‘JN858‘858585855“54‘##WUUUWUD‘UUUUMNNNNNNNNNNN-fi-fi-fi-l-O-O-l-fi-D-Dd-D-D—I-D Dim1 1.23689 -0.75829 2.02170 1.21585 1.49729 1.73167 0.75804 0.84623 0.93291 1.51691 -0.68660 0.40354 0.29617 0.82086 -0.82756 -0.30243 -0.16600 0.12349 0.06533 0.08098 -0.11048 -0.49635 -0.49051 -0.19041 -0.52136 -0.52881 -0.86516 -0.75018 -0.71515 -0.76130 0.38955 0.06466 -0.18565 -0.05454 0.13805 -0.05672 0.31686 0.22253 -0.84794 -0.63442 -0.80414 1.29258 1.28271 0.13249 -0.35995 1.15437 1.32838 1.56340 -0.35224 -0.61702 1.03840 0.73169 0.28614 -0.86683 -0.86517 -0.86843 -0.89392 0.32351 Row Coordinates Dim2 -0.01278 -0.26534 -1.86940 0.35442 -0.66938 -1.28778 0.38053 0.28223 0.92762 -1.03224 -0.16128 0.39688 0.75760 1.77871 -0.44990 0.42489 0.41674 0.73136 0.74153 0.72399 0.50486 0.11283 0.12702 0.58482 0.01386 0.09917 -0.49187 -0.21384 -0.28195 -0.26685 0.24519 0.87440 0.38898 0.66680 0.86891 0.54965 0.79551 0.59128 -0.46872 -0.09147 -0.35800 -0.20290 -0.66616 0.53027 0.14937 0.11982 -0.37295 -0.65209 -0.00720 -0.23632 0.37697 0.49321 0.48897 -0.52513 -0.48856 -0.49629 -0.53180 0.86879 70 Dim3 -0.28700 0.05779 0.30390 1.15781 ~0.02846 0.04032 '0.21732 -0.17890 0.68519 0.06063 0.05904 0.17292 0.52498 3.22497 0.18348 -0.32233 -0.24960 -0.35937 -0.49376 -0.27063 -0.24004 -0.20079 -0.14825 -0.34939 -0.16627 -0.14289 0.18462 -0.00851 0.09325 0.06102 -0.16523 -0.47116 0.48123 -0.26232 -0.41901 -0.37937 -0.45790 -0.39508 0.19234 -0.01892 0.11449 -0.34939 -0.19701 -0.35191 -0.03959 0.03649 70.01416 0.60330 -0.12073 0.07205 -0.02438 0.32380 -0.39723 0.21495 0.19167 0.19326 0.25955 0.68355 Dim4 -0.64936 0.00487 0.94650 0.12325 0.12646 0.69822 -0.20897 0.23470 -0.42752 0.68746 0.05651 -0.19229 -0.05208 0.19369 -0.21385 0.55413 0.28125 0.33886 0.51745 0.38979 0.33708 0.32192 0.31535 0.61727 0.18591 0.30574 -0.21421 0.12247 -0.09244 0.01905 0.09377 0.72392 0.08431 0.55917 0.58159 0.38084 0.18461 0.50658 -0.21442 0.12775 -0.07536 -0.50891 0.09820 -0.00557 0.07696 -0.56716 0.07163 0.21083 -0.15029 -0.16704 -0.65933 -0.57417 -0.20971 -0.27477 -0.21707 -0.22168 -0.28059 0.04253 ‘4‘4VV‘IN‘IO‘OOOOOOOOOOOGOOU‘MMUMMMMM 850240 7.”: V'U -0.70687 0.95761 -0.54773 0.10124 -0.61712 -0.43361 -0.74560 -0.29934 -0.83910 -0.85031 -0.83873 -0.43658 0.70917 -0.22472 '0.78771 0.08458 0.65946 0.85797 1.02478 '0.11821 -0.78276 -0.86288 -0.45128 -0.15526 -0.85028 -0.87993 -0.81918 -0.67690 -0.19719 0.00972 -0.57678 0.66757 0.63473 0.72545 1.18502 1.13729 0.31124 -0.26475 -0.31301 -0.67323 -0.16828 -0.05236 0.01576 Dim1 -0.80126 -0.39855 -0.65169 0.38299 0.88043 -0.00926 0.92900 1.68210 -0.23457 -0.50021 -0.01536 0.59834 ~0.02746 0.20513 -0.23286 0.22657 -0.39526 -0.44455 ~0.43176 0.11220 -0.44657 -0.01183 -0.30975 -0.15243 0.25102 0.35196 0.34514 0.33071 -0.31993 -0.47142 0.20181 0.51648 -0.44434 -0.49705 -0.41938 -0.40907 0.17529 0.15436 -0.02146 0.25173 0.51189 -0.32550 -0.56880 -0.12613 0.07553 0.12467 0.19292 -0.06006 0.37907 0.48599 0.40691 Dim2 -0.47485 0.13835 -0.17097 0.63478 1.17425 0.70530 0.16018 -1.18195 71 0.04847 -0.13449 -0.11265 -0.15300 -0.04725 -0.12261 0.07074 -0.10230 0.28625 0.16992 0.13682 -0.16391 '0.06217 '0.16755 0.04990 -0.16319 '0.48783 -0.49606 -0.41958 -0.35745 0.12287 0.24459 -0.06698 -0.18860 0.17003 0.23690 0.19364 0.14938 0.38907 -0.31930 0.11073 -0.38183 -0.45688 -0.17710 -0.07163 -0.01536 -0.29035 -0.06959 -0.13395 -0.12332 -0.25616 -0.24622 -0.34888 CA 0F THES1 DATA Column Coordinates Dim3 0.17979 -0.19922 0.09229 -0.14645 2.38114 ~0.39392 -0.27631 0.12884 ~0.00383 '0.32794 0.14059 0.10562 0.20657 0.32799 0.02829 0.14385 -0.17205 -0.16385 -0.13911 0.20604 -0.16442 -0.36367 0.00947 -0.31987 -1.05344 -0.88739 -0.87100 0.29598 -0.06278 -0.23418 0.34497 0.40200 -0.16369 -0.23908 -0.18853 -0.29246 -0.01249 -0.03615 0.08496 -0.56919 -0.78627 0.21118 -0.01660 -0.30595 -0.26283 -0.10155 0.14856 0.29269 0.23474 0.25915 0.02099 Dim4 -0.25109 0.26854 -0.02700 0.20778 -0.03302 0.56186 -0.69479 0.47600 Appendix C Quarter Township Codes for Appendices C-E The codes for the QT (quarter township) columns for all tables in Appendices C-E refer to the direction of that quarter township within the township: SW=southwest corner, NE=northeast corner and so on. For example, Table C2. species counts for ground moraines of fine textured till shows sample 2-10 in Osceola County was located in the northwest corner of T19N R7W. Sample 2- 12 crossed a county line between Clare and Gladwin counties. For instances such as this, the first location given (SE corner of T19N R2W in Clare County) contains six of the nine square miles, the second (SW corner of T19N R3W of Gladwin County) contains the last three square miles. The last example is for sample 2-13 where the code Sch refers to the southwest corner that has been corrected north by one square mile, in most cases to avoid a boundary with another landform type. Legend for Appedices C-E: Codes Given to Farrand's Landforms in Quaternary Geology of Michigan Map (Department of Geological Sciences, The University of Michigan, Ann Arbor. 