Monitoring of Terrestrial Vascular Plants and Structure in the Forested Regions of Alberta: Background, Indicators, and Protocols
Philip Lee and Stephen Hanus Alberta Research Council, Postal Bag 4000, Vegreville, AB. T9C 1T4 [email protected] [email protected]
October 1999
Disclaimer The views, statements, and conclusions expressed in this report are those of the authors and should not be construed as conclusions or opinions of the ABMP. Development of the ABMP has continued during the time since this report was produced. Thus, the report may not accurately reflect current ideas.
Abstract
This chapter focuses on the development of a monitoring program for terrestrial vascular plants within Alberta's forested regions. The steps in this process are 1) to establish objectives, 2) to select indicators, and 3) to develop protocols. We outline three objectives based on the spatial scales and types of changes likely to occur to terrestrial vascular vegetation. The first objective tracks the conversion of natural vegetation cover types to other types of cover including non-vegetation or heavily human-influenced vegetation cover types. It also includes the restoration or reclamation of developed land back to natural cover types. Six candidate indicators are identified for this objective. These indicators would be monitored at large spatial scales using remotely sensed data. The second objective tracks the impact of activities that while maintaining natural cover types potentially alters the sub-canopy assemblage of terrestrial vascular plants. Five candidate indicators are identified for this objective. The third objective tracks the integrity of forest structures produced by vascular plants. The underlying rationale for this objective is parallel to the second objective. Five candidate indicators are identified for this objective. The indicators for the second and third objectives require ground-level data collection. Various terrestrial plant and forest structure sampling methodologies were evaluated under a number of different criteria. A recommended protocol for the ground sampling of terrestrial vascular plants is outlined in Appendix 13.2. The protocol is similar to the ground methods for the National Forest Inventory. Appendix 13.1 is also included with a bibliography of references for monitoring terrestrial vascular plants.
Introduction
Monitoring vascular plants lies at the heart of sampling protocols for other biodiversity elements. Aside from being a large and diverse taxon, vascular plants also provide the basis for most ecological classifications. Though not as species rich as invertebrates, vascular plants are a significant portion of biodiversity of most ecosystems. In Alberta, there are 1,100 species of vascular plants (Anon. 1990) associated with the forested ecoregions. Moreover, ecological descriptions of Alberta’s landbase are based on vascular plant species. The system of ecosite, ecosite phase, and plant community classifications uses a combination of plants species and site characteristics (Archibald et al. 1996; Beckingham and Archibald 1996; Beckingham et al. 1996). At the highest levels of ecological organization, Alberta’s ecoregions and subregions are based primarily on predominant cover types (Anon. 1994).
Aside from their contribution to biodiversity and ecological classification, the physical structure produced by plants also serves as important habitat for other species. Wildlife species are often less tied to a particular species than to the structure of the habitat created by plants. Forest structures derived from plant materials that support other biota include vertical stratification of foliage layers, canopy characteristics (gaps, transparency, roughness), deadwood resources, large trees, and soil microtopography. As an example, about ¼ of the vertebrate species in Alberta are dependent upon large live and dead trees (Schieck and Roy 1995). The actual species of tree or snags is not as important as the physical parameters, e.g., height, diameter, decay condition. Though these forest attributes are not a component of biodiversity, they are critical elements of a functioning plant community.
A number of extensive reviews and bibliographies exist for monitoring terrestrial vascular plants (e.g., Elzinga et al. 1998). Based on these and other general literature on biodiversity monitoring, three development phases need to be considered (Silsbee and Petersen 1993; Stout 1993; Davis 1993; Hellawell 1991; Bunnell 1998): • Defining objectives • Selecting indicators
1
• Development of a sampling methodology
Objectives should be broad enough to encapsulate the impacts of future developments on the landscape, but cannot be written so vague as to be of little use in selecting indicators. Statements such as “provide better data for management” and “examine how the ecosystem functions” provide little guidance for identifying indicators. On the other hand, very specific objectives such as evaluating short–term impacts of specific management practices maybe too narrow in scope to guide long-term monitoring. This program’s overall objectives are to provide biodiversity trend data on the cumulative impacts of multiple landuse practices. The scale of the monitoring effort provides resolution at a regional scale (100’s km2) over a long period of time (i.e., decades). Hence, the specific objectives for terrestrial plants must provide data on comparable changes in different forested ecoregions. Furthermore, these indicators must broadly represent groups of taxa or forest structures that are likely to change over time.
If objectives are relatively unambiguous then indicator selection should flow easily. Indicators are represented by two classes: • Changes in a species, species group, or community whose abundances are most adversely affected by changes in land management. • Changes in a habitat variable, species, species group, or community whose response signals a change in the abundance of other, more sensitive species, species group, or community.
Difficulties in the selection of indicators usually reflect poorly stated or vague objectives. In practice, indicators must be logistically and financially feasible. Hence, in considering indicators one has to be mindful of the sampling methodologies available.
Once the indicators have been selected, sampling protocols can be developed. For terrestrial vascular plants, there is a long history and large body of literature associated with sampling and long term monitoring (Appendix 13.1). Our task is to select and integrate methods that are compatible and provide reasonable data quality. After these initial steps, most monitoring programs put forward pilot projects to test the statistical viability of linkages between indicators, methodology, and objectives (Hinds 1984).
The remainder of this chapter discusses the foreseeable changes on the forested landbase that will impact terrestrial vascular plant species. These probable changes are used to derive objectives for monitoring. In turn, the objectives are used to nominate candidate indicators. Lastly, we will assess methodologies for measurement of indicators and a tentative protocol is described. A number of caveats should be considered when reading the remainder of this chapter. This document does not present a comprehensive catalogue of potential land development impacts nor an exhaustive review of vascular plants or forest structures for Alberta. Instead, we attempt to provide some underlying rationale for the definition of objectives, selection of indicators, and development of protocols. As the initial investigation of this issue, we have focused on impacts with a historical record. By doing this we will miss some potential objectives and indicators, however, the monitoring program is intended to be dynamic. As the impact of current and future land uses become apparent through research, these objectives and indicators have every opportunity of being incorporated into this program.
Objectives in Monitoring Terrestrial Vascular Plants
Objective 1: Trends in Land Conversion over the Forested Regions
The largest scale of change for vascular plants is the direct conversion of the vegetation landbase as a result of anthropogenic activities. At its most severe, natural cover types (i.e., native tree species) are
2
converted to a non-vegetated cover (i.e., industrial, recreational, or municipal facilities), or to a heavily human-influenced cover type (i.e., agricultural lands, tree plantations, or some forms of enhanced forestry). A shortlist of activities responsible for land conversion includes: • Agricultural conversion • Enhanced forestry • Mining development • Municipal establishment and expansion • Oil/gas development • Recreational development • Transportation corridors • Waterway diversion
Though less frequent, developed lands may be converted back to natural cover. Activities featuring back conversion would include: • Abandonment of agricultural land • Enhanced forestry • Mining reclamation • Oil/gas reclamation
At gross levels, the area of cover type is the most important parameter in determining the diversity and abundances of species (Harris 1984; Franklin and Forman 1987). As a general rule, larger areas support more species (MacArthur and Wilson 1967). The conversion of landbase as measured by changes to cover type is a direct measurement of losses to vascular plants, particularly in forested regions. The success of back conversion to ease these losses depends upon the relative success of returning developed lands to natural cover types and having these plant communities support the pre-disturbance wildlife species and ecological functions.
The issue of human induced cover types is further complicated because it takes place against a background of natural succession. Species composition and structure can vary a great deal depending on stand age. In general, stands change greatly in composition and structure immediately after disturbance but slow down as stands age (e.g., Graham et al. 1963; Corns and La Roi 1976; Franklin and Hemstrom 1981). Because different stand ages support different assemblages of plants and animals, they need to be recognized and tracked as different entities (e.g., Ruggierio et al. 1991; Stelfox 1995).
Aside from the types of cover, time since disturbance needs to be a component of landbase classification and monitoring. Management practices that alter stand ages can place some unique communities (e.g., old seral stages) at risk. The combined effects of fire suppression and forest harvesting in Alberta’s Forested regions will potentially shift the current age-class distribution to a more regulated and constant distribution (Van Wagner 1978). In historic terms, the shift from a stochastic disturbance distribution to a regulated distribution has meant that stands are much less likely to reach old seral stages, i.e., old growth. Under current harvest practices in Alberta, rotation ages vary between 70 and 100 yrs depending on the forested ecosystem. As in other jurisdictions, technological advances in wood utilization, growth, and regeneration tend to further decrease rotation ages. As an example, advances in cutblock regeneration and shifts in timber markets to smaller stock, pushed the rotation of coastal Douglas fir (Pseudotsuga menziesii) from 120 yrs to 40 and 50 yrs on some ownerships in the United States Pacific Northwest (Curtis and Carey 1996). This subsequently resulted in declines of species associated with older seral stages (Carey 1989). There are current initiatives to rethink these relatively short rotations and develop options for longer rotations periods (Lippke et al. 1996).
3
Aside from the potential loss of older seral stages, there is a loss of wildfire-origin communities in early seral stages. Recent work in boreal forests indicates that these communities are unique and impossible to replicate through timber harvest practices (Crete et al. 1995; Schimmel and Granstrom 1996; Lynham et al. 1998; Hobson and Schieck et al. 2000). The loss of early wildfire-origin seral stages has already impacted a number of other forested ecosystems in North America (Pyne et al. 1996).
Despite our current efforts in Alberta, catastrophic wildfires still occur relatively frequently on the landscape. Furthermore, the ability to salvage burned stands particularly from catastrophic years, currently exceeds the abilities of the industrial sector. Hence, wildfire-origin stands still occur in Alberta. This may not be the case in the future as increasing sophistication of wildfire protection, fragmentation of fuel blocks, better access, and improved technology for salvage logging may potentially create declines in the proportion of wildfire communities able to naturally regenerate. Both, potential declines of young, wildfire-origin communities and older seral stage communities are important reasons to monitor the stand-origin and age-class distribution of forested cover types.
Monitoring the landbase and landbase conversion is probably best handled through remotely sensed data. As opposed to ground-based techniques, remotely sensed data more efficiently captures relatively large areas. Landscape monitoring can practically classify, measure, and track different cover types. This chapter will identify a number of important indicators for measuring landscape conversion. However, the protocol development is handled within Chapters 7 and 8.
Objective 2: Evaluate Species and Community-Level Heterogeneity in Intact Cover Types
A second level of impact on vascular plants is represented by more subtle changes in small-scale diversity (i.e., alpha diversity) of forests. The landbase may be temporarily disturbed for purposes of resource extraction or development activity. In this case, plant communities are either partially or completely removed with the expectation that they will be regenerated or reclaimed at a later date. Currently in Alberta, most forest management areas are in their first rotation of extensive timber harvest, and oil/gas and mining development continue to develop natural lands. The long-term goal for this landbase is either the repeated regeneration of the existing cover type or the eventual reclamation of facilities to natural cover types. Monitoring would focus on the question of whether the regeneration or reclamation activity was successful in recapturing the pre-disturbance diversity and abundance of species.
Alternatively, local cover types may remain unchanged but neighbouring areas are being affected by the outcome of anthropogenic activities. The opening of relatively isolated areas to access has historically produced a number of changes in the species assemblage of formally intact forests. This occurs despite no changes in the forest cover types (reviewed in Saunders and Hobbs 1991; Baker 1998; Forman and Alexander 1998). Some potential factors that may affect neighbouring intact forests include: • Changes in the broad flow patterns of nutrients and moisture • Changes in propagule sources • Introduction of pathogens • Introduction of herbivores • Introduction of exotic or weed species
The impact of roads and cutblocks on the local nutrient and hydrological pattern is well documented in a number of different forest types (Stoeckler 1965; Packer 1967; Hewlett 1982; Yousef et al. 1985; Kimmins 1987; Gilson et al. 1994; Jones and Grant 1996; Jurgenson et al. 1997). Changes in nutrient, moisture, and light levels shift the competitive advantage within most cutblocks at least temporarily
4
favoring shrub, grass species, and shade-intolerant trees such as aspen (e.g., Drew 1988; Hogg and Lieffers 1991; Bell et al. 1997). Once established, these perennial species can be very difficult and expensive to control. Furthermore, the dominance of these species displaces other species such as low herbs and non-vascular plants. The repeated cycle of harvest and regeneration allows an opportunity for these species to become established and persist in high abundance.
Nearby roads or other developments may facilitate access of agents potentially damaging to native vascular plants. These include pathogens, herbivores, and competitive weed species. Once established these species can be very destructive both ecologically and economically (Amor and Stevens 1976; Bennett 1991; Panetta and Hopkins 1991; Tyser and Worley 1992; Lonsdale and Lane 1994; Schmidt 1989). Furthermore, these species may lack natural pathogens and predators further inhibiting attempts to remove or control them once established. In a review of factors that produced endangered species, Wilcove et al. (1998) argued that introduction of exotics was the second most important reason behind overall loss of habitat.
In Alberta, the potential for the spread of invasive species is great. Relatively few operators work over a very large landbase. Currently, the number of access routes in the Green zone is relatively limited. That is, there are few corridors with heavy traffic patterns. Establishment of exotics in a few key locations would greatly increase the probability of spread through much of Alberta. The objective for monitoring is to track the impact of exotic agents on vascular plant communities.
Unlike the previous objective, this objective focuses on management activities that retain the natural cover type of the landbase while attempting to integrate human activities. The smaller spatial scale of changes may make them difficult to detect using remotely sensed data, thus monitoring would require ground plots. Repeated measures on permanent sample plots are the best method to collect this data (Austin 1981; Herben 1996). These ground plots should be linked to landscape data gathered over the matrix in which the plots are embedded. These data would potentially capture influences from neighbouring land development into the ground plot.
Objective 3: Evaluate Forest Structure Heterogeneity in Intact Cover Types
The third type objective in terrestrial vascular plant monitoring focuses on the use of forest structures by other organisms, i.e., habitat. The measurement of forest structure provides important explanatory variables to underlying patterns of other organisms (Spies 1998). Also, they potentially serve as an important early warning for changes in the abundances of difficult to measure species including soil organisms and endangered species. Forest structures considered in the program would include: • Deadwood resources, i.e., snags, logs, stumps, soil carbon • Vertical strata of shrubs • Canopy characteristics
Deadwood resources are a significant feature of forested ecosystems (Harmon et al. 1986). Until relatively recently, deadwood has been seen as a wasteful even undesirable component of forests. In part, this view has its roots in wildfire management, concerns over worker safety, disease and pest control, and the utilization dogma (Hagan and Grove 1999). While all of these are legitimate concerns, the ecological role of deadwood resources must be addressed. Deadwood forms a significant portion of total fibre volume within stands. As an example, in boreal systems, the total downed wood volume can equal the merchantable volume at rotation (Kirby et al. 1957; Lee et al. 1997). Due to its significance in forested systems, it is not surprising that a large number of forest species are dependent upon deadwood resources. In the northeastern United States, 28 species of birds, 18 mammals, 23 reptiles and amphibians and
5
hundreds of species of fungi and invertebrates make use of deadwood during their lifecycles (Degraaf and Rudis 1986; Keddy and Drummond 1996). In the Blue Mountains of Washington and Oregon, 45% of vertebrates are dependent upon fallen logs (Maser et al. 1979).
In forest systems where practices have led to a significant reduction in the density and sizes of snags and downed logs, significant losses of biota have occurred. Elimination of large snags from the Sierra Nevada forests of the western United States and Finland has reduced the abundance of cavity-nesting birds by 77% and 44%, respectively (Haapanen 1965; Raphael and White 1984). Losses of large downed logs from Swedish forests are believed to account for a considerable portion of threatened non-vascular plant species (Rydin et al. 1997). Lowered quantities of snags and downed logs eventually work their way through the ecosystem producing reduced inputs of carbon into the soil (Minderman 1968; Carey 1980). These losses are part of an ongoing debate concerning overall losses of nutrients in repeatedly harvested forests (Maser and Trappe 1984; Dutch 1993).
A number of current forestry practices can lead to the potential reduction of deadwood and soil carbon. These include loss of older seral stages through short-rotation logging, pre- and post-commercial thinning, burning of cutovers, and road-side processing. Losses within a single rotation may be relatively small, however, compounded over successive cycles of harvest and regeneration these losses have the potential to accumulate. In Sweden, losses of deadwood-dependent species has been one of the impacts of long-term timber harvest (Ostlund et al. 1997; Linder and Ostlund 1998). Of threatened species in Sweden, 21% were linked to snags (n~312 species) while 26% were linked to logs (n~390 species) (Berg et al. 1994). Clearly, maintenance of deadwood resources is an important long-term management issue. Monitoring on a long-term basis would provide critical data on losses and gains over the entire suite of land management practices.
Like deadwood resources, canopy structure has widespread ramifications on the function of the forested ecosystem and its suitability to support other species. Canopy structure includes canopy layering, canopy gaps, canopy transparency, and species composition. Canopy structure plays a significant role in the regeneration of trees as well as other understory species (e.g., Ehrenfeld, 1980; Collins and Pickett 1987; Gray and Spies 1997; Kneeshaw and Bergeron 1998; Wright et al. 1998). Canopy structure also influences a number of animal communities including invertebrates (Schowalter et al. 1981; Crossely et al. 1988; Schowalter and Sabin 1991; Reynolds and Crossley 1997), birds (Rodewal and Smith 1998; Buford and Capen 1999), arboreal mammals (Bendel and Gates 1987; Crome and Richards 1988; Crampton and Barclay 1998), ground-dwelling mammals (Waters and Zabel 1998), and ungulates (Demarchi and Bunnell 1993; Cook et al. 1998).
As with deadwood resources, a number of current and planned forest management initiatives in Alberta may alter canopy structure. A number of merchantable timber leave patterns are currently practiced or being considered by the government and forest companies. Similarly, residual clumps of merchantable trees are being incorporated into some silvicultural practices, e.g., shelterwood or selection cuts. Both types of residual patterns will produce distinctive heterogeneous, canopy patterns at the time of harvest and throughout succession. In contrast, some forestry practices favor systematic tree spacing and uniform growth. Practices such as pre- and post commercial thinning will produce more uniform canopies with a loss of vertical stratification. In turn, this promotes a more even canopy and removes the vertical structure of the canopy.
Lastly, much of Alberta’s forested landbase is a mixed canopy of deciduous and coniferous tree species (Anon. 1994). Despite a growing recognition of the ecological, economic, and administrative benefits of
6
mixedwood management, policies and practices still encourage the loss of the mixedwood landbase (Grover and Greenway 1999). Schieck et al. (2000) found that older, mixedwood forests maintained a higher diversity of species than either pure deciduous or coniferous forests alone. As with other sub- landscape level measures, monitoring of canopy composition would track changes in cover types that may signal changes in the composition of biotic communities.
In summary, we have defined three basic objectives to the monitoring of vascular plants and their communities. Firstly, the detection of broad land conversion from natural cover types to human- influenced cover types. Also, the conversion of human-influenced cover types back to natural cover types through reclamation or regeneration. Secondly, the detection of more subtle changes in terrestrial vascular species and communities due to cycles of human-influenced disturbance and succession, and the proximity of land conversion and access to natural cover types. Lastly, the detection of changes to the underlying forest structure derived from terrestrial vascular plants.
Candidate Terrestrial Vascular Plant and Forest Structure Indicators
This section puts forward three groups of indicators based on our objectives. Although a number of criteria for selecting indicators are available (Davis 1989; White and Bratton 1980), we have based our selection on known linkages between ongoing and future transformations to the landscape and potential changes in vascular plants and forest structure. The designation of indicators remains tentative with a number of steps remaining prior to final acceptance. These include further general discussion on the relative merits of each indicator, integration with indicators from other monitoring groups, and testing the feasibility of measurement during the pilot phase. In selecting indicators for vascular plants, we attempted to avoid overlap with other monitoring programs including rare elements, endangered species, and commercial timber species. The provincial government already mandates or operates programs to track both rare elements and endangered species in Alberta (Lee and Hanus 1998). Similarly, industry and the provincial government collect and maintain large databases on the productivity of commercial timber species in Alberta (Lee and Hanus 1998).
Objective 1 Indicators: Landscape Diversity
• Conversion of forested landbase to non-vegetation landbase • Conversion of forested landbase to non-forested but otherwise vegetated landbase • Conversion of non-vegetation landbase to forested landbase • Conversion of non-forested but otherwise vegetated landbase to forested landbase • Age-class distribution of forested cover types • Area of different natural and human-influenced cover types
Objective 2 Indicators: Species and Community Diversity
• Presence/absence of non-native or non-regional species i.e. weedy, invasive species • Composition and relative abundance of grass species • Composition and relative abundance of shrub species • Species richness • Diversity and abundance of understory community types e.g., plant community type
7
Objective 3 Indicators: Forest Structure Diversity
• Snag density, diameter, and condition • Down woody material diameter, size, and condition • Soil carbon • Canopy roughness (measured through remote sensing) • Canopy transparency
Methods for Sampling Terrestrial Vegetation and Forest Structure
This section presents a general review of methods available for ground sampling of vascular vegetation. Methodology for sampling the overall landbase and landbase conversion indicators are discussed in the remote sensing chapters and will not be further discussed in this section. In general, the procedures for ground sampling vascular vegetation is well developed and documented. This section will focus on the applicability of each protocol type for each of the indicators.
Plots (Ocular % Cover; direct density/volume estimation)
Plot-based sampling is a commonly used design for measuring terrestrial vegetation cover. Plant cover is defined as the area of ground that is occupied by the above-ground parts of each species when viewed from above (Kent and Coker 1992). All methods of plant cover estimation depend on the interception of the plant by a quadrat of a known area (Bonham 1989). Usually plants must be rooted within the quadrat to be recorded. Cover is typically estimated as a visual percentage; however, total cover estimates may exceed 100% as a result of multiple layering of the vegetation. Although species can be estimated from 1% to +100% over a uniform interval (e.g., +1%), the distribution of plant species often features a few very common and many rarer species. This has led to the development of a number of classification schemes biased towards lower cover values (Tables 13.4 - 13.6). These classification systems may simplify and speed visual estimation while retaining most of the crucial abundance information.
Plot size generally depends on vegetation type and limits of detection. In general, the less densely packed and larger the species, the larger the plot. Table 13.1 provides some common plot size ranges for different vegetation types. Protocols requiring the measurement of different species groups use nested plots, thereby capturing variances among different plant types. Figure 13.1 illustrates the nested plot design, which shows three different plot sizes embedded within one another.
Table 13.1 Common plot sizes for different plant types
Vegetation Type Plot Size Forbs and grasses 0.40 m2 to 1 m2 Low shrubs (<1 m) 1 m2 to 4 m2 Tall shrubs (>1 m) 4 m2 to 10 m2 Trees and snags 400 m2 to 1000 m2
8
Figure 13.1 Nested plot design
10m
4m 10m
4m 1m
1m
The number of plots depends upon the spatial distribution of species. Generally, the more clumped a species, the more plots that are required. Alternatively, the number of plots can be determined by calculating the desired proportion of the stand included in the sample design. Most studies adjust the quadrat size and number to include 1-20% of the stand into the sample (Barbour et al 1987). Table 13.2 lists the typical number of plots for various plant types.
Table 13.2 Typical plot size for different vegetation types
Vegetation Type Number of Plots Forbs and grasses 7 to 40 Low shrubs (<1 m) 5 to 20 Tall shrubs (>1 m) 5 to 10 Trees 1 to 4 Snags 4 to 10
Line Intercept
Line intercept is another common method used to determine plant percent cover. First described by Transley and Chipp (1926), the line intercept method is most often used for sampling woody species (Barbour et al 1987), grasslands (Willoughby 1998), and downed woody material (Harmon et al. 1986). It is applicable in areas with either sparse (Kent and Coker 1992) or dense (Bonham 1989) vegetation cover. As illustrated in Figure 13.2, this method involves laying down a line and measuring the length of
9
each species intersected (Bonham 1989). Figure 13.3 illustrates the belt transect method which is a modified line intercept. The belt transect method samples plants within a certain width of the line. For both variations of the line intercept method, species percent cover is determined by dividing the length of the line intercepted by a species with the total length of the line (Floyd and Anderson 1987). As with plot surveys, the total cover can exceed 100%.
Figure 13.2 Line Intercept Technique
d d
Plant Line Intercept
Percent Cover = sum d x 100 where, d = distance along transect tape length sum d = total intercept distance
10
Figure 13.3 The belt transect method
d d d
Plan t Belt Transect
Percent Cover = Sum d x 100 where d=distance along transect tape length Sum d = total intersect distance
The length of the line depends upon the species percent coverage and the logistics of handling the line in the field. In general, elements and species with 5-15% ground cover require at least 15 m of line while elements/species with less than 5% cover require at least 30 m of line. Table 13.3 outlines the common lengths associated with various plant types.
