Mapping the Best Farmland to Rebuild America’s Longleaf Pine Corridors

America’s great pine forest is broken. Longleaf pine (Pinus palustris) once blanketed an astonishing sweep of the southeastern United States, stretching from east Texas to southern Virginia and sheltering hundreds of unique plants and animals, many of which live nowhere else on Earth. Two centuries of deforestation for agriculture, relentless over-harvesting, and sprawling urban development have shredded that vast ecosystem into scattered fragments, leaving the species that depend on it increasingly isolated. Now, a team of researchers at the University of Georgia and Clemson University has developed a way to find precisely where replanting efforts would do the most good, turning a sprawling conservation challenge into a solvable mapping problem.

The new study, published in the journal Environmental Management, focuses on the Conservation Reserve Program, or CRP, a federally funded initiative administered by the United States Department of Agriculture’s Farm Service Agency. Under CRP, farmers agree to adopt conservation practices on their land, such as establishing wildlife habitat, riparian buffers, or tree plantings, and in exchange receive annual payments and cost-share assistance. Contracts run 10 to 15 years, and a field can remain enrolled for up to 30 years. Across the Southeast, many producers have used the program to plant longleaf pine, supported by regionwide initiatives from USDA agencies. The problem, the researchers argue, is that enrollment is often opportunistic, driven by which landowners happen to sign up rather than by where new forest would deliver the greatest environmental benefit at the landscape scale.

Benjamin C. Townsend, Nathan P. Nibbelink, and Puneet Dwivedi set out to change that by building a spatial prioritization approach that identifies future CRP opportunities capable of improving habitat connectivity for longleaf pine-associated wildlife. Their logic is elegant: rather than asking simply where longleaf pine could grow, they asked where planting it would most reduce the cost that animals pay to move through the landscape. Connectivity, in the language of landscape ecology, describes the ease with which wildlife can travel between habitat patches. When forests are fragmented, animals must cross hostile terrain, and the harder that journey becomes, the more isolated populations grow, with consequences that include reduced species richness, abundance, and genetic diversity.

The team’s workflow began with the raw geography of American agriculture. Using the USDA National Agricultural Statistics Service’s Crop Sequence Boundaries dataset, which algorithmically delineates individual crop fields across the contiguous United States, they screened every field in the South Atlantic Gulf region for CRP eligibility. Following the rules laid out in the 2018 Farm Bill and its extensions, a field qualified if it had been planted in commodity crops for a sufficient share of the preceding years. They then filtered those eligible fields for longleaf pine suitability using two independent datasets: LANDFIRE’s Environmental Site Potential, which maps the vegetation best fitted to each pixel’s biophysical profile, and the Florida Natural Areas Inventory’s Longleaf Sustainability Analysis, a Maxent-based model built from existing longleaf occurrences and variables covering climate, fire history, soils, and vegetation type. Only fields that passed both tests survived the cut.

Suitability alone was not enough. For restoration to genuinely improve connectivity, existing longleaf pine must already be present nearby. The researchers drew a 3-kilometer buffer around each candidate field, a distance chosen because it approximates the average juvenile dispersal range of the keystone, threatened, and culturally important species that inhabit longleaf ecosystems, from the gopher tortoise to the red-cockaded woodpecker. If at least 10 percent of that buffer, roughly 280 hectares, was already longleaf pine, the field was flagged as a prime opportunity. The result was a shortlist of 3,103 agricultural fields that are CRP eligible, suitable for longleaf pine, and positioned to stitch fragmented habitat back together.

The heart of the method lies in how the team measured the benefit of each field. They constructed resistance layers, raster maps in which every pixel carries a value representing how difficult it is for wildlife to cross. Longleaf pine received the lowest resistance value of 1, other pine species a modest 5, non-pine natural vegetation such as wetlands and hardwood forests a value of 10, agriculture 100, and developed land, roads, quarries, and open water the punishing maximum of 1,000. These values, adapted from prior connectivity studies, capture the relative permeability of each land cover type rather than absolute movement costs, which remain unknown for most species. For every candidate field, the researchers then built two cost surfaces: one representing the landscape as it stands today, and one simulating the field’s conversion to longleaf pine.

