Ecological Archives --D1
Michael F. Small, Joseph A. Veech, and Jennifer L. R. Jensen. 2012. Local landscape composition and configuration around North American Breeding Bird Survey routes. Ecology 93:2298. http://dx.doi.org/10.1890/12-0724.1
Wildlife monitoring programs, whether point counts or transect surveys, usually sample only a subset of the overall area for which inferences about populations are made. When designing surveys, it is critical that the surveyed area adequately represents the landscape characteristics of the greater area it is embedded in. Failure to accomplish this raises questions about the inferences made regarding trends in the populations purported to be sampled. Also, some large scale monitoring programs that sample large geographic areas often use roads as transects. The appeal of roads to survey large numbers of populations over broad extents relates to their ease of access, relative permanence, and the fact that they allow large areas to be traversed quickly. However, land cover composition immediately adjacent to roads may be different from areas further from roads, depending on the cover type (Veech et al. 2012).
The North American Breeding Bird Survey (BBS) was established in 1966 by the United States Geological Survey (USGS) Biological Resources Division in conjunction with the Canadian Wildlife Service. The BBS consists of more than 4,100 routes surveyed annually along roads in the continental USA and southern Canada (most of these routes are in the USA). The BBS is one of the most spatially and temporally extensive wildlife monitoring programs in the world. Each spring or early summer, BBS observers drive the 39.2 km routes and stop every 800 m to record the birds seen and heard within 400 m during a 3 minute observation period (Robbins et al. 1986, Sauer et al. 1994, Sauer et al. 2011). Breeding bird survey data have been widely used for a variety of purposes ranging from pure ecological/biological research to applied avian conservation; to date, at least 500 papers have been published that use BBS data (Pardieck and Ziolkowski 2009).
One particularly extensive use of BBS data in conservation involves coupling BBS routes with Bird Conservation Regions (BCRs). Bird Conservation Regions were developed network by the North American Bird Conservation Initiative (NABCI) and represents an ecologically distinct region with similar bird communities, habitats, and resource management issues. There are 30 BCRs located completely or partially within the lower 48 states. The BCR network is intended to facilitate planning for regional-level conservation of birds (U.S. NABCI 2000, Ruth et al. 2003, Sauer et al. 2003, Brennan and Kuvlesky 2005, Wells 2010). In addition, use of the BCR network is becoming more common in the formal statistical analysis of BBS data in published research papers (e.g., Sauer et al. 2003, Karanth et al 2006, Twedt et al. 2007, Tirpak et al. 2009, Jones-Farrand et al. 2011). Given the increasing use of BBS data in basic research and applied conservation, researchers and others could greatly benefit from having easily-accessible data on the composition and spatial configuration of land cover surrounding BBS routes (Veech et al. 2012). Previous studies have used GIS software to incorporate land cover data into analyses of BBS data (e.g., Vance et al. 2003, Schulte et al. 2005, Fearer et al. 2007, Pidgeon et al. 2007, Twedt et al. 2007, Nielson et al. 2008, Tirpak et al. 2009, Fleskes and Gregory 2010, Forcey et al. 2011, Jones-Farrand et al. 2011) and the practice could become more widespread if the time-consuming and technical GIS computational steps could be bypassed. Hence, our motivation in producing this data set is to make a set of consistent National Land Cover Database-derived data sets of landscape metrics available to a broader audience of researchers.
An excellent source of continent-wide land cover data is available from the Multi-Resolution Land Characteristics (MRLC) consortium, a group of federal agencies that compile land cover information for the continental USA from Landsat 5 Thematic Mapper (TM) and Landsat 7 Enchanced Thematic Mapper (ETM+) satellite imagery. The MRLC has generated a land cover map of the continental USA at a 30 × 30 m pixel spatial resolution. This map and the associated land cover data from the National Land Cover Database (NLCD; Fry et al. 2011) was most recently updated in 2006 (two previous datasets were made available in 1992 and 2001) and released several years later. National Land Cover Database 2006 includes 16 land cover categories that comprise the land cover of the conterminous USA. However, in a few instances pixels were categorized as "NO DATA". For whole BCRs this is most likely the result of persistent cloud cover. For individual routes this is probably a combination of persistent cloud cover and the fact that some route buffers extend into Canada or Mexico outside the extent of NLCD data. The image processing and interpretation phases in creating NLCD databases have been thoroughly detailed (Homer et al. 2004, 2007) and tests of the accuracy of the database have been conducted (Wickham et al. 2010).
