Item talk:Q318610

{

 "DOI": {
   "doi": "10.5066/p1dkq5rp",
   "identifiers": [],
   "creators": [
     {
       "name": "Laurence Clarfeld",
       "nameType": "Personal",
       "affiliation": [
         "Vermont Cooperative Fish and Wildlife Research Unit"
       ],
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           "nameIdentifier": "https://orcid.org/0000-0002-3927-9411",
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     },
     {
       "name": "Caroline Tang",
       "nameType": "Personal",
       "affiliation": [
         "Queens University"
       ],
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           "nameIdentifier": null,
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     },
     {
       "name": "Kaitlin Huber",
       "nameType": "Personal",
       "affiliation": [],
       "nameIdentifiers": [
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           "nameIdentifier": null,
           "nameIdentifierScheme": "ORCID"
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       ]
     },
     {
       "name": "Cathleen Balantic",
       "nameType": "Personal",
       "affiliation": [
         "Vermont Cooperative Fish and Wildlife Research Unit"
       ],
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     },
     {
       "name": "Therese M Donovan",
       "nameType": "Personal",
       "affiliation": [
         "United States Geological Survey"
       ],
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           "nameIdentifier": "https://orcid.org/0000-0001-8124-9251",
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       ]
     }
   ],
   "titles": [
     {
       "title": " AMMonitor: Remote monitoring of biodiversity in an adaptive framework. Version 2.0.0"
     }
   ],
   "publisher": "U.S. Geological Survey",
   "container": {},
   "publicationYear": 2024,
   "subjects": [
     {
       "subject": "autonomous monitoring units"
     },
     {
       "subject": "wildlife monitoring"
     },
     {
       "subject": "Program R"
     }
   ],
   "contributors": [],
   "dates": [],
   "language": null,
   "types": {
     "ris": "COMP",
     "bibtex": "misc",
     "citeproc": "article",
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     "resourceType": "Software",
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   "descriptions": [
     {
       "description": "Amid climate change and rapidly shifting land uses, effective methods\nfor monitoring natural resources are critical to support\nscientifically-informed resource management decisions. The\npractice of using Autonomous Monitoring Units (AMUs) to monitor wildlife\nspecies has grown immensely in the past decade, with monitoring projects\nacross species from birds, to bats, amphibians, insects, terrestrial\nmammals, and marine mammals.\nAMMonitor is an open source R package dedicated to collecting,\nstoring, and analyzing AMU information in a way that 1) is\ncost-effective, 2) can efficiently process and store information, and 3)\ncan take advantage of the vast and growing community of R analytics. We\ncreated AMMonitor for the Bureau of Land Management to monitor high\npriority wildlife across the southern California Solar Energy Zone\n(SEZ).\nIn broad terms, the AMMonitor approach starts with ecological\nhypotheses or natural resource management objectives (Figure 1).\nData are collected with Autonomous Monitoring Units (AMUs) to\ntest hypotheses or to evaluate the state of a resource with respect to a\nmanagement objective. Acoustic recordings and photos are collected and\ndelivered to the cloud. Raw and processed data are stored in a SQLite\ndatabase. The data can be analyzed with a wide variety of analytical\nmethods, often models of abundance or occupancy pattern. These analyses\ncan be stored, and resulting outputs can be compared with research and\nmonitoring objectives to track progress toward management goals. The\nfinal results are assessed with respect to hypotheses or objectives.",
       "descriptionType": "Abstract"
     }
   ],
   "geoLocations": [],
   "fundingReferences": [],
   "url": "https://code.usgs.gov/vtcfwru/ammonitor/-/tree/2.0.0?ref_type=tags",
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   "created": "2024-06-28T13:34:35Z",
   "registered": "2024-06-28T13:34:35Z",
   "published": null,
   "updated": "2024-06-28T14:35:13Z"
 }

}