Natural Quality Indicators

Monitoring the Natural Quality assesses how human-caused change affects ecological systems. Key indicators and measures monitor plants, animals, air and water, and ecological processes. This section provides detailed guidance for monitoring the following indicators and measures:

3.2 Indicator: Plants

3.2.1 Measure: Acres of Nonindigenous Plant Species

3.3 Indicator: Animals

3.3.1 Measure: Index of Nonindigenous Terrestrial Animal Species

3.3.2 Measure: Index of Nonindigenous Aquatic Animal Species

3.4 Indicator: Air and Water

3.4.1 Measure: Concentration of Ambient Ozone

3.4.2 Measure: Deposition of Nitrogen

3.4.3 Measure: Deposition of Sulfur

3.4.4 Measure: Amount of Haze

3.4.5 Measure: Index of Sensitive Lichen Species

3.4.6 Measure: Extent of Waterbodies With Impaired Water Quality

3.5 Indicator: Ecological Processes

3.5.1 Measure: Watershed Condition Class

3.5.1 Measure: Number of Animal Unit Months of Commercial Livestock Use

Section 3.6, Selecting Measures for the Natural Quality, provides recommendations for identifying and establishing locally developed measures in the Natural Quality. It discusses the general considerations for developing these measures, explains why certain types of measures are problematic, offers examples to clarify what are and are not appropriate measures, and provides a flowchart outlining the general process.

3.2 Indicator: Plants

This indicator focuses on threats to indigenous plant species and communities. There is one required measure for this indicator.

3.2.1 Measure: Acres of Nonindigenous Plant Species

This measure assesses the total number of acres, or the estimated percentage of acres, occupied by selected nonindigenous plant species in wilderness. Local units may select the appropriate protocol option as described in step 2 below. Data are compiled from a variety of local, state, regional, and national data sources. Local staff calculate the measure value. Table 2.3.1 describes key features for this measure.

Table 2.3.1—Summary of measure type, protocol options, local tasks, national tasks, and frequency of data reporting for measure "Acres of Nonindigenous Plant Species."

Protocol

Step 1: Develop a list of known nonindigenous plants in the wilderness and select species for monitoring. There may be many nonindigenous plants within a wilderness, but for practical reasons, it is recommended that local units select up to five species that pose the greatest ecological risk to native plant communities for use in this measure. Local units, however, may select as many species as they want but will need to balance practicality with the number of species selected considering the quality and availability of inventory data for the selected species. Selecting these species should consider the invasiveness or ability to spread and occupy new habitat, the amount of habitat at risk, and the potential impact of these species on indigenous plants and animals. If there is certainty that only natural vectors enabled a nonindigenous plant species to become established in a wilderness (i.e., via natural range expansion or movement), then that species would not be included. If, however, there is ambiguity about how the species was introduced (whether natural or human-caused), then the species would be included. Nonindigenous plant species that were present at the time of wilderness designation should be included for consideration in this measure. Consult the local botanist, invasive species program manager, ecologist, range conservationist, or other local sources of knowledge on nonindigenous plants to select species for this measure. Over time, new species can be added to the list of selected species, and species already on the list can be replaced with different species; any modification of the list of selected species should be considered carefully as changes in the acreage occupied by selected nonindigenous plant species may affect the trend in this measure.

Step 2: Determine the wilderness acreage currently occupied by each selected species and calculate the total number of acres, or the estimated percentage of acres, for all species. A variety of data sources may be necessary for this measure, and data sources may vary by species. Acreage data for each selected species can be based on actual surveys, observation, or professional knowledge. Current and past nonindigenous plant data are available from the NRM application for Threatened, Endangered, and Sensitive Plants, and Invasive Species (NRM-TESP-IS). To retrieve spatial data on selected species from NRM for this measure, consult a specialist familiar with the NRM application and GIS to perform the necessary queries. Examples of other sources of data concerning nonindigenous plant species for a particular area include the following:

• Forest Service resource specialist on the local unit where a wilderness is located (i.e., forest botanist, range specialist ecologist, or invasive species coordinator).
• Individual state Department of Natural Resources invasive species program.
• The Natural Heritage Program.
• Local weed associations by state or county.
• Forest Inventory and Analysis (FIA)

Data adequacy for all data sources likely varies greatly depending on the wilderness and the species of interest. It is strongly recommended that local natural resource specialists, such as forest botanists, ecologists, or invasive species coordinators, be consulted to validate the data, especially if national datasets are used. If the determination of species extent is based partially or entirely on professional judgment, include additional documentation (e.g., a brief narrative) explaining who made the assessment and their basis for the estimation.

To accommodate the reality that data may not be available for a wilderness, or that data adequacy may be insufficient to accurately assess the total number of acres for one or more selected species, the following section describes three protocol options for using this measure in order of decreasing data adequacy.

Protocol Option 1—Total Acres. The first protocol option assesses the total number of acres occupied by the selected species (e.g., 10 acres). Use this protocol option if the acreage of all selected species can be determined with sufficient data adequacy. Calculate the total number of wilderness acres occupied by one or more selected nonindigenous species to attain the measure value; do not double count acres if more than one of the species occur in the same location. For example, if there are four selected species that each occupy 10 acres and the distribution of all four species does not overlap, the total area reported would be 40 acres (10 for species a + 10 for species b + 10 for species c + 10 for species d). If the distribution of two of these species completely overlapped, the total area reported would be 30 acres (10 for species a + 10 for species b + 10 for the overlapped distribution of species c and species d).

Protocol Option 2—Categories Based Partially on Data. The second protocol option assesses the estimated percentage of acres occupied by selected nonindigenous plant species, using set "percent occupied" categories. Use this protocol option if data exist but there are concerns about how recent the data are, or about the quality or spatial coverage of the data. Similarly, if data adequacy is variable for different species, it may be appropriate to use this protocol option. For this protocol option, resource specialists must estimate the percentage of wilderness acres occupied by the selected nonindigenous plant species based on existing data as well as supplementary professional knowledge. Assign the applicable "percent occupied" amount from the seven categories described in the list below. These categories are scaled conservatively to emphasize the impact on the Natural Quality of wilderness character.

• None—0% of total wilderness acreage.
• Very Low—less than 1 percent of the total wilderness acreage.
• Low—1 to 5 percent of the total wilderness acreage.
• Moderate—6 to 20 percent of the total wilderness acreage.
• High—21 to 35 percent of the total wilderness acreage.
• Very high—36 to 50 percent of the total wilderness acreage.
• Extreme—greater than 50 percent of the total wilderness acreage.

Protocol Option 3—Categories Based on Professional Judgment: The third protocol option also assesses the estimated percentage of acres occupied by selected species, but uses broader "percent occupied" categories than the previous option. Use this protocol option when there are little or no data on which to base an estimate of the acreage of selected species and there is lower confidence in the estimate. Resource specialists must estimate the percentage of wilderness acres occupied by the selected nonindigenous plant species based on professional knowledge. Assign the applicable "percent occupied" amount from the following four categories:

• None—less than 1 percent of the total wilderness acreage.
• Low—1 to 5 percent of the total wilderness acreage.
• Moderate—6 to 20 percent of the total wilderness acreage.
• High—greater than 20 percent of the total wilderness acreage.

Step 3: Enter data in the WCMD. If protocol option 1 was selected, enter the total number of acres; if protocol options 2 or 3 were selected, enter the applicable "percent occupied" category of the estimated percentage of acres. The measure value is either the number of acres or the "percent occupied" category.

Caveats and Cautions

Comprehensive and systematic surveys in wilderness for nonindigenous terrestrial plants are typically lacking, with data coming from sporadic and infrequent visits from resource specialists who have the knowledge to identify these species. Wildernesses are typically remote and often viewed as not needing basic resource inventories to guide management so even if a systematic survey has been conducted, it may not be repeated. If either the second or third protocol option based on categories is used, resource specialists should note in a narrative if there are particular species that currently occur across less than 1 percent of the wilderness acreage but have the potential for significant spread and adverse impacts if environmental or other conditions change.

Data Adequacy

Data adequacy varies depending on the protocol option used. For the first protocol option that relies on existing data, overall data adequacy is generally considered medium or high; data quality is generally good (e.g., ground level inventory) or moderate (e.g., data from regional or national databases), and data quantity is partial or complete as there are likely data on selected species for most or all of a wilderness. For the second protocol option that relies on a combination of existing data and professional judgment, data adequacy is generally considered medium or low because data quality is likely moderate or poor and data quantity is likely partial or insufficient. For the third protocol option that relies extensively on professional knowledge, data adequacy is likely low because data quality is poor and data quantity is likely insufficient or partial. Data adequacy must be verified locally for all protocol options.

Frequency

Every 5 years, the spatial extent of selected nonindigenous plant species is assessed and the total number of acres (protocol option 1), or the applicable "percent occupied" category of the estimated percentage of acres (protocol options 2 and 3), is entered in the WCMD.

Threshold for Change

The threshold for meaningful change differs depending on the protocol option used. If the first protocol option is used, the threshold is a 5-percent change in the total number of acres occupied by selected nonindigenous plant species. Once there are five measure values, the threshold for meaningful change will switch to regression analysis. If either the second or third protocol option is used, the threshold is any change in categories. Either a decrease in the total acreage beyond the 5-percent threshold for meaningful change, or a change to a lower "percent occupied" category, results in an improving trend in the measure.

3.3 Indicator: Animals

This indicator focuses on threats to indigenous animal species and communities. There are two measures for this indicator and units are required to select at least one.

3.3.1 Measure: Index of Nonindigenous Terrestrial Animal Species

This measure is an index that assesses the geographic distribution and estimated impact of selected nonindigenous terrestrial animal species. Data are compiled from a variety of local, state, regional, and national data sources. The WCMD calculates the measure value. Table 2.3.2 describes key features for this measure.

Table 2.3.2—Summary of measure type, protocol options, local tasks, national tasks, and frequency of data reporting for measure "Index of Nonindigenous Terrestrial Animal Species."

Protocol

Step 1: Develop a list of known nonindigenous terrestrial animals in the wilderness and select species for monitoring. This includes all nonindigenous terrestrial animal species, and domestic (and feral) livestock, swine, horses, and burros; terrestrial insects such as Asian long-horned beetle, emerald ash borer, gypsy moth, and hemlock woolly adelgid; and terrestrial pathogens and diseases such as sudden oak death, chronic wasting disease, and white-nose syndrome. For some terrestrial animal species, distribution can vary seasonally and if the species occurs within a wilderness at any time, it could be included in this list. If there is certainty that only natural vectors enabled a nonindigenous animal species to become established in wilderness (i.e., via natural range expansion or movement), then that species would not be included; however, if there is ambiguity about how the species was introduced (whether natural or human-caused), then the species would be included. Nonindigenous animal species that were present at the time of wilderness designation should be included for consideration in this measure. Consult the local wildlife biologist, invasive species program manager, ecologist, or other local sources of knowledge to first identify nonindigenous terrestrial animals in a wilderness and then select species for inclusion in this measure.

As not all nonindigenous species have the same degree of ecological impact on wilderness, select species to monitor based on their potential to displace native species or do ecological harm to a wilderness environment. For practical reasons, local units may choose to limit the number of species selected based on their impact and data adequacy. Over time, new species can be added to the list of selected species, and species already on the list can be replaced with different species; any modification of the list of selected species should be considered carefully as changes in the index may affect the trend in this measure.

Step 2: Determine the distribution and impact of each selected species. The index used for this measure combines the numerical ratings for distribution and impact that are assigned for each selected species. These numerical ratings are based on defined distribution and impact categories, described below. If the determination of distribution or impact categories for any species is based partially or entirely on professional judgment, include additional documentation (e.g., a brief narrative) explaining who made the assessment and their basis for the estimation. Both distribution and impact must be reassessed for all selected species each monitoring cycle.

Distribution is the known or estimated geographic extent inside wilderness of each selected nonindigenous terrestrial animal species. For this measure, distribution is measured as the percentage of the total wilderness occupied by each selected species that permanently resides in a wilderness, or the maximum geographic extent of each selected species that occurs seasonally in a wilderness. A variety of data sources, including national and local data sources as well as professional knowledge, may be necessary for determining distribution, and data sources may vary by species. In order of priority, use surveys, observations, or professional knowledge to assess species distribution. A primary data source for this measure is the Forest Service Natural Resource Information System (NRIS). This application is a good starting place if data are available. Other relevant databases are NRM-Wildlife and NRM-TESP-IS. Examples of additional sources of data concerning nonindigenous terrestrial animal species include:

• Forest Service resource specialist on the local unit where a wilderness is located (i.e., wildlife biologist, ecologist, or invasive species coordinator).
• Individual state Department of Natural Resources invasive species program.
• NatureServe Explorer database (https://explorer.natureserve.org/) and its state Natural Heritage Program members.
• Local invasive species programs by county or city.
• Forest Service and BLM wild horse and burro herd data (http://www.fs.fed.us/rangelands/ecology/wildhorseburro/territories/index.shtml and https://www.blm.gov/programs/wild-horse-and-burro/herd-management).
• Forest Service Forest Health Protection mapping and reporting tools (https://www.fs.fed.us/foresthealth/).

Data adequacy for all data sources likely varies greatly depending on the wilderness and the species of interest. It is strongly recommended that local natural resource specialists, such as biologists, ecologists, or invasive species coordinators, be consulted to validate the data, especially for datasets that extend beyond a wilderness boundary.

Assign one of the following distribution categories for each selected species based on the known or estimated percent distribution over the entire wilderness:

• Trace—the species occupies less than 1 percent of a wilderness.
• Sparse—the species occupies 1 to 5 percent of a wilderness.
• Moderate—the species occupies 6 to 25 percent of a wilderness.
• Wide—the species occupies more than 25 percent of a wilderness.

These distribution categories are scaled conservatively to emphasize the ecological effects of increased distribution of nonindigenous species. Once the distribution category has been assigned for each selected species, note the associated numerical rating according to the following table 2.3.3.

Table 2.3.3—Numerical ratings for the distribution of terrestrial nonindigenous animal species.

Impact is the estimated relative effect of each selected nonindigenous terrestrial animal species on the Natural Quality of wilderness character. Impact may change over time due to a variety of changing ecological circumstances. Consult the local wildlife biologist, invasive species program manager, ecologist, or other local sources of knowledge to determine the impact of each species. Resource specialists should base their impact assessments for each species on the scientific literature or their professional observation or knowledge. Assign one of the following impact categories for each species:

• Low—the species has a relatively small or localized impact on the natural ecosystems and plant and animal communities.
• Moderate—the species has a noticeable effect on plant or animal communities or natural ecosystems and eradication efforts may or may not be in place because of uncertainty about impact.
• High—the species has a large or significant effect on plant or animal communities or natural ecosystems and plans for eradication or reduction are likely in place because of the known large impact of the species. Once the impact category has been assigned, note the associated numerical rating according to the following table 2.3.4.

Table 2.3.4—Numerical ratings for the impact category of nonindigenous terrestrial animal species.

Step 3: Enter data in the WCMD. The final measure value is derived through an index combining all selected species' numerical ratings for distribution and impact. While this index is described for reference, users will not be responsible for calculating the measure value themselves; instead, users will enter the assigned numerical distribution and impact ratings for each species in the WCMD, and the WCMD will calculate the measure value automatically. The measure value is the index value.

In calculating the index value for this measure, there are two basic steps. First, generate a component score for each selected species by multiplying the numerical rating for distribution by the numerical rating for impact. Second, sum the component scores for all species to produce the final index value. Table 2.3.5 provides an example showing how to calculate the index value for this measure.

Table 2.3.5—An example of how to calculate the index value for selected nonindigenous terrestrial animal species.

Caveats and Cautions

Comprehensive and systematic surveys in wilderness for nonindigenous terrestrial animals are typically lacking, with data coming from sporadic and infrequent visits from resource specialists who have the knowledge to identify these species. Even if a systematic survey is conducted, it may not be repeated.

Data Adequacy

Data quantity varies depending on the geographic area and the species of interest, and is generally expected to be insufficient to partial. Data quality also varies considerably across wildernesses because surveys and comprehensive, statistically robust inventories of nonindigenous terrestrial animal species in wildernesses are often lacking; data quality is therefore generally expected to be poor to moderate. Combining these two aspects yields an estimated low to medium data adequacy. Because of high variability, local units must verify these determinations for each data source used; for example, national data sources may have high data adequacy while evaluations based on professional judgment will often have low data adequacy.

Frequency

Every 5 years, assess the geographic distribution and estimated impact of selected nonindigenous terrestrial animal species. Enter the assigned distribution and impact ratings for each species into the WCMD. The measure value is automatically calculated by the WCMD based on the entered data.

Threshold for Change

The threshold for meaningful change is a 5-percent change in the measure value for all selected nonindigenous terrestrial animal species. Once there are five measure values, the threshold for meaningful change will switch to regression analysis. A decrease in the measure value beyond the threshold for meaningful change results in an improving trend in this measure.

3.3.2 Measure: Index of Nonindigenous Aquatic Animal Species

This measure is an index that assesses the geographic distribution and estimated impact of selected nonindigenous aquatic species (NAS), including amphibians, fish, crustaceans, mollusks, gastropods, aquatic insects, and aquatic pathogens and diseases. Data are compiled from a variety of local, state, regional, and national data sources. The WCMD calculates the measure value. Table 2.3.6 describes key features for this measure.

Table 2.3.6—Summary of measure type, protocol options, local tasks, national tasks, and frequency of data reporting for measure "Index of Nonindigenous Aquatic Animal Species."

Protocol

Step 1: Develop a list of known nonindigenous aquatic animals in the wilderness and select species for monitoring. This includes all NAS and water-borne pathogens and diseases such as whirling disease, iridoviruses, and chytrid fungus. Locally or regionally indigenous species introduced in fishless waters would also be included in this list. Stocking of indigenous species into waters where they may already occur, or introducing indigenous fish species into waters that already have other fish species, are unlikely to have a measurable effect on the Natural Quality and are therefore not included under this measure. If there is certainty that only natural vectors enabled a NAS to become established in a wilderness (i.e., via natural range expansion or movement), then that species would not be included; however, if there is ambiguity about how the species was introduced (whether natural or human-caused), then the species would be included. Nonindigenous aquatic animal species that were present at the time of wilderness designation should be included for consideration in this measure. Consult with forest and district resource specialists to first identify known NAS in a wilderness and then select species for inclusion in this measure.

As NAS have varying degrees of ecological impact on wilderness, select species to monitor based on their potential to displace native species or do ecological harm to the wilderness environment. For practical reasons, local units may choose to limit the number of species selected based on their impact and data adequacy. Over time, new species can be added to the list of selected species, and species already on the list can be replaced with different species; any modification of the list of selected species should be considered carefully as changes in the index will likely affect the trend for this measure.

Step 2: Determine the distribution and impact of each selected species. The index used for this measure combines the numerical ratings for distribution and impact that are assigned for each selected species. These numerical ratings are based on defined distribution and impact categories, described below. If the determination of distribution or impact categories for any species is based partially or entirely on professional judgment, include additional documentation (e.g., a brief narrative) explaining who made the assessment and their basis for the estimation. Both distribution and impact must be reassessed for all selected species each monitoring cycle.

Distribution is the known or estimated geographic extent inside wilderness of each selected NAS. For this measure, distribution is measured as the percentage of the total wilderness waterbodies occupied by each selected species that resides in a wilderness or the maximum geographic extent of each selected species that seasonally occurs in a wilderness.

A variety of data sources, including national and local data sources and professional knowledge, may be necessary for determining distribution, and data sources may vary by species. In order of priority, use surveys, observations, or professional knowledge to assess species distribution. Data adequacy for all data sources may vary greatly depending on the wilderness and the species of interest. Consult with local natural resource specialists, such as fisheries biologists, ecologists, or invasive species coordinators to validate the data, especially for datasets that extend beyond a wilderness boundary.

A useful source of data for this measure is the U.S. Geological Survey (USGS) national database for NAS, located at the Southeast Ecological Science Center and available at http://nas.er.usgs.gov/. This site is a central repository for accurate, spatially referenced biogeographic accounts of NAS in the United States. It provides detailed records, collection locations, and dates, and can searched by state, county, or watershed (Hydrologic Unit Code [HUC] 2 to HUC 8) for nonindigenous aquatic groups, taxa, and species. The Southeast Ecological Science Center can also be contacted to run specific queries for watersheds associated with an individual wilderness. After navigating to the website (http://nas.er.usgs.gov/), follow these steps to retrieve data:

1. Go to "Database & Queries" (top of home page).
2. Select "Search by Drainage Area [HUC 8]."
3. Select appropriate state from map of the U.S.
4. Select "All" groups and sort by "Taxonomic Group."
5. Select desired HUC 8 sub basin and records will be displayed.
6. Select "Collection Information" under the "More Information" column heading for each species listed.
7. Assess the distribution of each species using these results, especially the information in the "Locality" and "Year" columns. For additional details on a given collection or sighting, select "Specimen ID." This provides information on the collection date and accuracy, pathway, status, and any references that are available.

Examples of additional sources of data concerning nonindigenous aquatic animal species are listed below.

• Forest Service resource specialist on the unit where a wilderness is located (i.e., fisheries biologist, hydrologist, or invasive species coordinator).
• Forest Service NRM-TESP-IS.
• Forest Service regional aquatic invasive species databases (e.g., Region 4's database available at http://www.fs.usda.gov/detail/r4/landmanagement/resourcemanagement/?cid=fsbdev3_016101).
• Individual state Department of Natural Resources invasive species program, or state fish and game agencies (especially useful for obtaining fish stocking or fish assessment records).
• Regional, state, or local invasive aquatic species programs (e.g., the Portland State University Center for Lakes and Reservoirs has excellent data for the state of Oregon—Center for Lakes and Reservoirs - Portland State University; Michigan State University's Midwest Invasive Species Information Network [MISIN] has similar data for the Midwest—Midwest Invasive Species Information Network [MISIN]).

Assign one of the following distribution categories for each selected species based on the known or estimated percent distribution over the entire wilderness:

• Low—the species occupies 10 percent or less of the waterbodies in a wilderness.
• Moderate—the species occupies 11 to 20 percent of the waterbodies in a wilderness.
• Wide—the species occupies more than 20 percent of the waterbodies in a wilderness.

Once the distribution category has been assigned for each selected species, note the associated numerical rating shown in table 2.3.7.

Table 2.3.7—Numerical ratings for the distribution category of nonindigenous aquatic animal species.

Impact is the estimated relative effect of each selected nonindigenous aquatic animal species on the Natural Quality of wilderness character. Impact may change over time due to a variety of changing ecological circumstances. Assign one of the following an impact categories for each species:

• Low—the species has a relatively small or localized impact on the natural ecosystems and plant and animal communities.
• Moderate—the species has a noticeable effect on plant or animal communities or natural ecosystems and eradication efforts may or may not be in place because of uncertainty about impact.
• High—the species has a large or significant effect on plant or animal communities or natural ecosystems and plans for eradication or reduction are likely in place because of the known large impact of the species.

Once the impact category has been assigned, note the associated numerical rating according to the following, table 2.3.8.

Table 2.3.8—Numerical ratings for the impact category of nonindigenous aquatic animal species.

These recommended impact categories and numerical ratings are assigned to reflect relative impacts. The setting (location, climate, other species) plays a key role in influencing the relative impact of an individual NAS. As a general rule, nonindigenous fish should likely receive the highest impact rating because they are often the top predator in aquatic systems and can have significant and lasting effects on the character and function of aquatic systems. Indigenous fish introduced into fishless waters should be considered nonindigenous and assigned a rating of 3. Aquatic invasive species (including invasive nonindigenous aquatic pathogens) would generally be assigned the second highest rating due to their potential to increase in numbers and distribution relatively quickly and have significant impacts on indigenous species by direct competition for limited resources such as water, nutrients, food, and shelter (Office of Technology Assessment 1993). Low impact species would often include certain, nonindigenous aquatic organisms that are found at the current extreme edge of their range of conditions for survival where the stress of the environment limits their productivity and competitiveness. (Jim Capurso, U.S. Forest Service, R6 Fish Program Leader and Aquatic Invasive Species Coordinator, personal communication, December 1, 2016).

The general categories and ratings presented in table 2.3.8 may not fit local conditions or the specific circumstances found in an individual wilderness. Units are encouraged to adjust these ratings based on local information and professional knowledge. For example, the availability of a risk assessment for a particular invasive species, such as New Zealand mud snails or zebra mussels, could allow a local office to increase the impact rating to the maximum level of 3. Although there is no national database that provides relative risk ratings for invasive aquatic animals, such ratings may be available on a local, state, or regional level and could provide a basis for increasing the ratings for individual invasive species. Document the rationale for these adjustments.

Step 3: Enter data in the WCMD. The final measure value is derived through an index combining all selected species' numerical ratings for distribution and impact. While this index is described for reference, users will not be responsible for calculating the measure value themselves; instead, users will enter the assigned numerical distribution and impact ratings for each species in the WCMD, and the WCMD will then calculate the measure value automatically. The measure value is the index value. In calculating the index value for this measure, there are two basic steps. First, generate a component score for each selected species by multiplying the numerical rating for distribution by the numerical rating for impact. Second, sum the component scores for all species to produce the final index value. Table 2.3.9 provides an example showing how to calculate the index value for this measure.

Table 2.3.9—An example of how to calculate the index value for selected nonindigenous aquatic animal species.

