Landscape analysis of plant diversity

Transcription

1 Landscape Ecology 12: , Kluwer Academic Publishers. Printed in the Netherlands. Landscape analysis of plant diversity Thomas J. Stohlgren 1, Michael B. Coughenour 2, Geneva W. Chong 1, Dan Binkley 3, Mohammed A. Kalkhan 2, Lisa D. Schell 2, David J. Buckley 4 and Joseph K. Berry 5 1 Rocky Mountain Field Station, National Biological Service, Natural Resource Ecology Laboratory, Colorado State University, Fort Collins, CO 80523, U.S.A.; 2 Natural Resource Ecology Laboratory, Colorado State University, Fort Collins, CO 80523, U.S.A.; 3 Department of Forest Sciences, Colorado State University, Fort Collins, CO 80523, U.S.A.; 4 Innovative GIS Solutions, Inc., Suite 300, 2000 S. College Ave., Fort Collins, CO 80525, U.S.A.; 5 Berry & Associates, 19 Old Town Square, Fort Collins, CO 80524, U.S.A. Keywords: map accuracy assessment, geographic information systems, keystone ecosystems, plant species richness patterns, wildlife models, ecosystem models Abstract Studies to identify gaps in the protection of habitat for species of concern have been inconclusive and hampered by single-scale or poor multi-scale sampling methods, large minimum mapping units (MMU s of 2 ha to 100 ha), limited and subjectively selected field observations, and poor mathematical and ecological models. We overcome these obstacles with improved multi-scale sampling techniques, smaller MMU s (< 0.02 ha), an unbiased sampling design based on double sampling, improved mathematical models including species-area curves corrected for habitat heterogeneity, and geographic information system-based ecological models. We apply this landscape analysis approach to address resource issues in Rocky Mountain National Park, Colorado. Specifically, we quantify the effects of elk grazing on plant diversity, identify areas of high or unique plant diversity needing increased protection, and evaluate the patterns of non-native plant species on the landscape. Double sampling techniques use satellite imagery, aerial photography, and field data to stratify homogeneous and heterogeneous units and keystone ecosystems (ecosystems that contain or support a high number of species or have distinctive species compositions). We show how a multi-scale vegetation sampling design, species-area curves, analyses of within- and between-vegetation type species overlap, and geographic information system (GIS) models can be used to quantify landscape-scale patterns of vascular plant diversity in the Park. The new multi-scale vegetation plot techniques quickly differentiated plant species differences in paired study sites. Three plots in the Ouzel Burn area (burned in 1978) contained 75 plant species, while only 17 plant species were found in paired plots outside the burn. Riparian areas contained 109 plant species, compared to just 55 species in paired plots in adjacent forests. However, plant species richness patterns inside and outside elk exclosures were more complex. One elk exclosure contained more species than its adjacent open range (52 species inside and 48 species outside). Two elk exclosures contained fewer species inside than outside (105 and 41 species inside and 112 and 74 species outside, respectively). However, there was only 26% to 48% overlap (using Jaccard s Coefficient) of plant species composition inside and outside the exclosures. One elk exclosure had 13% cover of non-indigenous species inside the exclosure compared to 4% outside, but non-indigenous species cover varied by location. We compared plant diversity patterns from vegetation maps made with 100 ha, 50 ha, 2 ha, and 0.02 ha MMU s in the 754 ha Beaver Meadows study area using four ha and twenty-one 0.1 ha multi-scale vegetation plots. Preliminary data suggested that the 2 ha MMU provided an accurate estimate of the number of plant species ( 14%) for a study area, but the number of habitats (polygons) was reduced by 67%, and aspen, a unique and important habitat type, was missed entirely. We describe a hypothesis-driven approach to the design and implementation of geospatial databases for local resource monitoring and ecosystem management.

