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  Designing Sustainable Landscapes: Salt Marsh Ditching Metric

  Kevin McGarigal, Brad Compton, Ethan Plunkett, Bill DeLuca, and Joanna Grand

  The majority of salt marsh ditches in the Northeast have been ditched, both to facilitate harvest of salt marsh hay and to control mosquitoes (Smith and Niles 2016). Ditching changes the hydrology and flows of sediment and nutrients of marshes in ways that are not well understand, though ditched marshes may have altered invertebrate and shorebird communities, and may be less resilient to sea level rise (LeMay 2007). Marshes with intensive ditching (ca. 10 m spacing) appear to be most strongly affected (Vincent et al. 2013). The salt marsh ditching metric is an element of the ecological integrity analysis of the Designing Sustainable Landscapes (DSL) project (see technical document on integrity, McGarigal et al 2017). Consisting of a composite of 21 stressor and resiliency metrics, the index of ecological integrity (IEI) assesses the relative intactness and resiliency to environmental change of ecological systems throughout the northeast. As a stressor metric, salt marsh ditching provides an index of the relative intensity of ditching in salt marshes. Metric values range from 0 (no effect from nearby ditches) to 1 (severe effect). The metric is based on a custom image analysis process that identifies most ditches in salt marshes throughout the northeast from 1 m LiDAR (Light Detection And Ranging)-based DEMs (Digital Elevation Models). The algorithm, uses a kernel to identify local depressions that could be ditches. It then uses a morphological skeletonize algorithm to draw a 1 m-wide line through the middle of depressions, and then uses an original approach, “clockfacing,” to find linear features in these centerlines of depressions and connect disconnected sections. These potential ditches are converted from raster to linear features, and long, fairly straight sections are tagged as ditches. These linear ditches are converted back to a 1 m raster, then to a 30 m raster, with a value indicating ditch density in each cell. Finally, the ditching metric itself measures the intensity of ditches in the neighborhood of each salt marsh cell using a kernel estimator. Funding for this project was provided by the North Atlantic Landscape Conservation Cooperative and Department of the Interior Project #24, Decision Support for Hurricane Sandy Restoration and Future Conservation to Increase Resiliency of Tidal Wetland Habitats and Species in the Face of Storms and Sea Level Rise.

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  Designing Sustainable Landscapes: Sea level rise metric

  Kevin McGarigal, Brad Compton, Ethan Plunkett, Bill DeLuca, and Joanna Grand

  The sea level rise metric estimates the probability of the focal cell being unable to adapt to predicted inundation by sea level rise (SLR). Whether a site gets inundated by salt water permanently due to sea level rise or intermittently via storm surges associated with sea level rise determines whether an ecosystem can persist at a site and thus its ability to support a characteristic plant and animal community. Based on a sea level rise inundation model developed by USGS Woods Hole (Lentz et al. 2015). The sea level rise metric is an element of the ecological integrity analysis of the Designing Sustainable Landscapes (DSL) project (see technical document on integrity, McGarigal et al 2017). Consisting of a composite of 21 stressor and resiliency metrics, the index of ecological integrity (IEI) assesses the relative intactness and resiliency to environmental change of ecological systems throughout the northeast. As a stressor metric, sea level rise ranges from 0 (no effect from sea level rise) to 1 (severe effect). Sea level rise is only applied to future time steps; for 2010, the value of sea level rise is always zero.

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  Designing Sustainable Landscapes: Tidal Restrictions metric

