DMAP-CAL™ Decision Engine

DMAP-CAL™ (Define → Measure → Analyze → Predict → Choose → Act → Learn) is a structured reef decision-support framework designed to transform field observations into probability-based management decisions.

“Based on the available data, if we do this, what is most likely to happen—and with what level of confidence?”

From Observation to Action

Define

Identify reef condition and core problem (bleaching, algae, hypoxia).

Measure

Collect standardized environmental, biological, and impact data.

Analyze

Diagnose root causes and identify dominant stressors.

Predict

Model expected outcomes and assign probability of success.

Choose

Select optimal action based on impact, cost, and feasibility.

Act

Execute intervention in a controlled, repeatable manner.

Learn

Compare predicted vs actual outcomes and improve the model.

What the System Produces

Sample Reef Decision

Condition

Moderate coral stress with increasing algae presence

Problem

Thermal stress combined with nutrient-driven algae growth

Recommended Action

Reduce local nutrient input and monitor thermal conditions

Expected Outcome

Stabilization of coral condition and reduction in algae dominance

Probability of Success

68%

Confidence Level

Moderate (based on Level 2 data)

Data Gap

Dissolved Oxygen measurement required

Cross-Regional Decision Support

DMAP-CAL™ integrates evidence from multiple reef systems to identify transferable patterns, generate intervention options, and determine what actions are most likely to succeed under local conditions.

Target Reef

Mesoamerican Reef site showing declining coral cover, elevated bleaching, and increasing algae presence.

Evidence Sources

Mesoamerican Reef field data, Great Barrier Reef bleaching and recovery trends, and Red Sea thermal tolerance observations.

Observed Pattern

Elevated temperature leading to bleaching, followed by increased algae dominance and reduced recovery windows across all systems.

Problem Definition

Compounded stress: thermal stress combined with algae competition and insufficient recovery time between disturbance events.

Options Generated

1) Passive monitoring
2) Local stress reduction
3) Assisted intervention
4) Adaptive hybrid strategy

Selected Strategy

Adaptive hybrid approach combining local stress reduction, targeted protection, and continuous monitoring.

Why This Option

Balances feasibility, cost, and scalability while maintaining the highest probability of measurable improvement across varying reef conditions.

Implementation Steps

1) Establish baseline conditions
2) Reduce local stressors where possible
3) Implement repeat monitoring cycle
4) Compare outcomes to predicted response
5) Adjust actions based on measured results

Predicted Outcome

Stabilization in select reef sites, reduced algae dominance, and improved recovery potential over time.

Probability of Success

60–75% (moderate–high)

Confidence Level

Moderate — strong cross-system evidence, with local variability.

DMAP-CAL™ does not assume that what works in one reef system will work in another. It evaluates transferable evidence, local conditions, and operational constraints to determine what is most likely to succeed in the target environment.

DMAP-CAL™ Live Engine

Inputs

Decision Output

Awaiting input...

DMAP-CAL™ Live Decision Engine

Real-time multi-region reef decision synthesis and scenario modeling

For each reef site, the system produces a structured decision output:

Beyond Observation

Traditional reef monitoring focuses on documenting conditions. DMAP-CAL™ extends this into predictive, decision-based environmental management.

Instead of asking “What is happening?” the system answers:

“What should we do next—and what outcome should we expect?”

Field-Integrated System

This system is deployed through the Rim Run™ expedition and a distributed citizen science network across the Mesoamerican Reef and broader Caribbean coastal arc.

It integrates: