The objectives of a benthic invertebrate community survey for environmental effects monitoring (EEM) are to delineate the magnitude and geographic extent of habitat degradation due to effluent discharge, and to provide an evaluation of the aquatic food resources available for fish selected for the fish survey (see Chapter 3 of the present document). However, without a direct comparison between fish diet and the benthic invertebrate fauna, the benthic community survey is mainly aimed at examining habitat degradation. Therefore, the goal of the benthic community survey is to determine if there are structural differences (i.e., total invertebrate density, number of taxa, shifts in the kinds of dominance of taxa) in invertebrate communities in the vicinity of the mine effluent discharge points relative to reference communities. Design considerations will differ depending on whether the mines discharge into freshwater, estuarine or marine receiving waters; this issue is addressed in Section 4.3. It is also recognized that benthic invertebrate surveys will not always use the same study design as the adult fish or water quality surveys because of the different criteria and challenges inherent in the different sampling protocols.

If the benthic invertebrate community survey is conducted in an area where this is possible, sediment samples shall be collected and assessed for particle-size distribution and total organic carbon (Metal Mining Effluent Regulations [MMER], Schedule 5, subparagraph 16(a)(iii)). Water samples shall be taken from the sampling areas when the benthic invertebrate community survey is conducted (MMER, Schedule 5, subparagraph 7(a)(ii)). For more information on water and sediment sampling, see Chapters 5 and 7 of the present document.

The objective of this Chapter is to provide guidance on the study design and interpretation of results of a benthic invertebrate community survey in relation to EEM requirements. Specifically, this document expands upon 1) study design considerations, 2) standardization of methodologies and 3) data analyses appropriate to the study design. The Metal Mining Effluent Regulations (MMER, Schedule 5) set the requirements and timelines for the benthic invertebrate community surveys. The overall framework of the EEM program is presented in Chapter 1 of this guidance document.

The benthic invertebrate community descriptors used to determine effects (effect endpoints) include total benthic invertebrate density, taxa richness, evenness index (Simpson’s), and similarity index (Bray-Curtis) (MMER, Schedule 5, section 16 (iii)).

Additional community descriptors that could be calculated and reported to assist in data interpretation but that are not used in the determination of effects (supporting endpoints) include Simpson’s diversity index, taxon (i.e., family) density, taxon (i.e., family) proportion, and taxon (i.e., family) presence/absence. For more information on benthic invertebrate community effect endpoints and supporting endpoints refer to Section 4.9 of the present document.

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4.2 EEM Phases

4.2.1 First and Subsequent EEM Phases

The first phase of EEM is intended to characterize the benthic communities in major habitats that may be affected by mine effluent and to establish a baseline against which data from future phases can be compared. This phase will also allow for a critical assessment of the need to refine the study design in future phases or the need for the introduction of alternative monitoring techniques. To address the stated objectives of the benthic invertebrate community survey for Phase 1 mines, study design guidelines are presented below.

One specific objective of a Phase 1 survey is to define areas that are relatively homogeneous in terms of habitat class and that have specific ranges in level of exposure to mine effluent.

The study design for the first benthic invertebrate community survey should include:

Sampling during an ecologically relevant season
Sampling in both reference and high-exposure areas (e.g., area closest to effluent discharge point)
Sampling in ecologically relevant habitats
One of 7 site-specific sampling designs (Table 4-1)
Site-specific supporting variables
Standardization of field and laboratory methods

Subsequent EEM phases are intended to confirm the results of the previous phases, help refine monitoring techniques as needed, and determine the factors leading to any detected effect.

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4.2.2 Magnitude and geographic Extent

The objective of magnitude and geographic extent studies is to determine the spatial extent of effects on the benthic invertebrate community that are related to mine effluent. Chapter 1 provides information on mines conducting magnitude and geographic extent studies and the critical effects sizes that have been developed by Environment Canada to focus additional monitoring.

Magnitude and geographic extent study designs should include:

Study and sampling design elements similar to those of previous monitoring, but with more extensive geographic coverage (additional sampling areas)
An evaluation of the adequacy of previously sampled areas. The new geographic extent may include additional habitats and substrata such as higher-order streams and lakes or marine/estuarine areas ranging from intertidal to subtidal. If these new habitats were not represented in the reference areas used in previous monitoring, a re-assessment of the adequacy of these references areas is recommended
The sampling of additional ecologically relevant habitats, seasons or invertebrate life stages, if this is appropriate for assessing the magnitude of the effect
A consideration of other biotic indicators as tools to assess the magnitude of the effect if their use is appropriate and adds value. The list of optional indicators includes biomass and taxonomic composition of periphyton, phytoplankton, macrophyte or zooplankton communities; sampling of other invertebrate life stages, lower-level invertebrate identification, invertebrate biomass, secondary production, additional sensitive habitats or seasons; and toxicity tests on sediment and water

Magnitude and geographic extent surveys may ask the following questions:

Magnitude:

How many taxonomic groups are affected?
What is the magnitude (e.g., the amount of change in density) of the effect on the taxonomic groups affected?
Is there an effect on other benthic community members, such as periphyton or macrophytes, present in the reference area and expected to be present in the exposure area? Note that this is not a requirement of EEM but could be included in a study of investigation of cause.

Geographic extent:

What is the geographic area affected?
Are the benthic invertebrate communities at the sampling stations furthest from the effluent discharge similar to those living under reference conditions?

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4.2.3 Investigation of Cause

For information on investigations of cause (IOC), see Chapter 12 of the present guidance document.

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4.3 Study Design Considerations for the Benthic Invertebrate Community Survey

Discussed below are various considerations and recommendations which should be examined during the study design process. Benthic invertebrate community survey study designs will be site-specific. The 7 recommended study designs are outlined in section 4.3.5. They attempt to take into consideration factors and possible constraints related to the availability and spatial distribution of suitable reference areas and the spatial extent and heterogeneity of potential impact areas. It should be emphasized that these guidelines, although considered the most applicable generic designs available, are not an exhaustive list of the possible means and ways of conducting a benthic invertebrate community survey. It is assumed that each study leader has sufficient knowledge to apply these recommendations in a sound scientific manner and to determine if unique conditions exist which would warrant modification of the study designs.

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4.3.1 Power analysis and sample sizes

For detailed information on power analysis, refer to Chapter 8 of this guidance document.

For the first EEM phase it is recommended that the survey consist of the following:

At least 2 study areas: reference and high effluent exposure area
At least 5 replicate stations in each of the 2 study areas
A minimum of 3 field sub-samples to be taken at each station.

Note that, without a priori information on invertebrate density and variability within a station, the number of field sub-samples required to accurately reflect the true density at each station is arbitrarily set at 3. This amounts to a total recommended sampling effort for mines conducting their first monitoring (Phase 1) of 30 benthic samples. Where study designs other than the control/impact design are appropriate, the same minimum sampling effort should be used, although the distribution of areas, stations and samples may differ.

A further recommendation is that the stations be located such that only the dominant habitat class (see section 4.3.7) is sampled. Restricting sampling to the dominant habitat class reduces data variation. Study areas that have extremely heterogeneous habitats, or two habitats that are equally dominant, may require a greater sampling effort than the minimum previously suggested. Further increases in sampling effort, beyond the minimum, are recommended and could include any of the following: addition of one or more reference areas, addition of a low effluent exposure or a very low effluent exposure area, addition of more stations per area, or the addition of more field sub-samples per station. Increases in sampling effort should be determined in consultation with the Regional Coordinator.

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4.3.2 Confounding Factors

Note that the Metal Mining EEM Program does not mandate the Metal Mining Industry to investigate effects of other industries or pollution sources on the benthic invertebrate community under multiple discharge situations.

Are there confounding factors that can be resolved by modifying the study design?

The interpretation of benthic invertebrate community effects may be difficult if confounding factors exist within the study area. A careful review of historical or existing data and site characterization information to inform decisions about study and sampling design elements can often resolve problems with confounding factors. For additional information on confounding factors, see Hauer and Lamberti (1996), Culp et al. (2000), and Lowell et al. (2000).

Four categories of such factors include:

Environmental variables: Environmental variables can confound the interpretation of benthic invertebrate community effects if it is not possible to separate the effect of the mine effluent from the effects of differences in natural habitat variables. Augmenting the design to better characterize reference conditions with representation of all habitat types sampled may reduce the problem. This could include locating reference areas in adjacent or further-afield watersheds or by sampling additional reference areas (e.g., moving from a simple control/impact design to a more appropriate design; see figures 4-3 and 4-4 and Table 4-1). Some of the potentially confounding variables that may be dealt with by applying more appropriate study and sampling designs include depth gradients, substrate particle size, rapid effluent dilution, interannual and rare events, and seasonal and long-term variability in physical characteristics such as temperature and flow regimes. It may be possible to judge the influence of environmental or habitat variation by examining correlations between measurements of these factors and measurements of the benthic indicators.

Multiple discharges or historic effects: The potential for confounding effects exists if areas with varying levels of exposure to the mine effluent also have varying levels of exposure to other effluents or stressors, or from historic habitat modifications such as dams or impoundments. If feasible, changing the sampling design by modifying sampling locations may reduce the problem. The collection of sediment cores may also be useful in depositional environments to resolve confounding factors resulting from historic effects (see Chapter 7 for more details on sediment monitoring).

Time of sampling: The time of year or the particular year of sampling may confound the interpretation of benthic invertebrate community effects due to effluent. This can be assessed by knowledge of the phenology of benthic invertebrate community species (i.e., relation between climate and life history characteristics) and examination of data collected in previous years from reference areas.

Sampling methods: If standard methods (e.g., sampler types, mesh sizes, taxonomic levels) have not been used consistently within a study or in consecutive studies, any benthic invertebrate community response to the mine effluent may be obscured. It may be possible to examine the data in more detail and convert the data to a comparable level (i.e., convert all taxonomic identification levels to a higher common level). However, in many cases, a redesigned study ensuring that standard methods are consistently applied may be necessary to resolve these problems. Finally, if environmental or logistical conditions exist that preclude the safe and effective collection of samples, the applicability of alternative methods should be examined.

Currently, the only recommended alternative method for the benthic invertebrate component is the application of mesocosms to conduct on-site community bioassays. However, other scientifically defensible monitoring methods that can determine if the mine effluent is having an effect on the benthic invertebrate community may be proposed by the mine. Mesocosms are also useful as an investigation-of-cause tool (see Chapter 12), and their applicability and methodology are described in detail in Chapter 9. Other alternative methods are also described in Chapter 9.

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4.3.3 Standard Nomenclature

Standardized definitions for sampling location nomenclature are essential to the EEM program because these will aid national and regional assessments. The following standard terminology for sampling locations should be adopted and applied in a consistent and rigourous manner for all EEM studies with a benthic invertebrate community survey. A schematic representation of these terms is provided in Figure 4-1.

This section defines the terms field sub-sample and replicate station. Reference and exposure areas are defined in Chapter 2. For the basic analysis of variance (ANOVA) study designs (i.e., control/impact or multiple control/impact), where the objectives are to detect differences between or among areas, each reference or exposure area consists of a number of replicate stations (i.e., the replicates for an ANOVA). Each replicate station consists of a number of pooled field sub-samples. Similarly, gradient or reference condition (i.e., reference condition approach [RCA]) study designs use the replicate station as the spatial scale of replication, with field sub-samples being collected as appropriate. See section 4.3.5 for a description of these approaches.

The concept of area is not directly transferable to the gradient or RCA study designs. When using or developing a gradient or RCA study design, a balanced design with similar numbers of replicate stations located within reference and exposure areas is not the basis for comparisons. For example, in RCAstudy designs, exposure stations are individually compared to a distribution of reference stations, which represent appropriate reference conditions. For gradient designs, the lack of suitable reference or exposure areas may be the direct cause for selecting this study design, and thus the ANOVA type terminology is not directly applicable for this approach. Detailed guidance on dealing with these study designs is included in section 4.3.5.

Further guidance regarding the number of replicates and their allocation for different spatial scales and study designs is provided in sections 4.3.5 and 4.4.2.

Field sub-sample

Field sub-samples consist of individual area or time-limited collections of benthic invertebrates (e.g., a grab, core, cylinder, quadrat, kick or U-net sample). To ensure adequate spatial placement of field sub-samples within a station, they should be collected in a random or stratified-random pattern. For many of the statistical analyses used to assess effects in freshwater and marine environments (section 4.9), data from all field sub-samples within a station are pooled, providing a single value of each descriptor from each station.

Pooling of field sub-samples

The pooling of field sub-sample data can occur at several points in the monitoring program. The point at which pooling occurs will depend on several factors, including:

Field sample processing and storage efficiency (e.g., are field storage jars large enough to contain pooled samples?)
Laboratory sorting efficiency (e.g., is it more efficient to sort smaller samples?)
The potential to address study design issues

The first two factors, resulting in an actual physical pooling of the samples, are considered logistical in nature, and their applicability should be determined on a site-specific or method-specific basis. Note that once this physical pooling is done, the potential information from individual sub-samples is lost. In regards to factor 3, if there is a need for additional information to address study design issues (e.g., to examine species area curves or field sub-sampling precision), field sub-samples may be preserved and processed separately. The resulting unpooled data are then available to address the study design issues and can subsequently be pooled electronically for the appropriate statistical analyses. Electronic pooling for the endpoints should be done in such a manner as to be equivalent to results if field sub-samples were physically pooled. This is particularly important for the taxa richness endpoint. Sample calculations for pooled station density and richness are shown below.

For density endpoints, values should be calculated as follows:

Density from pooled field sub-samples = (# in field sub-sample a + # in sub-sample b + # in sub-sample c)/total area of field sub-samples a, b and c.

Note that the resulting number is also the same as calculating the density of each sub-sample and taking an average.

However, the calculation of taxa richness for a station is not equivalent to taking the average taxa richness for the three sub-samples. Station taxa richness should be calculated as follows:

Station taxa richness = all taxa observed at a station in all sub-samples, not the average number of taxa of the three sub-samples.

Replicate station

A replicate station is a specific, fixed sampling location within an area that can be recognized, re-sampled and defined quantitatively (e.g., latitude and longitude and a written description). For each habitat type, a number of replicate stations should be sampled, each resulting in a single composite sample, preferably consisting of ≥ 3 benthic invertebrate field sub-samples. Stations located within the exposure area should be positioned so as to ensure exposure to the effluent plume. Additionally, sufficient physical separation should exist between the replicate stations to allow them to be considered statistical replicates.

The recommended geographic extent of replicate stations for lakes, streams and rivers is as follows:

Lakes: The geographic extent of each replicate station should be at least 10 m × 10 m and separated by at least 20 m.

Rivers and streams: The geographic extent of each replicate station should encompass a longitudinal stretch of the river that includes one pool/riffle sequence. A river distance of six times the bankfull width should be adequate (Leopold et al. 1964; Newbury 1984; Leopold 1994) and allow a minimum separation of three times the bankfull width between stations of similar habitat. To ensure consistency of application for the EEM program, “bankfull width” is defined as in Newbury and Gaboury (1993) and in Chapter 5 of this guidance document. If it is not feasible to sample this length of river (e.g., large rivers or headwater streams with rapidly changing gradients), then an acceptable alternative approach would be to define the geographic extent of stations in a manner similar to that suggested for lakes (i.e., stations are re-visitable locations with predefined dimensions of at least 10 m x 10 m, with adequate separation).

