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Data from: Improved estimates of relative occurrence and abundance using opportunistic surveys and presence-only observations: A zero-inflated integrated species distribution model

Gonnerman, Matthew1; Sullivan, Jeffery2; Kent, Cody3; Prosser, Diann2; Mullinax, Jennifer1

Published Jul 27, 2026 on Dryad. https://doi.org/10.5061/dryad.pnvx0k729

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Jul 27, 2026 version files 1.79 GB

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Abstract

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 many options for addressing various sources of bias. Complex spatial processes associated with rare or clustered species can be accounted for using zero-inflated SDMs, while biased survey data can be integrated with additional data sources to support more accurate estimates. Each option addresses an important and common source of bias, but the two SDM frameworks have not previously been implemented together. We present a novel zero-inflated extension of a Poisson regression integrated SDM framework (ZI-iSDM), allowing for the estimation of independent occurrence and abundance processes by integrating opportunistic survey and presence-only data. We validated the performance of this ZI-iSDM using simulated datasets under different degrees of species rarity and density on the landscape. We also implemented this model for multiple wild bird species using publicly available survey data combined with open-access environmental information to describe habitat associations. We found that integration of 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 through a ZI term were better suited for approximating distributions of spatially clustered species.