These data document three classes of response variables: total abundance, total richness, and estimated richness for avian species in the coterminous United States. Each response variable was estimated for each year in the 2010-2014 time period and for all species, exotic species, native species, forest woodland species, grassland species, passerine species, exotic passerine species, native passerine species, forest woodland passerine species, and grassland passerine species. This data publication includes both the observed and estimated species richness for species detected using the 2016 Breeding Bird Survey routes. Estimates were calculated by COMDYN (https://www.usgs.gov/centers/pwrc/software). Also included are shapefiles containing Breeding Bird Survey (BBS) route centroids, BBS route start locations, and Bird Conservation Regions (BCR) for North America.Develop a modeling framework that will support the estimation of bird community response to environmental change stemming from shifts in climate and land use activities.

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-km2 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-km2 of CRP to cultivated land

Field "sr_p...

The Important Bird and Biodiveristy Areas (IBA) Programme is a BirdLife International Programme to conserve habitats that are important for birds. These areas are defined according to a strict set of guidelines and criteria based on the species that occur in the area. The Important Bird Areas of Southern Africa directory was first published 1998 and identified within South Africa 122 IBAs. In September 2015 a revised IBA Directory was published by BirdLife South Africa. All these IBAs were objectively determined using established and globally accepted criteria. An IBA is selected on the presence of the following bird species in a geographic area: • Bird species of global or regional conservation concern; • Assemblages of restricted-range bird species; \ • Assemblages of biome-restricted bird species; and • Concentrations of numbers of congregatory bird species. For more information see: http://www.birdlife.org.za/conservation/importantbird-areas/documents-and-downloads

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.

Methods 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.

Context

Invasive alien species have been pointed out as an important driver of biodiversity loss. Many policy responses are being developed to address this threat. Protected areas often represent and preserve hotspots of biological diversity and ensure the maintenance of ecosystem services crucial to human livelihoods. The impact of biological invasions can be particularly severe in protected areas and their occurrence and impact in such areas is an important element of the risk they pose. To address this, there is a need for data on the occurrence and extent of alien species invasions in protected areas.

Description

This dataset contains species occurrence and occupancy in protected areas of the Natura2000 network in Belgium (Special Conservation Areas sensu Habitat Directive and Special Protection Areas sensu Bird Directive). The dataset was generated using the Belgian occurrence cube at species level and the Belgian occurrence cube for non-native taxa (both containing GBIF data aggregated using Oldoni et al. 2020), the 1x1km EEA reference grid and the Natura2000 protected areas shapefiles from the European Environment Agency.

Data are grouped by protected area (SITECODE), year (year) and (infra)species (taxonKey, speciesKey). For each group, it provides the number of occurrences found in GBIF (n), the area of occupancy (aoo: number of 1 km² squares), the coverage (coverage: % of 1 km² squares), the minimum coordinateUncertaintyInMeters (min_coord_uncertainty), and the alien status (is_alien) based on the Global Register of Introduced and Invasive Species - Belgium. For infraspecific taxa in the latter, the alien status of the species is looked up and included.

The dataset is built on open science principles and intended to be completely reproducible:

• The input data are publicly available on Zenodo, with the download DOIs listed in the related identifiers of this dataset package.
• The code to process the data is publicly available and documented on GitHub.

Files

• protected_areas_species_occurrence.csv: number of occurrences (n), area of occupancy (aoo) and coverage of taxa (taxonKey) in Natura2000 areas of Belgium (SITECODE). Other columns included: speciesKey (for species is speciesKey = taxonKey), SITETYPE containing the site type of the Natura2000 area (one of A, B or C), min_coord_uncertainty with the lowest coordinate uncertainty in meters, is_alien containing the alien status (TRUE or FALSE) and remarks containing, if present, the infraspecific alien taxa whose occurrences contribute to the calculated aoo (only for species).
• protected_areas_species_info.csv: taxonomic information of taxa in protected_areas_species_occurrence.csv as retrieved from GBIF Backbone Taxonomy. Columns: taxonKey, speciesKey, scientificName, kingdom, phylum, order, class, genus, family, species, rank and includes. The latter contains the infraspecific taxa and synonyms whose occurrences contribute to the number of occurrences at species level.
• protected_areas_metadata.csv: protected area information for areas included in protected_areas_species_occurrence.csv. Columns: SITECODE as in protected_areas_species_occurrence.csv (BE*******), SITENAME containing the name of the protected area, SITETYPE as in protected_areas_species_occurrence.csv, flanders, wallonia and brussels containing whether the area is situated respectively in Flanders, Wallonia or Brussels-Capital Region (TRUE or FALSE). Field codes are in line with EEA element definitions for Natura 2000 sites.

