OUTDATED Blueprint 2.0 Priorities and Threats Map

May 5, 2015 (Last modified Nov 8, 2019)
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Description
BLUEPRINT 2.0

This map includes an older version of the South Atlantic Blueprint.

Description
The Blueprint prioritizes areas for shared conservation action in the South Atlantic geography. Priorities in Blueprint 2.0 are driven by natural and cultural resource indicator models and a connectivity analysis. Click here to learn more about the indicators.

The Conservation Blueprint is a living spatial plan for sustaining natural and cultural resources in the face of future change. More than 400 people from over 100 organizations have actively participated so far in developing the Blueprint. Click here to learn more about the Blueprint.

Background
The lands and waters of the South Atlantic are changing rapidly. Climate change, urban growth, and increasing human demands on resources are reshaping the landscape. While these forces cut across political and jurisdictional boundaries, the conservation community does not have a consistent cross-boundary, cross-organization plan for how to respond. The South Atlantic Conservation Blueprint is that plan.

In 2012, developing a Conservation Blueprint became the 3-5 year mission of the South Atlantic Landscape Conservation Cooperative (LCC). In 2013, the Cooperative adopted natural and cultural resource indicators as shared measures of success. In 2014, the Steering Committee approved Blueprint Version 1.0. This first version of the Blueprint was created by combining expert input from workshops with existing regional and state plans. Click here to explore Blueprint 1.0 in the Conservation Planning Atlas. Click here to see the simple Blueprint viewer.

Intended Uses
Due to its coarse resolution and broad scope, the Blueprint is not intended for use in isolation of other more locally and/or resource specific layers. Instead, it is designed to complement other more detailed information by identifying the best places for shared action at the ecosystem level.

Intended uses include: finding places to pool resources, raising new conservation dollars, guiding infrastructure development, developing conservation incentives, showing how local actions fit into a larger strategy, and locating places to build resilience to major disasters. Conservation practitioners have already started using the Blueprint to inform conservation action and investment across the geography.

Addressing Threats in Blueprint 2.0
Many ecosystems of the South Atlantic region are transitioning in response to sea-level rise and urbanization. As an adaptation strategy, the Blueprint represents a plan for responding to those threats. The urbanization and sea-level rise transition layers overlaying the Blueprint in this map are described in more detail below and were reviewed at the March and April 2015 Blueprint 2.0 workshops. 82% of workshop attendees, representing the broader South Atlantic conservation community, wanted to be part of conservation action in areas transitioning due to sea-level rise, urbanization, or both. 184 people from 64 different organizations participated in the Blueprint 2.0 workshops.

As a result, priority areas identified in the indicator and connectivity analysis that occur in transition areas remain in the Blueprint. These data layers were processed to simplify more detailed data available on the Conservation Planning Atlas and provide a basic binary depiction (yes or no) of whether an area is predicted to transition due to sea-level rise or urbanization by the year 2050. These simplified threat layers will likely be incorporated as filters in the Simple Viewer to allow users to identify those areas of the Blueprint likely to transition in the next 35 years.

Known Issues
Coarse and missing data for marine priorities
Indicator data for much of the marine environment was especially coarse, with large areas of missing data. Non-hardbottom nearshore areas and areas east of the shelf break should be viewed with particular caution.

Undervalues certain areas near high priority hubs with strong restoration potential
Some areas near, but not directly adjacent to, high priority hubs, have strong potential for restoration and broad landowner support for conservation actions. Ideally, these would have been prioritized more highly in the Blueprint. One example of where this occurred is the Black River Mingo Creek area near Winyah Bay.

Does not sufficiently incorporate benefits of upstream actions on downstream watersheds
In most cases, conservation actions in a particular watershed have a stronger ecosystem impact on downstream watersheds than upstream watersheds. This was not accounted for in the current Blueprint.

