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Vegetation characteristics and precipitation jointly influence grassland bird abundance beyond the effects of grazing management

Cite this dataset

Davis, Kristin; Augustine, David; Monroe, Adrian; Aldridge, Cameron (2021). Vegetation characteristics and precipitation jointly influence grassland bird abundance beyond the effects of grazing management [Dataset]. Dryad.


Grassland birds have experienced some of the steepest population declines of any guild of birds in North America. The shortgrass steppe contains some of North America’s most-intact grasslands, which makes the region particularly important for these species. Grassland birds differentially respond to variation in vegetation structure generated by spatiotemporally-varying disturbance like grazing management. However, understanding how species respond to characteristics beyond vegetation structure or grazing could better inform management for these species in the shortgrass steppe. We analyzed point count data for 5 grassland bird species breeding on the Central Plains Experimental Range in northeastern Colorado from 2013 – 2017 to examine the predictive capacity of models representing fine-scale (~3-ha) vegetation attributes (vegetation structure and cover type) and topography, combined with interannual precipitation variability (i.e. vegetation-abiotic models). We then compared these models to models based on grazing management treatments (applied to whole pastures, ~130 ha) and edaphic conditions (ecological sites), which represented information more generally available to rangeland managers. Precipitation, vegetation structure, and vegetation cover type influenced all species in a manner consistent with, but more nuanced than, vegetation structure alone. These models also explained more variation in abundance for species that responded to grazing management. Thus, while grazing management can be applied adaptively to improve habitat for these species, our more detailed vegetation-abiotic models identified species-specific habitat components that could be targeted for management. For example, not grazing pastures with extensive, homogenous stands of mid-height grasses (e.g, Hesperostipa comata) for an entire growing season during wet years could be one strategy to enhance Grasshopper Sparrow abundance and stockpile residual forage for future utilization by livestock. Our models provide a better understanding of, and reveal nuances in, the suite of environmental conditions to which grassland birds respond in shortgrass-steppe rangelands. 


The avian data were collected using 6-min, unlimited-radius point counts on days with appropriate weather conditions (i.e. wind speeds < 19 kph, no precipitation) between sunrise and ~10:30 am. Point counts were conducted twice during the breeding season between late May and the second week of June. Observers used rangefinders to measure the distance to all individual birds detected and recorded the distance and the method of detection (e.g., aural, visual) and sex (if determinable) of each individual.

The vegetation cover data were collected from a systematic grid of 4 25-m transects oriented north-south and spaced 106 m apart within each study monitoring plot using the line-point intercept method to quantify canopy and basal vegetation cover by species along each transect. We cumulated all vegetation species detected in these surveys into 8 cover groups that we predicted may influence the abundance of our focal species: shortgrass, midgrass, cactus, forb, shrub, subshrub, standing dead, and litter. We calculated mean absolute cover across all transects for each cover group per plot per year. The vegetation height-density data (i.e., vegetation obstruction, VO) were collected by placing a visual obstruction pole modeled after Robel et al. (1970; modified with 1-cm increments) at each of 8 locations spaced every 3 m along each transect. Technicians recorded the highest band on the pole that was partially or entirely obscured by vegetation, with two readings for each pole placement taken from locations perpendicular to the transect at a distance of 4 m from the pole and 1 m above the ground (Robel et al. 1970). We calculated mean VO for each plot based on the mean 16 readings from each of the 4 transects. 

The topographic data, topographic ruggedness index (TRI) and topographic wetness index (TWI), were calculated over 150-m buffers surrounding each point count location in ArcGIS version 10.2.2 (Environmental Systems Research Institute 2014) using a digital elevation model of the CPER developed by the National Ecological Observation Network and the “Roughness” tool in the Geomorphometry and Gradient Metrics toolbox for TRI (Evans et al. 2014), and the Landscape Connectivity and Pattern toolbox for TWI (Theobald 2007).

The precipitation data were collected from a rain gauge at the headquarters of CPER (located near the center of the site) that was checked daily Monday – Friday. We also used mean daily soil moisture between 10 and 20 cm from the USDA-SCAN station 2017, Nunn #1 to calculate average percent soil moisture.

Usage notes

Readme files include column and object descriptions for the .rds and .RData files.


National Institute of Food and Agriculture, Award: 2015-67019-23009

Agricultural Research Service

U.S. Geological Survey

Colorado State University