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A Guide to Lotic AIM Indicators, their Computation, and Example Applications

DRAFT – DO NOT CITE

Date Compiled: February, 2020

Table of Contents Introduction .................................................................................................................................................. 3

Intended Applications ............................................................................................................................... 3

Lotic AIM Analysis and Reporting Workflow ............................................................................................ 4

Field Method Overview............................................................................................................................. 5

Indicators for Lotic Systems ........................................................................................................................ 11

Overview ................................................................................................................................................. 11

Water Quality .......................................................................................................................................... 11

Background ......................................................................................................................................... 11

Example Applications (the big picture) ............................................................................................... 11

Indicators ............................................................................................................................................ 12

pH .................................................................................................................................................... 12

Specific Conductance ...................................................................................................................... 12

Water Temperature ........................................................................................................................ 12

Total Nitrogen and Phosphorous .................................................................................................... 13

Turbidity .......................................................................................................................................... 14

Watershed Function – Instream Habitat ................................................................................................ 15

Background ......................................................................................................................................... 15

Example Applications (the big picture) ............................................................................................... 16

Stream Channel Form, Function, and Habitat Indicators ................................................................... 16

Pool depth, length, and frequency ................................................................................................. 16

Thalweg ........................................................................................................................................... 18

Instream Habitat Complexity .......................................................................................................... 19

Large Wood ..................................................................................................................................... 20

Streambed Particle Sizes ................................................................................................................. 22

Relative Bed Stability ...................................................................................................................... 24

Bank Stability and Cover ................................................................................................................. 25

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Bank Angle ...................................................................................................................................... 26

Floodplain Connectivity and Channel Incision ................................................................................ 27

Biodiversity and Riparian Habitat ........................................................................................................... 29

Background ......................................................................................................................................... 29

Example Applications (the big picture) ............................................................................................... 29

Biodiversity and Riparian Habitat Indicators ...................................................................................... 29

Benthic Macroinvertebrates ........................................................................................................... 29

Percent Canopy Cover ..................................................................................................................... 32

Riparian Habitat Quality .................................................................................................................. 32

Covariates ............................................................................................................................................... 36

Background and Example Applications ............................................................................................... 36

Covariate Description and Computations ........................................................................................... 36

Bankfull Width and Wetted Width ................................................................................................. 36

Flood-prone Width .......................................................................................................................... 36

Slope ................................................................................................................................................ 37

Sinuosity .......................................................................................................................................... 37

Benchmarks: From Indicator Values to Management Decisions ................................................................ 38

What are Benchmarks and Why are They Needed? ............................................................................... 38

Approaches to Setting Benchmarks ........................................................................................................ 38

Limitations to Benchmark Approaches ................................................................................................... 40

Understanding Benchmarks Available in the Benchmark Tool ............................................................... 40

Water Quality Benchmark Development ............................................................................................ 42

Watershed Function, Instream Habitat, Riparian Habitat, and Biodiversity Benchmark Development ............................................................................................................................................................ 43

Appendix A. BLM AIM AquADat Local Feature Class Metadata ................................................................. 53

Appendix B. Indicators for Non-Wadeable Reaches ................................................................................... 62

Appendix C. Special Situations .................................................................................................................... 67

Appendix D. State priority native and nonnative lists ................................................................................ 72

Literature Cited ........................................................................................................................................... 83

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Introduction Intended Applications

This document is intended to help BLM resource specialists understand the types of indicators resulting from collection of the Assessment, Inventory, and Monitoring (AIM) field methods for wadeable lotic systems (TR 1735-2). It includes descriptions of how to calculate, interpret, and apply individual indicators to assess resource condition and trend. The provided information can be used by practitioners prior to collecting aquatic AIM data, such as when making decisions about what contingent indicators to collect, or as a reference document during data analysis and reporting. This document does not discuss indicator limitations but that information can be found here. The content was developed with the assumption that users will have some level of familiarity with lotic AIM and the associated field methods (TR 1735-1 and TR 1735-2). Therefore, we do not provide a rationale for why the core and contingent indicators were selected or detailed descriptions of field methods. If you are brand new to lotic AIM, the utility of this document will be increased by a review of the following three resources:

1. Technical Reference 1735-1: provides a detailed rationale for the development of the AIM-NAMF and the selection of core and contingent indicators

2. Technical Reference 1735-2: detailed field method protocol for wadeable lotic systems

3. AIM Implementation Website: walks practitioners through a series of implementation steps for AIM project from planning to design and analysis

The technical reference can be read cover to cover as a guide to the detailed metadata for lotic AIM indicator computation and application, but its greatest values is as a quick reference guide. Below are examples of using the document as a quick reference guide:

1. Indicator applications and computation: If you want to understand applications or computation specifics for an indicator of interest, use the table of contents to quickly reference the page number for the indicator of interest. Note that the indicators are organized by the BLM’s Fundamentals of Land Health.

2. Brief description of core and contingent methods and indicators: If you do not have much experience with lotic AIM methods or indicators, this document will provide a brief description of each method and the associated indicators (see pages 8-10 and Appendix A). Most of this document focuses on wadeable lotic AIM methods and indicators but most also applies to non-wadeable lotic AIM methods. Appendix B has a brief description of non-wadeable methods and indicators.

3. Contingent indicator selection: If you are just starting a lotic AIM project, you can use

this document to determine which contingent indicators you may want to collect in addition to the core indicators (see the table of contents for indicator page numbers).

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4. Determining benchmarks: If you are using the Benchmark Tool to make condition determinations and you want to better understand the default benchmarks, how the benchmarks are being applied in the tool, and whether they are appropriate, see pages 38-52.

Lotic AIM Analysis and Reporting Workflow

The AIM analysis and reporting workflow consists of three main steps: preparing for analysis, conducting analysis, and interpreting results (Figure 1). Understanding this general process and the resources available to assist with analysis and reporting will facilitate more effective and efficient communication among interdisciplinary teams making management decisions. This document addresses steps associated with ‘preparing for analysis’. Specifically, step 2 (indicator computation), step 3 (develop benchmarks), and step 5 (understand pertinent data) (Figure 1). These steps collectively work toward determining the condition of an individual stream reach, which might be the end goal of an analysis. However, if one seeks to combine condition estimates for multiple stream reaches to make inference to a larger area (e.g., BLM perennial streams in the Price Field Office, UT), more complex statistical analyses are required and we recommend consultation with the National AIM team (steps 9 and 10 in Figure 1). When preparing for and conducting analyses with lotic AIM data, there are two tools that can be of assistance: AquADat and the lotic AIM Benchmark Tool. AquADat is the BLM’s national database for lotic AIM, which stores the indicators described herein. Lotic AIM data can be obtained for analysis from AquADat following instructions here. Practitioners can then pair the data downloaded from AquADat with other analysis tools, such as the lotic AIM Benchmark Tool. The Benchmark Tool assists with assigning default (described in the Benchmark Section) or custom benchmarks for determining the condition of individual sampled stream reaches. Instructions for using the Benchmark Tool are contained within the tool.

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Figure 1. Analysis and reporting workflow to support assessments of resource condition and trend using the AIM Strategy. The BLM National Operations Center (NOC) assists with several parts of the workflow and serves as a central data storage location.

Field Method Overview

Technical reference 1735-1 identified core, contingent, and covariate methods for BLM assessment and monitoring efforts. Core methods are measurable ecosystem components that are applicable across many different ecosystems, management objectives, and agencies. Core lotic methods are recommended for application wherever the BLM implements monitoring and assessment of wadeable perennial streams. Contingent methods are measureable ecosystem component having the same characteristics of cross-program utility and consistent definition as core methods, but that are measured only where applicable. Contingent methods are not informative everywhere and, thus, are only measured when there is reason to believe they will be important for management purposes. Covariates are measured or derived parameters used to account for natural spatial or temporal variation in a core, contingent, or supplemental method (e.g., gradient); covariates help determine the potential of a given stream or river reach. Collectively, the core, contingent, and covariate methods and associated indicators provide multiple lines of evidence for quantifying the chemical, physical, and biological condition and trend of lotic resources. The core and covariate methods represent the minimum measurements required for reporting on the attainment of BLM lotic fundamentals of rangeland health in a quantitative manner (i.e., water quality, watershed function and instream habitat, biodiversity and riparian habitat quality, and ecological processes). Beyond the BLM fundamentals of land health, the core and contingent indicators have broad applicability to monitoring and assessment needs, including Clean Water Act attainment, the establishment of baseline conditions, assessing the

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efficacy of restoration or reclamation actions, and resource management plan (RMP) effectiveness monitoring. The use of AIM monitoring to evaluate RMP effectiveness was codified in IM 2016-139 and the Bureau’s Aquatic Habitat Management, Wildlife, and Rangeland Program is working to integrate AIM monitoring methods into their manuals and handbooks. The AIM monitoring methods for wadeable perennial streams and rivers include 11 core methods characterizing water quality (e.g., pH), instream habitat (e.g., percent fine sediment), and biodiversity and riparian habitat quality (e.g., macroinvertebrate biological integrity) (TR-1735-2) (Table 1). The core methods can be complemented by contingent field methods (e.g., nutrients, turbidity, bank angle, vegetative cover and composition) to address more specific management questions, as well as several covariates (e.g., flood-prone width, slope, and sinuosity) to help determine the potential of a stream or river to support a set of water quality, geomorphic, or biological conditions. The general field sampling design is to collect data along a length of stream called a reach. Reach lengths are scaled to the size of the stream and are measured as 20 times the average bankfull width, with a minimum of 150 m and a maximum of 4 km. Along the sample reach, 21 equally spaced transects consisting of 11 main transects and 10 intermediate transects, oriented perpendicular to the thalweg, are temporarily established (Figure 2). Field measurements consist of individual point measurements made at the center of the reach (e.g., water quality indicators), measurements at each of the 11 main transects (e.g., canopy cover) or all 21 transects (e.g., bank stability and cover), or measurements taken throughout the entire reach (e.g., slope and large woody debris) (Table 1). All field measurements are taken during base flow conditions between June 1st and September 30th (i.e., index period). For detailed descriptions of indicator specific field methods, refer to TR 1735-2 (Miller et al., 2017).

Figure 2. Typical reach setup with main transects (A–K; black lines) and 10 intermediate transects (gray lines) oriented perpendicular to the thalweg. Reach lengths are equal to 20 x bankfull width or a minimum of 150 m and a maximum of 4 km. Circled letters represent alternating benthic macroinvertebrate sampling locations for the reachwide protocol (USEPA 2009).

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AIM monitoring and assessments generally seek to make inference to an individual stream reach or a population of stream reaches through use of statistically appropriate sample designs (i.e., site selection). Regardless of the scope of inference, the unit of replication is the stream reach, and multiple sample reaches or samples through time are required to derive average indicator estimates and associated confidence intervals. Thus, where the field protocol prescribes multiple measurements throughout a reach (Table 1), the intent is to improve the accuracy of indicator values (e.g., bank stability) by describing spatial variability within the sample reach, and the individual measurements are not intended as statistical replicates. The use of multiple measurements per reach as replicates is subject to pseudo-replication, where the replicates are not statistically independent of each other (Hurlbert 1984). Pseudo-replication can lead to artificially low variance estimates and the detection of differences when they really do not exist (i.e., type II errors), for example.

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Table 1. Lotic AIM field methods and associated indicators. Appendix A contains more detailed descriptions of indicators.

Fundamentals of land health Field method Summary of field method Indicators computed from field

measurements B

iodi

vers

ity /

ripar

ian

habi

tat

Macroinvertebrate biological integrity

8 Surber/kicknet from multiple fast water habitats or 1 Surber/kicknet per each of 11 transects

InvasiveInvertSp, ObservedInvertRichness, ExpectedInvertRichness, OE_Macroinvertebrate, MMI_Macroinvertebrate

Canopy cover 6 densiometer readings at 11 transects (four measurements at mid-channel and one at each bank)

PctOverheadCover, BankOverheadCover

Frequency of occurrence of priority noxious vegetation

Presence of priority noxious species recorded for left and right bank within 10 X 10 m plots at 11 transects

NonNativeWoody, NonNativeHerb, Species PercentOfPlotsPresent

Frequency of occurrence of priority native woody riparian vegetation

Presence of priority native species recorded for left and right bank within 10 X 10 m plots at 11 transects

NativeWoody, NativeHerb, SedgeRush, Species PercentOfPlotsPresent

Ocular est. of riparian vegetative type, cover, and structure1

Ocular cover, structure, and type estimates for left and right bank within10 X 10 m plots at 11 transects

VegComplexity, RiparianVegComplexity,

Greenline vegetation composition1

Relative vegetation cover for left and right bank within 40 x 50 cm greenline-based quadrats at a minimum of 40 transects.2

Capacity to store and report this data is pending development - Use MIM excel app in the interim

Wat

er q

ualit

y

pH In-situ: multi-parameter sonde pH Specific conductance In-situ: multi-parameter sonde SpecificConductance Temperature In-situ: multi-parameter sonde or thermistor InstantTemp, MeanAugTemp

Total nitrogen and phosphorous1

50 ml water sample collected frozen within 24 hours and sent to the lab for quantification of total nitrogen and total phosphorus

TotalNitrogen, TotalPhosphorous

Turbidity1 In-situ: turbidometer or grab sample for lab analysis Turbidity

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Table 1 Continued. Lotic AIM field methods and associated indicators. Appendix A contains more detailed descriptions of indicators.

Fundamentals of land health Field method Summary of field method Indicators computed from field

measurements W

ater

shed

func

tion

- ins

tream

hab

itat

Pool depth, length, and frequency

Measure all qualifying pools within the reach with stick and tape

ResPoolDepth, PctPools, NumPools, PoolFreq

Large wood Size class counts of qualifying large wood (>0.1 m diameter for at least 1.5 m in length) over the entire reach

LWD_Freq, LWD_Vol, component of RelativeBedStability

Streambed particle size distribution

10 streambed particles measured at equal distances across the active channel (scour line to scour line) at 21 transects

PctFines2, PctFines6, D16, D84, D50, GeometricMeanParticleDiam, component of RelativeBedStability

Bank stability and cover

Ocular assessment of left and right banks at 21 transects. Bank type (erosional/depositional), bank foliar3 cover (50% covered or not), type of cover (bedrock, cobble, large wood, vegetation), and presence of unstable bank features (fracture, slumping, sloughing, eroding) are assessed.

BankCoverFoliar, BnkCoverBasal2, BankStability, BnkCoverFoliarStab, BnkCoverBedrock, BnkCoverCobble, BnkCoverLWD, BnkCoverFoliarVeg

Floodplain connectivity Bankfull and floodplain height measured at 11 transects BankfullHeight, FloodplainHeight,

ChannelIncision, FloodplainConnectivity

Bank angle1 Measured with a clinometer for left and right bank at 11 transects. Acute angles are overhanging banks and obtuse are laid back banks.

BankAngle

Thalweg depth profile1 101+ inter-transect measurements of thalweg water depth ThalwegDepthCV, ThalwegDepthMean,

PctDry, component of RelativeBedStability

Pool tail fines1

Number of intersections within a 36 X 36 cm grid (50 possible intersections) having fine sediment <2 mm or <6 mm for a pool tail. Assessed at 3 grids per pool tail (up to 10 pools).

PoolTailFines2, PoolTailFines6

Instream fish habitat complexity4

Ocular estimates of instream of concealment features (e.g., large wood, veg., undercuts, boulders) at 11 plots. Plots extend 5 m upstream and 5 m downstream of the transect and across the entire wetted width.

InstreamHabitatComplexity

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Table 1 Continued. Lotic AIM field methods and associated indicators. Appendix A contains more detailed descriptions of indicators

Fundamentals of land health Field method Summary of field method Indicators computed from field

measurements C

ovar

iate

s/ot

her

Bankfull width One tape measurement at each of 11 transects

BankfullWidth, Entrench, component of RelativeBedStability

Wetted width One tape measurement at each of 21 transects

WettedWidth, component of RelativeBedStability

Flood-prone width

Two tape measurements of floodplain valley width at riffles closest to the bottom and top of reach. Handlevels are used to ensure measurements are taken at a height of two times bankfull depth.

FloodWidth, Entrench

Slope Elevation change over entire reach length using transit and stadia rod, clinometer, or hand level

Slope, component of RelativeBedStability

Reach length GPS points of top and bottom of the reach and stick & tape measurement along the stream thalweg

Sinuosity

Photos

A minimum of 10 photos at sampled sites and 4 photos at non-sampled sites. Photo type, location, orientation, and description are recorded.

NA

Human influence Proximity weighted ocular estimates of human activities on left and right banks at each of 11 transects

HumanInfluence (in development)

1Contingent 2Methods for the quantification of riparian vegetative cover and composition follow Multiple Indicator Monitoring (MIM) methods. 3Basal cover collected prior to 2019 4Removed from wadeable protocol in 2019

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Indicators for Lotic Systems Overview

A suite of indicators can be computed from the core, contingent, and covariate methods (Table 1). Indicators are grouped in Table 1 and subsequent sections by the BLM’s fundamentals of land health and can be used to assess standard attainment in addition to a variety of other applications including assessing resource management plan effectiveness, quantifying the efficacy of restoration or reclamation actions, and completing grazing or other use-based permit renewals. The large number of indicators results from the fact that many indicators can be computed from one field method. For example, AquADat contains five different streambed particle size indicators that are all derived from field measurements of streambed particle sizes. Indicators are both computed and stored within the BLM’s AquADat database if they meet minimum data requirements specified in Appendix C. AquADat metadata and brief descriptions of each indicator are found in Appendix A. Water Quality

Background To assess water quality, lotic AIM identifies three core field methods (pH, specific conductance, and instantaneous temperature) and four contingent methods (total nitrogen, total phosphorus, continuous temperature, and turbidity). In most cases, the water quality field methods have a single indicator that can be derived from them, as what you measure is what you report; however, a variety of indicators can be derived from continuous temperature. The core and contingent indicators are not meant to be representative of all state water quality standards. Rather, the indicators are meant to help determine the common chemical stressors resulting from land uses, such as irrigation water withdrawals and return flows, grazing, mining, timber harvest, and other activities occurring on or adjacent to public lands. Furthermore, the methods in lotic AIM start with a one-time grab sample collected during base flow conditions. Such sampling is used to identify potential water quality exceedances requiring additional sampling or utilization of existing water quality monitoring networks to determine the temporal persistence of observed exceedances. In addition, one-time grab samples can be used to make correlations with macroinvertebrate biological integrity estimates to identify biologically relevant stressors. In contrast, monitoring to determine the attainment of state water quality standards requires the sampling frequency of each indicator to be consistent with state standards. Example Applications (the big picture)

• Identify priority water quality exceedances or stressors requiring additional sampling to assess standard attainment

• Assess attainment of state water quality standards (with increased sample frequency to match state standard operating procedures)

• Relate water quality conditions to observed biological condition as measured by benthic macroinvertebrates. Such correlations help to identify biologically relevant stressors (i.e., those water quality indicators that might be related to degraded biological conditions)

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• Relate water quality conditions to land uses or permitted activities in a correlative assessment to inform adaptive management

Indicators pH

A. Description and Applications: pH is a measure of hydrogen ions -log[H+] in a solution such as stream water. The more hydrogen ions in a solution the more acidic the solution, and the fewer hydrogen ions the more alkaline the solution. The acidification or alkalinification of aquatic systems can be detrimental to both the biota and ecosystem processes. Acidification can act as both a direct (e.g., reduction of survival rates) and indirect (e.g., mobilization of toxic metals) toxin to aquatic organisms, leading to reductions in species richness and density (Allan and Castillo 1995). Leaf litter breakdown rates, one of the primary energy sources of lotic food webs, are also known to decrease as pH decreases (Burton et al. 1985) due mainly to a reduction in microbial respiration and decreased number of invertebrate shedders (Dangles et al. 2004). Runnoff from anthropogenic activities such as mining and logging, and deposition of air pollutants from burning of fossil fuels can decrease the pH of surface waters leading to a reduced quality of aquatic resources (Allan and Castillo 1995). Acidification of aquatic systems is more commonly a concern than high alkalinity. High alkalinity can be caused by elevated concentrations of bicarbonate and/or increased photosynthetic activity, with pH varying diurnally by as much as 2.0 standard units. Therefore, high alkalinity during the daytime can be indicative of productive systems with high nutrient levels (Allan and Castillo 1995).

B. Indicator Computations: For pH, no indicator computations are required, as what one measures in the field is what is reported (Table 1).

Specific Conductance A. Description and Applications: Conductivity measures the capacity of water to conduct

an electrical charge. Distilled water contains very low levels of ions and is a poor conductor of electricity, which results in a low specific conductance (i.e., conductivity standardized to 25oC). Specific conductance increases with the concentrations of ions in solution (e.g., nitrates, chloride, phosphate, magnesium, calcium, iron) and concentrations can be elevated by anthropogenic activities that increase erosion and/or ion loading (e.g., irrigation water withdrawals, mining, grazing) (Miller et al. 2007, Vander Laan et al. 2013). Excessive conductivity degrades the quality of domestic and/or animal drinking water and can impact freshwater organisms through acute toxicity or less dramatically through disrupting osmoregulation. Altered osmoregulation can decrease organismal fitness and/or change species distribution ranges (Blasius and Merritt 2002, Miller et al. 2007, Vander Laan et al. 2013).

B. Indicator Computations: For specific conductance, no indicator computations are required, as what one measures in the field is what is reported (Table 1).

