We develop a general framework for analysing the distribution of resources in a population of targets under multiple independent search-and-capture events. Each event involves a single particle executing a stochastic search that resets to a fixed location xr at a random sequence of times. Whenever the particle is captured by a target, it delivers a packet of resources and then returns to xr, where it is reloaded with cargo and a new round of search and capture begins. Using renewal theory, we determine the mean number of resources in each target as a function of the splitting probabilities and unconditional mean first passage times of the corresponding search process without resetting. We then use asymptotic PDE methods to determine the effects of resetting on the distribution of resources generated by diffusive search in a bounded two-dimensional domain with N small interior targets. We show that slow resetting increases the total number of resources Mtot across all targets provided that ∑Nj=1G(xr,xj)<0
, where G is the Neumann Green’s function and xj is the location of the j-th target. This implies that Mtot can be optimized by varying r. We also show that the k-th target has a competitive advantage if ∑Nj=1G(xr,xj)>NG(xr,xk)
.