UBC Theses and Dissertations

UBC Theses Logo

UBC Theses and Dissertations

Plant community relationships to soil properties and topography in a southern interior BC grassland :… Lee, Robert N. 2011

Your browser doesn't seem to have a PDF viewer, please download the PDF to view this item.

Item Metadata

Download

Media
24-ubc_2012_spring_lee_robert.pdf [ 1.92MB ]
Metadata
JSON: 24-1.0072402.json
JSON-LD: 24-1.0072402-ld.json
RDF/XML (Pretty): 24-1.0072402-rdf.xml
RDF/JSON: 24-1.0072402-rdf.json
Turtle: 24-1.0072402-turtle.txt
N-Triples: 24-1.0072402-rdf-ntriples.txt
Original Record: 24-1.0072402-source.json
Full Text
24-1.0072402-fulltext.txt
Citation
24-1.0072402.ris

Full Text

PLANT COMMUNITY RELATIONSHIPS TO SOIL PROPERTIES AND TOPOGRAPHY IN A SOUTHERN INTERIOR BC GRASSLAND: A REFINEMENT by Robert N. Lee B.S., Pacific Lutheran University, 2007 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE in The Faculty of Graduate Studies (Botany) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver)  November 2011 © Robert N. Lee, 2011  Abstract The grasslands of British Columbia, Canada are an asset to the province’s biodiversity, economy, natural beauty, and recreation. Since the late 1800s, these areas have been largely modified and reduced, and they are currently threatened by climate change and exotic plant invasions. The ability of land managers to adapt to the effects of climate change and plant invasion depends on having the best possible understanding of these systems; however, little quantitative information is available on the strength of relationships between topography, soil properties, and plant community composition. I collected data on plant communities, selected soil properties, and topography on 31 sites in Lac du Bois Provincial Grasslands Park in southern interior British Columbia during May-July 2010. Cluster analysis (UPGMA) and multi-response permutation procedures (MRPP) of my data produced more homogeneous groups of sites by vegetation (A=0.29, p=0.001) and soil properties (A=0.22, p=0.001) than for two currently used grassland classification systems that are based on elevation ranges (A=0.17, p=0.001; A=0.15, p=0.001). Non-metric multidimensional scaling (NMS) confirmed the distinctiveness in overall species composition and main environmental correlates for the three main plant communities: bluebunch wheatgrass/big sage, bluebunch wheatgrass/rough fescue, and rough fescue/Kentucky bluegrass. The Mantel (multivariate) correlation between plant community composition and soil properties was stronger on north-facing sites (0.58) than on southfacing sites (0.19), while the Mantel correlation between topography and soil properties was stronger on south-facing sites (0.38) than on north-facing sites (n.s.). These correlations indicate stronger plant-soil feedbacks on north-facing slopes, while on south-facing slopes the effect of slope angle on heat and desiccation stress is the most important factor shaping plant communities. The highest occurrences of invasive species in the study area were at the higher elevations, likely in response to increasing precipitation, lower temperatures, and higher soil fertility at the higher elevations. The information provided in this thesis can aid land managers by adding to current knowledge about the relationships between plant communities, soil properties, and topography, which is important for predicting effects of future climate change and the spread of invasive plant species.  ii  Table of Contents Abstract ............................................................................................................ ii Table of Contents.............................................................................................. iii List of Tables ......................................................................................................v List of Figures ...................................................................................................vi Acknowledgements ..........................................................................................vii Chapter 1: Introduction ...................................................................................... 1 1.1  Distribution, natural history and climate of BC grasslands ......................................... 1  1.2  Importance of BC’s grasslands as rangeland.................................................................3  1.3  Vegetation of the southern interior BC grasslands .......................................................4  1.4  Soil of the southern interior BC grasslands...................................................................5  1.5  Climate change and invasive species: effects on grasslands.........................................6  1.6  Thesis overview...............................................................................................................9  Chapter 2: Materials and Methods .................................................................... 11 2.1  Study sites ..................................................................................................................... 11  2.2  Site selection ................................................................................................................. 12  2.3  Sampling, measurements, and analysis....................................................................... 13  2.3.1  Vegetation............................................................................................................. 13  2.3.2  Topography........................................................................................................... 15  2.3.3  Soil ........................................................................................................................ 15  2.3.3.1  Physical soil properties................................................................................16  2.3.3.2  Chemical soil properties .............................................................................. 17  2.4  Inclusion of sites sampled in 2006 .............................................................................. 17  2.5  Data assessment and transformation ..........................................................................18  2.6  Statistical analyses....................................................................................................... 20  2.6.1  Assessment and refinement of current classification ........................................ 20  2.6.2  Space-environment-vegetation relationships .................................................... 22  2.6.3  Predictors of exotic and/or invasive plant species in Lac du Bois .................... 23  Chapter 3: Results ............................................................................................25 3.1  Assessment and refinement of current classification .................................................25  3.2  Space-environment-vegetation relationships ............................................................. 31 iii  3.3  Predictors of exotic and/or invasive plant species in Lac du Bois..............................35  Chapter 4: Discussion....................................................................................... 41 4.1  Assessment and refinement of current classification .................................................41  4.2  Space-environment-vegetation relationships ............................................................ 42  4.3  Predictors of exotic and/or invasive plant species in Lac du Bois..............................45  4.4  Expectations for future change based on refinement of plant/soil/environment  relationships in Lac du Bois .................................................................................................... 46 Chapter 5: Conclusions.....................................................................................49 References ....................................................................................................... 51 Appendices....................................................................................................... 61 Appendix A Location coordinates (in decimal degrees) and topography of Lac du Bois rangeland study sites ................................................................................................................61 Appendix B List of all species encountered in Lac du Bois sampling sites, ordered by Daubenmire frame frequency.................................................................................................. 63 Appendix C Means (±SD) of soil, topography, and diversity properties for the Lac du Bois rangeland cluster groups based on plant community composition............................... 69  iv  List of Tables Table 2.1 Number of sites established and sampled at each aspect and grassland type combination of the Lac du Bois rangeland north of Kamloops, BC ............................................ 13 Table 3.1 Grouping of grassland sites based on different classification schemes................... 26 Table 3.2 Summary of multi-response permutation procedure (MRPP) using different group membership criteria ...........................................................................................................27 Table 3.3 Goodness-of-fit and p-value of nonmetric multidimensional scaling (NMS) correlation vectors on an ordination of sites in the Lac du Bois rangeland (Fig. 3.8) .............. 38  v  List of Figures Figure 2.1 Sampling plot layout diagram, showing orientation of vegetation survey and soil sampling transect lines .................................................................................................................14 Figure 2.2 Site layout diagram, showing 30 m x 50 m walkabout area divided into 20 equal segments (dashed lines), encompassing the 15 m x 30 m sampling plot. .................................. 15 Figure 2.3 Species frequency curve, based on walkabout species richness data, showing the rank-order proportion of the number of sites each species occurred at.....................................19 Figure 3.1 Reordered dendrogram of the vegetation data cluster analysis (Unweighted PairGroup Method using arithmetic Averages) for the 38 Lac du Bois rangeland study sites with a 3-group solution. ........................................................................................................................25 Figure 3.2 Nonmetric multidimensional scaling (NMS) of reduced and transformed vegetation data, with sites labeled by the BEC zone they would fall into based on elevation. . 28 Figure 3.3 Nonmetric multidimensional scaling (NMS) of reduced and transformed vegetation data, with sites labeled by vegetation cluster group. ................................................ 29 Figure 3.4 Nonmetric multidimensional scaling (NMS) of reduced and transformed vegetation data, with sites labeled by soil cluster group ............................................................ 30 Figure 3.5 Triangles depicting Mantel correlations between all combinations of three matrices (vegetation, space, and environment).......................................................................... 33 Figure 3.6 Triangles depicting Mantel correlations between all combinations of three matrices (vegetation, soil, and topography)................................................................................ 34 Figure 3.7 Nonmetric multidimensional scaling (NMS) of sites based on plant community composition, with open circles representing total vascular plant cover on the site and inner solid circles representing total invasive species over on the site ............................................... 36 Figure 3.8 Nonmetric multidimensional scaling (NMS) of sites based on plant community composition, with overlaid correlation vectors for environmental variables, ground cover types, exotic:native (E:N) species ratio, and percent cover of invasive species .........................37 Figure 3.9 “Domain space” polygons plotted for eight exotic species encountered in Lac du Bois rangeland study sites. Species are ordered by their total number of occurrences ............ 39  vi  Acknowledgements Financial support for this project was provided by the Future Forest Ecosystems Scientific Council (FFESC), an initiative of the BC Ministry of Forests, Lands and Natural Resource Operations. Many thanks to my co-advisors, Drs. Gary Bradfield and Maja Krzic, for the opportunity to do this project. Their guidance and advice on study design, analysis, and writing have been invaluable. I am also grateful for the help of committee member Dr. Roy Turkington. Thanks to Dr. Reg Newman, Brian Wallace, and Mike Ryan for their assistance with study design, sampling, and plant identification, without which I would have been lost in the grasslands. Thanks to Simon Zhao, Martin Hilmer, and Fernando Morell for their assistance with soil analysis. I would also like to thank Steve Henstra and Laurel Gardner for helpful and supportive conversation. Special thanks to my parents, without whose support I would not be where I am today.  vii  Chapter 1:  Introduction  In light of ongoing global changes, which include climate change, ecosystem shifts, and biodiversity loss, it is important that scientists and land managers have access to sitespecific classifications of various ecosystems including grasslands. This would allow for better prediction of potential responses to climate change. The majority of the world’s existing ecosystem classification systems are rather crude because they are based on broad assessments of vegetation and soil/climate parameters done on a large scale (UNESCO 1973, IUCN 1974, Bailey 1976, Grossman et al. 1998, di Gregorio and Jansen 2000). British Columbia (BC) is no exception. The provincial biogeoclimatic ecosystem classification (BEC) system (Krajina 1965, Pojar et al. 1987) is valuable for conservation and land management purposes on a regional scale, but given the size and vast diversity of regions within BC, it needs to be refined by inclusion of local scale sampling. Such refinement would provide grassland stewards with the most accurate and up-to-date information, which is critical in a time of threats such as expansion of exotic species’ distributions and a rapidly changing climate. This involves quantitatively assessing the relationships between vegetation, soil, and topography and exploring the ecological theory behind these relationships to more fully understand the grassland communities. BC’s grasslands are especially important ecosystems because they have high biodiversity (Wikeem and Wikeem 2004), are highly susceptible to change by anthropogenic use (Tisdale 1947, van Ryswyk and McLean 1989), and take a long time to recover after negative land-use impacts (BC Parks 2000). Locality-specific grassland classifications are required so the effects of future change can be more accurately predicted.  1.1  Distribution, natural history and climate of BC grasslands The grasslands in BC are mostly found in the Interior Plateau, which encompasses  most of south and central BC between the Rocky Mountains (to the east) and the Coast and Hazelton Mountains (to the west) (Wikeem & Wikeem 2004). A much smaller portion of grasslands is located north of 52°N latitude in the Peace River region of the province. British Columbia’s grasslands are very diverse ecosystems because they contain several habitat types, including riparian areas, aspen stands, and shrub-steppe. Because of this great diversity, almost 42% of the province’s 2854 vascular plant species are found in grasslands (Wikeem & Wikeem 2004). Grasslands in BC cover less than 1% of the province (Wikeem &  1  Wikeem 2004), and only about 7% of them are located within Provincial Crown parks or protected areas such as Lac du Bois Grasslands Provincial Park (GCC 2004). The South and North Thompson rivers create the defining topographic feature of the Interior Plateau; they cut deep (600-900 m) valleys into the plateau, whose topography is already quite irregular due to the past glacial activity (Wikeem & Wikeem 2004). The last glacial maximum in this area occurred about 17,000 years ago (Hebda 2007a). During the Pleistocene the whole Thompson-Pavilion area was covered by the Cordilleran ice sheet, the last of which melted by about 11,000 years ago (Tisdale 1947, Ryder 1978). Glaciation and subsequent melting in this time period resulted in deposits of both basal and ablation till, and fine silt. Vegetation that had been in un-glaciated grassland areas of Washington State moved north as the Cordilleran ice sheet was retreating (van Ryswyk and McLean 1989). Because of this the grasslands of BC are similar to the Palouse prairie in southeastern Washington and north central Idaho. According to pollen records, in the early Holocene (10,000 to 8,000 years ago) grasslands in BC existed up to 1,300 m above sea level, even up to 1,810 m in some areas (Hebda 2007b), but by approximately 4,000 years ago the climate had become moister and the grasslands shrunk toward their current range. The climate that favors the southern interior of BC grasslands is the result of a rain shadow created as air moves east over the Coast Mountains, making it too dry for trees to thrive (Wikeem & Wikeem 2004). Because of this rain shadow summers are hot and dry (van Ryswyk and McLean 1989, Nicholson et al. 1991). Occurrence of precipitation throughout the year is highest in December and January, and secondarily in June. The driest months are March and April (Nicholson et al. 1991). Most of the effective soil moisture comes from winter precipitation, as the water does not evaporate before it can seep in to the ground. In general, precipitation increases and temperature decreases with an increase in elevation (Tisdale 1947, van Ryswyk and McLean 1989). This leads to higher effective moisture at higher elevations. Although not as strong as the general effect of elevation on climate, there can be variation in microclimate even at the same elevation that is mostly affected by aspect and slope (Tisdale 1947). As a result of the strong influence of topography on the climate and subsequently the soil and plant communities, the grasslands in southern interior BC have been grouped as lower, middle and upper grassland (Tisdale 1947, van Ryswyk et al. 1966, BC Parks 2000, GCC 2004, Wikeem & Wikeem 2004). Elevation ranges for the zones have been given as roughly 350-600 m for the lower grasslands, 600-825 m for the middle grasslands, and 825-  2  975 m for the upper grasslands (van Ryswyk et al. 1966). Another set of ranges based more closely on BEC subzones is 335-700 m, 700-850 m, and 850-975 m for the lower, middle, and upper grasslands, respectively (BC Parks 2000). These ranges correspond with BGxh2 “Thompson Very Dry Hot Bunchgrass Variant”, BGxw1 “Nicola Very Dry Warm Bunchgrass Variant”, and IDFxh2a “Thompson Very Dry Hot Interior Douglas-fir Variant” (BC Parks 2000, Wikeem & Wikeem 2004).  