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I ll be talking about the Designing Sustainable Landscapes project today. This is a large integrated modeling effort our group at UMass has been working on for the past 5 years, in close collaboration with the North Atlantic LCC. Applied to 13 states in the northeast, Designing Sustainable Landscapes assesses current and future landscapes with both coarse and fine filters, with the goal of guiding strategic habitat conservation. Early on, we made the decision to expand beyond the NALCC boundary to the entirety of the 13 states that the NALCC falls in, as much conservation happens at the state level. 2
All modeling is based on a suite of GIS layers at 30 m. Landcover is represented by TNC s ecological systems map, with a number of modifications to include coastal wetlands, streams and lakes, and development. In parallel to the landcover map are about two dozen ecological settings variables, which are meant to represent the important drivers of ecological systems. These settings variables represent abiotic variables such as temperature, wetness, soils, and solar gain, biological variables such as biomass, and anthropogenic variables such as imperviousness and road traffic rates. We can run the landscape 70 years into the future by modifying landcover and settings variables with a succession/disturbance model, an urban growth model, climate change and sea level rise. That s all I m going to say about the future, as my colleague Ethan Plunkett will cover it in his talk, following mine. 3
We assess the landscape in two different ways. This first is with a coarse-filter model of ecological integrity. The index of ecological integrity, or IEI, is an integrated measure of intactness (that is, freedom from human disturbance), connectivity, and resilience for the ecosystem at each cell. IEI is based on a suite of about two dozen metrics that measure, for instance, the influence of road traffic, the amount of imperviousness in the watershed of a cell, and the connectivity of a cell to surrounding similar cells. Each ecological system can have a unique model in IEI, based on expert-derived weightings of the constituent metrics. IEI is quantile-scaled by ecological system, so interpretation is simple: values > 0.9 represent the best 10% of each system across the landscape. 4
We also assess the landscape with models for 30 representative wildlife species. Each species has a quantitative habitat capability model (and, for future timesteps, a climateniche model). 5
Although IEI and the species models provide a useful way to assess the landscape, and are being used by practitioners, the LCC wanted to build a more formal conservation design. We spent a year working closely with a group of some 30 stakeholders from state and federal agencies and NGOs on a pilot Landscape Conservation Design project covering the Connecticut watershed in 4 states, which the LCC has branded Connect the Connecticut. 6
At the heart of the Landscape Conservation Design pilot is a reserve network of cores with connectivity among them. The goal is to help target and focus conservation actions, as well as help conservationists envision what a 25% or 50% reserve network in the northeast might look like. The reserve network was built through an objective automated modeling processes, with lots of input from stakeholders on the approach. 7
We start with a selection index that consists of our Index of Ecological Integrity, TNC s Resilience, and state S1-S3 rare communities, as well as mapped floodplains. At each cell, we take the maximum quantile-scaled score of IEI and Resilience, so that sites that either model sees as important get a high value. IEI tends to emphasize larger intact, connected areas, while Resilience emphasizes diversity of geophysical settings. 8
Here s an example of the selection index, with higher scores in green. Note the solid green blobs: these are rare communities and floodplains, which get the maximum score, and thus are guaranteed to make it into the reserve network (unless they re tiny). The selection index is quantile-scaled. 9
We then take a slice of the top 5% of the selection index, shown here in yellow. These seeds form the basis of cores. Tiny seeds those smaller than 3.6 ha are dropped. Note that since we ve quantile-scaled, the seeds represent the best 5% of each ecological system and geophysical setting, proportional to representation. 10
We then spread outward from the seeds using resistant kernels, with the selection index used as resistance. They say that if all you have is a hammer, everything looks like a nail. Well, in our lab, resistant kernels are our hammer they are the basis of the connectedness metrics in IEI, the home range assessment in the species models, the cores, as shown here, and you ll see them twice more when we add species to the cores and when we connect cores. A resistant kernel works by spreading outward from a source cell or patch across a resistant surface, losing momentum as it spreads. Here, you can see the resistant kernels built from seeds in the high-valued undeveloped areas spread quite far, while those built from seeds in the developed valley at the center don t spread far the landscape is too resistant. 11
Now we quantile-scale the resistant kernels and take a slice here, the top 20% of the landscape. 12
The areas in baby blue are our ecosystem-based cores. In this application, resistant kernels allow us to build large cores in relatively undisturbed areas, and small cores in areas constrained by development. Now, these cores are based only on coarse-filter assessments, and thus are likely to omit habitat for some species, especially those with juxtaposition needs. 13
We use the species models to add another 5% of the landscape to the cores (resulting in cores that represent 25% of the landscape; we also did a 50% version). Each species was assigned a target by stakeholders in terms of the proportion of landscape capability captured in cores. 14