Table 13.3 Typical line intercept lengths associated with various vegetation types
Vegetation Type Line Lengths Forbs and grasses 10 m to 100 m Low shrubs (<1 m) 30 m to 200 m Tall shrubs (>1 m) 100 m to 200 m Downed woody material (DWM) 75 m to 200 m
Point Frame
Point frame sampling provides a more objective method than ocular estimation of cover. This method utilizes a group of pins distributed evenly in a frame. At each sampling point, the pins are dropped, and only the individuals in contact with the pins are counted. Usually frames contain 10 pins (Figure 13.4); however, any number of pins may be used depending on the growth pattern of the plants being sampled (Bonham 1989). The main disadvantages are an increase in effort per sample and the loss of rare species from the sample.
11
Figure 13.4 Point frame with 10 pins.
Pin Frame
Other Techniques
A number of other vegetation sampling designs exist, including distance methods (also called variable plot or plotless methods) and variable radius techniques. In practice, both methods are primarily used to measure large elements, such as trees, snags, and tall shrubs. The number of measurements depends upon the spatial distribution of the population.
Distance methods have been used since the 1950’s to measure plant density (Bonham 1989). These methods do not use rigid boundaries such as quadrats, lines, or point frames (Barbour et al 1987). Instead, plant density is estimated from the average distance between two plants or between a plant and a point. Some examples of distance methods include the nearest neighbour, nearest point, order methods, and quarter methods. These methods are reviewed in length by a number of authors including Cottam and Curtis (1956), Lindsey et al. (1958), Mueller-Dombois and Ellenberg (1974), and Bonham (1989). As an example, the nearest neighbour technique (Figure 13.5) is designed around a randomly selected point from which a plant is selected (plant 1). The distance from plant 1 to the nearest plant (plant 2) is measured to determine plant density. Overall, distance methods have been criticized for being overly sensitive to departures from a spatially random population. In general, methods that sample more than one individual per point, e.g., point-centred quadrat, are less sensitive to departures from randomness. However, these methods are also fairly complex and somewhat more time consuming.
12
Figure 13.5 Example of the distance method for vegetation sampling: the nearest neighbour technique
Randomly Located Point Plant 3
d
d = distance
Plant 2 Plant 1
The primary variable radius method was first developed by Bitterlich (1948) to examine canopy coverage. Later, the application of the variable radius technique was expanded to include tree, shrub, and grassland vegetation types (Bonham 1989). Each plant counted using this technique is considered as a separate plot. Generally, the variable radius method uses an angle gauge or basal area prism to count trees or shrubs within view from a centre point (Figure 13.6). Plants included in the sample are only those that can be observed from the sampling point, and those larger than the field of view of the angle gauge. Data collected from variable radius techniques are useful for calculating basal area in square meters per hectare.
Figure 13.6 The variable radius technique using an angle gauge
Plant Not Counted
Plant Counted Plant Counted
View Using an Angle Prism Sampling Point
Plant Counted
Plant Counted
Plant Not Counted
13
Techniques such as wandering point quadrats are not reliant on spatial distribution (Figure 13.7; Catana 1963). This design measures the distance of the nearest plant within a 90° angle of the first point. After the first plant is measured, it becomes the point from which the next plant is located. In general, these techniques are tied to specific mathematical analysis.
Figure 13.7 Example of a distance method free of spatial distribution: the wandering point technique
P2 P3 P1 P1 is nearest plant d3
P2 is nearest plant in 90 P2 degree angle
d2 P1
P2 P1 is nearest plant in 90 P1 degree angle
d1 90° d = distance measured from plant to plant
1st plant, randomly
selected
transect starting point
Assessment of Various Techniques
The selection of appropriate sampling protocols for measuring the terrestrial vascular plants and forest structures were based on the following criteria: ♦ Detectability of uncommon species ♦ Objectiveness of estimation ♦ Robustness for departures from random distribution ♦ Flexibility for analysis ♦ Historical datasets ♦ Simplicity of protocol ♦ Degree of training/standardization
Plot-based sampling is highly applicable to the AFBMP. A significant advantage of using plot cover estimates over other techniques lies in its ability to provide robust, comparable data to other studies and monitoring (Bonham 1989). Significant amounts of data have been collected using plot-based sampling, including Alberta Permanent Sample Plot Program established in the mid 1960’s (Lands and Forest
14
Service 1998), projects at the Alberta Research Council (e.g., Stelfox 1995; Lee 1999; Schieck et al. 2000), and research from the University of Alberta (e.g., Schmiegelow and Hannon 1999; EMEND 2000; TROLS 2000). Furthermore, plot-based sampling is robust because it can detect uncommon species, and a wide variety of statistical analyses can be applied to the data. Since data is gathered by ocular estimation, observer biases may exist. However, standardization of observers through training and experience can significantly reduce or eliminate biases. Observer bias can be greatly reduced by using a classification system (Tables 13.4 - 13.6). The technique itself is easily learned and relatively practical in a field environment. Actual plant identification to the genus and species level likely remains the most challenging aspect of vegetation sampling. This problem exists regardless of technique.
Table 13.4 The Braun-Blanquet cover-abundance scale
Rating Number of Plants Area Occupied by a Species + Sparsely or very sparsely present Very small 1 Plentiful Small 2 Very numerous 10-25% 3 Any number 25-50% 4 Any number 50-75% 5 Any number >75% From Braun-Blanquet, J. 1965. Plant Sociology: The Study of Plant Communities. Hafner, London.
Table 13.5 The Daubenmire cover scale
Cover Class Range of Cover (%) Class Midpoints (%) 1 0-5 2.5 2 5-25 15.0 3 25-50 37.5 4 50-75 62.5 5 75-95 85.0 6 95-100 97.5 From Daubenmire, R. F. 1959. A canopy coverage method. Northwest Science 33:43-64.
Line intercepts provide robust datasets and data gathering is more objective, repeatable, and standardized than plot data. Like the plot method, line intercept is easily learned. Some advantages of the line intercept over the plot method are its direct measurement of vegetation as opposed to visual estimation (Hanley 1978) and data can be collected more rapidly and more accurately in communities with different sized plants (Bonham 1989). The primary drawbacks of the line intercept technique include its tendency to miss rare species, limited number of analyses that can be applied to the data, and difficulty of application to herbaceous plant sampling in forested areas. Line intersects can be easily applied in the open habitats such as grasslands, but line lengths >50 m are difficult to lay out efficiently in areas with a dense understory or canopy trees.
15
Table 13.6 The Domin-Krajina cover-abundance scale
Rating Number of Plants of Each Species Cover (%) of the Species 10 Any number 100 9 Any number >75, but <100 8 Any number 50-75 7 Any number 33-50 6 Any number 25-33 5 Any number 10-25 4 Any number 5-10 3 Scattered 1-5 2 Very scattered <1 1 Seldom Insignificant + Solitary Insignificant From Krajina, V. J. 1969. Ecology of Forest Trees in British Columbia. In Ecology of Western North America. University of British Columbia Press, Vancouver.
Point frame methods include some of the least subjective techniques for sampling vegetation. It is also a technique that is easily learned by field technicians. However, point frames do not effectively capture non-dominant species. Also, data collection using point frames tend to be relatively time consuming.
Distance methods are most practical for sampling large elements such as trees and snags. Therefore, they have a relatively narrow application within AFBMP. Although distance methods are objective, replicable and easily learned, they tend not to be robust for variances from a spatially random distribution. Knowledge of the underlying spatial patterns is required or assumed; hence, methods applicable in one forest type may not be applicable in another. Furthermore, these techniques generally are inefficient for plant community based studies and do not effectively capture non-dominant elements. Analysis capabilities are reasonable and relate mainly to plant cover, density and basal area assessment.
Variable radius techniques provide objective data for woody species, namely trees and tall shrubs. Historically, this technique has not been used for measuring herbaceous plants, grass, and low shrubs, and as a result its application is not as broad as the plot and line intercept methods. Also, the variable radius technique is limited in its ability to detect non-dominant species and derive representative samples. From a technical perspective, these protocols require minimal training beyond the plant identification. Data analysis is generally limited to calculating basal area, a unit of measure more applicable to forestry practices than vegetation monitoring.
Summary of Methodology Assessment
Table 13.7 compares the different methods and ranks each one in terms of its applicability to the AFBMP. It suggests that the plot method is the most suitable technique for sampling vascular plants and stand structure. The line intercept method is most suitable for sampling downed woody material and soil microtopography, but also relatively effective for sampling herbs, grass, and tall shrubs. In contrast, pin frames may be applied to herbaceous plants, grass, low shrubs, and DWM. Relative to plots and line intercepts, they have significant disadvantages. Distance techniques are only suitable for measuring tall shrubs and snags, and the variable radius technique has relatively little utility beyond tree sampling.
16
Table 13.7 Applicability and comparison of different methods (ranked 1 to 4 with 1 being best suited, N/A = method not applicable)
Techniques Elements Plots Line Pin and Rack Distance Variable Radius Forbs/grasses 1 2 4 3 N/A Low shrubs 1 2 4 3 N/A Tall shrubs 1 3 N/A 2 4 Trees 1 4 N/A 3 2 Canopy openness 1 N/A N/A N/A N/A Snags 1 4 N/A 2 3 Down woody material 2 1 3 N/A N/A
Appendices 13.2 - 13.4 and Appendix 5.2 contain the recommended protocol and data sheets for terrestrial vascular plants.
References
Alberta Agriculture, Food and Rural Development. 1997. Weed Control Act. < www.agric.gov.ab.ca/ministry/acts/regs/ar138-80.html. > Alberta Natural Heritage Informatin Center. 2000. Plants of Conservation Concern. < www.gov.ab.ca/env/parks/anhic/definit.html > Amor, R. L. and P. L. Stevens. 1976. Spread of weeds from a roadside into sclerophyll forests at Dartmouth, Australia. Weed Research 16: 111-118. Anon. 1990. Alberta Vegetation Species List and Species Group Checklists. Alberta Energy, Forestry, Lands and Wildlife. Edmonton, Alberta. Anon. 1994. Natural Regions and Subregions of Alberta: Summary. Alberta Environmental Protection. Edmonton, Alberta. 18p. Archibald, J. H., G. D. Klappstein, and I. G. W. Corns. 1996. Field Guide to Ecosites of Southwestern Alberta. Natural Resources Canada, Canadian Forest Service, Northwest Region, Northern Forestry Centre. Edmonton, Alberta. Special Report 8. Austin, M. P. 1981. Permanent quadrats: An interface for theory and practice. Vegetatio 46: 1-10. Baker, B. 1998. Rethinking roads in the nation's forests. Bioscience 48: 156-166. Barbour, M., J. Burk, and W. Pitts. 1987. Terrestrial Plant Ecology. Ontario. The Benjamin/Cummings Publishing Company, Inc. 634p. Beckingham, J. D. and J. H. Archibald. 1996. Field Guide to Ecosites of Northern Alberta. Natural Resources Canada. Canadian Forest Service, Northwest Region, Northern Forestry Centre. Edmonton, Alberta. Special Report 5. Beckingham, J. D.I. G. W. Corns, and J. H. Archibald. 1996. Field Guide to Ecosites of West-Central Alberta. Natural Resources Canada. Canadian Forest Service, Northwest Region, Northern Forestry Centre. Edmonton, Alberta. Special Report 9. Bell, F. W., R. A. Lautenschlager, R. G. Wagner, D. G. Pitt, J. W. Hawkins, and K. R. Ride. 1997a. Motor-manual, mechanical and herbicide release affect early successional vegetation in northwestern Ontario. Forestry Chronicle 73(1): 61-68. Bendel, P. R., and J. E. Gates. 1987. Home range and microhabitat partitioning of the southern flying squirrel (Glaucomys volans). Journal of Mammalogy 68: 243-255.
17
Bennett, A. F. 1991. Roads, roadsides, and wildlife conservation: a review. Pp. 99-117 in Nature conservation 2: the role of corridors, eds. D. A. Saunders and R. J. Hobbs. Chipping Norton, Australia: Surrey Beatty and Sons. Berg, A., B. Ehnstrom, L. Gustafsson, T. Hallingback, M. Jonsell, and J. Weslien. 1994. Threatened plant, animal, and fungus species in Swedish forests: Distribution and habitat associations. Conservation Biology 8: 718-731. Biterlich, W. 1948. Die Winkelzahlprobe. Allg. Forst-und-Holzwirtsch. Ztg. 59: 4-5. Bonham, C. D. 1989. Measurements for Terrestrial Vegetation. John Wiley and Sons. Toronto. 338p. Braun-Blanquet, J. 1965. Plant Sociology: The Study of Plant Communities. Hafner, London. Buford, E. W., and D. E. Capen. 1999. Abundance and productivity of forest songbirds in a managed, unfragmented landscape in Vermont. Journal of Wildlife Management 63(1): 180-188. Bunnell, F. L. 1998. Setting goals for biodiversity in managed forests. Pp. 177-153 in The living dance: policy and practices for biodiversity in managed forests, eds. F. L. Bunnell and J. K. Johnson. Vancouver, B.C.: University of British Columbia Press. Carey, A. 1989. Wildlife associated with old-growth forests in the pacific northwest. Natural Areas Journal 9: 151-162. Carey, M. L. 1980. Whole tree harvesting in Sitka spruce. Possible implications. Irish Forestry 37: 48- 63. Catana 1963. REFERENCED IN TEXT Collins, B. S. and S. T. A. Pickett. 1987. Influence of canopy opening on the environment and herb layer in a northern hardwoods forest. Vegetatio 70: 3-10. Cook, J. G., L. L. Irwin, L. D. Bryant, R. A. Riggs and J. W. Thomas. 1998. Relations of forest cover and condition of elk: A test of the thermal cover hypothesis in summer and winter. Wildlife Monographs 62(4): 61. Corns, I. G. W. and G. H. La Roi. 1976. A comparison of mature with recently clear-cut and scarificed lodgepole pine forests in the Lower Foothills of Alberta. Canadian Journal Forest Research 6: 20- 32. Cottam, G., and J. T. Curtis. 1956. Use of distance measures in phytosociological sampling. Ecology 37: 451-460. Crampton, L. H. and R. M. Barclay. 1998. Selection of roosting and foraging habitat by bats in different- aged aspen mixedwood stands. Conservation Biology 12: 1347-1358. Crete, M., B. Drolet, J. Huot, M. J. Fortin, and G. J. Doucet. 1995. Post-fire sequence of emerging diversity among mammals and birds in the north of the boreal forest in Quebec. Canadian Journal of Forest Research 25(9): 1509-1518. Crome, F. H. J., and G. C. Richards. 1988. Bats and gaps: microchilropteran community structure in a Queensland rain forest. Ecology 69: 1960-1969. Crossley, D. A. Jr., C. S. Gist, W. W. Hargrove, L. S. Risley, T. D. Schowalter, and T. R Seastedt. 1988. Foliage consumption and nutrient dynamics in canopy insects. Pp. 193-205 in Forest hydrology and ecology at Coweeta, eds. W. T. Swank and D. A. Crossley, Jr. New York: Spring. Curtis, R. O. and A. B. Carey. 1996. Timber supply in the Pacific Northwest: managing for economic and ecological values. Journal of Forestry 94: 4-7, 35-37. Daubermire, R. F. 1959. A canopy coverage method. Northwest Science 33: 43-64. Davis, G. E. 1989. Design of a long-term ecological monitoring program for Channel Islands National Park, California. Natural Areas Journal 9(2): 80-89. Davis, G. E. 1993. Design elements of monitoring programs: the necessary ingredients for success. Environmental Monitoring and Assessment 26: 99-105. Degraff, R. D., and D. D. Rudis. 1986. New England wildlife: Habitat, natural history, and distribution. General Technical Report NE-108. Radnor, PA:USDA Forest Service. Demarchi, M. W., and F. L. Bunnell. 1993. Estimating forest canopy effects on summer thermal cover for Cervidae (deer family). Canadian Journal of Forestry Research 23: 2419-2426.
18
Drew, T. J. 1988. Managing white spruce in Alberta's mixedwood forest: the dilemma. In Management and utilization of northern mixedwoods, ed. J.K. Samoil. Can. For. Serv. North. For. Cent. Inf. Rep. NOR-X-296. Dutch, J. 1993. Intensive harvesting of forests: a review of nutritional aspects and sustainability implications. Pp. 41-57 in Assessing the effects of silvicultural practices on sustained productivity, eds. M. K. Mahendrappa, C. M. Simpson, and C. T. Smith. Proceedings of the IEA/BE workshop 1993. Canadian Forest Service, Fredericton. Elzinga, C. L., D. W. Salzer, and J. W. Willoughby. 1998. Measuring and Monitoring Plant Populations. Bureau of Land Management, Denver Colorado. BLM Technical Reference 1730-1. EMEND. 2000. Ecosystem Management by Emulating Natural Disturbances. Website
19
Hobson, K. A. and J. Schieck. 1999. Changes in bird communities within boreal mixedwood forest during the first thirty years: contrasting the effects of harvest and wildfire. Ecological Applications. In press. Hogg, E. H., and V. J. Lieffers. 1991. The impact of Calamagrostis canadensis on soil thermal regimes after logging in northern Alberta. Canadian Journal Forestry Research 21: 382-394. Jones, J. A. and G. E. Grant. 1996. Peak flow responses to clearcutting and roads in small and large basins, Western Cascades, Oregon. Water Resource Research 32: 959-974. Jurgensen, M. F., A. E. Harvey, R. T. Graham, D. S. Pagedumroese, J. R. Tonn, M. J. Larsen, and T. B. Jain. 1997. Impacts of timber harvesting on soil organic matter, nitrogen, productivity, and health of inland northwest forests [review]. Forest Science 43(2): 234-251. Keddy and Drummond (1996). Referenced in text. Kent, M., and P. Coker. 1992. Vegetation Description and Analysis. A practical approach. Belhaven Press. London. 363p. Kimmins, J. P. 1987. Forest Ecology. Macmillan Publishing Co. New York, New York. Kirby, C. L., W. S. Bailey, and J. G. Gilmour. 1957. The growth and yield of aspen in Saskatchewan. Saskatchewan Departement of Natural Resources, Forestry. Branch, Prince Albert, Saskatchewan. Technical Bulletin 3. Kneeshaw, D. D., and Y. Bergeron. 1998. Canopy gap characteristics and tree replacement in the southeastern boreal forest. Ecology 79(3): 783-94. Krajina, V. J. 1969. Ecology of Forest Trees in British Columbia. Ecology of Western North America 2: 1-146. Lands and Forest Service. 1998. Land and forest services permanent sample plot field procedures manual. Forest Management Division. Forest Resources Information Centre. Edmonton, AB. 110pp. Lee, P. (editor) 1999. Fire and Harvest Residual (FAHR) Project. Alberta Environment, Edmonton, Alberta. Publication No. T/495. 46pp. Lee, P., S. C. Crites, M. Nietfeld, H. Van Nguyen, and J. B. Stelfox. 1997. Characteristics and origins of deadwood material in aspen-dominated boreal forests. Ecological Applications 7: 691-701. Lee, P. and S. Hanus. 1998. Criteria and indicators for monitoring biodiversity in Alberta's forests: Part I: Review of legislation, policies, external agreements, and programs. Progress completion report prepared for Alberta Environmental Protection by the Alberta Research Council, Vegreville, AB. 123pp. Linder, P., and L. Ostlund. 1998. Structural changes in three mid-boreal Swedish forest landscapes, 1885-1996. Biological Conservation 85: 9-19. Lindsey, A. A., J. D. Barton, and S. R. Miles. 1958. Field efficiencies of forest sampling methods. Ecology 39: 428-444. Lippke, B. R., J. Sessions, and A. B. Carey. 1996. Economic analysis of forest landscape management alternatives. Joint Publication of Pacific Northwest Research Station, USDA Forest Service; Department of Natural Resources, Washington State; and College of Forest Resources, University of Washington. Printed by College of Forest Resources, University of Washington CINTRAFOR Special Paper 21. Lonsdale, W. M. and A. M. Lane. 1994. Tourist vehicles as vectors of weed seeds in Kakadu National Park, northern Australia. Biological Conservation 69: 277-283. Lynham, T. J., G. M. Wickware, and J. A. Mason. 1998. Soil changes and plant succession following experimental burning immature jack pine. Canadian Journal of Soil Science 78: 93-104. MacArthur, R. H. and E. O. Wilson. 1967. The theory of island biogeography. Monographs in Population biology, Princeton Univ. Press, Princeton, N.J. 203 pp. Maser, C. and J. M. Trappe. 1984. The seen and unseen world of the fallen tree. USDA Forest Service General Technical Report No. PNW-164. Maser, C., R. G. Anderson, and K. Cromack Jr. 1979. Dead and down woody material. Pp.78-95 in Wildlife habitats in managed forests: The Blue Mountains of Oregon and Washington, ed. J. Thomas. Agriculture Handbook #553. Washington, DC: USDA Forest Service.
20
Minderman, G. 1968. Addition, decomposition, and accumulation of organic matter in forests. Journal of Ecology 56: 355-362. Moss, E. H. 1994. Flora of Alberta. A manual of flowering plants, conifers, ferns and fern allies found growing without cultivation in the province of Alberta, Canada 3rd edition. Revised by J. G. Packer. Toronto: University of Toronto Press. Mueller-Dubois, D., and H. Ellenberg. 1974. Aims and methods of vegetation ecology. Wiley: New York. Ostlund, L., O. Zackrisson, and A. L. Axelsson. 1997. The history and transformation of a Scandinavian boreal forest landscape since the 19th century. Canadian Journal of Forest Research 27: 1198-1206. Packer, P. E. 1967. Criteria for designing and locating logging roads to control sediment. Forest Science 13: 01-18. Panetta, F. D. and A. J. M. Hopkins. 1991. Weeds in corridors: invasion and management. Pp. 341-351 in Nature conservation 2: the role of corridors, eds. D.A. Saunders and R.J. Hobbs. Chipping Norton, Australia: Surrey Beatty and Sons. Pyne, S. J., P. L. Andrews, and R. D. Laven. 1996. Introduction to Wildland Fire (2nd ed.). John Wiley and Sons, New York, New York. Pp. 566-596. Raphael, M. G., and M. White. 1984. Use of snags by cavity-nesting birds in the Sierra Nevada. Wildlife Monographs 86: 1-66. Reynolds, B. C., and D. A. Crossley, Jr. 1997. Spatial variation in herbivory by forest canopy arthropods along an elevation gradient. Environmental Entomology 26(6): 1232-1239. Rodewald, P. G., and K. G. Smith. 1998. Short-term effects of understory and overstory management on breeding birds in Arkansas oak-hickory forests. Journal of Wildlife Management 62(4): 1411-1417. Ruggerio, L. F., K. B. Aubrey, A. B. Carey, and M. Huff. (technical editors) 1991. Wildlife and vegetation of unmanaged Douglas-Fir forests. Pacific Northwest Research Station, USDA, General Technical Report, PNW-GTR-285. Rydin, H., M. Diekmann, and T. Hallingback. 1997. Biological characteristics, habitat associations, and distribution of macrofungi in Sweden. Conservation Biology 11: 628-40. Saunders, D. A. and R. J. Hobbs. (eds.) 1991. Nature Conservation 2: The Role of Corridors. Chipping Norton, Australia: Surrey Beatty. Schmiegelow, F. K. A. and S. J. Hannon. 1999. Forest-level effects of fragmentation on boreal songbirds: the Calling Lake Fragmentation Studies. Pp.? in Forest Fragmentation: Wildlife and Management Implications, ed. J. Wisniewski. Brill, Leiden. Schmidt, W. 1989. Plant dispersal by motor cars. Vegetatio 80: 147-152. Schieck, J. S. and L. D. Roy. 1995. Changes in vertebrate communities in relation to variation in forest community attributes: a comparison of bird and mammal communities. Pp. 241-261 in Relationships between stand age, stand structure, and biodiversity in aspen mixedwood forests in Alberta, ed. J. B. Stelfox. Jointly published by Alberta Environmental Centre (AECV95-R1), Vegreville, AB, and Canadian Forest Service (Project No. 0001A), Edmonton, AB. Schieck, J. S., S. Bradbury, S. Crites, L. Marinelli, T. Sorenson, C. Gray, and D. McKinnon. 2000. Biodiversity in Cutblocks with Very Few Versus Many Small Residual Patches of Trees and Snags. Alberta Research Council, Alberta. 153pp. Schimmel, J. and A. Granstrom. 1996. Fire severity and vegetation response in the boreal Swedish forest. Ecology 77: 1436-1450. Schowalter, T. D. and T. E. Sabin. 1991. Litter microarthropod responses to canopy herbivory, season and decomposition in litterbags in a regenerating conifer ecosystem in western Oregon. Biology and Fertiity of Soils 11: 93-96. Schowalter, T. D., J. W. Webb and D. A. Crossley, Jr. 1981. Community structure and nutrient content of canopy arthropods in clearcut and uncut forest ecosystems. Ecology 62(4): 1010-1019. Silsbee, D. G., and D. L. Peterson. 1993. Planning for implementation of long-term resource monitoring programs. Environmental Monitoring and Assessment 26: 177-185.