Water Temperature

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A. Description and Applications: Instantaneous temperature can be used to identify priority systems for continuous temperature monitoring and to provide context for other water quality indicators. However, to quantify the thermal regime and its viability for aquatic organisms, continuous temperature monitoring is needed. The thermal regime is among the most important abiotic drivers of biological patterns and processes in river systems (Vannote and Sweeney 1980). Over evolutionary time, organisms have evolved strategies to maximize fitness in response to different thermal regimes (Ward and Stanford 1982), while in the short term temperature constrains the distribution, development, and growth of aquatic organisms (Hauer and Benke 1987, Newbold et al. 1994). Consequently, activities such as grazing, dams, and water diversions that alter thermal regimes represent primary threats to lotic ecosystems (Dynesius and Nillson 1994).

B. Indicator Computations: For instantaneous temperature, no indicator computations are required, as what one measures in the field is what is reported (Table 1). For continuous temperature, a variety of indicators can be computed. Some of the more biologically relevant indicators include maximum daily temperature, maximum summer temperature, 7-day average of the maximum daily, and number of days above organismal thresholds such as 28°C for many trout species. Even if continuous temperature was not directly measured, models are available to predict site specific temperature based on other publicly available field data. AquADat reports site specific prediction of the 19 year mean August stream temperature for the period of 1993 – 2011, as derived from NorWest models (Isaak et al. 2016 https://doi.org/10.2737/RDS-2016-0033.)

Total Nitrogen and Phosphorous A. Description and Applications: Nitrogen and phosphorous are the two major nutrients

that influence rates of primary productivity in stream systems. The major natural sources of nitrogen in streams are derived from N-fixing soil microbes and decomposing vegetation entering the stream through runoff and groundwater inputs (reviewed in Allan and Castillo 2007). In contrast to nitrogen, the predominant natural phosphorous source is the weathering of soils and rocks, particularly the weathering of sedimentary rocks. For both nitrogen and phosphorous, anthropogenic activities such as logging, cattle grazing, accelerated erosion, and agriculture can significantly increased nutrient loading (reviewed in Allan and Castillo 1995), making it among the most ubiquitous freshwater stressors in the United States (Paulsen et al. 2008b). Excess nutrient loading can have adverse impacts on other water quality indicators and biological assemblages, as well as changing ecosystem processes such as food web structure and function (Citations). For example, excess nutrient loading can reduce dissolved oxygen concentrations through increased decomposition rates resulting from increased primary productivity (Citations)

B. Indicator Computations: All grab samples are sent to the Aquatic Biogeochemistry Lab at Utah State University for analysis. Lab Standard Operating Procedures can be found at http://canoeecology.weebly.com/uploads/2/1/0/0/21002098/abl_analytical_lab_manual.pdf. Detection limits are 25 µg/L and 10 µg/L for total nitrogen and phosphorus,

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respectively. Besides lab analysis, no indicator computations are required, as what one measures in the lab is what is reported (Table 1).

Turbidity

A. Description and Applications: Turbidity is a measure of the extent to which light is scattered when transmitted through water and can provide an approximation of suspended sediment loads. High suspended sediment loads and low water clarity can reduce the quality of drinking water for domestic livestock and impair habitat quality for aquatic organisms. For example, high suspended sediment loads can directly influence biota through gill abrasion, smothering of eggs, or a reduction in foraging efficiency (Henley et al. 2000). Indirectly, high suspended sediment loads can reduce photosynthetically active radiation, which in turn reduces rates of primary productivity and can have cascading effects within aquatic food webs (Allan and Castillo 1995).

B. Indicator Computations: Three turbidity measurements are taken in the field using a turbidimeter and then averaged to get the reported value.

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Watershed Function – Instream Habitat

Background Assessments of the physical functioning of stream and river systems are a major component of the BLM’s fundamentals of land health (43 CFR 4180.1). The watershed function fundamental calls for assessments of whether channel form and function are characteristic for the region (i.e., proper functioning condition), while the habitat fundamental requires the maintenance or improvement of aquatic habitat for threatened and endangered, proposed or candidate threatened and endangered, and other special status species (i.e., similar to the maintenance of cold- or warm-water fisheries under the Clean Water Act). To assess the condition and trend of instream habitat, lotic AIM identifies five core methods (pool depth, length, frequency; streambed particle sizes; bank stability and cover; floodplain connectivity; and large woody debris) and four contingent methods (bank angle, pool tail fines, ocular estimates of instream habitat complexity, and a thalweg depth profile) from which a variety of indicators can be computed (Table 1). Specifically, the indicators characterize five physical habitat attributes:

1. Habitat type and volume o Percent pools (length) o Number of pools in the reach o Pool frequency o Residual pool depth o Thalweg mean depth o Percent dry (length)

2. Habitat complexity and cover for aquatic biota o Thalweg depth CV o Instream habitat complexity o Large wood frequency o Large wood volume

3. Bed stability and the size of streambed particles as substrates for aquatic biota o Percent fines (<2 mm and <6 mm) o D50, D16, D84 o Geometric mean diameter o Pool tail fines o Relative bed stability

4. Bank type and condition o Bank cover- Percent cover of bedrock, cobble, large wood, or vegetation o Bank stability o Banks stable and covered o Bank angle

5. Floodplain interaction and channel dimensions o Bankfull height o Floodplain height o Floodplain connectivity o Channel incision

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Example Applications (the big picture) • Assess attainment of biological opinion conditions, land health standards, or other policy

standards for physical habitat • Relate physical habitat conditions to observed biological condition as measured by

benthic macroinvertebrates. Such correlations help to identify biologically relevant stressors (i.e., degraded physical habitat conditions that might be related to degraded biological conditions, such as excessive fine sediment loading)

• Relate physical habitat conditions to land uses or permitted activities in a correlative assessment to inform adaptive management

• Assess habitat viability for threatened, endangered, or other species of management concern

Stream Channel Form, Function, and Habitat Indicators Pool depth, length, and frequency

Description and Applications: Stream habitat can generally be classified into “riffles” and “pools” based on the morphological and hydraulic properties of the channel. Riffles are shallow, fast-water habitats, while pools are deep, concave shaped, slow-water habitats (Hawkins et al. 1993). In general, greater frequency of pool-riffle sequences can increase hydraulic and geomorphic complexity, thereby and increasing biological diversity (Citation). Due to the differences in hydraulic properties, riffles and pools generally have different sediment characteristics, as well as different water retention times and nutrient processing rates (Citations). From a biological perspective, riffles provide ideal habitat for benthic macroinvertebrates and spawning fishes due to the coarse bed material, lack of fine sediment, highly oxygenated water, and increased primary productivity (Citations). Subsequently, riffles are rich in food resources for fish. However, pools can provide important habitat for fishes, as deep water habitats can act as refugia from predators and fish expend less energy to swim in these slower water habitats. Additionally, pools can provide important refugia for invertebrates and fish during low flow periods when sections of streams may dry up (Citations). In many streams, anthropogenic activities have decreased the prevalence of the physical mechanisms that create and maintain pool habitat, thereby reducing the amount and quality of pool habitat. Pools are typically created by one of three mechanisms: the vertical force of water falling down over logs and boulders, high flow events that scour out sediment, or beaver activity (Archer et al. 2015). Decreased amounts of large trees and riparian vegetation within a watershed decreases the supply of pool-forming woody debris. Many human activities can increase fine sediment loading to streams, leading to increased deposition of fine sediment in pools and reduced pool habitat cover, complexity, and quality. Additionally, fine sediment can eventually fill in pools thereby reducing pool habitat availability. Flow reduction and alteration resulting from drought, water withdrawals, and dams can decrease the scouring ability of streams (Allan and Flecker 1993, Muotka and Syrjänen 2007). Beaver activity can be a significant process supporting pool formation, but beaver are considered a nuisance for human infrastructure, have been trapped for their pelts, and populations have been drastically reduced across the landscape. Recent research indicates that beaver population reduction has severely

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altered the physical structure of many streams across the West resulting in reduced pool frequency and quality (Citations). From both the core field methods of pools and the contingent field methods of thalweg depth profiles, we can compute indicators that characterize the amount of pool habitat (e.g., percent pools, residual pool depth, and pool frequency) and thereby also characterize the habitat and hydraulic complexity. Core methods use Archer et al. (2015)’s approach and define pools in the field, while contingent methods use the approach taken by Kaufmann et al. (1999) and use thalweg depth measurements and slope as a covariate to define pools post-hoc. Defining pools post-hoc is very difficult unless specific depth criteria can be easily applied and interpreted. Therefore, we recommend only using the contingent method of thalweg depth profile to characterize bed heterogeneity and to compute relative bed stability, rather than to characterize amount of pool habitat. The pool indicator computations below are only for the core method following Archer et al. (2015).

A. Indicator Computations: Pool habitat within the reach can be described by three indicators: percent pools, residual pool depth, and pool frequency. If there are no pools found in the reach, then Residual Pool Depth= NA, Pool Frequency= 0, and Percent Pools= 0.

• Percent Pools Percent of the sample reach (linear extent) classified as pool habitat. The lengths of all the pools in the reach are summed and divided by the reach length that was surveyed for pool habitat. The result is multiplied by 100 to express the value as a percent.

𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃 = ∑ 𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃ℎ(𝑚𝑚)𝑝𝑝𝑛𝑛𝑝𝑝=1

𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑃𝑃𝑆𝑆𝑃𝑃𝑆𝑆𝑆𝑆𝑃𝑃𝑆𝑆𝑃𝑃ℎ𝑃𝑃𝑃𝑃𝑃𝑃 (𝑚𝑚)∗ 100

Where p= 1….n and n is the total number of pools in the reach, SurveyedReachLen is the total field determined reach length.

If there is interrupted flow at a site, crews survey only the flowing section of the reach and record how much of the reach was flowing as the SurveyedReachLen. If the crew did not fill in SurveyedReachLen for sites that had interrupted flow or were partially sampled, PctPools is reported as “NA”.

• Number of pools in the reach A simple count of pools in the reach

• Pool frequency Frequency of pools in the reach (# pools/km)

𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑆𝑆𝑃𝑃𝑃𝑃 =𝐶𝐶𝑃𝑃𝑆𝑆𝑃𝑃𝑃𝑃 𝑃𝑃𝑜𝑜 𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃 𝑖𝑖𝑃𝑃 𝑃𝑃ℎ𝑃𝑃 𝑆𝑆𝑃𝑃𝑆𝑆𝑃𝑃ℎ 𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑃𝑃𝑆𝑆𝑃𝑃𝑆𝑆𝑆𝑆𝑃𝑃𝑆𝑆𝑃𝑃ℎ𝑃𝑃𝑃𝑃𝑃𝑃 (𝑚𝑚)

∗ 1000

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If there is interrupted flow at a site, crews survey only the flowing section of the reach and record how much of the reach was flowing as the SurveyedReachLen. If the crew did not fill in SurveyedReachLen for sites that had interrupted flow or were partially sampled, PoolFreq is reported as “NA”.

• Residual Pool Depth

Residual pool depth is a flow-independent measure of pool depth. Residual pool depth is calculated for each pool by subtracting the pool tail depth from the max depth.

𝑀𝑀𝑆𝑆𝑀𝑀𝑀𝑀𝑃𝑃𝑀𝑀𝑃𝑃ℎ𝑝𝑝 − 𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑆𝑆𝑖𝑖𝑃𝑃𝑀𝑀𝑃𝑃𝑀𝑀𝑃𝑃ℎ𝑝𝑝 = 𝑆𝑆𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑀𝑀𝑃𝑃𝑀𝑀𝑃𝑃ℎ𝑝𝑝 Residual pool depths for each pool are then averaged across all pools in the reach.

𝑆𝑆𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑀𝑀𝑃𝑃𝑀𝑀𝑃𝑃ℎ = 1𝑃𝑃 �𝑆𝑆𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑀𝑀𝑃𝑃𝑀𝑀𝑃𝑃ℎ𝑝𝑝

𝑛𝑛

𝑝𝑝=1

Where p= 1…...n and n is the total number of pools in the reach Thalweg

A. Description and Applications: The thalweg is the deepest part of the stream containing the most flow. The thalweg depth profile is a contingent method and indicators computed from thalweg can be used as measures of habitat volume, connectivity, and complexity. Thalweg depths can also be used as a covariate in the computation of other indicators including residual pool depth, length, and, frequency as well as relative bed stability (see above). Mean thalweg depth can be a general descriptor of average maximum depth of the stream at low-flow. Thalweg depths are systematically spaced measurements that also can be used to compute the percent of the reach that is dry. The coefficient of variation of thalweg depths can be used as a measure of stream bed heterogeneity and thereby habitat complexity. Indicators computed from thalweg depths include mean thalweg depth, percent of the reach that is dry, and thalweg depth CV.

B. Indicator Computations: • Mean Thalweg Depth: The mean of all thalweg depths collected. The number of

thalweg depths varies depending on the bankfull width. If thalweg measurements are missing, measurements are graphically interpolated; however, this interpolation has not been completed so at the present only sites with complete dataset have mean thalweg depth reported.

• Percent Dry: The percent of the reach that is dry is computed as the number of dry thalweg measurements divided by the total number of thalweg measurements taken.

• Thalweg Depth CV: The thalweg depth coefficient of variation is computed as

standard deviation of thalweg measurements divided by the mean thalweg depth. Thalweg CV is not computed for sites with any dry thalwegs because zeros could artificially inflate the CV.

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Instream Habitat Complexity A. Description and Applications: The complexity of instream habitat contributes to

biodiversity, energy dissipation during high flow events, habitat refugia during extreme conditions (i.e., high and low flow), and stream processes (e.g., nutrient retention) (Solazzi et al. 2000, Ward et al. 2002). Historic and current anthropogenic land use such as logging, splash dams, and water withdraw can decrease habitat complexity and volume by reducing inputs of large woody debris, channelizing streams, and reducing water levels and channel-forming flow (Allan and Flecker 1993, Muotka and Syrjänen 2007). Only one indicator is computed from this contingent method: an aggregate estimate of total instream habitat complexity, with an emphasis on cover provided for fish. This indicator is semi-quantitative because it is based on ocular estimates. It was included as a contingent indicator in the 2017 version of TR 1735-2 to complement more quantitative assessments of habitat complexity such as large wood frequency and volume. However, after doing two repeatability studies on precision of this indicator, this indicator was omitted from the 2020 version of TR 1735-2 because it has poor repeatability (see here for more information). Comprehensive instream habitat multimetric indexes based on the quantitative assessments of habitat complexity are in development but may vary in geographic applicability. Until these indexes are available the historic ocular estimates of instream habitat complexity will remain in AquADat but no new data is being collected using this ocular estimate method and condition assessments based on this indicator should be done with caution.

B. Indicator Computations: This indicator is an aggregate measure of average cover provided by boulders, overhanging vegetation, live trees and roots, large wood > 0.3 m diameter, small woody debris < 0.3 m diameter, and stream banks for stream fishes measured at 11 plots. In the field, crews assess cover using five cover categories: 0 = absent 0%, 1 = sparse: <10%, 2 = moderate: 10-40%, 3 = heavy: 40-75%, and 4 = very heavy >75% (Table 2). For analysis, the categories are converted to the mid-point of each category (0%, 5%, 25%, 57.5%, and 87.5%, respectively) and then converted to proportional cover (0.05, 0.25, 0.575, and 0.875 respectively). The proportional cover for each individual cover type (e.g., boulders) is averaged across all transects, and then these averages are summed across the six cover types (Table 3). Note that crews collect proportional cover of aquatic macrophytes, filamentous algae, and artificial structures but following Kaufmann et al. (1999) these cover values are not included in the aggregate instream-habitat complexity indicator.

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Table 2. Example raw instream habitat data from one site. Values represent cover classes ranging from 0 (absent) to 4 (>75% cover). TRANSECT BOULDER OVER_VEG TREE_ROOT LWD SWD BANKS

A 1 0 2 1 0 0 B 0 0 0 0 0 1 C 3 0 0 0 0 0 D 3 1 0 0 0 0 E 2 0 0 0 0 0 F 1 0 0 0 1 0 G 0 0 0 0 0 0 H 0 0 0 0 0 0 I 0 0 0 1 0 0 J 0 4 0 0 0 0 K 0 0 0 0 0 0

Table 3. Example calculation of instream habitat complexity from the raw data in Table 2. Table values are the mid-points of the five cover categories. The final instream habitat complexity for the example sample reach is 0.26, which is the sum of the average among cover categories. TRANSECT BOULDER OVER_VEG TREE_ROOT LWD SWD BANKS

A 0.05 0 0.25 0.05 0 0 B 0 0 0 0 0 0.05 C 0.575 0 0 0 0 0 D 0.575 0.05 0 0 0 0 E 0.25 0 0 0 0 0 F 0.05 0 0 0 0.05 0 G 0 0 0 0 0 0 H 0 0 0 0 0 0 I 0 0 0 0.05 0 0 J 0 0.875 0 0 0 0 K 0 0 0 0 0 0 SUM

AVERAGE 0.14 0.08 0.02 0.01 0.00 0.00 0.26 Large Wood

A. Description and Applications: Large wood is an important source of cover and velocity break for aquatic organisms such as fishes and amphibians (Whiteway et al. 2010; others). Geomorphically, Large wood plays a critical role in the creation and maintenance of complex geomorphic channel units such as pools and in the local storage of bed sediments (Montgomery et al. 1995; others). The relative role of large wood as habitat and in structuring channel morphology varies geographically as a function of climate and the capacity of an ecosystem to support tree growth. Despite such limitations, even small wood, live trees, shrub, or roots can create geomorphic heterogeneity and provide habitat for a diversity of aquatic organisms. Human activities such as timber harvest, grazing,

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and channelization can limit the production, recruitment, and storage of large wood within a stream or river system.

Large wood can serve as both an indicator and a covariate since it can provide critical habitat for aquatic organisms and influence geomorphic conditions. Regardless of the application, Large wood computations are the same.

B. Indicator and Covariate Computations: Large wood is defined as wood that is greater than 0.1 m in diameter for at least 1.5 m in length. Large wood is binned into size categories (see large wood volume below) based on whether it was 1) within the bankfull channel or 2) bridging above bankfull channel. Pieces of large wood that are considered “qualifying” are tallied for each size category and location. The number, size, and volume of large wood in each reach are computed from the large wood tallies. Specifically: the following indicators are computed:

• Large Wood Frequency: The number of pieces of large wood is counted, and this value is divided by the length of the reach surveyed for large wood. The resulting number is multiplied by 100 to get pieces per 100 m so that the number is approximately the number of pieces of wood within a standard reach length. All large wood size classes are included, but only large wood within the bankfull channel is included. The length of the reach surveyed for large wood is determined by counting the number of transects for which large wood was assessed at and then multiplying by the distance between transects.

𝑃𝑃𝐿𝐿𝑀𝑀_𝑃𝑃𝑆𝑆𝑃𝑃𝑃𝑃 = (100 𝑚𝑚𝑃𝑃𝑃𝑃𝑃𝑃𝑆𝑆𝑃𝑃)�∑ 𝐶𝐶𝑃𝑃𝑆𝑆𝑃𝑃𝑃𝑃𝑃𝑃𝐿𝐿𝑀𝑀𝑛𝑛 𝑝𝑝=1 𝑝𝑝

𝐶𝐶𝑃𝑃𝑆𝑆𝑃𝑃𝑃𝑃𝑃𝑃𝑆𝑆𝑆𝑆𝑃𝑃 ∗ 𝑃𝑃𝑃𝑃𝑃𝑃𝑆𝑆𝑃𝑃𝑆𝑆𝑃𝑃ℎ𝑃𝑃𝑃𝑃𝑃𝑃10

�

Where CountLWDp is the number of pieces of wood of size class “p”, Count Tran is the number of transects large wood was collected at, and TotReachLen is the total reach length in meters specified by the protocol based on average bankfull width for the reach.

• Large Wood Volume: Volume of large wood within the bankfull channel in the

reach expressed as m3/100 m. Where volume of each size class of large wood is computed using the following equation as taken from Robison (1998):

𝑉𝑉𝑃𝑃𝑃𝑃𝑆𝑆𝑚𝑚𝑃𝑃 = 𝜋𝜋 ��0.5 �𝑀𝑀𝑖𝑖𝑃𝑃𝑀𝑀𝑖𝑖𝑆𝑆𝑚𝑚𝑃𝑃𝑃𝑃𝑃𝑃𝑆𝑆 + 𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀−𝑀𝑀𝑀𝑀𝑛𝑛𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀3

��2� �𝑀𝑀𝑖𝑖𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃ℎ + 𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑛𝑛𝑀𝑀𝑀𝑀ℎ−𝑀𝑀𝑀𝑀𝑛𝑛𝑀𝑀𝑀𝑀𝑛𝑛𝑀𝑀𝑀𝑀ℎ

3�

Where MinDiameter and MaxDiameter are one of the following large wood diameter categories (large end):

MinDiameter (m) MaxDiameter (m) 0.1 0.3 0.3 0.6 0.6 0.8 0.8 2

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And MinLength and MaxLength are one of the following large wood length categories (considering the section of the large wood where the diameter is greater than 0.1 m):

MinLength (m) MaxLength (m) 1.5 3 3 5 5 15 15 30

The total volume of large wood is determined by multiplying the volume of each size class by the number of pieces observed within that size class and then summing across all size classes.

𝑃𝑃𝐿𝐿𝑀𝑀_𝑉𝑉𝑃𝑃𝑃𝑃 = �𝐶𝐶𝑃𝑃𝑆𝑆𝑃𝑃𝑃𝑃𝑃𝑃𝐿𝐿𝑀𝑀𝑝𝑝 ∗ 𝑉𝑉𝑃𝑃𝑃𝑃𝑆𝑆𝑚𝑚𝑃𝑃𝑛𝑛

𝑝𝑝=1

Where CountLWDp is the number of pieces of large wood of a particular size class and p= 1…...n and n is the total number of large wood size classes.