2 Introduction Several authors have recommended conserving biodiversity at the ecosystem level, rather than attempting to conserve individual species (Noss 1983; Agee and Johnson 1988; LaRoe 1993). Gap Analysis (Scott et al. 1993) is a method to identify gaps in the protection of habitat of species of special concern for the maintenance of biological diversity. The method is typically applied to large regions and states. However, landscape-scale analyses are needed to link ecological processes with habitat structure and population responses (Franklin 1993; Stohlgren 1994). Habitat reserves are embedded in, and interact with, a matrix of less protected and unprotected areas on the landscape. National parks and wilderness areas, wildlife refuges, and state and local preserves are not isolated from external threats, just as legal mandates to protect species and habitats do not guarantee species persistence (Stohlgren et al. 1995b). Spatially explicit landscape- and regional-scale analyses are needed for scientific ecosystem management and the conservation of biodiversity. Methods to identify gaps in the protection of biodiversity are typically applied to large areas with coarse minimum mapping units (MMU s of 100 ha, sometimes to 2 ha). These techniques (Scott et al. 1993; Strittholt and Boerner 1995) provide a suitable way to set priorities for the establishment of new parks and reserves at the state and national levels if one assumes that the scale, resolution, and completeness of the information is adequate to assess the status and trends of important species, habitats, ecosystems, and landscapes. Since most natural areas, parks, and wildlife refuges are small and most management decisions are made locally, two information gaps must be evaluated. First, do areas of high or unique diversity remain undetected because of poor resolution in the form of large MMU s? Second, does the use of broad cover types lead to a significant underestimate of diversity? Our study is designed to quantify the effects of these two gaps by providing systematic resource inventories at finer spatial resolutions (0.02 to 0.09 ha MMU) to develop the information needed to protect local plant and wildlife populations. Identifying keystone ecosystems and threatened resources in established parks and reserves is equally important to designating new parks and reserves. Outside Rocky Mountain National Park, Colorado (Fig. 1), elk habitat has been fragmented in some areas by urbanization, forest practices, agriculture, hunting-area regulations, and livestock grazing. Within the Park, elk habitat has been altered by fire suppression, visitor use, and elk-impacts on vegetation (particularly aspen stands). Understanding elk movements and their effects on plant diversity requires the linkage of plot-scale assessments of forage with larger-scale landscape patterns and the distribution and condition of natural communities. How does elk grazing affect plant species composition or richness? We are uncertain, for example, if native plant diversity is higher or lower in areas of high or low elk use. Are nonindigenous plant species invading heavily grazed areas, or are they more dominant in lesser grazed areas and elk exclosures? We provide a methodology that improves the measuring and mapping of plant diversity at local levels and provides a quantitative link between field ecology, landscape analysis, and regional resource mapping programs. The key features of the methodology include: (1) initial stratification of homogeneous and heterogeneous vegetation types and potential keystone ecosystems (see below); (2) a sampling design based on unbiased sampling at multiple spatial scales; (3) testing and validation of mathematical models to predict species richness; and (4) development of predictive ecosystem models for making management decisions that affect local plant and animal populations and ecosystems in the matrix of protected and less-protected landscapes. Our goal is to provide resource managers with a scientific basis to address pressing environmental challenges such as preserving biodiversity and keystone ecosystems, and managing wildlife in fragmented habitats. In this paper, we describe a hypothesis-driven approach to the design and implementation of geospatial databases for local resource monitoring and ecosystem management Keystone ecosystems: Aspen example A central idea in our approach involves identifying portions of landscapes that are particularly impor-

3 157 Fig. 1. Landscape analysis study area in and around Rocky Mountain National Park, Colorado (dashed line area) and the Beaver Meadows study area. tant for a given ecological or management question: keystone ecosystems (term coined by D. Binkley). These areas may be characterized by high native species diversity, unique or critical habitats, temporary habitats (e.g., early seral stage areas, ephemeral wetlands), or aesthetic value. In the central Rocky Mountains, most of the landscapes are dominated by coniferous forests. Aspen currently comprises less than 1% of the forested area within Rocky Mountain National Park, but this forest type is especially important for a wide range of reasons. The diversity of understory herbs is high (Peet 1988); 10 m 2 plots typically contain 10 to 15 species of graminoids, plus 20 to 40 herbaceous species, compared with 0 to 5 in lodgepole pine stands (Mueggler 1985). Aspen provide important wildlife habitat, particularly for ungulates, beavers, and a wide variety of birds. For example, Salt (1957) found that aspen stands near Jackson, Wyoming had 3 times the number of bird species found in other (mostly conifer) communities, with 85% of the bird biomass belonging to insectivorous species. The greatest variety of predatory birds in the Rocky Mountains inhabit landscapes with mixed stands of aspen and conifers (DeByle 1985a). Aspen habitat is very sensitive to the impacts of browsing by large ungulates (such as cattle and elk; DeByle 1985b). The occurrence and dominance of aspen in a landscape depend very heavily on fire, climate, and the amount of browsing (Romme et al. 1995). This leads to a dichotomy of aspen providing excellent habitat and forage for grazers, which may in turn lead to degradation or lack of regeneration of aspen stands. Aspen may be considered a