21
Spies, T. A. 1998. Forest structure: A key to the ecosystem. Northwest Science 72(Special Issue 2): 34- 39. Stelfox, J. B. 1995. Relationships between stand age, stand structure, and biodiversity in aspen mixedwood forests in Alberta. Jointly published by Alberta Environmental Centre (AECV95-R1), Vegreville, AB, and Canadian Forest Service (Project No. 0001A), Edmonton, AB. 301pp. Stoeckeler, J. H. 1965. Drainage along swamp forest roads: lessons from Northern Europe. Journal of Forestry 63: 771-776. Stout, B. 1993. The good, the bad and the ugly of monitoring programs: defining questions and establishing objectives. Environmental Monitoring and Assessment 26: 91-98. TROLS. 2000. Terrestrial and Riparian Organisms, Lakes, and Streams (TROLS) Project.
22
Appendix 13.1 Selected Bibliography for Monitoring of Terrestrial Vascular Plants
Concepts in Plant Monitoring Ausmus, B. 1984. An argument for ecosystem level monitoring. Environmental Monitoring and Assessment 4: 275-293. Austin, M. P. 1981. Permanent quadrats: an interface between theory and practice. Vegetatio 46: 1-10. Baker, J. P., H. Olff, J. H. Willems, and M. Zobel. 1996. Why do we need permanent plots in the study of long-term vegetation dynamics? Journal of Vegetation Science 7: 147-156. Baskin, J. M. and C. C. Baskin. 1986. Some considerations in evaluating and monitoring populations of rare plants in successional environments. Natural Areas Journal 6(3): 26-30. Bernes, C., B. Giege, K. Johannson, and J. E. Larson. 1986. Design of an integrated monitoring programme in Sweden. Environmental Monitoring and Assessment 6: 113-126. Buffington, J. D. 1980. A review of environmental data and monitoring. Pp. 5-7 in Biological monitoring for environmental effects, eds. D. L. Worf. Lexington, MA: Lexington Books, D.C. Health and Company. Davy. A. J., and R. L. Jeffries. 1981. Approaches to the monitoring of rare plant populations. Pp. 219- 232 in The biological aspects of rare plant conservation, eds. H. Synge. New York, NY: John Wiley & Sons. Duffy, J. J., G. Luders, and B. Ketshcke. 1981. Cost-benefit optimization for biota monitoring programs. Pp. 135-147 in Issues associated with impact assessment, eds. L. B. Jensen and M. D. Sparks. E.A. Communications. Eshelman, K. R. 1983. Vegetation monitoring and inventory on the public land. Pp. 79-83 in Renewable resource inventories for monitoring changes and trends: Proceedings of an international conference, eds. J. F. Belland T. Atterbury. 1983 August 15-19; Corvallis. OR. Corvallis, OR: Oregon State University, College of Forestry. Ferris-Kann, R. and G. S. Patterson. 1992. Monitoring vegetation changes in conservation management of forests. Forestry Commission Bulletin #108. 31pp. Given, D. R. 1989. Monitoring of threatened plants. Pp. 192-198 in Proceedings of a symposium on environmental monitoring in New Zealand with emphasis on protected natural areas, ed. B. Craig. 1988 May; Dunedin. Wellington: Department of Conservation. Goff, F. G., G. A. Dawson, and J. J. Rochow. 1982. Site examination for threatened and endangered plant species. Journal of Environmental Management 6: 307-316. Goldsmith, F. B. 1991. Vegetation monitoring. Pp. 77-86 in Monitoring for conservation and ecology, ed. F. B. Goldsmith. London: Chapman and Hall. Green, R. H., and R. C. Young. 1993. Sampling to detect rare species. Ecological Applications 3(2): 351-356. Hazlett, D. L. 1994. Vegetation monitoring guidelines for the intermountain wilderness area and ecosystem study. Fort Collins, CO: U.S. Department of Interior, National Biological Survey, Environmental Science and Technology Center. Holling, C. S., ed. 1978. Adaptive environmental assessment and management. New York, NY: John Wiley and Sons. 377p. Janz, K., and K. D. Sing. 1991. Assessment and monitoring of forest resources. Tenth World Forestry Congress, actes proceedings. Paris, France. 9-22pp. Kalton, G., and D. W. Anderson. 1986. Sampling rare populations. Journal of The Royal Statistical Society Series A 149: 65-82. Leak, W. B. 1992. Vegetation change as an index of forest environmental impact: considering a measure other than growth decline. Journal of Forestry 90: 32-35. Loeb, R. E. 1990. Measurement of vegetation changes through time by resampling. Bulletin of The Torrey Botanical Club 117: 173-175.
23
Owen, W. R., and R. Rosentreter. 1992. Monitoring rare perennial plants: techniques for demographic studies. Natural Areas Journal 12: 32-38. Ringold, P. L., J. Alergria, R. L. Czaplewski, B. S. Mulder, T. Tolle, and K. BurnettURNETT. 1996. Adaptive monitoring design for ecosystem management. Ecological Applications 6(3): 745-747. Schroeder, R. L., and M. E. Keller. 1990. Setting objectives – a prerequisite to ecosystem management. Pp. 1-4 in Ecosystem management: rare species and significant habitats, eds. R. S. Mitchell. NY State Museum Bull. 471. Albany, NY: New York State Museum. Stewart, G. H., P. N. Johnson, and A. F. Mark. 1989. Monitoring terrestrial vegetation for biological conservation. Pp. 199-208 in Proceedings of a symposium on environmental monitoring in New Zealand with emphasis on protected natural areas, ed. B. Craig. 1988 May; Dunedin. Wellington: Department of Conservation. Stohlgren, T. J. 1995. Planning long-term vegetation studies at landscape scales. Pp. 209-241 in Ecological time series, eds. T. M. Powell, and J. H. Steele. New York, NY: Chapman and Hall. Walters, C. J. 1986. Adaptive management of renewable resources. New York, NY: Macmillan. 374p. Wilson, J. B., and S. H. Roxburgh. 1994. A demonstration of guild-based assembly rules for a plant community, and determination of intrinsic guilds. Oikos 69: 267-276.
Large Scale Monitoring Design Armentano, T. V. 1981. Standardized ecological measurements in natural areas. Natural Areas Journal 1(2): 3-8. Auerbach, M., and A. Shmida. 1987. Spatial scale and the determinants of plant species richness. Trends in Ecology and Evolution 2(8): 238-242. Bourgeron, P. S., H. C. Humphries, and M. E. Jensen. 1994. General sampling design considerations for landscape evaluation. Pp. 109-120 in Ecosystem management, principles and applications, eds. Jensen, M.E., Bourgeron, P.W. Gen. Tech. Rep. PNW-318. Portland, OR: U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station. Curtis, R. O. 1983. Procedures for establishing and maintaining permanent plots for silvicultural and yield research. Gen. Tech. Rep. PNW-155. Olympia, WA: U.S. Department of Agriculture , Forest Service, Pacific Northwest Forest and Range Experiment Station. 56p. Guarnaccia Jr., D. 1961. A method of establishment and analysis of permanent growth plots. Bozeman, MT: Montana State University, 66 p. Thesis. Herbin, T. 1996. Permanent plots as tools for plant community ecology. Journal of Vegetation Science 7:195-202. Hunsaker, C. T., R. V. O'Neill, B. L. Jackson, S. P. Timmins, D. A. Levine, and D. J. Norton. 1994. Sampling to characterize landscape pattern. Landscape Ecology 9: 207-226. Hunt, E. V., and R. D. Baker. 1967. Practical point sampling. Bull. 14. Nacogdoches, TX: SAF State College. 43p. Labau, V.J. 1993. Regional monitoring with plot networks. Environmental Monitoring and Assessment 26: 283-294. Lesica, P., and B. M. Stelle. 1996. A method for monitoring long-term population trends: an example using rare arctic-alpine plants. Ecological applications 6(3): 879-887. Levy, P. S., and S. Lemeshow. 1991. Sampling of populations: methods and applications. New York, NY: Wiley and Sons. 420p. Lindsey, A. A. 1956. Sampling methods and community attributes in forest ecology. Forest Science 2: 287-296. Nyssonen, A. 1967. Remeasured sample plots in forest inventory. Vollebekk, Norway: Norwegian Forest Research Institute. 25p. Peterson, D. L., and S. W. Running. 1989. Applications in forest science and management. Pp. 429-473 in Theory and applications of optical remote sensing, ed. G. Asrar. New York, NY: John Wiley and Sons.
24
Ratliff, R. D., and S. E. Westfall. 1989. Monitoring plant community changes: an application of quadrat classification and discriminant analysis. Vegetatio 80: 1-9. Scott, C. T., A. R. Ek, and T. R. Zeisler. 1983. Optimal spacing of plots comprising clusters in extensive forest inventories. Pp. 707-710 in Renewable resource inventories for monitoring changes and trends: Proceedings of an international conference, eds. J. F. Bell, T. Atterbury; 1983 August 15-19; Corvallis, OR. Corvallis, OR: Oregon State University, College of Forestry. Shanks, R. E. 1954. Plotless sampling trials in Appalachian forest types. Ecology 35: 237-244. Shmida, A. 1984. Whittaker's plant diversity sampling method. Israel Journal of Botany 33: 41-46. Thompson, S. K. 1991. Adaptive cluster sampling: designs with primary and secondary units. Biometrics 47: 1103-1115. Thompson, S. K.. 1991. Stratified adaptive cluster sampling. Biometrika 78: 389-397. Turner, M. G. 1990. Spatial and temporal analysis of landscape patterns. Landscape Ecology 4: 21-30. Vanclay, J. K. 1992. Permanent plots for multiple objectives: defining goals and resolving conflicts. Pp. 157-163 in Remote sensing and permanent plot techniques for world forest monitoring: Proceedings of the IUFRO S4.02.05 Wacharakitti International Workshop, eds. H. G. Lunds, R. Paivinen, and S. Thammincha; 1992 January 13-17; Pattaya, Thailand. [Place of publication unknown] International Union of Forest Research Organizations.
Plot-level Techniques Aberdeen, J. E. C. 1958. The effect of quadrat size, plant size and plant distribution on frequency estimates in plant ecology. Australian Journal of Botany 7: 47-58. Aberdeen, J. E.. 1957. The uses and limitations of frequency estimates in plant ecology. Australian Journal of Botany 5: 86-102. Austin, M. P., and P. C. Heyligers. 1991. New approach to vegetation survey design: gradsect sampling. Pp. 31-36 in Nature conservation: cost effective biological surveys and data analysis, eds. C. R. Margules, and M. P. Austin, eds. Canberra, Australia: CSIRO (Commonwealth Scientific and Industrial Research Organization). Austin, M. P., and P. C. Heyligers. 1989. Vegetation survey design for conservation: gradsect sampling of forests in north-eastern New South Wales. Biological Conservation 50: 13-32. Awbrey, R. T. 1977. Locating random points in the field. Journal of Range Management 30: 157-158. Baker, R. L., and C. E. Thomas. 1983. A point frame for circular plots in southern forest ranges. Journal of Range Management 36(1): 121-123. Barkman, J. J. 1988. A new method to determine some characters of vegetation structure. Vegetatio 78: 81-90. Barrett, J. P., and H. P. Nevers. 1967. Slope correction when point-sampling. Journal of Forestry 65: 206-207. Batcheler, C. L. 1971. Estimation of density from a sample of joint point and nearest neighbor distances. Ecology 52: 703-709. Batcheler, C. L.. 1973. Estimating density and dispersion from truncated or unrestricted joint point- distance nearest-neighbor distances. Proceedings of the New Zealand Ecological Society 20: 131- 147. Battles, J. J., J. G. Dushoff, and T. J. Fahey. 1996. Line intercept sampling of forest canopy gaps. Forest Science 42(2): 131-138. Bentley, J. R., D. W. Seegrist, and D. A. Blakeman. 1970. A technique for sampling low shrub vegetation by crown volume classes. Res. Note PSW-215. Berkeley, CA: U.S. Department of Agriculture, Forest Service, Pacific Southwest Research Station, 11p. Bigwood, D. W., and D. W. Inouye. 1988. Spatial pattern analysis of seed banks: an improved method and optimized sampling. Ecology 69: 497-507. Binot, J. M., D. Pthier, and J. Lebel. 1995. Comparison of relative accuracy and time requirement between the caliper, the diameter tape, and an electronic tree measuring fork. Forestry Chronicle 71: 197-200.
25
Brady, W. W., J. E. Mitchell, C. D. Bonham, and J. W. Cook. 1995. Assessing the power of the point- line transect to monitor changes in basal plant cover. Journal of Range Management 48: 187-190. Brown, H. E., Worley, D. P. 1965. The canopy camera in forestry. Journal of Forestry 63: 674-680. Brown, J. K. 1976. Estimating shrub biomass from basal stem diameters. Canadian Journal of Forest Research 6: 153-158. Brown, J. K.. 1974. Handbook for inventoring downed woody material. Gen. Tech. Rep. INT-16. Ogden, UT: U.S. Department of agriculture, Forest Service, Intermountain Forest and Range Experiment Station. 24p. Brown, L. L. 1960. A plot marker that tells a story. Journal of Range Management 13: 40. Buckner, D. 1985. Point-intercept sampling in revegetation studies: maximizing objectivity and repeatability. Proceedings of the American Society of Surface Mining and Reclamation 1985 annual meeting. Denver, CO: American Society of Surface Mining and Reclamation 110-113. Burleson, W. H. 1975. A method of mounting plant specimens in the field. Journal of Range Management 28: 240-241. Butler, S. A., and L. L. McDonald. 1983. Unbiased systematic sampling plans for the line intercept method. Journal of Range Management 36: 463-468. Catchpole, W. R., and C. J. Wheeler. 1992. Estimating plant biomass: a review of techniques. Australian Journal of Ecology 17: 121-131. Catena, A. J. 1963. The wandering quarter method of estimating population density. Ecology 44: 349- 360. Coetsee, G., and D. P. Leroux. 1971. Polygonal plot method for estimating the canopy-spread cover of shrubs. Proceedings of The Grassland Society of Southern Africa 6: 176-180. Cooper, C. F. 1963. An evaluation of variable plot sampling in shrub and herbaceous vegetation. Ecology 44: 565-569. Cooper, C. F.. 1957. The variable plot method for estimating shrub density. Journal of Range Management 10: 111-115. Costello, D. F., and G. F. Klipple. 1939. Sampling intensity in vegetation surveys made by the square- foot density method. Agronomy Journal 31(9): 800-810. Cottam, G., and J. T. Curtis. 1956. The use of distance measure in phytosociological sampling. Ecology 37: 451-460. Cox, R. F. 1976. The robust estimation of the density of a forest stand using a new conditioned distance method. Biometrika 63: 493-499. Cunningham, G. M. 1975. Modified step-pointing: a rapid method of assessing vegetative cover. Journal of Soil Conservation 31: 256-265. Curtis, R. O., and D. Bruce. 1968. Tree heights without a tape. Journal of Forestry 66: 60-61. Daubenmire, R. F. 1959. Canopy-coverage methods of vegetation analysis. Northwest Science 33: 43- 64. De Becker, S., and D. Mahler. 1986. Photographing quadrats to measure percent vegetation cover. Natural Areas Journal 6(1): 67-69. Diggle, P. J. 1975. Robust density estimation using distance methods. Biometrika 62: 39-48. Driscoll, R. S. 1958. A loop method for measuring ground cover characteristics on permanent plots. Journal of Range Management 11: 94. Eberhardt, L. L. 1967. Some developments in "distance sampling". Biometrics 23: 207-216. Fisser, H. G. 1961. Variable plot, square foot plot and visual estimates for shrub crown cover measurements. Journal of Range Management 14: 202-207. Fisser, H. G., and G. M. Vandyne. 1960. A mechanical device for repeatable range measurements. Journal of Range Management 13: 40-42. Floyd, D. A., and J. E. Anderson. 1982. A new point frame for estimating cover of vegetation. Vegetatio 50: 185-186. Gilberts, N., and T. C. E. Wells. 1966. Analysis of quadrat data. Journal of Ecology 54: 675-685.
26
Gillson, A. N., and K. R. W. Brewer. 1985. The use of gradient directed transects of gradsects in natural resource survey. Journal of Environmental Management 20: 103-127. Grelen, H. E. 1959. The basal area method for measuring ground cover. Pp. 45-47 in Techniques and methods of measuring understory vegetation,ed. G. A. Tifton: U.S. Department of Agriculture, Forest Service, Southern Forest Experiment Station. Griffen, G. F. 1989. An enhanced wheel-point method for assessing cover, structure and heterogeneity in plant communities. Journal of Range Management 42:79-81. Harrington, G. N. 1979. Estimation of above-ground biomass of trees and shrubs. Australian Journal of Botany 27: 135-143. Heady, H. F. 1957. The measurement and value of plant height in the study of herbaceous vegetation. Ecology 38: 313-320. Heady, H. F., and L. Rader. 1958. Modifications of the point frame. Journal of Range Management 11: 95-96. Holgate, P. 1964. The efficiency of nearest neighbor estimators. Biometrics 20: 647-649. Holm, A. M., P. J. Curry, and J. F. Wallace. 1984. Observer differences in transect counts, cover estimates and plant size measurements on range monitoring sites in arid shrubland. Australian Rangeland Journal 6: 98-102. Hormay, A. L. 1949. Getting better records of vegetative changes with the line interception method. Journal of Range Management 2: 67-69. Hovind, H. J., and C. E. Rieck. 1970. Basal area and point-sampling: interpretation and application. Tech. Bull. 52. Madison, WI: Wisconsin Conservation Department. 52p. Hughes, H. G., L. W. Varner, and L. H. Blankenship. 1987. Estimating shrub production from plant dimensions. Journal of Range Management 40: 367-369. Hutchings, S. S., and R. C. Holmgren. 1959. Interception of loop-frequency data as a measure of plant cover. Ecology 40: 668-677. Ibrahim, K. M. 1971. Ocular point quadrat method. Journal of Range Management 24: 312. Iles, K. 1979. Some techniques to generalize the use of variable plot and line intersect sampling. Pp. 270-277 in Proceedings: forest resource inventories workshop, ed. W. E. Frayer. Fort Collins, CO:Colorado State University. Jackson, M. T., and R. O. Petty. 1973. A simple optical device for measuring vertical projection of tree crowns. Forest Science 19: 60-62. Jorgen, K., and K. Thomsen. 1994. A new method for measuring tree height in tropical rain forest. Journal of Vegetation Science 5: 139-140. Kaiser, L. 1983. Unbiased estimation in line-intercept sampling. Biometrics 39: 965-976. Kemp, C. D., and A. W. Kemp. 1956. The analysis of point quadrat data. Australian Journal of Botany 4: 167-174. Kendall, R. H., and L. Sayn-Wittgenstein. 1960. A rapid method of laying out circular plots. Forestry Chronicle 36: 230-233. Kenkel, N. C., P. Juhasz-Nagy, and J. Podani. 1989. On sampling procedures in population and community ecology. Vegetatio 83: 195-207. Kennedy, K. A., and P. A. Addison. 1987. Some considerations for the use of visual estimates of plant cover in biomonitoring. Journal of Ecology 75: 151-157. Kothmann, M. M., W. J. Waldrip, and G. W. Mathis. 1978. Rangeland vegetation of the Texas rolling plains: response to grazing management and weather. Pp. 606-609 in Proceeding of the 1st International Rangeland Congress; 1978; Denver, CO, ed. D. N. Hyder Denver, CO: Society for Range Management. Lesica, P. 1987. A technique for monitoring nonrhizomatous, perennial plant species in permanent belt transects. Natural Areas Journal 7(2): 65-68. Lindsey, A. A. 1955. Testing the line-strip method against tallies in diverse forest types. Ecology 36: 485-495.
27
Lindsey, A. A., J. D. Barton, Jr., and S. R. Miles. 1958. Field efficiencies of forest sampling methods. Ecology 39: 428-444. Long, G. A., P. S. Poissonet, J. A. Poissonet, P. M. Daget, and M. P. Godon. 1972. Improved needle point frame for exact line transects. Journal of Range Management 25: 228. Lucas, H. A., and G. R. Seber. 1977. Estimating coverage and particle density using the line intercept method. Biometrika 64: 618-622. Mark, A. F., and A. E. Esler. 1970. An assessment of the point-centered quarter method of plotless sampling in some New Zealand forests. New Zealand Ecological Society Proceedings 17: 106-110. McDonald, L. L. 1980. Line intercept for attributes other than coverage and density. Journal of Wildlife Management 44: 530-533. McIntyre, G. A. 1953. Estimation of plant density using line transects. Journal of Ecology 41: 319-330. McIntyre, G. A.. 1952. A method for unbiased selective sampling, using ranked sets. Journal of Agricultural Research 3: 385-390. Meese, R. J., and P. A. Tomich. 1992. Dots on the rocks: a comparison on percent cover estimation methods. Journal of Experimental Marine Biology and Ecology 165: 59-73. Meeuwig, R. O. 1981. Point sampling for shrub biomass. Pp. 423-326 in Arid land resource inventories: developing cost effective methods, tech coords. H. G. Lund, R. H. Caballero, R. H. Hamre, R. S. Driscoll, and W. Bonner. 1980. November 30 – December 6; La Paz, Mexico. Gen. Tech. Rep. WO- 28. Washington, DC: U.S. Department of Agriculture, Forest Service. Meeuwig, R. A., and J. D. Budy. 1981. Point and line-intersect sampling in pinyon-juniper woodlands. Gen. Tech. Rep. INT-104. Ogden, UT: U.S. Department of Agriculture, Forest Services, Intermountain Forest and Range Experiment Station. 38pp. Mitchell, J. E., W. W. Brady, and C. D. Bonham. 1994. Robustness of the point-line method for monitoring basal cover. Res. Note RM-528. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain Forest and Range Experiment Station. 6p. Morisita, M. 1957. A new method for the estimation of density by the spacing method applicable to non- randomly distributed populations. Physiology and Ecology 7: 134-144. Morrison, R. G., and G. A. Yarranton. 1970. An instrument for rapid and precise sampling of vegetation. Canadian Journal of Botany 48: 293-297. Myers, C. A. 1961. Variation in measuring diameter at breast height of mature ponderosa pine. Res. Notes RM-67. Fort Collins, CO: USDA Forest Service, Rocky Mountain Forest and Range Experiment Station. 3p. Neal, D. L., R. D. Ratliff, and S. E. Westfall. 1988. A quadrat frame for backcountry vegetation sampling. Journal of Range Management 41: 353-355. O'Brien, R., and D. D. Van Hooser. 1983. Understory vegetation inventory: an efficient procedure. Res. Pap. INT-323. Ogden, UT: U.S. Department of Agriculture, Forest Service, Intermountain Forest and Range Experiment Station. 6p. Owensby, C. S. 1973. Modified step-point system for botanical composition and basal cover estimates. Journal of Range Management 26: 302-303. Pakarinen, P. 1984. Cover estimation and sampling of boreal vegetation in northern Europe. Pp. 35-44 in Sampling methods and taxon analysis in vegetation science, ed. R. Knapp. Handbook of vegetation science, volume 4. The Hague: Junk. Park, G. N. 1973. Point height intercept analysis. New Zealand Journal of Botany 11: 103-114. Poissonet, P. S., P. M. Daget, J. A. Poissonet, and G. A. Long. 1972. Rapid point survey by bayonet blade. Journal of Range Management 25: 313. Rader, L., and R. D. Ratliff. 1962. A new idea in point frames. Journal of Range Management 15: 182- 183. Ripley, T. H., F. M. Johnson, and W. P. Thomas. 1960. A useful device for sampling understory woody vegetation. Journal of Range Management 13: 262-263. Roshier, D., S. Lee, and F. Boreland. 1997. A digital technique for recording of plant population data in permanent plots. Journal of Range Management 50(1): 106-109.