Does not include waterscapes aquatic connectivity indicators
The two waterscapes indicators (salt and freshwater connectivity, resident fish connectivity) were not used in this version of the Blueprint. They were removed due to technical challenges and counterintuitive results from using them in the prioritization. During validation, versions of the Blueprint using these indicators performed poorly when compared to comments and feedback from aquatic resources specialists at the Blueprint 2.0 workshops.

Insufficient aggregation of patches in some areas with multiple embedded ecosystems
In some areas, priorities within adjacent ecosystems diverged enough from each other that it created a speckling of high priority areas near hubs of highest priority. The area near Okefenokee National Wildlife Refuge is a good example of this issue. This speckling occurred despite the inclusion of landscape indicators covering multiple ecosystems.

Undervalues areas near some small rivers and streams that are important for aquatic endemic species, but occur in impacted watersheds
Many small rivers and streams in the South Atlantic that are important for aquatic endemics occur in watersheds strongly impacted by human use. While Blueprint 2.0 incorporated specific indicators for freshwater aquatics (riparian buffers and impervious surface), they were measured at subwatershed scales (HUC12) and didn't formally account for variations in aquatic endemism. That resulted in many small rivers and streams that are high priority for conservation organizations often falling into the medium priority class of the Blueprint.

Corridor routes do not account for locally specific opportunities
The current corridors simply take the least cost path through high priority areas. These costs are based on the priority itself and don't incorporate other costs of implementing a corridor. In many cases, locally specific opportunities could improve the route of the corridor. For example, in areas near cities, it will likely be easier to route corridors through areas important for that city's water supply. This is not accounted for in Blueprint 2.0.

Indicator models
The GIS depiction of each indicator has unique issues and limitations. More details on the modeling of each indicator is available in the metadata for each layer.

Some agricultural lands not easily assigned to an ecosystem may have been undervalued
A small amount of agricultural lands, particularly near the coast, could not be assigned to an ecosystem due to the challenge of determining what ecosystem would have existed there naturally. These areas were not prioritized using the indicators and could only contribute to the Blueprint as part of corridors. While these areas are not in pristine condition and probably would not have appeared in one of the other Blueprint priority categories, it is possible some specific areas were overlooked.

Development
The prioritization in Blueprint 2.0 is based on natural and cultural resource indicator models and a connectivity analysis. An earlier draft of Blueprint 2.0 was reviewed at workshops across the Southeast in March and April 2015, attended by about 185 experts from more than 60 organizations. A later draft for review reflected changes made in response to workshop feedback.This final version of 2.0 incorporates those post-workshop changes, as well as feedback received on draft Blueprint 2.0 during a one month comment period.

Input Data
The Blueprint 2.0 indicator folder on the Conservation Planning Atlas contains the data layers for all natural and cultural resource indicators used to create Blueprint 2.0. Some indicators not used in the Blueprint due to data limitations are classified as “additional data”. In some cases, the data layers used in the Blueprint and the State of the South Atlantic differ slightly, and the differences are explained in the metadata for each indicator.

Selection algorithm
For each ecosystem, except for the open water portion of estuaries and freshwater aquatic, we used the core area Zonation algorithm. This algorithm focuses on minimizing indicator loss and maintaining a balanced representation across all indicators. It begins by including all potential cells in the Blueprint and iteratively removes cells that will result in the least relative indicator loss. The first cell removed is the lowest priority and the final cell retained is the highest.

According to the Zonation manual, core area Zonation “tries to retain core areas of all [indicators] until the end of cell removal, even if the feature is initially widespread and common. Thus, at first only cells with occurrences of common features are removed. Gradually, the initially common features become more rare, and cells with increasingly rare feature occurrences start disappearing. The last site to remain in the landscape is the cell with the highest (weighted) richness. This is the site that would be kept last if all else was to be lost” (Moilanen et al. 2014).

Choosing run options and model stability
For each ecosystem, we chose run options that met two types of requirements: 1) run stability and 2) patch aggregation with minimal indicator loss.

