1018-0023 SS-B w tables and app - 2014

Supporting Statement B for
Paperwork Reduction Act Submission
Migratory Bird Harvest Surveys
Form numbers: 3-2056J, 3-2056K, 3-2056L, 3-2056M, 3-2056N, 3-165, 3-165A, 3165B, 3-165C, 3-165D, 3-165E
50 CFR 20.20
OMB Control Number 1018-0023

1. Describe (including a numerical estimate) the potential respondent universe and any
sampling or other respondent selection method to be used. Data on the number of
entities (e.g., establishments, State and local government units, households, or
persons) in the universe covered by the collection and in the corresponding sample
must be provided in tabular form for the universe as a whole and for each of the
strata in the proposed sample. Indicate expected response rates for the collection as
a whole. If the collection has been conducted previously, include the actual response
rate achieved.
Migratory Bird Harvest Information Program and Migratory Bird Hunter Survey: The
potential respondent universe is all licensed migratory bird hunters in the 49 States that have
migratory bird hunting seasons, approximately 3,800,000 individuals. The universe is stratified
by: (1) State, and (2) hunters' hunting activity and success the previous season. A systematic
sample is selected within each stratum from the names and addresses in the order in which
they are received. Stratum-specific universe and sampling data for forms 3-2056J, 3-2056K, 32056L, and 3-2056M, are given in Tables 1-4. Sampling rates vary by State, form, and success
strata, and range from <1 percent to 100 percent. Because sampling rates vary by State, form,
and success strata, weighted and unweighted response rates were calculated to each survey
form for 2012. Weighted and unweighted response rates for all five form types average 51
percent nationally. Response rates for the four HIP survey forms in 2012 were as follows:

Survey form
Waterfowl (3-2056J)
Dove (3-2056K)
Woodcock (3-2056L)
Snipe/Coot/Rail/Gallinule (3-2056M)

Unweighted
response rate
46%
51%
55%
52%

Weighted response
rate
51%
52%
51%
50%

About 4 percent of the nonresponse rate is due to undeliverable mail. We are investigating
address hygiene software, to determine if that is an economical way to reduce nonresponse due
to undeliverable mail.
Parts Collection Survey: In 2012, approximately 70,000 duck wings and 14,000 goose tails
were collected and examined by biologists out of a universe of 15,600,000 ducks and 3,200,000
geese harvested. These parts are obtained from approximately 6,500 successful waterfowl
hunters who return form 3-165 out of a universe of 1,115,000 active waterfowl hunters. Sample
sizes for waterfowl are given in Table 5.
The sample of hunters who will be sent form 3-165E consists of approximately 4,100 successful
mourning dove hunters from a sample universe of about 1 million active dove hunters. We

solicit wings from the first two weekends of the hunting season only, to allow estimation of
regional recruitment. We collect and examine about 25,000 wings from the first week of the
hunting season out of a universe of about 7,000,000 birds (about half of the total season
harvest) that are harvested during the first week of the mourning dove hunting season.
Sampling rates vary by State, and range from 55 percent to 100 percent of successful mourning
dove hunters responding to Form 3-2056K this previous year. Less than 1 percent of the
harvest during the first week is sampled. Sample sizes for mourning doves are listed in Table 6.
Appendix A contains a statistical review of the dove Parts Collection Survey. As the result of
this work, we reduced sample sizes for this survey for the 2013-14 hunting season.
The sample of hunters who are sent form 3-165B consists of approximately 2,000 successful
hunters from a sample universe of approximately 200,000 active hunters of other species of
migratory birds (woodcock [≈130,000 hunters], snipe [≈30,000 hunters], rail [≈10,000 hunters],
gallinule [≈3,000 hunters], and band-tailed pigeon hunters [≈15,000 hunters]. Approximately
15,000 wings are collected and examined out of a universe of approximately 500,000 birds
harvested. The percent of harvest sampled ranges from <1 percent to 5 percent for the species
or species groups, with the highest sampling rate applied to woodcock harvest. Sample sizes
for the woodcock, snipe, rail species, and band-tailed pigeons are listed in Table 7.
Sandhill Crane Harvest Survey: The universe for sampling is approximately 30,000 individuals
who obtain an annual permit to hunt sandhill cranes. Sampling rates are set by State, with 10
percent of the permittees randomly selected to receive questionnaires in Texas, 20 percent of
the permittees selected in Colorado and North Dakota, and 50 percent of the permittees
contacted in all other States except Wyoming. All permittees in Wyoming are contacted
because of the low number of permits issued. Pertinent sampling characteristics by State are
listed in Table 8. In 2009, the unweighted response rate for the crane survey was 62 percent,
and the weighted response rate was 56 percent.
2. Describe the procedures for the collection of information including:
* Statistical methodology for stratification and sample selection,
* Estimation procedure,
* Degree of accuracy needed for the purpose described in the justification,
* Unusual problems requiring specialized sampling procedures, and
* Any use of periodic (less frequent than annual) data collection cycles to reduce
burden.
Migratory Bird Harvest Information Program: Each State requires all migratory bird hunters
to identify themselves as such, and to provide their name, address, and date of birth, as a
condition for obtaining authorization to hunt migratory game birds in the State. We are seeking
permission to collect email addresses from States that already collect this information, so we
can send some reminders by email. All of the name, address, email address, and date of birth
information collection is done by the State’s hunting license vendors (agents) or by a State
contractor. State license agents or contractors ask each migratory game bird hunter to answer
a series of questions that allows us to stratify our sampling procedure. Each State collects the
information in a way that is most appropriate for that State, but all States ask some variation of
the following questions:
1)
2)
3)
4)
5)

Will you hunt migratory birds this year?
How many ducks did you bag last year?
How many geese did you bag last year?
How many doves did you bag last year?
How many woodcock did you bag last year?
2

6) Did you hunt coots or snipe last year?
7) Did you hunt rails or gallinules last year?
8) Will you hunt sandhill cranes this year?
9) Will you hunt band-tailed pigeons this year?
10) Will you hunt brant this year?
11) Did you hunt sea ducks last year?
States are responsible for development of adequate control procedures to ensure that agents
(1) account for all validated licenses; (2) promptly provide the State with names, addresses, and
other information; (3) have a low proportion of incomplete or illegible information; and (4) return
information from all migratory game bird hunters. We are continuing to work with States to
improve the quality of their sample frame (Appendixes B and C).
Migratory Bird Hunter Survey Procedures: Survey procedures are based on Dillman's Total
Design Method (Dillman, 1978, Mail and Telephone Surveys, the Total Design Method, Wiley).
This method has been shown to substantially reduce nonresponse in many situations.
a. States provide us with migratory game bird hunters' names, addresses, birth dates, and
their answers to the above questions in an acceptable form (electronic data or machinescannable paper form). We receive the first list of hunter names and addresses in
August prior to the migratory bird hunting seasons in each State. The States then send
us updated lists every 2 weeks until the end of the migratory bird hunting seasons within
each respective State. We need this information is needed in timely fashion to contact
survey participants and ask them to keep records of their migratory game bird hunting
throughout the hunting season. This also allows us to get survey forms to selected
hunters before the hunting season starts or shortly after the hunter purchased his or her
hunting license.
b. To protect hunters' privacy, it is our policy to use the names and addresses only for
conducting hunter surveys and for no other purpose. All records of hunters' names and
addresses are deleted after each year's survey results are finalized, and we do not
maintain a permanent record of names and addresses.
c. We use the answers to the above questions to assign each hunter to one of three activity
strata for duck, goose, dove, and woodcock hunting; and one of two hunting activity strata
for coots and snipe, rails and gallinules, band-tailed pigeons, brant, and sea duck
hunting. The three hunting activity strata for hunters of duck, goose, and dove hunters
are (1) no harvest; (2) low harvest; and (3) high harvest. Low harvest of ducks and geese
is defined as harvest of 1 to 10 birds the previous year; low harvest of doves is defined as
harvest of 1 to 30 birds the previous year. The two hunting activity strata for hunters of
woodcock, coots or snipe, rails or gallinules, band-tailed pigeons, brant, sea ducks are:
(1) will (did) hunt or (2) will (did) not hunt.
d. We select samples of hunters for receipt of one of four Migratory Bird Harvest Survey
forms: waterfowl (duck, goose, sea duck, and brant; form 3-2056J), dove and band-tailed
pigeon (form 3-2056K), woodcock (form 3-2056L), and snipe, rail, gallinule, and coot
(form 3-2056M). Similar species are grouped together on the same form to control
survey costs. Higher sampling rates are needed for successful hunters and for those
who hunt less-frequently hunted species. Hunters are not asked to participate in more
than one survey per State per year to minimize the burden on individual respondents.

3

e. Samples are stratified by survey form, State, and hunting activity. Stratification by State
is relevant because: (1) hunters must register for the Migratory Bird Harvest Information
Program in each State in which he/she hunts; (2) harvest regulations and species
distributions vary by State; and (3) response rates vary by State. Theoretically, there
could be up to (3)(3)(3)(2)(2)(2)(2)(2)(2) = 1,728 activity strata in each State, defined by
(number of duck hunting activity strata) X (number of goose hunting activity strata) X
(number of dove hunting activity strata) X (number of woodcock hunting activity strata) X
(number of coots/snipe success strata) X (number of rail/gallinule success strata) X
(number of band-tailed pigeon success strata) X (number of sea duck hunting success
strata) X (number of brant hunting success strata). However, individual States do not
allow hunting of all the species listed; therefore most States have fewer strata. For
example only 11 States have sea duck seasons, only 14 States have brant seasons, and
only 7 States have band-tailed pigeon seasons. We also consider the stratification of
each species/species group independently. Thus, there are a total of 705 strata in the 49
States, with the number of activity strata in individual States ranging from 10 to 17.
f. Samples are selected as the names are received so that migratory bird hunters can be
contacted and asked to keep records as soon as possible after the hunting season starts.
The first, eligible hunter in a file is selected, and then every nth hunter in each stratum is
selected thereafter, with (potentially) different sampling rates for each stratum. Sampling
without replacement is used, with high priority strata being sampled before lower priority
strata. Stratum priority is determined by: (1) biological need, and (2) desired precision
levels for the estimates.
g. Double sampling estimates (Hansen and Hurwitz, 1958, JASA) are used to account for
nonresponse (see Groves, 1989, Survey Errors and Survey Costs, Wiley, pages 165-169;
and Hansen, Hurwitz and Madow, 1953 Sample Survey Methods and Theory, Wiley, vol.
1, pages 468-475). Two response strata are defined by the respondents and
nonrespondents to the first wave of reminder letters. A second wave of reminders and
survey replacement forms is sent to all nonrespondents to the first wave of reminder
letters. Additionally, a third wave of reminder letters and survey replacement forms is
sent to all nonrespondents to the second wave of reminder letters.
For each species (e.g., mourning dove) or species-group (e.g., geese), the number of active
hunters, number of hunting days, and number of birds harvested are estimated from the
questionnaire responses using a ratio estimator with the harvest per hunter and the number of
migratory bird hunters reported, by stratum, by State. Species-, age-, and sex-specific harvests
are estimated using ratios estimated from the Parts Collection Survey.
Target 95 percent confidence intervals for harvest estimates at the management unit level (e.g.,
Flyway) are as follows: ducks, ± 5 percent; geese, ± 5 percent; mourning doves, ± 5 percent;
brant, woodcock, band-tailed pigeons, and white-winged doves, ± 10 percent; sea ducks, ± 25
percent; snipe, rails, gallinules, and coots, ± 50 percent. These target precision levels were
deemed appropriate by the Federal and State biologists who are charged with managing those
migratory bird species.
Surveys must be conducted annually because migratory bird harvests can change substantially
between years depending on the size of the fall flight and hunting pressure. Estimates are
required for annually promulgating hunting regulations.
Parts Collection Survey Procedures: Samples of successful hunters from the previous year’s
Migratory Bird Hunter Survey are asked to complete and return a postcard (forms 3-165A, C,
4

and E), volunteering to contribute wings and tails during the following hunting season. The
samples are randomly selected in proportion to the estimated harvest in each State, and rates
vary from 30 to 100 percent of successful hunters. Because it is difficult to find enough hunters
to be in the Parts Collection Survey each year and to mail out packages of survey forms,
hunters can remain in the survey for 3 (waterfowl)-10 (all others) years. Those who volunteer
are sent a cover letter with instructions and a supply of pre-addressed, postage-paid return
envelopes (forms 3-165, 3-165B, and 3-165E) for mailing in the wings and tails. Inner
envelopes to protect other mail from stains and seepage are enclosed with the instructions and
return envelopes. These packages are sent to survey volunteers before the hunting season
opens in their State. Throughout the hunting season, survey participants mail in parts to four
collection points (one in each flyway), where they are stored until they are examined. At the end
of the hunting season, biologists examine each part to determine species, age, and sex
composition of the sample; hunters cannot reliably determine this information. After those data
have been compiled, respondents are sent a personalized thank you letter detailing the species,
age, and sex of each bird from which they contributed a wing or a tail. The proportions of
species, age, and sex in the Parts Collection Survey are then applied to the total harvest
estimates from the Migratory Bird Hunter Survey, to allocate harvest estimates among groups.
The allocation is proportional to the State, because of different hunting regulations in States and
different sampling rates.
Sandhill Crane Harvest Survey: Sampling is stratified according to State of permit issuance;
sampling rates vary from 15 percent in States with many crane permittees (Texas, North
Dakota) to 100 percent in States with few crane permittees (e.g., Wyoming). No specialized
sampling procedures are required, and we use the standard estimation methods for stratified
random samples. Stratum-specific (State-specific) estimates of the proportion of permittees that
actually hunted cranes, the mean number of days hunted, and the mean number of cranes
harvested are derived from the responses. Those estimates are expanded by N (number of
permits issued) for each State to obtain State totals, which are then combined to provide
estimates of the number of active crane hunters, days of hunting, and cranes harvested for all
mid-continent sandhill crane hunting in the U.S. The 95 percent confidence interval for the
annual harvest estimate is about +5 percent, which is a precision level that is adequate to
ensure responsible harvest management (i.e., hunting regulations) decisions.
3. Describe methods to maximize response rates and to deal with issues of
nonresponse. The accuracy and reliability of information collected must be shown to
be adequate for intended uses. For collections based on sampling, a special
justification must be provided for any collection that will not yield "reliable" data that
can be generalized to the universe studied.
Response to the Migratory Bird Harvest Information Program is mandatory. We monitor
participation by reviewing trends in data transmission from each State, for which we have direct
information from 1999-present and indirect information from 1961-present. We also spot-check
compliance by following the registrations of individual hunters (Appendix B). We use standard
methods to encourage response to the Migratory Bird Harvest Survey, Parts Collection Survey,
and Sandhill Crane Survey. These include a cover letter that is addressed to the individual
hunter and signed by the Chief of the Division of Migratory Bird Management or the Chief of the
Branch of Harvest Surveys. The letter explains why the information is important and includes a
toll-free number to call and ask questions. The cover letters attempt to motivate the respondent
and stress the importance of participation. Forms are sent as early in the hunting season as
possible, to encourage participation. The forms are one page long and have been designed to
be as attractive and as easy to use as possible. All forms are sent to hunters with preaddressed, postage paid return envelopes. The Migratory Bird Hunter Survey and Sandhill
5

