Resource Optimization

Status: In Design — Architecture defined, seeking input on approach

Resource Optimization sits between manager intent and vegetation modeling. It takes real-world constraints (seed availability, labor, budget, logistics) and candidate management strategies, then drives Vegetation Modeling to run appropriate simulations and structures the comparison of outcomes.

The Problem

After characterizing a disturbance and building vegetation models, managers face a combinatorial explosion of choices:

• Where should we seed?
• Where should we plant juveniles?
• Should we remove invasive species first?
• How much labor can we invest per hectare?
• What if seed supply is limited?
• What if seedlings need to be planted near roads for watering access?

Each combination of treatment, location, timing, and intensity produces different projected outcomes. Even for a modest burn scar with a handful of treatment options, the number of permutations quickly exceeds what anyone can reason about intuitively.

Planned Approach

Resource Optimization will:

1. Accept constraints — seed availability, labor hours, budget, logistical requirements (e.g., road access for watering)
2. Define candidate strategies — combinations of interventions to compare
3. Drive simulations — trigger Vegetation Modeling runs for each strategy, with many replicates per scenario to characterize uncertainty
4. Structure comparisons — present outcomes side-by-side so managers can evaluate tradeoffs

What It Won’t Do

• Tell managers what to do — decisions involve values, risk tolerance, and local knowledge that can’t be automated
• Test unparameterized interventions — if you want to simulate an intervention not yet built into the vegetation model (e.g., caging seedlings to prevent herbivory), that requires model development first
• Solve the “optimal” problem — optimization requires agreement on objective criteria, which we can’t assume