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Biome-scale spatial patterns of avian abundance reveal proactive conservation opportunities in North American grasslands

North American grassland birds have experienced steeper population declines than any other avian guild, yet conservation efforts remain largely reactive and fragmented. We used nearly four decades of North American Breeding Bird Survey data to identify biome-scale spatial patterns (clustering) of grassland bird abundance for the Great Plains. Our results reveal an ecological core in the north-central Plains where community-level abundance is either increasing by >100% or remains high and stable, providing a strategic roadmap for a “Defend the Core” conservation approach. This approach flips the script from reactive triage centered on isolated population fragments to a proactive strategy of maintaining large-scale ecosystem integrity. Conversely, we found that population losses are more spatially clustered than wins, reflecting the relentless, one-way movement of woody encroachment and agricultural conversion. This asymmetry supports prioritizing intact landscapes, as current restoration rates are often outpaced by the scale of habitat loss. Notably, we found that community-level spatial clustering is a more robust indicator of biome condition than trends of individual flagship species, suggesting that managing for ecosystem integrity provides a more effective multi-species umbrella. Given our results, there is an opportunity for operationalizing a Great Plains Conservation Design that is ecosystem-centric and rooted in the sustainability of the private-land cattle production that maintains these open spaces. By leveraging avian abundance as a biological sensor, managers and producers can deploy a shared vision that matches the spatial scale of the threats, moving from reactive triage to proactive defense of core working grasslands in North America.

Great Plains biome

Where will the cat cross the road? Comparing camera and GPS-based models for identifying wildlife corridors

Designing effective wildlife corridors is a critical conservation challenge in fragmented landscapes. GPS-based step selection functions strongly predict dispersal corridors and connectivity, but GPS collaring can be expensive and invasive. Camera-based occupancy models are widely used for connectivity analyses but may involve trade-offs in data resolution. Despite widespread use of both approaches, few studies have directly compared them using concurrent datasets. We developed a stacked single-species, single-season occupancy model and a Circuitscape connectivity surface for mountain lions (Puma concolor) on Washington’s Olympic Peninsula, USA, and compared them with a connectivity surface from an existing integrated step selection function. Both models predicted mountain lion GPS locations well, with binned Spearman rank correlations of 1 for Circuitscape and 0.96 for the step selection function, though step selection better identified habitat use by dispersers. Connectivity predictions were moderately correlated across the landscape ( r = 0.26), but agreement was strongest in human-dominated areas most critical for corridor planning. We conclude that GPS-based approaches are advantageous when data collection is feasible and the focus is on dispersal or fine-scale movement. However, camera-based approaches may be preferable for multi-species monitoring, large spatial and temporal scales, noninvasive sampling, when resources are limited, or when fine-scale or dispersal-specific inference is not required.

Washington