CalBug: Digitizing Cali as long as nia’s Terrestrial Arthropods Summary Label Image Capture Georeferencing in addition to Mapping Collaborators: Bohart Museum – UC Davis, Cali as long as nia Academy of Sciences, Cali as long as nia State Collection of Arthropods, Entomology Research Museum – UC Riverside, Essig Museum of Entomology – UC Berkeley, LA County Natural History Museum, San Diego Natural History Museum, Santa Barbara Museum of Natural History

CalBug: Digitizing Cali as long as nia’s Terrestrial Arthropods Summary Label Image Capture Georeferencing in addition to Mapping Collaborators: Bohart Museum – UC Davis, Cali as long as nia Academy of Sciences, Cali as long as nia State Collection of Arthropods, Entomology Research Museum – UC Riverside, Essig Museum of Entomology – UC Berkeley, LA County Natural History Museum, San Diego Natural History Museum, Santa Barbara Museum of Natural History www.phwiki.com

CalBug: Digitizing Cali as long as nia’s Terrestrial Arthropods Summary Label Image Capture Georeferencing in addition to Mapping Collaborators: Bohart Museum – UC Davis, Cali as long as nia Academy of Sciences, Cali as long as nia State Collection of Arthropods, Entomology Research Museum – UC Riverside, Essig Museum of Entomology – UC Berkeley, LA County Natural History Museum, San Diego Natural History Museum, Santa Barbara Museum of Natural History

