Ecosystem Indicator Workflows

A modular, fit-for-purpose toolbox that supports the development of ecosystem-specific indicators.

https://doi.org/10.3565/j5sq-a757

🔧 Why this project?

Australia’s diverse ecosystems require tailored indicators for monitoring, assessment, and management.

Research infrastructure is critical to accelerate development of indicators for a wide range of Australia’s ecosystems

📦 Outcomes

🧩 Core Deliverables

  • Modular workflow toolbox
  • Scalable, adaptable indicator workflows
  • FAIR analysis‑ready datasets
  • Interoperable tools aligned to shared vocabularies
  • A connected and empowered user community

Aims & Impacts

A framework for developing fit-for-purpose ecosystem-specific indicators

  • Design modular, transparent, and reproducible workflow adaptable to:
    • Different datasets
    • Ecosystem types
    • Policy and reporting needs

Transparent, defensible, scientifically rigorous indicators

Mountain Ash Ecosystem Assessment

IUCN Red List of Ecosystems assessment by Burns et al. (2015).

Mountain Ash of the Black Spur Drive

Key processes: Vegetation-fire feedbacks, trees as foundation spp for other biota & functions

Key threats: Logging & fire regime shift, climate change

Reproducible workflow

Guru et al. (2016)

Data:

Scripts:

Environment:

CoESRA Desktop

Version control:

Alpine Sphagnum Bog and Associated Fen in the IUCN Red List of Ecosystems assessment of Australia’s Alpine and Subalpine Ecosystems(Rowland et al. 2026)

G Conceptual Ecosystem Model clusterBio Alpine Sphagnum bogs AE1 Water AE3 Peat AE1->AE3 CB1 Sphagnum moss AE1->CB1 CB2 Fauna (frogs, etc) AE1->CB2 AP2 Precipitation AP2->AE1 AP4 Fire AP4->CB1 TR2 Alien ungulates TR2->AE3 TR3 Pathogens TR3->CB1 TR4 Alien plants TR4->CB2 TR5 Infrastructure TR5->AE3 TR6 Climate change TR6->AP2 TR6->AP4 BP1 Evapo- transpiration TR6->BP1 BP1->AE1 CB1->AE3 CB1->CB2

G Conceptual Ecosystem Model clusterBio Alpine Sphagnum bogs AE1 Water AE3 Peat AE1->AE3 CB1 Sphagnum moss AE1->CB1 CB2 Fauna (frogs, etc) AE1->CB2 AP2 Precipitation AP2->AE1 AP4 Fire AP4->CB1 TR2 Alien ungulates TR2->AE3 TR3 Pathogens TR3->CB1 TR4 Alien plants TR4->CB2 TR5 Infrastructure TR5->AE3 TR6 Climate change TR6->AP2 TR6->AP4 BP1 Evapo- transpiration TR6->BP1 BP1->AE1 CB1->AE3 CB1->CB2

G Indicator development for subcriterion C1: Aridity Index cluster_RS Relative Severity & Extent of Decline cluster_uncertainty Uncertainty cluster_prep Data preparation cluster_CHELSA Data source: CHELSA Version 2.1 PREC Precipitation [mm/month] Raster, 30 arc-sec Time series 1979 – 2019 Monthly values ECO_PREC Precipitation [mm/month] Mean values Time series 1979 – 2019 Monthly values PREC->ECO_PREC extract and aggregate per polygon PET PET (Penman) [mm/month] Raster, 30 arc-sec Time series 1979 – 2019 Monthly values ECO_PET PET (Penman) [mm/month] Mean values Time series 1979 – 2019 Monthly values PET->ECO_PET extract and aggregate per polygon ANNUAL_PREC Annual Precipitation [mm/year] Data frame: polygon X year ECO_PREC->ANNUAL_PREC aggregate per year ANNUAL_PET Annual PET [mm/year] Data frame: polygon X year ECO_PET->ANNUAL_PET aggregate per year AI_CHELSA Aridity Index AI=PREC/PET Data frame: polygon X year ANNUAL_PREC->AI_CHELSA ANNUAL_PET->AI_CHELSA AI_smooth Smooth AI trend Linear model Per polygon Predict RS for 2024 AI_CHELSA->AI_smooth Threshold Threshold (CT): Years 2000-2002 Lower quantile of AI across polygons AI_CHELSA->Threshold ED Cummulative Extent of decline weighted formula RS Relative severity RS=(FV-IV)/(CT-IV) Per polygon ED->RS THR CT selection ED->THR SIZE Area of occurrences ED->SIZE TIME Annual variation RS->TIME RS->THR IV Initial value (IV): predicted AI 1975 AI_smooth->IV FV Final values (FV): predicted AI 2024 AI_smooth->FV Threshold->RS IV->RS FV->RS PRED Future projection 95% pred. interval

Data:

The LIST CHELSA

Scripts:

R Python

Documentation:

Jupyter Notebook quarto

Version control:

Transparent and reproducible

flowchart LR
    IND1>"Indicator 1"]:::WPtodo
    IND2>"Indicator 2"]:::WPtodo
    %%IND3>"Indicator 3"]:::WPtodo
    %%IND4>"Indicator 4"]:::WPtodo
    PLAT1[["Alpine bogs<br>assessment"]]:::WPtodo
    %%PLAT2[["Status of<br>Australian<br>peatlands"]]:::WPtodo
    WF1{WF1}:::WF
    WF2{WF2}:::WF
    %%WF1b{WF2*}:::WF
    ECO1(["Alpine<br>bogs"]):::Ecos
    %%ECO2(["Fleurieu<br>swamp"]):::Ecos
    %%ECO3(["other<br>peatlands"]):::Ecos
    RGA{{"Researcher<br>group A"}}:::RoleCore
    %%RGB{{"Researcher<br>group B"}}:::RoleCore
    RL4(("End Users<br>(state)")):::RoleExt
    %%RL6(("End Users<br>(national)")):::RoleExt
    
    RGA --> ECO1 --> WF1 --> IND1 --> PLAT1
    ECO1 --> WF2 --> IND2 --> PLAT1 
    %%WF2 -.-> WF1b
    %%RGB --> ECO2 & ECO3 --> WF1b --> IND3 & IND4 --> PLAT2
    %%PLAT2 --> RL6
    PLAT1 --> RL4
    classDef WPtodo fill:white,stroke:#E51875,color:#E51875, stroke-width:4px
    classDef WF fill:#E51875,stroke:#E51875,color:white, stroke-width:4px
    classDef Ecos fill:#dfd,stroke:#040,color:#040, stroke-width:2px
    classDef RoleExt fill:#fff,stroke:#8E489B,color:#8E489B, stroke-width:2px
    classDef RoleCore fill:white,stroke:#00B0D5,color:black, stroke-width:4px

  • FAIR methods and datasets
  • Update outputs with new data

Adopt and adapt indicators

flowchart LR
    IND1>"Indicator 1"]:::WPtodo
    IND2>"Indicator 2"]:::WPtodo
    IND3>"Indicator 2<br>Fleurieu swamp"]:::WPtodo
    IND4>"Indicator 2<br>other peatlands"]:::WPtodo
    PLAT1[["Alpine bogs<br>assessment"]]:::WPtodo
    PLAT2[["Status of<br>Australian<br>peatlands"]]:::WPtodo
    WF1{WF1}:::WF
    WF2{WF2}:::WF
    WF1b{WF2*}:::WF
    ECO1(["Alpine<br>bogs"]):::Ecos
    ECO2(["Fleurieu<br>swamp"]):::Ecos
    ECO3(["other<br>peatlands"]):::Ecos
    RGA{{"Researcher<br>group A"}}:::RoleCore
    RGB{{"Researcher<br>group B"}}:::RoleCore
    RL4(("End Users<br>(state)")):::RoleExt
    RL6(("End Users<br>(national)")):::RoleExt
    
    RGA --> ECO1 --> WF1 --> IND1 --> PLAT1
    ECO1 --> WF2 --> IND2 --> PLAT1 & PLAT2
    WF2 -.-> WF1b
    RGB --> ECO2 & ECO3 --> WF1b --> IND3 & IND4 --> PLAT2
    PLAT2 --> RL6
    PLAT1 --> RL4
    classDef WPtodo fill:white,stroke:#E51875,color:#E51875, stroke-width:4px
    classDef WF fill:#E51875,stroke:#E51875,color:white, stroke-width:4px
    classDef Ecos fill:#dfd,stroke:#040,color:#040, stroke-width:2px
    classDef RoleExt fill:#fff,stroke:#8E489B,color:#8E489B, stroke-width:2px
    classDef RoleCore fill:white,stroke:#00B0D5,color:black, stroke-width:4px

Data:

Enviro Data SA CHELSA

Scripts:

R Python

Documentation:

Jupyter Notebook quarto

Version control:

F2.5 Ephemeral freshwater lakes, e.g. Menindee Lakes, Lake Cowal, Peery Lake, etc.

A system of ephemeral, freshwater lakes which are fed by overflow of the Darling River in Australia

Key processes: Water volume-chemistry interactions & trophic web, river inflows, waterbird abundance

Key threats: Water extraction, water pollution, eutrophication, climate change

M1.1 Seagrass meadows, e.g. Shark Bay in Western Australia

Dugong near Marsa Alam, Egypt

Key processes: Interactions & trophic web, low-energy waves vs. storms, large herbivores

Key threats: Sedimentation, eutrophication, climate change

T1.1 Tropical-subtropical lowland rainforest, e.g. Gondwana rainforests, warm temperate rainforests, …

Gondwana rainforest at Dorrigo National Park (6611254443)

Key processes: Trophic web, gap dynamics, microclimate feedbacks, foundation species

Key threats: Climate change, fire regime shifts, invasive species, logging legacies