Streambed Particle Sizes

A. Description and applications: The size of particles composing streams and river beds plays a critical role in the type, diversity, and abundance of organisms inhabiting a given system (reviewed in Allan and Castillo 1995). For example, benthic macroinvertebrates are in close contact with the streambed during the egg, larvae, pupae, and in some cases adult life stages. Streambed particles and their interstices provide invertebrates cover from predators, a wide range of water velocities, substrate for algal growth, retention of organic matter, and attachment surfaces for feeding, matting, and egg laying (Citations). These same functions are provided to other aquatic organisms such as aquatic macrophytes, amphibians, and fishes (Citations). Consequently, bed particle size, type, and diversity are frequently measured to determine the habitat suitability for aquatic organisms.

Habitat suitability for aquatic organisms across BLM lands is frequently assessed in terms of the amount of fine sediment, average particle size, and the diversity of particles sizes available. Excessive fine sediment is among the most deleterious stressors to aquatic biota (Wood and Armitage 1997, Paulsen et al. 2008b, Bryce et al. 2010). Fine sediment can reduce food resource availability for benthic organisms (Henley et al. 2000), decrease benthic egg survival (Bjornn and Reiser 1991), and decrease habitat quality by filling interstical spaces, which are important micro-habitats for macroinvertebrates and smaller fishes (Cunjak and Power 1986, Gries and Juanes 1998). The average or diversity of available substrate sizes can influence the diversity and abundance of aquatic organisms (Citations). For these reasons, fine sediment is one of the dominant indicators used by the BLM to assess the quality of the streams or rivers. Similarly, we often seek to determine whether the sediment supply and transport of a stream or river is in balance (i.e., which helps determine whether fine sediment levels

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are natural or the result of poor land management). To assess this, we report on relative bed stability (See more detailed description below).

B. Indicator Computations: Six indicators summarize particle size distribution within a

sample reach (percent fines2, percent fines6, D16, D50, D84, geometric mean particle diameter) and are computed from the 210 substrate particle measurements taken from the active channel, defined as scour line on one side of the stream to scour line on the other side (10 per each of 21 transects)(Table 1). These values are computed to characterize substrate conditions within the entire sample reach, and because, in most instances, sample reaches are randomly located and transects are systematically spaced, the descriptive statistics can be interpreted as unbiased representations of substrate conditions.

The measured particle sizes are used to calculate the substrate indicators by ordering the substrate particles from smallest to largest and computing:

• Percent fines2 = percent of measured particles with a b-axis finer than 2 mm. This indicator is needed to calculate Al-Chokhachy et al. (2010) index of physical habitat condition for streams in the Columbia River basin.

• Percent fines6 = percent of measured particles with a b-axis finer than 6 mm.

• D16 = particle size corresponding to the 16th percentile of measured particles

• D50 = particle size corresponding to the 50th percentile of measured particles

• D84 = particle size corresponding to the 84th percentile of measured particles

• Geometric mean diameter= another measure of central tendency similar to that of the D50, but it is more heavily influenced by fine particle size classes. For example, Faustini and Kaufmann (2007) found that the geometric mean was, on average, half as large as the D50 at a site. While a D50 is a more commonly reported indicator for characterizing geomorphic and sediment transport, geometric means may be more biologically relevant because they are skewed towards fine sediment. For example, Kaufmann and Hughes (2006) found that the geometric mean was a significant covariate for a fish based index of biotic integrity. The geometric mean is computed as:

Geometric mean diameter =𝑃𝑃∑ log10 𝐵𝐵 𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 𝑃𝑃𝑎𝑎𝑃𝑃𝑃𝑃𝑎𝑎𝑃𝑃𝑃𝑃𝑃𝑃 𝑆𝑆𝑎𝑎𝑆𝑆𝑃𝑃𝑎𝑎𝑛𝑛𝑎𝑎=1

𝑛𝑛 Where: N = number of measured substrate particles X = individual particle b-axis size

Measured particles used in the above calculations include hardpan and bedrock, which are given values of 4098 mm and 4097 mm respectively; however, organic particles are excluded from the above calculations. Field protocol for data from 2013 differed significantly for stream bed particle sizes and should be compared with following years’

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data with caution. Specifically, particles were collected only within the wetted channel, particles were binned into size categories rather than measured, and only 110 particles were collected.

Pool Tail Fines

A. Description and Applications: Applications are similar to those listed above for substrate size but this indicator specifically quantifies the percent fine sediment in pool tails which are ideal fish spawning habitat. This contingent indicator is an important component of Al-Chokhachy et al's. (2010) index of physical habitat condition for streams in the Interior Columbia River basin, and therefore this contingent indicator should be collected if computation of this index is desired.

A. Indicator Computations: Pool tail fines is quantified using a 0.36m x 0.36m grid with 50 intersections placed at 3 locations along each pool tail (up to 10 pools). The percentage of particles <2 mm and <6 mm is calculated for each grid, averaged for each pool, then averaged for all pools within the reach.

𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑆𝑆𝑖𝑖𝑃𝑃𝑃𝑃𝑖𝑖𝑃𝑃𝑃𝑃𝑃𝑃2 =1𝑃𝑃𝑀𝑀�

𝑃𝑃𝑆𝑆𝑆𝑆𝑃𝑃𝑖𝑖𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃 < 2𝑀𝑀50 −𝑁𝑁𝑃𝑃𝑃𝑃𝑀𝑀𝑃𝑃𝑆𝑆𝑃𝑃𝑆𝑆𝑆𝑆𝑆𝑆𝑁𝑁𝑃𝑃𝑃𝑃𝑀𝑀

𝑀𝑀

𝑀𝑀=1

Where: g=1..i, and i is the number of grids collected at each pool tail, the max of i

is 3. Particles <2g is the count of the number of intersections with particles less than 2 mm. NonMeasurableg is the count of the number of intersections at grid (g) that are non-measurable (e.g. bedrock or organic matter).

The same calculation is used for PoolTailFines6 but using the number of intersections that had sediment < 6 mm present.

Relative Bed Stability (can only be computed if thalweg depth profile was collected)

A. Description and Applications: The size distribution of bed particles naturally varies across the landscape as a function of lithology, climate, watershed size, and slope (Wood and Armitage 1997). However, anthropogenic activities such as roads, dams, mining, logging, and grazing can increase fine sediment loading to streams. Imbalances in the supply and transport of bed sediments can alter channel structure and function, which in turn degrades the physical habitat for stream biota. A measure of stream bed stability is used to assess the degree to which sampled streams are in balance with the sediment and discharge regimes, (Kaufmann et al. 2008, 2009). Relative bed stability measures the competence of a stream to transport bed sediments given the stream size, slope, discharge and roughness. Values less than zero indicate a streambed consisting of particles that are finer and more mobile than would be expected, whereas values greater than zero indicate coarser or more stable bed particles than would be expected under natural conditions.

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B. Indicator Computations: Relative bed stability is calculated as the ratio of the median

observed substrate diameter (approximated by the geometric mean diameter) divided by the reach average for the largest particle that is mobile during bankfull flow. The calculation of the reach average for the largest particle that is mobile during bankfull flow indicator requires prior computation of the following indicators and covariates: percent slope, bankfull hydraulic radius as approximated by average bankfull width and height and corresponding thalweg depths, and measures of hydraulic roughness as approximated by pools delineated from the thalweg depth profile and LWD. Due to the computational intensity of this indicator, values are not currently available but will be available in the future for all sites that collected thalweg profile depth as a contingent method.

Bank Stability and Cover A. Description and Applications: Bank cover and stability measurements assess the

susceptibility of stream banks to both natural and accelerated erosion rates associated with anthropogenic activities. Streambank erosion overwhelmingly occurs during bankfull discharge events, which is the discharge associated with greatest amount of sediment transport and that has the largest influence on channel morphology (Wolman and Miller 1960). Anthropogenic activities that increase stream power (e.g., flow alteration, improperly sized culverts) or alter the composition and cover of stabilizing vegetation can increase bank erosion rates (Knapp and Matthews 1996, Coles-Ritchie et al. 2007, Herbst et al. 2012). Stream bank erosion is a source of fine sediment loading and channel widening. Elevated fine sediment loading can reduce the suitability of the stream bottom environment for aquatic organisms such as amphibians, macroinvertebrates, and fishes (Henley et al. 2000). Bank erosion can also alter channel morphology and subsequent habitat quality (e.g., width:depth ratios) through changing the balance between the sediment and water supply and thus the transport competence of a stream or river (citations). The composition and cover of vegetation and other stabilizing features such as large woody debris and boulders significantly influences streambank erosion. Therefore, the AIM protocol quantifies both streambank cover and the presence of any erosional features (e.g., fractures, slumps, sloughs).

B. Indicator Computations: The bank cover and stability field measurements from each of the 21 transects (42 plots) within a sample reach are used to compute the following three indicators following MIM (Multiple Indicator Monitoring) guidance:

a. Bank cover: the number of plots on both erosional and depositional banks classified as ‘covered’ are divided by the total number of plots and expressed as a percent. Cover constituents include perennial vegetation, wood greater than 10 cm in diameter, bedrock, and mineral substrate with a b-axis greater than 15 cm. A plot with greater than 50% cover from any one or a combination of these cover categories is considered ‘covered’. Note that in 2019 the AIM lotic protocol was switched to assess foliar cover rather than basal cover. Bank percentages computed using the two different cover methods are reported as separate indicators.

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b. Bank stability: the number of plots classified as ‘stable’ are divided by the total number of plots and expressed as a percent. The ‘stable’ designation results from the absence of erosion features (i.e., fracture, slump, slough, or eroding) on erosional banks or from covered depositional banks (Table 4).

c. Banks stable and covered: the number of plots that met both the ‘covered’ and ‘stable’ criteria, expressed as a percent.

Table 4. Possible bank stability ratings based on field data

Bank Type Cover category Erosional Feature Stability Rating

Erosional Covered Fracture, Eroding, Slump, or Slough Unstable

Erosional Covered Absent Stable Erosional Uncovered Absent Stable

Erosional Uncovered Fracture, Eroding, Slump, or Slough Unstable

Depositional Covered NA Stable Depositional Uncovered NA Unstable

In addition to these three indicators, we also report the observed aerial coverage for each of the four cover constituents. Aerial coverage by constituent is provided for practitioners to understand what contributed to a ‘covered’ or ‘uncovered’ rating.

a. Bedrock: average percent cover by bedrock among the plots. b. Cobble: average percent cover by cobble among the plots. c. Large woody debris: average percent cover by large woody debris among the

plots. d. Foliar cover by perennial vegetation: average percent foliar cover by perennial

vegetation among the plots. e. Basal cover by perennial vegetation: average percent basal cover by perennial

vegetation among the plots.

Bank Angle A. Description and Applications: Bank angle is the angle of the bank in degrees, as defined

by Archer et al. (2015). Bank angle can be used in conjunction with bank stability information to assess the natural erosional progression of banks from ≥ 90° to undercut ≤ 90°, and back to ≥ 90° (Fig. 3). Outside of sand-bed systems, streams should have some undercut banks, and these undercut banks can provide important fish cover and habitat. In contrast, an increase in bank angle (i.e., more laid back banks) may results from alterations to vegetative composition and cover by livestock trampling (Kauffman and Krueger 1984; Platts 1991), hydrologic alterations associated with roads (Furniss et al.

27

1991), or the destabilizing effects of forest harvest (Dose and Roper 1994). This contingent indicator is an important component of Al-Chokhachy et al's. (2010) index of physical habitat condition for streams in the Interior Columbia River basin, and therefore this contingent indicator should be collected if computation of this index is desired.

Figure 3. The general shape of obtuse (A) and acute (B) banks.

B. Indicator Computations: All bank angles <45 degrees are changed to 45 degrees following Archer et al. (2015). Then all bank angles are averaged across all banks and transects.

Floodplain Connectivity and Channel Incision

A. Description and Applications: The connectivity or access of a stream channel to its floodplain is critical for the maintenance and recruitment of riparian vegetation, the dissipation of energy during high flow events, and the creation of seasonal habitats during inundation (Naiman and Decamps 1997). Anthropogenic activities that alter the sediment and/or hydrologic regime or directly manipulate the stream channel can decrease the connectivity between streams and their adjacent floodplains (Citations). The floodplain connectivity and channel indicators are used to assess differences between bankfull height and that of the first flat depositional feature at or above bankfull. Channel incision is the difference between average floodplain height from the water’s surface and average bankfull height from the water’s surface. Floodplain connectivity (also known as Rosgen's Bank Height Ratio) measures the difference between these two surfaces but also takes into account water depth and therefore provides a better characterization of channel dimensions and access to the floodplain. For both indicators, when the difference between bankfull and floodplain heights is minor, the stream has access to the floodplain during annual or semi-annual high flow events and floodplain functionality is not expected to deviate from potential natural conditions. However, when the height of the first flat depositional feature significantly exceeds that of the bankfull elevation because of down-cutting or bed degradation, the frequency and duration of inundation are reduced and floodplain functionality deviates from potential natural conditions. It is important to note that naturally confined or high gradient systems are not expected to support floodplains and do not require floodplains for proper functionality. For such systems, the indicators are designed to set measured bankfull and floodplain heights to be one in the same and the system is considered to be properly functioning. Similarly, the flood-prone width covariate is used to assess the degree of valley confinement, the potential of the system to support a floodplain, and the potential extent of the system’s active floodplain.

B. Indicator Computations: • Bankfull height: average of 11 bankfull elevation heights measured from the

water’s surface • Floodplain height: average of 11 floodplain elevation heights measured from the

water’s surface

28

• Channel incision: In plain text, channel incision is the difference between average floodplain height from the water’s surface and average bankfull height from the water’s surface. Computationally,

Where: n = number of sampled bankfull and floodplain heights

• Floodplain connectivity: Floodplain connectivity is also known as Rosgen's Bank Height Ratio. It is similar to channel incision but takes into account water depth and therefore provides a better characterization of channel dimensions and access to the floodplain. Floodplain height and thalweg depth are added together for each transect. Similarly bankfull height and thalweg depths are added together for each transect. Then the ratio of these sums are calculated for each transect. Lastly the ratios are averaged across all 11 transects. Computationally,

𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑆𝑆𝑀𝑀𝑃𝑃𝑆𝑆𝑖𝑖𝑃𝑃 𝐶𝐶𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑖𝑖𝑆𝑆𝑖𝑖𝑃𝑃𝑆𝑆 =∑ 𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑆𝑆𝑀𝑀𝑃𝑃𝑆𝑆𝑖𝑖𝑃𝑃𝐹𝐹𝑃𝑃𝑖𝑖𝑃𝑃ℎ𝑃𝑃𝑀𝑀 + 𝑃𝑃ℎ𝑆𝑆𝑃𝑃𝑎𝑎𝑃𝑃𝑃𝑃𝑀𝑀𝑃𝑃𝑀𝑀𝑃𝑃ℎ𝑀𝑀

𝐵𝐵𝑆𝑆𝑃𝑃𝐵𝐵𝑜𝑜𝑆𝑆𝑃𝑃𝑃𝑃𝐹𝐹𝑃𝑃𝑖𝑖𝑃𝑃ℎ𝑃𝑃𝑀𝑀 + 𝑃𝑃ℎ𝑆𝑆𝑃𝑃𝑎𝑎𝑃𝑃𝑃𝑃𝑀𝑀𝑃𝑃𝑀𝑀𝑃𝑃ℎ𝑀𝑀𝑛𝑛𝑀𝑀

𝑃𝑃

Where: n = number of transects with floodplain height, bankfull height, and thalweg depth data.

𝑪𝑪𝑪𝑪𝑪𝑪𝑪𝑪𝑪𝑪𝑪𝑪𝑪𝑪 𝑰𝑰𝑪𝑪𝑰𝑰𝑰𝑰𝑰𝑰𝑰𝑰𝑰𝑰𝑪𝑪

= 𝑪𝑪𝑰𝑰𝒍𝒍𝟏𝟏𝟏𝟏 ��∑ 𝑭𝑭𝑪𝑪𝑰𝑰𝑰𝑰𝑭𝑭𝑭𝑭𝑪𝑪𝑪𝑪𝑰𝑰𝑪𝑪𝑭𝑭𝑪𝑪𝑰𝑰𝒍𝒍𝑪𝑪𝑭𝑭𝑰𝑰𝑪𝑪𝑰𝑰=𝟏𝟏

𝑪𝑪� -�

∑ 𝑩𝑩𝑪𝑪𝑪𝑪𝑩𝑩𝑩𝑩𝑩𝑩𝑪𝑪𝑪𝑪𝑭𝑭𝑪𝑪𝑰𝑰𝒍𝒍𝑪𝑪𝑭𝑭𝑰𝑰𝑪𝑪𝑰𝑰=𝟏𝟏

𝑪𝑪� + 𝟏𝟏.𝟏𝟏�

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Biodiversity and Riparian Habitat

Background Land health standards associated with the biodiversity and riparian habitat quality standards can be assessed by three core and one contingent indicators (Table 1). Macroinvertebrate biological integrity is used to quantify the intactness of instream aquatic assemblages to directly address the biodiversity fundamental, while also informing assessments of the water quality fundamental. Riparian habitat quality and intactness can be measured by either ocular or quantitative estimates of the riparian vegetative type, cover, and structure, depending on the particular monitoring application. Specifically, ocular estimates are recommended for use in regional-scale assessments (e.g., land use plan or larger spatial-scale monitoring) or where general information is sought regarding the intactness of riparian areas to buffer against anthropogenic stressors (e.g., thermal, sediment, or nutrient loading), to promote properly functioning channel form and function, or to provide wildlife habitat, among other functions. In contrast, quantitative estimates of riparian vegetative cover and composition (contingent indicator) are recommended for local, site-specific estimates of riparian condition and trend, to assess the impacts of a particular land use (e.g., grazing), or when conducting assessments for riparian obligate species. In addition to the ocular estimates of riparian vegetation, lotic AIM uses canopy cover as a core indicator. Canopy cover directly measures the capacity of riparian vegetation and other features such as cliff walls to shade the stream and mitigate thermal loading and, thus moderate stream temperatures (Beschta 1997; Johnson and Jones 2000). Canopy cover estimates also provide information regarding the amount of potential leaf litter and other terrestrial organisms that may be available to subsidize aquatic food webs (Cummins 1974; Baxter et al. 2005). Supplemental methods and indicators, such as periphyton and fishes, can incorporate additional lines of evidence regarding the biological integrity of instream assemblages, and should be added if management objectives explicitly address these forms of biological integrity. Example Applications (the big picture)

• Assess attainment of biological opinion terms and conditions, land health standards, or other policy standards for physical habitat

• Relate physical habitat conditions to observed biological condition as measured by benthic macroinvertebrates. Such correlations help to identify biologically relevant stressors (i.e., those degraded physical habitat conditions that might be related to degraded biological conditions, such as excessive fine sediment loading)

• Relate physical habitat conditions to land uses or permitted activities in a correlative assessment to inform adaptive management

• Assess habitat suitability for threatened, endangered, or other species of management concern

Biodiversity and Riparian Habitat Indicators Benthic Macroinvertebrates

A. Description and Applications: To address both the biodiversity and water quality fundamentals aquatic macroinvertebrates are sampled. Aquatic macroinvertebrates are ubiquitously used by state and federal agencies as the primary screening tool for

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assessing chemical, physical, and biological conditions, with all 50 states using macroinvertebrates in biomonitoring programs (USEPA 2002). Macroinvertebrates are used to compliment more traditional chemical and physical monitoring techniques (i.e., provide multiple lines of evidence regarding degradation) during baseline monitoring because they: 1. Are relatively long-lived and thus integrate conditions through time and space; 2. Are ubiquitously found in perennial stream systems; 3. Exhibit a variety of life history strategies, which can be used to discriminate among causes of impairment; and 4. Can be sampled and identified in an efficient and cost-effective manner (Bonada et al. 2006). Furthermore, macroinvertebrate sampling can be used to estimate food resource availability for higher trophic levels such as amphibians and fishes.