Crane Survey requests daily diary records, to minimize response bias. The form also includes
space to record season totals, for hunters who do not wish to record daily hunting activity. The
Migratory Bird Hunter Survey uses three waves of reminder mailings to contact nonrespondents
and encourage participation. The first wave includes a postcard and a letter sent by first class
mail. Second and third waves of reminders and replacement forms are sent to all
nonrespondents, also by first class mail. The Sandhill Crane Survey uses 1 wave of reminders,
because most sample frame information are not available until late winter and early spring, and
we have a limited time frame in which to analyze data and publish reports. The Parts Collection
Survey maximizes response rates by using forms 3-165A, C, and D to solicit volunteer
participants from a randomly selected sample of successful hunters. Solicitation forms are
mailed out well in advance of the opening of the hunting season, so that survey envelopes can
be mailed to them before the start of the hunting season. In these solicitation forms, we tell
hunters that we will send a report that contains all of the biological data on the specimens they
send in each year, as incentive to participate in the survey for the duration of the hunting
season. This report is sent in June of each year. As described in item B. 2.g. above, double
sampling estimates are used to detect and, if necessary, account for nonresponse.
We have a full-time administrative assistant to answer calls on the toll-free number that is
printed on all survey materials. This staff member can answer questions from the public, clarify
instructions, or order additional survey materials. If questions are too technical, the call can be
forwarded to a biologist. We also have an email address that is checked daily.
We are working on a contract with our Service’s Information Resources and Technology
Management to develop an online survey response platform, which we hope will increase
participation and save money on printing and mailing
4.
Describe any tests of procedures or methods to be undertaken. Testing is
encouraged as an effective means of refining collections of information to minimize
burden and improve utility. Tests must be approved if they call for answers to identical
questions from 10 or more respondents. A proposed test or set of tests may be
submitted for approval separately or in combination with the main collection of
information.
To test the accuracy of identification of species, age, and sex of duck wings, we ask six to
seven biologists each year to send known age- and sex-wings through the survey. These are
not identifiable by the biologists processing them. When these hunters receive their reports at
the end of the season, they tell us if we made any mistakes. This lets us know if we need to
focus attention on improving any particular species or area. To test the timeliness and accuracy
of our print contractor, we send a complete set of surveys from every mailing to the Chief of the
Branch. These mailings are reviewed for accuracy and timeliness.
5. Provide the name and telephone number of individuals consulted on statistical
aspects of the design and the name of the agency unit, contractor(s), grantee(s), or
other person(s) who will actually collect and/or analyze the information for the
agency.
The individual directly responsible for information collection and analysis is: Dr. Khristi Wilkins,
Chief, Branch of Harvest Surveys, Division of Migratory Bird Management, Laurel, MD 207084028 (301/497-5557).
The following statisticians have reviewed parts of the statistical design and analysis of these
surveys:
6

Dr. David Otis (retired), Leader, USGS Iowa Cooperative Fish and Wildlife Research Unit,
Department of Natural Resource Ecology and Management, Iowa State University, Ames, IA.
Dr. Christine M. Bunck, Deputy Center Director, USGS National Wildlife Health Center,
Madison, WI 53711 (608-270-2407).
Mr. Grey W. Pendleton, Statistician (Biology), Alaska Department of Fish and Game, Douglas,
AK 99824 (907-465-4353).
Dr. Robert E. Trost (retired), Division of Migratory Bird Management, U.S. Fish and Wildlife
Service, 911 N.E. 11th Avenue, Portland, OR 97232-4181.
Dr. Paul H. Geissler (retired), Biometrician, National Ecological Surveys Team, USGS Fort
Collins Science Center, Fort Collins, CO 80526.

7

Table 1. Potential respondent universe (N) and number of waterfowl hunters sampled (n) by stratum for Form 3-2056J, based on 2012 counts. Each hunter is assigned a duck, sea duck, goose, and brant stratum.
Duck hunters in stratum (N) and sample (n)
Sea duck hunters in stratum (N) and sample (n)
Goose hunters in stratum (N) and sample (n)
Bagged none
Bagged 1-10
Bagged >10
Do not hunt
Do hunt
Bagged none
Bagged 1-10
Bagged >10
State
N
n
N
n
N
n
N
n
N
n
N
n
N
n
N
n
AK
5,684
534
2,009
380
1,585
389
6,832
574
2,446
729
7,601
880
1,267
294
410
129
AL
141,521
1,428
6,337
325
6,548
849
0
0
0
0
149,700
2,004
2,928
304
1,778
294
AR
90,377
745
17,801
480
25,831
1,703
0
0
0
0
112,480
1,680
12,594
494
8,935
754
AZ
52,158
2,280
1,450
153
1,760
362
0
0
0
0
54,521
2,614
634
124
213
57
CA
124,240
687
15,405
808
26,860
1,805
165,966
3,083
539
217
142,115
1,448
15,630
1,016
8,760
836
CO
44,737
499
6,381
335
3,713
460
0
0
0
0
46,359
604
5,937
318
2,535
372
CT
2,973
104
1,148
207
305
122
2,973
104
1,453
329
3,201
136
973
200
252
97
DE
5,040
365
1,917
384
1,118
356
7,262
593
813
512
5,278
421
2,069
422
728
262
FL
92,499
927
5,483
260
4,847
550
0
0
0
0
102,829
1,737
0
0
0
0
GA
125,287
1,384
12,138
914
4,978
720
0
0
0
0
133,928
1,990
6,764
708
1,711
320
IA
103,597
1,345
4,303
168
4,013
300
0
0
0
0
105,541
1,398
4,171
201
2,201
214
ID
33,022
325
6,177
375
5,870
846
0
0
0
0
38,579
774
5,069
479
1,421
293
IL
71,237
641
12,506
361
9,200
469
0
0
0
0
76,270
739
12,004
447
4,669
285
IN
16,257
139
7,406
271
3,844
287
0
0
0
0
18,368
245
6,986
274
2,153
178
KS
36,368
311
5,498
188
6,895
494
0
0
0
0
38,724
411
5,332
198
4,705
384
KY
136,233
746
793
66
588
81
0
0
0
0
136,887
808
594
60
133
25
LA
169,544
2,891
13,771
982
12,195
1,518
0
0
0
0
152,798
1,611
17,803
803
24,909
2,977
MA
5,184
164
1,817
404
654
258
6,721
389
934
437
5,960
334
1,356
379
339
113
MD
24,834
336
11,032
651
5,750
454
41,122
1,340
494
101
38,919
1,121
1,835
197
862
123
ME
37,347
3,340
1,990
1,149
663
535
34,444
1,595
5,556
3,429
38,952
4,121
916
754
132
149
MI
108,702
812
19,309
534
11,381
630
0
0
0
0
114,697
1,037
17,856
551
6,839
388
MN
118,459
399
41,323
597
24,084
513
0
0
0
0
133,621
595
37,478
597
12,767
317
MO
54,179
262
8,138
313
10,058
505
0
0
0
0
61,739
580
6,474
255
4,162
245
MS
37,011
315
7,346
369
7,077
576
0
0
0
0
46,121
851
3,914
245
1,399
164
MT
43,898
425
4,378
360
3,176
372
0
0
0
0
45,489
528
4,039
357
1,924
272
NC
285,760
1,798
20,402
842
12,043
812
0
0
0
0
301,481
2,288
13,110
762
3,613
402
ND
30,050
375
12,311
713
13,593
902
0
0
0
0
36,064
682
13,850
799
6,040
509
NE
20,135
160
6,048
261
4,770
360
0
0
0
0
21,882
284
6,427
283
2,644
214
NH
5,570
260
1,188
377
344
216
6,932
716
170
137
6,087
386
873
355
142
112
NJ
10,461
517
3,217
361
2,115
391
14,902
1,061
891
208
11,932
718
2,331
289
1,530
262
NM
52,504
750
1,587
315
934
199
0
0
0
0
53,964
970
870
216
191
78
NV
5,964
101
1,386
168
1,432
312
0
0
0
0
7,641
294
942
234
200
53
NY
23,622
637
9,662
664
4,564
557
31,293
921
6,555
937
24,959
824
8,710
548
4,179
486
OH
19,867
98
5,933
203
2,516
163
0
0
0
0
20,710
139
6,084
213
1,522
112
OK
29,870
594
3,442
323
5,864
953
0
0
0
0
34,384
1,064
3,167
477
1,625
329
OR
30,000
324
6,899
268
10,101
1,221
46,569
2
431
7
36,499
748
6,011
402
4,490
663
PA
85,307
441
12,038
681
5,109
548
0
0
0
0
85,482
628
11,662
543
5,310
499
RI
718
95
340
156
169
112
862
135
365
228
775
99
349
201
103
63
SC
106,747
750
5,699
395
4,588
541
0
0
0
0
114,488
1,265
2,171
271
375
150
SD
27,783
272
7,185
310
6,385
424
0
0
0
0
28,512
381
9,107
350
3,734
275
TN
69,338
571
6,328
362
5,937
600
0
0
0
0
74,621
858
4,695
348
2,287
327
TX
578,647
958
20,533
885
23,500
1,388
0
0
0
0
608,890
2,322
9,308
505
4,482
404
UT
19,438
159
6,744
354
5,595
508
0
0
0
0
27,264
553
3,751
354
762
114
VA
31,443
279
6,037
498
3,299
430
39,464
943
1,315
263
33,556
435
5,450
490
1,773
282
VT
3,046
179
1,246
367
591
245
0
0
0
0
3,678
330
930
325
275
136
WA
24,259
198
6,832
314
8,899
940
24,259
198
15,731
1,254
31,185
639
5,820
459
2,985
354
WI
94,946
285
27,268
662
13,423
608
0
0
0
0
113,971
697
18,991
692
2,675
166
WV
2,752
219
1,100
110
302
70
0
0
0
0
2,865
229
1,050
119
239
51
WY
6,214
200
1,756
262
1,105
268
0
0
0
0
6,561
269
1,890
295
624
166
Total
3,244,829
3,276,453
391,037 411,922
320,171 348,093
429,601
441,255
37,693
46,481
3,498,128
3,543,897
312,172 331,379
145,737 161,192

Brant hunters in stratum (N) and sample (n)
Do not hunt
Do hunt
N
n
N
n
7,084
672
2,194
631
0
0
0
0
0
0
0
0
0
0
0
0
165,765
2,897
740
403
0
0
0
0
3,201
136
1,225
297
7,278
689
797
416
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
7,217
556
438
270
38,850
1,159
2,766
282
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
240,081
2,195
78,124
1,257
0
0
0
0
0
0
0
0
7,063
818
39
35
12,803
715
2,990
554
0
0
0
0
0
0
0
0
30,402
861
7,446
997
0
0
0
0
0
0
0
0
46,816
1,661
184
152
0
0
0
0
650
31
577
332
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
38,878
909
1,901
298
0
0
0
0
39,374
1,300
616
152
0
0
0
0
0
0
0
0
0
0
0
0
645,462
660,061
100,037 106,113

Table 2. Potential respondent universe (N) and number of mourning dove and band-tailed pigeon hunters sampled (n) by stratum for Form 32056K, based on 2012 counts.
Mourning dove hunters in stratum (N) and sample (n)
Band-tailed pigeon hunters in stratum (N) and sample (n)
Bagged none
Bagged 1-10
Bagged >10
Do not hunt
Do hunt
State
N
n
N
n
N
n
N
n
N
n
AL
120,666
1,633
24,682
498
9,058
450
0
0
0
0
AR
106,495
473
17,768
428
9,746
468
0
0
0
0
AZ
20,837
150
19,419
678
7,755
598
46,723
1,013
1,288
413
CA
111,993
756
42,197
818
12,315
691
163,549
1,667
2,956
598
CO
42,812
208
6,099
157
5,920
468
53,841
725
990
108
DE
6,228
198
1,468
148
379
75
0
0
0
0
FL
89,293
423
10,511
396
3,025
282
0
0
0
0
GA
95,992
707
36,946
803
9,465
406
0
0
0
0
ID
38,719
234
5,761
269
589
96
0
0
0
0
IL
74,631
411
15,541
259
2,771
90
0
0
0
0
IN
17,434
49
7,889
196
2,184
142
0
0
0
0
KS
27,676
83
13,685
28
7,400
26
0
0
0
0
KY
128,385
266
6,526
200
2,703
168
0
0
0
0
LA
190,966
915
4,174
151
370
24
0
0
0
0
MD
27,579
363
12,980
795
1,057
201
0
0
0
0
MN
174,221
303
6,648
240
2,997
278
0
0
0
0
MO
57,620
140
11,069
69
3,686
26
0
0
0
0
MS
32,192
347
14,234
300
5,008
344
0
0
0
0
MT
50,459
296
841
29
152
17
0
0
0
0
656
55,276
857
0
NC
253,200
9,729
254
0
0
0
ND
51,183
453
3,857
362
914
150
0
0
0
0
NE
20,421
122
8,347
414
2,185
170
0
0
0
0
NM
48,349
1,991
5,181
826
1,495
328
52,477
936
2,548
2,209
NV
6,464
69
2,067
161
253
90
0
0
0
0
OH
21,033
137
6,123
146
1,160
92
0
0
0
0
OK
28,230
362
8,232
328
2,714
266
0
0
0
0
OR
41,954
347
3,876
179
1,171
144
46,145
524
855
146
PA
84,259
244
15,518
433
2,677
148
0
0
0
0
RI
1,070
44
134
23
23
1
0
0
0
0
SC
99,480
430
13,052
224
4,502
187
0
0
0
0
SD
36,109
488
4,608
307
636
124
0
0
0
0
TN
59,918
299
14,435
287
7,250
285
0
0
0
0
TX
455,352
559
104,886
1,039
62,442
1,246
0
0
0
0
UT
26,552
140
4,797
361
428
61
30,271
479
1,506
83
VA
25,933
200
9,466
454
5,380
511
0
0
0
0
WA
36,459
360
3,005
391
526
39,872
747
118
4
WI
128,272
540
6,769
315
596
57
0
0
0
0
WV
2,697
147
1,397
115
60
13
0
0
0
0
WY
7,654
81
1,222
103
199
32
0
0
0
0
Total
2,848,787
15,624
530,686
13,787
190,920
9,009
432,878
6,091
10,261
3,561

Table 3. Potential respondent universe (N) and number of woodcock hunters (n)
sampled by stratum, for Form 3-2056L, based on 2012 counts.
Woodcock hunters in stratum (N) and sample (n)
Do not hunt
Do hunt
State
N
n
N
n

AL
AR
CT
DE
FL
GA
IA
IL
IN
KS
KY
LA
MA
MD
ME
MI
MN
MO
MS
NC
NE
NH
NJ
NY
OH
OK
PA
RI
SC
TN
TX
VA
VT
WI
WV
Total

153,502
127,726
3,984
7,965
100,937
138,770
109,227
91,705
27,086
48,444
137,540
182,280
7,059
41,581
38,745
123,019
174,376
71,423
49,646
311,775
30,758
6,055
15,139
36,217
27,815
39,051
97,922
1,157
116,468
79,616
622,436
40,125
4,335
126,933
3,273

391
291
315
218
148
307
278
198
179
320
352
729
429
270
409
628
505
111
214
487
85
116
202
263
131
144
537
45
253
150
294
261
61
434
126

904
6,283
442
110
1,892
3,633
2,686
1,238
421
317
74
13,230
596
35
1,255
16,373
9,490
952
1,788
6,430
195
1,047
654
1,631
501
125
4,532
70
566
1,987
244
654
548
8,704
881

205
383
159
45
197
247
46
126
115
90
15
652
221
17
752
533
575
98
482
102
13
211
115
230
52
51
386
22
122
185
14
200
51
484
93

3,194,090

9,881

90,488

7,289

Table 4. Potential respondent universe (N) and number of snipe/coot and rail/gallinule hunters sampled (n) by stratum for Form
3-2056M, based on 2012 counts. Each hunter is assigned to both a coot/snipe and rail/gallinule stratum.

