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Spatial synchrony, defined as the correlated fluctuations in abundance of spatially separated populations, can be caused by regional fluctuations in natural and anthropogenic environmental population drivers. Investigations into the geography of synchrony can provide useful insight to inform conservation planning efforts by revealing regions of common population drivers and metapopulation extinction vulnerability. We examined the geography of spatial synchrony and decadal changes in these patterns for grassland birds in the United States and Canada, which are experiencing widespread and persistent population declines. We used Bayesian hierarchical models and over 50 years of abundance data from the North American Breeding Bird Survey to generate population indices within a 2° latitude by 2° longitude grid. We computed and mapped mean local spatial synchrony for each cell (mean detrended correlation of the index among neighboring cells), along with associated uncertainty, for 19 species in 2, 26‐year periods, 1968–1993 and 1994–2019. Grassland birds were predicted to increase in spatial synchrony where agricultural intensification, climate change, or interactions between the 2 increased. We found no evidence of an overall increase in synchrony among grassland bird species. However, based on the geography of these changes, there was considerable spatial heterogeneity within species. Averaging across species, we identified clusters of increasing spatial synchrony in the Prairie Pothole and Shortgrass Prairie regions and a region of decreasing spatial synchrony in the eastern United States. Our approach has the potential to inform continental‐scale conservation planning by adding an additional layer of relevant information to species status assessments and spatial prioritization of policy and management actions. Our work adds to a growing literature suggesting that global change may result in shifting patterns of spatial synchrony in population dynamics across taxa with broad implications for biodiversity conservation. [ABSTRACT FROM AUTHOR]

Efforts to monitor and conserve populations and ecosystems in human‐dominated landscapes can benefit from an empirical social–ecological systems approach.Here we illustrate how latent variable structural equation modelling of regional time series data can effectively describe interconnected drivers of population fluctuations in dynamic landscapes and can help to reveal previously unknown system drivers.Using a declining farmland‐dependent bird species (Ammodramus savannarum) in the eastern United States (1994–2015) as a case study, our analysis reveals how farm management decisions drive population fluctuations (R2 = 20%), while management is in turn highly influenced by climate (R2 = 23%–51%), but not by regional conservation spending.Synthesis and applications. Structural equation modelling revealed potential social–ecological pathways for halting regional population declines in a grassland bird, the Grasshopper Sparrow. Lower population growth rates followed years of higher hay yields (~4 percentage points per metric ton increase in hay yield) and later harvests (~2 percentage points per 10‐day delay in harvest). Thus, one pathway for stabilising regional populations could involve compensating farmers for reducing hay harvests, potentially requiring a six‐fold increase in current annual agri‐environmental conservation spending. [ABSTRACT FROM AUTHOR]