A vegetation index of biotic integrity for small-order streams in southwest Montana and a floristic quality assessment for westem Montana wetlands. Prepared for: Montana Department of Environmental Quality and U.S. Environmental Protection Agency By: W. M. Jones Montana Natural Heritage Program Natural Resource Information System Montana State Library August 2005 MONTANA Natural Heritage Pix^^ram A vegetation index of biotic integrity for small-order streams in southwest Montana and a floristic quality assessment for westem Montana wetlands. Prepared for: Montana Department of Environmental Quality and U.S. Environmental Protection Agency Agreement Number: 203097 By: W. M. Jones j^" MONTANA 'l^ Natuial Heritage ^t ^-^ MUSTAFA j'j/'dAA Un-NTAllA ^T^itate If jIV Natural Kesouice ^ Library ^^jjjP Inioraiation System © 2005 Montana Natural Heritage Program P.O. Box 201800 • 1515 East Sixth Avenue • Helena, MT 59620-1800 • 406-444-5354 This document should be cited as follows: Jones, W. M. 2005. A vegetation index of biotic integrity for small-order streams in southwestern Montana and a floristic quality assessment for western Montana wetlands. Report to the Montana Department of Environmental Quality and U.S. Environmental Protection Agency, Montana Natural Heritage Program, Helena, Montana. 29 pp. plus appendices. Abstract This study evaluated the relationship between grazing-related disturbances and vegetation in first- through third-order montane streams in southwestern Montana. Eight vegetation metrics (relative cover of native graminoids, relative cover of exotic species, relative cover of hydrophytes, cover- weighted floristic quality index, cover- weighted mean bank stability rating, absolute combined cover of seedling and young willows, and willow seedling density) were found to respond to grazing-related disturbances. These metrics were combined into a multimetric index, the vegetation index of biotic integrity (VIBI), which responded strongly to a grazing-associated disturbance gradient. VIBI scoring thresholds were established that differentiated among three condition classes: reference condition, moderately impaired, and severely impaired. The VIBI can be used as an evaluation tool to assess riparian area condition. Coefficients of conservatism, which form the basis for floristic quality assessments, were assigned by an expert panel for plant species likely to occur in western Montana wetlands. IV Acknowledgments This study was funded through a U.S. Environmental Protection Agency wetland protection grant administered by the Montana Department of Environmental Quality. My sincere thanks to all who assisted with this project: Lynda Saul, for her tireless efforts as the linchpin for wetland conservation at Montana DEQ; Randy Apfelbeck, Anna Noson, and Bryce Maxell, for their fruitful involvement with the wetland monitoring and assessment work group; Steve Cooper, for his review of riparian assessment methods; and Greg Kudray and Coburn Currier for editing and formatting the final version of this report. My highest thanks also to Peter Lesica, John Pierce, Steve Shelly, Mary Manning, Steve Cooper, and Scott Mincemoyer for their invaluable help in assigning coefficients of conservation to western Montana wetland plants. Table of Contents Introduction 1 Methods 3 Study Area 3 Site Selection 3 Data Collection 3 Human Disturbance Gradient 6 Multimetric Analysis 7 Whole Community Analysis 11 Spatial Autocorrelation Analysis 12 Results 13 Human Disturbance Gradient 13 Metrics 14 VIBI 15 Whole Community 20 Spatial Autocorrelation 20 Discussion 21 Recommendations for Future Improvements 22 Literature Cited 24 Appendix A. Coefficients of conservation for selected wetland plants that occur in western Montana. Appendix B. List and attributes of sampled plant species. Appendix C. Location and condition rating of sample reaches. List of Figures Figure 1. Location and functional condition classes of sample reaches 4 Figure 2. Schematic showing placement of and data collected for subsamples within sample reaches 5 Figure 3. Graphical representation of the relationship between PFC categories and the composite disturbance gradient 13 Figure 4. Discriminatory power of selected metrics and their relationship with a composite human disturbance gradient 16-17 Figure 5. Scatterplot showing the relationship between the vegetation index of biotic integrity (VIBI) and a composite human disturbance gradient 15 Figure 6. Reference condition site 18 Figure 7. Moderately impaired site 18 Figure 8. Severely impaired site 18 Figure 9. Tree diagram showing VIBI scoring thresholds associated with disturbance categories and scatterplot of the composite disturbance gradient and VIBI 19 Figure 10. Graphical representation of the NMS ordination of sample reaches 20 List of Tables Table 1. Age classes for woody shrub and deciduous tree species 5 Table 2. Coefficient of conservatism scoring criteria 8 Table 3. Contribution of individual disturbance factors to a composite disturbance measure extracted by principal components analysis 13 vi List of Tables (Continued) Table 4. Candidate metrics considered for inclusion in the VIBI 14 Table 5. Formulas used to score metrics 15 Table 6. Accuracy assessment of VIBI scoring thresholds with regard to disturbance classes 18 Table 7. Species indicative of reference, moderately disturbed, and severely disturbed sites 19 VII Introduction The list of economic and environmental benefits provided by wetlands and riparian areas is long. These benefits include groundwater recharge, filtration and storage of sediments, nutrients, and pollutants, floodwater storage and attenuation, and unique habitat values (Brinson et al. 1981, Keddy 2000). Consequently, the importance of wetlands and riparian areas is disproportionate to their physical extent on the landscape, especially in semiarid regions such as Montana (Finch and Ruggiero 1993, Patten 1998). Despite their importance to both humans and wildlife, an estimated 25% of Montana's wetlands have been lost in the past 200 years (Dahl 1990). To improve wetland conservation in Montana, the Montana Department of Environmental Quality (DEQ) is developing a comprehensive statewide wetland monitoring and assessment program, of which this present study is a part. This program will use a three-tiered approach to characterize the condition and extent of wetlands in Montana. DEQ will combine landscape-level remotely sensed data, rapid site-level assessments, and detailed site-level evaluations of biota to evaluate wetland condition and to identify anthropogenic stressors that limit that condition. The purpose of the present study was to identify attributes of the riparian vegetation community of small-order streams that responded predictably to human disturbance. Such attributes could then be used as indicators of wetland condition for detailed site assessments as well as for validating and calibrating rapid assessment methods. I used a multimetric approach to identify vegetation indicators. Multimetric analysis attempts to determine the status of a wetland or stream reach by directly measuring the condition of one or more of its biotic components (Danielson 2002). This method is based on defining a relatively homogenous study environment and measuring the response of target biota across a human disturbance gradient (Karr and Chu 1999). Ideally, successful metrics should be attributes of the biota that change predictably with increasing human disturbance, are sensitive to a range of biological stresses, discriminate between human- caused perturbations and natural variability, and are easy to measure and interpret (Karr and Chu 1999). Successful metrics can then be combined into a multimetric index that reflects a diverse biotic response to human-related stressors and is an integrative measure of the site's biological condition (Karr and Chu 1999, Teels and Adamus 2002). Biological assessments can be accurate and cost-effective tools to assess wetland and stream condition and to measure impairment (Karr and Chu 1999). Since biota integrate multiple physical and chemical parameters, directly measuring a biotic community's response to anthropogenic stressors can be the most effective means to evaluate the effect of those stressors on wetland condition and function (Danielson 2002). The utility of using biota to assess wetlands has been demonstrated for numerous taxa, including fish (Karr 1981, Hughes et al. 1998, Mebane et al. 2003), diatoms (Fore and Grafe 2002), benthic and terrestrial macroinvertebrates (Kimberling et al. 2001, Blocksom et al. 2002, Klemm et al. 2003), birds (Bryce et al. 2002), and vegetation (DeKeyser et al. 2003, Mack 2004, Ferreira et al. 2005). This approach has been shown to be effective for perennial and seasonal depressional wetlands and ephemeral and intermittent streams in Montana (Apfelbeck 2001, Jones 2004). This study was conducted in southwestern Montana where the primary human-related stressors are livestock grazing and agriculture. Livestock grazing can influence numerous physical parameters in riparian systems, including stream channel and bank geomorphology and stability (Kauffman et al. 1983b, Clary 1999, Clary and Kinney 2002), floodplain microchannel sinuosity and drainage density (Flenniken et al. 2001), and soil bulk density, pore space, infiltration, and potential nitrification and mineralization rates (Kauffman and Krueger 1984, Wheeler et al. 2002, Kauffman et al. 2004). By altering these physical parameters as well as by directly removing plant biomass, grazing can significantly affect riparian vegetation. Livestock grazing can decrease belowground biomass (Kauffman et al. 2004), decrease the abundance of woody vegetation, especially willows (Kauffman et al. 1983a, Schulz and Leininger 1990, Clary 1999, Brookshire et al. 1 2002, Thome et al. 2005), and increase the elevations, agricultural land uses and their abundance of weedy species, such as Kentucky associated hydrologic modifications become bluegrass {Poa pratensis L.) (Schulz and Leininger important stressors on riparian systems; however, 1990, Green and Kauffman 1995), possibly by this study was conducted on smaller order streams altering competitive interactions with native that were largely unaffected by agricultural graminoids (Martin and Chambers 2001). At lower disturbances. Methods Study Area The study area encompassed portions of Beaverhead and Madison Counties in southwest Montana (Figure 1). This area lies within the Northern Rocky Mountain and Montana Valley and Foothill Prairies Ecoregions (Woods et al. 1999) and is characterized by broad intermontane valleys interspersed with isolated mountain ranges. The geology is a complex mixture of predominately Tertiary and Cretaceous sedimentary rocks with localized intrusions of Tertiary volcanics, Mississippian limestone, Proterozoic quartzite, and Archaean gneiss and schist; Pleistocene glacial deposits are locally abundant at higher elevations (Ruppel et al. 1993, Ruppel 1999, Lonn et al. 2000, Skipp and Janecke 2004). The climate is semiarid and continental. The weather station at Lima, Montana, which is representative of lower elevation sample locations, has recorded mean temperatures ranging from 16.8°F in January to 61.3°F in July and mean precipitation of 12.43 inches annually (Western Regional Climate Center 2005). Site Selection Potential sample locations were limited to small- order streams that had been previously evaluated for functional status by the Bureau of Land Management (BLM) and U.S. Forest Service (USFS) using standardized riparian assessments. BLM assessments used the proper functioning condition (PFC) methodology, which combines qualitative evaluations of hydrology, vegetation, and erosion/deposition to evaluate a stream reach (Prichard et al. 1998). USFS assessments evaluated a stream reach's degree of departure from reference condition using quantitative hydro- geomorphological parameters (Bengeyfield 1999). The output of both evaluation methods is to assign a stream reach into one of three condition classes: functioning (or proper functioning condition), functioning at risk, and nonfunctioning. To encompass variability in the degree of human- related disturbance, potential sample reaches were stratified by condition class. Rated reaches were displayed in a geographic information system (ArcGIS 8.3, ESRI, Redlands, California), and 11 functioning, 9 functioning at risk, and 10 nonfunctioning reaches were selected. All 30 stream reaches were sampled from June to August 2004. Sample reaches were first- through third-order, low gradient streams ranging in elevation from 6,000 to 7,900 feet above sea level; most reaches would be categorized as "E" type streams under Rosgen's (1996) classification system. All sample reaches were on tributaries to the Beaverhead and Red Rock Rivers on lands managed by the BLM or USFS and supported varying levels of willow cover, predominately Geyer's willow {Salix geyeriana Anderss.), Booth's willow (5. boothii Dorn), and Drummond's willow (5. drummondiana Barratt ex Hook.). Dominant herbaceous species included beaked sedge {Carex utriculata Boott), water sedge (C aquatilis Wahlenb.), Baltic rush (Juncus balticus Willd.), bluejoint reedgrass {Calamagrostis canadensis (Michx.) Beauv.), and Kentucky bluegrass. Data Collection The sampling method used to collect species abundance and environmental data was modified from the techniques outlined in Winward (2000) and Coles-Ritchie et al. (2004) and was selected based in part on a review by Cooper (2004). The sample unit was a 100-m stream reach that was subsampled using two types of systematically placed sample frames: 0.1-m^ (0.2-m x 0.5-m) quadrats and 4-m^ (1.13-m radius) plots. Sample frames were placed along transects running perpendicular and parallel to the stream channel, such that an area of 100-m x 8-m was sampled along each side of the stream channel (Figure 2). Streambank sampling was conducted along the greenline, which is defined as the first perennial vegetation that forms a lineal grouping of community types on or near the channel edge and usually occurs at or slightly below bankfuU discharge (Winward 2000). Greenline sampling consisted of 20 quadrats placed at 5-m intervals Figure 1. Locations and functional condition classes of sample reaches. Condition classes are PFC (proper functioning condition), FAR (functioning at risk), and NF (nonfunctioning) ■=> 0.2-m X 0.5-m quadrat (abundance of herbaceous vegetation, bare ground, height above bankfull discharge) QJ 4-m^ circular plot (abundance of woody vegetation, pugging/hummocking density, bank stability, browse intensity) Figure 2. Schematic showing placement of and data collected for subsamples within sample reaches. and 10 plots placed at 10-m intervals, with groups of four quadrats and one plot being placed on alternating sides of the channel, with the long ends of quadrats placed parallel to the channel. Five transects were also placed perpendicular to the valley slope at an interval of 20 m on alternating sides of the channel. Three quadrats (located 2.5, 5.0, and 7.5 m from the greenline with long ends parallel to the transect) and one plot (located 5.0 m from the greenline) were sampled at each transect. Species abundances were recorded using the cover estimation method described by Daubenmire (1959). Six cover classes were used to record species abundances: 1 (<5% cover), 2 (5-25% cover), 3 (25-50% cover), 4 (50-75% cover), 5 (75- 95% cover), and 6 (>95% cover). Herbaceous vegetation was sampled in quadrats and woody vegetation was sampled in plots. For woody species, both total cover and cover by age class (Table 1) were estimated. Mean height for each age class was estimated to the nearest 0.1 m. The number of woody seedlings present in each plot was also recorded. Species nomenclature follows the PLANTS database (version 3.5), which is the national naming standard used by the federal government (Natural Resources Conservation Service 2004). Five potential indicators of grazing-related stressors were measured: (1) amount of bare ground, (2) number of hoof shears (pugs) present in each plot, (3) number and mean depth of hummocks present in each plot, (4) bank stability at greenline plots, and (5) browse intensity. Bare ground was measured as the number of quadrat corners that intersected bare mineral soil. Bank stability was evaluated with a 0. 15-m wide plot running from the scour line to either twice bankfull height or a flat depositional surface, whichever was lower. A bank was considered unstable if less than 50% of the plot was covered by perennial vegetation ground cover or roots, rocks greater than 0.15-m diameter, or logs greater than 0.1 -m Table 1. Age classes for woody shrub and deciduous tree species. Description Age Class^ Woody Shrubs Deciduous Trees seedling 1 stem <0.3 m tall young 2-10 stems 0.3-2 m tall mature >10 stems, >V2 alive > 2 m tall, >V2 alive decadent/dead >10 stems, 2 m tall, 6). The square root modifier was proposed by Wilhelm and Ladd (1988) to dampen the effects of species richness on the index. This diminishes disparities between high quality species-poor sites and lower Metrics based on wetland indicator status - Wetland indicator status is a reflection of a species' affinity for wetland habitats. Species are placed into one of five ordinal categories that represent the Table 2. Coefficient of conservatism scoring criteria (after Andreas et al. 2004). C Criteria Plants with a wide range of ecological tolerances; often opportunistic invaders of natural areas or native taxa that are typically part of a disturbed community. 1-2 Widespread taxa that occur in a variety of communities, including disturbed sites. 3-5 Plants with an intermediate range of ecological tolerances that typify a stable phase of a native community, but that persist under some disturbance. 6-8 Plants with a narrow range of ecological tolerances that typify stable, relatively undisturbed communities. 9-10 Plants with a narrow range of ecological tolerances that exhibit high fidelity to narrow habitat require- ments. 8 likelihood of its occurring in wetlands versus non- wetlands. These categories, scored one through five, are: 1 = obligate upland (species occur almost exclusively in uplands), 2 = facultative upland (species usually occur in non-wetlands), 3 = facultative (species equally likely to occur in wetlands or non-wetlands), 4 = facultative wetland (species usually occur in wetlands), and 5 = obligate wetland (species occur almost exclusively in wetlands). Indicator status values were obtained from the 1988 national list and 1993 Pacific Northwest supplement published by the U.S. Fish and Wildlife Service (Reed 1993). Indicator values for the Pacific Northwest (Region 9) were used. These lists only identified obligate upland species if they occurred in wetlands in another region. Species sampled in this study that did not occur on the lists were coded as obligate upland species. Three potential metrics were calculated from wetland indicator values: relative cover of hydrophytes (species with an indicator value of obligate or facultative wetland), relative cover of upland species (species with an indicator value of obligate or facultative upland), and the cover- weighted mean wetland indicator value, which is calculated as: cWI. =I(WI.. xa.) iZa.. where cWI. is the cover- weighted mean wetland indicator value for site /, WL. is the wetland •/' ij indicator value of species / at site j, and a., is the abundance of species / at site j. Relative cover of hydrophytes and cWI were expected to decline with increasing disturbance, while the relative cover of upland-associated species was expected to increase. Metrics based on bank stability rating - The last category of metrics were derived from the ability of species to stabilize streambanks either with deep binding root masses or other mechanical means (e.g., Abernathy and Rutherfurd 2001, Simon and Collison 2002). Ordinal stability ratings were assigned to species based on similar categorizations in Crowe and Clausnitzer (1997, Appendix D), Hansen et al. (1995, Appendix A-7), and the author's judgment. Ratings were scored as 1 = poor, 2 = fair, 3 = good, and 4 = excellent. Two potential metrics were calculated: relative cover of stabilizing species (species with stability ratings of good or excellent) and the cover- weighted mean bank stability rating, which was calculated as: cSR.=I(SR.. xa.) /Za.. where cSR. is the cover- weighted mean vegetation stability rating for site j, SR.. is the stability rating of species / at site j, and a., is the abundance of species / at site j. Only data from greenline transects were used to calculate bank stability metrics. Both metrics were expected to decrease with disturbance. Stability ratings for species are listed in Appendix B . Metric Evaluation and Selection A three-step selection process was used to evaluate candidate metrics for inclusion in the VIBI, similar to Blocksom et al. (2002). The three criteria were the ability of metrics to discriminate between least and most disturbed sites, the overall relationship between metrics and the composite disturbance gradient, and redundancy among metrics. To test discriminatory power, I identified least disturbed sites (disturbance score <25* percentile of disturbance index) and most disturbed sites (disturbance score >75* percentile of disturbance index). Percentiles were calculated in the R statistical package using the method recommended by Hyndman and Fan (1996). Box plots were used to examine metric distributions. Metrics were scored based on their ability to differentiate between the two disturbance categories using the methodology described by Barbour et al. (1996). Metrics that had no overlap of interquartile range (middle 50% of observations) were scored 3, those that had no overlap of median and interquartile range were scored 2, those that had an overlap of one median and interquartile range were scored 1, and those where both medians overlapped with interquartile ranges were scored 0. Candidate metrics with scores of 2 or 3 were retained for further evaluation. The overall relationship between metrics and disturbance was evaluated by examining scatterplots and Spearman rank correlation coefficients (r ). Metrics with either Ir I >0.5 or a strong curvilinear relationship were retained. Finally, to ensure that metrics would not be providing redundant information to the VIBI, I examined correlations among the remaining candidate metrics. I used the high threshold recommended by the U.S. Environmental Protection Agency (IrJ >0.9) to determine redundancy (USEPA 1998). Where two or more metrics were found to be redundant, the one with the greatest discriminatory power and greatest response to disturbance was retained. Metric Scoring Metrics are usually scored by assigning value ranges to discrete categories depending on their deviation from an expected reference condition (Karr 1981, Wilcox et al. 2002, DeKeyser et al. 2003, Mack 2004). A commonly used scheme is to assign reference condition sites a score of 5, sites that deviate somewhat from reference condition a score of 3, and sites that strongly deviate from reference condition a score of 1 (Karr and Chu 1999). However, others have suggested that scoring metrics along a continuous scale would be more accurate, less variable, and easier to interpret (Minns et al. 1994, Hughes et al. 1998, McCormick et al. 2001, Mebane et al. 2003). Blocksom (2003) found that continuous scoring improved the overall performance of the multimetric index when compared to discrete scoring methods. Before scoring metrics I first identified the 95* percentile value of each metric (5* percentile value of metrics that increased in response to disturbance), which I used as the best expected value to reduce the effect of outliers (Barbour et al. 1999). Metrics were scored by linear interpolation. Scores of metrics that decreased in response to disturbance were calculated by dividing the observed value by the 95* percentile value; scores of metrics that increased in response to disturbance were calculated by dividing the difference between the maximum and observed value by the difference between the maximum and 5* percentile value. Percentile values were rounded to the nearest percent for metrics measured in percent cover, to the nearest hundredth for seedling density, and to the nearest tenth for cover- weighted averages. Resulting scores were truncated to range between [0, 1]. Metrics with a curvilinear response to the disturbance gradient were log-transformed prior to scoring to improve linearity in their response to the composite disturbance gradient. Log transformations were chosen based on the Box- Cox power transformation constrained by the disturbance gradient. The Box-Cox parameter was estimated using the MASS package (Venables and Ripley 2002) for R software. VIBI Scoring and Evaluation VIBI scores were calculated by averaging scores of selected metrics and multiplying by 100. The VIBI therefore ranged from to 100 regardless of the number of metrics found to be interpretable. The strength of the relationship between the VIBI and the composite disturbance gradient was evaluated using ordinary least squares regression. Assumptions of linear regression (normal distribution, constant variance, and independence of errors) were examined graphically. One application of the VIBI is to use it as a validation tool to assess the accuracy of rapid assessments. The output of the rapid assessment is an ordinal rating of wetland condition. To provide a congruent VIBI scoring system, I wanted to determine how many condition classes the VIBI could accurately distinguish and to identify scoring thresholds for those categories. To determine the number of condition classes, I first categorized the composite disturbance gradient into ^ = 3 to 5 groups. I used the 25* and 75* percentiles to partition the disturbance gradient into three disturbance categories, the 25*, 50*, and 75* percentiles to partition it into four disturbance categories, and the 20*, 40*, 60*, and 80* percentiles to partition it into five disturbance categories. One-way analysis of variance with multiple comparisons was used to test whether mean VIBI scores were different among disturbance categories and whether means for individual disturbance categories were different from one another. Significance values were modified with a Bonferroni correction. Only partitions where all VIBI means were different were considered useful. Analysis of variance assumptions were evaluated as described previously. 10 VIBI scoring thresholds that best predicted membership to disturbance classes was identified using classification trees. Given a dataset with predefined groups, classification trees recursively partition that dataset into increasingly homogenous subsets with regard to the groups (Breiman et al. 1984, Urban 2002). At each partition, the tree algorithm identifies the scoring threshold for the predictor variable that best predicts group membership. This process continues until a minimum node size is met. Classification tree analysis was implemented using Therneau and Atkinson's (2005) rpart package for R software. Minimum node size to be split was set at 15. Tree overfitting was controlled with an iterative 10-fold cross-validation procedure. Classification accuracy was evaluated by comparing predicted to actual group membership. Indicator species analysis was used to identify species that were strongly associated with VIBI condition categories. Indicator species analysis examines the frequency of occurrence and abundance of species within groups and assigns a group indicator value based on the specificity and fidelity of a species to that group (Dufrene and Legendre 1997). Group indicator values range from (no indication of group membership) to 100 (perfect indication). The strength of association was tested using a Monte Carlo randomization procedure with 10,000 iterations. Species with indicator values >25 and P-values <0. 