State
AK
AL
AR
AZ
CA
CO
CT
DE
FL
GA
IA
ID
IL
IN
KS
KY
LA
MA
MD
ME
MI
MN
MO
MS
MT
NC
ND
NE
NH
NJ
NM
NV
NY
OH
OK
OR
PA
RI
SC
SD
TN
TX
UT
VA
VT
WA
WI
WV
WY
Total

Coot/snipe hunters in stratum (N) and sample (n)
Do not hunt
Do hunt
N
n
N
n
7,172
30
2,106
310
153,186
396
1,220
187
128,070
278
5,939
98
39,994
58
8,017
1,061
163,216
726
3,289
453
53,003
310
1,828
50
4,362
235
64
55
6,467
260
1,608
770
96,366
411
6,463
363
130,792
322
11,611
1,813
109,110
211
2,803
275
44,738
204
331
21
90,264
209
2,679
118
26,787
83
720
250
48,476
255
285
156
130,924
291
6,690
51
187,842
958
7,668
307
188
7,467
484
125
19,773
93
21,843
2,301
34,684
213
5,316
994
134,450
272
4,942
100
170,779
513
13,087
315
70,707
178
1,668
20
26,592
121
24,842
112
51,150
207
302
63
259,200
415
59,005
448
54,990
243
964
540
28,781
123
2,172
63
7,021
197
81
21
15,481
393
312
67
53,267
519
1,758
10
8,577
175
206
108
33,290
281
4,558
475
27,363
68
953
38
38,904
166
272
72
43,793
185
3,207
133
100,107
182
2,347
170
1,070
58
157
22
116,362
536
672
80
40,970
196
280
383
79,975
70
1,628
70
622,110
592
570
48
25,080
252
6,697
260
39,941
488
838
230
4,843
114
40
25
39,990
145
0
3,660
128,977
207
6,660
380
3,853
239
301
132
8,918
352
157
92
3,719,234
13,598
229,447
17,708

Rail/gallinule hunters in stratum (N) and sample (n)
Do not hunt
Do hunt
N
n
N
n
9,278
340
0
0
153,740
455
666
128
130,009
281
4,000
95
39,935
61
8,076
1,058
165,736
991
769
188
54,554
344
277
16
4,362
232
64
58
7,255
653
820
377
99,006
479
3,823
295
131,250
332
11,153
1,803
109,293
230
2,620
256
45,069
225
0
0
91,100
210
1,843
117
27,350
269
157
64
48,516
257
245
154
126,799
271
10,815
71
1,039
4,829
226
190,681
71
7,551
538
104
41,156
2,255
460
139
34,684
208
5,316
999
134,450
272
4,942
100
171,989
511
11,877
317
71,653
182
722
16
26,835
125
24,599
108
51,452
270
0
0
262,003
425
56,202
438
55,954
783
0
0
29,305
129
1,648
57
7,102
218
0
0
15,488
360
305
100
53,359
525
1,666
4
8,701
226
82
57
33,588
285
4,260
471
28,053
90
263
16
39,118
212
58
26
47,000
318
0
0
100,725
233
1,729
119
1,083
59
144
21
116,455
548
579
68
41,353
476
0
0
80,332
83
1,271
57
622,482
620
198
20
31,777
512
0
0
40,133
489
646
229
4,883
139
0
0
39,990
3,805
0
0
130,240
250
5,397
337
3,889
248
265
123
8,775
276
300
168
3,775,491
22,339
173,190
8,967

Table 5. Potential sample universe for the Waterfowl Parts Survey Form 3-165, based on 2012 data.
State
AK
AL
AR
AZ
CA
CO
CT
DE
FL
GA
IA
ID
IL
IN
KS
KY
LA
MA
MD
ME
MI
MN
MO
MS
MT
NC
ND
NE
NH
NJ
NM
NV
NY
OH
OK
OR
PA
RI
SC
SD
TN
TX
UT
VA
VT
WA
WI
WV
WY
Total

Ducks
Harvest
Number of wings
55,900
527
210,900
313
1,328,700
2,888
51,000
823
1,586,800
6,513
87,900
880
13,400
446
45,700
297
257,900
2,469
162,600
461
117,700
652
277,700
1,817
401,200
1,062
131,600
607
174,600
1,097
42,300
270
2,762,700
5,894
17,800
639
112,200
620
31,400
450
320,200
1,574
749,300
1,276
445,000
1,936
306,700
727
120,100
858
394,400
1,529
459,300
2,544
155,500
1,119
14,200
212
70,600
721
29,800
388
51,000
767
192,800
2,142
106,800
573
342,100
2,113
389,000
4,342
91,800
987
4,800
174
235,500
893
220,300
1,038
249,000
282
1,491,600
5,272
274,500
2,060
134,000
844
20,500
688
490,900
2,758
350,700
2,018
10,300
116
41,700
429
15,632,400
69,105

Geese
Harvest
Number of wings
9,400
106
19,700
6
116,000
184
1,600
21
150,100
826
98,100
438
7,900
233
21,500
176
1,500
2
15,900
96
39,300
286
73,900
228
100,300
264
60,400
165
93,000
300
7,300
43
54,200
95
10,800
337
190,400
681
9,600
163
144,700
652
236,700
272
56,900
160
12,300
7
79,700
458
73,700
87
184,900
491
113,800
433
4,900
76
40,800
340
11,200
85
5,700
54
137,900
1,243
59,400
356
50,000
202
55,100
408
115,700
958
1,500
140
39,700
34
140,900
452
29,600
16
208,400
273
23,500
112
52,000
409
8,800
259
74,300
432
83,800
495
5,800
65
29,400
356
3,162,000
13,975

Table 6. Potential sample universe for the Mourning
Dove Parts Collection Survey for Form 3-165D, based
on 2012 data.
State
AL
AZ
AR
CA
CO
DE
FL
GA
ID
IL
IN
KS
KY
LA
MD
MN
MS
MO
MT
NE
NV
NM
NC
ND
OH
OK
OR
PA
RI
SC
SD
TN
TX
UT
VA
WA
WV
WI
WY
Total

Doves harvested

687,100
494,200
601,200
900,000
204,300
39,900
175,100
735,700
127,600
372,700
263,300
244,800
511,800
354,100
94,300
65,400
296,600
286,900
2,600
1,020,600
78,900
223,400
160,100
26,900
136,000
349,700
64,100
203,200
500
554,600
65,500
464,400
4,150,800
78,000
295,900
51,500
73,200
10,300
25,300
14,490,500

Dove wings collected
805
433
810
1,475
462
332
317
784
413
473
741
887
735
731
179
759
642
1,018
255
273
1,084
465
793
528
422
347
551
277
365
974
593
451
2,347
225
1,573
597
281
178
309
24,884

Table 7. Potential sample universe for the Other Migratory Game Bird Survey for Form 3-165B, based on 2012 data.
Woodcock
Snipe
Number of
Number of
wings
wings
State
Harvest
Harvest
2
.
AK
.
600
0
AL
3,500
10
1,800
0
AR
4,200
4
0
17
AZ
.
.
0
0
CA
.
.
6,300
0
CO
.
.
400
0
CT
1,700
262
200
5
DE
800
11
0
0
FL
12,600
0
10,600
14
GA
800
23
300
5
IA
0
10
1,100
0
ID
.
.
900
0
IL
1,900
2
100
0
IN
600
71
3,700
3
KS
1,300
0
100
0
KY
200
7
0
0
LA
20,000
189
7,400
3
1,900
MA
437
0
0
MD
2,400
131
1,300
0
ME
9,600
1,296
100
2
74,100
500
MI
3,631
11
MN
31,000
1,404
2,800
5
MO
900
143
800
0
200
0
MS
54
0
MT
.
.
100
0
13,400
800
NC
198
32
ND
.
.
200
0
NE
1,300
0
0
0
3,800
600
NH
906
1
NJ
3,100
195
700
0
NM
.
.
0
0
100
NV
.
.
0
NY
8,400
1,035
200
1
OH
1,500
65
0
0
600
4,000
OK
0
1
OR
.
.
3,400
9
13,500
100
PA
518
6
300
0
RI
7
0
SC
7,900
94
9,500
0
SD
.
.
100
0
TN
1,500
49
0
0
TX
.
.
800
0
UT
.
.
200
0
VA
1,200
172
1,000
0
VT
3,000
683
400
0
WA
.
.
2,200
0
WI
40,400
3,092
1,100
5
WV
2,000
40
0
1
WY
.
.
600
0
Total
269,600
14,739
65,100
121
1
Number of parts for rails is calculated as 3-year pooled total.
2
"." indicates no season available in the state.

Rail species 1
Number of
wings
Harvest
.
.
200
0
0
95
.
.
.
.
0
0
0
1
0
0
600
0
1,400
0
0
22
.
.
100
0
300
8
0
0
0
0
700
27
0
0
100
0
0
0
0
0
100
119
0
32
0
0

0
.

0
.

3,500
0
.

0
0
700
.

0
0
5,400
.

0
0
.

3,700
.
.

100
0
0
16,900

67
.
0
.
379
0
.
0
2
32
.
0
0
142
.
0
0
.
554
.
.
0
19
0
1,499

Band-tailed pigeon
Number of
wings
Harvest
.
.
.
.
.
.

1,300
9,100
1,100
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.

300
.
.
.
.

1,500
.
.
.
.
.
.

100
.
.

200
.
.
.
13,600

66
22
0
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
3
.
.
.
.
68
.
.
.
.
.
.
0
.
.
24
.
.
.
183

Table 8. Potential respondant universe, number of sandhill crane hunters,
and response rates for Form 3-2056N, based on 2012 counts.
Number of
Number
Number of
state
hunters sampled
responses Response rate
AK
3,599
745
459
62%
CO
801
684
321
47%
KS
571
336
191
57%
MN
1,032
518
343
66%
MT
186
93
72
77%
ND
8,519
4,232
3,065
72%
NM
859
859
486
57%
OK
451
1,306
683
52%
SD
343
347
271
78%
TX
14,083
2,812
1,514
54%
WY
102
102
70
69%
Total
30,546
12,034
7,475
62%

Appendix A. Alternative Sample Size Allocation Schemes for the Mourning Dove Parts
Collection (Wing) Survey
Addendum to Progress Report I
Dave Otis
February 27, 2013
I presented methods and results in Progress Report I for sample size and allocation when the
statistical objective was the precision of predicted Management Unit recruitment from a 5-year
moving average. This design was also discussed at the 2012 MODO Task Force meeting, and it
was agreed that Task Force representatives would take the information back to their respective
Technical Committees for discussion at their February/March meetings. In this Addendum, I
provide more specific details about this sampling design, the current design, and a third
alternative in an effort to provide the Management Unit technical committees with information
that can be used to make recommendations on a preferred alternative to the USFWS Harvest
Survey Branch. This effort does not necessarily preclude consideration of other alternatives,
but the designs described here do represent reasonably distinct choices.

Design 1. There would be no change in the sample allocations and total sample size that are
currently used. Expected state sample sizes are given in Table 1 in the “2007-2011 Average”
column.
Design 2. This is the design that was described in Progress Report I. Significant changes from
the original design were 1) evaluating sample size and the relative importance of recruitment
estimate precision in the context of a precision criterion for a predicted Management Unit
population growth rate (or abundance) from a demographic model, 2) state allocations based
on an index to breeding population size, and 3) Management Units analyzed separately.
Expected state sample sizes are given in Table 1 in the “Management Unit Optimum Allocation”
column. Total wing survey sample size is expected to be ~ 50% less than Design 1.
Design 3. This design was motivated from comments received from the 2013 EMU meeting.
They expressed interest in knowing a bit more about what the consequences would be for
recruitment estimates at a scale intermediate between the Management Unit and the
individual state. The thought was that development of harvest strategies based on population
balance equations might be impaired by decreased resolution in recruitment estimates at
regional (subunit) scales, since we know from data collected to date that biologically important
differences do occur within Management Units.
1

In response to this request, I evaluated the precision of predicted recruitment estimates for
regional subunits that I had suggested at the 2010 Task Force meeting. I began with the sample
sizes from Design II, and computed expected standard errors for each subunit (see Table 1 for
state assignments to subunits). The initial results informed me about the level of precision that
would be reasonably achievable for each of the subunits in each Management Unit, and I made
the somewhat arbitrary decision that a standard error of < 0.3 for predicted subunit
recruitment would be adequate to identify biologically important changes in recruitment over
time. These criteria together suggested target standard errors of 0.30, 0.25, and 0.25 for each
subunit in the EMU, CMU, and WMU, respectively. To achieve these target values, I made
relatively small additions to sample sizes in 1 WMU subunit and 2 CMU subunits (Table 1).
(Note: for the ME subunit in the CMU I could only reach a precision of 0.35). Achievement of
desired precision in the EMU required much more significant increases in sample size. I first
increased the optimum sample sizes for all EMU states by increasing the total Management
Unit sample size from 5000 (Design II) to 8200. One EMU subunit then received an additional
increase in sample size. (Note: reasonable precision was not achievable in the FL subunit.)
Expected state sample sizes are given in Table 1 in the “Management Unit Optimum Allocation
+ Subunit Supplement)” column. Total wing survey sample size for this design is expected to be
~ 30% less than Design 1.