1.2 Importance of BC’s grasslands as rangeland In BC there are over 10 million ha of land used as forage for livestock, within 11 different biogeoclimatic zones, which makes livestock and forage management quite complex. Crown rangeland, which is managed by the BC Ministry of Forests, Lands and Natural Resource Operations, makes up approximately 85% of this grazed area, and generally consists of enough native grasses and forbs that supplemental feed is not necessary (Wikeem et al. 1993). All three grassland levels are important for grazing, starting at the lower grasslands in early spring and moving to the upper grasslands by late fall (van Ryswyk et al. 1966, Nicholson et al. 1991, Wikeem et al. 1993). Forage production depends on the condition of the range, but generally high quality range production increases with elevation. The upper grasslands are some of the most productive in the province, producing up to 2700 kg biomass•ha-1 •yr-1 in favorable conditions. There is also an increase in average daily gains of cows and calves with increasing elevations (Wikeem et al. 1993). Bluebunch wheatgrass (Pseudoroegneria spicata (Pursh) A. Love) is the most productive forage species in the bunchgrass zone (lower and middle grasslands), but in the IDFxh2a (upper grassland) where rough fescue (Festuca campestris Rydb.) is the most productive forage species the yield is higher than almost any other grassland community type in western Canada (Looman 1969, Nicholson et al. 1991). Unfortunately, rough fescue is very susceptible to disturbances such as overgrazing. Rough fescue makes up as much as 95% of the vegetative cover in late seral upper grassland (Wikeem and Wikeem 2004) but easily loses its dominance under moderate grazing pressure (Looman 1969). The Kamloops-Merritt area is closely linked to ranching in BC, and has been central to the industry since the mid 1800s (van Ryswyk and McLean 1989). Cattle ranches were established in the Kamloops area by 1862 (Tisdale 1947). Utilization by domesticated livestock in these early times often led to overgrazing (van Ryswyk and McLean 1989). Such overgrazing, and concern that the grasslands would be permanently destroyed was the impetus for Tisdale’s (1947) study carried out in 1935-1940. Similar concerns also underlaid  3  the study of van Ryswyk et al. (1966), who wanted a better understanding of the grasslands in order to improve them. This interest in preserving the condition of forage resources comes largely from the economic contribution of BC’s beef industry to the province, currently estimated to be $500 million per year (BCCA 2011), and is also supported by other services that grasslands offer to the general public, such as aesthetic value and recreational opportunities (BC Parks 2000, Wikeem and Wikeem 2004).  1.3 Vegetation of the southern interior BC grasslands The reason that grasses, forbs, and shrubs dominate in southern interior BC grasslands, while trees are restricted to higher elevations, can be summarized as the effect of climate and geological history on moisture availability. Frequent summer drought in the grasslands is combined with warm to hot temperatures (Nicholson et al. 1991), and much of the soil overlies basal till, which is difficult for tree roots to penetrate. Because of these conditions trees cannot get the moisture they need, while grasses with their shallow roots are quite successful (van Ryswyk and McLean 1989). Many of the plants in the area are rare or endangered. For example, 55 red- and blue-listed vascular plant species are found in the Thompson-Pavilion region (Wikeem & Wikeem 2004). This fragile biodiversity highlights the importance of gathering more locality-specific data on grasslands in this region. The initial rigorous descriptions of the vegetation in southern interior BC grasslands were done by Tisdale (1947) and van Ryswyk et al. (1966). Both of their classification schemes identify lower, middle, and upper grassland communities. More current classifications examine the grassland by BEC subzones (BGxh2, BGxw1, and IDFxh2a) (Hope et al. 1991, Nicholson et al. 1991) and site units (Lloyd et al. 2005). The BEC subzones are approximately analogous to the grassland types characterized in previous studies, and the site units describe more specific plant associations within the subzones. Differences between site units are largely a result of topography, aspect, and drainage, which have been noted to have important influences on local vegetation (Nicholson et al. 1991). For example, a given BEC zone generally occurs at higher elevations on south-facing slopes than it does on north-facing slopes (Lloyd et al. 1990, Lloyd et al. 2005), and some site units only occur in areas with a certain slope or aspect (Lloyd et al. 2005). Despite such observations, overall patterns are not well understood because livestock grazing has changed much of the natural plant community composition (Nicholson et al. 1991). Aside from the influence of topography and grazing on plant species distributions, dispersal must also be considered. Dispersal ranges and strategies are important in  4  determining the structure of plant communities (Levine and Murrell 2003), in part because the distribution of plant species can be limited by these processes (Primack and Miao 1992). The importance of dispersal processes can be addressed by comparing the composition of plant communities and their location relative to each other in geographic space (“distance apart”) (Urban et al. 2002). Distance apart is independent of topographic variation and can capture local processes such as dispersal that are not likely to be autocorrelated with elevation or latitude.  1.4 Soil of the southern interior BC grasslands The climate and geological history of the southern interior BC grasslands are important soil formation factors resulting in Chernozemic soil (Mollisol/Haploboroll in the United States classification system), the typical soil of the world’s grasslands (Green and van Ryswyk 1982, van Ryswyk and McLean 1989). The Chernozemic soil order is one of 10 orders in the Canadian Soil Classification System (Soil Classification Working Group 1998). To be defined as Chernozem, a soil must have a diagnostic Ah horizon that is at least 10 cm thick and organic-rich, containing between 1 and 17% total C and with a C/N ratio of less than 17 (Soil Classification Working Group 1998). Chernozemic soil must also have a base saturation of at least 80% with calcium as the dominant cation (Soil Classification Working Group 1998). It occurs in subarid to subhumid climates with an annual average minimum temperature above 0°C and below 5.5°C (Soil Classification Working Group 1998, Valentine and Lavkulich 1978), typically under grassland vegetation and sometimes at the transition between grassland and forest (Valentine and Lavkulich 1978). Within the Chernozemic order there are four recognized soil great groups: Brown, Dark Brown, Black, and Dark Grey. These great groups are differentiated mostly by the color of their surface soil, which is determined by the accumulation of organic matter forming under certain climate and vegetation conditions (Green and van Ryswyk 1982, Soil Classification Working Group 1998). More specifically, shifts from one great group to another are caused by climate (i.e., precipitation/evaporation ratios), which in turn is correlated with different plant communities that provide different amounts and types of biomass (organic matter) inputs (van Ryswyk et al. 1966). As temperature decreases and precipitation increases, which generally happens at progressively higher latitudes and elevations, there is a shift from Brown to Black Chernozem (Tisdale 1947, van Ryswyk et al 1966). The surface horizon of a Brown Chernozem contains about 1% organic matter, while a Dark Brown Chernozem contains  5  about 4% and a Black Chernozem up to 10% (van Ryswyk and McLean 1989). Dark Grey Chernozems are primarily associated with the transition between forest and grassland, and subsequent leaching of organic matter and clay (Valentine and Lavkulich 1978, Krzic et al. 2007). Forest soils in southern BC contain less organic matter than grassland soils, in part because the amounts of carbon and nitrogen decrease much faster through forest soil profiles (Floate 1965). Grasslands have a massive network of fine roots with a greater surface area than that of shrubs or trees, and much of this dies off and decomposes within the soil at the end of each growing season (Floate 1965, Pawluk 1982). Up to 50,000 kg/ha of organic matter can be added to the soil by a grassland system over a single year, approximately half of which is from roots (Pawluk 1982). In a typical Chernozem soil there is a very thick humus layer; the top 25 cm of the soil profile contains only 25% of the total soil carbon (Buringh 1984). Plant biomass inputs not only affect soil chemical properties (carbon and nitrogen content), but also several physical properties such as aggregate stability and bulk density (Brady and Weil 1996). These physical and chemical properties, as well as others (pH, phosphorus content) are related to topography and its effects on temperature and soil moisture (Floate 1965, Brady and Weil 1996). Because these soil properties are closely related to topography and plant biomass, they are an important component of grassland community identification. Measuring these properties will help address the relationship between soil, topography, and plant communities.  1.5 Climate change and invasive species: effects on grasslands The latest report from the Intergovernmental Panel on Climate Change (IPCC) states that temperatures are expected to increase across the globe and weather events such as heat waves are expected to become more frequent by the end of the 21st century (IPCC WG I 2007). Warming and precipitation changes in BC are expected to be greater than the global averages predicted by IPCC (Spittlehouse 2008). Even under best-case scenarios, summer precipitation is expected to decrease by 10% in southern BC. Climate models for BC predict an increase of about 0.5°C per decade (Hamann & Wang 2006). Increases in temperature by up to 7°C (minimum of 2°C) are predicted by 2080 for southern BC; there is also a predicted decrease in summer precipitation and increase in winter precipitation for this region (Spittlehouse 2008). The percent change in area of Bunchgrass communities such as those found at the Lac du Bois rangeland is predicted to increase with these conditions more than any other biogeoclimatic zone in the  6  province (Hamann & Wang 2006). Hamann and Wang’s climate modeling predicted an increase in elevation for the bunchgrass-suitable climate zone of 104 m by 2025 and 243 m by 2085, which would have profound impacts on the plant communities and soils in these areas. Further complicating the situation, small areas within the predicted future bunchgrass zone do not have a climatic equivalent in present-day BC (Hamann & Wang 2006). Hamann and Wang (2006) point out that they have modeled changes based on the realized niche of species, and because the fundamental niche is unknown actual changes in species ranges and distributions may be less observable. Changes will not occur literally as they have predicted, but they still expect dramatic changes in the general manner of their predictions. Kimmins and Lavender (1992) suggest that because BC is so topographically varied any predicted climate changes may not be as extreme on a local scale as they would be in a flatter region. In a mosaic of vegetation types, a changing climate might simply result in the movement of many species to different locations (topographically) in the same area. The direct reaction of plant species to climate is individualistic, even though each species is an interacting component of its community. If the same associations of species move together or end up in the same place, their proportions and therefore community function may still change to form communities for which there is no current analog (Huntley 1991). Also, because interactions with other species play a large part in the actual range that is occupied, it is not likely that plant species movement will follow a linear expansion. There may be disruptions in currently important interactions, and new forced interactions as species boundaries shift in a changing climate (Walther 2010). Community-level interactions, even between trophic levels, are very important when trying to predict the outcome of climate change. Suttle et al. (2007) demonstrated that what seemed to be a beneficial addition of late spring rainfall early in the study period became increasingly detrimental to community richness and food web complexity by the end of five years, due to unforeseen interactions between species. It has also been demonstrated that expected future climate changes can have an effect that is stronger at the functional group level than at the species level (Zavaleta et al. 2003). With increasing temperatures, soil organic matter content and C:N ratios are expected to decrease because of increasing microbial activity and subsequently faster decomposition (Rosenzweig and Hillel 2000). At about 25°C the rate of microbial decomposition becomes higher than the rate of organic matter input from plants and the total amount of organic matter in the soil decreases (Brady and Weil 1996). Because organic matter has a positive effect on a variety of other soil properties such as aggregate stability,  7  water retention, nutrient availability, and bulk density (Brady and Weil 1996), a reduction in organic matter would have negative impacts on these properties. A large portion (almost 20%) of BC’s plant species are considered non-native (Wikeem & Wikeem 2004). Several of these species are found in the province over an estimated 100,000 ha of forest and grassland, including much of the valuable rangeland (Wikeem et al. 1993). The most problematic invasive species in the southern interior are diffuse and spotted knapweed (Centaurea diffusa Lam., Centaurea stoebe (Gugler) Hayek), Dalmatian toadflax (Linaria genistifolia (L.) Mill.), sulphur cinquefoil (Potentilla recta L.), houndstongue (Cynoglossum officinale L.), and leafy spurge (Euphorbia esula L.) (BC Parks 2000, GCC 2004, Wikeem & Wikeem 2004). Diffuse knapweed may have been introduced to the region as early as 1918 (Wikeem & Wikeem 2004). In southern interior BC grasslands, the presence of spotted knapweed has been shown to have detrimental effects on plant community composition (May and Baldwin 2011) and soil properties (Fraser and Carlyle 2011). Another plant species that poses a threat to local biodiversity is cheatgrass (Bromus tectorum L.), which may become a serious problem in the near future due to its high seed production (Bradley et al. 2009). It can also reduce nitrogen available to other plants by up to 50% (Rimer and Evans 2006), decrease the number of specialist soil fauna relative to generalists (Belnap and Phillips 2001), and increase fire hazard because it dries out early and is unpalatable (Tisdale 1947). BC’s grasslands historically occupied a much greater area than they do currently because the climate was hotter and drier shortly after the last glaciation (Hebda 2007b). Those initial grasslands, which formed immediately after the last glaciation, evolved in a more slowly changing, less globalized environment. The main issue with the synergy between present-day climate change, predicted range expansions, and exotic species is that many of these species have been spreading at unnatural rates due to aided migration by humans. This presents a challenge to native species that have coevolved for thousands of years. One way that invasive species have spread into grasslands in BC is through motorized vehicle use, especially the concentrated recreational use of off-road vehicles in designated areas, which leaves large spaces of exposed soil (BC Parks 2000). Some of these species have also spread through the effects of overgrazing. This can manifest in the plant community as an increase in the amount of species that are either weedy or unpalatable, and as a decrease in bunchgrasses (Nicholson et al. 1991).  