Assessing Ecological Restoration Potentials of Wisconsin (U.S.A.) Using Historical Landscape Reconstructions

Assessing Ecological Restoration Potentials of Wisconsin (U.S.A.) Using Historical Landscape Reconstructions Janine Bolliger, 1,2,3 Lisa A. Schulte, 2,4 Sean N. Burrows, 2 Theodore A. Sickley, 2 and David J. Mladenoff 2 Abstract Historical landscape reconstructions provide baseline information for evaluating current land management regimes and restoration potentials. We assessed the historical landscape composition and structure of the state of Wisconsin (U.S.A.). This knowledge forms a basis for delineation of potential spatial distribution of forest species and landscape structures before major humaninduced changes, quantification of the spatial extent and intensity of change in habitats and landscapes, and identification of target areas for ecological restoration (e.g., threatened ecosystems). Methods included two conceptually and methodologically different vegetation classifications. The classifications rely on the original U.S. Public Land Office Surveys conducted during the nineteenth century to sell land to Euro-American settlers. The subjective classification method we examined was R. W. Finley s Original Vegetation of Wisconsin. This classification accounts for qualitative information such as early land surveyors descriptions of ecosystems (e.g., distinctions into wet and dry prairies). However, the classification is hard to reproduce because some criteria are not strictly hierarchical or exclusive. Numerical cluster analysis was used as an objective classification method. This method offers reproducible, quantitative results and full hierarchical distinction between the classes. Qualitative information, however, is not accounted for in the objective numerical approach and may thus be viewed as less complete when representing local landscape details. Both classifications represent major vegetation characteristics consisting of a complex mosaic of forests (coniferous, mixed coniferous deciduous, deciduous, and swamps), savannas (oak and pine), and prairies. The objective classification indicates that savannas cover two times more (40%) and prairies six times less area (2%) compared with the subjective classification (savanna, 20%; prairie, 12%). We address the applications of these classifications to current and potential restoration projects, including eastern hemlock (Tsuga canadensis), wetland/riparian, savanna, and prairie ecosystems. Key words: Historical Landscape Reconstruction; classification method comparison, hierarchical cluster analysis, historical landscape reconstruction, Public Land Surveys, R. W. Finley s Original Vegetation of Wisconsin, restoration, Wisconsin (U.S.A.). 1 Swiss Federal Research Institute WSL, Zürcherstrasse 111, CH-8903 Birmensdorf. 2 Department of Forest Ecology and Management, University of Wisconsin Madison, Madison, WI 53706, U.S.A. 3 Address correspondence to J. Bolliger, email bolliger@wsl.ch 4 Present address: Natural Resource Ecology and Management, Iowa State University, 124 Science II, Ames, IA 50011, U.S.A. Ó 2004 Society for Ecological Restoration International Introduction The search for more self-sustaining forms of commercial forestry illustrates the need for stronger links between traditional practices and ecological principles (Franklin 1993; Mladenoff & Pastor 1993; Andersson et al. 2000). One such link, ecosystem management, has been used as a framework to conserve, protect, or restore entire ecological systems, including their structure and function, while accounting for economic and social concerns (Bormann 1993; Jensen & Everett 1994; Harrod et al. 1996). The framework is particularly important for applied science and land management, because scientists and managers increasingly confront a broad suite of issues ranging from maintaining biodiversity, conducting ecologically sustainable economics, or providing services and products for a variety of public needs. Successful ecosystem management will thus facilitate sustaining natural diversity and resources. However, the corresponding management objectives need to be tailored with respect to the specific ecosystem, thus pointing out the growing needs for baseline information on ecosystem structure and function. One way of obtaining such baseline information is through reconstructions of historical range of variability in ecosystems (Jensen & Everett 1994; Landres et al. 1999). Historical variability has been recognized as a valuable tool for understanding ecosystem response to change and as a means of preserving ecosystems into the future (Landres et al. 1999). Knowledge of historical landscape composition and structure before Euro-American impact are of particular 124 Restoration Ecology Vol. 12 No. 1, pp. 124 142 MARCH 2004

interest when studying long-term ecological processes and subsequent landscape pattern in North America. One data source to characterize historical vegetation is the original Public Land Survey (PLS) records that have been widely used to obtain baseline information. Despite biases, ambiguities, and inconsistencies (Bourdo 1956; Manies & Mladenoff 2000; Manies et al. 2001; Mladenoff et al. 2002), the PLS and similar surveys are considered to provide exceptional landscape scale information for a broad variety of vegetation analyses (Schulte & Mladenoff 2001), including local (Rodgers & Anderson 1979; Nelson 1997; Batek et al. 1999) and regional reconstructions (Curtis 1959; Marschner 1974; Finley 1976; Comer et al. 1995), historical disturbance events (Kline & Cottam 1979; White & Mladenoff 1994; Zhang et al. 2000), landscape change (Strittholt & Boerner 1995; Radeloff et al. 2000; Cowell & Jackson 2002), analysis of effects of human impact on land-use change (Foster et al. 1998; Axelsson & Östlund 2000; Bürgi & Russell 2001), and early socioeconomic trends (Silbernagel et al. 1997). One way to visualize areas of particular interest for restoration includes landscape classification. Objective approaches to classification, such as cluster analysis (Schulte et al. 2002) and fuzzy classification (Brown 1998a, 1998b), employ numerical criteria, whereas subjective approaches (Finley 1951, 1976; Marschner 1974; Küchler & Zonneveld 1988) mostly rely on mapping rules delineated by individuals. Because different classification approaches produce variable sets of solutions, the representations are strongly method dependent. A method s applicability varies depending on questions and objectives, and classification evaluation is therefore important to assess the limitations and utility of different approaches (Stehman 1999). This study is a comparative evaluation of two landscape classification methods that aim to assess the historical restoration potential of the landscape of Wisconsin (U.S.A.) before Euro-American settlement (approximately 1850). The methods differ conceptually in that one classification, R. W. Finley s Original Vegetation of Wisconsin (1951, 1976), is largely based on subjective criteria and qualitative use of data, whereas the second classification method relies on objective criteria and is strongly numerical in character. Both landscape classifications are based on data derived from the original U.S. PLS, collected in Wisconsin between 1832 and 1866. Objectives of this study include: (1) quantifying the landscape heterogeneity (landscape composition and structure); (2) relating the differences in patterning to the classification concepts and methodologies; and (3) assessing the utility of the two classifications for decisionmaking in land management, conservation, and restoration. Materials and Methods Study Area The study area encompasses Wisconsin, U.S.A. (138,000 km 2 ) (Fig. 1). The climate of the state is generally humid continental, and conditions range from hot, humid summers to cold, dry winters (Table 1). Differences in climatic patterns between the northern and southern parts of Wisconsin are due to the relatively strong influence of warmer, more humid air from the Gulf of Mexico in the south, whereas northern Wisconsin is mostly influenced by cooler conditions from the Arctic. Locations along the shores of Lake Superior and Lake Michigan are climatically milder, with cooler summers and warmer winters (Table 1; Fig. 1). Major landscape features in Wisconsin are of glacial origin and include moraines, outwash plains, drumlins, till, and lake plains (Table 1). The driftless area located in the southwestern part of the state (Fig. 1), however, has not been glaciated in recent episodes and exhibits stronger topographical gradients. Soils across the state include sands, clays, loams and loess, as well as peat (Table 1). Climatic differences between northern and southern Wisconsin are manifested in the vegetation physiognomy (Curtis 1959). Before Euro-American settlement, the southern Wisconsin landscape consisted of a mosaic of prairie, savanna, open forest, and closed forest (Curtis & MacIntosh 1951; Curtis 1959), influenced by aboriginal fire practices (Russell 1983). Most of southern Wisconsin has been transformed into agricultural land (dairy farms and crop fields). In contrast, northern Wisconsin was primarily covered by northern hardwood forests. Northern Wisconsin has undergone major changes in forest composition and structure, largely due to extensive logging activity and associated slash fires between the 1860s and 1930s (Fries 1951; Lorimer & Gough 1988). Current land uses include wood production for lumber and pulp, recreation, and some agriculture. The Public Land Surveys The U.S. General Land Office original PLS were carried out in the nineteenth century from OH to the west coast of U.S.A. The surveys first divided the land into 6 3 6mi (9.7 3 9.7 km) units called townships, which were in turn subdivided into 36 1 3 1 mi (1.6 3 1.6 km) units called sections (Stewart 1935). Along the grid, survey posts were set every half mile (quarter section corners) and every full mile (section corners). Meander corners were set at locations where survey lines intersected navigable rivers and lakes. Between one and four trees near corner posts were blazed (bearing or witness trees). Witness tree species, diameter, distance, and direction from the tree to the corner post were recorded by the surveyors in notebooks. Notes on ecosystem properties, such as swamps, burns, or windfall, were also recorded. Upon completion of an entire township, surveyors sometimes provided written descriptions of dominant over- and understory species, agricultural suitability of the soils, or Native Americans and early Euro-American settlements. Sometimes these features were sketched on maps that accompanied the descriptions. Despite various biases and constraints (Manies & Mladenoff MARCH 2004 Restoration Ecology 125

