Recent literature has shown citizen science data that include reported counts, such as observations from the eBird database, can be used to model the relative abundance of selected species. The objective of our study was to extend on both the methodology and the range of species considered by previous research on relative abundance modeling; we selected the Floridian peninsula as our geographical area of interest. We confirmed that, much like for other bird species and geographical areas, modeling such data with a classic Poisson generalized linear model was not appropriate due to overdispersion. Moreover, assuming linearity for the effects of all environmental and observer effort covariates on relative abundance was shown to be overly simplistic. These concerns led us to consider a variety of potentially suitable modeling techniques; the quasi‐Poisson generalized additive model displayed the best combination of flexibility, strong predictive performance, and ease of interpretation. Expanding on previous research for eBird data, we chose to explicitly incorporate spatial dependence into the modeling task by performing hierarchical generalized additive modeling with a spatial conditional autoregressive structure for random effects. This practice allows us to account for spatial factors not represented by model covariates, such as species dispersal patterns or colonial breeding habits. After conducting appropriate testing procedures and fitting spatially explicit models, we found that our data contain moderate spatial dependence and that spatially explicit models have overall superior predictive performance to models that do not account for spatial dependence. Upon closer inspection, spatially explicit models perform especially well compared with their nonspatial counterparts in identifying situations with low abundance, with a negligible trade‐off in prediction accuracy for situations with high abundance. We conclude that quasi‐Poisson hierarchical generalized additive models with spatial random effects provide the best representation of the relative abundance of bird populations. Moreover, our spatially explicit models are more realistic based on domain knowledge when regarding the impact of environmental covariates, which is important when considering conservation implications and future projections.