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A test of computer-assisted matching using the North Pacific humpback whale, Megaptera novaeangliae, tail flukes photograph collection.

Introduction

In the mid 1960's, researchers began to photograph individual marine mammals with the express purpose of using the images to identify individual animals on the basis of natural markings. Over time, researchers began to develop photo catalogs of individuals as they were sighted and photographed in different years and areas (Hammond et al., 1990). As the number of photographs has increased, so did the need for computer assistance to help with the collation and integration of the large collections.

Starting in the mid 1980's, computer-assisted systems began to be developed to aid in the identification of individual marine mammals (Hiby and Lovell, 1990; Mizroch et al., 1990). The system developed by Hiby and Lovell use a scanned image and a 3-dimensional computer model to interpret the photograph and to develop an identification algorithm. Their system is considered semi-automated because the computer system measures some of the photograph's characteristics independent of the system operator. The system developed by Mizroch and colleagues is categorical and requires that identification photographs be classified visually by a trained observer. This system is based on a categorization scheme of natural marks and scars, and data related to each photograph are entered into a computer database. The system operator controls all of the matching information and uses a computer to query the database for possible matching choices.

The NMFS National Marine Mammal Laboratory (NMML) has been developing and curating a collection of humpback whale, Megaptera novaeangliae, tail flukes photographs taken in North Pacific waters since 1985. This collection has grown from about 750 images in 1986 to about 24,000 in 1999, representing contributions from over 18 research groups from all regions in the North Pacific (Table 1). Unique NMML identification numbers (NMMLID) are assigned only when there are at least 2 photographs of a particular individual whale in the database. As of April 1999, 3,093 unique NMMLID numbers had been assigned and 12,057 tail flukes photographs had been assigned a NMMLID; 11,156 tail flukes photographs had not yet been assigned a NMMLID. Overall, the 23,213 tail flukes photographs evaluated in this paper may represent the sightings and resightings of no more than 6,000 individual humpback whales.

When conducting certain numerical studies using photo-identification data (e.g. capture-recapture analyses), it is important to segregate the photographic data strictly on photographic quality only (Hammond, 1986: Hammond et al., 1990; Mizroch et al., 1990). Photographs in the NMML database are given two different ratings: one based on photographic quality (focus, angle, distance), and the other based on recognition quality (distinctive pattern, marks, or scars) (Mizroch et al., 1990, provide more details). The analysis conducted in this paper stratified the photographs by three levels of photographic quality (hereafter simply referred to as photo quality), examples of which are shown in Figure 1. Matching was conducted using the system described in Mizroch et al. (1990), except that the patterns in use today (Fig. 2) have been simplified and improved. The tail flukes map (Fig. 3) has not been modified.

[FIGURES 1-3 OMITTED]

Tests of the NMML system (i.e. stratified by recognition quality) were first presented in Mizroch et al. (1990), when the database contained 9,353 photographs. Here, we present test results for the NMML database when it contained 12,000 photographs (using ad hoc tests conducted from 1991 to 1995), and tests with the database at its current size of nearly 24,000 photographs.

Methods

Categorizing Whale Tail Flukes

Humpback whale tail flukes have black and white pigment patterns that can match one or several categories (Fig. 2). For each photograph, a selection of patterns that most closely resembled the tail flukes was chosen. In general, the user selected between one and six patterns for each photo being matched, depending on what characteristics were visible on the photograph to be matched. In addition to selecting patterns, the user evaluated locations of natural markings, scars, or other unique marks on the tail flukes (Fig. 3), and selected any or all sectors that contained the markings (e.g. a distinctive line in Sector 5 and an open circle in Sector 6). If the mark extended across sectors, it was described in both. If it was not clear which sector to select, a mark was described as being in one or the other.