8  No matter how they have arrived, invasive plant species have traits that allow them to exploit novel or disturbed habitats. Global change in the form of altered atmospheric composition, warming temperatures, and changing precipitation regimes is expected to create such habitats for these species to exploit (Dukes and Mooney 1999, Bradley et al. 2009). For example, increased nitrogen deposition from several anthropogenic sources (e.g. fossil fuel combustion and application of fertilizers) increases nutrient availability and favors faster-growing plants, including invasive annuals (Dukes and Mooney 1999). These factors do not necessarily reflect a species’ competitive ability, and invasive species that disperse well into disturbed areas may not persist in the long term if the disturbance ends (Seabloom et al. 2003). Invasive species may also directly outcompete native species by more efficient acquisition of a critical resource (Funk and Vitousek 2007), by adapting more quickly if resource levels change (Shea and Chesson 2002), or because they have escaped the pressure of enemies from their natural habitat (Shea and Chesson 2002, Seabloom et al. 2003). Elton’s (1958) hypothesis suggests that higher diversity plant communities increase competition and lower the success of invasive plants spreading into the community. Many observational studies have suggested that this concept often does not hold up because of factors that covary with diversity and/or invasion (such as soil moisture or fertility) (Levine and D’Antonio 1999, Naeem et al. 2000, Shea and Chesson 2002). For example, various studies report that community invasibility increased with light penetration, site disturbance, soil phosphorus content, and soil moisture (Foster et al. 2002, Chong et al. 2006). In the former study, community invasibility also increased with diversity and the most “stable” sites were actually dominated by a single species, which is contrary to Elton’s hypothesis. Similarly, Rinella et al. (2007) found that a plant community’s ability to resist invasion may be determined by high productivity, not a balance of different functional groups. Because of these recent studies it is suggested that the most invasion-resistant plant communities are stable (resistant to change in relative species abundance) and productive, but not necessarily diverse.  1.6 Thesis overview The grasslands of the southern interior of BC are likely to experience various changes in the coming decades due to the influence of a changing climate and increasing pressure from invasive plant species. Given the ecological, aesthetic, recreational, and economic importance of rangelands to BC, it is important to understand the quantitative relationships  9  between plant communities, soils, and topography. This will improve our understanding of potential responses to a changing climate. The questions this thesis addresses are: 1) How well do van Ryswyk et al.’s (1966) and the more current biogeoclimatic grassland classification (Pojar et al. 1987) summarize grassland associations of Lac du Bois Grassland Provincial Park in relation to a multivariate assessment? 2) What is the relative importance of spatial location, soils, and topography in influencing grassland plant community composition? 3) Which topographic factors and soil properties are most strongly correlated with the presence of exotic and/or invasive plant species in Lac du Bois Grassland Provincial Park? 4) How might these relationships be expected to shift and influence the distribution of invasive species under future climate change?  10  Chapter 2: 2.1  Materials and Methods  Study sites Lac du Bois Provincial Park is a semi-arid temperate grassland located north of  Kamloops, BC in the Thompson Basin (50º 45’ N and 120º 25’ W). The park’s 15,000 ha include approximately 8,300 ha of grassland (van Ryswyk et al. 1966), including both Bunchgrass (BG) biogeoclimatic subzones of this region (BGxh and BGxw) and the IDFxh2a subzone within the Interior Douglas-Fir (BC Parks 2000, Gayton 2003). These three subzones correspond roughly with the lower, middle, and upper grassland of Lac du Bois as differentiated by van Ryswyk et al. (1966). The grasslands of Lac du Bois make up approximately 65% of the protected grasslands in the Thompson-Pavilion region (Wikeem & Wikeem 2004). The climate is warmer and drier at lower elevations in Lac du Bois, and becomes increasingly cooler and wetter at higher elevations (van Ryswyk et al. 1966, McLean and Marchand 1968, McLean 1970). Total precipitation during the growing season (most of which occurs in June and August) increases from 135 mm in the lower grasslands to 190 mm in the upper grasslands (BC Parks 2000). The average monthly growing season temperature has been shown to decrease from 15.5°C in the lower grasslands to 11.5°C in the upper grasslands (van Ryswyk et al. 1966). The lower grassland is characterized by bluebunch wheatgrass and big sagebrush (Artemisia tridentata Nutt.), widely spaced with lots of cryptogam crust, while the middle grassland includes bluebunch wheatgrass but almost no sagebrush (Nicholson et al. 1991). The upper grassland is typically represented by bluebunch wheatgrass and rough fescue, with Artemisia frigida Willd. (pasture sage), Koeleria macrantha (Ledeb.) Schult. (junegrass), and Poa pratensis L. (Kentucky bluegrass) also common (Hope et al. 1991). Rough fescue is also common on north-facing slopes in the lower and middle grasslands (Lloyd et al. 2005). Plant species richness and total cover tend to increase with elevation (Tisdale 1947, van Ryswyk et al. 1966, Nicholson et al. 1991). The soils in the Lac du Bois rangeland are developed from morainal deposits associated with basic volcanic and limestone bedrock, but are otherwise quite different geologically. The lower grasslands (300-600 m a.s.l.) are located on the loam to silt loam McKnight Orthic Brown Chernozem (Young et al. 1992). This soil is characterized as moderately alkaline and weakly saline, and has developed from medium-textured morainal deposits, with a thin, loamy aeolian veneer and slight to moderate stoniness.  11  Middle grasslands in Lac du Bois (450-950 m) are located on the silt loam to silty clay loam McQueen Orthic Dark Brown Chernozem (Young et al. 1992). This is a moderately alkaline soil developed from morainal deposits, with slight stoniness. The upper grasslands (910-1220 m) are located on the sandy loam to loamy sand Aylmer Orthic Black Chernozem (Young et al. 1992), which is moderately to strongly alkaline, developed from ablation moraine deposits, and has moderate to high stoniness. Because Lac du Bois occurs on an elevation gradient there is also a strong influence of climate and vegetation on the formation of Chernozemic soil. The darkness of the soil is related to the amount of organic carbon it contains, which increases with elevation due to greater plant biomass inputs (van Ryswyk et al. 1966).  2.2 Site selection Thirty one sites within the Lac du Bois rangeland were chosen using a random stratified procedure with the three grassland “levels” as strata. Because this study is concerned with the effects of climate change, sites were chosen on north and south-facing slopes to capture the potential variation in vegetation and soils with anticipated changes in temperature and soil moisture at the same elevation. The decision to differentiate between moderate and steep south-facing slopes was made because steeper south slopes were expected to have more exposed soil and less vegetation cover (M. Ryan, personal communication, 2010). The cutoff between moderate and steep slopes was approximately 30°; this was estimated visually before the site was sampled. The main reason for classifying moderate and steep slopes separately was to ensure that an adequate range of slope values was sampled. North aspect sites were not divided in this way because we did not expect a difference between moderate and steep slopes. A contour map of the study area was divided into a grid of 0.5 by 0.5 meter squares, and all squares with a north, south steep, or south moderate slope (as determined by contour lines and shape of the landscape as observed in Google Earth) were identified. Lines identifying the division between BEC zones were also present on the map, and potential sites were numbered within each zone. Approximately five of each site type (nine combinations of zone and aspect, for a total of about forty five potential sites) were chosen randomly, and these sites were then assessed in the field before sampling. If a site was subjectively determined to have characteristics such as overgrazing or other physical disturbance, saline or riparian conditions, it was replaced with another randomly chosen site. This was done to  12  keep the study focused on general environmental conditions rather than disturbance factors or relatively rare habitat types. Each site consisted of a 15 by 30 m (450 m2) plot inside a relatively homogeneous 30 by 50 m area, running parallel to the contour of the site. Sites were sampled starting in May 2010 at the lower elevations and working upward. Due to time constraints only one upper grassland site was sampled, however, seven upper grassland sites sampled in 2006 in the Lac du Bois rangeland (Newman et al. 2011) supplemented these data (Table 2.1). Table 2.1  Number of sites established and sampled at each aspect and grassland type combination of the Lac  du Bois rangeland north of Kamloops, BC. Lower/Middle/Upper grassland as defined by BEC variants. Supplemental sites from previous study (Newman et al. 2011) are shown in parentheses. Grassland Aspect  Lower  Middle  Upper  Total  North  5  6  1  12  South Moderate  6  4  0  10  South Steep  4  5  0  9  Total  15  15  1 (7)  31 (38)  2.3 Sampling, measurements, and analysis Sampling and field measurements were done from May to July 2010. 2.3.1  Vegetation Percent cover to the nearest 1% of all vascular plant species and ground cover types  (bare soil, rock, litter, cattle feces, lichen, and bryophytes) was estimated in each 15 by 30 m plot along four, 30-m long parallel transects 5 meters apart (Fig. 2.1). Visual estimation was accomplished with the aid of a Daubenmire (20 cm by 50 cm) frame (Daubenmire 1959), which was placed in a random location 10 times on each transect, always on the north side of the line, for a total of 40 estimates per plot. A “walkabout” zone extending the plot by 1050 m2 (233%) to a total area of 1500 m2 was also established to measure species richness outside of the cover estimate frames, and to obtain a site frequency count of invasive species. The walkabout zone was divided into a grid of 20, 7.5 m by 10 m rectangles (Fig. 2.2), and the presence of invasive species was recorded in each rectangle. Invasive species were defined as those included on the E-Flora BC website list of noxious, weedy, invasive and nuisance plant species compiled by Perzoff (2009).  13  Figure 2.1  Sampling plot layout diagram, showing orientation of vegetation survey and soil sampling transect  lines  14  Figure 2.2 Site layout diagram, showing 30 m x 50 m walkabout area divided into 20 equal segments (dashed lines), encompassing the 15 m x 30 m sampling plot.  2.3.2  Topography Slope was recorded using a clinometer (PM5/360PC, Suunto, Finland), aspect was  recorded using a compass (Ranger CL 515, Silva, Racine, WI), and both elevation and geographic coordinates were recorded using a GPS unit (GPSMAP 60CS, Garmin International, Inc., Olathe, KS) (Appendix A). The location for geographic coordinates of each site was recorded at the northwest corner of the plot. Total solar insolation on each site for June 2010 was estimated using the solar radiation analysis tools in ArcGIS Spatial Analyst (ESRI, Redlands, CA). 2.3.3  Soil Soil samples were collected along four, 30-m long transects laid out 5 meters apart.  The soil sampling transects were located parallel to and 2.5 m south of each vegetation transect (Fig. 2.1). The following subheadings detail on which transects each soil property was sampled for and how many samples were taken. All soil samples were collected at 0 – 7.5 cm depth.  15  2.3.3.1  Physical soil properties  Physical soil properties (e.g., aggregate stability and bulk density) are important for understanding the ecology of grasslands because they can be related both directly and indirectly to plant biomass inputs and topography; directly by the effect of slope on water infiltration, and indirectly by the effect of plant biomass on soil organic matter and subsequently on aggregation and soil bulk density. Aggregate stability samples were taken from ten locations along each transect to form a composite sample, for a total of four composite samples on each site. Aggregate stability samples were assessed in a similar method to that of Nimmo and Perkins (2002). Samples were maintained at 4°C until processing to inhibit microbial activity. Each sample was first passed through a 6 mm sieve and collected on a 2 mm sieve, resulting in aggregates within a diameter range of 2 to 6 mm. Directly before the wet-sieving, a sample of approximately 10 g was placed on a 2 mm sieve and humidified until saturation to eliminate air in pore spaces that might otherwise cause soil particles to break apart when submerged in water. Once humidified, the 2 mm sieve was placed on top of a nested set also containing sieves of 1 mm and 0.25 mm. Wet sieving was performed for 10 minutes using a motordriven instrument with a rate of 30 strokes per minute. Each stroke had a vertical component of 2.5 cm and oscillating motion through an angle of 30°. After wet-sieving, sieve sets with remaining material were oven-dried at 105° for 24 hours and weighed. To correct for non-aggregate particles, dry contents of each sieve were crushed and passed through the respective sieve again. Remaining particles were weighed and their mass subtracted from the total weight of sieve contents. Particle-free mass of each sieve’s contents was expressed as a percentage of the mass of the total particle-free aggregate sample. Soil lost during the wetsieving process was assumed to be aggregates of size class 0 to 0.25 mm and calculated as the mass remaining after all sieve contents were accounted for. Aggregate stability was expressed as mean weight density (MWD), in which the mean aggregate diameter of each size fraction is multiplied by its proportion of the total sample’s mass and these values are summed across all size fractions. Bulk density sampling was done at the center of transects 2 and 4 (two samples per site) using a double-cylinder drop-hammer sampler with core width 7.5 cm and height 7.5 cm for a total sample volume of 331.35 cm3. Bulk density cores were weighed the same day as samples were collected to determine wet weight. Cores were oven-dried at 105°C for 24 hours, then weighed before and after removal of coarse fragments greater than 2 mm in diameter. Volume of the sample was corrected for coarse fragments by subtracting their  16  estimated volume, assuming a density of 2.65 g cm-3. Bulk density was calculated as the mass of fragment-free dry soil divided by the corrected volume of the original field-moist sample. Soil samples of approximately 400 grams were taken from the same location as the bulk density samples, for a total of two samples per site. A portion of this sample was passed through a 2 mm sieve and used for particle size analysis. The percentages of sand, silt, and clay in each sample were assessed using the hydrometer method (Gee and Bauder 1986). 2.3.3.2  Chemical soil properties  Chemical soil properties (total nitrogen, total carbon, available phosphorus, and pH), as with physical soil properties, are related to vegetation and topography. Plant biomass inputs largely determine the amount of carbon, nitrogen, and phosphorus in the soil. Elevation, slope, and aspect indirectly influence pH because higher temperatures and lower moisture reduce leaching of base-forming cations from the surface horizon. Samples were air dried and passed through a 2 mm sieve, and then sent to a BC Ministry of Forests and Range soil testing laboratory in Victoria, BC. An automated elemental analyzer (LECO CNS-2000, Leco Corp., St. Joseph, MI) was used to determine total percent carbon (Nelson and Sommers 1982) and nitrogen (McGill and Figueiredo 1993) by dry combustion. Available phosphorus was determined by sodium bicarbonate (NaHCO3) extraction (Olsen et al. 1954). Soil pH in distilled water was determined using a slurry of 1:1 (v/v) soil to distilled water, and soil pH in calcium chloride (CaCl2) was determined using a slurry of 1:2 (v/v) soil to 0.01 M CaCl2 (Hendershot and Lalande 1993).  2.4 Inclusion of sites sampled in 2006 Data from sites in the upper grasslands, sampled in 2006 by Newman et al. (2011), were used to supplement this study. A better representation of the upper grassland was needed to achieve a more comprehensive picture of variation across the rangelands of Lac du Bois. Plot size of these sites was slightly larger, with sampling carried out on five, 30-m transect lines instead of four. Phosphorus and pH were not determined on these sites. These sites were revisited in 2010 to measure slope and aspect and perform “walkabouts”. All data from these sites were scaled to compatibility with data collected in 2010, e.g. species frequency data out of 50 Daubenmire frames per plot were multiplied by 0.8 to maintain frequency counts out of 40 frames.  