The importance of micro-topographic heterogeneity in determining species diversity of alpine plant communities of Glacier National Park, MT

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1 University of Iowa Iowa Research Online Theses and Dissertations Summer 2010 The importance of micro-topographic heterogeneity in determining species diversity of alpine plant communities of Glacier National Park, MT Jonathan Patrick Rose University of Iowa Copyright 2010 Jonathan Patrick Rose This thesis is available at Iowa Research Online: Recommended Citation Rose, Jonathan Patrick. "The importance of micro-topographic heterogeneity in determining species diversity of alpine plant communities of Glacier National Park, MT." MA (Master of Arts) thesis, University of Iowa, Follow this and additional works at: Part of the Geography Commons

2 THE IMPORTANCE OF MICRO-TOPOGRAPHIC HETEROGENEITY IN DETERMINING SPECIES DIVERSITY OF ALPINE PLANT COMMUNITIES OF GLACIER NATIONAL PARK, MT by Jonathan Patrick Rose A thesis submitted in partial fulfillment of the requirements for the Master of Arts degree in Geography in the Graduate College of The University of Iowa July 2010 Thesis Supervisor: Professor George Malanson

3 Copyright by JONATHAN PATRICK ROSE 2010 All Rights Reserved

4 Graduate College The University of Iowa Iowa City, Iowa CERTIFICATE OF APPROVAL MASTER S THESIS This is to certify that the Master s thesis of Jonathan Patrick Rose has been approved by the Examining Committee for the thesis requirement for the Master of Arts degree in Geography at the July 2010 graduation. Thesis Committee: George Malanson, Thesis Supervisor Stephen Hendrix Marc Linderman

5 ACKNOWLEDGEMENTS I would like to thank my parents for supporting me throughout my life. To my fiancée Stephanie, thank you for being so patient and supporting me during this process. I would also like to thank my advisor Dr. Malanson, and my committee members Dr. Hendrix and Dr. Linderman for guiding me in this study. Thanks to Darren Grafius and Melissa Hornbein for aid in collecting field data. Dan Fagre of the USGS Glacier Field Office helped arrange logistics. This project was supported by a grant from the UI Center for Global and Regional Environmental Research. ii

6 TABLE OF CONTENTS LIST OF TABLES LIST OF FIGURES iv vi CHAPTER I. OUTLINE OF STUDY 1 II. SPECIES COMPOSITION OF SITES AND ORDINATION 3 Introduction 3 Methods 6 Site Description 6 Data Collection 9 Ordination 9 Results 11 Discussion 15 III. INFLUENCE OF MICROTOPOGRAPHIC HETEROGENEITY ON ALPINE PLANT DIVERSITY 25 Introduction 25 Methods 32 Study Site 32 Data Collection 33 Statistical Analysis 34 Results 39 Species Diversity in 1 m 2 Plots 39 Quantile Regression 41 Boundary Tests 44 Discussion 46 IV. SUMMARY OF ANALSYES 107 REFERENCES 109 iii

7 LIST OF TABLES Table 1. Site Locations and Environmental Characteristics 18 Table 2. Table 3. Table 4. Table 5. Table 6. Table 7. Table 8. Table 9. Table 10. Table 11. Table 12. Table 13. Table 14. Table 15. Table 16. Table 17. Kendall Rank Correlations Between NMDS Axes and Environmental Variables 18 Kendall Rank Correlations Between NMDS Axes and Important Plant Species for All Plots 19 Kendall Rank Correlations Between NMDS Axes and Important Plant Species for Dryas Plots 19 Kendall Rank Correlations Between NMDS Axes and Important Plant Species for Non-Dryas Plots 20 Kendall Rank Correlations Between NMDS Axes 1 and 2 and Important Plant Species for All Plots with Damm s Data 20 Summary of Species Richness and Shannon-Weiner Diversity by Plot 54 Linear Regression of Species Richness vs. Environmental Variables for 1 m 2 Plots 55 Linear Regression of Shannon-Weiner Diversity vs. Environmental Variables for 1 m 2 Plots 56 Quantile Regression Results for Slope vs. Point Proportion of Richness Without Zeros 57 Quantile Regression Results for Slope vs. Point Proportion of Richness With Zeros 58 Quantile Regression Results for Negative Convexity vs. Point Proportion of Richness Without Zeros 59 Quantile Regression Results for Negative Convexity vs. Point Proportion of Richness With Zeros 60 Quantile Regression Results for Positive Convexity vs. Point Proportion of Richness Without Zeros 61 Quantile Regression Results for Positive Convexity vs. Point Proportion of Richness With Zeros 62 Quantile Regression Results for SD Elev vs. Window Proportion of Richness Without Zeros 63 Quantile Regression Results for SD Elev vs. Window Proportion of Richness With Zeros 64 Table 18. Boundary Line Test for SD Elev vs. Window Total Richness 65 iv

8 Table 19. Boundary Line Test for Slope vs. Point Richness 65 Table 20. Boundary Line Test for Neg Convex vs. Point Richness 66 Table 21. Boundary Line Test for Pos Convex vs. Point Richness 66 Table 22. Moran's I Statistic for Point Elevation and Richness by Plot 67 v