For each photograph matched, after the input criteria were selected, the matching program queried the database and brought up a subset of all photographs in the database that matched the input criteria and displayed each photograph sequentially on a television monitor, with related data for each photograph on a computer monitor. The operator compared each photograph on the television monitor to the photograph to be matched and determined if there was a match or not. In cases where the photograph on the television monitor was difficult to interpret, the operator pulled the original photograph from the files for further evaluation.

Testing with 12,000 Photographs

As part of data preparation for analyses of calf mortality and birth interval, humpback whale researchers in the North Pacific conducted an ad hoc matching test in the early 1990's. Researchers from Glacier Bay National Park and Preserve (1) (Gabriele), University of Alaska (2) (Straley), and North Gulf Oceanic Society (currently known as Eye of the Whale (3)) (von Ziegesar), working independently of each other and NMML staff (primarily A. Wolman), compared their catalogs to a catalog of known females prepared during a workshop on calf mortality (called here the "calf mortality" catalog, containing 352 individual whales, unpubl, data on file at the NMML). Their catalogs, which represented Alaska areas including Glacier Bay, portions of southeastern Alaska, and Prince William Sound, ranged in size from about 200 individuals to about 400 individuals. The tail flukes photograph collection at the NMML at the time of the matching exercise numbered about 12,000 photographs including photographs from all regions in the North Pacific. The matching success of computer-assisted matching at the NMML was compared with the matching success of each individual researcher visually inspecting their own hard-copy catalogs (Mizroch (4)).

Testing with 24,000 Photographs

A random selection of about 0.5% of the database (116 photographs) was made, stratified by photo quality codes (Table 2). Based on the stratification, there were 15 photo quality 1 (excellent) photos, 75 photo quality 2 (good or moderate) photos, and 26 photo quality 3 (poor) photos selected. The draw from the database was independent of recognition quality and of whether the animal had been matched previously.

At the time of the matching exercise, we did not know whether the photographs had been matched previously. For each photograph selected, the computer-assisted matching program was used to match each photograph to the entire collection, and matching was halted either when the first match was found, or when about 5% of the database (1,250) photographs had been examined. If the photograph was of a well-known animal, the match criteria used for this exercise were based strictly on the detail showing on the photograph drawn randomly, rather than on other known marks or scars that the individual may have accumulated over time.

Results

Testing with 12,000 Photographs

The Glacier Bay catalog (unpubl. data) numbered about 200 individual whales at the time of the matching exercise. Ten of the 12 matches between the "calf mortality" catalog and the Glacier Bay catalog were found independently by both Gabriele and Straley and by NMML staff. Gabriele and Straley found one match that NMML staff missed and NMML staff found one match that Gabriele and Straley missed (Table 3).

The southeastern Alaska catalog numbered about 400 individual whales at the time of the matching exercise. Both Straley and NMML staff found 19 of the 21 matches between the "calf mortality" catalog and the southeastern Alaska catalog independently. Straley found one match that was missed by NMML staff, and NMML staff found one match that was missed by Straley (Table 3).

The Prince William Sound catalog numbered about 200 individual whales at the time of the matching exercise. Both yon Ziegesar and NMML staff found 6 of the 10 matches found between the "calf mortality" catalog and the Prince William Sound catalog independently. Von Ziegesar found three matches that NMML staff missed and NMML staff found one that von Ziegesar missed. The number of matches missed from this set was somewhat larger than the others (Table 3). For at least one of the matches made by von Ziegesar and missed by NMML staff, the photo quality was poor, and the match was based mainly on trailing edge shape and detail, and not the marks, scars, and pigment patterns that were apparent on a good quality photograph of the tail.

Overall, 38 of the 43 total matches found (88%) were made using the computer-assisted system. There was no significant difference in matches found for each area (Chi-square = 4.37, P = 0.11).

Testing with 25,000 Photographs

Photo Quality 1

Of the 15 images in this category, matches were found for all 15 photographs. In l0 cases, the first match was found in the top 0.0027 of the database (fewer than 70 photographs evaluated). In all 15 cases, the first match was found in the top 0.031 of the database (Table 4, Fig. 4). On average, the first match was found in the top 0.0052 of the database (about 130 photographs) (SD = 0.0079).

