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Series of webinars across the country in 2014: LANDFIRE and consortia members of Joint Fire Science Program developed a series of webinars to provide information about LANDFIRE products and processes for large land management and planning. Started in January on the west coast and in the Rockies. Resumed in autumn with Tallgrass Prairie/Oak Savannah-Lake States, and the Southern Fire Exchange series. Requests to continue the series with fire exchange members and others in 2015 are coming in. Introduce Jennifer:

Jennifer earned her PhD in Ecology in 2010 from the University of North Carolina at Chapel Hill. Since then, she’s been working in the North Carolina Cooperative Fish and Wildlife Research Unit at NC State University. Jennifer has used LANDFIRE data for a number of projects, from assessing threats to vegetation and fire regimes in the Southeast, which she'll talk about today, to examining the potential impacts of bioenergy production scenarios to landscapes and wildlife across North Carolina.

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The first project was done in the North American Coastal Plain. The work had the goal of assessing vegetation modification in the North American Coastal Plain in order to make the case that it is a global biodiversity hotspot. In order to be a hotspot, a region has to have a high level of plant and animal species richness and endemism, but also must have a high degree of vegetation modification - over 70% modified. This is a map of the study area, showing two ways to define the Coastal Plain. My work was done in the US portion of the region (excluding the gray south of Texas.

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We wanted to know how much of the region has been modified, but also which habitats have been most modified. We did this analysis at a regional level and also for individual ecosystems.

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We know that the Coastal Plain contains some of the globe’s most endangered, modified ecosystems. There have been several past estimates of the extent of modification in the region, but all of these have been qualitative or based on data from only a subset of the region. A quantitative, regional, wall-to-wall assessment is needed, but there are few data sets that capture the specific types of modification we have in the Southeast.

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When we talk about modified vegetation, we’re definitely including conversion to other land uses – like urban areas or agriculture. It’s easy to see from aerial photos and satellite imagery like this where urban or suburban areas occur.

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Indeed, there are regional and national data sets – such as this map

from NLCD - that show urban and agricultural lands, so we can get an

idea of how much vegetation has been converted to those land uses.

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However, a large portion of vegetation in the coastal plain that has not

been converted has a structure or composition that is different from its

historic condition. Here’s an example the typical historic structure of

longleaf pine savannas, which have an open understory and a few

trees of varying ages. Today, some longleaf ecosystems can look like

the picture on the top right, which has many more trees, or the picture

on the bottom right, which has a dense understory. These types of

changes, especially the one on the bottom, come from fire

suppression.

I was interested in incorporating these types of modification into my

analysis.

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So, I defined vegetation modification as not only the portion converted, but also the portion with highly departed structure and fire regime.

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LANDFIRE is a source of data that does incorporate both conversion and many types of vegetation departure – especially departure in canopy structure. LANDFIRE data includes spatial data mapping Biophysical Settings, or BpS, which are ecosystems that may have been dominant on the landscape prior to European settlement. There are 159 BpS’s in the Coastal Plain. I used these as a reference for what unmodified vegetation would have looked like.

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Also, I used the state-and-transition models that were developed by LANDFIRE for each BpS. These models were developed by experts to represent pre-settlement vegetation dynamics. Here is an example of a model for a longleaf pine BpS. There are five “succession classes”, with two successional pathways. Succession and disturbance transitions are represented by arrows among state classes. Disturbances (especially fire) have probabilities that reflect pre-settlement. So, in this case, a stand that starts in early succession, and after 15 years, it proceeds along the bottom pathway to open conditions, as long as there is frequent fire. Replacement fires can reset succession, and surface fires (solid looped arrows) can also burn, but do not reset succession. An alternate pathway can take the stand to a closed state if fire is not present. Running these models over time lets you estimate the proportion of each BpS that would have typically existed in each box, or state class historically. So, I used these state-and-transition models as a reference for what the typical canopy structure of each vegetation type would have been in the Coastal Plain.