1982) of Fine Textured Till Coarse Textured Till of Coarse Textured Till Medium Textured Till of Medium Textured Till (not used) and Clay (not used) Fine Textured Till 1 = Outwash plains 2 = Ground Moraines 3 = Lacustrine Sands and Gravels 4 = Ice Contact Features 5 = End Moraines of 6 = Ground Moraines 7 = End Moraines of 8 = Ground Moraines 9 = Lacustrine Silt 10 = End Moraines of 11 = Dunes (not used) 12 = Peat and Muck (not used) Note: Landforms that covered relatively little area were not used in this study. The numbering system was developed for a preliminary study and kept for continuity. 74 Table C1. Species counts on non-swemplend for outwash plains. Data in these tables are the tallies of all witness and line trees surveyed on the uplands of each quarter township. The total number of indivimals comted for each sample was dependent upon the nulber of trees each surveyor recorded, the availability of bearing and line trees, and the exclusion of data points that occurred in wetlands. MAJOR SPECIES SAMPLE COUNTY LOCATION OT Sugar Hem- Beech 0hite Red 0hite Black Red Jack maple lock Pine Maple Oak Oak Pine Pine 1-1 P. Isle T330 R4E SN 0 0 0 15 4 2 1 52 20 1-2 Otsego 1300 R30 00 48 28 47 2 10 0 0 0 0 1-3 Crawford 1260 R30 NE 0 0 0 1 0 2 1 12 93 1-4 Mason T200 R150 00 0 0 0 25 0 18 2 16 21 1-5 Kalkaske 1250 R50 SE 0 ‘ 0 0 24 3 3 3 45 65 1.6 Roscommon T230 R10 NE 0 0 0 16 1 0 0 17 71 1-7 Roscommon T220 R20 NE 0 8 2 20 2 2 0 22 7 1-8 Manistee T210 R130 NE 0 5 5 55 3 2 2 22 22 1-9 Manistee T210 R150 80 0 0 0 37 1 17 4 34 4 1-10 Clara 1200 R60 SE 2 3 1 26 1 1 0 16 73 1-11 Lake T19N R110 SE 23 26 72 11' 2 0 3 0 0 1-12 Meson T200 R160 NE 0 6 20 20 3 6 0 21 5 1-13 Newaygo 1150 R130 SE 1 30 15 25 8 19 1 27 0 1-14 Newaygo T120 R120 NE 0 0 2 32 1 47 18 0 0 1-15 Kelkeska T270 R60 0E 67 20 38 4 3 0 0 0 0 total 141 126 202 313 42 119 35 284 381 MINOR SPECIES SITE COUNTY LOCATION 01 Fir Ash Cedar Elm Bas- Aspen Birch Yellow wood birch 1-1 P. Isle T330 R4E S0 2 0 0 0 0 1 2 0 1-2 Otsego T300 R30 00 0 0 0 2 0 0 5 10 1-3 Crawford T26N R30 NE 0 0 0 0 0 0 0 0 1-4 Mason 1200 R150 00 0 0 0 0 0 2 0 0 1-5 Kalkaske 1250 R50 SE 0 0 0 0 0 2 0 0 1-6 Rosconon T230 R10 NE 0 0 0 0 0 4 O 0 1-7 Roscomn 1220 R20 NE 0 0 3 0 0 8 0 0 1-8 Manistee T210 R130 NE 0 0 0 0 0 3 0 0 1-9 Manistee 1210 R150 SN 0 0 0 0 0 0 0 0 1-10 Clare T200 R60 SE 0 0 0 2 0 4 0 0 1-11 Lake T190 R110 SE 0 6 8 1 1 D 0 0 1-12 Mason 1200 R160 NE 0 0 0 1 1 0 0 0 1-13 Newaygo T150 R130 NE 0 2 0 0 0 2 0 0 1-14 Newaygo 1120 R120 NE 0 0 0 0 1 0 0 0 1-15 Kalkaska 1270 R60 NE 0 0 0 1 0 0 0 0 total 2 8 11 7 3 26 7 10 Table C2. Species Counts for Non-swamplend on Ground Moraines Composed of Fine Till MAJOR SPECIES SAMPLE CGINTY LOCATIGl 01’ Sugar ilem- Beech 1111113 Red Hhite Black Red Jack maple lock pine maple oak oak pine pine 2-3 13333 723fi115£ 311 s 31 11 13 3 o 1 o o 2-4 Iosco 1230 RSE SE 11 25 21 35 7 0 0 6 0 2-5 Iosco T220 RSE 00 9 8 13 53 17 0 0 10 0 2-6 Ogemaw 1220 R4E S0 2 18 1 20 9 0 0 5 0 2-7 Ogemaw T210 R4E 00 7 16 6 32 15 2 1 8 0 2-8 Ogemaw 1210 R4E NE 8 24 16 32 9 1 1 6 0 2-9 Clare T200 R60 00 12 46 25 11 3 0 0 . 2 0 2-10 Osceola 1190 R70 00 12 41 31 18 1 0 0 0 0 2-12 Cler/Glad 1190 R283 SEISH 3 36 24 39 17 0 2 0 0 2-13 Misseukee 1220 R70 S0cn 16 38 21 6 3 0 0 4 0 2-15 Missaukee 1230 R50 50 12 20 18 8 6 0 0 0 0 total 97 303 187 272 ' 93 3 5 41 0 MING SPECIES " SAMPLE COUNTY LOCATION 01 Fir Ash Cedar Elm Bas- Aspen Birch Yellow wood birch 2-3 Iosco 1230 RSE $0 3 4 3 2 0 5 2 0 2-4 Iosco 1230 RSE SE 1 4 0 0 0 0 0 0 2-5 Iosco 1220 RSE 00 0 0 1 1 1 O 2 0 2-6 Ogemaw 1220 R4E S0 0 4 3 2 1 0 2 0 2-7 Ogemaw 1210 R4E 00 0 2 0 2 2 0 0 0 2-8 Ogemaw 1210 R4E NE 0 1 3 1 0 0 1 0 2-9 Clara 1200 R60 00 2 3 2 2 4 0 0 2 2-10 Osceola 1190 R70 00 0 4 2 2 6 6 0 0 2-12 Clar/Glad 1190 R283 SE/S0 0 2 1 8 2 0 0 1 2-13 Misseukee 1220 R70 S0cn 0 1 0 0 3 1 2 0 2-15 Missaukee 1230 R50 S0 1 0 1 1 0 2 1 0 total 7 25 16 21 19 14 10 3 * Uncommon species cited for sample 2-10 : 2 black cherry, 3 ironwood 76 Table C3. Species Counts on Non-swampland for Lacustrine Sand and Gravel MAJOR SPECIES SAMPLE COUNTY LOCATION 01 Sugar Hem- Beech 0hite Red 0hite Black Red Jack maple lock pine maple oak oak pine pine 3-1 Cheboygan 1380 R30 SE 26 11 16 0 0 0 0 0 0 3-2 Alpene 1320 R7E SE 23 45 48 0 0 0 0 0 0 3-3 P. Isle 1340 R6E SE 16 11 22 3 0 0 1 1 0 3-5 Manistee 1240 R140 80 24 25 39 2 1 0 0 0 0 3-7 Ogemaw 1210 RZE NE 1 21 16 32 1 2 0 20 11 3-8 Iosco 1210 R6E NE 2 20 2 36 13 0 0 1 0 3-10 Mason 1200 R170 50 4 17 39 16 4 10 0 7 0 3-11 Gladwin 1200 R1E SEcn 2 32 3 23 6 2 0 3 0 3-12 Arenas 1200 R3E S0 3 11 2 33 8 0 0 2 0 3-13 Gladwin 1190 R2E NE 6 27 5 26 6 0 0 6 0 3-14 Gladwin 1190 RZE SE 1 11 4 34 7 0 0 13 0 3-15 Gladwin 1180 R2E $0 0 19 6 31 7 O 0 6 4 total 108 250 202 236 S3 14 1 59 15 MINOR SPECIES SAMPLE COUNTY LOCATION 01 Fir Ash Cedar Elm Bas- Aspen Birch Yellow wood birch 3-1 Cheboygan 1380 R30 SE 0 0 0 1 1 0 0 1 3-2 Alpene T320 R7E SE 2 1 7 1 1 0 2 0 3-3 P. Isle 1340 R6E SE 0 0 7 0 0 5 7 4 3-5 Manistee 1240 R140 S0 0 0 0 0 1 0 0 0 3-7 Ogemaw 1210 RZE NE 0 0 0 0 0 3 0 0 3-8 Iosco 1210 R6E NE 1 13 4 8 . 