ScholarWorks@UMass Amherst

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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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  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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  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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  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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  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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  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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  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.