Abstract The future health of ecosystems is arguably as dependent on urban sprawl as it is on human-caused climatic warming. Urban sprawl strongly impacts the urban ecosystems it creates and the natural and agro-ecosystems that it displaces and fragments. Here, we project urban sprawl changes for the next 50 years for the fast-growing Southeast U.S. Previous studies have focused on modeling population density, but the urban extent is arguably as important as population density per se in terms of its ecological and conservation impacts. We develop simulations using the SLEUTH urban growth model that complement population-driven models but focus on spatial pattern and extent. To better capture the reach of low-density suburban development, we extend the capabilities of SLEUTH by incorporating street-network information. Our simulations point to a future in which the extent of urbanization in the Southeast is projected to increase by 101% to 192%. Our results highlight areas where ecosystem fragmentation is likely, and serve as a benchmark to explore the challenging tradeoffs between ecosystem health, economic growth and cultural desires.

Citation: Terando AJ, Costanza J, Belyea C, Dunn RR, McKerrow A, Collazo JA (2014) The Southern Megalopolis: Using the Past to Predict the Future of Urban Sprawl in the Southeast U.S. PLoS ONE 9(7): e102261. https://doi.org/10.1371/journal.pone.0102261 Editor: Craig A. Layman, North Carolina State University, United States of America Received: March 20, 2014; Accepted: June 11, 2014; Published: July 23, 2014 This is an open-access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication. Data Availability: The authors confirm that all data underlying the findings are fully available without restriction. Data are in the USGS Geo Data Portal for public access and are available with the DOI: http://dx.doi.org/10.5066/F7BP00SX. Funding: This research was supported by the US Geological Survey (http://www.usgs.gov/) through the National Climate Change and Wildlife Science Center (https://nccwsc.usgs.gov/) and the Dept of Interior Southeast Climate Science Center (http://globalchange.ncsu.edu/secsc/) through grant agreement G11AC20524. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing interests: The authors have declared that no competing interests exist.

Introduction Cities are expanding, and as they do urban sprawl–low-density urban development outside the urban core–is expanding even more rapidly. In some regions, expansion of suburban habitats as a result of shifts to automobile-dependent living has led to increases in the urban footprint even where populations have not shown large increases [1]. Urban sprawl increases the connectivity among urban habitats while simultaneously fragmenting non-urban habitats such as forests and grasslands. These changes have a variety of effects on species and ecosystems, including impacts to water pollution, disturbance dynamics, local climate, and predator-prey relationships [2]–[5]. Urban sprawl will also, almost certainly, influence the ability of species to respond to climate change, in as much as it creates barriers to the movement of species that cannot survive in cities and corridors for those who can [6]. Knowledge about the potential future character of urban sprawl is thus useful to a variety of stakeholders, including resource managers, conservation organizations, and urban planners. Any hope of integrating the effects of urbanization into management plans (whether for humans or wildlife), will depend on projections of urban sprawl. Such projections are typically generated using urban-growth models. The challenge is how to generate projections of urbanization that are robust enough to inform management priorities, decisions, and actions. In this regard, the challenge is similar to that faced when projecting climate change. In both cases, human actions taking place over decades will determine the outcome, and individual actions (global greenhouse gas emissions in the case of climate change; population growth, automobile dependency, and housing preferences in the case of urban growth) are difficult to predict on the time-scales of interest to decision-makers. In other words, the future as it relates to human actions has more uncertainty than what can be realistically quantified in an individual model. A more cautious approach is to define scenarios that represent one or more particular kinds of futures, and then construct models to simulate the consequences of each scenario. For fast growing regions such as the Southeast US, the most relevant scenario for conservation and adaptation planning is the “business-as-usual” (BAU) scenario in which the net effect of growth is in line with that which has occurred in the past. While recent “Smart-Growth” initiatives that promote more intensive development and a return to a strong urban core are gaining popularity, this BAU scenario is still reflective of the primary development model. And without significant changes to the status quo, this type of growth will continue. Decision makers can use this information to see how the status quo, if continued, could affect and interact with the goals, objectives, and plans for the future. Once the scenario is chosen, the urban-growth models typically use some combination of population density, land cover trends, and demographic models to set the parameters for the simulation. In this approach, the assumption is that changes in population lead directly to increased urbanization (by increasing density). This strategy has recently been used to project potential urban-induced threats to water quality for the U.S. and to project changes to forest stands in the Southeast [7]–[8]. But for many regions such as the Southeast that are heavily dependent on cars, the geographic extent of urbanization (which is dependent not only on population size but also road networks and the location of often far-flung industrial and commercial activity centers), may be as relevant to conservation and other management decisions as the density of people. And because sprawling, fragmented, or “leapfrog” development has been the dominant form of development in the Southeast [9], population growth models may under-predict the future extent of urban areas in this region. Here we project urban growth to 2060 for the Southeast U.S. for a BAU scenario using a flexible cellular automata urban-growth model that focuses on changes in the extent of urban areas rather than the density of people within them. We use the SLEUTH model [10], which simulates patterns of urban expansion that are consistent with spatial observations of past urban growth and transportation networks. Natural and social land use controls, such as topographic barriers or regulatory restrictions in sensitive environmental areas are specified in the model parameterization and through resistance layers that reduce the likelihood of urbanization. More sophisticated urban growth models exist that may include more complex parameterizations or explicit links to economic and demographic theory (e.g. [11]–[12]). However, these models typically can only be used over limited spatial extents because of intensive data requirements, often reaching to the individual parcel-level (e.g. [13]). We also note that while the simpler SLEUTH model has known issues due to its structure and assumptions for how urban growth occurs, any attempt to model the dynamics of systems and phenomena as complex as cities will require a significant level of abstraction. Because our aim is to produce projections at a fine spatial resolution over a multi-state area containing many dozens of cities, we used the SLEUTH model to take advantage of its scalability, its use of commonly available datasets, and the ability to focus on patterns of suburban and exurban development. Our modeling approach has several advantages as it relates to projecting urbanization in this fast-growing region. The primary advantage is that street networks are used to define the urban extent, allowing for accurate mapping of suburban areas and enabling rapid updates to the model as conditions change. We also use a high spatial resolution for the projections (60 m) that better corresponds to typical suburban residential lot sizes than coarser scale models (e.g., 250 m), and reflects fine-scale impacts on habitat connectivity. We also use Monte Carlo simulation to better quantify the uncertainty in the model output. As modeled here, our projections reflect the most recent trends in the expansion of low-density urban areas. As such, they represent a BAU scenario depicting how urbanization may evolve in the Southeast U.S. given current policies, preferences and rates of growth. We analyze the results with respect to three questions of importance for conservation practitioners, land managers and urban planners: •1) Given recent trends, what is the projected rate of urban growth for the next 50 years for this fast-growing region? •2) Will this growth be uniform, or will some ecosystems and land cover types be more severely impacted than others? •3) Which areas can be expected to become new growth centers?