1 were reported. Indicator species analysis was performed using PC-ORD (McCune and Mefford 1999). Whole Community Analysis The vegetation metrics previously described represent the aggregated response of plant species with similar taxonomic or functional attributes to human disturbance. As a complement to the multimetric analysis, I also examined the simultaneous response of the entire vegetation community to human disturbance. Relationships among sample reaches in regard to the entire vegetation community were explored with nonmetric multidimensional scaling (NMS, Kruskal 1964, Mather 1976). NMS is an indirect ordination technique that attempts to describe underlying patterns of species composition by graphically summarizing complex relationships and displaying them in a few, usually two or three, dimensions (McCune and Grace 2002). NMS iteratively seeks the best representation of sample units in reduced space using an objective function, termed stress, that measures differences between ranked distances in the original multidimensional space and the reduced ordination space (Legendre and Legendre 1998). The global form of NMS was calculated using the Kulczynski distance measure (equivalent to the relativized form of the Bray- Curtis (= Steinhaus) distance measure). Dimensionality of the ordination was determined with PC-ORD 's autopilot mode using 40 runs with real data and 50 runs with randomized data. Dimensionality was chosen by selecting the highest number of dimensions that appreciably reduced stress and where the final stress was significantly lower than that for randomized data (McCune and Mefford 1999). The instability criterion to be achieved was set at 0.00001 after 500 iterations or within 50 continuous iterations. To reduce beta diversity (compositional heterogeneity among sample units (Whittaker 1972)) and improve the interpretability of results, species occurring in fewer than 5% of sample units were removed from the analysis. The Mantel test (Mantel 1967) was used to evaluate whether the whole vegetation community was significantly correlated with the composite disturbance gradient. The Mantel test calculates linear or rank correlations between distance matrices derived from the original data tables. For this test, the Kulczynski and Euclidean distance measures were used to calculate distances for the species composition and disturbance matrices, respectively. The standardized Mantel statistic, r^ which provides a measure of the strength of the correlation between the two matrices, was calculated on ranked distances and is equivalent to Spearman's rank correlation coefficient. Significance was tested by permutation with 10,000 iterations using the community ecology (vegan) package (Oksanen et al. 2005) for R software. 11 Spatial Autocorrelation Analysis Spatial autocorrelation can be broadly defined as a significant positive or negative correlation of the values of a variable as a function of distance (i.e., samples of a variable that are closer together in space having more similar values than those further away would be an example of positive spatial autocorrelation). Spatial autocorrelation is a very general phenomenon that operates at multiple scales for most ecological and environmental variables, and it is an important functional property of ecosystems (Legendre 1993). Autocorrelated data are problematic, however, because they violate an important assumption of many statistical tests, that observations of variables are independent from one another. The presence of positive autocorrelation between closely spaced observations distorts many tests and increases the likelihood of erroneous findings of statistical significance (Legendre and Legendre 1998). This has been observed for tests of normality (Dutilleul and Legendre 1992), analysis of variance (Legendre et al. 1990), and linear regression (Cliff and Ord 1981). However, Legendre et al. (2002) have shown that tests of significance for correlation and regression coefficients were valid unless both the response and predictor variables were spatially autocorrelated. I used two approaches to test for the presence of spatial autocorrelation. For environmental variables and derived vegetation variables (metrics), spatial autocorrelation was evaluated for each factor independently. Two statistics, Moran's / and Geary's c, were calculated using Rookcase software (Sawada 1999). These statistics are sensitive to departures from normality, and data were transformed as needed as previously described. Distances between sites were calculated from site coordinates projected in Euclidean space (Montana State Plane, 1983 North American Datum). Inter- site distances were divided into 10 classes and values for / and c were calculated for each class. The number of distance classes was chosen using Sturge's rule based on 30 samples and 435 pairwise comparisons (number of classes = 1 + 3.31og^Q(435) = 9.7) (Legendre and Legendre 1998). The significance of correlation coefficients was tested using a Monte Carlo randomization procedure with 10,000 iterations. Because the significance of coefficients was tested multiple times (once for each distance class), significance levels were adjusted with a Bonferroni correction. As the study area was relatively environmentally homogenous, second-order stationarity was assumed. Spatial structure of the entire vegetation community was examined with a multivariate Mantel correlogram. Using the method described by Legendre and Legendre (1998), based on Oden and Sokal (1986), standardized Mantel statistics were calculated for a multivariate species distance matrix (calculated with the Kulczynski distance measure) and model matrix based on inter- site distances. Mantel statistics were calculated for each distance class and significance values were calculated by Monte Carlo permutations with 9,999 iterations using PC-ORD. Because of multiple testing, significance values were corrected with a Bonferroni procedure. 12 Results Human Disturbance Gradient The composite disturbance gradient was calculated from a PC A of four variables: AUM, amount bare ground, bank stability, and browse intensity. The first principal component explained 58.8% of the variation in the data. It was considered interpretable as it explained more variation in the data than expected by chance. Subsequent principal components did not meet this criterion. The component was rescaled so that it ranged between [0, 1], with the least disturbed site scoring and the most disturbed site scoring 1, and was used to represent a composite human disturbance gradient for metric development. Table 3 shows the contributions of the original variables to the composite disturbance index. A PCA including road density was also run. It was rejected in favor of the four variable model because the addition of road density weakened the interpretability of the first principal component (component explained 46.9% of the variation, not much more than that expected by chance) while road density explained less than 1% of the variation of the component. Table 3. Contribution of individual disturbance factors to a composite disturbance measure extracted by principal components analysis. Variance Explained Factor (R') AUM bare ground bank stability browse intensity 0.223 0.346 0.252 0.179 Measures of pug and hummock density were not included in the composite human disturbance gradient. The relationship of these measures to grazing intensity appeared to be confounded by physical characteristics of the site, as the extent of pugging and hummocking is controlled to some extent by soil texture and geomorphology. Sites with finer texture soils and depositional surfaces at lower elevations relative to bankfuU discharge will likely be more susceptible to pugging and hummocking development. Although the relationship between pugging and hummocking and soil texture is only anecdotal for this dataset, there was a significant correlation between the elevation of the greenline relative to bankfuU discharge and hummock density (r^ = -0.42, P = 0.02) and mean hummock depth (r^ = -0.42, P = 0.02) and a weak correlation between greenline elevation and pug density (r^ = -0.34, P = 0.07). The composite disturbance gradient and PFC categories were positively associated, both for all sites (F^ 27 = 9.81, P = 0.0006) and BLM sites (F^ 2Q = 11.81, P = 0.0004). However, while composite disturbance scores were significantly different between functioning and functioning at risk categories (all sites, P = 0.003; BLM sites, P = 0.001) and between functioning and nonfunctioning categories (all sites, P = 0.002; BLM sites, P = 0.001), composite disturbance scores were not different between functioning at risk and nonfunctioning categories (all sites, P = 0.81; BLM sites, P = 0.93) (Figure 3, results from all sites analysis shown). PFC FAR NF Proper Functioning Condition Category Figure 3. Graphical representation of the relationship between PFC categories and the composite disturbance gradient. Points are disturbance gradient means within PFC categories; error bars are 95% confidence intervals. PFC categories are proper functioning condition (PFC), functioning at risk (FAR), and nonfunctioning (NF); higher disturbance gradient scores reflect greater disturbance. 13 Metrics Of the 27 candidate metrics evaluated, eight were selected for inclusion in the VIBI. Five metrics were removed for failing to discriminate between least and most disturbed sites, five were eliminated due to a poor relationship with the disturbance gradient, and nine were removed because of redundancies with the selected metrics. Groups of redundant metrics included relative cover of native perennials, exotic species, exotic grasses, and intolerant species; willow seedling density and cover of willow seedlings; cover of young willows and combined cover of young and seedling willows; cover- weighted mean C- values and cover- weighted FQAI; relative cover of hydrophytes and cover- weighted mean wetland indicator status; and relative cover of bank stabilizing species and cover- weighted mean bank stability rating. Table 4 shows the correlation of candidate metrics with the composite disturbance gradient, whether each metric was selected for inclusion in the VIBI or not, and the reason for removal of metrics not selected. Selected metrics were the relative cover of native graminoids, relative cover of exotic species, density of willow seedlings, combined cover of willow seedlings and young willows, cover- weighted FQAI, relative cover of hydrophytes, and cover- weighted mean bank stability rating. Table 4. Candidate metrics considered for inclusion in the VIBI, whether metrics were included and reason for removal if not selected, and metric response to composite disturbance gradient as measured by the Spearman rank correlation coefficient. Poor discriminatory power refers to the lack of difference in metric values between least and most disturbed sites; poor correlation with disturbance gradient refers to metrics with weak or no correlations with the composite disturbance gradient (r^ <0.5 for metrics with linear association; graphical evaluation for metrics with curvilinear association). Response to distur- Metric Selected/reason for removal bance gradient jv^) relative cover of native perennials relative cover of native graminoids t-^Vative cover of sedges rplntive cover of exotic species ?t,T^tive cover of exotic grasses relative cover of annuals/biennials willow seedling density absolute cover of willow seedlings absolute cover of young willows combined absolute cover of young and seedling willows absolute cover of willows willow age distribution Shannon diversity index Simpson diversity index mean C-value FQAI mean C-value (including exotic species) FQAI (including exotic species) mean cover-weighted C-value cover-weighted FQAI relative cover of disturbance tolerant species (C < 2) relative cover of disturbance intolerant species (C > poor correlation with disturbance gradi- 6) ent relative cover of hydrophytes selected relative cover of upland species redundant cover-weighted mean wetland indicator status redundant relative cover of bank stabilizing species redundant cover-weighted mean bank stability rating selected redundant -0.69 selected -0.59 poor correlation with disturbance gradi- ent -0.48 selected 0.70 redundant 0.56 selected 0.45 selected -0.50 redundant -0.52 redundant -0.39 selected -0.44 poor discriminatory power -0.13 poor correlation with disturbance gradi- ent -0.42 poor discriminatory power -0.15 poor discriminatory power -0.18 poor discriminatory power -0.08 poor correlation with disturbance gradi- ent -0.34 poor discriminatory power -0.27 poor correlation with disturbance gradi- ent -0.38 redundant -0.59 -0.59 redundant 0.65 -0.39 -0.60 0.32 -0.58 -0.54 -0.57 14 Formulas used to compute selected metrics and metric values for the 95* or 5* percentiles are shown in Table 5; Figure 4 (facing pages) displays the discriminatory power and relationships of selected metrics to the composite disturbance gradient. VIBI The VIBI showed a highly significant response to the composite disturbance gradient (VIBI = 85.08 - 47.14 X [disturbance score], F^ ^^ = 34.32, R2 = 0.55, P = 0.000003; Figure 5). However, Table 5. Formulas used to score metrics. Maximum and percentile values are rounded to nearest percent (relative cover metrics), hundredth (seedling density), or tenth (cover-weighted means), q^^^ and q^^^ refer to the 95* and 5^^ percentiles, respectively. Value Metric ctF Maximum 95 percentile 5 percentile Formula relative cover of native graminoids relative cover of exotic species relative cover of annuals/biennials^ willow seedling density (# / m^)*^ cover seedling+young willows'^ cover-weighted FQAI relative cover of hydrophytes cover-weighted mean bank stability rating 55 18 50 0.58 9 30.5 80 3.4 %ngram / qo.95 5 (max - % exotic) / (max - qo.05) (max - %ann) / (max - qo.05) sden / qo.95 %yngSalix / qo.95 cFQAI / qo.95 %hydro / qo.95 bank / qo.95 ^ values were transformed by logio(%ann -1- 1) prior to scoring ^ values were transformed by logio(sden + 0.01) + 2 prior to scoring ^values were transformed by logio(%yngSalix -1- 0.1) -1- 1 prior to scoring 99 > Disturbance Gradient Figure 5. Scatterplot showing the relationship between the vegetation index of biotic integrity (VIBI) and a composite human disturbance gradient. Disturbance gradient ranges from (least disturbed) to 1 (most disturbed); VIBI ranges from 100 (highest condition) to (lowest condition). Solid line represents the fitted linear relationship when the VIBI is regressed on the disturbance gradient using ordinary least squares. 