Appendix K: Specialized and Emerging InVEST Models

1 Purpose of this appendix

Appendices G–J covered the core InVEST services that dominate most Earth–economy applications.

This appendix completes the picture by introducing the remaining classes of InVEST models:

  • specialized production systems (pollination variants, rangelands, hydropower),
  • marine spatial planning tools (offshore wind),
  • coastal water-quality extensions,
  • and infrastructure-risk couplings,
  • along with the scenario engines that bind them together.

These models do not change the logic of InVEST.
They instantiate it in domains where sustainability is increasingly decided.

They show that Earth–economy modeling is not limited to forests and farms.
It now governs oceans, energy systems, and infrastructure.

2 Advanced pollination formulations

Beyond the core Pollination model, InVEST includes:

  • multi-guild pollinator formulations,
  • seasonal floral resource tracking,
  • crop-specific dependence curves.

Conceptually:

PollinatorAbundance_s = f(Nesting_s, Floral_s, Distance_s) CropYield = BaselineYield × g(PollinatorAbundance)

Where s indexes pollinator species or guilds.

These variants allow:

  • differentiation between wild bees, managed bees, and other pollinators,
  • modeling seasonal mismatches between bloom and habitat,
  • crop-specific sensitivity to pollination loss.

Earth–economy use:

  • distinguish vulnerability across cropping systems,
  • test habitat restoration timing,
  • integrate biodiversity composition into food security.

Here, species diversity becomes an economic variable.

3 Rangeland and forage production

Regional and partner InVEST variants model:

  • grassland productivity,
  • forage availability,
  • and grazing capacity.

Inputs:

  • precipitation and climate,
  • soil properties,
  • vegetation type,
  • grazing pressure.

Conceptually:

Forage = f(Rainfall, Vegetation, Soil) Stock_{t+1} = Stock_t + Growth − Grazing LivestockOutput = h(Forage)

Earth–economy use:

  • represent pastoral economies,
  • connect climate variability to livelihoods,
  • evaluate overgrazing and restoration,
  • treat rangelands as renewable capital.

This brings arid and semi-arid systems into Earth–economy space.

4 Hydropower production

Built on Water Yield and Seasonal Water Yield outputs, hydropower modules estimate:

  • flow reliability,
  • energy generation,
  • and vulnerability to land-use change.

Inputs:

  • streamflow,
  • dam locations,
  • head height,
  • turbine efficiency.

Conceptually:

Energy = ρ g Q H η

Where Q is modeled flow.

Earth–economy use:

  • link watershed protection to energy security,
  • integrate forests into power-system planning,
  • test tradeoffs between dams and ecosystems,
  • embed hydrology into macro energy pathways.

Forests become part of the energy grid.

5 Offshore wind energy (marine spatial planning)

The marine wind model evaluates:

  • wind resource,
  • bathymetry,
  • distance to grid,
  • shipping lanes,
  • fisheries and habitat conflict.

Conceptually:

Suitability = f(Wind, Depth, Distance, Exclusions, Conflict)

Outputs:

  • feasible development zones,
  • energy potential,
  • conflict maps.

Earth–economy use:

  • co-design energy and conservation,
  • integrate oceans into transition pathways,
  • prevent displacement of fisheries,
  • treat the seascape as economic space.

The energy transition becomes spatial economics.

6 Coastal water quality and eutrophication (emerging)

Extensions of Nutrient Delivery models propagate:

  • upstream nutrient loads,
  • through rivers,
  • into estuaries and reefs.

Conceptually:

N_load_coast = Σ WatershedExport EcosystemStress = f(N_load_coast) FishProductivity = g(EcosystemStress)

Earth–economy use:

  • connect agriculture to fisheries,
  • value upstream mitigation,
  • integrate land–sea coupling,
  • model hypoxia and reef loss.

Watersheds and oceans become a single system.

7 Infrastructure exposure and resilience (emerging)

Partner and experimental modules combine:

  • flood models,
  • landslide risk,
  • and infrastructure networks.

Outputs:

  • road and bridge exposure,
  • avoided damage from ecosystems,
  • critical corridor vulnerability.

Earth–economy use:

  • embed ecosystems in transport planning,
  • treat wetlands as asset protectors,
  • integrate resilience into national accounts,
  • internalize disaster risk.

Nature becomes a line item in infrastructure budgets.

8 Scenario engines and batch systems

Modern InVEST workflows include:

  • land-use scenario generators,
  • batch-run frameworks,
  • service comparison dashboards.

These allow:

Scenario_i → {Service_1, Service_2, …, Service_n}

Earth–economy use:

  • generate service vectors under futures,
  • feed them into CGE or IAM models,
  • compare portfolios of outcomes,
  • trace multi-service tradeoffs.

This is where InVEST becomes systemic.

9 Why these models matter

These remaining models extend InVEST into:

  • arid lands,
  • oceans,
  • energy systems,
  • infrastructure,
  • and species-level ecology.

They show that:

There is no “environmental sector.”
Every sector has an ecological production function.

Earth–economy modeling is the discipline that:

  • unifies these functions,
  • propagates them through markets,
  • and evaluates futures under limits.

10 Exercises

  1. Domain expansion.
    Choose one model from this appendix.
    Describe a real policy question it could answer.

  2. Coupled chain.
    Write a chain linking:

    • land or sea use,
    • an InVEST output,
    • an economic response,
    • and a stock change.

  3. Portfolio thinking.
    Pick one landscape or seascape.
    List:

    • one provisioning service,
    • one regulating service,
    • one cultural service
      that could be modeled with InVEST.

With Appendices G–K, InVEST now appears as:

  • a comprehensive library of ecological production functions,
  • spanning land, sea, city, and infrastructure,
  • feeding directly into Earth–economy systems.

At this point, no sector remains “outside” the model.

Only outside the future we choose to simulate.