Caveats and Cautions

Currently, comprehensive surveys for NAS, in both lakes and streams, are generally lacking, especially in wilderness. Where they do exist, data often are not entered into a national NAS database, but may be available in a local database. Also, there is often a lack of periodic follow-up sampling. Although progress has been made in the last few years, lack of data on NAS for many water bodies makes treatment, protection, and management extremely difficult. It also puts a premium on coordinated data gathering and data sharing among management, research interests, and users in general.

Data Adequacy

For data used in the NAS index, there is a fair degree of variability depending on a given geographic area and species of interest. Data quantity for the measure ranges from complete (e.g., fish stocking records) to insufficient (e.g., estimates and professional judgment), and is given an overall rating of partial. Data quality similarly ranges from high (e.g., fish stocking records) to low (e.g., estimates and professional judgment), resulting in an average moderate rating. This provides an overall data adequacy rating of medium. Because of high variability, local units must verify these determinations for each data source used.

Frequency

At least every 5 years, assess the geographic distribution and estimated impact of selected nonindigenous aquatic animal species. Enter the distribution and impact ratings for each species into the WCMD. The measure value is automatically calculated by the WCMD based on the entered data.

Threshold for Change

The threshold for meaningful change is a 5-percent change in the measure value for all selected nonindigenous aquatic animal species. Once there are five measure values, the threshold for meaningful change will switch to regression analysis. A decrease in the measure value beyond the threshold for meaningful change results in an improving trend in this measure.

3.4 Indicator: Air and Water

This indicator focuses on threats to air and water quality. There are six measures for this indicator: five measures on air quality from which units are required to select at least one, and one required measure on water quality.

Guidance for Selecting Air Quality Measures

Section 3.4 describes the five air quality measures:

1. Measure: Concentration of Ambient Ozone
2. Measure: Deposition of Nitrogen
3. Measure: Deposition of Sulfur
4. Measure: Amount of Haze
5. Measure: Index of Sensitive Lichens

Local units are required to select at least one of these air quality measures, or may select multiple measures if relevant to the individual wilderness. For all five measures, the central data analyst will complete the protocols by retrieving data from the Forest Service Air Resource Management Program or other national monitoring networks. The purpose of this section is to provide additional guidance for local units to consider when selecting air quality measures because some measures are more appropriate for certain geographic regions of the U.S., and available data may vary by geographic region.

Air quality measures are selected based on their relevancy to the local wilderness. Contact local or regional air resource specialists for assistance in determining which air quality measure(s) is/are most appropriate and feasible to monitor for each wilderness. Factors to consider include the availability of data as well as the relative impacts of various pollutants in a wilderness. Air quality monitoring plans for the forest or local unit may also identify the pollutant(s) most likely affecting a wilderness. The following general guidelines for each air quality measure may help guide the selection process.

• Concentration of Ambient Ozone—This measure will be particularly important for wildernesses located within or near areas that are exceeding the National Ambient Air Quality Standard (NAAQS) for ozone. Fortunately, these also are wildernesses most likely to have access to the data necessary to use this measure. There is limited data availability for the Pacific Northwest and Alaska.
• Deposition of Nitrogen—This measure will be of more interest to local units located west of the Mississippi River and in areas of the East where nitrogen deposition is of greater concern than sulfur deposition.
• Deposition of Sulfur—This measure will be of most interest for Forest Service Regions 8 and 9 in the eastern U.S. This is especially true for New England and the Appalachian Mountain range where sulfur has accumulated over decades of high deposition and continues to be released into, and negatively affect, watersheds and aquatic systems.
• Amount of Haze—This measure will be of interest to local units with noticeable haze or other impacts to visibility. Almost all wildernesses (except those in Alaska and Puerto Rico) have representative visibility data.
• Index of Sensitive Lichen Species—This measure is primarily for wildernesses where air pollution monitoring stations are limited or not available. It will be especially useful in Alaska where air quality monitoring equipment is very limited, and in Forest Service regions 1, 4, and 6 where lichen monitoring data are readily available. Use of this measure is limited to wildernesses with forested habitats. At this time, nitrogen and sulfur are the only pollutants modeled for lichen sensitivity.

Table 2.3.10: Recommended air measures for Forest Service regions. A dash (-) in the column generally means not relevant or not recommended.

Region	Concentration of ambient ozone	Deposition of nitrogen	Deposition of sulfur	Amount of haze	Index of sensitive lichen species
1	-	Yes	-	Yes	Yes
2	Yes	Yes	-	Yes	-
3	Yes	Yes	-	Yes	-
4	Yes	Yes	-	Yes	Yes
5	Yes	Yes	-	Yes	-
6	-	-	-	Yes	Yes
8	Yes	Yes	Yes	Yes	-
9	Yes	Yes	Yes	Yes	-
10	-	-	-	-	Yes

Table 2.3.10 shows the air quality measures that may be most relevant for each Forest Service region (Regions 1–10). While this table may help narrow the selection process, it should not replace recommendations of local or regional air resource specialists. In this table, the protocol options mentioned under nitrogen and sulfur are discussed in sections 3.4.2 and 3.4.3, respectively.

3.4.1 Measure: Concentration of Ambient Ozone

This measure assesses the 3-year rolling average of ozone concentration (fourth highest daily maximum 8-hour concentration) based on the Forest Service Air Resource Management Program's annual analyses of national ozone monitoring data. Unless stated otherwise, the protocol steps are intended to be completed by the central data analyst. Data are compiled from the Forest Service Air Resource Management Program NAAQS website. The central data analyst calculates the measure value. Table 2.3.11 describes key features for this measure.

Table 2.3.11—Summary of measure type, protocol options, local tasks, national tasks, and frequency of data reporting for measure "Concentration of Ambient Ozone.

Protocol

Step 1: Determine the ozone monitor that is representative of air quality for the wilderness. Although ozone data are available for all 50 states and Puerto Rico, not all wildernesses have a monitoring site located near them. Monitoring data from sites located within 25 miles of a wilderness boundary are generally considered representative (see Caveats and Cautions). If confronted with multiple viable monitors, a large disparity in elevation either between the monitoring site and a wilderness or across a wilderness, or any other questionable situations, contact an air resource specialist for assistance in selecting the single most representative site. If an air resource specialist confirms that there are no representative ozone monitors for a specific wilderness, do not use this measure and refer back to the local unit to select one of the other air quality measures.

In addition to consulting with air resource specialists, the central data analyst has the following two tools available to locate monitoring sites:

1. NRM-Air—This NRM application contains spatial layers for ozone monitoring sites (called fixed equipment sites) and wilderness boundaries. Use these layers within the Geospatial Interface (GI) ArcMap extension to buffer a wilderness and identify monitoring sites within 25 miles. Consult a GIS specialist or a specialist familiar with the application for assistance if necessary.
2. Forest Service Air Resource Management Program mapping tool—This tool is available online at https://webcam.srs.fs.fed.us/maps/index.php and displays locations of ozone monitors as dots on the map.

Figure 2.3.1 is a screen capture from the Forest Service Air Resource Management Program online tool showing ozone monitoring site locations. When the user zooms in on the map, forest and wilderness boundaries and scale are revealed. Information is available for all 50 states and Puerto Rico. The tool allows users to select the area of interest to see whether any ozone monitoring sites are located within approximately 25 miles of a wilderness boundary.

Figure 2.3.1—Screen capture from the Forest Service Air Resource Management Program online tool showing ozone monitoring site locations for the Boundary Waters Canoe Area Wilderness.

Once a representative monitoring site is identified for a wilderness, record the last five digits of the monitor ID as well as the state and county it is located in.

Step 2: Retrieve ozone data from the Forest Service Air Resource Management Program. Navigate to the Forest Service Air Resource Management Program NAAQS website (http://webcam.srs.fs.fed.us/graphs/o3calc/health.php) to access ozone summary data (shown in fig. 2.3.2). In the boxes under "Select a New Location" (found in the upper right hand corner of the page), enter the state, county, and monitor ID for the selected monitoring site; ignore the check box for "Class 1 only" and click "Load Data."

Figure 2.3.2—An example of a summary graph for the 3-year average ozone statistic from the Forest Service Air Resource Management Program website.

The first graph in the summary report depicts the NAAQS for ozone: the annual fourth highest daily maximum 8-hour concentration, averaged over 3 calendar years. The 3-year averages are calculated using values from the current and previous two years of data (e.g., the 3-year average for 2018 combines data from 2016, 2017, and 2018), and are represented in the graph by red triangles. Note that there may be up to a year delay in posting data. To retrieve the data depicted in the graph, click on "NAAQS Results" to the right of the graph. The data appear in columns, with each row representing a single year. To identify which column contains the 3-year rolling averages, click on the "Readme" (metadata) file (located below "NAAQS Results").

Record the "3-year average (parts per million [ppm])" data for all relevant years since the year of wilderness designation. For example, for a wilderness designated in 2000, the first 3-year average to record would be from 2002 (combining data from 2000— the year of designation, 2001, and 2002). Not all ozone monitoring sites have legacy data dating from the year of designation, in which case begin recording the ozone data when monitoring began. Only ozone data from 1990 forward are considered valid for this measure, even though some monitoring sites may have data from earlier years. Since the ozone monitoring network was expanded and became more stable around 1990, using data from that year forward minimizes the amount of missing data that could adversely affect the trend analysis. For wildernesses designated from 1964 to 1989, therefore, the first 3-year average to record should be from 1992 at the earliest (combining data from 1990, 1991, and 1992).

Step 3: Enter data in the WCMD. Enter the 3-year average fourth highest daily maximum 8-hour ozone concentration, rounded to the nearest tenth (i.e., 0.1), for all recorded years. If a null value is recorded for a certain year (i.e., a value of "-999" indicating missing annual data), include documentation of the null value but do not enter data for that year in the WCMD. The measure value is the 3-year average ozone statistic.

Caveats and Cautions

One problem with this measure is that ozone monitors are frequently located near urban areas, and not all Forest Service wildernesses have a representative monitor. Wildernesses without a representative monitor will not be able to use this measure.

There are cases when a monitor located more than 25 miles away may be considered representative of a wilderness. Many factors determine how broad an area a single ozone monitor can represent, including topography, elevation, and distance to major pollution sources. Ozone monitors located further than 25 miles from a wilderness may still be representative if the air mass is similar to that over a wilderness, or if the terrain is relatively flat and the monitor is located at a similar elevation and downwind distance from major air pollution sources.

It is acceptable to use ozone data from a monitor that may represent only a portion of a wilderness, a situation that may arise for very large wildernesses and those with highly complex terrain. Ozone data from one monitor may not accurately evaluate ozone levels in all areas of a wilderness, but the data from one well-managed monitor should provide a representative ozone trend for a wilderness. The goal of this measure is to evaluate the trend in ozone concentration over time, not to establish exact ozone concentrations for a particular location in a wilderness.

If there is any question about the representativeness of a monitoring site, consult an air resource specialist to help identify the most representative monitor to use for this measure. Finally, the Forest Service Air Resource Management Program website does not have up to date ozone data or graphics. Until the lapse in maintenance ends, annual ozone concentration data are sourced from EPA [1] and trends are calculated by the WCM Central Team using the protocol described above.

Data Adequacy

The ozone data used in this measure comes from a network of permanent monitoring sites managed by the EPA and other federal, state, tribal, and local air quality agencies (including some national forests that participate in cooperative ozone monitoring with state or local air regulatory agencies). The data collected from these monitoring sites receive rigorous quality assurance (QA) and quality control (QC) review before being entered into the EPA's Air Quality System (AQS) database, from which the Forest Service Air Resource Management Program pulls and analyzes the data. The method of analysis used by the Forest Service Air Resource Management Program follows national protocols from the EPA and state and local air regulators.

Data adequacy must be verified for each wilderness individually. While data quality is considered good for all ozone monitoring sites, data quantity may vary and this will affect the data adequacy rating. Data quantity is considered complete only if there is a continuous data record. If there are data gaps of more than 2 years, data quality is moderate. Ozone monitoring sites with complete data will have a high data adequacy rating. Sites with partial data will have a medium data adequacy rating.

Frequency

Every 5 years, obtain the most current ozone data from the Forest Service Air Resource Management Program (that draws the data from the EPA) and enter these data in the WCMD. Although the data are released annually, data compilation, analysis, and entry of all new years may take place on a 5-year interval rather than annually (i.e., rather than retrieving, analyzing, and entering data every year, the central data analyst may retrieve, analyze, and enter 5 years of data at a time). There can be up to a year delay in ozone data being available (e.g., 2014 data may not be available until the end of 2015) and the central data analyst should plan to compile data for this measure just prior to the reporting interval.

Threshold for Change

The threshold for meaningful change is statistical significance as determined by regression analysis. A statistically significant decreasing trend in the 3-year rolling average of the fourth highest 8-hour ozone concentration results in an improving trend in the measure.