9 LIST OF FIGURES Figure 1. Location of Study Sites Within Glacier National Park 21 Figure 2. NMDS Ordination of All Plots Labeled by Site 22 Figure 3. NMDS Ordination of All Plots Labeled by Presence of Dryas 22 Figure 4. NMDS Ordination of Dryas Plots 23 Figure 5. NMDS Ordination of Non-Dryas Plots 23 Figure 6. Figure 7. Figure 8. Axis 1 and Axis 2 of NMDS Ordination of All Plots with Data from Damm 24 Axis 1 and Axis 3 of NMDS Ordination of All Plots with Data from Damm 24 Total Richness vs. SD Elev for 3 x 3 Windows from Divide Mtn Plot Figure 9. Linear Regression of Plot Species Richness on D for All Plots 69 Figure 10. Linear Regression of Plot Species Richness on % Non-Vegetated Ground for All Plots 70 Figure 11. Figure 12. Figure 13. Figure 14. Linear Regression of Plot Species Richness on D for Dryas Plots 71 Linear Regression of Plot Species Richness vs. Mean Soil Depth for Dryas Plots 72 Linear Regression of Plot Species Richness vs. % Dryas Cover for Dryas Plots 73 Linear Regression of Shannon-Weiner Index vs. % Rocky Ground for Dryas Plots 74 Figure 15. Linear Regression of Plot Species Richness vs. % Non-Vegetated Ground for Non-Dryas Plots 75 Figure 16. Plot Species Richness vs. D for Baring Basin 76 Figure 17. Plot Species Richness vs. D for Hidden Lake 76 Figure 18. Plot Species Richness vs. D for Lunch Creek 77 Figure 19. Plot Species Richness vs. D for Scenic Point 77 Figure 20. Plot Species Richness vs. D for Divide Mountain 78 Figure 21. Plot Species Richness vs. D for White Calf Mountain 78 vi

10 Figure 22. Plot Species Richness vs. D for Lee Ridge 79 Figure 23. Plot Species Richness vs. D for Siyeh Pass 79 Figure 24. Figure 25. Figure 26. Figure 27. Figure 28. Figure 29. Figure 30. Figure 31. Figure 32. Figure 33. Figure 34. Figure 35. Figure 36. Figure 37. Figure 38. Figure 39. Figure 40. Quantile Regression of Slope vs. Point Proportion of Richness for All Plots with Zeros Removed 80 Quantile Regression of Slope vs. Point Proportion of Richness for All Plots with Zeros 81 Quantile Regression of Slope vs. Point Proportion of Richness for Dryas Plots with Zeros Removed 82 Quantile Regression of Slope vs. Point Proportion of Richness for Non-Dryas Plots with Zeros 83 Quantile Regression of Slope vs. Point Proportion of Richness for Non-Dryas Plots with Zeros Removed 84 Quantile Regression of Slope vs. Point Proportion of Richness for Non-Dryas Plots with Zeros 85 Quantile Regression of Neg Convex vs. Point Proportion of Richness for All Plots with Zeros Removed 86 Quantile Regression of Neg Convex vs. Point Proportion of Richness for All Plots with Zeros 87 Quantile Regression of Neg Convex vs. Point Proportion of Richness for Dryas Plots with Zeros Removed 88 Quantile Regression of Neg Convex vs. Point Proportion of Richness for Dryas Plots with Zeros 89 Quantile Regression of Neg Convex vs. Point Proportion of Richness for Non-Dryas Plots with Zeros Removed 90 Quantile Regression of Neg Convex vs. Point Proportion of Richness for Non-Dryas Plots with Zeros 91 Quantile Regression of Pos Convex vs. Point Proportion of Richness for All Plots with Zeros Removed 92 Quantile Regression of Pos Convex vs. Point Proportion of Richness for All Plots with Zeros 93 Quantile Regression of Pos Convex vs. Point Proportion of Richness for Dryas Plots with Zeros Removed 94 Quantile Regression of Pos Convex vs. Point Proportion of Richness for Dryas Plots with Zeros 95 Quantile Regression of Pos Convex vs. Point Proportion of Richness for Non-Dryas Plots with Zeros Removed 96 vii

11 Figure 41. Figure 42. Figure 43. Figure 44. Figure 45. Figure 46. Figure 47. Figure 48. Figure 49. Quantile Regression of Pos Convex vs. Point Proportion of Richness for Non-Dryas Plots with Zeros 97 Quantile Regression of SD Elev vs. Window Proportion of Richness for All Plots with Zeros Removed 98 Quantile Regression of SD Elev vs. Window Proportion of Richness for All Plots with Zeros 99 Quantile Regression of SD Elev vs. Window Proportion of Richness for Dryas Plots with Zeros Removed 100 Quantile Regression of SD Elev vs. Window Proportion of Richness for Dryas Plots with Zeros 101 Quantile Regression of SD Elev vs. Window Proportion of Richness for Non-Dryas Plots with Zeros Removed 102 Quantile Regression of SD Elev vs. Window Proportion of Richness for Non-Dryas Plots with Zeros 103 SD Elevation vs.window Total Richness for All Plots with Estimated Boundary Line 104 SD Elevation vs. Window Total Richness for a Simulated Dataset from All Plots with Estimated Boundary Line 105 Figure 50. Huston's Dynamic Equilibrium Model 106 viii