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LANDFIRE also mapped current s-classes, so we can use these as a measure of the current canopy structure in the region. Class A – Class E refers to the five boxes in each LANDFIRE state-and-transition model. The s-class data also includes agriculture and urban pixels, so we can calculate the current proportion of each ecosystem that has been converted.

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For each BpS, then, it is possible to calculate the proportions in each S-class historically (in light blue) and currently (in dark blue).

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Comparing the two is a measure of vegetation departure. However, the LANDFIRE models distinguish canopy structure, but do not usually distinguish between open and closed understory, or the condition of the understory.

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To capture changes in understory structure and composition, I added a metric of fire regime departure to the vegetation departure metric. I compared the annual areas burned from recent wildfire records from across the region to annual areas burned when running the LANDFIRE state-and-transition models. The result was that recent wildfire records only showed 15% of the area burned in the LANDFIRE reference models. Therefore, we can say that the current fire regime is 85% departed from presettlement. To be conservative, I only applied this departure metric to high fire frequency ecosystems, where I could be certain that recent fire conditions would affect the vegetation. In systems with less frequent fires, I am less certain that lack of fire recently would affect the vegetation.

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Vegetation departure, fire departure, and conversion were combined to assess modification. The portion of each BpS that had an average vegetation and fire regime departure greater than 66% was considered highly departed. I added the highly departed portion to the proportion converted to get modification. I summarized the level of modification by BpS, also by broad habitat types (groups of BpS’s) and for the entire region.

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Here are the results for the entire region (on the left) and three broad habitat types on the right. The numbers indicate the total modification. According to the results, the Coastal Plain is 85% modified overall. It does qualify as a global biodiversity hotspot! About half of that is converted to urban or agriculture, and half is not urban or ag, but is highly departed vegetation. However, modification varies by habitat type. There is a lower rate of modification for forests, but for grasslands and savannas/woodlands, the total modification rate is much higher. Those high rates generally match the estimates I showed in an earlier slide. Also, the proportions converted and departed vary by habitat type. In forests, a greater portion is converted, while in savannas and woodlands, a greater proportion of the modification comes from highly departed vegetation. Management and restoration of savannas and woodlands that have not been converted are greater, and often easier than for those that have been converted.

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Here are a few examples of the same numbers for individual BpS’s. Those that correspond to longleaf pine savannas/woodlands have a high degree of total modification, while forests have a range of modification rates. As was evident in the previous slide for habitat groups, the rates of high departure and conversion vary by BpS. I have results like these for every BpS in the Coastal Plain. This assessment can provide a starting point to determine potential conservation strategies for individual BpS’s. Perhaps starting with ecosystems that have the highest levels of modification would be a good option. Or perhaps determining which systems have had the highest levels of conversion makes sense.

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The second portion of my talk focuses on a smaller ecoregion on the Coastal Plain of Alabama, Georgia, and Florida. Here is a map showing the current distribution of longleaf, agriculture, planted pine, and urbanization in the ecoregion.

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We already saw this graph characterizing the past and present distributions of s-classes in the longleaf pine ecosystem. In this project, the question is, what will the future be?

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In particular, there have been some recent recommendations for the future of longleaf ecosystems. For example, there were two goals, or recommendations, from the Range-Wide Conservation Plan in 2009. That report recommended increasing the total area of the longleaf ecosystem by 135%, and doubling the current area of open longleaf. The recommended general management strategies included a focus on maintaining open stands, and a secondary emphasis on improving and restoring degraded stands.

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In particular, there have been some recent recommendations for the future of longleaf ecosystems. For example, there were two goals, or recommendations, from the Range-Wide Conservation Plan in 2009. That report recommended increasing the total area of the longleaf ecosystem by 135%, and doubling the current area of open longleaf. The recommended general management strategies included a focus on maintaining open stands, and a secondary emphasis on improving and restoring degraded stands.