3.4.2 Measure: Deposition of Nitrogen

This measure assesses the amount of nitrogen deposition in a wilderness by using either the average total deposition (based on nationally modeled or measured spatial data) or the trend in wet deposition (based on the Forest Service Air Resource Management Program's annual analyses of spatially interpolated data). Local units may select the appropriate protocol option as described in step 1 below. Unless stated otherwise, the protocol steps are intended to be completed by the central data analyst. Data are compiled from either the NADP website, the Forest Service Air Resource Management Program website, or other local or regional databases. The central data analyst calculates the measure value. Table 2.3.12 describes key features for this measure.

Table 2.3.12—Summary of measure type, protocol options, local tasks, national tasks, and frequency of data reporting for measure "Deposition of Nitrogen."

Measure type	Protocol options	Local tasks	National tasks	Frequency
Required to select at least one of the five air quality measures	Protocol Option 1: Total Deposition Protocol Option 2: Wet Deposition	Validate the nationally selected protocol option.	Step 1: Determine which protocol option is appropriate for the wilderness. Step 2: Retrieve and process the deposition data. Step 3: Enter data in the WCMD.	5 years

Protocol

Step 1: Determine which protocol option is appropriate for the wilderness. The two protocol options for this measure are described below. Consult with an air resource specialist to confirm which protocol option is most appropriate. While the central data analyst may make a preliminary recommendation, local units must validate and approve the selected protocol option, and may choose to use local or regional deposition data if available and appropriate.

Protocol Option 1—Total Deposition. This protocol option uses modeled spatial data to assess the average total nitrogen deposition in a wilderness. These data are available for wildernesses in the lower 48 states. Use this protocol option unless more accurate, regionally refined deposition information is available.

Protocol Option 2—Wet Deposition. This protocol option uses spatially interpolated data to assess the trend in wet deposition of nitrogen. The wet deposition data used for this protocol option are interpolated at a finer resolution than for protocol option 1, and therefore better reflect variation in deposition across the landscape and provide a more accurate average deposition value. These data are only available for eastern wildernesses in the continental U.S. where wet deposition trends mirror total deposition trends.

In addition to the two protocol options described in this technical guide, other local or regional nitrogen deposition data may be available for a given wilderness. For example, El Toro Wilderness in Puerto Rico is not covered by the data described in protocol option 1 or 2, however there is a National Atmospheric Deposition Program (NADP) monitoring site (PR20) located near the wilderness and those data could be used to describe wet deposition trends. An air resource specialist should be consulted to assist with this analysis. Similarly, forests in Regions 1, 4, 6, and 10 have access to a regionally specific alternative to the protocol options described in this section: nitrogen deposition estimates based on lichen sampling and elemental analysis of lichen tissue. These are considered the best nitrogen deposition data for the Pacific Northwest and Alaska where deposition-monitoring sites are scarce and the extremely complex (i.e., mountainous) terrain located adjacent to the ocean makes air quality modeling difficult. Wilderness-specific nitrogen deposition trends based on lichen elemental analyses are available for units in Washington, Oregon, Montana, and Alaska on the Forest Service National Lichens and Air Quality Database and Clearinghouse at http://gis.nacse.org/lichenair/. Local Forest Service units in Regions 6 and 10 should consider using these nitrogen deposition trends as well as the air pollution scores described in the measure Index of Sensitive Lichens (section 3.4.5 in part 2) to monitor wilderness air quality.

Other local or regional deposition data sources might be preferred for a wilderness if they are available at a finer spatial resolution, especially in areas of mountainous terrain. If a local unit is considering using regionally refined deposition data other than those described in protocol options 1 and 2, consult with an air resource specialist to ensure that the data are relevant and used appropriately.

Step 2: Retrieve and process the deposition data. This step is described below for each protocol option.

Protocol Option 1—Total Deposition. The best total nitrogen deposition values available nationally are the result of a hybrid approach that combines measured and modeled deposition into spatial coverages (Schwede and Lear 2014). This approach combines monitoring data with output from the Community Multiscale Air Quality modeling system, giving priority to measurement data near the location of the monitor and priority to modeled data in areas where monitoring data are not available. The Total Deposition (TDEP) Science Committee of the NADP http://nadp.slh.wisc.edu/committees/tdep/ creates these deposition values. Although TDEP products include values for many components of deposition, this protocol option uses only the annual total nitrogen data. Annual nitrogen deposition data are available from 2000 forward. GIS analysis will be required to calculate annual total nitrogen deposition within each wilderness for each year of interest.

If protocol option 1 is selected, TDEP data are obtained from the NADP through the website: http://nadp.slh.wisc.edu/committees/tdep/tdepmaps/. To retrieve data, navigate to the website and follow these steps:

1. Open the "README file for data" (found on the bottom of the page) and record the TDEP version number. The version number consists of a 4-digit year and a 2-digit release number (e.g., 2016.01), and can be found in the lower left corner of the ReadMe file. It is critical to document which TDEP version is used because each subsequent release updates all of the previous years to reflect modifications and enhancements in the underlying model.
2. Return to the bottom of the main page and click "Download Grids." Next, click on the folder labeled "n_tw" that contains the total wet and dry deposition data. Other similarly named folders contain different nitrogen statistics, so it is important to use the "n_tw" folder and no other.
3. Download the zip files for all years of interest. The first time data are compiled for this measure, and every time a new version is released, all available years of data since the year of wilderness designation must be downloaded and analyzed. If the version number has not changed since the previous data compilation, only new years of data must be downloaded. Be advised that the most recent years posted will usually be 1 to 2 years behind the current date.

Consult with a GIS specialist to analyze the downloaded spatial data. Once the files have been unzipped and imported from the .e00 extension, each will show a gridded coverage of the modeled estimates of total nitrogen deposition for the calendar year, at a resolution of 12 kilometers by 12 kilometers. The GIS specialist will need to buffer the wilderness boundary by 12 kilometers before clipping the data.

Because the total deposition estimates are in 12 kilometer squares (approximately 35,600 acres), there likely will be a significant number of wildernesses that are entirely encompassed in a single square (e.g., many of the 270 Forest Service wildernesses smaller than 35,600 acres). For wildernesses contained within a single square, use that value as the wilderness average. For wildernesses that take up more than one square, however, the average TDEP value for a wilderness will need to be calculated. Record the wilderness average total deposition for all years of downloaded data.

Protocol Option 2—Wet Deposition. If protocol option 2 is selected, wet deposition data are obtained through the Forest Service Air Resource Management Program website at http://webcam.srs.fs.fed.us/graphs/dep/ (see fig. 2.3.3). In the boxes under "Select a New Location," enter the state, national forest, and wilderness, and click "Load Data" (ignore the check box for "Class 1 only").

Figure 2.3.3—An example of a summary for wet total nitrogen deposition from the Forest Service Air Resource Management Program website.

Relevant information for this measure is found in the second section of the summary titled "Wet Total Nitrogen," which includes both a graphic presentation of the data and an explanatory narrative. (In this case, total refers to the sources of nitrogen, rather than the type of deposition.) The graph depicts the average total wet deposition for a wilderness (in kilograms per hectare) for each calendar year, and contains either a red regression estimate line or a blue historical mean line. Note that there may be up to a year delay in posting data.

Determine whether wet nitrogen deposition has increased, decreased, or remained stable over time by using both the graph and the explanatory narrative. A blue line on the graph indicates a stable (not statistically significant) trend in the data. A red regression line on the graph indicates a statistically significant trend in the data, either increasing or decreasing. Look at the first sentence in the narrative to confirm the direction of the data; this sentence will read: "Deposition has decreased on average..." or "Deposition has increased on average…." Be aware that these analyses are based upon the entire data record, whereas WCM determines trend comparing the most recent measure value with the baseline measure value. As a result, the central data analyst will need to consult with an air resource specialist to determine whether it is more appropriate to use the narrative description or estimate the trend from the year of designation to the present day. Assign the applicable trend category from the options described in the following list. For the measure baseline year, the stable wet deposition of nitrogen category should be selected. If there is any question about which category to assign, contact an air resource specialist for assistance in interpreting the graph and narrative.

• Decreasing wet deposition of nitrogen—there is a statistically significant decreasing trend in the average annual wet deposition.
• Stable wet deposition of nitrogen—there is no statistically significant trend in the average annual wet deposition.
• Increasing wet deposition of nitrogen—there is a statistically significant increasing trend in the average annual wet deposition.

Step 3: Enter data in the WCMD. For protocol option 1, enter the wilderness average total deposition values, rounded to the nearest tenth (i.e., 0.1), for all years that were assessed. For protocol option 2, enter the assigned trend category for wilderness wet deposition. The measure value is either the average total deposition or the trend category for wet deposition.

Caveats and Cautions

The Forest Service will soon be able to use exceedance of identified critical loads (CL) to monitor the trend in nitrogen deposition. A CL is the amount of pollutant loading, below which negative impacts to sensitive resources do not occur. In other words, a CL is a threshold for air pollution effects. By comparing a CL to total deposition (and determining whether the CL has been exceeded) it is possible to directly address effects of pollution on natural resources within a wilderness and not just the pollution trend, as is used currently. Use of total deposition estimates from TDEP, as outlined in protocol option 1 of the current guidance, sets the stage for an easy transition to using CL exceedance in the future when units have identified CLs for nitrogen. For more information on CLs, see the Forest Service Air Portal, available at http://www.srs.fs.usda.gov/airqualityportal/critical_loads/index.php, and the EPA Global Change Impacts & Adaptation CLs Mapper (currently in beta version and regularly updated), available at https://clmapper.epa.gov/.

Data Adequacy

For protocol option 1, data quantity is considered complete and data quality is considered good, resulting in a high data adequacy rating. TDEP is considered the best available approach for estimating total deposition of nitrogen, in part because it maximizes the use of measured data from nationally recognized monitoring networks. The included monitoring networks produce high quality measurements following documented protocols for monitor site selection, equipment maintenance, sample collection and handling, sample analysis, data processing, and data reporting.

For protocol option 2, data quantity is considered complete and data quality is considered good, resulting in a high data adequacy rating for the continental eastern U.S. The regionally refined spatial interpolations of wet deposition created by Grimm and Lynch (2004) are considered the best available approach for tracking deposition in the eastern U.S. This approach uses measured deposition data (similar to what is described for TDEP), measured precipitation, and topography to model wet deposition. The resulting product has a finer resolution than TDEP estimates, which better reflects variation in deposition across the landscape and provides a more accurate average deposition value for each wilderness.

Frequency

Every 5 years, the amount of nitrogen deposition is assessed and the total deposition annual averages (protocol option 1) or applicable trend category (protocol option 2) are entered in the WCMD. For protocol option 1, although the data are released annually, data compilation, analysis, and entry of all new years may take place on a 5-year interval rather than annually (i.e., rather than retrieving, analyzing, and entering data every year, the central data analyst may retrieve, analyze, and enter 5 years of data at a time). The central data analyst should plan to compile data for either protocol option of this measure just prior to the 5-year trend reporting interval because there can be up to a year delay in posting national air quality data to websites (e.g., 2014 data may not be available until the end of 2015).

Threshold for Change

The threshold for meaningful change differs depending on the protocol option used. For protocol option 1, the threshold is statistical significance as determined by regression analysis. For protocol option 2, the threshold is any change in categories. A statistically significant decreasing trend in the data, or a change in categories towards decreasing deposition, results in an improving trend in the measure.

3.4.3 Measure: Deposition of Sulfur

This measure assesses the amount of sulfur deposition in a wilderness by using either the trend in wet deposition (based on the Forest Service Air Resource Management Program's annual analyses of spatially interpolated data) or the average total deposition (based on nationally modeled spatial data). Local units may select the appropriate protocol option as described in step 1 below. Unless stated otherwise, the protocol steps are intended to be completed by the central data analyst. Data are compiled from either the Forest Service Air Resource Management Program website, the NADP website, or other local or regional databases. The central data analyst calculates the measure value. Table 2.3.13 describes key features for this measure.

Table 2.3.13—Summary of measure type, protocol options, local tasks, national tasks, and frequency of data reporting for measure "Deposition of Sulfur."

Measure type	Protocol options	Local tasks	National tasks	Frequency
Required to select at least one of the five air quality measures	Protocol Option 1: Wet Deposition Protocol Option 2: Total Deposition	Validate the nationally selected protocol option.	Step 1: Determine which protocol option is appropriate for the wilderness. Step 2: Retrieve and process the deposition data. Step 3: Enter data in the WCMD.	5 years

Protocol

Step 1: Determine which protocol option is appropriate for the wilderness. The two protocol options for this measure are described below. Protocol Option 1—Wet Deposition, applies to the eastern U.S. (the area most likely to select this measure), while Protocol Option 2—Total Deposition, applies to the rest of the continental U.S. Consult with an air resource specialist to confirm which protocol option is most appropriate. While the central data analyst may make a preliminary recommendation, local units must validate and approve the selected protocol option, and may choose to use local or regional deposition data if available and appropriate.