17  2.5 Data assessment and transformation Vegetation data (Appendix B) from the 15 by 30 m sampling plots was represented in a matrix with sites as rows and species as columns. The intersection of each plot and plant species contained the mean percent cover value of the given species on that site. Most (80%) of the cells in this vegetation community matrix were zeroes, indicating absence of most species on most sites. This reflects the fact that, especially across long gradients covering different communities, there is high turnover in the species assemblage (Whittaker 1967). The “walkabout” data were included in a presence/absence matrix expanded to include all the species found only in walkabouts (28) as well as those found in the percent cover surveys (107) for a total of 135 species. Most of the cells in this matrix were zeroes. Only a few species were present in >80% of the sites, while most plant species encountered were relatively rare (Fig. 2.3). Species frequency data (the occurrence of each species on each site, out of a maximum 40 Daubenmire frames) and invasive species frequencies from the walkabouts were also compiled in separate matrices. Ground cover types, although assessed along with the vegetation by percent cover estimation, were included with the environmental data. The ground cover types were mostly abiotic, and the two living components (bryophytes and lichen) are non-vascular and were not identified to the species level. Hence the presence of bryophytes and lichen serves more as an indicator than as a component of the vascular plant community.  18  Figure 2.3  Species frequency curve, based on walkabout species richness data, showing the rank-order  proportion of the number of sites each species occurred at. Species above 80% are Pseudoroegneria spicata, Poa secunda, Achillea millefolium, Calochortus macrocarpus, and Lomatium macrocarpum.  Rare species have the potential to contribute unnecessary “noise” to datasets during statistical analysis. It is unwise to remove them from the data if the analysis is concerned with patterns of overall species diversity, but as in this case where the concern is primarily of communities and specific species responses to the environment, removing rare species can clarify the results. I chose to remove all species occurring on only 1 or 2 plots in the percent cover matrix (i.e., on less than 5% of the total 38 plots). The resulting species percent cover (abundance) matrix contained 69 species instead of 107, and is hereafter referred to as the reduced matrix. The vegetation data were then natural log-transformed to reduce the variation among species, so that dominant species would not have unduly large effects on any results. The transformation function used was “log1p” from the vegan package in the R statistical computing software (Oksanen et al. 2011). This method adds 1 to each value before natural log transformation, hence zero values are still zero after the transformation. 19  Soil and topographic data were square root transformed to reduce the variation among these factors because they are measured in units that differ by several orders of magnitude (e.g., solar insolation with a range of 124,531 – 275,761 WH/m2 and bulk density with a range of 0.50 – 1.40 g/cm3). Relativization is a data manipulation technique often used in cases where variables have been measured in different units (McCune and Grace 2002). This involves dividing rows or columns by the row or column total, respectively, which scales every value to a maximum of 1. In other words, with species abundance data it erases the effect of among-site variations in abundance and only measures proportions. Because this study is concerned with how distributions and abundances of species change across the landscape, the vegetation data were not relativized. Soil and topography data were relativized to eliminate the possibility of variables that are measured on different scales having a disproportionate effect on the analysis (such as solar insolation and bulk density as in the example mentioned above). Total nitrogen was removed from the soil data because it was found to have an extremely high correlation with total carbon (adj. R2 = 0.99, p < 2.2e-16) and is therefore redundant. Soil properties that were measured in 2010 but not on all of the sites sampled in 2006 (pH, texture, and available P) were removed from the soil data for analyses involving all 38 sites. These properties were retained for analyses only involving the 31 sites sampled in 2010.  2.6 Statistical analyses All statistical analyses were performed using the R statistical computing software version 2.9.2 (R Development Core Team 2009). A different set of statistical analyses was performed to address each of the study questions. 2.6.1  Assessment and refinement of current classification A dissimilarity-based grouping was desired to see how the 38 sites would be divided  based on their plant community composition, and to compare this with current classifications of the grasslands. Cluster analysis (the grouping of similar sample units) is an appropriate way to classify data in this manner. Three commonly used agglomerative hierarchical clustering methods were assessed to see which produced the strongest sets of groups. The clustering methods assessed were single linkage, complete linkage, and UPGMA (Unweighted Pair-Group Method using arithmetic Averages). A distance matrix was created from the reduced and transformed vegetation data, and this was used by the cluster analysis  20  to determine how closely related any two sites are. A Bray-Curtis coefficient was used as the distance measure, because it has been supported numerous times as an ecologically meaningful distance measure (McCune and Grace 2002). The efficacy of the clustering methods and optimal group number was chosen using cophenetic correlation, Manteloptimal number of clusters, and silhouette plots (Borcard et al. 2011). Once the optimal number of vegetation cluster groups was determined, K-means clustering was performed on the soil data using a Euclidean distance dissimilarity matrix derived from the transformed soil property data (bulk density, MWD, and total C). K-means clustering forces the data into a specified number of groups to create the lowest sum of within-group sums-of-squares. The same number of cluster groups was desired for both vegetation and soil to compare them more easily and find out if the sites would be distributed into similar groups (i.e., if they seem to be responding to each other). Euclidean distance was used for the soil data matrix because environmental variables have a continuous distribution across all sites. The strength of cluster groups was compared to that of the previously published elevation ranges using multi-response permutation procedure (MRPP). The test was run on each set of groups (BEC zone elevation ranges, van Ryswyk zone elevation ranges, UPGMA vegetation cluster groups, and k-means soil cluster groups) using 999 permutations. The test for each set of groups was done using a Bray-Curtis distance matrix derived from the reduced and transformed vegetation data. All of the MRPP analyses were performed by fitting the groups in question to the vegetation distance matrix so that the A-values from each analysis could be compared. The MRPP test statistic (A), chance-corrected within-group agreement, is analogous to an R2 value in a linear model and illustrates how homogeneous the groups are as compared to random permutations of the distance matrix (McCune and Grace 2002). MRPP is well suited to ecological data because it does not require multivariate normality or homogeneity of variances. To visually compare the results of the vegetation and soil cluster analyses with the BEC classification, nonmetric multidimensional scaling (NMS or NMDS) was performed. NMS is a multivariate ordination technique that differs from other ordination techniques in that it is a ranked distance technique, instead of relying on a more quantitative analysis of how individual species are correlated as in PCA (Principal Components Analysis) (Clarke 1993, McCune and Grace 2002). The NMS method ranks dissimilarity of all site pairs and attempts to plot the sites in relation to each other with the lowest “stress” possible. This  21  stress is minimized when the distances between sites in the resulting plot are as close to the original dissimilarity ranks as possible. Use of NMS is well supported in the literature as an informative method, especially for community ecology data with high diversity and nonlinearity (Clarke 1993, McCune & Grace 2002, Urban et al. 2002). NMS was performed using the reduced and transformed vegetation data. 2.6.2  Space-environment-vegetation relationships Analyzing the relationship between two different sets of data collected on the same  sample units (e.g., vegetation and soil) requires a comparison of the matrices containing these data. One common class of multivariate methods for this type of analysis is constrained ordination such as CCA (Canonical Correspondence Analysis) or RDA (Redundancy Analysis). These analyses only address the variation in one matrix that can be explained by what was measured in the other (McCune and Grace 2002). Further, they assume a specific type of relationship (linear or unimodal depending on the method) between the variables in the two matrices. With ecological data it is rarely appropriate to assume that these relationships exist, especially in complex terrain such as Lac du Bois Provincial Park. The Mantel test (Mantel 1967) does not make these assumptions about distributions, and addresses the question of how strongly the structure of two distance matrices is correlated. It is a less constrained approach to comparing two sets of variables and is more appropriate for the exploratory nature of this study. A Mantel test calculates the correlation between two distance matrices. Each matrix is derived from a different set of variables measured on the same sample units, in this case the sampling plots. The significance of the correlations was calculated by comparing the observed values with the results of 9,999 permutations of the matrices. All of the Mantel tests were performed using data from only the 31 sites sampled in 2010. This allowed for assessing north and south-facing sites separately to see if there was an effect of aspect on the relationships between matrices. The matrices used were: reduced and transformed vegetation abundance matrix (“species matrix”); environment matrix (bulk density, MWD, total C, elevation, slope, aspect southness, and percent cover of ground cover types); location matrix (distance between sites based on x-y coordinates, “space matrix”); soil matrix (bulk density, MWD, total C, available P, pH in water, percentages of sand and silt, and percent cover of bare soil and exposed rock); and topography matrix (slope, elevation and aspect southness). Euclidean distance was used for all matrices except the vegetation matrix, for which Bray-Curtis distance was used. Two different sets of matrices  22  were assessed: “species-space-environment” and “species-soil-topography”. Within each set the Mantel correlation was calculated for every combination of matrices for all 2010 sites, south-facing sites only, and north-facing sites only. Using a spatial location (geographic distance) matrix is useful for addressing issues of spatial autocorrelation and testing for the significance of this phenomenon in the study area. Space-vegetation relationships can arise from localized patterns of species dispersal that are independent of the environment, and space-environment relationships indicate autocorrelation of environmental variables (Urban et al. 2002). The second set of matrices excludes geographic distance and breaks the environmental variables into topographic and soil variables to look at the effect of these factors individually. 2.6.3  Predictors of exotic and/or invasive plant species in Lac du Bois The distinction between terms such as “exotic” and “invasive” species can be vague  and confusing (Colautti and MacIsaac 2004). For the purposes of this study “exotic” means a species that is not native to BC grasslands and “invasive” refers specifically to plant species included in the list of noxious, weedy, invasive and nuisance species in BC, as compiled on the E-Flora BC website (Perzoff 2009). This distinction between terms is made and both are addressed because although many exotic plants are not species of concern, they may become a concern (“invasive”) as the climate changes (Dukes and Mooney 1999). A visually intuitive way to see how the presence of exotic and invasive plants is related to various environmental properties is to overlay correlation vectors on a multivariate ordination. Correlation vectors were overlaid on the NMS plot created from the reduced and log-transformed vegetation data, using “envfit” in R’s vegan package. The “envfit” analysis fits vectors to the results of an ordination that have maximum correlation with each of the specified variables, and calculates R2 and p-values for each vector using 999 permutations of the data. Correlation vectors for exotic:native (E:N) species ratio, invasive species abundance, and environmental factors (topography, soil properties, and ground cover types) were plotted. By looking at how the E:N ratio and invasive abundance vectors are positioned relative to the environmental vectors, conclusions can be drawn about which environmental factors are most strongly related to the presence of exotic and invasive plants. These vectors can also be used to generalize key properties of the three vegetation cluster groups, because  23  they point toward the sites that have generally higher values of the property that is represented by the vector. To illustrate the range of an individual exotic species for all measured environmental variables we developed a method for visualizing species’ “domain space.” This method is more specific and less abstract than a multivariate ordination figure. A species may be more tolerant of wide ranges in certain variables and restricted by others. To show this, relativized variables were used so they could be compared and plotted in the same space. Variables were ordered on the x-axis by performing a principal components analysis (PCA) with the transformed and relativized environmental data and recording each variable’s loading on PCA axis 1. The variables were then ordered in the domain space graph by placing the highest loading variable at the far left of the x-axis and subsequently decreasing to the right. For every species plotted, its range of each variable was determined by finding the minimum and maximum values of that variable from all sites on which the species occurs (based on walkabout/species richness data). A polygon was drawn around these points and the area of this polygon is the domain space. This space-value has no intrinsic scale, but can be compared to other species measured on the same sites in the same study. The species used for this analysis are those from the list of invasive species on the EFlora BC website (Perzoff 2009) that were encountered on more than one site in this study (Appendix B). The following variable set was used for comparison: bulk density, MWD, total C, elevation, slope, percent cover of bryophyte, lichen, litter, rock and soil, and solar insolation. The main question being addressed in this approach is which of these environmental variables have the strongest distributional constraints on each plant species. This can be used to infer how climate change may affect the range and distribution of each species.  24  Chapter 3:  Results  3.1 Assessment and refinement of current classification UPGMA linkage with 3 groups was chosen as the final dendrogram for vegetation cluster analysis (Fig. 3.1). The Mantel- and silhouette-optimal number of groups was between 3 and 6, but 3 was chosen as an intuitive solution because it is better suited for comparison with the existing regional grassland classifications which also include 3 groups.  Figure 3.1  Reordered dendrogram of the vegetation data cluster analysis (Unweighted Pair-Group Method  using arithmetic Averages) for the 38 Lac du Bois rangeland study sites with a 3-group solution. Site codes are displayed on the leaves of the dendrogram. Cluster groups are separated by red boxes.  K-means cluster analysis was then performed on the soil data matrix to force the sites into three groups. K-means clustering was also performed with two to ten groups, and three groups was the best out of all regardless of the vegetation clustering results (lowest sum of within-group sums-of-squares).  25  The cluster groups were assessed in comparison with the more traditional groupings of Lower, Middle and Upper grassland, to confirm that the cluster analysis formed relatively strong groups. Group membership numbers (N) of the sites in this study varied slightly depending on the elevation ranges used (Table 3.1). Elevation ranges given for UPGMA clusters are based on the minimum and maximum elevation sites in each group, and there is considerable overlap between clusters. Sites were assigned to the other two classifications’ groups based on their corresponding elevation ranges published by van Ryswyk et al. (1966) and the government of BC (BC Parks 2002). Tisdale (1947) also referred to the Lower, Middle and Upper grassland but did not specify elevation ranges. The BC Parks grassland elevation ranges are based closely on the BEC variants: BGxh “Thompson Very Dry Hot Bunchgrass Variant”, BGxw “Nicola Very Dry Warm Bunchgrass Variant”, and IDFxh2a “Thompson Very Dry Hot Interior Douglas-fir Variant” (BC Parks 2002). Table 3.1  Grouping of grassland sites based on different classification schemes. Sites were assigned to BEC  and van Ryswyk levels based on published elevation ranges. Elevation ranges for vegetation and soil cluster groups were defined by highest and lowest elevation of sites contained in each group. Lower Grassland  Middle Grassland  Upper Grassland  Elevation (m)  No. of sites  Elevation (m)  No. of sites  Elevation (m)  No. of sites  BEC  335 – 700  15  700 – 850  12  850 – 975  11  van Ryswyk  350 – 600  10  600 – 825  17  825 – 975  11  UPGMA (veg.)  372 – 715  10  498 – 896  17  757 – 982  11  k-means (soil)  372 - 778  12  498 - 896  13  674 - 982  13  The BEC and van Ryswyk classifications show similar group strength (Table 3.2), while UPGMA vegetation cluster groups are about twice as strongly homogeneous. The kmeans soil cluster groups are more homogeneous than the current classifications, but less than the vegetation groups. This shows that the cluster analysis did form stronger groups; stronger than those of the currently accepted classification system. The biggest difference is that the cluster groups allowed a great deal of elevation range overlap, and included many lower elevation north-facing sites in the middle elevation group.  