2

Table 1.

Three alternative sample size allocation schemes for the Mourning Dove Parts
Collection Survey. Highlighted cells indicate states that receive additional wings to
achieve the subunit precision objective. In the EMU, this supplment is in addition to
the overall increase in Management Unit sample size.

Management
Unit
WMU

2007-2011
Average
1579
1292
304
169
715
370
278
4707

Management Unit
Optimum
Allocation

Management Unit
Optimum
Allocation +
Subunit
Supplement
759
718
318
337
222
204
241

State
AZ
CA
ID
OR
WA
NV
UT
Total

Subunit
AZ
CA
NW
NW
NW
MC
MC

AR
MO
IA
MN
ND
SD
KS
NE
OK
TX
CO
MT
WY
NM
Total

ME
ME
NE
NE
NE
NE
MW
MW
MW
TX
W
W
W
NM

CMU

518
796
58
102
264
523
994
830
817
1859
1164
273
356
552
9106

218
229
339
209
362
470
777
375
345
807
322
325
101
123
5000

418
429
339
209
362
470
777
375
345
807
522
525
301
123
6000

AL
GA
LA
MS
NC
SC
TN
FL
DE
KY
MD
PA
VA
WV
IL
IN
OH
WI
Total

S
S
S
S
S
S
S
FL
MC
MC
MC
MC
MC
MC
N
N
N
WI

EMU

662
327
254
573
967
1157
370
372
225
566
449
374
1238
190
948
1298
507
243
10720

568
490
208
279
545
266
210
466
13
353
54
86
157
55
410
330
315
195
5000

932
804
341
457
893
436
345
765
21
578
88
140
257
92
673
541
517
620
8500

24533

12500

17300

Grand Total

759
718
218
237
122
204
241
2500

2800

Sample Size Derivation for the Mourning Dove Parts Collection (Wing) Survey
Progress Report I
Dave Otis
September, 2012
This report describes an approach for determining the total sample size necessary to achieve a
desired level of precision of the harvest age ratio at the Management Unit (MU) scale, and the
optimum allocation of the sample to individual states within the MU.

Notation
Let
HY = number of sampled juvenile birds in a state,
AHY = number of sampled adult birds in a state,
n = HY + AHY.
Then the proportion of juveniles in the sample = p = HY/n, and the age ratio R = HY/AHY = p/(1p).

Prediction Variance of 𝑅�

Let 𝑅� be a sample age ratio in a given year and state. Then

� = 𝐸�𝑉𝑎𝑟�𝑅��𝑅�� + 𝑉𝑎𝑟[𝐸�𝑅� �𝑅�]
𝑉𝑎𝑟(𝑅)
= 𝑠2 + 𝜎 2,

where s2 is the average sampling error of 𝑅� and σ2 is the temporal process error, i.e., true

natural variation) of R.

Define the predicted value of the age ratio (𝑅�0 ) in year T+1 as the running average ( 𝑅� ) of the

previous T years. The variance of this individual predicted value is
1

𝑉𝑎𝑟�𝑅� − 𝑅�0 � = 𝑉𝑎𝑟�𝑅� � + 𝑉𝑎𝑟�𝑅�0 �
=

𝑠2+ 𝜎 2
+ (𝑠 2 + 𝜎 2 ).
𝑇

Prediction variance at the Management Unit scale
Let 𝑅�𝑖𝑡 be the age ratio estimate in the ith state in the tth year. The states are treated as strata
with weights {wi} that are proportional to population abundance. The index to abundance is

the same as used in the current harvest management assessment, i.e., the product of the area
of dove habitat and the running average mourning dove Call Count Survey index. So,
1
1
𝑅� = 𝑇 ∑𝑇𝑡=1 ∑𝑍𝑖=1 𝑤𝑖 𝑅�𝑖𝑡 = 𝑇 ∑𝑍𝑡=1 𝑅�𝑡 ,

Now the prediction variance of 𝑅� is

𝑤here Z = number of states.

𝑍

𝑇+1
𝑉𝑎𝑟�𝑅� � = � 𝑤𝑖2 (𝜎𝑖2 + 𝑠𝑖2 ) �
�.
𝑇
𝑖=1

(1)

Now, for the sample size exercise we assume a true long-term average proportion of HY birds in
the ith state = pi and let ni = number of (known age) wings sampled. We then have
𝑠𝑖2 =

𝑓(𝑝𝑖 )
, 𝑤ℎ𝑒𝑟𝑒
𝑛𝑖

𝑝𝑖
𝑝𝑖 2
2𝑝𝑖
𝑓(𝑝𝑖 ) = �
� �1 + �
� +
�.
1 − 𝑝𝑖
1 − 𝑝𝑖
1 − 𝑝𝑖

(2)

(See Appendix for derivation.)

From finite sampling theory (Cochran 1977), the optimum allocation of N wings to the Z states
in a Management Unit is then

2

𝑛𝑖 = 𝑁

𝑤𝑖 �𝑓 (𝑝𝑖 )

∑𝑍𝑖=1 𝑤𝑖 �𝑓 (𝑝𝑖 )

.

(3)

Sample Size for a Desired Precision of the Harvest Age Ratio
Data
All subsequent calculations are based on SAS codes and datasets of the 2007 – 2011 mourning
dove wingbee provided by B. Raftovich (USFWS, 4/25/12).
Calculations
As an initial exercise, I used the above equations to calculate expected variance of R as a
function of N = total MU sample size for T = 5. I used the average value of p for each state to
substitute into f(p). Because this effectively assumes that sampling variance is constant, I used
the naïve estimates of process error (Burnham et al. 1987):
𝑇

𝑇

𝑡=1

𝑖=1

1
1
𝜎 =
�(𝑅�𝑡 − 𝑅� )2 − � 𝑠𝑖2 .
𝑇−1
𝑇
2

For CMU states that are geographically split between the Central and Pacific Flyways (MT, WY,
CO NM), I used only the CMU wing data. If σ2 < 0, then I set σ2 = 0.00001. I used IL values of (R,
σ2) for IA, and the Burnham et al. (1987) unequal sampling variance process error calculation
for MN because of the extreme range in sampling variation in that state.
The above exercise assumed that all wings were of known age. But we know that a significant
proportion of sample wings are classified as ‘unknown’ based on the standard age key, and this
was the motivation for development of a technique for assigning ‘unknown’ wings to an age
class (Miller and Otis 2010). So how do we adjust variance calculations for this adjustment?
The only available information is contained in Miller (2008, p.27), which reports results of a
simulation exercise that suggests that the variance of the adjusted estimate is ~ 2.5 times
3

greater than a corresponding estimate that assumes all wing ages are known. (Note that even
though the sample size is greater when unknown wings are included, the variance also
increases substantially. This is the ‘cost’ of the adjusting the naïve age ratio estimator for bias.)
Therefore, I inflated all of the f(p) values by 2.5. I also calculated the average proportion of
unknown wings in each state and adjusted each calculated state sample size by 1/(1-proportion
unknown). The result represents the number of total wings (known + unknown) needed.
Results
Required sample size began to reach asymptotic values for N ≈ 3000 for the EMU and N ≈ 2000
for the CMU and WMU (Fig. 1). Minimum achievable SE values were approximately 0.20 for the
EMU and 0.10 for the CMU and WMU. Proportional allocations of total sample size to the
individual states are given in Table 1.

Figure 1. Total Management Unit sample size (N) required to achieve a desired level of precision of the predicted
harvest age ratio.

4

Wing Survey Sample Size in the Context of a Population Balance Equation
The primary rationale for the dove PCS is to provide annual estimates of recruitment that are
used in a more informed harvest management strategy (Anonymous 2005). Thus, it makes
sense to consider the sample size problem in the context of a desired precision of a prediction
from a simple population balance equation. This approach is similar to that used in the
Mourning Dove Banding Needs Assessment (Otis 2009), which used the basic population model
described in the Mourning Dove National Strategic Harvest Management Plan (Anonymous
2005):
𝜆 = 𝑆𝐴𝐻𝑌 + 𝑆𝐻𝑌 ∗ 𝑅 ′ ,

where λ = the relative change in population abundance in the subsequent year, 𝑆𝐴𝐻𝑌 and 𝑆𝐻𝑌

are annual survival rates, and 𝑅 ′ is the annual per capita recruitment rate, which is estimated

by adjusting the harvest age ratio for the relative harvest vulnerability (𝑉) of the HY to AHY age
class, i.e., 𝑅 ′ = 𝑅 ⁄𝑉 . This adjustment factor is typically calculated from age- class recovery

rates produced by standard band recovery models (Brownie et al. 1985). Based on previous
band recovery analyses (Otis, unpubl. data) I chose to use a grand average value of V = 1.3 for
present calculations. I also assume that the relative contribution of the sampling variance of V
in the population balance equation will be negligible, i.e., V is treated as a constant.
The variance of a prediction from this equation is given as
2
𝑉𝐴𝑅�𝜆̂� = 𝑉𝐴𝑅�𝑆̂𝐴𝐻𝑌 � + 𝑅� ′2 𝑉𝐴𝑅�𝑆̂𝐻𝑌 � + 𝑆̂𝐻𝑌
𝑉𝐴𝑅�𝑅� ′ � + 𝑉𝐴𝑅�𝑆̂𝐻𝑌 � 𝑉𝐴𝑅�𝑅� ′ �

+ 2 𝑅� ′ 𝐶𝑂𝑉�𝑆̂𝐴𝐻𝑌 , 𝑆̂𝐻𝑌 �.

For current purposes, I assumed that a harvest strategy that used a population balance

(4)

equation with this basic structure for making annual regulation decisions would use estimated
average survival rates and associated variances derived from a standard band recovery analysis.
(These survival rates could be interpreted as survival in the absence of harvest, which would
then be adjusted for a predicted harvest rate in the balance equation). Thus, a desired level of
precision for the predicted λ becomes strictly a function of 𝑅� ′ and 𝑉𝐴𝑅�𝑅� ′ �.
5

Survival rate calculations and results
I used 2003 – 2011 band recovery data provided by M. Seamans (USFWS, 5/16/12) and Program
MARK to calculate state specific survival rate estimates. For each state, I fit only 3 models using
the Brownie et al. dead-recovery model: 1) the standard age and time-specific global model, 2)
constant age, age and time-specific recovery rates, 3) constant age, age and time-additive
recovery rates. I used the estimated c-hat from the global model to adjust for binomial overdispersion, but considered only Models 2 and 3 for estimation of survival rates.
Based on comparison of AIC values, Model 3 was best for nearly all states. Precision of survival
rate estimates was generally inflated significantly by c-hat values (𝑥� = 5.0, range = 2.3 – 8.2).
In several states band recovery data was insufficient to produce reliable estimates, so I made
the following substitutions: DE = MD; NM, MT, WY = CO; OR = WA.

6

Sample size calculations
We now have all of the empirical estimates necessary for using Eq. (4) to calculate the expected
variance of the predicted population growth rate as a function of N.

Figure 2. Total Management Unit sample size (N) required to achieve a desired level of precision of the predicted
population growth rate (λ).

Achievable precision asymptotes occur at approximately N = 2500 (WMU) and N = 5000 (EMU,
CMU). Values of SE( λ ) at these values are approximately 0.087 (WMU), 0.065 (CMU) and .055
(EMU).
Discussion
Results suggest that significant reductions in the current survey sampling effort (𝑥̅𝐸𝑀𝑈 =
10,800; 𝑥̅ 𝐶𝑀𝑈 = 9,100; 𝑥̅𝑊𝑀𝑈 = 4,500) could be made with negligible loss of precision in
population balance equation prediction. The relative importance of reducing the sampling
variance of the harvest age ratio is determined by both the magnitude of the true natural
7

variation in annual recruitment ( 𝜎𝑅2′ ) and the relative magnitude of the variance components
associated with survival rates. Survival rate estimates in the EMU are estimated much more
precisely than in the CMU and WMU, but natural variation in recruitment is nearly an order of
magnitude larger in the EMU than in the CMU and WMU (Fig. 1). The end result is that
population balance equation predictions can be estimated most precisely in the EMU, but not
by a huge margin (Fig. 2). Conversely, natural variation in annual WMU recruitment is relatively
small and therefore WMU recruitment can be predicted relatively precisely with appropriate
sample sizes (Fig. 1). However, WMU survival rates are estimated with much less precision,
which to a large degree swamps the small variation in predicted recruitment, and results in the
least precise balance equation predictions (Fig. 2). Relationships between variance
components in the CMU are intermediate between the EMU and WMU.
I envision that the next steps in the project will be 1) discussions with FWS Harvest Survey
Section staff and the Mourning Dove Task Force about the acceptability of and/or modifications
in the approach outlined in this report, and 2) initial discussions about various attributes of the
sampling design used to achieve the desired sample sizes.

Literature Cited
Anonymous. 2005. Mourning dove national strategic harvest management plan. U. S. Fish and
Wildlife Service, Laurel, MD, USA.
Brownie, C., D. R. Anderson, K. P. Burnham, and D. S. Robson. 1985. Statistical inference from
band recovery data – a handbook. Resource Publication 131. 2nd edition. U. S. Fish and
Wildlife Service, Washington DC. 305pp.
Burnham, K. P., D. R. Anderson, G. C. White, C. Brownie, and K. H. Pollock. 1987. Design and
analysis methods for fish survival experiments based on release-recapture. American
Fisheries Society. Monograph 5.
Cochran, W. G. 1977. Sampling techniques. 3rd ed. Wiley and Sons, New York, New York.

