DOI: 10.5061/dryad.r7sqv9sch
title: Vegetation characteristics and precipitation jointly influence grassland bird abundance beyond the effects of grazing management

description: 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. 
description: The avian data were collected using 6-min, unlimited-radius point
 counts on days with appropriate weather conditions (i.e. wind speeds
 &lt; 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.
description: Readme files include column and object descriptions for the .rds
 and .RData files.