Marine coastal environments: Each of the replicate stations should be a defined location with re-visitable dimensions (e.g., 10 m × 10 m). Replicate stations may be spaced 50 m apart or more, depending on the size of the area. In some estuaries, a replicate station should encompass a longitudinal stretch, which includes the major habitat to be sampled (e.g., a distance of 6 times the bankfull width). If this length of river is not feasible for large estuaries, an alternative definition would be similar to that suggested for coastal areas.

Area

General information and definitions of reference and exposure areas are presented in Chapter 2.

Sufficient geographic coverage for a single benthic invertebrate study area is recommended for lakes, streams and rivers, as follows:

Lakes: The spatial extent of the study area should be at least 100 m x 100 m and large enough to adequately accommodate the necessary number of replicate stations with sufficient separation.

Rivers and streams: The spatial extent of the study area is defined in terms of stream or river morphology and should encompass a length of river that is adequate to accommodate the necessary number of replicate stations with sufficient separation. The total length of river comprising an area would therefore be defined by the number of replicate stations multiplied by 6 times the bankfull width, the river length, on average, in which one pool riffle sequence is expected to occur (Newbury 1984).

Estuary: For low-salinity, relatively homogeneous estuaries, area is defined in the same way as for rivers. For long, narrow marine regions such as narrow bays or fjords in which a control/impact type design is to be used, the area should be large enough to encompass the homogeneous habitat being sampled, as well as the defined exposure range. This will be at least 100 m × 100 m and large enough to adequately accommodate the necessary number of replicate stations.

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4.3.4 Reporting of Field Station Positions

Refer to Chapter 2 for general information on the reporting of field station positions.

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4.3.5 Recommended Sampling Program Designs

The design of the benthic invertebrate community survey is site-specific, and one of 7 benthic sampling program designs listed below is recommended.

Control-impact design (C-I)
Multiple control-impact design (MC-I)
Before/after control-impact (BACI)
Simple gradient (SG) design
Radial gradient (RG) design
Multiple gradient design (MG)
Reference condition approach (RCA)

Examples of these designs are illustrated in Figures 4-2, 4-3 and 4-4.

These designs fall into three basic categories with different “philosophical” approaches, as follows:

The C-I or MC-I designs (including BACI) are ANOVA-type designs used to detect differences between discrete exposure and reference areas.
The gradient (SG, RG or MG) designs are intended to examine changes in community structure along a physical and/or effluent gradient, and are better suited to regression analyses or analysis of covariance (ANCOVA).
The multivariate approach of the RCA compares potential “impaired” or test stations to a selection of appropriate reference stations.

It should be noted that there may be some circumstances where ANOVA analyses are applicable to b) and c) above. Alternative monitoring methods (e.g., mesocosms) are also recommended but must be scientifically defensible. A summary of the attributes, applicability and limitations of the sampling designs is presented in Table 4-1 and described in more detail below.

The following descriptions apply primarily to the design of the first and subsequent phases, although special applications for determining the magnitude and geographic extent of an effect are indicated, where applicable.

Control-impact design

The simplest study design for use in EEM is the control-impact (or reference-exposure) design (Green 1979). In rivers and estuaries, this consists of no less than one reference area and a series of downstream exposure areas. For regular monitoring, this should include, at a minimum, one high effluent exposure area. Levels of exposure to mine effluent differ between exposure and reference areas, but should be similar between the stations within each area. Habitat classes sampled should be consistent among areas and, with the exception of exposure level, these areas are to be as similar as possible in terms of substrate, depth, current velocity, water properties, environmental gradients, land use, etc. The first study design employs ANOVA comparisons among areas and is recommended for simple, homogeneous rivers and streams without confounding upstream or near-site discharges from other sources.

The mine may propose modifications to this basic ANOVA approach providing that the modified design is scientifically defensible and addresses the appropriate monitoring questions. For example, if a reference area cannot be located upstream or in adjacent watersheds due to a confounding factor, but a C-I design would otherwise be applicable, a modification of the C-I design may be appropriate. In this case, the design could be modified so that the reference area is “downstream” instead of “upstream” of the point source. The downstream reference area would have to be outside of the exposure area and meet the same reference area criteria as other designs.

This first study design is also recommended for simple, homogeneous estuaries or narrow inlets or bayswithout confounding upstream or near-site discharges from other sources or where the ecologically relevant habitat occurs in spatially discrete but homogeneous patches (i.e., intermittent rocky outcroppings).

Magnitude and geographic extent

The C-I design can be used to ascertain the geographic extent of an effect by first making use of rapid bioassessment protocols (Plafkin et al. 1989) or other available information to approximate how far the effect extends. Following this, a C-I monitoring program can be used that includes the high effluent exposure area and targets additional exposure areas in localities where the effect is suspected to be dissipating (e.g., additional exposure areas located so as to bracket the suspected furthest reach of the effluent effects, together with the previous reference and exposure areas). ANOVA comparisons among areas can then be made to determine the geographic extent of an effect at a given significance level.

Multiple control-impact design

Two of the major problems associated with the use of a single reference area are 1) it can be easily confounded by other factors, and 2) there is a lack of independence among the stations in a single reference area (pseudoreplication) (Hurlbert 1984). In systems where an appropriate reference area is not available due to confounding factors or where it is determined, after a review of historical information, that more reference areas are desirable, the MC-I design should be used. Schematic diagrams of this design for application in mines discharging to large rivers, lakes or coastal waters are presented in Figures 4-2d, e and f. Sampling schemes should be devised so that additional reference areas are located in adjacent watersheds or bays and that comparable habitat classes spanning the range of habitats found within the exposure area are selected.

The design philosophy of both the C-I and MC-I designs is that a specific difference in magnitude of effect between a series of areas is being examined. This lends itself to a classic ANOVA design with associated power analyses. These methods are statistically tractable and can provide indicators as to whether or not there is a biological effect from the mine effluent on the benthic invertebrate community. These designs assume that effluent exposure and habitat conditions are relatively homogeneous among all stations within a sampling area or that effluent exposure is within an acceptable range for a particular defined area.

Before/after control-impact designs

An improvement to the above C-I and MC-I designs is possible when data can be collected both before and after initiation of effluent discharge into the receiving water area. The same considerations discussed above apply for choice of reference (control) and exposure (impact) areas. But the design is further enhanced by collecting data both before and after the facility becomes operational. This kind of monitoring design has been termed a before/after control-impact (BACI) design (Schmitt and Osenberg 1996). Use of a BACI design helps to distinguish effluent effects from natural differences between reference and exposure areas that may have existed before the initiation of effluent discharge.

Detailed descriptions of several kinds of BACI designs and their statistical analyses are available in Green (1979), Schmitt and Osenberg (1996), Underwood (1997), and references therein. In its simplest form, a BACI design entails collecting monitoring data at least once, both before and after initiation of effluent discharge, in both a reference and an exposure area, after which the data are analyzed using an area-by-time factorial ANOVA (Green 1979). In this situation, evidence for an effluent effect is inferred when the area-by-time interaction term in the ANOVA is significant. When the reference and exposure areas have been sampled repeatedly during both the before and after periods, it is possible to use a BACI paired-series analysis; in this case, potential effects are investigated by testing for a change in delta (difference between reference and exposure) from the before to the after period (Schmitt and Osenberg 1996). The design can be further improved by incorporating multiple reference areas (Schmitt and Osenberg 1996; Underwood 1997). Refer to Chapter 2, section 2.2.2.2.2for additional information on baseline data.

Simple gradient and radial gradient designs

Simple and radial gradient designs (Figures 4-3a, b and c) are suitable for situations where rapid effluent dilution precludes the selection of an exposure area that is comparatively homogeneous in terms of effluent concentration. As with the C-I design, gradient designs can be used in cases where no suitable reference areas are available upstream or in adjacent watersheds or bays. Gradient designs are also useful for determining how far along an effluent path effects are observed (i.e., objective of magnitude and geographical extent).

Philosophically, the gradient approach examines departures from expected (non-impacted) “patterns” of correlated biotic and environmental factors over spatial gradients. This is more suited to a regression type of analysis (or equivalent) in which replication (i.e., five stations within an area) is less appropriate than expending a similar effort to obtain accurate measurements of biotic and habitat variables over a sufficiently broad range of the gradient conditions. In the simplest case, a statistically significant effect would be declared if the slope of the regression of a response variable against distance from the effluent source is significantly different than 0. In this approach, a point-source discharge is expected to have a “declining” gradient of effects away from the source, and it is not always feasible to make the judgment that there either “is” or “is not” an effect at a given station. At a certain point along the gradient it is necessary to judge that this effect is no longer measurable or important. Therefore, in gradient designs, reference information is obtained from the stations furthest away from the effluent source.

A gradient does not necessarily imply straight lines or the even spacing of stations within areas. The spacing of stations may be more or less continuous on a gradient away from the discharge, with less emphasis on distinctly different dilution zones and more on adequate geographic coverage, as compared to a C-I design. There are often no “blank” spaces between distinct sample areas, but rather a continuum of sampling stations along the gradient. However, if a change in effluent dilution within the receiving environment is abrupt, more sampling effort may be desirable over these stretches to accurately track rapid changes in mine effects.

SG designs are particularly appropriate for narrow water bodies such as rivers and streams. In wider water bodies such as lakes or open coastal areas, a radial gradient design may be more appropriate. Sampling is conducted away from the effluent source along several gradient transects. As in the MC-I approach, the use of an RG design will provide a larger number of reference sites. Furthermore, a broader geographic area will be sampled, which can be important in non-homogeneous, open lakeshore or marine areas, which often have complex current and circulation patterns or a variety of equally important major habitat classes or gradients.

For RGs, a comparison of regression patterns for each gradient (e.g., regressions of faunal abundance versus distance from the outfall) may help to illuminate the direction and extent of effects. Alternatively, all data from all gradients can be included in one regression, if the comparison is between biotic and physical factors unrelated to geographic or natural habitat factors. If sufficient sampling is done (e.g., RGs), it may be possible to pick and choose unconfounded replicate stations (e.g., homogeneous habitat conditions) to regress a biotic versus a mine-related variable.

Wherever possible, the exposure gradient should be de-coupled or independent from any environmental gradients. A declining exposure gradient may fall along a path with varying depths, but an SG or RG approach may still be feasible if the exposure and depth gradients are not correlated and the differences in depth are not so great as to obscure any effluent effects. In cases where the exposure gradient is correlated with a co-occurring environmental gradient, an MG design may be more appropriate (see next section). Alternatively, a multivariate approach may be necessary to remove the confounding influence of varying depth.

Gradient designs and magnitude and geographic extent

Due to the layout of sampling stations, gradient designs are particularly well suited for determining the geographic extent of an effect. The simplest design for magnitude and geographic extent would be to allocate sampling stations along a gradient from more to less exposed, ensuring that the most distant stations are located well beyond the likely extent of effects. The geographic extent of effects could then be determined graphically by plotting the response variables against distance from the mine and inspecting the data for an inflection point where the response variable asymptotes to the reference condition. Data from sampling stations arrayed in this manner could also be used, together with measured physicochemical data, in a multivariate analysis (e.g., ordination or clustering) used to identify which distant stations tend to group with reference stations and which tend to group with clearly impacted stations. Both of these approaches (graphical plotting and multivariate analysis) look for patterns in the data to determine the approximate extent of an effect; that is, they do not entail hypothesis testing and therefore a power analysis would not be applicable in these cases (in contrast to the C-I approach to magnitude and geographic extent described above).

It is also possible to design a hypothesis-testing gradient program for examining the geographic extent of an effect. This would entail using field sub-samples as replicates (treating stations as areas) and making station-by-station ANOVA comparisons along a gradient to determine where an effect disappears at a given significance level. However, this latter approach might require extensive sampling effort, depending upon the number of stations along the gradient and the required (by power analysis) number of field sub-samples per station.

Multiple-gradient design

In some cases, it may also be useful to compare reference gradients to those exposed to mine effluent. This would be the case when a co-occurring environmental gradient confounds an effluent gradient in the exposure area. By using a MG design (see charts d) and e) of Figure 4-3), it is possible to make statistical comparisons of the exposure area gradient to a similar environmental gradient in a reference area. The reference gradients should be as similar as possible in depth and habitat to the exposure gradient. Potential effluent impacts would be tested for by using ANCOVA to factor out the influence of the co-occurring environmental gradient.

Reference condition approach

The fundamental concept of the RCA is to establish a database of sites that represents unimpaired conditions (reference stations) at which biological and environmental attributes are measured. This database is used to develop predictive models that match a set of environmental variables to biological conditions. These predictive models then allow a set of environmental measurements to be made at a new station and used in the model to predict the station’s expected biological condition (i.e., the biological conditions of the group of reference stations with similar environmental attributes). A comparison of the actual biological condition at the new station with the predicted conditions allows an assessment of the condition of the new station to be made.

The RCA can reduce the need to find nearby comparable reference sites when studying an impacted system, which can be a problem in some traditional approaches. Rather than identifying and sampling upstream reference sites in a river system or next-bay-over reference sites in a lake, the RCA uses a set of biologically equivalent reference sites selected from an existing database to evaluate an exposure site. Provided that it is kept up-to-date, the reference condition database can be used over a number of EEM phases.

The reference condition database is established by an initial standardized sampling program at a wide variety of geographic scales. The same benthic invertebrate community sampling protocol is used in as many ecoregions and stream orders or lakes as are available in a catchment. A number of environmental variables are measured in conjunction with invertebrate sampling. The data are then subjected to a 3-step multivariate analysis in which:

a number of invertebrate groups are formed based on similarity of community structure;
biological data are correlated with environmental attributes and an optimal set of environmental variables is identified that can be used to predict group membership; and
the biological condition of test (exposure) stations is assessed by using the optimal set of environmental variables to predict group membership. How the test station fits, relative to the group to which it is predicted to belong, establishes whether, and to what degree, the station is different from the reference group. Assessment can be made by either the use of the community descriptors, by determining if the site is within the prescribed range of variation observed at reference sites (2 standard deviations [SDs]), or by the use of ordination methods and determining if the exposure site is within the 95% probability ellipse of the matched reference sites.

Depending on the timing and location of the sampling program, it may also be possible to use the resulting database to make ANOVA comparisons between reference and exposure areas.

Once the reference database is established, the RCA can be used as a rapid bioassessment method and to deal with national and local issues using the same database and software. Due to the intensive initial sampling effort required, the RCA would not be considered a practical approach for use by a single mine in a remote location if a reference database is not already available; however, it may be applicable in areas where multiple industries (including different EEM industrial sectors) are located. In this case it may be practicable and cost-effective for multiple users to collaborate in the development of the reference database. Additional information on the RCA can be found in Bailey et al. (2003).

To assist industry in locating suitable reference sites for the EEM program, the Cooperative Freshwater Ecology Unit of Laurentian University has led the Northern Ontario Benthic Invertebrate Reference Condition Approach (RCA) Biomonitoring Network (Northern Ontario RCA Network). For additional information on this network refer to the following website.