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I started with the same state-and-transition model for longleaf that we saw before, with five state classes, and succession and disturbance transitions among them. The LANDFIRE model represented presettlement conditions, so I first modified the fire probabilities to reflect those in recent prescribed fire data, similar to the approach for fire regime departure that I described earlier. According to recent fire records in the Dougherty Plain, surface fires occurred with an annual probability of 2%. That is much lower than the 33% probability that was originally in the LANDFIRE model.

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The next modification to the state-and-transition model I made was to include the effect of climate change on those wildfire probabilities. Colleagues and I developed a statistical relationship between recent annual area burned and climate variables. We then used climate projections to project the future area burned under the same relationship. The change in area burned from today was used as a “multiplier” to the wildfire probabilities in the state-and-transition model. We did this for two climate emissions scenarios. In the graph, the middle line shows the multiplier values. Note that it is fairly close to 1, indicating little change in wildfire probabilities through time under this scenario. The result was the same for both scenarios.

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Another modification I made to the sate-and-transition models was to add in the possibility of longleaf being urbanized. I used recent projections of urbanization for the region to determine the annual probability of any part of the landscape being urbanized.

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I added transitions to a new “urban” class from all of the longleaf pine state classes.

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Finally, I added three types of management. First, Maintenance management is represented by the looped arrows. It involved prescribed burning of early and open canopy longleaf to maintain the fire frequency in those stands, and prevent them from becoming closed canopy stands. Second, Improvement involved a series of prescribed burns and other management to transition closed canopy stands to open canopy. Third, Restoration also involved a series of management steps to transition planted pine and agricultural lands to early succession stands. I determined annual probabilites, including failure rates, for these from literature.

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Once the state-and-transition models were modified, I developed a series of scenarios that incorporated climate change, urbanization, and management. The first six have no management included, only combinations of climate emissions scenarios, with and without urbanization. Eight have management included. Of those, half have maintenance burning only, since the first focus according to the Range-Wide Plan should be maintaining open stands. The other half have a portfolio of all three management activities. For each of those, I included a level of management that was the same as recent levels, as well as a doubling of management effort. To determine areas treated in each scenario, I used data and literature on recent levels of prescribed burning and how those areas are allocated.

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I ran the modified state-and-transition model through 2100 for all 14 of those scenarios. These are graphs of area of the longleaf state classes through time. The blues are early and open longleaf, and the reds are closed longleaf. First, notice that the scenarios without management (along the top and second row) generally resulted in a build-up of closed canopy state classes. That is because even with climate change, fire is relatively infrequent in the landscape. The other scenarios that incorporate management have more open canopy classes. Second, only the scenarios that include a portfolio of management activities result in more total longleaf on the landscape through time. That is because those are the only scenarios in which non-longleaf is restored to longleaf. Those scenarios also result in more total area of longleaf than the other management scenarios.

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Here is an alternative way to look at the results, tying them directly to the conservation goals outlined in the Range-Wide Plan. First, colors correspond to scenarios. All six of the scenarios without management overlap one another and are represented by the orange dots. The blues are scenarios with management, and the type of management in each is written to the right of the graph. For each color, triangles are scenarios including urbanization, and circles are without urbanization. On the x-axis is change in total area of longleaf. Scenarios to the right have an increase in total longleaf area. On the y-axis is hange in area of open longleaf. Scenarios toward the top have an increase in area of open longleaf. Notice first that all scenarios with management (even those including urbanization) again did a better job than those without management. They show an increase of at least one of the longleaf area variables. The conservation goal of doubling the area of open longleaf is shown in red. Several scenarios accomplished that goal and appear above that red line. The other conservation goal of increasing the total area of longleaf by 135% is not shown. It would be a vertical line off of the right margin of the graph. No scenario came close to meeting that goal.

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The bottom line is that any management was better than no management, even with urbanization included. Additional scenarios should be considered, along with available costs and resources.

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Customizing Data preview

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