**4. Methods**

*Water Quality - Science, Assessments and Policy*

Methyl mercury: toxic form of mercury that bioaccumulates in the lake food chain

Total mercury: indicates potential exposure and

availability of mercury to lake biota

for release of algal toxins

**Table 1.**

Microcystin: direct measure of algal toxin concentration present on day of sampling

Cyanobacteria: includes organisms responsible

*Indicators and sampling locations for the national lakes assessment.*

**Indicator and rationale Sample location**

Collected from the top 2 cm of sediment from a core taken from the bottom of the lake. Concentrations were

Collected from the top 2 cm of sediment from a core taken from the bottom of the lake. Concentrations were

Collected from a vertically integrated sample of the upper water column at the open-water site. We report on detection; measured concentrations were compared to the World Health Organization (WHO) algal toxin

Collected from a vertically integrated sample of the upper water column at the open-water site. Concentrations were compared to WHO algal toxin

compared to a benchmark

compared to a benchmark

benchmark for recreation

benchmark for recreation

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**Figure 1.**

sites sampled to the entire target population of approximately 112,000 lakes targeted by the survey within the conterminous USA. The spatial distribution of sampled lakes in the 2012 survey is shown in **Figure 1**. For more details on survey designs as

*Distribution of lakes sampled for the 2012 National Lakes Assessment. Circles represent sites selected as part of the probability-based survey design. Squares represent lakes hand selected as additional candidate "leastdisturbed" reference sites for use in assigning lake condition categories. Aggregated ecoregions are based on* 

The way in which an individual lake is sampled for the various indicators is considered the "response design" [19]. In some cases, as with water samples, this is rather simple. For other indicators, such as physical habitat indicators, the response

applied to aquatic resources, see [21, 26–30].

**3.3 Response design**

*Omernik level 3 ecoregions.*

The methods for the NLA are described in great detail in its supporting documentation (e.g., see [30–34]). A brief summary of critical elements of the approach follows.

#### **4.1 Data acquisition (field and laboratory)**

The NLA has developed field protocols intended to be applied consistently at all lakes and reservoirs sampled. This is in contrast to the approach implemented in the European Union to accomplish the objectives of the Water Framework Directive, which employs various methods to arrive at analogous assignments of water body condition (e.g., see [35]). The NLA protocols are also designed to be implemented by field crews who are not all experienced limnologists or aquatic biologists. Many (80–90) field crews (comprised of state and contractor crew employees) are required to sample the selected lakes during a summer sampling window (index period) from June through September. It is important to note that inferences made from the data are estimates of condition found during that index period and do not apply, necessarily, to other parts of the year. In essence, these are "snapshots" of conditions in the lake population during the summer growing season. Standardized field and laboratory protocols are used to collect and process the samples. Standardized field forms, either paper or electronic, are used by the crews to record measurements and observations. The samples that are collected are sent to processing laboratories for analyses. The field and laboratory data are sent to a central repository for inclusion into the data sets (see [30] for details). A comprehensive quality assurance program is developed and implemented for all field, laboratory, data analysis, and data management activities in the NLA to ensure that results are of known and adequate quality to be used in the assessment (e.g., see [33]).

#### **4.2 Indicator development and evaluation**

For the benthic macroinvertebrate and zooplankton samples, a comprehensive analysis and evaluation process was used to construct a multimetric index (MMI) of biological integrity for that assemblage. The process was based on general approaches described in [36, 37]. Metrics were developed using autecology information, taxonomic composition, taxonomic diversity, functional feeding groups, habitat preferences and tolerance to disturbance. The rationale and descriptions for each of these indicators can be found in [30, 38–42].

The approach used to measure and describe various dimensions of littoral and riparian physical habitat is described in [43–46]. These measurements result in indicators of lake habitat complexity, shallow water habitat alteration, riparian vegetation cover, lakeshore disturbance, and lake drawdown exposure in the littoral zone [30, 45, 46]. The shallow water habitat alteration indicator is based on visual estimates of the areal cover of several types of natural cover (e.g., snags, macrophytes, overhanging vegetation) observed in the littoral zone around each lake. The riparian vegetation cover indicator is based on visual estimates of vegetation cover and structure in three layers of riparian vegetation observed around each lake. The lakeshore disturbance indicator is based on visual estimates of the presence and proximity of several types of human disturbance (e.g., agricultural activities, residences, marinas) to the lake margin observed around each lake. The lake habitat complexity indicator is based on the mean value of the shallow water habitat alteration and riparian vegetation cover indicators.

