Avian Indicators of Climate Change Based on the North American Breeding Bird Survey

Matthew J Clement, James E Hines, James D Nichols, Keith L Pardieck, David Ziolkowski
Posted on: 7/18/2022 - Updated on: 7/17/2023

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Appropriate ecological indicators of climate change can be used to measure concurrent changes in ecological systems, inform management decisions, and potentially to project the consequences of climate change. However, many of the available indicators for North American birds do not account for imperfect observation. We proposed to use correlated-detection occupancy models to develop indicators from the North American Breeding Bird Survey data. The indicators were used to test hypotheses regarding changes in range and distribution of breeding birds. The results will support the Northeast Climate Science Center’s Science Agenda, including the science priority: researching ecological vulnerability and species response to climate variability and change.

Project Products:

  • Estimating indices of range shifts in birds using dynamic models when detection is imperfect (not open access)
    • There is intense interest in basic and applied ecology about the effect of global change on current and future species distributions. Projections based on widely used static modeling methods implicitly assume that species are in equilibrium with the environment and that detection during surveys is perfect. We used multiseason correlated detection occupancy models, which avoid these assumptions, to relate climate data to distributional shifts of Louisiana Waterthrush in the North American Breeding Bird Survey (BBS) data. We summarized these shifts with indices of range size and position and compared them to the same indices obtained using more basic modeling approaches. Detection rates during point counts in BBS surveys were low, and models that ignored imperfect detection severely underestimated the proportion of area occupied and slightly overestimated mean latitude. Static models indicated Louisiana Waterthrush distribution was most closely associated with moderate temperatures, while dynamic occupancy models indicated that initial occupancy was associated with diurnal temperature ranges and colonization of sites was associated with moderate precipitation. Overall, the proportion of area occupied and mean latitude changed little during the 1997–2013 study period. Near-term forecasts of species distribution generated by dynamic models were more similar to subsequently observed distributions than forecasts from static models. Occupancy models incorporating a finite mixture model on detection – a new extension to correlated detection occupancy models – were better supported and may reduce bias associated with detection heterogeneity. We argue that replacing phenomenological static models with more mechanistic dynamic models can improve projections of future species distributions. In turn, better projections can improve biodiversity forecasts, management decisions, and understanding of global change biology.


Matthew J. Clement, James E. Hines, James D. Nichols, Keith L. Pardieck, David J. Ziolkowski Jr, 2016-05-13, Estimating indices of range shifts in birds using dynamic models when detection is imperfect. Global Change Biology, 22 (10): 3273-3285.

Affiliated Organizations

The USGS is a science organization that provides impartial information on the health of our ecosystems and environment, the natural hazards that threaten us, the natural resources we rely on, the impacts of climate and land-use change, and the core science systems that help us provide timely, relevant, and useable information.

The Northeast Climate Adaptation Science Center is part of a network of nine Climate Adaptation Science Centers managed by the U.S. Geological Survey National Climate Adaptation Science Center. We work with natural and cultural resource managers to gather the scientific information and build the tools needed to help fish, wildlife, and ecosystems adapt to the impacts of climate change.

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