The Weekly Phenology of Bees of the Mid-Atlantic States: MD, VA, WV, DC, PA, DE Sam Droege [email_address]

Objectives of This Presentation A quick coarse look at the week-by-week phenologies of common genera and species of bees in the Mid-Atlantic area using collections from the USGS bee database Corrections for effort and better graphics were NOT the objective (too time consuming, for Sam), but anyone who would like to make those efforts is welcome to have the dataset We also want to demonstrate one of the many values of keeping detailed data on ALL the bees that are caught not just the “good” ones.

A quick coarse look at the week-by-week phenologies of common genera and species of bees in the Mid-Atlantic area using collections from the USGS bee database

Corrections for effort and better graphics were NOT the objective (too time consuming, for Sam), but anyone who would like to make those efforts is welcome to have the dataset

We also want to demonstrate one of the many values of keeping detailed data on ALL the bees that are caught not just the “good” ones.

Where Do These Data Come From? They come from a joint database that is maintained by Sam Droege and Discoverlife ( www.discoverlife.org ) Many collectors were involved but the primary ones have been Sam Droege, Jane Whitaker, Heather Harmon, and Cathy Strager

They come from a joint database that is maintained by Sam Droege and Discoverlife ( www.discoverlife.org )

Many collectors were involved but the primary ones have been Sam Droege, Jane Whitaker, Heather Harmon, and Cathy Strager

Who Can Use These Graphs? Anyone who wants to, we are public servants and this is public data and we want it widely used, but it would be very nice if you acknowledged the work of the people listed in this presentation, and then followed up by doing something nice for someone else, perhaps calling a relative who you haven’t talked to in a while…that sort of thing

Anyone who wants to, we are public servants and this is public data and we want it widely used, but it would be very nice if you acknowledged the work of the people listed in this presentation, and then followed up by doing something nice for someone else, perhaps calling a relative who you haven’t talked to in a while…that sort of thing

Data Facts Data were collected primarily by bowl traps and netting Data reflect projects by the participants and therefore effort varied from year-to-year and week-to-week Data from April 4, 2003 were not used in the analysis of phenology due to the capture of 4000 individuals on one day (750 traps set!), these data tended to have too great an impact on the resulting graphs With perhaps a few exceptions (none come to mind) all bees collected were entered in the database

Data were collected primarily by bowl traps and netting

Data reflect projects by the participants and therefore effort varied from year-to-year and week-to-week

Data from April 4, 2003 were not used in the analysis of phenology due to the capture of 4000 individuals on one day (750 traps set!), these data tended to have too great an impact on the resulting graphs

With perhaps a few exceptions (none come to mind) all bees collected were entered in the database

Some General Site Information Data came from 3121 site records, and approximately 57,000 captured individuals Most of the time a site record would be a unique collecting event at a unique location, however, sometimes data were split into several experimental subunits at a site and each subunit given a separate site record

Data came from 3121 site records, and approximately 57,000 captured individuals

Most of the time a site record would be a unique collecting event at a unique location, however, sometimes data were split into several experimental subunits at a site and each subunit given a separate site record

Number of Records By State

Geographic Locations of Sampling Sites

Record Locations Records were weighted toward sampling locations around Washington D.C., Delaware, the eastern part of West Virginia (not in early spring, however) and in the remainder of the region it was more scattered, at different times and locations. If we had enough data it would be likely that spring data from the mountains would be shifted a bit later than that coming from the Coastal Plain

Records were weighted toward sampling locations around Washington D.C., Delaware, the eastern part of West Virginia (not in early spring, however) and in the remainder of the region it was more scattered, at different times and locations. If we had enough data it would be likely that spring data from the mountains would be shifted a bit later than that coming from the Coastal Plain