--Run stability: when running core area Zonation within each ecosystem, there were often multiple valid solutions to the prioritization defined in the run. This is common in spatial prioritization where there are multiple ways to design a landscape and produce the same outcomes. However, to ensure the data-driven Blueprint process was transparent and replicable, we tried to select run options that would result in similar results if repeated using the same data by another group. We defined stable run options as a set of options where the average indicator coverage in the top 20% of priorities changes no more than 5% with multiple runs with indicators input in different orders.

--Patch aggregation with minimal indicator loss: aggregation is already incorporated in some of the indicators. In testing model runs with additional aggregation, our testing indicated that for some ecosystems additional aggregation better captured other species and habitat characteristics of interest not selected as indicators. Habitat patch quality, quantity, and configuration directly affect species’ long-term viability and ecosystem processes within each ecosystem. Aggregation is also helpful when uncertainty exists (Moilanen and Wintle 2006) and to reduce overall management costs (Moilanen et al. 2014). Furthermore, the additional constraints imposed by patch aggregation often helped improve run stability in certain ecosystems.

We evaluated 2 different run options for patch aggregation in Zonation, boundary length penalty (BLP) and edge removal. BLP is computationally fast and penalizes networks with greater boundary lengths. Edge also removal decreases overall computation time and only removes cells from edges of existing patches. These two options are typically used together within Zonation.

--Rule set for selecting run options: for ecosystems covering large areas (marine, forested wetland, pine, and upland hardwood), we began by running edge removal with no BLP. If runs were stable, we continued to increase the BLPs until there was less than a 5% loss in average indicator coverage. If runs were not stable, we increased the BLP until the runs stabilized. In the marine environment, based on reviews of earlier drafts and to further improve stability, we included an additional layer in the model runs representing ecological depth zones produced by The Nature Conservancy’s South Atlantic Bight Marine Assessment. In the pine ecosystem, due to computational limitations, we used an option (warp factor) to remove blocks of 10 pixels at a time from the prioritization. In forested wetlands, also due to computational limitations and the embedded nature of the ecosystem, we did not include a BLP.

For ecosystems covering smaller areas (maritime forest, beaches, freshwater wetland, estuarine marsh), we began by running without edge removal and without a BLP. These ecosystems tended to be small, embedded in other ecosystems, and computationally fast, so they were less appropriate candidates for edge removal. If runs were stable, we continued to increase the BLPs until there was less than a 5% loss in average indicator coverage. If runs were not stable, we increased the BLP until the runs stabilized.

Approach for freshwater aquatic and open water estuaries
Freshwater aquatic and the open water part of estuaries were treated differently than the other ecosystems. Freshwater aquatic indicators were summarized at a coarser resolution (catchments and HUC12),and ensuring high values for both indicators was particularly important in the prioritization. For these reasons, we used HUC12s as the selection unit (instead of 30 m pixels) and used the additive benefits algorithm in Zonation. Additive benefits maximizes the overall indicator representation (as opposed to core area Zonation, which all attempts to maintain a balance across indicators).

The open water portion of the estuaries was only covered by one indicator (the Coastal Condition Index) and therefore could not be prioritized within Zonation. Instead, we used areas rated 4.0 and above (“good”) to define the highest priority areas.

Integrating freshwater aquatic with other ecosystems
The coarse scale of freshwater aquatic indicators made it particularly challenging to integrate their prioritization with other ecosystems. To combine the freshwater aquatic indicators with other ecosystems, we first retained the top 10% of the other ecosystems. Then, for the remaining priority levels, we averaged the freshwater aquatic and other ecosystem priorities.

Connectivity Analysis
We used Linkage Mapper in ArcGIS to do the connectivity analysis.