As part of multiple studies, we are evaluating how well BBS route-landscapes (0.4 km buffer or sample area along a route) represent the land cover composition of larger landscapes (e.g., 2, 5, and 10 km buffers) that they are embedded in. Previously, we have referred to the comparison of a smaller "daughter" landscape to a larger "mother" landscape as an assessment of local representativeness (Veech et al 2012). Our analyses are based on the NLCD 2006 data. As a result of the analyses, we have derived a large data set of pixel counts for 16 NLCD 2006 land cover types at 30 x 30 m resolution for buffer distances of 0.2, 0.4, 1, 2, 5, and 10 km on either side of a BBS route. We also calculated three landscape configuration metrics (patch density, largest patch index, and aggregation index) for buffer distances of 0.4, 2, 5, and 10 km on either side of each BBS route. We have data for 3,980 BBS routes organized by BCR. We have also determined the pixel counts of the cover types for each BCR as a whole.
Although our initial use of these data dealt with evaluating survey schemes, they have further and equally important implications. The National Land Cover Database (whether it be the 1992, 2001, or 2006 version) has been used to evaluate habitat preference of birds (Fuller et al. 2005, Collins et al. 2010) as well as habitat change as it relates to bird guilds (Radovic et al. 2011). Additionally, NLCD data have been used in avian conservation (Pennington et al. 2008), modeling of distribution patterns (Virkkala et al. 2005) and assessment of climate change (Barbet-Massin et al. 2012).
Until now, no one has linked all the BBS routes to any national land cover database in a way that makes the land cover data easily accessible and useful. Given that most bird species are affected by land cover composition and configuration at multiple spatial scales (Wiens and Rotenberry 1981, Newton 1998, Coppedge et al. 2001, Donovan and Flather 2002, Veech 2006, Ribic et al. 2009, Rotenberry and Wiens 2009) we believe that it is definitely worthwhile (sometimes imperative) to include land cover data in many analyses of BBS data. The strategic planning document for the BBS calls for a greater recognition of the importance of habitat availability as it affects BBS data and greater integration of land cover data in virtually all studies that use BBS data (USGS 2007). Indeed, all types of bird surveys benefit greatly by quantifying the habitat or land cover composition of the surveyed area (Bibby and Buckland 1987, Hutto and Young 2002, Diefenbach et al. 2003, Norvell et al. 2003, Gregory et al. 2004). All of this points to the need to link BBS data to standardized, relatively accurate, and extensive land cover data such as the NLCD.
At this point a cautionary note is necessary. First, the data derive from remote-sensing imagery collected between 2005 and 2007 only, although made available in 2010. Consequently, applicability of the data to different time periods should be considered carefully by the user. Consideration should also be given to the number of land cover classes (16) and the degree of resolution (30 × 30 m pixel size) and if these are appropriate for the intended use. Misclassification of similar cover types or feature attributes can occur during the image processing phase of any land characterization database. With regard to error in NLCD data, users of the data should consult Wickham et al. (2010). We also recommend Gallant (2009) for a critical discussion of the use of remotely-sensed land cover data in ecological research. Ultimately, it is the responsibility of the person utilizing the data to ensure they are being used judiciously.
Class I. Data set descriptors
A. Data set identity: Breeding bird survey route land cover: Pixel counts of 16 NLCD 2006 land cover categories and landscape configuration metrics at various buffer distances around breeding bird survey routes.
B. Data set description
Data consist of 10 comma-delimited text files; six containing pixel counts of 16 NLCD 2006 land cover categories at 0.2, 0.4, 1, 2, 5, and 10 km buffers around BBS survey routes and three containing landscape configuration metrics for patch density, largest patch index, and aggregation index. Files are labeled as:
There is also one comma-delimited text file containing pixel counts of 16 NLCD 2006 land cover categories for each of 30 BCRs as a whole (All_BCRs_whole.txt) and one text file (var_descrip.txt) that provides descriptions of all column headings in the other files. Note that landscape metrics pertaining to entire BCRs are not provided as the FRAGSTATS program is unable to process such extensive, large landscape files.