0 1 2 1 3-10 Mason 1200 R170 S0 0 0 0 0 0 0 0 0 3-11 Gladwin 1200 R1E SEcn 5 5 4 1 0 1 2 0 3-12 Arenac 1200 R3E $0 0 3 4 0 0 2 1 0 3-13 Gladwin T19N RZE NE 1 1 1 2 0 1 3 0 3-14 Gladwin T190 R2E SE 1 1 3 0 0 5 3 0 3-15 Gladwin T180 R2E S0 0 0 3 0 0 1 5 0 total 10 24 33 13 3 19 25 6 '_ Cl“ I'J- . ' Table C4. Species Counts on Non-swampland for Ice Contact Features MAJOR SPECIES SAMPLE COUNTY LOCATION 01 Sugar Hem- Beech 0hite Red 0hite Black Red Jack maple lock pine maple oak oak pine pine 4-1 Emmet 1340 R40 SE 60 21 43 2 1 0 0 0 0 4-2 Crawford 1280 R30 NEce 25 43 64 13 2 0 0 1 0 4-3 Crawford 1280 R30 S0 48 35 56 2 0 0 0 0 0 4~4 Oscoda 1280 R4E 00 0 1 0 21 1 O 1 55 29 4-5 Oscoda 1280 R4E SEcn 0 10 1 10 1 0 0 28 36 4-6 Alcona 1280 RBE S0 4 10 19 30 10 0 1 19 0 4-7 Alcona 1270 R8E SE 8 12 24 9 5 1 0 5 0 4-8 Ogemaw 1230 R1E SE 0 0 3 21 0 6 4 45 18 4-9 Crawford 1270 R20 NE 0 0 2 29 0 3 1 32 40 4-10 Oscoda 1270 R2E 00 0 2 0 11 2 0 0 35 64 4-11 Ogemaw 1240 R38 NE 24 28 29 13 2 0 5 15 0 4-12 Ogemaw 1240 R4E N0 34 18 36 10 0 0 7 5 0 4-13 Ogemaw 1220 R1E 00 0 4 0 21 .1 6 8 44 10 4-14 Ogemaw 1220 R1E $0 2 3 11 20 3 11 8 40 6 4-15 Roscommon 1210 R10 00cs 0 16 21 32 7 2 6 33 1 13131 205 203 309 233 35 29 31 7‘57 203 MINOR SPECIES SAMPLE COUNTY LOCATION 01 Fir Ash Cedar Elm Bas- Aspen Birch Yellow wood birch 4-1 Emmet 1340 R40 SE 0 0 0 2 3 0 0 0 4-2 Crawford 1280 R30 NEce 0 0 0 0 1 0 5 0 4-3 Crawford 1280 R30 S0 0 0 0 2 0 0 10 0 4.4 Oscoda 1280 R4E 00 0 0 0 0 0 11 1 0 4-5 Oscoda 1280 R4E SEcn 0 0 0 0 0 14 1 0 4-6 Alcona 1280 R8E S0 0 0 0 0 0 1 2 0 4-7 Alcona 1280 R8E SE 0 0 0 1 3 1 0 0 4-8 Ogemaw 1230 R1E SE 0 0 0 0 0 0 0 0 469 Crawford 1270 R20 NE 0 0 0 0 0 5 0 0 4-10 Oscoda 1270 RZE N0 0 0 0 0 0 8 11 0 4-11 Ogemaw 1240 R3E NE 0 0 0 1 4 1 1 0 4-12 Ogemaw 1240 R4E 00 0 0 0 1 7 1 0 0 4-13 Ogemaw 1220 R1E 00 0 0 0 0 0 3 0 0 4-14 Ogemaw 1220 R1E S0 0 0 0 0 0 2 0 0 4-15 Roscommon 1210 R10 00cs 0 0 0 O 0 5 4 0 total 0 O 0 7 18 52 35 0 Table C5. Species Counts on Non-swampland for End Moraines of Coarse-textured Till MAJOR SPECIES SAMPLE COUNTY LOCATION 01 Sugar Hem- Beech 0hite Red 0hite Black Red Jack maple lock pine maple oak oak pine pine 5-1 Cheboygan 13 R40 SE 65 20 30 2 0 0 1 0 0 5-2 B.Treverse 1250 R100 00 56 21 38 0 1 0 0 0 0 5-3 B.Traverse 1250 R100 NE 60 24 40 0 1 0 0 0 0 5-4 Osceola 1200 R80 00 55 11 60 D 0 0 0 0 0 5-5 Clare 1190 R50 SE 6 15 14 45 5 20 13 17 0 5-6 B.Traverse 1270 R90 NEcw 38 36 47 8 0 0 0 1 0 5-8 Alcona 1270 RSE $0 6 6 10 5 2 1 2 38 28 5-9 Newaygo 1160 R110 50 26 45 47 9 9 0 3 6 0 5-10 Newaygo 1150 R110 00 2 13 40 52 16 4 2 23 0 5-11 Osceola 1170 R80 80 11 28 46 9 3 0 0 0 0 5-12 Mecosta 1160 R80 S0 6 34 37 15 6 1 2 2 0 5-13 Missaukee 1240 R70 S0 30 26 66 4 5 0 0 0 0 5-14 Isabella 1160 R60 NE 6 32 36 14 6 2 0 10 0 5-15 Oceana 71311 R170 115 23 3 70 o '2 1 2 o 0 total 393 315 581 163 56 29 25 97 28 MINOR SPECIES * SAMPLE COUNTY LOCATION 01 Fir Ash Cedar Elm Bes- Aspen Birch Yellow wood birch 5-1 Cheboygan 1330 R40 SE 0 0 0 18 6 0 0 0 5-2 G.1raverse 1250 R100 00 0 1 0 5 3 0 3 1 5-3 B.Traverse 1250 R100 NE 0 0 0 10 9 0 1 1 5-4 Osceola 1200 R80 00 0 2 0 10 4 0 0 0 5-5 Clara 1190 R50 SE 0 0 1 0 0 7 2 0 5-6 B.Traverse 1270 R90 NEcw 0 0 0 2 1 2 0 0 5-8 Alcona 1270 R5E S0 0 0 0 0 2 0 0 0 5-9 Newaygo 1160 R110 S0 0 3 1 1 0 5 3 0 5-10 Newaygo 1150 R110 00 0 2 2 0 0 0 2 0 5-11 Osceola 1170 R80 S0 0 0 0 1 4 1 2 0 5-12 Mecosta 1160 R80 50 0 1 0 4 0 0 1 0 5-13 Missaukee 1240 R70 S0 0 0 0 4 1 0 1 0 5-14 Isabella 1160 R60 NE 0 1 0 0 1 0 2 0 5-15 Oceana 1140 R170 NE 0 1 0 6 8, 0 1 0 total 0 11 4 61 39 15 18 2 * Additional Species seldom recorded 5-1 3 ironwood 5-10 2 ironwood 5-2 1 ironwood 5-11 1 butternut 5-6 1 ironwood 5-13 2 ironwood 5-9 2 ironwood 5-15 4 ironwood Table C6. Species Counts on Non-swampland for Ground Moraines of Coarse-textured Till MAJOR SPECIES SAMPLE COUNTY LOCATION 01 Sugar Hem- Beech 0hite Red 0hite Black Red Jack maple lock pine maple oak oak pine pine 6-1 P. Isle 1350 R2E S0 42 21 41 0 0 0 0 0 0 6-2 Cheboygan 1340 R10 00 51 30 35 0 0 0 0 0 0 6-3 Cheboygan 1340 R1E SE 12 28 19 12 2 0 0 3 0 6-4 P. Isle 1330 R2E 0E 19 8 8 17 1 1 4 32 30 6-5 Alpene T300 R6E NE 3 11 21 2 0 0 1 