Protocol Option 1—Wet Deposition. This protocol option uses spatially interpolated data to assess the trend in wet deposition of sulfur. The wet deposition data used for this protocol option are interpolated at a finer resolution than data for Protocol Option 2–Total Deposition, and therefore better reflect variation in deposition across the landscape and provide a more accurate average deposition value. These data are only available for eastern wildernesses in the continental U.S. where wet deposition trends mirror total deposition trends. Wildernesses in Forest Service Regions 8 and 9 (excluding Puerto Rico) should strongly consider using this protocol option.

Protocol Option 2—Total Deposition. This protocol option uses modeled spatial data to assess the average total sulfur deposition in a wilderness. These data are available for wildernesses in the lower 48 states. Use this protocol option unless more accurate, regionally refined deposition information is available.

In addition to the two protocol options described in this technical guide, other local or regional sulfur deposition data may be available for a given wilderness. For example, sulfur deposition trends at El Toro Wilderness is Puerto Rico are not covered by protocol options 1 or 2 but could be described using data from the nearby NADP wet deposition monitoring site (PR20). Other local or regional deposition data sources might be preferred if they are available at a finer spatial resolution, especially in areas of mountainous terrain. If a local unit is considering using regionally refined deposition data other than those described here, consult with an air resource specialist to ensure that the data are relevant and used appropriately.

Step 2: Retrieve and process the deposition data. This step is described for each protocol option.

Protocol Option 1—Wet Deposition. If the first protocol option is selected, wet deposition data are obtained through the Forest Service Air Resource Management Program website at http://webcam.srs.fs.fed.us/graphs/dep/ (shown in fig. 2.3.4). In the boxes under "Select a New Location," enter the state, national forest, and wilderness, and click "Load Data" (ignore the check box for "Class 1 only").

Figure 2.3.4—An example of a summary for wet sulfate deposition from the Forest Service Air Resource Management Program website.

Relevant information for this measure is found in the second section of the summary titled "Wet Sulfate," which includes both a graphic presentation of the data and an explanatory narrative. The graph depicts the average total wet deposition for a wilderness (in kilograms per hectare) for each calendar year, and contains either a red regression estimate line or a blue historical mean line. Note that there may be up to a year delay in posting data.

Determine whether wet sulfate deposition has increased, decreased, or remained stable over time by using both the graph and the explanatory narrative. A blue line on the graph indicates a stable (not statistically significant) trend in the data. A red regression line on the graph indicates a statistically significant trend in the data, either increasing or decreasing. Look at the first sentence in the narrative to confirm the direction of the data; this sentence will read: "Deposition has decreased on average..." or "Deposition has increased on average…." Be aware that these analyses are based upon the entire data record, whereas WCM determines trend comparing the most recent measure value with the baseline measure value. As a result, the central data analyst will need to consult with an air resource specialist to determine whether it is more appropriate to use the narrative description or estimate the trend from the year of designation to the present day. Assign the applicable trend category from the options described in the following list. For the measure baseline year, the stable wet deposition of sulfur category should be selected. If there is any question about which category to assign, contact an air resource specialist for assistance in interpreting the graph and narrative.

• Decreasing wet deposition of sulfur—there is a statistically significant decreasing trend in the average annual wet deposition.
• Stable wet deposition of sulfur—there is no statistically significant trend in the average annual wet deposition.
• Increasing wet deposition of sulfur—there is a statistically significant increasing trend in the average annual wet deposition.

Protocol Option 2—Total Deposition. If the second protocol option is selected, TDEP data are obtained from the NADP through the website http://nadp.slh.wisc.edu/committees/tdep/tdepmaps/. To retrieve data, navigate to the website and follow these steps:

1. Open the "README file for data" (found on the bottom of the page) and record the TDEP version number. The version number consists of a 4-digit year and a 2-digit release number (e.g., 2016.01), and can be found in the lower left corner of the ReadMe file. It is critical to document which TDEP version is used because each subsequent release updates all of the previous years to reflect modifications and enhancements in the underlying model.
2. Return to the bottom of the main page and click "Download Grids." Next, click on the folder labeled "s_tw" that contains the total (wet and dry) sulfur deposition data. Other similarly named folders contain different sulfur statistics, so it is very important to use the "s_tw" folder and no other.
3. Download the zip files for all years of interest. The first time data are compiled for this measure, and every time a new version is released, all available years of data since the year of wilderness designation must be downloaded and analyzed. If the version number has not changed since the previous data compilation, only new years of data must be downloaded. Be advised that the most recent years posted will usually be 1 to 2 years behind the current date.

Consult with a GIS specialist to analyze the downloaded spatial data. Once the files have been unzipped and imported from the .e00 extension, each will show a gridded coverage of the modeled estimates of total sulfur deposition for the calendar year, at a resolution of 12 kilometers by 12 kilometers. The GIS specialist will need to buffer the wilderness boundary by 12 kilometers before clipping the data.

Because the total deposition estimates are in 12 kilometer squares (approximately 35,600 acres), there likely will be a significant number of wildernesses that are entirely encompassed in a single square (e.g., many of the 270 Forest Service wildernesses smaller than 35,600 acres). For wildernesses contained within a single square, use that value as the wilderness average. For wildernesses that take up more than one square, however, the average TDEP value for a wilderness will need to be calculated. Record the wilderness average total deposition for all years of downloaded data.

Step 3: Enter data in the WCMD. For protocol option 1, enter the assigned trend category for wilderness wet deposition. For protocol option 2, enter the wilderness average total deposition values, rounded to the nearest tenth (i.e., 0.1), for all years that were assessed. The measure value is either the trend category for wet deposition or the average total deposition.

Caveats and Cautions

The Forest Service will soon be able to use exceedance of identified CLs to monitor the trend in sulfur deposition. By comparing a CL to total deposition (and determining whether the CL has been exceeded) it is possible to directly address effects of pollution on natural resources within wilderness and not just the pollution trend, as is used currently. This is especially important for areas where pollution trends are decreasing but resources continue to be negatively affected by accumulated pollutants. A prime example can be found in the southern Appalachians, where sulfur emissions and deposition have decreased dramatically since 2006, but the accumulated sulfur in some watersheds slows recovery from acidification. Therefore, even with decreasing trends in sulfur deposition, sensitive resources may still show negative effects from acidification. Use of total deposition estimates from TDEP, as outlined in protocol option 2 of the current guidance, sets the stage for an easy transition to using CL exceedance in the future when units have identified CLs for sulfur. See the Forest Service Air Portal for more information on CLs, available at http://www.srs.fs.usda.gov/airqualityportal/critical_loads/index.php.

Data Adequacy

For the protocol option 1, data quantity is considered complete and data quality is considered good, resulting in a high data adequacy rating for the continental eastern U.S. The regionally refined spatial interpolations of wet deposition created by Grimm and Lynch (2004) are considered the best available approach for tracking deposition in the eastern U.S.

For the protocol option 2, data quantity is considered complete and data quality is considered good, resulting in a high data adequacy rating. TDEP is considered the best available approach for estimating total deposition of sulfur, in part because it maximizes the use of measured data from nationally recognized monitoring networks.

Frequency

Every 5 years, the amount of sulfur deposition is assessed and the applicable trend category (protocol option 1) or total deposition annual averages (protocol option 2) are then entered in the WCMD. For protocol option 2, although the data are released annually, data compilation, analysis, and entry of all new years may take place on a 5-year interval rather than annually (i.e., rather than retrieving, analyzing, and entering data every year, the central data analyst may retrieve, analyze, and enter 5 years of data at a time). The central data analyst should plan to compile data for either protocol option of this measure just prior to the 5-year trend reporting interval because there can be up to a year delay in posting national air quality data to websites (e.g., 2014 data may not be available until the end of 2015).

Threshold for Change

The threshold for meaningful change differs depending on the protocol option used. For protocol option 1, the threshold is any change in categories. For protocol option 2, the threshold is statistical significance as determined by regression analysis. A change in categories towards decreasing deposition, or a statistically significant decreasing trend in the data, results in an improving trend in the measure.

3.4.4 Measure: Amount of Haze

This measure assesses the trend in average deciview for the 20 percent most impaired days, based on the Forest Service Air Resource Management Program's annual analyses of national visibility monitoring data. Unless stated otherwise, the protocol steps are intended to be completed by the central data analyst. Data are compiled from the Forest Service Wilderness Air Quality website. The central data analyst calculates the measure value. Table 2.3.14 describes key features for this measure.

Table 2.3.12—Summary of measure type, protocol options, local tasks, national tasks, and frequency of data reporting for measure "Deposition of Nitrogen."

Measure type	Protocol options	Local tasks	National tasks	Frequency
Required to select at least one of the five air quality measures	None	None	Step 1: Retrieve visibility data from the Forest Service Air Resource Management Program. Step 2: Enter data in the WCMD.	5 years

Protocol

Step 1: Retrieve visibility data from the Forest Service Air Resource Management Program. The visibility data used for this measure are collected through the IMPROVE (Interagency Monitoring of Protected Visual Environments) program and analyzed by staff in the Forest Service Air Resource Management Program. Data will be uploaded to the Forest Service Wilderness Air Quality website (http://www.fs.fed.us/air/wilderness_monitoring.htm) once development of this site is completed. Each wilderness with a representative monitoring site will be listed with a link to the summary of visibility data from that monitor.

Relevant information for this measure will be found in the visibility graph (shown in fig. 2.3.5). The graph depicts IMPROVE data of the average deciview for the 20 percent most impaired days within a calendar year, averaged over 5 years. Deciview is a measurement of the amount of haze, with higher values of deciview indicating increased haze and greater levels of visibility impairment. Five year averages are calculated using values from the current and previous four years of data (e.g., the 5-year average for 2015 combines data from 2011, 2012, 2013, 2014, and 2015), and are represented in the graph by blue dots. Note that there may be up to a year delay in posting data.

The Forest Service Air Resource Management Program conducts a statistical analysis of the IMPROVE 5-year averages using a non-parametric regression technique: the Theil-Sen slope. This technique minimizes the influence of data outliers (e.g., so that a 5 year period with very favorable weather conditions for sulfate aerosol formation does not unduly affect the trend calculation). The statistical analysis is based on the available data record since the year of wilderness designation. For example, for a wilderness designated in 2000, the first 5-year average to be included in the statistical analysis would be from 2004 (combining data from 2000—the year of designation, 2001, 2002, 2003, and 2004). Not all IMPROVE monitoring sites will have legacy data dating from the year of wilderness designation, in which case the statistical analysis would use the entire data record available.

Figure 2.3.5—Visibility trends for the Bridger Wilderness, 2000–2015.

For this measure, a p-value less than 0.10 indicates a statistically significant trend in the data. The p-value from the most recent statistical analysis is shown on the graph. If the p-value is less than 0.10, use the trend line on the graph (the blue dotted line) to determine if average deciview has increased or decreased over time. For example, in figure 2.3.5, the low p-value and downward trend line indicate a statistically significant decreasing trend in deciview. Assign the applicable trend category from the options described in the following list. For the measure baseline year, the stable deciview category should be selected. If there is any question about which category to assign, contact an air resource specialist for assistance in interpreting the graph.

• Decreasing deciview—there is a statistically significant decreasing trend in the 5-year average deciview for the 20 percent most impaired days.
• Stable deciview—there is no statistically significant trend in the 5-year average deciview for the 20 percent most impaired days.
• Increasing deciview—there is a statistically significant increasing trend in the 5-year average deciview for the 20 percent most impaired days.

Step 2: Enter data in the WCMD. Enter the assigned trend category for the average deciview in the WCMD for this measure. The measure value is the trend category for deciview.

Caveats and Cautions

Representative IMPROVE sites were assigned to each Class I wildernesses based on distance and elevation criteria established by the IMPROVE Steering Committee. Subsequent studies of the spatial variability in IMPROVE data and model results suggest that most Class I wildernesses are well represented by their regulatorily assigned IMPROVE site. Based on the same criteria, most Class II wildernesses are also well represented by an IMPROVE site, but there are several, especially in southeast Alaska, which cannot be reasonably represented by IMPROVE data. Wildernesses without a representative monitor will not be able to use this measure.

IMPROVE sites that represent Class I areas are likely to remain operational in some capacity until 2064. However, a small number of other sites will most likely move or be shut down over time, in which case wildernesses will be evaluated for representativeness at a different IMPROVE monitor site. Gaps in the data record should not affect the regression.

While higher haze values indicate a less natural air quality condition, the EPA's Regional Haze Rule is designed to make steady progress towards natural conditions by 2064. As a result, the trend is a more important measure for WCM than the absolute impairment value.

Complete visibility data were not available on the Forest Service Wilderness Air Quality website during the initial implementation years of WCM, so visibility trends were calculated by the Central Team from tabular data provided by the IMPROVE coordinator. Because the website was still under development when this technical guide was published, there may need to be some reconciliation between the protocol currently described here for retrieving the haze data and the approach used once the website is finalized.