The Canadian Aquatic Biomonitoring Network (CABIN) is a collaborative program developed and maintained by Environment Canada to establish a network of reference sites through the RCA. This information is available to all users interested in assessing the biological health of freshwater in Canada. For additional information on CABIN, please refer to their website.

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**Table 4-1**: Recommended sampling program designs (text description)
Design Type	Receiving Environment	Reference/Control Area	Impact Area	Statistics
Control-impact (C-I) Figure 4-2	Freshwater rivers or lakes, homogeneous or low-salinity estuaries	A single reference area, upstream of mine effluent outfall	High effluent exposure area (additional exposure areas are added in magnitude and geographic extent)	ANOVA
Multiple control-impact (MC-I) Figure 4-2 d,e,f	Freshwater rivers or lakes with geographically homogeneous lake shores, homogeneous estuaries and coastal zones	Multiple reference areas in the same or environmentally similar adjacent watersheds or bays	High effluent exposure area (additional exposure areas are added in magnitude and geographic extent)	ANOVA
Before/after control-impact (BACI)	Same as C-I and MC-I	Same as C-I and MC-I, but with sampling done both before and after initiation of effluent discharge	Same as C-I and MC-I, but with sampling done both before and after initiation of effluent discharge	ANOVA
Simple gradient (SG) Figure 4-3a, b	Freshwater rivers or geographically restricted lakes, non-homogeneous, narrow estuaries or geographically restricted marine bays, inlets or fjords	A series of reference stations with little or no effluent, situated towards the end of a declining gradient of mine effluent	Single gradient through declining levels of effluent in the receiving environment	Regression/ ANCOVA
Radial gradient (RG) Figure 4-3 c	Lakes, non-homogeneous open marine bays and coastal areas	As above, but situated near the end of several radially oriented transects	As above, but repeated in a radially oriented design	As above
Multiple gradient (MG) Figure 4-3 d, e	Freshwater lakes or rivers Non-homogeneous open marine bays and coastal areas	A series of reference stations with no effluent situated on a transect along the same kind of environmental gradient observed in the exposure area	Gradient through declining levels of effluent and a co-occurring environmental gradient in the receiving environment	ANCOVA, with reference and exposure transects considered as treatment groups
Reference condition approach (RCA) Figure 4-4	Freshwater rivers or lakes, particularly for cooperative investigations or where there is an existing reference database	Multiple series of reference stations with little or no effluent situated in similar drainage basins within the same ecoregion	Series of stations within the exposure area which are tested individually against the reference station distribution	Multivariate/ ANOVA (if possible)

Note: Multivariate analyses can be performed on data collected using any of the above designs to look for patterns (i.e., not hypothesis tests) that may be useful for highlighting potential areas of concern.

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Figure 4-1: Examples of area, replicate station and field sub-sample spatial scales for a basic control-impact design (text description)

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Figure 4-2: Control-impact designs (text description)

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Figure 4-3: Gradient designs(text description)

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Figure 4-4: Reference condition approach (text description)

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4.3.6 Reference and Exposure Area Consideration for the EEM program phases

The allocation of reference and exposure areas is dependent on the site-specific study design and the phase of the EEM program.

For Phase 1, the objective is to determine whether there is an effect on the benthic invertebrate community in the high effluent exposure area where an effect is more likely to occur. This spatial limitation is designed to concentrate sampling effort in a cost-effective manner. The study design and allocation of reference and exposure areas should be based on this objective.

For subsequent phases, the objectives are to confirm results, detect changes and allow for trend monitoring data. As these objectives are similar in geographic scale to Phase 1, the selection criteria for reference and exposure areas should be the same. However, as with any ongoing monitoring program, the appropriateness of reference and exposure area selection should be re-evaluated as additional information is gained.

For magnitude and geographic extent the objective is to determine the spatial extent of previously identified effects. Thus, sampling should be conducted at exposure areas located farther away from the mine effluent discharge point, until a return to reference conditions is reached. The physical allocation of multiple exposure areas and stations is dependent on the study design. If a confounding factor is encountered before the reference condition is reached in the low effluent exposure area, and this factor cannot be resolved by modifying the study design (see Table 4-1), then the exposure area may be defined to extend only as far as the confounding factor is encountered. Alternative, cost-effective study designs or methods may be applicable (see the Chapter 9 and Table 4-1).

In addition, as part of the review of monitoring information, reference areas sampled in previous monitoring should be re-evaluated to assess whether they are adequate for the magnitude and geographic extentprogram. The new geographic extent may naturally include additional habitats such as higher-order streams or lakes. If these new habitats were not represented in the reference areas that were used for previous monitoring, a reassessment of the adequacy of these reference areas will be necessary. The addition of reference areas should also be considered to allow a more balanced design between the number of reference and exposure areas.

If an RCAstudy design was used during previous monitoring, additional reference areas may not be necessary (assuming they adequately represent the habitat types), but it is recommended that a subset be re-sampled to examine the effects of natural temporal variation.

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4.3.7 Selection of Ecologically Relevant Habitats

4.3.7.1 General Guidance for Habitat Selection

The most ecologically relevant habitats should be sampled within the exposure areas, and similar habitats should be located and sampled within the reference areas. The selection of the appropriate habitat types requires consideration of the following questions:

Which habitat type is present in the highest proportion in the exposure area?
Which habitat, in the absence of human influences, supports the richest assemblages of invertebrates (benthic invertebrate diversity) within the study area?
In which habitat are the invertebrates most likely to be exposed to sediment or water-borne contaminants for extended periods of time?
Is historical information available for a particular habitat?

The first consideration is to sample the habitat that accounts for the greatest proportion of the exposure area. However, other factors can override the importance of geographically dominant habitat including the ecological relevance of sampling highly sensitive and diverse habitats, even if they comprise a lower proportion of the study basin. In streams, riffles can support a diverse assemblage of species that display a wide range of sensitivities to water-quality changes. Therefore, the community in this habitat has the potential for greater change than less species-rich communities. In contrast, the fauna of depositional areas, which are generally less rich taxonomically, are of interest during biomonitoring exercises because they may be directly exposed to concentrations of sediment-borne contaminants for longer periods. Consequently, communities in depositional areas may respond to contaminants differently than the more sensitive but less exposed riffle habitats. For additional guidance regarding stream habitat selection, refer to Cuffney et al. (1993), Plafkin et al. (1989), and Meador et al. (1993).

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4.3.7.2 Habitat Considerations for the EEM program

The decision about which habitat to sample should be based on site-specific considerations. Decisions about the sampling of more than one ecologically relevant habitat during the same survey depend upon the phase of the EEM program.

For Phase 1 and subsequent phases, the objective is to determine if there is an effect on the benthic invertebrate community; therefore, the habitat most likely to exhibit these effects should be sampled. If more than one habitat is determined to be ecologically relevant, effort could be expended during magnitude and geographic extent monitoring to sample all ecologically relevant habitats. This may lessen the potential for missing an effect on a sensitive habitat and/or the necessity of expanding the survey to additional habitats during future monitoring efforts. If questions regarding magnitude and extent can be addressed by additional sampling during the same field trip, it may be cost-effective to do so.

Sampling of additional ecologically relevant habitat types should not be at the expense of reduced sampling effort in the primary habitat of interest. For most biomonitoring studies, sampling a single habitat is intended to reduce the variability inherent in sampling natural substrates. This variability would be even greater if the same level of effort were spread over a range of habitat types.

For magnitude and geographic extent monitoring, the most ecologically relevant habitats should be sampled within the exposure areas, and similar habitats should be located and sampled within the reference areas. The decision regarding number and type of habitats to sample is made based on a review of the previous monitoring results, site-specific considerations and the objectives of magnitude and geographic extent. For example, to determine the geographic extent of an observed effect, additional habitats such as higher-order streams or lakes may become important. On the other hand, if, during previous monitoring, a number of habitat types were sampled but one particular habitat appeared to show responses and the others did not, this habitat type could be targeted during magnitude and geographic extent monitoring.

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4.3.7.3 Habitat Considerations for Marine/Estuarine Habitats

A decision needs to be made to sample either depositional or erosional habitats in the estuarine/marine receiving environment. In addition, decisions about sampling intertidal vs. subtidal substrates for estuarine/marine mines will depend on which is the appropriate receiving environment and on the feasibility of obtaining useful samples. In marine/estuarine habitats, the selection of the appropriate habitat types therefore requires consideration of the following questions:

What habitats are feasible to sample?

The habitat that is most common geographically and most likely to be affected by the effluent should be selected. However, selection of major habitats is partly related to viability of sampling. For example, if the major habitat is a vertical rock cliff to a depth of 300 m immediately outside the effluent discharge outfall, this is rarely feasible for benthic invertebrate sampling without extraordinary equipment. As another extreme example, if the major habitat is intertidal but consists of a steep rock cliff with heavy wave and wind exposure or ice build-up, sampling may not be possible. When multiple habitats are available and appropriate, some choices need to be made. In some cases, more than one habitat may have to be sampled (radial gradient or similar design). Where there is a choice, sampling of soft substrates is preferred because methods are generally more quantitative.

What is the habitat that is most biologically active or “important?”

When the subtidal environment most exposed to mine effluent consists of both consolidated and unconsolidated sediments, then either both substrates need to be sampled or a decision on which to sample must be made. All other factors being equal, the unconsolidated sediment is more efficiently sampled quantitatively. However, when it is obvious that a coarse sand substrate is almost devoid of macrofauna within the top 10 cm (or the depth of penetration of a sampling device), whereas the nearby rocky reef is extremely rich and an obvious haven for many fish, it is the most “active.” Similarly, if there is an important fishery resource in one major habitat type that is directly exposed to mine effluent, it may be considered the more biologically important.

Can the ecologically relevant habitat be “classified” according to recognized physical type and characteristic species?

Habitat classification systems have been discussed and reviewed by many researchers and can be useful for comparing “expected” biotic factors with actual biotic factors present in the mine vicinity. Some marine examples include a comprehensive draft document presented to DFO for delineating the Strait of Georgia on the west coast of Canada and northwestern U.S. (Watson 1997). Some relevant references for marine classification for worldwide shorelines to deep coastal areas include Frith et al. (1993), Booth et al. (1996), Robinson and Levings (1995), Hay et al. (1996), and Robinson et al. (1996). Specifically, estuarine classification has been reviewed by Matthews (1993), Scott and Jones (1995), Finlayson and van der Valk (1995), and Levings and Thom (1994). In the U.S., the most widely used system is that of Cowardin et al. (1979) and Cowardin and Golet (1995), with expansions proposed by other authors.

Is the effluent discharge depth and/or buoyancy most likely to affect the intertidal or subtidal regions?

If the effluent discharge is and remains mainly intertidal, then this should be the targeted habitat. However, if the effluent affects both intertidal and subtidal habitats, then the subtidal is the preferred habitat, because this area is most likely to show impacts in fish. If suitable, both habitats may be sampled. This question should also take into consideration seasonal water column stability changes, which can affect intertidal areas.

What habitat type is present in the highest proportion?

In many cases, coastal shorelines will be mixed silt, sand, gravel and rock substrates. In bays near freshwater discharge points, there tends to be accumulations of sandy or silty sediment. Estuarine mine sediments will usually have dominantly soft substrates from river-borne material. If there are similar percentages of both depositional and erosional habitats, the preferred habitat to sample is depositional, because this type will accumulate the discharged material from mine effluents and is more likely to present deleterious effects. Erosional substrates tend to be kept “clean” by high current action or wave or ice scouring.

However, if the percentage of solid substrate habitat is much greater than the soft substrate habitat, or if a previously “clean” rocky shoreline has begun to accumulate sediment related to mine discharge, then this may be the preferred habitat to sample.

Are there confounding factors that may affect benthic communities?

Benthic communities in naturally or anthropogenically confounded sampling areas are problematic to use for interpreting effects from mines. Obviously they should be avoided. For example, in situations where consolidated and unconsolidated substrates are present, only one of these may be outside the influence of the confounding factors. One source of confounding factors that is particularly important in Arctic areas is the seasonal or year-round effects of freezing or ice scour, particularly in intertidal or estuarine areas, which may seriously disrupt surficial communities.

What is the environment affected by subtidal discharges?

Obviously, the environment most exposed to effluent should be the targeted sampling area and will also determine the type of sampling design used. In an estuary, if the discharge occurs at the surface where there is a strong and permanent surface freshwater layer with little intrusion of saltwater at high tide at depth, then the habitat to be sampled is downstream from the mine. However, where there is a strong tidal intrusion, sampling will have to go upstream and downstream. There are numerous other factors of this type to consider, all of which require detailed site-specific information about the habitats and the pattern of effluent dispersion.

In summary, if there is a choice of habitats to sample, it is recommended first that subtidal habitats be sampled because they tend to have higher diversity and less patchiness in fauna than the intertidal, due to less extreme or harsh habitat conditions. This is particularly true in Arctic regions, where extreme wintertime conditions may eliminate most of the longer-lived fauna that tend to more clearly integrate the effects of contaminants. Second, if there are a variety of suitable habitats, depositional habitats should be chosen, particularly for subtidal areas, because the methodology allows easier and more quantitative sampling procedures. Depositional areas also tend to accumulate contaminants over time, whereas erosional areas may not.

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4.3.8 Selection of Ecologically Relevant Sampling Seasons

4.3.8.1 General Guidance for Sampling Season Selection

All benthic invertebrate community surveys should be completed during the most ecologically relevant season. Sampling should occur during a period of effluent discharge and after the receiving environment has been exposed to the effluent for a sufficient period during which effects would reasonably be expected to occur (i.e., generally within 3-6 months).

The preferred seasonal period for sampling is when biological diversity is highest. In general, this corresponds with the seasonal recruitment cycles of benthic organisms (generally related to climate and food abundance). Many insects with freshwater stages reproduce in the spring and fall, although others have multiple cohorts throughout the open-water period. For many lotic habitat types, sampling is conducted during the fall (September/October), when the majority of taxa are present and/or large enough to be collected by the sampling equipment and flow regimes allow access for sampling. In large lakes where the benthic community is often dominated by annelids, crustaceans and molluscs, insect emergence periods and hydrologic regimes are of less importance in determining the sampling period (Rosenberg and Resh 1993).

If historic benthic invertebrate community surveys exist for the system under investigation, it is useful to examine the data and, if appropriate, conduct the survey during similar periods so that the surveys can be compared. Other factors that may influence the sampling period include seasonal flow disruption such as extreme high- or low-flow conditions, freezing and ice scour, mine effluent discharge conditions, type of sampling gear, and feasibility of sampling and field crew safety. Sampling during periods when effluent is not being discharged should be avoided. An understanding of the seasonal patterns and life cycles of the taxa along with changes in the hydrologic regime found within the specific system is also helpful to determine the appropriate timing for the survey. Rosenberg and Resh (1993), Johnson et al. (1993), Rees (1984), Malley and Reynolds (1979), Barber and Kevern (1974), and Jonasson (1955) provide information that may assist in the selection of a sampling period.