26  Table 3.2  Summary of multi-response permutation procedure (MRPP) using different group membership  criteria. All analyses were done using 999 permutations. Chance-corrected within-group  p-value  agreement statistic (A) BEC zones  0.1487  0.001  van Ryswyk zones  0.1690  0.001  UPGMA veg. groups  0.2945  0.001  k-means soil groups  0.2246  0.001  Because the strength of the soil cluster groups was analyzed against a distance matrix derived from the vegetation data, this inherently results in a lower A-value than if they were assessed against a distance matrix derived from the soil data (cluster groups will fit best against the data they were derived from). However, this still shows that the soil cluster groups are stronger than the existing classifications, which indicates that the soil properties determined in this study play an important role in assessing how sites are different from each other. The top two species by abundance in each vegetation cluster group (for the purposes of naming the groups) are Group 1: Pseudoroegneria spicata - Artemisia tridentata, Group 2: P. spicata - Festuca campestris, and Group 3: F. campestris - Poa pratensis (Bluebunch wheatgrass-Big sage, Bluebunch wheatgrass-Rough fescue, and Rough fescue-Kentucky bluegrass, respectively). For abbreviation purposes the groups are hereafter referred to as Wheatgrass-Sage, Wheatgrass-Fescue, and Fescue-Bluegrass. When NMS was performed on the reduced and transformed vegetation matrix, the resulting stress of the two-dimensional solution was 9.67, which is considered to signify low risk of misinterpretation (McCune and Grace 2002). With the points on the ordination identified by their BEC zone based on elevation, the division between zones is somewhat apparent, but still quite scattered (Fig. 3.2). When the points in the same ordination are identified by their vegetation cluster groups, the division is much more clear (Fig. 3.3). Separation of groups is of course the goal of cluster analysis, so it may seem circular to compare the results of a cluster-based and non-clusterbased classification in ordination space, because the results of NMS are based on the same distance matrix used for the cluster analysis. However, this is only meant to highlight how the BEC classification system may lead to placing sites with very similar plant communities into different zones if the classification is done using elevation ranges alone.  27  Figure 3.2  Nonmetric multidimensional scaling (NMS) of reduced and transformed vegetation data, with sites  labeled by the BEC zone they would fall into based on elevation.  28  Figure 3.3  Nonmetric multidimensional scaling (NMS) of reduced and transformed vegetation data, with sites  labeled by vegetation cluster group.  29  Figure 3.4  Nonmetric multidimensional scaling (NMS) of reduced and transformed vegetation data, with sites  labeled by soil cluster group. The six sites that have switched groups (compared to the vegetation cluster groups) are identified with red circles.  When the soil cluster groups were compared with the vegetation cluster groups, 32 out of 38 sites were placed in the same three groups. 27 out of 38 sites are grouped the same in the vegetation cluster groups and BEC classification. Identifying the points (again in the same ordination) by their soil cluster groups, the result is mostly the same as the vegetation cluster groups except that 6 sites have changed group identity (Fig. 3.4). One site switched from the Wheatgrass-Sage group to the Wheatgrass-Fescue. The Wheatgrass-Fescue group lost 5 members: 3 to the wheatgrass-sage and 2 to the Fescue-Bluegrass. Because the Wheatgrass-Fescue lost the most sites to other groups upon re-classification, this suggests that the other two groups have a stronger link  30  between their plant community composition and soil properties. For a summary of cluster group soil properties and diversity characteristics, see Appendix C. The Wheatgrass-Sage vegetation cluster group consists entirely of south-facing sites, while the Fescue-Bluegrass cluster group consists of north-facing sites plus the seven sites sampled in 2006, of which the majority is northeast-facing. These two groups can be summarized as south-facing and primarily north-facing, respectively. Meanwhile the Wheatgrass-Fescue vegetation cluster group is almost evenly split between north-facing (eight) and south-facing (nine) sites. When the NMS ordination labeled by vegetation cluster groups was relabeled by soil cluster groups, two of the three sites that moved from Wheatgrass-Fescue to Wheatgrass-Sage were south-facing, and the two sites that moved from Wheatgrass-Fescue to Fescue-Bluegrass were north-facing.  3.2 Space-environment-vegetation relationships The Mantel test results are divided into two sets of three (Figs. 3.5 and 3.6), where each set contains an analysis of south-facing sites only (Figs. 3.5a and 3.6a), north-facing sites only (Figs. 3.5b and 3.6b), and south plus north sites (Figs. 3.5c and 3.6c). This was done to check if the relationships between various factors influencing the plant community are different in varying microclimate conditions, as represented by opposite aspect. In the first set of diagrams (Figs. 3.5a-c), distance apart can influence vegetation and environment, but distance cannot be affected by other factors because the sites are fixed in place. Hence, any Mantel correlations associated with the distance between sites must be an effect of this distance and not the other way around. The cause of correlations between vegetation and environment could be in either direction, or in both directions, depending on the relative effects of topography, soil properties, and ground cover types within the environment data. In the second set of diagrams (Figs. 3.6a-c), topography and soil properties have been separated, and the space component is removed. Similar to distance apart, topography cannot be affected by the other factors (vegetation or soils), hence any Mantel correlations associated with topography must be an effect of topography and not the other way around. Soil-vegetation correlations could be an effect of one on the other or a more mutual interaction. Spatial proximity is not correlated with environmental similarity on south-facing sites (Fig. 3.5a). Environment is only slightly correlated with vegetation. However, spatial proximity has a fairly strong Mantel correlation (0.66) with vegetational similarity on southfacing sites.  31  There are strong correlations between all combinations of space, vegetation, and environment on north-facing sites (Fig. 3.5b). However, the Mantel correlation between space and environment (0.50) is lower than the correlations involving plant community composition. The correlations of space-environment and vegetation-environment for all 31 sites sampled in 2010 (Fig. 3.5c) are intermediate to the separate results of south and northfacing sites. The space-environment correlation seems to be rather tenuous (0.17), while vegetation and environment have a high correlation (0.71), close to that of the north-facing sites (0.76). The vegetation-space correlation for all sites is lower than for either north- or south-facing sites alone.  32  3.5a  South-facing only (n=19)  3.5b  3.5c Figure 3.5  North-facing only (n=12)  All 31 sites  Triangles depicting Mantel correlations between all combinations of three matrices (vegetation, space, and environment). Mantel correlation values  are given on the bar connecting any two ovals, and the thickness of the bar reflects this value. All correlations are significant at p ! 0.05; non-significant connections (n.s.) are shown by a dashed line.  33  3.6a  South-facing only (n=19)  3.6b  3.6c  Figure 3.6  North-facing only (n=12)  All 31 sites  Triangles depicting Mantel correlations between all combinations of three matrices (vegetation, soil, and topography). Mantel correlation values are  given on the bar connecting any two ovals, and the thickness of the bar reflects this value. All correlations are significant at p ! 0.05; non-significant connections (n.s.) are shown by a dashed line.  34  When the environmental properties were split into soil and topography components, some different relationships emerged. On south-facing sites (Fig. 3.6a), there is a weak Mantel correlation (0.19) between vegetation and soil. Stronger correlations exist for topography with vegetation (0.33) and topography with soil (0.38). These relationships should be considered keeping in mind that the Mantel correlation between environment and vegetation in the first set of diagrams (Fig. 3.5a) was low to begin with (0.25). Therefore comparing the respective correlations of topography and soil with vegetation indicates that plant community composition is influenced more by topography than by soil properties, but their correlation as a whole with vegetation is still weak. On north-facing slopes (Fig. 3.6b), the strongest Mantel correlation is between soil and vegetation (0.58). There is a lesser correlation between topography and vegetation (0.34), and no significant correlation between topography and soil. This lack of a correlation between soil properties and topography is interesting given that their combined properties have a strong correlation with plant community composition on north-facing slopes (0.76, Fig. 3.5b). When all 31 sites sampled in 2010 are considered together, all Mantel correlations are higher than for either south- or north-facing sites alone (Fig. 3.6c). The topography-soil correlation was closer to that of south-facing sites, and the vegetation-soil correlation was closer to that of north-facing sites.  3.3 Predictors of exotic and/or invasive plant species in Lac du Bois The same NMS ordinations (used for Figs. 3.2-3.4) based on the reduced and transformed vegetation abundance data are used again here to illustrate how the percent cover of invasive species as well as the total plant abundance vary between vegetation cluster groups (Fig. 3.7). The lowest total cover is in the Wheatgrass-Sage group, and the highest total cover is in the Fescue-Bluegrass group. There is quite low presence of invasive species in both the Wheatgrass-Sage and Wheatgrass-Fescue groups, with only a few occurrences. All three sites with relatively major occurrences of invasive species are in the FescueBluegrass group, and several other sites in this group have minor occurrences as well.  35  Figure 3.7  Nonmetric multidimensional scaling (NMS) of sites based on plant community composition, with  open circles representing total vascular plant cover on the site and inner solid circles representing total invasive species over on the site. Some sites total to greater than 100% cover because of the additive nature of estimating percent cover over different heights of vegetation. Cluster groups are indicated by 95% confidence ellipses based on standard deviation. In general, sites toward the right of the ordination have higher total cover of vegetation as well as higher cover of invasive species. From left to right, the cluster groups are Wheatgrass-Sage, WheatgrassFescue, and Fescue-Bluegrass.  36  Figure 3.8  Nonmetric multidimensional scaling (NMS) of sites based on plant community composition, with  overlaid correlation vectors for environmental variables, ground cover types, exotic:native (E:N) species ratio, and percent cover of invasive species. Arrow lengths of environmental variables and ground cover types are scaled according to their relative correlation with the points in the ordination; arrow lengths of E:N ratio and invasive % cover are also scaled relative to each other.  Correlation vectors are helpful in identifying how the different cluster groups are associated with different environmental properties (Fig. 3.8). In general, the WheatgrassSage group is associated with higher values of soil bulk density, lichen, and exposed rock. Bare soil and south-facing aspects also seem to be associated with this group, as well as with the Wheatgrass-Fescue group (probably just the south-facing sites in the latter). The FescueBluegrass group is generally associated with higher elevation, percent cover of mosses, litter  37  and invasive species, MWD, and total soil C. It seems based on the asp.southness vector (Fig. 3.8) that the Wheatgrass-Fescue group is separated so that the south-facing sites are toward the top of the figure and the north-facing sites are toward the bottom. Because it contains sites from opposite aspects, its center is near the origin. The south-facing sites are spread toward the asp.southness vector and the Wheatgrass-Sage group, and the northfacing sites are opposite this vector closer to the Fescue-Bluegrass group. The E:N plant species ratio vector is facing in the opposite direction as the slope vector, which implies that there is a higher E:N ratio on gentler slopes. The abundance of invasive species seems to be closely associated with higher elevation and to some degree with the percent cover of moss, although the moss (bryo) vector has a relatively low R2 value (Table 3.3). Table 3.3  Goodness-of-fit and p-value of nonmetric multidimensional scaling (NMS) correlation vectors on an  ordination of sites in the Lac du Bois rangeland (Fig. 3.8) 2  Factor  R  p-value  Bulk density  0.7588  0.001  MWD  0.7690  0.001  Total soil C  0.8035  0.001  Elevation  0.7490  0.001  Slope  0.2398  0.016  Aspect southness  0.7081  0.001  Bryophyte  0.2081  0.015  Lichen  0.2481  0.006  Litter  0.7808  0.001  Exposed rock  0.2138  0.007  Bare soil  0.5346  0.001  E:N ratio  0.2198  0.009  Invasive % cover  0.2113  0.008  38  Figure 3.9  “Domain space” polygons plotted for eight exotic species encountered in Lac du Bois rangeland  study sites. Species are ordered by their total number of occurrences. TRDU=Tragopogon dubius; POPR=Poa pratensis; BRTE=Bromus tectorum; LIGE=Linaria genistifolia; CEDI=Centaurea diffusa; CEBI=Centaurea stoebe; VETH=Verbascum thapsus; POCO=Poa compressa. The X-axes are a set of environmental variables ordered by their loadings on PCA axis 1. Y-axes are relativized values of the environmental properties, calculated by dividing all values of each variables on sites where the species occurred by the highest value of that variable on sites where the species occurred by the highest value of that variable across all sites. Note that some properties do not begin at 0 because their lowest value is greater than 0.  In the domain space analysis (Fig. 3.9), the ordering of the variables by PCA has placed them so that the left side of the x-axis contains variables that have higher values on sites in the Fescue-Bluegrass group. The correlation vectors in Fig. 3.8 that correspond to these variables are all pointing in the direction of sites included in the Fescue-Bluegrass 39  group. The right side of the x-axis contains variables that have higher values on sites in the Wheatgrass-Sage group. This analysis does not indicate absolute species limits, but species ranges based on the study sites. A few results stand out in Fig. 3.9. TRDU (Tragopogon dubius Scop., yellow salsify) and POPR (Kentucky bluegrass) have high maximum values toward the left side of the xaxis. BRTE (cheatgrass) has high maximum values toward the right side of the axis. The left side of the axis corresponds with the Fescue-Bluegrass group and the right side of the axis corresponds with the Wheatgrass-Sage group. However, this does not confirm that (for example) TRDU was most abundant in the Fescue-Bluegrass group, only that it occurred on at least one site with a high value for each of the properties associated with this group. The shape of the other species’ polygons are not as clear, but some species such as CEBI (spotted knapweed) have very narrow regions of the polygon, indicating that they were only found in a relatively small range of values for those variables. When interpreting the species domain space it is tempting to compare the overall shapes of the polygons, but it is difficult to draw accurate conclusions in this manner. In general, the species with a higher frequency of occurrence have a greater domain space value, but the difference becomes less pronounced as more occurrences are added.  