Ecosystem · McKinsey Solve

The McKinsey Ecosystem game: complete strategy guide

Updated 2026 · ~9 min read

Overview & objective

In the Ecosystem module you're given a pool of species — producers (plants / algae) and animals — and environmental data for each. Your job is to (1) pick a starting location and a set of producers, (2) build a matrix of species and predator–prey relationships, and (3) derive the longest viable food chain (up to 8 species) that maximizes total calories flowing through the chain.

Each species has:

  • Calories provided — energy available to predators.
  • Calories required — energy the species itself needs to survive (0 for producers).
  • Depth/elevation range and temperature range — environmental constraints (producers only).

A predator can only eat a prey if it's listed as able to do so. The calories a prey provides minus the calories the predator requires gives the "net" energy available to the next level up.

Related: This guide covers the Ecosystem game. For the other McKinsey Solve game, see the Sea Wolf guide; for the overall assessment, see McKinsey Solve. Want to practise? Try our Ecosystem Solver ($15/solve) or grab the bundle for both solvers.

Step 1 — Pick the optimal producers

Producers are grouped by their environmental range (depth/elevation + temperature). The optimal starting location is the environmental cell shared by the most producers that maximizes total calories provided. In practice: group producers with identical ranges, sum the calories in each group, and pick the group with the highest total.

The Ecosystem Solver finding the optimal starting location and producer set with the highest total calories.
Ecosystem Solver · Step 1 result. Producers are grouped by environmental cell, the highest-calorie group is locked in, and the set is sorted by calories. Open the solver →
Selection rule: Group by (min depth, max depth, min temp, max temp). For each group, sum the calories of its producers. The group with the highest calorie sum is the optimal starting location, and its producers are locked in.

Step 2 — Build the species matrix

With producers locked in, you add the remaining animal species. Each animal has calories provided and calories required. You then mark which species eat which in a predator–prey matrix. A "×" in cell (row, column) means the row species eats the column species.

Constraints:

  • No species eats itself.
  • A predator must be able to get enough net calories from its prey to survive.

Step 3 — Derive the optimum food chain

The optimum food chain is up to 8 species long, with producers at the base. The algorithm sorts species by calories provided (descending), greedily places producers, then for each predator selects the highest-calorie available prey whose remaining calories (after subtracting the predator's requirement) are still non-negative. If the best prey can't sustain the predator, it falls back to the next-best available prey.

The Ecosystem Solver optimum food chain result with up to 8 species chained predator-to-prey.
Ecosystem Solver · Step 3 result. The optimum food chain (8 species) with total calories and producers placed at the base. Open the solver →
Net calorie rule: If a prey has 5,000 provided and the predator requires 4,000, the prey "carries forward" 1,000 to the next level. A predator can only be added if at least one of its listed prey has non-negative remaining calories.

How you're scored

  • Chain length — longer is better (max 8).
  • Total calories in the chain — higher is better.
  • Producer set quality — did you pick the highest-calorie group?
  • Move efficiency — how quickly you completed the steps.

Optimal strategy

  1. Always group producers by identical environment. Don't mix depth/temperature ranges — it dilutes your calorie total.
  2. Pick the group with the highest calorie sum, not the single highest-calorie producer.
  3. In the matrix, be comprehensive. Mark every true predator–prey relationship you can find — the algorithm can't use a relationship you didn't enter.
  4. Work fast. The selection algorithm itself is deterministic; the score-killer is time spent second-guessing.

Practice with the Ecosystem Solver

Our solver implements the exact producer-grouping and food-chain logic. $15 per solve, runs in your browser.