From Microbial Data to Decisions: A KPI Framework for Measuring Biological Performance
How decision-ready microbial indicators can strengthen remediation, mining, asset integrity, water, agriculture, and wastewater management
Microbial measurements are most valuable when they help answer a practical question: Is the biological system performing well enough to achieve the project objective? Microbial Insights’ Microbial Performance KPI Framework organizes biological signals into decision-ready indicators that can be reviewed alongside chemistry, process data, operational history, and site objectives.
Why Microbial KPIs Matter
Chemistry and process measurements describe important outcomes, but they do not always reveal whether the microbial community has the abundance, composition, or functional capacity needed to sustain those outcomes. Microbial KPIs add a biological line of evidence by translating molecular measurements into a defined target, performance scale, and key decision question.
The framework is not intended to replace chemistry, field observations, engineering data, or regulatory criteria. Instead, it creates a consistent way to place microbial information into the same performance-management conversation. Depending on the application, the underlying measurements may include CENSUS® quantitative polymerase chain reaction (qPCR), QuantArray® testing, next generation sequencing (NGS), host-specific markers, community-similarity measures, diversity indices, or relationships between functional genes and process outcomes.
From Detection to Performance Management
A detection result confirms that a target is present. A performance indicator goes further by evaluating what the result may mean in the context of the system. MI’s framework uses four broad performance categories:
| Performance | General meaning |
| Excellent | Biological measurements support or exceed the desired performance conditions. |
| Good | The system appears biologically capable of meeting the current objective. |
| Marginal | One or more indicators warrant continued evaluation, optimization, or additional lines of evidence. |
| Poor | The biological condition may be insufficient or may indicate elevated operational risk. |
These categories become most meaningful when thresholds are supported by the application, reference condition, system history, or relationship to measured outcomes. Site-specific context remains essential.
Examples Across the KPI Portfolio
1. Remediation: Move Beyond “Are the Microbes Present?”
For chlorinated ethene remediation, Dehalococcoides abundance is used as an indicator of complete reductive dechlorination potential. The framework identifies a target above 1 × 10⁴ cells/mL and asks whether the microbial community can completely detoxify PCE and TCE through to ethene. This helps distinguish simple detection from a population level associated with more efficient dechlorination and a reduced likelihood of cDCE or vinyl chloride accumulation.
For anaerobic BTEX biodegradation, the framework connects bssA gene abundance with BTEX concentrations through the MI Toluene Biodegradation Calculator. The resulting KPI asks whether the native community is degrading BTEX rapidly enough to reduce concentrations and control plume migration.
2. LNAPL: Add a Biological Line of Evidence for NSZD
Traditional LNAPL monitoring describes concentrations and plume behavior, but it does not directly characterize the microorganisms responsible for hydrocarbon destruction. QuantArray®-NSZD evaluates key hydrocarbon-biodegradation and methane-cycling genes, providing a biological line of evidence that can complement temperature-based or carbon dioxide flux rate estimates. The KPI focuses the discussion on whether biological degradation is sufficient to reduce LNAPL mass and support closure objectives.
3. Asset Integrity: Translate Microbial Consortia into Corrosion Risk
Microbiologically influenced corrosion is driven by interacting microbial groups and pathways rather than a single organism. The MIC Corrosion Risk Index correlates corrosion-associated functional gene abundance with measured indicators such as corrosion rate, metal loss, pit density, leak frequency, coupon data, or inline inspection results. The goal is to establish system-specific thresholds that can provide earlier warning of increasing risk.
4 & 5. Mining: Measure Recovery and Culture Stability
For mine closure and ecosystem recovery, NGS and the Shannon Diversity Index can track microbial community diversity relative to an appropriate reference or pre-disturbance condition. This complements vegetation and soil chemistry by evaluating whether the community is recovering the diversity and resilience needed to support nutrient cycling, organic matter turnover, and long-term ecosystem function.
For bioleaching, community similarity can help verify culture integrity before application and during continued use. The Bioleaching Culture Integrity and Stability Index uses an 80% similarity target and can reveal culture drift, contamination, or the loss of important community members before process performance is affected.
6. Water Quality: Strengthen Fecal Contamination Source Attribution
Microbial source tracking is most actionable when it does more than identify a marker. MI’s MST Confidence Index organizes host-specific marker evidence around the confidence of source attribution and the identification of a dominant contamination source. This helps stakeholders distinguish among human, ruminant, swine, avian, and other fecal sources and direct corrective actions where they can have the greatest impact.
7. Agriculture and Wastewater: Evaluate Functional Capacity
In agricultural soils, QuantArray®-BGC can assess genes associated with nutrient-cycling capacity. Linking those functions with crop performance can reveal biological limitations that may not be apparent from a chemistry snapshot alone.
In wastewater systems, the same performance-management concept can be applied to FOG treatment and biological nutrient removal. A methanogen-to-fermenter ratio, interpreted with FOG loading and methane production, can indicate whether biological processing capacity is approaching overload. Nitrogen-cycling genes, interpreted with effluent ammonia, nitrate, and total nitrogen, can help identify declining nitrification or denitrification capacity before changes appear in effluent chemistry.
Build the KPI Around the Decision
The strongest KPI programs begin with the management question rather than the laboratory method. A practical development process includes:
- Define the outcome that matters, such as plume control, source depletion, asset protection, restoration, source attribution, crop productivity, or treatment stability.
- Select a microbial measurement that is scientifically connected to that outcome.
- Establish a target or reference condition using applicable evidence, system history, and site-specific data.
- Interpret the result with chemistry, engineering, operating conditions, and trend data.
- Review performance over time and refine the threshold as the site-specific relationship becomes stronger.
The Result: Microbial Data That Can Be Managed
Microbial data should not sit apart from the rest of a project’s performance information. When biological measurements are organized around targets, performance ranges, and key decision questions, they become easier to communicate, trend, and incorporate into management decisions.
The Microbial Performance KPI Framework provides a starting point for turning CENSUS®, QuantArray®, NGS, and CENSUS® MST results into practical indicators across diverse applications. Each KPI should be interpreted with relevant chemistry, process performance, operational history, and site-specific reference data.