4 158 fire-adapted species, because it regenerates readily after fires. However, aspen forests are much less flammable than pine forests (Jones and DeByle 1985), and landscapes with large aspen components may have very different wildfire regimes (less severe, and patchier fires) than conifer landscapes. Other examples of keystone ecosystems include those associated with rare geologic features (e.g., rock outcrops, mineral licks) and distinctive or transient vegetation communities (early successional seres, rare plant associations). Narrow riparian zones and seeps hidden by overstory trees, for example, are small in total area, and are rarely detected in regional GIS databases. However, they are often areas of high species richness, unique species assemblages, and high productivity; and they provide vulnerable linkages between terrestrial and hydrological portions of the landscape. Our objectives are to quantify the contribution of these areas to landscape diversity and understand the structural and functional differences between these keystone ecosystems and their surrounding environments in light of conservation issues. 2. General methods and specific hypotheses 2.1. General methods General methods include: (1) initial stratification of homogeneous and heterogeneous vegetation types and potential keystone ecosystems; (2) a study design based on unbiased sampling and accuracy assessment at multiple spatial scales; (3) developing and validating mathematical models to predict species richness; and (4) developing and validating predictive ecosystem models for decision making to conserve local plant and animal populations and ecosystems in the matrix of protected and less-protected landscapes Initial stratification of homogeneous and heterogeneous vegetation types, and potential keystone ecosystems Initial stratification of homogeneous units (e.g., large expanses of lodgepole pine), heterogeneous units (e.g., burned versus unburned lodgepole pine), and keystone ecosystems (e.g., small aspen stands within other forest types) might greatly increase the accuracy of estimates of plant diversity for two reasons. First, low resolution surveys may lump large, species-poor areas with a few, small, species-rich areas. Because these areas are small and likely to be clustered on the landscape, simple random sampling would generally miss the species-rich areas. Second, if these species-rich areas contain fairly unique plant species (e.g., if plant lists have little overlap with the species-poor areas in the same general vegetation type), the speciesarea curves might be far less-steep than if the species-poor areas were not combined. We classified land cover and land use types using Landsat Thematic Mapper data (30 m pixels based primarily on bands 3, 4, and 7) and color aerial photography. For the Beaver Meadows pilot study area, for example, we used 1:15,840 natural color aerial photographs taken 28 September We stratified the vegetation to include lodgepole pine, ponderosa pine, wet meadow, dry meadow, and aspen communities. The ponderosa pine community was further stratified into burned (prescribed fire in September 1994) and unburned ponderosa pine. We had a minimum mapping unit of 0.02 ha so that small patches of aspen and burned ponderosa pine would be included A study design based on unbiased sampling and accuracy assessment at multiple spatial scales Accuracy assessments are based on double sampling techniques (Kalkhan et al. 1995) where separate error matrices are developed between the classifications on the satellite image and the aerial photography, and between the aerial photography and the ground observations. The error matrices consist of a series of rows and columns containing the number of sample units, such as pixels. Each unit is assigned to a particular category relative to its actual type based on a set of reference data. The error matrix provides the user with information on the accuracy of individual categories, and both errors of commission and omission in the classification (Congalton 1991). Errors of commission relate to user accuracies, while errors of omission represent the accuracy of the remotely sensed data. In short, sample variances are analyzed sequentially to meet predetermined levels of precision (Krebs 1989) so one can understand the strengths and limitations of the data.

5 159 Fig. 2. Modified-Whittaker nested vegetation plot design. Validating the accuracy of vegetation maps using double sampling requires accurate estimates of bias and variance at multiple spatial scales. For this, we use a composite estimator developed by Mayback (1979, detailed in: Kalkhan et al. 1995). Based on previous studies (Kalkhan 1994), we are using Pielou s index of segregation and double sampling as alternative measures for assessing the accuracy of vegetation maps. Pielou s index of segregation (Pielou 1961, 1977) was developed to measure the spatial association between two or more categories. Pielou s index has been used extensively in ecological studies for testing spatial segregation. These methods work especially well with ecological studies that deal with an asymmetrical error matrix. We used the multi-scale Modified-Whittaker plot design (Fig. 2) for ground-truth sampling of understory vegetation (Stohlgren et al. 1995a) at the points selected using the above method. The Modified-Whittaker technique reduces bias and underreporting of species richness due to spatial autocorrelation. The new plot technique was applied in various vegetation types under various land use regimes (e.g., elk grazed and ungrazed, burned and unburned, riparian and non-riparian) to quantify landscape-scale species richness patterns, to identify keystone ecosystems, and to test the accuracy assessment method. We conducted extensive high and low resolution inventories of a 754 ha pilot study area with minimum mapping units of 0.02 ha, 2 ha, 50 ha, and 100 ha Developing and validating mathematical models to predict species richness Species-area curves allow one to estimate the number of species that could be found in an area larger than the one sampled. Cumulative species data from the 1 m 2, 10 m 2, and 100 m 2 subplots from each 1000 m 2 Modified-Whittaker plot were fit to species-log(area) curves. This semi-log relationship was reported to be a robust species-area curve (Shmida 1984; Stohlgren et al. 1995a). Because replicate vegetation plots rarely (never?) have identical species compositions, the average specieslog(area) curve would underestimate the combined species richness from replicate plots. Species-area curves based on replicate plots must be corrected for within vegetation type heterogeneity. First, we developed average species-log(area) curves for each vegetation type, then we used Jaccard s Coefficient (J) to correct the slope of the average species-log(area) curve as: y = (m/j)(logx) + b where y is the number of species, X is the combined plot area, b is the constant, m is the slope, and J is Jaccard s Coefficient. Jaccard s Coefficient accounts for the overlap between complete species lists from paired sites (Krebs 1989). Jaccard s Coefficient (J) is defined as: J = A/(A + B + C) where A = the number of species found in both paired sites, B = species in site 1 but not in site 2, and C = species in site 2 but not in site 1. In other words, Jaccard s Coefficient is the proportion of the two sites combined diversity that is shared. A comparison of species lists for two sites resulting in a similarity coefficient of 1.0 would indicate complete overlap (i.e., identical species lists), while a value of 0.0 would indicate no overlap. For each vegetation type, we calculated the average Jaccard s Coefficient from pairwise comparisons between plots. We validated the corrected species-area curves using observed values from 1 m 2, 10 m 2, and 100 m 2 subplots in the replicate 1000 m 2 plots in each vegetation type. We also calculated Jaccard s Coefficient for the 1000 m 2 plot data to further refine the average Jaccard s Coefficient and improve the accuracy of the species-log(area) curves for each vegetation type Developing and validating predictive ecosystem models for decision making The real potential of our study lies in developing