40  Chapter 4:  Discussion  4.1 Assessment and refinement of current classification The similarity of how sites in my study were divided into soil and vegetation cluster groups (Figs. 3.3 and 3.4) indicates that the soils and vegetation generally vary in accordance with each other across the landscape. This is corroborated by the 0.65 Mantel correlation of soil and vegetation (Fig. 3.6c). The sites that moved from the mixed-aspect vegetation cluster group (Wheatgrass-Fescue) to a different soil group generally followed a pattern of south-facing sites moving to the south-facing group and north-facing sites moving to the primarily north-facing group. This suggests that aspect may have a stronger effect on soil properties than it does on plant community association, which could explain a portion of the disconnection between soil properties and plant communities. Such disconnect is not surprising given the complexity of ecosystems, but it does highlight one difficulty of establishing a classification system based on both soils and vegetation. The overlap in elevation ranges for cluster groups highlights the potential danger in making a strict interpretation of elevation ranges for largely heterogeneous areas, especially those with great topographic variation. The striking difference in vegetation communities on north and south slopes in this region even at the same elevation was initially acknowledged by Tisdale (1947). The difference between my cluster groups and the groups based strictly on reported elevation ranges is probably inflated because using these elevation-based groups did not allow consideration of site-specific vegetation or soils in the grouping process. Thus, assessment of an area using the BEC system should not be done in a top-down manner by making generalizations about a locality based on lines that were drawn from relatively coarse sampling. Rather, assessment of a locality should be done in a bottom-up manner that involves more thorough sampling and interpretation of the area. In this way, knowledge of plant community and soil relationships can be locally refined. Better prediction of site conditions using only maps would then be possible because more fine-grained information would be available. Soil properties determined in this study all fell within ranges and trends expected based on previous studies (Tisdale 1947, Floate 1965, van Ryswyk et al. 1966). As the mean elevation of the cluster groups increases, so do mean values for MWD, total carbon, total nitrogen, and total vegetation cover (Appendix C). Bulk density and solar insolation decrease over the same range. The changes in carbon and nitrogen are most likely a result of  41  increasing precipitation and decreasing temperature, which increases plant growth and decreases the rate of microbial decomposition (Floate 1965, Wildung et al. 1975). An ongoing study (L. Fraser et al., unpublished data) in Lac du Bois has also found this climatic trend. The results of the cluster analysis and its comparison with the elevation ranges of published BEC subzones are not suggestive that a refinement of the current classification system is needed, because of the way that I tested the relationship between these groups. Rather, my results point to an assessment of how the BEC system is applied, in that its use should be approached with caution as far as generalizing about specific areas based on coarse scale sampling. In other words, whenever possible, field studies in the area of interest should be the basis of gathering information as opposed to making assumptions based on GIS map layers unless the area of interest is too large to sample. In this case, even limited field sampling may help refine knowledge of the area.  4.2 Space-environment-vegetation relationships Our results indicate that closer sites within either north or south-facing slopes have more similar plant communities, probably because the population expansion of plant species is often limited by their ability to successfully disperse into new locations (Primack and Miao 1992). North-facing sites that are closer together tend to have similar environmental properties and a high correlation between vegetation and environment. This suggests that there is a much stronger plant-environment feedback on north-facing sites than there is on south-facing sites. For example, greater total biomass in north-facing plant communities that tend to be dominated by rough fescue also leads to more surface litter, which in turn leads to higher total soil C and N as well as MWD, and lower soil bulk density. Perhaps north-facing sites are more stable because they tend to be more “buffered” from extreme climatic conditions such as high temperature and low moisture. The idea of litter strongly influencing grassland communities is supported by Lamb (2008), who found that species richness and evenness declined with increasing amounts of litter. Possible mechanisms for these effects of litter include physical interference, shading, or a shift in the importance of below- and above-ground competition (Lamb 2008). The relationship between site proximity and similarity of plant communities was lower for all sites than for south- or north-facing sites alone. This is most likely because south- and north-facing sites are intermixed across the landscape, and their respective plant communities and environmental properties are markedly different. Two adjacent sites may  42  be of opposite aspect, which has the overall effect of diminishing the correlation as opposed to focusing on only one aspect at a time. The correlation between environmental and vegetative similarity is still high for north- and south-facing sites combined, even though it is low on south-facing sites alone. This correlation remains high because it is not confounded by “distance apart”. Perhaps the plant communities and environment are so different on north- and south-facing sites that a stronger relationship emerges when they are combined in the same analysis. On south-facing sites, the topography-soil and topography-vegetation relationships are higher than the relationship of soil and vegetation. This indicates that topography influences both vegetation and soil properties more than they influence each other. This effect of topography is likely related to slope and elevation. Steeper slopes receive greater amounts of solar insolation and also drain faster, which could have plant communityaltering effects depending on the heat and desiccation tolerance ranges of different species. Lower elevation sites in this region are also subjected to more heat stress relative to higher elevation sites because they experience higher temperatures and less precipitation. The slope and elevation of south-facing sites also appears to be affecting soil properties, and this is also likely related to varying temperature and moisture. Drier sites tend to have higher pH, and lower C and N content. The weak correlation between soil and vegetation on south-facing sites suggests that the soil properties determined in this study are not particularly important in determining the composition of south-facing plant communities, and vice versa. Other properties such as soil fauna composition and micronutrient quantities might be more important. However, this could also be an issue of scale, in which a relatively homogeneous group (i.e. south-facing sites) does not display as strong a correlation as it would when juxtaposed with north-facing sites. On north-facing sites, topography has a similar correlation with vegetation as on south-facing sites. This is probably again an effect of slope and elevation on temperature, precipitation, solar insolation, and water infiltration, and consequently on plant communities based on the individualistic responses of species to their environment. Unlike south-facing sites, the Mantel correlation between topography and soil properties on northfacing sites is non-significant. However, there is a fairly strong correlation between soil and vegetation. Because soil and topography are both correlated with vegetation, but uncorrelated with each other, perhaps they are influencing plant community composition independently of one another. Another possibility is that topography is affecting vegetation, which in turn is affecting the soil properties. Plant biomass inputs (which are generally much  43  higher on north-facing sites) have a strong effect on soil properties because of the high additions of organic matter through decomposing leaf litter, especially from perennial bunchgrasses such as rough fescue. All relationships of vegetation, soil, and topographic similarity were higher for northand south-facing sites combined than for either aspect examined separately. It is possible that the strong differences between conditions on north- and south-facing sites are exaggerating the strength of the correlations when all sites are included in the analysis. This could be likened to performing two separate linear regressions of data points, neither of which has a particularly strong R2 value. However, when all data points are plotted together, the correlation is stronger because each set of points has a completely different range for the respective units of measure. Overall, we have shown that the correlations among plant community, soil properties, topography, and geographic location can be highly dependent on aspect. It is known that site conditions are dramatically affected by aspect (Tisdale 1947, Lloyd et al. 2005), but previously there has been no focus on how vegetation, soils, and topography interact as a consequence of aspect. With future climate change, the relationships among topography, soil properties, and vegetation have important implications for the fate of plant communities. Plant species found primarily on north slopes have low tolerance limits to heat and desiccation stress, pressures that are expected to increase in the coming decades. If some of these species cannot survive under future conditions, especially the dominant rough fescue, north-facing plant communities will be significantly altered. The gaps opened by reduced production of rough fescue would present opportunities for exotic species that were formerly unable to inhabit these communities because of the dense, stable plant cover, and abundant leaf litter. This in turn could lead to a loss of the strong plant-soil feedback on north-facing slopes, which would likely shift the relationships among vegetation, soil properties, and topography. Presumably these relationships would become more similar to those currently found on south-facing slopes, meaning topography would have a greater impact on north-facing plant communities. Topography will also affect how rapidly any changes occur. Slope affects both solar insolation and water infiltration, which will be critical factors for plant growth especially if the temperature on north-facing sites becomes similar to the current conditions on south-facing sites.  44  4.3 Predictors of exotic and/or invasive plant species in Lac du Bois Many of the highly invaded sites were those surveyed in 2006 and this probably confounds the relationship of aspect to the environmental variables because these previously sampled sites were not north- or south-facing. These seven sites also included most of the highest elevation sites, and likely also those with the highest percent cover of moss. This connection between increasing invasive species abundance and percent cover of moss to elevation most likely reflects precipitation increasing with elevation in Lac du Bois (van Ryswyk et al. 1966). Several studies have also shown that invasive species compete more successfully when they have access to increased amounts of water (Hobbs and Mooney 1991, Chong et al. 2006, Tannas 2009). Higher total C, MWD, and percent cover of litter are strongly associated with northfacing sites, while higher bulk density and percent cover of bare soil, lichen, and rock are associated with south-facing sites. The fact that these soil and ground cover properties are more strongly associated with aspect than they are with elevation indicates that aspect has the greater effect on plant communities and soils, despite the fact that many crucial environmental factors also covary with elevation. It is true that variables such as precipitation and temperature increase with elevation, but the ultimate effect of these variables is highly dependent on aspect and the resulting solar insolation. The influence of aspect is also suggested by the polarization of these soil and ground cover variables on the x-axis of the domain space analysis. However, since most invasive species’ domain space polygons did not have a clear pattern in regards to this polarization, the most useful interpretation of this analysis is to compare the domain space values of different species. This can be considered a relative measure of how well each species tolerates variation in the environment. In some cases it may be useful to identify the narrowest points of a polygon to get an idea of the environmental properties that are most restricting the distribution of a particular species. For example, CEBI, VETH, and POCO (spotted knapweed, great mullein (Verbascum thapsus L.), and Canada bluegrass (Poa compressa L.), respectively) all were observed only in a narrow range of elevation. If it can be generalized that a greater domain space is equivalent to a higher tolerance of environmental variation, then yellow salsify, Kentucky bluegrass, and cheatgrass seem to be the species with the greatest capacity to spread with environmental change. It is also possible that some of the more recently arrived species of concern, such as LIGE (dalmatian toadflax), have not yet had time to spread to the extent of their tolerance. In this case the potential of a species to expand its range with climate change may be underestimated.  45  In the domain space analysis abundances (percent cover) of the invasive species are not taken into account, thus it is important to also examine where each species is presently most abundant. For example, Kentucky bluegrass is quite prevalent in the Fescue-Bluegrass group, as is reflected by the name of the group, and cheatgrass is most abundant in the Wheatgrass-Sage group. Because the interface between the upper grassland and the lower extent of the interior Douglas-fir forests is expected to shift upward with climate change (Hamann and Wang 2006), this is the area that would be opened to the expansion of the grasslands in Lac du Bois and similar areas in the region. Species that already occur in relatively high abundance at these high grassland elevations would be the most likely to move into the newly opened space that is created. Because cheatgrass currently is most abundant at the lower elevations in Lac du Bois, it is probably not a species of concern for these higher elevation areas in the near future. However, Kentucky bluegrass may be one of the first species to take advantage of tree die-off and aggressively colonize new spaces, because it is relatively abundant at high elevations in Lac du Bois. The Fescue-Bluegrass group, which contained the highest amounts of Kentucky bluegrass (and total invasive percent cover), also had the highest mean diversity and mean cover of invasive species (Appendix C). This is intriguing because it contradicts both Elton’s hypothesis that greater diversity excludes invasive plant species (Elton 1958) and more recent suggestions that productivity excludes invasives (Rinella et al. 2007). A greater understanding of how invasive plant species are more successful in the most diverse and productive grassland community type in Lac du Bois would require experiments targeting the mechanisms assumed to underlie these interactions, especially competition and resource availability.  