  Kevin McGarigal, Brad Compton, Ethan Plunkett, Bill DeLuca, and Joanna Grand

  Tidal restrictions include undersized culverts and bridges, tide gates, dikes, and other structures that interfere with normal tidal flushing in estuarine systems. Effects can range from mild changes in species composition and cycling of sediment and nutrients to wholesale conversion of ecological systems, such as conversion of Spartina-dominated salt marshes to Phragmites australis, or, in extreme cases, to freshwater wetlands (Roman et al. 1984, Ritter et al. 2008). The tidal restrictions metric is an element of the ecological integrity analysis of the Designing Sustainable Landscapes (DSL) project (see technical document on integrity, McGarigal et al 2017). Consisting of a composite of 21 stressor and resiliency metrics, the index of ecological integrity (IEI) assesses the relative intactness and resiliency to environmental change of ecological systems throughout the northeast. As a stressor metric, tidal restrictions uses an estimate of the historic loss of mapped salt marshes in areas where they should occur given elevation and tidal regime to indicate the location and magnitude of potential tidal restrictions. The metric estimates the effect of potential tidal restrictions on upstream wetland systems, including intertidal systems such as salt marshes, as well as freshwater systems and low-lying nonforested uplands that may have once been intertidal. Metric values range from 0 (no effect from downstream tidal restrictions) to 1 (severe effect). The metric is based on an estimate of the salt marsh loss ratio above each potential tidal restriction (road-stream and railroad-stream crossings). Note that tide gates not associated with roads are excluded as potential tidal restrictions, as they are not comprehensively mapped throughout the region. The salt marsh loss ratio is the proportion of a basin above a crossing that is modeled as potential salt marsh (from tide range and elevation) but not mapped as existing salt marsh in the National Wetlands Inventory (NWI) maps. Funding for this project was provided by the North Atlantic Landscape Conservation Cooperative and Department of the Interior Project #24, Decision Support for Hurricane Sandy Restoration and Future Conservation to Increase Resiliency of Tidal Wetland Habitats and Species in the Face of Storms and Sea Level Rise.

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  Designing Sustainable Landscapes: Traffic metric

  Kevin McGarigal, Brad Compton, Ethan Plunkett, Bill DeLuca, and Joanna Grand

  The traffic metric assesses the effect of road (and railroad) traffic on animal populations due to road mortality. It integrates the distance to and traffic intensity of roads in the neighborhood of the focal cell. The traffic metric (Fig. 1) is an element of the ecological integrity analysis of the Designing Sustainable Landscapes (DSL) project (see technical document on integrity, McGarigal et al 2017). Consisting of a composite of 21 stressor and resiliency metrics, the index of ecological integrity (IEI) assesses the relative intactness and resiliency to environmental change of ecological systems throughout the northeast. As a stressor metric, Traffic values range from 0 (no effect from road traffic) to 1 (severe effect; although in real landscapes, the metric never reaches 1).

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  Designing Sustainable Landscapes: Wind exposure settings variable

  Kevin McGarigal, Brad Compton, Ethan Plunkett, Bill DeLuca, and Joanna Grand

  Wind exposure is one of several ecological settings variables that collectively characterize the biophysical setting of each 30 m cell at a given point in time (McGarigal et al 2017). Wind exposure gives the mean sustained wind speed (m/s) at 50 m height. High wind speeds can shape natural communities, especially on exposed high peaks.

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  Designing Sustainable Landscapes: Watershed habitat loss, watershed imperviousness, road salt, sediment, nutrients, and dam intensity metrics

  Kevin McGarigal, Brad Compton, Ethan Plunkett, Bill DeLuca, and Joanna Grand

  This document describes a suite of stressor metrics that assess the various effects of development in the watershed of the focal cell, as opposed to a (usually) circular window around the focal cell, as with the other metrics. These metrics are used for lotic, lentic, and wetland systems. All effects are weighted by a the time of flow from each stressor source to the focal cell, thus, stressor sources that fall within a stream have a greater effect than those in distant uplands within the watershed. These share a common algorithm, but each has unique parameters. These metrics are elements of the ecological integrity analysis of the Designing Sustainable Landscapes (DSL) project (see technical document on integrity, McGarigal et al 2014). Consisting of a composite of 21 stressor and resiliency metrics, the index of ecological integrity (IEI) assesses the relative intactness and resiliency to environmental change of ecological systems throughout the northeast. These stressor metrics range from 0 (no effect) to maximum values that differ for each metric (severe effect). See Table 1 for parameters for each metric.

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  Designing Sustainable Landscapes: Water salinity settings variable

  Kevin McGarigal, Brad Compton, Ethan Plunkett, Bill DeLuca, and Joanna Grand

  Water salinity is one of several ecological settings variables that collectively characterize the biophysical setting of each 30 m cell at a given point in time (McGarigal et al 2017). Salinity, which varies from 0‰ in freshwater to 30‰ in seawater, is a major driver of aquatic systems, as very few organisms can survive across this full range.

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  Designing Sustainable Landscapes: Topographic wetness and Flow volume settings variables

  Kevin McGarigal, Brad Compton, Ethan Plunkett, Bill DeLuca, and Joanna Grand

  Topographic wetness and flow volume are two of several ecological settings variables that collectively characterize the biophysical setting of each 30 m cell at a given point in time (McGarigal et al 2017). These variables are two ways of assessing the flow of water; they share an underlying algorithm. Topographic wetness gives an estimate of the amount of moisture at any point in the landscape based on topography, which has a major effect on species habitat, soils, and the nutrient cycle. It ranges, in arbitrary units, from low values at hilltops and steep upper slopes to high values in low, flat areas with high flow accumulation. All lotic and lentic waterbodies share the same maximum value. Flow volume ranges in arbitrary units from 0 in uplands to a maximum in large rivers. It estimates the amount of water flowing into and through aquatic and wetland systems, which, along with gradient, largely determines species habitat and sediment transport. Flow volume is often coarsely estimated by stream order.