6 160 Fig. 3. A conceptual diagram of the modeling environment where the ecosystem model (with submodels) is linked to an empirical biodiversity model, and resource managers are provided with a friendly GIS interface and decision support system. and validating predictive ecosystem models. Here, various scenarios can be tested non-destructively. Conceptual and predictive models are an important step in avoiding the preventable degradation of biotic resources and populations of special concern. The models also can be powerful tools in planning, teaching, training, and public outreach. Each Modified-Whittaker plot serves as a geo-referenced, long-term study point to validate model predictions in the coming years and decades. A GIS-based ecosystem model provides the ideal tool for synthesizing existing information and making it useful to resource managers. For example, elk and moose may rely heavily on forage provided by aspen and willow (keystone ecosystems), while the vegetation may require fire or periodic flooding for successful regeneration. Thus, a model may be initiated with current locations and abundances of aspen and willow. Subsequent changes of locations and abundances through time, created by fire and hydrologic incidents, can be modeled with information from sampling biodiversity in areas in various stages of recovery from disturbance (chronosequences). We are using a spatially explicit ecosystem model, Savanna (Coughenour 1993), linked to the ARC/INFO GIS system via the RAPiD/Arc development environment (Buckley et al. 1993) to investigate links between biodiversity and ecosystem function (Fig. 3). The Savanna model was first developed for a pastoral ecosystem in Kenya (Coughenour 1991, 1992), but it has since been modified, made more elaborate, and applied to temperate zone ecosystems like Elk Island National Park, Alberta, Canada (Buckley et al. 1993) and Yellowstone National Park (Coughenour and Singer 1991). Savanna includes process oriented models of carbon flows through three trophic levels, plant and soil water budgets, and plant and animal population dynamics. The model is spatially explicit in the sense that spatial subunits are simulated in parallel, and organisms and water can move or be redistributed across the landscape during a simulation. The use of the RAPiD/Arc development environment not only provides seamless integration of the Fortran-based Savanna model with the ARC/INFO GIS system, but it also allows field-based resource managers easy access to ecosystem modeling and output analysis. Again, our long-term study plots will provide data to validate the model predictions.

7 Specific hypotheses and methods To address site-specific, landscape-scale conservation issues related to patterns of plant diversity and the importance of keystone ecosystems, we have four testable hypotheses Hypothesis 1 The accuracy of estimates of vascular plant species richness using species-area curves is influenced greatly by the amount of species overlap among vegetation plots. We developed average species-area curves for each vegetation type, then used Jaccard s Coefficient to correct the slope of the average speciesarea curve (as described above). The number of species per total area sampled (i.e., the total area of three, four, or five 1000 m 2 plots) for each community was estimated based on the semi-log relationship of the average number of species recorded in 1 m 2, 10 m 2, and 100 m 2 subplots. We tested this hypothesis directly by quantifying species-area relationships in homogeneous, hetereogeneous, and keystone ecosystems and comparing species overlap (using Jaccard s Coefficient), coefficients of determinations for species-area curves, and the differences between observed and expected species richness values based on species-area curves for various vegetation types Hypothesis 2 Estimates of plant diversity (indicated by the number of vegetation types recognized, the number of polygons or habitat patches by type, and vascular plant species richness) decrease non-linearly with increases in the size of the minimum mapping unit used in resource analysis. Understanding the gain in information at various levels of resolution can help resource managers plan vegetation and wildlife studies at the scale(s) appropriate for decision making. We tested this hypothesis by comparing plant diversity patterns from vegetation maps made with 100 ha, 50 ha, 2 ha, and 0.02 ha MMU s. We selected a 754 ha study area in Rocky Mountain National Park and established three to five Modified-Whittaker plots in each of six vegetation types. We developed and tested species-log(area) curves, correcting the curves for within-vegetation-type heterogeneity with Jaccard s Coefficient. Total species richness in the study area was estimated from vegetation maps at each resolution (MMU) based on the corrected species-area curves, total area of the vegetation type, and species overlap among vegetation types. At a landscape scale, we know that large (100 ha) MMU s will produce fewer polygons and recognize fewer vegetation types than small MMU s, and thus result in lower estimates of vascular plant species richness. However, we do not know if there is a linear or non-linear decrease in species richness and habitat complexity (i.e., the number of vegetation types recognized and the number of polygons per type). Whatever map resolution is selected, resource managers need to know the strengths and limitations of the data, and the costs and benefits of collecting finer-resolution data Hypothesis 3 Growing numbers of elk in Rocky Mountain National Park have greatly affected plant diversity, and declines in plant diversity are further exacerbated by modern land uses (e.g., hunting, lack of natural predation, fire suppression, habitat fragmentation, and urbanization/development) that affect elk distributions in the Colorado Rocky Mountains. We are measuring height and percent cover of native and non-native plant species at local and landscape scales. At the local scale, we are evaluating patterns of plant diversity inside and outside elk exclosures to document differences in species richness, species composition, and cover (dominance) of native and non-native species. At the landscape scale, we can evaluate native species persistence and the spread of non-native plants over broad areas. We will accept the hypothesis that elk have greatly effected plant diversity if: (1) native species richness is lower in heavily grazed versus ungrazed areas (local extirpation of plant species has occurred); (2) non-native species cover is significantly greater in heavily grazed areas; and (3) the condition of plant and soil resources is declining significantly. Interactions between elk distributions and regional plant diversity patterns, ecosystem processes and functions, and modern land use practices will be modeled by linking a GIS with simulation models (Coughenour 1992), where predictions are cor-