Connectivity between open water estuaries and marine
To define hubs in open water estuaries, we performed a region group function (4-neighbor) on the Coastal Condition Index indicator with values rated 4.0 and above (“good”) and selected all aggregates of ≥100 cells (400 ha). Instead of defining multiple hubs in the marine environment, we selected the single largest linear hub from the top 10% of the marine prioritization. This area overlaps two major ecologically important features: the continental shelf break and the Gulf stream. To delineate the hub, we performed a region group function (4-neighbor) on the top 10% of the marine prioritization and retained the largest group.

To define resistance in the open water estuaries and marine, we rescaled and inverted the Coastal Condition Index indicator (estuaries) and the marine prioritization (marine) to ensure they used the same scale and that high values represented high resistance.

To identify the top 20% of corridors, we ran Linkage Mapper with the default options. We then retained pixels within the top 20% of the least cost corridors.

Connectivity across all other ecosystems
To define hubs within the LCC boundaries, we combined the indicator priorities from all other ecosystems and selected 2000 ha (5000 acre) aggregations within the top 10% of the priorities. This size threshold from Hoctor et. al (2000) is also used by the neighboring LCC to the South for connectivity purposes.

To define hubs and resistance outside of the LCC boundaries, we used local connectedness from The Natural Conservancy’s Southeast Resilience and Northeast Resilience projects. For hubs, we selected 2000 ha aggregations both with a connectedness score at least 1 standard deviation above average, and located within 26.1 km of the LCC boundary. The distance of 26.1 km is based on dispersal distances of subadult black bears (White et al. 2000), a species that disperses from within the LCC into all other adjacent LCCs.

To create a single resistance layer, we rescaled and inverted the indicator priorities (within the LCC boundaries) and the local connectedness metric (outside the LCC boundaries) to ensure they used the same scale and that high values represented high resistance.

To identify the top 20% of corridors, we first ran Linkage Mapper with the default options, plus settings to prune corridors if a hub was connected to more than 4 of its nearest neighbors. We then removed the bottom 5% of resulting corridors based on cost-weighted distance. The bottom 5% of corridors tended to be particularly long corridors moving through a large amount of high resistance areas. We then retained pixels within the top 20% of the remaining least cost corridors.

Priority Categories
Highest priority for shared action
The top 10% of areas generated by the indicator prioritization are defined as highest priority for shared action. This collection of areas makes the greatest contribution toward the persistence of the natural and cultural resource indicators. They will likely remain important places for collaborative conservation into the future.

High priority for shared action
The top 10-25% of areas generated by the indicator prioritization are defined as high priority for shared action. This collection of areas makes a large contribution toward the persistence of the natural and cultural resource indicators. While this supports their current importance for collaborative conservation, their priority level may change in future versions of the Blueprint.

High priority corridors
The top 20% of corridors identified in the connectivity analysis are defined as high priority corridors. This set of corridors covers about 5% of the South Atlantic geography and brings the total area covered by the Blueprint to 30%. These areas depict the best connections between highest priority areas in the Blueprint.

Medium priority for shared action
All areas in the top 50% generated by the indicator prioritization---not already defined as highest, high, or corridors---are considered medium priority. This category identifies areas in better-than-average condition that are potential candidates for restoration.

Literature Cited
Hoctor, T. S., M. H. Carr, P. D. Zwick. 2000. Identifying a linked reserve system using a regional landscape approach: the Florida ecological network. Conservation Biology 14:984-1000.

Moilanen, A., and B. A. Wintle. 2006. Uncertainty analysis favours selection of spatially aggregated reserve networks. Biological Conservation 129:427-434.

Moilanen, A., L. Meller, J. Leppänen, F.M. Pouzols, H. Kujala, A. Arponen. 2014. Zonation Spatial Conservation Planning Framework and Software V4.0, User Manual.​

White, T.H., JR., J.L. Bowman, B.D. Leopold, H.A. Jacobson, W.P. Smith, and F.J. Vilella. 2000. Influence of Mississippi alluvial valley rivers on black bear movements and dispersal: Implications for Louisiana black bear recovery. Biological Conservation 95:323–331.