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4.3.8.2 Sampling Season Considerations for the EEM Program

It is recommended that efforts be concentrated within a single seasonal sampling period, unless previous data indicate that there is more than one critical time period for the benthic community in the study basin. As this seasonal period should then be used in subsequent studies, it is important to make this decision after compiling all available site-specific data regarding taxa life history characteristics and hydrologic discharge regimes.

Similarly, the sampling season for magnitude and geographic extent monitoring should be the same as previous monitoring unless, upon review of the previous results, there is scientific or logistical justification for a change. Furthermore, additional seasons may be warranted to help determine the magnitude of the response of the benthic community. For example, if the sampling is done at a time when the life stage of a particular invertebrate is not present, then an additional sampling season may be necessary to determine if effects are seen for this specific invertebrate. Bivalves, for example, are not easily sampled in the fall, which is often a critical period for many other invertebrates. In this case an additional season could be added to the monitoring program with the sampling program designed to answer this site-specific concern (i.e., an additional summer sampling trip where methods designed for bivalve sampling are used).

For most marine or estuarine areas the sampling season could be anytime from spring through mid-fall. For temperate marine environments, benthic sampling is usually conducted in late summer or fall as some benthic forms have planktonic larval stages that do not settle to the bottom until later in the season when populations with spring recruits have stabilized. For Arctic areas, the appropriate time period would likely be late summer or early fall, when the long day-length and warmer temperature have allowed some time for growth and development of flora and fauna and there is no sea-surface ice to contend with. In general, reproductive periods and patterns of abundance of benthic species are related to tidal cycles, season and abundance of food supply.

4.4 Statistical Considerations for Study Design

General statistical guidance (e.g., selecting a and b levels and determining sampling effort) is discussed in Chapter 8. This section provides specific guidance on benthic invertebrate statistics including sampling effort for RCA designs and the use of ordination probability ellipses for RCA designs. In addition, a discussion is included which provides guidance on determining the number of field sub-samples which should be taken at a given station and how this field sub-sample data could be used to improve future study designs.

It should be noted here that although a RCA can be used to present results of a benthic invertebrate communauty survey, mines should also submit information related to the effect endpoints required under the MMER (see section 4.9).

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4.4.1 Determination of Sampling Effort for RCA Designs

The issue of replication is somewhat different when using the RCA. Replication is at the station scale and, since variation within a station is often much lower than among stations, single samples are taken at stations and variation among stations is used to describe the reference condition. The number of reference replicates is determined by the number of stations in the group to which the exposed station is predicted to belong using the RCA. This is determined when forming the groups of reference stations in the initial classification (see section 4.3), but has been set to a minimum of 10 stations. The variation among the reference stations forming the reference group determines the Type I error, which has been set at 0.1 by using a 90% probability ellipse. Because, in this approach, single-exposure stations are compared to multiple reference stations (minimum of 10), it is not possible to set Type II error, which requires an estimate of the variance associated with a single station. A surrogate can be applied by taking more than one sample at the exposed station, but this is estimating within-station error rather than the appropriate variation at the among-station level. Clearly, Type II station error cannot be determined when there is only one member of the population of exposed stations. Therefore, the power analyses referred to above would not be applicable for the RCA study design. Consequently, RCAstudies should be designed in a way that provides an accurate and precise determination of reference conditions so as to maximize the likelihood of detecting departures from reference conditions at exposure stations, when they exist.

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4.4.2 Determination of Sampling Effort for Field Sub-sampling

The objective of multiple field sub-samples at each replicate station is to ensure that the sampling effort will produce an accurate reflection of all the metrics of interest (e.g., taxa richness, density) for each station that is sampled. This is necessary because species may not be homogeneously distributed throughout a station (which is much bigger than the size of the physical sampling apparatus being used). Inadequate sub-sampling 1) gives an imprecise estimate of the true mean for each station and 2) can contribute to an inflated estimate of the true among-station variance, thereby decreasing power.

Therefore, the allocation of field sub-samples within a replicate station depends on the following two inter-related factors that should be considered during any benthic sampling design exercise. However, in the absence of background information, the recommended minimum number of field sub-samples to obtain from each station is 3.

1. The abundance (or density) and degree of aggregation of organisms in relation to the desired level of precision for station estimates

For a given station, the number of field sub-samples needs to be sufficient to give a mean and variance that provide confidence that a representative number of animals has been captured (for a review, see Burd et al. 1990). The more aggregated a community, the higher the variance of mean abundance for each replicate station. Elliott (1977) and Holme and McIntyre (1984) suggested the same simple method of determining the number of field sub-samples to obtain a predefined level of precision. Elliott (1977) suggests that toleration of an index of precision (D) of 20% (i.e., that the standard error is equal to 20% of the mean) is acceptable for most bottom samples. The number of field sub-samples can then be calculated as follows:

n = s²/D² X̄²

where

X̄ = the sample mean
n = the number of field sub-samples
s² = the sample variance
D = the index of precision (i.e., 0.20)

Thus, to determine how many field sub-samples (i.e., grabs) per replicate station will provide an estimate with 20% precision, previous data can be used to determine the mean and variance and, thus, the appropriate number of field sub-samples. This determination may vary from location to location along with changes in the mean-to-variance ratio. It is recommended that the number of field sub-samples be calculated for locations that exhibit the highest variability and that the resulting sample size be applied equally to all areas to standardize sampling effort. Although this recommendation will produce better precision in the less variable habitats, it is a conservative approach and maintains equal sampling effort between areas and replicate stations. Also of note is that, with aggregated populations, although the overall mean should remain the same, depending on the scale of the aggregation in relation to sampler size, variance will change with the size of the sampler. Therefore, sample size estimates using preliminary data are only relevant to a sampling program that would employ the same type and size of sampler with which the preliminary data were obtained. In cases where this sampling effort cannot be determined from a previous phase’s data, counting organisms in field sub-samples from the current survey as they are processed and calculating means and variances will allow determination of how many grabs should be processed in the laboratory. However, this a posteriori approach necessitates that a sufficient number of grabs were obtained during the field survey in the first place, so the effort to calculate the sample size within a replicate station a priori should minimize problems due to insufficient sampling effort.

A related approach uses abundance and variance to determine sub-sampling effort and precision and can be used for determining the number of field sub-samples at all replicate stations. It is derived from the relationship between within-station mean abundance and variance across all replicate stations in the area or gradient being evaluated. Downing (1979, 1986) used Taylor’s power law (1961) to estimate aggregation in a freshwater benthic community and thus determine the sampling effort required to reduce variance to an acceptable level. In effect, a given number of organisms are required for each replicate station in order to produce the precision of within-station mean abundance from sampling. Vezina (1988) used the same approach to determine empirically the degree of aggregation inherent in marine benthic communities. This approach entails calculating a power regression equation that describes the log/log relationship between within-station mean abundance and variance across all stations; this provides a formula that is then used to determine the estimated variance expected for a given abundance of organisms in that survey region. From this, the estimated variance for each mean abundance at each replicate station is calculated and then used in the same way as Elliott (1977) to estimate the number of field sub-samples at that replicate station. The difference between the methods of Elliott (1977) and Downing (1979) is that in Elliot’s method the variance used in the equation to determine the number of field sub-samples is based on sample variance, while in Downing’s method the variance used in the equation is based on the variance calculated for the sample mean from the power regression equation for all the samples in the survey region. Furthermore, the index of aggregation (slope) from the power regression equation can then be used to determine the most appropriate data transformation for parametric statistical analyses. Unfortunately, this method assumes that the overall assemblage has a uniform aggregation throughout the study area, which may or may not be true when an external environmental stressor is applied. However, the degree of goodness of fit of the mean and variance data to the log regression equation provides a good indication of how true the homogeneous aggregation assumption is. If there are extreme outliers, they should be taken out of the analysis to avoid skewing the results. Because the aggregation of benthic communities can change as environmental conditions change either naturally or unnaturally, it is wise to review the relationship between mean and variance every time benthic samples are collected. Finally, it should be noted in the above discussion that the “power regression equation” used here to calculate the number of field sub-samples is unrelated to the “power analyses” used to determine the number of replicate stations discussed in the previous sections.

2. The number and distribution of different species in relation to obtaining a representative collection

To determine if sufficient species have been sampled, simple rarefaction methods such as the “species abundance curve” or species/sampling area curve can be used (for a review, see Burd et al. 1990), which compare the number of species obtained vs. number of individuals for different numbers of pooled replicates. This analysis is particularly important in Arctic areas, where diversity may be high, but only on a geographic scale much larger than is feasible to sample (i.e., number of species relative to abundance is high, but abundance is quite low--this can also occur in the deep sea). Because of assumptions inherent in the underlying distribution of fauna related to logarithmic species abundance curves, a more sophisticated approach is the “similarity/sampling area” curve, which uses similarity indices on presence/absence data to determine the sampling effort to obtain an acceptable overall faunal similarity between replicate stations (Weinberg 1978; Kronberg 1987).

If preliminary data are unavailable or unsuitable for determination of the number of field sub-samples to obtain a representative collection of species, a check on sampling effort could be performed very simply. If it is estimated that X number of grab samples per replicate station is sufficient to achieve a data quality objective of retrieving 95% of benthic species present at any replicate station, more grabs can be collected at a few select replicate stations and analyzed. Determination of a taxa richness plateau from these extra samples determines whether the number of grabs were sufficient to achieve the 95% objective (using a species area curve).

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4.4.3 The Use of Ordination Probability Ellipses for RCADesigns

A large-scale water quality survey on rivers conducted in the U.K. in 1990 provided the impetus for the development of methods to circumscribe the continuum of responses into a series of bands that represented grades of biological quality (Clarke et al. 1992). The study produced a simplification of the continuum of responses in sites ranging from good to poor biological quality. It was seen as an appropriate mechanism for obtaining a simple statement of biological quality, which allows broad comparisons in either space or time that are useful for management purposes. From a management perspective it is desirable to assign a degree of impairment. This can be done by setting response categories from mild to severe impairment. In the study by Clarke et al. (1992), a number of schemes for categorizing the response were considered and tested. The threshold between unstressed and stressed sites (band A) was set at the 90% probability level (SD = 1.64) for number of taxa and the biological monitoring working party (BMWP) score and 95% for the average score per taxon (ASPT). In Australia, the threshold is set at 2 SD from the reference site mean for the number of taxa. Finally, 95% is frequently set as the limit for determining a biological effect for univariate data and single community descriptors (Lowell 1997). The strategy employed in the U.K. (Wright 1995) to discriminate between degrees of impairment was to quantify the thresholds for stressed and non-stressed sites via the setting of 3 equal-sized bands, as Wright (1995) argued that there was no logical basis for an alternative scheme for dividing up the continuum of sites.

A similar approach can be adopted for defining degrees of impact using multivariate ordination. The reference invertebrate assemblage can be described by its distribution in ordination space, and the assemblage at any given site is characterized by its position in that XY space (Figure 4-5). The greater the similarity between sites, the closer together they are in XY space. Using this approach to set effect size for an invertebrate assemblage, all the reference sites are plotted in XY space together with a test site. The likelihood of the test site being the same as the reference site is quantified by constructing probability ellipses for the reference site only. Reynoldson et al. (1995) selected the 90% probability ellipse as representing the first band, the threshold for a site being considered equivalent to reference. The rationale for using the 90% ellipse rather than the more typical 95% was based on the fact that a multivariate approach will tend to be noisier than univariate measures and therefore a more conservative threshold was deemed appropriate. Sites located in ordination space inside this smallest ellipse (90% probability) would be considered as equivalent to reference and therefore unstressed. Two other probability ellipses are used (Figure 4-5), which are equal in width, to describe further divergence from the reference state, following the argument used by Wright and co-workers (Clarke et al. 1992; Wright 1995). Sites between the smallest (90%) and next ellipse (99% probability) would be considered possibly different; there is a 1 in 10 chance that sites will fall in this band through normal variability. Sites between the 99% and the largest ellipse (99.9% probability) are considered different: there is a 1 in 100 chance that these sites would incorrectly be described as different. And finally, sites located outside the 99.9% ellipse are designated as very different.

Figure 4-5: Impairment stress levels derived for reference sites in hybrid multidimensional scaling ordination space (text description)

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4.5 Field Methods for Benthic Invertebrate Community Monitoring

4.5.1 Sampler Mesh Sizes

Benthic samples typically contain varying amounts of fine sediment and debris. To expedite transfer to sample containers, storage, and shipping, these samples should be reduced in the field by sieving. Field sieving should be done, wherever possible, immediately after sample retrieval and before preservation, as many organisms become fragile and brittle after preservation. Various techniques for sieving are available, but most involve washing the sample with a sieve or sieve bucket device.

The recommendation for sieve and/or mesh size for all freshwater mines is 500 µm.

In fresh water, macroinvertebrates are defined as those retained by mesh sizes of 200–500 micrometres (µm) (Slack et al. 1973; Weber 1973; Wiederholm 1980; Suess 1982), although immature life stages of some taxa may be smaller and some adult life stages may be larger.

Note that these mesh sizes are applicable to all equipment used in the field and laboratory (i.e., both the Nitex mesh on the benthic samplers and sieving apparatus).

In some site-specific circumstances it may be desirable for the field samples to be screened for smaller organisms by using a smaller sieve size (less than 500 mm). For example:

for comparative purposes, where historic benthic surveys for the system under investigation utilized smaller mesh sizes, or
if sampling needs to be conducted, for logistical reasons, at times when organisms are very small. Rees (1984), Barber and Kevern (1974), and Jonasson (1955) provide information on seasonal effects of mesh size.

In these aforementioned cases, it is highly recommended that a stack of screens be used which minimally have the mandatory sieve sizes and then any other smaller sizes, as appropriate. This procedure simultaneously allows site-specific concerns to be addressed and fulfills EEM objectives by allowing for national or regional comparisons to be conducted on the standardized mesh sizes. Sieving with the finest-scale sieve can be done in the field so long as the appropriate fractionation of the sample is performed in the laboratory before processing.

For marine organisms, samples should be sieved with seawater rather than freshwater, since the osmotic shock of freshwater may cause cell bursting and gross distortion of the animals. Where appropriate, field water used to sieve should be screened for ambient organisms with a mesh smaller than the required minimum screen size used for the study. In addition, extreme care should be taken during washing of samples to avoid breakage of specimens, which can greatly reduce taxonomic efficiency and cost-effectiveness. Methods have been described to reduce breakage, particularly in marine samples (Gray et al. 1990).

In marine systems,it is recommended to use astacked set of 1000-mm and 500-mmscreens in the field, with the 500-mm samples being archived and processed only if appropriate.Marine macrobenthos are typically those retained by sieves with 500–1000-µm mesh (Reish 1959; Thiel 1975; Pearson 1975; Holme and McIntyre 1984; Gray et al. 1990). It is estimated that a 1000-mm sieve will retain about 95% of the biomass of marine macrofauna (Reish 1959), while reducing the numbers of juvenile taxa and meiofauna present in samples that respond functionally differently to environmental perturbation than do adult macrofauna (Schwinghamer 1981, 1983; Warwick 1986).