8 162 roborated with remotely sensed data (Coughenour 1991). This model links plant diversity and ecosystem functions in a format that can be simulated before management actions are taken so that resource managers can gain insights into possible outcomes of specific decisions. Once a management action is taken, model results may be validated through ongoing monitoring of both vegetation and animal use. We are strategically locating longterm monitoring plots at use ecotones (areas that are likely to be developed for human residences and use), vegetation ecotones, and in areas that are likely to be corridors of areas of concentrated use for elk Hypothesis 4 Keystone ecosystems play a disproportionately large role in the preservation of native biological diversity and species of special concern (e.g., elk, wetland vegetation) in the Colorado Rocky Mountains. We are developing a case study of realized and potential aspen distribution, and its function related to elk habitat and native understory diversity, based on a synthesis of many new and old data sets. Our first objective is to quantify the relative contribution of aspen habitat to landscape scale plant diversity. We will accept the first part of the hypothesis if aspen habitat has both higher plant species richness and higher numbers of unique species than most other vegetation types. At the landscape to regional scales, we are using the GIS-simulation modeling approach to examine: (1) how much the aspen ecosystem contributes to biological diversity and species of special concern in the Colorado Rocky Mountains; (2) how climate, fire, grazing, and land use scenarios may alter biodiversity and the distribution of species of special concern (and how future scenarios may alter future diversity); and (3) how ecosystem function and diversity are linked through keystone ecosystem processes. We are validating predictions by quantifying vegetation change and land use change since the last aerial photographs, and by establishing a series of paired, long-term monitoring plots in developed and undeveloped areas. The current distribution of aspen stands in our study area is limited in part by heavy browsing, as evidenced by the dramatic difference in aspen presence and biomass within and outside ungulate exclosures (see also debates about Yellowstone: Chadde and Kay 1991; Coughenour and Singer 1991). What would the implications be of reduced aspen influence on landscapes in our study area, or of increased aspen? The aspen case study focuses on determining some implications of varying the cover of aspen across the landscape. We are developing several scenarios of aspen abundance: (1) current extent and distribution of aspen stands; (2) reduction of current aspen by 90%; (3) doubling of aspen extent, centered on expansion of current stands; and (4) a maximum aspen scenario, based on aspen occupying all suitable sites (defined based on soil type, aspect, topography, and elevation). We can analyze each scenario for a variety of characteristics, including understory species diversity, ungulate habitat characteristics, fire hazard (using existing, topographically and spatially explicit models), and water yields. 3. Preliminary results and discussion 3.1. General results Standardizing sampling protocols for conducting systematic plant surveys As reported in Stohlgren et al. (1995a), we recently compared the widely used Whittaker plot (Shmida 1984) that collects species richness data at multiple spatial scales, using 1 m 2, 10 m 2, and 100 m 2 subplots within a 20 m 50 m (1000 m 2 ) plot, with a Modified-Whittaker plot. The Modified-Whittaker plot minimizes the statistical problems of the original design, while maintaining many of its attractive attributes. Stohlgren et al. (1995a) tested the two techniques by overlaying them in forest and prairie vegetation types in Larimer County, Colorado USA (n = 13 sites) and Wind Cave National Park, South Dakota USA (n = 19 sites). The modified design often returned significantly higher (p < 0.05) species richness values in the 1 m 2, 10 m 2, and 100 m 2 subplots. Species-area relationships, using the Modified-Whittaker design, conformed better to published semilog relationships, explaining, on average, 92% of the variation. Using the original Whittaker design, the semilog species-area relationships were not as strong, ex-

