In 1998, MCHD completed its first annual collection of benthic macroinvertebrates from streams throughout the county. Benthic macroinvertebrates are animals lacking backbones (invertebrate), which can be seen with the naked eye (macro), and live part of their lives on or in the bottom (benthos) of a body of water. There are many advantages of using benthic macroinvertebrates to assess the quality of a stream. Monitoring of the biological communities is relatively inexpensive in comparison to the cost of assessing chemical or bacterial parameters. It also has minimal detrimental effect on the resident biota.
The benthic macroinvertebrates are good indicators of localized conditions, as many of the animals have limited migration patterns or a sessile mode of life. Most species have a complex life cycle of one year or more. Sensitive life stages will respond quickly to stress; the overall community will respond more slowly.
WHAT ARE METRICS?
Metrics or indices are the number values used to interpret the site data. The data analysis used by the MCHD integrates several community, population and functional parameters into the interpretation. Each parameter, or metric, measures a different component of community structure and has a different range of sensitivity to pollution stress. This approach provides a more valid assessment due to the variety of parameters evaluated.
METRICS USED FOR MCHD MACROINVERTEBRATE PROJECT
HBI or Sensitive taxa index: The modified Hilsenhoff Biotic Index in which individual taxa are classified on the basis of their tolerance or intolerance to various levels of domestic wastes. The HBI is Calculated by multiplying the number of organisms in each Insecta and/or Crustacea taxon by the pollution tolerance value assigned to each Insecta and/or Crustacea taxon, adding these for all Insecta and/or Crustacea individuals represented in the sample, and dividing by the total number of individuals in the sample. A high index number is not good.
HBI index = S (Xi*t)/n
Where:
S = the summation of Xi*t
Xi = the number of individuals in each taxon
t = tolerance value for each taxon in the sample
n = number of individuals in the sample
Description/Designation Explanation
Family level biotic index
0.00-3.75 Excellent
3.76-4.25 Very Good
4.25-500 Good
5.01-5.75 Fair
5.76-6.50 Fairly Poor
6.51-7.25 Poor
7.26-10.00 Very Poor
Shannon-Weaver Mean Diversity: A mean diversity measurement of species composition. The calculation is affected by richness of species and by the distribution of individuals among the species (species composition) and may range from zero to 3.321928 log N.
Mean diversity (d) is calculated using the formula:
d = C (N log10 N - Sni log10 ni) /N
where C = 3.321928 (converts base 10 log to base 2); N = total number of individuals ; and ni = total number of individuals in the ith species.
Number of Taxa: Count of the number of taxa (families) found in the sample. A high variety is good.
Number of Individuals: Total count of individuals collected in sample.
% Dominant Taxon: Measure of the percent composition of the most abundant family from the sample. A high percent dominance is not good.
EPT Count: Count of the number of individuals in the three generally pollution-sensitive orders – Ephemeroptera (mayflies), Plecoptera (stoneflies), and Trichoptera (caddisflies). A high variety is good.
EPT Index: Count of the number of taxa (families) in each of the three generally pollution-sensitive orders – Ephemeroptera (mayflies), Plecoptera (stoneflies), and Trichoptera (caddisflies). A high variety is good.
EPT/Total Count: EPT count divided by the total number of individuals in the sample. A higher number is good.
Chironomid Count: Total of individual chironomids (midge larvae) collected in sample.
EPT abundance/Chironomid abundance: EPT count divided by Chironomid count. A lower chironomid abundance is good.
% abundance of Chironomid larvae: Compares the number of chironomids to the total number of organisms in the sample. (The number of organisms in the chironomidae family is divided by the total number of organisms in the sample to calculate a percent composition. A low percentage is good.