8

Miller, D. A. 2008. Reproductive ecology of the mourning dove: large-scale patterns in
recruitment, breeding endocrinology, and developmental plasticity. Ph.D. Dissertation.
Iowa State University.
Miller, D.A., and D.L. Otis. 2010. Calibrating recruitment estimates for mourning doves from
harvest age ratios. Journal of Wildlife Management 74:1070-1079.
Otis, D. L. 2009. Mourning dove banding needs assessment. U.S. Fish and Wildlife Service.
Unpublished report. 22pp. Online:
http://www.fws.gov/migratorybirds/NewReportsPublications/Dove/Mourning%20Dove
%20Banding%20Needs%20Assessment.pdf

9

Table 1. Optimum state sample size
allocation proportions (from Eq. 3).
State
AZ
CA
ID
NV
OR
UT
WA
AR
CO
IA
KS
MN
MO
MT
NE
NM
ND
OK
SD
TX
WY
AL
DE
FL
GA
IL
IN
KY
LA
MD
MS
NC
OH
PA
SC
TN
VA
WI
WV

Management Unit
WMU

CMU

EMU

0.280
0.280
0.094
0.085
0.103
0.106
0.052
0.039
0.069
0.073
0.155
0.044
0.044
0.072
0.079
0.025
0.077
0.065
0.098
0.139
0.022
0.102
0.003
0.057
0.098
0.091
0.071
0.074
0.042
0.011
0.059
0.114
0.069
0.019
0.055
0.044
0.033
0.045
0.012

10

APPENDIX. Derivation of sampling variance of R = juvenile to adult age ratio.

Let HY = number of juvenile birds in the sample,
AHY = number of adult birds in the sample,
n = HY + AHY.
Then the proportion of juveniles in the sample = p = HY/n, and R = HY/AHY = p/(1-p).
Using the delta method we have
𝑝
𝑉𝑎𝑟(𝑅) = 𝑉𝑎𝑟 �
�
1−𝑝
1
𝑝2
𝑝
=
𝑉𝑎𝑟(𝑝)
+
𝑉𝑎𝑟(1 − 𝑝) − 2
𝐶𝑜𝑣(𝑝, 1 − 𝑝)
2
4
(1 − 𝑝)
(1 − 𝑝)
(1 − 𝑝)3
𝑝2
1
1 𝑝
�
� �1 +
−2
�−𝑝(1 − 𝑝)��
2
(1 − 𝑝)
(1 − 𝑝)2
𝑛 1−𝑝
=

𝑅
(1 + 𝑅 2 + 2𝑅)
𝑛
=

𝑅
(𝑅 + 1)2 .
𝑛

11

Participation Rates and Envelope Issue Allocation for the Mourning Dove Parts
Collection (Wing) Survey
Progress Report II
Dave Otis
April, 2013
Progress Report I focused on the problem of optimum allocation of wing sample size to
Management Units (MUs) and states, using a statistical criterion based on the variance of a
predicted population growth rate from a demographic balance equation. This progress report
is concerned with the next logical step in the survey protocol, i.e., the amount of total effort (in
terms of the number of wing envelopes issued to hunters) necessary to achieve a given desired
number of total wings from a MU, and the efficient allocation of this effort to the states within
the MU.
The allocation process depends on 3 parameters:
1. Volunteer rate: the proportion of hunters that are contacted who indicate they would
be willing to participate in the wing survey.
2. Participation rate: the proportion of volunteers that actually contribute wings to the
survey.
3. Average number of wings contributed by a participant (Parts per hunter [PPH]).
In late spring of each year, USFWS Branch of Harvest Surveys (BHS) staff creates a list of
potential participants from successful hunters who reported hunting on their HIP diary survey
the previous year. Letters are sent to a random sample of these hunters to ask if they would be
willing to participate in the dove Parts Collection Survey (PCS). Sampling rates are set to
achieve minimum state sample sizes. Contacted hunters that respond positively are then sent 2
envelopes and asked to return wings from their first 2 hunts during the first 2 weeks of the
season.
Values for volunteer and participation rates used in the design of the first dove wing survey in
2007 were taken from the long-standing waterfowl Parts Collection Survey. These values
remain essentially unchanged in the current survey protocol. The original expected number of
wings submitted by hunters was calculated from 2005 HIP data diaries and values are now
updated annually. The specific objectives in this report are to 1) update the state-specific
participation rate and average wings submitted by actual participants using 2007 – 2011 dove
wing survey (data are not available for updating volunteer rates), 2) use these estimates
together with optimum wing sample size allocations derived in Progress Report I to calculate
1

the most cost-efficient allocation of envelopes issued to volunteers, 3) compare the costefficiency of asking participants to return separate envelopes from 2 hunts with asking a larger
sample of participants to return only a single envelope from their first hunt.

DATA
Analyses were based on the 2007 – 2011 ‘Wingbee’ Access database, which includes state,
year, hunter number, issued envelope numbers, location, and wing data, and a 2007-2011
‘Issue’ Excel database, which contains state, year, hunter number, and year of participation of
volunteers that were issued envelopes. Databases were provided by B. Raftovich (FWS Harvest
Survey Branch).
RESULTS
Participation rates
Average state participation rates exhibited substantial variation, ranging from 0.284 to 0.648
(Table 1). The annual averages over all states were consistent, with a grand average = 0.483,
which is somewhat less than the value of 0.55 taken from the waterfowl parts collection survey.
However, intra-state annual variation in participation rates was substantial, with an average
range = 0.17.

Expected wings per envelope issue
The annual operational cost of the survey is primarily determined by the cost of mailing
(issuing) envelopes to volunteers. Therefore, a logical parameter of interest is the number of
wings that we can expect to receive from each hunter (PPH). This expectation is estimated by
the product of the participation rate and the average total number of wings submitted to the
wingbee by participants. Average state PPH exhibited substantial variation, ranging from 2.379
to 10.374 (Table 2). The annual averages over all states were consistent, with a grand average
= 6.804. Intra-state PPH annual variation was modest, with an average range = 3.039.
2

Participation rates of volunteer holdovers
To increase sample sizes, in 2010 the PCS protocol for selecting volunteers was altered by
automatically including all hunters that had submitted wings the previous year. Comparison of
the grand average participation rate of randomly selected first year volunteers ( 𝑋�2010 = 0.452,
n = 2738; 𝑋�2011 = 0.461, n= 2533) to holdover participants ( 𝑋�2010 = 0.517, n = 1361; 𝑋�2011 =

0.483, n= 1286) revealed rate increase of ~ 0.04.
Optimum issue allocation

Given the set of state-specific optimum proportional allocation for wing sample sizes presented
in Progress Report I and the corresponding PPH values in Table 2, it becomes straightforward to
calculate the optimum number of envelope issues for each state for any desired MU total wing
sample size. For example, Table 3 presents the results using the current MU total sample sizes.
Cost comparison of one versus two requested wing samples
The premise of this exercise is that it would be less expensive, in terms of mailing costs, to
achieve a desired number of wings by sending one return envelope to 2X willing participants
instead of sending 2 return envelopes to X willing participants (the current protocol), if the
expected number of wings received from the second hunt envelope is sufficiently smaller. Note
that the current protocol engenders cost of 3 mailed envelopes per hunter (the envelope sent
by the BHS + 2 return envelopes) while the alternative protocol engenders cost of 2 envelopes.
Let
X = expected number of wings received from the first hunt,
Y=

“

“

“

“

“

second

C = 1 unit of envelope mailing cost,
W = cost/wing of current protocol,
W” = cost/wing of alternative protocol,
N = number of envelopes sent in the current protocol.
Then

3

“,

W=

𝑁(3𝐶)

𝑁(𝑋+𝑌)

W” =

2𝑁(2𝐶)

𝑊"

2𝐶

𝑊

=

2𝑁𝑋
𝑋

∗

=

=

3𝐶

𝑋+𝑌
2𝐶
𝑋

𝑋+𝑌
3𝐶

2

= * (1 + Y / X ).
3

Therefore, if Y / X < 0.33, then the alternative protocol is cheaper. However, wingbee data
revealed that the overall average proportion of wings submitted for the second hunt compared
to the first hunt number (= Y / X ) was 0.56, with very little variation among years. Thus, the
conclusion is that the current protocol is more cost efficient.
DISCUSSION
Methods and results presented in Progress Reports I (and Addendums) and II can be used to
increase cost and efficiency of the Mourning Dove Parts Collection Survey. The next important
step will be to continue work with the FWS Branch of Harvest Surveys and the
Flyway/Management Unit Technical Committees to reach consensus on modification of survey
protocols in time for implementation in the 2013 survey.

4

5

6

Appendix B. Overview of HIP Sample Frame Problems
Background. The Harvest Information Program (HIP) was initiated to provide reliable estimates of hunter
activity and harvest for all migratory game birds. Under this program, the states annually collect the names and
addresses of individuals who specify they intend to hunt migratory game birds that year, and send that name
and address information to the USFWS. The states’ databases of migratory game bird hunters are then used as
the sample frames for surveys that generate annual hunter activity and harvest estimates. The HIP was initially
implemented in a few states in 1992, and has been fully operational in the 48 contiguous states and Alaska since
1999.
So far, HIP survey results have been used primarily to provide the public and migratory bird managers with state,
regional, and national estimates of hunting activity and harvest. However, the migratory bird management
community believes more appropriate harvest regulations can be developed by using harvest estimates and
banding data to index or estimate abundance of species for which we do not have surveys designed to estimate
population size. For example, the Mourning Dove Task Force has recommended the use of such methods,
rather than the Call Count Survey or the Breeding Bird Survey (which estimate only trends in birds observed), as
the basis for harvest strategies that may be implemented in all 3 dove management units as early as 2014.
Similarly, goose managers have begun using harvest and band-recovery data to estimate the abundance of midcontinent light geese.
The Problem. If the management community uses HIP harvest estimates explicitly as part of an informed
decision-making process, we need to ensure that the estimates are unbiased and precise. The key to reliable
results from any survey is the sample frame. A complete list of migratory bird hunters from each state is
essential to the success of the HIP because it enables the USFWS to select and survey representative samples of
hunters in each state. If a state’s HIP sample frame excludes certain groups of hunters (e.g., lifetime license
holders or nonresident hunters) and the success (harvest) of excluded groups is different than that of hunters
included in the sample frame, the survey sample will not be representative and the survey results will likely be
biased. More important is the fact that the sample frame is also the source of expansion factors. A harvest
survey estimates the average number of birds shot per hunter and that average is then multiplied by the
number of hunters in the sample frame (the expansion factor) to obtain an estimate of total harvest. Obviously,
if a state’s sample frame only includes 75% (or some unknown percentage) of the migratory bird hunters in the
state, the resulting harvest estimates will only be 75% (or some unknown percentage) of the actual harvest.
Conversely, a state also can identify too many hunters for HIP. That is, if hunters who do not intend to hunt
migratory birds are included in a state’s list, it is much harder to “find” specifically migratory bird hunters to
send surveys. These inflated sample frames are also common, and can result from several practices. The
problem is particularly acute in states that have electronic licensing systems but do not charge a fee for a
separate migratory bird (i.e., HIP) permit. In some of those states, many license vendors HIP-certify license
purchasers without asking them the required HIP questions that are used to identify the migratory bird hunters.
Hunters who purchase “sportsmen’s” or “combo” licenses that give them all of the hunting and fishing privileges
the state offers, including migratory bird hunting, are often automatically HIP-certified even though they have
no intention to hunt migratory birds. In both cases the consequence is that the USFWS receives name and
address data for hundreds of thousands of “migratory bird hunters” who are really only anglers, or only deer

hunters, etc. Because they have been erroneously identified as migratory bird hunters by the states’ HIP
certification processes, many of these people are sampled and asked to participate in HIP surveys. This
dramatically increases the costs of the HIP to obtain desired levels of precision (i.e., many more hunters from
states’ lists need to be sampled to ensure an adequate number of migratory bird hunters receive surveys), and
ultimately undermines public confidence in the HIP and the agencies (state and USFWS) that manage the
program (e.g., a deer hunter, non-hunter, or angler receives a survey and wonders why, and sees the effort as a
waste of tax dollars).
Often sample frame problems stem from a change in contractor, changes in the state’s licensing system, and/or
changes in the automated methods used to extract the HIP data for submission to the USFWS. When such
changes result in inflated or incomplete sample frames, it usually takes a few years and considerable effort to
identify the cause of the problem and correct it, but it can be done with cooperation from contractors and/or
licensing and IT personnel. Sample frames that are inconsistent for no apparent reason are much more difficult
to “fix,” but must be addressed if we expect the results of surveys using those samples frames to be the basis for
promulgating management regulations.
In addition to problems associated with identifying the correct hunters to survey, we also have experienced
problems in getting accurate information from sampled hunters due to delays in receiving lists of names from
the states. HIP survey forms are in diary format; they ask the sampled hunter to report the date, location, and
number of birds shot for every hunt. The diary design is an attempt to reduce response bias (both memory and
prestige bias), but its effectiveness depends on our collective ability to get the forms in the hands of selected
hunters before or as soon as possible after they begin hunting. The operational agreement between the Service
and the states when the HIP was implemented was that each year the state agencies would send their first
batch (i.e., those hunters who purchased a migratory bird hunting license prior to the opening of migratory bird
hunting seasons in September) of HIP name and address data to the USFWS in August before the first hunting
seasons start. Once seasons commence, they would send additional data twice each month (on the first and
third Wednesday of every month) as they HIP-certify additional hunters, until hunting seasons end in the state.
This schedule enables the USFWS to send sampled hunters survey forms that they receive no later than four
weeks after being HIP-certified. However, in any given year only about half of the states send HIP sample frame
data to the USFWS twice a month, and some don’t send data in August or prior to the opening of their state’s
hunting seasons. Some send the data monthly; others send only two files, the first in the middle or near the end
of the hunting season and the second after the season closes. This limits the effectiveness of the diary format
and likely results in response bias that compromises survey results.
In summary, by using the data gathered from HIP to estimate abundance of doves, the management community
is increasing the efficiency of that data collection program to improve management decisions and the
benefit:cost ratio of the survey. Over time doing so should reduce federal and state costs of monitoring efforts
needed to promulgate hunting regulations. However, we must strive to ensure the data collected from these
efforts are reliable. Although some of the needed changes to HIP may take some time and resources to resolve
(e.g., ensuring representative samples of hunters are surveyed), others can occur quickly and likely with minimal
costs (e.g., sending name lists to the USFWS twice each month as was initially agreed upon).