[FIGURE 4 OMITTED]

Examples of two of the photo quality 1 matches, including the pattern and marks selections are presented in Figures 5 and 6. Figure 5 shows a match that was found after making one change in selection criteria and evaluating 69 photographs. Figure 6 shows a whale that had no apparent marks, and the match was found after evaluating 793 photographs.

Photo Quality 2

Of these 75 images, matches were found for 45 photographs. Of these 45, in 27 cases the first match was found in the top 0.0027 of the database (70 or fewer photographs evaluated) (Table 5, Fig. 4). On average, the first match was found in the top 0.0056 of the database (about 130 photographs) (SD = 0.0072).

In only three cases, known matches of photo quality 2 photos were missed, due to the following reasons (Fig. 7):

1) For photograph 5889, the flecked markings (speckled or streaked pigment markings which were present in both Sectors 5 and 8) did not appear to be present in Sector 5 on the photograph missed in the database, so the matching photograph was not selected in any of the matching selections.

2) For photograph 50363, the matching photograph lacked any detail, and would have been found only after looking at more than 1,250 photographs, the arbitrary cut-off point for this exercise, because of where it was on the list of photos selected from the database.

3) For photograph 61147, the distinctive circle in Sector 6 was present but not coded as such on the photograph in the database, so the matching photograph was not selected in any of the matching selections.

[FIGURE 7 OMITTED]

Examples of two of the photo quality 2 matches, including the pattern and mark selections, are presented in Figure 8. Figure 8 shows a match that was found after making two changes in selection criteria and evaluating 764 photographs.

Photo Quality 3

Of these 26 images, matches were found for 14 photographs. Of these 14 photographs, in 9 cases the first match was found in the top 0.0034 of the database (85 or fewer photographs evaluated) (Table 6, Fig. 4). On average, the first match was found in the top 0.0052 of the database (about 125 photographs) (SD = 0.0071).

In only two cases, known matches of photo quality 3 photographs were missed due to the following reasons (Fig. 7):

1) For photograph 9774, only part of one tail fluke was showing, and there were very few distinguishing marks present.

2) For photograph 34697, the photo quality was so poor that the match could only be confirmed by the researcher who took the photo.

An example of a photo quality 3 match, including the pattern and marks selections (Fig. 9) shows a match that was found after making two changes in selection criteria and evaluating 101 photographs.

Results for photos of qualities 1 though 3 were surprisingly similar. In Figure 10, results are presented independent of photo quality, sorted by match success, with recognition quality plotted for each photograph. Recognition quality is based on the presence of distinctive markings or pigmentation, which should affect one's ability to recognize the individual even if photo quality is very poor. There did not appear to be a trend in recognition quality with respect to known matches that were missed. Also, there did not appear to be a trend with respect to the photographs as yet unmatched (Fig. 11).

[FIGURES 10-11 OMITTED]

Overall, matches were found for 74 of the 116 photographs, and on average, the first match was found in the top 0.0054 of the database (about 130 photographs) (SD = 0.0073).

Discussion

Testing with 12,000 Photographs

This exercise confirmed that computer-assisted matching was an effective tool, especially considering that NMML staff was comparing the "calf mortality" catalog to a collection of over 12,000 photographs and not to individual catalogs ranging in size from 200-400 photographs.

Testing with 25,000 Photographs

Figure 10 indicates no trend in match results with respect to recognition quality, which may mean that even the less distinctive tail flukes photographs have enough detail so matches can be found.

Of the 116 photographs selected at the time the matching exercise began, only 52 had been previously matched (i.e. assigned a NMMLID). New matches were found for 26 of the photographs and 38 remain without known matches. Overall, only five known matches were missed.