N Superior coastal plain Northwest lowlands Northwest sands Northcentral forest Northern highland Northeast hills Northeast sands Western prairie Farm forest transition Central sand plains Northeast plains Northern Lake Michigan coastal MN IA MO C a n a d a Lake Superior WI MI Lake Michigan IL IN OH Western coulees and ridges Southwest savanna Central sand hills Southeast glacial plains Southern Lake Michigan coastal 0 50 100 km Figure 1. Study area of the state of Wisconsin (U.S.A.) with ecoregions derived from the USDA Forest Service Hierarchical Land Classification System (Keys et al. 1995; WiDNR 1999). 2000; Manies et al. 2001; Mladenoff et al. 2002), the surveys are widely recognized to provide reliable quantitative and qualitative information over landscapes. Subjective Classification: Finley s Original Vegetation of Wisconsin R. W. Finley s Original Vegetation of Wisconsin (1951, 1976) map is based on data contained within the surveys (Fig. 2a & 2b). First, the data were recorded and assembled in a comprehensive manner. Finley manually transcribed most of the information provided by the surveys (descriptive survey notes, witness tree information, and surveyor sketches). The data were then interpreted, analyzed, and put into a map form. The following descriptions are based on Finley s (1976) thesis. Nonspecific Survey Notes. Interpretations had to be made in cases of nonspecific survey notes. In many cases the surveyor ambiguously identified trees, naming them generically (e.g., pine) or by obscure common name (e.g., bastard pine) instead of assigning the tree to a known species occurring in the area (e.g., jack pine; Pinus banksiana). Finley (1951, 1976) dealt with the problem by subjectively assigning the most likely species present based on the following clues: (1) neighboring trees; (2) abiotic site characteristics (soil, topography); (3) agreement among other, more specific surveyors working in the same area. The final map does not indicate where Finley found ambiguities nor is it known how many nonspecific trees he identified. Data Processing. To assemble the data Finley generated separate sheets for each township (6 3 6 mi survey unit) within Wisconsin. The ecological information recorded along each linear mile of survey transect was transcribed from the surveyors notebooks to the corresponding line on the township grid. Transcribed information included the characteristics of witness trees (species, diameter, and distance) as well as qualitative descriptions of the mile transect. Qualitative notes sometimes included handdrawn sketch maps. Other information of relevance was the general summary of each township, year of survey, and the names of the surveyors. Vegetation Compositional Classification. To analyze and interpret the data, the survey notes were organized into plant communities. Plant communities were identified based on a combination of dominant species composition 126 Restoration Ecology MARCH 2004

Table 1. Climate, landform, and soil characteristics of landscapes of Wisconsin. Landscapes are section-level ecoregions from the hierarchical USDA Forest Service Vegetation Classification (Albert 1995; Keys et al. 1995; WiDNR 2002). Ecoregion Climate Landform Soil Superior coastal plain Northwest lowlands Northwest sands Climate heavily moderated by Lake Superior; moderate snow Northcentral forest Continental climate; shorter growing season; moderate snow Glacial lake plain and waterreworked moraine Climate somewhat moderated Lake Superior lobe ground by Lake Superior; lighter snows moraine and drumlin fields Climate somewhat moderated Pitted outwash plain; flat terraces by Lake Superior; lighter snows intersected by hummock sediments Till plains, end moraines, and outwash channels; kettle lakes and wetlands Leached calcareous red loams and clays; peat extensive in wetlands Acidic loams Deep loamy sands low in organic matter Acidic and rocky loamy sand, sandy loam, or silt loam; fragipans common; peat extensive in wetlands Northern highland Winters long and cold; heavy Pitted outwash plain with Acidic sands snowfall; along with northeast coarse-textured moraines hills, has shortest growing season in study area Northeast hills Continental climate; variable Rolling drumlins; parallel end Neutral sandy loam to loam, snowfall; along with Northern moraines separated by outwash may have silt cap of loess, Highland, has shortest growing channels; swampy depressions small inclusions of peat season in study area Northeast sands Climate somewhat moderated Bedrock knobs surrounded by Droughty outwash sands by Lake Michigan; heavy extensive outwash plains, snowfall some pitted Northeast plains Climate somewhat influenced Undulating till plain underlain Rocky, podsolized pink sandy by Lake Michigan; moderate by limestone dolomite bedrock loams interspersed with peat snow and muck Northern Lake Michigan coastal Climate moderated by Lake Michigan and Green Bay Flat-to-rolling plain of lacustrine clays, ground and end moraines, some underlain by limestone and dolomite Leached red calcareous clays, sands and loams, calcareous and poorly drained where bedrock near surface Farm forest transition Western prairie Western coulees and ridges Southwest savanna Central sand plains Central sand hills Southeast glacial plains Southern Lake Michigan coastal Continental climate; moderate snow Continental climate: moderate snow Continental climate: hot, humid summers Continental climate: hot, humid summers Continental climate: hot, humid summers Continental climate: hot, humid summers Continental climate: hot, humid summers; low winter snow cover Continental climate: hot, humid summers, moderated by Lake Michigan; long growing season Stagnation and end moraines; dissected glacial till plains Loess plains over bedrock or till; topography generally gently rolling with more dissected ravines at southeastern edge Loess-caped, unglaciated, and dissected landscape Neutral sandy loam or silt loams, often poorly drained, with small inclusions of peat Loess, underlain by eroded sands or gravels; heavily influenced by prairie and savanna vegetation Mix of leached loess, silt loams, and sandy loams over sandstone Loess-caped, unglaciated, and dissected landscape Silt loam (loess) over dolomite; fertile valley floors Sand lake and outwash plain Sands and poorly drained mineral soils/peats Diverse landforms with pitted Sands and loamy sands outwash, hummocky end, and ground moraines Rolling-to-hilly drumlins, end moraines and outwash Rolling ground moraine with outwash channels at the end moraines Silt loams on the surface with calcareous loams (till) in the subsoils Silt loam loess soils cover the loamy and clayey tills on line descriptions and the apparent local abiotic physiognomy (soil properties and topography). Because the survey was performed on a rectangular grid, the vegetation information was sampled along the section lines. To map the vegetation, however, generalizations were required to cover the areas between the section lines. Finley MARCH 2004 Restoration Ecology 127