Appendix C. HIP sample frame reports – summary of requests and recommendations
Alabama: (1) We request that you work with us to detemine whether we are missing some of your
sample frame data for 2005, 2007, 2008, and 2009. (2) We recommend that you take steps to minimize
the number of unnecessary HIP registrations issued through your electronic licensing system. This can
usually be accomplished by charging a small fee for HIP registration, as recommended by AFWA in 2002,
or by providing more explicit instructions to your license vendors. (3) We request that you take steps to
ensure that Alabama HIP data are sent to us on schedule, starting with the data for 2012.
Alaska: (1) We request that you work with us to detemine whether you are sending us complete sample
frames. An incomplete sample frame could be caused by any of several factors, such as poor
compliance by hunters, poor compliance by license vendors (failure to send ADFG the completed HIP
forms), or omission of incomplete or very late (but valid) HIP forms. If you have state survey estimates
of waterfowl harvest that we could compare with HIP estimates, or information from law enforcement
on hunter compliance rates, it would help us assess this together. (2) We request that you ensure more
timely transmission of the HIP sample frame data by (a) increasing the frequency at which license
vendors are required to send in HIP forms, particularly early in the year (prior to October), and (b)
elevating the priority of HIP form data entry by your staff and/or contractors.
Arizona: (1) We request that you send us counts of the number of migratory bird permits and
waterfowl permits sold each license year from 2005-2011. This will enable us to use legitimate
expansion factors when we generate the final hunter activity and harvest estimates for those years. (2)
We request that you ensure more complete and timely transmission of the HIP sample frame data by (a)
increasing the frequency at which license vendors are required to send in HIP forms, particularly early in
the year (prior to October), and (b) elevating the priority of HIP form data entry by your staff and/or
contractors.
Arkansas: (1) We request that you work with us to detemine whether we are missing some of your
sample frame data for 2007. (2) We request that you work with us to detemine whether you are
sending us complete sample frames. An incomplete sample frame could be caused by any of several
factors, such as poor compliance by hunters or omission of some type(s) of HIP-registered hunters from
the HIP data you send us. If you have state survey estimates of migratory bird harvest that we could
compare with HIP estimates, or information from law enforcement on hunter compliance rates, it would
help us assess this together. (3) “take steps to minimize the number of unnecessary HIP registrations”
(see Alabama #2).
California: (1) We request that you work with us to detemine how much of your sample frame data for
2003 we are missing, and whether we are missing any of the 2010 data. (2) We recommend that you
take steps to minimize the number of unnecessary HIP registrations that are issued. This can usually be
accomplished by charging a small fee for HIP registration, as recommended by AFWA in 2002, or by
providing explicit instructions to your license vendors about which hunters need HIP registration and
which ones don’t. (3) We request that you take steps to ensure that you include HIP registration data
from all sources, including internet license sales and HIP registrations of lifetime license holders who

hunt migratory birds, and send us the electonic data twice a month, in accordance with the established
schedule.
Colorado: (1) We request that you work with us to detemine the actual number of HIP registrations that
your contractor issued for the 2009 hunting season. (2) We request that you take steps to ensure that
your contractor sends us all of the Colorado HIP data on schedule, starting with the data for 2012.
Connecticut: We request that you work with us to detemine whether we are missing some of your
sample frame data from 2005-2011. Please check your accouting records and if possible, send us the
number of migratory bird (HIP) permits you sold each month from January 1, 2005 through February 28,
2011.
Delaware: (1) We request that you work with us to detemine the actual number of HIP registrations
that your contractor issued for the 2008 and 2010 hunting seasons. (2) “timely data transfers” (see
Colorado #2).
Florida: (1) “take steps to minimize the number of unnecessary HIP registrations” (see Alabama #2). (2)
“detemine whether you are sending us complete sample frames” (see Arkansas #2).
Georgia: (1) We request that you work with us to detemine the correct number of HIP registrations
Georgia issued for the 2003, 2009, and 2010 hunting seasons. (2) “take steps to minimize the number of
unnecessary HIP registrations” (see Alabama #2). (3) “detemine whether you are sending us complete
sample frames” (see Arkansas #2).
Idaho: We request that you work with us to detemine the correct number of HIP registrations Idaho
issued for the 2007 hunting season.
Illinois: (1) We request that you work with us to detemine the correct number of HIP registrations your
telephone contractor issued for the 2003 hunting season. (2) “take steps to minimize the number of
unnecessary HIP registrations” (see Alabama #2).
Indiana: “detemine whether you are sending us complete sample frames” (see Arkansas #2).
Iowa: (1) “take steps to minimize the number of unnecessary HIP registrations” (see Alabama #2). (2)
“detemine whether you are sending us complete sample frames” (see Arkansas #2).
Kansas: We request that you work with us to detemine the correct number of HIP registrations Kansas
issued for the 2004 and 2005 hunting seasons. This will enable us to use legitimate expansion factors
when we generate the final hunter activity and harvest estimates for those years.
Kentucky: (1) We request that you work with us to detemine the correct number of HIP
registrations/permits that Kentucky issued for the 2005, 2006, 2007, and 2008 hunting seasons. (2) We
recommend that you take steps to minimize the number of unnecessary HIP registrations issued
through your electronic licensing system. This can perhaps be accomplished by providing more explicit

instructions to your license vendors about which hunters need to be HIP-registered and which ones do
not. (3) “timely data transfers” (see Alabama #3).
Louisiana: (1) “take steps to minimize the number of unnecessary HIP registrations” (see Alabama #2).
(2) “detemine whether you are sending us complete sample frames” (see Arkansas #2).
Maine: We appreciate the cooperation we have enjoyed with Maine in examining sample frame issues,
and we request that you continue to work with us to detemine whether you are sending us complete
sample frames. We also recommend that you take steps to minimize the number of unnecessary HIP
registrations issued through your electronic licensing system. This can perhaps be accomplished by
providing more explicit instructions to your license vendors.
Maryland: “timely data transfers” (see Alabama #3).
Massachusetts: (1) We request that you work with us to detemine the correct number of HIP
registrations Massachusetts issued for the 2007 hunting season. (2) “detemine whether you are sending
us complete sample frames” (see Arkansas #2).
Michigan: (1) “take steps to minimize the number of unnecessary HIP registrations” (see Alabama #2).
Minnesota: (1) “take steps to minimize the number of unnecessary HIP registrations” (see Alabama #2).
Mississippi: (1) We request that you work with us to detemine whether we are missing some of your
sample frame data for 2003, 2005, 2007, 2008, 2009, and 2010. (2) “detemine whether you are sending
us complete sample frames” (see Arkansas #2). (3) “take steps to minimize the number of unnecessary
HIP registrations” (see Alabama #2). (4) “timely data transfers” (see Alabama #3).
Missouri: None. Missouri’s execution of the state’s HIP responsibility is an outstanding example of how
this program can and should be conducted. Please just keep doing what you’re doing.
Montana: (1) We request that you work with us to detemine whether we are missing some of your
sample frame data for 2008. (2) “take steps to minimize the number of unnecessary HIP registrations”
(see Alabama #2). (3) “timely data transfers” (see Alabama #3).
Nebraska: (1) We request that you work with us to detemine the actual number of HIP registrations
that your contractor issued for the 2009 hunting season. (2) “detemine whether you are sending us
complete sample frames” (see Arkansas #2). (3) “timely data transfers” (see Colorado #2).
Nevada: (1) We request that you work with us to detemine whether we are missing some of your
sample frame data that your contractor collected for 2007 and 2008. (2) “detemine whether you are
sending us complete sample frames” (see Arkansas #2).
New Hampshire: (1) We request that you work with us to detemine whether we are missing some of
your sample frame data from 2003, 2004, and 2006. (2) “timely data transfers” (see Alabama #3).

New Jersey: We request that you take steps to ensure that you and/or your contractors send us the
data for all New Jersey HIP registrations and that the data are sent to us on schedule, starting with the
2012-13 season.
New Mexico: We request that you work with us to detemine whether we are receiving complete
sample frames for New Mexico. An incomplete sample frame could be caused by any of several factors,
such as poor compliance by hunters, poor compliance by license vendors (failure to send in the
completed HIP forms), or, if you provide hunters the opportunity to get their HIP registration on-line,
failure to transfer electronic HIP data to us. If you have state survey estimates of migratory bird harvest
that we could compare with HIP estimates, or information from law enforcement on hunter compliance
rates, it would help us assess this together.
New York: (1) “detemine whether you are sending us complete sample frames” (see Arkansas #2). (2)
We request that you take steps to ensure that you and/or your contractors send us the data for all New
York HIP registrations and that the data are sent to us on schedule, starting with the 2012-13 season.
North Carolina: (1) We request that you continue to work with us to detemine whether you are sending
us complete sample frames. (2) “take steps to minimize the number of unnecessary HIP registrations”
(see Alabama #2). (3) “timely data transfers” (see Alabama #3).
North Dakota: “timely data transfers” (see Alabama #3).
Ohio: (1) “take steps to minimize the number of unnecessary HIP registrations” (see Alabama #2). (2)
“timely data transfers” (see Alabama #3).
Oklahoma: (1) “detemine whether you are sending us complete sample frames” (see Arkansas #2). (2)
“timely data transfers” (see Alabama #3).
Oregon: “timely data transfers” (see Alabama #3).
Pennsylvania: “timely data transfers” (see Alabama #3).
Rhode Island: (1) We request that you send us counts of the number of HIP registrations issued in
Rhode Island in each of the following license years: 2003, 2005, and 2007-2011. This will enable us to
use legitimate expansion factors when we generate the final hunter activity and harvest estimates for
those years. (2) We request that you ensure more complete and timely transmission of the HIP sample
frame data by (a) increasing the frequency at which license vendors are required to send in HIP forms,
particularly early in the year (prior to October), and (b) elevating the priority of HIP form data entry by
your staff and/or contractors.
South Carolina: (1) We request that you work with us to detemine the correct number of HIP
registrations South Carolina issued for the 2003 hunting season. This will enable us to use legitimate
expansion factors when we generate the final hunter activity and harvest estimates for that year. (2)
“take steps to minimize the number of unnecessary HIP registrations” (see Alabama #2).

South Dakota: We request that you work with us to detemine whether we received the data from all of
the HIP registrations South Dakota issued for the 2006 and 2007 hunting seasons.
Tennessee: (1) We request that you work with us to detemine the correct number of HIP
registrations/permits that Tennessee issued for each year from 2003-2010. This will enable us to use
legitimate expansion factors when we generate the final hunter activity and harvest estimates for those
years. (2) “detemine whether you are sending us complete sample frames” (see Arkansas #2). (3)
“timely data transfers” (see Alabama #3).
Texas: (1) We request that you take steps to minimize the number of unnecessary HIP registrations
issued through your electronic licensing system. This could perhaps be accomplished by requiring super
combo license purchasers to “activate” their migratory bird hunting privilege by registering for HIP,
and/or by providing more explicit instructions to your license vendors about which hunters need to be
HIP-registered and which do not. (2) “timely data transfers” (see Alabama #3).
Utah: (1) We request that you work with us to detemine the actual number of HIP registrations that
your contractor issued for the 2009 hunting season. (2) “detemine whether you are sending us
complete sample frames” (see Arkansas #2). (3) “timely data transfers” (see Colorado #2).
Vermont: (1) We request that you work with us to detemine whether we are missing some of the HIP
registration data that you collected for 2003, 2005, 2006, and 2010. (2) “detemine whether you are
sending us complete sample frames” (see Arkansas #2). (3) “timely data transfers” (see Rhode Island
#2)
Virginia: (1) “detemine whether you are sending us complete sample frames” (see Arkansas #2). (2)
“timely data transfers” (see Colorado #2).
Washington: (1) We request that you work with us to detemine the correct number of HIP
registrations/migratory bird permits that Washington issued for each year from 2006-2010. This will
enable us to use legitimate expansion factors when we generate the final hunter activity and harvest
estimates for those years. (2) “detemine whether you are sending us complete sample frames” (see
Arkansas #2). (3) “timely data transfers” (see Alabama #3).
West Virginia: (1) “take steps to minimize the number of unnecessary HIP registrations” (see Alabama
#2). (2) We request that you take steps to ensure that all of West Virginia’s HIP data are sent to us on
schedule, particularly after September 1 when hunting seasons are underway.
Wisconsin: (1) “take steps to minimize the number of unnecessary HIP registrations” (see Alabama #2).
(2) We request that you send us the first file of your annual HIP data on schedule in late August, starting
in 2012.
Wyoming: (1) “detemine whether you are sending us complete sample frames” (see Arkansas #2). (2)
“timely data transfers” (see Alabama #3).

Harvest Information Program (HIP) Review 
Report to the Migratory Shore & Upland Game Bird Working Group 
August 22, 2013 
Khristi Wilkins (USFWS), Dave Morrison (TXPWD), and Brad Bortner (USFWS) 

Background 
The Harvest Information Program (HIP) was implemented nation‐wide in 1998 to provide a sample 
frame for estimating harvest of waterfowl, doves, band‐tailed pigeons, woodcock, snipe, coots, rails, 
gallinules, and sandhill cranes.  Prior to 1998, purchasers of the Migratory Bird Hunting and 
Conservation Stamp (a.k.a. Duck Stamp) provided the sample frame for estimating harvest of these 
species/species groups (except sandhill cranes).  The wildlife management community had long 
recognized that not all migratory bird hunters purchase Duck Stamps (e.g., dove hunters who do not 
hunt waterfowl).  The cooperative State‐Federal HIP was developed to obtain unbiased estimates of 
harvest of all migratory game birds nationwide.  Under this program, hunters are required to register for 
HIP every year in each State in which they hunt migratory birds.  State agencies are responsible for 
collecting the hunter information and forwarding it to the United States Fish and Wildlife Service 
(USFWS), and the USFWS is responsible for implementing the survey, analyzing data, and making results 
available to States and the public.   
In 2000, the Migratory Shore and Upland Game Bird Working Group (MSUGBWG) struck an ad hoc 
committee and charged it with conducting a complete review of all aspects of HIP to determine whether 
the program was functioning as intended, and to make recommendations for improvements where 
necessary.  This committee of State and Federal biologists, State licensing administrators, and 
biometricians reviewed and evaluated:  
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)

the purpose and history of HIP, 
the efficacy of the screening (i.e., registration) questions, 
types, impacts, and scope of vendor non‐compliance, 
the scope and impact of hunter non‐compliance, 
the role and impact of large chain stores in the overall success of HIP, 
the statistical reliability of HIP surveys, 
the impacts of HIP on State harvest surveys, 
the impacts of State license systems, and 
the cost of implementation. 

The committee issued a report of its findings and recommendations in 2002 (Ver Steeg and Elden, 2002).  
The committee determined that the Harvest Information Program was necessary to improve the 
reliability of annual harvest estimates for all migratory game birds, especially for doves, woodcock, and 
marsh birds (snipe, coot, rails, and gallinules).   However, the committee recommended several actions 
to improve the quality of the sample frame and increase compliance by hunters and license vendors.  
They also recommended that the USFWS investigate using reduced stratification (i.e. reducing the 
number of screening questions [up to 10 depending on species hunted in the State] and answers [2‐
4/question]).  Recommendations and follow‐up actions are summarized in Table 1.    
1 

Table 1.  Summary of results from HIP evaluation 2002 (Versteeg and Elden, 2002). 
Area 
Future 

Recommendation 
Continue HIP with significant improvements 

To 
USFWS 

Information & 
Education 

Improve hunter education about HIP 

USFWS & States 

Communication 
with States 

Work closely with States collectively to improve agency 
acceptance of HIP 

USFWS 

Communication 
Work with States individually to determine any special 
with States 
needs related to implementing HIP 
Screening questions  Investigate the feasibility of a reduced level of stratification, 
primarily by examining an additional year of survey data 
Screening questions  Pilot a shorter series of screening questions 
Enforcement of 
Stricter enforcement of the HIP registration requirement in 
Hunter Compliance  States where such enforcement has been minimal or 
nonexistent 
Enforcement of 
Provide a more consistent level of enforcement throughout 
Hunter Compliance  the country 

Enforcement of 
Hunter Compliance 
Vendor Compliance 
Vendor Compliance 

Vendor Compliance 

USFWS 

Minimal 

USFWS 
States 

None 
Varies by State 

USFWS 

New agents receive training on HIP, including 
examples of what to look for on licenses of 
several States, and fines for non‐compliance 
are standard across most federal enforcement 
districts 
? 