An advantage of computer-assisted matching is the ability to compare new photographs to the entire North Pacific collection and the potential to find matches to whales photographed in other regions. No bias is introduced based on expectation of resightings within or between specific summer or winter grounds. Another advantage in using computer-assisted matching is that by matching to the entire collection, no bias is introduced based on expectation of behavioral role (e.g. matching "known" females to "known" females).

At this time, the NMML computer matching system is able to match images effectively with a database of over 25,000 photographs to choose from. The computer-assisted system has continued to be an efficient matching system for such a large number of photographs because the matching criteria are always controlled by a human operator and because database performance is not constrained by size. Data entry is fast (between 100-200 photographs entered per day). Image capture and retrieval is last. with the capability of capturing 5,000 images per day on a videodisc that holds 54,000 images. Image retrieval time ranges from a traction of a second to perhaps 2 seconds, depending on the distance between images on the videodisc.

Conclusions

Since the NMML system has been in use, there has been a desire to develop computer-assisted systems that are more "automated." The NMML system takes advantage of the human brain's ability to instantly rotate, adjust, compensate, and recognize similar images. Computer technology cannot yet compete with the image processing power of the human brain, and it is not so advanced that a completely automated system is possible. Both the categorical systems used here and the other systems developed by Hiby take some operator training and intervention.

New systems are being developed for identifying individual Alaska harbor seals which should provide a direct comparison of categorical versus semi-automated systems. Future sample sizes will likely be large enough to compare the two approaches with rigor.
Table 1.--Major contributing research groups and primary contact
people.

Research group/affiliation Primary contact

Center for Coastal Studies D. Mattila
Cascadia Research Collective J. Calambokidis, G. Steiger
Center for Whale Research K. Balcomb, D. Claridge
Center for Whale Studies D. Glockner-Ferrari, M. Ferrari
Glacier Bay National Park and Preserve C. Gabriele
 U.S. Dep. Interior, Gustavus
Hawaii Whale Research Foundation D. Salden
J. Straley Investigations J. Straley
Kewalo Basin Marine Mammal Laboratory L. Herman, A. Craig
 University of Hawai'i
Moss Landing Marine Labs S. Cerchio
California State Universities
North Gulf Oceanic Society O. von Ziegesar, C. Matkin
National Marine Mammal Laboratory S. Mizroch
 NMFS, NOAA, Seattle
Okinawa Expo Aquarium S. Uchida, N. Higashi
Pacific Biological Station G. Ellis
 Dep. Fish. Oceans, Nanaimo
SeaSearch C. and S. Jurasz
Univ. Autonoma de Baja Calif. Sur J. Urban
Univ. Nacional Autonoma de Mexico M. Salinas, J. Jacobsen
West Coast Whale Research Foundation J. Darling, E. Mathews,
 D. McSweeney, K. Mori

Table 2.--Number of photographs in the database stratified by
photo quality (focus, etc.) (Fig. 1) and recognition quality
(distinctiveness).

 Recognition quality
 Total 1% of 0.5% of
Photo quality 1 2 3 0 (1) photos database database

1, excellent 2,742 420 40 3,202 30 15
2, good 7,255 6,627 1,642 1,5,524 160 80
3, poor 1,032 2,152 2,434 84 5,702 60 30
Total 11,029 9,199 4,116 84 24,428 250 125

(1) Category 0 means that the recognition quality cannot be evaluated
due to poor photo quality

Table 3.--Comparisons of computer-assisted matches and matches from
each Alaska research group, matching the "calf mortality" catalog to
each independent collection. The "calf mortality" catalog included
photographs of about 350 individual whales, and the NMML database
contained about 12,000 tail fluke photographs at the time of this
matching exercise.

 Observed by
 Approx. both NMML Total no.
 sample and research of matches
Catalog size group found

Glacier Bay (Gabriele) 200 10 12
Southeastern Alaska (Straley) 400 19 21
Prince William Sound (von Ziegesar) 200 6 10

Table 4.--Photo quality 1 results, including numbers of photographs
examined and origin of each photo.