Data Adequacy

Visibility data are routinely collected and reported through the IMPROVE program. QA of the data is extensive. The number of observations per site is quite high, and the data record is generally greater than 10 years. In addition, data completeness for each site and year is determined as part of the calculation of the 5-year rolling average deciview, and site-years that do not meet standard completeness criteria are removed from the statistical analysis. For wildernesses with representative IMPROVE sites, data quantity is considered complete and data quality is considered good, resulting in a high data adequacy rating.

Frequency

Every 5 years, the trend in the 5-year average deciview for the 20 percent most impaired days is assessed and the applicable trend category is then entered in the WCMD. The central data analyst should plan to compile data for this measure just prior to the 5-year trend reporting interval because there can be up to a year delay in posting data to the Forest Service Air Resource Management Program website (e.g., 2014 data may not be available until the end of 2015).

Threshold for Change

The threshold for meaningful change is any change in categories. A change towards decreasing deciview results in an improving trend in the measure.

3.4.5 Measure: Index of Sensitive Lichen Species

This measure assesses the trend in air pollution scores for nitrogen and sulfur derived from the presence and abundance of sensitive lichen species, based on the Forest Service Air Resource Management Program's analyses of local biomonitoring data. Unless stated otherwise, the protocol steps are intended to be completed by the central data analyst. Data are compiled from the Forest Service National Lichens and Air Quality database. The central data analyst calculates the measure value. Table 2.3.15 describes key features for this measure.

Table 2.3.15—Summary of measure type, protocol options, local tasks, national tasks, and frequency of data reporting for measure "Index of Sensitive Lichen Species."

Measure type	Protocol options	Local tasks	National tasks	Frequency
Required to select at least one of the five air quality measures	None	None	tep 1: Retrieve lichen data from the Forest Service Air Resource Management Program. Step 2: Conduct a statistical analysis to determine the trend in the data. Step 3: Enter data in the WCMD.	5 years

Protocol

Step 1: Retrieve lichen data from the Forest Service Air Resource Management Program. Navigate to the Forest Service National Lichens and Air Quality database (http://gis.nacse.org/lichenair/index.php) and follow these steps:

1. Go to "Database Queries" on the left and click on "Lichen Plot Data." Select the desired wilderness from the list in the rightmost box (fig. 2.3.6) and click "Minimize."
   

   Figure 2.3.6—Example of lichen plot data from the National Lichens and Air Quality Database.
2. Scroll down to the section titled "Select Database Fields to Include in Query." Check the boxes for "Field collection date," "Plot name" and "Air pollution score" (fig. 2.3.7), then click "Retrieve Tabular Data."
   

   Figure 2.3.7—An example of the database fields included in the query for this measure.
3. Record relevant air pollution scores. In the table that appears, each row records a different plot number (fig. 2.3.8). The three fields selected for the query are found as columns on the far right of the table. Air pollution scores for nitrogen and sulfur are derived from an analysis of the lichen community and lichen abundances. Higher (more positive) scores indicate that more pollution (i.e., nitrogen and sulfur) is impacting the lichens on the plot. Plots without an air pollution score have not yet had their data analyzed; they will be updated in 2017 to reflect the most up to date information available. Sulfur air scores will also be calculated and uploaded here so that forests in the western U.S. and eastern U.S. have two scores. Click on the title of the "Plot name" column to sort the plots alphabetically, and identify which plots have been sampled more than once by comparing the plot name and field collection date. For example, in the figure 2.3.8, Plot 1142184 was sampled in 1995 and 2005 (first two rows) while Plot 1140188 has only been sampled once in 1996 (third row). Record the air pollution scores for all plots that have multiple field collection dates (table 2.3.16); ignore plots that have only been sampled once.

Figure 2.3.8—An example of the tabular summary of lichen plot data.

Step 2: Conduct a statistical analysis to determine the trend in the data. Consult a statistician or an air resource specialist for assistance with this step to determine if the number of sites is adequate and if the use of these statistical methods is appropriate. Use a two-tailed, paired t-test with an alpha level of 0.05 to determine if the air pollution scores are significantly different from one year to another. The earliest field collection date after the year of wilderness designation should be compared to the most recent field collection date to complete this analysis. There may be instances where a wilderness has multiple sampling dates across a period of up to 10 years (e.g., in fig. 2.3.8 the first year of sampling for each plot varies from 1995 to 1997). These wilderness areas may compare values from the earliest 10-year period to the most recent 10-year period. For example, in table 2.3.14, air pollution scores from 1990–1999 could be compared to air pollution scores from 2000–2010 for the three sites with more than 1 year of sampling. A p-value greater than 0.05 (as in the example in table 2.3.16) indicates that air pollution scores have not changed significantly over time, while a p-value less than or equal to 0.05 would be reasonable statistical evidence that the air pollution scores from the most recent field collection date are significantly different than those from the earliest field collection date. If a statistically significant difference is found, determine whether the air pollution scores are increasing or decreasing over time by comparing the mean air pollution score for the first field collection year with the mean score for the most recent year.

Table 2.3.16—An example of data retrieved from the tabular summary of lichen plot data placed into a period of the decade the data were collected.

Air pollution scores—Badger Creek Wilderness
Plot number	1990–1999	2000–2010
Plot 1142184	-0.2678	-0.4108
Plot 1140180	-0.244	-0.5537
Plot 1140176	-1.154	-0.0884
Average	-0.5553	-0.2920
p-value	0.6461

Assign the applicable trend category from the options described in the list below. For the measure baseline year—that is, the first period of data collection after the year of wilderness designation (e.g., 1990–1999 in the table above)—the stable air pollution category should be selected. If there is any question about which category to assign, contact an air resource specialist for assistance in interpreting the air pollution scores.

• Decreasing air pollution—there is a statistically significant decreasing trend in the air pollution scores.
• Stable air pollution—there is no statistically significant trend in the air pollution scores.
• Increasing air pollution—there is a statistically significant increasing trend in the air pollution scores.

Step 3: Enter data in the WCMD. Enter the assigned trend category for air pollution in the WCMD for this measure. The measure value is the trend category for air pollution.

Caveats and Cautions

Sensitive lichen species respond to both air pollutants as well as changes in climatic conditions, such as prolonged drought. Consult with an air resource specialist to understand if changes in sensitive lichen species are a response to changes in air pollution, climate, or both.

The data collection rate, or the amount of time between field collection dates, may be a concern for this measure. Plots are generally sampled on a 10-year monitoring cycle, and the data may not be updated on the same timeline as needed for WCM. In many wildernesses in the western U.S., lichen air plots are co-located with FIA plots, which are evenly spaced across a sampling grid that covers the country. Larger wildernesses may have more lichen biomonitoring plots because there are more FIA plots. Units may choose to locate more lichen biomonitoring plots in a wilderness to fill in the data gaps if threats due to air pollution are detected. For the Pacific Northwest, the goal is to establish one plot per 20,000 acres of wilderness, and a minimum of three plots in wildernesses under 40,000 acres.

In 2019, data will be uploaded that will result in two air pollution scores: one for nitrogen and one for sulfur. When this transition occurs, local units will need to select the metric that best describes the air condition of their forest based on individual wilderness air concerns. In general, nitrogen is more of a concern in the western U.S. whereas sulfur is a greater concern in the eastern U.S., but local conditions may vary greatly. Consult an air resource specialist for assistance in selecting which metric to use.

Data Adequacy

Data quantity for this measure is considered to be partial with a moderate degree of confidence that all data records have been gathered. Data quality is considered to be good due to a high degree of confidence that the quality of the data can reliably access trends in the measure. These ratings indicate that overall data adequacy is medium for this measure. Some wildernesses may have more lichen biomonitoring plots than others, and some plots are monitored more frequently than others based on when they were first established, funding cycles, and accessibility. Ideally, there would be one lichen biomonitoring plot per 20,000 wilderness acres even though in many cases this standard will not be met. As expected, more plots and more frequent plot remeasurments will mean that air pollution trends from a particular area will be more representative of the air conditions. Data adequacy of the lichen biomonitoring plots should be verified with the appropriate air quality specialist.

Frequency

Every 10 years, lichen data are analyzed and the applicable trend category is then entered in the WCMD. Be aware that this is the only measure based on a 10-year monitoring cycle. If this measure is selected, trends in wilderness character will not be determined until 10 years after the WCM baseline year. If deteriorating air pollution trends are detected, the frequency could be shortened to every 5 years. Consult with an air resource specialist to determine if a 5-year frequency may be appropriate.

Threshold for Change

The threshold for meaningful change is any change in categories. A change towards decreasing air pollution results in an improving trend in the measure.

3.4.6 Measure: Extent of Waterbodies With Impaired Water Quality

This measure assesses the miles of streams or number of lakes inside wilderness with impaired water quality, based on national or state 303(d) list of impaired water bodies or local monitoring data. Local units may select the appropriate protocol options as described in step 1 below. Data are compiled from national or state 303(d) databases, or other local, state, regional, or national data sources. Local staff calculate the measure value. Table 2.3.17 describes key features for this measure.

Table 2.3.17—Summary of measure type, protocol options, local tasks, national tasks, and frequency of data reporting for measure "Extent of Waterbodies with Impaired Water Quality."

Protocol

Step 1: Determine which protocol option is appropriate for the wilderness. The two protocol options for this measure are described below:

Protocol Option 1—Miles of Streams. Assesses the miles of streams with impaired water quality within wilderness.

Protocol Option 2—Number of Lakes. Assesses the number of lakes with impaired water quality within wilderness.

Local units should select the protocol option that is most relevant for a wilderness. Wildernesses with both streams and lakes should also consider the quality and availability of impairment data (see step 2) when selecting which protocol option to use. If there is any question about which protocol option to select, local units should consult with a hydrologist, fishery biologist, or other water resource specialist to help identify which is most relevant for a wilderness.

Step 2: Determine which wilderness streams or lakes are impaired. All levels of impairment are counted equally for this measure—waterbodies are either impaired or not impaired. Impaired streams and lakes that are only partially inside wilderness also are included in this measure. Given the variability in threats to water quality for each wilderness, units are encouraged to identify locally relevant water quality impairment metrics and data sources. For example, for Protocol Option 2— Number of Lakes, units could use local data on mercury levels in fish to determine whether lakes are impaired. The type of local metric and the data source(s) should be determined by a hydrologist, fishery biologist, or other water resource specialist based on both relevancy and data adequacy.

In lieu of a locally-specific impairment metric, the simplest way to assess impaired waters is by using 303(d) listings. The 303(d) refers to the section of the Clean Water Act that requires the listing of impaired waters, including streams and lakes. The central data analyst can assist local units in compiling and processing 303(d) impairment data. Methods for retrieving these data are described below:

• Spatial 303(d) data can be downloaded from the EPA's website (https://www.epa.gov/waterdata/waters-geospatial-data-downloads, select ESRI 10.x File Geodatabase under the "303(d) Listed Impaired Waters" heading). A "rad_303d.mxd" ARCMAP project contains the relevant feature classes:
  • rad_303d_a—depicts impaired lakes (the "a" is for area).
  • rad_303d_l—depicts impaired streams (the "l" is for line).
  • rad_303d_p—depicts impaired points (the "p" is for point); for example, fish sampling may yield impaired points if pollutants are found in fish tissue. Impaired points are expected to be rare but may be relevant for either the miles of streams or number of lakes protocol options. If there are impaired points inside a wilderness, consult a hydrologist, fishery biologist, or other water resource specialist to determine whether and how those points should be included in counting the mileage of impaired streams or number of impaired lakes.
• An interactive map of the 303(d) data is also available through the EPA's "How's My Waterway" website (http://watersgeo.epa.gov/mywaterway/map.html). This website is recommended for quick initial assessments of how many streams or lakes are impaired, but it may be difficult to extract impairment data for further analysis. Detailed information on each waterbody, such as the cause of impairment, can be found through the list view by clicking on the name of the waterbody and selecting "Technical Report(s)." Note that some records may include multiple waterbodies, and the same waterbody may be included under multiple records; make sure to note the waterbody ID if there is any confusion.
• Many states have their own websites with 303(d) data. State websites will usually provide similar information as the EPA websites listed above, but may be more up to date and may contain additional references to segment-specific reports or other data.

In many cases the 303(d) listings will be sufficient to determine which waterbodies are impaired; if necessary, however, they may be supplemented with other water quality data. Consult a hydrologist, fishery biologist, or other water quality specialist to validate the national or state 303(d) data and determine whether any other water quality data are necessary, appropriate, and available to supplement those data. Examples of other sources of water quality data include the following:

• Considerable information on lake and stream impairment may be available from local unit staff, NEPA documents, wilderness plans, and various reports, publications, and university and private sector databases. Many Forest Service regions and national forests also have extensive monitoring databases for wilderness lakes.
• Forest Service monitoring data and reports for wilderness lake chemistry can be retrieved from the Federal Land Manager Environmental Database at http://views.cira.colostate.edu/fed/.
• Other national or regional water quality data may also be available with monitoring sites near wilderness areas (e.g., the USGS maintains a water quality data mapping tool at http://maps.waterdata.usgs.gov/mapper/index.html). Because instrumentation within wilderness is limited, water quality data for streams are often collected downstream from a wilderness boundary. Consult a hydrologist, fishery biologist, or other water quality specialist to help interpret what impairment of streams adjacent to a wilderness area (upstream or downstream) means for water quality impairment inside a wilderness.
• If there are limited or no data on water quality for a wilderness, a local hydrologist, fishery biologist, or other water quality specialist may use their professional knowledge to determine which waterbodies are impaired.