 Law Enforcement Committee of the IAFWA to develop 
uniform enforcement guidelines as soon as possible for 
adoption by the Service and the States 
Employ better techniques to monitor vendor compliance 
Examine, and possibly adopt, HIP‐certification systems that 
eliminate or minimize the service of license vendors 

States & AFWA 

Consider a standardized, multi‐State system for HIP data 
collection and possibly other permits 

States 

2 

USFWS 

Action taken since 2002 
Developing ways to better to manage data and 
assess quality 
USFWS – info on HIP on FWS website; States – 
most have info on their websites and/or in their 
hunting regulations booklets 
States have accepted HIP as necessary and 
routine, but some States would like to see 
progress on efforts to streamline 
This has been an ongoing process 

States 
States 

Minimal 
Many have implemented on‐line licensing and 
HIP registration, and usage by hunters is 
growing 
None 

HIP registration fees  Each State with the authority to require a fee for HIP 
registration should do so 

States 

Exempted Hunters 
Mechanics 

States 
USFWS 

In 2009, 15/49 States were charging for HIP 
(although some States charge for point of sale 
permits and offer online permits for free) 
Minimal change 
Minimal 

USFWS 

None 

USFWS 

None 

USFWS 

Not applicable (no changes made to design) 

Mechanics 
Mechanics 

Mechanics 
Funding 

Funding 

Minimize the number of hunters exempted from HIP 
Investigate potential sources of bias and evaluate the 
impact of such biases on the harvest estimates 
Develop and maintain data to determine trends and 
variations in bias over time 
Conduct additional research over a number of years to 
determine if the bias is consistent and to ensure that 
current harvest estimates are correlated with reality 
Determine the impact on harvest survey results when 
changes are made to the survey design 
 Support an increase in the Service’s budget of $300,000 
annually to assist the Division of Migratory Bird 
Management (MBM) with research and outreach efforts 
related to many of these recommendations 
 $150,000 of the $300,000 for at least the next 10 years to 
support efforts directed specifically at improving the HIP 
survey design methodology 

3 

AFWA & USFWS  That funding was eliminated by the time the 
report was released in 2002 

USFWS 

That funding was eliminated by the time the 
report was released in 2002 

Requested actions 
At this time, we do not feel that the USFWS and States need to repeat an intensive review of the HIP, 
because we expect that the outcome and recommendations would be similar.  We recommend focus on four 
areas.  Recommendation #1 would provide a long‐term fix; recommendations 2‐4 are needed to improve HIP 
as it currently is implemented. 
(1) Long‐term: Consider making HIP a Federal permit.  When HIP was initially proposed by IAFWA in 
1990, it was envisioned as a national permit that would be required by the USFWS but administered 
by the States.  However, some State agencies and the Office of Management and Budget (OMB) were 
concerned about the Federal government getting involved in State licensing, and OMB would not 
approve what it perceived to be a Federal permit.  Thus, when phased‐in implementation began in 
1994, HIP had evolved into its current form: HIP permits (or the equivalent) issued by State natural 
resources agencies and forwarded to the USFWS.  If HIP registration was accomplished through a 
Federal permit issued by the USFWS, the quality of the sample frame would be improved and States 
would be relieved of the burden of collecting these data.  However, some States charge a fee for 
their HIP permit and might not want to lose that revenue.  Also, we do not know how OMB would 
react to a proposal to create a Federal permit, or if the USFWS would be able to secure the necessary 
funding to operate the HIP program. 
(2) Ensure game bird managers are involved with the HIP data collection, coding, and transfer 
processes in each State.   The quality of the HIP sample frame is dependent upon receipt of correct, 
complete data in a timely manner.  Incomplete or late sample frames have limited the utility of the 
harvest estimates for management, especially the harvest estimates of mourning doves (see 
Appendix A for description of sample frame problems and Appendix B for summary of requested 
actions for each state).   Inaccurate coding of hunters’ responses to the HIP screening questions has 
also been a problem (e.g., many hunters assigned to the wrong strata regardless of their answers to 
the screening questions, because of a programming error).  The latter problem is nearly impossible to 
detect until after the bulk of the hunting registrations have been received.   At that point it is too late 
to make corrections even if the problem is recognized because most hunters have already been 
surveyed.  State agency biologists have limited ability to prevent and correct problems when 
licensing or IT personnel or contractors have sole responsibility for HIP registration systems and for 
compiling and transferring the HIP data to the USFWS.  In our experience, the quality of the data is 
improved when people with a vested interest in the survey results (i.e., game bird managers) are 
closely involved with the HIP process within each State agency.  Therefore, we request that each 
State agency make the quality of its HIP sample frame data a high priority, and appoint a migratory 
game bird manager to be responsible for the quality of HIP data from his/her agency.  This person 
would work with the USFWS’s Branch of Harvest Surveys to ensure that HIP data are coded correctly, 
compiled from all license types and sources (e.g., online, telephone, point of sale), and transferred to 
the USFWS in a timely fashion.   
(3) Review efficacy of stratification questions.  The HIP registration process takes several minutes (see 
Appendix C for list of screening questions).  Reducing the number of questions and answers would 
make the process quicker and would likely increase hunter and license vendor compliance.  This 
change would also have the added benefit of clearing up the misconception that the screening 
questions are the harvest survey.  In our conversations with hunters, we have found that many 
4 

hunters believe this to be the case, and when they see non‐compliance, they think that the survey is 
not providing useful information.  Analyses of data from 1999 showed that the screening questions 
reduced necessary sample size somewhat (VerSteeg and Elden, 2002), but current data need to be 
analyzed in a cost‐benefit context, keeping in mind that a slight loss of efficiency might be beneficial 
to the overall success of the HIP.   
(4) Get permission from State agencies to include email addresses with HIP registrations.  The design 
of the HIP survey is based on the Dillman Total Design Method (Dillman 1978, Dillman 1991).  
Hunters are mailed a complete survey packet (diary form and cover letter) within 1‐3 weeks of 
receipt of name and address.  A hunter who does not return his/her survey form shortly after the 
close of the season is sent a reminder post card.  About 3 weeks after this mailing, a complete follow‐
up packet is sent to non‐respondents.  Finally, 3‐4 weeks, later an additional survey packet is sent to 
remaining non‐respondents.  Because some States collect email addresses as part of the licensing 
process, the USFWS would like to investigate the feasibility of sending some reminder mailings by 
email.  This would allow us to reduce printing and mailing costs, as well as reduce our carbon 
footprint in support of the USFWS Sustainability Initiative.  Postage costs are the largest part of the 
HIP survey program, and anything we can do to lessen these costs will protect the long‐term viability 
of the survey.  However, before the USFWS can investigate including email addresses with HIP 
registrations, approval is needed from State agencies and the Office of Management and Budget.  
Approval from the States is the first step.        
Citations 
Dillman, D.A.  1978.  Mail and telephone surveys: the Total Design Method.  Wiley & Sons, New York USA. 
Dillman, D.A.  1991.  The design and administration of mail surveys.  Annual Review of Sociology 17: 225‐249. 
Ver Steeg, J., and R.C. Elden, compilers.  2002.  Harvest Information Program: Evaluation and 
Recommendations.  International Association of Fish and Wildlife Agencies, Migratory Shore and 
Upland Game Bird Working Group, Ad Hoc Committee on HIP, Washington, DC.  100 pp. 

5 

Appendix A.  Overview of HIP Sample Frame Problems  
(Note: This document was given to each state agency’s Flyway Technical Representative in June 2012). 
Background.  The Harvest Information Program (HIP) was initiated to provide reliable estimates of hunter 
activity and harvest for all migratory game birds.  Under this program, the states annually collect the names 
and addresses of individuals who specify they intend to hunt migratory game birds that year, and send that 
name and address information to the USFWS.  The states’ databases of migratory game bird hunters are then 
used as the sample frames for surveys that generate annual hunter activity and harvest estimates.  The HIP 
was initially implemented in a few states in 1992, and has been fully operational in the 48 contiguous states 
and Alaska since 1999. 
So far, HIP survey results have been used primarily to provide the public and migratory bird managers with 
state, regional, and national estimates of hunting activity and harvest.  However, the migratory bird 
management community believes more appropriate harvest regulations can be developed by using harvest 
estimates and banding data to index or estimate abundance of species for which we do not have surveys 
designed to estimate population size.  For example, the Mourning Dove Task Force has recommended the 
use of such methods, rather than the Call Count Survey or the Breeding Bird Survey (which estimate only 
trends in birds observed), as the basis for harvest strategies that may be implemented in all 3 dove 
management units as early as 2014.  Similarly, goose managers have begun using harvest and band‐recovery 
data to estimate the abundance of mid‐continent light geese.  
The Problem.  If the management community uses HIP harvest estimates explicitly as part of an informed 
decision‐making process, we need to ensure that the estimates are unbiased and precise.  The key to reliable 
results from any survey is the sample frame.  A complete list of migratory bird hunters from each state is 
essential to the success of the HIP because it enables the USFWS to select and survey representative samples 
of hunters in each state.  If a state’s HIP sample frame excludes certain groups of hunters (e.g., lifetime 
license holders or nonresident hunters) and the success (harvest) of excluded groups is different than that of 
hunters included in the sample frame, the survey sample will not be representative and the survey results 
will likely be biased.  More important is the fact that the sample frame is also the source of expansion 
factors.  A harvest survey estimates the average number of birds shot per hunter and that average is then 
multiplied by the number of hunters in the sample frame (the expansion factor) to obtain an estimate of total 
harvest.  Obviously, if a state’s sample frame only includes 75% (or some unknown percentage) of the 
migratory bird hunters in the state, the resulting harvest estimates will only be 75% (or some unknown 
percentage) of the actual harvest.   
Conversely, a state also can identify too many hunters for HIP.  That is, if hunters who do not intend to hunt 
migratory birds are included in a state’s list, it is much harder to “find” specifically migratory bird hunters to 
send surveys.  These inflated sample frames are also common, and can result from several practices.  The 
problem is particularly acute in states that have electronic licensing systems but do not charge a fee for a 
separate migratory bird (i.e., HIP) permit.  In some of those states, many license vendors HIP‐certify license 
purchasers without asking them the required HIP questions that are used to identify the migratory bird 
hunters.  Hunters who purchase “sportsmen’s” or “combo” licenses that give them all of the hunting and 
fishing privileges the state offers, including migratory bird hunting, are often automatically HIP‐certified even 
though they have no intention to hunt migratory birds.  In both cases the consequence is that the USFWS 
6 

receives name and address data for hundreds of thousands of “migratory bird hunters” who are really only 
anglers, or only deer hunters, etc.  Because they have been erroneously identified as migratory bird hunters 
by the states’ HIP certification processes, many of these people are sampled and asked to participate in HIP 
surveys.  This dramatically increases the costs of the HIP to obtain desired levels of precision (i.e., many more 
hunters from states’ lists need to be sampled to ensure an adequate number of migratory bird hunters 
receive surveys), and ultimately undermines public confidence in the HIP and the agencies (state and USFWS) 
that manage the program (e.g., a deer hunter, non‐hunter, or angler receives a survey and wonders why, and 
sees the effort as a waste of tax dollars). 
Often sample frame problems stem from a change in contractor, changes in the state’s licensing system, 
and/or changes in the automated methods used to extract the HIP data for submission to the USFWS.  When 
such changes result in inflated or incomplete sample frames, it usually takes a few years and considerable 
effort to identify the cause of the problem and correct it, but it can be done with cooperation from 
contractors and/or licensing and IT personnel.  Sample frames that are inconsistent for no apparent reason 
are much more difficult to “fix,” but must be addressed if we expect the results of surveys using those 
samples frames to be the basis for promulgating management regulations. 
In addition to problems associated with identifying the correct hunters to survey, we also have experienced 
problems in getting accurate information from sampled hunters due to delays in receiving lists of names from 
the states.  HIP survey forms are in diary format; they ask the sampled hunter to report the date, location, 
and number of birds shot for every hunt.  The diary design is an attempt to reduce response bias (both 
memory and prestige bias), but its effectiveness depends on our collective ability to get the forms in the 
hands of selected hunters before or as soon as possible after they begin hunting.  The operational agreement 
between the Service and the states when the HIP was implemented was that each year the state agencies 
would send their first batch (i.e., those hunters who purchased a migratory bird hunting license prior to the 
opening of migratory bird hunting seasons in September) of HIP name and address data to the USFWS in 
August before the first hunting seasons start.  Once seasons commence, they would send additional data 
twice each month (on the first and third Wednesday of every month) as they HIP‐certify additional hunters, 
until hunting seasons end in the state.  This schedule enables the USFWS to send sampled hunters survey 
forms that they receive no later than four weeks after being HIP‐certified.  However, in any given year only 
about half of the states send HIP sample frame data to the USFWS twice a month, and some don’t send data 
in August or prior to the opening of their state’s hunting seasons.  Some send the data monthly; others send 
only two files, the first in the middle or near the end of the hunting season and the second after the season 
closes.  This limits the effectiveness of the diary format and likely results in response bias that compromises 
survey results. 
In summary, by using the data gathered from HIP to estimate abundance of doves, the management 
community is increasing the efficiency of that data collection program to improve management decisions and 
the benefit:cost ratio of the survey.  Over time doing so should reduce federal and state costs of monitoring 
efforts needed to promulgate hunting regulations.  However, we must strive to ensure the data collected 
from these efforts are reliable.  Although some of the needed changes to HIP may take some time and 
resources to resolve (e.g., ensuring representative samples of hunters are surveyed), others can occur quickly 
and likely with minimal costs (e.g., sending name lists to the USFWS twice each month as was initially agreed 
upon). 
7 

