  Citizen science databases that consist of volunteer-led sampling efforts of
species communities are relied on as essential sources of data in ecology.
Summarizing such data across counties with frequentist-valid prediction sets
for each county provides an interpretable comparison across counties of varying
size or composition. As citizen science data often feature unequal sampling
efforts across a spatial domain, prediction sets constructed with indirect
methods that share information across counties may be used to improve
precision. In this article, we present a nonparametric framework to obtain
precise prediction sets for a multinomial random sample based on indirect
information that maintain frequentist coverage guarantees for each county. We
detail a simple algorithm to obtain prediction sets for each county using
indirect information where the computation time does not depend on the sample
size and scales nicely with the number of species considered. The indirect
information may be estimated by a proposed empirical Bayes procedure based on
information from auxiliary data. Our approach makes inference for under-sampled
counties more precise, while maintaining area-specific frequentist validity for
each county. Our method is used to provide a useful description of avian
species abundance in North Carolina, USA based on citizen science data from the
eBird database.