To ensure confidence in tracking trends, impairment data must be tracked consistently over time. Given the amount of variability in data sources and protocol options for this measure, it is essential that local units document the data compilation strategy for each wilderness (including the metric of impairment, primary data source including the date accessed, and any supplemental data sources). If professional judgment is used, additional documentation (e.g., a brief narrative) explaining who made the assessment and their basis for the determination should also be included. Documentation may be stored locally, on shared drives, or uploaded to the WCMD.

Step 3: Assess the miles of impaired streams or number of impaired lakes in the wilderness. This is described below for each protocol option. Wildernesses with no impaired streams or lakes may skip this step and proceed to step 4.

Protocol Option 1—Miles of Streams. Calculate the total miles of impaired streams inside wilderness, as determined from the data sources described above. For impaired streams that continue beyond a wilderness boundary, only the mileage inside wilderness should be counted for this measure. Where impaired streams flow through lakes, the mileage distance through the lake should not be counted. Each monitoring cycle, document which streams were counted as impaired. Table 2.3.18 provides an example summary of how the impaired miles of streams could be documented for the wilderness. The measure value is the total miles of impaired streams inside wilderness.

Table 2.3.18—An example summary of impaired miles of streams.

Regardless of the metric or data source(s) used to determine impairment, a spatial analysis is likely to be the simplest way to assess the total miles of impaired streams. Consult a GIS specialist for assistance with the spatial analysis if necessary. The central data analyst can also assist local units in analyzing the national or state 303(d) impairment data. The following steps provide an example of how to complete the spatial analysis using the spatial 303(d) data downloaded from the EPA's website at https://www.epa.gov/waterdata/waters-geospatial-data-downloads.

1. Intersect the wilderness boundary (available from the Enterprise Data Warehouse [EDW]) and the rad_303d_l (i.e., impaired streams) feature classes.
2. Remove impaired stream segments flowing through lakes, if necessary, by erasing the rad_303d_a (i.e., impaired lakes) feature class from the intersect output.
3. Add a "Miles" field and calculate the mileage of each stream segment using the calculate geometry tool.
4. Copy the records to a spreadsheet similar to table 2.3.16 and sum results to derive the total miles of impaired streams inside wilderness.

Protocol Option 2—Number of Lakes. Count the total number of wilderness lakes with impaired water quality, as determined from the data sources described in step 2. A lake partially inside wilderness is counted as one lake, it is not assessed proportionally based on the percentage of area inside wilderness. Each monitoring cycle, document which lakes were counted as impaired. Table 2.3.19 provides an example summary of how the impaired number of lakes could be documented for the wilderness. The measure value is the total number of impaired lakes.

Table 2.3.19—An example summary of the number of lakes with impaired water quality.

Step 4: Enter data in the WCMD. For protocol option 1, enter the total mileage of impaired streams. For protocol option 2, enter the total number of impaired lakes. If there are no streams or lakes with impaired water quality in a wilderness, enter zero in the WCMD. The measure value is either the miles of streams or the number of lakes.

Caveats and Cautions

Although some wildernesses only have a small number of lakes, or a small number of impaired lakes, they may still select protocol option 2 if that is considered to be most relevant for a wilderness. For wildernesses that have a measure baseline value of 20 or fewer impaired lakes, it may be helpful to note that the 5-percent threshold for change effectively means that any change in the number of impaired lakes is considered meaningful change. For example, if a wilderness has a measure baseline value of 10 impaired lakes, a 5-percent change would be equal to 0.5 lakes; because an increase or decrease of just a single impaired lake would exceed 0.5 lakes, any change in the total number of impaired lakes would therefore result in a change in trend for the measure.

Data Adequacy

The 303(d) assessment procedures are fairly rigorous in most states so the impaired databases are generally good. However, the data adequacy varies greatly by state, and consideration of 303(d) status on NFS lands is not necessarily thorough, particularly for wildernesses where assessment information is limited. Some of the data sources for assessment information are old and should be reviewed by a local specialist for current applicability. The EPA and states are working constantly to improve the accuracy of 303(d) lists and to prepare and implement Total Maximum Daily Load plans for rehabilitation work, which will ultimately allow removal of some of the impaired waterbodies from listing. Data quantity is considered to be partial and data quality is considered to be moderate, resulting in a medium data adequacy rating for 303(d) listings.

Data adequacy for other data sources varies widely. Professional judgment typically has low data adequacy. In many cases, historical water quality data and reports are dated and of limited use. For other sources, the water quality protocols, analytical methods, and data QC may not be well documented. In a few cases, such as a proposed mining operation near a wilderness, extensive recent water quality data may have been collected. Because of this high variability, the data adequacy of all data sources must be assessed for each wilderness individually.

Frequency

Every 5 years, assess water quality impairment in wilderness streams or lakes, and enter the total mileage of impaired streams or the total number of impaired lakes in the WCMD.

Threshold for Change

The threshold for meaningful change is a 5-percent change in either the total mileage of impaired streams or the total number of impaired lakes. Once there are five measure values, the threshold for meaningful change will switch to regression analysis for both protocol options. A decrease in the amount of impaired waterbodies beyond the threshold for meaningful change results in an improving trend in this measure.

3.5 Indicator: Ecological Processes

This indicator focuses on threats to ecological processes that affect biotic and abiotic components of wilderness ecological systems. There are two measures for this indicator and units are required to select at least one.

3.5.1 Measure: Watershed Condition Class

This measure assesses the average wilderness watershed condition class, based on Forest Service Watershed Condition Classification (WCC) data. Unless stated otherwise, the protocol steps are intended to be completed by the central data analyst. Data are compiled from the Forest Service Watershed Condition Framework website and validated locally. The WCMD calculates the measure value. Table 2.3.20 summarizes key features for this measure.

Table 2.3.20—Summary of measure type, protocol options, local tasks, national tasks, and frequency of data reporting for measure "Watershed Condition Class."

Protocol

Step 1: Identify wilderness watersheds. The most efficient way to determine which watersheds are inside wilderness is to use GIS to overlay a wilderness boundary over a watershed layer. Watershed and wilderness layers are available on the Forest Service T drive (T:\FS\Reference\GIS drive). Watershed layers can also be downloaded from http://www.fs.fed.us/biology/watershed/condition_framework.html by clicking on the link to "download a shapefile with WCC and Prioritization information." Make sure to use the 6th code HUC (HUC 12) watershed layer rather than a different HUC level. For each watershed partially or entirely within wilderness, determine the acreage inside wilderness. Consult a GIS specialist for assistance with the spatial analysis if necessary. Record the watershed names/codes and area inside wilderness for all watersheds that are partially or entirely within wilderness.

Step 2: Retrieve watershed condition class data from the Forest Service Watershed Condition Framework (WCF) website. The WCF website (http://www.fs.fed.us/biology/watershed/condition_framework.html) provides several methods for accessing watershed condition information: an interactive map, tabular data, and spatial data. The links to the following methods are on the website:

• Interactive map—The USDA Forest Service Watershed Condition and Prioritization Interactive map (fig. 2.3.9) can be accessed at https://apps.fs.usda.gov/wcatt/.
• Tabular data—Download a table containing the WCC and prioritization information for the entire NFS summarizing watershed class, watershed score, and metric (attribute) and watershed class scores (fig. 2.3.10) at http://www.fs.fed.us/biology/resources/pubs/watershed/maps/USDAFS-WCF2011.htm.
• Spatial data—For GIS application, users can download a shapefile with WCC and Prioritization information. (This is the same link described in step 1 to download watershed layers.)

Figure 2.3.9—A screenshot of the Forest Service watershed condition and prioritization interactive map for portions of Idaho, Montana, Oregon, and Washington.

Figure 2.3.10—Example WCC table with watershed condition ratings for several national forests.

All three links provide information at the 6th code HUC (HUC 12) watershed level, for all national forests and grasslands. Use whatever method is easiest to obtain the condition class data. Condition class may be described or titled differently for each method; for example, the table uses the heading "Watershed_Condition_FS_Area" while the spreadsheet uses "Watershed_Class_FS_Land." There are only three viable watershed condition classes: 1, 2, or 3. Equivalent descriptions for these three condition classes are displayed in table 2.3.21.

Table 2.3.21—Equivalent watershed condition class descriptions.

The listed condition class is based on an assessment of the entire watershed. As watershed boundaries often extend beyond a wilderness boundary, a watershed may therefore be classified as "functioning at risk" or "impaired function" based on conditions outside wilderness. For watersheds that are only partially within a wilderness, a local hydrologist or other water resource specialist must validate that the listed condition class is appropriate for the portion of a watershed inside wilderness. Hydrologists or other water resource specialists may use professional judgment or the best available data to assess the listed condition class for these partial wilderness watersheds. If a water resource specialist determines that the wilderness portion of a watershed should be assigned a different condition class than the whole watershed, they may modify the condition class for that watershed. Additional documentation (e.g., a brief narrative) explaining who made the assessment and their basis for the determination should be included if a condition class is modified.

Once the data have been validated locally and any changes have been documented, the information is sent back to the central data analyst for data entry. Record the watershed condition class (using whole numbers 1, 2, or 3) for all wilderness watersheds identified in step 1.

Step 3: Enter data in the WCMD. Enter each watershed's name, area inside wilderness, and condition class in the WCMD, and the WCMD will then calculate the average wilderness condition class automatically. Local units are not responsible for calculating the average condition class themselves, but the formula is described below for reference. The measure value is the average wilderness watershed condition class.

The calculation for the average wilderness watershed condition class consists of two basic steps. First, the WCMD multiplies the wilderness acreage in each watershed by the condition class rating for that watershed. Second, the WCMD sums these calculated values and divides the result by the total number of wilderness acres. Table 2.3.22 provides a hypothetical example of how to calculate the average wilderness watershed condition class.

Table 2.3.22—An example of how to calculate the average wilderness condition class.

Caveats and Cautions

As stated above, the watershed condition class is for an entire 6th code HUC (HUC 12). As watersheds often extend beyond a wilderness boundary, conditions outside a wilderness may drive the condition class listed for a watershed. Local knowledge and professional judgment must be applied to determine if the listed condition class is appropriate for the wilderness portion of a watershed.

Data Adequacy

The WCC database provides a complete dataset for all national forests and grasslands using consistent rating protocols with nationwide maps and data tables. Local units periodically update the database, with nationwide updates conducted at 5-year intervals. While the WCC condition class ratings are moderately subjective, the quality of data should improve with future updates. Data adequacy is complete for data quantity and moderate for data quality for an overall data adequacy rating of medium. If professional judgment or other data sources are used to modify a watershed condition class, additional subjectivity is added to the data quality. Data adequacy must therefore be verified locally for each wilderness.

Frequency

Every 5 years, watershed condition class is assessed, and the condition class for each wilderness watershed is then entered in the WCMD.

Threshold for Change

The threshold for meaningful change is any change in the average wilderness watershed condition class. A decrease in the average condition class score results in an improving trend in this measure.

3.5.2 Measure: Number of Animal Unit Months of Commercial Livestock Use

This measure assesses the 3-year rolling average of commercial livestock use, based on an annual count of Animal Unit Months (AUMs) within a wilderness. Local data are compiled and entered in NRM-Range annually and are automatically retrieved by NRM-WCM. NRM-WCM calculates the annual value. The WCMD calculates the annual value and the 3-year rolling average (the measure value). Table 2.3.23 summarizes key features for this measure.

Table 2.3.23—Summary of measure type, protocol options, local tasks, national tasks, and frequency of data reporting for the measure "Number of Animal Unit Months of Commercial Livestock Use."

Protocol

Step 1: Retrieve and validate data on annual count of AUMs within wilderness from NRM. Livestock use is evaluated by monitoring the number of permitted AUMs of livestock grazing that are authorized for allotments located entirely or partially within wilderness. AUMs – the quantity of forage required by one mature cow and her calf (or the equivalent in sheep or horses) for 1 month – are the preferred unit of measurement rather than head months.