Appendix B. HIP sample frame reports – summary of requests and recommendations 
(Note: This document was given to each state agency’s Flyway Technical Representative in June 2012, 
along with a detailed report for each state.) 
Alabama:  (1) We request that you work with us to detemine whether we are missing some of your sample 
frame data for 2005, 2007, 2008, and 2009.  (2) We recommend that you take steps to minimize the number 
of unnecessary HIP registrations issued through your electronic licensing system.  This can usually be 
accomplished by charging a small fee for HIP registration, as recommended by AFWA in 2002, or by providing 
more explicit instructions to your license vendors.  (3) We request that you take steps to ensure that 
Alabama HIP data are sent to us on schedule, starting with the data for 2012. 
Alaska:  (1) We request that you work with us to detemine whether you are sending us complete sample 
frames.  An incomplete sample frame could be caused by any of several factors, such as poor compliance by 
hunters, poor compliance by license vendors (failure to send ADFG the completed HIP forms), or omission of 
incomplete or very late (but valid) HIP forms.  If you have state survey estimates of waterfowl harvest that 
we could compare with HIP estimates, or information from law enforcement on hunter compliance rates, it 
would help us assess this together.  (2) We request that you ensure more timely transmission of the HIP 
sample frame data by (a) increasing the frequency at which license vendors are required to send in HIP 
forms, particularly early in the year (prior to October), and (b) elevating the priority of HIP form data entry by 
your staff and/or contractors. 
Arizona:  (1) We request that you send us counts of the number of migratory bird permits and waterfowl 
permits sold each license year from 2005‐2011.  This will enable us to use  legitimate expansion factors when 
we generate the final hunter activity and harvest estimates for those years.  (2) We request that you ensure 
more complete and timely transmission of the HIP sample frame data by (a) increasing the frequency at 
which license vendors are required to send in HIP forms, particularly early in the year (prior to October), and 
(b) elevating the priority of HIP form data entry by your staff and/or contractors.  
Arkansas:  (1) We request that you work with us to detemine whether we are missing some of your sample 
frame data for 2007.  (2) We request that you work with us to detemine whether you are sending us 
complete sample frames.  An incomplete sample frame could be caused by any of several factors, such as 
poor compliance by hunters or omission of some type(s) of HIP‐registered hunters from the HIP data you 
send us.  If you have state survey estimates of migratory bird harvest that we could compare with HIP 
estimates, or information from law enforcement on hunter compliance rates, it would help us assess this 
together.  (3) “take steps to minimize the number of unnecessary HIP registrations” (see Alabama #2).  
California:  (1) We request that you work with us to detemine how much of your sample frame data for 2003 
we are missing, and whether we are missing any of the 2010 data.  (2) We recommend that you take steps to 
minimize the number of unnecessary HIP registrations that are issued.  This can usually be accomplished by 
charging a small fee for HIP registration, as recommended by AFWA in 2002, or by providing explicit 
instructions to your license vendors about which hunters need HIP registration and which ones don’t.  (3) We 
request that you take steps to ensure that you include HIP registration data from all sources, including 
internet license sales and HIP registrations of lifetime license holders who hunt migratory birds, and send us 
the electonic data twice a month, in accordance with the established schedule.  
8 

Colorado:  (1) We request that you work with us to detemine the actual number of HIP registrations that 
your contractor issued for the 2009 hunting season.  (2) We request that you take steps to ensure that your 
contractor sends us all of the Colorado HIP data on schedule, starting with the data for 2012.  
Connecticut:  We request that you work with us to detemine whether we are missing some of your sample 
frame data from 2005‐2011.  Please check your accouting records and if possible, send us the number of 
migratory bird (HIP) permits you sold each month from January 1, 2005 through February 28, 2011.  
Delaware:  (1) We request that you work with us to detemine the actual number of HIP registrations that 
your contractor issued for the 2008 and 2010 hunting seasons.  (2) “timely data transfers” (see Colorado #2). 
Florida:  (1) “take steps to minimize the number of unnecessary HIP registrations” (see Alabama #2).  (2) 
“detemine whether you are sending us complete sample frames” (see Arkansas #2).  
Georgia:  (1) We request that you work with us to detemine the correct number of HIP registrations Georgia 
issued for the 2003, 2009, and 2010 hunting seasons.  (2) “take steps to minimize the number of unnecessary 
HIP registrations” (see Alabama #2).  (3) “detemine whether you are sending us complete sample frames” 
(see Arkansas #2). 
Idaho:  We request that you work with us to detemine the correct number of HIP registrations Idaho issued 
for the 2007 hunting season. 
Illinois:  (1) We request that you work with us to detemine the correct number of HIP registrations your 
telephone contractor issued for the 2003 hunting season.  (2) “take steps to minimize the number of 
unnecessary HIP registrations” (see Alabama #2).   
Indiana:  “detemine whether you are sending us complete sample frames” (see Arkansas #2). 
Iowa:  (1) “take steps to minimize the number of unnecessary HIP registrations” (see Alabama #2).  (2) 
“detemine whether you are sending us complete sample frames” (see Arkansas #2). 
Kansas:  We request that you work with us to detemine the correct number of HIP registrations Kansas 
issued for the 2004 and 2005 hunting seasons.  This will enable us to use  legitimate expansion factors when 
we generate the final hunter activity and harvest estimates for those years. 
Kentucky:  (1) We request that you work with us to detemine the correct number of HIP 
registrations/permits that Kentucky issued  for the 2005, 2006, 2007, and 2008 hunting seasons.  (2) We 
recommend that you take steps to minimize the number of unnecessary HIP registrations issued through 
your electronic licensing system.  This can perhaps be accomplished by providing more explicit instructions to 
your license vendors about which hunters need to be HIP‐registered and which ones do not.  (3) “timely data 
transfers” (see Alabama #3).  
Louisiana:  (1) “take steps to minimize the number of unnecessary HIP registrations” (see Alabama #2).  (2) 
“detemine whether you are sending us complete sample frames” (see Arkansas #2). 
Maine:  We appreciate the cooperation we have enjoyed with Maine in examining sample frame issues, and 
we request that you continue to work with us to detemine whether you are sending us complete sample 
9 

frames.  We also recommend that you take steps to minimize the number of unnecessary HIP registrations 
issued through your electronic licensing system.  This can perhaps be accomplished by providing more 
explicit instructions to your license vendors. 
Maryland:  “timely data transfers” (see Alabama #3). 
Massachusetts:  (1)  We request that you work with us to detemine the correct number of HIP registrations 
Massachusetts issued for the 2007 hunting season.  (2) “detemine whether you are sending us complete 
sample frames” (see Arkansas #2). 
Michigan:  (1) “take steps to minimize the number of unnecessary HIP registrations” (see Alabama #2). 
Minnesota:  (1) “take steps to minimize the number of unnecessary HIP registrations” (see Alabama #2).   
Mississippi:  (1) We request that you work with us to detemine whether we are missing some of your sample 
frame data for 2003, 2005, 2007, 2008, 2009, and 2010.  (2) “detemine whether you are sending us complete 
sample frames” (see Arkansas #2).  (3) “take steps to minimize the number of unnecessary HIP registrations” 
(see Alabama #2).  (4) “timely data transfers” (see Alabama #3). 
Missouri:  None.  Missouri’s execution of the state’s HIP responsibility is an outstanding example of how this 
program can and should be conducted.  Please just keep doing what you’re doing. 
Montana:  (1) We request that you work with us to detemine whether we are missing some of your sample 
frame data for 2008.  (2) “take steps to minimize the number of unnecessary HIP registrations” (see Alabama 
#2).  (3) “timely data transfers” (see Alabama #3). 
Nebraska:  (1) We request that you work with us to detemine the actual number of HIP registrations that 
your contractor issued for the 2009 hunting season.  (2) “detemine whether you are sending us complete 
sample frames” (see Arkansas #2).  (3) “timely data transfers” (see Colorado #2). 
Nevada:  (1) We request that you work with us to detemine whether we are missing some of your sample 
frame data that your contractor collected for 2007 and 2008.  (2) “detemine whether you are sending us 
complete sample frames” (see Arkansas #2). 
New Hampshire:  (1) We request that you work with us to detemine whether we are missing some of your 
sample frame data from 2003, 2004, and 2006.  (2) “timely data transfers” (see Alabama #3). 
New Jersey:  We request that you take steps to ensure that you and/or your contractors send us the data for 
all New Jersey HIP registrations and that the data are sent to us on schedule, starting with the 2012‐13 
season. 
New Mexico:  We request that you work with us to detemine whether we are receiving complete sample 
frames for New Mexico.  An incomplete sample frame could be caused by any of several factors, such as poor 
compliance by hunters, poor compliance by license vendors (failure to send in the completed HIP forms), or, 
if you provide hunters the opportunity to get their HIP registration on‐line, failure to transfer electronic HIP 
data to us.  If you have state survey estimates of migratory bird harvest that we could compare with HIP 

10 

estimates, or information from law enforcement on hunter compliance rates, it would help us assess this 
together.   
New York:  (1) “detemine whether you are sending us complete sample frames” (see Arkansas #2).  (2) We 
request that you take steps to ensure that you and/or your contractors send us the data for all New York HIP 
registrations and that the data are sent to us on schedule, starting with the 2012‐13 season. 
North Carolina:  (1) We request that you continue to work with us to detemine whether you are sending us 
complete sample frames.  (2) “take steps to minimize the number of unnecessary HIP registrations” (see 
Alabama #2).  (3) “timely data transfers” (see Alabama #3). 
North Dakota:  “timely data transfers” (see Alabama #3). 
Ohio:  (1) “take steps to minimize the number of unnecessary HIP registrations” (see Alabama #2).  (2) 
“timely data transfers” (see Alabama #3). 
Oklahoma:  (1) “detemine whether you are sending us complete sample frames” (see Arkansas #2).  (2) 
“timely data transfers” (see Alabama #3). 
Oregon:  “timely data transfers” (see Alabama #3). 
Pennsylvania:  “timely data transfers” (see Alabama #3). 
Rhode Island:  (1) We request that you send us counts of the number of HIP registrations issued in Rhode 
Island in each of the following license years: 2003, 2005, and 2007‐2011.  This will enable us to use  
legitimate expansion factors when we generate the final hunter activity and harvest estimates for those 
years.  (2) We request that you ensure more complete and timely transmission of the HIP sample frame data 
by (a) increasing the frequency at which license vendors are required to send in HIP forms, particularly early 
in the year (prior to October), and (b) elevating the priority of HIP form data entry by your staff and/or 
contractors. 
South Carolina:  (1) We request that you work with us to detemine the correct number of HIP registrations 
South Carolina issued for the 2003 hunting season.  This will enable us to use  legitimate expansion factors 
when we generate the final hunter activity and harvest estimates for that year.  (2) “take steps to minimize 
the number of unnecessary HIP registrations” (see Alabama #2). 
South Dakota:  We request that you work with us to detemine whether we received the data from all of the 
HIP registrations South Dakota issued for the 2006 and 2007 hunting seasons. 
Tennessee:  (1) We request that you work with us to detemine the correct number of HIP 
registrations/permits that Tennessee issued  for each year from 2003‐2010.  This will enable us to use 
legitimate expansion factors when we generate the final hunter activity and harvest estimates for those 
years.  (2) “detemine whether you are sending us complete sample frames” (see Arkansas #2).  (3) “timely 
data transfers” (see Alabama #3).  
Texas:  (1) We request that you take steps to minimize the number of unnecessary HIP registrations issued 
through your electronic licensing system.  This could perhaps be accomplished by requiring super combo 
11 

license purchasers to “activate” their migratory bird hunting privilege by registering for HIP, and/or by 
providing more explicit instructions to your license vendors about which hunters need to be HIP‐registered 
and which do not.  (2) “timely data transfers” (see Alabama #3). 
Utah:  (1) We request that you work with us to detemine the actual number of HIP registrations that your 
contractor issued for the 2009 hunting season.  (2) “detemine whether you are sending us complete sample 
frames” (see Arkansas #2).  (3) “timely data transfers” (see Colorado #2). 
Vermont:  (1) We request that you work with us to detemine whether we are missing some of the HIP 
registration data that you collected for 2003, 2005, 2006, and 2010.  (2) “detemine whether you are sending 
us complete sample frames” (see Arkansas #2).  (3) “timely data transfers” (see Rhode Island  #2) 
Virginia:  (1) “detemine whether you are sending us complete sample frames” (see Arkansas #2).  (2) “timely 
data transfers” (see Colorado #2). 
Washington:  (1) We request that you work with us to detemine the correct number of HIP 
registrations/migratory bird permits that Washington issued  for each year from 2006‐2010.  This will enable 
us to use legitimate expansion factors when we generate the final hunter activity and harvest estimates for 
those years.  (2) “detemine whether you are sending us complete sample frames” (see Arkansas #2).  (3) 
“timely data transfers” (see Alabama #3). 
West Virginia:  (1) “take steps to minimize the number of unnecessary HIP registrations” (see Alabama #2).  
(2) We request that you take steps to ensure that all of West Virginia’s HIP data are sent to us on schedule, 
particularly after September 1 when hunting seasons are underway. 
Wisconsin:  (1) “take steps to minimize the number of unnecessary HIP registrations” (see Alabama #2).  (2) 
We request that you send us the first file of your annual HIP data on schedule in late August, starting in 2012. 
Wyoming:  (1) “detemine whether you are sending us complete sample frames” (see Arkansas #2).  (2) 
“timely data transfers” (see Alabama #3). 

12 

Appendix C.  Standard HIP registration questions.  States can omit questions that are not relevant to 
seasons in their States (e.g., cranes, brant, sea ducks).   

Q1. Will you hunt migratory birds this year?   
A. Yes or no. 

Q2. How many ducks did you harvest last year?  
A. Did not hunt, 0, 1‐10, or more than 10. 

Q3. How many geese did you harvest last year?  
A. Did not hunt, 0, 1‐10, or more than 10. 

Q4. How many doves did you harvest last year?   
A. Did not hunt, 0, 1‐30, or more than 30. 

Q5a. How many woodcock did you harvest last year?  
A. Did not hunt, 0, 1‐30, or more than 30.   
or 
Q5b.  Did you hunt woodcock last year?   
A. Yes or no. 

Q6.  Did you hunt coots or snipe last year?   
A. Yes or no. 

Q7.  Did you hunt rails or gallinules last year?   
A. Yes or no. 

Q8. Will you hunt cranes this year?  
A. Yes or no. 

Q9. Will you hunt band‐tailed pigeons this year?  
A. Yes or no. 

Q10. Will you hunt brant this year?  
A. Yes or no.  

Q11. Did you hunt sea ducks last year? (NOTE: Some States have changed this to “Will you hunt sea 
ducks this year?”  to identify out‐of‐state sea duck hunters.) 
A. Yes or no. 

13