For each of the physical, chemical, and biological indicators used in the assessment, a set of benchmarks or thresholds was developed against which to evaluate the quality of the lake relative to that indicator. For the NLA, expected values were developed for each indicator within each of the 9 aggregated ecoregions shown in **Figure 1** based on the distribution of measured values (observed scores), or observed/expected values (calculated scores) of the indicator in the set of leastdisturbed reference lakes within that region. Condition thresholds were developed using the 5th and 25th (or 95th and 75th) percentiles of the distribution of the indicator scores in the set of regional reference sites, as described in the NLA 2012 technical report [30], and all sampled sites were assigned to good, fair, or poor condition based on those thresholds. More detailed discussions of the concepts underpinning behind the use of reference sites to model regional or individual lake expected indicator values in least-disturbed reference sites can be found in [25, 45, 47, 48].

#### **4.3 Population estimates**

The analytical goal of the assessment is to produce estimates of the number of lakes (or percent of lake number) falling into a condition class or stressor level based on the indicator data and the weights from the survey design [49]. Examples of how this was done for lakes and wetlands are presented in [21, 50]. The weight assigned to an individual lake is an estimate of the number of lakes in the target population represented by that lake and is used to develop a cumulative picture of the total target population. Status of the total lake population can be assessed for each of the indicators measured, whether they are biological, chemical, or physical. These population estimates represent the assessment of biological, chemical, and physical integrity goals expressed in the CWA.

## **4.4 Ranking of stressors**

The final element of the assessment is intended to answer another key NLA question—"What is the relative importance of the different stressors impacting lakes?" This element ranks the potential stressors to biological condition that were measured during the survey. This assessment element is not intended to determine the "cause" of poor conditions at an individual lake but rather to evaluate and then rank the relative improvement in national status that might be gained, biologically, if one were to eliminate the adverse influence of each stressor through policy changes or management efforts. The quantitative approach

**97**

**Figure 2.**

*Jewels across the Landscape: Monitoring and Assessing the Quality of Lakes and Reservoirs…*

borrowed from the medical literature to derive relative rankings is outlined in [51, 52]. This approach first requires a "relative extent" estimate (for each stressor) represented by the proportion of lakes in poor condition for that stressor. Then, the "relative risk" to biological indicators associated with poor conditions of each stressor indicators is calculated. Relative risk is the ratio of the percentage of lakes in poor biological condition in the subset of lakes that have high stress (poor condition), divided by the percentage of lakes in poor biological condition in the subset of lakes with stressor condition not classified as poor. Combining relative risk with relative extent of lakes with poor biological condition allows the calculation of "attributable risk," that is, the potential reduction in the percentage of lakes with poor biological condition if all of the lakes with poor stressor condition were to be restored so that they would be in good or fair stressor condition. These estimates are calculated for each stressor indicator and ranked relative to one another to see where the greatest improvement in biological

The results presented here are examples of a few of the ways to present and interpret the results from the NLA. We do not present a comprehensive assessment of lake condition based on NLA results here (see [34]). The first objective of the NLA is to describe the biological integrity of lakes within the conterminous USA. Based on a pelagic zooplankton multimetric index (MMI) of biological integrity, only 53 ± 7% of lakes in the conterminous USA ("National") are considered to be in good condition (**Figure 2**). A greater percentage of the natural lakes are in good condition (61 ± 10%) when compared with man-made lakes (43 ± 8%; **Figure 2**).

*Status of lake biological condition for the 2012 National Lakes Assessment based on a multimetric index (MMI) for the zooplankton assemblage. Results are presented nationally and by lake origin type (natural versus man-made) in the conterminous United States (i.e., lower 48 states). Estimates are presented as percent of lakes in each condition class (good, fair, or poor relative to regional determination of least-disturbed condition) and as the absolute numbers of lakes. Values in parentheses are the estimated number of target lakes* 

*in the population. Error bars are 95% confidence intervals.*

*DOI: http://dx.doi.org/10.5772/intechopen.92286*

condition might be expected.

**5. National and regional status estimates**

*Jewels across the Landscape: Monitoring and Assessing the Quality of Lakes and Reservoirs… DOI: http://dx.doi.org/10.5772/intechopen.92286*

borrowed from the medical literature to derive relative rankings is outlined in [51, 52]. This approach first requires a "relative extent" estimate (for each stressor) represented by the proportion of lakes in poor condition for that stressor. Then, the "relative risk" to biological indicators associated with poor conditions of each stressor indicators is calculated. Relative risk is the ratio of the percentage of lakes in poor biological condition in the subset of lakes that have high stress (poor condition), divided by the percentage of lakes in poor biological condition in the subset of lakes with stressor condition not classified as poor. Combining relative risk with relative extent of lakes with poor biological condition allows the calculation of "attributable risk," that is, the potential reduction in the percentage of lakes with poor biological condition if all of the lakes with poor stressor condition were to be restored so that they would be in good or fair stressor condition. These estimates are calculated for each stressor indicator and ranked relative to one another to see where the greatest improvement in biological condition might be expected.