Studying smaller benthic organisms for magnitude and geographic extent in marine systems may include assessment of meiofauna such as nematodes, copepods and smaller oligochaetes or it may include assessment of living and dead foraminifera (Schwinghamer 1981, 1983) or it may include more detailed assessment of juvenile forms of macrofauna. All of these approaches require the use of smaller mesh sizes and/or different samplers (cores may be more appropriate than grabs: see Holme and McIntyre 1984) than are currently recommended here. However, if smaller forms are important, simply adding an additional sieve may not fulfill this function. The appropriateness of the sampling techniques should be assessed for smaller forms. For marine environments, Gray et al. (1990) noted that meiobenthos are most appropriately collected with core samplers, which are not recommended sampling devices for the EEM program. Thus, before simply screening for smaller organisms, appropriate protocols should be implemented.

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4.5.2 Sampling Equipment

Two major considerations for benthic surveys are mesh size (see previous section) and quantitative sampling equipment. Quantitative sampling of benthic communities is carried out using devices that sample a known area or volume of habitat, such as grab samplers or stream net samplers. Each sampling device should be non-selective and suitable for a particular substrate. Benthic samples collected from natural substrates provide an indication of past and current stressors. Therefore, samplers that collect benthic communities from the bottom sediments are recommended unless this is not possible due to physical constraints. Samplers are to be consistent within a habitat class among all stations and areas. However, different samplers may be used in the same survey if they are used to sample different habitat classes. For example, if both erosional and depositional habitat classes are sampled, it would be reasonable to use one of the recommended grabs for the exposure and reference depositional habitat, but a Hess-type sampler for the exposure and reference erosional habitat. It is recommended that grab samplers with screens on the top and top-opening gates be used so that the bow-wave ahead of them is reduced and less substrate is lost, and for examination (and possibly sediment chemical analysis) of undisturbed surface layer in sediment samples.

Standardization of benthic samplers facilitates regional and national comparison of benthic invertebrate survey data. Recommendations of samplers appropriate to the various habitat classes encountered during EEM benthic surveys are provided below. Eleftheriou and Holme (1984), Klemm et al. (1990), and Scrimgeour et al. (1993) discuss the options as they pertain to different receiving environments and summarize the advantages and disadvantages of the recommended samplers. The selection of a sampler may also be influenced by the type used in previous surveys of particular systems. To ensure that surveys can be compared with previous historical surveys, it would be useful to use similar sampling equipment. For more detailed information, the reader is referred to the bibliographies on quantitative samplers and appropriate methodologies prepared by Klemm et al. (1990), Eleftheriou and Holme (1984), Elliott and Tullett (1978, 1983), Rosenberg (1978), Downing (1984), and Mason (1991). See also Rabeni and Gibbs (1978) and Alberta Environment (1990).

The standardization of techniques applies not only to the sampling equipment but also to the level of expertise required to correctly deploy the sampler. Crew members should be properly trained in the use of sampling equipment to minimize variation introduced by operator error. For example, when sampling erosional zones in rivers, the depth to which the substrate is disturbed within the net-sampler should be standardized since some individuals may be more energetic in regards to stirring up the substrate than others. The study leader should be well versed in benthic invertebrate sampling and conduct effective training sessions with the crew members performing the field sampling. If training is done effectively, operator error can be eliminated (Reynoldson and Rosenberg 1996).

Depositional habitats: freshwater

Grab samplers are devices with spring-loaded or gravity-activated jaws that “bite” into unconsolidated substrates (sand, silt, mud, etc.) to enclose a defined surface area of the bottom. These devices are generally lowered on a line or cable from a survey vessel to the bottom, sometimes with the aid of a winch. If the sampler type chosen is not suited to the substratum present, it can affect sampling efficiency. Factors that may affect grab sampling include depth of penetration, completeness of closure of jaws, and subsequent loss of material during retrieval. In depositional zones of freshwater rivers or lakes, Ponar or Ekman grabs are suggested as standard samplers for EEM benthic invertebrate surveys. See Eleftheriou and Holme (1984), Klemm et al. (1990), and Scrimgeour et al. (1993) for additional information on samplers.

Erosional habitats: freshwater

Stream-net samplers are devices used for collecting benthic invertebrates in erosional riverine environments. They use mesh of various sizes (but see Section 4.5.1 for discussion on mesh size) to sieve organisms from water flowing through the mesh after disturbance of a known area of the substrate. It is recommended that erosional habitats in freshwater environments be sampled with Neill-Hess cylinder-type samplers that allow unit area (typically 0.1 m²) estimates to be made. One drawback to cylinder samplers in streams is a potential incompatibility with size of substrate. In some systems, mean particle size may be too great for the Neill-Hess cylinder to effectively sample the benthic invertebrates. In such cases, a U-net sampler (Scrimgeour et al. 1993) can provide area-limited samples and be adjusted accordingly to the size of the substrate. This sampler has been used successfully for a range of substrate sizes (Glozier 1989) and can sample either individual stones or a defined area. Kick-net samplers do not provide an area-delimited estimate, but have been used widely in the United Kingdom, the United States, Australia and Canada in large-scale monitoring programs (Reynoldson et al. 1995). Kick sampling is particularly appropriate for the reference condition approach, where many stations are sampled. A timed kick sample is taken at each station to estimate benthic community descriptors. Standardization of kick-sampling techniques is essential for comparative purposes and can be obtained with minimal training (Reynoldson and Rosenberg 1996). The kick-sampling method involves a single composite sample collected at each station by a 3-minute travelling kick method (Reynoldson et al. 1997). Note that separately preserved field sub-samples are not required for the kick-sampling technique recommended for RCA.

For difficult habitats (e.g., very deep, slow-flowing areas or areas with hard substrates) alternatives such as the metal quadrat or airlift system may be available. However, for national or regional comparative purposes, the list of recommended samplers should be sufficient for sampling the majority of ecologically relevant habitats. If habitats are extremely difficult to sample, alternative approaches may be considered.

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4.5.3 Artificial Substrates

The use of artificial substrates for benthic invertebrate collections is generally not recommended as a sampling protocol in EEM.

There is no advantage to be gained from using artificial substrates where conventional sampling techniques provide at least as reliable data without the many drawbacks and difficulties of artificial substrates (AETE 1995). Artificial substrates do not collect a representative sample of the indigenous benthic invertebrate community at the site where they are placed, but rather select for mobile, drift-prone species of hard substrata. In addition, artificial substrates do not effectively monitor the effects of sediments or sediment-bound contaminants on aquatic biota because sediment-dwelling taxa tend to be under-represented in artificial substrate samples. The invertebrate community represented by artificial substrates indicates conditions during the period of exposure only and does not integrate long-term effects. Therefore, the use of artificial substrates for benthic invertebrate collections may fail to indicate the effects from effluents, particularly where non-mobile species, sediment-bound contaminants or longer-term integration of effects are important. However, it is recognized that there may be a limited number of cases where there is either a long history of artificial substrate use in a particular ecosystem or extreme habitat conditions (e.g., very deep, fast-water systems) where the use of artificial substrates is the only feasible field method available. In these cases, the use of artificial substrates may be considered along with other alternatives--provided this method can determine if there are effects on the benthic invertebrate community in a scientifically defensible manner.

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4.5.4 Marine/Estuarine Habitat Sampling Equipment

Depositional Habitats: Marine/Estuarine

Depositional habitats in marine environments can be sampled with the Smith-McIntyre grab, a modified Van Veen grab, which is suitable and available in Canada. A good review of marine sampling methods is available in Eleftheriou and Holme (1984). However, in shallow subtidal areas where there is not enough water depth to allow the deployment of the larger grabs, a smaller or mini (petite) Ponar grab can be used. This grab is deployable from small inflatable boats and can be retrieved by hand.

Intertidal soft substrates may be sampled using any device that demarcates an area of at least 0.1 m². The soft substrate is then removed to a standard depth of 10 cm using an appropriate device. Note that, in general, the lowest intertidal level available for sampling is preferred because less harsh physical conditions promote higher species richness and abundance.

Erosional Habitats: Marine/Estuarine

In marine/estuarine environments, large unconsolidated sediments such as gravel may be sampled with grab samplers. If not, hard substrates in erosional habitats (intertidal and subtidal) should be sampled using quadrats with a minimum area of 0.1 m². However, some other quantitative techniques may be recommended to collect marine shellfish and other large species. These may include hand collection by divers, remote sensing techniques from defined surface areas (Eleftheriou and Holme 1984; Gray et al. 1990), and collection from defined boundaries along transects. An outline of a marine sampling protocol is described in a series of Puget Sound Estuary Program Reports (Tetra Tech 1986a, 1986b, 1987). When done properly, photographic surveys can be quantitative, at least for larger epibenthic organisms (c.f. Burd et al. 1990). Processing costs tend to be considerably less than for soft-bottom surveys using grabs or cores.

The intertidal zone should be sampled if the effluent plume impinges substantially on it. Determining the tidal level of greatest interest for examining mine impacts will involve logistical considerations. Basically, the lower in the intertidal area the surveys can be conducted, the better, since less harsh conditions create less patchiness and higher diversity in flora and fauna (inter-sample variability). Coastal plants and animals in this habitat typically exhibit vertical distributions that reflect gradients in environmental parameters such as air exposure, temperature (including freezing), salinity, light intensity and daylength, abrasion due to logs or ice, and wave shock. These gradients should be considered in planning and undertaking biological surveys in the intertidal environment. Sampling protocols for this area will be somewhat different from those described in the earlier sections (for review see Gray et al. 1990). Wherever possible, semi-quantitative surveys using quadrat areas of 0.1 m² should be done. Determining the substrate or habitat type to be sampled depends on sampling limitations and the dominant habitat present (see section 4.3.7 for discussion of dominant habitat selection). However, if it is not feasible or ecologically sound to collect samples, then visual surveys are recommended. If approved, a visual survey would include approaches such as recording and mapping (at a gross scale of 1:5000) the major biological features for assessment of gross changes in the biological community.

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4.5.5 Sample Containers

Environment Canada’s Guidelines for Monitoring Benthos in Freshwater Environments (EVS Environment Consultants 1993) specify that sample containers should:

be large enough to ensure the sample takes up no more than 50% of the container volume, with the remainder of the space allocated for preservative;
be sturdy enough for routine handling and transportation;
be leak-proof;
have physical and chemical properties that are not affected by the fixative/preservative; and
conform to regulations concerning the transportation of dangerous goods.

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4.5.6 Specimen Fixation and Preservation

All samples should be fixed in the field in a 10% buffered formalin solution to prevent damage to freshwater and marine worms. Formalin is also important for the proper preservation of most aquatic insects. Preservation directly in ethanol often results in soft, difficult-to-handle specimens. After preservation in the field, samples should be gently mixed several times to ensure that the preservative has thoroughly penetrated any fine material that may be present in the sample. Because formalin is a carcinogen and an irritant to workers, gloves and protective eye gear are needed and should be considered mandatory safety equipment. Furthermore, unbreakable sample jars should be sealed with parafilm, double-bagged for transport back to the laboratory facilities and adequately labelled. The samples should be preserved as soon as is practical after sampling to prevent predatory invertebrates from preying on others in the samples.

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4.5.7 QA/QC for Benthic Invertebrate Field Operations

An outline of the quality assurance/quality control (QA/QC) recommendations for the field components of the benthic invertebrate community survey is presented below.

Field sampling is the first stage of data collection. QA/QC procedures for the benthic invertebrate survey are outlined in the study design and should be followed precisely to maintain high data quality. Field standard operating procedures (SOPs) should specify sampling equipment and protocols appropriate to the study. A QA/QC plan for field sampling has many components. Some of the main procedures are listed below:

All personnel involved in the field sampling should have appropriate training and experience with field equipment and objectives.
All safety measures should be identified, understood and adhered to.
Collection equipment should be appropriate for the specific water body and selected invertebrate group, and should be checked frequently and maintained regularly.
There should be some a priori criteria for acceptability of samples obtained and clear directions if acceptability guidelines fail (i.e., when to retake a sample; grab sample penetrations of 10-cm depth would be considered an acceptable sample, Gray et al. 1990). Also, sampling methods need to be consistent throughout the study.
A visual description of benthic grab samples should be recorded to describe sediment color, odour, texture and debris.
Contamination during chemical sampling should be checked by means of trip blanks and equipment rinsates.
Field sieving, if necessary, should be done as soon as possible after retrieval of samples.
Samples should be stored in appropriate containers with appropriate preservative to prevent breakage and spoilage.
All sample containers should be appropriately labelled.
Detailed field notes should be maintained in a bound waterproof notebook.
Chain-of-custody forms and appropriate shipping and storage procedures should be applied.

For further information regarding all aspects of QA/QC procedures for benthic invertebrate programs, refer to the 1999 AETE report (Beak 1999).

4.6 Laboratory Methods

For information pertaining to sample sorting and sub-sampling, please refer to the Revised Guidance for Sample Sorting and Subsampling Protocols for EEM Benthic Invertebrate Community Surveys, which can be obtained from the EEM website.

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4.6.1 NABS Certification Program

The accurate identification of aquatic benthic invertebrates is crucial to monitoring programs like the metal mining EEM program. The North American Benthological Society (NABS) implemented a certification program for benthic invertebrate identification. The program tests the candidate’s knowledge and skills in aquatic invertebrate taxonomy and ensures that individuals are providing high-quality identifications. It is recommended that the identification of aquatic benthic invertebrates be conducted by an individual who has completed the NABS certification program. For additional information, please refer to the following website.

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4.6.2 Taxonomic Level of Identification

Identification of the benthic invertebrates sampled should be adequate to meet the objectives of the assessment program. Research indicates that family-level identification provides sufficient taxonomic resolution to detect community responses to human disturbances (Warwick 1988a, 1988b; Bowman and Bailey 1997). As discussed below, the level of taxonomic resolution used may vary across the different monitoring phases, with finer taxonomic resolution needed to detect more subtle environmental impacts.

The recommended level of taxonomic identification is family for the first and subsequent monitoring of freshwater systems. All summary statistics and descriptive metrics should be calculated and reported at the family level for submission to the first and subsequent monitoring interpretative reports. Organisms that cannot be identified to the desired level of taxonomic precision should be reported as a separate category in the fundamental data set. It is recommended that investigators use taxonomic keys appropriate to the geographic region of study. Table 4-2lists taxonomic references typically used for various groups of freshwater organisms.

For some phases, a lower taxonomic level may be recommended, depending on the questions and objectives of the study. The lowest practical level (LPL) has been defined as genus for most insects and the lowest level possible without special procedures (dissection, microscopy) or reliance on specialist for other groups (Taylor 1997). This definition can be used as a guide if lower level identifications are desired for magnitude and extent and investigation of cause monitoring.

There may be site-specific conditions that warrant a lower taxonomic level for some or all familial groups. For example, historic benthic invertebrate information may be identified to lower taxonomic levels, and it may be desirable to identify subsequent surveys to a similar level for comparative purposes. If a lower taxonomic level has been used, either in historic data or during a current survey, the summary statistics and descriptive metrics can be reported at this level, provided a summarized data set at the family level is also included.