15 >> CM 1 "D in O) " 7=5 o W ^ O ^ o • sO o^ ^ q _ 1 1 Least Most RelatiN^ Disturbance — I 1 1 r 0.2 0.4 0.6 0.8 1.0 Disturbance Gradient Figure 4. Discriminatory power of selected metrics and their relationship with a composite human disturbance gradient. Boxplots compare vegetation attribute values between least and most disturbed sites. Boxes show the range of the middle 50% each metric's distribution; thick lines within boxes represent median values. Vertical lines (whiskers) show metric values within 1.5 quartiles of the box; dots show more extreme values. Dashed lines in scatterplots show the fitted linear relationship when attributes are regressed on disturbance using ordinary least squares; solid lines show a locally weighted nonparametric smoother. 16 C^ LO _ q _ LO d q _ 1 o 1 1 1 • c^ ^ • • • LO _ ~t -• \ s^ • • -^ ^^.^^ • • • • ^C^-^^ • ^ • • • '^^^^ V. • LO 1 1 1 1 0.2 0.4 0.6 < O — I 1 1 r 0.2 0.4 0.6 O.E 2 « >r CO 1 stabili c ^ 1 T3 1 Q) 0) 1 Least Most Relative Disturbance 0.2 0.4 0.6 O.E Disturbance Gradient Figure 4. (Continued) 17 Table 6. Accuracy assessment of VIBI scoring thresholds with regard to disturbance classes. Predicted disturbance class Actual class Least disturbed Moderately disturbed Most disturbed Actual total Least disturbed Moderately disturbed Most disturbed 8 1 12 1 2 6 8 15 7 Predicted total 9 13 8 30 Overall accuracy 87% VIBI scores could reliably only differentiate three condition classes (F^ ^^ = 23.09, P = 0.0000001, all pairwise comparisons significant at the 0.001 level after Bonferroni correction). Overall, the VIBI was relatively robust in its ability to differentiate between these classes (Table 6). While analyses of variance of the four- and five-category partitions of the composite disturbance gradient were significant, VIBI means were not strongly differentiated among all disturbance categories. Sites with VIBI scores above 70 were considered to be reference condition (Figure 6), sites with scores from 48 to 70 were considered to be moderately impaired (Figure 7), and sites with scores below 48 were considered to be severely impaired (Figure 8). Figure 9 graphically displays VIBI scoring thresholds, condition classes, and misclassified cases. Species indicative of each condition class are shown in Table 7. Figure 7. Moderately impaired site; channel shows evidence of past incisement but is stable. *^^ ^ m *^?^: Figure 8. Severely impaired site; note incised and unstable banks. Figure 6. Reference condition site. 18 Least VIBM48.44 0.4 0.6 0.8 Disturbance Gradient Figure 9. (A) Tree diagram showing VIBI scoring thresholds associated with disturbance categories. Least = least disturbed, Moderate = moderately disturbed, Most = most disturbed. (B) Scatterplot of the composite disturbance gradient and VIBI. Symbols represent disturbance categores: • = least disturbed sites, ■ = moderately disturbed sites, ^= most disturbed sites. Colors represent VIBI classes: green = reference condition, blue = moderately impaired, red = severely impaired. Table 7. Species indicative of reference, moderately disturbed, and severely disturbed sites. Indicator value represents the strength of indication (0 = no indication, 100 = perfect indication). P- values were calculated with a Monte Carlo permutation test. Species with indicator values >25 and P <0.1 are reported. Indicator P- Scientific Name Common Name Condition Class Value value Agrostis scabra rough bentgrass reference 39.4 0.049 Car ex aquatilis water sedge reference 58.5 0.022 Car ex utriculata beaked sedge reference 50.3 0.044 Galium trifidum threepetal bedstraw reference 46.6 0.073 Juncus ensifolius swordleaf rush reference 67.4 0.004 Salix drummondiana Drummond's willow reference 50.6 0.008 Iris missouriensis Rocky Mountain iris moderately impaired 37.7 0.058 Maianthemum stellatum starry false hly of the valley moderately impaired 47.1 0.057 Mertensia ciliata tall fringed bluebells moderately impaired 39.0 0.063 Muhlenbergia richardsonis mat muhly moderately impaired 37.5 0.088 Potentilla gracilis slender cinquefoil moderately impaired 61.3 0.012 Pyrola asarifolia liverleaf wintergreen moderately impaired 34.5 0.061 Trifolium longipes long stalk clover moderately impaired 54.0 0.047 Car ex nebrascensis Nebraska sedge severely impaired 29.4 0.087 Cirsium vulgare bull thistle severely impaired 37.5 0.017 Poa pratensis Kentucky bluegrass severely impaired 46.8 0.011 Ranunculus abortivus littleleaf buttercup severely impaired 50.5 0.036 Rosa woodsii Woods' rose severely impaired 64.5 0.003 Trifolium rep ens white clover severely impaired 68.4 0.001 Triglochin palustre marsh arrowgrass severely impaired 28.7 0.089 19 Whole Community Spatial Autocorrelation Relationships among sample units are graphically displayed in Figure 10, which shows the results from the NMS ordination (three-dimensional solution, stress = 12.56, instability <0.00001, 83 iterations). The ordination diagram shows that vegetation is differentiated along the composite disturbance gradient, as evidenced by the relatively distinct groupings of VIBI condition classes. The vegetation community was significantly correlated with the disturbance gradient (r = 0.15, P = 0.02). No significant spatial autocorrelation was observed for either environmental or vegetation- derived variables at any distance class after Bonferroni corrections. Autocorrelation in the vegetation community trended from positive to negative over increasing distances; however, these results were nonsignificant after a Bonferroni correction. Condition Categories w Reference ■ IVIoderately Impaired A Severely Impaired Disturbance Gradient Axis 1 Figure 10. Graphical representation of the NMS ordination of sample reaches. Points represent aggregated species cover and composition data for each sample reach. Distance between points is proportional to dissimilarity between samples (i.e., samples with similar species composition are plotted closer together). Axis 1, which corresponds to the composite disturbance gradient, represents 20% of the variation in the data; Axis 2 accounts for 47% (total variation explained = 67%). The vector represents the strength of the relationship between Axis 1 and the composite disturbance gradient (R^ = 0.31). Condition categories refer to VIBI condition classes. 20 Discussion The goal of this study was to find attributes of the riparian vegetation community that responded predictably to human disturbance and could be used to assess site condition. Eight such attributes were identified and combined into a vegetation index of biotic integrity. Overall, this multimetric index demonstrated a robust response to grazing-related stressors, and VIBI scores could be used to classify a site into one of three disturbance categories with relatively high accuracy. Within the reference domain considered - small-order montane streams able to support woody vegetation - the VIBI appears to be a good indicator of site condition. This is consistent with other multimetric vegetation studies that have found plants to be good indicators of wetland and riparian condition (DeKeyser et al. 2003, Mack 2004, Jones 2004, Ferreira et al. 2005). Both a strength and complication of using vegetation as an indicator of site condition is the large number of species often involved. For example, 178 species of vascular plants were sampled in the course of this study, and the mean richness was 43 + 7 species per site. A strength of the multimetric approach is that species are grouped by the expected similarity of their response to disturbance or stress. This makes use of redundancies in species' responses within groups and can thereby reduce the noise often generated when the response of all species is considered simultaneously. This study made use of species groups based on functionality, taxonomy, and nativity. The utility of vegetation classifications based on common attributes, adaptations, or responses of species to environmental factors, has long been recognized (Raunkiaer 1934, Grime 1977, Grime 1988, Lavorel and Gamier 2002, Pausas and Lavorel 2003). Functional groups in particular have been shown to be an effective approach to evaluating vegetation response to grazing-related disturbances (Friedel et al. 1988, Mclntyre et al. 1995, Lavorel et al. 1997, Landsberg et al. 1999). In this study, the VIBI showed a much stronger response to the disturbance gradient than did the whole community analysis. A major output of this project was the extension of the floristic quality assessment methodology to western Montana wetlands. Although not strictly a functional classification, the concept of floristic quality, which is based on the fidelity of plant species to high-integrity habitats, can be used as a broadly integrative measure of site condition. It is especially pertinent for measuring human- associated stresses, as the tolerance of plant species to anthropogenic disturbance is an implicit criterion in the assignment of C- values. The utility of the floristic quality assessment index as a vegetation metric has been demonstrated in diverse wetland settings (Lopez and Fennessy 2002, DeKeyser et al. 2003, Cohen et al. 2004). The FQAI has been criticized for the subjective assignment of C- values. In a study of prairie potholes using C- values assigned by expert opinion for the Dakotas (Northern Great Plains Floristic Quality Assessment Panel 2001), Mushet et al. (2002) found that subjectively assigned C-values were good indicators of species response and gave comparable results to C-values that had been objectively derived. Although the C-values used in this study have not been independently verified, they are likely to be similarly robust. One surprising finding was the relatively poor performance of the floristic quality assessment index, at least as traditionally calculated. The FQAI is usually computed based on species presence/absence data, and this approach has been found to be a good indicator of site condition (Lopez and Fennessy 2002, Cohen et al. 2004). However, in this study, the species richness-based FQAI exhibited a weak correlation with disturbance. Including exotic species in the richness-based FQAI provided a marginal improvement. In contrast, the FQAI weighted by each species' relative cover was strongly correlated with the disturbance gradient. This is in contrast to Cohen et al. (2004) who found no improvement in FQAI performance when using frequency- weighted abundance values. The improvement in FQAI performance with cover- weighted values in this study may be due in part to the increased dominance of a relatively few exotic species with low C-values in disturbed sites. These species include Kentucky bluegrass, redtop (Agrostis gigantea Roth), white clover (Trifolium 21 repens L.), and common dandelion {Taraxacum officinale G.H. Weber ex Wiggers). Although in this study the FQAI was used as a component in a multimetric index, floristic quality assessments should have broader applicability. In assigning C-values, the expert panel was not limited to the species sampled in this study but considered all species likely to occur in western Montana wetlands (the species list was taken from Lesica and Husby (2001, Appendix A)). Thus, the FQAI and related measures of floristic quality can be tested and applied as a stand-alone indicator of site condition to all wetland types in western Montana, not just the limited subset considered here. Further testing should be done to compare the relative utility of the presence-absence and cover-weighted formulations of the FQAI. Recommendations for Future Improvements An important next step is to validate the VIBI and to expand its applicability. This study examined vegetation response to a single, albeit complex, stressor. The VIBI should be validated at additional environmentally similar sites where grazing is the primary human stressor. However, to be broadly applicable, the VIBI will need to be generalized so that it is responsive to other anthropogenic stressors, especially those that modify hydrology. Some applicability of the VIBI as formulated here should be expected, as one of the effects of overgrazing can be bank erosion and stream channel downcutting, which can affect hydrology and make a site "drier." Functionally, there may be some overlap between grazing- induced stresses on the vegetation community and other stressors that cause hydrologic alterations. Several of the metrics developed here, including willow seedling density, absolute cover of willow seedlings and young willows, and relative cover of hydrophytes, should also be responsive to hydrologic stressors. The site selection procedure used in this study could also be improved. Sites were initially selected based on proper functioning condition assessments. PFC categories, which were assumed to be indicative of general site condition. were used to establish a broad disturbance gradient for sampling purposes. However, PFC assessments may not adequately differentiate between moderately and highly disturbed sites, at least when grazing is the primary stressor. This is evidenced by the lack of difference in mean PCA- derived disturbance scores between the functioning at risk and nonfunctioning categories. This lack of association may reflect in part the different purposes of these measures of disturbance: the composite disturbance gradient was constructed by finding linear combinations of variables that were expected to measure different aspects of grazing- associated stresses, while the PFC is a more general method to evaluate site condition. Another aspect of the site selection process should be reconsidered: in defining the site selection criteria for this study, the sampling universe was restricted to sites able to support tall woody vegetation (i.e., willows). Site potential was verified by either previous BLM surveys, which characterized sites by Hansen et al.'s (1995) vegetation community classification or by review of U.S. Geological Survey digital orthophoto