B. Indicator Computations: • O/E or MMI indices: Stream and river bioassessments are based on comparisons

of observed biota at sample sites with estimates of the biological potential at each site. Two common tools for conducting bioassessment of lotic systems are observed/expected (O/E) and Multi-Metric (MMI) indices. O/E models compare the macroinvertebrate taxa observed at sample sites of unknown condition to the assemblages expected to occur in the absence of anthropogenic stressors (Hawkins et al. 2000, Hawkins 2006). In contrast, MMI models aggregate multiple macroinvertebrate assemblage composition and structure metrics (e.g., total richness, proportion of tolerant individuals, combined richness of mayflies, stoneflies, and caddisflies) to assess biological condition (Stoddard et al. 2008). Metrics that differentiate between reference and degraded conditions are selected, rescaled to standardized scale (e.g., 0 to 100), and aggregated into a single measure of biological condition. Metrics are computed for sample sites of unknown condition and compared to metric values predicted to occur in the absence of anthropogenic activities (Hawkins et al. 2010a, Vander Laan and Hawkins 2014). Both O/E and MMI use empirical models built with data from a network of reference sites to predict conditions of sample sites in the absence of anthropogenic impacts. O/E scores range from 0 to approximately 1, with a score of zero indicating that sample sites have no taxa in common with expected reference conditions. In contrast, an O/E score of 1 occurs when macroinvertebrate assemblages at sample sites are equal to those of reference conditions. Biological condition of sample sites is assessed based on the precision of the reference site model used to predict the expected number and type of taxa. Specifically, the standard deviation (SD) of predicted reference site O/E scores, with sample sites scoring less than one SD below the mean of reference sites having ‘minimal departure’ from reference; sites scoring between one SD and two SD having ‘moderate departure’; and sites scoring more than two SD below the mean of reference sites having ‘major departure’ from reference conditions. Final aggregate MMI scores commonly range from 0 to 100, with lower scores indicating that sample sites significantly deviated from reference sites across all metrics. In contrast, higher MMI scores occur when all macroinvertebrate metrics

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are equal to or greater than those of reference conditions. MMI scores computed for sample sites are compared to reference site scores, with a percentile approach commonly used to assign different degrees of departure or condition categories (e.g., sample sites falling below the 5th percentile of reference MMI distribution are considered to have major departure). Caution should be exercised when interpreting O/E or MMI scores to ensure sample or laboratory error does not have an undue influence on final scores. For example, taxonomic richness of macroinvertebrate assemblages typically increases asymptotically with the number of individuals in a sample (Vinson and Hawkins 1996). Therefore, indices commonly recommend a minimal number of individuals per sample to minimize this sample artifact (e.g., 200 individuals). Low samples counts can result from sampling and/or laboratory processing errors, but can also be a signal of degraded biological condition. For samples with low counts, additional samples should be collected to verify the precision of ‘major’ or ‘moderate’ departures from reference. Additionally, care should be taken to ensure the environmental conditions of sample sites are similar to those of the reference conditions used to developed biological indices. This can be determined from the “ModelApplicability” field in AquADat, which reports whether or not the sample site's environmental conditions are within the range of experience of the model. A “fail” indicates the model had to extrapolate, rather than interpolate, to accommodate one or more of the habitat variables. O/E or MMI scores and condition ratings should be interpreted cautiously if a site failed the test for range of experience of the model. The NOC can currently compute state O/E or MMI bioassessment indices for the following states: UT, NV, CA, CO, and OR. If a state model is not available, a BLM Westwide model can be used for all other states. , with the exception of Idaho, which falls within the PIBO regional model (see below). Additionally, the following two regional models are available on request if applicable to the sample locations and if needed to meet policy requirements: for CA, WA, and OR, the Northwest Forest Plan Region (model developed for AREMP program); and for MT, ID, and OR, the Columbia River Basin (model developed for the PIBO program) can be applied. Model specific metadata is in development but more information about different bioassessment indices can be found at http://www.qcnr.usu.edu/wmc/bioassessments/. All macroinvertebrate samples are sorted and identified by NAMC, with the exception of midges, which are sent to EcoAnalysts if identification to genus or species level is needed (e.g., models for CO and NV). NAMC sorting and taxonomic SOPs can be found at www.usu.edu/buglab/.

• Invasive Species: In addition to computing state bioassessment indices from the macroinvertebrate identification and enumeration data, we also compute the presence of invasive invertebrates. The list of considered invertebrates includes:

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1. All individuals within the crayfish family Cambaridae; 2. The Asian clam (Corbicula fluminea); 3. Zebra mussels (Dreissena polymorpha); 4. Quagga mussels (Dreissena rostriformis); 5. New Zealand mud snail (Potamopyrgus antipodarum) and 6. The red-rimmed melania snail (Melanoides tuberculatus).

Percent Canopy Cover A. Description and Applications: Percent canopy cover measurements are an indicator of

the capacity of riparian vegetation to mitigate thermal loading (i.e., provide shade) and thus moderate stream temperatures (Beschta 1997, Johnson and Jones 2000). The extent of the riparian canopy also provides information on the amount of potential leaf litter to subsidize aquatic food webs (Cummins 1974). We use a modified convex densiometer to quantify percent canopy cover at the left bank, stream center, and right bank of the 11 main transects following the methods of the USEPA (2009).

B. Indicator Computations: • Percent Overhead Cover: Convex densiometers assess canopy cover as the

percentage of a total number of 17 intersections on a grid that are covered. Four densiometer measurements are taken in the center of the stream looking upstream, downstream, left, and right. The percent overhead cover is an average of the percent cover at these four locations.

• Bank Overhead Cover: Bank overhead cover is computed similarly to percent overhead cover except only two densiometer measurements are averaged: those taken at left and right banks.

Riparian Habitat Quality A. Description and Applications: Riparian zones are transitional areas between terrestrial

and aquatic ecosystems that provide important habitat for organisms and influence many ecological processes (Naiman and Decamps 1997). Healthy riparian zones provide refugia during periods of stress (e.g., drought or floods), influence ecosystem microclimates, provide energy and nutrients to the stream (e.g., leaf litter), shade surface water, stabilize banks, filter runoff and groundwater inputs, and provide a corridor for plants and animals (Hauer and Lamberti 1998). However, these functions can be altered by land use such as grazing and road development, which is one of the most pervasive land uses in western riparian ecosystems (Fleischner 1994, Beschta et al. 2012). Riparian habitat quality is estimated using visual estimates of vegetation type and cover at 11 transects and left and right banks.

B. Indicator Computations: • Frequency of Occurrence of Priority Native and Non-native Species

o Woody: The presence of priority non-native and native woody vegetation is assessed at 11 transects on left and right banks. The number of plots with non-native native woody vegetation present is divided by the number of plots assessed and multiplied by 100 to get the percent of plots with non-native woody species present. The percent of plots with priority native vegetation is computed similarly.

33

o Herbaceous: The presence of non-native and native/other herbaceous vegetation is assessed and calculated the same as detailed above for nonnative and native woody vegetation. Aquatic: Starting in 2019 the presence of aquatic non-native species was also assessed with plots going from the center of the stream up to the furthest extent of the riparian vegetation plot. Calculations follow those above.

Starting in 2019 each BLM administrative state developed standardized priority native and non-native species lists to inform the above data collection. This allowed frequency of occurrence for a given genus or species to be recorded for priority native woody and non-native vegetation as well. Species lists for each state can be found in Appendix D.

• Frequency of Occurrence of Sedges and Rushes: The presence of sedges or rushes is also assessed in riparian plots and calculations follow those above.

• Vegetative Complexity and Riparian Vegetative Cover: These indicators are ocular assessments of cover and as such it should only be used for very coarse resolution analyses. Indicator precision studies done for these indicators in the lower 48 states show that these indicators are not precise enough for most data applications. In 2019 following this indicator precision study, these indicators were changed to only be a contingent indicators for Alaska. They have been useful in Alaska where some reaches are completely devoid of vegetation while others have healthy riparian vegetation. See here for more information regarding the precision of these indicators. Vegetative complexity it is an aggregate measure of the average vegetative cover provided by three different vegetative height categories: canopy (>5m), understory (0.5-5m), and ground cover (<0.5m). The canopy category only applies to woody vegetation, while the understory and ground cover categories are divided into two vegetation types: woody or non-woody. In the field, crews assess cover for each height category and vegetation type using five cover classes: 0 = absent 0%, 1 = sparse: <10%, 2 = moderate: 10-40%, 3 = heavy: 40-75%, and 4 = very heavy >75% (Table 5). For analysis, the cover classes are assigned mid-points (0%, 5%, 25%, 57.5%, and 87.5% respectively) and then converted to proportional cover (0.05, 0.25, 0.575, and 0.875 respectively). Proportional cover is then summed across the two vegetation types and three heights, and finally averaged across the left and right banks of 11 transects (Table 6). Riparian vegetative cover indicators are all computed similarly to vegetative complexity. They are measures of the cover provided by riparian species ONLY within each respective layer: canopy (> 5m), understory (0.5-5m), and ground (<0.5m). In the field, crews assess cover for each height category using five cover classes: 0 = absent 0%, 1 = sparse: <10%, 2 = moderate: 10-40%, 3 = heavy: 40-75%, and 4 = very heavy >75%. For analysis, each cover class is assigned a mid-

34

point (0%, 5%, 25%, 57.5%, and 87.5% respectively) and then converted to proportional cover (0.05, 0.25, 0.575, and 0.875 respectively). Proportional cover is then averaged across 11 transects and right and left banks. These calculations are identical to those detailed above for vegetative complexity except cover is not assessed separately for different vegetation types (e.g. woody, non-woody) and each layer (canopy, understory, and ground) is kept separate rather than summing across layers.

Table 5. Example raw vegetative cover data from one site. Values are an ordinal scale from 0 (absent) to 4 (>75% cover).

Canopy (> 5m) Understory Ground Cover

Transect Bank Big trees Small trees Woody Non-

woody Woody Non-

woody A Left 1 0 3 1 3 2 A Right 0 0 3 0 3 1 B Left 3 0 2 0 3 3 B Right 3 1 3 0 2 2 C Left 2 0 2 4 2 2 C Right 1 0 1 2 2 2 D Left 0 0 0 1 1 1 D Right 0 0 0 0 2 0 E Left 0 0 0 1 4 1 E Right 0 4 4 0 4 1 F Left 0 0 1 1 1 1 F Right 0 0 0 2 3 1 G Left 0 0 0 4 4 1 G Right 0 0 0 4 4 1 H Left 0 0 0 4 4 1 H Right 1 0 3 0 2 2 I Left 0 1 2 4 2 2 I Right 0 0 1 2 2 2 J Left 0 0 1 1 1 1 J Right 0 0 0 2 3 1 K Left 0 0 1 4 2 1 K Right 0 0 0 0 1 4

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Table 6. Example calculation of vegetative complexity from the raw data in Table 5. Table values are mid-points of the five cover classes. The final vegetative complexity value is the sum of cover at a given transect (left and right bank) averaged across all transects.

Canopy (> 5m) Understory Ground Cover

Transect Bank Big trees Small trees Woody Non-

woody Woody Non-

woody Sum A Left 0.05 0 0.575 0.05 0.575 0.25 1.5 A Right 0 0 0.575 0 0.575 0.05 1.2 B Left 0.575 0 0.25 0 0.575 0.575 1.975 B Right 0.575 0.05 0.575 0 0.25 0.25 1.7 C Left 0.25 0 0.25 0.875 0.25 0.25 1.875 C Right 0.05 0 0.05 0.25 0.25 0.25 0.85 D Left 0 0 0 0.05 0.05 0.05 0.15 D Right 0 0 0 0 0.25 0 0.25 E Left 0 0 0 0.05 0.875 0.05 0.975 E Right 0 0.875 0.875 0 0.875 0.05 2.675 F Left 0 0 0.05 0.05 0.05 0.05 0.2 F Right 0 0 0 0.25 0.575 0.05 0.875 G Left 0 0 0 0.875 0.875 0.05 1.8 G Right 0 0 0 0.875 0.875 0.05 1.8 H Left 0 0 0 0.875 0.875 0.05 1.8 H Right 0.05 0 0.575 0 0.25 0.25 1.125 I Left 0 0.05 0.25 0.875 0.25 0.25 1.675 I Right 0 0 0.05 0.25 0.25 0.25 0.8 J Left 0 0 0.05 0.05 0.05 0.05 0.2 J Right 0 0 0 0.25 0.575 0.05 0.875 K Left 0 0 0.05 0.875 0.25 0.05 1.225 K Right 0 0 0 0 0.05 0.875 0.925 Average 1.20

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Covariates

Background and Example Applications The chemical, physical, and biological potential of stream and river systems naturally varies across the landscape (reviewed in Allan 2004). Six covariates can be computed from the core methods to begin to account for this natural variability: bankfull width, wetted width, flood-prone width, entrenchment, slope, and sinuosity (Table 1). Covariates such as slope and bankfull width are critical for computing and interpreting several of the watershed function – instream habitat indicators, particularly residual pool depth, length, and frequency and relative bed stability. In addition to field-based covariates, site coordinates and geographic information systems are used to compute a large number of covariates (e.g., watershed area, precipitation, geology, soil types) for use in the computation of O/E type indices and general data interpretation. Photos can be important in reviewing the accuracy of many computed indicators too, as well as for reporting and communicating results. Qualitative assessment of the extent and type of anthropogenic impacts adjacent to or within the assessed reaches can also be useful in general data interpretation, but this indicator has not been made a priority to compute because much of this information is available from readily available geospatial layers.

Covariate Description and Computations Bankfull Width and Wetted Width Bankfull width is used in the calculation of floodplain connectivity, entrenchment ratio, and relative bed stability. It is also provides a coarse estimate of stream size. Stream size is a natural factor that influences many indicators such as canopy cover and bed particle size distributions. Therefore, stream size along with ecoregion are important covariates to consider prior to applying benchmarks (see below). Wetted widths are stage dependent; however, they can provide important context for other indicators such as instream habitat complexity, which is rated as zero if the transect is dry. Additionally wetted width can provide a coarse assessment of available habitat at low flows and can be paired with pool data to determine residual pool volume. Hydraulic retention can also be approximated by the width-depth product, which has been shown to be an important covariate for fish IBIs (Citation). Reported values for bankfull width and wetted width are the average value across all sampled transects (11 and 21 for bankfull and wetted width, respectively). Wetted width for dry transects equals zero.

Flood-prone Width • Flood-prone Width: Average flood-prone width is defined as the valley width at two

times maximum bankfull height. Flood-prone width is an approximation of the size of the floodplain and can be compared to bankfull width to compute entrenchment ratio (see below). Two flood-prone width measurements are taken within riffle habitats per sample reach, one near the top and bottom of the sample reach. Reported flood-prone width is the average of the two measured widths.

• Entrenchment Ratio: Entrenchment ratio equals average flood-prone width divided by the average of two bankfull widths taken in the same location as the flood-prone widths. Ratios of 1-1.4 represent entrenched streams; 1.41-2.2 moderately entrenched streams; and ratios greater than 2.2 indicate rivers only slightly entrenched in a well-developed floodplain (Rosgen 1996). This entrenchment value can be used with other covariates

37

such as slope and sinuosity to determine stream type and the potential of the system for floodplain formation (Rosgen 1996).

Slope Slope is used in the computation of residual pool depth, length, and frequency when computing these indicators from thalweg measurements. Slope is also used in the computation of stream bed stability and is an important factor in determining stream velocity and power (the ability of the stream to move sediment). For example, slope can be used to help develop context for the potential of a stream system to support a given number of pools (Al-Chokhachy et al. 2010). Slope is measured as the total change in elevation from the top to the bottom of the sample reach divided by the distance of the reach along the thalweg and then multiplied by 100 to convert to a percentage. To ensure precision of this measurement, most crews measure the total elevation change until they get two measurements that are within 10% of one another. The reported value for slope uses the average of two measurements that are within 10% of each other for the total change in elevation.

Sinuosity Sinuosity is a key descriptive characteristic of stream type that is correlated with sediment size and slope. Sinuosity can also be a form of habitat complexity, creating features such as backwaters and oxbows. Sinuosity is computed as the reach length along the thalweg divided by the straight line distance between bottom of reach (BR) and top of reach (TR) coordinates. Reach length along the thalweg is measured by crews, and the straight line distance between the bottom of reach and top of reach coordinates is computed as:

𝑆𝑆𝑃𝑃𝑆𝑆𝑆𝑆𝑖𝑖𝑃𝑃ℎ𝑃𝑃𝑃𝑃𝑖𝑖𝑃𝑃𝑃𝑃𝑀𝑀𝑖𝑖𝑃𝑃𝑃𝑃 = acos �𝑠𝑠𝑀𝑀𝑛𝑛(𝑀𝑀𝐿𝐿𝑇𝑇𝐵𝐵𝐵𝐵)𝜋𝜋

180� ∗ sin(𝑃𝑃𝐿𝐿𝑃𝑃𝑇𝑇𝑇𝑇) 𝜋𝜋

180+ cos �𝑀𝑀𝐿𝐿𝑇𝑇𝐵𝐵𝐵𝐵 𝜋𝜋

180� ∗ cos (𝑀𝑀𝐿𝐿𝑁𝑁𝑇𝑇𝐵𝐵𝜋𝜋

180− 𝑀𝑀𝐿𝐿𝑁𝑁𝐵𝐵𝐵𝐵𝜋𝜋

180) ∗ 6371000

Sinuosity is not computed for sites that were partially sampled because crews were not consistent with recording the partial reach length with matching coordinates.

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Benchmarks: From Indicator Values to Management Decisions What are Benchmarks and Why are They Needed?

Monitoring objectives are quantitative statements about desired resource conditions that help translate information to action in natural resource management. These quantitative statements are critical to answering questions such as: Was the management goal achieved? Are watersheds, streams, and rivers functioning properly? Are management actions maintaining the health of public lands? A key component of monitoring objectives is benchmarks. Benchmarks are indicator values, or ranges of values used to indicate the need for change or conversely, project success. For example, total nitrogen values characterize ambient nutrient concentrations at a single point in time, but without appropriate benchmarks, such measurements lack context and cannot be used to assess water quality condition and potential eutrophication. Benchmark attainment or exceedance can trigger the need to adjust management practices, to collect additional data, or indicate project success among other applications. The importance of establishing benchmarks, either formal or informal, cannot be understated. In a review of judicial decisions, Fischman and Ruhl (2016) found the failure to establish benchmarks as one of the leading causes for adaptive management to be ruled against in U.S. courts. In the absence of quantifiable benchmarks, management agencies struggle to make objective and decisive decisions as to when current management strategies should be reviewed, amended, or changed all together. Approaches to Setting Benchmarks

There are many different sources of benchmarks and methods for their development (reviewed in Hawkins et al. 2010). However, in aquatic systems benchmarks are generally set relative to some approximation of the environmental conditions expected in the absence of anthropogenic impacts. Because of the paucity of knowledge regarding pre-European stream and river conditions and it being unrealistic to manage for such conditions, least disturbed or minimally impacted sites are commonly used to establish reference conditions (Hughes et al. 1994, Stoddard et al. 2006). This does not mean though that the end goal of all management is the attainment of reference condition. For example, the Federal Land Policy Management Act requires the BLM to manage public lands under a multiple use mandate, which differs from the preservation mandate of the National Park Service. Thus, benchmarks can be used to assess the degree of departure from reference condition and managers must decide whether such departures are sustainable given management objectives. In other words, it is best practice to set benchmarks relative a reference condition and change the degree of allowable departure from that benchmark based on management objectives, as opposed to varying benchmarks based on management objectives. Such an approach is critical to facilitate comparable condition estimates among management units or geographic areas. A central challenge of environmental monitoring is the ability to discriminate between natural environmental gradients and those resulting from anthropogenic activities. Traditionally, regulatory and land management agencies established benchmarks using narrative criteria, professional judgment, or by selecting ‘paired’ reference site(s). Although these approaches are feasible and have been effectively used, they have significant drawbacks capable of limiting the

39

repeatability and inference drawn from monitoring assessments. These include: (1) the subjective interpretation of narrative standards; (2) the considerable expense of identifying paired reference and test reaches; (3) the susceptibility of paired reference sites to pseudo-replication and (4) the difficulty of identifying replicate stream reaches given the multitude of confounding factors that can occur (reviewed in Hawkins et al. 2010). Where available, the AIM-NAMF recommends using benchmarks set by policy (e.g., state water quality standards, biological opinions), as these encompass the legal commitments made by the Bureau. However, in many cases, policy does not include objective, quantifiable benchmarks or policy objectives do not exist for a given indicator. In these instances and where available, the AIM-NAMF recommends utilizing networks of least disturbed sites established by state and federal agencies to define reference conditions and subsequent benchmarks (Hughes et al. 1994, Stoddard et al. 2006, Hawkins et al. 2010). Benchmarks established outside of policy should be considered monitoring benchmarks used to alert practitioners of potential problems requiring additional investigation before changing management or making policy decisions. Networks of least disturbed reference sites consist of stream or river monitoring locations screened for the presence or density of anthropogenic impacts using a mix of field- and/or GIS-based variables (e.g., road density, dams, artificial channel, agricultural land uses)(e.g., Herlihy et al. 2008, Ode et al. 2016). The use of reference site networks is advantageous because natural spatial and temporal gradients are more likely to be adequately represented, thus minimizing the chance of confounding natural and anthropogenic gradients. However, given the natural environmental heterogeneity among streams and rivers, one must ensure that monitoring sites of unknown condition are compared to reference sites of similar potential (Hawkins et al. 2010b). In aquatic monitoring, there are two widely used methods for setting benchmarks based on networks of reference sites: predicted natural conditions and percentiles of regional reference. Predicted natural conditions use empirical models based on geospatial predictors to understand the spatial variability among reference sites for a given indicator. For example, Hill et al. (2013) were able to use nine GIS-derived variables (e.g., air temperature, watershed area, reservoir index) to explain 87% of the spatial variability in mean summer stream temperature (root-mean-square deviation of 1.9oC) among reference sites throughout the conterminous U.S. In addition to stream temperature, models have been developed for macroinvertebrate biological integrity through the use of observed/expected or multimetric indices (e.g., Hawkins 2006; Hargett et al. 2007; Vander Laan et al. 2013), total nitrogen and phosphorous (Olson 2012), conductivity (Olson and Hawkins 2012), and instream habitat complexity (Al-Chokhachy et al. 2010). Such models account for natural environmental gradients and are used to make predictions of chemical, physical, or biological values expected at a site in the absence of anthropogenic impairment. Condition is then determined based on the deviation of the observed indicator value from the site specific predicted value. If this deviation is greater than specified percentiles of model error (e.g., 75th and 95th), the value is assigned a condition of moderate or major departure respectively. Predictive modeling approaches are advantageous because they result in site specific predictions with known levels of accuracy and precision. However, predictive models have not been built for all indicators. For indicators lacking predictive models, the BLM can start by using the natural

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range of variability among regional reference site networks to set expectations and assign benchmarks (i.e., percentiles of regional reference conditions) (Hughes et al. 1986; Paulsen et al. 2008). Specifically, distributions of reference site indicator values can be used to characterize the natural range of variability from which test sites can be compared and subsequent exceedances identified. Reference site networks are typically grouped by physiographic boundaries (e.g., level III ecoregions; Omernik 1987) to account for differences in reference site distributions resulting from factors such as climate and topography. For example, the 90th and 70th percentiles of reference site fine sediment values for the Colorado Plateau ecoregion can be used as benchmarks to classify the condition of a monitoring site as “major departure,” “moderate departure,” or “minimal departure” from reference conditions, respectively. In other words, a site would be categorized as having major departure from reference conditions if the fine sediment value for a sample site is greater than that observed among 90% of reference sites in the Colorado Plateau ecoregion. Where protocols are concordant and empirical models have not been developed, the BLM can use the percentiles of regional reference condition approach established by the EPA’s National Rivers and Streams Assessment program to make a first pass at setting benchmarks (e.g., Stoddard et al. 2005). Limitations to Benchmark Approaches

All approaches for setting benchmarks are subject to error and the potential to under (type I errors) or over protect (type II errors) natural resources. An important part of developing and applying benchmarks is to be aware of potential limitations. The two main approaches described herein, percentiles of regional reference conditions and predicted natural conditions, are no exception and have important limitations that can influence the interpretation of monitoring data. The National AIM team is currently working on additional content to be provided here that explains the limitations of both of these approaches. Understanding Benchmarks Available in the Benchmark Tool

As stated in the introduction, the AIM-NAMF recommends using benchmarks set by policy (e.g., state water quality standards, biological opinions), as these encompass the legal commitments made by the Bureau. However, in many cases, policy does not include objective, quantifiable benchmarks or policy objectives do not exist for a given indicator. Therefore, the national AIM team has compiled example benchmarks for a majority of indicators that can serve as a starting point for indicator interpretation (Table 7). Additionally, the national AIM team has developed an Excel based tool that allows practitioners to apply these or any other benchmarks to assist with the interpretation of AIM data. Again, the provided benchmarks should only be used after fully understanding their limitations and vetting the benchmark values with a BLM interdisciplinary team. Lastly, the benchmark methods described in Table 7 are only applicable to a subset of computed indicators because not all indicators make sense to assign a condition category or have readily available methods to determine benchmarks.