12 1 CHAPTER I OUTLINE OF STUDY Alpine plant communities provide an exceptional system in which to test hypotheses about the determinants of species diversity. Plants in this environment are exposed to extremely cold temperatures, short growing seasons, and severe soil disturbance (77). Short stature, dense prostrate growth forms, and a perennial life history are all adaptations made by plants to survive in the alpine (77). Despite this "stressful" environment, remarkable spatial patterns of species diversity are evident. Due to sharp environmental gradients, very different plant communities can be found within a few meters of one another (10, 11, 97). Also, plant diversity in alpine communities may be much higher at a fine scale (e.g. < 1 m 2 ) than other temperate plant communities (75). This could simply be due to greater subdivision of space owing to the small growth form of many alpine species (55, 77). Alternatively, it may be that high species diversity in alpine plants is the result of fine scale spatial heterogeneity in the environment (84, 122). The soil surface is often topographically heterogeneous at a fine scale in alpine tundra, and topography greatly influences the climate plants experience (77). Therefore it is possible that fine scale topographic heterogeneity or variability promotes high species diversity of alpine plants. The ability of environmental heterogeneity to promote coexistence and therefore diversity depends on the importance of competition in structuring the community (112, 122). Theoretically, the degree of competition between individual plants depends on the productivity of the community (46, 58), which varies to a large degree between nearby alpine plant assemblages (129). Greater competition has been found in more productive alpine plant communities (25, 28, 102). The goal of this study was to test if micro-topographic heterogeneity and/or variability is correlated with the species diversity of alpine plants at a fine scale. This test encompasses two separate analyses of data collected from alpine plant communities in Glacier National Park, MT. The first involves characterizing the similarity between sample plots in their species composition. In Chapter 2, I perform an ordination of my

13 2 plant community data, and analyze species composition to place these samples within the alpine vegetation units defined by May and Webber (97). Rather than directly measuring climatic and other environmental variables, I use the similarity in species composition as a surrogate for the similarity in abiotic environment between different sites. From the ordination results, I can separate my samples into two distinct groups based on their species composition, and make qualitative judgments as to the relative productivity of these groups. The second part of my analysis in Chapter 3 involves measuring topographical heterogeneity and variability within 1 m 2 and examining the relationship between these variables and species richness and diversity. To do so, I collected 100 point elevation measures in each of 32 plots where I sampled plant species diversity. From these elevation measures, I model the surface and calculate several statistics that describe the heterogeneity and variability in micro-topography. I use a variety of techniques including linear regression, quantile regression, and boundary tests to describe the impact of topographic heterogeneity on plant species diversity. Finally, I discuss the implications of my results with regards to niche based and neutral explanations of high micro-scale plant diversity in alpine tundra.

14 3 CHAPTER II SPECIES COMPOSITION OF SITES AND ORDINATION Introduction The species composition of an ecological community provides insight into both the abiotic environment in which it is found and the biotic processes important in shaping that community. Many studies of plant communities have investigated how species composition changes along an environmental gradient. For example, Whittaker (131, 132) characterized how vegetation changed along elevational and topographical gradients in mountain ecosystems. Because each species is adapted to certain environmental conditions (61), species respond differently to environmental gradients such as temperature and moisture (131). The plant community at a local site then is the combination of species that are adapted to survive and reproduce in that particular environment. Certain dominant species may be more important in driving biotic interactions and characterizing plant associations than others due to their high abundance, frequency, or size (133). If the environmental associations of such dominant species are known, one can infer the abiotic environment at a site by sampling its plant community. For example, a preponderance of succulents indicates low soil moisture at a site, while dense cover of tall grass species might signify a more productive environment with greater soil moisture. If the relationship between environmental gradients and plant communities is well established, the species composition of a site is sufficient for a coarse characterization of its abiotic environment. In the alpine tundra of western North America, plant communities are often characterized by their position along a meso-topographic gradient (10, 11). The combination of precipitation, topography, and wind creates a gradient of soil moisture from rocky fellfields and dry meadows on wind exposed slopes to moist and wet meadows in sheltered sites (97). The moist and wet meadows are located down slope from snow banks, and receive moisture inputs from melting snow throughout the growing

15 4 season, while the fellfields and dry meadows have much drier soils due a lack of snow cover and high winds (129, 130). In general, productivity increases as soil moisture increases along this gradient up to a point where the growing season is limited by longlasting snow pack (16, 128). This increase in productivity is due in large part to greater microbial activity in wetter soils, which in turn increases nutrient levels available to plants (16). The full length of this gradient may occur over a few hundred meters or less, and is accompanied by a steep vegetational gradient (10). At wetter sites, graminoids (grasses, sedges, and rushes) are abundant and species rich (9) along with drought intolerant forbs. Drier sites on exposed slopes and ridges are dominated by woody dwarf shrubs, cushion plants, and drought tolerant forbs (130). Because of this predictable vegetation gradient, it is possible to indirectly characterize the environment at a site by measuring the identity, growth form, and abundance of plant species found there. The ultimate goal of my study is to characterize the effect of micro-topographic heterogeneity on species diversity at a fine (< 1 m 2 ) scale in the alpine plant communities of Glacier National Park. I sampled sites from throughout the park to capture the variability in alpine plant communities that are found there. The number of species at a fine scale can only be a subset of the larger species pool of the community. For example, it is intuitive that fewer species will be found within 1 m 2 at a site that contains 20 total species than at a species rich site where 80 species are present. A study of European alpine plants found that diversity is highest in areas of intermediate snow cover (123), so the position of a community along the meso-topographical gradient will in part determine the number of species. Also, the environmental conditions at a site will determine which processes are most important in structuring local communities. Competition is predicted to be a driving biotic process in more productive communities (58) including some alpine plant associations (117), while facilitation may be more important in less productive, more stressful environments (9, 25).

