Item talk:Q302549

{

 "USGS Publications Warehouse": {
   "@context": "https://schema.org",
   "@type": "Article",
   "additionalType": "Journal Article",
   "name": "Data management challenges in species distribution modeling",
   "identifier": [
     {
       "@type": "PropertyValue",
       "propertyID": "USGS Publications Warehouse IndexID",
       "value": "70191673",
       "url": "https://pubs.usgs.gov/publication/70191673"
     },
     {
       "@type": "PropertyValue",
       "propertyID": "USGS Publications Warehouse Internal ID",
       "value": 70191673
     }
   ],
   "journal": {
     "@type": "Periodical",
     "name": "Bulletin of the Technical Committee on Data Engineering",
     "volumeNumber": "36",
     "issueNumber": "4"
   },
   "inLanguage": "en",
   "isPartOf": [
     {
       "@type": "CreativeWorkSeries",
       "name": "Bulletin of the Technical Committee on Data Engineering"
     }
   ],
   "datePublished": "2013",
   "dateModified": "2017-10-17",
   "abstract": "An important component in the fields of ecology and conservation biology is understanding the environmental\nconditions and geographic areas that are suitable for a given species to inhabit. A common tool\nin determining such areas is species distribution modeling which uses computer algorithms to determine\nthe spatial distribution of organisms. Most commonly the correlative relationships between the organism\nand environmental variables are the primary consideration. The data requirements for this type of\nmodeling consist of known presence and possibly absence locations of the species as well as the values\nof environmental or climatic covariates thought to define the species habitat suitability at these locations.\nThese covariate data are generally extracted from remotely sensed imagery, interpolated/gridded\nhistorical climate data, or downscaled climate model output. Traditionally, ecologists and biologists\nhave constructed species distribution models using workflows and data that reside primarily on their\nlocal workstations or networks. This workflow is becoming challenging as scientists increasingly try to\nuse these modeling techniques to inform management decisions under different climate change scenarios.\nThis challenge stems from the fact that remote sensing products, gridded historical climate, and\ndownscaled climate models are not only increasing in spatial and temporal resolution but proliferating\nas well. Any rigorous assessment of uncertainty requires a computationally intensive sensitivity analysis\naccounting for various sources of uncertainty. The scientists fitting these models generally do not have\nthe background in computer science required to take advantage of recent advances in web-service based\ndata acquisition, remote high-powered data processing, or scientific workflow systems. Ecologists in the\nfield of modeling are in need of a tractable platform that abstracts the inherent computational complexity\nrequired to incorporate the burgeoning field of coupled climate and ecological response modeling.\nIn this paper we describe the computational challenges in species distribution modeling and solutions\nusing scientific workflow systems. We focus on the Software for Assisted Species Modeling (SAHM) a\npackage within VisTrails, an open-source scientific workflow system.",
   "description": "10 p.",
   "publisher": {
     "@type": "Organization",
     "name": "IEEE"
   },
   "author": [
     {
       "@type": "Person",
       "name": "Talbert, Colin talbertc@usgs.gov",
       "givenName": "Colin",
       "familyName": "Talbert",
       "email": "talbertc@usgs.gov",
       "identifier": {
         "@type": "PropertyValue",
         "propertyID": "ORCID",
         "value": "0000-0002-9505-1876",
         "url": "https://orcid.org/0000-0002-9505-1876"
       },
       "affiliation": [
         {
           "@type": "Organization",
           "name": "Fort Collins Science Center",
           "url": "https://www.usgs.gov/centers/fort-collins-science-center"
         }
       ]
     },
     {
       "@type": "Person",
       "name": "Talbert, Marian mtalbert@usgs.gov",
       "givenName": "Marian",
       "familyName": "Talbert",
       "email": "mtalbert@usgs.gov",
       "identifier": {
         "@type": "PropertyValue",
         "propertyID": "ORCID",
         "value": "0000-0003-0588-0265",
         "url": "https://orcid.org/0000-0003-0588-0265"
       },
       "affiliation": [
         {
           "@type": "Organization",
           "name": "National Climate Change and Wildlife Science Center",
           "url": null
         },
         {
           "@type": "Organization",
           "name": "North Central Climate Science Center",
           "url": null
         }
       ]
     },
     {
       "@type": "Person",
       "name": "Morisette, Jeffrey T. morisettej@usgs.gov",
       "givenName": "Jeffrey T.",
       "familyName": "Morisette",
       "email": "morisettej@usgs.gov",
       "identifier": {
         "@type": "PropertyValue",
         "propertyID": "ORCID",
         "value": "0000-0002-0483-0082",
         "url": "https://orcid.org/0000-0002-0483-0082"
       },
       "affiliation": [
         {
           "@type": "Organization",
           "name": "North Central Climate Science Center",
           "url": null
         },
         {
           "@type": "Organization",
           "name": "Southwest Climate Science Center",
           "url": null
         }
       ]
     },
     {
       "@type": "Person",
       "name": "Koop, David",
       "givenName": "David",
       "familyName": "Koop"
     }
   ],
   "funder": [
     {
       "@type": "Organization",
       "name": "Fort Collins Science Center",
       "url": "https://www.usgs.gov/centers/fort-collins-science-center"
     }
   ]
 }

}