DOI: 10.5061/dryad.vt4b8gtqz
title: Protected area, easement, and rental contract data reveal five communities of land protection in the United States

description: Land protection efforts represent large societal investments and are
 critical to biodiversity conservation. Land protection involves a complex
 mosaic of areas managed by multiple organizations, using a variety of
 mechanisms to achieve different levels of protection. We develop an
 approach to synthesize, describe, and map this land protection diversity
 over large spatial scales. We use cluster analysis to find distinct
 “communities” of land protection based on the organizations involved, the
 strictness of land protection, and the protection mechanisms used. We also
 associate identified land protection communities with socioenvironmental
 variables. Applying these methods to describe land protection communities
 in counties across the coterminous United States (US), we recognize five
 different land protection communities. Two land protection communities
 occur in areas with low human population size at higher elevations and
 include a large amount of protected land primarily under federal
 management. These two community types are differentiated from one another
 by the particular federal agencies involved, the relative contributions of
 smaller actors, and the amount of protection by designations vs.
 conservation easements or covenants. Three remaining land protection
 communities have less overall protection. Land in one community is
 primarily protected by federally managed rental contracts and government
 managed easements; another is managed by a diversity of non-federal actors
 through fee-ownership and easements; and the third stands out for having
 the lowest amount of formally recorded protection overall. High elevation
 and poor quality soils are over-represented in US protected lands. Rental
 contracts help fill in gaps in counties with high productivity soil while
 the US Fish and Wildlife Service fills in gaps in low-elevation counties.
 Counties with large numbers of threatened species have more and stricter
 protection, particularly by regional entities like water management
 districts. The ability to synthesize and map land protection communities
 can help conservation planners tailor interventions to local contexts,
 position local agencies to approach collaborations more strategically, and
 suggest new hypotheses for researchers regarding interactions among
 different protection mechanisms.
description: The following is from the Methods
 section of the related journal article. Please see the article and its
 accompanying appendix to read more about these
 data. We considered the spatial
 configuration of land protection in the coterminous US. Specifically, we
 used all 3108 US counties as our units of analysis (median county area in
 coterminous US = 1670 km<sup>2</sup>). While other choices of
 spatial unit would have been possible, counties provide meaningful spatial
 units for many smaller conservation actors, a convenient reporting unit
 for relevant socioenvironmental data, and a large enough area to encompass
 a range of conservation actors. Protected area data Protected area data were obtained
 from the PAD-US 2.0 (US Geological Survey Gap Analysis Project 2018). Data
 for lands managed by the Bureau of Indian Affairs were collected from
 PAD-US 1.4 (US Geological Survey Gap Analysis Project 2016) because those
 data are absent from PAD-US 2.0. Data for rental contract lands managed by
 the USDA Farm Service Agency (FSA) under the Conservation Reserve Program
 in 2016 were collected from the USDA Conservation Reserve Program
 Statistics (US Department of Agriculture Farm Services Agency 2018). All
 easement data were contained in PAD-US 2.0, which gathered its data from
 the National Conservation Easement Database in February 2018. We note that
 this database has been updated since February 2018 to include data missing
 from our analysis. Just between April 2019 and September 2020, the
 hectares listed under easement in the National Conservation Easement
 Database grew by 32%, or 3.2 million ha. We consider protected areas along a
 continuum, from those managed strictly for biodiversity outcomes (GAP 1
 and 2) on one end to those for which conservation is not a primary
 objective (GAP 4) or for which protection is temporary on the other (not
 given GAP status). GAP 3 lands are multi-use lands with mixed conservation
 and social objectives. Only lands under GAP 1, 2, and 3 status would be
 considered “protected areas” under the IUCN definition (Dudley et al.