Two objectives of the magnitude and geographic extent survey may require different levels of taxonomic resolution. Determination of the geographical extent of the effect may be addressed adequately with family identification. Family-level identification would provide the information necessary for calculating and reporting the required summary and descriptive statistics related to extent of the effect. This first objective is similar in scope to Phase 1 and subsequent monitoring, the major difference being the addition of low effluent exposure areas further from the effluent discharge.

The second objective of magnitude and geographic extent monitoring, when determining the magnitude of the effect, may use family identification or it could warrant investigation at a lower taxonomic level. The question of magnitude of effect with regard to taxonomic level can be addressed using the following question:

What is the magnitude of the effect on specific taxonomic groups that may be sensitive to the site-specific mine effluent characteristics (e.g., how many groups within a sensitive family are affected)?

Addressing the magnitude of effect during magnitude and geographic extent monitoring can be accomplished by using one of the options outlined below:

Identify all samples collected to the lowest practical level. Establishing the magnitude of effect in this way provides additional information that may be useful for the study design exercise at the outset of the investigation of cause.
Re-analyze families that were significantly affected during the first monitoring to identify indicator taxa that can be used to assess the magnitude of effect at stations farther afield. For example, if an effect during the first monitoring was observed for the family Baetidae (order Ephemeroptera), all Baetidae could be identified to a lower level (e.g., genus) for the magnitude and geographic extent monitoring program. This approach would catalog the “sensitive” taxa within the family, and the magnitude of effects would be established by examining this subset of sensitive taxa.
Other scientifically defensible approaches may be used to identify magnitude of effect as required.

In marine/estuarine environments, it is recommended that all benthic invertebrate organisms be identified to the family level. In interpretative reports, all summary statistics are calculated and reported to the family level. Various authors have examined the utility of using higher taxonomic classifications for environmental monitoring of organically polluted sites in Europe (cf. Warwick and Clarke [1993] and references therein). For marine benthos, juvenile or non-adult fauna should be identified and enumerated separately from adults, as they show different patterns of response to environmental effects.

Though mines may proceed with benthic invertebrate identifications to a lower level, the recommended level of identification for data reporting and determination of effects is the family level. There may also be site-specific conditions that warrant a lower taxonomic level for some or all familial groups. For example, historic benthic invertebrate information may be identified to lower taxonomic levels and it may be desirable to identify subsequent surveys to a similar level for comparative purposes. If a lower taxonomic level has been used, either in historic data or during a current survey, the summary statistics and descriptive metrics can be reported at this level--provided a summarized data set at the family level is also included.

For marine samples it is suggested that, if sufficient numbers of specimens are available in the reference collections, they could be used for a further purpose: to develop a size and biomass database for each mine as another indicator or tool (see section 4.11.4). For these purposes, 5 to 10 representative specimens per taxa are recommended, with mean width, lengths and blotted wet weights recorded for each group.

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4.6.3 Reference Collections

Consistency in taxonomic identifications within and between surveys is essential to obtaining useful information on environmental effects monitoring. Therefore, for comparative purposes and quality control of taxonomic identification, the maintenance of a reference collection of organisms is recommended. In addition, it is recommended that an independent professional taxonomist verify the identifications in the collection. Museums are sometimes prepared to perform this service when remote areas are included in the study and new specimens or distribution records are likely. Reference collections have several benefits, including their use in confirming identifications, ensuring consistent taxonomy between surveys, and the training of personnel. Protocols for establishing and maintaining reference collections for benthic invertebrates are detailed in a report prepared for Environment Canada’s Fraser River Action Plan (Green 1994). Following the recommendations of this report, each mine (or group of mines) should compile and archive a complete reference collection with several specimens of representative-sized individuals for each taxon. The collection should encompass representative organisms from each area in the survey, be labelled according to the location and date of collection, and updated as appropriate (i.e., when a taxon is collected). This type of reference collection will not occupy a large space: a small cupboard should be sufficient and should be in the custodianship of the mine. If a mine does not have the facilities or personnel to maintain their own reference collection, universities or museums may be willing to fulfill this function. However, since considerable effort is involved in the long-term maintenance of preserved biological material, the quantity of material submitted should be minimized.

**Table 4-2**: Taxonomic keys for benthic invertebrate taxonomic identification in freshwater environments (text description)
Taxon	Taxonomic Reference Typically Used
General Keys	Merritt and Cummins 1984, 1996; Peckarsky et al. 1990; Pennak 1978; Thorp and Covich 1991
Regional Keys	Clifford 1991 (Alberta) Lehmkuhl 1975a, 1975b, 1976, 1979 (Saskatchewan) Laplante et al. 1991 (Quebec)
Taxon-specific Keys
Annelida Oligochaeta Hirudinea	Brinkhurst 1986 Klemm 1972, 1985, 1991
Crustacea Amphipoda Decapoda Cladocera Copepoda	Bousfield 1958 Brandlova et al. 1972 Dussart 1969 Crocker and Barr 1968 Fitzpatrick 1983
Insecta	Chu and Cutkomp 1992; Hilsenhoff 1995
Plecoptera (stoneflies)	Fullington and Steward 1980; Harper and Stewart 1984; Hitchcock 1974; Stewart and Stark 1993
Ephemeroptera(mayflies)	Bednarik and McCafferty 1979; Edmunds et al. 1976; Lewis 1974; Morihara and McCafferty 1979; McCafferty and Waltz 1990; Waltz 1994
Odonata (dragonflies and damselflies	Hilsenhoff 1995; Westfall and May 1996; Walker 1933, 1953, 1958; Walker and Corbet 1978
Trichoptera (caddisflies)	Schefter and Wiggins 1986; Schuster and Etnier 1978; Wiggins 1996
Coleoptera (beetles)	Hilsenhoff and Schmude 1992
Diptera (flies)	Hilsenhoff 1995; Johannsen 1977; Oliver et al. 1978; Saether 1975, 1977; Simpson and Bode 1980; Wiederhom 1983, 1986; Wood et al. 1963
Gastropoda (snails)	Burch 1989; Clarke 1981
Pelecypoda (clams, mussels)	Mackie et al. 1980; Clarke 1981; Burch 1975a, 1975b; Mackie and Huggins 1983

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Table 4-3 lists recommended levels of taxonomy desirable for major taxonomic groups of marine benthic organisms. In general, the level of taxonomy should be consistent in each major group for all samples from a survey and also from survey to survey. Organisms that cannot be identified to the desired level of taxonomic precision should be reported as a separate category in the fundamental data set at the finest level of taxonomic resolution possible. Since the accuracy of the taxonomic work depends on the availability of up-to-date taxonomic literature, a basic library of identification keys is essential. Keys appropriate to the geographic region of study are recommended. A detailed list of taxonomic references for marine and estuarine habitat is found in Table 4-4. Microscope slide mounts should be prepared for taxa requiring detailed microscopic examination for identification. This may involve various steps, including dissection, clearing and staining. Slide preparation techniques are listed in Klemm et al. (1990). For marine benthos, juvenile or non-adult fauna should be identified and enumerated separately from adults, as they show different patterns of response to environmental effects. All identifications should be carried out or verified by a qualified and experienced taxonomist. Existing reference collections may be useful as well. An example is the Atlantic Reference Centre at Huntsman Marine Station in St. Andrews, New Brunswick. Photographic iconographs have been used to advantage (Camburn et al. 1984–1986).

Table 4-3: Recommended level of taxonomic precision for benthic invertebrates in marine environment (for lowest practical taxonomic level approach) (text description)

Taxon	Level
Porifera	Class
Cnidaria	Genus
Turbellaria	Genus
Nemertea	Genus
Nematoda	(not to be included in analyses*)
Sipuncula	Species
Priapulida	Species
Brachiopoda	Genus
Bryozoa	Family
Mollusca
• Aplacophora • Gastropoda • Bivalvia • Polyplacophora • Scaphopoda	Genus Species Species Genus Species
Annelida
• Polychaeta • Oligochaeta	Species (except some immature) Genus
Arthropoda
• Pycnogonida • Cephalocarida • Malacostraca • Copepoda • Cirripedia	Family Sub-class Species (remove from analyses*) Species
Ascidiacea	Family
Echinodermata	Species

*Nematodes and copepods (e.g. harpacticoida) are meiofauna, and only a fraction of specimens will be captured by a 500 μm or 1000 μm screen. Therefore, numbers are not representative and should be excluded from analyses (Holme and McIntyre 1984).

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Table 4-4: List of marine and estuarine taxonomic benthic invertebrate keys for Canada (text description)

References

Abbot 1974 (seashells)
Abbott et al. 2001 (Mollusca)
Appy et al. 1980 (Bay of Fundy polychaetes)
Austin 1985 (Pacific invertebrates)
Baker 1980 (Tubificid species)
Banse 1972; Banse and Hobson 1974 (polychaetes)
Berkeley and Berkeley 1952 a,b (Pacific Annelida)
Blake 1971 (Polydora, East Coast)
Blake 1991 (Polychaeta, North Atlantic)
Blake 1988 (Phyllodocidae [Polychaeta], Atlantic)
Bousfield 1960 (Atlantic seashells)
Bousfield and Hendryks 1994, 1995 a, 1995b (Pacific Amphipoda)
Bousfield and Hoover 1995 (Pacific amphipods)
Bousfield and Kendall 1994 (Pacific amphipods)
Bousfield 1973 (Amphipoda, Atlantic)
Brinkhurst 1982 (Oligochaetes)
Brinkhurst and Baker 1979 (Marine Tubificidae) (Oligochaeta)
Brunel et al. 1998 (Catalogue of the invertebrates of the Gulf of St. Lawrence)
Butler 1983 (Pacific shrimps)
Clark 1924 (Holothuroidea)
Clark 1915 (Ophiuroidea)
Coates 1980 (B.C. Enchytraeidae)
Coe 1912 (Echinodermata, Atlantic)
Coe 1943 (Nemertea, Atlantic)
Cutler 1973 (Sipuncula)
Fauchald 1977 (Polychaeta)
Fournier and Petersen 1991 (Polychaeta)
Gibson 1994 (Nemertea)
Gosner 1971
Graham 1988 (Gastropoda)
Hart 1982 (B.C. crabs)
Hobson and Banse 1981 (B.C. polychaetes)
Hyman 1940 (Polycladida [Turbellaria, Atlantic])
Hyman 1944 (Turbellaria, Atlantic)
Keen and Coan 1974 (Mollusca)
Knight-Jones 1978 (Spirorbidae [Polychaeta], Pacific and Atlantic)
Knight-Jones 1983 (Sabellidae [Polychaeta])
Kozloff 1987 (Pacific N.W. invertebrates)
Lambert 1981 (B.C. sea stars)
Laubitz 1972 (Caprellidae)
Light 1977 (Spionidae [Polychaeta], Pacific)
Morris 1951 (Mollusca, Atlantic)
Pettibone 1963 (Polychaeta, Atlantic)
Pettibone 1992 (Pholoidea, Polychaeta)
Pettibone 1993 (Polynoidae, Polychaeta)
Pohle 1990 (Decapoda, Atlantic)
Sars 1895 (Amphipoda)
Sars 1899 (Isopoda)
Sars 1900 (Cumacea)
SBMNH 1994a,b,c; 1995a,b,c; 1996a,b,c; 1997a,b
Schultz 1969 (Isopod crustaceans)
Smith 1964 (Marine invertebrate keys, Atlantic)
Squires 1990 (Decapoda, Atlantic)
Steele and Brunel 1968 (Amphipoda)
Tattersall and Tattersall 1951 (Mysidacea)
Thorp and Covich 1991(Freshwater invertebrate keys)
Ushakov 1955 (Polychaeta)
Wallace 1919 (Bay of Fundy Isopoda)
Watling 1979 (Cumacea, Atlantic)
Weiss 1995 (Marine macrofauna)

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4.6.4 QA/QC for Benthic Invertebrate Laboratory Operations

In the laboratory, invertebrate samples are processed and counts of the various taxa are made. It is recommended that the sorted, preserved samples from each survey be retained in an appropriate storage facility for at least 6 years, or until it is determined that no further information will be required from the samples. Samples should be processed in a consistent manner to minimize experimental error in counts. To minimize processing error, the following items should be included in the QA/QC program:

All personnel involved in the sample processing and analyses should have appropriate training. NABS implemented a certification program for aquatic invertebrate taxonomists. For additional information see this website.
The effects of sub-sampling (if done) on abundance estimates should be examined on a minimum of 10% of the samples, and the effects of sub-sampling on the sample estimates should be documented.
Re-sorting of randomly selected samples should be done to determine the success of the initial sorting (see detailed discussions below).
Appropriate taxonomic references should be used for the type of habitat and geographic location.
A complete reference collection for each mine should be compiled and verified by an external taxonomic expert and updated as appropriate (i.e., when new taxa are recorded).
A system for archiving samples should be outlined.
Detailed sample processing and laboratory notes should be maintained.

Ecological sample processing involves, as a first step, sorting organisms from debris and, possibly, sub-sampling sorted organisms for detailed identification. Inevitably, processing errors are associated with these activities and should be estimated (e.g., Kreis 1986, 1989).

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4.6.4.1 Sorting Efficiency

Verification of sorting efficiency is easily performed on a spot-check basis if the leftover debris from a sample is retained. It is recommended that at least 10% of all samples be re-sorted and that the criterion for an acceptable sort be that ≤ 10% of the total number of organisms were missed. This estimate should be reported in the interpretative report. If ≥ 10% of the total number were missed during the re-sort, then all the samples within that group of samples should be re-sorted.

A re-sort would also be required if an entire group of benthic invertebrates was missed by the sorter (i.e., not recognized as an organism), even if the missed organism constituted < 10% of the total. The factors to consider when determining similar groups of samples include: 1) sampling area, 2) habitat class and 3) individual sorters. The QA/QC guidelines apply independently to each group of samples sorted. Sorted and unsorted fractions are to be retained until taxonomy and sorting efficiency are confirmed.

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4.6.4.2 Sub-sampling

Sub-sampling of invertebrate samples in the laboratory is acceptable, providing that the quantitative method is used. Large samples or samples with large amounts of sediment debris may require laboratory sub-sampling prior to sorting. Readers are referred to the Revised Guidance for Sample Sorting and Subsampling Protocols for EEM Benthic Invertebrate Community Surveys (Environment Canada 2002), which can be obtained from the EEM website. The detailed reporting of sub-sampling accuracy and precision for all methods is essential to the QA/QC of EEM benthic invertebrate programs. The criterion for an acceptable sub-sampling protocol is that the estimates of each group of samples should be within 20% of the true counts. If the error exceeds 20% for a particular sub-sampling technique or type of samples (i.e., type and amount of organic matter), the technique should be modified to achieve this level of precision, or all samples within that group should be completely sorted to ensure the sub-sampling process is not compromising data integrity. The estimates are then compared to the actual counts from the sample, and the accuracy of the estimates and the precision between sub-samples can be calculated using the following equation:

% error in the estimate = [1 – (estimated # in sample / actual # in sample)] × 100

The accuracy should be reported in the interpretative report.

