1. Landscape dynamics in the Southern Atlantic Coastal Plain in response to climate change, sea level rise and urban growth
Todd S. Earnhardt, Biology Department, NCSU
Alexa McKerrow, USGS-Biological Resources Discipline
Adam Terando, Pennsylvania State University
Matt Rubino, Biology Department, NCSU
Steve Williams, Biology Department, NCSU
August 5, 2010
Pittsburgh, PA
Ecological Society of America

2. DSL Project Objectives
Assess the current capability of habitats in ecoregions in the eastern United States to support sustainable bird populations
Predict the impacts of landscape-level changes (e.g., from urban growth, conservation programs, climate change) on the future capability of these habitats to support bird populations
Target conservation programs to effectively and efficiently achieve objectives in State Wildlife Action Plans and bird conservation plans and evaluate progress under these plans
Enhance coordination among partners during the planning, implementation and evaluation of habitat conservation through conservation design
The overall objective of this proposal is to develop a consistent methodology and to enhance the capacity of states, joint ventures and other partners to assess and design sustainable landscape conservation for birds and other wildlife in the eastern United States. Specifically…
BaSIC/NCCoop is responsible for Obj. 1 & 2 as well as administering the project.
This work supports a broader project that involves designing sustainable landscapes (DSL) with respect to migratory bird populations in the SE Coastal Plain.
The overall goal of the DSL work is to develop a consistent methodology and to enhance the capacity of states, joint ventures and other partners to formulate conservation design schemes at landscape levels to sustain bird populations and other wildlife in the eastern United States.

3. Project Extent South Atlantic Migratory Bird Initiative (SAMBI)
SE-GAP data set serves as the base dataset
GAP data serves as a fundamental starting point for project

4. Output Summary ~ 2.5 million polygons prior to grid conversion
20 study areas based on Level IV ecoregions
291,757,276 pixels
262,581 km2
408 state class conditions
82 ecological systems
29 anthropogenic or “unprojectable”
3 SRES CO2 emission scenarios (a2, a1b, b1)
100 year simulation with decadal output

5. Modeling Landscape Change
Existing Landscape
Conditions
Succession &
Disturbance
Models
Range of Future
Landscape Conditions
(+25, +50, +75, +100 yrs)
Global Climate
Models
Overview
Urban Growth
Models

6. Vegetation Dynamics Models
VDDT
State transition models
Succession
Disturbance
Model controls
State transition probabilities
Management activities
Forcing scenarios
Trend patterns
TELSA
Spatial simulations
Polygon based
VDDT and Telsa

7. Vegetation modeling inputs
Deterministic and Probabilistic transitions (from VDDT)
Cover Type (e.g., Upland Longleaf pine)
Canopy Structure (open, closed)
Stage (early, mid, late)
Age from FIA distribution
Temporal and trend disturbance multipliers, polygon forcing (urban growth), etc.

8. Sequence of slides showing objects, land cover and structure characterization. How we get the polygons.

9. How do Global Climate Model variables relate to landscape changes?
Temperature
Precipitation
Sea Level Rise
Evapotranspiration
Drought
GCM “a2” scenario:
-“business as usual” emission scenario (900 ppm CO2)
Fire Frequency
Disease
Insect Outbreak
Habitat Loss &
Shifts in Habitat
3 Scenarios: A2, A1B, B2

10. Projected Fire Changes
A2 Weighted Mean
A1b Weighted Mean
B1 Weighted Mean
Hindcast Weighted Mean
The results of the fire model where we developed relationships between climate variables and the number of acres burned in the SAMBI region. Once the relationships are developed we apply them to the Global Climate Model (GCM) output to project the changes in acres burned in the SAMBI region by About 15 climate models were used. We develop the ‘best’ prediction by calculating a weighted mean of all GCMs for each emissions scenario (the dashed lines and the solid black line). The black line is the weighted GCM mean of the ‘hindcasted’ acres burned. The solid colored lines are the smoothed versions of the projected acres burned. The smoothed versions are what we input into the landscape model to predict changes in the fire probability. So for instance notice that the smoothed line from the A2 scenario shows roughly 50% more acres burned in year 2100 compared to So when we run the landscape change model, by year 2100, it will assume a 50% higher probability of fire each year across the SAMBI region.

11. Incorporating probability multipliers
Trend multipliers for fire in TELSA

12. Urban Growth Model Gigalopolis - SLEUTH
Slope,
Land Cover,
Exclusion,
Urbanization,
Transportation, &
Hillshade
Slope (%) – is a resistance parameter (0 – 100 %)
Land Cover – Land cover has different transition probabilities (categorical)
Excluded – Public ownership probability that it could be developed (0-100%)
Urban – Initial urban extent at the start of the modeling (urban/non-urban)
Transportation – can be binary, road/non-road or have relative weighting based on accessibility
Hillshade – just for looks, gives a backdrop for the build out scenarios – and makes SLEUT sound better.

15. Distribution of Atlantic CP Upland Longleaf Pine in SAMBI region

16. Summary
Have a consistent method for modeling and mapping vegetation dynamics into the future
Vegetation models can be adapted to reflect predicted responses to climate change
Completed land cover mapping for SAMBI region out to 2100 and currently using this data for predicted species distribution modeling
Predicted habitat maps are being used in DSL work at Auburn University (Moody, COS-111-4)

18. Thank you