To evaluate connectivity from every direction rather than between specific patches, the team employed an omnidirectional approach. Fifty nodes were placed at even spacing around the ring of each field’s 3-kilometer buffer, and least-cost paths, calculated with Dijkstra’s algorithm through the gdistance package in R, were computed for all 1,225 possible node pairs. Averaging these cost surfaces yielded a single map of mean movement cost for each pixel from any direction. The difference between the pre-CRP and post-CRP scenarios produced a measure the authors call uplift: the absolute reduction in movement cost that planting longleaf pine on that field would deliver. The higher the uplift, the greater the potential connectivity gain.

The geographic results are striking. High-uplift opportunities cluster in the Central Florida Ridges and Uplands, the Florida Panhandle, and the Carolina Sandhills, with Florida alone hosting 1,283 of the identified fields, followed by South Carolina with 808 and North Carolina with 796. At the county level, Aiken and Lexington in South Carolina and Levy, Okaloosa, and Santa Rosa in Florida each offer more than 200 opportunities. Central Florida’s Marion and Levy counties posted the highest median uplifts, at 2,598.7 and 904.6 respectively, while the Ocklawaha, Withlacoochee South, and Waccasassa watersheds emerged as connectivity hotspots. Crucially, many of these opportunities overlap the ranges of multiple focal species, including the federally threatened frosted flatwoods salamander, meaning a single well-placed field can benefit a wide suite of wildlife.

The statistical analysis revealed a counterintuitive pattern with real strategic value. In a linear regression explaining uplift, all five landscape variables proved significant, together accounting for 46 percent of the variation. Larger fields delivered more uplift, with each percentage increase in field size raising uplift by 1.4 percent. But the strongest driver was the presence of high-resistance land: every one-unit increase in the share of roads and developed areas within the buffer boosted uplift by 7 percent. Fields surrounded by clustered longleaf pine and aggregated land cover also performed better, with patch cohesion and contagion both showing positive relationships. In other words, the best places to plant are not the wildest landscapes but the fragmented ones, where new forest can punch a low-resistance corridor through an otherwise hostile matrix and link existing stands together.

The authors are careful to note the limits of their framework. Uplift represents a best-case scenario, since least-cost modeling captures only the single easiest path of movement, and the models assume a mature longleaf landscape that will take decades to materialize, a caveat that matters especially for the red-cockaded woodpecker, which requires trees at least 80 years old for its cavity nests. The approach also does not address the human dimension of enrollment, since landowners must voluntarily participate, and data limitations mean some suitable fields were inevitably missed. Still, the method’s reach extends well beyond the Southeast. The researchers point out that the same prioritization logic could be adapted to China’s Grain for Green Program or the European Union’s pledge to plant 3 billion additional trees by 2030, where choosing the right parcels of abandoned farmland could mean the difference between tree planting that helps biodiversity and planting that does not. For a foundation species clinging to fragments of its former empire, that kind of precision may be the lifeline it needs.

Subject of Research: Spatial prioritization of Conservation Reserve Program enrollment to enhance longleaf pine ecosystem habitat connectivity in the southeastern United States

Article Title: A Spatial Prioritization Approach for Identifying Future Conservation Reserve Program Opportunities to Enhance Longleaf Pine (Pinus palustris) Ecosystem Connectivity

Article References: Townsend, B. C., Nibbelink, N. P., & Dwivedi, P. (2026). A Spatial Prioritization Approach for Identifying Future Conservation Reserve Program Opportunities to Enhance Longleaf Pine (Pinus palustris) Ecosystem Connectivity. Environmental Management, 76(9), Article 303. https://doi.org/10.1007/s00267-026-02593-z

Image Credits: AI Generated

DOI: 10.1007/s00267-026-02593-z

Keywords: longleaf pine, habitat connectivity, Conservation Reserve Program, spatial prioritization, habitat fragmentation, landscape ecology, least-cost path, foundation species, ecosystem restoration, Southeastern United States, conservation planning, resistance surfaces