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Research about Bald Knob National Wildlife Refuge, Cache River National Wildlife Refuge, Camp Robinson Special Use Area, Holla Bend National Wildlife Refuge

Source-linked reports with geographic coverage including Bald Knob National Wildlife Refuge, Cache River National Wildlife Refuge, Camp Robinson Special Use Area, Holla Bend National Wildlife Refuge.

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Finding the (small) cores: Spatial covariance tracks grassland bird community occupancy in fragmented grasslands

Grasslands are an imperiled ecosystem, and grassland bird abundance is declining across North America. One of the strongest drivers for these declines is woody plant encroachment of grasslands. In the Great Plains and Sagebrush biomes of North America, spatial covariance—a remote-sensing metric for tracking boundaries between vegetation types—is emerging as a new method to identify and strategize conservation of grassland cores in the face of woody plant encroachment. However, the relationship between spatial covariance and grassland bird community occupancy is unknown. Here, we used Bayesian multispecies occupancy models to understand how occupancy probability of six declining grassland species responded to spatial covariance at three scales (0.81, 7.29, and 65.61 ha) and tree cover in fragmented grasslands of Arkansas, USA. Model selection revealed that the smallest spatial scale (0.81 ha) best explained grassland bird occupancy. Tree cover alone was a poor predictor of grassland bird occupancy compared to models that included spatial covariance at the 0.81- and 7.29-ha scales. Grassland bird occupancy declined at tree-grass boundaries (negative spatial covariance at the 0.81-ha scale) and increased in grassland cores (near-zero or slightly positive spatial covariance at the 0.81-ha scale). At low tree cover, Dickcissel ( Spiza americana ), Eastern Kingbird ( Tyrannus tyrannus ), Loggerhead Shrike ( Lanius ludovicianus ), Northern Bobwhite ( Colinus virginianus ), and Scissor-tailed Flycatcher ( Tyrannus forficatus ) occupancy probability more than doubled in grassland cores (where spatial covariance approached zero). Eastern Meadowlark ( Sturnella magna ) had the weakest relationship with spatial covariance. Our results suggest that spatial covariance can identify grassland cores and serve as a powerful predictor of grassland bird community occupancy, even in highly fragmented grasslands. Identifying grassland cores empowers defending core grasslands from woody plant encroachment and then growing cores via active restoration.

Arkansas↗

Comparing spatial covariance to common field vegetation metrics

Spatial covariance is a metric used to delineate grassland cores and track state transitions driven by woody encroachment in rangelands, and it does so by calculating the degree of spatial coexistence between functional groups like grasses and trees at different scales. Spatial covariance is being used in biome-scale frameworks aimed at combatting woody plant encroachment and grassland conservation, but little is known about how spatial covariance relates to on-the-ground vegetation metrics. Here, we begin to address this knowledge gap by comparing tree-grass spatial covariance (0.81 ha scale) to a variety of established, field vegetation structure metrics. Generalized additive models revealed that relationships between spatial covariance and field-based vegetation metrics were weak to moderate (deviance explained ranging from 10% – 47%). Tree density had the strongest relationship with spatial covariance (deviance explained = 47%): tree density nonlinearly decreased to near-zero when spatial covariance became positive. Average visual obstruction and visual percent grass cover had the weakest relationships with spatial covariance (deviance explained = 13% and 10%, respectively). The stronger tie between tree density and spatial covariance and the very weak link between dominant vegetation cover aligns with the intended usage of spatial covariance in rangeland management, which is to locate treeless grassland cores (i.e., spatial covariance values near zero) and prioritize them for defending “core” rangeland habitats. This also aligns with current applications of spatial covariance in grassland conservation as a metric of early warning, variation, intactness, and boundary strength, not of central tendencies or dominance. Additional testing at different scales and in different ecosystem contexts will help to further understand how spatial covariance relates to other field vegetation metrics.

Arkansas↗