16 5 Determining the best explanation of the diversity of alpine plants at a fine scale depends on the relative importance of deterministic processes such as competition and stochastic processes such as dispersal in structuring communities. The first step in my study of fine scale alpine plant diversity is to characterize the species composition of my sites. I can then measure the similarity between these sites in their species composition, and use this similarity as a surrogate for similarity in the local environment and similarity in ecological interactions. For example, if two sites separated by several kilometers are both dominated by low growing shrub species, it is likely that they both are located in stressful environments with dry soils and limited snow cover throughout the year. If geographically distant sites both contain many species of graminoids, then they most likely represent productive, moist turf where competitive interactions between species are strong. To characterize the similarity in species composition between my samples I used ordination. Ordination is a useful method to describe the response of plant communities to measured environmental gradients known as Gradient analysis (131, 132, 134) and the similarity in species composition between different plant communities (Indirect gradient analysis, 6, 134). Ordination is a data reduction technique, which takes complex multivariate data sets and reduces them to simple, easy to interpret diagrams in a few dimensions (98). In the case of ecological community data, if there are n species in the entire dataset, each sample can be thought of as having a position in n-dimensional space where its coordinates are based on the abundance value of each species in that sample. After the ordination, the Euclidean distance between sample points in the 2 or 3 dimensional ordination space is representative of the similarity in species composition between the samples. By interpreting this 2 or 3 dimensional representation, a researcher can make qualitative statements about the similarity of plant communities from different sites.

17 6 In this study, my goal is to measure the similarity in species composition of eight alpine plant communities throughout Glacier National Park. I use ordination as a form of indirect gradient analysis. The advantage of indirect gradient analysis is that it not only reflects environmental differences between samples, but also historical factors and biotic interactions (6). Although I am not comparing this ordination to a measured environmental gradient (i.e. elevation), I expect that the distance between sites in ordination space will reflect their position along the meso-topographical gradient. Also, I will test for correlation between the position of a sample plot on the ordination axes and a few environmental variables related to surface cover and micro-topography. Methods Site Description I collected data on alpine plant communities from four plots within each of eight sites in Glacier National Park (GNP), Montana, USA in August of 2009 (Figure 1). This park covers almost 4100 km 2 of the Northern Rockies, including the Lewis and Livingston mountain ranges. The mean minimum temperature in January is approximately -14 C and mean maximum for July is 26 C for East Glacier. Mean annual precipitation ranges from 59 to 76 cm in the eastern side of the Park, though higher elevations may receive up to 250 cm (83). Site locations and characteristics are summarized in Table 1. Data was collected at 8 sites, ranging from 2160 to 2370 m elevation (Table 1). Sites ranged from Scenic Point near East Glacier in the southern part of the park, to Lee Ridge near the Canadian border in the north (Figure 1). Seven of my study sites were located east of the continental divide, and one site was west of the divide. My goal when choosing sites to sample was to include a variety of alpine plant communities that exist throughout the park. Baring Basin is a sheltered southeast facing site dominated by rocky ground cover with moist soils underneath. Common species at this site included the spike-moss Selaginella scopulorum, several graminoids such as grasses of the genus Poa, and forbs

18 7 such as Sagina saginoides, Arnica diversifolia, Potentilla diversifolia, Erigeron peregrines, and Achillea millefolium. Shrubs and mat forming species are not abundant at this site. The 4 th plot at Baring Basin had very little vegetation, with approximately 90 % rock cover, while the other three had much greater plant cover. Divide Mountain is a wind exposed, east facing slope with high vegetative cover composed of low growing, prostrate shrubs. The dwarf shrub Arctostaphylos uva-ursi is by far the dominant plant species, covering almost 90 % of one 1 m 2 sample. Another dwarf shrub, Dryas octopetala was abundant, as well as the shrub Dasiphora fruticosa, and the sedge Carex rupestris. Many other forbs are present, but in low abundance. Hidden Lake is a west facing slope located just below the continental divide on the west side, with moist soils and a large amount of bare soil at the surface compared to the other sites. Graminoids are abundant at this site, including Poa secunda, Poa alpina, Carex scirpoidea, Carex paysonis, Danthonia intermedia, and Kobresia myosuroides. Common forbs at Hidden Lake were Hypericum formosum, Potentilla diversifolia, Senecio cymbalaria and Sibbaldia procumbens. The willow-shrub Salix arctica and the spike-moss S. scopulorum were also present. No one species covered more than 30 % of a 1 m 2 plot. My sample transect at Lee Ridge was located on a southwest facing slope. Vegetative cover was high, with no plot consisting of more than 15 % bare rock at the surface. D. octopetala was the most abundant plant species in all four plots, ranging from 40 to nearly 70 % cover. Other abundant species included Hedysarum sulphurescens, Lupinus argenteus, and Dasiphora fruticosa. The sedge Carex rupestris was the second most abundant species in the first plot; a few other graminoids were present in low abundances at this site. The spike-moss Selaginella scopulorum was only present in the first plot where it was the third most abundant species. The study site at Lunch Creek was on a bench near the bottom of a west facing slope. The amount of surface rock cover was consistent in all 4 study plots, ranging