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Table 7. Overview of indicator benchmark methods that can be used in the absence of benchmarks established by policy. The provided benchmarks were developed using one of three approaches: predicted natural conditions, percentiles of regional reference, or best professional judgement. All benchmarks should be reviewed by an interdisciplinary team prior to using results to inform management decisions.

Indicator(s) Citation Condition Benchmarks Appropriateness of

benchmarks Predicted natural conditions Total Nitrogen Olson

and Hawkins 2013

Predicted natural condition plus 75th (moderate) and 95th (major) percentiles of model error, 52.1 µg/L and 114.7 µg/L, respectively.

Use these benchmarks if no state water quality standards are available.

Total Phosphorous Olson and Hawkins 2013

Predicted natural condition plus 75th (moderate) and 95th (major) percentiles of model error, 9.9 µg/L and 21.3 µg/L, respectively.

Specific Conductance Olson and Hawkins 2012

Predicted natural condition plus 75th (moderate) and 95th (major) percentiles of model error, 27.1 µg/L and 74.5 µg/L, respectively.

OE_Macroinvertebrate, MMI_Macroinvertebrate

Varies depending on the model (see www.usu.edu/buglab/ for more information). In general, most model's benchmarks are the mean of the reference distribution plus 1SD (moderate) or 2SD (major).

Should always be appropriate to meet state regulations, if a state based model was used.

Percentiles of regional reference conditions VegComplexity, LWD_Freq, LWD_Vol, InstreamHabitatComplexity, PctOverheadCover, BankOverheadCover

Kaufmann et al. 1999; Stoddard et al. 2005

30th (moderate) and 10th (major) percentiles of regional reference conditions defined by 23 groups of EPA hybrid level III ecoregions and a combination of stream size (> or ≤ 10 m bankfull width) and sampling protocol (wadeable vs. boatable). See Tables 10-11 for specific values.

Use these if no other policy benchmarks are available. Benchmarks should be carefully reviewed based on local knowledge of environmental conditions and thus site potential.

PctFines2, ChannelIncision

Kaufmann et al. 1999; Stoddard et al. 2005

70th (moderate) and 90th (major) percentiles of regional reference conditions defined by 23 groups of EPA hybrid level III ecoregions and a combination of stream size (> or ≤ 10 m bankfull width) and sampling protocol (wadeable vs. boatable). See Tables 10-11 for specific values.

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Water Quality Benchmark Development

Under the water quality fundamental, BLM land health standards specify the attainment of state water quality standards as a management objective; therefore, the development of water quality benchmarks for assessing condition is relatively straightforward for those indicators for which state standards have been developed. Practitioners should consult with their state regulatory agency to determine the availability of state standards and the required field sample frequency to assess standard attainment. For those states or indicators for which standards have not been identified, we recommend use of the predicted national conditions approach for setting benchmarks due to the site specific nature of these predictions, as well as known levels of accuracy and precision (see introduction to benchmark section). Specific conductance benchmarks can be established using methods in Olson et al. (2012). This model uses 15 GIS-derived variables (e.g., % calcium carbonate in local geology, air

Table 7 continued.

Indicator(s) Citation Condition Benchmarks

Determining if these benchmarks are appropriate

Best professional judgement InvasiveInvertSp Assumed all sites with invasive species

present had major departure from reference and if no invasive species present had minimal departure from reference.

Consult management objectives to determine if invasive species presence is acceptable.

pH Kaufmann et al. 1999

Acidic (7, 6.5) and alkaline (8.5, 9) for moderate and major departure from reference respectively.

Use these benchmarks if no state standards are available. Otherwise use state standards.

BankCover, BankStability, BnkCoverStab

80% of banks stable and/or covered (moderate) and 69% of banks stable and/or covered (major) for all Hybrid III ecoregions except Plains or the southern xeric and eastern xeric ecoregions. These ecoregions should have naturally lower bank stability so thresholds were 70% of banks stable and/or covered (moderate) and 50% of banks stable and/or covered (major).

Use these benchmarks if no other policy or information available, but carefully examine for your sites and verify condition ratings with local knowledge of physiographic conditions and thus site potential.

Floodplain Connectivity Rosgen 1996

Mean floodplain height 1.3 (moderate) and 1.5 (major) times mean bankfull height

Consult your management objectives to determine if these defaults are applicable

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temperature, precipitation) to explain 71% of the spatial variability in base-flow specific conductance concentrations (root-mean-square error 84.2 µS/cm) among reference sites throughout the western U.S. Benchmarks were then established by taking the site-specific predicted natural conditions from the model and adding the 75% and 95% of model error, 27.1 µg/L and 74.5 µg/L respectively, to the prediction. We are currently working to refine this approach by differentially assigning model error to small and large streams given expectations for naturally higher specific conductance values for larger systems. Total nitrogen and total phosphorus benchmarks can be established using methods in Olson and Hawkins (2013). The total nitrogen model uses 12 GIS-derived variables (e.g., atmospheric nitrogen deposition, air temperature, precipitation) to explain 23% of the spatial variability in base-flow total nitrogen values (root-mean-square error 80.1 µg/L). The total phosphorus model uses 15 GIS-derived variables (e.g., % calcium carbonate in local geology, air temperature, precipitation) to explain 46% of the spatial variability in base-flow total phosphorus values (root-mean-square error 20.5 µg/L) among reference sites throughout the western U.S. Benchmarks for total nitrogen and total phosphorus were established similar to specific conductance. The 75% and 95% of model error are 52.1 µg/L and 114.7 µg/L respectively for total nitrogen and are 9.9. µg/L and 21.3 µg/L respectively for total phosphorus. We are currently working to refine this approach by differentially assigning model error to small and large streams given expectations for naturally higher nutrient concentrations for larger systems.

Watershed Function, Instream Habitat, Riparian Habitat, and Biodiversity Benchmark Development Overview We combine descriptions of benchmark methods for all indicators that address the watershed function, instream habitat quality, biodiversity, and riparian habitat quality fundamentals of land health because benchmarks for most of these indicators were developed very similarly. The exception is biodiversity. For our main indicators of biodiversity, OE_Macroinvertebrate and MMI_Macroinvertebrate, benchmarks are largely determined using state bioassessment indices, which include benchmarks for assigning condition categories. Because of the large number of states and associated models, we do not go into specific methods here. More information about OE and MMI score interpretation see http://www.qcnr.usu.edu/wmc/bioassessments/. All instream habitat and riparian indicators generally lack predictive models and therefore the provided benchmarks are based on the percentiles of regional reference conditions. Specifically, we used data from 1096 reference sites throughout ten hybrid level II/III ecoregions across the west to characterize the natural range of indicator variability expected to occur in the absence of anthropogenic impairment (Figure 4; Table 8) (Stoddard et al. 2006). Benchmarks were established at the extremes of reference site distributions to identify significant departures from reference for each of three stream sizes: small wadeable reaches ≤10 m bankfull width, large wadeable reaches >10 m bankfull width, and boatable reaches. For example, the 70th and 90th percentiles of reference site percent fine sediment values (<2 mm) for the small wadeable streams in the Eastern Xeric Basin ecoregion, 44 and 73% respectively, were used to separate minimal, moderate, and major departure from reference conditions, respectively. In other words,

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sites were categorized as having major departure if fine sediment measurements exceeded levels observed among 90% (73% fine sediment) of reference sites. Subsequent text explains details about reference site selection and screening, as well as detailed methods used to determine ecoregion/stream size groupings of references sites. Lastly, we present benchmark values and number of references sites used for a given ecoregion/stream size.

Figure 4. EPA hybrid level II/III ecoregions used to group 1096 reference sites for determining the natural ranges of variability among indicators and subsequent benchmarks. Figure developed by Stoddard et al. 2005.

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Table 8. Translation table between EPA hybrid level II/III ecoregions and EPA level III ecoregions. EPA Hybrid Level II/III Ecoregion EPA Level III Ecoregion Northern Xeric Basin Columbia Plateau

Snake River Plain Northern Basin and Range

Pacific Northwest Coast Range Puget Lowland Willamette Valley Cascades Sierra Nevada Eastern Cascades Slopes and Foothills North Cascades Klamath Mountains

Xeric California Southern and Central California Chaparral and Oak Woodlands Central California Valley

Northern Rockies Blue Mountains Northern Rockies Idaho Batholith Middle Rockies Canadian Rockies

Southern Rockies Wasatch and Uinta Mountains Southern Rockies

Eastern Xeric Basin Wyoming Basin Colorado Plateaus Arizona/New Mexico Plateau

Southern Xeric Basin Central Basin and Range Mojave Basin and Range Chihuahuan Deserts Madrean Archipelago Sonoran Basin and Range

Southwest Mountains Southern California Mountains Arizona/New Mexico Mountains

Northern Cultivated Plains High Plains Rangeland Plains Southwestern Tablelands

Northwestern Glaciated Plains Northwestern Great Plains

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Reference sites The national AIM team used an EPA dataset to identify the range of variability among least disturbed sites (i.e., reference) by hybrid Omernick level II/III ecoregions. The reference dataset was comprised of 1096 sites sampled between 2000 and 2009 as part of EPA’s Wadeable Streams Assessment (WSA), Western Environmental Monitoring and Assessment Program (EMAP-West), and National Rivers and Streams Assessment (NRSA) surveys (Stoddard et al. 2005, Olsen and Peck 2008, Paulsen et al. 2008a, EPA 2016). The EPA screening process for reference site designations differed slightly among surveys, but we sought to use all three datasets to maximize sample sizes within each ecoregion. In general, the EPA used multiple lines of evidence to screen sampled sites for those in least disturbed conditions. At the broadest scale, GIS derived metrics of land use (e.g., row crop and urban land use) and other anthropogenic activities (e.g., dams and impoundments) were used to screen sites. At the site-scale, field observations of the magnitude and proximity of streamside human activities such as roads, agricultural, and urban development and riparian disturbance were used as described by Kaufmann et al. (1999). Instream habitat variables such as habitat complexity and fine sediment levels were also utilized. Lastly, GIS and field-based observations were used in conjunction with water chemistry data, as described by Herlihy et al. (2008), to designate sites as least, moderately, and highly disturbed relative to other sampled sites. To avoid circularity in above process, no measures directly related to a given indicator were used to screen sites. For example, field measurements of riparian vegetation, sediment, or instream habitat complexity were not used to screen sites and determine ranges of variability for any instream indicators. The only exceptions were the use of riparian conditions for designations of instream habitat indicators and visa-versa. We are compiling further detail on reference screening criteria, including values used, and this information is forthcoming. Development of Ecoregion/Size Groupings Given a lack of predictive models of instream habitat and riparian indicators, the national AIM team attempted to minimize natural variability associated with ecoregions and stream size. Similar to the approach taken by the EPA in the Western Environmental Monitoring and Assessment Program (EMAP-West) surveys, we used EPA hybrid level II/III ecoregions to divide reference sites into relatively homogenous physiographic regions. Then within a given ecoregion, we used bankfull width to separate reference sites into small streams (≤ 10 m bankfull width) and large streams (> 10 m). We chose 10 m as an arbitrary cutoff based on balancing sample sizes and maximizing discriminatory efficiency for individual indicators between groups. Boatable AIM data is collected using a slightly different protocol and these sites were more similar to each other than wadeable streams of similar bankfull widths. Therefore, boatable sites were considered their own stream size category. Using this approach, most indicators had a substantial difference between benchmark values for small streams and large streams that made ecological sense (Table 9 & 10), while still providing adequate sample sizes for most indicators for a given ecoregion and stream size (e.g., > 20 sites)

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(Table 11 & 12). For example, the benchmark for major departure from reference for PctOverheadCover in the Northern Rockies is 20.9% for small streams (which generally support more overhead cover than large streams) but 2.3% for large (Table 9). When subdividing the dataset by both ecoregion and stream size, we attempted to maintain >30 reference sites in each grouping. However, this wasn’t always possible, and we had to lump ecoregions in some cases in an effort to increase sample sizes (see specific bullets below). This approach enabled us to use all available reference site data for a given indicator. For ecoregion and stream size groupings where minimal sample sizes were not achieved, extreme care should be exercised when using the provided benchmarks, especially if sample sizes are <20. Groups or indicators with low sample sizes included:

• Boatable reaches within all ecoregions o Boatable ecoregions were lumped into most similar groups (Table 10 and 12).

• Large streams in the Xeric Basin Ecoregion o These values were different enough from other ecoregions that we didn’t feel

justified in lumping these sites with another ecoregion, but care should be exercised when using the provided benchmarks.

• Wadeable small and large streams within the Xeric California ecoregion o Wadeable AIM sites sampled in the Xeric California to date have been on the

border of Pacific Northwest ecoregion and so Pacific Northwest reference sites have been used in the current benchmark tool to set benchmarks for these sites rather than lumping reference sites from these two diverse ecoregions to obtain benchmarks.

• Channel Incision for both wadeable and boatable sites o Sample sizes were not egregiously low for most ecoregions, so ecoregions were

not lumped for wadeable sites, but care should be exercised when using the provided benchmarks.

o Sample sizes were much lower for boatable than wadeable sites, and there was little difference among ecoregions so all ecoregions were lumped.

• LWD_Vol, which had inadequate sample sizes across the board. o No benchmarks were developed for LWD_Vol.

The national AIM team is in the progress of refining these benchmarks further and attempting to develop models for physical habitat indicators. Our most immediate attempts at refining these benchmarks are in the Columbia River Basin were models exist for habitat MMIs, percent fines, pool tail fines, bank angle, and percent pools (Al-Chokhachy et al. 2010). We are currently working on assessing the applicability of these models to BLM sampled sites.

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Table 9. Benchmarks used to assign condition ratings of minimal, moderate, or major departure for wadeable reaches determined by quantiles of regional values by Omernik hybrid level III/IV ecoregions and stream size: small wadeable streams ≤ 10 m or large wadeable streams > 10 m. All benchmarks should be reviewed by an interdisciplinary team prior to using results to inform management decisions.

Northern Xeric

Basin

Pacific Northwest and

Xeric California

Northern Rockies

Southern Rockies

Indicator Response Benchmark Percentile Small Large Small Large Small Large Small Large

PctOverheadCover Decreases with stress

Moderate 30th 47.2 7.6* 65.8 37.6 51.2 15.0 40.7 19.0 Major 10th 12.2 0.0* 38.1 18.3 20.9 2.3 12.2 6.3

BankOverheadCover Decreases with stress

Moderate 30th 69.0 55.1* 84.5 73.8 76.5 61.1 73.1 66.5 Major 10th 32.1 25.2* 67.8 56.8 53.9 38.3 52.7 58.6

VegComplexity Decreases with stress

Moderate 30th 1.03 0.76* 1.14 1.03 1.06 0.84 1.02 0.99 Major 10th 0.60 0.73* 0.83 0.76 0.79 0.57 0.90 0.78

Pctfines2 Increases with stress

Moderate 70th 45 44* 15 12 29 15 23 22 Major 90th 66 81* 33 26 48 27 37 36

InstreamHabitat Complexity

Deceases with stress

Moderate 30th 0.41 0.19* 0.32 0.29 0.43 0.33 0.64 0.49 Major 10th 0.16 0.11* 0.17 0.16 0.28 0.21 0.34 0.27

LWD_Freq Decreases with stress

Moderate 30th 4.70 0.00* 0.00 0.00 0.00 1.35 0.00 0.00 Major 10th 0.00 0.00* 0.00 0.00 0.00 0.00 0.00 0.00

ChannelIncision Increases with stress

Moderate 70th -0.09 0.11* -0.31 0.20 -0.26 -0.20 -0.09 -0.13 Major 90th 0.22 0.22* 0.26 0.54 0.00 0.01 0.17 0.09

*Sample size less than 20

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Table 9 continued. Benchmarks used to assign condition ratings of minimal, moderate, or major departure for wadeable reaches determined by quantiles of regional values by Omernik hybrid level III/IV ecoregions and stream size: small wadeable streams ≤ 10 m or large wadeable streams > 10 m. All benchmarks should be reviewed by an interdisciplinary team prior to using results to inform management decisions.

Eastern Xeric

Basin Southern Xeric

Basin Southwest Mountains

Northern Cultivated

Plains Rangeland

Plains Indicator Response Benchmark Percentile Small Large Small Large Small Large Small Large Small Large

PctOverheadCover Decreases with stress

Moderate 30th 23.9 0.9 70.6 0.9 64.6 11.9 26.9 6.3 5.3 0.1 Major 10th 10.0 0.0 51.9 0.0 33.0 9.5 3.7 0.0 0.6 0.0

BankOverheadCover Decreases with stress

Moderate 30th 70.9 27.1 81.8 28.4 85.8 58.1 76.1 50.8 60.5 34.9 Major 10th 39.8 10.4 65.5 6.2 64.4 32.7 63.3 26.7 31.4 17.6

VegComplexity Decreases with stress

Moderate 30th 0.83 0.56 1.01 0.62 0.89 0.48 0.79 0.68 0.88 0.72 Major 10th 0.71 0.41 0.67 0.26 0.59 0.33 0.49 0.53 0.55 0.50

Pctfines2 Increases with stress

Moderate 70th 44 46 54 64 26 28 77 84 84 72 Major 90th 73 82 77 84 41 52 96 99 100 93

Instream HabitatComplexity

Deceases with stress

Moderate 30th 0.42 0.12 0.46 0.14 0.36 0.24 0.21 0.16 0.19 0.09 Major 10th 0.12 0.05 0.27 0.08 0.23 0.11 0.08 0.05 0.08 0.03

LWD_Freq Decreases with stress

Moderate 30th 1.80 0.00 0.45 0.50 0.95 5.02 1.60* 8.07 2.92 0.00 Major 10th 0.00 0.00 0.00 0.00 0.00 0.54 0.26* 1.84 0.00 0.00

Channel Incision Increases with stress

Moderate 70th 0.11 0.09 0.11 0.23* -1.00 0.26* 0.07* 0.30* 0.30 0.26 Major 90th 0.66 0.50 0.43 0.44* 0.03 0.65* 0.29* 0.37* 0.61 0.50

*Sample size less than 20

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Table 10. Benchmarks used to assign condition ratings of minimal, moderate, or major departure for boatable reaches determined by quantiles of regional values by groupings of Omernik hybrid level III/IV ecoregions. Note that PctOverheadCover is not collected at boatable sites and is therefore not included in this table. All benchmarks should be reviewed by an interdisciplinary team prior to using results to inform management decisions.

Indicator Response Benchmark Percentile

Eastern and Southern

Xeric Basin

Northern and Southern Rockies

and Northern Xeric Basin

Pacific Northwest

Northern Cultivated

Plains Rangeland

Plains

BankOverheadCover Decreases with stress

Moderate 30th 14.9 10.1 16.6 19.0 7.8 Major 10th 4.3 3.5 6.6 4.9 1.0

VegComplexity Decreases with stress

Moderate 30th 0.72 0.78 1.17 1.13 0.67 Major 10th 0.54 0.63 0.54 0.72 0.49

Pctfines2 Increases with stress

Moderate 70th 40 3 14 93 69 Major 90th 97 35 98 100 99

InstreamHabitat Complexity

Deceases with stress

Moderate 30th 0.13 0.14 0.14 0.09 0.10 Major 10th 0.08 0.07 0.06 0.06 0.05

LWD_Freq Decreases with stress

Moderate 30th 1.34 0.00 3.58 3.89 1.05 Major 10th 0.00 0.00 0.00 0.13 0.00

ChannelIncision* Increases with stress

Moderate 70th 0.22 0.22 0.22 0.22 0.22 Major 90th 0.40 0.40 0.40 0.40 0.40

* All ecoregions combined due to low sample sizes

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Table 11. Number of reference sites for wadeable reach benchmark development and subsequent condition rating assignments of minimal, moderate, or major departure. Samples sizes presented by Omernik hybrid level III/IV ecoregions and stream size: small wadeable streams ≤ 10 m or large wadeable streams > 10 m.