Potential use of the dataset

Currently, there is no comprehensive reporting system for invasive alien species in Natura 2000 sites. This dataset provides a baseline as to which species occur in which protected area. We envisage this dataset can be an interesting starting point for various types of analyses on alien species in protected areas in Belgium, but that it can also be used in complement to other data on alien species in protected areas to study more general patterns. Some examples of research questions:

• Which protected areas are most invaded by alien species
• Which alien species are most distributed in protected areas and which traits do they have
• How does the proportion of alien species in protected areas change in time
• How does the occurrence/occupancy of alien species in protected areas match lists of regulated species (e.g. Union List, EPPO lists)
• To what extent can the network of protected areas contribute to providing safe refuge to native species from the impacts of invasive alien species
• How widespread are the impacts of certain alien species on protected areas

Acknowledgements

This work has been funded under the Belgian Science Policies Brain program (BelSPO BR/165/A1/TrIAS), the European Union's LIFE program (LIFE19 NAT/BE/000953 - LIFE RIPARIAS).

Hexagonal framework: There are many datasets that can go into a regional greenprint. In order for them to be used most effectively, they need to be organized in a manner that allows queries across the data. A regular hexagonal grid shapefile was chosen to aggregate the many datasets. The hexagons were 10 hectares (24.7 acres) in extent. Aggregating the data in a shapefile such as this also enables analysis using Marxan optimization software. Ecological and infrastructure data were aggregated as fields within the shapefile.Greenprint data:Greenprint data were identified through team members’ knowledge of available data,internet searches, and discussions with stakeholders and others. These were downloaded or otherwise acquired and added to the hexagonal database (see below for specific sampling methods for each greenprint element).Connectivity. Four connectivity assessments were included in the greenprint: California Essential Habitat Connectivity (CEHC; Spencer et al. 2010), Bay Area Critical Linkages (BACL; Penrod et al. 2013), a Central Coast conservation network design (Thorne; Thorne et al. 2006), and a Central Valley conservation network design (Huber; Huber et al. 2010). CEHC is a relatively coarse‐scale, statewide analysis of important connectivity areas and covers the full study area. BACL used many of the same methodologies as CEHC, but is finer‐scale. However, it does not cover San Luis Obispo and Santa Barbara counties. Thorne uses a different methodology than the previous analysis; it covers the full study area. Finally, Huber only addresses the easternmost sections of the study area. It depicts areas of connectivity potentially linking the Central Coast and Central Valley ecoregions. In addition to these terrestrial datasets, a fish passage barrier was included in order to address aquatic connectivity issues. The California Fish Passage Assessment Database Project (PAD) documents the location and other details of barriers to fish passage on waterways across California. Hexagons were attributed as belonging to these datasets if their centroid was located within the boundary.Critical Habitat. U.S. Fish and Wildlife Service (USFWS) has delineated Critical Habitat for 29 federally‐listed species within the study area (Table 1). This is less than one third of the listed species in the study area. Critical Habitat data are available for both terrestrial and aquatic species, and taxa include plants, mammals, birds, reptiles and amphibians, fish, and invertebrates. Hexagons were attributed as belonging to these datasets if their centroid was located within the boundary.ACE II. California Department of Fish and Wildlife (CDFW) developed a database with numerous biodiversity measurements for the state of California. These Areas of Conservation Emphasis (ACE II) is a hexagonal dataset measuring species richness, rarity, and other biodiversity metrics. Weighted rarity scores from ACE II were assigned to hexagons based on centroid location.Other conservation priorities. The Nature Conservancy’s ecoregional priorities and Audubon society’s Important Bird Areas (IBA) were included as other conservation priorities covering the full study region.Land cover. Unfortunately, there is no single, fine scale land cover dataset covering the full study region. Land cover information was combined from several to create an overall land cover dataset. CalVeg was used as the base layer. In areas that were not covered by CalVeg, we used a combination of Landfire land cover data and FRAP land cover data. In addition, the Nature Conservancy provided fine scale land cover data for the Salinas and San Benito river riparian areas.Habitat models. The team compiled existing spatial data on the locations of state and federally‐listed species (threatened and endangered). These were selected from the statewide California Natural Diversity Database (CNDDB). Points selected were those that were listed as “Presumed Extant” and from 1980‐present. Each of these points was buffered by two and four miles. Using the California Wildlife Habitats Relationships model (CWHR), land cover types scored as “High” for use by that species were selected from within the buffered points.Watersheds. Hexes were attributed for their inclusion in HUC 8, 10, and 12 digit watersheds.Counties. Hexes were attributed for their inclusion in the six counties in the planning region.Existing conservation. California Protected Areas Database (CPAD) and the National Conservation Easement Database (NCED) were used to identify existing conservation lands.Local datasets. A total of 54 local, site‐specific datasets were included in the database. Examples include City of Santa Barbara biological features, Santa Cruz County blueprint, and mountain lion collar data.