19 8 between 20 and 30 %. No single plant species exhibited greater than 20 % cover in any of the 4 plots. The most abundant species were S. scopulorum, the cushion plant Silene acaulis, and the cinquefoil P. diversifolia. Several other forbs were present including Pedicularis contorta, H. sulphurescens, and Solidago multiradiata. There is also an abundance of graminoid species at this site, with grasses (e.g. P. alpina and P. glauca, D. intermedia), sedges (Carex lachenalii, C. scirpoidea, and C. rupestris) and rushes (Luzula spicata) all present. At Scenic Point, my transect ran along a sheltered portion of an otherwise wind exposed south facing slope. This site was moderately rocky with a high amount of bare soil covering the surface of the first plot. The dwarf shrub D. octopetala was the dominant plant species in the first plot, but absent from all other plots. S. scopulorum was present in the first plot and the most abundant species in the other three plots. Other abundant species at this site are the graminoids L. spicata, Calamagrostis purpurascens, Carex phaeocephala, C. rupestris, and Festuca idahoensis and the forbs Antennaria umbrinella, L. argenteus, Arenaria capillaris, and Eriogonum flavum Siyeh Pass consists of rocky patterned ground on a windblown southwest facing slope between two peaks, and has been classified as a fellfield community (83). The amount of ground surface covered by bare rock ranged between 55 and 70 % for the four sample plots. The most abundant species at this site was the dwarf shrub Salix reticulata, followed by D. octopetala. S. scopulorum is present in all four plots. The only abundant graminoid species is C. rupestris, common forbs include Polygonum viviparum, Minuartia obtusiloba, and the cushion forming S. acaulis. The sample transect on White Calf Mountain crossed a rocky, windblown northeast facing ridge. The amount of surface rock was low in the 4 th plot, but for the other 3 ranged from 30 to 45 % of surface cover. D. octopetala was the dominant species in every plot, ranging from about 20 to 70 % cover. L. argenteus was the next most abundant species, other abundant forbs included Oxytropis sericea and D. fruticosa.

20 9 Several other forb species were present in low abundance. Two graminoid species were abundant at Siyeh Pass, the sedge C. rupestris and the grass C. purpurascens. Data Collection I recorded general characteristics such as overall slope (in degrees), aspect, elevation, latitude and longitude at each study site. At each site, I set a 50 m transect and laid out 1 m x 1 m quadrats at the 0, 12, 24, and 36 m marks. Inside the quadrat I arranged a grid of string dividing it into cm x 10 cm cells to aid in visual estimates of surface cover. The quadrat had adjustable legs so I could account for the degree of slope. I measured soil depth at 5 points within each quadrat (1 in each corner and 1 in the middle) to the nearest 0.5 cm by inserting a metal pin 1/8 inch in diameter until resistance was met. Rather than measuring total soil depth, this method captures soil stoniness and effective soil depth available to plants (93). The topographic surface of the plot was estimated from 100 point elevation measures. A full explanation of the topographic sampling method is presented in Chapter 3. Several measures of topographic heterogeneity such as fractal dimension (D) and Moran s I statistic (100) for elevation, and variability such as standard deviation of elevation (SD Elev), and degree of slope were calculated for each 1 m 2 plot. I identified all vascular plants rooted within the quadrat to species. If identification to species was not possible, I recorded the genus of the plant. Note that while S. scopulorum is commonly referred to as a spike moss, it is a vascular plant and not a bryophyte. Species names follow the nomenclature used in Lesica (83). I visually estimated the percent cover of each species within the plot to the nearest one percent using the string grid as a guideline. I also estimated the amount of surface area covered by rock, soil, and litter. To prevent erroneous values, I confirmed all cover estimates with a second observer. While it is unlikely that cover estimates are accurate to one percent, this should not introduce error because of the ordination method I used. Ordination

21 10 I used Non-metric Multidimensional Scaling (NMDS) (78, 79) to construct ordinations from my species composition data. NMDS iteratively searches for the configuration of sample units in a few dimensions that best reflects their original position in n-dimensional space (98). The difference between the dissimilarity of samples (as measured by one of several indices) and the distance between samples on the ordination axes is called the stress of the solution, and the best ordination is one that minimizes this stress. NMDS is particularly appropriate for use with ecological data because its use of ranked distances allows it to handle data that is not normally distributed or is on an arbitrary scale (98). Unlike other ordination methods (e.g. PCA), NMDS is not an eigenvalue-eigenvector technique where the 1st axis always explains the most variance (98). In NMDS any axis may explain the most variance in the data. I performed all of my ordinations using PC-ORD software (103) which follows the NMDS method of Kruskal (79) and Mather (95). The ordinations were run using the Sorensen (Bray- Curtis) distance measure (17) to characterize similarity between samples, with starting coordinates from random numbers. The ordination began with six axes and stepped down to one axis. The dimensionality of the final solution is chosen based on the amount an additional dimension would reduce the stress of the final configuration (98). For a single run, the positions of each sample are iteratively rearranged until either the stability of the solution is equal to or the maximum of 400 iterations is reached. This process is repeated for 50 runs with different starting configurations, and the final solution with the lowest stress is chosen as the best ordination. A Monte Carlo randomization test with 250 simulated runs was used to evaluate the significance of each solution. After the ordination was performed, I recorded several values to evaluate the effectiveness of the ordination. These include the number of axes in the final configuration, the percentage of variance in the data represented by each axis, and the stress and instability of the final configuration. I then used PC-ORD to calculate the