Northern

Xeric Basin

Pacific Northwest and

Xeric California Northern Rockies

Southern Rockies

Indicator Small Large Small Large Small Large Small Large PctOverheadCover 30 11 138 66 143 42 53 33

BankOverheadCover 30 11 138 66 143 42 53 33 RiparianVegComplexity 30 11 138 66 143 43 53 33

Pctfines2 30 11 137 65 138 42 53 33 InstreamHabitatComplexity 30 11 137 65 138 42 53 33

LWD_Freq 30 11 136 64 135 41 52 32 ChannelIncision 22 6 117 55 115 38 39 27

Table 11 continued. Number of reference sites for wadeable reach benchmark development and subsequent condition rating assignments of minimal, moderate, or major departure. Samples sizes presented by Omernik hybrid level III/IV ecoregions and stream size: small wadeable streams ≤ 10 m or large wadeable streams > 10 m.

Eastern Xeric

Basin

Northern Cultivated

Plains Rangeland

Plains Southern

Xeric Basin Southwest Mountains

Indicator Small Large Small Large Small Large Small Large Small Large PctOverheadCover 40 29 24 31 75 74 34 29 31 22

BankOverheadCover 40 29 24 31 75 74 34 29 31 22 RiparianVegComplexity 40 29 24 31 75 74 34 29 31 23

Pctfines2 40 29 23 28 73 68 34 29 31 23 InstreamHabitatComplexity 40 29 23 28 73 68 34 29 31 23

LWD_Freq 40 28 19 24 70 64 33 29 31 23 ChannelIncision 27 21 10 13 44 25 21 13 27 16

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* All ecoregions combined due to low sample sizes

Table 12. Number of reference sites for boatable reach benchmark development and subsequent condition rating assignments of minimal, moderate, or major departure determined by quantiles of regional values by Omernik hybrid level III/IV ecoregions. Note that PctOverheadCover is not collected at boatable sites and is therefore not included in this table.

Indicator

Eastern and Southern

Xeric Basin

Northern and Southern

Rockies and Northern

Xeric Basin Pacific

Northwest

Northern Cultivated

Plains Rangeland

Plains BankOverheadCover 29 52 46 36 26

RiparianVegComplexity 29 52 46 36 26 Pctfines2 29 49 45 35 26

InstreamHabitatComplexity 29 51 45 36 26 LWD_Freq 29 51 45 36 26

ChannelIncision* 65 across all ecoregions

53

Appendix A. BLM AIM AquADat Local Feature Class Metadata Description Abstract: This feature class includes monitoring data collected nationally to understand the status, condition, and trend of resources on BLM lands. Data are collected in accordance with the BLM Assessment, Inventory, and Monitoring (AIM) Strategy. The AIM Strategy specifies a probabilistic sampling design, standard core indicators and methods, electronic data capture and management, and integration with remote sensing. Attributes include the BLM aquatic core indicators: pH, conductivity, temperature, pool depth, length, frequency, streambed particles sizes, bank stability and cover, floodplain connectivity, large woody debris, macroinvertebrate biological integrity, ocular estimates of vegetative type, cover, and structure and canopy cover. In addition, the contingent indicators of total nitrogen and phosphorous, turbidity, bank angle, thalweg depth profile and quantitative vegetation estimates (see the Data Structure and Attribute Information section for exact details on attributes). Data were collected and managed by BLM Field Offices, BLM Districts, and/or affiliated field crews with support from the BLM National Operations Center. Data are stored in a centralized database (AquADat) at the BLM National Operations Center. Purpose: This dataset was created to monitor the status, condition and trend of national BLM resources in accordance with BLM policies. The methodology used for the collection of these data can be found in TR 1735-2 (AIM National Aquatic Monitoring Framework: Field Protocol for Wadeable Lotic System). These data should not be used for statistical or spatial inferences without knowledge of how the sample design was drawn or without calculating spatial weights for the points based on the sample design. Update frequency: Annually Data Access Constraints Access constraints: NON-PUBLIC. BLM INTERNAL USE ONLY. Unverified Dataset. These data will be restricted to internal BLM staff, contractors and partners directly involved with developing the associated planning documents. These data might contain sensitive information, and may only be accessed by the public by filing a FOIA request, which may or may not be granted depending on the applicable FOIA exemption(s). Use constraints: "NON-PUBLIC, BLM INTERNAL USE ONLY. NOT FOR DISTRIBUTION. NO WARRANTY IS MADE BY BLM AS TO THE ACCURACY, RELIABILITY, OR COMPLETENESS OF THESE DATA FOR INDIVIDUAL USE OR AGGREGATE USE WITH OTHER DATA. The User is cautioned that these data have not been verified, and have not been approved for release. The User should take reasonable measures to ensure that these data are protected from disclosure. Although these data might be available to internal BLM staff, contractors or partners; the quality and fit for use of these data should be considered unknown. The User is advised that the content of the metadata file associated with these data might be incomplete. The User assumes the entire risk associated with its use of these data. The BLM shall not be held liable for unintentional disclosure; nor for any use or misuse of the data described or contained herein. Further, the BLM assumes no liability for the current accuracy, reliability, completeness or utility of these data on any system or for any general or scientific purposes. The User bears all responsibility in determining whether these data are fit for the User's intended use. These data are neither legal documents nor land surveys, and must not be used as such. Official records can be referenced at most BLM offices. Please report any errors in the data to the BLM office from which it was obtained. Any products derived from these data should clearly identify the source as unverified data. They must also include the statement ""REVIEW AND/OR DISPLAY COPY - NOT FOR DISTRIBUTION.” The BLM should be cited as the data source in any products derived from these data. Any Users wishing to modify the data are obligated to describe within the process history section of the metadata the types of modifications they have performed. The User specifically agrees not to misrepresent the data, nor to imply that changes made were approved or endorsed by BLM. This data may be updated by the BLM without notification."

Spatial Domain Boundary Coordinates- Unprojected (geographic) West -157.514027 (longitude) East -102.365554 (longitude) North 70.761066 (latitude) South 32.0226 (latitude)

Point Of Contact Bureau of Land Management Scott Miller Director, National Aquatic Monitoring Center (720) 545-8367 BLM National Operations Center; Denver Federal Center, Building 50 Lakewood, CO 80225

Citation Title: BLM AIM AquADat Local Feature Class Originators: US Dept of Interior, Bureau of Land Management Publication date: 20170101 Data type: vector digital data Dataset credit: US Department of the Interior - Bureau of Land Management Assessment, Inventory, and Monitoring Project Team; NAMC

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Data Structure and Attribute Information (most commonly used columns are in light gray)

Data Type Indicator or Column Heading Description

Site

Des

crip

tors

SiteCode

Site code assigned during the design process and used by field crews and project leads to track samples. SiteCodes should be used to track design specific information; whereas MasterCode should be used to query information about a site across multiple designs or revisits.

StreamName Stream name based off the USGS National Hydrography Dataset (NHD) layer

MasterCode Code used to identify a unique location. SiteCode may (or may not) change on site revisits. However, MasterCode will remain the same across all site visits.

UID Unique code for an individual site visit. This is the database primary key. Date Sample date (units: m/d/yyyy)

MergeSiteCodes List of existing monitoring sites that fall in the same location as this site. All such sites have been screened for merging using the site scouting protocol

VisitNumber A sequential number indicating the number of times the site has been visited up to the date of this sample. MidLat Latitude of the reach midpoint in NAD 83 (units: decimal degrees) MidLong Longitude of the reach midpoint in NAD 83 (units: decimal degrees) Project Project associated with data collection Protocol Protocol used for collecting the data (wadeable or boatable) Stratum The original design stratum for the site

DesignType

Specifies whether the site was a part of a spatially balanced random design (RandomGRTS), a systematic random design (RandomSystematic), or whether it was selected as a targeted site to address a specific management concern (Targeted)

IndicatorsCollected Specifies whether all core indicators (CoreIndicators) or only a subset of core indicators (SubsetCoreIndicators) were collected

StreamOrder Strahler stream order of the site

StreamSizeOrder

Stream size category as defined in the design by grouping Strahler stream orders together. Generally, SS- Small Streams (Stream Order:1-2) , LS-Large Streams (Stream Order:3-4), RV-Rivers (Stream Order >5), RM-River Major (only streams designated as major rivers).

55

Data Type Indicator or Column Heading Description

Site

Des

crip

tors

StreamSizeBankfull

Stream size category as defined by bankfull width and protocol. SmallWadeable: wadeable streams <10 m bankfull width, LargeWadeable: wadeable stream >10 m bankfull width, Boatable: All boatable streams regardless of bankfull width.

NAMC_Benchmark NAMC assigned category used to determine default benchmarks for making indicator specific condition ratings. This field is a combination of EcoregionHybrid10 and StreamSizeBankfull.

Ecoregion Level II ecoregion or EPA hybrid level III ecoregion Climate EPA climatic zone (Mountain, Xeric, Plains) BRLat Bottom of reach latitude in NAD 83 (units: decimal degrees) BRLong Bottom of reach longitude in NAD 83 (units: decimal degrees) TRLat Top of reach latitude in NAD 83 (units: decimal degrees) TRLong Top of reach longitude in NAD 83 (units: decimal degrees)

TotRchlen

Total length of the reach (m) measured along the thalweg as calculated by 20 times average bankfull width (wadeable) or 40 times wetted width (boatable), with a min of 150 m and a max of 4000 m. This field is provided for context for the site but sampled reach lengths may differ from this total reach length for partially sampled sites (FieldStatus= Sampled - Partial). (units: m, min: 150, max: 4000, n=1)

FieldStatus

Whether the reach was fully sampled, partially sampled, or sampled with interrupted flow. Data from partially sampled sites or sites with interrupted flow should be examined carefully to insure crews followed the modified protocols properly.

56

Data Type Indicator or Column Heading Description

Biod

iver

sity

and

Ripa

rian

Habi

tat Q

ualit

y

PctOverheadCover Average % overhead cover provided by stream banks, vegetation, or other objects measured mid-channel (looking 4 directions) across 11 transects (units: %, min: 0, max: 100, n= 44)

BankOverheadCover Average percent overhead cover provided by stream banks (left and right), vegetation or other objects measured at the scour line of the left and right banks across 11 transects (units: %, min: 0, max: 100, n= 22)

VegComplexity

Aggregate measure of the average vegetative cover provided by three different vegetative height category: Canopy (>5m), Understory (0.5-5m), and Ground (<0.5m). Each vegetative height category is then divided into two vegetation types (e.g. woody or nonwoody). Proportional cover was binned into four classes (0.875, 0.575, 0.25, and 0.05) per vegetation type, summed across the three heights, and then averaged across the left and right banks of 11 transects. (units: none, min: 0, max: 2.6, n= 132)

RiparianVegCanopyCover

Measure of the average riparian vegetative cover provided by canopy vegetation (>5m). Proportional cover was binned into four classes (0.875, 0.575, 0.25, and 0.05) and then averaged across the left and right banks of 11 transects. (units: none, min: 0, max: 0.88, n= 22)

RiparianVegUnderstoryCover

Measure of the average riparian vegetative cover provided by understory vegetation (0.5-5m). Proportional cover was binned into four classes (0.875, 0.575, 0.25, and 0.05) and then averaged across the left and right banks of 11 transects. (units: none, min: 0, max: 0.88, n= 22)

RiparianVegGroundCover

Measure of the average riparian vegetative cover provided by the ground cover vegetation (<0.5m). Proportional cover was binned into four classes (0.875, 0.575, 0.25, and 0.05) and then averaged across the left and right banks of 11 transects. (units: none, min: 0, max: 0.88, n= 22)

NonNativeWoody Percent of 22 vegetation plots with invasive woody vegetation present (units: %, min: 0, max: 100, n= 22)

NativeWoody Percent of 22 vegetation plots with native woody vegetation present (units: %, min: 0, max: 100, n= 22)

NonNativeHerb Percent of 22 vegetation plots with invasive herbaceous vegetation present (units: %, min: 0, max: 100, n= 22)

NativeHerb Percent of 22 vegetation plots with native herbaceous vegetation present (units: %, min: 0, max: 100, n= 22)

SedgeRush Percent of 22 vegetation plots with sedges and rushes present (units: %, min: 0, max: 100, n= 22)

57

Data Type Indicator or Column Heading Description

Biod

iver

sity

and

Ripa

rian

Habi

tat Q

ualit

y

InvasiveInvertSp Presence or absence of invasive macroinvertebrates

ObservedInvertRichness Observed macroinvertebrate richness standardized to model specific operational taxonomic units (OTU) (units: # of taxa)

ExpectedInvertRichness Expected macroinvertebrate richness in the absence of anthropogenic impacts from the O/E model (units: # of taxa)

OE_Macroinvertebrate

Biological condition was assessed using an observed/expected (O/E) index. O/E models compare the macroinvertebrate taxa observed at sites of unknown biological condition (i.e., ‘test sites’) to the assemblages expected to be found in the absence of anthropogenic stressors (see Hawkins et al. 2000 for details). The specific model used can be found in the OE_MMI_ModelUsed column and the model specific metadata can be found at www.usu.edu/buglab/. (units: none, min: 0, max: 1.5)

MMI_Macroinvertebrate Biological condition was assessed using the MMI (MultimetricIndex) model specified in the OE_MMI_ModelUsed column.

OE_MMI_ModelUsed

The O/E or MMI model used to determine biological integrity. NAMC currently has the following models available UT, NV, CA, CO, OR, regional models for areas sampled by AREMP or PIBO programs (Northwest Forest Plan or Columbia River Basin), and a West-wide model. Generally, State based models are used if available, otherwise the West-wide model is used.

MacroinvertebrateCount

This field indicates whether or not the site's environmental gradients were within the range of experience of the model. A fail indicates the model potentially had to extrapolate, rather than interpolate, to accommodate one or more of the habitat variables. O/E scores and condition ratings should be interpreted cautiously if a site failed the test of experience.

ModelApplicability

Number of macroinvertebrates identified and resampled to a standardized fixed count (i.e. rarefaction). Samples with counts less than 200 macroinvertebrates can result from sampling and/or laboratory processing errors, but low counts can also be a signal of degraded biological condition. Additional samples should be taken to verify Major or Moderate departure from reference. (units: # of individuals, min: 0, max: 400)

58

Data Type

Indicator or Column Heading Description

Wat

er Q

ualit

y TotalNitrogen Measured total nitrogen value (units: µg/L, n=1) PRD_TotalNitrogen Site specific predicted values for reference nitrogen concentrations (Olson and Hawkins 2013) (units: µg/L) TotalPhosphorous Measured total phosphorous value (units: µg/L, n=1)

PRD_TotalPhosphorous Site specific predicted values for reference phosphorus concentrations (Olson and Hawkins 2013) (units: µg/L)

SpecificConductance Measured specific conductance value. The specific conductance is conductivity standardized to 25 degrees C. (units: µS/cm, min: 0, max: 65500, n=1)

PRD_SpecificConductance Site specific predicted values for reference specific conductance values (Olson and Hawkins 2012) (units: µS/cm, min: 0, max: 65500)

pH Measured pH value (units: SU, min: 0, max: 14, n=1) InstantTemp Instantaneous water temperature measurement (units: degrees C, n=1)

MeanAugTemp Site specific prediction of 19 year mean August stream temperature for the period of 1993 – 2011 as derived from NorWest models (Isaak et al. 2016 https://doi.org/10.2737/RDS-2016-0033) (units: degrees C, n=1)

Turbidity Average water clarity as measured by the suspended solids in the water column (units: NTU, n=3)

Wat

ersh

ed F

unct

ion

and

Inst

ream

Hab

itat

Qua

lity

PctPools Percent of the sample reach (linear extent) classified as pool habitat as assessed using the core pool method (units: %, min: 0, max: 100, n=1)

ResPoolDepth Average residual pool depth as assessed using the core pool method (units: m, n= variable depending on number of pools)

PoolFreq Frequency of pools in the reach as assessed using the core pool method (units: # pools/km, n=1) LWD_Freq Frequency of large woody debris within the bankfull channel of the reach (units: # pieces/ 100 m, n= 1) LWD_Vol Volume of LWD within the bankfull channel of the reach (units: m^3/100 m, n=1) PctFines2 Percent of 210 particles with a b-axis < 2 mm (units: %, min: 0, max: 100, n=210) PctFines6 Percent of 210 particles with a b-axis < 6 mm (units: %, min: 0, max: 100, n=210)

D16 Particle size corresponding to the 16th percentile of measured particles (units: mm, min: 1, max: 4098, n=210)

D84 Particle size corresponding to the 84th percentile of measured particles (units: mm, min: 1, max: 4098, n=210)

D50 Particle size corresponding to the 50th percentile of measured particles (units: mm, min: 1, max: 4098, n=210)

59

Data Type Indicator or Column Heading Description

Wat

ersh

ed F

unct

ion

and

Inst

ream

Hab

itat Q

ualit

y

GeometricMeanParticleDiam

Geometric mean bed particle diameter= exponential function[mean(log(particle diameter)]. This is a less frequently used metric of characterizing central tendency of substrate sizes, but is the main metric used by the EPA for determining relative bed stability. It is less variable than a D50 and more biologically meaningful because it is more influenced by fine sediment. (units: mm, min: 1, max: 4098, n=210)

PoolTailFines2 Average percent fine sediment (< 2mm) on the pool tail (units %, min: 0, max: 100, n= 3 per pool) PoolTailFines6 Average percent fine sediment (< 6mm) on the pool tail (units %, min: 0, max: 100, n=3 per pool)

BankCoverFoliar Percent of 42 erosional banks with greater than 50% foliar cover provided by perennial vegetation, wood or mineral substrate > 15 cm (units: %, min: 0, max: 100, n= 42)

BankCoverBasal Percent of 42 erosional banks with greater than 50% basal cover provided by perennial vegetation, wood or mineral substrate > 15 cm (units: %, min: 0, max: 100, n= 42)

BankStability Percent of 42 banks lacking visible signs of active erosion (e.g., slump, slough, fracture) (units: %, min: 0, max: 100, n= 42)

BnkCoverFoliar_Stab

Percent of 42 banks both stable (lacking visible signs of active erosions (e.g., slump, slough, fracture)) and covered (greater than 50% foliar cover provided by perennial vegetation, wood or mineral substrate > 15 cm) (units: %, min: 0, max: 100, n= 42)

BnkCoverBedrock Average bank cover composed of bedrock (units: %, min: 0, max: 100, n= 42) BnkCoverCobble Average bank cover composed of cobble > 15 cm (units: %, min: 0, max: 100, n= 42) BnkCoverLWD Average bank cover composed of LWD (units: %, min: 0, max: 100, n= 42) BnkCoverFoliarVeg Average bank foliar cover composed of vegetation (units: %, min: 0, max: 100, n= 42) BnkCoverBasalVeg Average bank basal cover composed of vegetation (units: %, min: 0, max: 100, n= 42) BankfullHeight Average bankfull height measured from water surface across 11 transects (units: m, n = 11) FloodplainHeight Average floodplain height measured from water surface across 11 transects (units: m, n = 11)

ChannelIncision Logarithm of the difference between average bankfull height and average floodplain height= log(FloodplainHeight - BankHeight + 0.1) (units: none, min: -1, max: 2, n=11)

FloodplainConnectivity

The ratio of average floodplain height to average bankfull height taken from the thalweg = (floodplain height + thalweg depth) / (bankfull height + thalweg depth). This is also known as Rosgen's Bank Height Ratio (units: none, n= 11)

60

Data Type Indicator or Column Heading Description

Wat

ersh

ed F

unct

ion

and

Inst

ream

Ha

bita

t Qua

lity

InstreamHabitatComplexity

Aggregate measure of average cover provided by boulders, overhanging vegetation, live trees and roots, LWD, small woody debris, and stream banks for stream fishes measured at 11 plots. Proportional cover was binned into four classes (0.875, 0.575, 0.25, and 0.5), averaged across transects, and then summed across six types of cover. (units: none, min: 0, max: 2.3, n= 66)

BankAngle Measured angle of the stream bank; banks with obtuse angles = >90° and undercut banks with acute angles = <90° (units: degrees, min: 0, max: 180, n= 22)

ThalwegDepthCV Indicator of bed heterogeneity computed as the coefficient of variation of 100-300 thalweg depth measurements (units: none, n= 1)

ThalwegDepthMean Mean thalweg depth. Metric of how deep water was at the site. (units: m, min: 0, max: none, n variable depending on reach length (100 - 300))

PctDry

Percent of the reach that was dry. This is calculated as the number of dry thalweg measurements divided by the total number of thalweg measurements collected and expressed as a percentage. (units: %, min: 0, max: 100, n= variable depending on reach length (100-300))

61

Data Type Indicator or Column Heading Description

Cova

riate

s/ O

ther

BankfullWidth Average bankfull width across 11 transects (units: m, n= 11) WettedWidth Average wetted width across 21 transects (units: m, n= 21)

FloodWidth Average flood prone width as defined as valley width at 2 times bankfull height. The larger the value the larger the floodplain is. (units: m, n= 2)

Entrench

Entrenchment ratio = average floodprone width divided by average bankfull width. Ratios of 1-1.4 represent entrenched streams; 1.41-2.2 represent moderately entrenched streams; and ratios greater than 2.2 indicate rivers only slightly entrenched in a well-developed floodplain (Rosgen 1996). This entrenchment value can then be used with other ancillary site data such as slope and incision to determine stream type (Rosgen 1996) and the potential of the system for floodplain formation. (units: none, min: 1, max: 3, n= 1)

Slope Reach slope measured from the water's surface. In most cases, the reported value is an average of 2 independent measurements that were within 10% of one another. (units: %, min: 0, max: ~45, n= 2)

Sinuosity Reach sinuosity (reach length along the thalweg/straight line distance between BR and TR coordinates) (units: none, min: 1, max: NA, n= 1)

BeaverFlowMod Qualitative visual assessment of extent of beaver flow modifications within the reach (NONE, MINOR, MAJOR)

BeaverSign Qualitative visual assessment of frequency of beaver signs (e.g. chewed logs) within the reach (ABSENT, RARE, COMMON)

SideChannels Presence of absence of side channels WaterWithdrawal Presence or absence of water withdrawals DateChange Date that the site's data was updated and changed (units: m/d/yyyy) ReasonChange Reason for change and field changes; detailing what data was changed and why

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Appendix B. Indicators for Non-Wadeable Reaches Overview An AIM-NAMF protocol for non-wadeable reaches is still being developed. In the interim, AIM-NAMF field crews have used the EPA’s non-wadeable protocol used for the National River and Stream Assessment supplemented by the BLM’s wadeable methods in TR 1735-2 when it is logistically feasible to do so. A draft AIM-NAMF non-wadeable protocol can be requested from NAMC if desired. Key differences between the AIM-NAMF wadeable and non-wadeable protocols are summarized below. Detailed descriptions of differences between wadeable and non-wadeable indicators currently calculated within AquADat are also provided.

