HISTORICAL FOODHABITS

Goals for the Historical Food Habits project

a) Safeguard data (electronic storage)• Scan all cards to hard drives (pdf image files)

b) Safeguard cards (physical storage)• After scanning, move cards to safe long-term storage

c) Transform currently inaccessible data into globally accessible usable information (i.e. searchable online database)

Data Fields for each card Header (general data: easy to read and comprehend):

• Biological data for the collected specimen (genus, species; sex and age in some cases)

• Card number (accession number)

• Locality (town, state)

• Where killed (habitat)

• Date (month, day, year)

• Hour (time of death)

Food Habits (‘stomach’ contents - specialist data):

• Characterized contents (either percentages or counts of specific plant and animal material e.g. Gastropoda, C. florida )

• Collector (name and sometimes a collector number)

• Condition of stomach/gullet (how full)

• Percentage of:

- animal matter - vegetable matter - Gravel, rubbish etc.

• Examination date, examiner name

Example cards, with entertaining oddities

- Card number (42444) is high for the very early collection date (1876)- Is this an error? There are other cards with similar

dates/numbers - 24 years elapsed between sample collection and food contents analysis

(1910). That’s a long time to float in formalin!- Can YOU read Beal’s handwriting?

Example of food habits data* (Eastern Meadowlark – non-plant food)

• Rhyncophorus• Sphenophorus• Melanoplus• Systena• Coenus• Sitones• Armadillidium• Melampus• Casnonia• Harpalus• Phytonomus• Tenebrio• Leucania• Aphodius• Epicauta• Lachnosterna• Omophron• Myrmica• Gryllus

PopilliaChlaeniusAnomalaAmaraAnisodactylusCorticanaTiphiaMetrionaMonocrepidiusSynetaPlatypusCarabusMusJulusPeromyscusEuphoriaGryllotalphaMelanotusEntamiaCorymbitesNecrophorusCillenum

TettixSpizellaCotinisDesmocerusCampanotusLebiaMicrotusOedionychisTettigideaCrepidoderaBarisLimoniusOdontotaChaetocnemaMacropsAgrotisDiboliaEpitrixGraphopsCentrinusApinaTanypus

CorimelaenaAndrenaProtoparceColaspisCoriscusLygusWacrosiphum

* Frequent “???! due to Beal’s illegibility omitted

Feasibility trial to evaluate data entry• Select high profile species (Wood thrush) with representative card complexity DONE• Review summary sheets to outline extent of dataset DONE• Scan cards DONE, pdf format • Enter header data DONE, via Excel• Review food habits data entered via Access DONE*

• Share resultant dataset with Consortium colleagues to determine best:• online host for ~250,000 pdfs (est. 75 gb) for transcription• data entry system for generalist header data transcribed from

pdfs• data entry system for specialist food habits data transcribed from

pdfs

* Thanks to Haas, Gorman et al.

Hylocichla mustulina (Wood Thrush)Submissions by state

Canada

AL CT DC FL GA IL KS MAME MI MS NC NJ NY PA SC TN TX VA WI0

10

20

30

40

50

60

Total = 180Gender: Unknown

81Female 38Male 61

Draft

Hylocichla mustulina (Wood Thrush)Submissions by year (180 total)

1876

1879

1882

1885

1888

1891

1894

1897

1900

1903

1906

1909

1912

1915

1918

1921

1924

1927

1930

1933

1936

0

2

4

6

8

10

12

14

16

18

20

Draft

Citations collected during HFHabits study demonstrate its perspective : how do these species affect humans?

Birds of CT – Bishop - 1913

1912

Header field issues affecting data entry system designHeader (general data):

• Biological data:

• Species name changes (Turdus mustelina became Hylocichla mustelina)

• Gender noted ~ 50% of the time; age rarely (juvenile/immature or adult)

• Card number (accession).

• Never omitted. Number unrelated to species, date, location. (Likely collectors were handed out-of-sequence stacks of cards whenever they ran out. )

• Locality (town, state)

• Rarely omitted but inconsistent names.

• Where killed (habitat).

• Neither specific nor consistent terminology e.g. : Woods, woodland and field, upland grove, timber, cedar grove, rocky woods, oak grove, thicket, low woods, hemlock woods, hardwoods, near woods, deep woods, deciduous woods, near woodlands.

• Date (month, day, year)

• Consistently included.

• Hour (time of death)

• Included much more often than gender of sample unfortunately.

Gordian knot approach for data entry

To transform currently inaccessible data to globally accessible, accurately transcribed and usable information:

Separate the entry process into two independent but linked

parts :

1. Header - generalist data fieldsa) Themselves valuable research data

b) Predominantly clearly written and straight-forward therefore easy to enter (Can be read, entered and proof-read by just about anybody)

c) Once entered online they characterize the entire database, opening it to any investigator including specialists

d) Focus on these fields first

2. Food habits - specialist data fieldsa) Predominantly unclearly written and require specialist (entymologist,

botanist, etc.) expertise to interpret and enter online

b) Principal investigators focused on this area of research are the most likely to commit resources (grants, grad students, etc.) to wringing out the information – we don’t have the resources

Please let us know:

Thoughts at this point on the best …

• Online host for scanned images and linked evolving database? • American Bird Network? SORA? Biodiversity Universe?

• Data entry system linked with scanned cards for both:

1. Header data (generalist input)

2. Food habits data (specialist input)

• Access? • Zooniverse and its Scribe system?• Other?