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  Designing Sustainable Landscapes: Tides settings variable

  Kevin McGarigal, Brad Compton, Ethan B. Plunkett, Bill DeLuca, and Joanna Grand

  Tides is one of several ecological settings variables that collectively characterize the biophysical setting of each 30 m cell at a given point in time (McGarigal et al 2017). Tides estimates the probability that a point is intertidal or subtidal. It is derived from a logistic regression model using tide range and elevation to distinguish mapped salt marshes from uplands.

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  Designing Sustainable Landscapes: Terrestrial barriers settings variable

  Kevin McGarigal, Brad Compton, Ethan B. Plunkett, Bill DeLuca, and Joanna Grand

  Terrestrial barriers is one of several ecological settings variables that collectively characterize the biophysical setting of each 30 m cell at a given point in time (McGarigal et al 2017). Terrestrial barriers measures the relative degree to which roads and railroads may physically impede movement of terrestrial organisms. It is derived by assigning an expertderived score to each road/railroad class to reflect the increasing physical impediment of larger roads, and adjusting these scores at road-stream crossings (i.e., bridge or culvert) based either on a custom algorithm applied to field measurements of the crossing structure or predictions from a statistical model (see below for details) to reflect increased passability of terrestrial organisms through the crossing structure. Terrestrial barriers is scaled 0-5, where roads and railroads are assigned values >0 (indicating the relative degree of impediment) and all other cells are assigned 0.

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  Designing Sustainable Landscapes: Substrate mobility settings variable

  Kevin McGarigal, Brad Compton, Ethan B. Plunkett, Bill DeLuca, and Joanna Grand

  Substrate mobility is one of several ecological settings variables that collectively characterize the biophysical setting of each 30 m cell at a given point in time (McGarigal et al 2017). Substrate mobility measures the realized mobility of the physical substrate, due to both substrate composition (e.g., sand) and exposure to forces (wind and water) that transport material. This is an important attribute of certain dynamic systems (e.g., coastal dune systems); given as a simple index of mobility (1 = stable, 10 = highly mobile). Substrate mobility is assigned by landcover class, derived from expert opinion. This settings variable is dynamic, changing with urban growth.

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  Designing Sustainable Landscapes: Stream temperature settings variable

  Kevin McGarigal, Brad Compton, Ethan Plunkett, Bill DeLuca, and Joanna Grand

  Stream temperature is one of several ecological settings variables that collectively characterize the biophysical setting of each 30 m cell at a given point in time (McGarigal et al 2017). Several fish species (e.g., brook trout) can only survive in coldwater streams, which have higher levels of dissolved O2, while other fish species are adapted to warmer streams. At the same time, ectotherms such as aquatic insects and fish can develop more quickly in warmer streams. Stream temperature is a coarse classification of streams by mean annual temperature.

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  Designing Sustainable Landscapes: Soil available water supply, Soil depth to restrictive layer, and Soil pH settings variables

  Kevin McGarigal, Brad Compton, Ethan Plunkett, Bill DeLuca, and Joanna Grand

  These three soils variables are among several ecological settings variables that collectively characterize the biophysical setting of each 30 m cell at a given point in time (McGarigal et al 2017). Soils are important drivers of natural communities. We picked three soil attributes that represent the most important factors: depth, chemistry, and water-holding capacity. Depth to resistant layer measures the depth of soils to a restrictive layer (e.g., bedrock) that limits root depth. Areas with shallow soils (usually on steep slopes or ridgetops) can’t support deep-rooted plants. Soil pH strongly affects nutrient uptake by plants. In the east, soils with higher pH (e.g., in areas with limestone bedrock) tend to support a greater diversity of plants, including a number of species that typically grow only in sweet soils. Conversely, some groups of plants (such as members of Ericaceae) are specialized to acidic soils, where generalist species grow poorly at best. Available water supply (AWS) measures the water-holding capacity of soils. It is measured as the total volume of water that available to plants when the soil, inclusive of rock fragments, is at field capacity. Soils with a high AWS are more drought-resistant, supporting plant growth through periods without rain—for instance, good agricultural soils have a high AWS. AWS is calculated as the available water capacity times the thickness of each soil horizon to a specified depth (25 cm in this case). Note that AWS is distinct from our topographic wetness settings variable, which estimates the amount of water delivered to the soil at each point.

