  We consider an ergodic harvesting problem with model ambiguity that arises
from biology. To account for the ambiguity, the problem is constructed as a
stochastic game with two players: the decision-maker (DM) chooses the `best'
harvesting policy and an adverse player chooses the `worst' probability
measure. The main result is establishing an optimal strategy (also referred to
as a control) of the DM and showing that it is a threshold policy. The optimal
threshold and the optimal payoff are obtained by solving a free-boundary
problem emerging from the Hamilton--Jacobi--Bellman (HJB) equation. As part of
the proof, we fix a gap that appeared in the HJB analysis of [Alvarez and
Hening, {\em Stochastic Process. Appl.}, 2019], a paper that analyzed the
risk-neutral version of the ergodic harvesting problem. Finally, we study the
dependence of the optimal threshold and the optimal payoff on the ambiguity
parameter and show that if the ambiguity goes to 0, the problem converges, to
the risk-neutral problem.
