  1. Citizen and community-science (CS) datasets have great potential for
estimating interannual patterns of population change given the large volumes of
data collected globally every year. Yet, the flexible protocols that enable
many CS projects to collect large volumes of data typically lack the structure
necessary to keep consistent sampling across years. This leads to interannual
confounding, as changes to the observation process over time are confounded
with changes in species population sizes.
  2. Here we describe a novel modeling approach designed to estimate species
population trends while controlling for the interannual confounding common in
citizen science data. The approach is based on Double Machine Learning, a
statistical framework that uses machine learning methods to estimate population
change and the propensity scores used to adjust for confounding discovered in
the data. Additionally, we develop a simulation method to identify and adjust
for residual confounding missed by the propensity scores. Using this new
method, we can produce spatially detailed trend estimates from citizen science
data.
  3. To illustrate the approach, we estimated species trends using data from
the CS project eBird. We used a simulation study to assess the ability of the
method to estimate spatially varying trends in the face of real-world
confounding. Results showed that the trend estimates distinguished between
spatially constant and spatially varying trends at a 27km resolution. There
were low error rates on the estimated direction of population change
(increasing/decreasing) and high correlations on the estimated magnitude.
  4. The ability to estimate spatially explicit trends while accounting for
confounding in citizen science data has the potential to fill important
information gaps, helping to estimate population trends for species, regions,
or seasons without rigorous monitoring data.
