Bioassessment Indicies

Standardization of raw taxonomic data

Prior to calculation of the below indices. Raw taxonomic data must be standardized. Standardization differs slightly among indices but in general includes the following steps:

  • Development of a standard taxonomic resolution across samples (i.e. operational taxonomic units (OTU))(only required for O/E indices) or standardization of taxonomic resolution for a given sample (i.e. unique taxa per sample)(all other indices) (Ostermiller and Hawkins 2004).
  • Subsampling number of individuals in a sample to a standard number (typically 300 count) to correct for differences in sample effort (Vinson and Hawkins 1996, Ostermiller and Hawkins 2004).

Attribution of taxa with trait data

Traits such as tolerance values, functional feeding groups, or thermal preferences may be used in bioassessment index calculations below. Three primary invertebrate trait databases exist for North America and are used by NAMC for bioassessment index development:

  • Poff, N. L., J. D. Olden, N. K. M. Vieira, D. S. Finn, M. P. Simmons, and B. C. Kondratieff. 2006. Functional trait niches of North American lotic insects: Traits-based ecological applications in light of phylogenetic relationships. Journal of the North American Benthological Society 25:730–755.
  • Twardochleb, L., E. Hiltner, M. Pyne, and P. Zarnetske. 2021. Freshwater insects CONUS: A database of freshwater insect occurrences and traits for the contiguous United States. Global Ecology and Biogeography 30:826–841.
  • USEPA. 2012. Freshwater traits database. Global Change Research Program, National Center for Environmental Assessment, Washington, DC; EPA/600/R-11/038F. https://cfpub.epa.gov/ncea/global/traits/search.cfm.

Bioassessment indices

Four types of indices are commonly used in aquatic biological assessments:

  1. Single metric indices such as diversity or species richness
  2. Biotic indices, which are typically calculated as the weighted average of the tolerance values of the different species inhabiting a waterbody (e.g., Chandler 1970)
  3. Multimetric indices (MMIs), which aggregate measures of several different assemblage attributes into a single value (e.g., Karr 1986)
  4. RIVPACS-type O/E indices, which measure biodiversity loss by comparing observed taxonomic composition (O) with that expected at individual sites (E) (e.g., Moss et al. 1987)

Below we detail how to calculate the above indices and pros and cons of each index (but see review by Hawkins and Carlisle 2022 for more information).

Single metric indices

Single metric indices such as diversity or species richness were the first bioassessment indices used. However, they only represent single aspects of biological communities, and initial computations of these metrics did not consider natural gradients across reference sites in diversity and richness. While the comparability and interpretability of diversity metrics are still in question (Jost 2010), modeling efforts have improved performance of richness indices (Bailey et al. 1998). Richness indices computed as observed richness divided by predicted richness are also intuitive indices that capture fundamental aspects of biological integrity.

Biotic indices

Biotic indices (also called Average Score per Taxon (ASPT) indices were some of the first indices used and include indices such as the Hilsenhoff Biotic index (Hilsenhoff 2017). These indices assign tolerance values to individual taxa and then calculate simple or weighted averages of tolerance values among all taxa present at a site. Tolerance values however can vary with natural environmental variation so it is important to account for this variability through modeling techniques detailed below (Clarke et al. 1996).

MMIs

Multimetric indices aggregate measures of several different assemblage attributes into a single value. Individual biological assemblage metrics such as richness of clingers and relative abundance of Ephemeroptera are rescaled to a common scale (e.g. 0-100). Then individual metrics are averaged across to obtain a single score. MMIs are the most common index used in the U.S. and elsewhere because they are thought to best capture multiple components of ecological integrity (e.g., Karr 1986, Ruaro et al. 2020). However, initial development of MMIs used ecoregional classification approaches to account for natural gradients of reference sites across the landscape. Index performance using this approach is inferior to continuous modeling of natural gradients across the landscape (Hawkins et al. 2000a) so modeled MMI approaches are becoming more common (Stoddard et al. 2008, Hawkins et al. 2010, Vander Laan and Hawkins 2014, Mazor et al. 2016). Despite improved index performance, MMIs remain difficult to interpret due to the standardization and aggregation of multiple metrics (Hill et al. 2020). MMIs also require more steps in development than other indices. Development of modeled MMIs require the following steps:

  1. Identification of reference and degraded sites for which macroinvertebrate data is available.
  2. Standardization of taxonomic data to unique taxa for a given sample and subsampling of count data to a standardized count (typically 300 individuals)
  3. Calculation of desired candidate metrics which may include both taxonomic groups as well as metrics based on traits such as functional feeding groups or thermal tolerances.
  4. Calculation of natural environmental gradients to use as candidate predictors of macroinvertebrate assemblages.
  5. Building separate models (typically random forest) for each candidate metric using the environmental gradients identified in step 4 to explain natural variation in metric values among reference sites.
  6. Calculation of residual metric values (subtracting observed metric values from predicted metric values in step 5).
  7. Ordination of residual metric values to determine which metrics are least correlated with each other. Including metrics that are highly correlated with one another can decrease performance (Van Sickle 2010).
  8. Calculation of t-values for residual metric values to determine which metrics best discriminate between reference and degraded sites.
  9. Selection of final metrics using results of step 7 and 8 to select metrics that are least correlated and also best discriminate among reference and degraded sites.
  10. Rescaling of metrics to a common scale.
  11. Averaging across final metrics to generate a single score.

O/E indices

O/E indices assess taxa loss by measuring the proportion of predicted taxa under reference conditions (E) that were observed (O) in a sample. These indices were developed in the United Kingdom where they are also known as RIVPACS (River Invertebrate Prediction and Classification System). Because they are an intuitive measure of biological integrity, they are increasingly being used in the U.S. (Hawkins et al. 2000b, Hawkins 2006). However, studies in arid regions or nonperennial streams have found that these indices may not perform well due to difficulties predicting specific taxonomic composition in these dynamic systems (Vander Laan and Hawkins 2014). In contrast to MMIs, O/Es are built using only reference site data. Development of O/E indices require the following steps:

  1. Identification of reference sites with macroinvertebrate data available.
  2. Standardization of taxonomic data to OTUs and subsampling of count data to a standardized count (typically 300 individuals).
  3. Hierarchical cluster analysis to group sites by macroinvertebrate assemblages and selection of number of groups
  4. Calculation of natural environmental gradients to use as candidate predictors of macroinvertebrate assemblages.
  5. Building of models (random forest is used for newer models but discriminant function analysis was used in the past) to predict group membership of a given site based on its environmental gradients.
  6. Calculation of frequency of occurrence for a given taxa across reference sites.
  7. Probabilities of group membership from step 5 are then used to weight frequency of occurrence values in step 6. These weighted values are then summed to calculate expected number of taxa (E). Taxa found at less than 50% of sites are frequently excluded from E calculations to improve index performance (Ostermiller and Hawkins 2004, Van Sickle et al. 2005, Hawkins 2006). However, if few taxa are present at sites, excluding rare taxa may decrease performance.
  8. Calculation of observed taxa. Only observed taxa that are also observed at reference sites and have a probability of capture >0.5 for a given sample are included in calculations.
  9. Observed number of expected taxa (O) is then calculated and divided by E.