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  Designing Sustainable Landscapes: Slope settings variable

  Kevin McGarigal, Brad Compton, Ethan Plunkett, Bill DeLuca, and Joanna Grand

  Slope is one of several ecological settings variables that collectively characterize the biophysical setting of each 30 m cell at a given point in time (McGarigal et al 2017). Slope gives the percent slope at each cell. High slopes indicate a propensity for gravityinduced physical disturbance (e.g., talus slopes), which can limit plant development. Slope ranges from 0% for flat areas to theoretically infinity for absolutely vertical cliffs, though the actual maximum occurring in our landscape is 440%.

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  Designing Sustainable Landscapes: Potential dominant life form settings variable

  Kevin McGarigal, Brad Compton, Ethan Plunkett, Bill DeLuca, and Joanna Grand

  Potential dominant life form is one of several ecological settings variables that collectively characterize the biophysical setting of each 30 m cell at a given point in time (McGarigal et al 2017). Potential dominant life form (unvegetated, herbaceous, shrubland, woodland, forest) represents the structure of vegetative community at a site and is used, for example, to distinguish early successional forest from permanent grassland or shrubland. Potential dominant life form is assigned by landcover class, derived from expert opinion. This settings variable is dynamic, changing with urban growth.

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  Designing Sustainable Landscapes: Imperviousness settings variable

  Kevin McGarigal, Brad Compton, Ethan Plunkett, Bill DeLuca, and Joanna Grand

  Imperviousness is one of several ecological settings variables that collectively characterize the biophysical setting of each 30 m cell at a given point in time (McGarigal et al 2017). Imperviousness measures the percentage of the ground surface area that is impervious to water infiltration, which is an indicator of intensive development and thus an important determinant of ecological communities. This is a dynamic settings variable, increasing with future urban growth.

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  Designing Sustainable Landscapes: Incident solar radiation settings variable

  Kevin McGarigal, Brad Compton, Ethan Plunkett, Bill DeLuca, and Joanna Grand

  Incident solar radiation is one of several ecological settings variables that collectively characterize the biophysical setting of each 30 m cell at a given point in time (McGarigal et al 2017). The amount of sun affects temperature, moisture, and plant growth, affecting the communities found in each place.

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  Designing Sustainable Landscapes: Mean annual temperature, Growing season degree days, Heat index, Minimum winter temperature, and Maximum summer temperature settings variables

  Kevin McGarigal, Brad Compton, Ethan Plunkett, Bill DeLuca, and Joanna Grand

  These five temperture variables are among several ecological settings variables that collectively characterize the biophysical setting of each 30 m cell at a given point in time (McGarigal et al 2017). The temperature regime strongly affects species composition, as well as rates of ecological processes such as nutrient cycling. We’ve chosen five variables to represent different aspects of temperature. All five variables have future versions that incorporate climate change via General Circulation Models (GCMs) (as described in the technical document on climate, McGarigal et al 2017).

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  Designing Sustainable Landscapes: Traffic settings variable

  Kevin McGarigal, Brad Compton, Ethan Plunkett, Bill DeLuca, and Joanna Grand

  Traffic is one of several ecological settings variables that collectively characterize the biophysical setting of each 30 m cell at a given point in time (McGarigal et al 2017). Traffic measures the estimated probability of an animal crossing the road being hit by a vehicle given the mean traffic rate, an important determinant of landscape connectivity for mobile terrestrial organisms. It is based on an empirical model of mean vehicles per day, using point counts of traffic, and a transformation to estimate the mortality rate for road crossings. Traffic is a dynamic settings variable, increasing in future timesteps with urban growth.

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  Designing Sustainable Landscapes: Stream gradient settings variable

  Kevin McGarigal, Brad Compton, Ethan Plunkett, Bill DeLuca, and Joanna Grand

  Stream gradient is one of several ecological settings variables that collectively characterize the biophysical setting of each 30 m cell at a given point in time (McGarigal et al 2017). Stream gradient is a measure of the percent slope of a stream, which is a primary determinate of water velocity and thus sediment and nutrient transport, and habitat for aquatic plants, invertebrate, fish, and other organisms. Stream gradient is often approximated by categories such as pool, riffle, run, and cascade. Stream gradient is 0% for lentic waterbodies, palustrine, and uplands. It ranges from 0% to infinity (theoretically) for streams.