 2013). GAP 4 protection and protection under temporary rental contracts,
 however, often also support conservation objectives and might be
 classified as “other effective area-based conservation measures”
 (IUCN-WCPA 2019). With a few adjustments, we used the
 categories defined by PAD-US 2.0 to describe land protection by strictness
 of protection, managing agency, and protection mechanism (Table 1). Land
 protection agencies were placed in groups according to PAD-US “Agency
 Type” categories: federal (FED), state, regional districts, city and
 county governments (hereafter local governments), non-governmental
 organizations (NGO), Native American tribes (hereafter tribes), private
 entities, and unknown agencies. When an agency was the fee-owner, easement
 holder, and/or designating agency for more than 10 million ha of land, we
 specified the individual agency by name, including the US Fish and
 Wildlife Service (FWS), the Bureau of Land Management (BLM), the US Forest
 Service (USFS), the Bureau of Indian Affairs (BIA), the US Department of
 Agriculture Farm Services Agency (FSA), and the National Park Service
 (NPS). These were all federal agencies. We combined the PAD-US “joint”
 agency category (&lt; 0.5% of protected lands) with the unknown agency
 category (0.13% of protected lands). We added a “no reported protection”
 category to describe the area of a county not covered by any recorded
 protection (i.e., not recorded by PAD-US or FSA). These lands are likely
 to be managed by nonfederal actors, especially private entities, local
 governments, and small NGOs, for whom records are less complete.
 We retained spatial overlaps
 in protected area data. For example, a portion of the Beaverbrook
 Watershed in Clear Creek County, Colorado is fee-owned by the US Forest
 Service (GAP 3), is designated as a watershed protection area by the
 county (GAP 3) and is protected by an easement held by an NGO (GAP 2).
 Tiering of conservation activity, with multiple actors involved in
 protection on the same land, is common in other countries as well
 (Eigenbrod et al. 2010, Scullion et al. 2014) and provides additional
 information about the conservation community in an area. Retaining
 overlapping protection types means that the sum of the area covered by
 different protected area types in a county can add up to more than the
 total area of the county in our dataset. We identified 116 protected area
 types for our analysis, where a protected area type is a unique
 combination of strictness, managing agency, and protection mechanism (see
 Appendix S1: Table S1). There are only 116 types because several potential
 combinations of these aspects do not occur in the data. Several other
 combinations exist but are so uncommon (present in &lt; 0.13% of
 counties) that they were also excluded and removed from county totals.
 Similarly rich complexes of land protection activities have been
 documented elsewhere (Shwartz et al. 2017, Donald et al. 2019). Although
 some protected areas in our dataset could have been “slivers”, or
 artifacts of geoprocessing errors, most are likely to represent real
 protected areas (Baldwin and Fouch 2018). One benefit of the update from
 PAD-US 1.4 to 2.0 is a reduction in the number of slivers (https://www.sciencebase.gov/ ). To control for variations in county area, we focus on the relative abundance of each protected area type. In community ecology, relative abundance is a measure of how common or rare a species (or, in our case, protection type) is relative to other types in a given location (Hubbell 2001). Socioenvironmental variables Socioenvironmental variables were also summarized by county and included average elevation, average soil productivity, state population density in 1900, county population density in 2010, number of IUCN-listed threatened species whose range overlaps with a county, median household income in 2018, and the percentage of the adult population over age 25 with a bachelor’s degree (five year average from 2013-2017). Spatial variables included county size, longitude, and latitude. Details about how we put together our dataset are in Appendix S1: Table S2. A description of correlations among socioenvironmental predictors can be found in Appendix S1: Fig. S1.  
description: Description of each
 column This is a brief summary of columns. The README
 file contains column by column descriptions. A
 county_fips_code: B county_name: The name of the county
 as of 2016 (including change of Shannon County to Oglala Lakota). Many
 counties of the same name are found in different states.
 C state_name: The name of the state in which the county
 resides.  D-DO [116 columns]: Hectares of each
 protected area type in a county, where protected area type is a unique
 combination of strictness (GAP status), agency, and mechanism. (see Table
 1 and Appendix S1: Table 1 for more details) <br>
 DQ-DU [5 columns]: cluster membership scores given a 5 cluster partition
 based on fuzzy clustering (see article)DP ha_total_area_of_county: size of
 the county in hectares DV dominant_cluster: The cluster
 for which a county has the highest cluster membership score.
 DW-EF [10 columns]: socioenvironmental variables by county (see
 Appendix S1: Table S2 for descriptions and sources)