performed these generalizations within each 1-mi grid, using soil associations and topography to expand the range of influence of the corner data. As composition and density distributions follow continua, the decision had to be made at what point one species ceases to be dominant and where a new species becomes dominant based on tree-species frequencies. Discrete vegetation boundaries were drawn, where one or more dominant species no longer consistently occurred among the other dominants (Finley 1976), resulting in 14 vegetation classes (Fig. 2a). Vegetation Structural Classification. Finley s distinction between vegetation composition and structural features was not always consistent. Sometimes multiple facets of vegetation were included in a single-class vegetation name, such as in the class Oak openings: Bur oak White oak Black oak giving information not only on structural information like tree density (openings) but also on the species composition (various species of oaks). In other cases, such as the oak class: White oak Bur oak Black oak, only species compositions and no structural features were indicated by Finley. Because the subjective classification does not distinguish hierarchically among different vegetation and structural properties, we assigned structural landscape features in accordance with Curtis (1959). First, we identified the Forest class in all cases where Finley did not explicitly indicate any structural characteristics. These included mesic (e.g., Beech Hemlock Sugar maple Yellow birch White pine Red pine), but also pine (e.g., White pine Red pine) and oak forests (White oak Bur oak Black oak) (Fig. 2b). Second, the classes Swamp Conifers and Lowland Hardwoods (Fig. 2b) were added to the Forest class because they belong structurally to forests (Curtis 1959), although they represent a particular type of forest. Third, we grouped Finley s classes Oak openings and Barrens (Fig. 2b) into a Savanna class, because oak openings indicate open forest structure, whereas barrens in Wisconsin are usually associated with a pine-dominated type of savanna (Curtis 1959). Fourth, the grassland-type vegetation was grouped into a Prairie class, consisting of Finley s Prairie (likely to indicate dry prairie), Brush (indicating grasslands scattered with shrubby oak), and Marsh Sedge Meadow (Fig. 2b). Objective Classification: Hierarchical Agglomerative Clustering The map resulting from the objective classification is based on numerical information from the surveys (Fig. 3a & 3b), digitally compiled from the PLS records (Sickley 2000). Nonspecific Survey Notes. Nonspecific tree records (e.g., where surveyors named trees by genus only) were differentiated into species using a probabilistic method developed by Mladenoff et al. (2002). The approach uses tree-species associations and environmental predictors in a logistic regression to identify ambiguous trees (Mladenoff et al. 2002). There were 44,972 nonspecific trees in the study area. Of 1,739 ashes we identified 1,232 (71%). Of 9,355 maples we identified 7,213 (96%). Of 229 oaks we identified 184 (80%). Of 7,170 pines we identified 7,142 (99.6%) and of 26,479 birches we identified 25,148 (98%). Evaluations of the model indicate accuracies of 87% and higher (Mladenoff et al. 2002). For the genera maple, birch, and ash in southern Wisconsin and elm throughout Wisconsin, the model could not be trained appropriately. Three species in each of maple, birch, and ash occur in southern Wisconsin, but the surveyors only consistently named two of the species; the surveyors never consistently distinguished elm species. Because most of these unspecific trees were of lower abundance and sparsely distributed, we left them named at the genus-level; however, in a few locations, clumps of unspecific maples and birches did occur, and preliminary analysis showed that these clumps affected the classification by producing single-species Maple or Birch classes. In these few areas we qualitatively differentiated maple and birch to species using a combination of data: proximity to unambiguous maple and birch, neighboring tree species, and tree size. In the case of maple, distance to river was also used to differentiate silver maple (Acer saccharinum), a riparian tree species (Curtis 1959), from sugar maple (A. saccharum). Data Processing. We chose (1) relative tree dominance to represent species patterns and (2) absolute tree density as a measure of vegetation structure. Relative dominance uses diameter of the tree at breast height (1.4 m) measurements taken from the PLS records and is a commonly used forest metric that expresses basal area of a species in comparison with all other species at a given site (Cottam & Curtis 1956; He et al. 2000). In determining absolute tree densities from the U.S. General Land Office PLS records we used the point-based sampling methodology developed by Cottam & Curtis (1956), modified to measure density over area, in this case over PLS sections. For each section we first calculated the mean witness tree distance per section corner or quarter section corner and then multiplied the resultant mean distance by a correction factor based on the number of trees at the corner. For the number of witness trees n 5 1, 2, 3, or 4 we applied a correction factor c 5 0.50, 0.66, 0.81, or 1.00, respectively (Cottam & Curtis 1956). The corrected mean distances were then averaged by section, and the new averages were used to calculate absolute density for each section. Density estimates are generally best expressed over broad spatial scales as witness trees were not always the closest trees to the corners (Almendinger 1997; Nelson 1997). Both measures, relative tree dominance and absolute tree density, were derived from a modified version of MWINDOWS (He et al. 2000), a computer program that calculates a variety of vegetation indices (density, basal area, and relative importance) at user-defined scales (1 3 1, 2 3 2, 3 3 3, or 6 3 6 sections) based on a moving windows technique (He et al. 2000). To match the 128 Restoration Ecology MARCH 2004

resolution of the surveys we calculated relative dominance and absolute density of each 1 mi 2 PLS section unit, using all trees recorded on the bounding section lines. Vegetation Compositional Classification. Hierarchical agglomerative clustering was used to develop compositional land cover classes. Cluster analysis is a statistical process that numerically groups data based on distance measures, so that similar entities are combined into the same cluster (Everitt 1980). We used a two-step clustering procedure to handle the large data set (175,000 records) representing the state of Wisconsin (Schulte et al. 2002). In the first step, the data were fed into FASTCLUS (SAS Institute 2001), which preclusters the data set. In a second step, we fed the FASTCLUS output into a hierarchical agglomerative clustering method to achieve final clusters (Schulte et al. 2002). For this study we chose Ward s method (Ward 1963), as it produced plausible results in the form of close proximity of similar ecological groups within the dendrogram output (dendrograms graphically depict the statistical relationship of clusters) (Fig. 4). The hierarchical method allows analysis on both general and detailed levels and quantifies the relationships among the classified entities (Gauch 1982). Criteria for naming our final classes included: (1) in single-species classes, the highest ranking species represented at least 30% of the relative dominance and was at least two times as dominant as the second ranking species; (2) double-species classes were clusters that did not meet the criteria for a single-species class, but for which the sum of the relative dominance of the two highest ranking species was greater than 50% and the third ranking species was less than half as dominant as either of the first two; and (3) the remainder of the classes were defined as mixed and named according to the top four ranking species. The criteria were selected subjectively according to natural thresholds in the data (Schulte et al. 2002) but could be easily renamed given different objectives using the results presented in this article (Table 2; Fig. 4). Vegetation Structural Classification. Ecologically, prairie savanna forest transitions follow gradients of tree densities. For classification comparison, however, discrete structural types are advantageous. We applied a structural classification based on absolute tree densities developed by Anderson & Anderson (1975) for use with the PLS data in the Midwest of the US. Absolute tree densities were calculated using a modified version of the MWINDOWS program (He et al. 2000). Prairies are defined as having less than 0.5 trees/ha, Savannas between 0.5 and 47 trees/ha, Open Woodlands between 47 and 99 trees/ha, and Closed Forests as having densities greater than 99 trees/ha (Anderson & Anderson 1975). Comparing and Quantifying Differences in Compositional and Structural Characteristics We compared the landscape heterogeneity as represented by the two classification approaches based on vegetation composition and structure. We use section-level ecoregions within the U.S. Forest Service Ecological Classification System (ECS) as a geographic basis for spatial comparisons (Fig. 1) (Keys et al. 1995; Cleland et al. 1997; WiDNR 1999). The ECS hierarchically divides the U.S. into regions that are relatively homogenous in some ecological feature (i.e., climate, landform, soil, and potential vegetation), and section-level ecoregions are defined based on similarities in regional climate, landform, soil orders, and potential natural vegetation (Cleland et al. 1997). We developed categories that generalize the two classifications to a sufficiently high level to make them ecologically comparable. Composition. The generalized vegetation composition is represented by three categories: Pine, Oak, and Mesic species. The pine and oak categories represent drier environmental conditions, while hardwoods characterize mesic conditions (Curtis 1959). Finley s subjective classification was generalized as follows:. Pine: (a) White pine Red pine; (b) Barrens: Jack Pine Northern pin oak.. Oak: (a) White oak Black oak Bur oak and (b) Oak openings: Bur oak White oak Black oak.. Mesic (a) Hemlock Sugar maple Yellow birch White pine Red pine; (b) Sugar maple Basswood Red oak White oak Black oak Sugar maple Yellow birch White pine Red pine; (c) Beech Sugar maple Basswood Red oak White oak Black oak; (d) Beech Hemlock Sugar maple Yellow birch White pine Red pine; (e) Lowland hardwoods: Silver maple Boxelder Ash Elm Cottonwood River birch; and (f) Swamp conifers, White cedar Black spruce Tamarack Hemlock. The objective classification was generalized into Pine, Oak, and Mesic in the following way:. Pine: (a) White pine; (b) Red pine; (c) Jack pine.. Oak: (a) White oak; (b) Bur oak; (c) Black oak; (d) Red oak, White oak; (e) Mixed oak.. Mesic: (a) Aspen; (b) Elm White oak Sugar maple Basswood; (c) Basswood Sugar maple White oak Black ash; (d) Sugar maple; (e) Yellow birch Hemlock Sugar maple White pine; (f) Hemlock; (g) Beech; (h) Silver maple Birch Ash Elm; (i) Tamarack. Structure. The generalized structural classes developed for classification comparison include: Prairie, Savanna, Open Woodland, and Forest. The subjective classification is classified as follows:. Closed Forest: (a) White spruce Balsam fir Tamarack White cedar White birch Aspen; (b) Beech Hemlock Sugar maple Yellow birch White pine Red pine; (c) Hemlock Sugar maple Yellow birch White pine Red pine; (d) Sugar maple Yellow birch White pine Red pine; (e) White pine Red pine; (f) Aspen White birch Pine; (g) Beech Sugar MARCH 2004 Restoration Ecology 129