4.4 Expectations for future change based on refinement of plant/soil/environment relationships in Lac du Bois South-facing aspects receive more solar insolation, which in turn causes higher temperature and lower moisture, with the net result of higher evapotranspiration (van Ryswyk et al. 1966). This difference in evapotranspiration rates probably drives much of the community structure, both directly through available soil water content and indirectly through plant species tolerances to desiccation. Community structure is also strongly driven by aspect and elevation, which act as a proxy for several underlying environmental variables that are indirectly affected by climate such as soil bulk density, MWD, and total carbon. Some studies have suggested that these underlying factors should be decoupled from elevation especially when the major questions deal with climate change, because all of the  46  factors currently correlated with elevation will not necessarily change in the same direction as the climate changes (Urban et al. 2002). The difference in the amount of organic matter of forest versus grassland soil is potentially important because the transitional zone between grasslands and forests is expected to be the area of the future BEC zone shift. Grassland soils tend to contain greater amounts of organic matter than forest soils, especially deeper in the soil profile (Floate 1965). Aside from the amount of organic matter, there is also a difference in the quality of organic matter. It has been shown that high amounts of nitrogen and low amounts of lignin increase decomposition rates (Aber and Melillo 1989, Smith and Bradford 2003). Grass and other herbaceous litter has a low C:N ratio (high relative amounts of nitrogen), so its decomposition will be faster than coniferous forest litter. This contributes to the higher organic matter in grassland soil. Grassland soils also have a higher pH than forest soils (Floate 1965). Plant species with the highest success in colonizing formerly forested sites will likely be those with a greater tolerance for lower fertility and more acidic soils. Lac du Bois is topographically heterogeneous, thus it seems reasonable to expect that most plant species will be able to move within the landscape and find new suitable habitat as the climate changes, as long as they are not too dispersal-limited. Plant species restricted to north-facing communities will move upward in elevation as the climate warms because they require cooler temperatures and higher moisture availability. Many species currently associated with south-facing communities could simply move around the hills on which they occur to the north side. From the lowest to the highest elevation of Lac du Bois there is only a 4°C decrease in monthly mean temperature (van Ryswyk et al. 1966), and the expected overall increase by the year 2080 is at least 2°C, even by the most conservative models (Kimmins and Lavender 1992). This means that temperatures in the upper grasslands of Lac du Bois will be at least as high as they currently are in the middle grasslands, and temperatures in the middle grasslands will be at least as high as they currently are in the lower grasslands. Such an increase in temperatures has severe implications for plant community change and associated changes in soil properties. If temperatures in the high elevation grasslands of Lac du Bois are eventually the same as current temperatures in the middle elevation grasslands, it follows that many plant species currently inhabiting the high elevation grasslands will no longer be able to do so. Such a change in climate also has the potential to decouple the plant-soil relationships found on north-facing slopes in the lower and middle grasslands in this study because the required temperature and moisture conditions for many plant species may no longer exist at these elevations. This will likely  47  result in decreased grassland productivity on these sites, and consequently a decrease in soil fertility. The BEC system’s ability to predict future change is based on static, linear models, and it is argued (Haeussler 2011) that the system should be developed as a more dynamic model that allows for multiple potential outcomes. Changes that occur in temperature or precipitation will probably not be able to accurately predict large BEC zone shifts in a linear fashion, as there are many contingencies involved, and new combinations of conditions could create novel BEC subzones or site units. The bottom-up approach advocated in previous sections of this discussion is recommended, because plant species and their associations with soils and topography will be affected by future change individually.  48  Chapter 5:  Conclusions  The van Ryswyk et al. (1966) and BEC (Pojar et al. 1987) classification systems were developed to predict the plant community and soil association most likely to occur in a given area. In contrast, this thesis was meant to assess the relationships between plant communities, soil properties, and topography without assuming the existence of predetermined community types. This information was then used to refine understanding of the current systems. An important finding in this thesis relates to the significant role of aspect in the evaluation of relationships among soils and plant communities, particularly in the context of improving the accuracy of current classifications for future climate change predictions. Applying current classifications based solely on elevation ranges did not capture important effects of slope and aspect on grassland plant communities and soils. The collection and analysis of locality-specific data, which addresses the mechanisms underlying community associations, will aid the refinement of current understanding. A strong correlation was detected between plant communities and soil properties on north-facing sites; however, these sites may be affected by future climate change to the point that they become climatically similar to current conditions on south-facing sites. This can be expected to cause increased stress for many plant species that are restricted to north-facing slopes, leading them to disperse to higher elevations. These expected stresses on northfacing plant communities will likely lead to a reduction in overall vegetative cover. It is postulated that slope and elevation will become relatively more important factors in shaping these climate-altered communities, mirroring the stronger correlation between topography and soil properties on south-facing sites as compared to north-facing sites. Invasive species were more prevalent at higher elevations in the study area. As the interface between grassland and forest moves up in elevation with climate change, plant species currently occupying the upper grasslands may also move upward as they become stressed by lower moisture and higher temperatures in their current habitat. Invasive species will likely be the most successful at colonizing this newly opened habitat because they have characteristics that allow them to exploit environmental change. Some invasive species were more environmentally constrained than others, and by different variables. Yellow salsify, Kentucky bluegrass, and cheatgrass were the least constrained species, and are thus expected to persist more successfully under conditions of future change.  49  Continued monitoring of the sites established in this thesis is recommended to track the ongoing effects of climate change on topography, plant, and soil relations. As well, the further establishment of north- and south-facing sites in the IDFxh2a (upper grassland) would enhance the depth of knowledge and allow plant-soil-topography relationships to be assessed more thoroughly across all the grassland communities at Lac du Bois. This thesis has provided several ideas about how soil properties, plant communities, and topography are related in a southern interior BC grassland, and the implications future climate change has for these relationships. The concepts and ideas developed and presented can be of assistance to the management of these valuable natural areas.  50  References Aber, J.D. and J.M. Melillo. 1982. Nitrogen immobilization in decaying hardwood leaf litter as a function of initial nitrogen and lignin content. Canadian Journal of Botany 60: 2263-2269. Bailey, R.G. 1976. Ecoregions of the United States (map). Ogden, Utah: USDA Forest Service, Intermountain Region. 1:7,500,000. BC Parks. 2000. Lac du Bois Grasslands Park: management plan background document. Ministry of Environment, Lands and Parks, Kamloops, B.C. 118 pp. BCCA. 2011. BC Beef Cattle Industry. British Columbia Cattlemen's Association, Kamloops, BC. http://www.cattlemen.bc.ca (Accessed July 20, 2011.) Belnap, J. and S.L. Phillips. 2001. Soil biota in an ungrazed grassland: response to annual grass (Bromus tectorum) invasion. Ecological Applications 11(5):1261-1275. Borcard, D., F. Gillet, and P. Legendre. 2011. Numerical Ecology with R. Springer, New York, NY, USA. 306 pp. Bradley, B.A., D.M. Blumenthal, D.S. Wilcove, and L.H. Ziska. 2009. Predicting plant invasions in an era of global change. TREE 30: 1-9. Brady, N.C. and R.R. Weil. 1996. The Nature and Properties of Soils (Eleventh Edition). Prentice Hall, Upper Saddle River, NJ. 740 pp. Buringh, P. 1984. Organic carbon in soils of the world. P. 91-109 in The role of terrestrial vegetation in the global carbon cycle (G.M. Woodwell, ed.) Scientific Committee on Problems of the Environment Publication 23. John Wiley and Sons, Chichester, England.  51  Chong, G.W., Y. Otsuki, T.J. Stohlgren, D. Guenther, P. Evangelista, C. Villa, and A. Waters. 2006. Evaluating plant invasions from both habitat and species perspectives. Western North American Naturalist 66(1): 92-105. Clarke, K.R. 1993. Non-parametric multivariate analyses of changes in community structure. Australian Journal of Ecology 18: 117-143. Colautti, R.I. and H.J. MacIsaac. 2004. A neutral terminology to define ‘invasive’ species. Diversity and Distributions 10: 135-141. Daubenmire, R.F. 1959. A canopy-coverage method of vegetational analysis. Northwest Science 33: 43-66. Dukes, J.S. and H.A. Mooney. 1999. Does global change increase the success of biological invaders? TREE 14: 135-139. Elton, C.S. 1958. The ecology of invasions by animals and plants. Methuen, London. Floate, M.J.S. 1965. Distribution of organic matter and phosphorus fractions in a topographic sequence of soils in southern British Columbia. Canadian Journal of Soil Science 45: 323-329. Foster, B.L., V.H. Smith, T.L. Dickson and T. Hildebrand. 2002. Invasibility and compositional stability in a grassland community: relationships to diversity and extrinsic factors. Oikos 99: 300-307. Fraser, L.H. and C.N. Carlyle. 2011. Is spotted knapweed (Centaurea stoebe L.) patch size related to the effect on soil and vegetation properties? Plant Ecology 212: 975-983. Funk, J.L. and P.M. Vitousek. 2007. Resource-use efficiency and plant invasion in lowresource systems. Nature 446: 1079-1081. Gayton, D.V. 2003. British Columbia grasslands: Monitoring vegetation change. FORREX Series 7. Forest Research Extension Partnership, Kamloops, BC. 49 pp.  52  Gee, G.W. and Bauder, J.W. 1986. Particle-size analysis. p. 383-411. in A. Klute (ed.) Methods of soil analysis. Part 1. 2nd ed., Agronomy Monograph. 9. ASA-SSSA, Madison, WI. Grasslands Conservation Council (GCC). 2004. B.C. grasslands mapping project: a conservation risk assessment (Final Report). Grasslands Conservation Council of BC, Kamloops, B.C. 116 pp. Green, A., and A.L. van Ryswyk. 1982. Chernozems: their characterization and distribution. Pages 95-112 in A. Nicholson, A. McLean, and T. Baker, eds. Grassland ecology and classification. Symposium Proceedings. BC Ministry of Forests, Victoria, BC. 353 pp. Gregorio, A. di, and L.J.M. Jansen. 2000. Land Cover Classification System, LCCS. FAO. http://www.fao.org/DOCREP/003/X0596E/X0596E00.htm (Accessed September 30, 2011.) Grossman, D. H., D. Faber-Langendoen, A. S. Weakley, M. Anderson, P. Bourgeron, R. Crawford, K. Goodin, S. Landaal, K. Metzler, K. D. Patterson, M. Pyne, M. Reid, and L. Sneddon. 1998. International classification of ecological communities: terrestrial vegetation of the United States. Volume I. The National Vegetation Classification System: development, status, and applications. The Nature Conservancy, Arlington, Virginia, USA. 126 pp. Haeussler, S. 2011. Rethinking biogeoclimatic ecosystem classification for a changing world. Environmental Reviews 19: 254-277. Hamann, A. and T. Wang. 2006. Potential effects of climate change on ecosystem and tree species distribution in British Columbia. Ecology 87(11): 2773-2786. Hebda, R. 2007a. Biodiversity: geological history in British Columbia. Biodiversity BC Technical Subcommittee for the Report on the Status of Biodiversity in BC. http://www.biodiversitybc.org/assets/Default/BBC%20Biodiversity%20and%20Geo logical%20History.pdf (Accessed October 4, 2011).  53  Hebda, R. 2007b. Ancient and Future Grasslands: Climate change and insights from the fossil record and climate models. BC Grasslands 11: 14-16. Hendershot, W., and Lalande, H. 1993. Soil reaction and exchangeable acidity. Pages 141–145 in M.R. Carter (ed.) Soil sampling and methods of analysis. Canadian Soil Science Society, Lewis Publishers, Boca Raton, FL. Hobbs, R.J. and H.A. Mooney. 1991. Effects of rainfall variability and gopher disturbance on serpentine annual grassland dynamics. Ecology 72: 59-68. Hope, G.D., W.R. Mitchell, D.A. Lloyd, W.R. Erickson, W.L. Harper and B.M. Wikeem. 1991. Interior Douglas-fir zone. Pp. 153-166 in D.V. Meidinger and J. Pojar (eds.) Ecosystems of British Columbia. BC Ministry of Forests, Research Branch. Victoria, BC. Special Report Series 06. http://www.for.gov.bc.ca/hfd/pubs/Docs/Srs/Srs06.htm (Accessed August 10, 2011). Huntley, B. 1991. How plants respond to climate change: migration rates, individualism and the consequences for plant communities. Annals of Botany 67(supp1): 15-22. IPCC WG I. 2007. Climate change 2007: The physical science basis: summary for policymakers. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, Geneva, Switzerland. http://www.ipcc.ch (Accessed July 20, 2011.) IUCN. 1974. Biotic Provinces of the World. IUCN Occasional Paper No 9, Morges, Switzerland. 57 pp. Kimmins, J.P. and D.P. Lavender. 1992. Ecosystem-level changes that may be expected in a changing global climate: a British Columbia perspective. Environmental Toxicology and Chemistry 11:1061-1068.  54  Krajina, V.J. 1965. Biogeoclimatic zones and classification of British Columbia. in Krajina, V. J., ed. Ecology of western North America. Vancouver: University of British Columbia: 1-17. Krzic, M., S. Grand, and T. Ballard. 2007. Lab manual for introduction to soil science. APBI 200, Univ. of British Columbia, Vancouver, B.C. 90 pp. Lamb, E.G. 2008. Direct and indirect control of grassland community structure by litter, resources, and biomass. Ecology 89: 216-225. Levine, J.M. and C.M. D'Antonio. 1999. Elton revisited: a review of evidence linking diversity and invasibility. Oikos 87: 15-26. Levine, J.M. and D.J. Murrell. 2003. The community-level consequences of seed dispersal patterns. Annual Review of Ecology, Evolution, and Systematics 34: 549-574. Lloyd, D., K. Angove, G. Hope, and C. Thompson. 1990. A guide to site interpretation and identification for the Kamloops forest region. BC Ministry of Forests, Research Branch, Victoria, BC. Land Management Handbook Number 23. http://www.for.gov.bc.ca/hfd/pubs/docs/Lmh/Lmh23.htm (Accessed October 2, 2011.) Lloyd, D., M. Ryan, N. Brand, M. Doney, V. Larson, and J. MacDonald. 2005. Site classification for 52 biogeoclimatic units in the southern interior forest region. Draft. British Columbia Ministry of Forests and Range, Kamloops, BC. 91 pp. Looman, J. 1969. The fescue grasslands of western Canada. Vegetatio 19:128-145. McCune, B., and J.B. Grace. 2002. Analysis of Ecological Communities. MjM Software Design, Gleneden, OR, USA. 300 pp. McGill, W. B., and Figueiredo, C. T. 1993. Total nitrogen. Pages 201–211 in M.R. Carter (ed.) Soil sampling and methods of analysis. Canadian Soil Science Society, Lewis Publishers, Boca Raton, FL.  55  McLean, A. 1970. Plant communities of the Similkameen Valley, British Columbia, and their relationships to soils. Ecological Monographs 40: 403-423. McLean, A. and L. Marchand. 1968. Grassland ranges in the southern interior of British Columbia. Publication 1319. AAFC Research Station, Kamloops, BC. Mantel, N. 1967. The detection of disease clustering and a generalized regression approach. Cancer Research 27: 209-220. May, L. and L.K. Baldwin. 2011. Linking field based studies with greenhouse experiments: the impact of Centaurea stoebe (=C. maculosa) in British Columbia grasslands. Biological Invasions 13: 919-931. Naeem, S., J.M.H. Knops, D. Tilman, K.M. Howe, T. Kennedy and S. Gale. 2000. Plant diversity increases resistance to invasion in the absence of covarying extrinsic factors. Oikos 91: 97-108. Nelson, D. W., and Sommers, L. E. 1982. Total carbon, organic carbon, and organic matter. Pp. 539-579 in A. L. Page, R. H. Miller, and D. R. Keeney (ed.) Methods of soil analysis. Part 2, vol. 2. ASA-SSSA, Madison, WI. Newman, R., M. Krzic, and B. Wallace. 2011. Linking range health assessment methodology with science: rough fescue grasslands of British Columbia. BC Ministry of Forest and Range, Forest Science Program. Victoria, BC Technical Report 062. www.for.gov.bc.ca/hfd/pubs/Docs/Tr/Tr062.htm (Accessed April 12, 2011.) Nicholson, A., E. Hamilton, W.L. Harper, and B.M. Wikeem. 1991. Bunchgrass zone. Pp. 125137 in D.V. Meidinger and J. Pojar (eds.) Ecosystems of British Columbia. BC Ministry of Forests, Research Branch. Victoria, BC. Special Report Series 06. http://www.for.gov.bc.ca/hfd/pubs/Docs/Srs/Srs06.htm (Accessed August 10, 2011.)  56  Nimmo, J., and K.S. Perkins. 2002. Aggregate stability and size distribution. Pp. 317-328 in J.H. Dane and G.C. Topp (eds.) Methods of soil analysis, Part 4: Physical Methods. Soil Science Society of America, Madison, WI. Oksanen, J., F.G. Blanchet, R. Kindt, P. Legendre, R.B. O’Hara, G.L. Simpson, P. Solymos, H.H. Stevens, and H. Wagner. 2011. vegan: Community ecology package. R package version 1.17-10. http://CRAN.R-project.org/package=vegan (Accessed July 13, 2011). Olsen, S. R., Cole, C. V., Watanabe, F. S., and L.A. Dean. 1954. Estimation of available phosphorus in soils by extraction with sodium bicarbonate. USDA circ, 939: 1–19. Pawluk, S. 1982. Soils of grasslands at climax: their processes and dynamics. Pp. 113-132 in A. Nicholson, A. McLean, and T. Baker, eds. Grassland ecology and classification. Symp. Proc., B.C. Minist. of For., Victoria, B.C. 353 pp. Perzoff, Tania. 2009. Invasive, noxious and problem plants of British Columbia (September, 2009). In: Klinkenberg, Brian (Editor). 2009. E-Flora BC: Atlas of the plants of British Columbia [www.eflora.bc.ca]. Lab for Advanced Spatial Analysis, Department of Geography, University of British Columbia, Vancouver. Pojar, J., K. Klinka, and D. Meidinger. 1987. Biogeoclimatic ecosystem classification in British Columbia. Forest Ecology and Management 22: 119-154. Primack, R.B. and S.L. Miao. 1992. Dispersal can limit local plant distribution. Conservation Biology 6: 513-519. R Development Core Team. 2009. R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3- 900051-07-0, URL http://www.R-project.org. 2.9.1 (Accessed July 6, 2009.) Rimer, R.L. and R.D. Evans. 2006. Invasion of downy brome (Bromus tectorum L.) causes rapid changes in the nitrogen cycle. American Midland Naturalist 156: 252-258.  57  Rinella, M.J., M.L. Pokorny and R. Rekaya. 