28
Rothery, P. 1974. The number of pins in a point quadrat frame. Journal of Applied Ecology 11: 745- 754. Schumacher, F. X., and R. A. Chapman. 1948. Sampling methods in forestry and range management. Bull. 7. Durham, NC: Duke University School of Forestry. 22p. Sharrow, S. H., and D. A. Tober. 1979. A simple, lightweight point frame. Journal of Range Management 32: 75-76. Sikora, P. C. 1983. Efficient tree height measurement utilizing a hand-held calculator. Pp. 690-692 in Renewable resource inventories for monitoring changes and trends: Proceedings of an international conference; 1983 August 15-19; Corvallis, OR, eds. J. F. Bell, and T. Atterbury. Corvallis, OR: Oregon State University, College of Forestry. Stage, A. R., and J. C. Rennie. 1994. Fixed-radius plots or variable-radius plots? Journal of Forestry 92(12): 20-24. Stanton, F. W. 1960. Ocular point frame. Journal of Range Management 13: 153. Stohlgren, T. J., M. B. Falkner, and L. D. Schell. 1995. A modified-Whittaker nested vegetation sampling method. Vegetatio 117: 113-121. Strong, C. W. 1966. An improved method of obtaining density from line-transect data. Ecology 47: 311- 313. Taha, F. K., H. G. Fisser and R. E. Ries. 1983. A modified 100-point frame for vegetation inventory. Journal of Range Management 36: 124-125. Ursic, S. J., and D. C. McClurkin. 1959. Small plots for measuring vegetation composition and cover. Techniques and methods of measuring understory vegetation. Tifton, GA: U.S. Department of Agriculture, Forest Service: 70-78. Vandyne, G. M. 1960. A procedure for rapid collection, processing and analysis of line intercept data. Journal of Range Management 13: 247-251. Vandyne, G. M.. 1965. A further note of random locations for sample units in circular plots. Journal of Range Management 18: 150-151. White, W. E., and C. E. Lewis. 1982. Establishing circular plot boundaries with a wedge prism and an adjustable target pole. Journal of Range Management 35: 677-680. Wilde, S. A. 1954. Floristic analysis of ground cover vegetation by a rapid chain method. Journal of Forestry 52: 499-502. Zamora, B. A. 1981. An approach to plot sampling for canopy volume in shrub communities. Journal of Range Management 34: 155-156. Zeide, B. 1980. Plot size optimization. Forest Science 26: 251-257.
Comparisons Between Techniques Ahmed, J., C. D. Bonham, and W. A. Laycock. 1983. Comparison of techniques used for adjusting biomass estimates by double sampling. Journal of Range Management 36: 217-221. Arzani, H., and G. W. King. 1994. Comparison of wheel point and point frame methods for plant cover measurement of semiarid and arid rangeland vegetation of New South Wales. Rangeland Journal 16: 94-105. Beasom, S. L., and H. Haucke. 1975. A comparison of four distance sampling techniques in south Texas live oak mottes. Journal of Range Management 28: 142-144. Birdsey, R. A. 1995. A brief history of the "straddler plot" debates. Forest Science Monograph 31, Supplement 41(3): 7-11. Bormann, G. E. 1953. The statistical efficiency of sample plot size and shape in forest ecology. Ecology 34: 474-487. Bourdeau, P. F. 1953. A test of random versus systematic ecological sampling. Ecology 34: 499-512. Bricknell, J. E. 1970. More on diameter tape and calipers. Journal of Forestry 68: 169-170. Brummer, J. E., J. T. Nichols, R. K. Engel, and K. M. Eskridge. 1994. Efficiency of different quadrat sizes and shapes for sampling standing crop. Journal of Range Management 47: 84-89.
29
Brun, J. M., and W. T. Box. 1963. Comparison of line intercepts and random point frames for sampling desert shrub vegetation. Journal of Range Management 16: 21-25. Buell, M. F., and J. E. Cantlon. 1950. A study of two communities of New Jersey Pine Barrens and a comparison of methods. Ecology 31: 567-586. Clymo, R. S. 1980. Preliminary survey of the peat-bog Knowe Moss using various numerical methods. Vegetatio 42: 129-148. Cook, C. W., and T. W. Box. 1961. A comparison of the loop and point methods of analyzing vegetation. Journal of Range Management 14: 22-27. Dethier, M. N., E. S. Graham, S. Cohen, and L. M. Tear. 1993. Visual versus random-point percent cover estimations: "objective" is not always better. Marine Ecology Progress Series 96: 93-100. Eddelman, L. E., E. Remmenga, and R. Ward. 1964. An evaluation of plot methods for alpine vegetation. Bulletin of the Torrey Botanical Club 91: 439-450. Ellison, L. 1942. A comparison of methods of quadrating vegetation. Journal of Agricultural Research 64: 595-614. Floyd, D. A., and J. E. Anderson. 1987. A comparison of three methods for estimating plant cover. Journal of Ecology 75: 221-228. Ganey, J. L., and W. M. Block. 1994. A comparison of two techniques for measuring canopy closure. Western Journal of Applied Forestry 9(1): 21-23. Grosenbaugh, L. R. 1957. Point sampling compared with plot-sampling in southeast Texas. Forestry Science 3: 2-14. Hanley, T. A. 1978. A comparison of the line-interception and quadrat estimation methods of determining shrub canopy coverage. Journal of Range Management 31: 60-62. Heady, H. F., R. P. Gibbens, and R. W. Powell. 1959. A comparison of the charting, line intercept, and line point methods of sampling shrub types of vegetation. Journal of Range Management 12: 180- 188. Kinsinger, F. E., R. E. Eckert, and P. O. Currie. 1960. A comparison of the interception, variable plot and loop methods as used to measure shrub-crown cover. Journal of Range Management 13: 17-21. Kothmann, M. M., W. J. Waldrip, and G. W. Mathis. 1978. Rangeland vegetation of the Texas rolling plains: response to grazing management and weather. Pp. 606-609 in Proceeding of the 1st International Rangeland Congress; 1978; Denver, CO, ed. D. N. Hyder. Denver, CO: Society for Range Management. O'Brien, R. 1989. Comparison of overstory canopy cover estimates on forest survey plots. Res. Pap. INT-417. Ogden, UT: U.S. Department of Agriculture, Forest Service, Intermountain Research Station. 5p. Oldemeyer, J. D., and W. L. Regelin. 1980. Comparison of nine methods for estimating density of shrubs and saplings in Alaska. Journal of Wildlife Management 44(3): 662-666. Sudia, T. W. 1954. A comparison of forest ecological sampling techniques with the use of a known population. Columbus, OH: Ohio State University. Dissertation.
Analytical and Design Considerations Aitken, M. 1981. Regression models for repeated measurements. Biometrics 37: 831-832. Ashby, E. 1935. The quantitative analysis of vegetation. Annals of Botany 49: 779-802. Austin, M. P. 1991. Vegetation: data collection and analysis. Pp. 37-41 in Nature conservation: cost effective biological surveys and data analysis, eds. C. R. Margules, and M. P. Austin. Canberra, Australia: CSIRO (Commonwealth Scientific and Industrial Research Organization). Bannister, P. 1966. The use of subjective estimates of cover-abundance as the basis for ordination. Journal of Ecology 54: 665-674. Barrett, J. P. 1969. Estimating averages from point-sample data. Journal of Forestry 67: 185. Barrett, J. P., and L. GOLDSMITH. 1976. When is n sufficiently large? American Statistician 30(2): 67-70.
30
Bernstein, B. B., and J. Zalinski. 1983. An optimal sampling design and power tests for environmental biologists. Journal of Environmental Sciences 16:35-43. Caswell, H., and J. R. Weinberg. 1986. Sample size and sensitivity in the detection of community impact. Institute of Electrical and Electronic Engineers Oceans 1986 conference proceedings. Washington, DC: Marine Technology Society/Institute of Electrical and Electronic Engineers: 1040- 1045. Croy, C. D., and R. L. Dix. 1984. Notes on sample size requirements in morphological plant ecology. Ecology 65: 662-666. Dale, V. H., R. L. A. Franklin, W. M. Post, and R. H. Gardner. 1991. Sampling ecological information: choice of sample size. Ecological Modelling 57: 1-10. Dixon, P. M., and K. A. Garrett. 1993. Sampling ecological information: choice of sample size reconsidered. Ecological Modelling 68: 67-73. Dutilleul, P. 1993. Spatial heterogeneity and the design of ecological field experiments. Ecology 74: 1646-1658. Eckblad, J. W. 1991. How many samples should be taken? BioScience 41: 346-348. Fairweather, P. G. 1991. Statistical power and design requirements for environmental monitoring. Australian Journal of Marine and Freshwater Research 42: 555-567. Finney, D. J. 1948. Random and systematic sampling in timber surveys. Forestry 22: 64-99. Fisser, H. G., and G. M. Vandyne. 1966. Influence of number and spacing of points on accuracy and precision of basal cover estimates. Journal of Range Management 19: 205-211. Gaiser, R. N. 1951. Random sampling within circular plots by means of polar coordinates. Journal of Forestry 49: 916-917. Gerrodette, T. 1987. A power analysis for detecting trends. Ecology 68: 1364-1372. Green, R. H. 1989. Power analysis and practical strategies for environmental monitoring. Environmental Research 50: 195-205. Green, R. H.. 1984. Statistical and non-statistical considerations for environmental monitoring studies. Environmental Monitoring and Assessment 4: 293-301. Hoel, P. G. 1943. The accuracy of sampling methods in ecology. Annals of Mathematics and Statistics 14: 289-300. Hurlbert, S. H. 1984. Pseudoreplication and the design of ecological field experiments. Ecological Monographs 54: 187-211. Kenkel, N. C., and J. Podani. 1991. Plot size and estimation efficiency in plant community studies. Journal of Vegetation Science 2: 539-544. Kupper, L. L., and K. B. Haftner. 1989. How appropriate are popular sample size formulas? American Statistician 43: 101-105. Laferriere, J. E. 1987. A central location method for selecting random plots for vegetation surveys. Vegetatio 71: 75-77. Larson, L. L., and P. A. Larson. 1987. Use of microsite sampling to reduce inventory sample size. Journal of Range Management 40: 378-379. Leps, J., and V. Hadicova. 1992. How reliable are our vegetation analyses? Journal of Vegetation Science 3: 119-124. Lipsey, M. W. 1990. Design sensitivity: statistical power for experimental research. Newbury Park, CA: Sage Publications. 205pp. Matern, B. 1972. The precision of basal area estimates. Forestry Science 18: 123-125. Mertaugh, P. A. 1996. The statistical evaluation of ecological indicators. Ecological Applications 6(1): 132-139. Orloci, L. 1975. Multivariate analysis in vegetation research. The Hague, the Netherlands: Junk. 276pp. Palmer, M. W. 1993. Potential biases in site and species selection for ecological monitoring. Environmental Monitoring and Assessment 26: 277-282.
31
Rice, E. L. 1967. A statistical method for determining quadrat size and adequacy of sampling. Ecology 48: 1047-1049. Scheuder, H. T., and J. Alegria. 1995. Stratification and plot selection rules: misuses and consequences. Res. Note RM-RN-536. Ft. Collins, CO: U.S. Department of Agriculture Forest Service, Rocky Mountain Experiment Station. 4pp. Scheuder, H. T., and R. L. Czaplewski. 1993. Long-term strategy for the statistical design for a forest health monitoring system. Environmental Monitoring and Assessment 27: 81-94. Skalski, J. R. 1987. Selecting a random sample of points in circular field plots. Ecology 68: 749. Smartt, P. F. M. 1978. Sampling for vegetation survey: a flexible systematic model for sample location. Journal of Biogeography 5: 43-56. Smartt, P. F. M., and J.E.A. GRAINGER. 1974. Sampling for vegetation survey: some aspects of the behavior of unrestricted and stratified techniques. Journal of Biogeography 1: 193-206. Wang, D. 1986. Use of statistics in ecology. Bulletin of The Ecological Society of America 67: 10-12.
Archiving Data Byrne, J. C., and M. D. Sweet. 1992. Managing data from remeasured plots: an evaluation of existing systems. Res. Pap. INT-451. Ogden, UT: U.S. Department of Agriculture, Forest Service, Intermountain Research Station. 26pp. Jenkins, R. E. 1996. Natural heritage data center network: managing information for managing biodiversity. Pp. 176-192 in Biodiversity in managed landscapes, eds. R. C. Szaro, and D. W. Johnson New York, NY: Oxford University Press. Stafford, S. G. 1993. Data, data everywhere, but not a byte to read: managing monitoring information. Environmental Monitoring and Assessment 26: 125-143.
General Texts and General Papers of Interest Bailey, N. T. J. 1995. Statistical methods in biology. 3rd ed. New York, NY: Cambridge. 272pp. Berger, A. R. 1992. A special role of parks and protected areas in long-term environmental monitoring. Pp. 385-390 in Science and the management of protected areas, eds. J. H. M. Willison, S. Bondrup- Nielsen, C. Drysdale, T. B. Herman, N. W. P. Munro, and T. L. Pollock. New York, NY: Elsevier. Berkowitz, A. R., K. Kolosa, R. H. Peters, and S. T. A. Pickett. 1989. How far in space and time can the results from a single long-term study be extrapolated? Pp. 192-198 in Long-term studies in ecology: approaches and alternatives, ed. Likens, G.E. New York: Springer-Verlag. Bonham, C. D. 1989. Measurements for terrestrial vegetation. New York, NY: John Wiley and Sons. 338pp. Bonham, C. D., L. L. Larson, and A. Morrison. 1980. A survey of techniques for measurement of herbaceous and shrub production, cover and diversity on coal lands in the west. Contract 17090435. Denver, CO: Office of Surface Mining, Region V. 182pp. Bonnor, G. M. 1972. Forest sampling and inventories: a bibliography. Ottawa: Forest Management Institute. 27pp. Botkin, D. B. 1977. Long-term ecological measurements. Washington, DC: National Science Foundation. 44pp. Bratton, S. P. 1989. Environmental monitoring in wilderness. Wilderness benchmark 1988: proceedings of the national wilderness colloquium; 1988 January 13-14; Tampa, FL. Gen. Tech. Rep. SE-51. Ashville, NC: U.S. Department of Agriculture, Forest Service, Southeastern Forest Experiment Station: 103-112. Brower, J. E., and J. H. Zar. 1990. Field and laboratory methods for general ecology. 3rd ed. Debuque, IA: Wm. C. Brown Publishers. 237pp. Brown, D. 1954. Methods of surveying and measuring vegetation. Hurley, Berkshire, England: Commonwealth Bureau of Pastures and Field Crops. 223pp. Burley, F. W. 1988. Monitoring biological diversity for setting priorities in conservation. Pp. 227-230 in Biodiversity, ed. E. O. Wilson. Washington, DC: National Academy Press.
32
Burns, R. M. 1984. Importance of baseline information to the research natural area program. Pp. 50-52 in Research natural areas: baseline monitoring and management; 1984 March 21; Missoula, MT, coordinators J. L. Johnson, J. F. Franklin, and R. G. Krebill. Gen. Tech. Rep. INT-173. Ogden, UT: U.S. Department of Agriculture, Forest Service, Intermountain Forest and Range Experiment Station. Buttrick, S. C. 1984. Biological monitoring: The Nature Conservancy's perspective. Pp. 59-64 in Research natural areas: baseline monitoring and management; 1984 March 21; Missoula, MT, coordinators J. L. Johnson, J. F. Franklin, and R. G. Krebill. Gen. Tech. Rep. INT-173. Ogden, UT: U.S. Department of Agriculture, Forest Service, Intermountain Forest and Range Experiment Station. Cain, S. A., and G. M. Castro. 1959. Manual of vegetation analysis. New York, NY: Harper and Bros. 325pp. Cairns, J. 1986. The myth of the most sensitive species. BioScience 36: 670-672. Cairns, J., P. V. McCormick, and B. R. Niederlehner. 1993. A proposed framework for developing indicators of ecosystem health. Hydrobiologia 263: 1-44. Cairns, J., G. P. Patil, and W. E. Waters, eds. 1979. Environmental biomonitoring, assessment, prediction and management. Patil, G.P., series ed. Statistical ecology series, vol. 11. Fairland, MD: International Cooperative Publishing House. 469pp. Campbell, R.C. 1989. Statistics for biologist. 3rd ed. New York, NY: Cambridge University Press. 446pp. Chambers, J. C., and R. W. Brown. 1983. Methods for vegetation sampling and analysis on revegetated mined lands. Gen. Techn. Rep. INT-151. Ogden, UT: U.S. Department of Agriculture, Forest Service, Forest and Range Experiment Station. 57pp. Cochran, W. G. 1977. Sampling techniques. New York, NY: John Wiley and Sons. 428pp. Coffin, D. P., and W. K. Lauenroth. 1989. Disturbance and gap dynamics in a semiarid grassland: a landscape-level approach. Landscape Ecology 3: 19-27. Conkling, B. L., and G. E. Byers. 1992. Forest health monitoring field methods guide (internal report). Las Vegas, NV: U.S. Environmental Protection Agency. Conover, S. A. M. 1985. Environmental effects monitoring and environment Canada: a synthesis of the findings of four workshops. Ottawa, Ontario: Environment Canada. Cook, C. W., and C. D. Bonham. 1977. Techniques for vegetation measurements and analysis for pre- and post-mining inventory. Range Science Department Range Science Series 28. Fort Collins, CO: Colorado State University. 93pp. Cormack, R. M., G. P. Patil, and D. S. Robsen, eds. 1979. Sampling biological populations. Patil, G.P., series ed. Statistical ecology series, volume 5. Fairland, MD: International Cooperative Publishing House. 425pp. Croze, H. 1984. Monitoring within and outside protected areas. Pp. 628-633 in National parks, conservation and development: the role of protected areas in sustaining society, eds. J. A. McNeely, and K. R. Miller. Washington, DC: Smithsonian Press. Davis, G. E. 1993. Design elements of monitoring programs: the necessary ingredients for success. Environmental Monitoring and Assessment 26: 99-105. Department of the Environment and Countryside Commission. 1986. Monitoring landscape change. Borehamwood: Huntingdon Technical Services. Durr, P. C., L. Richmond, and C. Eagar. 1988. Site classification and field measurements: methods manual. Gatlinburg, TE: U.S. Department of Interior, National Park Service, Great Smoky Mountains National Park. Forthofer, R. N., and E. S. Lee. 1995. Introduction to biostatistics: a guide to design, analysis and discovery. New York, NY: Academic Press. 567pp. Fowler, J., and L. Cohen. 1990. Practical statistics for field biology. Philadelphia, PA: Open University Press. 227pp. Franklin, J. F. 1989. Importance and justification of long-term studies in ecology. Pp. 3-19 in Long- term studies in ecology – approaches and alternatives, ed. G. E. Likens New York, NY: Springer- Verlag.
33
Franklin, J. F.. 1981. Wilderness for baseline ecosystem studies. Proceedings XVII IUFRO World Congress; 1981; Japan. [Place of publication unknown]: International Union of Forest Research Organizations. 37-48pp. Franklin, J. F., C. S. Bledsoe, and J. T. Callahan. 1990. Contributions of the long-term ecological research program. BioScience 40(7): 509-523. Goldsmith, F. B., ed. 1991. Monitoring for conservation and ecology. London: Chapman and Hall. 275pp. Graves, B. M., and P. L. Dittberner. 1986. Variables for monitoring aquatic and terrestrial environments. Biol. Rep. 86(5). Washington, DC: U.S. Department of Interior, Fish and Wildlife Service. 55pp. Gray, J. S., and K. Jensen. 1993. Feedback monitoring: a new way of protecting the environment. Trends In Ecology and Evolution 8: 267-268. Greig-Smith, P. 1983. Quantitative plant ecology. 3rd ed. Berkeley, CA: University of California Press. 347pp. Hagglund, B. 1983. The new national forest survey. Pp. 298-303 in Renewable resource inventories for monitoring changes and trends: Proceedings of an international conference; 1983 August 15-19; Corvallis, OR, eds. Bell, J.F., Atterbury, T. Corvallis, OR: Oregon State University, College of Forestry. Halvorson, W. L., S. D. Veirs Jr., R. A. Clark, and D. D. Borgais. 1988. Terrestrial vegetation monitoring handbook. Ventura, CA: U.S. Department of Interior, National Park Service, Channel Islands National Park. 11pp. Heyer, W. R., M. A. Donnelly, R. W. McDiarmid, S. C. Hayek, and M. S. Foster. 1994. Measuring and monitoring biological diversity. Washington, DC: Smithsonian Institution Press. 364pp. Hinds, W. T. 1984. Towards monitoring of long-term trends in terrestrial ecosystems. Environmental Conservation 11: 11-18. Hirsch, A. 1980. Monitoring cause and effects of ecosystem change. Pp. 137-142 in Biological monitoring for environmental effects, ed. D. L. Worf. Lexington, MA: Lexington Books, D.C. Health and Company. Hunsaker, C. T., D. E. Carpenter, and J. Messer. 1990. Ecological indicators for regional monitoring. Bulletin of The Ecological Society of America 71: 165-172. Hunsaker, C. T., and D. E. Carpenter, eds. 1990. Environmental monitoring and assessment program – ecological indicators. EPS/600/3-90/060. Washington, DC: U.S. Environmental Protection Agency, Office of Research and Development. Izrael, Y. A., and R. E. Munn. 1986. Monitoring and assessment of ecological change. Pp.360-374 in Sustainable development of the biosphere, eds. W. C. Clark, and R. E. Munn. Cambridge, Massachusetts: Cambridge University Press. Jassby, A. D., and T. M. Powell. 1990. Detecting change in ecological time series. Ecology 71: 2044- 2052. Jeffers, J. N. R. 1989. Environmental monitoring. Biologist 36: 171. Jenkins, R. E., and W. B. Bedford. 1973. The use of natural areas to establish environmental baselines. Biological Conservation 5: 168-174. Johnson, J. L., J. F. Franklin, and R. B. Krebill, tech coords. 1984. Research natural areas: baseline monitoring and management. 1984 March 21; Missoula, MT. Gen. Tech. Rep. INT-173. Ogden, UT: U.S. Department of Agriculture, Forest Service, Intermountain forest and Range Experiment Station. 84pp. Jones, K. B. 1986. The inventory and monitoring process. Pp. 1-9 in Inventory and monitoring of wildlife habitat, eds. A. Y. Cooperider, R. J. Boyd, and H. R. Stuart. Denver, CO: U.S. Department of Interior, Bureau of Land Management. Jones, K. B., and B.R. RIDDLE. 1996. Regional scale monitoring of biodiversity. Pp. 193-209 in Biodiversity in managed landscapes, eds. R. C. Szaro, , and D. W. Johnson. New York, NY: Oxford University Press.
34
Karr, J. R. 1987. Biological monitoring and environmental assessment: a conceptual framework. Environmental Management 11: 249-256. Keddy, P. A. 1991. Biological monitoring and ecological prediction: from nature reserve management to national state of the environment. Pp. 249-268 in Monitoring for conservation and ecology, ed. F. B. Goldsmith. London: Chapman and Hall. Keith, L. H. 1990. Environmental sampling: a summary. Environmental Science and Technology 24: 610-617. Keith, L. H.. 1992. Principles of environmental sampling. A practical guide. Chelsea, MI: Lewis Publishers. 143pp. Kelly, J. R., and M. A. Harwell. 1990. Indicators of ecosystem recovery. Environmental Management 14: 527-545. Kershaw, K. A. 1985. Quantitative and dynamic plant ecology. 3rd ed. London: Edward Arnold. 287pp. Kirby, K. J. 1988. Woodland survey handbook. Research and Survey in Nature Conservation 11. Peterborough: Nature Conservancy Council. 164pp. Krebs, C. J. 1989. Ecological methodology. New York, NY: Harper and Row. 654pp. Kremen, C. 1992. Assessing the indicator properties of species assemblages for natural areas monitoring. Ecological Applications 2: 203-217. Landres, P. B. 1992. Ecological indicators: panacea or liability? Pp. 1295-1318 in Ecological indicators, eds. D. H. McKenzie, D. E. Hyatt, and V. I. McDonald. Amsterdam: Elsevier Applied Scientific Publishers. Landres, P. B.. 1995. The role of ecological monitoring in managing wilderness. Trends 32(1): 10-13. Landres, P. B., D. N. Cole, and A. Watson. 1994. A monitoring strategy for the national wilderness preservation system. Pp. 192-197 in International wilderness allocation, management, and research, eds. J. C. Hendee, and J. C. Martin. Fort Collins, CO: International Wilderness Leadership Foundation. Likens, G. E., ed. 1989. Long-term studies in ecology. New York, NY: Springer-Verlag. 214pp. Ludwig, J. A., and J. F. Reynolds. 1988. Statistical ecology. New York, NY: Wiley-Interscience. 337p. Lund, H. G. 1992. How to watch the forests. IUFRO guides for world forest monitoring. Revista SELPER 8(2): 40-44. MacDonald, L. H., and A. Smart. 1993. Beyond the guidelines: practical lessons for monitoring. Environmental Monitoring and Assessment 26: 203-218. Maki, A. W. 1980. Methods issues in biological monitoring. Pp. 203-211 in Biological monitoring for environmental effects, ed. D. L. Worf. Lexington, MA: Lexington Books, D.C. Health and Company. Merigliano, L. 1987. The identification and evaluation of indicators to monitor wilderness conditions. Moscow, ID: University of Idaho. 273 p. Thesis. Moore, P. D., and S. D. Chapman. 1986. Methods in plant ecology. Oxford: Blackwell Scientific. 589p. Munn, R. E. 1988. The design of integrated monitoring systems to provide early indications of environmental/ecological changes. Environmental Monitoring and Assessment 11: 203-217. Noble, I., and G. Norton. 1991. Economic aspects of monitoring for national park management. Pp. 69- 73 in Nature conservation: cost effective biological surveys and data analysis, eds. C. R. Margules, and M. P. Austin. Canberra, Australia: CSIRO (Commonwealth Scientific and Industrial Research Organization). Noss, R. 1990. Indicators for monitoring biodiversity: a hierarchical approach. Conservation Biology 4(4): 355-364. Oderwald, R. G. 1981. Point sampling and plot sampling – the relationship. Journal of Forestry 79: 377-378. Oliver, I., and A. J. Beattie. 1993. A possible method for the rapid assessment of biodiversity. Conservation Biology 3: 562-568.