Retrieve data for this measure in NRM-WCM by accessing the "commercial livestock" option under the "Natural" quality in the "Navigator" tab. This will display the annual count of AUMs for the wilderness. The following attributes are automatically uploaded to NRM-WCM from NRM-Range:

• Range Management Unit Name
• Range Management Unit ID
• Total Allotment Acres
• Acres in Wilderness
• Percent in Wilderness
• Authorized AUM
• Wilderness AUM

NRM-WCM will display each of these attributes for every allotment on record in NRM-Range. Each allotment listed in NRM-WCM has an "Include" option, in which allotments can be unselected if they no longer are active. NRM-WCM also contains a "Remarks" tab in order to record specific details about each range management unit (i.e. allotment A was in non-use this year). The Local wilderness staff must review and validate all of the information pulled for each of these attributes for accuracy and completeness. If data are incorrect, work with range specialists to correct the original data in NRM-Range.

Step 2: Calculate the annual value. The NRM-WCM application will automatically calculate the annual count of AUMs within wilderness. The method NRM-WCM uses to calculate these values is described below for reference.

The calculation for the annual number of authorized wilderness AUMs consists of three basic steps. First, NRM-WCM determines which allotments are completely within the wilderness boundary and which allotments extend outside the wilderness boundary. For the allotments that extend outside the wilderness boundary NRM-WCM determines the percentage of allotment acres located inside wilderness. Next, NRM-WCM calculates the wilderness AUMs for each allotment by multiplying the number of authorized AUMs by the percentage of the allotment inside wilderness. Lastly, NRM-WCM sums the number of wilderness AUMs for all allotments to produce the total amount of authorized livestock use in wilderness for the fiscal year. Table 2.3.24 provides a hypothetical example of how to calculate the annual number of authorized wilderness AUMs.

Table 2.3.24—Example of how to calculate the total number of authorized wilderness animal unit months (AUMs).

If NRM-WCM cannot be used to retrieve data on authorized AUMs, the data may be determined by a range specialist evaluating range allotment maps, range annual operating instructions, or actual use reports. This type of evaluation relies on estimation and is less accurate, but can provide data to determine the trend in the measure if used consistently over time. Additional documentation (e.g., a brief narrative) explaining who made the assessment and their basis for the determination should be included if data are compiled this way. If local units only track head months, they should convert those units to AUMs using factors relating to days of use, livestock kind, and class. Consult a range specialist for assistance with this conversion if necessary.

Step 3: Enter data in the WCMD. Enter each allotment's name, percentage inside wilderness, and number of authorized AUMs retrieved from NRM-WCM in the WCMD. The WCMD will also automatically calculate the total number of wilderness AUMs authorized for the fiscal year. Make sure this calculation matches the NRM-WCM calculation. The WCMD will also automatically calculate 3-year rolling averages based on these annual values. The measure value is the 3-year rolling average number of authorized wilderness AUMs.

Caveats and Cautions

This measure does not directly monitor the ecological impacts of livestock grazing in wilderness; however, for the purposes of this measure and WCM, the number of AUMs is considered a good proxy for assessing impacts to the Natural Quality from livestock use. In addition, the protocol for determining the number of wilderness AUMs may or may not accurately reflect actual wilderness use. Assessing wilderness use as a simple proportion of the total allotment use, and using authorized AUMs rather than actual use, may not capture on-the-ground wilderness use due to a variety of factors, including rotational grazing programs, seasonality, difficult terrain, and lack of forage.

Data Adequacy

The data quantity for this measure is generally considered to be complete, as the data are available for all Forest Service grazing allotments. The data quality is considered to be moderate due to some uncertainty of actual use inside wilderness on an annual basis. As the overall use on the allotment is apportioned based on acres of wilderness in the allotment, actual use may vary from year to year and is difficult to ascertain. Therefore, the overall data adequacy is considered to be medium. If locally stored data or professional judgment are used in lieu of data from NRM-Range, and NRM-WCM data adequacy is likely to be lower. Data adequacy must be verified locally for each wilderness.

Frequency

Each year, data are compiled and calculated on the amount of authorized livestock use in NRM-WCM. The percentage of allotment acres inside wilderness and the number of authorized AUMs for each wilderness allotment are then entered in the WCMD.

Threshold for Change

The threshold for meaningful change is a 5-percent change in the 3-year rolling average number of authorized wilderness AUMs. Once there are five measure values, the threshold for meaningful change will switch to regression analysis. A decrease in the 3-year rolling average beyond the threshold for meaningful change results in an improving trend in this measure.

3.6 Selecting Measures for the Natural Quality

This section provides recommendations for selecting locally developed measures for the Natural Quality, based on Keeping It Wild 2 (Landres et al. 2015). It discusses the general considerations for selecting these measures, explains why certain types of measures are problematic, offers examples to clarify what are and are not appropriate measures, and provides a flowchart outlining the general selection process.

The essential requirement for all measures in WCM is the ability to assign a degrading, improving, or stable trend based on changes in their condition. Applying this seemingly straightforward idea to the Natural Quality can be fraught because ecological conditions typically do not have a single natural state from which a trend can be assigned. Instead, ecological systems are complex. Individuals of a species tend to move around, and ecological conditions and processes vary over time from one location to another (e.g., species come and go, some years are warm and some are cold, snowfields and glaciers expand and melt).

Natural change over time and from one place to another is a fundamental aspect of ecological systems, and is an essential aspect of the Natural Quality of wilderness character. To allow for this change, the Natural Quality should not be used to recreate historical conditions from an arbitrary point in time (e.g., pre-European settlement or the date of wilderness designation), target a subjective set of desired conditions (e.g., a specific game species population), or otherwise maintain unchanging ecological conditions. When combined with the Untrammeled Quality, the basic legal and philosophical tenet in wilderness is to watch what happens and not direct this change. This tenet means that there is no target for the species that occur there, or for abiotic conditions such as temperature or precipitation.

Given this principle, the most direct and simple measures in the Natural Quality are those that quantify known direct threats to the ecological system. For example, air pollutants or nonindigenous species are known threats that generally have good reference information. Even these threats, however, require sufficient understanding of whether changes are primarily natural or anthropogenic (e.g., separating the effects of volcanic air pollutants from human-caused pollutants, or the natural dispersal of nonindigenous species from human-caused spread). Today, many changes in the Natural Quality are due to the interacting effects of natural variation and humancaused threats, and our ability to distinguish between the two is frequently lacking. Moreover, even if these interactions are understood on a global or regional scale, this knowledge may be lacking for the smaller spatial scale of a wilderness. Therefore, measures of threats should be selected only if they are determined (either by data or professional judgment) to be primarily anthropogenic and if they can show meaningful change within the timeframe that is appropriate for WCM (i.e., 5–10 years) as opposed to requiring decades or centuries of data collection.

The Forest Service currently collects much natural resource information, and in some cases this information may be directly used in WCM. The data collected from resource programs provide valuable insight into regional and local ecosystems, but may not be appropriate or feasible to include in WCM. Importantly, not all threats or features of the natural environment important to wilderness character need to be included as measures in WCM if other resource programs already monitor these threats or features. In such cases, only those measures that are appropriate and the highest priority would be included, typically selected because they quantify threats to features that are truly integral to and representative of the area's wilderness character.

There are some cases in which a measure is inappropriate to monitor under the Natural Quality but is clearly integral to wilderness character. For example, the return of extirpated bears and wolves to wildernesses may be, from a wilderness perspective, a significant improvement in the Natural Quality. Counting populations of naturally occurring species, however, does not monitor a human-caused threat, nor can a trend in the measure be assigned without assuming a target ecological state. For such cases, the importance of the measure that was not selected should be acknowledged in the Wilderness Character Narrative (required under the WSP Wilderness Character Baseline element) or by including it in other monitoring programs.

Occasionally, a measure may be included under the Other Features of Value Quality instead of the Natural Quality. For example, the Other Features of Value Quality may include measures related to iconic features (e.g., glaciers) or species (e.g., wolves) that define how people think about wilderness or are specifically identified in the enabling legislation. This can be appropriate because trends in measures under the Other Features of Value Quality may be defined by human values (e.g., the presence of the feature or the species within a wilderness), whereas trends in measures under the Natural Quality are defined by the more stringent criterion of whether the ecological system is free from the effects of modern civilization.

Examples of Appropriate and Inappropriate Measures

The following examples show how measures are and are not appropriate, based on the guidelines presented in this section.

Appropriate Measures

Appropriate measures are those that meet four criteria: (1) they are current or potential threats to the ecological systems in wilderness, (2) they are primarily human-caused, (3) they do not rely on a static or target ecological state to make an assessment about trend, and (4) they can show change within 5–10 years. The discussion below describes two example measures and includes a brief explanation of why each measure is appropriate for use in WCM.

1. Example Measure: Index of Nonindigenous Terrestrial Animal Species.
   1. Nonindigenous species are a direct and significant threat to ecological systems in wilderness.
   2. Nonindigenous species are most commonly introduced or spread in wilderness by humans. Even populations of nonindigenous invasive species that are spreading naturally into a wilderness were likely initially introduced outside of a wilderness by humans. In most cases, therefore, changes in the data result primarily from human agency.
   3. This measure monitors an effect of modern civilization and does not reference a specific ecological state (any ecological state is natural so long as it is substantially unaffected by human-caused introductions of nonindigenous invasive species). A trend can be assigned for the measure such that increasing distribution or impact of nonindigenous species degrades the Natural Quality and decreasing distribution or impact improves it.
   4. A meaningful trend in the measure value can be observed in a short timeframe.
2. Example Measure: Concentration of Ambient Ozone.
   1. Ozone in the lower atmosphere is a pollutant formed primarily from reactions involving emissions from cars, industrial facilities, power plants, and other types of combustion. It can have a significant effect on ecological components, structures, and functions and is therefore a threat to the Natural Quality.
   2. Air pollutants such as ozone are a by-product of modern civilization and changes in the data result primarily from human agency.
   3. This measure monitors an effect of modern civilization and does not reference a specific ecological state (any ecological state is natural so long as it is substantially unaffected by human-caused air pollution). A trend can be assigned for the measure such that an increasing concentration of ozone degrades the Natural Quality and decreasing concentration improves it.
   4. A meaningful trend in the data can be observed in a short timeframe.

Inappropriate Measures

Inappropriate measures are those that do not meet the criteria described above for appropriate measures. The discussion below describes two example measures and includes a brief explanation of why the measure is inappropriate for use in WCM.

1. Example Measure: Average Annual Summer or Winter Temperature (related to climate change).
   1. Temperature naturally varies within a wilderness from year to year without necessarily degrading wilderness character. Although changes in global temperature reflect human agency, making that determination for local change—especially in the short term—may not be feasible.
   2. Changing average temperature simply represents change, and cannot be considered to improve or degrade wilderness character. To state that any change in average temperature would degrade the Natural Quality sets a static target for what "natural" is in a wilderness.
   3. If data are not already being collected in close proximity to a wilderness, a long-time scale would be required before a meaningful trend in the data could be observed.
   4. Established climatology monitoring programs already exist within wilderness managing agencies and other federal agencies. This science is complex, nuanced, time-consuming, and already conducted by specialists at a much higher level than is generally possible for an individual wilderness. WCM should not duplicate or create new monitoring programs.
2. Example Measure: Index of Animal (or Plant) Species of Concern (primarily state or federally listed threatened or endangered species).
   1. Monitoring a listed species does not directly monitor the threat to the Natural Quality. A species may be listed because of threats occurring outside a wilderness, and change in the abundance or distribution of such species in a wilderness may not be indicative of a threat inside a wilderness.
   2. Measures that quantify the loss of an indigenous species must be able to determine that the change in species abundance or distribution is due primarily to anthropogenic impacts and not to natural variation. Few wildernesses have adequate historical or current data to make this determination.
   3. Change in a population of an indigenous species does not necessarily improve or degrade the Natural Quality of wilderness character because populations change naturally over time. Identifying a trend in the measure would require setting a static historical, current, or desired abundance and distribution as a target state, which is inappropriate in wilderness.
   4. Determining if there is a change in species abundance and distribution would require sampling over periodic intervals and over a large area, which may be difficult to accomplish for a wilderness. The sampling protocol would also need to account for annual and seasonal migrations and probable immigration-emigration dispersal patterns.

Flowchart

The flowchart depicted in figure 2.3.11 provides general guidelines, using a series of questions, for selecting measures for the Natural Quality. The first question is whether the measure is a threat to the Natural Quality, with threat defined as human agency in directly or indirectly causing a significant change to the composition, structure, and functioning of ecological systems in wilderness (Landres et al. 2009). The second question is whether the measure will provide an interpretable trend. This question, based on the discussion above, can be summarized as asking the following: (1) whether the measure holds a wilderness to a static or target ecological state, (2) if changes can be primarily attributed to human agency, and (3) if there is sufficient information or data to make a reasonable assessment of trend within approximately 5–10 years. For this flowchart, it is assumed that all measures being considered have already been determined to be integral to wilderness character, significant or meaningful to understanding change in the indicator of the Natural Quality, and vulnerable to human-caused threats. It also is assumed that measures are able to be reliably monitored with a high degree of confidence in the data, and can feasibly be monitored into the future.

Figure 2.3.11—Flowchart for selecting measures for the Natural Quality.