9 163 Table 1. Observed and estimated numbers of species, based on species-area curves (S-A Curves) from 1 m 2, 10 m 2, and 100 m 2 subplot data, and percent accuracy (difference between observed and expected values) for vegetation types in the Beaver Meadows area of Rocky Mountain National Park, Colorado. J-Corr. = slope of the species-area curve corrected with Jaccard s Coefficient, no Corr. = no correction. Estimated number of species Percent accuracy Community type No. Observed S-A Curve S-A Curve S-A Curve S-A Curve Plots No. spp. (no Corr.) (J-Corr.) (no Corr.) (J-Corr.) Aspen % 99.3% Lodgepole % 96.6% Burned ponderosa % 98.3% Wet meadow % 98.0% Dry meadow % 95.1% Ponderosa % 96.6% 1 Two outliers of Jaccard s Coefficient removed (n = 8 values left). plaining only 83% of the variation, on average. We now use the Modified-Whittaker plot design for measuring species composition, analyzing plant diversity patterns at multiple spatial scales, and for trend analysis. Subtle, long-term changes in plant diversity may not be detectable in the five-year study period so the establishment of a series of long-term study plots in many areas is essential. Each Modified- Whittaker plot serves as a long-term, nested vegetation monitoring plot for assessing temporal trends in plant diversity patterns Hypothesis-specific results and discussion Hypothesis 1 The accuracy of estimates of vascular plant species richness using species-area curves is influenced greatly by the amount of species overlap among vegetation plots. Preliminary data from six vegetation types in the Beaver Meadows area strongly suggest that species-area curves corrected with Jaccard s Coefficient (as described above) greatly improve estimates of species richness (Table 1). Species-area curves not corrected for species overlap had accuracy levels between 31.8% and 55.5%. Speciesarea curves that were corrected for species overlap had accuracy levels between 95.1% and 99.3%. Species overlap between the replicate plots within a vegetation type ranged from 19.9% (Jaccard s Coefficient 100) in the wet meadow and lodgepole types to 31.8% in the burned ponderosa type. Regression analyses of the mean Jaccard s Coefficient values to coefficients of determination for species-area curves and percent accuracy revealed no significant relationships. Correcting species-area curves with Jaccard s Coefficient should have broad application for homogeneous and heterogeneous vegetation types. However, further study is needed to see if this approach is successful in other mountain vegetation types, with various numbers of sample plots, and in different biomes. The testing of the methods continues in plains vegetation (e.g., short grass steppe) Hypothesis 2 Estimates of plant diversity (indicated by the number of vegetation types recognized, the number of polygons or habitat patches by type, and vascular plant species richness) decrease non-linearly with increases in the size of the minimum mapping unit used in resource analysis. We developed and tested species-log(area) curves for six vegetation types in the Beaver Meadows area of Rocky Mountain National Park, correcting the curves for within-vegetation-type heterogeneity with Jaccard s Coefficient. Total species richness in the study area was estimated from vegetation maps at four resolutions (MMU s) based on the corrected species-area curves, total area of the vegetation type, and species overlap among vegetation types. With the 0.02 ha MMU, all six initially recognized vegetation types were recovered, resulting in an estimated 552 species in the 754 ha study area (Table 2). With the 2 ha MMU, five vegetation types were recognized, resulting in an estimated 473 species for the study area. With the 50 ha

10 164 Table 2. Effects of minimum mapping unit size on number of vegetation types recognized, number of habitat patches (polygons recognized), and estimated species richness for the 754 ha Beaver Meadows study area in Rocky Mountain National Park. Percent of 0.02 ha (100%) MMU data in parentheses. Plant Minimum mapping unit size diversity characteristic 100 ha 50 ha 2 ha 0.02 ha Vegetation types (33%) (67%) (83%) (100%) Habitat patches (3%) (5%) (33%) (100%) Plant species (62%) (80%) (86%) (100%) MMU, 439 plant species were estimated for the four vegetation types recognized in the study area. With the 100 ha MMU, only three vegetation types were recognized, resulting in an estimated 341 plant species for the study area. Locally rare species and keystone ecosystems (areas of high or unique plant diversity) were missed at the 2 ha, 50 ha, and 100 ha scales. Describing vegetation patterns with 50 ha or 100 ha MMU s may significantly underestimate plant diversity, the number and area of habitat patches, and community diversity. The vegetation types that were undetected when the resolution was decreased were those that had the greatest proportion of total and unique species. Increasing the MMU from 0.02 ha to 100 ha resulted in an exponential decrease in the total number of polygons (y = e x, r 2 = 0.97), a weak linear decrease in the total number of species estimated (y = 52x + 569, r 2 = 0.91), and a linear decrease in the number of vegetation types recognized (y = x + 7, r 2 = 1.0). The non-linear decrease in the number of polygons is a particularly significant effect of decreasing map resolution because it creates gaps in the recognition of habitat patches that may determine whether an area is important for conservation efforts. As another example of the loss of important habitat types during mapping, we compared aspen cover before and after the Forest Service and Rocky Mountain National Park staffs combined their vegetation maps. They reclassified the park s 348 vegetation classification categories into the 19 used by the Forest Service. Aspen, for example, was reduced from 23 dominant cover-types (602 ha) and 11 co-dominant cover-types (173 ha) to one dominant cover-type (608 ha) on the regional map. Thus, the area with aspen was reduced by 22% simply by merging and collapsing the data. We suspect that aspen was already under-represented by the 2 ha minimum mapping unit since it often occurs as isolated, small clumps Hypothesis 3 Growing numbers of elk in Rocky Mountain National Park have greatly affected plant diversity, and declines in plant diversity are further exacerbated by modern land uses (e.g., hunting, lack of natural predation, fire suppression, habitat fragmentation, and urbanization/development) that affect elk distributions in the Colorado Rocky Mountains. We established a pair of Modified-Whittaker plots at the Deer Ridge elk exclosure in Rocky Mountain National Park. This 0.5 ha exclosure was established in After 31 years without large ungulate grazing, total species richness in the study plots inside (52 species/0.1 ha plot) and outside (47 species/0.1 ha plot) the exclosure were similar (Fig. 4b). Species overlap, measured with Jaccard s Coefficient (J) was intermediate (J = 33% overlap). Two other elk exclosures contained fewer species inside than outside (105 and 41 species inside and 112 and 74 species outside, respectively). Land uses and management activities also affect plant cover evenness (i.e., how percent cover is distributed among species) and the dominance of non-indigenous species. At the Deer Ridge and Upper Beaver Meadows elk exclosures, percent plant cover was much more even outside the exclosures than inside. At Upper Beaver Meadows nonindigenous plants provided 13% of the total cover inside and 4% of the total cover outside the elk