It is recommended that a minimum number of 300 organisms be removed from a sample in any sub-sampling program to provide additional standardization. If any sampling stations have not reached the recommended minimum number of organisms during sub-sampling (i.e. 300) or have poor accuracy, the sample should be flagged when reported.

For further information regarding all aspects of QA/QC procedures for benthic invertebrate programs, readers are referred to the 1999 AETE report (Beak 1999).

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4.7 Data Assessment and Interpretation

4.7.1 Data Handling Methods

4.7.1.1 QA/QC for Data Input and Verification

After data entry, the first step in data analysis is to check for transcription errors. Failure to do this invalidates further analyses. All computer entries should be verified by checking a hard copy of the file against the raw data sheets. Someone other than the person who originally entered the data should do this cross-checking. Double entry systems and transcription checks against the original data records are useful QC techniques. Missing data should be clearly distinguished from taxon absence by use of unique non-zero missing value codes with code definitions built into each file. Read-only files help to ensure data integrity. QA/QC concerns regarding data analysis include data verification and validity, repeatability and robustness of statistical analyses, and rigour and defensibility of analysis. EVS Environment Consultants (1993) suggest that other investigators should be able to arrive at the same conclusions if they were to use the methods and data set found in the report. Other considerations regarding the data verification and analyses are listed below:

Use trained and experienced personnel.
Conduct screening exercises to identify transcription errors, outliers and other suspicious data points.
Provide raw data in an electronic database format and appendices to reports that summarize the data.
Document the methods (specific statistical tests) and software (if applicable) used for analysis.
Maintain detailed notes regarding the analyses of the survey data.

For further information regarding all aspects of QA/QC procedures for benthic invertebrate programs, readers are referred to the 1999 AETE report (Beak 1999).

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4.7.1.2 Dealing with Outliers

Assuming the data are entered correctly, data should be summarized, screened for erroneous values and outliers, assessed for normality, and transformed if necessary (EVS Environment Consultants 1993). Visual screening techniques such as box-and-whisker plots, normal-probability plots and stem-leaf diagrams can be used to identify extreme values (true outliers and/or data entry errors) (see Tukey 1977). Norris and Georges (1993) recommend examining abundance estimates for each taxon to determine if numbers are reasonable. They also recommend calculating means and standard deviations because aberrantly high or low values can indicate errors. Extreme values or outliers that are not errors of some kind should not be removed from the data set because this will result in the loss of an observation and a loss of power to the benthic invertebrate community survey. Instead, extreme values should be identified in the report and the influence of the extreme value on the results should be determined by reanalyzing the data minus the extreme value.

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4.7.1.3 Unknown, Immature and Non-benthic Organisms

There have been several instances where non-benthic organisms have been submitted as part of the metal mining EEM program. If it is documented that a given family of organisms can at some point become benthically attached (e.g., Simocephalus), then it is acceptable to include the organism within the benthic invertebrate community. However, species such as planktonic Daphnia should be removed from the data set.

Some samples may contain immature individuals that cannot be identified to the recommended level of taxonomic precision. A similar situation could also occur when samples are improperly preserved and identifying features are destroyed (e.g., mollusc shells dissolve due to unbuffered formalin). For the purposes of correctly reporting the raw data, these unidentified taxa and their abundances should be provided within the electronic raw data and report appendices. However, for data analysis, investigators need to decide whether or not to apportion the unknown individuals according to the ratio of known specimens. This assumes that the ratio of unidentified specimens is similar to the ratio of identified specimens, which may or may not be true. The choices include:

not incorporating immature or damaged forms at all
pooling all specimens (i.e., mature/immature, identified/unidentified) and lumping them into one category at the next highest taxonomic level
keeping unidentified taxa as a separate category in the analysis

Option (1) is not preferred if the “problem” taxa represent a large proportion of the total benthic invertebrate community. Option (2) assumes that all taxa within a higher taxonomic level respond the same way to effluent-related stressors, which may or may not be true. Option (3) will have variable effects on data interpretation depending on the abundance of unidentified taxa. Whatever choice is made will depend on the expertise and experience of the individual investigator; however, it should be fully documented in the Methods section of the interpretative report.

For marine surveys, it is recommended that immature and juveniles be counted and enumerated separately from adults, whether or not they can be identified to the species level, so that the adult assemblage can be analyzed without the confounding influence of transient juveniles. Thus, data analyses should show results both with and without immatures included. This is because newly settled benthic forms have different survival characteristics than adults, which have been present in the sediment much longer and integrate the effects of habitat perturbations over time. Depending on the timing of sampling, newly settled juveniles may be abundant in samples, but may all die within days due to habitat stressors, predation or competition. This is not to say that data on immatures are not important. Dramatic variations in immature settlement between nearby samples within physically homogeneous habitats may be indicative of varying levels of stress. It is just important to avoid confounding the results by mixing groups together for analysis.

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4.7.1.4 Data Reduction and Transformation

Data transformation is often performed without consideration of the effects it has on the interpretation of results. For general information on transformations, see Chapter 8. Transformation should only be applied with a complete understanding of its effect on the data and their interpretation, and only if it is necessary to aid in statistical analyses. Transformations should:

make heterogeneous variances homogeneous or make the variance independent of the mean for parametric analyses
normalize distributions
linearize relationships among variables
reduce the effects of extremely dominant taxa within a data set on a multivariate analysis (or ordination)
reduce the analytical problem of too many zeros in a data matrix (see Clarke and Green 1988).

Data reductions should be done only to aid in statistical or multivariate analyses, and for the same reasons as data transformation. Data reductions can include eliminating or rolling-up rare taxa or reducing field sub-samples by pooling or averaging. Protocols for data reductions for marine communities are varied, but subsequent interpretations of data analyses should take these reductions into account. For example, elimination of rare taxa may result in the elimination of 90% or more of the biomass within a given station if those rare taxa are large. In some cases, rare taxa are rolled up into higher groups, which prevents loss of information but adds assumptions about the uniform behaviour of mixed taxonomic groups. Reviews of standard methods of data reductions are given in Stephenson and Cook (1980), with some ecological consideration in Burd et al. (1990).

Logarithmic transformations have often been used for benthic invertebrate data because organism abundance typically varies exponentially (Green 1979). A log transformation will reduce the importance of the numerically dominant members and improve the likelihood of resolving structure when differences are due to medium-abundance or rare taxa. However, a log transformation is quite extreme. Other researchers have advocated the use of other geometric conversions such as square root, cube root, fourth-root, natural log, etc. (for reviews, see Hoyle 1973; Tukey 1977; Hoaglin et al. 1983; Downing 1981). Downing (1979) showed empirically that the best overall transformation for stabilizing variance in freshwater benthos was the fourth-root (x^0.25), because this greatly improves the performance of parametric multivariate methods such as ordinations. Vezina (1988) repeated the exercise for marine subtidal communities, concluding that they were empirically less aggregated than their freshwater counterparts and require a less extreme transformation (e.g., x^0.4). However, both researchers emphasize that the mean and variance relationships of any given community need to be analyzed to determine the most appropriate transformation. In this way it is possible to check whether or not the transformation used has stabilized the variance.

4.8 Data Reporting Guidelines

Data are submitted in the electronic database format and in hard copies (the interpretative report), as outlined and provided by Environment Canada (see Chapter 10 of the present document for additional information on electronic reporting). The complete fundamental data set, including rare and highly variable taxa and ambiguous identifications, should be stored in this manner, even if data filtering has been applied prior to calculation of community descriptors. Other approaches to data filtering, calculation of community descriptors, and analysis can be employed in reanalysis or meta-analysis. A list of the relevant details for the field, laboratory and data analysis components of the EEM benthic invertebrate survey is provided below; these details should be included and submitted with the interpretative report.

Field reporting

field sheets should be retained for six years
replicate station location (grid coordinates)
date and time of sampling
field crew members
habitat descriptions, including measures of the supporting environmental variables
sampling method used, including type and size of sampler and sieve or mesh size

Laboratory reporting

bench sheets should be retained for six years
raw data reported for each individual or pooled field sub-sample, listing taxa present and numbers of individuals
method and level of sub-sampling applied in the laboratory sorting process
sorting efficiency achieved
taxonomic authorities used
location of reference collection and report on taxonomic verification

Data analysis reporting

tabular listing of the number of individuals per taxon in each sample as an appendix
tabular summaries of calculated descriptors with variance estimates
estimates of power obtained for the survey
effects of outliers or extreme values on the results (if any)
a summary of adherence to data quality objectives, standard operating procedures and sampling protocols, and identification of any QA/QC problems

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4.9 Effect Endpoints and Supporting Endpoints for the Benthic Invertebrate Community

Total invertebrate density: The total number of individuals of all taxonomic categories collected at the station expressed per unit area (e.g., numbers/m²). Values should be reported for each station, as well as the arithmetic mean ± standard error (SE), ± standard deviation (SD), median, minimum and maximum for the area.

Taxa (i.e., family) richness: The total number of different taxonomic categories collected at the station, and the arithmetic mean ±SE, ±SD, median, minimum and maximum for the area.

Evenness index (Simpson’s Evenness Index) (equitability): Evenness (E) can be quantified for each station, and mean E ±SE, ±SD, median, minimum and maximum for the area should be reported. Evenness is calculated as in Smith and Wilson (1996):

where:
E = evenness
p_i = the proportion of the i^th taxon at the station
S = the total number of taxa at the station

Similarity index (Bray-Curtis [B-C] Index):The B-C Index is a distance co-efficient that reaches a maximum value of 1 for two sites that are entirely different and a minimum value of 0 for two sites that possess identical descriptors. Distance coefficients measure the amount of association between sites, and the B-C Index is a member of the class of distance coefficients known as a semimetric that some prefer to call dissimilarity coefficients. The B-C Index measures the percentage of difference between sites (Legendre and Legendre 1983), where the distance statistic is calculated as below:

where:

B-C = Bray-Curtis distance between sites 1 and 2
Y_i1 = count for taxon i at site 1
Y_i2 = count for taxon i at site 2
n = total number of taxa present at the two sites

The Bray-Curtis distance (B-C) from a calculated reference median will be reported for each station, and the arithmetic mean ±SE, ±SD, minimum and maximum B-C distance is reported for the area. As the use of this index for determination of effects may be novel to some, a brief literature summary and a detailed example is provided below.

Most of the invertebrate community statistics discussed above are measures of total density and taxa richness and provide no quantitative information on what kind of organisms are present. A similarity index is also recommended, as it summarizes the overall difference in community structure between reference and exposed sites in a single number, requires no preconceived assumptions about the nature of the community and only varies in one direction (Taylor and Bailey 1997). Of the various indices available, many reviewers have indicated that the Bray-Curtis Index (Bray and Curtis 1957) is the most reliable (Pontasch et al. 1989; Jackson 1993; Bloom 1981). The Bray-Curtis Index is also unaffected by the nature of the communities being compared (Bloom 1981), and differences contribute the same to the Bray-Curtis (B-C) Index regardless of whether the taxon is rare or abundant. Bloom (1981) showed that, of 4 indices examined, only the B-C Index accurately reflected the true resemblance over its range.

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Example of Bray-Curtis Index for use in the EEM program

The following steps use an example data set to illustrate how the Bray-Curtis Index should be used for the evaluation of effects in the EEM program. In this example, 5 stations were sampled from an exposure area and a reference area, with a total of 5 taxa found to be present.

Taxa density is entered into a table.

For the reference stations, the median taxa density is determined (see example below).

Table showing median taxa densities for reference stations
	Taxa Density
Reference Stations	Taxon 1	Taxon 2	Taxon 3	Taxon 4	Taxon 5
Ref 1	2	3	2	3	1
Ref 2	3	5	2	4	3
Ref 3	9	1	1	1	1
Ref 4	4	6	3	4	1
Ref 5	5	4	2	3	2

Reference Median	4	4	2	3	1

A similar table is constructed for the exposure stations without the median calculation.

Table showing median taxa densities for exposure stations
Exposure Stations	Taxon 1	Taxon 2	Taxon 3	Taxon 4	Taxon 5
	Taxa Density
Exp 1	23	4	2	10	1
Exp 2	12	2	2	8	3
Exp 3	14	6	1	6	2
Exp 4	13	1	3	12	2
Exp 5	15	3	2	4	1

The distance of each station (reference and exposure) from the reference median is calculated as illustrated by the following example for reference station 1.

For this approach, the reference median for particular taxa becomes y_i2, the taxon count for site 2 in the above equation.

Table showing the distance of each station (reference and exposure) from the reference median
	Taxa 1	Taxa 2	Taxa 3	Taxa 4	Taxa 5
Ref 1 (y_i1)	2	3	2	3	1
Reference median (y_i2)	4	4	2	3	1

\| y_i1-y_i2 \| or Ref 1- reference median	2	1	0	0	0

(y_i1+y_i2)	6	7	4	6	2

Substituting into the B-C equation gives:

5. The B-C distance from the reference median is calculated for each station in this manner.

6. The result of this calculation should be reported for each station, along with the mean (±SE) for the area. The sample data set would result in the following B-C distances:

Table showing B-C distances
Station	Σ\| y_i1-y_i2\|	Σ(y_i1+y_i2)	B-C distance from median	Mean ± SE
Ref 1	3	25	0.12	0.18 ± 0.06
Ref 2	5	31	0.16
Ref 3	11	27	0.41
Ref 4	4	32	0.13
Ref 5	2	30	0.07

Exp 1	26	54	0.48	0.43 ± 0.03
Exp 2	17	41	0.41
Exp 3	17	43	0.40
Exp 4	23	45	0.51
Exp 5	13	39	0.33

7. Finally, for the purposes of determining an effect at the exposure area, the mean B-C distance between the reference stations and the reference median (0.18 ±0.06) can be compared statistically to the mean distance between the exposure stations and the reference median (0.43 ± 0.03).

Simpson’s Diversity Index: Simpson’s Diversity Index (D) takes into account both the abundance patterns and taxonomic richness of the community. This is calculated by determining, for each taxonomic group at a station, the proportion of individuals that it contributes to the total in the station. D for each station and mean (±SE, ±SD), median, minimum and maximum D for the area should be reported. Simpson’s Diversity Index is calculated as (Krebs 1985):

where:
D = Simpson’s index of diversity
S = the total number of taxa at the station
p_i = the proportion of the ith taxon at the station

Taxa (i.e., family) density: The number of individuals of each family expressed per unit area (e.g., numbers/m²). Values should be reported for each taxon at each station and as the mean (±SE) of each taxon for the area.

Taxa (i.e., family) proportion: The percentage abundance for each taxon at each station and the mean (±SE) percentage abundance of each taxon for the area.

Taxa (i.e., family) presence/absence: A matrix indicating the presence and absence of each taxon at the sampling stations should be reported. The matrix will consist of stations (columns) and taxa (rows).

In addition to the benthic invertebrate endpoints, the sediment monitoring variables are also to be reported (see Chapter 7).

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4.10 Evaluation of Results

4.10.1 Effect on the Benthic Invertebrate Community

The objective of the benthic invertebrate component of the EEM program is to answer the following question:

“Is there an effect on the benthic invertebrate community?”

The definition of effect is described in Schedule 5, section 1 of the Metal Mining Effluent Regulations.