Franklin, Bernard, Host has reference to this Academic Journal, PHwiki organized this Journal CalBug: Digitizing Cali as long as nia’s Terrestrial Arthropods Peter T Oboyski, Joan Ball, Rosemary Gillespie, Joyce Gross, Traci Grzymala, Gordon Nishida, Kipling Will Essig Museum of Entomology, University of Cali as long as nia at Berkeley ,USA Summary Databasing of entomology collections has lagged behind that of other disciplines primarily due to large collection sizes in addition to the highly abbreviated in addition to inconsistent data on very small specimen labels. CalBug is a National Science Fundation funded collaboration of the eight major entomology collections in Cali as long as nia that intends to capture 1.1 million specimen-level data records from our combined holdings. Data from all institutions will be combined in a single online cache. We will analyze these data using geospatial technology to explore the relationship between changes in distribution in addition to habitat modification. Developing time-saving methods in addition to technology as long as getting data from specimen labels into databases is paramount. We have focused on developing in addition to testing methods in addition to workflows to increase the rate of data capture, while maximizing data quality. Digital imaging of labels provides an easy-to-view verbatim archive of specimen data in addition to allows remote data entry from image files through manual entry, crowd-sourcing, in addition to automated OCR in addition to data parsing. Specimen h in addition to ling remains a significant obstacle as long as efficient data capture from entomological collections because of costs in time in addition to risk to specimens. Georeferencing is also a challenge due to the highly abbreviated in addition to inconsistent nature of location data on specimen labels. To address these challenges we are exploring strategies that combine computer in addition to human data h in addition to ling. Label Image Capture Georeferencing in addition to Mapping Collaborators: Bohart Museum – UC Davis, Cali as long as nia Academy of Sciences, Cali as long as nia State Collection of Arthropods, Entomology Research Museum – UC Riverside, Essig Museum of Entomology – UC Berkeley, LA County Natural History Museum, San Diego Natural History Museum, Santa Barbara Museum of Natural History Figure 6. Annual average high temperatures under a high emissions scenario of climate change (Source: Cal-Adapt in addition to the Public Interest Energy Research program, Cali as long as nia Energy Commission). Records of arthropod collections over the past 100 years along with projections of future climates will be used to predict the impact of climate change on arthropod distributions. Methods Taxa in addition to localities to database: Priority species were selected to address urgent environmental issues in addition to target localities to examine changes in biodiversity at sites with long-term sampling, including Natural Area Reserves. Sort specimens by location in addition to date (optional): A “carry-over” function reduces time spent typing when consecutive specimens have similar data. Digital imaging: DinoLite® digital microscopes (Figure 1) capture images of label data in JPEG as long as mat. Manual data entry into MySQL database: Label data are interpreted in addition to entered into appropriate database fields (Figure 4). Error checking: Records are successively sorted by locality in addition to date to identify typographic errors/inconsistencies. Georeference locality data: Database records are uploaded to BioGeomancer georeferencing software (Figure 5) which suggests coordinates in addition to an error radius as long as each locality based on st in addition to ardized protocols. Upload data to cache (in development): At the completion of the project each institution will upload records to a central cache as long as inter-institution analyses (Figure 4). Temporospatial analyses (in development): GIS tools will be used to correlate species distributions with climate in addition to habitat factors in addition to to predict changes in species distributions based on climate change projections (Figure 6). Workflow Optional step In development Database Assessment in addition to Progress Specimen h in addition to ling: A significant time expenditure includes retrieval of individual specimens, positioning of labels as long as viewing, adding a catalog number label, in addition to returning the specimen to its unit tray. Digital Imaging: Protocols as long as entering data directly from specimens into a verbatim field followed by parsing into interpreted fields proved slow. Digital imaging of specimen labels provides advantages, including a true verbatim digital archive, the ability to enlarge labels onscreen, in addition to the opportunity as long as remote data entry in addition to /or Optical Character Recognition (OCR) to automate data extraction. Using a naming convention that includes the specimen catalog number, digital images are automatically linked to database records. Each specimen takes ~2 seconds to photograph, but naming in addition to saving files adds ~7-10 seconds/specimen. Databasing: Several fields, including higher taxonomy in addition to “higher geography” are automatically filled names already in the database. Data are carried-over from one specimen to the next (yellow fields in Figure 1). These features, along with pick lists in addition to controlled fields, reduce errors. Progress: 27,000 Hymenoptera; 8,400 Odonata; 7,000 Lepidoptera entered into Essig Database. 4,000 specimens fully georeferenced. 36,000 images taken with 24,000 awaiting data entry. Improving image & data acquisition Minimize imaging time: We are currently developing high-throughput assembly lines to increase the rate of image capture by spatial arrangement of h in addition to ling tasks in addition to automating file naming in addition to saving. Online crowd-sourcing: We are collaborating with the Zooniverse citizen science program to engage thous in addition to s of volunteers in label data entry from digital images. Multiple volunteers enter data multiple times as long as each label, which are then compared as long as consistency (as a proxy as long as accuracy). OCR in addition to automated data parsing: We are developing user dictionaries as long as Optical Character Recognition software to increase percent recognition in addition to accuracy. We are also looking as long as programmers to create a “smart” parsing program that can assign data elements to appropriate database fields based on context in addition to dictionary terms. Developing a data cache: Data from each collaborating institution will be added to a combined online cache (see required fields in Figure 4). Collecting Event Data eventID (DC) country (DC) stateProvince (DC) county (DC) locality (DC) minimumElevationMeters (DC) maximumElevationMeters (DC) decimalLatitude (DC) decimalLongitude (DC) coordinateUncertaintyMeters (DC) geodeticDatum (DC) verbatimCoordinateSystem (DC) georeferenceSources (DC) georeferencedBy (DC) georeferencedDate georeferenceRemarks (DC) collectionBeginDate () collectionEndDate () recordedBy (DC) = collectors samplingProtocol (DC) associatedTaxa (DC) sex (DC) individualCount (DC) Specimen Data catalogNumber (DC) institutionCode (DC) kingdom (DC) phylum (DC) class (DC) order (DC) family (DC) genus (DC) specificEpithet (DC) subspecies taxonIDCertainty scientificNameAuthorship (DC) identifiedBy (DC) dateIdentified (DC) eventID (DC) Bold = required Normal = recommended (DC) = Darwin Core field () = Darwin Core recommends one field that accommodates several date options. We prefer “begin” in addition to “end” dates. Figure 4. Each institution uses its own database system. Records will be collected into a Darwin Core-compliant, flat-file, cache with required fields as long as collecting event data in addition to specimen data as indicated in the above tables from the Essig database. Labels are often highly abbreviated – unrecognized abbreviations are entered “as is” in addition to bulk updated after data entry is completed. Figure 1. (upper left) DinoLite® digital microscope in addition to software used to capture images of specimens in addition to labels. (upper right) Essig database data entry screen with specimen image – clicking on image icon makes image appear in a separate movable window. Yellow fields are carried-over to the next specimen. (lower right) Dragonfly with labels removed as long as imaging. Figure 5. Semi-automated programs, such as BioGeomancer, estimate latitude-longitude coordinates with an adjustable error radius based on text descriptions (above example: 15 miles E of Cloverdale, CA). Queries of georeferenced specimens are mapped “on-the-fly” using Berkeley Mapper (right example: specimens near Sacramento, Cali as long as nia of Libellula luctuosa Burmeister dragonflies in the Essig Database). Figure 3. General workflow as long as image capture, databasing, georeferencing, in addition to analysis. See Methods as long as workflow details. © Joyce Gross © Joyce Gross © Joyce Gross © PT Oboyski © PT Oboyski Response to climate change

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