THREATS

Urban Growth by 2050

Reason for Incorporating Threat
Urban growth directly and indirectly threatens ecosystems with high ecological integrity in the South Atlantic. Direct threats include loss of habitat for species and loss of greenways and open space for people. Indirect threats include increased fragmentation of wildlife and fisheries populations and barriers for connectivity.

Input Data
Terrando et al. (2014) created an urban growth model for the Southeastern United States using the SLEUTH concept (short for Slope, Land use, Excluded, Urban, Transportation and Hillshade), which uses data on slope, land use, exclusion, urban extent, and transportation (Jantz et al. 2004, Jantz et al. 2010). They forecasted urban growth based on road infrastructure increases from 2000-2009. Urbanization was projected to increase 139% in the next 50 years for a total of 216,900 sq km of urbanization in the region; larger increases are expected for areas of the Piedmont where major cities are located. We used the SLEUTH projection of urban growth by 2050.

Mapping Steps
For the SLEUTH urban growth 2050 scenario, we defined areas with any probability of urbanization (≥ 2.5%) by 2050 as threatened by urbanization. It also includes areas that were classified as existing urban in the SLEUTH data.

GIS Processing
All indicators were initially computed, or in the case of existing data, were resampled to 1 ha spatial resolution using the nearest neighbor method. For computational reasons, we then used the Spatial Analyst-Aggregate function to rescale the resolution to 200 m. The aggregate function avoided loss of detail by taking the maximum value of each cell in the conversion (e.g., urban growth = 1; no urban growth = 0).

Known Issues
SLEUTH projections assume past growth (2000-2009) will continue at the same rate until 2050. In areas that have reached their capacity for growth due to cultural and/or environmental reasons (e.g., rural areas without major infrastructure), the model will tend to overestimate urban growth. The model is also limited in projecting new areas of urban growth and does not incorporate the impact of smart growth policy alternatives.

Literature Cited
Jantz, C. A., S. J. Goetz, D. Donato, and P. Claggett. 2010. Designing and implementing a regional urban modeling system using the SLEUTH cellular urban model. Computers, Environment and Urban Systems 34:1-16.

Jantz, C. A., S. J. Goetz, and M. K. Shelley. 2004. Using the SLEUTH urban growth model to simulate the impacts of future policy scenarios on urban land use in the Baltimore-Washington metropolitan area. Environment and Planning B 31:251-272.

Terando, A. J., J. Costanza, C. Belyea, R. R. Dunn, A. McKerrow, and J. A. Collazo. 2014. The southern megalopolis: using the past to predict the future of urban sprawl in the southeast us. PLoS One 9:e102261.

Sea-level Rise Transition Areas by 2050 with a projected rate of 0.9 m by 2100

Reason for Incorporating Sea-level Rise Transition Areas
Sea-level rise is projected to have a profound impact on the people and natural communities of the South Atlantic coast. Both the transition of ecosystems and complete loss of land are projected to occur by 2050. Rather than planning for worst-case inundation scenarios, a recent focus has been placed on managing retreat of coastal wetlands as marshes migrate inland and ecosystems transition (Amundsen et al. 2010, Nicholls and Cazenave 2010, Rogers et al. 2014). This data layer depicts areas where transitions are likely to occur, but given the great uncertainty involved, does not attempt to distinguish between particular transitions (e.g., forested wetlands to open water are treated the same as forested wetlands to salt marsh). Local conditions, such as management actions, subsidence, organic accretion, sedimentation, plant productivity zones, storms, and other factors will determine how ecosystems transition.