imagery. This was done to focus on the most typical stream reaches (which do support woody vegetation) and to reduce environmental heterogeneity by excluding forested, sagebrush, or herbaceous-dominated stream reaches (i.e., sedge meadows). Although reducing environmental heterogeneity is an important design consideration when developing multimetric indices (Teels and Adamus 2002), an unfortunate result of this stratification was the potential undersampling of extremely disturbed sites where grazing had completely removed woody cover. All the sites sampled in this study, even the most heavily disturbed, supported willow cover, although at heavily disturbed sites this cover was usually exclusively provided by mature or senescent willows. Another improvement would be to develop a model- or rule-based scoring method to measure a site's level of disturbance. The PCA-based method employed in this study had the benefit of providing a quantitative and objective measure of site disturbance, and it was a good first step to understand the relative importance of and interactions between the measured disturbance variables. However, a limitation to this approach is 22 that the specific results are idiosyncratic to the collected dataset. A next step would either be to model the composite disturbance gradient (e.g., generalize the results of the PCA by finding explanatory equations) or to develop a rule-based procedure. Lopez and Fennessy (2002) used a rule-based approach to describe wetland disturbance: wetlands were ordered into one of 24 categories based on buffer conditions and presence of hydrologic modifications. Ohio EPA used their rapid assessment method as a measure of site disturbance (Mack et al. 2000). (This last approach would be somewhat circular, as the VIBI is meant to be used to validate DEQ's rapid assessment.) Developing a more generalized disturbance measure will become more of an issue as the VIBFs reference domain broadens to include greater environmental and anthropogenic heterogeneity. A parallel issue is to limit disturbance factors to variables that are measurable at all sites. For example, three of the disturbance factors used here, amount bare ground, bank stability, and browse intensity, were measured on-site. The fourth, livestock use (AUM), was readily available only because sites were sampled on public land. Therefore, AUM is not likely to be easily generalized and should probably be removed from future studies. Finally, there is the question of improving the broader utility of the VIBI. As the VIBI is sufficiently validated (and possibly modified), it will become a useful tool to assess riparian area condition and will provide validation for rapid assessments. 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A. Nesser, J. Shelden, and S. H. Azevedo. 1999. Ecoregions of Montana. (1:1,500,000 map), U.S. Geological Survey, Reston, Virginia. 29 Appendix A. Coefficients of conservation for selected wet- land PLANTS THAT OCCUR IN WESTERN MONTANA Appendix A. Coefficients of conservation for selected wetland plants that occur in western Montana. Coefficients of conservatism were assigned to 747 plant species known to occur in wetlands in western Montana. Species were selected based on the species list in Lesica and Husby (2001, Appendix A). Coefficients for a few additional non-wetland species were assigned because they were sampled in the course of the study. Coefficients were determined by a panel of expert botanists. Panel members were Stephen Cooper (Vegetation Ecologist, Montana Natural Heritage Program), Marc Jones (Ecologist, Montana Natural Heritage Program), Peter Lesica (Botanical Consultant), Mary Manning (Vegetation Ecologist, U.S. Forest Service), Scott Mincemoyer (Botanist, Montana Natural Heritage Program), John Pierce (Botanical Consultant), and Steve Shelly (Regional Botanist, U.S. Forest Service). Coefficients for 345 species were assigned by the entire committee; coefficients for the remaining 402 species were assigned by Marc Jones, Peter Lesica, and John Pierce. Nomenclature follows the federal naming standard (Natural Resources Conservation Service 2004). For unfamiliar names, a partial synonymy can be found by consulting the PLANTS database at http://plants.usda.gov . Literature Cited Lesica, P. and P. Husby. 2001. Field guide to Montana's wetland vascular plants. Montana Wetlands Trust, Helena, Montana. Natural Resources Conservation Service. 2004. The PLANTS database, version 3.5. U.S. Department of Agriculture, Natural Resources Conservation Service, National Plant Data Center. Available at http://plants.usda.gov. Appendix A -1 Coefficients of conservatism (C) for 747 wetland plants species known to occur in western Montana. C Scientific Name Common Name 4 Acer glabrum Torr. 2 Acer negundo L. 6 Achnatherum nelsonii (Scribn.) Barkworth 5 Aconitum columbianum Nutt. 7 Actaea rubra (Ait.) Willd. 7 Adiantum aleuticum (Rupr.) Paris 5 Agoseris aurantiaca (Hook.) Greene 4 Agoseris glauca (Pursh) Raf. 3 Agrostis exarata Trin. 1 Agrostis gigantea Roth 8 Agrostis humilis Vasey 2 Agrostis scabra Willd. 6 Allium brevistylum S. Wats. 6 Allium schoenoprasum L. 6 Alnus incana (L.) Moench 6 A//2w^ viridis (Vill.) Lam. & DC. 4 Alopecurus aequalis Sobol. 6 Alopecurus alpinus Sm. 4 Alopecurus carolinianus Walt. 2 Alopecurus geniculatus L. Alopecurus pratensis L. Amaranthus blitoides S. Wats. 7 Amaranthus californicus (Moq.) S. Wats. 3 Ambrosia psilostachya DC. 1 Ambrosia trifida L. 10 Amerorchis rotundifolia (Banks ex Pursh) Hulten 2 Androsace filiformis Retz. 6 Anemone parviflora Michx. 5 Angelica arguta Nutt. 7 Angelica dawsonii S. Wats. 4 Angelica pinnata S. Wats. 3 Antennaria corymbosa E. Nels. 3 Antennaria microphylla Rydb. 4 Apocynum cannabinum L. 6 Aquilegia caerulea James 6 Aquilegia formosa Fisch. ex DC. Arenaria serpyllifolia L. 3 Argentina anserina (L.) Rydb. 5 Arnica amplexicaulis Nutt. 5 Arnica chamissonis Less. 7 Arnica longifolia D.C. Eat. 6 Arnica mollis Hook. 2 Artemisia biennis Willd. 4 Artemisia cana Pursh 7 Artemisia lindleyana Bess. 3 Artemisia ludoviciana Nutt. 5 Artemisia tridentata Nutt. ssp. tridentata 3 Artemisia tridentata Nutt. ssp. vaseyana (Rydb.) Beetle Rocky Mountain maple boxelder Columbia needlegrass Columbian monkshood red baneberry Aleutian maidenhair orange agoseris pale agoseris spike bentgrass redtop alpine bentgrass rough bentgrass shortstyle onion wild chives gray alder green alder shortawn foxtail boreal alopecurus Carolina foxtail water foxtail meadow foxtail mat amaranth California amaranth Cuman ragweed great ragweed roundleaf orchid filiform rockjasmine smallflowered anemone Lyall's angelica Dawson's angelica small-leaf angelica flat-top pussytoes littleleaf pussytoes Indianhemp Colorado blue columbine western columbine thymeleaf sandwort silverweed cinquefoil clasping arnica Chamisso arnica spearleaf arnica hairy arnica biennial wormwood silver sagebrush Columbia River wormwood white sagebrush basin big sagebrush mountain big sagebrush Appendix A -2 C Scientific Name Common Name 4 Artemisia tridentata Nutt. ssp. wyomingensis Beetle & Young Asclepias speciosa Torr. 3 Astragalus agrestis Dougl. ex G. Don 6 Astragalus americanus (Hook.) M.E. Jones 3 Astragalus canadensis L. 5 Athyrium filix-femina (L.) Roth AtriplexpatulaL. 5 Atriplex truncata (Torr. ex S. Wats.) Gray 7 Bacopa rotundifolia (Michx.) Wettst. 4 Barbarea orthoceras Ledeb. Barbarea vulgaris Ait. f. 4 Beckmannia syzigachne (Steud.) Fern. 7 Berula erecta (Huds.) Coville 8 Betula nana L. 5 Betula occidentalis Hook. 4 Bidens cernua L. 6 Bidens tripartita L. 6 Bidens vulgata Greene 6 Botrychium lanceolatum (Gmel.) Angstr. 4 Botrychium lunaria (L.) Sw. 8 Botrychium multifidum (Gmel.) Trev. 7 Botrychium pinnatum St. John 6 Botrychium simplex E. Hitchc. 7 Boykinia major Gray 6 Bromus ciliatus L. Bromus inermis Leyss. 5 Bromus marginatus Nees ex Steud. 5 Calamagrostis canadensis (Michx.) Beauv. 6 Calamagrostis stricta (Timm) Koel. 6 Callitriche hermaphroditica L. 3 Callitriche heterophylla Pursh 7 Caltha leptosepala DC. 6 Calypso bulbosa (L.) Oakes 6 Camassia quamash (Pursh) Greene 5 Camissonia subacaulis (Pursh) Raven 7 Campanula parryi Gray 3 Campanula rotundifolia L. 9 Campanula uniflora L. 6 Canadanthus modestus (Lindl.) Nesom 7 Cardamine breweri S. Wats. 3 Cardamine oligosperma Nutt. 3 Cardamine pensylvanica Muhl. ex Willd. 5 Carex amplifolia Boott 6 Carex aperta Boott 5 Carex aquatilis Wahlenb. 6 Carex arcta Boott 5 Carex atherodes Spreng. 4 Carex athrostachya Olney Wyoming big sagebrush showy milkweed purple milkvetch American milkvetch Canadian milkvetch common ladyfem spear saltbush wedgescale saltbush disk waterhyssop American yellowrocket garden yellowrocket American sloughgrass cutleaf waterparsnip dwarf birch water birch nodding beggartick threelobe beggarticks big devils beggartick lanceleaf grapefem common moonwort leathery grapefem northern moonwort little grapefem large boykinia fringed brome smooth brome mountain brome bluejoint slims tem reedgrass northern water- starwort twoheaded water- starwort white marsh marigold fairy slipper small camas diffuseflower evening-primrose Parry's bellflower bluebell bellflower arctic bellflower giant mountain aster Brewer's bittercress little western bittercress Pennsylvania bittercress bigleaf sedge Columbian sedge water sedge northem cluster sedge wheat sedge slenderbeak sedge Appendix A -3 c Scientific Name Common Name 1 Carex atratiformis Britt. scabrous black sedge 6 Carex aurea Nutt. golden sedge 7 C<3r^x /?^/?/?// Olney ex Fern. Bebb's sedge 4 Carex brevior (Dewey) Mackenzie shortbeak sedge 8 Carex brunnescens (Pers.) Poir. brownish sedge 8 Carex buxbaumii Wahlenb. Buxbaum's sedge 8 Carex canescens L. silvery sedge 8 Carex capillaris L. hairlike sedge 7 Carex capitata L. capitate sedge 9 Carex chordorrhiza Ehrh. ex L. f. creeping sedge 7 Carex comosa Boott longhair sedge 6 Carex crawei Dewey Crawe's sedge 7 Carex cusickii Mackenzie ex Piper & Beattie Cusick's sedge 6 Carex deweyana Schwein. Dewey sedge 8 Carex diandra Schrank lesser panicled sedge 6 Carex disperma Dewey softleaf sedge 8 Carex echinata Murr. star sedge 7 Carex flava L. yellow sedge 7 Carex foenea Willd. dryspike sedge 9 Carex gynocrates Wormsk. ex Drej. northern bog sedge 7 Carex heteroneura W. Boott different nerve sedge 5 Carex hystericina Muhl. ex Willd. bottlebrush sedge 7 Carex idahoa Bailey Idaho sedge 7 Carex illota Bailey sheep sedge 8 Carex interior Bailey inland sedge 9 Carex lachenalii Schkuhr twotipped sedge 9 Carex lacustris Willd. hairy sedge 5 Carex laeviconica Dewey smoothcone sedge 6 Carex laeviculmis Meinsh. smoothstem sedge 7 Carex lasiocarpa Ehrh. wooUyfruit sedge 5 Carex lenticularis Michx. lakeshore sedge 8 Carex leptalea Wahlenb. bristlystalked sedge 9 Carex limosa L. mud sedge 9 Carex livida (Wahlenb.) Willd. livid sedge 7 Carex luzulina Olney woodrush sedge 3 Carex mertensii Prescott ex Bong. Mertens' sedge 3 Carex microptera Mackenzie smallwing sedge 3 Carex nebrascensis Dewey Nebraska sedge 7 Carex nelsonii Mackenzie Nelson's sedge 7 Carex neurophora Mackenzie alpine nerve sedge 7 Carex nigricans C.A. Mey. black alpine sedge 8 Carex norvegica Retz. Norway sedge 7 Carex nova Bailey black sedge 7 Carex pachystachya Cham, ex Steud. chamisso sedge 5 Carex parry ana Dewey Parry's sedge 4 Carex pellita Muhl ex Willd. woolly sedge 7 Carex podocarpa R. Br. shortstalk sedge 7 Carex praeceptorium Mackenzie early sedge 4 Carex prae gracilis W. Boott clustered field sedge Appendix A -4 c Scientific Name Common Name 4 Carex praticola Rydb. meadow sedge 7 Carex pyrenaica Wahlenb. Pyrenean sedge 7 Carex sartwellii Dewey Sartwell's sedge 8 Carex saxatilis L. rock sedge 7 Carex scoparia Schkuhr ex Willd. broom sedge 8 Carex scopulorum Holm mountain sedge 8 Carex simulata Mackenzie analogue sedge 7 Carex spectabilis Dewey showy sedge 8 Carex sprengelii Dewey ex Spreng. Sprengel's sedge 4 Carex stipata Muhl. ex Willd. owlfruit sedge 8 Carex sychnocephala Carey manyhead sedge 10 Carex tenuiflora Wahlenb. sparseflower sedge 9 Carex torreyi Tuckerman Torrey's sedge 3 Carex utriculata Boott Northwest Territory sedge 5 Carex ve sic aria L. blister sedge 8 Carex viridula Michx. little green sedge 6 Carex vulpinoidea Michx. fox sedge 4 Castilleja miniata Dougl. ex Hook. giant red Indian paintbrush 3 Castilleja minor (Gray) Gray lesser Indian paintbrush 7 Castilleja occidentalis Torr. western Indian paintbrush 7 Castilleja rhexiifolia Rydb. splitleaf Indian paintbrush 7 Castilleja sulphurea Rydb. sulphur Indian paintbrush 3 Catabrosa aquatica (L.) Beauv. water whorlgrass 3 Ceratophyllum demersum L. coon's tail 1 Chamerion angustifolium (L.) Holub fireweed Chenopodium album L. lambsquarters 3 Chenopodium rubrum L. red goosefoot 4 Chrysosplenium tetrandrum (Lund ex Malmgr.) Th. Fries northern golden saxifrage 7 Cicuta bulbifera L. bulblet-bearing water hemlock 4 Cicuta douglasii (DC.) Coult. & Rose western water hemlock 3 Cicuta maculata L. spotted water hemlock 5 Circaea alpina L. small enchanter's nightshade Cirsium arvense (L.) Scop. Canada thistle 5 Cirsium scariosum Nutt. meadow thistle 4 Cirsium undulatum (Nutt.) Spreng. wavy leaf thistle Cirsium vulgare (Savi) Ten. bull thistle 2 Claytonia perfoliata Donn ex Willd. miner's lettuce 5 Claytonia sibirica L. Siberian springbeauty 6 Coeloglossum viride (L.) Hartman longbract frog orchid 3 Collomia linearis Nutt. tiny trumpet 7 Comarum palustre L. purple marshlocks Conium maculatum L. poison hemlock 8 Corallorrhiza trifida Chatelain yellow coralroot 9 Corallorrhiza wisteriana Conrad spring coralroot 6 Coreopsis tinctoria Nutt. golden tickseed 7 Cornus canadensis L. bunchberry dogwood 5 Cornus sericea L. redosier dogwood 5 Crataegus douglasii Lindl. black hawthorn 5 Cr^p/^- runcinata (James) Torr. & Gray fiddleleaf hawksbeard Appendix A -5 C Scientific Name Common Name 10 10 5 3 7 6 7 5 4 9 5 7 5 7 9 5 6 6 4 4 7 9 8 7 4 5 5 6 5 5 3 5 7 2 6 3 4 6 6 7 Cynoglossum officinale L. Cyperus schweinitzii Torr. Cypripedium fasciculatum Kellogg ex S. Wats. Cypripedium parviflorum Salisb. Cypripedium passerinum Richards. Cystopteris montana (Lam.) Bemh. ex Desv. Danthonia intermedia Vasey Dasiphora floribunda (Pursh) Kartesz, comb. nov. ined. Delphinium depauperatum Nutt. Delphinium glaucum S. Wats. Deschampsia caespitosa (L.) Beauv. Deschampsia danthonioides (Trin.) Munro Deschampsia elongata (Hook.) Munro Dichanthelium acuminatum (Sw.) Gould & C.A. Clark var. fasciculatum (Torr.) Freckmann Distichlis spicata (L.) Greene Dodecatheon jeffreyi Van Houtte Dodecatheon pulchellum (Raf.) Merr. Draba aurea Vahl ex Hornem. Dryopteris cristata (L.) Gray Echinochloa muricata (Beauv.) Fern. Echinocystis lobata (Michx.) Torr. & Gray Elaeagnus angustifolia L. Elatine californica Gray Elatine rubella Rydb. Eleocharis acicularis (L.) Roemer & J.A. Schultes Eleocharis palustris (L.) Roemer & J.A. Schultes Eleocharis quinqueflora (F.X. Hartmann) Schwarz Eleocharis rostellata (Torr.) Torr. Elodea bifoliata St. John Elodea canadensis Michx. Elodea nuttallii (Planch.) St. John Elymus canadensis L. Elymus glaucus Buckl. Elymus repens (L.) Gould Elymus submuticus (Hook.) Smyth & Smyth Elymus trachycaulus (Link) Gould ex Shinners Epilobium anagallidifolium Lam. Epilobium ciliatum Raf. Epilobium glaberrimum Barbey Epilobium palustre L. Equisetum arvense L. Equisetum fluviatile L. Equisetum hyemale L. Equisetum laevigatum A. Braun Equisetum palustre L. Equisetum pratense Ehrh. Equisetum scirpoides Michx. gypsyflower Schweinitz's flatsedge clustered lady's slipper lesser yellow lady's slipper sparrowegg lady's slipper mountain bladderfern timber oatgrass shrubby cinquefoil slim larkspur Sierra larkspur tufted hairgrass annual hairgrass slender hairgrass western panicgrass inland saltgrass Sierrra shootingstar darkthroat shootingstar golden draba crested woodfem rough barnyardgrass wild cucumber Russian olive California waterwort southwestern waterwort needle spikerush common spikerush fewflower spikerush beaked spikerush twoleaf waterweed Canadian waterweed western waterweed Canada wildrye blue wildrye quackgrass Virginia wildrye slender wheatgrass pimpernel willowherb fringed willowherb glaucus willowherb marsh willowherb field horsetail water horsetail scouringrush horsetail smooth horsetail marsh horsetail meadow horsetail dwarf scouringrush Appendix A -6 C Scientific Name Common Name 5 Equisetum variegatum Schleich. ex F. Weber & D.M.H. Mohr 4 Eragrostis hypnoides (Lam.) B.S.P. 4 Eragrostis pectinacea (Michx.) Nees ex Steud. 2 Ericameria nauseosa (Pallas ex Pursh) Nesom & Baird 5 Erigeron acris L. 6 Erigeron coulteri Porter 3 Erigeron flagellaris Gray 6 Erigeron gracilis Rydb. 8 Erigeron humilis Graham 4 Erigeron lonchophyllus Hook. 7 Erigeron peregrinus (Banks ex Pursh) Greene 3 Erigeron philadelphicus L. 10 Eriophorum scheuchzeri Hoppe 5 Eupatorium maculatum L. 7 Euthamia graminifolia (L.) Nutt. 7 Euthamia occidentalis Nutt. 4 Festuca idahoensis Elmer 1 Festuca rubra L. 7 Festuca subulata Trin. 3 Fragaria virginiana Duchesne 4 Galium boreale L. 2 Galium mexicanum Kunth 5 Galium palustre L. 6 Galium trifidum L. 6 Galium triflorum Michx. 8 Gaultheria humifusa (Graham) Rydb. 8 Gaultheria ovatifolia Gray 6 Gentiana affinis Griseb. 9 Gentiana algida Pallas 7 Gentiana calycosa Griseb. 10 Gentiana glauca Pallas 6 Gentiana prostrata Haenke 3 Gentianella amarella (L.) Boemer 10 Gentianella propinqua (Richards.) J. Gillett 6 Gentianella tenella (Rottb.) Boerner 9 Gentianopsis simplex (Gray) litis 8 Gentianopsis thermalis (Kuntze) litis 5 Geranium richardsonii Fisch. & Trautv. 4 Geranium viscossisimum Fisch. & Trautv. 6 Geum aleppicum Jacq. 5 Geum macrophyllum Willd. 7 Geum rivale L. 5 Glaux maritima L. 6 Glyceria borealis (Nash) Batchelder 7 Glyceria grandis S. Wats. 6 Glyceria striata (Lam.) A.S. Hitchc. 3 Glycyrrhiza lepidota Pursh 3 Gnaphalium palustre Nutt. variegated scouringrush teal lovegrass tufted lovegrass rubber rabbitbrush bitter fleabane large mountain fleabane trailing fleabane quill fleabane arctic alpine fleabane shortray fleabane subalpine fleabane Philadelphia fleabane white cottongrass spotted joepyeweed flat-top goldentop western goldentop Idaho fescue red fescue bearded fescue Virginia strawberry northern bedstraw Mexican bedstraw common marsh bedstraw threepetal bedstraw fragrant bedstraw alpine spicywintergreen western teaberry pleated gentian whitish gentian Rainier pleated gentian pale gentian pygmy gentian autumn dwarf gentian fourpart dwarf gentian Dane's dwarf gentian oneflower fringed gentian Rocky Mountain fringed gentian Richardson's geranium Sticky geranium yellow avens largeleaf avens purple avens sea milkwort small floating mannagrass American mannagrass fowl mannagrass American licorice western marsh cudweed Appendix A -7 c Scientific Name Common Name 8 Gratiola ebracteata Benth. ex A. DC. bractless hedgehyssop 8 Gratiola neglecta Torr. clammy hedgehyssop 2 Grindelia howellii Steyermark Howell's gumweed 2 Grindelia squarrosa (Pursh) Dunal curlycup gumweed 7 Gymnocarpium dryopteris (L.) Newman western oakfern 6 Helenium autumnale L. common sneezeweed 7 Helianthus nuttallii Torr. & Gray Nuttall's sunflower 5 Heliotropium curassavicum L. salt heliotrope 5 Heracleum maximum Bartr. common cowparsnip 9 Hesperochiron pumilus (Dougl. ex Griseb.) Porter dwarf hesperochiron 8 Hierochloe hirta (Schrank) Borbas northern sweetgrass 6 Hippuris vulgaris L. common mare's-tail 5 Hordeum brachyantherum Nevski meadow barley 2 Hordeum jubatum L. foxtail barley 9 Howellia aquatilis Gray water howellia 8 Hypericum majus (Gray) Britt. large St. Johnswort 7 Hypericum scouleri Hook. Scouler's St. Johnswort 3 Impatiens ecalcarata Blank. spurless touch-me-not 2 Iris missouriensis Nutt. Rocky Mountain iris Iris pseudacorus L. paleyellow iris 7 Isoetes bolanderi Engelm. Bolander's quillwort 8 Isoetes howellii Engelm. Howell's quillwort 3 Iva axillaris Pursh povertyweed 3 Iva xanthifolia Nutt. giant sumpweed 5 Juncus acuminatus Michx. tapertip rush 9 Juncus albescens (Lange) Fern. northern white rush 7 Juncus alpinoarticulatus Chaix northern green rush 7 Juncus articulatus L. jointleaf rush 3 Juncus balticus Willd. Baltic rush 9 Juncus biglumis L. twoflowered rush 1 Juncus bufonius L. toad rush 9 Juncus castaneus Sm. chestnut rush 2 Juncus compressus Jacq. roundfruit rush 2 Juncus confusus Coville Colorado rush 7 Juncus drummondii E. Mey. Drummond's rush 6 Juncus effusus L. common rush 4 Juncus ensifolius Wikstr. swordleaf rush 7 Juncus filiformis L. thread rush 6 Juncus hallii Engelm. Hall's rush 5 Juncus longistylis Torr. longstyle rush 7 Juncus mertensianus Bong. Mertens' rush 6 Juncus nevadensis S. Wats. Sierra rush 8 Juncus nevadensis Watson Sierra rush 5 Juncus nodosus L. knotted rush 7 Juncus parryi Engelm. Parry's rush 3 Juncus tenuis Willd. poverty rush 5 Juncus torreyi Coville Torrey's rush 6 Juncus tracyi Rydb. Tracy's rush 8 Juncus triglumis L. threehuUed rush Appendix A -8 C Scientific Name Common Name 7 9 3 4 7 8 2 5 5 6 7 7 9 4 9 7 8 7 7 5 6 8 5 5 6 7 8 9 6 10 7 8 6 9 4 4 4 6 7 Kobresia myosuroides (Vill.) Fiori Kobresia simpliciuscula (Wahlenb.) Mackenzie Kochia scoparia (L.) Schrad. Lactuca biennis (Moench) Fern. Lactuca tatarica (L.) C.A. Mey. Ledum glandulosum Nutt. Leersia oryzoides (L.) Sw. Lemna minor L. Lemna trisulca L. Leptarrhena pyrolifolia (D. Don) R. Br. ex Ser. Leptochloafusca (L.) Kunth ssp.fascicularis (Lam.) N. Snow Leymus cinereus (Scribn. & Merr.) A. Love Ligusticum canbyi Coult. & Rose Ligusticum tenuifolium S. Wats. Ligusticum verticillatum (Hook.) Coult. & Rose ex Rose Lilium philadelphicum L. Limosella aquatica L. Listera borealis Morong Listera caurina Piper Listera convallarioides (Sw.) Nutt. ex Ell. Listera cordata (L.) R. Br. ex Ait. f. Lloydia serotina (L.) Reichenb. Lobelia kalmii L. Lolium prat ens e (Huds.) S.J. Darbyshire Lomatogonium rotatum (L.) Fries ex Fern. Lonicera caerulea L. Lonicera involucrata Banks ex Spreng. Lotus corniculatus L. Lupinus aridus Dougl. Lupinus polyphyllus Lindl. Luzula parviflora (Ehrh.) Desv. Luzula piperi (Coville) M.E. Jones Lycopodium alpinum L. Lycopodium annotinum L. Lycopodium clavatum L. Lycopodium complanatum L. Lycopus americanus Muhl. ex W. Bart. Lycopus asper Greene Lycopus uniflorus Michx. Lysichiton americanus Hulten & St. John Lysimachia ciliata L. Lysimachia thyrsiflora L. Lythrum salicaria L. Maianthemum racemosum (L.) Link Maianthemum stellatum (L.) Link Marsilea vestita Hook. & Grev. Melampyrum lineare Desr. Melica spectabilis Scribn. Bellardi bog sedge simple bog sedge Mexican-fireweed tall blue lettuce blue lettuce western Labrador tea rice cutgrass common duckweed star duckweed fireleaf leptarrhena bearded sprangletop basin wildrye Canby's licorice-root Idaho licorice-root northern licorice-root wood lily water mudwort northern twayblade northwestern twayblade broadlipped twayblade heartleaf twayblade common alplily Ontario lobelia meadow ryegrass marsh felwort sweetberry honeysuckle twinberry honeysuckle birdfoot deervetch desert lupine bigleaf lupine smallflowered woodrush Piper's woodrush alpine clubmoss stiff clubmoss running clubmoss groundcedar American water horehound rough bugleweed northern bugleweed American skunkcabbage fringed loosestrife tufted loosestrife purple loosestrife feathery false lily of the vally starry false lily of the vally hairy waterclover narrowleaf cowwheat purple oniongrass Appendix A -9 C Scientific Name Common Name 3 Mentha arvensis L. Mentha spicata L. 6 Mertensia ciliata (James ex Torr.) G. Don 7 Mertensia paniculata (Ait.) G. Don 3 Mimulus breviflorus Piper 3 Mimulus floribundus Lindl. 5 Mimulus guttatus DC. 7 Mimulus lewisii Pursh 3 Mimulus moschatus Dougl. ex Lindl. 10 Mimulus primuloides Benth. 7 Mimulus tilingii Regel 5 Minuartia rubella (Wahlenb.) Hiern. 7 Mitella breweri Gray 8 Mitella nuda L. 7 Mitella pentandra Hook. 6 Mitella stauropetala Piper 5 Moehringia lateriflora (L.) Fenzl Mollugo verticillata L. 8 Moneses uniflora (L.) Gray Monolepis nuttalliana (J.A. Schultes) Greene 4 Montia chamissoi (Ledeb. ex Spreng.) Greene 2 Montia dichotoma (Nutt.) T.J. Howell 2 Montia parvifolia (Moc. ex DC.) Greene 7 Muhlenbergia asperifolia (Nees & Meyen ex Trin.) Parodi 4 Muhlenbergia filiformis (Thurb. ex S. Wats.) Rydb. 8 Muhlenbergia glomerata (Willd.) Trin. 2 Muhlenbergia minutissima (Steud.) Swallen 4 Muhlenbergia richardsonis (Trin.) Rydb. Myosotis arvensis (L.) Hill 7 Myosotis asiatica (Vesterg.) Schischkin & Sergievskaja 4 Myosotis laxa Lehm. Myosotis scorpioides L. 4 Myosurus apetalus C. Gay 4 Myosurus minimus L. 4 Myriophyllum verticillatum L. 7 Najas flexilis (Willd.) Rostk. & Schmidt 6 Navarretia intertexta (Benth.) Hook. Nepeta cataria L. 5 Nuphar lutea (L.) Sm. Nymphaea odorata Ait. 9 Nymphaea tetragona Georgi 2 Oenothera flava (A. Nels.) Garrett 1 Oenothera villosa Thunb. 4 Ophioglossum pusillum Raf. 7 Oplopanax horridus Miq. 4 Osmorhiza berteroi DC. 6 Osmorhiza occidentalis (Nutt. ex Torr. & Gray) Torr. 6 Osmorhiza purpurea (Coult. & Rose) Suksdorf 7 Packera cymbalarioides (Buck) W.A. Weber & A. Love wild mint spearmint tall fringed bluebells tall bluebells shortflower monkeyflower manyflowered monkeyflower seep monkeyflower purple monkeyflower muskflower primrose monkeyflower Tiling's monkeyflower beautiful sandwort Brewer's miterwort naked miterwort fivestamen miterwort smallflower miterwort bluntleaf sandwort green carpetweed single delight Nuttall's poverty weed water minerslettuce dwarf minerslettuce littleleaf minerslettuce scratchgrass puUup muhly spiked muhly annual muhly mat muhly field forget-me-not Asian forget-me-not bay forget-me-not true forget-me-not bristly mousetail tiny mousetail whorl-leaf watermilfoil nodding waternymph needleleaf navarretia catnip yellow pond-lily American white waterlily pygmy waterlily yellow evening-primrose hairy evening-primrose northern adderstongue devilsclub sweetcicely western sweetroot purple sweetroot cleftleaf groundsel Appendix A -40 C Scientific Name Common Name 8 Packera debilis (Nutt.) W.A. Weber & A. Love 6 Packera indecora (Greene) A.& D. Love 5 Packera paupercula (Michx.) A.& D. Love 7 Packera pseudaurea (Rydb.) W.A. Weber & A. Love 1 Panicum capillare L. 7 Parnassia fimbriata Koenig 9 Parnassia kotzebuei Cham, ex Spreng. 9 Parnassia palustris L. var. parviflora (DC.) Boivin 7 Parnassia palustris L. var. tenuis Wahlenb. 3 Pascopyrum smithii (Rydb.) A. Love 7 Pedicularis groenlandica Retz. Pennisetum glaucum (L.) R. Br. 5 Penstemon attenuatus Dougl. ex Lindl. 5 Penstemon procerus Dougl. ex Graham 9 Petasitesfrigidus (L.) Fries 8 Petasites sagittatus (Banks ex Pursh) Gray Phalaris arundinacea L. 10 Phippsia algida (C.J. Phipps) R. Br. 7 Phleum alpinum L. Phleum pratense L. 5 Phlox kelseyi Britt. 4 Phragmites australis (Cav.) Trin. ex Steud. 7 Phyllodoce empetriformis (Sm.) D. Don 7 Phyllodoce glanduliflora (Hook.) Coville 7 Physostegia parviflora Nutt. ex Gray 4 Picea engelmannii Parry ex Engelm. 9 Pinguicula macroceras Link 6 Piperia unalascensis (Spreng.) Rydb. 2 Plagiobothrys scouleri (Hook. & Arn.) LM. Johnston 3 Plantago elongata Pursh 7 Plantago eriopoda Torr. Plantago lanceolata L. 1 Plantago major L. 5 Platanthera dilatata (Pursh) Lindl. ex Beck 8 Platanthera hyperborea (L.) Lindl. 10 Platanthera obtusata (Banks ex Pursh) Lindl. 9 Platanthera orbiculata (Pursh) Lindl. 7 Platanthera stricta Lindl. 