Michael F. Small
Wildlife Ecology Program
Department of Biology
Texas State University - San Marcos
San Marcos, TX 78666
Joseph A. Veech
Wildlife Ecology Program
Department of Biology
Texas State University - San Marcos
San Marcos, TX 78666
Jennifer L. R. Jensen
Texas Center for Geographic Information Science
Department of Geography
Texas State University - San Marcos
San Marcos, TX 78666
Abstract: Large-scale monitoring of avian species is often conducted by observers driving rural roads and highways. Surveys along roads allow for easy and reliable access over time and the ability to survey large geographic areas in a relatively short period of time. However, if the landscape directly adjacent to these roadside transects is not representative of the greater landscape inhabited by the bird species, then estimates of abundance along the route may not be accurate for the greater landscape. Moreover, inferences regarding abundance, distribution, population trend, and habitat use in the greater landscape may not be valid. Using land cover data from the National Land Cover Database (NLCD), we recently conducted an assessment that revealed a satisfactorily high level of similarity in land cover composition between landscapes immediately adjacent to North American Breeding Bird Survey (BBS) routes (buffer distance of 0.4 km on either side of route) and the larger landscapes (buffer distance of 10 km) in which they are embedded. Thus, local representativeness was high as was regional representativeness (a comparison of similarity between combined local landscapes and a larger region containing the routes). Motivated by this positive outcome, we now provide a data set of land cover composition in six and land cover configuration in four different-sized landscapes surrounding each of 3980 BBS routes. We used a geographic information system (GIS) to produce 30 × 30 m raster maps of NLCD 2006 land cover types for each route in each of 30 Bird Conservation Regions. For each BBS route, the data set consists of pixel counts for each of 16 land cover types. These pixel counts are suitable for conversion into proportions for comparison of landscapes of different sizes. Additionally, we provide three measures of landscape configuration for each BBS route. The data set will be useful to researchers analyzing BBS bird data as it represents the first time that a vast majority of BBS routes have been individually linked to a land cover database.
D. Key words: Bird Conservation Region; bird survey; landscape; National Land Cover Database; North American Breeding Bird Survey; road survey.
Class II. Research origin descriptors
A. Overall project description
Identity: Land cover data are from the Multi-Resolution Land Characterization consortium NLCD 2006. Breeding bird survey routes are from the North American Breeding Bird Survey. Bird Conservation Regions are from the North American Bird Conservation Initiative.
Originator: Original concept and rationale for this project are from Michael F. Small and Joseph A. Veech. Jennifer L. R. Jensen contributed GIS expertise.
Period of Study: Land cover data are derived from Landsat 5 TM and Landsat 7 ETM+ satellite images acquired between February 11, 2005 and October 03, 2007.
Objectives: Our primary objective in this report is to provide land cover composition data for six different-sized landscapes surrounding routes of the North American Breeding Bird Survey.
B. Specific subproject description
Site description: Conterminous United States.
Data Sources, Software, and Map Creation
We used geographic information systems software (ArcGIS 10.0, Environmental Systems Research Institute, Redlands, CA) to create a map of the conterminous USA from a single raster file download of NLCD 2006 data in ERDAS IMAGINE (Intergraph Corporation, Madison, AL) image format (available at http://earthexplorer.usgs.gov/). The NLCD image data has a 30 × 30 m pixel resolution and each pixel is assigned to one of the NLCD land cover categories. We then added a shapefile layer to the map of BCRs (available at http://www.nationalatlas.gov/). We next used the Spatial Analyst tool "clip management" in ArcToolbox to create separate raster land cover map layers of each BCR. After each new raster file was created we used the "build attribute table" function in ArcGIS to update the attribute table associated with each image.
We added a shapefile layer of BBS routes to the map file (available at http://www.nationalatlas.gov/) (each BBS route is "assigned" to a particular BCR). We then made sure all layer files (including rasters) were displayed in meters using "USA_Contiguous_Albers_Equal_Area_Conic_USGS_version" projection.
To extract pixel count land cover data for each route we used the "buffer (analysis)" function in ArcToolbox to create buffers around each BBS route at distances of 0.2, 0.4, 1, 2, 5, and 10 km. We then used "dissolve" in Data Management > Generalization window of ArcToolbox to eliminate any overlap caused by non-linear BBS routes. With this step, we avoided all double-counting of pixels that would have occurred had overlapped areas not been dissolved. We obtained pixel counts for each of the 16 NLCD land cover categories for each of the buffer distances by using the "isectpolyrst" and "export.csv" commands in the Geospatial Modeling Environment (Beyer 2012) software package for ArcGIS 10.x (http://www.spatialecology.com/). Ultimately this provided us with 382,080 unique pixel counts (3,980 BBS routes × 16 land cover categories × 6 buffer distances). These counts are the dataset provided in this report.