12 0 6-6 P. Isle T340 RSE S0 24 26 26 O 0 0 0 0 0 6-7 Montmorency 1290 R3E S0 21 21 14 11 0 0 4 25 8 6-8 Montmorency 1290 R3E SE 5 14 6 11 0 0 3 61 2 6-9 Alcona 1280 R6E SE 1 3 5 20 3 0 7 53 5 6-10 Alcona 1270 R7E SE 2 0 0 29 1 1 6 60 9 6-11 Clare 1190 R60 SE 8 53 10 24 4 0 0 12 2 6-12 Lake 1170 R110 0E 37 25 62 4 1 0 0 0 0 6-13 Osceola 1170 R100 S0 45 9 69 2 0 0 0 0 0 6-14 Isabella 1140 R60 S0 14 41 35 19 '7 2 1 0 0 6-15 Montcalm 1120 R70 NE 1 20 42 S6 3 0 3 0 0 total 285 310 393 207 22 4 29 258 56 MINOR SPECIES * SAMPLE COUNTY LOCATION 01 Fir Ash Cedar Elm Bas- Aspen Birch Yellow wood birch 6-1 P. Isle 1350 R2E S0 1 0 6 3 3 2 0 4 6-2 Cheboygan 1340 R10 00 0 0 6 2 4 1 9 0 6-3 Cheboygan T340 R1E SE 2 0 3 0 1 15 3 2 6-4 P. Isle T330 02E NE 0 0 0 1 1 4 3 0 6-5 Alpene T300 R6E NE 0 0 0 0 0 0 0 0 6-6 P. Isle 1340 RSE S0 0 0 4 3 2 0 0 1 6-7 Montmorency 1290 R3E S0 0 0 0 0 3 1 1 0 6-8 Montmorency 1290 R3E SE 1 0 1 0 0 3 0 0 6-9 Alcona 1280 R6E SE 2 0 0 0 2 6 2 1 6-10 Alcona 1270 R7E SE 0 0 0 0 0 3 1 1 6-11 Clare 1190 R60 SE 0 1 1 1 3 5 2 1 6-12 Lake 1170 R110 NE 0 0 0 0 2 0 4 0 6-13 Osceola 1170 R100 S0 0 0 0 22 6 0 0 0 6-14 Isabella 1140 R60 S0 0 0 0 0 3 0 3 0 6-15 Montcalm 1120 R70 NE 0 0 0 0 1 0 1 0 total 6 1 21 32 31 40 29 10 ' Additional Species seldom recorded 6-12 3 ironwood 6-13 6 ironwood 6-14 3 ironwood Table C7. Species Counts on Non-swempland for End Moraines of Medium-textured Till MAJOR SPECIES SAMPLE COUNTY LOCATION 01 Sugar Hem- Beech 0hite Red 0hite Black Red Jack maple lock pine maple oak oak pine pine 7-1 Otsego 1300 R20 NE 52 26 51 0 0 0 0 0 0 7-2 Otsego 1300 R10 00 52 14 65 0 0 0 0 0 0 7-3 Montmorency 1300 R1E N0 41 14 45 4 0 0 D 0 0 7-4 Montmorency 1300 R1E SE 37 12 40 3 0 O 0 6 1 7-5 Alcona 1250 R6E $0 12 24 29 12 0 8 2 9 2 7-6 Alcona 1250 R6E NE 6 43 17 15 0 0 0 23 4 7-7 Manistee 1220 R160 NE 8 16 65 7 6 2 2 6 0 total 208 149 312 41 6 10 4 44 7 MINOR SPECIES SAMPLE COUNTY LOCATION 01 Fir Ash Cedar Elm Bas- Aspen Birch Yellow wood birch 7-1 Otsego 1300 R20 NE 0 0 0 5 1 0 0 3 7-2 Otsego 1300 R10 00 0 0 0 4 2 0 0 4 7-3 Montmorency T300 R1E N0 0 0 1 4 4 3 0 5 7-4 Montmorency T300 R1E SE 0 0 0 0 3 1 0 2 7-5 Alcona 1250 R6E S0 0 0 1 2 0 2 2 2 7-6 Alcona 1250 R6E NE 0 2 1 0 0 0 2 0 7-7 Manistee 1220 R160 NE 0 3 0 0 0 0 0 0 total 0 5 3 15 1O 6 4 16 81 Table C8. Species Counts on Non-swampland for End Moraines of Fine-textured Till MAJOR SPECIES SAMPLE COUNTY LOCATION 01 Sugar Hem- Beech 0hite Red 0hite Black Red Jack maple lock pine maple oak oak pine pine 10-1 Alcona 1260 R6E N0 7 9 3 26 0 0 3 40 7 10-3 Alcona 1260 R6E SE 6 6 4 33 0 0 7 49 0 10-4 Oscoda 1260 RZE SE 3 25 2 10 1 1 0 19 23 10-5 Oscoda 1260 R3E S0cn 0 19 4 10 0 0 4 44 48 10-6 Oscoda 1260 R4E SE00 2 1 3 16 3 4 6 36 23 10-7 Oscoda 1250 R4E 0E 11 16 12 16 5 0 3 29 8 10-8 Oscoda 1250 R4E S0/E 14 24 38 19 3 1 4 14 0 10-9 Ogemaw T23N RZE NE/H 10 31 16 14 0 1 0 6 0 10-10 Ogemaw 1220 R2E NEcs 11 45 35 1 2 0 0 0 0 10-11 Ogemaw 1230 R4E 0E 13 20 8 24 5 0 2 5 0 10-12 Ogemaw 1230 R4E SEcw 0 36 9 16 2 2 2 10 0 10-13 Arenac 1200 R4E 00cs 5 31 14 26 2 0 0 18 0 total 82 263 148 211 23 9 31 270 109 MINOR SPECIES * SAMPLE COUNTY LOCATION 01 Fir Ash Cedar Elm Bas- Aspen Birch Yellow wood birch 10-1 Alcona 1260 R6E N0 0 0 0 0 1 3 1 0 10-3 Alcona 1260 R6E SE 0 0 0 0 0 2 1 3 10-4 Oscoda 1260 R2E SE 0 0 1 1 1 16 9 0 10-5 Oscoda 1260 R3E S0cn 0 0 2 1 0 4 5 0 10-6 Oscoda 1260 R4E SEcw 0 0 0 0 0 6 1 0 10-7 Oscoda 1250 R4E NE 0 1 0 0 0 3 0 1 10-8 Oscoda 1250 R4E S0/E O 4 0 0 0 0 1 1 10-9 Ogemaw 1230 RZE NE/0 O 0 3 3 1 2 6 1 10-10 Ogemaw 1220 R2E NEcs 0 1 1 1 1 0 1 0 10-11 Ogemaw 1230 R4E NE 1 1 3 1 1 1 4 1 10-12 Ogemaw 1230 R4E SEcw 3 1 3 0 0 0 3 0 10-13 Arenac 1200 R4E 00cs 0 4 3 1 1 4 0 0 total 4 12 16 8 6 41 32 7 * Additional species seldom cited 10-5 3 "dead“ oak Appendix D 82 Table>D1. Citation frequency for non-swampland on outwash plains. Citation frequency is the count for a species at a site divided by the total count for all species at that site, then multiplied by 100 so that the sun of all citation frequencies for all sites equal 100. MAJOR SPECIES SAMPLE COUNTY LOCATION 01 Sugar Hem- Beech 0hite Red 0hite Black Red Jack maple lock pine maple oak oak pine pine 1-1 P. Isle T330 R4E S0 0.0 0.0 0.0 15.2 4.0 2.0 1.0 52.5 20.2 1-2 Otsego 1300 R30 00 31.6 18.4 30.9 1.3 6.6 .0 0.0 0.0 0.0 1-3 Crawford 1260 R30 0E 0.0 0.0 0.0 0.9 0.0 1.8 0.9 11.0 85.3 1-4 Mason 1200 R150 00 0.0 0.0 0.0 29.1 0.0 20.9 2.3 18.6 24.4 1-5 Kalkaske 1250 R50 SE 0.0 0.0 0.0 16.6 2.1 2.1 2.1 31.0 44.8 1-6 Roscommon 1230 R10 NE 0.0 0.0 0.0 14.7 0.9 0.0 0.0 15.6 65.1 1-7 Roscommon 1220 R20 0E 0.0 10.7 2.7 26.7 2.7 2.7 0.0 29.3 9.3 1-8 Manistee 1210 R130 NE 0.0 4.2 4.2 46.2 2.5 1.7 1.7 18.5 18.5 1-9 Manistee 1210 R150 $0 0.0 0.0 0.0 38.1 1.0 17.5 4.1 35.1 4.1 1-10 Clare 1200 R60 SE 1.6 2.3 0.8 20.2 0.8 0.8 0.0 12.4 56.6 1-11 Lake 1190 R110 SE 14.7 16.7 46.2 7.1 1.3 0.0 1.9 0.0 0.0 1-12 Mason 1200 R160 NE 0.0 7.1 23.8 23.8 3.6 7.1 0.0 25.0 6.0 1-13 Newaygo 1150 R130 00 0.8 23.1 11.5 19.2 6.2 14.6 0.8 20.8 0.0 1-14 Newaygo 1120 R120 0E 0.0 0.0 2.0 31.7 1.0 46.5 17.8 0.0 0.0 1-15 Kalkaska 1270 R60 NE 48.2 14.4 27.3 2.9 2.2 0.0 0.0 0.0 0.0 average 6.5 6.5 10.0 19.6 2.3 7.9 2.2 18.0 22.3 standard deviation 13.9 7.8 14.3 13.0 2.0 12.3 4.3 14.7 26.8 MINOR SPECIES SAMPLE COUNTY LOCATION 01 Fir Ash Cedar Elm Bas- Aspen Birch Yellow wood birch 14 9. 