35
Olson, C. M. 1992. Monitoring species diversity: a sampling approach. Pp. 30-35 in Symposium on biodiversity of northwestern California, ed. H. M. Kerner. Berkeley, CA: University of California, Wildland Resources Center, Division of Agriculture and Natural Resources. Omule, S. A. Y. 1980. Personal bias in forest measurements. Forestry Chronicle 56(5): 222-224. O'Neill, R. V., C. T. Hunsaker, and D. A. Levine. 1992. Monitoring challenges and innovative ideas. Pp. 1443-1460 in Ecological indicators, eds. D. H. McKenzie, D. E. Hyatt, and V. I. McDonald. Amsterdam: Elsevier Applied Scientific Publishers. Ott, W. R. 1978. Environmental indices. Ann Arbor, Michigan: Ann Arbor Science. Palmer, M. E. 1987. A critical look at rare plant monitoring in the United States. Biological Conservation 39: 113-127. Parker, I. 1983. Effective reporting of monitored changes and trends. Pp. 664-667 in Renewable resource inventories for monitoring changes and trends: proceedings of an international conference, eds. J. F. Bell, and T. Atterbury. 1983 August 15-19; Corvallis, OR. Corvallis, OR: Oregon State University, College of Forestry. Patil, G. P., and C. Taillie. 1982. Diversity as a concept and its measurement. Journal of The American Statistical Association 77: 548-567. Pavlik, B. M. 1987. Autecological monitoring of endangered plants. Pp. 385-390 in Conservation and management of rare and endangered plants, ed. T. S. Elias. Sacramento, California: The California Native Plant Society. Peet, R. K., D. C. Glenn-Lewin, and J. Walker-Wolf. 1983. Prediction of man's impact on plant species diversity – a challenge for vegetation science. Pp. 41-54 in Man's impact on vegetation, eds. W. Holzner, M. J. A. Werger, and I. Ikusima. The Hague: Junk. Pickett, S. T. A. 1991. Long-term studies: past experience and recommendations for the future. Pp. 71- 85 in Long-term ecological research: an international perspective, ed. P. G. Risser. Scientific Committee on Problems of the Environment (SCOPE) 47. New York, NY: John Wiley and Sons. Pieper, R. D. 1973. Measurement techniques for herbaceous and shrubby vegetation. Las Cruces, NM: New Mexico State University, Department of Animal, Range, and Wildlife Sciences. 190p. Reid, W. F., J. A. McNeeley, D. B. Tunstall, D. A. Bryant, and M. Winograd. 1993. Biodiversity indicators for policy-makers. World Resources Institute Report. Gland, Switzerland: International Union of Conservation and Nature. 42p. Risser, P. G. 1991. Long-term ecological research: an international perspective. Scientific Committee on Problems of the Environment (SCOPE) 47. New York, NY: John Wiley and Sons. 294p. Risser, P. G., J. M. Melillo, and J. R. Gosz. 1991. Current status and future of long-term ecological research. Pp. 275-285 in Long-term ecological research: an international perspective, ed. P. G. Risser. Scientific Committee on Problems of the Environment (SCOPE) 47. New York, NY: John Wiley and Sons. Ritchie, J. C. 1995. Current trends in studies of long-term plant community dynamics. New Phytologist 130: 469-494. Rugh, J. C., and D. L. Peterson. 1992. Inventory and monitoring in the national parks: forging a plan. Park Science 12(4): 1-4. Shampine, W. J. 1993. Quality assurance and quality control in monitoring programs. Environmental Monitoring and Assessment 26: 143-151. Silsbee, D. G., and D. L. Peterson. 1993. Planning for implementation of long-term resource monitoring programs. Environmental Monitoring and Assessment 26: 177-185. Smith, S. B., ed. 1982. Symposium on environmental monitoring. Edmonton, Alberta: Alberta Society of Environmental Biologists. 259p. Snedecor, G. W., and W. G. Cochran. 1989. Statistical methods. 8th ed. Ames, IA: Iowa State University Press. 524p. Sokal, R. R., and F. J. Rohlf. 1981. Biometry. San Francisco, CA: Freeman and Co. 859p. Spellerberg, I. F. 1991. Monitoring ecological change. New York, NY: Cambridge University Press. 334p.
36
Stevens, D. L. 1994. Implementation of a national monitoring program. Journal of Environmental Management 42: 1-29. Stohlgren, T. J., D. Binkley, T. T. Veblen, and W. L. Baker. 1995. Attributes of reliable long-term landscape-scale studies: malpractice insurance for landscape ecologists. Environmental Monitoring and Assessment 36: 1-25. Stout, B. B. 1993. The good, the bad and the ugly of monitoring programs: defining questions and establishing objectives. Environmental Monitoring and Assessment 26: 91-98. Strand, G. 1996. Detection of observer bias in ongoing forest health monitoring programmes. Canadian Journal of Forest Research 26: 1692-1696. Strayer, D., J. S. Glitzenstein, C. G. Jones, J. Kolasa, G. E. Likens, M. J. McDonnel, G. G. Parker, and S. T. A. Pickett. 1986. Long-term ecological studies: an illustrated account of their design, operation, and importance to ecology. The Institute of Ecosystem Studies Occasional Publ. 2. New York, NY: The New York Botanical Garden. 28p. Stuart, A. 1976. Basic ideas of scientific sampling. Second edition. London: Charles Griffen. 106p. Sutter, R. D. 1986. Monitoring rare plant species and natural areas – ensuring the protection of our investment. Natural Areas Journal 6: 3-5. Thomas, J. M., D. H. McKenzie, and L. L. Eberhardt. 1981. Some limitations of biological monitoring. Environment International 5: 3-10. Thomas, L. 1996. Monitoring long-term population changes: why are there so many analysis methods? Ecology 77: 49-58. Thompson, S. K. 1992. Sampling. New York, NY: John Wiley and Sons. 368p. Travis, J., and R. Sutter. 1986. Experimental designs and statistical methods for demographic studies of rare plants. Natural Areas Journal 6(3): 4-12. Tyrrell, L. E., D. T. Funk, C. T. Scott, M. Smith, L. Parker, and T. E. Demeo. 1994. Research natural area monitoring field guide. St. Paul, MN: U.S. Department of Agriculture, Forest Service, Region 9. 136p. Underwood, A. J. 1991. Biological monitoring for human impact: how little it can achieve. Pp. 105-123 in Proceedings of the 29th Congress of the Australian Society of Limnology; Jabiru, Australia, Canberra, Australia, ed. Hyne, R.V. Australian Government Publishing Service. White, P. S., and S. P. Bratton. 1981. Monitoring vegetation and rare plant populations in U.S. national parks and preserves. Pp. 265-278 in The biological aspects of rare plant conservation, ed. H. Synge. New York, NY: John Wiley and Sons Ltd. Wiersma, G. B., G. J. White, and D. A. Bruns. 1991. Global environmental monitoring in wilderness and protected areas. Pacific rim forestry, bridging the word: Proceedings of the 1991 Society of American Foresters national convention; 1991 August 4-7; San Francisco, CA. Bethesda, MD: Society of American Foresters. 225-230pp. Williams, B. G. 1993. Biostatistics, concepts and applications for biologists. New York, NY: Chapman and Hall. 201p. Williams, B. 1978. A sampler on sampling. New York, NY: John Wiley and Sons. 254p. Williams, B. L., and B. G. Marcot. 1991. Use of biodiversity indicators for analyzing and managing forest landscapes. Transactions of The North American Wildlife and Natural Resources Conference 56: 613-627. Woodley, S., and J. Theberge. 1992. Monitoring for ecosystem integrity in Canadian national parks. Pp. 369-377 in Science and the management of protected areas, eds. J. H. M. Willison, S. Bondrup- Neilson, C. Drysdale, T. B. Herman, N. W. P. Munro, and T. L. Pollock. Amsterdam, The Netherlands: Elsevier Scientific Publishers. Worf, D. L., ed. 1980. Biological monitoring for environmental effects. Lexington, MA: Lexington Books, D. C. Health and Company. 227p. Zar, J. H. 1984. Biostatistical analysis. Englewood Cliffs, NJ: Prentice Hall. 717p.
37
Case studies Barton, J. D., and D. V. Schmelz. 1987. Thirty years of growth records in Donaldson's Woods. Proceedings of the Indiana Academy of Science 96: 209-214. Belsky, A. J. 1985. Long-term vegetation monitoring in the Serengeti National Park, Tanzania. Journal of Applied Ecology 22: 449-460. Botkin, D. B. 1978. A pilot program for long-term observations and study of ecosystems in the United States. Washington, DC: National Science Foundation. Brewer, R. 1980. A half-century of changes in the herb layer of a climax deciduous forest in Michigan. Journal of Ecology 68: 223-232. Burgess, R. L. 1980. The national biological monitoring inventory. Pp. 153-165 in Biological monitoring for environmental effects, ed. D. L. Worf. Lexington, MA: Lexington Books, D.C. Health and Company. Burkman, W. G., and G. D. Hertel. 1992. Forest health monitoring. Journal of Forestry 90(9): 26-27. Cain, S. A. 1934. Sample plot technique applied to alpine vegetation in Wyoming. American Journal of Botany 30: 240-247. Canham, C. D., G. G. Parker, and T. G. Siccama. 1992. A directory of long-term studies of vegetation. Occasional publ. of the Institute of Ecosystem Studies 7. Millbrook, NY: New York Botanical Garden. Davis, G. E. 1989. Design of a long-term monitoring program for Channel Islands National Park, California. Natural Areas Journal 9(2): 80-89. Davis, G. E., and W. L. Halvorson. 1989. Natural resources monitoring program development plan, Western Region, National Park Service. Western Region Working Group on Natural Resources Monitoring, Ventura, CA: U.S. Department of Interior, National Park Service. French, N. R., and J. E. Mitchell. 1983. Long-term vegetation changes in permanent quadrats at the Idaho National Engineering Laboratory Site. Forest, Wildlife and Range Exp. Sta. Bull. 36. Moscow, ID: University of Idaho. 42p. Greene, S. E. 1984. Botanical baseline monitoring in research natural areas in Oregon and Washington. Pp. 6-10 in Research natural areas: baseline monitoring and management, tech. coords. J. L. Johnson, J. F. Franklin, and R. G. Krebill. 1984 March 21; Missoula, MT. Gen. Tech. Rep. INT-173. Ogden, UT: U.S. Department of Agriculture, Forest Service, Intermountain Forest and Range Experiment Station. Johnson, W. C., and S. P. Bratton. 1978. Biological monitoring in UNESCO biosphere reserves with special reference to the great Smoky Mountain National Park. Biological Conservation 13: 105-115. Lang, G. E., D. H. Knight, and D. A. Anderson. 1971. Sampling the density of tree species with quadrats in a species rich tropical forest. Forest Science 17: 395-400. Max, T. A., H. T. Scheuder, J. W. Hazard, D. D. Oswald, J. Teply, and J. Alegria. 1996. The Pacific Northwest Region vegetation and inventory monitoring system. Res. Pap. PNW-493. Portland, OR: U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station. 22p. Meurk, C. D. 1989. Vegetation monitoring, with special reference to the subantarctic islands of New Zealand. Pp. 209-219 in Proceedings of a symposium on environmental monitoring in New Zealand with emphasis on protected natural areas, ed. B. Craig; 1988 May; Dunendin. Wellington: Department of Conservation. Moir, W. H., and J. F. Franklin. 1972. Baseline measurement programs on federal research natural areas. Rep. 1 (Review Draft). Corvallis, OR: Pacific Northwest Natural Areas Committee. 20p. National Science Foundation. 1977. Long-term measurements: report of a conference. Washington, DC: National Science Foundation Directorate for Biological, Behavioral, and Social Sciences. National Swedish Environment Protection Board. 1985. Monitor 1985 – the national Swedish environmental monitoring programme. Stockholm, Sweden: National Swedish Environmental Protection Board.
38
Palmer, C. J., K. H. Ritters, J. Strickland, D. C. Cassell, G. E. Byers, M. L. Papp, and C. I. Liff. 1991. Monitoring and research strategy for forests – Environmental Monitoring and Assessment Program (EMAP). EPA/600/4-91/012. Washington, DC: U.S. Environmental Protection Agency. Peterken, G. F., and C. Backmeroff. 1988. Long-term monitoring in unmanaged woodland nature reserves. NCC Research and Survey in Nature Conservation 11. Peterborough: Nature Conservancy Council. Peterson, D. L., D. G. Silsbee, and D. L. Schmoldt. 1994. Developing inventory and monitoring programs in national parks: a planning approach. Natural Resources Rep. Washington, DC: U.S. Department of Interior, National Park Service, Cooperative Park Studies Unit, University of Washington. Pickart, A. J. 1991. The evolution of a rare plant monitoring program: a case study at the Lanphere- Christensen Dunes Preserve. Natural Areas Journal 11(4): 187-189. Silsbee, D. G., and D. L. Peterson. 1991. Designing and implementing comprehensive long-term inventory and monitoring programs for National Park System lands. Natural Resources Rep. NPS/NRUW/NRR-91/04. Washington, DC: U.S. Department of Interior, National Park Service. 13p. Synnott, T. J. 1979. A manual of permanent plot procedures for tropical rain forest. Tropical For. Pap. 14. Oxford, England: University of Oxford, Commonwealth Forestry Institute. 67p. U.S.Department of Interior, National Park Service. 1990. Shenandoah National Park, long-term ecological monitoring system. NPS/NRSHEN/NRTR-90/02. Denver, CO: U.S. Department of Interior, National ark Service.
39
Appendix 13.2 Recommended Protocols for Terrestrial Vascular Plants and Associated Forest Structures
This appendix contains the vascular plant protocols with the revisions suggested in the pilot project (Figure A13.1). Sampling is divided into spring and summer sampling packages. The spring package includes large trees (≥ 25 cm DBH), snags (≥ 10 cm DBH), and downed woody material (DWM), and is integrated with plot header methodology, and terrestrial vertebrate and avifauna sampling (Figure A13.2). The summer package samples low understory vegetation (≤0.5 m height), tall shrubs (>0.5 m height), trees (>0.5 m height), and canopy openness (Figure A13.3). The timing of these protocols corresponds with the flowering and fruiting of forest vegetation and is integrated well with non-vascular plant and terrestrial invertebrate sampling.
Spring Protocol Package
Plot Header Protocol
The plot header characterizes location, topography and forest cover attributes for each site. Site access information includes directions and distances of vehicle, all terrain vehicle, and foot travel to site center. In addition to a written description, this data is plotted onto aerial- or ortho-photographs, as well as topographic maps (1:50,000). The geographic location of site centre is determined by the use of a high quality global positioning system (GPS) receiver. Basic synoptic topographic and forest cover attributes includes: slope; elevation; drainage; dominant canopy type (height and diameter at breast height); stand age; overall site phenology; and stand origin (e.g., fire- or harvest-origin) at each location.
Topographic and forest cover attributes are collected within 15 m of site centre. Slopes are determined using a laser hypsometer (Laser Technology, Inc., Englewood, Colorado) positioned at site centre and sighted to reference points 10 m in each cardinal direction. Dominant canopy types are described and measurements of height, diameter at breast height (DBH; 1.3 m), and tree age were recorded for six representative canopy trees. Tree height is determined using the laser hypsometer. DBH calipers or tapes were used to measure DBH, and trees are cored at DBH to estimate age. In young stands, or when canopy trees are too small to be cored, trees are cut at the base to yield stump age.
Large Tree and Snag Protocol
A 50 m x 100 m rectangular plot is centered on the site marker (Figure A13.2). Large trees are identified as any bole (≥25 cm DBH) with green leaves, buds or cambium, while snags (≥10 cm DBH) are dead woody stems greater than 1.3 m tall (i.e., breast height), leaning no more than 45°. The plot is divided into quarters (25 m x 50 m) and sampled in an east to west zigzag fashion (Figure A13.4). For each tree and snag, the species, diameter at breast height (DBH), and condition (alive or dead) is recorded. In addition, a decay class is assigned to each snag (Figure A13.5). Decay classes are based on the size of branches attached to the bole, top condition, wood softness, and percent bark cover.
40
Down Woody Material (DWM) Protocol
A line-intercept transect method (Figure A13.2) is used to sample DWM (>10 cm dia.) within a 1 ha area surrounding site centre. Each site location consists of four 25 m transects radiating in sub-ordinal directions (315°, 45°, 135° and 225°), for a total length of 100 m. Species, diameter, length, and decay class are measured and recorded. DWM decay classification depends on wood hardness, plant colonization, and the condition of branches attached to the bole. Table 4.1 outlines the decay classification scheme for DWM.
Summer Protocol Package
Understory Community Measurements
Each site consists of 16 low understory plots (0.5 m x 0.5 m), for a total of 4 m2. Plots are placed at 20 m and 40 m along four ordinal transects and four sub-ordinal transects (Figure A13.3). A brief habitat description is completed for each plot, which includes information on: the dominant overstory; dominant understory; soil moisture regime; and types of anthropogenic disturbance (e.g., cut block, cut line, road). More generalized habitat characteristics (e.g., stream bank, bog, canopy gap) are also noted. Within each plot, the following variables are recorded: percent cover of total vascular plants; litter (dead vegetation material, DWM < 2 cm in diam.); wood (DWM ≥ 2 cm diam., bole of live trees > 1.3 m height); total moss; total lichen; total grass; and bare ground. These general measurements are followed by an estimation of percent cover for each species (+ 1%). If a species has >1% presence then it is recorded as present only. Only plants rooted in the plot are included.
Species Richness and Rare Species Measurements
Species presence is recorded in a walking search along 40 m of each ordinal and sub-ordinal transect, searching 1 m on each side for a total area of 640 m2. Lists of target species aid in species identification (Appendices 13.3-13.5).
Some vascular plants, such as willows, grasses, and sedges, require significant amounts of time for identification to the species level. We recommend the collection of voucher specimens and the use of taxonomic expertise to accurately identify these specimens.
Tall Shrub and Sapling Tree Protocol
Two shrub and sapling tree sample plots envelope understory plots at 20 m and 40 m along four transects radiating in ordinal and sub-ordinal directions from site centre (Figure A13.3). Each plot is 2 m x 2 m for a total of 64 m2 per site. Species and stem counts for all shrubs and sapling tree species are recorded. For stems >1.3 m in height, DBH is recorded. Only specimens rooted inside the quadrat are counted.
Tree Protocol
Small (>1.3 m height, ≤7 cm DBH) and medium (>7 cm to 25 cm DBH) -sized trees are sampled using 10 circular plots per site. Plots are located at site centre and at 45 m along the four ordinal transects. Plots for small and medium trees have a radius of 3 m (22.5 m2) and 5 m (78.5 m2), respectively (Figure A13.3). Species and DBH are recorded for each stem.
41
LFH Soil Carbon Protocol
Ten soil core samples (5 cm in diam.) are removed per site (Figure A13.3). Two soil pits coincide with each of the five tree plots. Pits are randomly placed on either side of the ordinal transect lines within the radius of the live tree plot (5 m radius). The thickness of the LFH layer to mineral soil was recorded (Appendix 5.2.9).
Canopy Openness Protocol
Four readings are taken in each ordinal direction at site centre (Figure A13.3). The observer is positioned with their back to site centre, and holds the spherical densiometer level at approximately 1.3 m. In the pilot project, we used a spherical densiometer manufactured by Forest Densiometers (Bartlesville, OK).
Figure A13.2 Spring package protocol layout
N
100m
Legend 100m
Plot Header
Down Woody Material Transect
Large Tree and Snag Plot
42
Figure A13.3 Summer package protocol layout. Note that figure is not to scale.
N
100m
Legend
Site Perimeter Live Tree Plot (>7 cm DBH, <25cm DBH) Low Vegetation Plot
Tall Shrub and Sapling Tree Live Tree Plot Plot (>1.3 m height, ≤7 cm DBH) Stand Photographs LFH Soil Carbon Pit Tree Canopy Cover
43
Figure A13.4 Suggested sampling route for large trees and snags
N
Suggested Sampling Route
Figure A13.5 Snag decay classification scheme
f
Stage 1 Stage 2 Stage 3 Stage 4 recently killed some twigs and branches major branch stubs bole broken below canopy remaining, wood hard remaining, wood beginning height, wood condition to decay, bole relatively variable, includes broken intact, tip may be missing canopy stage 1 to stage 3.
44
Table A13.1 Key characteristics of coarse (>10 cm diam.) downed woody material (DWM) decay classes.
Decay classes Key Characteristics
1 Wood hard to slightly decayed; twigs and bark may be present; no plants colonizing log
2 Wood decayed, some hard wood may be present; non-vascular plants beginning to colonize; no vascular plants present
3 Wood mostly well decayed; log completely moss-covered and colonized by various vascular plants; may be hard to define as a log.
45
Figure A13.1 Schematic flowchart of objectives (red), indicators (blue), methods (yellow), and metrics (green) for monitoring terrestrial vascular plants and associated forest structures.
Objective 1: Land Conversion Objective 2: Sepcies and Objective 3: Forest Structure of the Forested Region Community Diversity Diversity
Overall Target Understory Deadwood Canopy Remote Sensing Diversity Species Communities Resources Attributes
Grass and Soil Canopy Canopy Species Rare Shrub Community Snag Logs Richness Species Weedy Species Diversity Carbon Texture Transparency Species
Area Search Cover Stem Counts Bole Count Log Soil Remote Ocular 640 m² Estimate 4 m² 569 m² 5,000 m² Intercept Cores Sensing Estimation 100 m Densiometer transect
Relative Community Volume and Volume Density % Cover Number of Abundance of Type e.g. Density of and of Soil Species Target ecosite, Snags Density of Carbon Species ecosite phase, Logs plant community
1
Appendix 13.3.1 Rare Species Data Sheet for the Boreal Forest
Alberta Forest Biodiversity Monitoring Program Pilot
Non-vascular and Vascular Plant Elements of Conservation Concern
Site Location:______Date:______
Page____of____ Crew:______
Transect Checklist: North Northeast East Southeast South Southwest West Northwest Genus Species Common Name Alberta Global Area Search Rank Rank 1=Presence, 0=Absence 1 Calypogeia muelleriana liverwort S? G5 2 Calypogeia neesiana liverwort S? G5 3 Cephalozia bicuspidata liverwort S? G5 4 Cephaloziella hampeana liverwort S? G5 5 Riccardia multifida liverwort S3? G5 6 Riccia cavernosa S? G5 7 Aloina brevirostris short-beaked rigid screw S2 G3G5 moss 8 Aloina rigida aloe-like rigid screw moss S2 G3G5 9 Anomodon minor S1 G5 10 Aongstroemia longipes S2 G? 11 Atrichum undulatum undulated crane's bill moss S1S2 G5 12 Brachythecium acutum SU G?Q 13 Brachythecium rutabulum S2? G5 14 Bryum algovicum S2 G4G5 15 Bryum pallens S2 G4G5 16 Bryum uliginosum S1 G3G5 17 Bryum cyclophyllum S1S2 G4G5 18 Campylium polygamum S3 G5 19 Campylium radicale S2 G3G5 20 Conardia compacta S2 G3G5 21 Cynodontium tenellum S2 G? 22 Desmatodon heimii long-stalked beardless S1 G5 moss 23 Dicranum tauricum broken-leaf moss S1S2 G4 24 Didymodon fallax fallacious screw moss S2 G5 25 Drepanocladus brown moss S2 G? crassicostatus 26 Drepanocladus sendtneri brown moss S2 G? 27 Entodon concinnus S2S3 G4G5 28 Entodon schleicheri S1 G3G5 29 Fontinalis antipyretica S1 G5 30 Herzogiella turfacea S1 G4G5 31 Hygroamblystegium SU G4 noterophilum
1
32 Hygroamblystegium tenax S1 G5 33 Hypnum callichroum S1 G? 34 Hypnum pallescens S1 G5 35 Leskeella nervosa S2 G5 36 Meesia longiseta S1 G3G4 37 Neckera pennata S1 G5 38 Phascum cuspidatum cuspidate earth moss S2 G5 39 Physcomitrium pyriforme urn moss S1 G5 40 Pohlia atropurpurea S1 G4G5 41 Pohlia bulbifera S1 G4G5 42 Pohlia sphagnicola S2 G4 43 Polytrichum longisetum slender hairy-cap S1 G5 44 Pseudoleskeella sibirica S2 G? 45 Rhodobryum ontariense S2 G5 46 Schistostega pennata luminous moss S1S2 G4 47 Seligeria calcarea chalk brittle moss S1 G4? 48 Sphagnum balticum peat moss S1 G? 49 Sphagnum compactum neat bog moss S1S2 G5 50 Sphagnum fimbriatum fringed bog moss S2S3 G5 51 Sphagnum lindbergii Lindberg's bog moss S2S3 G5? 52 Sphagnum contortum twisted bog moss S1 G5 53 Sphagnum fallax peat moss S2 G5 54 Splachnum ampullaceum flagon-fruited splachnum S2 G4 55 Splachnum rubrum red collar moss S2 G3 56 Tayloria serrata slender splachnum S2 G4 57 Weissia controversa green-cushioned weissia S2 G5 58 Warnstorfia tundrae brown moss S2 G? 59 Warnstorfia brown moss S1 G2G3 pseudostraminea 60 Bryobrittonia longipes S3 G3 61 Limprichtia cossonii SU G? 62 Schistidium agassizii elf bloom moss S1 G3G5 63 Pseudobryum S1 G5 cinclidioides 64 Rhizomnium S1 G3G5 andrewsianum 65 Cyphelium tigillare S2 G? 66 Ramalina obtusata S2 G? 67 Ramalina sinensis SU G? 68 Dermatocarpon moulinsii S2 G? 69 Arthonia patellulata S3? G? 70 Biatora vernalis S2 G? 71 Cladonia cyanipes S2 G? 72 Cladonia ramulosa S1 G? 73 Cladonia symphycarpa S2 G? 74 Flavopunctelia soredica S2 G? 75 Heterodermia speciosa S2 G? 76 Hypocenomyce friesii S2 G? 77 Imshaugia placorodia S2 G?