11 165 Fig. 4. (a) Sample data on multi-scale species richness comparing paired plots inside and outside the Ouzel Burn area, Rocky Mountain National Park. (b) Sample data on multi-scale species richness comparing paired plots inside and outside the Deer Ridge elk exclosure, Rocky Mountain National Park. (c) Sample data on multi-scale species richness comparing paired plots inside and outside a riparian zone (Michigan River, Colorado). exclosures. Patterns were site-specific, however. In the Lower Beaver Meadows elk exclosure percent cover of non-indigenous species varied by location inside and outside the exclosure. On the drier sites, 1% of the cover inside was from non-indigenous species, while they covered 0% outside. At the ecotone between dry and wet sites non-indigenous species provided 27% of the cover inside and 33% outside. At the wet sites non-indigenous species provided 40% cover inside and 42% cover outside. The Upper Beaver Meadows exclosure may be providing a refuge for the non-indigenous Bromus inermis, but an interaction between the abundant aspen and understory species is also likely (i.e., aspen may be shading out perennial herbaceous plants). The non-indigenous cover at the Lower Beaver Meadows exclosure suggests that the species are responding to the grazing regime and to environmental gradients. We are continuing to investigate plant height, cover, composition, and species richness, and non-indigenous species occurrence inside and outside other elk exclosures. The exclosures may produce a refuge effect (i.e., protection from ungulate grazing/browsing) for some non-indigenous species. In the 754 ha Beaver Meadows study area (where there is heavy elk use and visible differences inside and outside two elk exclosures), we recorded 330 plant species (~1/3 the number of plants recorded in the 1074 km 2 Park) in the 2.2 ha area sampled with the randomly placed Modified- Whittaker plots. This represents a sampling intensity of just 0.29% of the 754 ha study site. We estimated 552 plant species (~1/2 the number of plants recorded in the Park) in the 754 ha sampling area. Thus, about 1/2 of the Parks recorded plant species might be found on just 0.7% of the Park suggesting extremely high landscape diversity and redundancy of plant diversity even on heavily grazed areas of the landscape. These preliminary results suggest that elk browsing may have positive effects on landscape-scale native plant diversity and neutral effects on local species richness, but continued negative effects on some plant species such as aspen and willow. Preliminary data also suggest that elk grazing may decrease non-indigenous species cover in some areas, while ungrazed areas (exclosures) may provide a refuge for invasive species. Thus, elk management may have significant, predictable effects on plant diversity Hypothesis 4 Keystone ecosystems play a disproportionately large role in the preservation of native biological diversity and species of special concern (e.g., elk, wetland vegetation) in the Colorado Rocky Mountains.

12 166 Table 3. Total numbers of plant species and unique species observed (OBS.; in plots) or estimated (EST.) in the study area, and the estimated number of new observations of plant species per ha of habitat in the 754 ha study area (as an index of the relative contribution of vegetation types to the diversity of plants in the study area). Vegetation type # Spp. # Unique # Spp. # Spp./ha OBS. Spp. OBS. EST. EST. Dry meadow Wet meadow Aspen Ponderosa Burned ponderosa Lodgepole pine Totals (duplicates removed) Before quantifying the relative contribution of aspen habitat to landscape-scale plant diversity, we tested our field methods by comparing different keystone ecosystems with their more homogeneous surroundings. In the first field season (1994), we established three pairs of Modified-Whittaker plots in and adjacent to the Ouzel Burn near Ouzel Lake in Rocky Mountain National Park. Plots were randomly located within the 390 ha burn area with paired plots established randomly outside the burn at similar elevations ( m), slopes, and aspects. Preliminary results showed that 17 years after the fire, understory species richness was 3.6 times greater in the burned area study plots (47 species/0.1 ha plot versus 13 species/0.1 ha in the unburned area; Fig. 4a). Plant species composition overlap between burned and unburned sites was quite low (14%). We established two Modified- Whittaker plots in the riparian zone of the Michigan River in the Colorado State Forest, and two paired plots in adjacent forest stands. The riparian sample plots contained almost twice as many species as plots in the surrounding forest (77 and 38 species, respectively, per 0.1 ha plot, Fig. 4c). Plant species composition overlap between the riparian plots and adjacent forests was low to intermediate (24%). Still, this paired plot approach could not tell us how much these ecosystems contributed to plant diversity at landscape scales. In the second field season (1995), we developed and tested species-log(area) curves for six vegetation types in the 754 ha Beaver Meadows study area (as described in Hypothesis 2). By incorporating the areal coverage estimates of the vegetation types into the species area curves, we could estimate the total species richness for each vegetation type in the study area (Table 3). We estimated, for example, that the 8.8 ha of aspen type would contain approximately 221 plant species, while the ha of lodgepole pine would contain 233 plant species. The ponderosa pine and dry meadow types would contain a low number of species relative to the landscape cover of the type. Seventy-six of the 330 plant species encountered were found only in the wet meadow type. Fifty plant species were found only in the aspen type. On a per-area basis, the aspen added about 25 plant species/ha of habitat to the plant checklist for the 754 ha study area, while the dry meadow type contributed just 0.4 plant species/ha to the checklist. Thus, aspen habitat had both higher plant species richness and higher numbers of unique species than most other vegetation types. The burned ponderosa type, though quite small (8.9 ha) contributed 10 species/ha of habitat to the plant checklist. Fire creates ecosystems that may be temporary, yet important, for maintaining biological diversity. Like the aspen type, this relatively small area contained a disproportionately large component of the landscape s species richness. Fire suppression and the lack of natural fire could greatly reduce local biological diversity, because the diversity of plant species probably influences the diversity of habitat use by ungulates, birds, insects, and other organisms. However, many additional plots are needed to test whether the unique-totype plant species reported here truly have strong affinities to overstory types. The study area will be enlarged ten times to evaluate the complex patterns of plant diversity discovered in the first two years. 4. Planned synthesis: landscape modeling and verification We are designing the models to allow resource managers to address general landscape-scale issues and specific hypotheses (see above). The addition of fine-scale, understory vegetation data to the ecological model will provide resource managers with a tool for making management decisions that protect and enhance biodiversity from landscape to regional scales.