Discussion Our results point to a future where urban areas occupy a much greater portion of the landscape of the Southeast U.S. The projected region-wide increase in urban area would constitute a doubling or tripling of land devoted to urban and suburban uses. With this increase will come greater need for urban infrastructure, but also an increase in all of those ecological features associated with urbanization including urban run-off, urban warming and habitat fragmentation. The tremendous growth in urbanization will come at the expense of natural areas as well as agricultural and silvicultural landscapes. Furthermore, the growth will be uneven and focused in areas that have few geographic and socioeconomic constraints, or in areas with high aesthetic value that act as strong attractants for development. As such, the largest urban expansions are projected in Blue Ridge, Ridge and Valley, Southern Coastal Plain, and Piedmont ecoregions. We also project new urban centers in the Appalachian Mountains and central Florida, while the more aggressive model simulations show large new areas of urbanization north of the Everglades region. The greatest expansion, projected to occur in the Piedmont ecoregion, reflects a combination of growth attractors such as the existence of large urban areas, a lack of geographic constraints on growth, auto-oriented residential development, and proximity to natural amenities (Appalachian Mountains and the Atlantic Ocean). The rapid urbanization projected to occur in the mountainous regions also results in greater wetland losses because topographic constraints and the abundance of protected forest areas limit the supply of alternative land use types. Undoubtedly our model simulations do not capture the full range of uncertainty, and our focus on a single BAU scenario does not consider alternative policies that could promote different urbanization patterns. However, the broad patterns of growth do reflect the recent trends, both in terms of the speed at which urbanization has progressed in the Southeast, and in the locations that are most affected by it. Other studies operating on similar temporal and spatial scales have shown lower rates of urbanization [7], [24]. One possible reason is that our more aggressive urbanization scenario is not constrained by population projections that previously have underestimated this region’s population growth [25]. Furthermore, our model calibration covers a period of very rapid expansion of suburban development (along with the beginning period of the global recession that prompted a similarly rapid retreat from building new housing). In effect this is a true Business As Usual scenario, albeit one that likely portrays an upper bound benchmark for urban growth, which visualizes the consequences of continuing current land use policies and implicitly reflects factors that have contributed to rapid development in this region (e.g., favorable climate and a cultural tendency toward sprawling growth). We are projecting changes in the spatial footprint of urban areas, and in doing so, do not model the differences in the types of urbanization within those urban areas. Not all areas classified as “urban” are alike: cities are heterogeneous in terms of their land use, population density, and impacts [26]–[27]. Still, our focus on the spreading frontier of development underscores the increasing connectedness and favorable conditions for urban-adapted species, while a whole host of species and ecosystems will experience reduced habitat area and increased difficulty in migration and dispersal. The changes we project would have significant and lasting effects on the region’s ecosystems. The increasingly fragmented natural landscape would reduce habitat availability, suppress natural disturbance processes (such as wildfires), hinder management actions that come into conflict with urban areas, and likely eliminate existing corridors. Furthermore, all these impacts could occur simultaneously, posing a particularly devastating threat to already vulnerable species and systems. Such is the case for the endangered Red-cockaded Woodpecker (Picoides borealis) in the longleaf pine (Pinus palustris) ecosystem, where planned stepping-stone corridors are expected to be negatively impacted by encroaching urbanization, and will likely make management of existing fire-suppressed habitat difficult [28], [3]. At the same time, urban corridors will expand and become less fragmented which will promote the establishment of novel habitats that favor an entirely different assemblage of species (e.g., [29]–[30]). For example our results show the emergence of a new, completely connected megalopolis in the Piedmont region by 2060, extending from Raleigh, NC to Atlanta, GA (Figure 1d). Not only will habitats and corridors for wildlife be eliminated, but the continuous urban corridor will have a warmer climate than surrounding rural areas. Urban heat islands in this region are 0.5°–1.5°C warmer than rural areas [31], meaning that the new megalopolis would effectively extend the warmer southern and coastal climates to the (formerly cooler) Piedmont, which could be 2°–6°C warmer due to climate change [32]. Others have shown that these urban and suburban habitats are already acting as corridors for the expansion of invasive species that take advantage of urban heat island conditions [4]; a phenomenon likely to accelerate as these urban corridors expand in a warming climate. Projections of the future have, for good reasons, tended to focus on global warming. However, global warming scenarios will be superimposed on or even act synergistically with urbanization scenarios. In the Southeast US, the effects of global warming are expected to be modest compared with many regions, however our results suggest that the effects of urbanization, given business-as-usual will not be. Given that urbanization has many consequences for how both humans and other species live, optimizing such growth could become a key national and regional priority, where optimization includes providing for biodiversity as well as economic development and cultural desires. However, history suggests humans, in contrast to ants and slime molds (e.g. [33]), rarely optimize growth, particularly when multiple objectives such as profit, equity, and ecological integrity come into conflict. Given this reality, and the not unlikely possibility that the recent urbanization path will continue, our model suggests the template around which natural resource managers, urban planners and everyone else whose job relates to the distribution of wild places or humans, will need to respond.