1:13 13fi3e $0 2.0 0.0 0.0 0.0 0.0 1.0 2.0 0.0 1-2 Otsego 1300 R30 00 0.0 0.0 0.0 1.3 0.0 0.0 3.3 6.0 1-3 Crawford 1260 R30 NE 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1-4 Mason 1200 R150 00 0.0 0.0 0.0 0.0 0.0 2.3 2.3 0.0 1-5 Kalkaska 1250 R50 SE 0.0 0.0 0.0 0.0 0.0 1.4 0.0 0.0 1-6 Roscommon 1230 R10 NE 0.0 0.0 0.0 0.0 0.0 3.7 0.0 0.0 1-7 Roscommon 1220 R20 0E 0.0 0.0 4.0 0 0 0.0 10.7 1.3 0.0 1-8 Manistee 1210 R130 0E 0.0 0.0 0.0 0.0 0.0 2.5 0.0 0.0 1-9 Manistee 1210 R150 S0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1-10 Clare 1200 R60 SE 0.0 0.0 0.0 1.6 0.0 3.1 0.0 0.0 1-11 Lake 1190 R110 SE 0.0 3.8 5.1 0.6 0.6 0.0 1.9 0.0 1-12 Mason 1200 R160 0E 0.0 0.0 0.0 1.2 1.2 0.0 1.2 0.0 1-13 Newaygo 1150 R130 00 0.0 1.5 0.0 0.0 0.0 1.5 0.0 0.0 1-14 Newaygo 1120 R120 NE 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 1-15 Kelkeske 1270 R60 NE 0.0 0.0 0.0 0.7 0.0 0.0 0.7 3.6 average 0.1 0.4 0.6 0.4 0.2 1.7 0.9 0.7 standard deviation 0.5 1.0 1.6 0.5 0.4 2.7 1.1 1.8 Table 02. Citation Frequency for Non-swampland for Ground Moraines of Fine-textured Till MAJM SPEC I ES SAMPLE COUNTY LOCATION 01 Sugar Hem- Beech 0hite Red 0hite Black Red Jack maple lock Pine maple oak oak p1ne pme 2-3 Iosco 1230 RSE $0 5.5 34.1 12.1 19.8 6.6 0.0 1.1 0.0 0.0 2-4 Iosco 1230 RSE SE 10.0 22.7 19.1 31.8 6.4 0.0 0.0 5.5 0.0 2-5 Iosco 1220 RSE 00 7.8 7.0 11.3 46.1 14.8 0.0 0.0 8.7 0.0 2-6 Ogemaw 1220 R4E $0 3.0 26.9 1.5 29.9 13.4 0.0 0.0 7.5 0.0 2-7 Ogemaw 1210 R4E 00 7.5 17.2 6.5 34.4 16.1 2.2 1.1 8.6 0.0 2-8 Ogemaw 1210 R4E 0E 7.8 23.3 15.5 31.1 8.7 1.0 1.0 5.8 0.0 2-9 Clare 1200 R60 00 10.5 40.4 21.9 9.6 2.6 0.0 0.0 1.8 0.0 2-10 Osceola 1190 R70 00 9.8 33.3 -25.2 14.6 0.8 0.0 0.0 0.0 0.0 2-12 Clar/Glad 1190 R2830 SE/0 2.2 26.7 17.8 28.9 12.6 0.0 1.5 0.0 0.0 2-13 Missaukee 1220 R70 S0cn 16.8 40.0 22.1 6.3 3.2 0.0 0.0 4.2 0.0 2-15 Missaukee 1230 R50 S0 17.1 28.6 25.7 1.4 8.6 0.0 0.0 0.0 0.0 average 8.9 27.3 16.2 24.0 8.5 0.3 0.4 3.8 0.0 standard deviation 4.6 9.4 7.4 11.9 4.9 0.7 0.6 3.4 0.0 MINOR SPECIES SAMPLE COUNTY LOCATION 01 Fir Ash Cedar Elm Bas- Aspen Birch Yellow wood birch 2-3 Iosco 1230 RSE S0 3.0 4.0 3.0 2.0 0.0 5.0 2.0 0.0 2-4 Iosco 123M R5E SE 1.0 4.0 0.0 0.0 0.0 0.0 0.0 0.0 2-5 Iosco 1220 R5E 00 0.0 0.0 1.0 1.0 1.0 0.0 2.0 0.0 2-6 Ogemaw 1220 R4E $0 0.0 4.0 3.0 2.0 1.0 0.0 2.0 0.0 2-7 Ogemaw 1210 R4E 00 0.0 2.0 0.0 2.0 2.0 0.0 0.0 0.0 2-8 Ogemaw 1210 R4E NE 0.0 1.0 3.0 1.0 0.0 0.0 1.0 0.0 2-9 Clare 1200 R60 00 2.0 3.0 2.0 2.0 4.0 0.0 0.0 2.0 2-10 Osceola 1190 R70 00 0.0 4.0 2.0 2.0 6.0 6.0 0.0 0.0 2-12 Clare/Glad 1190 R2830 SE/0 0.0 2.0 1.0 8.0 2.0 0.0 0.0 1.0 2-13 Missaukee 1220 R70 S0cn 0.0 1.0 0.0 0.0 3.0 1.0 2.0 0.0 2-15 Missaukee 1230 R50 80 1.0 0.0 1.0 1.0 0.0 2.0 1.0 0.0 average 0.6 2.3 1.5 1.9 1.7 1.3 0.9 0.3 standard deviation 1.0 1.5 1.2 2.1 1.9 2.1 0.9 0.0 Jack Red oak pine pine Red 0hite Black pine maple oak lock MAJOR SPECIES Sugar Hem- Beech 0hite maple OT LOCATION Citation Frequencies on Non-swampland for Lacustrine Sand and Gravel SAMPLE COUNTY Table D3. 00003000.onwnu.9. 20 6 1 000000000004 7 003070249173 3 mmLm&LL32757 ii ww lr 802000000000 1 MM 105001000000 003000000000 1 Y mmmemmmmmmm mm h m fifiJfififlfilJJJJ 5 “ 019001021336 000090320000 28 mmmmmelmmmm LL W 005080019202 1 B 0060LL¢LLL6L 000195176145 MM 333333333333 . 880100000000 000102461788 5’” s ssssssssssss el el all 100100000000 0.0w9.29.65fl~8.6.0.8. 56 .. m 880007010400 0032”“6570L7 26 l ............ 124343 21 E 100007010200 $36.4AN321M33RW3 23 .1 . . 04100805826J 8 .8.25¢|.nv.32514.wl. 8S ........... 241 4 1.1 3 C 059003045133 1 6632.6.2509832 75 C ..... . . E h 080005063220 myth-Emma 5.133 27 D. S ............ 12111 13.12 2 s A 000002054110 1 IN-I.81..9.°uelz3el.20. 16 M .1 05000006AU.220 I I I a‘ O ...... O L0.60.1u14.214.wl.el.0. 13 M F 0L0001050.1.3l.0 122 11 SSS "N ssuss o SSS "N ssus ”0 mm mm 12 113222 2 113222 RRRRRRRRRRR "II. RR RRRRRRRR NNNNNNNNN A "N NNNNN 2M4110009W“ a 2M IIOOOWW" 3 2222221111 3 22222111 TTTTTTTTTTT L T... TTTTTTTT Cheboygan 1380 R30 Manistee 1240 R140 50 Alpene P. Isle Cheboygan 1380 R30 Ogemaw Iosco Mason Gladwin Arenac Gladwin Gladwin Gladwin Alpene P. Isle Manistee Ogemaw Iosco Mason Gladwin Arenac Gladwin Gladwin Gladwin average standard SAMPLE COUNTY 333333333333 333333333333 standard average Jack pine pine oak oak Red white Black Red pine maple Beech White 85 Hem- lock MAJOR SPECIES Sugar maple Citation Frequencies on Non-suampland for Ice Contact Features LOCATION OT SAMPLE COUNTY Table D4. nU.nU.0.2A~0.0..6.