Historical Landscape Reconstructions of Wisconsin (USA) Classification (% area) Hemlock, sugar maple, yellow birch, white pine, red pine (17.3) Oak: white oak, black oak, bur oak (13.9) Oak openings: bur oak, white oak, black oak (9.5) Swamp conifers, white cedar, black spruce, tamarack, hemlock (9.4) Sugar maple, basswood, red oak, white oak, black oak (8.7) Jack pine, scrub oak forests and barrens (6.6) Sugar maple, yellow birch, white pine, red pine (6.1) White pine, red pine (5.4) Prairie (4.7) Beech, sugar maple, basswood, red oak, white oak, black oak (3.6) Marsh, sedge meadow, wet prairie, lowland shrubs (3.3) Beech, hemlock, sugar maple, yellow birch, white pine, red pine (2.7) Brush (2.2) White spruce, balsam fir, tamarack, white cedar, white birch, aspen (1.5) Aspen, white birch, pine (1.1) Lowland hardwoods, willow, soft maple, box elder, ash, elm, cottonwood (0.9) No data and open water Lake Winnebago a N 0 50 100 km Figure 2a. Subjective landscape classification of Wisconsin based on Finley (1976): Vegetation composition. Classification (% area) White oak (13.7) White pine (12.4) Hemlock (10.7) Bur oak (9.4) Yellow birch, hemlock, sugar, maple, white pine (8.6) Black oak (7.4) Sugar maple (7.0) Tamarack (5.3) Basswood, sugar maple, white oak, black ash (5.1) Red pine (4.9) Elm, white oak, sugar maple, basswood (2.9) Aspen (2.6) Jack pine (2.4) Beech (2.3) Red oak, white oak (1.9) Prairie (1.8) Mixed oak (1.3) Silver maple, birch, ash, elm (0.4) Lake winnebago No data a N 0 50 100 km Figure 3a. Objective landscape classification of Wisconsin: Vegetation composition. 130 Restoration Ecology MARCH 2004

b Forest Savana Prairie Figure 2b. Subjective landscape classification of Wisconsin based on Finley (1976): Structural composition. maple Basswood Red oak White oak Black oak; (h) Sugar maple Basswood Red oak White oak Black oak; (i) Swamp conifers White cedar Black spruce Tamarack Hemlock; (j) Lowland hardwoods Willow River maple Box elder Ash Elm Cottonwood River birch.. Savanna: (a) Jack pine Northern pin oak forests, and barrens; (b) Oak openings: Bur oak White oak Black oak.. Prairie: (a) Prairie; (b) Brush; (3) Marsh and sedge meadow Wet prairie and Lowland shrubs. For this classification, Open Woodlands could not be distinguished from Savanna and Forest. In contrast to Finley (1951, 1976) the objective approach treats vegetation structure separately from vegetation composition. The structural classification is based solely on tree-density distributions, and classes include Closed Forest (<99 trees/ha), Open Woodland (47 99 trees/ha), Savanna (0.5 47 trees/ha), and Prairie (treeless areas with <0.5 trees/ ha), according to Anderson & Anderson (1975). Because composition and structure are treated separately, a single compositional class (e.g., Beech White oak) can fall b Closed forest Open forest Savanna Prairie Figure 3b. Objective landscape classification of Wisconsin: Structural composition. MARCH 2004 Restoration Ecology 131

Table 2. Relative dominance value of tree species within each vegetation class in the objective classification. Vegetation Classes Tree Species Jack Pine Red Pine White Pine Black Oak Bur Oak White Oak Northern Red, Oak, White Oak Mixed Oak Aspen Elm, White Oak,Sugar Maple, Basswood Basswood, Sugar Maple, White Oak, Black Ash Sugar Maple Yellow Birch, Hemlock, Sugar Maple, White Pine Hemlock Beech Silver Maple, Birch, Ash, Elm Tamarack Jack pine 70.1 8.8 1.6 1.4 0.3 0.3 1.0 0.7 1.9 0.1 0.5 0.1 0.0 0.0 0.0 0.1 1.4 Red pine 8.6 53.6 5.6 0.4 0.1 0.2 1.3 1.0 3.4 0.2 0.7 0.4 0.3 0.6 0.1 0.3 2.4 White pine 3.9 13.9 54.4 0.5 0.3 0.4 4.1 0.8 6.7 2.6 5.9 3.7 5.6 5.4 2.5 1.1 8.2 Tamarack 2.2 4.3 4.1 1.3 0.2 0.7 1.2 1.1 3.9 1.8 2.5 2.5 4.3 4.0 1.6 1.0 33.7 Spruce spp. 0.2 1.2 1.3 0.0 0.0 0.0 0.2 0.0 1.9 0.3 1.1 0.7 1.7 1.3 0.2 0.1 7.1 Eastern hemlock 0.1 0.9 3.4 0.0 0.0 0.0 0.6 0.0 1.3 1.7 3.0 5.1 14.5 48.7 4.9 0.5 5.5 Balsam fir 0.1 0.4 0.8 0.0 0.0 0.0 0.2 0.0 0.9 0.2 1.2 0.9 2.0 1.4 0.1 0.0 2.5 N. white cedar 0.3 1.0 1.5 0.0 0.0 0.0 0.4 0.0 1.3 0.8 4.1 1.9 3.2 4.7 6.0 0.5 9.7 Aspen spp. 1.7 4.1 3.1 2.0 0.8 1.8 4.8 2.0 44.9 2.9 2.9 1.4 0.9 0.9 0.9 0.9 4.2 E. cottonwood 0.0 0.0 0.0 0.0 0.0 0.1 0.2 0.0 0.1 0.1 0.3 0.0 0.0 0.0 0.0 0.4 0.0 Willow spp. 0.0 0.0 0.0 0.1 0.1 0.1 0.0 0.0 0.0 0.2 0.4 0.0 0.0 0.0 0.0 2.2 0.0 Hickory spp. 0.0 0.0 0.0 0.4 0.4 0.5 0.3 0.6 0.1 0.5 0.8 0.3 0.0 0.0 0.1 0.3 0.0 White birch 0.7 2.9 3.3 0.3 0.1 0.2 1.5 0.5 4.7 1.4 4.2 2.7 3.5 3.4 1.1 2.1 5.4 Yellow birch 0.2 1.0 3.9 0.1 0.0 0.1 1.8 0.1 1.8 3.1 4.0 8.0 39.5 11.8 2.3 3.3 5.6 Birch spp. 0.0 0.0 0.1 0.1 0.0 0.1 0.0 0.2 0.2 0.5 0.2 0.2 0.0 0.0 0.1 18.5 0.2 Am. beech 0.1 0.1 0.8 0.1 0.0 0.2 0.6 0.5 0.4 1.5 2.5 1.9 0.3 1.8 45.4 0.1 0.5 White oak 1.6 1.4 2.4 18.1 12.4 60.8 18.1 22.3 6.5 11.3 8.8 4.5 0.4 0.1 1.9 4.6 1.1 Bur oak 2.4 0.8 0.8 15.3 71.3 12.9 7.1 18.1 3.6 5.2 2.3 1.0 0.2 0.1 0.1 5.4 0.6 N. red oak 0.4 1.1 1.5 0.5 0.9 1.6 34.9 0.7 2.2 1.1 2.0 1.7 0.9 0.4 1.1 0.4 0.7 Black oak 4.9 0.7 0.8 54.6 9.8 11.5 3.7 7.4 2.9 3.8 3.0 1.5 0.1 0.0 0.5 4.9 0.9 N. pin oak 1.2 0.7 0.4 1.0 1.5 1.5 1.4 40.9 1.4 0.6 0.7 0.2 0.0 0.0 1.1 0.8 0.2 Elm spp. 0.4 0.4 1.4 1.0 0.6 1.9 3.0 0.7 2.3 32.6 6.3 5.5 3.8 2.2 3.4 5.5 1.5 Sugar maple 0.2 1.3 5.4 0.8 0.2 2.1 6.7 0.6 3.6 10.7 13.7 43.6 12.7 8.4 13.4 3.6 4.1 Silver maple 0.0 0.1 0.2 0.1 0.0 0.1 0.2 0.1 0.2 0.2 0.2 0.1 0.1 0.1 0.1 24.3 0.1 Red maple 0.0 0.0 0.1 0.0 0.0 0.0 0.2 0.0 0.1 0.3 0.8 0.3 0.4 0.4 0.5 0.0 0.2 Am. basswood 0.1 0.1 1.0 0.5 0.2 1.2 2.3 0.3 1.1 5.6 14.8 6.0 2.5 1.9 3.5 0.8 0.8 White ash 0.0 0.1 0.3 0.3 0.1 0.4 0.8 0.2 0.4 1.9 2.3 0.9 0.3 0.3 1.9 4.0 0.2 Black ash 0.3 0.4 1.0 0.3 0.1 0.5 1.1 0.4 0.8 3.5 6.1 2.0 1.6 1.5 5.6 0.8 2.0 Ash spp. 0.0 0.0 0.0 0.1 0.1 0.1 0.2 0.1 0.2 0.5 0.2 0.2 0.0 0.0 0.0 9.7 0.0 Other 0.2 0.7 0.7 0.6 0.4 1.0 1.9 0.6 1.2 4.5 4.6 2.6 0.8 0.7 1.7 3.7 1.0 Sum 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 Bold indicates species that were used in naming land cover classes derived from hierarchical agglomerative clustering.