During the first phases and in magnitude and geographic extent phases of the monitoring program, the following effect endpoints are calculated, reported and used to determine if there is an effect on the benthic invertebrate community:

Total benthic invertebrate density
Taxa (i.e., family) richness
Evenness index (Simpson’s)
Similarity index (Bray-Curtis)

For the benthic invertebrate component, it is recommended that the following supporting endpoints also be calculated and reported:

Simpson’s Diversity Index
Taxa (i.e., family) density
Taxa (i.e., family) proportion
Taxa (i.e., family) presence/absence

All these endpoints, described into details in the previous section are largely summary metrics selected to encompass the range of effects that may be a result of mine effluent.

Many other benthic invertebrate descriptive metrics are available in the literature and serve to address a wide range of questions regarding benthic invertebrate communities. If desired, additional site-specific descriptors may be calculated and used to support the interpretation of effects. For guidance on selecting these optional descriptive metrics and discussion of their applicability, readers are referred to the reviews by Resh et al. (1995).

For the statistical analyses and determination of sufficient power, the recommendations developed for setting of effect size, a and b and presented in section 8.6.1 are also applicable. The recommendation in this previous section was to set a and b equally at 0.10 or less. The appropriate method of analysis for each of the study design options (e.g., ANOVA, ANCOVA, regression, multivariate analysis) is indicated in Table 4-1.

A final caveat regarding effects on the benthic invertebrate community: it is essential for the mine to select a site-specific study design to allow for an appropriate evaluation. Critical to the study design is the selection of an appropriate reference area or areas. The importance of proper reference area selection is underscored by the following, potentially frequent, example. If a mine performs a simple control-impact design with the reference area placed upstream, then differences between upstream and downstream communities will be those determining the presence or absence of effects. However, if the downstream benthic communities are modified due to a factor such as the restoration of an upstream flow disruption (e.g., from a dam), then these communities, although different from upstream communities, may be more similar to (but perhaps not exactly the same as) the communities at a reference area chosen in a drainage basin adjacent to (or even further afield than) the mine drainage basin. In this example, selection of an additional reference area (see Figure 4-2d for an example) may well be worth the extra cost involved so that site-specific interpretation and the appropriate assessment of effects can be accomplished. Note that this example of significant upstream-downstream differences may not necessarily be considered an effect if sufficient additional evidence suggests otherwise.

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4.10.2 Next Step

Once the monitoring data have been analyzed, decisions regarding the next step in the EEM program are made. The next step in the monitoring program is dependent on the relationship between several key factors, which are briefly discussed below.

The statistical outcome of the previous benthic invertebrate survey

There are 3 possible statistical outcomes of the benthic invertebrate community survey:

no effect is detected but power is not sufficient (i.e., power < 0.90)
no effect is detected and power is sufficient (i.e, power ≥ 0.90)
an effect is detected

If any of the effect endpoints (total benthic invertebrate density, taxa richness, evenness index (Simpson’s) and similarity index (Bray-Curtis)) demonstrate a statistical difference between exposure and reference areas (or along a gradient), then the conclusion is that there is an effect on the benthic invertebrate community. This result can be obtained by various statistical methods; the choice of methods depends on the study design of the monitoring program.

If the power was insufficient, the mine may reconsider the number of sampling stations or the sampling design that was used, in order to design a study with sufficient power in the next survey.

EEM program options after an effect has been established

If an effect on the benthic invertebrate community is found, the next question to be addressed is:

Is the effect mine-related?

An assessment of whether the effect is mine-related could include asking the following questions:

Is the cause of the effect known or suspected?
Can the effect be related to a natural change in the aquatic receiving environment?
Can the effect be reasonably correlated to an anthropogenic cause other than the mine effluent?
Is there a weight-of-evidence approach that can indicate a causal link? (See section 4.11)

This series of questions is provided as an example of the type of approach that may allow for the determination of whether or not the observed effect is mine-related. If the presence of confounding factors makes it difficult to determine the effect of mine effluent on the benthic invertebrate community, the mine should reconsider the study design for the next phase. If the effect has been confirmed, and the cause of the effect is unknown, the mine proceeds to the next step of data assessment and interpretation: determining the magnitude and geographic extent of the effect.

Are the magnitude and geographic extent known?

If an effect has been confirmed (see Chapter 1 for details on confirmed effects), and the cause of the effect is unknown, then the mine should proceed to the next step and determine the magnitude and geographic extent of the effect. For additional information refer to section 4.2.2.

4.11 Additional Tools for Focused Monitoring, Weight-of-Evidence Approaches and/or Investigation of Cause

There are a number of alternative approaches and tools possible for investigations of cause in the EEM program. Methods provided in this guidance document are not meant to be exhaustive, and mines may propose additional scientifically defensible approaches. Tools should be cost-effective, recognized in the primary literature, readily available from consulting, academic or government laboratories, and applicable to the EEM program.

Additional information can be found in Chapters 9 and 12 of this document.

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4.11.1 Use of Weight-of-Evidence Approaches to Establish Cause of Effects

Distinguishing among the cumulative impacts of multiple stressors (which sometimes have confounding effects) requires the establishment of a definitive causal link to the mine effluent under evaluation. The environmental assessment of an aquatic ecosystem is particularly prone to impediments because such ecosystems often receive multiple, interactive effluent discharges. Assessments of monitoring results often rely, in large part, on field monitoring data that can only show correlations rather than clear cause and effect between mine effluent and a presumed effect. Establishing a strong causal link, however, can benefit from a weight-of-evidence approach that combines information from a variety of sources. For additional information on the use of weight-of-evidence approaches, readers are referred to chapters 9 and 12 of the present document.

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4.11.2 Lethal and Sublethal Toxicity Tests

Lethal and sublethal toxicity test methods can be applied during magnitude and geographic extent and investigation-of-cause surveys when an effect has been identified or when previous work failed to provide a satisfactory explanation of cause. These methods provide a direct determination of lethal or sublethal toxicity and can verify that alterations in benthos are due to the toxicity of the mine effluent rather than confounding factors. For example, adverse effects on benthic community structure may be due to factors other than effluent toxicity, including differences in environmental regime. Concurrent impairment of benthic community structure and toxicity implicates the effluent itself as the cause of changes in the benthos. These methods also provide important information for interpreting field effects in situations where benthic community data are inconclusive, or if only pollution-tolerant species are present in both impacted and reference sites.

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4.11.3 Analysis of Sediment Cores for Historic Trends

Sedimentary records from depositional areas of water bodies can be used to indicate limnological conditions in recent and ancient history (Frey 1988). Precise dating of sediments, combined with an inventory of the remains of certain organisms and plant material (e.g., diatoms, zooplankton, insects), provides a chronology of changes that often can be linked to the period of anthropogenic influence. In addition to the water body itself, the history of the watershed and airshed may be deduced, and the influences of natural events may be distinguished from anthropogenic impacts. A substantial volume of literature is available on the subject, with a useful synthesis of the science provided by Frey (1988). Due to the level of expertise needed to undertake this type of analysis, the availability of paleolimnological services is limited. In addition, the analyses are restricted to resolving trends over longer time frames (multiple years to decades) as a result of sedimentation processes such as bioturbation. The costs of the technique will be site-specific.

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4.11.4 Other Benthic Invertebrate Measures and Organisms

Benthic invertebrates are recommended as the primary indicator organisms for use in an EEM program for monitoring effects on fish habitat. However, the level of identification and measures recommended in the main text of the guidance document are not an exclusive list of measures for which benthic invertebrates can be evaluated. Additional measures include biomass, lower level of identification, secondary production, and population fitness parameters.

Benthic invertebrate biomass in marine environments can provide additionally useful information because it is related to the availability of energy to other trophic levels (e.g., fish). For marine communities, some investigators suggest that an analysis of benthic abundance and biomass together provide a sensitive indicator of changes in the composition of the benthic community (e.g., Warwick 1986; Warwick et al. 1987; Clarke 1990; Burd et al. 1990). For example, in marine samples, it is in the measurement of distributions of biomass that the three main functional groups of benthic organisms--microfauna (grain surface dwellers), meiofauna (interstitial organisms) and macrofauna (burrowers and epifauna)--can be distinctly separated (Schwinghamer 1981, 1983). Because these three groups of organisms have different reproductive modes, metabolic rates, life histories and habitat adaptations, they respond differently to habitat perturbation. This could be particularly important in Arctic subtidal habitats, where abundance may be low, but individuals may be large. However, because precise biomass measurements are time-consuming and problematic (cf. Crisp 1984) unless collected in more detail and more often than is feasible for EEM requirements, it is only possible to determine relative changes in biomass of samples for the EEM surveys. This is easily done by taking blotted wet-weight measurements of representative-sized adult specimens of each species for each survey. Since the method is non-destructive, the reference collection may be used for this purpose prior to external verification or archiving. The mean weight of a given species can then be used to transform species abundance data to relative species biomass data for further summary or statistical analyses. These data show relative, large-scale changes only, and cannot be used to infer production or trophic flow rates within benthic communities.

In addition to benthic invertebrates, several other types of aquatic biota were considered for use in the EEM program. The most relevant ones were 1) phytoplankton, 2) macrophytes, and 3) periphyton.

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4.12 References

Abbott RT. 1974. American seashells. 2nd ed. New York (NY): Van Nostrand Reinhold Co.

Abbott RT, Zim HS, Sandström GF. 2001. Seashells of North America: a guide to field identification. Golden Field Guides from St. Martin’s Press. New York (NY): St. Martin’s Press.

[AETE] Aquatic Effects Technology Evaluation Program. 1995. Field evaluation of aquatic effects monitoring methods - pilot study. Volume 1. AETE Project 4.1.1. Ottawa (ON): Canada Centre for Mineral and Energy Technology.

Alberta Environment. 1990. Selected methods for the monitoring of benthic invertebrates in Alberta rivers. Environmental Quality Monitoring Branch, Alberta Environment.

Appy TD, Linkletter IF, Dadswell MJ. 1980. A guide to the marine flora and fauna of the Bay of Fundy: Annelida: Polychaeta. St. Andrews (NB): Fisheries and Marine Service. Technical Report No. 920.

Austin WC. 1985. An annotated checklist of marine invertebrates in the cold temperate northeast Pacific. Vol. 1, 2 and 3. ,Cowichan Bay (BC): Khoyatan Marine Laboratory. Report for Fisheries and Oceans Canada.

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Bailey RC, Norris RH, Reynoldson TB. 2003. Bioassessment of freshwater ecosystems. Boston (MA): Kluwer.

Baker HR. 1980. Key to the common Tubificid species of the northeast Pacific. Manuscript from Oligochaeta workshop.

Banse K. 1972. Redescription of some species of ChoneKröyer and Euchone Malmgren, and three new species (Polychaeta: Sabellidae). Fish Bull 70(2):459-495.

Banse K, Hobson KD. 1974. Benthic errantiate polychaetes of British Columbia and Washington. (Bulletin of Fisheries Research Board of Canada 185). Ottawa (ON): Fisheries and Marine Service.

Barber WE, Kevern NR. 1974. Seasonal variation of sieving efficiency in a lotic habitat. Freshwat Biol 4:293-300.

Beak International Inc. 1999. Quality assurance program for assessing mine-related effects using benthic invertebrate communities. Ottawa (ON): Natural Resources Canada, Canada Centre for Mineral and Energy Technology. Aquatic Effects Technology Evaluation Program project 2.1.4.

Bednarik AF, McCafferty WP. 1979. Biosystematic revision of the genus Stenonema (Ephemeroptera, Heptageniidae). Can Fish Aquat Sci Bull 201. Ottawa: Department of Fisheries and Oceans.

Berkeley C, Berkeley E. 1952a. Canadian Pacific fauna, 9. Annelida 9b (1) Polychaeta Errantia. (Bulletin of Fisheries Research Board of Canada).Toronto (ON): University of Toronto Press.

Berkeley C, Berkeley E. 1952b. Canadian Pacific fauna 9. Annelida 9b (2) Polychaeta Sedentaria. (Bulletin of Fisheries Research Board of Canada).Toronto (ON): University of Toronto Press.

Blake JA. 1971. Revision of the genus Polydora from the east coast of North America (Polychaeta: Spionidae). Smithson Contr Zool 75:1-32.

Blake JA. 1988. New species and records of Phyllodocidae (Polychaeta) from Georges Bank and other areas of the western North Atlantic. Sarsia 73:245-257.

Blake JA. 1991. Revision of some genera and species of Cirratulidae (Polychaeta) from the western North Atlantic. In Petersen ME, Kirkegaard JB, editors. Systematics, biology and morphology of world Polychaeta. Proceedings of the Second International Polychaete Conference, Copenhagen, August 18-23, 1986. Ophelia Supplement 5: 1-723. p. 17-30.

Bloom SA. 1981. Similarity indices in community studies: potential pitfalls. Mar Ecol Prog Ser 5:125-128.

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Figures and Tables

Table 4-1 outlines recommended sampling program designs. Based on the design type, the receiving environment, reference or control area, impact area, and statistics are identified.

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Figure 4-1 is a schematic representation of the spatial scales of reference and exposure areas, replicate stations and field sub-samples for a basic control-impact design.

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Figure 4-2 illustrates six examples of control impact designs. Image (a) shows a control-impact design for simpler freshwater rivers and streams or for homogeneous estuarine habitat. Image (b) illustrates a modified C-I design with downstream reference area for streams, rivers, or estuaries. Image (c) shows a magnitude and geographic extent monitoring design. Image (d) illustrates a multiple control-impact design for freshwater rivers and streams with two reference areas. Image (e) shows a multiple control-impact design for freshwater rivers and streams with multiple reference areas in adjacent drainage. Finally, Image (f) illustrates geographically homogeneous lakes, marine bays or inlets, with habitat characteristics similar to the exposure area.

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Figure 4-3 is an illustration of five gradient design examples. Image (a) shows a simple gradient design for freshwater rivers, streams and estuaries. Image (b) illustrates a simple gradient design for lake or costal sites situated in narrow bays or fjords. Image (c) shows a radial gradient design for lake or coastal situations. Image (d) illustrates a multiple gradient design for freshwater rivers. Finally, image (e) shows a multiple gradient design for lake or coastal sites.

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Figure 4-4 is a schematic representation illustrating how the reference and exposure stations are located relative to the effluent input from multiple sources in a reference condition approach.

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Figure 4-5 is a graph illustrating the impairment stress levels derived for reference sites in hybrid multidimensional scaling ordination space. Bands, based on 90, 99, and 99.9% probability ellipses, are identified as A (unstressed), B (possible stressed), C (stressed) and D (severely stressed).

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Table 4-2 outlines the taxonomic keys for benthic invertebrate taxonomic identification in freshwater environments. Each taxon is provided with taxonomic references typically used.

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Table 4-3 provides the recommended level of taxanomic precision for benthic invertebrates in a marine environment, for the lowest practical taxonomic approach. Each taxon is aligned with a level. Levels include family, class, sub-class, genus, and species.

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Table 4-4 provides a list of marine and estuarine taxonomic benthic invertebrate keys for Canada. References are listed in alphebetical order.

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Date modified:: 2022-07-31