~I.1.0.0.~).~I.8 000450085800050 134 0.6.0.8.J834.6.3224.7.0. nU.flwnU.5~I.9.7. 8.6.214.5~I..6. 421 221 432 nU.0.0.8.nU.nw0..l.9.0.0..9.35—7. nU.nU.AU.0.nU.al.nw14.0.0.4.,58.~I.I.-.. n~0.0.0.n~0.427.nwo.0.24.6. 0.0.0.nm0.0.16.20.0.0.6.0.1. 8.30.8.0.4.20.nw56.0.nw85 0..l.nU.nU.1.0.7.0.0.1.1.nv.1.7m5“ 514.35330.6.n7.36.l4.£w°”2 1.8.l.7.9.1.31.58.nmfim1.flm5 3122 1 212 Aw.6..l.0.flw8.81flw0.6.30.14.5 9.39.8.AJIJIM0.AU.S8.I..I.8w.6. 572090700125422 122 21 524002600056090 U I O 6 I I I I I O I I D e n 8 susussssunuuusn wwwnuuammnnummm RRRRRRRRRRRRRRR muuuuunmmmmmuam 3222222 2222222 TTTTTTTTTTTTTTT dd d fr DI oowmmmuomuuuum t‘l‘l f u. m. mmmmc munccmm n. « scammaamcmmmmwa 444444444444444 birch Aspen Birch Yellow Ash Cedar Elm Bas- HOOd MINOR SPECIES Fir LOCATION OT average standard deviation SAMPLE COUNTY 0.0.0.0.0.0.nw0.0.0.nu.0.0.0.0. 0.0.0.0.0.0.nw0.0.0.0.0.0.0.0. $358.0.1d0.0.0.1~6.0.0.0.a|. 0.3.6.0.L2nWflm0.8&0.0.0.3 0.nw0.29.0.5nw50.8.nw19.9. 0.0.nw9.31.1.nwlm.6.nu.0.313 3An0.0.0.0.l4.0.nw0.3n9.0.0.0. 20.nwnw0.nU.14.0.0.0.350.0.nw 50.30.0.0.I4.0.0.0.8.8.0.0.0. 10.a|.0.0.0.al.0.0.0.nw0.0.0.n0. 000000000000000 000000000000000 0000000000000.0.0. 0000000000000.0.nw 0.0.00000000.0.0.0.nw0. 6 n 8 C C “wwwawaauwuwwwm mmwuuuammnxuumm RRRRRRRRRRRRRRR ummmuunumnummmm 32222222222222.1- TTTTTTTTTTTTTTT dd .6 rr pl oomwmmuomumuflm tff ‘- u. m. m mm. mnnccmm n. » eccmWAAmcmmmmwa 444444444444444 00 00 00 I O 00 00 00 standard deviation average Table 05. Citation Frequencies on Non-swampland for End Moraines of Coarse-textured Till MAJOR SPECIES SAMPLE COUNTY LOCATION OT Sugar Hem- Beech Hhite Red Hhite Black Red Jack maple lock pine maple oak oak pine pine 5-1 Cheboygan T33N R4H SE 46.1 14.2 21.3 1.4 0.0 0.0 0.0 0.0 0.0 5-2 G.Traverse T25N R10H NH 43.4 16.3 29.5 0.0 0.8 0.0 0.0 0.0 0.0 5-3 G.Traverse T25N R1OH NE 41.1 16.4 27.4 0.0 0.7 0.0 0.0 0.0 0.0 5-4 Osceola TZON R8H NH 38.7 7.7 42.3 0.0 0.0 0.0 0.0 0.0 0.0 5-5 Clare T19N RSH SE 4.14 10.3 9.7 31.0 3.4 13.8 9.0 11.7 0.0 5-6 G.Traverse T27N R9H NEcw 28.1 26.7 34.8 5.3 0.0 0.0 0.0 0.7 0.0 5~8 Alcona 127N RSE SH 6.0 6.0 10.0 5.0 2.0 1.0 2.0 38.0 28.0 5-9 Newaygo T16N R11H SH 16.5 28.5 29.7 5.7 5.7 0.0 1.9 3.8 0.0 5-10 Newaygo T15N R11H NH 1.3 8.2 25.3 32.9 10.1 2.5 1.3 14.6 0.0 5-11 Osceola T17N R8H SH 10.5 26.7 43.8 8.6 2.9 0.0 0.0 0.0 0.0 5-12 Mecosta T16N R8H SH 5.5 31.2 33.9 13.8 5.5 0.9 1.8 1.8 0.0 5-13 Missaukee T24N R7H SH 21.9 19.0 48.2 2.9 3.6 0.0 0.0 0.0 0.0 5-14 Isabella T16N R6H NE 5.5 29.1 32.7 12.7 5.5 1.8 0.0 9.1 0.0 5-15 Oceana T14N R17H NE 21.5 3.3 57.9 0.0 1.6 0.8 1.6 0.0 0.0 average 20.7 17.4 31.9 8.6 3.0 1.5 1.3 5.7 2.0 standard deviation 15.6 9.2 13.0 10.5 2.8 3.5 2.3 10.1 7.2 MINOR SPECIES SAMPLE COUNTY LOCATION OT Fir Ash Cedar Elm Bas- Aspen Birch Yellow Hood birch 5-1 Cheboygan T33N’R4H SE 0.0 0.0 0.0 12.8 4.3 0.0 0.0 0.0 5-2 G.Traverse TZSN R10H NH 0.0 0.8 0.0 3.9 2.3 0.0 2.3 0.8 5-3 G.Traverse T25N R1OH NE 0.0 0.0 0.0 6.8 6.2 0.0 0.7 0.7 5-4 Osceola T20N R8H NH 0.0 1 4 0.0 7.0 2.8 0.0 0.0 0.0 5-5 Clare T19N RSH SE 0.0 0.0 0.7 0.0 0.0 4.8 1.4 0.0 576 G.Traverse T27N R9H NEcw 0.0 0.0 0.0 1.5 0.7 1.5 0.0 0.0 5-8 Alcona TZTN R5E SH 0.0 0.0 0.0 0.0 2.0 0.0 0.0 0.0 5-9 Newaygo T16N R11H SH 0.0 1.9 0.6 0.6 0.0 3.2 1.9 0.0 5-10 Newaygo T1SN R11H NH 0.0 1.3 1.3 0.0 0.0 0.0 1.3 0.0 5-11 Osceola T17N R8H SH 0.0 0.0 0.0 1.0 3.8 1.0 1.9 0.0 5-12 Mecosta T16N R8H SH 0.0 0.9 0.0 3.7 0.0 0.0 0.9 0.0 5-13 Missaukee TZ4N R7H SH 0.0 0.0 2.9 0.7 0.0 0.7 0.0 0.0 5-14 Isabella T16N R6H NE 0.0 0.9 0.0 0.0 0.9 0.0 1.8 0.0 5-15 Oceana T14N R17H NE 0.0 0.8 0.0 5.0 6.6 0.0 0.8 0.0 average 0.0 0.6 0.2 3.2 2.2 0.7 1.0 0.1 standard deviation 0.0 0.6 0.4 3.6 2.2 1.4 0.8 0.3 87 Table 06. Citation Frequency for Non-suampland for Ground Moraines of Coarse-textured Till MAJOR SPECIES SAMPLE COUNTY LOCATION OT Sugar Nem- Beech Hhite Red Hhite Black Red Jack maple lock pine maple oak oak pine pine 6-1 P. Isle T35N R2E SH 34.1 17.1 33.3 0.0 0.0 0.0 0.0 0.0 0.0 6-2 Cheboygan T34N R1H NH 37.0 21.7 25.4 0.0 0.0 0.0 0.0 0.0 0.0 6-3 Cheboygan T34N R1E SE 11.8 27.5 18.6 11.8 2.0 0.0 0.0 2.9 0.0 6-4 P. Isle T33N R2E NE 14.7 6.2 6.2 13.2 0.8 0.8 3.1 24.8 23.3 6-5 Alpena T30N R6E NE 6.0 22.0 42.0 4.0 0.0 0.0 2.0 24.0 0.0 6-6 P. Isle T34N RSE SH 27.9 30.2 30.2 0.0 0.0 0.0 0.0 0.0 0.0 6-7 Montmorency T29N R3E SH 19.3 19.3 12.8 10.1 0.0 0.0 3.7 22.9 7.3 6-8 Montmorency T29N R3E SE 4.7 13.1 5.6 10.3 0.0 0.0 2.8 57.0 1.9 6-9 Alcona