22 11 Kendall rank correlations (tau) between each axis and several environmental variables including the mean soil depth, the percent cover of rock and soil, SD Elev, D, Moran s I of elevation, and slope. I also recorded the correlation between each axis and the abundance of each species in the dataset. For brevity s sake, eight letter species codes are used to represent species names when correlation results are reported in Tables 3-6. Note that the first axis of the ordination is synonymous with the x-axis of my 2D ordination figures, and the second axis is equivalent to the y-axis. Results A total of 110 vascular plant species were identified from the 32 1 m 2 sample plots. Lunch Creek and Lee Ridge were the most species rich sites with 61 and 60 species respectively (Table 1). Baring Basin contained 52 species, Hidden Lake had 47 species, both Scenic Point and White Calf Mountain had 43 species, and Divide Mountain contained 36 species. Siyeh Pass was the least diverse site with only 30 total vascular plant species present. The NMDS ordination of all sites together is presented in Figure 2. This solution is significant according to the Monte Carlo test (p < 0.01), has two dimensions and a stress value of 14.33, which is within the acceptable range according to Clarke s (29) rules of thumb. The first axis represents 38 % of the variance; the second represents 26% for a total of 64 % of the variance in the original data represented in the ordination. Most plots from the same site tend to group together, with the exception of Scenic Point plot 1, which is closest to plots from Lee Ridge (specifically Lee Ridge plot 1). As illustrated in Figure 3, there is a clear separation between those plots that contain the dwarf shrub D. octopetala and those that do not. Divide, Scenic Point plot 1, White Calf, Lee Ridge, and Siyeh Pass all have D. octopetala in them and group together in the lower left quadrant of the ordination space. Baring Basin, Hidden Lake, Lunch Creek, and Scenic Point plots 2-4 all group in the upper right region of the ordination and do not contain D. octopetala. Baring Basin plot 4 is the lone outlier in the ordination. The dissimilarity between this

23 12 plot and all others is most likely because it only contained 7 species due to the high cover of bare rock. No environmental variables show strong correlations with the positions of sites on either ordination axis (Table 2); the largest correlation on the first axis is soil depth, while SD Elev has the highest correlation with the second axis. Table 3 presents correlations between species abundances and ordination axes for all sites for the 10 most highly correlated species for each axis. For the first axis, D. fruticosa is indicative of a negative value and S. procumbens and S. scopulorum correlate to a positive value. O. sericea and Arnica alpina show the strongest correlation to the second axis with negative values. Two low growing, mat-forming species, S. procumbens and D. octopetala, are highly correlated to each axis. D. octopetala is correlated with negative values on both the first and second axes, while S. procumbens is correlated with positive values on each axis. Unlike D. octopetala, S. procumbens is often found in areas of high snow accumulation (83), suggesting that the Non-Dryas sites exhibit high snow cover. Graminoids are characteristic of the Non-Dryas plots based on the position of these plots in the ordination diagram and the correlations between graminoids and ordination axes. P. secunda, L. spicata and Carex paysonis are correlated with positive values on the first axis and P. secunda and P. alpina are correlated with positive values on the second axis. The lone exception to this is Carex rupestris, which is negatively correlated to the second axis. Unlike many graminoids, this species is often found on exposed slopes and ridges (83). Because of this apparent distinction between plots with and without D. octopetala I ran separate NMDS ordinations of each group. The ordination of plots containing D. octopetala (hereafter referred to as Dryas plots) is presented in Figure 4. The final two dimensional configuration of this ordination was significant (p < 0.01) with a final stress of The first axis represents 59 % of the variance in the data and the second axis 32 %, for a total of 91 % variance represented by this configuration. Plots from White Calf Mtn, Lee Ridge, and Scenic Point plot 1 group closely to one another. Plots from Siyeh

24 13 Pass and Divide Mtn form separate groups, with negative and positive x-axis values respectively. Correlations between ordination axes and environmental variables were much higher for this ordination than the ordination of all sites (Table 2). The first axis was highly correlated to the percent of the surface that was non-vegetated; this was mainly due to the contribution of rock cover. This result is easy to understand in the context of the ordination figure, as plots from Siyeh Pass have high amounts of rocky, non-vegetated cover and Divide Mtn plots have very high vegetative cover. The first axis was correlated to Soil cover, D and Moran s I of elevation to a lesser degree. SD Elev and slope show the highest correlation to the second axis followed by Moran's I of elevation and D. This result indicates that the species composition of Dryas plots is correlated to topographic variability and heterogeneity at a fine scale, though to a lesser degree than the amount of non-vegetated cover. The species with the highest correlations to the ordination axes for Dryas plots are dwarf shrubs, cushion plants, and forbs (Table 4). A few graminoids exhibit low correlations. This is a reflection of the low diversity of graminoids in these plots, and the low abundance of the graminoids that are present compared to forbs and shrubs. The forb Sedum roseum, and the shrubs A. uva-ursi, S. reticulata, and D. fruticosa show the highest correlation with the first axis. These species separate Siyeh Pass (high cover of S. reticulata and presence of S. roseum) and Divide Mtn (high cover of D. fruticosa and A. uva-ursi). Second axis positions are strongly correlated to D. octopetala abundance, with sites of high cover having positive values. A. uva-ursi and the forbs P. diversifolia and Anemone lithophila are also highly correlated to the second ordination axis. The ordination of plots that did not contain D. octopetala (Non-Dryas plots) also resulted in a 2 dimensional configuration (Figure 5) which was significant (p < 0.01) with a final stress of The first axis represents 50 % of the variance, while the second represents 19 % for a total of 69 % of the variance represented in this configuration. The study plots from Scenic Point form a tight cluster in the upper left quadrant of the