Potentially of Interest to Someone: Numbers of Individuals By County Count state county 129 Delaware 6895 Delaware Kent 1430 Delaware New Castle 6620 Delaware Sussex 3728 District of Columbia 748 Maryland 5889 Maryland Anne Arundel 87 Maryland Baltimore 6198 Maryland Calvert 825 Maryland Caroline 19 Maryland Carroll 53 Maryland Cecil 935 Maryland Charles 208 Maryland Dorchester 25 Maryland Frederick 688 Maryland Garrett 19 Maryland Harford 573 Maryland Howard 40 Maryland Kent 447 Maryland Montgomery 53 Maryland P.G and A.A. 1804 Maryland Prince Georges 20251 Maryland Prince George's 40 Maryland Prince George's County 2889 Maryland Queen Anne's 158 Maryland Queen's Town 180 Maryland Somerset 11 Maryland Somerset and Worchester Counties 78 Maryland Sussex 2053 Maryland Talbot 1 Maryland Washington 217 Maryland Wicomico 365 Maryland Worcester 9 Maryland Worchester 678 Pennsylvania 74 Pennsylvania Berks 7 Pennsylvania Cambria 14 Pennsylvania Clearfield 169 Pennsylvania Delaware 19 Pennsylvania Lancaster 8 Pennsylvania McKean 94 Pennsylvania Philadelphia 185 Pennsylvania Pike 773 Virginia 1725 Virginia Accomack 2697 Virginia Albemarle 5 Virginia Essex 3350 Virginia Fairfax 73 Virginia Fluvanna 81 Virginia Frederick 110 Virginia Greene 3 Virginia Isle of Wight 40 Virginia King George 142 Virginia Madison 3 Virginia Prince William 1 Virginia Richmond 14 Virginia Shenandoah 41 Virginia Suffolk City 5 Virginia Virginia Beach City 336 West Virginia 12 West Virginia Barbour 947 West Virginia Berkeley 22 West Virginia Cabell 20 West Virginia Doddridge 134 West Virginia Grant 7347 West Virginia Hampshire 4092 West Virginia Hardy 11 West Virginia Harrison 215 West Virginia Jefferson 32 West Virginia Lincoln 2 West Virginia Mason 52 West Virginia Mineral 157 West Virginia Pendleton 43 West Virginia Pleasants 72 West Virginia Pocahontas 6 West Virginia Preston 21 West Virginia Raleigh 125 West Virginia Randolph 230 West Virginia Ritchie 76 West Virginia Tucker 16 West Virginia Tyler 1 West Virginia Wayne

Count state county

129 Delaware

6895 Delaware Kent

1430 Delaware New Castle

6620 Delaware Sussex

3728 District of Columbia

748 Maryland

5889 Maryland Anne Arundel

87 Maryland Baltimore

6198 Maryland Calvert

825 Maryland Caroline

19 Maryland Carroll

53 Maryland Cecil

935 Maryland Charles

208 Maryland Dorchester

25 Maryland Frederick

688 Maryland Garrett

19 Maryland Harford

573 Maryland Howard

40 Maryland Kent

447 Maryland Montgomery

53 Maryland P.G and A.A.

1804 Maryland Prince Georges

20251 Maryland Prince George's

40 Maryland Prince George's County

2889 Maryland Queen Anne's

158 Maryland Queen's Town

180 Maryland Somerset

11 Maryland Somerset and Worchester Counties

78 Maryland Sussex

2053 Maryland Talbot

1 Maryland Washington

217 Maryland Wicomico

365 Maryland Worcester

9 Maryland Worchester

678 Pennsylvania

74 Pennsylvania Berks

7 Pennsylvania Cambria

14 Pennsylvania Clearfield

169 Pennsylvania Delaware

19 Pennsylvania Lancaster

8 Pennsylvania McKean

94 Pennsylvania Philadelphia

185 Pennsylvania Pike

773 Virginia

1725 Virginia Accomack

2697 Virginia Albemarle

5 Virginia Essex

3350 Virginia Fairfax

73 Virginia Fluvanna

81 Virginia Frederick

110 Virginia Greene

3 Virginia Isle of Wight

40 Virginia King George

142 Virginia Madison

3 Virginia Prince William

1 Virginia Richmond

14 Virginia Shenandoah

41 Virginia Suffolk City

5 Virginia Virginia Beach City

336 West Virginia

12 West Virginia Barbour

947 West Virginia Berkeley

22 West Virginia Cabell

20 West Virginia Doddridge

134 West Virginia Grant

7347 West Virginia Hampshire

4092 West Virginia Hardy

11 West Virginia Harrison

215 West Virginia Jefferson

32 West Virginia Lincoln

2 West Virginia Mason

52 West Virginia Mineral

157 West Virginia Pendleton

43 West Virginia Pleasants

72 West Virginia Pocahontas

6 West Virginia Preston

21 West Virginia Raleigh

125 West Virginia Randolph

230 West Virginia Ritchie

76 West Virginia Tucker

16 West Virginia Tyler

1 West Virginia Wayne

General Format for All Phenology Charts The Week Number (1-52) of the year is on the X-Axis The height of the bars along the Y-Axis indicates the number of individuals captured for each week Ignore the categories “blank” and “value” it took too much time to get rid of them so I left them in Note that in order to “trick” Excel into consistently displaying all the weeks of the year I used and Excel macro to add a single individual count to each week every time I made a chart, thus the real number of individuals for any week is whatever is displayed minus 1. OK, this is not the slickest way to make the graphs, but it was the fastest way I could think of at the time, if you like we can send you the data and you can make better ones!