We used an iterative model that built a joint selection index for all species, weighted by how far short of their goal each species was. In this example of a species selection index, we re at an iteration in which riparian areas (in green) are rated highly, which suggests that this iteration is focusing on Louisiana Waterthrush and wood turtle. We pick the highest scored cell at each iteration (the red dot), and then build a resistant kernel to select an area to add to the cores, here in yellow. 15
We iterate until we ve picked an additional 5% of the landscape (here in yellow), giving us cores that cover 25% of the landscape, representing high-valued areas for both ecosystems and species. This approach represents a trade-off among efficiency, cohesiveness, complementarity, and core size. It s not an optimization, and it s hard to imagine optimizing for so many competing criteria, but it works pretty well. There are a number of parameters that control core sizes and a number and other aspects of the design, and we spent an inordinate amount of time doing different runs until the stakeholder team was satisfied. 16
Now that we have a network of cores, we want to connect them. We start by taking each core one at a time, designated a to-core, and taking each neighboring core, designated a from-core. We want the paths between cores to be context-dependent; thus a from-core that s mostly wetland should connect via wetlands and lowlands to wetlands in the to-core, and a core that s mostly mountain tops should connect via ridge-tops to the highlands in the to-core. To achieve this, we need context-dependent resistant surfaces. 17
We get this by building a unique resistant landscape for each focal cell, based on the ecological distance between the focal cell and neighboring cells. We take the two dozen ecological settings variables. 18
and measure the distance in multidimensional settings space between the focal cell and each neighbor this gives us the ecological distance, and thus the resistance. This is an approach that we also use in the connectedness metric that s part of IEI. 19
Thus, here s a resistant landscape for a particular forest cell. Note that other forest cells will have somewhat different resistant landscapes, depending on whether they re wetter or sunnier or steeper. Development has a high ecological distance from all natural systems, and roads have an even greater distance the more traffic there is, the more resistant roads are. 20
Here s a resistant landscape for an acidic rocky outcrop. 21
And here s one for a stream. 22
For each formation that s represented in the from-core, we build a resistant kernel from 100 random points in that formation in the to-core. Resistant kernels encode least-cost paths, so we can pick a random point in the current formation in the from-core and build a least-cost path to the to-core. 23
However, there are a number of problems with least-cost paths. They re quite sensitive to GIS data errors, and ignore perfectly-good sub-optimal alternatives. 24
Our approach, instead, is to build a number of random low-cost paths from random points in the from-core to the first cell in the corresponding formation of the to-core. Random low-cost paths fall somewhere between least-cost paths and random walks. The idea is that a large number of random low-cost paths can do a better job of depicting connectivity that a least-cost path. We build 1000 random low-cost paths between every pair of cores. 25
The paths between each pair of cores are weighted by the path-cost and the size and quality of the cores, which gives us conductance. Conductance is a measure of the connectivity between cores, dependent on the composition of each core. Cores with a lot of wetlands will tend to have high conductance through wet areas connecting them to other cores, while mountainous cores will tend to have high conductance along ridge-tops. 26
The stakeholders wanted something simpler than conductance to connect cores they wanted binary corridors. So we kept the best paths between each pair of cores and buffered them to get connectors. Pairs of cores that have high conductance will have more connectors, while cores that are distant or poorly connected will have fewer connectors, or maybe none. 27
The cores and connectors give us a blueprint that can help target conservation. It s unlikely that we ll see this reserve network enacted as it s depicted here. There are too many local considerations in protecting land, and the process of protecting this much land will take many decades, during which both the landscape and our criteria for protecting land will change. I think one of the values of a complete design such as this is to provide a framework for thinking about how much land we want to protect, about the configuration we want to target, and especially to pay more attention to the composition of reserves and how reserves complement each other. Approaches like this can encourage thinking strategically about land conservation. 28
Something I ve become aware of through this process is how diverse the needs and goals of conservation practitioners are from federal to state agencies, to local and regional land trusts. I ve gained an appreciation for how sophisticated many people who are protecting land on the ground have become. As such, a one size fits all approach is inadequate. It s important to keep in mind that the constituent tools ecological integrity, species models, conductance, and others can be very useful for conservationists. We re now working on expanding the Landscape Conservation Design to the 13 northeastern states, under the Regional Conservation Opportunity Areas project of the NALCC. We should have a draft product this summer. Finally, I didn t mention landscape change at all it s a key aspect of the Designing Sustainable Landscapes project. Ethan Plunkett will have plenty to say about landscape change in a few minutes. 29
I ll end here, with a link to our website, where tons of documentation and all of the regional results are up, as well as results from the Connecticut pilot. There are several people to thank; I ll focus on Andrew Milliken and Scott Schwenk at the North Atlantic LCC, without whom this work would never have happened. Thank you.