Literature Cited

  • Bailey, R. C., M. G. Kennedy, M. Z. Dervish, and A. R. M. Taylor. 1998. Biological assessment of freshwater ecosystems using a reference condition approach: Comparing predicted and actual benthic invertebrate communities in Yukon streams: Bioassessment of Yukon streams. Freshwater Biology 39:765–774.
  • Chandler, J. R. 1970. A biological approach to water quality management. Water Pollution Control 69:415-422.
  • Clarke, R. T., M. T. Furse, J. F. Wright, and D. Moss. 1996. Derivation of a biological quality index for river sites: Comparison of the observed with the expected fauna. Journal of Applied Statistics 23:311–332.
  • Hawkins, C. P. 2006. Quantifying biological integrity by taxonomic completeness: its utility in regional and global assessments. Ecological Applications 16:1277–1294.
  • Hawkins, C. P., Y. Cao, and B. Roper. 2010. Method of predicting reference condition biota affects the performance and interpretation of ecological indices. Freshwater Biology 55:1066–1085.
  • Hawkins, C. P., R. H. Norris, J. Gerritsen, R. M. Hughes, S. K. Jackson, R. K. Johnson, and R. J. Stevenson. 2000a. Evaluation of the use of landscape classifications for the prediction of freshwater biota: Synthesis and recommendations. Journal of the North American Benthological Society 19:541–556.
  • Hawkins, C. P., R. H. Norris, J. N. Hogue, and J. W. Feminella. 2000b. Development and evaluation of predictive models for measuring the biological integrity of streams. Ecological Applications 10:1456–1477.
  • Hawkins, C. P., and D. M. Carlisle. 2022. Biological assessments of aquatic ecosystems. Pages 525–536 in Mehner, T. and K. Tockner (Eds). Encyclopedia of Inland Waters 2nd edition. Elsevier. Oxford, United Kingdom.
  • Hill, R., C. Moore, J. Doyle, S. G. Leibowitz, P. Ringold, and B. Rashleigh. 2020. Valuing aquatic ecosystem health at a national scale: modeling biological indicators across space and time. 20-04.
  • Hilsenhoff, W.L. 1987. An improved biotic index of organic stream pollution. The Great Lakes Entomologist 20:31-39.
  • Jost L (2010) The relation between evenness and diversity. Diversity 2: 207–232.
  • Karr, J. R., Fausch, K. D., Angermeier, P .L., Yant, P. R. and I. J. Schlosser. 1986. Assessing biological integrity in running waters. A method and its rationale. Illinois Natural History Survey, Champaign, Special Publication 5:1-28.
  • Mazor, R. D., A. C. Rehn, P. R. Ode, M. Engeln, K. C. Schiff, E. D. Stein, D. J. Gillett, D. B. 
  • Herbst, and C. P. Hawkins. 2016. Bioassessment in complex environments: Designing an index for consistent meaning in different settings. Freshwater Science 35:249–271.
  • Moss, D., M. T. Furse, J. F. Wright, and P. D. Armitage. 1987. The prediction of the macro-invertebrate fauna of unpolluted running-water sites in Great Britain using environmental data. Freshwater Biology 17:41–52.
  • Ostermiller, J. D., and C. P. Hawkins. 2004. Effects of sampling error on bioassessments of stream ecosystems: Application to RIVPACS-type models. Journal of the North American Benthological Society 23:363–382.
  • Ruaro, R., É. A. Gubiani, R. M. Hughes, and R. P. Mormul. 2020. Global trends and challenges in multimetric indices of biological condition. Ecological Indicators 110:105862.
  • Stoddard, J. L., A. T. Herlihy, D. V. Peck, R. M. Hughes, T. R. Whittier, and E. Tarquinio. 2008. A process for creating multimetric indices for large-scale aquatic surveys. Journal of the North American Benthological Society 27:878–891.
  • Vander Laan, J. J., and C. P. Hawkins. 2014. Enhancing the performance and interpretation of freshwater biological indices: An application in arid zone streams. Ecological Indicators 36:470–482.
  • Van Sickle, J. 2010. Correlated metrics yield multimetric indices with inferior performance. Transactions of the American Fisheries Society 139:1802–1817.
  • Van Sickle, J., C. P. Hawkins, D. P. Larsen, and A. T. Herlihy. 2005. A null model for the expected macroinvertebrate assemblage in streams. Journal of the North American Benthological Society 24:178–191.
  • Vinson, M. R., and C. P. Hawkins. 1996. Effects of sampling area and subsampling procedure on comparisons of taxa richness among streams. Journal of the North American Benthological Society 15:392–399.