Modeling tools for estimating and forecasting shifts in species distributions are becoming increasingly valuable for conservation planning and response. This is especially true for wild bird populations, which have been declining across habitats and regions. Species distribution models (SDM) represent a diverse set of tools with options for addressing various sources of bias. Complex spatial processes associated with rare or clustered species can be accounted for using zero-inflated SDMs, whereas biased survey data can be integrated with additional data sources to improve estimates. Each option addresses an important and common source of bias, but the two SDM frameworks have not been implemented together previously. We present a novel zero-inflated extension of an integrated SDM framework for a Poisson regression (ZI-iSDM), allowing for the estimation of independent occurrence and abundance processes by integrating opportunistic survey and presence-only data. We validated performance of this ZI-iSDM using simulated datasets under different degrees of species rarity and density on the landscape as well as sampling bias in opportunistic datasets. We additionally applied this model to real survey data for multiple wild bird species using publicly available observation data combined with open-access environmental information to describe habitat associations. We found that integrating presence-only data, such as banding or harvest events, can compensate for potential deficiencies in opportunistic surveys by expanding sampling to be more representative of available and used habitat. Additionally, models that first differentiated occurrence and abundance using a ZI term were better suited for approximating distributions of spatially clustered species.