0.20 0.15 Semipartial r 2 0.10 0.05 0.00 Elm, white oak, sugar maple, basswood Northern red oak, white oak Bur oak White oak Black oak Tamarack Beech Silver maple, birch, ash, elm Basswood, sugar maple, white oak, black ash Mixed oak Aspen Sugar maple Jack pine Red pine Yellow birch, hemlock, sugar maple, white pine Hemlock White pine Figure 4. Dendrogram produced through hierarchical agglomerative clustering on tree-species-relative dominance data. into one or more structural classes (e.g., Closed Forest, Savanna). Quantification of Vegetation Patterns. Differences in vegetation patterns were quantified using (1) the individual patch area and (2) cover type diversity (Shannon Weaver diversity). Low values indicate low diversity and high values indicate high diversity. The metric is calculated for entire landscapes and takes the form: D ¼ Xm i¼1 P i ln P i where P i is the proportion of the landscape occupied by patch type i. The metrics were calculated using APACK (Mladenoff & DeZonia 1999). Results Subjective Classification The subjective classification (Finley 1951, 1976) derives quantitative and qualitative information from the survey records of classification (Table 3). Quantitative information involves the number and size of trees of each species. In addition to this survey-based information, Finley (1951, 1976) used environmental data to distinguish among different community types and to delineate their boundaries (Table 3). Qualitative information includes written descriptions and sketches of vegetation types and their boundaries; however, surveyors only recorded these data for selected areas within Wisconsin (Table 3). Thus, some areas received more detailed treatment in delineating community types and boundaries than others. Finley (1951, 1976) distinguished 14 vegetation classes in Wisconsin using a combination of compositional and structural characteristics (Fig. 2). The four major vegetation divisions included: (1) coniferous, deciduous, and mixed coniferous deciduous forest types; (2) wetlands (Swamp, Marsh, Sedge Meadow) (3) savannas (Barrens and Oak openings); and (4) Upland Prairie and Brush. All of Finley s 14 vegetation classes are multiple-species classes (Fig. 2a). For this reason, his classification likely represents vegetation patterns on a relatively high hierarchical level (i.e., vegetation communities). Because Finley accounted for the variety of numerical and descriptive information provided by the surveys, as well as ancillary data sources (soil and topography), he was able to distinguish fine-scale ecosystem types such as marsh, sedge meadow, or brush. Thus, Finley s resolution is generally finer than 1 mi 2, providing a detailed and comprehensive picture of specific vegetation features. However, the spatial resolution is not standard across the state because the treatment was spatially inconsistent due to uneven surveyor descriptions, better soil maps in some locations resulting in better understanding of soil vegetation relationships for some cover types in some locations, but also in coarser and more general representations (polygons of 2,099,538 ha) (Table 3). MARCH 2004 Restoration Ecology 133

Table 3. Comparison of subjective and objective classification methods. Classification Type Information Used to Identify Unspecific Survey Notes Information Used for Mapping Classes Subjective Associated neighboring trees Abiotic site characteristics (soil and topography) Agreement between other, more specific surveyors working in the same area Surveyor notes and sketches Abiotic site characteristics (soil and topography) Objective Neighboring tree species, tree density, tree size, ecoregions (Mladenoff et al. 2002) Subjective assignment using associated species and distance to river (maple and birch in southern WI only) Tree-species dominance (He et al. 2000) and tree density (Anderson & Anderson 1975) and Ward s clustering algorithm Classification Procedure Based on subjective criteria Based on objective numerical classification using FASTCLUS (SAS Institute 2001) Classification Scale Classification Result Advantages Limitations Community Locally detailed reproduction of qualitative and Generally finer resolution than 1 m 2 (polygons ranging from 32.8 ha to 130 160 ha), with individual large ones (maximal 2,099,538 ha). Thus the resolution is not standard across the landscape (uneven surveyor descriptions, better soil maps in some locations, better understanding of the soil vegetation relationships for some cover types) quantitative landscape features The classification criteria and generalizations are not equally applied throughout the landscape; some areas received more detailed treatment than others due to unequal distribution of the qualitative information Not fully reproducible Individual species 1m 2 standard resolution across the entire landscape Spatially uniform The classification exclusively accounts Fully reproducible for numerical landscape descriptions and thus does not represent qualitative details

