DOI: 10.5061/dryad.m905qfv0h
title: Effect of data source on estimates of regional bird richness in northeastern United States

description: Standardized data on large-scale and long-term patterns of species
 richness are critical for understanding the consequences of natural and
 anthropogenic changes in the environment.  The North American
 Breeding Bird Survey (BBS) is one of the largest and most widely used
 sources of such data, but so far, little is known about the degree to
 which BBS data provide accurate estimates of regional richness. Here we
 test this question by comparing estimates of regional richness based on
 BBS data with spatially and temporally matched estimates based on state
 Breeding Bird Atlases (BBA). We expected that estimates based on BBA data
 would provide a more complete (and therefore, more accurate)
 representation of regional richness due to their larger number of
 observation units and higher sampling effort within the observation units.
 Our results were only partially consistent with these predictions: while
 estimates of regional richness based on BBA data were higher than those
 based on BBS data, estimates of local richness (number of species per
 observation unit) were higher in BBS data. The latter result is attributed
 to higher land-cover heterogeneity in BBS units and higher effectiveness
 of bird detection (more species are detected per unit time).
 Interestingly, estimates of regional richness based on BBA blocks were
 higher than those based on BBS data even when differences in the number of
 observation units were controlled for. Our analysis indicates that this
 difference was due to higher compositional turnover between BBA units,
 probably due to larger differences in habitat conditions between BBA units
 and a larger number of geographically restricted species. Our overall
 results indicate that estimates of regional richness based on BBS data
 suffer from incomplete detection of a large number of rare species, and
 that corrections of these estimates based on standard extrapolation
 techniques are not sufficient to remove this bias. Future applications of
 BBS data in ecology and conservation, and in particular, applications in
 which the representation of rare species is important (e.g., those
 focusing on biodiversity conservation), should be aware of this bias, and
 should integrate BBA data whenever possible.
description: Overview This is a
 compilation of second-generation breeding bird atlas data and
 corresponding breeding bird survey data. This contains presence-absence
 breeding bird observations in 5 U.S. states: MA, MI, NY, PA, VT, sampling
 effort per sampling unit, geographic location of sampling units, and
 environmental variables per sampling unit: elevation and elevation range
 from (from SRTM), mean annual precipitation & mean summer
 temperature (from PRISM), and NLCD 2006 land-use data.
 Each row contains all observations per sampling unit, with
 additional tables containing information on sampling effort impact on
 richness, a rareness table of species per dataset, and two summary tables
 for both bird diversity and environmental variables. 
 The methods for compilation are contained in the supplementary
 information of the manuscript but also here:
 Bird data For BBA data,
 shapefiles for blocks and the data on species presences and sampling
 effort in blocks were received from the atlas coordinators. For BBS data,
 shapefiles for routes and raw species data were obtained from the Patuxent
 Wildlife Research Center
 (https://databasin.org/datasets/02fe0ebbb1b04111b0ba1579b89b7420 and
 https://www.pwrc.usgs.gov/BBS/RawData). Using ArcGIS
 Pro© 10.0, species observations were joined to respective BBS and BBA
 observation units shapefiles using the Join Table tool. For both BBA and
 BBS, a species was coded as either present (1) or absent (0). Presence in
 a sampling unit was based on codes 2, 3, or 4 in the original volunteer
 birding checklist codes (possible breeder, probable breeder, and confirmed
 breeder, respectively), and absence was based on codes 0 or 1 (not
 observed and observed but not likely breeding). Spelling inconsistencies
 of species names between BBA and BBS datasets were fixed. Species that
 needed spelling fixes included Brewer’s Blackbird, Cooper’s Hawk,
 Henslow’s Sparrow, Kirtland’s Warbler, LeConte’s Sparrow, Lincoln’s
 Sparrow, Swainson’s Thrush, Wilson’s Snipe, and Wilson’s Warbler. In
 addition, naming conventions were matched between BBS and BBA data. The
 Alder and Willow Flycatchers were lumped into Traill’s Flycatcher and
 regional races were lumped into a single species column: Dark-eyed Junco
 regional types were lumped together into one Dark-eyed Junco,
 Yellow-shafted Flicker was lumped into Northern Flicker, Saltmarsh Sparrow
 and the Saltmarsh Sharp-tailed Sparrow were lumped into Saltmarsh Sparrow,
 and the Yellow-rumped Myrtle Warbler was lumped into Myrtle Warbler
 (currently named Yellow-rumped Warbler). Three hybrid species were
 removed: Brewster's and Lawrence's Warblers and the Mallard x
 Black Duck hybrid. Established “exotic” species were included in the
 analysis since we were concerned only with detection of richness and not
 of specific species. The resultant species tables with
 sampling effort were pivoted horizontally so that every row was a sampling
 unit and each species observation was a column. This was done for each
 state using R version 3.6.2 (R© 2019, The R Foundation for Statistical
 Computing Platform) and all state tables were merged to yield one BBA and
 one BBS dataset. Following the joining of environmental variables to these
 datasets (see below), BBS and BBA data were joined using rbind.data.frame
 in R© to yield a final dataset with all species observations and
 environmental variables for each observation unit. 
 Environmental data Using
 ArcGIS Pro© 10.0, all environmental raster layers, BBA and BBS shapefiles,
 and the species observations were integrated in a common coordinate system
 (North_America Equidistant_Conic) using the Project tool. For BBS routes,
 400m buffers were drawn around each route using the Buffer tool. The
 observation unit shapefiles for all states were merged (separately for BBA
 blocks and BBS routes and 400m buffers) using the Merge tool to create a
 study-wide shapefile for each data source. Whether or not a BBA block was
 adjacent to a BBS route was determined using the Intersect tool based on a
 radius of 30m around the route buffer (to fit the NLCD map resolution).
 Area and length of the BBS route inside the proximate BBA block were also
 calculated. Mean values for annual precipitation and summer temperature,
 and mean and range for elevation, were extracted for every BBA block and
 400m buffer BBS route using Zonal Statistics as Table tool. The area of
 each land-cover type in each observation unit (BBA block and BBS buffer)
 was calculated from the NLCD layer using the Zonal Histogram
 tool.