13 The Savanna model The Savanna model (Coughenour 1993) simulates processes at landscape through regional spatial scales over annual to decadal time scales appropriate spatial and temporal scales for landscape analysis. Savanna is a spatially explicit, processoriented model of grassland, shrubland, savanna, and forested ecosystems. The model is composed currently of hydrologic, plant biomass production, plant population dynamics, ungulate herbivory, ungulate spatial distribution, ungulate energy balance, ungulate population dynamics, and wolf predation submodels. Since Savanna is process-oriented rather than empirical or rule-based, it provides realistic, general, and explanatory representations of ecological change as opposed to descriptions of ecological states or prescribed responses Biodiversity-ecosystem linkages We are developing and using empirical models of biodiversity (using vascular plant diversity as an index) and linking these models to the ecosystem process model. Diversity, described using vegetation structure and plant species composition and richness, is being modeled as an empirical function of landscape, climate, and biotic variables. The empirical models include structural vegetation attributes such as overstory cover, overstory tree functional group composition and height, dominant herbaceous layer functional group composition, and biomass. These variables are predicted by the ecosystem model. The empirical diversity model is then coupled to the ecosystem model to simulate spatial and temporal patterns of biodiversity (again using vegetation structure and plant species richness as indicators of total diversity), as they are affected by modeled vegetation attributes. Simulation experiments will be conducted to study potential implications of climate change, fire regimes, and grazing on ecosystem processes and subsequent diversity. Landscape-level plant diversity will influence the spatial distribution of ungulates, disturbance regimes (Boeresma et al. 1991) and nutrient recycling rates (Chapin et al. 1986; Pastor et al. 1987; Holland et al. 1992; McInnes et al. 1992). We expect our research will point the way towards future empirical studies of the effects of diversity and species composition on energy and carbon flows, nutrient cycling, and hydrology. Metapopulation dynamic theory is used increasingly to analyze the effects of habitat fragmentation and to design effective conservation strategies (e.g., Levin 1976; Brown 1984; Pulliam 1988; Pulliam et al. 1992; Burgman et al. 1993; Liu 1993). This is an excellent opportunity to conduct empirical metapopulation studies given that we will have developed a spatial ecosystem model and developed or linked databases for spatial distributions of elk and key aspects of biodiversity. We are conducting simulations to explore the effects of spatial resolution, patterns of reserve design (e.g., wildlife corridors between protected patches), potential and historic land use patterns, fire regimes, and herbivory on biodiversity. Resource managers can use the ecological model and submodels to evaluate links between regional habitats and changing land uses, and potential consequences for high profile wildlife species. The fire submodel, for example, will allow resource managers to evaluate how fire suppression has affected hydrological regimes, particularly runoff, and, thus, downstream ecosystems. 5. A generalized landscape-scale analysis system for end-users Our biggest, long-term task is the development of user-friendly, GIS-based, predictive ecosystem models. We are modifying the prototype Ecosystem Management Model (EMM) developed for Elk Island National Park, Canada (Buckley et al. 1993) for our study area. The RAPiD/Arc application development toolkit (Innovative GIS Solutions, Inc.) is used to overlay the EMM on the GRID modeling subsystem of ARC/INFO. RAPiD/Arc provides a full suite of customization tools, providing the enduser with a friendly, pick and click graphical user interface to view and query the GIS data sets, and to initiate simulation model runs (Fig. 5). The beauty of this system is that technical GIS expertise is unnecessary. It allows on-site resource managers (end-users, non-gis specialists) an interactive modeling tool to aid in making management

14 168 Fig. 5. The Ecosystem Management Model Development Environment is layered over the grid modeling system of Arc/Info. A custom menu system allows users to view, query, and initiate simulation model runs (Buckley et al. 1993). decisions. Simulation results are readily accessible at many points during the model run, which allows the resource manager to pinpoint where simulated management actions are taking effect (Berry 1993). Such interactive, predictive, ecosystem models are destined to be the resource management tools of choice. This system will allow the resource manager to take advantage of the powerful engine provided by ARC/INFO without requiring the resource manager to be a trained mechanic. It provides a means to experiment on an ecosystem before actually manipulating it. Perhaps most important is the extensive model validation program that combines the use of new field data to refine and validate short-term model predictions, and a series of strategically-placed, long-term study plots to validate longer-term predictions. Because most management decisions are made at the landscape scale without sufficient data, we aim to develop a complete landscape-scale analysis