- Implementing this protocol requires a minimum of three people rather than two. - All data at non-wadeable reaches are collected in one pass down the river, since going

back upstream is often not an option. - The order of the transects is reversed - A is at the upstream end of the reach and K is at

the downstream end of the reach (Figure 1). Reach setup is done via Google Earth and not in the field.

- There are no intermediate transect measurements taken when using the non-wadeable protocol.

- Some transect data are only collected one side of the river, rather than both. - Bank cover is not recorded by cover category. - Substrate data are collected from the littoral zone and thalweg rather than along each

transect. - Substrate size is visually or tactilely estimated rather than directly measured. - Wetted and bankfull widths are measured using a laser rangefinder rather than a tape

measure. - Bankfull and floodplain heights are often measured using a stadia rod and a hand level,

rather than depth rods. - Pools are not measured in the field with the core pools method. - Because pools cannot be obtained from the core pools methods, thalweg depth profile is

always measured and is not considered a contingent indicator for non-wadeable reaches. - Riparian plots are larger (20 m x 10 m) than at wadeable reaches and begin where the

perennial vegetation begins or at bankfull, rather than scour line. - Instream habitat complexity (fish cover) is only measured 10 m out into the channel

instead of across the entire channel. - Slope is not measured on non-wadeable systems. - No side channels are sampled due to logistical reasons.

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Figure 1. Non-wadeable reach set-up. Large boxes indicate the side of the stream that the boat is on and represent instream, bank, and riparian plots assessed. Whereas small boxes on the opposite bank, represent bank and riparian plots that are assessed visually from the opposite side of the river.

64

Indicator Calculations Macroinvertebrates

• Macroinvertebrates are collected in the littoral zone using a kicknet and reachwide methods. There are no differences in macroinvertebrate indicator calculations.

Riparian Habitat Complexity and Cover • There are no differences in the methods or calculations of non-wadeable vs. wadeable

riparian habitat complexity and cover indicators. However, non-wadeable data is expected to be less precise and accurate than wadeable data because crews are visually estimating values from opposite banks at half of plots.

Percent Canopy Cover

• Mid-channel overhead cover is not measured at non-wadeable reaches so Percent Overhead Cover cannot be calculated. Bank overhead cover is measured but four measurements exist instead of two. All four are averaged to calculate the bank overhead cover.

Water Quality

• There are no differences in water quality indicators between wadeable and nonwadeable reaches.

Pool depth, length, and frequency

• Core pool methods used in wadeable reaches are not logistically feasible in non-wadeable reaches. Therefore, the contingent method of thalweg should be used to assess pool depth, length, and frequency. However, due to the complexity of this data and computations the National AIM team is still working on computing these indicators for non-wadeable reaches.

Pool tail fines

• Pool tail fine methods used for wadeable reaches are not logistically feasible or appropriate for non-wadeable reaches.

LWD

• These indicators are computed identical for wadeable and non-wadeable reaches. However, wood size classes differ between the two protocols. Large wood size classes are as follows: Where MinDiameter and MaxDiameter are one of the following LWD diameter categories (large end):

MinDiameter (m) MaxDiameter (m) 0.3 0.6 0.6 0.8

0.8 1.0 1.0 2.0

And MinLength and MaxLength are one of the following LWD length categories (considering the section of the LWD where the diameter is greater than 0.1 m):

65

MinLength (m) MaxLength (m) 5 15 15 30 30 75

Streambed Particle Sizes

• Particle sizes from non-wadeable reaches are approximated by sound and feel from 100 systematically spaced locations along the thalweg (for more detail see wadeable thalweg spacing requirements). Particles are binned into the following size classes:

Diameter (mm) Size Class Min Max Bedrock/Hardpan 4000 8000 Boulder 250 4000 Cobble 64 250 Gravel 2 64 Sand 0.06 2 Slit/Clay/Muck 0.001 0.06

Percent fines is calculated as the percent of the 100 collected particles that are Slit/Clay/Muck. The National AIM team is still working on translating the above bins into size classes that can be used to calculate the other substrate indicators that are currently being calculated for wadeable reaches. Similarly, wadeable data that was collected in 2013 was collected in a binned fashion rather than measuring particles so additional substrate indicators for these sites are also forth coming. Note that currently no indicators are being calculated from littoral dominant and subdominant estimates of particle sizes, but this data can be provided upon request.

Bank Stability and Cover • Bank cover and stability is assessed on both banks at all main transects for non-wadeable

reaches. However, there are no intermediate transects so only 21 bank stability and cover plots are assessed at non-wadeable reaches compared to 42 plots at wadeable reaches. While bank stability and cover is assessed on both banks, crews do not physically visit both banks so cover is not subdivided into cover types, rather an approximation is made of whether the bank is at least 50 covered or not. However, actual bank stability and cover indicators for non-wadeable reaches are calculated identical to wadeable reaches.

Floodplain Connectivity

• Floodplain connectivity is measured and computed identically for wadeable and non-wadeable reaches.

Instream Habitat Complexity

• This indicator is computed identical for wadeable and non-wadeable reaches. However, fish cover is only assessed in littoral plots rather than across the entire channel.

66

Bank Angle • Angle is only assessed on the bank that the crew visits and is only approximated. Angle

is binned into one of the following categories: Flat (<5 degrees) Gradual (5-30 degrees) Steep (30-75 degrees) Near Vertical/Undercut >75 degrees)

Because this data is collected so differently than wadeable reaches and is so coarse, we do not currently report these values within AquADat but data can be obtained upon request.

Thalweg

• This indicator is computed identical for wadeable and non-wadeable reaches. However, for some reaches, depths have to be approximated, and the maximum likely depth able to be approximated is roughly 6 m (slightly longer than then length of most stadia rods).

Bankfull and wetted width

• A laser range finder is used to measure floodprone width but there are no differences in the indicator calculations between non-wadeable and wadeable reaches.

Flood-prone width

• A laser range finder is used to measure floodprone width but there are no differences in the indicator calculations between non-wadeable and wadeable reaches.

Slope

• Is not measured at non-wadeable reaches. Values may be estimated topographically on maps but the National AIM team does not currently provide any estimates of slope for these reaches.

Sinuosity

• There are no differences in the calculations of non-wadeable vs. wadeable sinuosity, although total reach length is obtained using measurements on google earth.

67

Appendix C. Special Situations The field protocol (TR-1735-2) has five appendices for special situations: interrupted flow, side channels, beaver-impacted reaches, braided systems, and partial data collection. In general, no modifications are made to indicator computations for reaches that have been sampled using special situation protocols but a few specific things to note are called out below: Interrupted flow In general, no modifications are needed for indicator computations in interrupted flow situations and data are used from dry transects and wet transects alike. Thalweg is the only exception to this and thalweg depths of 0 are excluded from ThalwegDepthCV because this could unduly influence bed heterogeneity values. Side channels The side channel special situations protocol was modified significantly in 2019 to collect data on both dry and wet side channels, whereas previously measurements were only taken on side channels with >15% of the flow. This should be considered when comparing data pre vs. post 2019. This modification was made to make the protocol less flow dependent. Which field methods are collected on side channels has also changed throughout the years, but historic data has been recomputed to standardize data as much as possible. Table 1 shows what data is currently collected on side channels and how this data is included in computations of each indicator. A separate appendix is in development on how field methods for all indicators have changed through time. Beaver-impacted reaches No modifications are made to indicator computations for beaver-impacted reaches, but given how variable these systems may be, reach averages may not adequately characterize actual conditions. Therefore, special attention should be paid to raw data at these sites. Braided systems No modifications are made to indicator computations for braided systems, but given how variable these systems may be, reach averages may not adequately characterize actual conditions. Therefore, special attention should be paid to raw data at these sites. Partial data collection Any site with partial data collection should be treated more skeptically and field comments and notes should be examined to determine why it was a partial reach. The fewer the measurements the less precise the data is likely to be. Similarly, measurements not collected because of dense vegetation, safety concerns, or time limitations may bias the data in different ways. Table 2 gives the minimum data the NOC considers acceptable, but for highly contentious decisions full data collection is recommended.

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Table 1. Whether side channels are sampled for each field method and how/if the sampled data is used in indicator computations if it is sampled. Note only one side channel is sampled per transect and sampled side channels may or may not have water. Field Method Computed Indicators Side Channel

Macroinvertebrate biological integrity

InvasiveInvertSp, ObservedInvertRichness, ExpectedInvertRichness ,OE_Macroinvertebrate, MMI_Macroinvertebrate

Not sampled

Canopy cover PctOverheadCover, BankOverheadCover

Averaged across main and side channels

Frequency of occurrence of priority noxious vegetation

NonNativeWoody, NonNativeHerb, Species PercentOfPlotsPresent

Averaged across main and side channels

Frequency of occurrence of priority native woody riparian vegetation

NativeWoody, NativeHerb, SedgeRush, Species PercentOfPlotsPresent

Averaged across main and side channels

Ocular est. of riparian vegetative type, cover, and structure

VegComplexity, RiparianVegComplexity,

Averaged across main and side channels

pH pH Not sampled

Specific conductance SpecificConductance Not sampled Temperature InstantTemp, MeanAugTemp Not sampled Total nitrogen and phosphorous TotalNitrogen, TotalPhosphorous Not sampled

Turbidity Turbidity Not sampled

Pool depth, length, and frequency

ResPoolDepth, PctPools, NumPools, PoolFreq Not sampled

Pool tail fines PoolTailFines2, PoolTailFines6 Not sampled

Large woody debris Frequency of LWD, Volume of LWD, component of RelativeBedStability

Summed across main and side channels

Streambed particle size distribution

PctFines, D16, D84, D50, GeometricMeanParticleDiam, component of RelativeBedStability

All particles included in calculations regardless of location

69

Table 1 continued. Whether side channels are sampled for each field method and how/if the sampled data is used in indicator computations if it is sampled. Note only one side channel is sampled per transect and sampled side channels may or may not have water. Field Method Computed Indicators Side Channel

Bank stability and cover

BankCoverFoliar, BnkCoverBasal2, BankStability, BnkCoverFoliarStab, BnkCoverBedrock, BnkCoverCobble, BnkCoverLWD, BnkCoverFoliarVeg, BnkCoverBasal2

Only data from outer banks used

Floodplain connectivity

BankfullHeight, FloodplainHeight, ChannelIncision, FloodplainConnectivity

Not sampled; No measurements taken on side channel outer banks or islands. Only outer bank of main channel measured at transects where side channels are present.

Ocular estimate of instream habitat complexity

InstreamHabitatComplexity Averaged across main and side channels

Bank angle BankAngle Only data from outer banks used

Thalweg depth profile

ThalwegDepthCV, ThalwegDepthMean, PctDry, component of RelativeBedStability

Not sampled

Bankfull width BankfullWidth, Entrench, component of RelativeBedStability

Summed across main and side channels

Wetted width WettedWidth, component of RelativeBedStability

Summed across main and side channels

Flood-prone width FloodWidth, Entrench Not sampled

Slope Slope, component of RelativeBedStability Not sampled

Reach length Sinuosity Not sampled

Human impacts None currently available Averaged across main and side channels

70

Table 2. Minimum data requirements for each field method to compute and store indicators within AquADat. Field Method Computed Indicators Minimum Data Requirements

Macroinvertebrate biological integrity

InvasiveInvertSp, ObservedInvertRichness, ExpectedInvertRichness, OE_Macroinvertebrate

Data suspect if less than 8 nets composited but included with flags if at least 6 nets present

Canopy cover PctOverheadCover, BankOverheadCover

4 or 2 locations at each of 5 transects for overhead and bankoverhead cover, respectively

Frequency of occurrence of priority noxious vegetation

NonNativeWoody, NonNativeHerb, Species PercentOfPlotsPresent

10 banks (2 banks at each of 5 transects)

Frequency of occurrence of priority native woody riparian vegetation

NativeWoody, NativeHerb, SedgeRush, Species PercentOfPlotsPresent

10 banks (2 banks at each of 5 transects)

Ocular est. of riparian vegetative type, cover, and structure

VegComplexity, RiparianVegComplexity, 10 banks (2 banks at each of 5 transects)

pH pH Probe calibrated within 8 days

Specific conductance SpecificConductance Data suspect if probe not calibrated within 8 days but included with flags if calibrated at least some point during field season

Temperature InstantTemp, MeanAugTemp NA Total nitrogen and phosphorous

TotalNitrogen, TotalPhosphorous NA

Turbidity Turbidity Three replicates must be within 30% of one another.

Pool depth, length, and frequency

ResPoolDepth, PctPools, NumPools, PoolFreq

At least 45% of total reach length assessed and had flowing water.

Pool tail fines PoolTailFines2, PoolTailFines6

2 grids per pool; Note all particles can be nonmeasurable (i.e. bedrock or organic matter)

Large woody debris Frequency of LWD, Volume of LWD, component of RelativeBedStability

4 intertransects (e.g. A to B)

Streambed particle size distribution

PctFines, D16, D84, D50, GeometricMeanParticleDiam, component of RelativeBedStability

100 particles

71

Table 2 continued. Minimum data requirements for each field method to compute and store indicators within AquADat. Field Method Computed Indicators Minimum Data Requirements

Bank stability and cover

BankCoverFoliar, BnkCoverBasal2, BankStability, BnkCoverFoliarStab, BnkCoverBedrock, BnkCoverCobble, BnkCoverLWD, BnkCoverFoliarVeg, BnkCoverBasal2

18 banks (2 banks at 5 main and 4 intermediate transects)

Floodplain connectivity

BankfullHeight, FloodplainHeight, ChannelIncision, FloodplainConnectivity

5 transects

Ocular estimate of instream habitat complexity

InstreamHabitatComplexity 5 transects

Bank angle BankAngle 10 banks (2 banks at each of 5 transects)

Thalweg depth profile

ThalwegDepthCV, ThalwegDepthMean, PctDry, component of RelativeBedStability

80% of data collected for full reaches and at least 80% of data collected for each intertransect (A to B) sampled at partially sampled reaches

Bankfull width BankfullWidth, Entrench, component of RelativeBedStability

5 transects

Wetted width WettedWidth, component of RelativeBedStability 9 transects (5 main and 4 intermediate)

Flood-prone width FloodWidth, Entrench 1 floodprone width and corresponding bankfull width measurement

Slope Slope, component of RelativeBedStability

At least 1 pass for at least 45% of the total reach length; If 2 or more passes present, slope estimates must be within 20% of each other

Reach length Sinuosity Sinuosity not computed for partially collected reaches due concerns about precision

Human impacts HumanInfluence (in development) 10 banks (2 banks at 5 transects)

72

Appendix D. State priority native and nonnative lists

State Type CommonName ScientificName AK NonNativeAquatic CANADIAN WATERWEED Elodea canadensis AK NonNativeAquatic EURASIAN WATERMILFOIL Myriophyllum spicatum AK NonNativeAquatic PURPLE LOOSESTRIFE Lythrum salicaria AK NonNativeAquatic REED CANARY GRASS Phalaris arundinacea AK NonNativeHerb AUSTRIAN FIELDCRESS Rorippa austriaca AK NonNativeHerb BIRD VETCH Vicia cracca AK NonNativeHerb CANADA THISTLE Cirsium arvense AK NonNativeHerb COUCH GRASS Elymus repens AK NonNativeHerb GALINSOGA Galinsoga parviflora AK NonNativeHerb HEMPNETTLE Galeopsis tetrahit AK NonNativeHerb HOARY CRESS_WHITETOP Cardaria spp. AK NonNativeHerb HORSENETTLE Solanum carolinense AK NonNativeHerb JAPANESE KNOTWEED Polygonum cuspidatum AK NonNativeHerb LEAFY SPURGE Euphorbia esula AK NonNativeHerb PERENNIAL PEPPERWEED Lepidium latifolium AK NonNativeHerb RUSSIAN KNAPWEED Acroptilon repens AK NonNativeHerb SOWTHISTLE Sonchus spp. AK NonNativeHerb WHITE SWEET CLOVER Melilotus officinalis AK NonNativeWoody EUROPEAN BIRD CHERRY Prunus padus

State Type CommonName ScientificName AZ NativeWoody ASPEN Populus tremuloides AZ NativeWoody BOXELDER Acer negundo AZ NativeWoody COTTONWOOD Populus spp. AZ NativeWoody DESERT BROOM Baccharis spp. AZ NativeWoody NEW MEXICO PRIVET Forestiera spp. AZ NativeWoody WILLOW Salix spp. AZ NonNativeWoody SALTCEDAR Tamarix spp.

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State Type CommonName ScientificName CA NativeWoody ASPEN Populus tremuloides CA NativeWoody CHOKECHERRY Prunus virginiana CA NativeWoody COTTONWOOD Populus spp. CA NativeWoody CURRANT Ribes spp. CA NativeWoody DOGWOOD Cornus spp. CA NativeWoody ELDERBERRY Sambucus nigra CA NativeWoody ROSE Rosa spp. CA NativeWoody WILLOW Salix spp. CA NonNativeAquatic CURLY PONDWEED Potamogeton crispus CA NonNativeAquatic EURASIAN WATERMILFOIL Myriophyllum spicatum CA NonNativeAquatic HYDRILLA Hydrilla verticillata CA NonNativeAquatic PURPLE LOOSESTRIFE Lythrum salicaria CA NonNativeAquatic WATERHYACINTH Eichhornia crassipes CA NonNativeHerb BULBOUS BLUEGRASS Poa bulbosa CA NonNativeHerb BULL THISTLE Cirsium vulgare CA NonNativeHerb CANADA THISTLE Cirsium arvense CA NonNativeHerb COCKLEBUR Xanthium spp. CA NonNativeHerb HOUNDSTONGUE Cynoglossum officinale CA NonNativeHerb MEDITERRANEAN SAGE Salvia aethiopis CA NonNativeHerb PERENNIAL PEPPERWEED Lepidium latifolium CA NonNativeHerb POISON HEMLOCK Conium maculatum CA NonNativeHerb SCOTCH THISTLE Onopordum acanthium CA NonNativeHerb WHITE HOREHOUND Marrubium vulgare CA NonNativeHerb YELLOW NUTSEDGE Cyperus esculentus CA NonNativeWoody FRENCH BROOM Genista monspessulana CA NonNativeWoody HIMALAYAN BLACKBERRY Rubus armeniacus CA NonNativeWoody RUSSIAN OLIVE Elaeagnus angustifolia CA NonNativeWoody SALTCEDAR Tamarix spp. CA NonNativeWoody SCOTCH BROOM Cytisus scoparius CA NonNativeWoody TREE OF HEAVEN Ailanthus altissima

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State Type CommonName ScientificName CO NativeWoody ALDER Alnus spp. CO NativeWoody ASPEN Populus tremuloides CO NativeWoody SAGEBRUSH Artemisia spp. CO NativeWoody BIRCH Betula spp. CO NativeWoody BOXELDER Acer negundo CO NativeWoody CHOKECHERRY Prunus virginiana CO NativeWoody COTTONWOOD Populus spp. CO NativeWoody DOGWOOD Cornus spp. CO NativeWoody DOUGLAS FIR Pseudotsuga spp. CO NativeWoody FIR Abies spp. CO NativeWoody GAMBELS OAK Quercus gambelii CO NativeWoody GREASEWOOD Sarcobatus vermiculatus CO NativeWoody HAWTHORN Crataegus rivularis CO NativeWoody JUNIPER Juniperus spp. CO NativeWoody MOUNTAIN MAHOGANY Cercocarpus montanus CO NativeWoody NEW MEXICO PRIVET Forestiera spp. CO NativeWoody PINE Pinus spp. CO NativeWoody RUBBER RABBITBRUSH Ericameria nauseosa CO NativeWoody SERVICEBERRY Amelanchier alnifolia CO NativeWoody SILVER BUFFALOBERRY Shepherdia argentea CO NativeWoody SNOWBERRY Symphoricarpos spp. CO NativeWoody THREE LEAF SUMAC Rhus trilobata CO NativeWoody WILLOW Salix spp. CO NonNativeAquatic CURLY PONDWEED Potamogeton crispus CO NonNativeAquatic EXOTIC BUR-REED Sparganium erectum CO NonNativeAquatic FLOWERING RUSH Butomus umbellatus CO NonNativeAquatic GIANT SALVINIA Salvinia molesta CO NonNativeAquatic HYDRILLA Hydrilla verticillata CO NonNativeAquatic NARROWLEAF CATTAIL Typha angustifolia CO NonNativeAquatic PURPLE LOOSESTRIFE Lythrum salicaria CO NonNativeAquatic WATERMILFOIL Myriophyllum spp. CO NonNativeHerb CANADA THISTLE Cirsium arvense CO NonNativeHerb COMMON REED Phragmites australis CO NonNativeHerb GIANT REED Arundo donax CO NonNativeHerb HAIRY WILLOW-HERB Epilobium hirsutum CO NonNativeHerb HOARY CRESS_WHITETOP Cardaria draba CO NonNativeHerb HOUNDSTONGUE Cynoglossum officinale CO NonNativeHerb JAPANESE KNOTWEED Polygonum cuspidatum CO NonNativeHerb KNAPWEED Centaurea spp. CO NonNativeHerb PERENNIAL PEPPERWEED Lepidium latifolium CO NonNativeHerb POISON HEMLOCK Conium maculatum CO NonNativeHerb SCOTCH THISTLE Onopordum acanthium CO NonNativeHerb TEASEL Dipsacus spp. CO NonNativeWoody RUSSIAN OLIVE Elaeagnus angustifolia CO NonNativeWoody SALTCEDAR Tamarix spp.