For land cover configuration metrics, each BBS route was buffered by 0.4, 2, 5 and 10 km based on the aforementioned objectives. Then, each buffered route polygon was used to clip the NLCD base map and generate individual land cover rasters for all routes and buffer extents. The individual land cover rasters corresponding to each buffered route were used as landscape files in FRAGSTATS v3.4 (McGarigal et al. 2002). The FRAGSTATS software program was designed to calculate landscape metrics based on thematic map patterns. We selected three landscape metrics to evaluate landscape representativeness based on configuration for this study. None of the metrics is adversely sensitive to buffer extent or size of the landscape.
Using an 8-neighbor patch rule, the following metrics were calculated at the landscape level: patch density (PD), largest patch index (LPI), and aggregation index (AI). PD represents the number of patches on the landscape divided by the total landscape area, expressed per km2. Since PD is expressed on a per unit area basis, it is a useful metric for comparisons of varying landscape extents. LPI represents the percent of the landscape comprised by the largest patch. High values of LPI indicate landscape homogeneity in that a single large contiguous patch dominates the landscape. AI is based on whether adjacent cells (pixels) represent the same cover type. These are referred to as "like adjacencies". The first step is to calculate the ratio of observed like adjacencies to the maximum possible number of like adjacencies that would exist if all pixels of the given cover type consisted of one single compact patch. This ratio is then multiplied by the proportion of the cover type in the landscape (buffer). Ratios for each cover type are calculated and summed. The summation is then multiplied by 100 so that AI is expressed as a percentage. High values of AI indicate aggregation of pixels of the same cover type whereas low values indicate dispersion and interspersion of different cover types. More detailed descriptions of the metrics can be obtained from McGarigal et al (2002).
Again, a word of caution is warranted at this point. While all BBS routes are approximately 39.2 km in length, some routes "appear" longer because they contain portions that are not surveyed for various reasons, including traffic and noise. However, the GIS shapefile does not reflect these un-surveyed portions as gaps in the route, but reflect each route as continuous. Consequently, many routes are depicted as substantially longer than 39.2 km (one route is depicted as > 80 km) and our buffer areas, and subsequent pixel counts, are based on buffering of the routes as represented by the shapefile. Thus, for some routes our data include small portions of the route where birds were not surveyed. The data we provide, however, is based on the best information available for route paths.
Class III. Data set status and accessibility
Latest update: April 2012
Metadata status: Metadata are complete. Full metadata for all data are provided in the file Data_paper.txt.
Data verification: Numerous verification procedures were taken in the compilation of these data (see research methods, above) including screening of data for potential typographical errors and improbable values.
Storage location and medium: Ecological Society of America data archives, URL published in each issue of its journals.
Michael F. Small
Wildlife Ecology Program
Department of Biology
Texas State University - San Marcos
San Marcos, Texas, 78666
Email: email@example.com (or alternatively: firstname.lastname@example.org).
Copyright restrictions: None.
Proprietary restrictions: None.
Class IV. Data structural descriptors
A. Data Set File
Identity and size:
(385 kb, md5: c7a26726979a94178a59d27af3ea99d2)
(399 kb, md5: fb8718b64b55f6ff7fd5356b60a76b53)
(418 kb, md5: 08340cb8c3ec1833b183ffe5d4119a30)
(438 kb, md5: 5e1c1f939583a6edccbd0745496e9ace)
(465 kb, md5: 952a0636769f2b1a167d7481085a9967)
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(4 kb, md5: 1ee18e923fb5add18df833da7bab56d3)
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(6 kb, md5: f59abc46cd443f50fdfdb1ca08efafdf)
Format and storage mode: Comma-delimited text files.
Header information: The metadata file var_descrip.txt contains all explanatory header information for the main data files.
B. Variable information
The metadata file var_descrip.txt contains all explanatory header information for the main data files.
While GIS data sets (ArcInfo coverages or grids) are not included with this report, the projection information may be of interest:
Central Meridian: -96
Standard Parallel 1: 29.5
Standard Parallel 2: 45.5
Latitude of Origin: 23
False Easting: 0
False Northing: 0
Class V. Supplemental descriptors
A. Computer programs and data processing algorithms: GIS data were derived using the ArcInfo© version 10.0 suite of software, Geospatial Modeling Environment for ArcGIS, and FRAGSTATS v. 3.4. Any use of trade, product, or firm names is for descriptive purposes only and does not imply endorsement.
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