Objective Classification This approach relies exclusively on quantifiable information derived from the surveys, such as tree dominance calculated from basal area (Table 3). Using a numerical clustering algorithm (Schulte et al. 2002), the objective approach distinguishes among 17 vegetation classes and four structural classes (Table 2; Fig. 3). Six vegetation classes are multiple-species classes and 11 are singlespecies classes. The classification distinguishes among three pine classes, five oak classes, seven mesic classes (e.g., Beech, Elm White oak Sugar maple Basswood) and two lowland classes (Tamarack, Silver maple Birch Ash Elm) (Table 2; Fig. 3a). Because procedures and the data used are treated as spatially uniform and because classes are numerically differentiated, this classification is fully reproducible across the landscape (i.e., independent of who is performing the analysis). The only irreversible qualitative decision made during the objective classification process was to use Ward s method for hierarchical agglomerative clustering (decision made in preliminary analysis based on qualitatively more plausible relationships among clusters compared with other clustering methods). Other rule-based decisions such as further grouping cluster output to obtain a smaller number of classes or naming classes based on relative dominance values can be revisited and updated by other users given, respectively, the dendrogram (Fig. 4) and tabular summary (Table 2) provided here. Comparison Between the Subjective and the Objective Classification Approach The subjective and the objective classifications reveal a complex and diverse vegetation composition and structure across the state of Wisconsin (Figs. 2 & 3) and indicate dominance of closed forests and savannas (Figs. 2 & 3). Diversity measures for the vegetation composition are 2.5 for the objective and 2.6 for the subjective classification. In this respect the classifications are similar. Structurally the extent of prairie and savanna are nearly equal in the subjective classification (Fig. 5a). The objective classification indicates that savannas are two times more frequent and prairies are six times less frequent compared to the subjective classification (Fig. 5b). Most of these differences are likely to have originated from different mapping techniques. Finley (1951, 1976) manually generalized and delineated the vegetation types and their boundaries using surveyors descriptive notes; thus, ecosystem details such as prairie, swamp, and marsh can be distinguished and accounted for smaller resolutions than 1mi 2 (Table 3). But this approach is prone to the caprice of surveyors naming conventions and consistency as well as to inconsistent handling across the landscape (e.g., sizes of polygons ranging from fine-scale 32.8 to coarse-scale 2,099,538 ha) (Table 3). Compositional Vegetation Patterns as Classified into Pine, Oak, and Mesic. Differences between the classifications are observed when comparing patterns of the major species groups of Pine, Oak, and Mesic; however, the overall Figure 5. Area coverage of the structural landscape features of (a) the subjective and (b) the objective landscape classification. MARCH 2004 Restoration Ecology 135

vegetation diversity of the subjective and the objective classifications does not differ (both 0.9). The subjective classification describes mesic species as dominating (60%) and coexisting with Pines and Oaks of equal frequency (20%). The objective classification, however, describes mesic species as less dominant (40%) and Oaks and Pines cover more relative area (30%) than depicted in the subjective classification. The relative area of mesic species is thus over-represented by the subjective classification, whereas Oaks and Pines are similarly represented by both classifications. Structural Vegetation Patterns as Classified into Forest, Savanna, and Prairie. The area representation of the individual structural patterns between the two classifications disagrees (Fig. 5). The subjective classification indicates strong dominance of the Forest class (70%) and lesser coverage of Prairies and Savannas (19 and 12%). The objective classification indicates almost equal area coverage between the categories Forest and Savanna (45 and 43%), whereas Prairies account for only about 2% of the area. The inclusion of descriptive notes allows the subjective classification to depict a wealth of detail on the ecosystems: 16.4% of the Forest class is covered with wet forests such as swamps and lowland hardwoods (Fig. 5a). The savanna class distinguishes between almost equal dominance of Oak openings and Barrens. The Prairie class consists of 67% dry and 33% wet prairie (Fig. 5a). The objectively classified landscape based on density information assigns 10% of the total area to Open forests and 2% to Prairies (Fig. 5b). Because no descriptive surveyor notes were included in the objective classification, details on the ecosystem properties such as the distinction into wet and dry prairie cannot be delineated from this classification. The overall structural pattern diversity is higher for the objective classification (1.0) compared with the objective classification (0.7). There is a trend for the subjective classification to exhibit more spatially aggregated structural patterns, which results in a lower overall pattern diversity. Although required for comparison and quantification of landscape features, categorization of originally continuous landscape features such as the continuum between prairies, savannas, and forests is challenging and inherently subjective. Independent evaluation can be addressed partly by qualitatively comparing the categorized landscape with independent sources. In the case of southern Wisconsin for example, Hoyt s (1860) map of the vegetation of Wisconsin and Curtis s (1959) maps are in accordance with our classification based on Anderson & Anderson (1975) showing that oak and pine savannas were dominant in southern Wisconsin (Fig. 3b). Vegetation Composition and Structure General Description One of the most distinct features is the subdivision of the state into northern (Laurentian Mixed Forest Province) and southern (Eastern Deciduous Forest Province) regions based on vegetation characteristics (Figs. 2 & 3). The boundary between the northern forests and the southern savanna prairie regions runs northwest to southeast across the state and is referred to as the tension zone (Curtis 1959). The location of the tension zone is primarily climatically driven and manifests itself as a species-limiting line in addition to a boundary in vegetation physiognomy (Curtis 1959). Both classifications indicate that mesic forest types dominated northern Wisconsin, locations along Lake Michigan, and much of the driftless area (located in the eastern western coulees and ridge ecoregion) (Fig. 1) and oak savannas dominated much of the south (Figs. 2 & 3). The high abundance of oak and more open vegetation in the south is likely an indicator of more frequent fires (Curtis 1959). Qualitative comparisons of these landscape features with independent early descriptions such as Hoyt s (1860) and Curtis s (1959) maps generally show agreement in representing these major landscape features. Both classifications show that the landscape of Wisconsin before Euro-American settlement primarily consisted of Closed Forests and Savannas (Figs. 2a & 2b, 3a & 3b, 5). Forests comprised 45 69% of the total area, Savannas covered 20 43%, and Prairies 2 12% of the total area (Fig. 5). Structural patterns for the objective classification indicate that Closed Forests were exclusively dominated by mesic classes such as hemlock, yellow birch, sugar maple, and beech. Savannas were largely characterized by white oak, bur oak, black oak, mixed oak, and jack pine (Fig. 6). Silver maple Birch Ash Elm are usually considered a lowland forest type as it is a representative of commonly inundated areas along rivers and streams (Curtis 1959); however, based on purely structural attributes this class is more savanna-like in character (Fig. 6). Open forests indicate the transition between closed forests and savannas and comprise between 2 and 8% of a cover type (Fig. 6). This transition class is characteristic of all compositional classes, however, more so for classes that tend to be dominated by Closed Forests (e.g., hemlock and yellow birch). Although prairies are dominated by herbaceous vegetation, occasional bur oak, black oak, and jack pine trees do occur (Fig. 6). The Northwest Lowlands and Superior Coastal Plains are areas of glacial outwash and till (Table 1; Fig. 1). Conifers dominate the western part of the area (spruce, balsam fir, tamarack, and cedar) interchanging with pines (red pine and white pine) and mesic forest species (sugar maple, yellow birch, hemlock, and red oak) (Figs. 2a & 3a). Aspen-dominated patches and northern hardwood forests with hemlock as dominant species become important in the east. Structurally the area ranges from swampy or lower to higher density forests (Figs. 2b & 3b). In general the subjective and objective classifications draw similar pictures of this part of Wisconsin. The subjective classification, however, indicates spatially patchier structure than the objective classification. 136 Restoration Ecology MARCH 2004