Supporting Information Figure S1. Stem-plots of commission and omission error percentages for 32 sampled CSAs. The two stem-plots in the left column are results for sampled urban pixels in the CSAs and the two-stem plots in the right column are results for sampled rural pixels. Color-coded numbers indicate the number of points out of the 272 randomly sampled points in each CSA that were classified as urban or rural during manual photo interpretation. https://doi.org/10.1371/journal.pone.0102261.s001 (TIFF) Table S1. Pooled accuracy assessment results for 32 sampled CSAs. https://doi.org/10.1371/journal.pone.0102261.s002 (DOCX) File S1. Detailed Description of Model Calibration and Accuracy Assessment. https://doi.org/10.1371/journal.pone.0102261.s003 (DOCX)

Acknowledgments We thank S Williams for helpful input on the urban growth modeling, D Blodgett with the USGS Office of Water Information, Center for Integrated Data Analytics (CIDA) for assistance in serving the model output, and the thoughtful comments and suggestions provided by three colleagues and one anonymous reviewer. Any use of trade, product, or firms names is for descriptive purposes only and does not imply endorsement by the U.S. government.

Author Contributions Conceived and designed the experiments: AT JC AM JAC. Performed the experiments: AT JC CB. Analyzed the data: AT JC CB. Contributed reagents/materials/analysis tools: JAC. Wrote the paper: AT JC CB RRD AM JAC.