2
78 Lecania dubitans S2 G? 79 Lecanora cateilea S2 G? 80 Lepraria incana S2 G? 81 Leptogium furfuraceum S2 G? 82 Melanelia multispora S2? G? 83 Melanelia olivacea S1 G? 84 Melanelia subelegantula S2 G? 85 Mycobilimbia sabuletorum S2 G? 86 Mycocalicium subtile S2 G? 87 Nephroma bellum S2 G? 88 Ramalina calicaris S1? G? 89 Scoliciosporum S2 G? chlorococcum 90 Sphinctrina turbinata S1 G? 91 Arctoparmelia seperata S1 92 Hypogymnia rugosa S1S2 93 Melanelia fuliginosa S1 94 Physconia enteroxantha S1? 95 Physconia isidiigera S2 96 Usnea scabiosa SU 97 Pannaria conoplea S1 G3G4 98 Phaeophyscia hirsuta S1 G3 99 Phaeophyscia nigricans S2 G4 100 Physcia dimidiata S1 G3 101 Physcia tenella S2 G4 102 Peltigera collina S1 G3G4 103 Peltigera horizontalis S1S2 G5 104 Peltigera polydactyla S1S2 G5 105 Bryoria nadvornikiana old man's beard S2 G? 106 Bryoria simplicior old man's beard S2S3 G? 107 Bryoria trichodes old man's beard SU G3G5 108 Cladonia bacilliformis S2S3 G3G4 109 Cladonia bellidiflora S2S3 G5 110 Cladonia squamosa S2 G5 111 Cladina stygia S1 G5 112 Anaptychia setifera S2 G3G4 113 Artemisia borealis northern wormwood S2 G5? 114 Artemisia tilesii Herriot's sagewort S2 G5 115 Aster umbellatus flat-topped white aster S2 G5 116 Aster x maccallae S1S2 HYB 117 Erigeron hyssopifolius wild daisy fleabane S1 G5 118 Eupatorium maculatum spotted Joe-pye weed S1S2 G5 119 Lactuca biennis tall blue lettuce S2 G5 120 Tanacetum bipinnatum ssp Indian tansy S1 G4G5Q huronense 121 Arabidopsis salsuginea mouse-ear cress S1 G4G5 122 Barbarea orthoceras American winter cress S2 G5 123 Cardamine parviflora small bitter cress S1 G5 124 Cardamine pratensis meadow bitter cress S1S2 G5
3
125 Lobelia dortmanna water lobelia S1 G4 126 Silene antirrhina sleepy catchfly SE? G5 127 Spergularia salina salt-marsh sand spurry S2 G5 128 Stellaria crispa wavy-leaved chickweed S2 G5 129 Hypericum majus large Canada St. John's- S2 G5 wort 130 Drosera linearis slender-leaved sundew S2 G4 131 Elatine triandra waterwort S1 G5 132 Vaccinium uliginosum bog bilberry S2 G5 133 Astragalus bodinii Bodin's milk vetch S1 G4 134 Gentianopsis detonsa ssp northern fringed gentian S1 G4T? raupii 135 Lomatogonium rotatum marsh felwort S2 G5 136 Geranium carolinianum Carolina wild geranium S1 G5 137 Physostegia ledinghamii S2 G3? 138 Pinguicula villosa small butterwort S1 G4 139 Utricularia cornuta horned bladderwort S1 G5 140 Monotropa hypopithys pinesap S2 G5 141 Nymphaea leibergii S1 G5 142 Epilobium halleanum willowherb S1 G5 143 Epilobium lactiflorum willowherb S2 G5 144 Boschniakia rossica ground-cone S1 G5 145 Polygala paucifolia fringed milkwort S1 G5 146 Plantago canescens western ribgrass S2 G4G5 147 Plantago maritima sea-side plantain S1 G5 148 Pyrola grandiflora Arctic wintergreen S2 G5 149 Potentilla multifida branched cinquefoil S1 G5 150 Rubus x paracaulis hybrid dwarf raspberry S1 HYB 151 Hedyotis longifolia long-leaved bluets S2 G4G5 152 Salix sitchensis Sitka willow S1 G5 153 Sarracenia purpurea pitcher-plant S2 G5 154 Parnassia parviflora small northern grass-of- S2 G4 parnassus 155 Pedicularis sudetica purple rattle S1 G5 156 Viola pallens Macloskey's violet S1 G5T5 157 Sagittaria latifolia broad-leaved arrowhead S1 G5 158 Carex adusta browned sedge S1 G5 159 Carex arcta narrow sedge S1 G5 160 Carex backii Back's sedge SU G4 161 Carex capitata capitate sedge S2 G5 162 Carex heleonastes Hudson Bay sedge S2 G4 163 Carex hookerana Hooker's sedge S2 G4? 164 Carex houghtoniana sand sedge S2 G5 165 Carex hystericina porcupine sedge S1 G5 166 Carex lacustris lakeshore sedge S2 G5 167 Carex mertensii purple sedge S1 G5 168 Carex oligosperma few-fruited sedge S1 G4 169 Carex pedunculata S1 G5 170 Carex petasata pasture sedge S1S2 G5
4
171 Carex pseudocyperus cyperus-like sedge S2 G5 172 Carex retrorsa turned sedge S2 G5 173 Carex rostrata beaked sedge S2 G5 174 Carex tincta tinged sedge S1 G4G5 175 Carex vulpinoidea fox sedge S2 G5 176 Eleocharis tenuis slender spike-rush SU G5 177 Rhynchospora capillacea slender beak-rush S1 G5 178 Trichophorum clintonii Clinton's bulrush S1 G4 179 Bolboschoenus fluviatilis river bulrush S1 G5 180 Scirpus pallidus pale bulrush S1 G5 181 Blysmus rufus Red Bulrush S1 G5 182 Trichophorum pumilum dwarf bulrush S2 G5 183 Elodea bifoliata S1 G4G5 184 Sisyrinchium pale blue-eyed grass S2S3 G3G4 septentrionale 185 Juncus brevicaudatus short-tail rush S2 G5 186 Juncus filiformis thread rush S2S3 G5 187 Juncus stygius var marsh rush S2 G5T5 americanus 188 Luzula acuminata wood-rush S1 G5 189 Luzula rufescens reddish wood-rush S1 G5 190 Wolffia columbiana watermeal S2 G5 191 Streptopus roseus rose mandarin S1 G5 192 Najas flexilis slender naiad S1S2 G5 193 Cypripedium acaule stemless lady's-slipper S2 G5 194 Malaxis monophylla white adder's-mouth S2 G5 195 Malaxis paludosa bog adder's-mouth S1 G4 196 Spiranthes lacera northern slender ladies'- S1 G5 tresses 197 Arctagrostis arundinacea polar grass S1 G? 198 Danthonia spicata poverty oat grass S1S2 G5 199 Panicum leibergii Leiberg's millet S1 G5 200 Glyceria elata tufted tall manna grass S2 G4G5 201 Muhlenbergia racemosa marsh muhly S1 G5 202 Oryzopsis canadensis Canadian rice grass S1 G5 203 Spartina pectinata prairie cord grass S1 G5 204 Sphenopholis obtusata prairie wedge grass S2 G5 205 Potamogeton foliosus leafy pondweed S2 G5 206 Potamogeton natans floating-leaf pondweed S2 G5 207 Potamogeton obtusifolius blunt-leaved pondweed S2 G5 208 Potamogeton praelongus white-stem pondweed S2 G5 209 Potamogeton robbinsii Robbins' pondweed S1 G5 210 Potamogeton strictifolius linear-leaved pondweed S2 G5 211 Sparganium glomeratum bur-reed S1 G4? 212 Pellaea glabella ssp S2 G5T4? simplex 213 Cystopteris montana mountain bladder fern S2 G5 214 Dryopteris cristata crested shield fern S1 G5 215 Gymnocarpium SU G4
5
disjunctum 216 Isoetes echinospora northern quillwort S1 G5? 217 Diphasiastrum sitchense ground-fir S2 G5 218 Huperzia selago mountain club-moss S1 G5 219 Botrychium multifidum leather grape fern S2 G5T4? var intermedium 220 Botrychium minganense S2S3 G4 221 Botrychium pinnatum S1 G4? 222 Polypodium virginianum rock polypody S2? G5 223 Polypodium sibiricum S1? G5? 224 Phegopteris connectilis northern beech fern S2 G5 * Plants of conservation concern were defined as having one or more mapped occurrences in or within 2.5 km of the Boreal Forest Natural Region. This species list was provided by the Alberta Natural Heritage Information Centre, updated 22 November 2000.
6
Appendix 13.3.2 Rare Species Data Sheet for the Canadian Shield
Alberta Forest Biodiversity Monitoring Program Pilot
Non-vascular and Vascular Plant Elements of Conservation Concern
Site Location:______Date:______
Page____of____ Crew:______
Transect Checklist: North Northeast East Southeast South Southwest West Northwest Genus Species Common Name Alberta Global Area Search Rank Rank 1=Presence, 0=Absence 1 Campylium polygamum S3 G5 2 Cynodontium strumiferum S2 G3G5 3 Fissidens adianthoides maidenhair moss S2S3 G5 4 Leskeella nervosa S2 G5 5 Neckera pennata S1 G5 6 Sphagnum balticum peat moss S1 G? 7 Sphagnum contortum twisted bog moss S1 G5 8 Tayloria serrata slender splachnum S2 G4 9 Ulota curvifolia S1 G3G5 10 Warnstorfia tundrae brown moss S2 G? 11 Pseudobryum cinclidioides S1 G5 12 Ramalina intermedia S1 G? 13 Arctoparmelia centrifuga S2 G? 14 Cladonia macrophylla S2 G? 15 Imshaugia placorodia S2 G? 16 Lasallia pensylvanica S1 G? 17 Umbilicaria muehlenbergii S2 G? 18 Arctoparmelia seperata S1 19 Stereocaulon condensatum S1 G4 20 Cetraria laevigata SU G? 21 Cladonia bacilliformis S2S3 G3G4 22 Artemisia borealis northern wormwood S2 G5? 23 Tanacetum bipinnatum ssp Indian tansy S1 G4G5Q huronense 24 Barbarea orthoceras American winter cress S2 G5 25 Cardamine pratensis meadow bitter cress S1S2 G5 26 Lobelia dortmanna water lobelia S1 G4 27 Sagina nodosa pearlwort S1 G5 28 Silene antirrhina sleepy catchfly SE? G5 29 Stellaria arenicola sand-dune chickweed S1 G3 30 Hypericum majus large Canada St. John's- S2 G5 wort 31 Vaccinium uliginosum bog bilberry S2 G5 32 Physostegia ledinghamii S2 G3? 33 Pinguicula villosa small butterwort S1 G4 34 Utricularia cornuta horned bladderwort S1 G5
7
35 Potentilla hookeriana Hooker's cinquefoil S2 G4 36 Potentilla multifida branched cinquefoil S1 G5 37 Carex capitata capitate sedge S2 G5 38 Carex heleonastes Hudson Bay sedge S2 G4 39 Carex houghtoniana sand sedge S2 G5 40 Carex lenticularis var dolia lens-fruited sedge S1 G5T3Q 41 Carex oligosperma few-fruited sedge S1 G4 42 Carex pseudocyperus cyperus-like sedge S2 G5 43 Carex rostrata beaked sedge S2 G5 44 Carex supina Weak Sedge S1 G5 45 Sisyrinchium septentrionale pale blue-eyed grass S2S3 G3G4 46 Juncus brevicaudatus short-tail rush S2 G5 47 Juncus filiformis thread rush S2S3 G5 48 Luzula groenlandica wood-rush S1 G4 49 Cypripedium acaule stemless lady's-slipper S2 G5 50 Spiranthes lacera northern slender ladies'- S1 G5 tresses 51 Danthonia spicata poverty oat grass S1S2 G5 52 Leymus mollis American dune grass S1 G5 53 Potamogeton natans floating-leaf pondweed S2 G5 54 Potamogeton obtusifolius blunt-leaved pondweed S2 G5 55 Potamogeton robbinsii Robbins' pondweed S1 G5 56 Sparganium fluctuans bur-reed S1 G5 57 Gymnocarpium jessoense northern oak fern S1 G5 58 Isoetes echinospora northern quillwort S1 G5? 59 Lycopodiella inundata bog club-moss S1 G5 60 Botrychium multifidum var leather grape fern S2 G5T4? intermedium 61 Polypodium virginianum rock polypody S2? G5 62 Polypodium sibiricum S1? G5? * Plants of conservation concern were defined as having one or more mapped occurrences in or within 2.5 km of the Canadian Shield Natural Region. This species list was provided by the Alberta Natural Heritage Information Centre, updated 22 November 2000.
8
Appendix 13.3.3 Rare Species Data Sheet for the Foothills
Alberta Forest Biodiversity Monitoring Program Pilot
Non-vascular and Vascular Plant Elements of Conservation Concern
Site Location:______Date:______
Page____of___ Crew:______
Transect Checklist: North Northeast East Southeast South Southwest West Northwest Genus Species Common Name Alberta Global Area Search Rank Rank 1=Presence, 0=Absence 1 Cephaloziella hampeana liverwort S? G5 2 Aloina rigida aloe-like rigid screw S2 G3G5 moss 3 Amblyodon dealbatus S2 G3G5 4 Andreaea rupestris black rock moss S2 G5 5 Anoectangium aestivum S1 G3G5 6 Aongstroemia longipes S2 G? 7 Aulacomnium androgynum S2 G5 8 Barbula coreensis S1 G? 9 Brachythecium nelsonii S2 G? 10 Brachythecium rutabulum S2? G5 11 Bryum algovicum S2 G4G5 12 Bryum arcticum S1 G? 13 Bryum muehlenbeckii S1 G4G5 14 Bryum pallens S2 G4G5 15 Bryum purpurascens S1 G3G4 16 Bryum turbinatum SU G5 17 Bryum uliginosum S1 G3G5 18 Buxbaumia aphylla bug on a stick S2 G4 19 Campylium polygamum S3 G5 20 Campylium radicale S2 G3G5 21 Cirriphyllum cirrosum S2 G? 22 Conardia compacta S2 G3G5 23 Cynodontium alpestre S1 G3G5 24 Cynodontium schisti S1 G3G5 25 Cynodontium strumiferum S2 G3G5 26 Cynodontium tenellum S2 G? 27 Desmatodon heimii var S1 G5T? heimii 28 Desmatodon leucostoma S2 G? 29 Desmatodon systylius S2 G4G5 30 Dichelyma falcatum S1 G4G5 31 Dicranella crispa curl-leaved fork moss S2 G? 32 Dicranella heteromalla silky fork moss S1 G? 33 Dicranella palustris drooping-leaved fork S1? G?
9
moss 34 Dicranella subulata awl-leaved fork moss S2S3 G? 35 Dicranum spadiceum cushion moss S2S3 G? 36 Dicranum tauricum broken-leaf moss S1S2 G4 37 Didymodon johansenii S2 G? 38 Didymodon rigidulus rigid screw moss S2 G5 39 Didymodon tophaceus blunt-leaved hair S1 G5 moss 40 Didymodon S2 G? subandreaeoides 41 Didymodon fallax fallacious screw moss S2 G5 42 Discelium nudum naked weissia S1 G3G4 43 Drepanocladus brown moss S2 G? crassicostatus 44 Drepanocladus brevifolius brown moss S1 G?Q 45 Drepanocladus brown moss S1 G? capillifolius 46 Encalypta brevicolla candle-snuffer moss S2 G4 47 Encalypta intermedia candle-snuffer moss S2 G? 48 Entodon concinnus S2S3 G4G5 49 Fissidens adianthoides maidenhair moss S2S3 G5 50 Fontinalis dalecarlica S1 G3G5 51 Fontinalis missourica S1 G2G4 52 Fontinalis neomexicana S2 G3G5 53 Grimmia donniana Donian grimmia S2 G4G5 54 Grimmia montana sun grimmia S2 G? 55 Gymnostomum tufted rock beardless S2S3 G5 aeruginosum moss 56 Homalothecium nevadense S1 G4 57 Homalothecium S2? G4 pinnatifidum 58 Hygroamblystegium tenax S1 G5 59 Hygrohypnum bestii S2S3 G4 60 Hypnum procerrimum S3 G3G4 61 Hypnum recurvatum S2 G3G5 62 Orthothecium strictum S1 G? 63 Orthotrichum affine SU G3G5 64 Orthotrichum pallens S2 G5 65 Philonotis marchica S1 G5 66 Physcomitrium pyriforme urn moss S1 G5 67 Pogonatum dentatum hair-like pogonatum S2S3 G3G5 68 Pogonatum urnigerum urn-like pogonatum S2S3 G5 69 Polytrichum longisetum slender hairy-cap S1 G5 70 Pseudoleskeella sibirica S2 G? 71 Racomitrium sudeticum S1S2 G? 72 Schistostega pennata luminous moss S1S2 G4 73 Scouleria aquatica S2 G4 74 Seligeria campylopoda S2 G3G5 75 Seligeria donniana Donian beardless S2 G4G5
10
moss 76 Sphagnum compactum neat bog moss S1S2 G5 77 Sphagnum lindbergii Lindberg's bog moss S2S3 G5? 78 Sphagnum contortum twisted bog moss S1 G5 79 Splachnum ampullaceum flagon-fruited S2 G4 splachnum 80 Splachnum rubrum red collar moss S2 G3 81 Splachnum vasculosum large-fruited S1S2 G? splachnum 82 Stegonia pilifera S2 G? 83 Tayloria acuminata point-leaf small-kettle SU G3G4 moss 84 Tayloria hornschuchii small-kettle moss S1 G? 85 Tayloria serrata slender splachnum S2 G4 86 Tayloria splachnoides splanchnoid cyrtodon S1 G2G3 87 Tortella inclinata bent screw moss S2 G4G5 88 Anomobryum filiforme S1 G4 89 Warnstorfia tundrae brown moss S2 G? 90 Bryobrittonia longipes S3 G3 91 Limprichtia cossonii SU G? 92 Schistidium pulvinatum S1 G5 93 Schistidium tenerum thread bloom moss S1 G? 94 Coscinodon calyptratus sieve-toothed big S2 G? calyptra moss 95 Jaffueliobryum raui S1 G4? 96 Jaffueliobryum wrightii S2 G3G4 97 Chaenotheca S2 G? chrysocephala 98 Cladonia umbricola S1 99 Physcia phaea S2S3 G4G5 100 Cladina portentosa S1 G? 101 Ramalina intermedia S1 G? 102 Ramalina obtusata S2 G? 103 Ramalina sinensis SU G? 104 Collema nigrescens S1 G? 105 Hypogymnia vittata S1S2 G? 106 Leptogium hirsutum S1? G? 107 Acarospora arenacea S2 G? 108 Acarospora veronensis S2 G? 109 Aspicilia pergibbosa S1 G? 110 Aspicilia supertegens S2 G? 111 Calicium trabinellum S2 G? 112 Caloplaca flavovirescens S2 G? 113 Caloplaca sinapisperma S2 G? 114 Chaenotheca stemonea S1 G? 115 Chaenotheca trichialis S2 G? 116 Cladonia cyanipes S2 G? 117 Cladonia macrophylla S2 G? 118 Cladonia metacorallifera S2 G?