 No. photographs
 examined until
 Recognition first match
Accession no. quality was found

10087 1 4
848 1 11
28207 1 12
23827 1 17
28892 1 45
29233 1 56
2810 1 58
23407 1 61
5330 1 65
2053 1 69
45598 1 107
9115 1 153
28841 1 227
9768 2 288
25436 2 793
Average (Standard Deviation) 131.0667

 Proportion of Geographic
 the database origin
Accession no. examined of photo

10087 0.000158648 Hawaii
848 0.000436283 Hawaii
28207 0.000475945 Hawaii
23827 0.000674255 Hawaii
28892 0.001784794 Hawaii
29233 0.002221076 Hawaii
2810 0.002300401 Mexico
23407 0.002419387 Hawaii
5330 0.002578035 Alaska
2053 0.002736683 Mexico
45598 0.004243842 California
9115 0.006068298 California
28841 0.009003292 Hawaii
9768 0.011422679 California
25436 0.031452029 Alaska
Average (Standard Deviation) 0.005198 (0.007949)

Table 5.--Photo quality 2 results, including numbers of photographs
examined and origin of each photo.

 No. photographs
Accession Recognition examined until
number quality first match found

29213 1 1
135 2 2
37195 1 3
40317 2 5
6832 1 7
5507 2 7
39389 1 9
36384 1 12
28227 1 16
29724 2 16
39914 2 20
22558 1 24
23683 2 25
116 1 26
39138 1 28
37658 3 28
60184 2 38
22749 1 39
34584 1 42
24291 2 42
36179 2 61
8112 1 63
16240 1 66
75991 1 67
38357 1 69
22377 1 70
23914 2 101
1585 1 108
5502 3 118
114 2 143
28574 1 182
23945 3 191
39955 2 208
1194 1 223
50236 1 228
7535 1 247
39102 1 249
23980 2 272
25855 2 275
38704 2 292
44091 2 302
18044 2 346
9078 1 375
5842 1 764
12102 2 897
1547 2 No match
2003 2 No match
2935 2 No match
5380 2 No match
5889 1 No match
10465 1 No match
10592 1 No match
10848 2 No match
10973 1 No match
11171 2 No match
14802 3 No match
16300 1 No match
16327 1 No match
17430 1 No match
23506 1 No match
27102 2 No match
30394 2 No match
37170 3 No match
37410 2 No match
39090 3 No match
40418 2 No match
44567 2 No match
45217 3 No match
45651 3 No match
50363 2 No match
50400 2 No match
60328 3 No match
60620 2 No match
61147 2 No match
99914 2 No match
Average (Standard Deviation) 133.4127