5 Poa alpina L. 4 Poa arida Vasey 8 Poa leptocoma Trin. 1 Poa palustris L. Poa pratensis L. 3 P6>6z secunda J. Presl 6 Polemonium occidentale Greene 6 Polygonum amphibium L. 1 Polygonum aviculare L. 6 Polygonum bistortoides Pursh Polygonum convolvulus L. weak groundsel elegant groundsel balsam groundsel falsegold groundsel witchgrass fringed grass of Parnassus Kotzebue's grass of Parnassus smallflower grass of Parnassus marsh grass of Parnassus western wheatgrass elephanthead lousewort pearl millet sulphur penstemon littleflower penstemon arctic sweet coltsfoot arrowleaf sweet coltsfoot reed canary grass icegrass alpine timothy timothy Kelsey's phlox common reed pink mountainheath yellow mountainheath western false dragonhead Engelmann spruce California butterwort slender- spire orchid Scouler's popcomflower prairie plantain redwool plantain narrowleaf plantain common plantain scentbottle northern green orchid bluntleaved orchid lesser roundleaved orchid slender bog orchid alpine bluegrass plains bluegrass marsh bluegrass fowl bluegrass Kentucky bluegrass Sandberg bluegrass western polemonium water knotweed prostrate knotweed American bistort black bindweed Appendix A -41 C Scientific Name Common Name 3 1 1 4 7 5 5 5 5 7 10 1 7 6 8 7 6 9 4 6 6 4 1 5 4 5 8 2 3 3 8 4 6 6 8 4 4 2 3 6 7 4 9 Polygonum douglasii Greene Polygonum erectum L. Polygonum lapathifolium L. Polygonum persicaria L. Polygonum polygaloides Wallich ex Meisn. Polygonum viviparum L. Populus xacuminata Rydb. (pro sp.) Populus angustifolia James Populus balsamifera L. ssp. trichocarpa (Torr. & Gray ex Hook.) Brayshaw Populus deltoides Bartr. ex Marsh, ssp. monilifera (Ait.) Eckenwalder Populus tremuloides Michx. Portulaca oleracea L. Potamogeton alpinus Balbis Potamogeton amplifolius Tuckerman Potamogeton crispus L. Potamogeton friesii Rupr. Potamogeton gramineus L. Potamogeton obtusifolius Mert. & Koch Potamogeton pusillus L. Potamogeton richardsonii (Benn.) Rydb. Potamogeton zosteriformis Fern. Potentilla biennis Greene Potentilla diversifolia Lehm. Potentilla glandulosa Lindl. Potentilla gracilis Dougl. ex Hook. Potentilla norvegica L. Potentilla paradoxa Nutt. Potentilla rivalis Nutt. Primula incana M.E. Jones Primula parryi Gray Prunella vulgaris L. Prunus virginiana L. Pseudognaphalium stramineum (Kunth) W.A. Weber Psilocarphus brevissimus Nutt. Puccinellia distans (Jacq.) Pari. Puccinellia nuttalliana (J. A. Schultes) A.S. Hitchc. Pyrola asarifolia Michx. Pyrola chlorantha Sw. Pyrrocoma integrifolia (Porter ex Gray) Greene Pyrrocoma lanceolata (Hook.) Greene Pyrrocoma uniflora (Hook.) Greene Ranunculus abortivus L. Ranunculus acriformis Gray Ranunculus acris L. Ranunculus alismifolius Geyer ex Benth. Ranunculus aquatilis L. Ranunculus cardiophyllus Hook. Douglas' knotweed erect knotweed curlytop knotweed spotted ladysthumb milkwort knotweed alpine bistort lanceleaf cottonwood narrowleaf cottonwood black cottonwood plains cottonwood quaking aspen little hogweed alpine pondweed largeleaf pondweed curly pondweed Fries' pondweed variableleaf pondweed bluntleaf pondweed small pondweed Richardson's pondweed flatstem pondweed biennial cinquefoil varileaf cinquefoil sticky cinquefoil slender cinquefoil Norwegian cinquefoil Paradox cinquefoil brook cinquefoil silvery primrose Parry's primrose common selfheal chokecherry cottonbatting plant short wooUyheads weeping alkaligrass Nuttall's alkaligrass liverleaf wintergreen greenflowered wintergreen manysted goldenweed lanceleaf goldenweed plantain goldenweed littleleaf buttercup sharpleaf buttercup tall buttercup plantainleaf buttercup Whitewater crowfoot heartleaf buttercup Appendix A -42 C Scientific Name Common Name 3 Ranunculus cymbalaria Pursh 7 Ranunculus eschscholtzii Schlecht. 4 Ranunculus flammula L. 4 Ranunculus glaberrimus Hook. 4 Ranunculus gmelinii DC. 9 Ranunculus hyperboreus Rottb. 6 Ranunculus inamoenus Greene 5 Ranunculus macounii Britt. 8 Ranunculus orthorhynchus Hook. 9 Ranunculus pedatifidus Sm. 7 Ranunculus populago Greene 9 Ranunculus pygmaeus Wahlenb. Ranunculus repens L. 4 Ranunculus sceleratus L. 2 Ranunculus uncinatus D. Don ex G. Don 9 Ranunculus verecundus B.L. Robins, ex Piper 4 Rhamnus alnifolia L'Her. 8 Rhodiola rhodantha (Gray) Jacobsen 8 Rhododendron albiflorum Hook. 6 Ribes americanum P. Mill. 5 Ribes aureum Pursh 7 i^/fc^^" hudsonianum Richards. 5 Ribes inerme Rydb. 6 Ribes lacustre (Pers.) Poir. 6 Ribes oxyacanthoides L. 10 Romanzoffia sitchensis Bong. 4 Rorippa alpina (S. Wats.) Rydb. 4 Rorippa curvipes Greene 4 Rorippa nasturtium- aquaticum (L.) Hayek 4 Rorippa palustris (L.) Bess. 3 Rotala ramosior (L.) Koehne 10 Rubus arcticus L. ssp. acaulis (Michx.) Focke 7 Rubus pubescens Raf. 5 Rudbeckia laciniata L. 4 Rudbeckia occidentalis Nutt. Rumex acetosella L. 7 Rumex aquaticus L. Rumex crispus L. 6 Rumex maritimus L. 7 Rumex salicifolius Weinm. 8 Ruppia cirrhosa (Petag.) Grande 1 Sagina procumbens L. 3 Sagina saginoides (L.) Karst. 7 Sagittaria cuneata Sheldon 7 Sagittaria latifolia Willd. 7 Salicornia rubra A. Nels. 7 fc/Zx amygdaloides Anderss. 7 Salix arctica Pallas 8 Salix barclayi Anderss. alkali buttercup Eschscholtz's buttercup greater creeping spearwort sagebrush buttercup Gmelin's buttercup high northern buttercup graceful buttercup Macoun's buttercup straightbeak buttercup surefoot buttercup popular buttercup pygmy buttercup creeping buttercup cursed buttercup woodland buttercup wetslope buttercup alderleaf buckthorn redpod stonecrop Cascade azalea American black currant golden currant northern black currant whitestem gooseberry prickly currant Canadian gooseberry Sitka mistmaiden alpine yellowcress bluntleaf yellowcress watercress bog yellowcress lowland rotala dwarf raspberry dwarf red blackberry cutleaf coneflower western coneflower common sheep sorrel western dock curly dock golden dock willow dock spiral ditchgrass birdeye pearlwort arctic pearlwort arumleaf arrowhead broadleaf arrowhead red swampfire peachleaf willow arctic willow Barclay's willow Appendix A -43 c Scientific Name Common Name 10 Salix barrattiana Hook. Barratt's willow 4 Salix bebbiana Sarg. Bebb willow 6 &//x boothii Dorn Booth's willow 6 5a//x brachycarpa Nutt. shortfruit willow 9 5a//x Candida Fluegge ex Willd. sageleaf willow 7 5(3//x commutata Bebb undergreen willow 5 fc/Zx drummondiana Barratt ex Hook. Drummond's willow 4 Salix exigua Nutt. narrowleaf willow 7 Salix farriae Ball Farr's willow Salix fragilis L. crack willow 6 &//x geyeriana Anderss. Geyer's willow 7 &//x glauca L. gray leaf willow 6 &//x lemmonii Bebb Lemmon's willow 5 fc/k lucida Muhl. ssp. caudata (Nutt.) E. Murr. greenleaf willow 6 &//x /wr^a Nutt. yellow willow 5 fc/k melanopsis Nutt. dusky willow 7 Salix planifolia Pursh diamondleaf willow 7 Salix prolixa Anderss. MacKenzie's willow 7 Salix pseudomonticola Ball false mountain willow 4 &//x scouleriana Barratt ex Hook. Scouler's willow 9 &//x serissima (Bailey) Fern. autumn willow 8 Salix sitchensis Sanson ex Bong. Sitka willow 7 Salix tweedyi (Bebb ex Rose) Ball Tweedy's willow 7 Salix vestita Pursh rock willow 7 Salix wolfii Bebb Wolfs willow 6 Saxifraga adscendens L. wedgeleaf saxifrage 6 Saxifraga caespitosa L. tufted alpine saxifrage 8 Saxifraga cernua L. nodding saxifrage 8 Saxifraga ferruginea Graham russethair saxifrage 6 Saxifraga integrifolia Hook. wholeleaf saxifrage 7 Saxifraga lyallii Engl. redstem saxifrage 7 Saxifraga mertensiana Bong. wood saxifrage 7 Saxifraga nidifica Greene peak saxifrage 6 Saxifraga occidentalis S. Wats. Alberta saxifrage 7 Saxifraga odontoloma Piper brook saxifrage 6 Saxifraga oregana T.J. Howell Oregon saxifrage 7 Saxifraga rhomboidea Greene diamondleaf saxifrage 8 Saxifraga rivularis L. weak saxifrage 10 Scheuchzeria palustris L. rannoch-rush 5 Schoenoplectus acutus (Muhl. ex Bigelow) A.& D. Love hardstem bulrush 8 Schoenoplectus heterochaetus (Chase) Sojak slender bulrush 6 Schoenoplectus maritimus (L.) Lye cosmopolitan bulrush 6 Schoenoplectus pungens (Vahl) Palla common threesquare 9 Schoenoplectus subterminalis (Torr.) Sojak swaying bulrush 6 Schoenoplectus tabernaemontani (K.C. Gmel.) Palla softstem bulrush 1 Scirpus cyperinus (L.) Kunth woolgrass 5 Scirpus microcarpus J.& K. Presl panicled bulrush 9 Scirpus nevadensis S. Wats. Nevada bulrush 4 Scrophularia lanceolata Pursh lanceleaf figwort Appendix A -44 C Scientific Name Common Name 6 7 5 5 7 5 6 5 3 6 6 6 6 1 3 6 7 7 6 6 6 8 7 5 6 7 7 6 6 6 5 4 6 7 7 7 7 5 3 6 7 Scutellaria galericulata L. Senecio crassulus Gray Senecio hydrophiloides Rydb. Senecio hydrophilus Nutt. Senecio integerrimus Nutt. Senecio serra Hook. Senecio sphaerocephalus Greene Senecio triangularis Hook. Senecio vulgaris L. Sidalcea oregana (Nutt. ex Torr. & Gray) Gray Silene menziesii Hook. Silene uralensis (Rupr.) Bocquet Sisyrinchium idahoense Bickn. Sisyrinchium montanum Greene Sisyrinchium septentrionale Bickn. Slum suave Walt. Smilax lasioneura Hook. Solanum dulcamara L. Solidago canadensis L. Solidago gigantea Ait. Sonchus arvensis L. Sonchus asper (L.) Hill Sparganium angustifolium Michx. Sparganium eurycarpum Engelm. ex Gray Sparganium natans L. Spartina gracilis Trin. Spartina pectinata Bosc ex Link Spergularia salina J.& K. Presl Sphenopholis obtusata (Michx.) Scribn. Spiraea douglasii Hook. Spiranthes romanzoffiana Cham. Spirodela polyrrhiza (L.) Schleid. Sporobolus airoides (Torr.) Torr. Stachys pilosa Nutt. Stellaria borealis Bigelow Stellaria calycantha (Ledeb.) Bong. Stellaria crassifolia Ehrh. Stellaria crispa Cham. & Schlecht. Stellaria longifolia Muhl. ex Willd. Stellaria longipes Goldie Stellaria umbellata Turcz. ex Kar. & Kir. Stenanthium occidentale Gray Streptopus amplexifolius (L.) DC. Stuckenia pectinatus (L.) Boerner Suaeda calceoliformis (Hook.) Moq. Suaeda moquinii (Torr.) Greene Suksdorfia ranunculifolia (Hook.) Engl. Suksdorfia violacea Gray Swertia perennis L. marsh skullcap thickleaf ragwort tall groundwel water ragwort lambstongue ragwort tall ragwort ballhead ragwort arrowleaf ragwort old-man-in-the-Spring Oregon checkerbloom Menzies' campion apetalous catchfly Idaho blue-eyed grass strict blue-eyed grass northern blue-eyed grass hemlock waterparsnip Blue Ridge carrionflower climbing nightshade Canada goldenrod giant goldenrod field sowthistle spiny sowthistle narrowleaf bur-reed broadfruit bur-reed small bur-reed alkali cordgrass prairie cordgrass salt sandspurry prairie wedgescale rose spirea hooded ladies'-tresses common duckmeat alkali sacaton hairy hedgenettle boreal starwort northern starwort fleshy starwort curled starwort longleaf starwort longstalk starwort umbrella starwort western featherbells claspleaf twistedstalk sago pondweed Pursh seepweed Mojave seablite buttercup suksdorfia violet suksdorfia felwort Appendix A -45 C Scientific Name Common Name 3 Symphoricarpos albus (L.) Blake 4 Symphoricarpos occidentalis Hook. 2 Symphyotrichum chilense (Nees) Nesom 5 Symphyotrichum ciliatum (Ledeb.) Nesom 5 Symphyotrichum eatonii (Gray) Nesom 6 Symphyotrichum ericoides (L.) Nesom var. pansum (Blake) Nesom 5 Symphyotrichum foliaceum (DC.) Nesom 4 Symphyotrichum f rondo sum (Nutt.) Nesom 4 Symphyotrichum lanceolatum (Willd.) Nesom 5 Symphyotrichum spathulatum (Lindl.) Nesom 6 Symphyotrichum subspicatum (Nees) Nesom Tamarix chinensis Lour. Taraxacum ojficinale G.H. Weber ex Wiggers 9 Thalictrum alpinum L. 5 Thalictrum dasycarpum Fisch. & Ave-Lall. 5 Thalictrum occidentale Gray 5 Thalictrum sparsiflorum Turcz. ex Fisch. & C.A. Mey. 6 Thermopsis montana Nutt. 7 Thuja plicata Donn ex D. Don 7 Tofieldia glutinosa (Michx.) Pers. 7 Torreyochloa pallida (Torr.) Church 4 Toxicodendron rydbergii (Small ex Rydb.) Greene 8 Trautvetteria caroliniensis (Walt.) Vail Tribulus terrestris L. 4 Trifolium beckwithii Brewer ex S. Wats. Trifolium fragiferum L. 6 Trifolium longipes Nutt. 1 Trifolium microcephalum Pursh 7 Trifolium parryi Gray Trifolium repens L. 8 Triglochin maritimum L. 7 Triglochin palustre L. 1 Triodanis perfoliata (L.) Nieuwl. Tripleurospermum perforata (Merat) M. Lainz 7 Trollius laxus Salisb. 7 Typha angustifolia L. 3 Typha latifolia L. 3 Urtica dioica L. 10 Utricularia minor L. 8 Vaccinium uliginosum L. 7 Vahlodea atropurpurea (Wahlenb.) Fries ex Hartman 5 Valeriana dioica L. 7 Valeriana edulis Nutt. ex Torr. & Gray 7 Valeriana occidentalis Heller 7 Valeriana sitchensis Bong. 7 Veratrum viride Ait. 5 Veronica americana Schwein. ex Benth. 4 Veronica anagallis-aquatica L. common snowberry western snowberry Pacific aster rayless alkali aster Eaton's aster manyflowered aster alpine leafybract aster short-rayed alkalai aster white panicle aster western mountain aster Douglas aster fivestamen tamarisk common dandelion alpine meadow-rue purple meadow-rue western meadow-rue fewflower meadow-rue mountain goldenbanner western red cedar sticky tofieldia pale false mannagrass western poison ivy Carolina bugbane puncturevine Beckwith's clover strawberry clover longstalk clover smallhead clover Parry's clover white clover seaside arrowgrass marsh arrowgrass clasping Venus' looking-glass scentless false mayweed American globeflower narrowleaf cattail broadleaf cattail stinging nettle lesser bladderwort bog blueberry mountain hairgrass marsh valerian tobacco root western valerian Sitka valerian green false hellebore American speedwell water speedwell Appendix A -46 c Scientific Name Common Name 1 Veronica cusickii Gray Cusick's speedwell 4 Veronica peregrina L. neckweed 6 Veronica scutellata L. skullcap speedwell 2 Veronica serpyllifolia L. thymeleaf speedwell 7 Veronica wormskjoldii Roemer & J.A. Schultes American alpine speedwell 8 Viburnum edule (Michx.) Raf. squashberry 5 Viola adunca Sm. hookedspur violet 8 y/6>/a macloskeyi Lloyd small white violet 8 y/6>/a nephrophylla Greene northern bog violet 7 y/6>/a palustris L. marsh violet 8 y/6>/6z renifolia Gray white violet 5 y/r/^ riparia Michx. riverbank grape 7 Wolffia brasiliensis Weddell Brazilian watermeal 7 Woljfia Columbiana Karst. Columbian watermeal 2 Wyethia helianthoides Nutt. sunflower mule-ears 2 Xanthium strumarium L. rough cockleburr 8 Zannichellia palustris L. horned pondweed 5 Zigadenus venenosus S. Wats. meadow deathcamas 6 Zizania palustris L. northern wildrice 7 Z/z/a aptera (Gray) Fern. meadow zizia Appendix AA7 Appendix B. List and attributes of sampled plant species. Appendix B. List and attributes of sampled plant species. Wetland Bank Growth Indicator Stability Scientific Name Common Name Form^ Duration'' Nativity" Status^ Rating" Achillea millefolium L. Achnatherum nelsonii (Scribn.) Barkworth Actaea rubra (Ait.) Willd. Agoseris glauca (Pursh) Raf . Agrostis gigantea Roth Agrostis scabra Willd. Allium brevistylum S. Wats. Allium schoenoprasum L. Alnus incana (L.) Moench Alopecurus aequalis Sobol. Alopecurus alpinus Sm. Alopecurus pratensis L. Androsace filiformis Retz. Angelica arguta Nutt. Antennaria corymbosa E. Nels. Antennaria microphylla Rydb. Argentina anserina (L.) Rydb. Arnica mollis Hook. Artemisia cana Pursh Artemisia frigida Willd. Artemisia ludoviciana Nutt. Artemisia tridentata Nutt. ssp. tridentata Artemisia tridentata Nutt. ssp. vaseyana (Rydb.) Beetle Artemisia tridentata Nutt. ssp. wyomingensis Beetle & Young Astragalus agrestis Dougl. ex G. Don Beckmannia syzigachne (Steud.) Fern. Bromus