5  High-Level Workflow Requirements and Constraints

5.1 Purpose

Ecosystem Indicator Workflows (EIWs) are documented, transparent, and reproducible computational workflows developed to generate ecosystem-specific and fit-for-purpose indicators. The workflows are intended to support consistent indicator development across ecosystem types while providing sufficient flexibility to address different management, monitoring, assessment and reporting needs.

All workflows are designed to align with FAIR principles (Findable, Accessible, Interoperable and Reusable) and support reproducible research practices.

5.2 Scope and Structure

The development of an Ecosystem Indicator Workflow must begin with a clearly defined scope. The scope establishes the context, purpose and intended application of the workflow and provides the basis for all subsequent workflow development activities.

Each workflow must define the following four attributes:

1. Target Ecosystem Definition: The workflow must identify the ecosystem or ecosystem group that will be assessed.
2. Intended Purpose: The workflow must clearly state the purpose of the indicator and the decision-making context in which it will be used. Examples may include: Environmental reporting, Natural capital accounting, Ecosystem risk assessment, Ecosystem recovery and integrity assessment, Conservation planning.
3. Diagnostic Mechanism: The workflow must describe the ecological mechanism through which the selected indicator provides information about ecosystem status, condition or change.
4. Indicator Variable Selection: The workflow must identify one or more measurable variables that can be used to quantify the selected ecosystem characteristic.

All Ecosystem Indicator Workflows will follow a common modular structure consisting of four core workflow components.

1. Ecosystem Data Preparation: This module prepares ecosystem spatial data and associated ecosystem information required for indicator calculation. Dataset selection decisions must be documented and justified.
2. Indicator Data Preparation: This module prepares the data required to quantify the selected indicator variable. All processing steps must be transparent and reproducible.
3. Indicator Calculation: This module implements the analytical methods required to generate the ecosystem indicator. Calculation methods should be designed to allow consistent application across assessment periods where appropriate.
4. Validation and Uncertainty Assessment: This module assesses the performance, reliability and limitations of the indicator. Validation outcomes must be documented alongside indicator results.

5.3 Design Principles

The Ecosystem Indicator Workflow program will be guided by the following principles:

1. Scientific credibility – indicators are based on sound ecological understanding and defensible methods.
2. Fitness for purpose – indicators are developed to address clearly defined user needs and decision contexts.
3. Transparency – methods, assumptions and data sources are fully documented.
4. Reproducibility – workflows can be repeated and independently verified.
5. Interoperability – workflows align with recognised standards and classification frameworks.
6. Scalability – workflows can be adapted to additional ecosystems and indicator types over time.
7. Accessibility – tools and outputs are usable by a broad community of researchers, practitioners and decision makers.
8. Continuous improvement – workflows can be updated as new data, methods and user requirements emerge.

5.4 High-Level Workflow Requirements

5.4.1 Scope Definition Requirements

Scope		Requirements
Target Ecosystem Definition
	1.1	Ecosystem definitions must align with the IUCN Global Ecosystem Typology.
	1.2	The ecosystem classification level must be appropriate to the intended application of the indicator.
	1.3	The ecosystem definition must be documented and traceable throughout the workflow.
Intended Purpose
	2.1	The workflow must clearly state the purpose of the indicator.
	2.2	The workflow must clearly state the decision-making context in which it will be used.
	2.3	The intended purpose must guide data selection, methodology and validation requirements.
Diagnostic Mechanism
	3.1	The diagnostic mechanism should document the ecological process being assessed
	3.2	The diagnostic mechanism should document the relationship between the indicator and ecosystem characteristics
	3.3	The diagnostic mechanism should document key assumptions underpinning indicator interpretation
Indicator Variable Selection
	4.1	Indicator variables should be scientifically defensible
	4.2	Indicator variables should be measurable using available data sources.
	4.3	Indicator variables should be appropriate to the intended purpose.
	4.4	Indicator variables should support repeatable assessment through time.

5.4.2 Workflow Structure Requirements

Module	Code	Requirements
Ecosystem Data Preparation
	1.1	Identification and acquisition of ecosystem datasets.
	1.2	Documentation of ecosystem classification systems and mapping methods.
	1.3	Standardisation and preprocessing of ecosystem spatial data.
	1.4	Recording of all assumptions and transformations applied to source data.
	1.5a	Where available, ecosystem datasets should utilise the best available information from State and Territory jurisdictions.
	1.5b	Federal spatial datasets or global datasets may be used when: (a) They provide a better fit for the intended purpose; (b) Regional or jurisdictional datasets are unavailable; and/or (c) Regional datasets lack sufficient coverage, consistency or quality.
Indicator Data Preparation
	2.1	Identification and acquisition of indicator datasets.
	2.2	Assessment of data quality and suitability.
	2.3	Documentation of data provenance.
	2.4	Data cleaning, transformation and standardisation.
	2.5	Management of temporal and spatial alignment between ecosystem and indicator datasets.
Indicator Calculation
	3.1	Documentation of all mathematical and analytical methods.
	3.2	Explicit parameter definitions.
	3.3	Recording of assumptions and methodological choices.
	3.4	Repeatable execution using documented computational procedures.
	3.5	Generation of standard outputs suitable for interpretation and reporting.
Validation and Uncertainty Assessment
	4.1	Validation against independent datasets where available.
	4.2	Documentation of methodological limitations.
	4.3	Identification of data quality issues and potential biases.
	4.4	Quantification of uncertainty where feasible.
	4.5	Transparent communication of confidence and limitations associated with outputs.

5.4.3 Software and Technical Requirements

The Ecosystem Indicator Workflow framework must be supported by software tools and computational environments that promote transparency, accessibility and reproducibility. Workflow implementation should minimise barriers to adoption and facilitate long-term maintenance.

This will be achieved through:

• Use of open-access or widely accessible software where feasible.
• Use of documented and version-controlled analytical processes.
• Support for workflow portability across organisations and jurisdictions.
• Ability to adapt workflows for alternative ecosystems and use cases.
• Transparent documentation of software dependencies, packages and computational environments.

5.4.4 FAIR and Reproducibility Requirements

All workflows, tools and outputs must align with FAIR principles and reproducible research practices.

Findable

Workflow documentation, code, metadata and outputs should be discoverable through appropriate repositories and documentation systems.

Accessible

Workflow components and outputs should be accessible to authorised users through documented and sustainable access mechanisms.

Interoperable

Data, metadata and outputs should use recognised standards, formats and classification systems to support integration with other workflows, platforms and reporting systems.

Reusable

Workflow documentation must provide sufficient detail to enable independent application, review, adaptation and reuse by other practitioners.

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5.5 Constraints

The development and implementation of Ecosystem Indicator Workflows will be subject to several constraints.

• Data Availability
  • Indicator development is dependent on the availability, quality and coverage of ecosystem and indicator datasets.
  • Data availability will vary across ecosystem types, jurisdictions and time periods.
  • Some workflows may require the use of proxy variables where direct measurements are unavailable.
• Data Consistency
  • Differences in spatial scale, classification systems, temporal coverage and collection methodologies may affect comparability across datasets.
  • Additional data harmonisation may be required to support national-scale assessments.
• Computational Constraints
  • Computational methods should balance scientific rigour with practical implementation requirements.
  • Workflows should remain maintainable by future users and project teams.
• Resource Constraints
  • Workflow development priorities must reflect available funding, staff capacity and stakeholder demand.
  • Not all desired functionality may be delivered within the initial development period.
• Scientific Constraints
  • Ecological processes are inherently complex and may not be fully represented by individual indicators.
  • All indicators should clearly communicate assumptions, limitations and uncertainty associated with interpretation.