This Conservation Site shapefile contains spatial and other information of over 750 sites of conservation, scientific, and ecological interest distributed across all of Idaho’s landscapes. Sites represent a variety of ecosystems and typically have intact ecological processes, exemplary native plant communities, unique geologic processes, or important habitat for species (e.g., Important Bird Areas). Conservation site boundaries often include most of the land area necessary to maintain the ecological processes of interest. For most areas, site boundaries also include a variable width buffer, but do not necessarily include an entire watershed. In some situations site boundaries nearly match those of a special management area, such as IDFG Wildlife Management Area (WMA), Research Natural Area (RNA), or USFWS National Wildlife Refuge (NWR). Corresponding descriptions for each site polygon in this shapefile describe the site, its location, size, design considerations, biological or other natural significance, ecological processes and functions, ecological condition and integrity, conservation or protection status, stewardship concerns, and known occurrences of communities and rare species. Approximately 475 of the sites in the Conservation Site shapefile contain significant wetland or riparian habitat. The majority of these sites were identified between 1996 and 2007, when IDFG completed wetland inventories across approximately two-thirds of Idaho’s river basins (wetland conservation strategies for most basins are available at https://fishandgame.idaho.gov/content/page/wetlands-publications-idaho-natural-heritage-program). These projects involved field surveys of wetland and riparian areas to document their condition, function, and biodiversity value. Field surveys were supplemented with interpretation of aerial imagery and National Wetland Inventory maps. Wetland sites were mapped relatively broadly, but typically finer than a HUC 12 scale (i.e., they include adjacent upland buffers). Wetland sites were typically classifiedaccording to habitat diversity, biodiversity significance, condition, and landscape context or viability into these conservation priority categories: Class I—highest priority; relatively undisturbed; often support unique or rare wetland types that are very sensitive to disturbance; often supports high concentrations of globally and state rare plant or animal species, and high diversity of common plant associations in excellent ecological condition; provide a high level of diverse wetland functions (i.e., hydrologic processes, water quality, etc. are intact); impacts should be avoided as these sites may be impossible to replace within a human lifetime; alteration may result in significant degradation that is not easily mitigated or restored; conservation efforts should focus on full protection including maintenance of hydrologic regimes. Class II—second highest priority; differentiated from Class I sites based on condition or biological significance; often support globally or state rare plant or animal species and/or contains rare or unique wetland types; human influences are apparent (i.e., portions of wetland are in excellent condition, however drier, accessible sites are impacted); moderate to high diversity of common plant associations in good to excellent ecological condition; wetland functions are intact; impacts and hydrologic modification should be avoided; mitigation and restoration may be possible, but may involve significant investments to be successful; improved stewardship may be necessary to alleviate low level impacts (e.g., improper livestock grazing).Reference—support common plant associations in good ecological condition, contain rare or unique wetland types in fair condition, and/or support state rare plant or animal species; human impacts are present, but functions are mostly intact; these wetlands may be the best remaining examples in areas of relatively high human influence and are therefore sometimes useful for monitoring the progress of restoration or enhancement of similar wetland types; they may also serve as donor sites for plant material used in restoration or enhancement; improved stewardship is often needed to maintain or improve function and condition.Habitat—provide moderate to outstanding wetland functions, such as food chain support, maintenance of important (and scarce) plant and wildlife habitat, or water quality support; provide numerous ecological services, although ecological condition is often impaired due to human activities; restoration, enhancement, and/or management may be necessary to improve or maintain wetland functions and condition; may have high potential for designation as, or expansion of, existing wildlife refuges or publically managed areas.Restoration Opportunity—After 2005, the Restoration Opportunity classification was added; currently support, or has the high likelihood of supporting, at least several important or rare (at local watershed scale) wetland functions and values, such as habitat for common and/or rare species, unique wetland types, or other locally important functions (e.g., water quality), but where human disturbance has notably decreased all functions and ecological condition; however, functions and condition are restorable with moderate levels of investment and coordination and a mix of public and private ownership (with willing landowners); often in areas with completed watershed or water quality management or improvement plans.PurposeConservation Sites are intended to help guide conservation planning, restoration, and implementation of protective measures using the full range of existing conservation tools, ranging from governmental programs to voluntary incentives. For example, Class I and Class II federal lands could be designated as RNAs, Special Interest Areas, Areas of Critical Environmental Concern, or Wildlife Refuge. Class I and Class II private lands could be conserved establishment of conservation easements, acquisition, and/or legal management agreements. Avoidance of impacts to these wetlands (and mitigation if unavoidable) may be required when obtaining permits under S. 404 of the Clean Water Act. Reference sites may rely on voluntary conservation, legal protection, avoidance of impacts, and/or application of Best Management Practices by the current landowner or manager. Habitat sites can be targeted for voluntary protection through incentives to private landowners. Reference and Habitat sites often benefit from improved stewardship, active enhancement, and application of Best Management Practices by landowners.