  We consider an optimization problem in which a (single) bat aims to exploit
the nectar in a set of $n$ cacti with the objective of maximizing the expected
total amount of nectar it drinks. Each cactus $i \in [n]$ is characterized by a
parameter $r_{i} > 0$ that determines the rate in which nectar accumulates in
$i$. In every round, the bat can visit one cactus and drink all the nectar
accumulated there since its previous visit. Furthermore, competition with other
bats, that may also visit some cacti and drink their nectar, is modeled by
means of a stochastic process in which cactus $i$ is emptied in each round
(independently) with probability $0 < s_i < 1$. Our attention is restricted to
purely-stochastic strategies that are characterized by a probability vector
$(p_1, \ldots, p_n)$ determining the probability $p_i$ that the bat visits
cactus $i$ in each round. We prove that for every $\epsilon > 0$, there exists
a purely-stochastic strategy that approximates the optimal purely-stochastic
strategy to within a multiplicative factor of $1 + \epsilon$, while exploiting
only a small core of cacti. Specifically, we show that it suffices to include
at most $\frac{2 (1 - \sigma)}{\epsilon \cdot \sigma}$ cacti in the core, where
$\sigma = \min_{i \in [n]} s_{i}$. We also show that this upper bound on core
size is asymptotically optimal as a core of a significantly smaller size cannot
provide a $(1 + \epsilon)$-approximation of the optimal purely-stochastic
strategy. This means that when the competition is more intense (i.e., $\sigma$
is larger), a strategy based on exploiting smaller cores will be favorable.