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State Type CommonName ScientificName ID NativeWoody ALDER Alnus spp. ID NativeWoody ASPEN Populus tremuloides ID NativeWoody BANEBERRY Actaea rubra ID NativeWoody BIRCH Betula spp. ID NativeWoody CHOKECHERRY Prunus virginiana ID NativeWoody CINQUEFOIL Potentilla fruticosa ID NativeWoody COTTONWOOD Populus spp. ID NativeWoody CURRANT Ribes spp. ID NativeWoody DOGWOOD Cornus spp. ID NativeWoody DOUGLAS FIR Pseudotsuga spp. ID NativeWoody FIR Abies spp. ID NativeWoody JUNIPER Juniperus spp. ID NativeWoody MAPLE Acer spp. ID NativeWoody PINE Pinus spp. ID NativeWoody ROSE Rosa spp. ID NativeWoody SERVICEBERRY Amelanchier alnifolia ID NativeWoody SNOWBERRY Symphoricarpos spp. ID NativeWoody SPRUCE Picea spp. ID NativeWoody WILLOW Salix spp. ID NonNativeAquatic CURLY PONDWEED Potamogeton crispus ID NonNativeAquatic EURASIAN WATERMILFOIL Myriophyllum spicatum ID NonNativeAquatic FLOWERING RUSH Butomus umbellatus ID NonNativeAquatic HYDRILLA Hydrilla verticillata ID NonNativeAquatic PURPLE LOOSESTRIFE Lythrum salicaria ID NonNativeHerb CANADA THISTLE Cirsium arvense ID NonNativeHerb COMMON REED Phragmites australis ID NonNativeHerb HOARY CRESS_WHITETOP Cardaria draba ID NonNativeHerb HOUNDSTONGUE Cynoglossum officinale ID NonNativeHerb KNOTWEED Polygonum spp. ID NonNativeHerb LEAFY SPURGE Euphorbia esula ID NonNativeHerb MUSK THISTLE Carduus nutans ID NonNativeHerb PERENNIAL PEPPERWEED Lepidium latifolium ID NonNativeHerb POISON HEMLOCK Conium maculatum ID NonNativeHerb RUSSIAN KNAPWEED Acroptilon repens ID NonNativeHerb SCOTCH THISTLE Onopordum acanthium ID NonNativeHerb TOADFLAX Linaria spp. ID NonNativeWoody HIMALAYAN BLACKBERRY Rubus armeniacus ID NonNativeWoody RUSSIAN OLIVE Elaeagnus angustifolia ID NonNativeWoody SALTCEDAR Tamarix spp. ID NonNativeWoody SCOTCH BROOM Cytisus scoparius

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State Type CommonName ScientificName OR-Eastern NativeWoody ALDER Alnus spp. OR-Eastern NativeWoody ASPEN Populus tremuloides OR-Eastern NativeWoody BIRCH Betula spp. OR-Eastern NativeWoody CHOKECHERRY Prunus virginiana OR-Eastern NativeWoody COTTONWOOD Populus spp. OR-Eastern NativeWoody CURRANT Ribes spp. OR-Eastern NativeWoody DOGWOOD Cornus spp. OR-Eastern NativeWoody DOUGLAS FIR Pseudotsuga spp. OR-Eastern NativeWoody ELDERBERRY Sambucus nigra OR-Eastern NativeWoody HAWTHORN Crataegus rivularis OR-Eastern NativeWoody MAPLE Acer spp. OR-Eastern NativeWoody MOCK ORANGE Philadelphus lewisii OR-Eastern NativeWoody PINE Pinus spp. OR-Eastern NativeWoody ROSE Rosa spp. OR-Eastern NativeWoody SERVICEBERRY Amelanchier alnifolia OR-Eastern NativeWoody WALNUT Juglans spp. OR-Eastern NativeWoody WILLOW Salix spp. OR-Eastern NonNativeAquatic CURLY PONDWEED Potamogeton crispus OR-Eastern NonNativeAquatic EURASIAN WATERMILFOIL Myriophyllum spicatum OR-Eastern NonNativeAquatic HYDRILLA Hydrilla verticillata OR-Eastern NonNativeAquatic PURPLE LOOSESTRIFE Lythrum salicaria OR-Eastern NonNativeHerb BULL THISTLE Cirsium vulgare OR-Eastern NonNativeHerb CANADA THISTLE Cirsium arvense OR-Eastern NonNativeHerb HOUNDSTONGUE Cynoglossum officinale OR-Eastern NonNativeHerb KNOTWEED Polygonum spp. OR-Eastern NonNativeHerb LEAFY SPURGE Euphorbia esula OR-Eastern NonNativeHerb PERENNIAL PEPPERWEED Lepidium latifolium OR-Eastern NonNativeHerb POISON HEMLOCK Conium maculatum OR-Eastern NonNativeHerb SCOTCH THISTLE Onopordum acanthium OR-Eastern NonNativeHerb SPINY COCKLEBUR Xanthium spinosum OR-Eastern NonNativeHerb ST. JOHNSWORT Hypericum perforatum OR-Eastern NonNativeHerb TEASEL Dipsacus spp. OR-Eastern NonNativeHerb TOADFLAX Linaria spp. OR-Eastern NonNativeHerb WHITETOP Cardaria spp. OR-Eastern NonNativeHerb YELLOW NUTSEDGE Cyperus esculentus OR-Eastern NonNativeWoody HIMALAYAN BLACKBERRY Rubus armeniacus OR-Eastern NonNativeWoody RUSSIAN OLIVE Elaeagnus angustifolia OR-Eastern NonNativeWoody SALTCEDAR Tamarix spp.

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State Type CommonName ScientificName OR-Western NativeWoody ALDER Alnus spp. OR-Western NativeWoody COTTONWOOD Populus spp. OR-Western NativeWoody DOGWOOD Cornus spp. OR-Western NativeWoody DOUGLAS FIR Pseudotsuga spp. OR-Western NativeWoody FIR Abies spp. OR-Western NativeWoody HAZELNUT Corylus cornuta OR-Western NativeWoody HEMLOCK Tsuga heterophylla OR-Western NativeWoody MAPLE Acer spp. OR-Western NativeWoody NINEBARK Physocarpus capitatus OR-Western NativeWoody OCEANSPRAY Holodiscus discolor OR-Western NativeWoody SALMONBERRY Rubus spectabilis OR-Western NativeWoody SPRUCE Picea spp. OR-Western NativeWoody WESTERN REDCEDAR Thuja spp. OR-Western NativeWoody WILLOW Salix spp. OR-Western NonNativeAquatic BRAZILIAN WATERWEED Egeria densa OR-Western NonNativeAquatic CURLY PONDWEED Potamogeton crispus OR-Western NonNativeAquatic HYDRILLA Hydrilla verticillata OR-Western NonNativeAquatic PURPLE LOOSESTRIFE Lythrum salicaria OR-Western NonNativeAquatic WATERMILFOIL Myriophyllum spp. OR-Western NonNativeHerb BULL THISTLE Cirsium vulgare OR-Western NonNativeHerb CANADA THISTLE Cirsium arvense OR-Western NonNativeHerb COMMON BUGLOSS Anchusa officinalis OR-Western NonNativeHerb ENGLISH IVY Hedera helix OR-Western NonNativeHerb GARLIC MUSTARD Alliaria petiolata OR-Western NonNativeHerb HOUNDSTONGUE Cynoglossum officinale OR-Western NonNativeHerb KNOTWEED Polygonum spp. OR-Western NonNativeHerb LEAFY SPURGE Euphorbia esula OR-Western NonNativeHerb POISON HEMLOCK Conium maculatum OR-Western NonNativeHerb ST. JOHNSWORT Hypericum perforatum OR-Western NonNativeHerb TOADFLAX Linaria spp. OR-Western NonNativeHerb WHITETOP Cardaria spp. OR-Western NonNativeHerb YELLOW FLAG IRIS Iris pseudacorus OR-Western NonNativeHerb YELLOW NUTSEDGE Cyperus esculentus OR-Western NonNativeWoody BUTTERFLY BUSH Buddleja davidii OR-Western NonNativeWoody DOG ROSE Rosa canina OR-Western NonNativeWoody HIMALAYAN BLACKBERRY Rubus armeniacus OR-Western NonNativeWoody RUSSIAN OLIVE Elaeagnus angustifolia

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State Type CommonName ScientificName MT NativeWoody ALDER Alnus spp. MT NativeWoody ASH Fraxinus spp. MT NativeWoody ASPEN Populus tremuloides MT NativeWoody BIRCH Betula spp. MT NativeWoody BLACK OAK Quercus macrocarpa MT NativeWoody BUNCHBERRY Cornus canadensis MT NativeWoody CHOKECHERRY Prunus virginiana MT NativeWoody COTTONWOOD Populus spp. MT NativeWoody DOGWOOD Cornus spp. MT NativeWoody DOUGLAS FIR Pseudotsuga spp. MT NativeWoody FIR Abies spp. MT NativeWoody GAMBELS OAK Quercus gambelii MT NativeWoody HEMLOCK Tsuga heterophylla MT NativeWoody ROSE Rosa spp. MT NativeWoody SERVICEBERRY Amelanchier alnifolia MT NativeWoody SNOWBERRY Symphoricarpos spp. MT NativeWoody SPRUCE Picea spp. MT NativeWoody THIMBLEBERRY Rubus parviflorum MT NativeWoody WESTERN REDCEDAR Thuja spp. MT NativeWoody WILLOW Salix spp. MT NonNativeAquatic CURLY PONDWEED Potamogeton crispus MT NonNativeAquatic FLOWERING RUSH Butomus umbellatus MT NonNativeAquatic HYDRILLA Hydrilla verticillata MT NonNativeAquatic PURPLE LOOSESTRIFE Lythrum salicaria MT NonNativeAquatic WATERMILFOIL Myriophyllum spp. MT NonNativeHerb CANADA THISTLE Cirsium arvense MT NonNativeHerb HOARY CRESS_WHITETOP Cardaria spp. MT NonNativeHerb HOUNDSTONGUE Cynoglossum officinale MT NonNativeHerb JAPANESE KNOTWEED Polygonum cuspidatum MT NonNativeHerb LEAFY SPURGE Euphorbia esula MT NonNativeHerb POISON HEMLOCK Conium maculatum MT NonNativeHerb YELLOW FLAG IRIS Iris pseudacorus MT NonNativeWoody HIMALAYAN BLACKBERRY Rubus armeniacus MT NonNativeWoody RUSSIAN OLIVE Elaeagnus angustifolia MT NonNativeWoody SALTCEDAR Tamarix spp.

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State Type CommonName ScientificName NM NativeWoody ASH Fraxinus spp. NM NativeWoody BOXELDER Acer negundo NM NativeWoody COTTONWOOD Populus spp. NM NativeWoody DESERT BROOM Baccharis spp. NM NativeWoody WALNUT Juglans spp. NM NativeWoody WILLOW Salix spp. NM NonNativeAquatic CURLY PONDWEED Potamogeton crispus NM NonNativeAquatic HYDRILLA Hydrilla verticillata NM NonNativeAquatic PURPLE LOOSESTRIFE Lythrum salicaria NM NonNativeAquatic WATERCRESS Nasturtium officinale NM NonNativeAquatic WATERMILFOIL Myriophyllum spp. NM NonNativeHerb BERMUDA GRASS Cynodon dactylon NM NonNativeHerb BULL THISTLE Cirsium vulgare NM NonNativeHerb CANADA THISTLE Cirsium arvense NM NonNativeHerb DIFFUSE KNAPWEED Centaurea diffusa NM NonNativeHerb GIANT REED Arundo donax NM NonNativeHerb POISON HEMLOCK Conium maculatum NM NonNativeHerb SCOTCH THISTLE Onopordum acanthium NM NonNativeHerb TOADFLAX Linaria spp. NM NonNativeWoody RUSSIAN OLIVE Elaeagnus angustifolia NM NonNativeWoody SALTCEDAR Tamarix spp. NM NonNativeWoody SIBERIAN ELM Ulmus pumila NM NonNativeWoody TREE OF HEAVEN Ailanthus altissima

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State Type CommonName ScientificName NV NativeWoody ALDER Alnus spp. NV NativeWoody ASPEN Populus tremuloides NV NativeWoody BIRCH Betula spp. NV NativeWoody BOXELDER Acer negundo NV NativeWoody CHOKECHERRY Prunus virginiana NV NativeWoody COTTONWOOD Populus spp. NV NativeWoody CURRANT Ribes spp. NV NativeWoody DOGWOOD Cornus spp. NV NativeWoody DOUGLAS FIR Pseudotsuga spp. NV NativeWoody ELDERBERRY Sambucus nigra NV NativeWoody FIR Abies spp. NV NativeWoody GAMBELS OAK Quercus gambelii NV NativeWoody HAWTHORN Crataegus rivularis NV NativeWoody JUNIPER Juniperus spp. NV NativeWoody MAPLE Acer spp. NV NativeWoody MOCK ORANGE Philadelphus lewisii NV NativeWoody PINE Pinus spp. NV NativeWoody ROSE Rosa spp. NV NativeWoody SERVICEBERRY Amelanchier alnifolia NV NativeWoody SILVER BUFFALOBERRY Shepherdia argentea NV NativeWoody SNOWBERRY Symphoricarpos spp. NV NativeWoody WALNUT Juglans spp. NV NativeWoody WILLOW Salix spp. NV NonNativeAquatic CURLY PONDWEED Potamogeton crispus NV NonNativeAquatic EURASIAN WATERMILFOIL Myriophyllum spicatum NV NonNativeAquatic GIANT SALVINIA Salvinia molesta NV NonNativeAquatic HYDRILLA Hydrilla verticillata NV NonNativeAquatic PURPLE LOOSESTRIFE Lythrum salicaria NV NonNativeHerb BERMUDA GRASS Cynodon dactylon NV NonNativeHerb BULL THISTLE Cirsium vulgare NV NonNativeHerb CANADA THISTLE Cirsium arvense NV NonNativeHerb HOARY CRESS_WHITETOP Cardaria draba NV NonNativeHerb HOUNDSTONGUE Cynoglossum officinale NV NonNativeHerb LEAFY SPURGE Euphorbia esula NV NonNativeHerb MUSK THISTLE Carduus nutans NV NonNativeHerb PERENNIAL PEPPERWEED Lepidium latifolium NV NonNativeHerb POISON HEMLOCK Conium maculatum NV NonNativeHerb RUSSIAN KNAPWEED Acroptilon repens NV NonNativeHerb SCOTCH THISTLE Onopordum acanthium NV NonNativeHerb TOADFLAX Linaria spp. NV NonNativeWoody RUSSIAN OLIVE Elaeagnus angustifolia NV NonNativeWoody SALTCEDAR Tamarix spp. NV NonNativeWoody TREE OF HEAVEN Ailanthus altissima

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State Type CommonName ScientificName UT NativeWoody ALDER Alnus spp. UT NativeWoody ASPEN Populus tremuloides UT NativeWoody BIRCH Betula spp. UT NativeWoody BOXELDER Acer negundo UT NativeWoody CHOKECHERRY Prunus virginiana UT NativeWoody CINQUEFOIL Potentilla fruticosa UT NativeWoody COTTONWOOD Populus spp. UT NativeWoody CURRANT Ribes spp. UT NativeWoody DOGWOOD Cornus spp. UT NativeWoody FIR Abies spp. UT NativeWoody GAMBELS OAK Quercus gambelii UT NativeWoody HAWTHORN Crataegus rivularis UT NativeWoody NETLEAF HACKBERRY Celtis reticulata UT NativeWoody NEW MEXICO PRIVET Forestiera spp. UT NativeWoody PINE Pinus spp. UT NativeWoody ROSE Rosa spp. UT NativeWoody SERVICEBERRY Amelanchier alnifolia UT NativeWoody SILVER BUFFALOBERRY Shepherdia argentea UT NativeWoody SNOWBERRY Symphoricarpos spp. UT NativeWoody SPRUCE Picea spp. UT NativeWoody THREE LEAF SUMAC Rhus trilobata UT NativeWoody WILLOW Salix spp. UT NonNativeAquatic CURLY PONDWEED Potamogeton crispus UT NonNativeAquatic HYDRILLA Hydrilla verticillata UT NonNativeAquatic PURPLE LOOSESTRIFE Lythrum salicaria UT NonNativeAquatic WATERMILFOIL Myriophyllum spp. UT NonNativeHerb BERMUDA GRASS Cynodon dactylon UT NonNativeHerb CANADA THISTLE Cirsium arvense UT NonNativeHerb COMMON REED Phragmites australis UT NonNativeHerb DALMATIAN TOADFLAX Linaria spp. UT NonNativeHerb GIANT REED Arundo donax UT NonNativeHerb HOARY CRESS_WHITETOP Cardaria draba UT NonNativeHerb JAPANESE KNOTWEED Polygonum cuspidatum UT NonNativeHerb KNAPWEED Centaurea spp. UT NonNativeHerb LEAFY SPURGE Euphorbia esula UT NonNativeHerb PERENNIAL PEPPERWEED Lepidium latifolium UT NonNativeHerb POISON HEMLOCK Conium maculatum UT NonNativeHerb RUSSIAN KNAPWEED Acroptilon repens UT NonNativeHerb SCOTCH THISTLE Onopordum acanthium UT NonNativeWoody CAMELTHORN Alhagi maurorum UT NonNativeWoody RUSSIAN OLIVE Elaeagnus angustifolia UT NonNativeWoody SALTCEDAR Tamarix spp.

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State Type CommonName ScientificName WY NativeWoody ALDER Alnus spp. WY NativeWoody AMERICAN ELM Ulmus Americana WY NativeWoody ASPEN Populus tremuloides WY NativeWoody BIRCH Betula spp. WY NativeWoody CHOKECHERRY Prunus virginiana WY NativeWoody CINQUEFOIL Potentilla fruticosa WY NativeWoody COTTONWOOD Populus spp. WY NativeWoody CURRANT Ribes spp. WY NativeWoody DOGWOOD Cornus spp. WY NativeWoody FIR Abies spp. WY NativeWoody HAWTHORN Crataegus rivularis WY NativeWoody MAPLE Acer spp. WY NativeWoody PINE Pinus spp. WY NativeWoody ROSE Rosa spp. WY NativeWoody SERVICEBERRY Amelanchier alnifolia WY NativeWoody SILVER BUFFALOBERRY Shepherdia argentea WY NativeWoody SNOWBERRY Symphoricarpos spp. WY NativeWoody SPRUCE Picea spp. WY NativeWoody THREE LEAF SUMAC Rhus trilobata WY NativeWoody WILLOW Salix spp. WY NonNativeAquatic CURLY PONDWEED Potamogeton crispus WY NonNativeAquatic EXOTIC BUR-REED Sparganium erectum WY NonNativeAquatic HYDRILLA Hydrilla verticillata WY NonNativeAquatic NARROWLEAF CATTAIL Typha angustifolia WY NonNativeAquatic PURPLE LOOSESTRIFE Lythrum salicaria WY NonNativeAquatic WATERMILFOIL Myriophyllum spp. WY NonNativeHerb BULL THISTLE Cirsium vulgare WY NonNativeHerb CANADA THISTLE Cirsium arvense WY NonNativeHerb COMMON REED Phragmites australis WY NonNativeHerb FOXTAIL BARLEY Hordeum jubatum WY NonNativeHerb HOARY CRESS_WHITETOP Cardaria draba WY NonNativeHerb HOUNDSTONGUE Cynoglossum officinale WY NonNativeHerb JAPANESE KNOTWEED Polygonum cuspidatum WY NonNativeHerb LEAFY SPURGE Euphorbia esula WY NonNativeHerb MUSK THISTLE Carduus nutans WY NonNativeHerb PERENNIAL PEPPERWEED Lepidium latifolium WY NonNativeHerb POISON HEMLOCK Conium maculatum WY NonNativeHerb SCOTCH THISTLE Onopordum acanthium WY NonNativeHerb SOWTHISTLE Sonchus spp. WY NonNativeHerb TOADFLAX Linaria spp. WY NonNativeWoody RUSSIAN OLIVE Elaeagnus angustifolia WY NonNativeWoody SALTCEDAR Tamarix spp.

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