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  Designing Sustainable Landscapes: All Ecological Settings

  Kevin McGarigal, Brad Compton, Ethan Plunkett, Bill DeLuca, and Joanna Grand

  The ecological settings products include a broad suite of static as well as dynamic abiotic and biotic variables representing the natural and anthropogenic environment at each location (cell). Static variables are those that do not change over time (e.g., elevation, incident solar radiation). Dynamic settings are available for 2010 and 2080; static settings are available for 2010. Dynamic variables are those that change over time in response to succession and the drivers (e.g., growing season degree days, traffic rate). Most of the settings variables are continuous and thus represent landscape heterogeneity as continuous (e.g., slope, biomass), although some are categorical and thus represent heterogeneity as discrete (e.g., developed, hard development). Importantly, the settings variables include a broad but parsimonious suite of attributes that can be used to define the ecological system at any point in time; they are considered primary determinants of ecosystem composition, structure and function, and determine the ecological similarity between any two locations. As such, they play a key role in the ecological integrity assessment, they are used in species' habitat models to represent important habitat components, as appropriate, and are sometimes used in other model components. The settings provide a rich, multivariate representation of important landscape attributes.

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  Designing Sustainable Landscapes: Development settings variable, Hard development settings variable

  Kevin McGarigal, Brad Compton, Ethan Plunkett, Bill DeLuca, and Joanna Grand

  Development and hard development are two of several ecological settings variables that collectively characterize the biophysical setting of each 30 m cell at a given point in time (McGarigal et al 2017). Development represents all development, scaled from 0 to 10 by development intensity. Hard development is a subset of development, with a value of 1 for very high intensity development only. Both layers come from DSLland, the primary landcover map. These are dynamic settings variables, increasing with future urban growth.

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  Designing Sustainable Landscapes: Biomass settings variable

  Kevin McGarigal, Brad Compton, Ethan Plunkett, Bill DeLuca, and Joanna Grand

  Vegetation biomass is an effective descriptor of the net primary productivity of an ecosystem. As such, it is a fundamental component of the ecosystem's trophic dynamics. In addition, vegetation biomass is an effective proxy for the successional development (or seral stage) of vegetation following a disturbance. Biomass is one of several ecological settings variables that collectively characterize the biophysical setting of each 30 m cell at a given point in time (McGarigal et al 2017). Specifically, biomass measures the estimated above-ground live biomass (Mg/ha) of undeveloped forested (including forested wetlands) cells in 2010 based primarily on a spectral analysis of Landsat imagery by USGS Woods Hole. Note that for forested ecosystems, we also model the predicted change in biomass between 2010-2080 using on a custom succession model trained using Forest Inventory and Analysis (FIA) plot data (see technical document on disturbance and succession, McGarigal et al 2017).

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  Designing Sustainable Landscapes: CaCO3 content settings variable

  Kevin McGarigal, Brad Compton, Ethan Plunkett, Bill DeLuca, and Joanna Grand

  Calcium carbonate (CaCO3) content is one of several ecological settings variables that collectively characterize the biophysical setting of each 30 m cell at a given point in time (McGarigal et al 2017). CaCO3 buffers acidity in soil and water, increasing nutrient uptake by plants, and providing a ready source of calcium for organisms such as aquatic insects. CaCO3 content (Fig. 1), affects the composition of natural communities both directly and indirectly, such that areas with high calcium have increased species richness and support a number of unique species.

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  Designing Sustainable Landscapes: aquatic barriers settings variable

  Kevin McGarigal, Brad Compton, Ethan Plunkett, Bill DeLuca, and Joanna Grand

  Aquatic barriers is one of several ecological settings variables that collectively characterize the biophysical setting of each 30 m cell at a given point in time (McGarigal et al 2017). Aquatic barriers measures the relative degree to which road-stream crossings (i.e., bridges and culverts) and dams may physically impede upstream and downstream movement of aquatic organisms, particularly fish. It is derived from a custom algorithm (see below for details) applied to dams and derived road-stream crossings. Briefly, each dam has an aquatic barrier score based either on dam height or attributes indicating whether the dam has a partial/complete breach. Similarly, each road-stream crossing has an aquatic barrier score based either on an algorithm applied to field measurements of the crossing structure or predictions from a statistical model based on GIS data. Aquatic barriers is scaled 0-1, where dams and road-stream crossing are assigned values >0 (with 1=complete barrier) and all other cells (including terrestrial) are assigned 0.