Historical Landscape Reconstructions of Wisconsin (USA) Closed forest Open forest Savanna Prairie 100 90 Percent of total area 80 70 60 50 40 30 20 10 Bur oak Black oak Mixed oak White oak Jack pine Silver maple, birch, ash, elm Red pine Northern red oak, white oak Aspen Elm, white oak, sugar maple, basswood White pine Tamarack Basswood, sugar maple, white oak, black ash Sugar maple Beech Yellow birch, hemlock, sugar maple, white pine Hemlock 0 Compositional classes Figure 6. Vegetation and structural characteristics of the objective landscape classification. Pine species are frequently found on glacial sands, some of the poorest soils in the state, such as the Northwest Sands, Northern Highland, Northeast Sands, and Central Sand Plains Ecoregions (Table 1; Fig. 1). Dominant species in these areas include jack pine, red pine, and white pine, and structurally these areas are predominantly savannas (Figs. 2a & 2b, 3a & 3b). The Northwest Sands and the Central Sand Plains have been described as dominated by jack pine (Curtis 1959; Radeloff et al. 1999), and both classifications indicate that jack pine was prevalent in these areas (Figs. 2a & 3a). The subjective classification represents jack pine more patchy and white and red pine are rarely dominant (Fig. 2a). The objective classification attributes white and red pine as large areas of dominance, particularly in the Northern Highlands and the Northeast Sands. Thus the objectively classified landscape indicates that the glacial plains were not generally homogenous (Fig. 2a), but rather a mixture of pine species with smaller areas of single-pine dominance (i.e., jack pine). Structurally the glacial sands are characterized by savannas with forests and prairies (Figs. 2b & 3b). Mesic hardwood-mixed coniferous forests are predominant in the North Central Forest and Northeast Hills. Dominant species include hemlock, sugar maple, basswood, white pine, and yellow birch. Structurally the area MARCH 2004 Restoration Ecology is closed forest (Figs. 2b & 3b). The hardwood-mixed coniferous patches interchange with aspen, pine, or swamp conifers including northern white cedar, black spruce, or tamarack (Figs. 2a & 3a). These lowlands structurally belong to Open forest (Figs. 2b & 3b). Within the Farm Forest Transition landscape, the subjective classification delineates discrete and homogeneous patches of mesic classes of sugar maple, yellow birch, red oak, and white oak, interchanging with patches of pines (jack, white, and red pine) (Fig. 2a). The objective classification, however, indicates dominance of white and red pine interplaying with mesic forests of hemlock, yellow birch, sugar maple, and basswood. The largest contrast between the classifications is exhibited in this area. The Northeast Plains and Northern Lake Michigan Coastal landscapes are dominated by beech, hemlock, sugar maple, yellow birch, red oak, white oak, white, and red pine (Figs. 2a & 3a). According to the subjective classification the beech-dominated forests interchange with patches of swamp conifers (black spruce and tamarack) or silver maple, birch, and elm (Figs. 2a & 3a). The objective classification, however, represents the area more heterogeneously, with Beech forests interchanging with patches of Hemlock and Yellow birch Hemlock Sugar maple White pine forests in the east and Basswood 137

Sugar maple White oak Black ash, and White oak patches in the west (Fig. 3a). These oak patches are also represented by the subjective classification (Fig. 2a), but are attributed more spatial homogeneity. Again the subjective classification exhibits more distinct and homogenous patches (Fig. 2a) compared with the objective classification (Fig. 3a). The Western Prairie, Western Coulees and Ridges, Southeast Glacial Plains, Southwest Savannas, and Southern Lake Michigan Coastal landscapes are dominated by oak species (bur oak, black oak, white oak, and red oak), forming open woodlands or savanna patterns (Figs. 2a & 2b, 3a & 3b), likely due to higher fire frequencies (Curtis 1959). The patterns of oak woodlands and savannas interchange with treeless prairies and distinct mesic forest patches in the Western Coulees and Ridges (Figs. 2a & 2b, 3a & 3b). The mesic forest patches in southern Wisconsin occur on higher elevation sites such as ridges, moraines, and in the driftless area (southwestern WI). They are characterized by sugar maple, basswood, and yellow birch. The extent of these patches is more pronounced and homogenous in character when looking at the subjectively classified landscape (Fig. 2a); more heterogeneous patterns result from the objectively classified landscape (Fig. 3a). The Western Prairie Ecoregion is not only dominated by treeless areas and brush, but also by species such as aspen, white oak, and white birch (Figs. 2a & 3a). Both the open condition and the species composition indicate higher fire frequency. Discussion Conceptual and Methodological Comparison Between the Subjective and the Objective Classification Science and management require an analysis of the effect of ecological processes on spatial vegetation patterns or recognition of higher level aggregation of vegetation on landscapes (Haines-Young & Chopping 1996; Andersson et al. 2000). Because research objectives usually involve quantitative descriptions of landscape change through time, including directions of change and their drivers, general baseline information on ecosystem pattern and processes are required. The process of generalization involves aggregation of spatial data. Aggregation introduces uncertainties stemming from sources such as data collection, data processing, and choice of classification model. Because the intention behind the use of spatial data in environmental management is to ultimately make decisions, it is relevant to relate aspects of spatial uncertainty to the decision-making process. Dealing with uncertainty can be accomplished by stating assumptions or data limitations. In this article we address uncertainties originating from the way surveys are utilized (content used and scale of analysis) and from different classification approaches (subjective and objective). We relate these uncertainties to the resulting landscape representations. Major sources of uncertainty in the subjective classification approach based on Finley (1951, 1976) include nonreproducible subjective decisions, which makes replication of the mapping effort difficult. Although Finley (1976) defined classification criteria to be applied throughout the state, these criteria were not applied consistently, and it remains unclear how completely the overall landscape is represented. Subjectivity influences Finley s (1976) landscape as follows: (1) identification of ambiguous trees (e.g., pine instead of white, red, or jack pine); (2) assembling the species into higher level community associations; and (3) location of discrete boundaries between community types, partly based on other sources of information such as soil or topographic maps. One of the consequences of this subjectivity is nonuniform classes (e.g., compositional or structural classes) that are not strictly hierarchical and are also not necessarily exclusive. For example, more closed canopies are expected in Finley s Oak class (assumed to be oak forest) than in his Oak opening class. However, the Oak forest class is on the same hierarchical level as classes such as Basswood Sugar maple White oak Black ash. The latter, however, does not exhibit any structural properties in the class name. Whereas Oak indicates more closed canopy structures, this class may contain subgroups of Oak forests such as Oak openings that were only distinguished where surveyors happened to mention open forests. This indicates that Finley s classification may not generally be strictly hierarchical and does not consistently distinguish among compositional and structural characteristics of the vegetation. The objective approach represents the quantitative aspects of the surveys more completely than the subjective approach. However due to the exclusion of qualitative information, the objective approach does not represent local detail included in the surveys, for example it does not distinguish fine-scale structural details such as barrens, brush, dry, or wet prairie. The objective classification can, however, highlight areas of single-species dominance yielding spatial distributions of trees that are species and not community specific and that can be quantitatively compared with more compositionally diverse areas facilitated by a standardized 1mi 2 resolution for the entire classification. The resulting classification from the objective approach thus lacks specific details; however, it is highly reproducible, flexible, and independent from other input data sources such as topographic or soil maps (Schulte et al. 2002). All criteria for classification including: (1) identifying ambiguous trees; (2) clustering species into vegetation types; and (3) identifying boundaries of the vegetation types rely on objective numerical and statistical differentiation. Classification results clearly distinguish hierarchies among the vegetation and structural classes, and the quantitative numerical classification allows tests for robustness and reproducibility. Trade-offs for the objective approach include the exclusion of qualitative information from the 138 Restoration Ecology MARCH 2004

