
Capture Fisheries
Indonesia's waters are among the most biologically diverse marine ecosystems in the world, supporting a wide variety of fish species, particularly high-value resources such as large pelagic fish, small…
- Evidence-led
- Traceable assumptions
- Decision-ready outputs
- Methods proportionate to risk
One-Page Visual Summary for Quick Briefing
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Use this leaflet as a concise visual entry point before moving into the more detailed technical explanation.



Clarity before a decision is made
Capture Fisheries
Clarity before a decision is made
Indonesia's waters are among the most biologically diverse marine ecosystems in the world, supporting a wide variety of fish species, particularly high-value resources such as large pelagic fish, small…
This variability represents one of the greatest challenges for the capture fisheries industry. The distribution and abundance of commercially important fish species are closely linked to the physical, chemical, and biological characteristics of the surrounding marine environment.

Decision Supported
Define the approach, priorities, and actions for capture fisheries using traceable evidence.

Risk Controlled
Environmental impact, design failure, operational disruption, uncontrolled cost, and weak assumptions.

Success Criteria
Comparable options, quantified risk, and implementable recommendations.
Choose the area that matches your need

Coastal Capture Fisheries
Coastal capture fisheries refer to fishing activities conducted in waters that are still strongly influenced by terrestrial inputs and nearshore environmental processes.
Learn more →
Offshore Capture Fisheries
Indonesia's marine waters exhibit exceptionally high spatial and temporal variability, with each marine region possessing its own distinctive oceanographic characteristics.
Learn more →A traceable evidence base

Observations
Field surveys, in-situ measurements, laboratory results, historical records, and operating information as required.

Remote sensing & GIS
Satellite imagery, mapping, spatial analysis, temporal change, and integration of multiple data sources.

Modeling & scenarios
Model setup, calibration, validation, existing–planned–extreme scenarios, and sensitivity analysis.

Quality assurance
Metadata, quality controls, assumptions, limitations, data versions, and processing lineage are documented.
Decision-ready information

Initial assessment & data gaps
Objectives, study area, available data, additional needs, initial risks, and recommended level of detail.

Datasets, maps & indicators
Quality-controlled data, thematic maps, time series, indicators, and comparable visualizations.

Scenarios & risk evaluation
Comparison of existing conditions, alternatives, extremes, sensitivities, consequences, and mitigation options.

Report & executive brief
Methods, results, limitations, recommendations, action priorities, and stakeholder presentation materials.
Benefits for decision makers and policy leaders

Reduce uncertainty
Assumptions, data, variability, and limitations are stated so decision risk is not hidden.

Compare options objectively
Alternative locations, designs, operations, or policies are assessed using consistent indicators.

Optimize cost and time
Data needs and analysis depth are proportionate to risk so resources are used efficiently.

Increase stakeholder confidence
Findings and recommendations are transparent for technical, management, regulatory, and partner review.
A clear process from need to recommendation
- 01

Need definition
Objectives, users, location, project phase, problems, constraints, and the decision to support.
- 02

Scope & work plan
Methods, data, surveys, models, schedule, team, deliverables, review gates, and resource estimate.
- 03

Acquisition & quality control
Collection, inspection, harmonization, documentation, and data-sufficiency assessment.
- 04

Analysis & scenario testing
Processing, modeling, validation, option comparison, sensitivity, and risk evaluation.
- 05

Recommendation & handover
Maps, report, executive brief, presentation, supporting data, and follow-up plan.
Full technical basis and contextOpen this section to read the complete source technical narrative.
Indonesia's waters are among the most biologically diverse marine ecosystems in the world, supporting a wide variety of fish species, particularly high-value resources such as large pelagic fish, small pelagic fish, demersal fish, and shrimp. This diversity is primarily driven by the unique environmental characteristics of each marine region. Consequently, the abundance of individual species varies significantly from one location to another. In addition, the presence and abundance of a particular fish species fluctuate over time, with populations sometimes becoming highly concentrated and, at other times, declining substantially or even disappearing from a given area.
This variability represents one of the greatest challenges for the capture fisheries industry. The distribution and abundance of commercially important fish species are closely linked to the physical, chemical, and biological characteristics of the surrounding marine environment. Therefore, successful fishing operations require a comprehensive understanding of changing oceanographic conditions and ecosystem dynamics that influence fish distribution.
Marine modeling technology provides an effective means of studying and predicting changes in ocean conditions. Capture fisheries assessments can be supported through integrated numerical modeling of the physical, chemical, and biological processes that govern marine ecosystems. These modeling capabilities enable fisheries operators to make faster, more accurate, and scientifically informed decisions when planning fishing strategies, reducing operational risks while improving efficiency and productivity.
Share the need, location, available data, and the decision to be supported.
The CORZ team will review the objective, scope, data availability, risk level, schedule, and required outputs to prepare a proportionate approach.
- Location and project phase
- Decision or objective to support
- Primary problems and risks
- Available data
- Expected outputs and schedule