11
119 Dactylina beringica S2S3 G? 120 Dermatocarpon S2 G? intestiniforme 121 Heterodermia speciosa S2 G? 122 Hypocenomyce friesii S2 G? 123 Lecanora cateilea S2 G? 124 Lecidea plana S2 G? 125 Lepraria incana S2 G? 126 Leptogium furfuraceum S2 G? 127 Leptogium lichenoides S2S3 G? 128 Melanelia multispora S2? G? 129 Melanelia subelegantula S2 G? 130 Micarea assimilata S2 G? 131 Mycoblastus affinis S2 G? 132 Mycoblastus sanguinarius S2 G? 133 Mycocalicium subtile S2 G? 134 Nephroma bellum S2 G? 135 Pertusaria borealis S1 G? 136 Pertusaria sommerfeltii S1? G? 137 Physconia perisidiosa S1 G? 138 Ramalina dilacerata S2 G? 139 Rinodina exigua S1 G? 140 Rinodina polyspora S1 G? 141 Usnea ceratina old man's beard S1 G? 142 Varicellaria rhodocarpa S2 G? 143 Xylographa vitiligo S2 G? 144 Evernia perfragilis SU 145 Leptogium subtile S1? 146 Nephroma isidiosum S1 147 Psora tuckermanii S2 148 Usnea scabiosa SU 149 Xanthoparmelia S2 G5 subdecipiens 150 Phaeophyscia nigricans S2 G4 151 Phaeophyscia sciastra S2 G4 152 Physcia biziana S1S2 G5 153 Peltigera polydactyla S1S2 G5 154 Bryoria nadvornikiana old man's beard S2 G? 155 Bryoria simplicior old man's beard S2S3 G? 156 Cladonia bacilliformis S2S3 G3G4 157 Cladonia bellidiflora S2S3 G5 158 Cladonia squamosa S2 G5 159 Hypogymnia S2 G4 enteromorpha 160 Baeomyces rufus S2 G4G5 161 Anaptychia setifera S2 G3G4 162 Antennaria corymbosa corymbose S1 G5 everlasting 163 Antennaria aromatica scented everlasting S2 G4
12
164 Arnica amplexicaulis stem-clasping arnica S2 G4 165 Artemisia borealis northern wormwood S2 G5? 166 Erigeron flagellaris creeping fleabane S1 G5 167 Erigeron radicatus dwarf fleabane S2 G3 168 Erigeron trifidus trifid-leaved fleabane S1S2 G2G3Q 169 Lactuca biennis tall blue lettuce S2 G5 170 Prenanthes alata white lettuce S1 G5 171 Barbarea orthoceras American winter cress S2 G5 172 Cardamine pratensis meadow bitter cress S1S2 G5 173 Cardamine umbellata mountain cress S2 G? 174 Draba fladnizensis whitlow-grass S1 G4 175 Draba porsildii Porsild's whitlow- S2 G3G4 grass 176 Draba ventosa whitlow-grass S2 G3 177 Lesquerella arctica var northern bladderpod S2 G4T? purshii 178 Campanula uniflora alpine harebell S2 G4 179 Stellaria crispa wavy-leaved S2 G5 chickweed 180 Cornus unalaschkensis S? G5? 181 Drosera linearis slender-leaved S2 G4 sundew 182 Rhododendron Lapland rose-bay S2 G5 lapponicum 183 Astragalus bodinii Bodin's milk vetch S1 G4 184 Oxytropis campestris var S2? G5T3 davisii 185 Gentiana glauca alpine gentian S2 G4G5 186 Lomatogonium rotatum marsh felwort S2 G5 187 Monotropa hypopithys pinesap S2 G5 188 Epilobium clavatum willowherb S2 G5 189 Epilobium lactiflorum willowherb S2 G5 190 Epilobium leptocarpum willowherb S1 G5 191 Epilobium saximontanum Rocky Mountain S1 G5 willowherb 192 Papaver radicatum ssp alpine poppy S2 G3?Q kluanense 193 Primula egaliksensis primrose S2 G4 194 Pyrola grandiflora Arctic wintergreen S2 G5 195 Anemone quinquefolia wood anemone S1 G5 196 Aquilegia formosa Sitka columbine S2 G5 197 Ranunculus uncinatus hairy buttercup S2 G5 198 Potentilla hookeriana Hooker's cinquefoil S2 G4 199 Potentilla villosa hairy cinquefoil S2 G4 200 Potentilla multisecta smooth-leaved S2 G3G4Q cinquefoil 201 Salix alaxensis var Alaska willow S2 G5T? alaxensis 202 Salix commutata changeable willow S2 G5
13
203 Salix raupii Raup's willow S1 G2 204 Boykinia heucheriformis telesonix S2 G4 205 Parnassia parviflora small northern grass- S2 G4 of-parnassus 206 Saxifraga flagellaris ssp spiderplant S2 G5T? setigera 207 Saxifraga nelsoniana ssp Nelson's saxifrage S2 G5T3T4 porsildiana 208 Saxifraga nivalis alpine saxifrage S2 G4G5 209 Castilleja lutescens stiff yellow S2S3 G4G5 paintbrush 210 Pedicularis capitata large-flowered S2 G4 lousewort 211 Pedicularis flammea flame-colored S2 G3G5 lousewort 212 Pedicularis lanata woolly lousewort S2 G4G5 213 Viola pallens Macloskey's violet S1 G5T5 214 Thuja plicata western red cedar S1S2 G5 215 Sagittaria latifolia broad-leaved S1 G5 arrowhead 216 Carex adusta browned sedge S1 G5 217 Carex aperta open sedge S1 G4 218 Carex arcta narrow sedge S1 G5 219 Carex lachenalii two-parted sedge S2 G5 220 Carex capitata capitate sedge S2 G5 221 Carex heleonastes Hudson Bay sedge S2 G4 222 Carex houghtoniana sand sedge S2 G5 223 Carex incurviformis var seaside sedge S2 G4G5T? incurviformis 224 Carex lacustris lakeshore sedge S2 G5 225 Carex mertensii purple sedge S1 G5 226 Carex pedunculata S1 G5 227 Carex petricosa stone sedge S2 G4 228 Carex podocarpa alpine sedge S2 G4G5 229 Carex preslii Presl sedge S2 G4 230 Carex retrorsa turned sedge S2 G5 231 Carex rostrata beaked sedge S2 G5 232 Carex umbellata umbellate sedge S1 G5 233 Trichophorum clintonii Clinton's bulrush S1 G4 234 Trichophorum pumilum dwarf bulrush S2 G5 235 Sisyrinchium pale blue-eyed grass S2S3 G3G4 septentrionale 236 Juncus brevicaudatus short-tail rush S2 G5 237 Juncus filiformis thread rush S2S3 G5 238 Juncus stygius var marsh rush S2 G5T5 americanus 239 Luzula acuminata wood-rush S1 G5 240 Luzula rufescens reddish wood-rush S1 G5 241 Streptopus roseus rose mandarin S1 G5
14
242 Streptopus streptopoides twisted-stalk S1 G5 243 Listera convallarioides broad-lipped S2 G5 twayblade 244 Agrostis exarata spike redtop S2 G5 245 Calamagrostis lapponica Lapland reed grass S1 G5 246 Deschampsia elongata slender hair grass S1 G5 247 Festuca altaica northern rough fescue S2 G5 248 Glyceria elata tufted tall manna S2 G4G5 grass 249 Anthoxanthum monticola alpine sweet grass S2 G5 250 Sphenopholis obtusata prairie wedge grass S2 G5 251 Potamogeton natans floating-leaf S2 G5 pondweed 252 Potamogeton praelongus white-stem pondweed S2 G5 253 Sparganium hyperboreum northern bur-reed S1 G5 254 Pellaea glabella smooth cliff brake S2 G5 255 Pellaea glabella ssp S1 G5T? occidentalis 256 Pellaea glabella ssp S2 G5T4? simplex 257 Pellaea gastonyi S1 G2G4 258 Cystopteris montana mountain bladder fern S2 G5 259 Woodsia glabella smooth woodsia S1 G5 260 Diphasiastrum sitchense ground-fir S2 G5 261 Huperzia haleakalae S2 G4? 262 Huperzia selago mountain club-moss S1 G5 263 Botrychium multifidum var leather grape fern S2 G5T4? intermedium 264 Botrychium minganense S2S3 G4 265 Botrychium ascendens ascending grape fern S1 G2G3 266 Botrychium pinnatum S1 G4? 267 Botrychium spathulatum S2 G3G4 268 Phegopteris connectilis northern beech fern S2 G5 * Plants of conservation concern were defined as having one or more mapped occurrences in or within 2.5 km of the Foothills Natural Region. This species list was provided by the Alberta Natural Heritage Information Centre, updated 22 November 2000.
15
Appendix 13.3.4 Rare Species Data Sheet for the Rocky Mountains
Alberta Forest Biodiversity Monitoring Program Pilot
Non-vascular and Vascular Plant Elements of Conservation Concern
Site Location:______Date:______
Page____of____ Crew:______
Transect Checklist: North Northeast East Southeast South Southwest West Northwest Genus Species Common Name Alberta Global Area Search Rank Rank 1=Presence, 0=Absence 1 Calypogeia muelleriana liverwort S? G5 2 Cephaloziella hampeana liverwort S? G5 3 Diplophyllum taxifolium liverwort S1 G5 4 Jungermannia atrovirens liverwort S2 G4G5 5 Jungermannia leiantha liverwort S? G5 6 Jungermannia sphaerocarpa liverwort S2 G5 7 Lophozia ascendens liverwort S2 G4 8 Porella cordaeana liverwort S2 G4 9 Porella platyphylla liverwort S1 G5 10 Scapania curta liverwort S2 G5 11 Scapania subalpina liverwort S2 G4G5 12 Aloina brevirostris short-beaked rigid screw S2 G3G5 moss 13 Aloina rigida aloe-like rigid screw S2 G3G5 moss 14 Amblyodon dealbatus S2 G3G5 15 Amphidium mougeotii S1 G5 16 Andreaea blyttii S1 G5 17 Andreaea nivalis red rock moss S2 G5 18 Andreaea rupestris black rock moss S2 G5 19 Andreaea alpestris S1 G? 20 Anoectangium aestivum S1 G3G5 21 Aongstroemia longipes S2 G? 22 Arctoa fulvella S1 G3G5 23 Atrichum undulatum undulated crane's bill S1S2 G5 moss 24 Aulacomnium acuminatum S1 G3? 25 Aulacomnium androgynum S2 G5 26 Bartramia halleriana S1 G4G5 27 Blindia acuta sharp-pointed weissia S1S2 G5 28 Brachythecium acutum SU G?Q 29 Brachythecium calcareum S1 G3G4 30 Brachythecium frigidum SU G4 31 Brachythecium hylotapetum S2 G? 32 Brachythecium nelsonii S2 G? 33 Brachythecium plumosum S2 G5
16
34 Brachythecium reflexum S1 G4G5 35 Brachythecium rutabulum S2? G5 36 Bryoerythrophyllum red leaf moss S1 G4 ferruginascens 37 Bryum algovicum S2 G4G5 38 Bryum calophyllum S1 G? 39 Bryum knowltonii S1 G3G4 40 Bryum lonchocaulon SU G? 41 Bryum muehlenbeckii S1 G4G5 42 Bryum pallens S2 G4G5 43 Bryum schleicheri S1 G? 44 Bryum turbinatum SU G5 45 Bryum uliginosum S1 G3G5 46 Bryum cyclophyllum S1S2 G4G5 47 Bryum dichotomum S1 G5? 48 Bryum flaccidum SU G5 49 Bryum amblyodon S1 G? 50 Bryum calobryoides S1 G3 51 Bryum stirtonii S1S2 G? 52 Buxbaumia aphylla bug on a stick S2 G4 53 Buxbaumia viridis green shield moss S1S2 G4 54 Campylium polygamum S3 G5 55 Campylium radicale S2 G3G5 56 Cirriphyllum cirrosum S2 G? 57 Claopodium bolanderi S2 G4 58 Conardia compacta S2 G3G5 59 Cynodontium alpestre S1 G3G5 60 Cynodontium schisti S1 G3G5 61 Cynodontium strumiferum S2 G3G5 62 Cynodontium tenellum S2 G? 63 Cynodontium glaucescens glaucous shield moss S1 G3G4 64 Desmatodon heimii var heimii S1 G5T? 65 Desmatodon laureri S1 G? 66 Desmatodon leucostoma S2 G? 67 Desmatodon systylius S2 G4G5 68 Dichelyma falcatum S1 G4G5 69 Dichodontium olympicum S1 G? 70 Dicranella crispa curl-leaved fork moss S2 G? 71 Dicranella heteromalla silky fork moss S1 G? 72 Dicranella palustris drooping-leaved fork S1? G? moss 73 Dicranella subulata awl-leaved fork moss S2S3 G? 74 Dicranum angustum cushion moss S1S2 G? 75 Dicranum majus greater fork moss S1 G4G5 76 Dicranum pallidisetum alpine curly heron's bill S1 G? moss 77 Dicranum spadiceum cushion moss S2S3 G? 78 Dicranum tauricum broken-leaf moss S1S2 G4 79 Dicranum ontariense cushion moss S1 G4G5
17
80 Didymodon asperifolius S1 G3G5 81 Didymodon johansenii S2 G? 82 Didymodon rigidulus rigid screw moss S2 G5 83 Didymodon tophaceus blunt-leaved hair moss S1 G5 84 Didymodon subandreaeoides S2 G? 85 Didymodon vinealis S1 G5 86 Didymodon fallax fallacious screw moss S2 G5 87 Didymodon nigrescens S1 G? 88 Ditrichum montanum S1 G? 89 Drepanocladus crassicostatus brown moss S2 G? 90 Drepanocladus brevifolius brown moss S1 G?Q 91 Dryptodon patens spreading fringe moss S2 G4G5 92 Encalypta brevicolla candle-snuffer moss S2 G4 93 Encalypta brevipes candle-snuffer moss S1 G3 94 Encalypta longicolla candle-snuffer moss S1 G3G4 95 Encalypta vulgaris common extinguisher S1S2 G5 moss 96 Encalypta intermedia candle-snuffer moss S2 G? 97 Encalypta spathulata candle-snuffer moss S2 G3 98 Entodon concinnus S2S3 G4G5 99 Fissidens adianthoides maidenhair moss S2S3 G5 100 Fissidens grandifrons narrow-leaved Chinese S2 G3G5 phoenix moss 101 Fissidens limbatus S1 G3G5 102 Fontinalis antipyretica S1 G5 103 Funaria muhlenbergii Muhlenberg's cord moss S1 G4 104 Grimmia donniana Donian grimmia S2 G4G5 105 Grimmia elatior large grimmia S1 G? 106 Grimmia incurva black grimmia S1 G4G5 107 Grimmia montana sun grimmia S2 G? 108 Grimmia alpestris alpine grimmia S2 G3G5 109 Grimmia teretinervis S1 G3G5 110 Grimmia torquata twisted-leaved grimmia S2 G3G5 111 Grimmia trichophylla hair-pointed grimmia S1 G5? 112 Grimmia anomala mountain forest grimmia S2 G5 113 Gymnostomum aeruginosum tufted rock beardless S2S3 G5 moss 114 Herzogiella seligeri S1 G4 115 Herzogiella turfacea S1 G4G5 116 Heterocladium dimorphum S1 G4G5 117 Homalothecium nevadense S1 G4 118 Homalothecium pinnatifidum S2? G4 119 Hygroamblystegium tenax S1 G5 120 Hygrohypnum alpestre S1 G3G5 121 Hygrohypnum bestii S2S3 G4 122 Hygrohypnum cochlearifolium S1 G? 123 Hygrohypnum molle S1 G4G5 124 Hygrohypnum ochraceum S2 G5 125 Hygrohypnum smithii S1 G3G5
18
126 Hygrohypnum styriacum S1 G? 127 Hypnum callichroum S1 G? 128 Hypnum pallescens S1 G5 129 Hypnum procerrimum S3 G3G4 130 Hypnum recurvatum S2 G3G5 131 Kiaeria blyttii Blytt's fork moss S1S2 G5 132 Kiaeria starkei alpine broom moss S2 G5 133 Leptodictyum humile S2 G5 134 Lescuraea saxicola S1 G4G5 135 Leskeella nervosa S2 G5 136 Meesia longiseta S1 G3G4 137 Mielichhoferia macrocarpa S1 G2 138 Mnium ambiguum S1S2 G5 139 Myurella sibirica S1 G4? 140 Neckera pennata S1 G5 141 Oligotrichum aligerum S1 G5 142 Oligotrichum hercynicum Hercynian hair moss S2 G5 143 Oligotrichum parallelum S1 G5 144 Oreas martiana S1 G5? 145 Orthothecium intricatum S1 G4G5 146 Orthothecium strictum S1 G? 147 Orthotrichum affine SU G3G5 148 Orthotrichum pallens S2 G5 149 Orthotrichum pumilum S1S2 G5 150 Orthotrichum pylaisii S2 G4G5 151 Phascum cuspidatum cuspidate earth moss S2 G5 152 Phascum vlassovii S1 G2? 153 Philonotis marchica S1 G5 154 Plagiobryum demissum S1 G? 155 Plagiobryum zieri S2 G3G4 156 Platydictya minutissima SU G3 157 Pogonatum dentatum hair-like pogonatum S2S3 G3G5 158 Pogonatum urnigerum urn-like pogonatum S2S3 G5 159 Pohlia annotina S1 G4G5 160 Pohlia atropurpurea S1 G4G5 161 Pohlia crudoides S1 G? 162 Pohlia drummondii S2 G3G4 163 Pohlia longicolla S1 G4G5 164 Pohlia obtusifolia S1 G? 165 Pohlia filum S1 G4G5 166 Pohlia andalusica S1 G? 167 Pohlia brevinervis S1 G1G2 168 Polytrichum lyallii hair cap moss S2 G? 169 Polytrichum sexangulare northern hair moss S2 G4 170 Pseudoleskea atricha S2 G5 171 Pseudoleskea patens S2 G5 172 Pseudoleskea stenophylla S1S2 G? 173 Pseudoleskeella sibirica S2 G? 174 Pterygoneurum ovatum hairy-leaved beardless S1 G5
19
moss 175 Pterygoneurum subsessile S1 G4? 176 Racomitrium aciculare S1S2 G5 177 Racomitrium fasciculare S1 G5 178 Racomitrium heterostichum S2? G5 179 Racomitrium microcarpon S1? G?Q 180 Racomitrium elongatum S1 G? 181 Racomitrium sudeticum S1S2 G? 182 Rhodobryum ontariense S2 G5 183 Rhytidiadelphus squarrosus pipecleaner moss S1 G4G5 184 Scouleria aquatica S2 G4 185 Seligeria calcarea chalk brittle moss S1 G4? 186 Seligeria campylopoda S2 G3G5 187 Seligeria donniana Donian beardless moss S2 G4G5 188 Seligeria subimmersa S1 G? 189 Seligeria tristichoides SU G4 190 Sphagnum compactum neat bog moss S1S2 G5 191 Sphagnum lindbergii Lindberg's bog moss S2S3 G5? 192 Splachnum ampullaceum flagon-fruited splachnum S2 G4 193 Splachnum vasculosum large-fruited splachnum S1S2 G? 194 Stegonia pilifera S2 G? 195 Tayloria acuminata point-leaf small-kettle SU G3G4 moss 196 Tayloria froelichiana Froelichian splachnum S1 G? 197 Tayloria hornschuchii small-kettle moss S1 G? 198 Tayloria lingulata tongue-leaf small-kettle S2S3 G3G5 moss 199 Tayloria serrata slender splachnum S2 G4 200 Tetraplodon urceolatus alpine lemming moss S1 G? 201 Thamnobryum neckeroides S1 G? 202 Timmia norvegica S2 G4? 203 Timmia sibirica S1 G? 204 Tortella inclinata bent screw moss S2 G4G5 205 Tortula bartramii S1 G2G4 206 Tortula subulata S1 G? 207 Tortula caninervis S1 G? 208 Ulota curvifolia S1 G3G5 209 Voitia nivalis hidden kettle moss S1 G4 210 Weissia controversa green-cushioned weissia S2 G5 211 Anomobryum filiforme S1 G4 212 Plagiomnium rostratum S1 G5 213 Loeskypnum badium S1 G4G5 214 Warnstorfia tundrae brown moss S2 G? 215 Oxystegus tenuirostris acid-soil moss S1 G5 216 Bryobrittonia longipes S3 G3 217 Limprichtia cossonii SU G? 218 Schistidium agassizii elf bloom moss S1 G3G5 219 Schistidium pulvinatum S1 G5 220 Schistidium heterophyllum SH G3
20
221 Schistidium tenerum thread bloom moss S1 G? 222 Schistidium trichodon S1 G? 223 Coscinodon calyptratus sieve-toothed big S2 G? calyptra moss 224 Jaffueliobryum raui S1 G4? 225 Jaffueliobryum wrightii S2 G3G4 226 Hylocomiastrum pyrenaicum S2? G4G5 227 Rhizomnium andrewsianum S1 G3G5 228 Rhizomnium nudum S2 G? 229 Cyrtomnium hymenophylloides S1S2 G5? 230 Buellia lactoidea S2 231 Chaenotheca chrysocephala S2 G? 232 Xylographa parallela Black woodscript lichen S2 233 Agrestia hispida S2S3 G3 234 Phylliscum demangeonii S1 G? 235 Physcia phaea S2S3 G4G5 236 Cladina portentosa S1 G? 237 Cladonia norvegica S1 G3 238 Ramalina sinensis SU G? 239 Collema crispum S2 G? 240 Umbilicaria angulata S1S2 G2 241 Umbilicaria cylindrica S2 G3 242 Umbilicaria lambii S2 G? 243 Umbilicaria phaea S2 G? 244 Arctoparmelia incurva S1 G? 245 Bryocaulon divergens S1 G? 246 Hypogymnia vittata S1S2 G? 247 Lecanora pringlei S1S2 G? 248 Leptogium hirsutum S1? G? 249 Arthrorhaphis citrinella S2 250 Catillaria subnegans S1 251 Pyrrhospora elabens S1 252 Mycobilimbia hypnorum S1 253 Farnoldia hypocrita S1 254 Schaereria fuscocinerea S2 255 Dermatocarpon moulinsii S2 G? 256 Acarospora arenacea S2 G? 257 Acarospora fuscata S2 G? 258 Acarospora heppii S1 G? 259 Allantoparmelia alpicola S2 G? 260 Arctoparmelia centrifuga S2 G? 261 Arctoparmelia subcentrifuga S1 G? 262 Arthonia patellulata S3? G? 263 Arthopyrenia punctiformis S1 G? 264 Aspicilia anseris S2 G? 265 Aspicilia arctica S1 G? 266 Aspicilia pergibbosa S1 G? 267 Aspicilia sublapponica S1 G? 268 Aspicilia supertegens S2 G?
21
269 Bellemerea cinereorufescens S2 G? 270 Buellia semitensis S1 G? 271 Buellia turgescens S1 G? 272 Buellia vilis S1 G? 273 Calicium trabinellum S2 G? 274 Caloplaca approximata S1 G? 275 Caloplaca citrina powdery jewel lichen S1 G? 276 Caloplaca epithallina S2 G? 277 Caloplaca flavorubescens S2 G? 278 Caloplaca flavovirescens S2 G? 279 Caloplaca sinapisperma S2 G? 280 Caloplaca trachyphylla S2 G? 281 Candelariella canadensis S2 G? 282 Candelariella xanthostigma S1 G? 283 Catillaria chalybeia S2 G? 284 Catillaria globulosa S1 G? 285 Chaenotheca stemonea S1 G? 286 Chaenotheca trichialis S2 G? 287 Chaenotheca xyloxena S1 G? 288 Chrysothrix chlorina S1 G? 289 Cladonia acuminata S1? G? 290 Cladonia cyanipes S2 G? 291 Cladonia digitata S2 G? 292 Cladonia glauca S1 G? 293 Cladonia humilis S1 G? 294 Cladonia macrophylla S2 G? 295 Cladonia metacorallifera S2 G? 296 Cladonia polycarpoides S1? G? 297 Cladonia ramulosa S1 G? 298 Cladonia rei S2 G? 299 Cladonia robbinsii S2S3 G? 300 Cladonia symphycarpa S2 G? 301 Cladonia turgida S1 G? 302 Cliostomum griffithii S1S2 G? 303 Collema cristatum S1 G? 304 Collema flaccidum S1 G? 305 Collema multipartitum S1 G? 306 Collema subflaccidum S2 G? 307 Cyphelium notarisii S2 G? 308 Dactylina beringica S2S3 G? 309 Dactylina madreporiformis S2 G? 310 Dermatocarpon intestiniforme S2 G? 311 Endocarpon pusillum S2 G? 312 Endocarpon tortuosum S2 G? 313 Flavopunctelia soredica S2 G? 314 Hypocenomyce friesii S2 G? 315 Hypogymnia subobscura S2 G? 316 Lasallia pensylvanica S1 G? 317 Lecanora chlarotera S2 G?
22
318 Lecanora crenulata S1 G? 319 Lecanora hypoptoides S2 G? 320 Lecanora intricata S1 G? 321 Lecanora wisconsinensis S1 G? 322 Lecidea lithophila S2 G? 323 Lecidea plana S2 G? 324 Lecidea umbonata S2 G? 325 Lecidella carpathica S1S2 G? 326 Lecidella elaeochroma S1 G? 327 Lecidella patavina S1S2 G? 328 Lecidella wulfenii S2 G? 329 Lepraria incana S2 G? 330 Leprocaulon subalbicans S2 G? 331 Leptogium furfuraceum S2 G? 332 Leptogium gelatinosum S2 G? 333 Leptogium lichenoides S2S3 G? 334 Leptogium tenuissimum S2 G? 335 Lopadium pezizoideum S1 G? 336 Melanelia disjuncta S2 G? 337 Melanelia infumata S2S3 G? 338 Melanelia olivacea S1 G? 339 Melanelia panniformis S1 G? 340 Melanelia sorediata S1S2 G? 341 Melanelia subelegantula S2 G? 342 Micarea assimilata S2 G? 343 Mycobilimbia sabuletorum S2 G? 344 Mycoblastus sanguinarius S2 G? 345 Mycocalicium subtile S2 G? 346 Nephroma bellum S2 G? 347 Nodobryoria subdivergens old man's beard S1 G2 348 Ochrolechia frigida S1 G? 349 Ochrolechia inaequatula S1S2 G? 350 Orphniospora moriopsis S1 G? 351 Parmelia omphalodes S2 G? 352 Pertusaria panyrga S1 G? 353 Pertusaria sommerfeltii S1? G? 354 Physconia perisidiosa S1 G? 355 Porpidia glaucophaea S1 G? 356 Porpidia thomsonii S1 G? 357 Psora globifera S1S2 G? 358 Rhizocarpon badioatrum S1 G? 359 Rhizocarpon bolanderi S2 G? 360 Rhizocarpon riparium S2 G? 361 Rhizocarpon superficiale S2 G? 362 Rinodina archaea S2 G? 363 Rinodina bischoffii S1 G? 364 Rinodina calcigena S2 G? 365 Rinodina mniaraea S2 G? 366 Schaereria cinereorufa S1 G?
23
367 Solorina octospora S2 G? 368 Solorina spongiosa S2 G? 369 Solorinella asteriscus S1 G? 370 Spilonema revertens S2 G? 371 Tephromela atra black-eye lichen S2 G? 372 Thelidium decipiens S2 G? 373 Thelidium pyrenophorum S1 G? 374 Thrombium epigaeum S2 G? 375 Tremolecia atrata S2 G? 376 Umbilicaria polyphylla S2 G? 377 Varicellaria rhodocarpa S2 G? 378 Verrucaria fuscella S1 G? 379 Verrucaria muralis S2 G? 380 Verrucaria nigrescens S2 G? 381 Caloplaca atroalba S1 382 Collema undulatum var. S2 granulosum 383 Dermatocarpon rivulorum S1 384 Dermatocarpon schaechtelinii S1 385 Evernia perfragilis SU 386 Hypogymnia metaphysodes S2 387 Hypogymnia rugosa S1S2 388 Phaeophyscia decolor S1 389 Psora cerebriformis S1 390 Psora tuckermanii S2 391 Umbilicaria americana S2 392 Umbilicaria cinereorufescens S1 393 Usnea scabiosa SU 394 Usnea stuppea SU 395 Xanthoparmelia lineola S1 G5 396 Xanthoparmelia subdecipiens S2 G5 397 Stereocaulon botryosum S1 G4 398 Stereocaulon glareosum S1 G5 399 Stereocaulon grande S1 G5 400 Stereocaulon rivulorum S2 G5 401 Ephebe lanata S1 G5 402 Pannaria conoplea S1 G3G4 403 Melanelia stygia S2 G4G5 404 Phaeophyscia nigricans S2 G4 405 Phaeophyscia sciastra S2 G4 406 Physcia biziana S1S2 G5 407 Physcia tenella S2 G4 408 Buellia elegans S2 G? 409 Thamnolia vermicularis S2 G? 410 Peltigera collina S1 G3G4 411 Bryoria friabilis SR G3 412 Bryoria nitidula S2 G? 413 Bryoria simplicior old man's beard S2S3 G? 414 Bryoria tenuis S1 G?
24
415 Bryoria trichodes old man's beard SU G3G5 416 Cetraria arenaria S1 G4 417 Cetraria laevigata SU G? 418 Cladonia bacilliformis S2S3 G3G4 419 Cladonia bellidiflora S2S3 G5 420 Cladonia merochlorophaea S2 G2G3 421 Cladonia squamosa S2 G5 422 Cladonia transcendens S1 G5 423 Cladina stygia S1 G5 424 Hypogymnia enteromorpha S2 G4 425 Alectoria imshaugii S1 G5 426 Lobaria hallii S1 G4 427 Baeomyces rufus S2 G4G5 428 Dermatocarpon luridum S2 G5 429 Anaptychia setifera S2 G3G4 430 Placidium lachneum S2? G5 431 Verrucaria glaucovirens S2 432 Lomatium cous biscuit-root S1S2 G5 433 Osmorhiza longistylis smooth sweet cicely S2 G5 434 Osmorhiza purpurea purple sweet cicely S2 G4G5 435 Adenocaulon bicolor pathfinder S2S3 G5? 436 Agoseris lackschewitzii pink false dandelion S2 G4 437 Antennaria corymbosa corymbose everlasting S1 G5 438 Antennaria luzuloides silvery everlasting S1 G5 439 Antennaria monocephala one-headed everlasting S2 G4G5 440 Antennaria aromatica scented everlasting S2 G4 441 Arnica amplexicaulis stem-clasping arnica S2 G4 442 Arnica longifolia long-leaved arnica S2 G5 443 Arnica parryi nodding arnica S2 G5 444 Artemisia borealis northern wormwood S2 G5? 445 Artemisia furcata var furcata forked wormwood S1 G4T? 446 Artemisia tridentata big sagebrush S2 G5 447 Aster campestris meadow aster S2 G5 448 Aster eatonii Eaton's aster S2 G5 449 Aster x maccallae S1S2 HYB 450 Brickellia grandiflora large-flowered brickellia S2 G5 451 Cirsium scariosum thistle SU G5 452 Crepis atribarba hawk's-beard S2 G5 453 Crepis intermedia intermediate hawk's- S2 G5 beard 454 Crepis occidentalis small-flowered hawk's- S2 G5 beard 455 Erigeron divergens fleabane S1 G5 456 Erigeron flagellaris creeping fleabane S1 G5 457 Erigeron ochroleucus var buff fleabane S2 G5T5 scribneri 458 Erigeron pallens pale alpine fleabane S2 G2?Q 459 Erigeron radicatus dwarf fleabane S2 G3 460 Erigeron trifidus trifid-leaved fleabane S1S2 G2G3Q
25
461 Erigeron lackschewitzii S1 G3 462 Gnaphalium viscosum clammy cudweed SH G5 463 Hieracium cynoglossoides woolly hawkweed S2S3 G5? 464 Microseris nutans nodding scorzonella S2S3 G5 465 Nothocalais cuspidata prairie false dandelion S1 G5 466 Prenanthes sagittata purple rattlesnakeroot S2 G3G4 467 Saussurea americana American saw-wort S1 G5 468 Packera subnuda ragwort S2 G5 469 Townsendia condensata alpine townsendia S2 G4 470 Townsendia exscapa low townsendia S2 G5 471 Cryptantha minima small cryptanthe S1 G5 472 Cryptantha macounii Macoun's cryptanthe S2 G3G5 473 Mertensia lanceolata lance-leaved lungwort S2 G5 474 Mertensia longiflora large-flowered lungwort S2 G4G5 475 Arabis lemmonii Lemmon's rock cress S2 G5 476 Barbarea orthoceras American winter cress S2 G5 477 Braya humilis var maccallae leafy braya S1 G5T3?Q 478 Braya humilis var porsildii S1 G5T?Q 479 Braya purpurascens alpine braya S1 G4G5Q 480 Cardamine bellidifolia alpine bitter cress S2 G5 481 Cardamine umbellata mountain cress S2 G? 482 Draba densifolia whitlow-grass S1S2 G5 483 Draba fladnizensis whitlow-grass S1 G4 484 Draba glabella whitlow-grass S1 G4G5 485 Draba kananaskis Kananaskis whitlow- S1 G1Q grass 486 Draba longipes whitlow-grass S1S2 G4 487 Draba macounii Macoun's whitlow-grass S2 G3G4 488 Draba porsildii Porsild's whitlow-grass S2 G3G4 489 Draba ventosa whitlow-grass S2 G3 490 Lesquerella arctica var purshii northern bladderpod S2 G4T? 491 Rorippa curvipes yellow cress SU G5 492 Rorippa tenerrima slender cress S1 G5 493 Campanula uniflora alpine harebell S2 G4 494 Downingia laeta downingia S1S2 G5 495 Arenaria longipedunculata sandwort S1 G3Q 496 Minuartia elegans purple alpine sandwort S1 G4G5 497 Sagina nivalis pearlwort SU G5 498 Silene involucrata alpine bladder catchfly S1S2 G5 499 Spergularia salina salt-marsh sand spurry S2 G5 500 Stellaria americana American chickweed S1 G3? 501 Stellaria crispa wavy-leaved chickweed S2 G5 502 Stellaria obtusa chickweed S1 G5 503 Stellaria umbellata chickweed S1 G5 504 Chenopodium incanum goosefoot S1 G5 505 Hypericum scouleri ssp scouleri western St. John's-wort S1 G5T? 506 Cornus unalaschkensis S? G5? 507 Sedum divergens spreading stonecrop S2 G5? 508 Drosera linearis slender-leaved sundew S2 G4
26
509 Loiseleuria procumbens alpine azalea S1S2 G5 510 Rhododendron lapponicum Lapland rose-bay S2 G5 511 Vaccinium ovalifolium oval-leaved blueberry S2 G5 512 Vaccinium uliginosum bog bilberry S2 G5 513 Lupinus minimus least lupine S1 G3G4 514 Lupinus polyphyllus large-leaved lupine S1 G5 515 Lupinus wyethii Wyeth's lupine S1 G5 516 Oxytropis campestris var davisii S2? G5T3 517 Gentiana glauca alpine gentian S2 G4G5 518 Gentiana fremontii S2S3 G4 519 Lomatogonium rotatum marsh felwort S2 G5 520 Geranium erianthum geranium SH G5 521 Ribes laxiflorum mountain currant S2 G5 522 Philadelphus lewisii mock orange S1 G5 523 Hippuris montana mountain mare's-tail S1 G4 524 Ellisia nyctelea waterpod S2 G5 525 Nemophila breviflora small baby-blue-eyes S1S2 G5 526 Phacelia linearis linear-leaved S2 G5 scorpionweed 527 Phacelia lyallii Lyall's scorpionweed S2 G3 528 Romanzoffia sitchensis Sitka romanzoffia S2 G4 529 Iliamna rivularis mountain hollyhock S2 G5 530 Monotropa hypopithys pinesap S2 G5 531 Epilobium clavatum willowherb S2 G5 532 Epilobium glaberrimum ssp willowherb S1 G5T? fastigiatum 533 Epilobium lactiflorum willowherb S2 G5 534 Epilobium leptocarpum willowherb S1 G5 535 Epilobium luteum willowherb S1 G5 536 Epilobium mirabile willowherb S? G4Q 537 Epilobium saximontanum Rocky Mountain S1 G5 willowherb 538 Gayophytum racemosum low willowherb S1 G5 539 Oenothera flava low yellow evening- S2 G5 primrose 540 Orobanche uniflora one-flowered cancer-root S2 G5 541 Papaver pygmaeum alpine poppy S2 G3 542 Papaver radicatum ssp alpine poppy S2 G3?Q kluanense 543 Eriogonum pauciflorum SU G5 544 Koenigia islandica koenigia S1 G4 545 Polygonum minimum least knotweed S2 G5 546 Polygonum polygaloides ssp Watson's knotweed S2 G4G5T3 confertiflorum T4 547 Rumex paucifolius alpine sheep sorrel S1 G4 548 Linanthus septentrionalis linanthus S2 G5 549 Phlox gracilis ssp gracilis slender phlox S1 G5T5 550 Plantago canescens western ribgrass S2 G4G5 551 Lewisia pygmaea var pygmaea dwarf bitter-root S2 G5T5
27
552 Lewisia rediviva bitter-root S1 G5 553 Montia linearis linear-leaved montia S1 G5 554 Montia parvifolia small-leaved montia S1 G4G5 555 Douglasia montana mountain dwarf-primula S1 G4? 556 Primula egaliksensis primrose S2 G4 557 Primula stricta erect primrose S1 G4 558 Pyrola grandiflora Arctic wintergreen S2 G5 559 Pyrola picta white-veined S1 G4G5 wintergreen 560 Aquilegia formosa Sitka columbine S2 G5 561 Aquilegia jonesii Jones' columbine S2 G4 562 Ranunculus glaberrimus early buttercup S2 G5 563 Ranunculus nivalis snow buttercup S1 G5 564 Ranunculus occidentalis var western buttercup S2 G5T5 brevistylis 565 Ranunculus uncinatus hairy buttercup S2 G5 566 Physocarpus malvaceus mallow-leaved ninebark S1 G4G5 567 Potentilla drummondii Drummond's cinquefoil S2 G5 568 Potentilla hookeriana Hooker's cinquefoil S2 G4 569 Potentilla paradoxa bushy cinquefoil S2 G5 570 Potentilla subjuga S1 G4 571 Potentilla villosa hairy cinquefoil S2 G4 572 Potentilla macounii S1 G2? 573 Potentilla multisecta smooth-leaved S2 G3G4Q cinquefoil 574 Spiraea splendens pink meadowsweet S1 G5 575 Galium bifolium two-leaved Bedstraw S1 G5 576 Salix alaxensis var alaxensis Alaska willow S2 G5T? 577 Salix commutata changeable willow S2 G5 578 Salix lanata ssp calcicola woolly willow S1 G4T4 579 Salix raupii Raup's willow S1 G2 580 Salix sitchensis Sitka willow S1 G5 581 Salix stolonifera willow S1 G4G5 582 Boykinia heucheriformis telesonix S2 G4 583 Conimitella williamsii conimitella S2 G3? 584 Heuchera glabra alpine alumroot S1 G5 585 Lithophragma glabrum rockstar S2 G4G5 586 Lithophragma parviflorum small-flowered rockstar S2 G5 587 Parnassia parviflora small northern grass-of- S2 G4 parnassus 588 Saxifraga ferruginea saxifrage S2 G5 589 Saxifraga flagellaris ssp spiderplant S2 G5T? setigera 590 Saxifraga nelsoniana ssp Nelson's saxifrage S2 G5T3T4 porsildiana 591 Saxifraga nivalis alpine saxifrage S2 G4G5 592 Saxifraga odontoloma saxifrage S1 G5 593 Saxifraga oregana var Oregon saxifrage SU G4G5T? montanensis Q
28
594 Suksdorfia ranunculifolia suksdorfia S2 G5 595 Suksdorfia violacea blue suksdorfia S1 G4 596 Tellima grandiflora fringe-cups S1 G5 597 Castilleja cusickii yellow paintbrush S2S3 G4G5 598 Castilleja lutescens stiff yellow paintbrush S2S3 G4G5 599 Castilleja pallida SU G5 600 Mimulus breweri S1 G5 601 Mimulus floribundus small yellow S1 G5 monkeyflower 602 Mimulus guttatus yellow monkeyflower SU G5 603 Pedicularis capitata large-flowered lousewort S2 G4 604 Pedicularis flammea flame-colored lousewort S2 G3G5 605 Pedicularis lanata woolly lousewort S2 G4G5 606 Pedicularis langsdorfii ssp Arctic lousewort S2 G4T4 arctica 607 Pedicularis racemosa leafy lousewort S1 G5 608 Penstemon fruticosus var shrubby beardtongue S2 G4T? scouleri 609 Parietaria pensylvanica American pellitory S2 G5 610 Viola pallens Macloskey's violet S1 G5T5 611 Viola praemorsa ssp linguifolia S2 G5T5 612 Thuja plicata western red cedar S1S2 G5 613 Larix occidentalis western larch S2 G5 614 Pinus monticola western white pine SU G4G5 615 Tsuga heterophylla western hemlock S1 G5 616 Taxus brevifolia western yew S1 G4 617 Carex adusta browned sedge S1 G5 618 Carex aperta open sedge S1 G4 619 Carex arcta narrow sedge S1 G5 620 Carex backii Back's sedge SU G4 621 Carex bicolor SU G5 622 Carex lachenalii two-parted sedge S2 G5 623 Carex capitata capitate sedge S2 G5 624 Carex crawei Crawe's sedge S2 G5 625 Carex glacialis glacier sedge S2 G5 626 Carex heleonastes Hudson Bay sedge S2 G4 627 Carex heteroneura var blackened sedge S1 G5T? epapillosa 628 Carex hookerana Hooker's sedge S2 G4? 629 Carex illota small-headed sedge S1 G4G5 630 Carex incurviformis var seaside sedge S2 G4G5T? incurviformis 631 Carex leptopoda S1 G5 632 Carex mertensii purple sedge S1 G5 633 Carex misandra nodding sedge S1S2 G5 634 Carex parryana var parryana Parry's sedge S1S2 G4T4 635 Carex paysonis Payson's sedge S1S2 G4 636 Carex petasata pasture sedge S1S2 G5 637 Carex petricosa stone sedge S2 G4
29
638 Carex platylepis broad-scaled sedge S1S2 G4? 639 Carex podocarpa alpine sedge S2 G4G5 640 Carex preslii Presl sedge S2 G4 641 Carex scoparia broom sedge S1 G5 642 Carex supina Weak Sedge S1 G5 643 Carex tincta tinged sedge S1 G4G5 644 Carex vesicaria blister sedge S1 G5 645 Eleocharis engelmannii Engelmann's spike-rush S1? G4? 646 Eleocharis compressa var flattened spike-rush SU G5T5 borealis 647 Eriophorum callitrix beautiful cotton grass S2 G5 648 Trichophorum pumilum dwarf bulrush S2 G5 649 Iris missouriensis western blue flag S1 G5 650 Sisyrinchium septentrionale pale blue-eyed grass S2S3 G3G4 651 Lilaea scilloides flowering-quillwort S1 G5? 652 Juncus biglumis two-glumed rush S2 G5 653 Juncus brevicaudatus short-tail rush S2 G5 654 Juncus confusus few-flowered rush S2S3 G5 655 Juncus nevadensis Nevada rush S1 G5 656 Juncus parryi Parry's rush S2 G4G5 657 Juncus regelii Regel's rush S1 G4? 658 Allium geyeri Geyer's onion S2 G4G5 659 Camassia quamash var blue camas S2 G5T? quamash 660 Triantha occidentalis ssp S1 G5T4 brevistyla 661 Triantha occidentalis ssp S1 G5T? montana 662 Trillium ovatum western wakerobin S1 G5 663 Cypripedium montanum mountain lady's-slipper S2 G4G5 664 Listera caurina western twayblade S1 G4? 665 Listera convallarioides broad-lipped twayblade S2 G5 666 Malaxis paludosa bog adder's-mouth S1 G4 667 Platanthera stricta slender bog orchid S2 G5 668 Agropyron x brevifolium S? HYB 669 Agrostis exarata spike redtop S2 G5 670 Agrostis humilis low bent grass S1 G4 671 Agrostis mertensii northern bent grass S2 G5 672 Agrostis thurberiana Thurber's bent grass S2 G5 673 Alopecurus alpinus alpine foxtail S2 G5 674 Arctagrostis arundinacea polar grass S1 G? 675 Bromus vulgaris woodland brome S2S3 G5 676 Bromus latiglumis Canada brome S1 G5 677 Calamagrostis lapponica Lapland reed grass S1 G5 678 Deschampsia elongata slender hair grass S1 G5 679 Panicum acuminatum SU G5 680 Elymus virginicus Virginia wild rye S1 G5 681 Elymus scribneri Scribner's wheat grass S2 G5 682 Festuca altaica northern rough fescue S2 G5
30
683 Festuca occidentalis western fescue S1 G5 684 Festuca subulata fescue S1 G5 685 Festuca lenensis SU G4 686 Festuca minutiflora tiny-flowered fescue S2 G5 687 Festuca viviparoidea ssp S1 G4G5T? krajinae 688 Glyceria elata tufted tall manna grass S2 G4G5 689 Anthoxanthum monticola alpine sweet grass S2 G5 690 Melica smithii melic grass S1S2 G4 691 Melica spectabilis onion grass S2 G5 692 Muhlenbergia racemosa marsh muhly S1 G5 693 Oryzopsis exigua little rice grass S1 G5 694 Poa gracillima Pacific bluegrass S2 G4 695 Poa lettermanii Letterman's bluegrass S1 G4 696 Poa nevadensis Nevada bluegrass SU G5 697 Poa stenantha bluegrass SU G5 698 Torreyochloa pallida var few-flowered salt- S1 G5?T5? pauciflora meadow grass 699 Trisetum cernuum nodding trisetum S2 G5 700 Trisetum cernuum var tall trisetum S1 G5T? canescens 701 Trisetum montanum mountain trisetum S1 G4G5 702 Trisetum wolfii awnless trisetum S1 G4 703 Potamogeton foliosus leafy pondweed S2 G5 704 Potamogeton natans floating-leaf pondweed S2 G5 705 Potamogeton praelongus white-stem pondweed S2 G5 706 Sparganium hyperboreum northern bur-reed S1 G5 707 Adiantum aleuticum S2 G5? 708 Cheilanthes gracillima lace fern S1 G4G5 709 Cryptogramma stelleri Steller's rock brake S2 G5 710 Pellaea glabella smooth cliff brake S2 G5 711 Pellaea glabella ssp S1 G5T? occidentalis 712 Pellaea glabella ssp simplex S2 G5T4? 713 Pellaea gastonyi S1 G2G4 714 Athyrium alpestre var alpine spleenwort S1 G4G5 americanum 715 Cystopteris montana mountain bladder fern S2 G5 716 Dryopteris filix-mas male fern S1 G5 717 Gymnocarpium disjunctum SU G4 718 Woodsia glabella smooth woodsia S1 G5 719 Isoetes bolanderi var bolanderi Bolander's quillwort S1 G4T4 720 Isoetes maritima S1 G3? 721 Isoetes occidentalis S1 G4G5 722 Isoetes x truncata S1 HYB 723 Diphasiastrum sitchense ground-fir S2 G5 724 Huperzia haleakalae S2 G4? 725 Botrychium lanceolatum lance-leaved grape fern S2 G5 726 Botrychium multifidum var leather grape fern S2 G5T4?
31
intermedium 727 Botrychium simplex dwarf grape fern S2 G5 728 Botrychium paradoxum paradoxical grape fern S1 G2 729 Botrychium crenulatum S1 G3 730 Botrychium minganense S2S3 G4 731 Botrychium ascendens ascending grape fern S1 G2G3 732 Botrychium pedunculosum S1 G2? 733 Botrychium pinnatum S1 G4? 734 Botrychium spathulatum S2 G3G4 735 Botrychium x watertonense S1 HYB 736 Polypodium hesperium western polypody S1S2 G5 737 Selaginella wallacei Wallace's little club- S1 G5 moss 738 Phegopteris connectilis northern beech fern S2 G5 * Plants of conservation concern were defined as having one or more mapped occurrences in or within 2.5 km of the Rocky Mountain Natural Region. This species list was provided by the Alberta Natural Heritage Information Centre, updated 22 November 2000.
32
Appendix 13.4 Rare Non-Vascular and Vascular Plant Species Ranking
Rank Frequency/Distribution Concerns/Comments G=Global; S=Alberta S1/G1 5 or fewer occurrences or only a few May be especially vulnerable to extirpation remaining individuals because of some factor of its biology S2/G2 6-20 or fewer occurrences or with many May be especially vulnerable to extirpation individuals in fewer locations because of some factor of its biology S3/G3 21-100 occurrences, may be rare and May be susceptible to extirpation because of local throughout it's range, or in a large scale disturbances restricted range (may be abundant in some locations) S4/G4 Typically >100 occurrences Apparently secure S5/G5 Typically >100 occurrences Demonstrably secure * Defined by the Alberta Natural Heritage Information Center, last updated 27 January 2000.
33
Appendix 13.5 Weed Species Data Sheet
Alberta Forest Biodiversity Monitoring Program Pilot
Restricted, Noxious and Nuisance Weed Species
Site Location: ______Date: ______
Page _____of_____ Crew: ______
Transect Checklist: Area Search Noxious Weed Area Search Species North Northeast 1=Presence, 1=Presence, East Southeast 0=Absence 0=Absence South Southwest West Northwest Restricted Weed Species Odontites serotina Cardaria spp. (Red bartsia) (Hoary cress) Centaurea diffusa Scleranthus annuus (Diffuse knapweed) (Knawel) Centaurea maculosa Sonchus arvensis (Spotted knapweed) (Perennial sow thistle) Carduus nutans Euphorbia cyparissias (Nodding thistle) (Cypress spurge) Myriophyllum spicatum Euphorbia esula (Eurasian water-milfoil) (Leafy spurge) Cuscuta spp. Erodium cicutarium (Dodder) (Stork’s-bill) Centaurea solstitialis Cirsium arvense (Yellow starthistle) (Canada thistle) Noxious Weed Area Search Linaria vulgaris Species 1=Presence, (Toad-flax) 0=Absence Centaurea repens Lolium persicum (Russian knapweed) (Persian darnel) Convolvulus arvensis Matricaria maritima (Field bindweed) (Scentless chamomile) Lychnis alba Tanacetum vulgare (White cockle) (Common tansy) Silene cucubalus Echium vulgare (Bladder campion) (Blue devil) Galium aparine and Galium Apocynum spurium androsaemifolium (Cleavers) (Spreading dogbane)
34
Noxious Weed Area Search Nuisance Weed Area Search Species Species 1=Presence, 1=Presence, 0=Absence 0=Absence Knautia arvensis Potentilla norvegica (Blue buttons) (Rough cinquefoil)
Cynoglossum officinale Saponaria vaccaria (Hound’s-tongue) (Cow cockle) Chrysanthemum Descurainia sophia leucanthemum (Flixweed) (Ox-eye daisy) Ranunculus acris Setaria viridis (Tall buttercup) (Green foxtail) Lythrum salicaria Agropyron repens (Purple loosestrife) (Quack grass) Nuisance Weed Species Area Search Crepis tectorum 1=Presence, (Annual hawk’s- beard) 0=Absence Linaria dalmatica Galeopsis tetrahit (Dalmatian toadflax) (Hemp nettle) Raphanus raphanistrum Lamium amplexicaule (Wild radish) (Henbit) Campanula rapunculoides Polygonum persicaria (Garden bluebell) (Lady’s thumb) Convolvulus sepium Malva rotundifolia (Hedge bindweed) (Round-leaved mallow) Lappula echinata Neslia paniculata (Blue-bur) (Ball mustard) Bromus tectorum Erucastrum gallicum (Downy chess) (Dog mustard) Fagopyrum tartaricum Descurainia pinnata (Tartary buckwheat) (Green tansy mustard) Polygonum convolvulus Sinapis arvensis (Wild buckwheat) (Wild mustard) Silene cserei Erysimum cheiranthoides (Biennial campion) (Wormseed mustard) Silene noctiflora Avena fatua (Night-flowering catchfly) (Wild oats) Stellaria media Amaranthus retroflexus (Common chickweed) (Red-root pigweed) Cerastium arvense Capsella bursa-pastoris (Field chickweed) (Shepherd’s-purse) Cerastium vulgatum Sonchus oleraceus (Mouse-eared chickweed) (Annual sow thistle)
35
Nuisance Weed Species Area Search * Species list derived from the Weed Control Act 1=Presence, (Alberta Agriculture, Food and Rural 0=Absence Development 1997) and Moss (1994). Spergula arvensis (Corn spurry)
Thlaspi arvense (Stinkweed) Salsola pestifer (Russian thistle) Taraxacum officinale (Common dandelion)
36