 Proportion of Geographic
Accession database origin
number examined of photo

29213 3.96621E-05 Hawaii
135 7.93242E-05 Alaska
37195 0.000118986 Alaska
40317 0.00019831 Hawaii
6832 0.000277635 Alaska
5507 0.000277635 Alaska
39389 0.000356959 Hawaii
36384 0.000475945 Alaska
28227 0.000634593 Hawaii
29724 0.000634593 Hawaii
39914 0.000793242 Hawaii
22558 0.00095189 Hawaii
23683 0.000991552 Hawaii
116 0.001031214 Alaska
39138 0.001110538 Hawaii
37658 O.001110538 Alaska
60184 0.001507159 Hawaii
22749 0.001546821 Hawaii
34584 0.001665807 Hawaii
24291 0.001665807 Hawaii
36179 0.002419387 Alaska
8112 0.002498711 Hawaii
16240 0.002617697 Mexico
75991 0.002657359 Alaska
38357 0.002736683 Alaska
22377 0.002776346 Hawaii
23914 0.00400587 Hawaii
1585 0.004283505 Hawaii
5502 0.004680125 Alaska
114 0.005671677 Alaska
28574 0.007218498 Hawaii
23945 0.007575457 Hawaii
39955 0.008249712 Hawaii
1194 0.008844644 Hawaii
50236 0.009042954 Hawaii
7535 0.009796534 Alaska
39102 0.009875858 Hawaii
23980 0.010788086 Hawaii
25855 0.010907072 Alaska
38704 0.011581327 Alaska
44091 0.011977948 Hawaii
18044 0.013723079 Alaska
9078 0.01487328 California
5842 0.030301828 Alaska
12102 0.035576885 Alaska
1547 0.05 Hawaii
2003 0.05 Mexico
2935 0.05 Mexico
5380 0.05 Alaska
5889 0.05 California
10465 0.05 Hawaii
10592 0.05 Hawaii
10848 0.05 Hawaii
10973 0.05 Hawaii
11171 0.05 Hawaii
14802 0.05 Mexico
16300 0.05 Mexico
16327 0.05 Mexico
17430 0.05 Alaska
23506 0.05 Hawaii
27102 0.05 Hawaii
30394 0.05 Japan
37170 0.05 Alaska
37410 0.05 Alaska
39090 0.05 Hawaii
40418 0.05 Hawaii
44567 0.05 Hawaii
45217 0.05 California
45651 0.05 Oregon
50363 0.05 Hawaii
50400 0.05 Hawaii
60328 0.05 Hawaii
60620 0.05 Hawaii
61147 0.05 Hawaii
99914 0.05 Colombia
Average (Standard Deviation) 0.00556 (0.00729)

Table 6.--Photo quality 3 results, including numbers of photographs
examined and origin of each photo.

 Recognition No. photographs examined
Accession no. quality until first match found

29288 2 1
34937 3 2
25519 3 3
80029 2 9
70044 2 12
174 2 16
75263 0 17
5755 0 19
22809 3 85
2658 1 101
22281 1 194
9418 2 416
23141 2 473
37034 1 491
1783 1 No match
9774 2 No match
10725 2 No match
22031 2 No match
23785 3 No match
28185 3 No match
29292 3 No match
34549 2 No match
34697 3 No match
37237 3 No match
46410 3 No match
50102 2 No match
Average (Standard Deviation) 125.0375

 Proportion of Geographic
Accession no. database examined origin of photo

29288 3.96621E-05 Hawaii
34937 7.93242E-05 Hawaii
25519 O.000118986 Alaska
80029 0.000356959 Mexico
70044 0.000475945 Mexico
174 0.000634593 Hawaii
75263 0.000674255 Alaska
5755 0.00075358 Mexico
22809 0.003371277 Hawaii
2658 0.00400587 Mexico
22281 0.007694443 Hawaii
9418 0.016499425 California
23141 0.018760163 Hawaii
37034 0.019474081 Alaska
1783 0.05 Hawaii
9774 0.05 California
10725 0.05 Hawaii
22031 0.05 Hawaii
23785 0.05 Hawaii
28185 0.05 Hawaii
29292 0.05 Hawaii
34549 0.05 Hawaii
34697 0.05 Hawaii
37237 0.05 Alaska
46410 0.05 California
50102 0.05 Hawaii
Average (Standard Deviation) 0.005210 (0.007131)

Figure 5.--Example of the evaluation of photo accession number 2053,
coded as photo quality 1.

Patterns used to Number of photographs
find the match Marks/Scars used evaluated

54, 55 XL in 11 57

54, 55 L in 5 and 11 12

 69

Figure 6.--Example of the evaluation of photo accession number 25436,
coded as photo quality 1.

Patterns used to Number of photographs
find the match Marks/Scars used evaluated

26 none 793

Figure 8.--Example of the evaluation of photo accession number 5842,
coded as photo quality 2.