ciliatus L. Bromus marginatus Nees ex Steud. Calamagrostis canadensis (Michx.) Beauv. Calamagrostis stricta (Timm) Koel. Campanula rotundifolia L. Canadanthus modestus (Lindl.) Nesom common yarrow F P N FACU poor Columbia needlegrass G P N UPL n/a red baneberry F P N UPL poor pale agoseris F P N FAC poor redtop G P E FAC fair rough bentgrass G P N FAC fair shortstyle onion F P N UPL poor wild chives F P N FACW poor gray alder S P N FACW good shortawn foxtail G P N OBL poor boreal alopecurus G P N FACW fair meadow foxtail G P E FACW fair filiform rockjasmine F A/B N FACW poor Lyall's angelica F P N FACW fair flat-top pussytoes F P N FAC poor littleleaf pussytoes F P N UPL poor silverweed cinquefoil F P N OBL fair hairy arnica F P N FAC poor silver sagebrush S P N FAC fair prairie sagewort S P N UPL n/a white sagebrush S P N FACU n/a basin big sagebrush S P N FACU poor mountain big sagebrush S P N UPL n/a Wyoming big sagebrush S P N UPL n/a purple milkvetch F P N FACW poor American sloughgrass G A/B N OBL fair fringed brome G P N FAC fair mountain brome G P N UPL n/a bluejoint G P N FACW excellent slimstem reedgrass G P N FACW excellent bluebell bellflower F P N FACU n/a giant mountain aster F P N FAC poor Appendix B - 1 Wetland Bank Growth Indicator Stability Scientific Name Common Name Form' Duration*' Nativity" Status" Rating" Cardamine oligosperma Nutt. Cardamine pensylvanica Muhl. ex Willd. Car ex aquatilis Wahlenb. Carex aurea Nutt. Car ex canescens L. Carex disperma Dewey Carex foenea Willd. Carex lenticularis Michx. Carex microptera Mackenzie Carex nebrascensis Dewey Carex norvegica Retz. Carex pellita Muhl ex Willd. Carex prae gracilis W. Boott Carex praticola Rydb. Carex simulata Mackenzie Carex utriculata Boott Carex vesicaria L. Carex L. Castilleja miniata Dougl. ex Hook. Catabrosa aquatica (L.) Beauv. Cerastium nutans Raf . Chamerion angustifolium (L.) Holub Cirsium arvense (L.) Scop. Cirsium scariosum Nutt. Cirsium vulgare (Savi) Ten. Collomia linearis Nutt. Cynoglossum officinale L. Danthonia intermedia Vasey Dasiphora floribunda (Pursh) Kartesz, comb. nov. ined. Deschampsia caespitosa (L.) Beauv. Descurainia sophia (L.) Webb ex Prantl Eleocharis palustris (L.) Roemer & J.A. Schultes Eleocharis quinqueflora (F.X. Hartmann) Schwarz little western bittercress F A/B N FACW poor Pennsylvania bittercress F A/B N FACW poor water sedge G P N OBL excellent golden sedge G P N FACW fair silvery sedge G P N FACW good softleaf sedge G P N FACW fair dryspike sedge G P N UPL fair lakeshore sedge G P N FACW good smallwing sedge G P N FAC good Nebraska sedge G P N OBL excellent Norway sedge G P N FACW n/a woolly sedge G P N OBL excellent clustered field sedge G P N FACW excellent meadow sedge G P N FACW n/a analogue sedge G P N OBL excellent Northwest Territory sedge G P N OBL excellent blister sedge G P N OBL excellent sedge G P N n/a fair giant red Indian paintbrush F P N FAC poor water whorlgrass G P N OBL fair nodding chickweed F A/B N FACU poor fireweed F P N FACU poor Canada thistle F P E FACU fair meadow thistle F A/B N UPL poor bull thistle F A/B E FACU poor tiny trumpet F A/B N FACU poor gypsyflower F A/B E FACU poor timber oatgrass G P N FACU n/a shrubby cinquefoil S P N FAC fair tufted hairgrass G P N FACW fair herb sophia F A/B E UPL n/a common spikerush G P N OBL excellent fewflower spikerush G P N OBL n/a Appendix B - 2 Wetland Bank Growth Indicator Stability Scientific Name Common Name Form' Duration*' Nativity" Status" Rating" Elymus rep ens (L.) Gould Elymus trachycaulus (Link) Gould ex Shinners Epilobium anagallidifolium Lam. Epilobium ciliatum Raf. Epilobium palustre L. Equisetum arvense L. Equisetum laevigatum A. Braun Ericameria nauseosa (Pallas ex Pursh) Nesom & Baird Erigeron gracilis Rydb. Erysimum cheiranthoides L. Festuca idahoensis Elmer Festuca rubra L. Fragaria virginiana Duchesne Galium boreale L. Galium trifidum L. Galium triflorum Michx. Gentiana ajfinis Griseb. Geranium richardsonii Fisch. & Trautv. Geranium viscosissimum Fisch. & C.A. Mey. ex C.A. Mey. Geum macrophyllum Willd. Geum rivale L. Geum triflorum Pursh Glyceria grandis S. Wats. Glyceria striata (Lam.) A.S. Hitchc. Heracleum maximum Bartr. Hordeum brachyantherum Nevski Hordeum jubatum L. Iris missouriensis Nutt. Juncus balticus Willd. Juncus ensifolius Wikstr. Juncus longistylis Torr. Juncus mertensianus Bong. Juncus L. quackgrass G P E FACU excellent slender wheatgrass G P N FAC n/a pimpernel willowherb F P N FACW poor fringed willowherb F P N FACW poor marsh willowherb F P N OBL poor field horsetail F P N FAC good smooth horsetail F P N FACW fair rubber rabbitbrush S P N UPL n/a quill fleabane F P N UPL n/a wormseed wallflower F A/B E FACU poor Idaho fescue G P N FACU n/a red fescue G P N FAC good Virginia strawberry F P N FACU poor northern bedstraw F P N FACU poor threepetal bedstraw F P N FACW poor fragrant bedstraw F P N FACU poor pleated gentian F P N FACU n/a Richardson's geranium F P N FAC poor sticky purple geranium F P N FACU poor largeleaf avens F P N FACW poor purple avens F P N FACW n/a old man's whiskers F P N FACU n/a American mannagrass G P N OBL fair fowl mannagrass G P N OBL fair common cowparsnip F P N FAC fair meadow barley G P N FACW fair foxtail barley G P N FAC poor Rocky Mountain iris F P N FACW fair Baltic rush G P N OBL excellent swordleaf rush G P N FACW poor longstyle rush G P N FACW fair Mertens' rush G P N OBL fair rush G P N n/a n/a Appendix B - 3 Wetland Bank Growth Indicator Stability Scientific Name Common Name Form' Duration*' Nativity" Status" Rating" Leymus cinereus (Scribn. & Merr.) A. Love Ligusticum tenuifolium S. Wats. Lolium pratense (Huds.) S.J. Darbyshire Lupinus polyphyllus Lindl. Lupinus sericeus Pursh Luzula parviflora (Ehrh.) Desv. Lycopus asper Greene Maianthemum stellatum (L.) Link Mentha arvensis L. Mertensia ciliata (James ex Torr.) G. Don Mimulus guttatus DC. Moehringia lateriflora (L.) Fenzl Montia chamissoi (Ledeb. ex Spreng.) Greene Muhlenbergia richardsonis (Trin.) Rydb. Osmorhiza berteroi DC. Packera pseudaurea (Rydb.) W.A. Weber & A. Love Parnassia fimbriata Koenig Pas copy rum smithii (Rydb.) A. Love Pedicularis groenlandica Retz. Penstemon procerus Dougl. ex Graham Phleum alpinum L. Phleum pratense L. Picea engelmannii Parry ex Engelm. Plantago major L. Platanthera stricta Lindl. Poa arida Vasey Poa palustris L. Poa pratensis L. Poa secunda J. Presl Polemonium occidentale Greene Polygonum aviculare L. Polygonum douglasii Greene Populus tremuloides Michx. basin wildrye G P N FAC n/a Idaho licorice-root F P N FACW poor meadow ryegrass G P E FACU n/a bigleaf lupine F P N FAC poor silky lupine F P N UPL n/a smallflowered woodrush G P N FAC poor rough bugleweed F P N OBL poor starry false lily of the vally F P N FAC poor wild mint F P N FACW poor tall fringed bluebells F P N FACW fair seep monkeyflower F A/B N OBL poor bluntleaf sandwort F P N FAC poor water minerslettuce F P N OBL poor mat muhly G P N FAC poor sweetcicely F P N UPL poor falsegold groundsel F P N FACW fair fringed grass of Parnassus F P N OBL poor western wheatgrass G P N FACU good elephanthead lousewort F P N OBL n/a littleflower penstemon F P N UPL n/a alpine timothy G P N FAC fair timothy G P N FAC fair Engelmann spruce T P N FAC fair common plantain F P N FAC poor slender bog orchid F P N FACW poor plains bluegrass G P N FAC fair fowl bluegrass G P E FAC fair Kentucky bluegrass G P E FAC poor Sandberg bluegrass G P N FACU n/a western polemonium F P N FACW poor prostrate knotweed F A/B E FACW n/a Douglas' knotweed F A/B N FACU n/a quaking aspen T P N FAC good Appendix B - 4 Wetland Bank Growth Indicator Stability Scientific Name Common Name Form' Duration*' Nativity" Status" Rating" Potentilla gracilis Dougl. ex Hook. Potentilla rivalis Nutt. Pyrola asarifolia Michx. Ranunculus abortivus L. Ranunculus acriformis Gray Ranunculus aquatilis L. Ranunculus cymbalaria Pursh Ranunculus macounii Britt. Ranunculus L. Rhus trilobata Nutt. Ribes L. Rosa woodsii Lindl. Rudbeckia occidentalis Nutt. Rumex aquaticus L. Rumex crispus L. Salix bebbiana Sarg. Salix boothii Dorn Salix drummondiana Barratt ex Hook. Salix exigua Nutt. Salix farriae Ball Salix geyeriana Anderss. Salix lemmonii Bebb Salix lucida Muhl. ssp. caudata (Nutt.) E. Murr. Salix planifolia Pursh Saxifraga oregana T.J. Howell Saxifraga L. Senecio hydrophiloides Rydb. Senecio serra Hook. Senecio sphaerocephalus Greene Silene menziesii Hook. Sisyrinchium idahoense Bickn. Sisyrinchium montanum Greene Solidago canadensis L. slender cinquefoil brook cinquefoil liverleaf wintergreen littleleaf buttercup sharpleaf buttercup Whitewater crowfoot alkali buttercup Macoun's buttercup buttercup skunkbush sumac currant Woods' rose western coneflower western dock curly dock Bebb willow Booth's willow Drummond's willow narrowleaf willow Farr's willow Geyer's willow Lemmon's willow greenleaf willow diamondleaf willow Oregon saxifrage saxifrage tall groundwel tall ragwort ballhead ragwort Menzies' campion Idaho blue-eyed grass strict blue-eyed grass Canada goldenrod F F F F F F F F F S S S F F F S S S S S S S S S F F F F F F F F F P A/B P P P P P P P P P P P P P P P P P P P P P P P P P P P P P P P N FAC poor N FACW poor N FACU poor N FACW poor N FACW poor N OBL poor N OBL poor N OBL poor N n/a poor N UPL good N FACU good N FAC good N FACW poor N FAC fair E FACW fair N FACW excellent N FACW excellent N OBL excellent N OBL excellent N FACW excellent N FACW excellent N FACW excellent N OBL excellent N FACW n/a N FACU poor N n/a poor N FACW fair N FACU fair N FACW fair N FAC poor N FACW poor N FACW n/a N FACU fair Appendix B - 5 Wetland Bank Growth Indicator Stability Scientific Name Common Name Form' Duration*' Nativity" Status" Rating" Stellaria crassifolia Ehrh. Stellaria longifolia Muhl. ex Willd. Stellaria L. Symphyotrichum foliaceum (DC.) Nesom Symphyotrichum spathulatum (Lindl.) Nesom Symphyotrichum subspicatum (Nees) Nesom Symphyotrichum Nees Taraxacum officinale G.H. Weber ex Wiggers Thalictrum occidentale Gray Thermopsis montana Nutt. Thlaspi arvense L. Tragopogon dubius Scop. Trifolium longipes Nutt. Trifolium rep ens L. Triglochin palustre L. Urtica dioica L. Veronica americana Schwein. ex Benth. Veronica serpyllifolia L. Viola nephrophylla Greene Viola L. fleshy starwort F P N FACW poor longleaf starwort F P N FACW poor starwort F P N n/a poor alpine leafybract aster F P N FACW n/a western mountain aster F P N FAC n/a Douglas aster F P N FACW n/a aster F P N n/a poor common dandelion F P E FACU poor western meadow-rue F P N FACU poor mountain goldenbanner F P N UPL fair field pennycress F A/B E UPL poor yellow salsify F A/B E UPL n/a longstalk clover F P N FAC poor white clover F A/B E FAC poor marsh arrowgrass F P N OBL poor stinging nettle F P N FAC fair American speedwell F P N OBL poor thymeleaf speedwell F P E FAC poor northern bog violet F P N FACU poor violet F P N n/a poor ^ F = forb/fem, G = graminoid, S = shrub, T = tree "^ A/B = annual/biennial, P = perennial " E = exotic, N = native "" OBL = obligate v^etland, FACW = facultative w^etland, " rating w^as calculated for species occurring in greenline FAC = facultative, FACU = facultative upland, UPL = obligate upland samples only Appendix B - 6 Appendix C. Location and condition rating of sample REACHES. Appendix C. Location and condition rating of sample reaches. Location PEC VIBI VIBI Condition Disturbance Disturbance Site Code Stream Latitude Longitude Rating^ Score Class Score Category CAMP Camp Cr. 45.68156616 -112.56099008 FAR 0.60 moderately impaired 0.59 moderate MORRISON Morrison Cr. 44.70083328 -113.05396895 FAR 0.64 moderately impaired 0.80 most NF_EVRSN North Fork Everson Cr. 44.90777384 -113.33167515 PEC 0.75 reference 0.14 least EF_BLACK East Fork Blacktail Deer Cr. 44.84571892 -112.20396350 PEC 0.61 moderately impaired 0.36 moderate WF_BLACK West Fork Blacktail Deer Cr. 44.78252959 -112.31075800 NF 0.37 severely impaired 0.80 most PRICE_DN Middle Fork Price Cr. 44.57434567 -112.12498066 PEC 0.94 reference 0.04 least L_BEAVER Little Beaver Cr. 44.52808267 -112.47752761 FAR 0.59 moderately impaired 0.40 moderate L_SHEEP Little Sheep Cr. 44.58333903 -112.67295781 NF 0.47 severely impaired 0.53 moderate L_SAGE Little Sage Cr. 44.79545642 -112.52678974 FAR 0.47 severely impaired 0.77 most MUDDY_TR Tributary of Muddy Cr. 44.72151009 -112.89286223 NF 0.64 moderately impaired 0.71 moderate MCNINCH McNinch Cr. 44.69827957 -112.87387689 FAR 0.68 moderately impaired 0.40 moderate NICHO_DN Nicholia Cr. 44.54776607 -112.82693010 PEC 0.70 moderately impaired 0.73 moderate TENDOY Tendoy Cr. 44.45170686 -112.92159908 NF 0.53 moderately impaired 0.56 moderate NICHO_UP Nicholia Cr. 44.45793453 -112.91187976 PEC 0.54 moderately impaired 0.51 moderate COW Cow Cr. 44.65020038 -112.95523105 NF 0.68 moderately impaired 0.47 moderate INDIAN Indian Cr. 44.60515310 -113.00577929 PEC 0.76 reference 0.00 least PRICE_UP Middle Fork Price Cr. 44.56140133 -112.12400492 PEC 0.82 reference 0.30 least L_SAGE_T Tributary of Little Sage Cr. 44.81923495 -112.43812202 NF 0.54 moderately impaired 0.41 moderate EAST East Cr. 44.86921884 -112.54489046 NF 0.86 reference 0.28 least BL_CANYN Black Canyon Cr. 44.86336458 -113.32877526 PEC 0.80 reference 0.08 least NF_DIVDE North Fork Divide Cr. 44.81897227 -113.32126491 FAR 0.88 reference 0.52 moderate HORSE_PR Horse Prairie Cr. 44.81718742 -113.20700767 PEC 0.71 reference 0.19 least SHENON Shenon Cr. 44.92784061 -113.22862124 FAR 0.68 moderately impaired 0.67 moderate RAPE Rape Cr. 44.97097330 -113.21309485 NF 0.47 severely impaired 0.88 most TAYLR_UP Taylor Cr. 45.27322615 -112.98248908 NF 0.23 severely impaired 0.94 most TAYLR_DN Taylor Cr. 45.22943582 -112.99539160 PEC 0.50 moderately impaired 0.28 moderate BLD_DICK Bloody Dick Cr. 45.06979468 -113.42391657 PEC 0.96 reference 0.09 least BG_HOLLW Big Hollow Cr. 45.01306739 -113.35803380 FAR 0.34 severely impaired 1.00 most SF_WAT_U South Fork Watson Cr. 45.09592367 -113.19631118 NF 0.41 severely impaired 0.91 most SF_WAT_D South Fork Watson Cr. 45.07747150 -113.19649282 FAR 0.40 severely impaired 0.52 moderate " FAR = functioning at risk, PEC = proper functioning condition, NF = nonfunctioning Appendix C - 1