ScholarWorks@UMass Amherst

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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.

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  Northeast Invasive Plants Data

  Tyler Cross, John T. Finn, and Bethany Bradley

  The data are distribution and ranked abundance data for thirteen invasive plants in the Northeast US compiled from various spatial repositories for invasive species. iMAP invasives data are not included in this dataset because they are not publicly available. iMAP data can be requested from individual states.

  These data form the basis of analyses presented in Cross et al. 2017. "Frequency of invasive plant occurrence is not a suitable proxy for abundance in the Northeast US Ecosphere".

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  Data for "Interplay Between Ion Transport, Applied Bias and Degradation under Illumination in Hybrid Perovskite p-i-n Devices"

  Emily C. Smith, Christie L.C. Ellis, Hamza Javaid, Lawrence A. Renna, Yao Liu, Thomas P. Russell, Monojit Bag, and Dhandapani Venkataraman

  We studied ion transport in hybrid organic inorganic perovskite p-i-n devices as a function of applied bias under device operating conditions. Using electrochemical impedance spectroscopy (EIS) and equivalent circuit modeling, we elucidated various resistive and capacitive elements in the device. We show that ion migration is predictably influenced by a low applied forward bias, characterized by an increased capacitance at the hole transporting (HTM) and electron transporting material (ETM) interfaces, as well as through the bulk. However, unlike observations in n-i-p devices, we found that there is a capacitive discharge leading to ion redistribution in the bulk at high forward biases. Furthermore, we show that a chemical double layer capacitance buildup as a result of ion accumulation impacts the electronic properties of the device, likely by either inducing charge pinning or charge screening, depending on the direction of the ion induced field. Lastly, we extrapolate ion diffusion coefficients (~10-7 cm2 s-1) and ionic conductivities (~10-7 S cm-1) from the Warburg mass (ion) diffusion response, and show that, as the device degrades, there is an overall depletion of capacitive effects coupled with an increased ion mobility.

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  Distribution Locations of Invasive Species (Out of the Weeds? Reduced Plant Invasion Risk with Climate Change in the Continental United States)

  Bethany Bradley and Jenica Allen

  The comma-delimited data file includes the species code (see Supplemental Online Table S2 for full species names), latitude, and longitude in decimal degrees (WGS84) for the occurence points used in model fitting. Some data use agreements prohibit the publication of coordinate data and those points have been removed.

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  Species Potential Range Predictions (Out of the Weeds? Reduced Plant Invasion Risk with Climate Change in the Continental United States)

  Bethany Bradley and Jenica Allen

  This PDF file contains the binary potential range prediction maps for each species in the dataset under current climate. The prediction map for each species lists the species code (see Supplemental Online Table S2 for full species names), areas predicted to be climatically suitable/unsuitable under current climate, and the occurrence points for the species. See the main publication for model fitting details.

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  Ramsey-Musolf According data

  Darrel Ramsey-Musolf

  This CSV file contains the data pertaining to my research on housing plan quality and low-income housing production that was published in Urban Science.

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  The Evidence for Traffic (Data Tables)

  Eric E. Poehler

  The file, "Evidence_for_Traffic_at_Pompeii", is a spreadsheet containing the description of 1024 curbstones, guard stones, and stepping-stones, upon which the evidence for ancient traffic at Pompeii were inscribed. These data underlie and support the tables, figures, and arguments of Poehler, Eric. E. 2017 The Traffic System of Pompeii. (New York: Oxford University Press) and should be used in consultation with that work. Using tabs in the spreadsheet, these data are combined as a single table (Traffic_Evidence_All) and also divided into each object type. Field names are intended to be both human readable and in formats ready for use in other software environments. Copies of these data in more sustainable formats, such as .csv and .txt, are appended as additional files, but include only the combined data table.

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  Anthropogenic Ignitions

  Emily J. Fusco, John Abatzoglou, Jennifer K. Balch, John T. Finn, and Bethany Bradley

  This dataset contains ignition points derived from the MODIS Burned Area Product (MCD45) from 2000-2012), It also contains a random subset of unburned points. Both ignition and unburned points have associated anthropogenic feature data.

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  Model of cheatgrass (Bromus tectorum) distribution across the Great Basin, USA

  Bethany Bradley

  A description of the methods associated with this model can be found in:

  Bradley, B.A., C.A. Curtis, E.J. Fusco, J.T. Abatzoglou, J.K. Balch, S. Dadashi, and M.N. Tuanmu. “Cheatgrass (Bromus tectorum) distribution in the intermountain western United States and its relationship to fire frequency, seasonality, and ignitions”, In Press, Biological Invasions

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  Ignition Cause

  Emily J. Fusco, John Abatzoglou, Jennifer K. Balch, John T. Finn, and Bethany Bradley

  This dataset contains ignition points derived from the MODIS Burned Area Product (MCD45) from 2000-2012), It also contains the determined cause for each ignition.

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  Cheatgrass (Bromus tectorum) percent cover data

  Bethany Bradley

  A compilation of cheatgrass (Bromus tectorum) percent cover data across the western U.S. used to train a regional land cover map as well as assess relationships to fire.

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  Lights, Camera...Citizen Science: Assessing the Effectiveness of Smartphone-based Video Training in Invasive Plant Identification dataset

  Jared Starr, Charles M. Schweik, Nathan Bush, Lena Fletcher, John T. Finn, Jennifer Fish, and Charles T. Bargeron

  The rapid growth and increasing popularity of smartphone technology is putting sophisticated data-collection tools in the hands of more and more citizens. This has exciting implications for the expanding field of citizen science. With smartphone-based applications (apps), it is now increasingly practical to remotely acquire high quality citizen-submitted data at a fraction of the cost of a traditional study. Yet, one impediment to citizen science projects is the question of how to train participants. The traditional “in-person” training model, while effective, can be cost prohibitive as the spatial scale of a project increases. To explore possible solutions, we analyze three training models: 1) in-person, 2) app-based video, and 3) app-based text/images in the context of invasive plant identification in Massachusetts. Encouragingly, we find that participants who received video training were as successful at invasive plant identification as those trained in-person, while those receiving just text/images were less successful. This finding has implications for a variety of citizen science projects that need alternative methods to effectively train participants when in-person training is impractical. This file is the raw data that accompanies the PLoS article.