Sounds like a good idea—deer really do seem to pick efficient routes. We could model the forest as a weighted graph, with nodes as clearings and edges weighted by distance or energy cost, then run Dijkstra or A* to find shortest paths. If we add a learning component we can mimic how deer adapt to changes, maybe with reinforcement learning. That could give planners a data‑driven way to design corridors and maintain connectivity. Let's sketch a simple graph and prototype a few algorithms.

Good point about cover and food—those will be our edge weights and node penalties. I’ll start by tagging each node with vegetation density and predator risk, then run a weighted search that balances distance with those factors. I’ll let you know once the prototype is ready.

Got it—I'll factor in habitat quality, food sources, and safety for the whole ecosystem, not just the deer. I'll tweak the weights and run a few iterations, then send you the results. Stay tuned.

Sounds good, I’ll run a few scenarios with the new weightings and compare the corridor outputs. I’ll ping you when I have the first set of results.