2007. Grassland invader responses to realistic changes in native species richness. Ecological Applications 17(6): 1824-1831. Rosenzweig, C. and D. Hillel. 2000. Soils and global climate change: challenges and opportunities. Soil Science 165(1): 47-56. Ryder, J.M. 1978. Geology, landforms and surficial materials in Valentine, K.W.G., Sprout, P.N., Baker, T.E., Lavkulich, L.M. (eds.), 1978, The soil landscapes of British Columbia. BC Ministry of Environment, Resource Analysis Branch. Victoria, BC. 197 pp. Seabloom, E.W., W.S. Harpole, O.J. Reichman, and D. Tilman. 2003. Invasion, competitive dominance, and resource use by exotic and native California grassland species. PNAS 100(23): 13384-13389. Shea, K. and P. Chesson. 2002. Community ecology theory as a framework for biological invasions. Trends in Ecology & Evolution 17(4): 170-176. Smith, V.C. and M.A. Bradford. Litter quality impacts on grassland litter decomposition are differently dependent on soil fauna across time. Applied Soil Ecology 24: 197-203. Soil Classification Working Group. 1998. The Canadian system of soil classification. 3rd ed. Agriculture and Agri-Food Canada. Publication 1646. National Resources Council of Canada, Ottawa, Ontario. 187 pp. Spittlehouse, D.L. 2008. Climate change, impacts, and adaptation scenarios: climate change and forest and range management in British Columbia. BC Ministry of Forest and Range, Research Branch, Victoria, BC. Technical Report 045. 38 pp. Suttle, K.B., M.S. Thomsen and M.E. Power. 2007. Species interactions reverse grassland responses to changing climate. Science 315: 640-643.  58  Tannas, S. 2009. Does fertilization, watering, and defoliation aid Kentucky bluegrass invasion in fescue grasslands. http://www.ualberta.ca/~sctannas/index.html. Accessed Oct. 2, 2011. Tisdale, E.M. 1947. The grasslands of the southern interior of British Columbia. Ecology 28(4): 346-382. UNESCO. 1973. International classification and mapping of vegetation. UNESCO, Paris. Urban, D., S. Goslee, K. Pierce, and T. Lookingbill. 2002. Extending community ecology to landscapes. Écoscience 9(2): 200-212. Valentine, K.W.G., and L.M. Lavkulich. 1978. The soil orders of British Columbia. Pp. 67-95 in K.W.G. Valentine, P.N. Sprout, T.E. Baker, and L.M. Lavkulich (eds.) The soil landscapes of British Columbia. BC Ministry of Environment, Resource Analysis Branch, Victoria, BC. 197 pp. van Ryswyk, A.L. and A. McLean. 1989. Geology and soils of grassland ranges, Kamloops, British Columbia. Rangelands 11:2 (65-67). Van Ryswyk, A.L., A. McLean, and L.S. Marchand. 1966. The climate, native vegetation, and soils of some grasslands at different elevations in British Columbia. Canadian Journal of Plant Science. 46: 35-50. Walther, G. 2010. Community and ecosystem responses to recent climate change. Philosophical Transactions of the Royal Society B 365: 2019-2024. Whittaker, R.H. 1967. Gradient analysis of vegetation. Biological Reviews 42: 207-264. Wikeem, B.M., A. McLean, A. Bawtree, and D. Quinton. 1993. An overview of the forage resource and beef production on crown land in British Columbia. Canadian Journal of Animal Science 73: 779-794.  59  Wikeem, B.M. and S. Wikeem. 2004. The grasslands of British Columbia. Grasslands Conservation Council of BC, Kamloops, BC. 479 pp. Wildung, R.E., T.R. Garland, and R.L. Buschbom. 1975. The interdependent effects of soil temperature and water content on soil respiration rate and plant root decomposition in arid grassland soils. Soil Biology and Biochemistry 7: 373-378. Young, G., A.M. Fenger, and H.A. Luttmerding. 1992. Soils of the Ashcroft Map Area. Report no. 26, BC Soil Survey. BC Environment, Integrated Management Branch, Victoria, BC. 233 pp. Zavaleta, E.S., M.R. Shaw, N.R. Chiariello, B.D. Thomas, E.E. Cleland, C.B. Field and H.A. Mooney. 2003. Grassland responses to three years of elevated temperature, CO2, precipitation, and N deposition. Ecological Monographs 73(4): 585-604.  60  Appendices  Appendix A Location coordinates (in decimal degrees) and topography of Lac du Bois rangeland study sites. Site codes are: LG=lower grassland, MG=middle grassland, UG=upper grassland, N=north-facing, SM=south-facing moderate slope, SS=south-facing steep slope. Last seven codes are for sites sampled in 2006. AGW=Agriculture Canada weather station, DpLk=Deep Lake, LDB Fert=Lac du Bois fertilization trial site, G=grazed (outside exclosure), UG=ungrazed (inside exclosure).  Site Code  Latitude  Longitude  Elevation (m)  Slope (°)  Aspect (°)  LGN1  50.7242  -120.4301  507  59  23  LGN2  50.7232  -120.3930  624  35  358  LGN3  50.7224  -120.3806  498  56  356  LGN4  50.7289  -120.4201  674  62  349  LGN5  50.7244  -120.4134  618  59  338  LGSM1  50.7284  -120.4349  579  22  166  LGSM2  50.7205  -120.4163  555  22  157  LGSM3  50.7170  -120.4123  532  28  158  LGSM4  50.7285  -120.5262  405  4  210  LGSM5  50.7405  -120.4030  668  21  220  LGSM6  50.7169  -120.4365  372  21  206  LGSS1  50.7320  -120.4394  631  48  170  LGSS2  50.7239  -120.4373  495  34  192  LGSS3  50.7228  -120.4212  579  48  155  61  Site Code  Latitude  Longitude  Elevation (m)  Slope (°)  Aspect (°)  LGSS4  50.7143  -120.3918  436  37  159  MGN1  50.7550  -120.4126  732  50  8  MGN2  50.7438  -120.4305  753  33  339  MGN3  50.7403  -120.4441  756  25  354  MGN4  50.7924  -120.3971  861  26  354  MGN5  50.7806  -120.4238  823  32  14  MGN6  50.8115  -120.3875  757  21  338  MGSM1  50.7557  -120.4129  742  20  174  MGSM2  50.7581  -120.3904  786  22  193  MGSM3  50.7553  -120.4059  738  13  198  MGSM4  50.7754  -120.3969  856  12  190  MGSS1  50.7554  -120.4133  755  29  187  MGSS2  50.7565  -120.4351  737  34  177  MGSS3  50.7750  -120.3927  778  30  166  MGSS4  50.7860  -120.4354  896  43  199  MGSS5  50.7307  -120.4136  715  44  194  UGN1  50.7877  -120.4373  899  20  335  AGW G  50.7866  -120.4489  919  13  237  AGW UG  50.7866  -120.4489  919  14  249  DpLk1 G  50.7930  -120.3802  903  24  44  DpLk1 UG  50.7930  -120.3802  903  34  54  LDB Fert 1 G  50.8023  -120.4311  982  11  76  LDB Fert 1 UG  50.8023  -120.4311  982  12  64  DpLk3 G  50.7920  -120.3778  872  37  62  62  Appendix B List of all species encountered in Lac du Bois sampling sites, ordered by Daubenmire frame frequency. Life cycle: A=annual, B=biennial, P=perennial; Growth form: F=forb, Fe=fern, G=graminoid, S=shrub, Sp=spikemoss, T=tree; Endemism: E=exotic, N=native; Weed status: A=abundant in disturbed areas, I=invasive, Nu=nuisance, Nx=noxious, *=included on BC’s Invasive Alien Plant Program list. Note that some species have a frame frequency and average % cover of zero because they were only found in the walkabout survey and were not quantified in any Daubenmire frames.  Species  Family  Life  Growth  Weed  cycle  form  Endemism  status  Site frequency / Frame frequency / Avg. % cover / 38  1520  frame  Pseudoroegneria spicata  Poaceae  P  F  N  37  1025.6  38.4  Festuca campestris  Poaceae  P  G  N  22  663  69.4  Poa secunda  Poaceae  P  G  N  32  658  8.9  Koeleria macrantha  Poaceae  P  G  N  29  417.2  8.2  Astragalus miser  Fabaceae  P  F  N  16  262  11.5  Achillea millefolium  Asteraceae  P  F  N  31  239  8.1  Poa pratensis  Poaceae  P  G  E  19  238  18.0  Artemisia tridentata  Asteraceae  P  S  N  26  223  40.9  Descurainia pinnata  Brassicaceae  A  F  N  19  186  3.0  Calochortus macrocarpus  Liliaceae  P  F  N  31  176  1.6  Collinsia parviflora  Scrophulariaceae  A  F  N  18  144.4  1.2  Arabis holboellii  Brassicaceae  B/P  F  N  27  124  1.3  Antennaria microphylla  Asteraceae  P  F  N  11  122.2  12.9  Antennaria dimorpha  Asteraceae  P  F  N  15  121  4.0  Antennaria sp.  Asteraceae  P  F  N  18  118  5.4  Castilleja thompsonii  Scrophulariaceae  P  F  N  28  116.2  5.6  I  63  Species  Family  Life  Growth  Weed  cycle  form  Endemism  status  Site frequency / Frame frequency / Avg. % cover / 38  1520  frame  15  116  16.1  27  112  3.6  Astragalus collinus  Fabaceae  P  F  N  Tragopogon dubius  Asteraceae  P  F  E  Myosotis stricta  Boraginaceae  A  F  E  13  98  1.3  Crepis atrobarba  Asteraceae  P  F  N  29  97.4  5.1  Hesperostipa comata  Poaceae  P  G  N  24  97.2  7.3  Achnatherum richardsonii  Poaceae  P  G  N  9  88.6  13.8  Vulpia occidentalis  Poaceae  A  F  N  14  85  1.7  Artemisia frigida  Asteraceae  P  F  N  21  84  6.6  Achnatherum nelsonii  Poaceae  P  G  N  12  82.4  7.4  Taraxacum officinale  Asteraceae  P  F  E  25  79.4  3.3  Centaurea stoebe  Asteraceae  B/P  F  E  6  78  18.9  Cerastium arvense  Caryophyllaceae  P  F  N  10  74.4  3.8  Lomatium macrocarpum  Apiaceae  P  F  N  31  73.2  5.0  Phlox gracilis  Polemoniaceae  A  F  N  6  71  1.1  Agoseris glauca  Asteraceae  P  F  N  9  66.6  12.2  Campanula rotundifolia  Campanulaceae  P  F  N  15  66.2  3.3  Erigeron filifolius  Asteraceae  P  F  N  15  60  6.0  Polygonum douglasii  Polygonaceae  A  F  N  12  57.6  4.1  Lithospermum ruderale  Boraginaceae  P  F  N  27  57.4  8.4  Erigeron corymbosus  Asteraceae  P  F  N  23  54  4.6  Bromus tectorum  Poaceae  A  G  E  17  52  7.4  Penstemon procerus  Scrophulariaceae  P  F  N  9  47  4.6  Balsamorhiza sagittata  Asteraceae  P  F  N  14  46.8  23.3  Heuchera cylindrica  Saxifragaceae  P  F  N  13  45  4.3  Zigadenus venenosus  Liliaceae  P  F  N  17  42.4  5.3  Nu/*  I/Nx/*  I/*  64  Species  Family  Life  Growth  Weed  cycle  form  Endemism  status  Site frequency / Frame frequency / Avg. % cover / 38  1520  frame  Galium boreale  Rubiaceae  P  F  N  11  39.6  9.4  Juncus balticus  Juncaceae  P  G  N  5  38  2.5  Draba verna  Brassicaceae  A  F  E  11  36  1.1  Allium cernuum  Liliaceae  P  F  N  14  34  4.2  Sporobolus cryptandrus  Poaceae  P  G  N  11  33  11.8  Androsace septentrionalis  Primulaceae  A  F  N  4  31  1.3  Rosa acicularis  Rosaceae  P  S  N  12  30.8  13.7  Linaria genistifolia  Scrophulariaceae  P  F  E  16  30  3.0  Erigeron flagellaris  Asteraceae  P  F  N  4  30  18.2  Alyssum desertorum  Brassicaceae  A  F  E  3  30  2.3  Rhinanthus minor  Scrophulariaceae  A  F  N  10  29.8  7.2  Linum lewisii  Linaceae  P  F  N  10  27  0.8  Bromus squarrosus  Poaceae  A  G  E  14  25  6.2  Carex petasata  Cyperaceae  P  G  N  13  24.4  5.0  Arenaria serpyllifolia  Caryophyllaceae  A  F  E  5  22.8  5.7  Eriogonum heracleoides  Polygonaceae  P  F  N  23  22.6  5.7  Opuntia fragilis  Cactaceae  P  F  N  15  21  5.7  Dodecatheon pulchellum  Primulaceae  P  F  N  5  21  3.1  Centaurea diffusa  Asteraceae  A/B/P  F  E  13  20  4.6  Erigeron pumilis  Asteraceae  P  F  N  10  19  3.8  Saxifraga integrifolia  Saxifragaceae  P  F  N  5  18  3.1  Ericameria nauseosa  Asteraceae  P  S  N  22  17  18.1  Comandra umbellata  Santalaceae  P  F  N  17  16  2.9  Antennaria umbrinella  Asteraceae  P  F  N  1  14  10.4  Geum triflorum  Rosaceae  P  F  N  18  10.8  8.2  A/*  I/Nx/*  65  Species  Family  Life  Growth  Weed  cycle  form  Endemism  status  Site frequency / Frame frequency / Avg. % cover / 38  1520  frame  Astragalus beckwithii  Fabaceae  P  F  N  5  9  3.3  Antennaria parvifolia  Asteraceae  P  F  N  1  9  12.0  Verbascum thapsus  Scrophulariaceae  B  F  E  6  8  9.3  Stellaria nitens  Caryophyllaceae  A  F  N  1  8  1.1  Aster campestris  Asteraceae  P  F  N  6  7.4  7.0  Poa compressa  Poaceae  P  G  E  4  7  1.9  Cirsium undulatum  Asteraceae  B/P  F  N  15  6  6.3  Poa juncifolia  Poaceae  P  G  N  5  6  6.8  Erigeron compositus  Asteraceae  P  F  N  11  5  1.2  Artemisia campestris  Asteraceae  B/P  F  N  6  5  14.2  Draba nemorosa  Brassicaceae  A  F  N  4  5  1.2  Ranunculus glaberrimus  Ranunculaceae  P  F  N  3  4.8  2.9  Tetradymia canescens  Asteraceae  P  S  N  10  4  5.0  Viola adunca  Violaceae  P  F  N  2  3.2  3.1  Medicago sativa  Fabaceae  P  F  E  16  3  2.3  Pseudotsuga menziesii  Pinaceae  P  T  N  6  3  68.0  Chenopodium sp.  Chenopodiaceae  A  F  N  4  3  1.0  Vicia americana  Fabaceae  P  F  N  2  3  11.7  Amelanchier alnifolia  Rosaceae  P  S/T  N  11  2.8  36.8  Agropyron cristatum  Poaceae  P  G  E  4  2.6  13.9  Bromus japonicus  Poaceae  A  G  E  1  2.4  10.0  Camelina microcarpa  Brassicaceae  A  F  E  4  2  3.5  Antennaria rosea  Asteraceae  P  F  N  3  2  6.0  Arnica fulgens  Asteraceae  P  F  N  3  2  3.0  Fragaria virginiana  Rosaceae  P  F  N  2  2  7.5  Nu/*  I  66  Species  Family  Life  Growth  Weed  cycle  form  Endemism  status  Site frequency / Frame frequency / Avg. % cover / 38  1520  frame  Potentilla glandulosa  Rosaceae  P  F  N  2  2  8.5  Fritillaria pudica  Liliaceae  P  F  N  15  1  1.0  Crepis modocensis  Asteraceae  P  F  N  6  1  4.0  Sisymbrium altissimum  Brassicaceae  A  F  E  3  1  1.0  Lotus denticulatus  Fabaceae  A  F  N  3  1  1.0  Erysimum inconspicuum  Brassicaceae  B/P  F  N  2  1  2.0  Potentilla argentea  Rosaceae  P  F  E  2  1  7.0  Cirsium sp.  Asteraceae  B/P  F  E  1  1  1.0  Microseris nutans  Asteraceae  P  F  N  1  1  2.0  Sedum lanceolatum  Crassulaceae  P  F  N  1  1  1.0  Achnatherum hymenoides  Poaceae  P  G  N  1  1  6.0  Populus tremuloides  Salicaceae  P  T  N  1  1  2.0  Gaillardia aristata  Asteraceae  P  F  N  11  0.8  35.0  Erigeron linearis  Asteraceae  P  F  N  4  0.8  2.5  Aster ericoides  Asteraceae  P  F  N  1  0.8  2.5  Heterotheca villosa  Asteraceae  P  F  N  1  0.8  3.8  Selaginella densa  Selaginellaceae  P  Sp  N  2  Delphinium nuttallianum  Ranunculaceae  P  F  N  7  Lactuca serriola  Asteraceae  A/B  F  E  5  Solidago sp.  Asteraceae  P  F  N  5  Juniperus scopulorum  Cupressaceae  P  T  N  5  Orobanche fasciculata  Orobanchaceae  P  F  N  3  Lomatium dissectum  Apiaceae  P  F  N  2  Filago arvensis  Asteraceae  A  F  E  2  Juniperus communis  Cupressaceae  P  S  N  2  67  Species  Family  Life  Growth  Weed  cycle  form  Endemism  status  Site frequency / Frame frequency / Avg. % cover / 38  Pinus ponderosa  Pinaceae  P  T  N  2  Potentilla pensylvanica  Rosaceae  P  F  N  2  Cichorium intybus  Asteraceae  P  F  E  Conyza canadensis  Asteraceae  A  F  E  1  Grindelia squarrosa  Asteraceae  B/P  F  N  1  Hieracium sp.  Asteraceae  P  F  N  1  Lappula occidentalis  Boraginaceae  A  F  N  1  Schoenocrambe linifolia  Brassicaceae  P  F  N  1  Sisymbrium loeselii  Brassicaceae  A  F  E  1  Symphoricarpos albus  Caprifoliaceae  P  S  N  1  Woodsia oregana  Dryopteridaceae  P  Fe  N  1  Astragalus agrestis  Fabaceae  P  F  N  1  Astragalus purshii  Fabaceae  P  F  N  1  Geranium viscosissimum  Geraniaceae  P  F  N  1  Phacelia hastata  Hydrophyllaceae  P  F  N  1  Epilobium angustifolium  Onagraceae  P  F  N  1  Bromus inermis  Poaceae  P  G  E  Festuca rubra  Poaceae  P  G  N  1  Collomia linearis  Polemoniaceae  A  F  N  1  Nu/A/*  I  1520  frame  1  1  68  Appendix C Means (±SD) of soil, topography, and diversity properties for the Lac du Bois rangeland cluster groups based on plant community composition.  Cluster Group Wheatgrass-Sage  Wheatgrass-Fescue  Fescue-Bluegrass  Bulk density (g / cm )  1.23 (0.11)  0.95 (0.16)  0.7 (0.13)  Aggregate stability (mean-wt. density)  0.97 (0.48)  1.37 (0.57)  2.76 (0.57)  Carbon (% by weight)  1.1 (0.26)  2.64 (1.25)  6.63 (1.49)  Nitrogen (% by weight)  0.1 (0.02)  0.23 (0.09)  0.54 (0.12)  529.9 (105.68)  712.82 (106.14)  892.73 (65.03)  30.8 (14.11)  35.47 (16.54)  22.18 (9.27)  0.97 (0.02)  0.52 (0.5)  0.26 (0.26)  219137.92 (23040.16)  203686.46 (43757.65)  195610.09 (49978.58)  Bryophyte (% cover)  3.04 (2.14)  6.09 (6.13)  12.78 (9.96)  Lichen (% cover)  25.1 (17.51)  28.77 (20.62)  2.18 (2.81)  Litter (% cover)  39.57 (10.14)  55.9 (16.76)  95.24 (4.18)  Exposed rock (% cover)  15.83 (20.7)  5.03 (9.31)  0.53 (1.00)  Bare soil (% cover)  17.59 (7.87)  9.01 (9.39)  0.93 (1.19)  Shannon diversity (H')  1.18 (0.15)  1.56 (0.45)  1.42 (0.68)  Shannon evenness  0.48 (0.06)  0.48 (0.11)  0.44 (0.2)  Simpson diversity (D)  0.42 (0.07)  0.39 (0.16)  0.46 (0.25)  (a) Environmental variables 3  Elevation (m) Slope (°) Aspect southness 2  Solar insolation (WH / m )  (b) Diversity statistics  69  Cluster Group Wheatgrass-Sage  Wheatgrass-Fescue  Fescue-Bluegrass  Richness (by plot data)  12.3 (3.2)  25.88 (6.65)  23.55 (4.5)  Total richness (including walkabout)  21 (8.47)  41.29 (7.88)  36 (6.47)  3.64 (3.88)  16.51 (10.33)  22.22 (14.70)  Grasses  52.60 (11.48)  61.59 (15.47)  90.88 (14.77)  Shrubs  17 (10.16)  3.94 (4.89)  1.12 (2.69)  0 (0)  0.15 (0.64)  0.23 (0.75)  73.25 (12.91)  82.19 (16.62)  114.45 (12.91)  Exotic richness  2.7 (2.67)  8 (3.62)  5.27 (1.9)  Exotic:Native species ratio  0.16 (0.14)  0.24 (0.1)  0.17 (0.05)  Invasive species (% cover)  0.8 (2.18)  1.42 (2.58)  12.64 (13.41)  (c) Vegetation by class (% cover) Forbs  Trees Total vegetation  (d) Exotic/Invasive statistics  70  

Cite

Citation Scheme:

        

Citations by CSL (citeproc-js)

Usage Statistics

Share

Embed

Customize your widget with the following options, then copy and paste the code below into the HTML of your page to embed this item in your website.
                        
                            <div id="ubcOpenCollectionsWidgetDisplay">
                            <script id="ubcOpenCollectionsWidget"
                            src="{[{embed.src}]}"
                            data-item="{[{embed.item}]}"
                            data-collection="{[{embed.collection}]}"
                            data-metadata="{[{embed.showMetadata}]}"
                            data-width="{[{embed.width}]}"
                            async >
                            </script>
                            </div>
                        
                    
IIIF logo Our image viewer uses the IIIF 2.0 standard. To load this item in other compatible viewers, use this url:
http://iiif.library.ubc.ca/presentation/dsp.24.1-0072402/manifest

Comment

Related Items