T28N R6E SE 0.9 2.7 4.5 18.2 2.7 0.0 6.4 48.2 4.5 6-10 Alcona 127N R7E SE 1.8 0.0 0.0 25.7 0.9 0.9 5.3 53.1 8.0 6-11 Clare T19N R6H SE 6.3 41.7 7.9 18.9 3.1 0.0 0.0 9.4 1.0 6-12 Lake T17N R11H NE 27.4 18.5 45.9 3.0 0.7 0.0 0.0 0.0 0.0 6-13 Osceola T17N R10H SH 29.4 5.9 45.1 1.3 0.0 0.0 0.0 0.0 0.0 6-14 Isabella T14N R6H SH 11.2 32.8 28.0 15.2 5.6 1.6 0.8 0.0 0.0 6-15 Montcalm T12N R7H NE 0.8 15.7 33.1 44.1 ' 2.4 0.0 2.4 0.0 0.0 average 15.5 18.3 22.6 11.7 1.2 0.2 1.8 16.2 3.1 standard deviation 14.2 18.4 21.8 12.5 1.3 0.2 1.9 17.3 3.3 MINOR SPECIES SAMPLE COUNTY LOCATION 0T Fir Ash Cedar Elm Bas- Aspen Birch Yellow Hood birch 6-1 P. Isle T35N R2E SH 0.8 0.0 4.9 2.4 2.4 1.6 0.0 3.3 6-2 Cheboygan T34N R1H NH 0.0 0.0 4.3 1.4 2.9 0.7 6.5 0.0 6-3 Cheboygan T34N R1E SE 2.0 0.0 2.9 0.0 1.0 14.7 2.9 2.0 6-4 P. Isle T33N R2E NE 0.0 0.0 0.0 0.8 0.8 3.1 2.3 0.0 6-5 Alpena T30N R6E NE 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 6-6 P. Isle T34N RSE SH 0.0 0.0 4.7 3.5 2.3 0.0 0.0 1.2 6-7 Montmorency T29N R3E SH 0.0 0.0 0.0 0.0 2.8 0.9 0.9 0.0 6-8 Montmorency T29N R3E SE 0.9 0.0 0.9 0.0 0.0 2.8 0.0 0.0 6-9 Alcona 128N R6E SE 1.8 0.0 0.0 0.0 1.8 5.5 1.8 0.9 6-10 Alcona T27N R7E SE 0.0 0.0 0.0 0.0 0.0 2.7 0.9 0.9 6-11 Clare T19N R6H SE 0.0 0.8 0.8 0.8 2.4 3.9 1.6 0.8 6-12 Lake T17N R11H NE 0.0 0.0 0.0 0.0 1.5 0.0 3.0 0.0 6-13 Osceola T17N R10H SH 0.0 0.0 0.0 14.4 3.9 0.0 0.0 0.0 6-14 Isabella T14N R6H SH 0.0 0.0 0.0 0.0 2.4 0.0 2.4 0.0 6-15 Montcalm T12N R7H NE 0.0 0.0 0.0 0.0 0.8 0.0 0.8 0.0 average 0.4 0.1 1.2 1.6 1.7 2.4 1.5 0.6 standard deviation 0.3 0.1 1.0 1.5 1.6 2.4 1.7 0.4 Table 07. Citation Frequency for Non-swampland for End Moraines of Medium-textured Till MAJOR SPECIES SAMPLE COUNTY LOCATION OT Sugar Hem- Beech Hhite Red Hhite Black Red Jack maple lock pine maple oak oak pine pine 7-1 Otsego T30N R2H NE 37.7 18.8 37.0 0.0 0.0 0.0 0.0 0.0 0.0 7-2 Otsego T30N R1H NH 36.9 9.9 46.1 0.0 0.0 0.0 0.0 0.0 0.0 7-3 Montmorency T30N R1E NH 33.9 11.6 37.2 3.3 0.0 0.0 0.0 0.0 6.0 7-4 Montmorency T30N R1E SE 35.2 11.4 38.1 2.9 0.0 0.0 0.0 5.7 1.0 7—5 Alcona T25N R6E SH 1.2 22.4 27.1 11.2 0.0 7.5 1.9 8.4 1.9 7-6 Alcona T25N R65 NE 5.3 38.1 15.0 13.3 0.0 0.0 0.0 20.4 3.5 7-7 Manistee T22N R16H NE 7.0 13.9 56.5 6.1 5.2 1.7 1.7 5.2 0.0 average 23.9 18.0 36.7 5.2 0.7 1.3 0.5 5.7 0.9 standard deviation 14.0 9.2 12.2 4.9 1.8 2.6 0.8 6.8 1.3 MINOR SPECIES SAMPLE COUNTY LOCATION OT Fir Ash Cedar Elm Bas- Aspen Birch Yellow wood birch 7-1 Otsego T30N R2H NE 0.0 0.0 0.0 3.6 0.7 0.0 0.0 2.2 7-2 Otsego T30N R1H NH 0.0 0.0 0.0 2.8 1.4 0.0 0.0 2.8 7-3 Montmorency T30N R1E NH 0.0 0.0 0.8 3.3 3.3 2.5 0.0 4.1 7-4 Montmorency T30N R1E SE 0.0 0.0 0.0 0.0 2.9 1.0 0.0 1.9 7-5 Alcona T25N R6E SH 0.0 0.0 0.9 1.9 0.0 1.9 1.9 1.9 7-6 Alcona TZSN R66 NE 0.0 1.8 0.9 0.0 0.0 0.0 1.8 0.0 7‘7 Manistee T22N R16H NE 0.0 2.6 0.0 0.0 0.0 0.0 0.0 0.0 average 0.0 0.6 0.4 1.7 1.2 0.8 0.5 1.8 standard deviation 0.0 1.0 0.4 1.5 1.3 1.0 0.8 1.4 89 Table 08. Citation Frequency on Non-swampland for End Moraines of Fine-textured Till MAJOR SPECIES SAMPLE COUNTY LOCATION OT Sugar Hem- Beech Hhite Red Hhite Black Red Jack maple lock - pine maple oak oak pine pine 10-1 Alcona T26N R6E NH 7.0 9.0 3.0 26.0 0.0 0.0 3.0 40.0 7.0 10-3 Alcona T26N R6E SE 5.4 5.4 3.6 29.7 0.0 0.0 6.3 44.1 0.0 10-4 Oscoda T26N R2E SE 2.7 22.3 1.8 8.9 0.9 0.9 0.0 17.0 20.5 10-5 Oscoda T26N R3E Sch 0.0 13.5 2.8 7.1 0.0 0.0 2.8 31.2 34.0 10-6 Oscoda T26N R4E SEcw 2.0 1.0 3.0 15.8 3.0 4.0 5.9 35.6 22.8 10-7 Oscoda 125N R4E NE 10.6 15.4 11.5 15.4 4.8 0.0 1.9 27.9 7.7 10-8 Oscoda T25N R4E SE/H 11.4 19.5 30.9 15.4 2.4 0.8 3.3 11.4 0.0 10-9 Ogemaw T23N RZE NE/H 10.6 33.0 17.0 14.9 0.0 1.1 0.0 6.4 0.0 1-10 Ogemaw T22N R2E NEcs 11.1 45.5 35.4 1.0 2.0 0.0 0.0 0.0 0.0 10-11 Ogemaw T23N R4E NE 14.6 22.5 9.0 27.0 5.6 0.0 1.1 5.6 0.0 10-12 Ogemaw T23N R4E SEcw 0.0 41.4 10.3 18.4 2.3 2.3 2.3 11.5 0.0 10-13 Arenac T20N R4E NHcs 4.6 28.4 12.8 23.9 1.8 0.0 0.0 16.5 0.0 average 6.7 21.4 11.8 17.0 1.9 0.8 2.2 20.6 7.7 standard deviation 4.7 13.2 10.6 8.3 1.8 1.2 2.1 14.1 11.2 MINOR SPECIES SAMPLE COUNTY LOCATION OT Fir Ash Cedar Elm Bas- Aspen Birch Yellow wood birch 10-1 Alcona T26N R6E NH 0.0 0.0 0.0 0.0 1.0 3.0 1.0 0.0 10-3 Alcona T26N R6E SE 0.0 0.0 0.0 0.0 0.0 1.8 0.9 2.7 10-4 Oscoda T26N RZE SE 0.0 0.0 0.9 0.9 0.9 14.3 8.0 0.0 10-5 Oscoda T26N R3E Sch 0.0 0.0 1.4 0.7 0.0 2.8 3.5 0.0 10-6 Oscoda 126N R4E SEcw 0.0 0.0 0.0 0.0 0.0 5.9 1.0 0.0 10-7 Oscoda T25N R4E NE 0.0 1.0 0.0 0.0 0.0 2.9 0.0 1.0 10-8 Oscoda T25N R485 SE/H 0.0 3.3 0.0 0.0 0.0 0.0 0.8 0.8 10-9 Ogemaw 123N R283 NE/H 0.0 0.0 3.2 3.2 1.1 2.1 6.4 1.1 1-10 Ogemaw TZZN RZE NEcs 0.0 1.0 1.0 1.0 1.0 0.0 1.0 0.0 10-11 Ogemaw T23N R4E NE 1.1 1.1 3.4 1.1 1.1 1.1 4.5 1.1 10-12 Ogemaw T23N R4E SEcw 3.4 1.1 3.4 0.0 0.0 