classification in favor of reproducibility and flexibility and quantitative tests for robustness (Schulte et al. 2002). Differences between the classifications are attributed to the variable type and amount of data, as well as dissimilar use of these data in classification. When comparing the two approaches, a trade-off between comprehensive detail and reproducibility and objectivity is manifested. The subjective classification includes the complete set of survey information available (qualitative and quantitative). Abiotic landscape properties (soil and topography) were additionally used to increase the spatial resolution of the classification. Thus the subjective classification (Finley 1951, 1976) may represent the historical vegetation communities more completely. But because the classification follows subjective criteria, it is not strictly consistent and is hard to reproduce. The objective classification technique, however, employs quantitative information only and the classifications are based on purely statistical criteria. The classification results are spatially uniform in the sense that identical information is used in the same way across the entire landscape, and the results are fully reproducible. The objective approach, however, lacks nonquantifiable information that results in locally more detailed classes. Because the generalization of landscape elements always involves introduction of trade-offs and uncertainty, we agree with Batek et al. (1999) in suggesting that several classifications be considered and that the applicability and limitations of each classification should be analyzed and compared. Such comparisons increase the reliability and interpretability of landscape classifications and allow the choice of methods relevant to the research questions under investigation. Applicability for Management and Restoration Current biological communities provide valuable sources of genetic, structural, and functional biodiversity. The challenge is to retain the range of biodiversity still present and regain lost biodiversity through restoration. Historical landscape reconstructions are helpful tools to identify the historical spatial extent, distribution core, and boundaries of the ecosystems to be protected (Cissel et al. 1994; Ogle & DuMond 1997; Fulé et al. 2002). This study provides baseline information for restoration and management by identifying opportunities for enhancing and retaining biological diversity. The utility of the subjective and objective classifications is addressed by five restoration and management objectives for Wisconsin. Hemlock Hardwoods Management and Restoration. Current motivation exists to restore conifer ecosystems to the northern Wisconsin landscape (Mladenoff & Stearns 1993; Crow et al. 1994). Widespread logging followed by wildfire in the late 1800s and early 1900s reduced the prominence of white pine, red pine, and eastern hemlock on the landscape, largely replacing them with hardwoods (Stone & Thorne 1961; Mladenoff & Pastor 1993). Eastern hemlock, in particular, has had long-lasting regeneration problems that severely affect its populations in the western Great Lakes Region (Mladenoff & Stearns 1993). Many factors potentially limiting the regeneration success of hemlock have been investigated, including the role of deer browsing (Anderson & Loucks 1979) and climate change (Mladenoff & Stearns 1993; WiDNR 1995). Our maps for northern Wisconsin show that hemlock forests were dominant across much of northern Wisconsin before Euro-American settlement. Both classifications agree on the geographic range of this species; however, if we were to restore entire hemlock ecosystems, the applicability of the two classification models differ. The subjective classification indicates that hemlock shares dominance with sugar maple, yellow birch, white pine, and red pine throughout its core area. These mixed stands interchange with swamp conifers (white cedar, black spruce, and tamarack) and other mesic forest types such as Sugar Maple Yellow birch White, and Red pine. The objective classification, however, draws a less homogenous picture and less clustered hemlock stands. Other mesic forest types such as Yellow birch Hemlock Sugar maple White pine, Sugar maple, or White pine are more frequent in this classification scheme. Rather than grouping hemlock northern hardwood forests into a uniform vegetation type, the objective classification reveals the complex patterning of these mesic forests and splits areas of hemlock occurrence into multiple levels of dominance. For example hemlock was the primary component of presettlement forests in broad areas of north central Wisconsin, but it also was found at lower levels of dominance within Yellow birch Hemlock Sugar maple White pine, and Sugar maple forests. Thus if we were to restore the spatial distribution of hemlock, the large-scale distribution can be assessed by using results from both classification models; however, given the local challenges to hemlock regeneration in addition to the regional ones, the finer detail provided by the objective classification can help target areas where the restoration potential of this species is high, and therefore target restoration efforts for success. Savanna Management and Restoration. Savannas have been reported to rank among the most threatened ecosystems in the world (Lorimer 1993; WiDNR 1995; Leach & Givnish 1999). In practice savanna represents an arbitrarily defined segment of a continuum between prairie and forest that is not inherent in the vegetation; an agreed upon definition of what savanna means is required for successful restoration efforts (Mendelson et al. 1992). Regardless, savanna-type vegetation covered millions of acres across Wisconsin for the past several thousand years and, in the US at the time before settlement, Wisconsin had an estimated 5.5 millions acres of oak savanna and pine barrens (Curtis 1959). The recovery potential for oak savannas in Wisconsin is considered substantial, primarily because large portions of land MARCH 2004 Restoration Ecology 139

potentially useful for restoration are already in state possession (WiDNR 1995). As with hemlock forest, the spatial extent of oak savannas is comparable for both classification schemes. The subjective classification draws discrete boundaries around the vegetation patches, creating more uniform (less patchy) structures in these areas. Furthermore, the area potentially covered by oak savannas is divided into Oak and Oak openings. This indicates that the hierarchical distinction between the structural categories may not be strict. Rather, oak openings represent areas that the surveyors explicitly labeled open oak savannas. The Oak class may thus represent areas where the surveyors did not find structural particularities and its structural properties may represent everything from open oak woodland to closed oak forests. In contrast the objective classification draws a patchy picture of oak savannas with less precise boundaries and highlights areas where different oak species were dominant. The structural classification here is also more unambiguous based on purely numerical density values and shows a gradation from open ecosystem types with a few oak trees to closed forest vegetation. Although the objective classification may not represent local detail contained within the surveyors description, it provides a different view of the heterogeneity in oak savannas as they existed before Euro-American settlement. A similar situation exists for pine barren ecosystems. Although the two classifications show similar trends in structural characteristics, the objective classification provides a broader picture of species dominance within the Northwest Sands Ecoregion of Wisconsin, an area where restoration efforts are currently active (Parker 1997; Radeloff et al. 2000). Wetland or Riparian Management and Restoration. Increases in flood levels, problems in water quality, decline of many amphibian species, and infestation of exotic invasives have brought attention to wetland and riparian areas of the Midwest (WiDNR 2000). Although cumulatively abundant in Wisconsin, these areas tend to have small, limited spatial extents (0.01 100 ha) (WiDNR 1992) in comparison with uplands. For this reason, reproducing both their extent and composition is difficult using the PLS records by themselves. The resolution of the survey as used in the objective classification is at the PLS section level or approximately 1 mi 2 (2.56 km 2 ). For this reason we recommend using the subjective classification while considering the management or restoration of these areas. The subjective classification, through its consideration of the local detail provided by the surveyors, and its use of topographical data, provides more complete representation of these land cover types. Prairie Management and Restoration. As with savannas, prairies are some of the most threatened ecosystems today. Although prairies were not the dominant structural type in Wisconsin before Euro-American settlement, they were some of the first to be converted to agriculture (Curtis 1959). The decline of this land cover type, and many of the species that depend on it, has made prairie restoration and management a focus for the State Department of Natural Resources (Sample & Mossman 1997; Cochrane & Iltis 2000), as well as many conservation groups. The subjective and objective classifications agree in the geographic location of prairies in presettlement Wisconsin, although there are some differences in their extent. These differences are likely due to Finley s consideration of qualitative information in the surveyor records and his use of ancillary data, such as soil characteristics. We recommend using both of these classifications when considering prairie restoration and management. The objective classification more consistently represents prairie according to the structural definition of <0.5 trees/ha (Anderson & Anderson 1975) and reveals gradation of this structural type into others. But in many areas, especially southwestern Wisconsin, prairies were a very local phenomenon, located on southern and western facing slopes within the Western Coulees and Ridges and the Southwest Savanna Ecoregions. With its coarse resolution (2.59 km 2 ) the objective classification cannot represent these fine-scale features well. As with wetland and riparian areas the subjective classification can help distinguish small patches of prairie through the incorporation of qualitative surveyor descriptions and topography. Conclusions Ecosystem management will be a key topic within conservation and management circles during the next decade or two (Andersson et al. 2000). To provide baseline information for ecosystem management we evaluated the historical landscape of Wisconsin (U.S.A.) and provided reconstruction potentials based on a subjective and an objective landscape classification. Trade-offs between comprehensive detail and reproducibility and objectivity are manifested in the amount and characteristics of the data used for classification. The subjective classification includes the complete set of information available from the surveys (qualitative and quantitative); however, the approach is hard to reproduce due to inconsistencies in data handling (e.g. inconsistent resolutions across the landscape), criteria application, and subjective decisions. The objective classification relies exclusively on quantitative information and an objective statistical classification approach with a 1 mi 2 standard resolution across the entire landscape. This makes the objective classification fully reproducible (i.e., independent of the person who performs the analysis), however, locally less complete due to the exclusion of qualitative surveyor descriptions. The classifications are applied to assess the restoration potential for regional land planning, hemlock hardwoods, savanna, wetland/riparian, and prairie management in 140 Restoration Ecology MARCH 2004

Wisconsin. The subjective classification addresses vegetation communities rather than individual species and includes subjective descriptions on specific ecosystem properties, revealing that this classification is best applied for conserving or restoring species or areas where local detail is requested and where spatial consistencies do not matter. The objective classification accounting for species distributions, rather than entire vegetation communities is best applied to restore or conserve individual species and for making cross-landscape comparisons. Acknowledgments We thank J. McKeefry for technical support. Helpful and much appreciated comments were provided by F. Kienast and two reviewers. This study was supported by a grant from the Swiss National Science Foundation for prospective scientists (J. Bolliger) and grants by the Department of Natural Resources (D. Mladenoff and L. Schulte). LITERATURE CITED Almendinger, J. C. 1997. Minnesota bearing tree data base. 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