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A common challenge for studying wildlife populations occurs when different survey methods provide inconsistent or incomplete inference on the trend, dynamics, or viability of a population. A potential solution to the challenge of conflicting or piecemeal data relies on the integration of multiple data types into a unified modeling framework, such as integrated population models (IPMs). IPMs are a powerful approach for species that inhabit spatially and seasonally complex environments. We provide guidance on exploiting the capabilities of IPMs to address inferential discrepancies that stem from spatiotemporal data mismatches. We illustrate this issue with analysis of a migratory species, the American Woodcock (Scolopax minor), in which individual monitoring programs suggest differing population trends. To address this discrepancy, we synthesized several long‐term data sets (1963–2015) within an IPM to estimate continental‐scale population trends, and link dynamic drivers across the full annual cycle and complete extent of the woodcock's geographic range in eastern North America. Our analysis reveals the limiting portions of the life cycle by identifying time periods and regions where vital rates are lowest and most variable, as well as which demographic parameters constitute the main drivers of population change. We conclude by providing recommendations for resolving conflicting population estimates within an integrated modeling approach, and discuss how strategies (e.g., data thinning, expert opinion elicitation) from other disciplines could be incorporated into ecological analyses when attempting to combine multiple, incongruent data types.

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Habitat loss is the greatest threat to the persistence of forest-dependent amphibians, but it is not the only factor influencing species occurrences. The composition of the surrounding matrix, structure of stream networks, and topography are also important landscape characteristics influencing amphibian distributions. Tropical forests have high diversity and endemism of amphibians, but little is known about the specific responses of many of these species to landscape features. In this paper, we quantify the response of amphibian species and communities to landscape-scale characteristics in streams within the fragmented Brazilian Atlantic Forest. We surveyed amphibian communities during a rainy season in 50 independent stream segments using Standardized Acoustic and Visual Transect Sampling (active) and Automated Acoustic Recorders (passive) methods. We developed a hierarchical multi-species occupancy model to quantify the influence of landscape-scale characteristics (forest cover, agriculture, catchment area, stream density, and slope) on amphibian occurrence probabilities while accounting for imperfect detection of species using the two survey methods. At the community level, we estimated an overall mean positive relationship between amphibian occurrence probabilities and forest cover, and a negative relationship with agriculture. Catchment area and slope were negatively related with amphibian community structure (95% credible interval [CI] did not overlap zero). The species-level relationships with landscape covariates were highly variable but showed similar patterns to those at the community level. Species detection probabilities varied widely and were influenced by the sampling method. For most species, the active method resulted in higher detection probabilities than the passive approach. Our findings suggest that small streams and flat topography lead to higher amphibian occurrence probabilities for many species in Brazil's Atlantic Forest. Our results combined with land use and topographic maps can be used to make predictions of amphibian occurrences and distributions beyond our study area. Such projections can be useful to determine where to conduct future research and prioritize conservation efforts in human-modified landscapes.