Division of Ecological and Water Resources

Lake Index of Biological Integrity

One measure of a lake's health is the community of fish, plants and aquatic life it sustains. Certain species can't survive without clean water and a healthy habitat while other species are tolerant of degraded conditions. These species are considered "indicators" of the health of a lake. An index of biological integrity (IBI) is a score that compares the types and numbers of fish or plants observed in a lake to what is expected for a healthy lake.

The biological communities of a lake are a reflection of the cumulative effects of natural and human-caused influences on the lake. These communities change in predictable ways in response to degrading water quality or loss of shoreline habitat. Scientists are able to judge the overall health of a lake based on measurements of these biological communities.

Fish-Based Index of Biological Integrity

The DNR developed fish-based IBIs by sampling a wide range of lakes, from high-quality lakes to those with significantly degraded water quality or shoreline habitat. A statistical analysis found a relationship between biological populations and water quality and habitat characteristics. Separate analyses were done for different categories of lakes because healthy shallower lakes have different indicator species than deeper lakes. Lakes with more complex shaped shorelines have more fish species than lakes with a rounder shaped shoreline.

Lakes used for fish-based IBI

DNR researchers developed four fish-based IBIs based on different types of lakes.

Lake Type

Species Diversity

Fish sensitive to water quality and habitat changes

Fish tolerant of degraded habitat conditions

Very deep, large lakes with complex shoreline in central and northern Minnesota

Highest species diversity expected

  • Bluegill, Pumpkinseed and Rock Bass

  • Cisco
  • Bass, Northern Pike, Muskellunge and/or Walleye

  • Darter species

  • Several shiner species

  • Common Carp

  • Black Bullhead

  • Green Sunfish

Moderately deep lakes with a rounder shoreline shape in Central and northern Minnesota

Intermediate species diversity expected

  • Bluegill, Pumpkinseed and Rock Bass

  • Cisco

  • Bass, Northern Pike and/or Walleye

  • One or more darter species

  • One or more shiner species

  • Common Carp

  • Black Bullhead

  • Green Sunfish

Intermediate depth, naturally productive lakes in central and northern Minnesota

Low species diversity expected

  • Bluegill and Pumpkinseed

  • Bass, Northern Pike and/or Walleye

  • One or more shiner species

  • Common Carp

  • Black Bullhead

  • Green Sunfish

Shallow (80% - 100% of the lake area less than 15 feet deep), naturally productive lakes in southern and central Minnesota

Fewest species expected

  • Bluegill

  • Northern Pike

  • One or more darter species

  • Common Carp

  • Black Bullhead

  • Orangespotted or Green Sunfish

  • Fathead Minnow


Fish sampling

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There are many different methods to sample fish in a lake and each method targets different kinds of fish. The DNR uses gill netting, trap netting, seining and backpack electrofishing to develop a full picture of the fish community living in a lake.

Once the fish have been collected, they are identified to species, counted, weighed, measured and released back into the lake when possible. Information on the number of species and quantity of individuals collected are used to calculate the IBI score.

The fish-based IBI scores incorporate several measurements of the fish community. When the measurements are analyzed together, they are given a score that reflects the lake's health.

Each IBI score is generated using between eight and 15 calculated measurements. Examples include: the number of intolerant species sampled, the number of tolerant species sampled, the number of vegetation-dwelling species sampled, the percentage of small benthic individuals captured in seining and electrofishing sampling gears, and the percentage of insect-eating species sampled in trap nets.

These measurements are known to correspond with human-induced stress, such as watershed land use patterns and quality of aquatic habitat. The fish-based IBI score places lakes into five designations: exceptional, fully supporting, vulnerable, not supporting, and insufficient information. The designations can be used by the Minnesota Pollution Control Agency (MPCA), local governments and conservation groups, lake associations and homeowners to guide future lake management actions.



Management Implication


Minnesota's highest quality lakes

Top priority for protection, especially for lakes at risk of land use change or shoreline development

Fully Supporting

Healthy lakes that support diverse fish populations

Protection needed; some restoration may also be required


Lakes that are somewhat degraded but still support some sensitive species

Top priority for restoration, protection of remaining quality habitat

Not Supporting

Lakes that are degraded and where sensitive species can no longer live

Restoration, sometimes extensive, will be needed; lake will be added to the impaired waters list

Insufficient Information

Sampling did not yield enough data to make a clear determination about lake health

Additional data collection is needed


Assessing Minnesota lakes

The fish-based IBI score is an important component of the MPCA's water quality assessment process.

The MPCA uses physical measurements of water quality such as water clarity and amount of phosphorus to determine the type of recreation a lake supports. These measurements represent a snapshot in time of lake conditions whereas the biological community reflects changes to the lake over several years or more. The IBI is used as a biological measurement to determine if the lake supports the expected aquatic life. These biological assessments provide important information to guide Clean Water planning and restoration.

The DNR supports the MPCA's lake assessments with expertise and data on fish and plant communities in Minnesota lakes to provide this important component to the assessment process.