Sea-level Rise Projections
We used the SRES (Special Report on Emissions Scenarios) A1B scenario for guiding sea-level rise projections, as it is a balanced approach between the fossil fuel intensive (A1FI) and lowest emission scenario (B2). The 2013 report from the Intergovernmental Panel on Climate Change (IPCC) shows the A1B scenario has a likely sea-level rise range of 0.42–0.80 m from 1996 to 2100 (Church et al. 2013). Other sources report sea-level rise rates with the A1B scenario ranging from 0.32–1.56 m by 2100 (Vermeer and Rahmstorf 2009, Grinsted et al. 2010, Jevrejeva et al. 2010). The U.S. National Climate Assessment (Parris et al. 2012) projected 0.2- 2.0 m sea-level rise by 2100 with the highest rate projected with maximum possible glacier and ice sheet loss. In addition to the sea-level rise scenario presented here, others will soon be available under "additional data layers" on the Conservation Planning Atlas website.

Input Data
To project sea-level rise transition areas, we used NOAA Coastal Services Center's Marsh Impacts/Migration data from the Sea Level Rise and Coastal Flooding Impacts Viewer (accessed 1 February 2015). This data is best described as a modified bathtub approach that accounts for local and regional tidal variability in mean higher high water (see website for specific details and disclaimers). For the South Atlantic LCC region, current sea-level rise rates range from a 1.76 mm/yr in Apalachicola, Florida, 3.11 mm/yr in Charleston, South Carolina, to a long-term average of 4.57 mm/yr in Duck, North Carolina (NOAA Tides and Currents, Sea Level Trends, accessed 20 April 2015). Rates are projected to accelerate over time. NOAA uses the sea-level rise acceleration curve from the A1B scenario to provide estimates of sea-levels for different time steps, and we used the projection to 2050 for consistency with the urbanization threat model to define one planning horizon. Surface elevation change may vary locally and regionally, but data is lacking throughout the region. Therefore, we used the NOAA data with a 0 mm/year accretion rate (4 mm/year scenarios will soon be available).

Mapping Steps
Land cover classifications from NOAA's marsh migration data, via the Sea Level Rise and Coastal Flooding Impacts Viewer, were simplified to correspond with the ecosystems defined by the South Atlantic LCC. The changes are also consistent with the level of detail mapped with relatively high accuracy. Developed open space, low, medium, and high intensity developments were reclassified into a single "developed" class. Scrub/shrub wetlands were included with "forested wetlands." Brackish and estuarine marshes were reclassified together into "estuarine
marsh." Unconsolidated shore and open water were classified together as open water, as both land cover types are indicative of wetland loss. Classifications of uplands and freshwater emergent wetlands did not change.

Each cell was classified as a transition zone or no transition zone. Transition zones were defined as any projected change in a land cover type (see classifications above). For example, estuarine marsh to open water transitions were defined the same as a forested wetland to estuarine marsh transitions. NOAA quantified land cover transitions at a 10 m horizontal resolution with LiDAR elevation data; however, the data were converted to a 200 m resolution with a nearest neighbor method to incorporate the scenarios with the Blueprint. While vertical accuracy of elevation data are critical to determine land cover transitions, elevations are relatively consistent with horizontal distances up to 200 m. As coastal zone management is generally applied well beyond 10 m scales, a 200 m scale also characterizes a more realistic interpretation of the data.

NOAA's land cover types did not perfectly overlap with the South Atlantic LCC ecosystems due to slight differences in the USGS's NLCD (National Land Cover Database) and NOAA's CCAP (Coastal Change Analysis Program Regional Land Cover and Change). For example, forested wetlands along water bodies were sometimes classified as open water by NOAA CCAP data and transitions were not possible. To remedy the mismatch, we identified areas where NOAA classified open water was designated as a terrestrial ecosystem in NLCD. We reclassified only these particular areas based on a 5x5 focal maximum (ArcGIS-Spatial Analyst) and mosaicked these areas to the original transition areas.

Known Issues
NOAA states: "…model results contain inherent uncertainties that may not be evident but may alter the effectiveness of management decisions." They further advise that "to provide appropriate information for management, outputs of a model need to be interpreted based on the assumptions, simplifications, and uncertainties included in the model."