The Week Number (1-52) of the year is on the X-Axis

The height of the bars along the Y-Axis indicates the number of individuals captured for each week

Ignore the categories “blank” and “value” it took too much time to get rid of them so I left them in

Note that in order to “trick” Excel into consistently displaying all the weeks of the year I used and Excel macro to add a single individual count to each week every time I made a chart, thus the real number of individuals for any week is whatever is displayed minus 1. OK, this is not the slickest way to make the graphs, but it was the fastest way I could think of at the time, if you like we can send you the data and you can make better ones!

A Couple of Excel Notes Data from this presentation were dumped from an Access program into Excel. The date field was converted to a week field using the WEEKNUM function of Excel Graphs were produced using the PIVOTCHART feature of Excel

Data from this presentation were dumped from an Access program into Excel.

The date field was converted to a week field using the WEEKNUM function of Excel

Graphs were produced using the PIVOTCHART feature of Excel

The Week Number at the Start of Each Month – So You Can Handily Keep Track of Where You Are in the Year

Presentation Order The results by genera are presented first and then for the more common species Unless otherwise noted the males and the females are presented together. The phenology of males and females is almost always at least slightly different; time, however, did not permit presenting the differences by sex in this presentation, but others are welcome to do so and we can send you the data

The results by genera are presented first and then for the more common species

Unless otherwise noted the males and the females are presented together. The phenology of males and females is almost always at least slightly different; time, however, did not permit presenting the differences by sex in this presentation, but others are welcome to do so and we can send you the data

A Graph of All Individuals Captured

Osmia

Triepeolus

Svastra

Sphecodes

Pseudopanurgus

Perdita

Panurginus

Nomada

Melissodes

Megachile

Lasioglossum

Lasioglossum females

Hylaeus

Hoplitis

Heriades

Halictus

Eucera

Epeolus

Colletes

Coelioxys

Ceratina

Bombus

Andrena

Agapostemon

Agapostemon sericeus

Agapostemon splendens

Agapostemon texanus

Agapostemon virescens

Andrena asteroides

Andrena banksi

Andrena barbara

Andrena bradleyi

Andrena carlini

Andrena fenningeri

Andrena cressonii

Andrena erigeniae

Andrena imitatrix

Andrena miserabilis

Andrena nasonii

Andrena nigrae

Andrena perplexa

Andrena personata

Andrena pruni

Andrena tridens

Andrena vicina

Andrena violae

Andrena ziziaeformis

Anthidiellum notatum

Anthidium oblongatum

Anthophora abrupta

Anthophora plumipes

Augochlora pura

Augochlorella aurata

Augochlorella metallica

Bombus bimaculatus

Bombus citrinus

Bombus fervidus

Bombus griseocolis

Bombus impatiens

Bombus pensylvanicus

Bombus perplexus

Bombus vagans

Calliopsis andreniformis

Ceratina calcarata

Ceratina dupla

Ceratina strenua

Coelioxys sayi

Colletes americanus

Colletes inaequalis

Colletes thoracicus

Dieunomia heteropoda

Eucera dubitata

Eucera hamata

Habropoda laboriosa

Halictus confusus

Halictus rubicundus

Hoplitis pilosifrons

Hoplitis producta

Hoplitis simplex

Hoplitis spoliata

Hylaeus mesillae

Hylaeus ornatus

Lasioglossum males – Note that very few males of the Dialictus groups were identified to species and thus do not appear in the Lasioglossum species graphs

Lasioglossum bruneri

Lasioglossum cattellae

Lasioglossum coeruleum

Lasioglossum coreopsis

Lasioglossum cressonii

Lasioglossum fuscipenne

Lasioglossum illinoense

Lasioglossum imitatum

Lasioglossum leucozonium

Lasioglossum macoupinense

Lasioglossum nelumbonis

Lasioglossum nymphaearum

Lasioglossum pectorale

Lasioglossum pilosum

Lasioglossum platyparium

Lasioglossum quebecensce

Lasioglossum tegulare

Lasioglossum vierecki

Lasioglossum zephyrum

Megachile brevis

Megachile campanulae

Megachile exilis

Megachile mendica

Megachile rotundata

Megachile sculpturalis

Melissodes bimaculata

Melissodes comptoides

Melissodes denticulata

Melissodes dentiventris

Melissodes desponsa

Melissodes druriella

Melissodes trinodis

Melitoma taurea

Osmia atriventris

Osmia bucephala

Osmia collinsiae

Osmia conjuncta

Osmia cornifrons

Osmia georgica

Osmia lignaria

Osmia pumila

Osmia taurus

Panurginus atramontensis

Panurginus potentillae

Peponapis pruinosa

Ptilothrix bombiformis

Xylocopa virginica