0.0 3.4 0.0 10-13 Arenac T20N R4E NHcs 0.0 3.7 2.8 0.9 0.9 3.7 0.0 0.0 average 0.4 0.9 1.3 0.7 0.5 3.1 2.6 0.6 standard deviation 1.0 1.2 1.4 0.9 0.5 3.8 2.5 0.8 E x i d n e D. D. A Table E1 . Cululative comt data for swaups on outwash plains. Data in these tables represent the cumlative corner and line trees surveyed in swaup or wetlands in each township. The percent of lines that fel l in wetlands is presented, however, the nmber of wet areas is not. SAMPLE CGJNTY XAREA Spruce Ash Fir Tam- Cedar Nem- Birch Hhite Aspen Elm SUI SHAMP arack lock pine \-1 P. Isle 31.9 --- --- --- --- --- --- --- --- --- --- --- * 1-2 Otsego 0.0 0 0 0 0 0 0 0 0 0 0 1-3 Crawford 5.0 0 0 1 2 6 0 1 0 0 0 10 1-4 Mason 7.5 1 2 0 0 3 0 0 2 0 1 9 1-5 Kalkaska 1.2 --- --- --- --- --- --- --- --- --- --- --- * 1-6 Roscommon 23.3 8 2 0 16 8 0 1 .2 3 0 40 1-7 Rosconlnon 46.7 8 0 1 22 29 4 5 2 6 0 77 1-8 Manistee 2.1 0 2 0 0 0 1 0 0 0 0 3 1-9 Manistee 3.1 0 0 0 0 1 1 0 0 1 0 3 1-10 Clare 1.7 1 2 0 4 0 0 0 1 0 0 8 1-11 Lake 2.5 0 0 0 0 0 1 1 0 0 0 2 1-12 Mason 14.6 1 1 0 0 4 4 0 0 0 0 10 1-13 Newaygo 14.6 ~-- ~-- --- --- --- --- --- --- * 1-14 Newaygo 6.0 0 0 0 0 2 0 0 1 3 0 6 1-15 Kalkaska 10.4 0 1 0 8 5 1 0 4 0 0 19 * swam data were not recorded for this sanple 91 Table 62. Cumulative Comt Data for Swaups on Gromd Moraines of Fine-textured Till SAMPLE COJNTY XAREA Spruce Ash Fir Tam- Cedar Nem- Birch Hhite Aspen Elm Sun SHAMP arack lock pine 2-3 Iosco 18.4 1 2 1 7 2 4 0 3 1 0 21 2-4 Iosco 7.3 0 0 0 1 1 3 0 0 0 0 5 2-5 Iosco 7.3 0 0 0 5 1 0 0 0 0 0 6 2-6 Ogemaw 34.6 5 12 1 17 7 5 0 5 2 0 54 2-7 Ogemaw 23.6 0 5 1 11 0 0 0 3 0 0 20 2-8 Ogemaw 14.8 --- --- --- --- -- --- --- --- --- --- --- * 2-9 Clare 22.9 1 3 2 4 19 11 0 3 ‘0 0 43 2-10 Osceola 17.1 0 8 1 1 5 6 0 3 3 2 29 2-12 Clar/Olad 8.3 0 8 0 4 5 4 0 0 0 0 21 2-13 Missaukee 20.8 4 3 0 2 19 5 0 0 0 0 33 2-15 Missaukee 48.3 7 1 1 15 27 18 0 5 0 0 74 * swam) data were not recorded for this sanple 92 Table 63. Cumlative Count Data for Swanps on Lacustrine Sand 8 Gravel SAMPLE CQJNJTY iAREA Spruce Ash Fir Tam- Cedar Nem- Birch Hhite Aspen Elm SUI SHAMP arack lock pine 3-1 Cheboygan 51.3 12 2 0 25 36 13 2 1 0 0 91 3-2 Alpene 17.5 0 0 0 1 22 7 3 1 0 2 36 3-3 P. Isle 38.3 3 8 4 16 29 3 0 1 0 0 ‘ 64 3-5 Manistee 27.1 0 8 0 0 14 2 2 0 0 0 26 3-7 Ogemaw 25.4 0 2 0 0 19 2 0 3 0 0 26 3-8 Iosco 10.4 0 3 0 0 0 1 1 4 0 0 9 3-10 Mason 22.1 0 3 0 3 9 4 1 2 0 0 22 3-11 Gladwin 15.4 0 2 0 2 5 2 1 0 0 0 12 3-12 Arenac 21.3 3 1 0 12 6 2 1 5 0 0 30 3-13 Gladwin 25.8 0 3 0 11 9 1 3 2 0 2 31 3-14 Gladwin 22.9 1 0 2 8 7 0 2 8 0 0 28 3-15 Gladwin 15.0 3 6 1 3 6 0 0 5 0 0 24 93 Table E4. Cumulative Count Data for Swamps on Ice Contact Features XAREA Spruce Ash Fir Tam- Cedar Hem- Birch Hhite Aspen Elm SUM pine lock arack SHAMP SAMPLE COUNTY 1O 21 35 25 000000000000000 000034000000000 000012000000002 000000000000000 oooozsnsoooooo-J oooozafioooooooa 0000129000000040l 000010000000002 000000000000000 000000000000000 0.8.0.0.5J090000621 0.0.nmnm22520000147. 122 1 Crawford Crawford Oscoda Oscoda Alcona Alcona Ogemaw Crawford Oscoda Ogemaw Ogemaw Ogemaw Osmu Euuun ‘IQIQIQIQ‘“IQI~IQIHIVIQIQ 94 Table E5. Cumulative Count Data for Swamps on End Moraines of Coarse-textured Till SAMPLE COUNTY XAREA Spruce Ash Fir Tam- Cedar Nem- Birch Hhite Aspen Elm SUM SHAMP arack lock pine Charlevoix 0. Traverse C. Traverse Osceola Clare 0. Traverse Alcona Newaygo Newaygo Osceola Mecosta Missaukee Isabella Oceana 8‘3‘Ul 5m0 I ad‘dddouomfiufigd m5MN-DO OOOOOOOOO ONOOOW-I-DO OOOOOOOOO OO-‘dOONOO 090000000 000000000 I a... Old-PUNONJ‘UIJ‘UUIOO I O I I C I I O I I 0000: I I I I I I I I I I I I I I I I I I I I I OOMHMD-PO‘GNUOUO uuumum?uuumuum OdOMIOOOOOOOdO coca-OONMOaaao coo-aooaaooaoo OdOuIONOOOOOOO O-POUI I 0000 O-POO 000° GOOD I * swamp data were not recorded for this sample Table E6. Cumulative Count Data for Swamps on Ground Moraines of Coarse-textured Till SAMPLE COUNTY XAREA Spruce Ash Fir Tam- Cedar Nem- Birch Hhite Aspen Elm SUM SHAMP arack lock pine 6-1 P. Isle 29.1 4 3 0 3 21 5 0 2 2 1 41 6-2 Cheboygan 15.4 --- --- ... --- --- --- --- --- --- --- --- 6-3 Cheboygan 40.6 6 0 0 10 22 12 5 6 2 0 63 6-4 P. Isle 16.7 4 1 0 10 14 1 0 3 2 0 35 6-5 Alpene 49.6 4 2 0 8 39 2 3 1 2 0 61 6-6 P. Isle 41.6 7 3 0 7 37 8 3 1 0 0 66 6-7 Montmorency 10.8 3 0 0 4 8 0 0 1 0 0 16 6-8 Montmorency 13.3 1 1 1 5 8 0 0 1 0 0 17 6-9 Alcona 6.3 0 0 0 1 5 1 0 1 0 0 8 6-10 Alcona 5.4 --- --- --- --- --- --- --- --- --- --- --- 6-11 Clare 12.5 0 3 1 5 4 4 2 4 0 2 25 6-12 Lake 5.6 0 0 0 2 0 1 1 1 0 1 6 6-13 Osceola 17.3 2 7 0 4 1 12 1 0 0 0 27 6-14 Isabella 0.0 0 0 0 0 0 0 0 0 0 0 --- 6-15 Montcalm 17.5 0 0 0 2 5 3 1 0 0 0 11 * swamp data were not recorded for this sample Table E7. 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