25 14 ordination, as do the plots from Lunch Creek. Hidden Lake's samples are somewhat more dispersed in the upper right portion of the ordination. Samples from Baring Basin show the greatest separation, with one sample in the upper left part of the ordination, two grouped near each other in the lower right, and one sample located in the extreme lower right region of the ordination. The first axis is most strongly correlated with the amount of bare soil cover and D, the fractal dimension The second axis is most strongly correlated with the amount of rock cover which is no doubt due to the extremely negative value for Baring plot 4 on this axis. Soil depth is also highly positively correlated to the second axis. The species with the highest correlation to the ordination axes are very different for Non-Dryas plots than for the Dryas plots. The grass P. secunda shows the greatest correlation with the first axis, followed by Silene parryi and the rush L. spicata (Table 5). Unlike its congeneric species S. acaulis, S. parryi is not cushion forming and is found in grasslands and meadows (83). The second axis is most correlated with the cover of Polygonum bistortoides, the sedge C. phaeocephala, and Lupinus argenteus. The spike moss S. scopulorum is also positively correlated to the second axis. P. bistortoides is strongly correlated to both axes, and is known to occur in moist grasslands and meadows at high elevations in GNP (83). Several graminoids are important species in the ordination of Non-Dryas plots, notably absent are dwarf shrubs and mat forming plants. I also performed an ordination combining my samples with data from a phytosociological study of alpine plant communities in GNP performed by Damm (31) to determine if the separation between Dryas and Non-Dryas plots holds when the complete range of GNP's alpine communities are considered. This resulted in a 3 dimensional solution (Figures 6 and 7) which was significant (p < 0.01) with a stress of The higher stress is the result of the large number of plots (594) included in the ordination. The first axis represents 20.4% of the variance, the second 16.1%, and the third 16.4% for a total of 53% of the variance in the data represented by this configuration. I did not run

26 15 correlations with environmental variables for this ordination because I recorded different variables than Damm. The Dryas and Non-Dryas plots do not separate out on the first two axes, and the correlations with plant species for these axes (Table 6) are not particularly informative in distinguishing these two groups. On the third axis, there is a clear distinction between Dryas and Non-Dryas plots with the former exhibiting positive values and the latter negative values (Figure 7). This is exemplified in the species correlations for the third axis, D. octopetala shows a high, positive correlation, while S. procumbens and the rushes Juncus parryi and Luzula hitchcockii have high negative correlations. This ordination demonstrates that even when the whole range of alpine plant communities in GNP are considered, the separation between Dryas and Non-Dryas plots is noticeable. Discussion The results of the ordination studies performed show that there is a separation between sites where the mat-forming dwarf shrub D. octopetala is present and where it is absent. This species is often the most abundant species on exposed slopes and ridges with rocky soil in GNP, and is much less common in moist turf (83). The presence of D. octopetala is also indicative of down slope soil creeping due to frost heaving processes (83) which is exemplified in vegetation striping at Siyeh Pass (5). It is likely then that the plots that contain D. octopetala are subject to similar soil disturbance due the interaction between slope and soil freeze-thaw processes. The intensity and frequency of these disturbance processes could influence the species composition (77) and the diversity of species at a site (30, 45). While I have focused on D. octopetala as being diagnostic of the difference between these plots, it is clear that other species are important in separating these two groups as well. The Non-Dryas plots have both greater species richness and greater abundance of graminoids than the Dryas plots. In particular L. spicata, P. secunda and P. alpina are diagnostic of Non-Dryas plots, while dwarf shrubs such as A. uva-ursi and S.

27 16 reticulata and the shrubby herb D. fruticosa are diagnostic of Dryas plots. Also, the matforming S. procumbens is found primarily in my Non-Dryas plots, this species is usually present in areas of long lasting snow cover in GNP (83). Graminoids are the dominant growth form of wetter portions of the meso-topographic gradient (10, 97) and contribute more to total biomass in meadows than rocky fellfields (129). These differences between sites in their species makeup suggest that they differ in some environmental factors as well. None of the topographical and environmental variables I measured were well correlated to the axes from the ordination of all plots, suggesting that these plant communities are aligned with environmental variables or gradients that I did not measure. Based on the differences in species composition between Dryas and Non-Dryas plots it is likely that this distinction corresponds to different soil moisture regimes. Dryas plots likely have drier soils and less snow cover, while Non-Dryas plots experience greater snow cover and therefore have wetter soils. The result that one plot from Scenic Point groups closer to plots from other sites than those only a few meters away illustrates how variable microclimate can be at a fine scale. There can be large differences in productivity between different alpine plant communities (16). Many studies have demonstrated a correlation between the mesotopographic gradient in soil moisture and a gradient of productivity in alpine plant communities. Walker et al. (129) found that aboveground phytomass increased from snow bed to wet meadow to moist meadow, with the highest phytomass found in fellfields. The high values in fellfields may be misleading as an indicator of annual productivity. The cushion and mat-forming plants that dominate fellfields may be many years old, and there is no way to distinguish between the current year's growth and biomass remaining from previous years (129). Fisk et al. (36) also found greater productivity in wet alpine tundra than dry tundra. Nutrient addition studies have shown that the productivity of dry alpine sites is more nutrient limited than wet sites (14). Therefore more biomass is produced on an annual basis by forbs and graminoids in moist

28 17 meadow alpine communities than the dwarf shrubs and mats in dry fellfields. The results of my ordination along with these established relationships between productivity and species composition suggest that my Dryas sample plots are less productive than the Non-Dryas plots. The importance of competition in structuring plant communities and determining species diversity may be dependent on the productivity of a community (46, 58), with greater competition taking place in more productive environments. Therefore I expect that competition will be a stronger force in structuring communities in the Non- Dryas plots than the Dryas plots. My analysis of similarity in species composition between alpine sites in GNP has revealed that my plant community samples can be placed in one of two distinct groups based on the presence of D. octopetala. While I did not sample along a mesotopographic gradient at one site, it appears that my samples from different sites encompass different positions along this gradient. The abiotic processes (such as disturbance regimes) and biotic interactions (competition, facilitation) experienced by plants are probably more similar between plots from the same group than those from different groups. These processes may in turn influence the species diversity within a small plot. It is because of these differences that in the following chapter, I study the relationship between species diversity and micro-topographic variability for all plots together, and Dryas and Non-Dryas plots separately.