Patterns used to Number of photographs
find the match Marks/Scars used evaluated

13, 40, 41, 43 X in 11 or 13 170

13, 40, 41, 43 L in 5 and S in 13 344

13, 40, 41, 43 F in 6 250

 764

Figure 9.--Example of the evaluation of photo accession number 2658,
coded as photo quality 3.

Patterns used to Number of photographs
find the match Marks/Scars used evaluated

12, 13, 40 XS in 11 74

12, 13, 40 XC in 11 4

12, 13, 40 XC or XS in 12 23

 101


Acknowledgments

Thanks are due to Allen Wolman, who did most of the matching for the ad hoc study, to Sitha Hoy and Melissa Dolan, who did most of the data entry of the photographs in the database and provided many of the matches known to-date, and to Dave Rugh and Janice Waite for their help with photo quality coding. The paper was improved due to thoughtful reviews by NMML researchers Merrill Gosho, Sue Moore, and Janice Waite.

In addition, we thank the many research groups whose photographs are part of the research collection (see Table 1), including those groups who allowed us to use their photos as examples in this paper (photo credits in parentheses), including Cascadia Research Collective (Fig. 1: photo 45598: Fig. 7: photos 5889, 5924, 9774, and 45364), Center for Whale Research (Fig. 7: photos 5889 and 5924), Center for Whale Studies (Fig. 1: photos 23141 and 23407: Fig. 7: photos 50363 and 50364), Glacier Bay National Park and Preserve (Fig. 6: photo 18502), Hawaii Whale Research Foundation (Fig. 1: photos 50236 and 60328: Fig. 7: 6l147 and 61148), J. Straley Investigations (Fig. 8: photo 5842), J. Jacobsen and Universidad Nacional Autonoma de Mexico (Fig. 5: photo 14262; Fig. 9: photos 2658 and 2722), Sal Cerchio and Moss Landing Marine Labs (Fig. 7: photo 34540 and 34697), National Marine Mammal Laboratory (Fig. 6: photo 25436), NMFS, Alaska Region (Fig. 8: photo 25013), Jorge Urban currently of Universidad Autonoma de Baja California Stir (Fig. 5: photo 2053), West Coast Whale Research Foundation (Fig. 1: photo 10465).

(1) Humpback Whale Monitoring Program. Glacier Bay National Park and Preserve, P.O. Box 140. Gustavus, AK 99826.

(2) University of Alaska Southeast Sitka Campus. 1332 Seward Avenue. Sitka, AK 99835.

(3) Eye of the Whale. P.O. Box 15191, Fritz Creek. AK 99603.

(4) Mizroch, S. A. Report of the workshops on the estimation of calf mortality in North Pacific humpback whales. 38 p., Unpubl. data.

Literature Cited

Hammond, P. 1986. Estimating the size of naturally marked whale populations rising capture-recapture techniques. Rep. Int. Whaling Comm. Spec. Iss. 8:253-282.

--. S. A. Mizroch, and G. Donovan. 1990. Report of the workshop on individual recognition and the estimation of cetacean population parameters. Rep. Int. Whaling Comm. Spot. Iss. 12:3-40.

Hiby, A. R., and R Lovell. 1990. Computer aided matching of natural markings: a prototype system for gray seals. Rep. Int. Whaling Comm. Spec. Iss. 12:57-61.

Mizroch, S. A., J. A. Beard, and M. Lynde. 1990. Computer assisted photo-identification of humpback whales. Rep. Int. Whaling Comm. Spec. Iss. 12:63-70.

The authors are with the National Marine Mammal Laboratory, Alaska Fisheries Science Center, National Marine Fisheries Service, NOAA, 7600 Sand Point Way NE, Seattle, WA, 98115 [e-mail: sally.mizroch@noaa.gov].
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Title Annotation:services of National Marine Mammal Laboratory
Author:Mizroch, Sally A.; Harkness, Suzanne A.D.
Publication:Marine Fisheries Review
Geographic Code:1U9WA
Date:Jun 22, 2003
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