Important assumptions of the approach include no future change in coastal geomorphology and the effects of detailed hydrological characteristics are negligible (e.g., ditches, engineering structures). The modeling does not show marsh migration across developed lands, as classified by C-CAP "developed" land cover classes. Great uncertainty exists in these areas because of the human decisions involved. The data do not consider complex natural processes such as freshwater influence, subsidence, sediment erosion dynamics, and storm impacts.
Most notably, many factors are associated with surface elevation change at local and regional levels, but detailed data are not available across the region. Surface elevation change includes dynamics based on organic vertical accretion / plant productivity and biological feedbacks, sediment availability and deposition, tidal range, flooding conditions, and subsidence (Reed 1995; Morris et al. 2002; Cahoon 2006; Schile et al. 2014).

The sea-level rise scenario does not incorporate beach movements and barrier island rollover. Indicators of Beaches and Dunes ecological integrity characterize areas of potential or likely barrier island rollover.

Literature Cited
Amundsen, H., F. Berglund, and H. Westskog. 2010. Overcoming barriers to climate change adaptation—a question of multilevel governance? Environment and Planning C Government and Policy:276-289.

Cahoon, D. R. 2006. A review of major storm impacts on coastal wetland elevations. Estuaries and Coasts 29:889-898.

Church, J. A., P. U. Clark, A. Cazenave, J. M. Gregory, S. Jevrejeva, A. Levermann, M. A. Merrifield, G. A. Milne, R. S. Nerem, P. D. Nunn, A. J. Payne, W. T. Pfeffer, D. Stammer, and A. S. Unnikrishnan. 2013. Sea Level Change. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA.

Grinsted, A., J. C. Moore, and S. Jevrejeva. 2010. Reconstructing sea level from paleo and projected temperatures 200 to 2100 AD. Climate Dynamics 34:461-472.

Jevrejeva, S., J. Moore, and A. Grinsted. 2010. How will sea level respond to changes in natural and anthropogenic forcings by 2100? Geophysical Research Letters 37.

Morris, J. T., P. Sundareshwar, C. T. Nietch, B. Kjerfve, and D. R. Cahoon. 2002. Responses of coastal wetlands to rising sea level. Ecology 83:2869-2877.

Nicholls, R. J., and A. Cazenave. 2010. Sea-level rise and its impact on coastal zones. Science 328:1517-1520.

Parris, A., P. Bromirski, V. Burkett, D. Cayan, M. Culver, J. Hall, R. Horton, K. Knuuti, R. Moss, J. Obeysekera, A. Sallenger, and J. Weiss. 2012. Global Sea Level Rise Scenarios for the US National Climate Assessment. NOAA Tech Memo OAR CPO-1. 37 pp.

Reed, D. J. 1995. The response of coastal marshes to sea‐level rise: Survival or submergence? Earth Surface Processes and Landforms 20:39-48.

Rogers, K., N. Saintilan, and C. Copeland. 2014. Managed retreat of saline coastal wetlands: challenges and opportunities identified from the Hunter River Estuary, Australia. Estuaries and Coasts 37:67-78.

Schile, L. M., J. C. Callaway, J. T. Morris, D. Stralberg, V. T. Parker, and M. Kelly. 2014. Modeling tidal marsh distribution with sea-level rise: Evaluating the role of vegetation, sediment, and upland habitat in marsh resiliency. PLoS One 9:e88760.

Vermeer, M., and S. Rahmstorf. 2009. Global sea level linked to global temperature. Proceedings of the National Academy of Sciences 106:21527-21532.
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South Atlantic Landscape Conservation Cooperative. 2015. The Conservation Blueprint Version 2.0.
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SouthAtlantic LCC
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The South Atlantic LCC encompasses and ecologically diverse 89 million acres across portions of six states, from southern Virginia to northern Florida. The geography also includes the marine environment within the federal Exclusive Economic Zone The South Atlantic region is a place where major urban...