29 18 Table 1. Site Locations and Environmental Characteristics Site Baring Basin Divide Mtn Hidden Lake Lee Ridge Lat N N N N Long W W W W Elevation (m) Aspect Slope Richness Site Lunch Creek Scenic Pt. Siyeh Pass White Calf Mtn Lat N N N N Long W W W W Elevation (m) Aspect Slope Richness Table 2. Kendall Rank Correlations Between NMDS Axes and Environmental Variables All Dryas Non-Dryas Variable Axis 1 Axis 2 Axis 1 Axis 2 Axis 1 Axis 2 D Slope SD Elev Soil Depth Moran's I Non-Veg Rock Cover Soil Cover

30 19 Table 3. Kendall Rank Correlations Between NMDS Axes and Important Plant Species for All Plots Species Axis 1 Species Axis 2 SIBBPROC OXYTSERI DASIFRUT ARNIALPI SELASCOP CARERUPE DRYAOCTO ERIGPERE ANDRCHAM DRYAOCTO LUZUSPIC SIBBPROC CAREPAYS POA_ALPI ARCTUVAU POA_SECU POTEDIVE MINUROSS POA_SECU MINUOBTU Table 4. Kendall Rank Correlations Between NMDS Axes and Important Plant Species for Dryas Plots Species Axis 1 Species Axis 2 DASIFRUT DRYAOCTO ARCTUVAU POTEDIVE 0.57 SALIRETI ARCTUVAU SEDUROSE ANEMLITH DRABINCE DRABINCE AGOSGLAU ANDRCHAM SILEACAU MINUOBTU CALAPURP SEDULANC SENECYMB TRISSPIC CARESCIR CARENARD 0.344

31 20 Table 5. Kendall Rank Correlations Between NMDS Axes and Important Plant Species for Non-Dryas Plots Species Axis 1 Species Axis 2 POA_SECU 0.62 POLYBIST SILEPARR CAREPHAE LUZUSPIC LUPIARGE OXYTSERI ANTEUMBR POLYBIST SELASCOP 0.51 SEDULANC ARNIDIVE ANTEUMBR ASTRBOUR ARENCAPI CAMPROTU LUPIARGE POA_CUSI AGOSGLAU LUZUSPIC 0.4 Table 6. Kendall Rank Correlations Between NMDS Axes and Important Plant Species for All Plots with Damm s Data Species Axis 1 Species Axis 2 Species Axis 3 EPILALPI DASIFRUT ERIGPERE CARENIGR CARENIGR DRYAOCTO LUZUSPIC ACHIMILL DASIFRUT SELASCOP GALIBORE HIERGRAC ARENOBTU SIBBPROC SEDUROSE SOLIMULT CAMPROTU SIBBPROC SEDULANC JUNCDRUM SILEACAU CARESPEC ERIGPERE ZIGAELEG VEROWORM SENECYMB JUNCPARR JUNCDRUM CERAARVE 0.38 LUZUHITC

32 Figure 1. Location of Study Sites Within Glacier National Park 21

33 Divide Baring Hidden Lunch Scenic White Lee Siyeh -1.5 Figure 2. NMDS Ordination of All Plots Labeled by Site No Dryas Dryas Figure 3. NMDS Ordination of All Plots Labeled by Presence of Dryas

34 Divide Scenic White Lee Siyeh Figure 4. NMDS Ordination of Dryas Plots Baring -0.5 Hidden -1 Lunch Scenic Figure 5. NMDS Ordination of Non-Dryas Plots

35 24 Figure 6. Axis 1 and Axis 2 of NMDS Ordination of All Plots with Data from Damm Figure 7. Axis 1 and Axis 3 of NMDS Ordination of All Plots with Data from Damm

36 25 CHAPTER III INFLUENCE OF MICROTOPOGRAPHIC HETEROGENEITY ON ALPINE PLANT DIVERSITY Introduction Much effort has been put into exploring the factors that control species diversity at local, regional, and global scales. Explanations for large scale patterns such as the latitudinal gradient in diversity invoke a complex of potential factors including environmental stability (65, 113), climate/energy (136), historical influences (105), land area (107), and environmental heterogeneity (90). At much smaller spatial scales, authors often cite the importance of competition between individuals in determining the number of species that can coexist (120). Of the theories that have been put forward, two groups are distinguishable: those based on the concept of the niche, and those based on neutral processes such as dispersal and demographic stochasticity (55). Niche theories emphasize the differences between species to explain their coexistence, while neutral theories assert that it is the similarity between species that allows them to coexist. Whether the niche is defined as the requirements for a species survival (47, 61) or a species ecological role and impact in a community (35, 91), niche theories of diversity have been entrenched in ecology since the early 20 th century. The niche concept is associated with the concept of competitive exclusion (40), which states that no two species with the same niche can coexist in the same area (52). Even the slightest difference in population growth rates between species will lead to one species dominating the other over time. Competitive exclusion follows from theoretical formulations of population growth under competition and predation (87, 127). Niche explanations of diversity often assume that competitive interactions between individuals, especially individuals of different species, are fundamental in structuring a community. Species make trade-offs in their life history and competitive ability for different resources such that no one species can be the best competitor for a set of resources in all situations. It is