DOI: 10.5061/dryad.9zw3r22f3
title: Landscape-scale conservation mitigates the biodiversity loss of grassland birds

description: The decline of biodiversity from anthropogenic landscape modification is
 among the most pressing conservation problems world-wide.  In
 North America, long-term population declines have elevated the recovery of
 the grassland avifauna to among the highest conservation
 priorities.  Because the vast majority of grasslands of the Great
 Plains are privately owned, the recovery of these ecosystems and bird
 populations within them depend on landscape-scale conservation strategies
 that integrate social, economic, and biodiversity objectives. 
 The Conservation Reserve Program (CRP) is a voluntary program for private
 agricultural producers administered by the United States Department of
 Agriculture that provides financial incentives to take cropland out of
 production and restore perennial grassland.  We investigated
 spatial patterns of grassland availability and restoration to inform
 landscape-scale conservation for a comprehensive community of grassland
 birds in the Great Plains.  The research objectives were to 1)
 determine how apparent habitat loss has affected spatial patterns of
 grassland bird biodiversity, 2) evaluate the effectiveness of CRP for
 offsetting the biodiversity declines of grassland birds and 3) develop
 spatially explicit predictions to estimate the biodiversity benefit of
 adding CRP to landscapes impacted by habitat loss.  We used the
 Integrated Monitoring in Bird Conservation Regions program to evaluate
 hypotheses for the effects of habitat loss and restoration on both the
 occupancy and species richness of grassland specialists within a continuum
 modelling framework.  We found the odds of community occupancy
 declined by 37% for every 1 Standard Deviation (SD) decrease in grassland
 availability [loge(km2)] and increased by 20% for every 1 SD increase in
 CRP land cover [loge(km2)].  There was 17% turnover in species
 composition between intact grasslands and CRP landscapes, suggesting
 grasslands restored by CRP retained considerable, but incomplete
 representation of biodiversity in agricultural landscapes. 
 Spatially explicit predictions indicated absolute conservation outcomes
 were greatest at high latitudes in regions with high biodiversity, whereas
 the relative outcomes were greater at low latitudes in highly modified
 landscapes.  By evaluating community-wide responses to landscape
 modification and CRP restoration at bioregional scales, our study fills
 key information gaps for developing collaborative strategies, and
 balancing conservation of avian biodiversity and social well-being in
 agricultural production landscapes of the Great Plains.
description: <strong>Sampling
 design</strong> The study area corresponded to the Great
 Plains sampling frame from the Integrated Monitoring in Bird Conservation
 Regions (IMBCR) program (Pavlacky et al. 2017).  The sampling frame was
 developed by superimposing a 1 km ×1 km grid over four BCRs in the study
 area, stratified by state and partner defined-regions, and
 1-km<sup>2</sup> sampling units were selected within each
 stratum using Generalized Random-tessellation Stratified (GRTS) sampling
 (Stevens and Olsen 2004).  We sampled Bird Conservation Regions (BCR) 11,
 BCR 17 and BCR 18 in eastern Colorado every year from 2010 through 2018,
 but except for a small number of isolated strata, sampling in the greater
 BCR 18 and 19 began in 2016.  We sampled the set of sampling units in
 successive years, but because annual sampling intensity within strata
 varied, some units were not sampled in successive years.  We sampled 4,140
 1-km<sup>2</sup> sampling units within the study area from
 2010 through 2018.  The IMBCR design sampled vegetation types in
 proportion to availability within strata (Pavlacky et al. 2017), and we
 included all data in the analysis. The sampling
 protocols for avian monitoring involved a two-stage design with systematic
 sub-samples of 16 point count plots located 250 m apart and ≥125 m from
 grid cell boundaries (Pavlacky et al. 2017).  We monitored the occurrence
 of bird species at 44,849 point count plots on one visit per year from
 2010 through 2018 using 6-min counts from one-half hour before sunrise to
 five hours after sunrise at each accessible point count location (Pavlacky
 et al. 2017).  Field technicians measured distances to each bird detection
 using a laser rangefinder and we truncated distances &lt; 125 m to
 specify 4.9-ha, non-overlapping point count plots (Pavlacky et al. 2012). 
 We used a removal sampling protocol to estimate incomplete detection
 (MacKenzie et al. 2018), and binned the 6-min point count intervals into
 three, 2-min time occasions to maintain a constant detection rate in each
 occasion and ensure a monotonic decline in the detection frequency through
 time (Pavlacky et al. 2012). <strong>Landscape
 covariates</strong> We measured 3 continuous
 landscape composition covariates in 3 km × 3 km (9
 km<sup>2</sup>) square landscape buffers surrounding the
 1-km<sup>2</sup> sampling units using remotely sensed data. 
 We selected a 3 km × 3 km landscape buffer based on the eight 1
 km<sup>2</sup> grid cells neighboring the IMBCR sampling unit
 (Pavlacky et al. 2017) to allow a design-based hierarchal structure for
 predictions.  The 9 km<sup>2</sup> landscape buffer was
 similar in size to a grid of point counts buffered by the mean of the
 best-supported landscape radii for 6 grassland bird species (10
 km<sup>2</sup>) studied by Niemuth et al. (2017).  We
 quantified the area of grassland and shrubland vegetation in the
 9-km<sup>2</sup> landscapes using the LANDFIRE Existing
 Vegetation Type (EVT) spatial data layer (USGS 2016) using a Geographic
 Information System (GIS; ArcGIS Version 10.1, Environmental Systems
 Research Institute, Redlands, CA, USA), and the raster and spatialEco
 packages in the R statistical computing environment (R Version 3.5.2,
 www.r-project.org).  We classified landscape composition as grassland or
 shrubland vegetation according to the EVT System Group Physiognomy field,
 except we reclassified three grassland types, two conifer-hardwood types
 and one hardwood type as shrubland based on review of the vegetation
 types.  The grassland vegetation was composed of native grassland
 vegetation, as well as agricultural grasslands such as pastures and hay
 fields.  In addition, we measured the area of CRP in the
 9-km<sup>2</sup> landscapes using Common Land Unit spatial
 data (USDA 2014).  We included only the CRP conservation practices that
 involved grassland or wetland cover types, and removed practices involving
 tree cover and parcels containing missing practice information across all
 years.  For missing practice information within a particular year,
 including all CRP raster data from 2008 - 2010, we updated values with
 data from the closest available year, with the exception of CRP parcels
 with an expiration date &gt; 15 years after the data year or parcels
 with a missing expiration date.  When possible, we replaced missing parcel
 data at the county or state level with data from the closest available
 year.  We intersected the annual CRP land-cover data and replaced the
 intersected land cover with CRP to arrive at seamless annual vegetation
 mosaics composed of grassland, shrubland and CRP land cover.  In addition
 to the landscape composition covariates, we used GIS to calculate latitude
 and longitude for the centroid of the 1-km<sup>2</sup>
 sampling units. <strong>Statistical
 analysis</strong> We
 log<i><sub>e</sub></i> transformed the land cover
 covariates [log<i><sub>e</sub></i>(1 +
 km<sup>2</sup>)] to allow non-linear and threshold responses
 to landscape features, and centered and standardized all covariates using
 the <i>z</i>-transformation (Schielzeth 2010).
 Literature Cited Pavlacky D. C.
 Jr., J. A. Blakesley, G. C. White, D. J. Hanni, and P. M. Lukacs. 2012.
 Hierarchical multi-scale occupancy estimation for monitoring wildlife
 populations. Journal of Wildlife Management 76:154-162. Pavlacky, D. C.,
 Jr., P. M. Lukacs, J. A. Blakesley, R. C. Skorkowsky, D. S. Klute, B. A.
 Hahn, V. J. Dreitz, T. L. George, and D. J. Hanni. 2017. A statistically
 rigorous sampling design to integrate avian monitoring and management
 within Bird Conservation Regions. PLOS ONE
 <b>12</b>:e0185924. Stevens, D. L.,
 Jr., and A. R. Olsen. 2004. Spatially balanced sampling of natural
 resources. Journal of the American Statistical Association
 <b>99</b>:262-278. MacKenzie, D. I.,
 J. D. Nichols, J. A. Royle, K. H. Pollock, L. L. Bailey, and J. E. Hines.
 2018. Occupancy estimation and modeling: inferring patterns and dynamics
 of species occurrence. Second edition. Academic Press, London,
 UK. Niemuth, N. D.,
 M. E. Estey, S. P. Fields, B. Wangler, A. A. Bishop, P. J. Moore, R. C.
 Grosse, and A. J. Ryba. 2017. Developing spatial models to guide
 conservation of grassland birds in the U.S. Northern Great Plains. The
 Condor: Ornithological Applications
 <b>119</b>:506-525. Schielzeth, H.
 2010. Simple means to improve the interpretability of regression
 coefficients. Methods in Ecology and Evolution
 <b>1</b>:103-113. United States
 Department of Agriculture [USDA]. 2014. Common Land Unit geospatial data.
  Memorandum of understanding between the USDA and Bird Conservancy of the
 Rockies, signed 4 August 2014. USDA, Farm Service Agency, Economic and
 Policy Analysis, and Commodity Credit Corporation, Washington, D. C.,
 USA. United States
 Geological Survey [USGS]. 2016. Landfire 1.4.0: existing vegetation cover
 layer. United States Department of the Interior, Geological Survey, Sioux
 Falls, South Dakota, USA.
 &lt;http://landfire.cr.usgs.gov/viewer&gt;. Accessed 23 January
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description: Two data files are provided: point-count_data_dryad.csv contains
 presence-absence data at point-count plots for 44 grassland species in the
 study area.  landscape_covariate_data_dryad.csv
 contains landscape covariate data. We omitted Conservation Reserve
 Program (CRP) data for private lands enrolled in confidential Farm Service
 Agency agreements.  In addition, we omitted coordinates for primary
 sampling units from the Integrated Monitoring in Bird Conservation Regions
 program that require a data sharing agreement through Bird Conservancy of
 the Rockies. A shapefile for species richness
 predictions is provided:  sampling_frame_sr_pred_dryad.zip
 contains a shapefile for species richness predictions to
 9-km<sup>2</sup> landscapes in the study area Field "sr_all" is
 estimated species richness of 44 grassland specialist bird
 species Field
 "sr_p1_all" is estimated species richness from
 adding 1-km<sup>2</sup> of CRP to cultivated land 
 Field "sr_p1_all_"
 is predicted proportional increase in species richness from adding
 1-km<sup>2</sup> of CRP to cultivated land Field "sr_p1_all1" is
 predicted percentage increase in species richness from adding
 1-km<sup>2</sup> of CRP to cultivated land