Classifying Sea Lamprey Marks on Great Lakes Lake Trout: Observer Agreement, Evidence on Healing Times between Classes, and Recommendations for Reporting of Marking Statistics

J. Great Lakes Res. 29 (Supplement 1):283–296 Internat. Assoc. Great Lakes Res., 2003

Classifying Sea Lamprey Marks on Great Lakes Lake Trout: Observer Agreement, Evidence on Healing Times between Classes, and Recommendations for Reporting of Marking Statistics Mark P. Ebener1,*, James R. Bence2, Roger A. Bergstedt3, and Katherine M. Mullett4 1Inter-Tribal

Fisheries and Assessment Program Chippewa/Ottawa Resource Authority 179 W. Three Mile Road Sault Ste. Marie, Michigan 49783

2Department

of Fisheries and Wildlife Michigan State University 13 Natural Resources Building East Lansing, Michigan 48824

3U.S.

Geological Survey, Great Lakes Science Center Hammond Bay Biological Station 11188 Ray Road Millersburg, Michigan 49759 4U.

S. Fish & Wildlife Service Marquette Biological Station 1924 Industrial Parkway Marquette, Michigan 49855

ABSTRACT. In 1997 and 1998 two workshops were held to evaluate how consistent observers were at classifying sea lamprey (Petromyzon marinus) marks on Great Lakes lake trout (Salvelinus namaycush) as described in the King classification system. Two trials were held at each workshop, with group discussion between trials. Variation in counting and classifying marks was considerable, such that reporting rates for A1–A3 marks varied two to three-fold among observers of the same lake trout. Observer variation was greater for classification of healing or healed marks than for fresh marks. The workshops highlighted, as causes for inconsistent mark classification, both departures from the accepted protocol for classifying marks by some agencies, and differences in how sliding and multiple marks were interpreted. Group discussions led to greater agreement in classifying marks. We recommend ways to improve the reliability of marking statistics, including the use of a dichotomous key to classify marks. Laboratory data show that healing times of marks on lake trout were much longer at 4°C and 1°C than at 10°C and varied greatly among individuals. Reported A1–A3 and B1–B3 marks observed in late summer and fall collections likely result from a mixture of attacks by two year classes of sea lamprey. It is likely that a substantial but highly uncertain proportion of attacks that occur in late summer and fall lead to marks that are classified as A1–A3 the next spring. We recommend additional research on mark stage duration. INDEX WORDS:

Sea lamprey, lake trout, classification of sea lamprey marks, healing.

INTRODUCTION Observations of sea lamprey (Petromyzon marinus) marks have been used to either infer the degree *Corresponding

of predation on Great Lakes fish (e.g., Fry 1953, Berst and Wainio 1967, Berst and Spangler 1970, Christie and Kolenosky 1980, Henderson 1986, Sitar et al. 1997, Rutter and Bence 2003), or to estimate sea lamprey-induced mortality experienced by

author. E-mail: [email protected]

283

284

Ebener et al.

economically or socially important Great Lakes fish (e.g., Spangler et al. 1980, Pycha 1980, Sitar et al. 1999). Sea lamprey marking statistics can provide important information on the relative numbers of parasitic sea lampreys and the success of ongoing control efforts. Thus, they provide one potential means for estimating how damage, in terms of dead fish, responds to costly control efforts for sea lampreys in the Great Lakes (Bence et al. 2003). Such estimates of damage to Great Lakes fish, and use of them to optimize the control of sea lampreys in the Great Lakes (Larson et al. 2003, Lupi et al. 2003), depends on reliable and consistent classification of sea lamprey marks. Since marking statistics in the Great Lakes are collected by state, provincial, federal, or tribal fishery agencies and combined in lakewide or regional marking databases, the potential for inconsistencies in classifying marks is high. King (1980) developed a guide for classifying sea lamprey marks on Great Lakes lake trout (Salvelinus namaycush) that recognized type-A and type-B marks and four stages of healing (1–4). Type-A marks indicate that the sea lamprey penetrated the skin and muscle of the fish, whereas type-B marks indicate the sea lamprey did not penetrate the skin and muscle of the fish. Stage 1 indicates no healing while stage 4 indicates the mark is completely healed, and stage 2 and stage 3 represent intermediate stages of healing. Eshenroder and Koonce (1984) recommended that agencies report sea lamprey marking rates as the sum of the number of large A1, A2, and A3 marks per 100 fish observed for the purpose of standardizing the reporting of sea lamprey marking of Great Lakes fishes. After evaluating the ability of the staff from several agencies to identify King’s eight types of sea lamprey marks, Eshenroder and Koonce (1984) realized that classification of marks among individuals was not precise. Therefore, they recommended that A1 to A3 marks be combined because they believed this combined category roughly represented marks-of-the-year, because these marks were generally distinguishable from type-B marks, and because stage-4 marks were not confused with newer stages. The reporting system was subsequently adopted by most Great Lakes fishery agencies during 1984–1986. Unfortunately, training of staff to use the King classification system was left to the discretion of each agency and no additional inter-agency training sessions were held. The lack of formal training and the need for additional inter-agency training sessions became apparent in reporting of marking data from Lake

Michigan during 1994–1996. The U.S. Geological Survey (USGS) and Wisconsin Dept. of Natural Resources (WIDNR) both sampled lake trout at roughly the same two sites and time periods in both 1994 and 1995 and reported marking rates on lake trout > 532 mm long. Marking rates reported by WIDNR were two-fold greater than rates reported by USGS in 1994, while in 1995 marking rates reported by WIDNR were three-fold less than marking rates reported by USGS. In late summer to early fall of 1996, USGS, Grand Traverse Band of Ottawa and Chippewa Indians (GTB), Chippewa/Ottawa Resource Authority (CORA), and Michigan Dept. of Natural Resources (MIDNR), all sampled lake trout > 532 mm long from northern Lake Michigan and reported sea lamprey marking rates of 20, 10, 6, and 2 marks per 100 lake trout, respectively. While it is possible that the differences in marking described during 1994–1996 were simply the result of sampling variability, the question remained as to whether the reported marking rates captured the true nature of sea lamprey-lake trout interactions, or whether the marking data represent different ways of classifying marks by each of the agencies. Assuming that issues of consistency in classifying marks can be resolved, other evidence suggests that extended healing times may create seasonal differences in reliability of assigning mark stages to the parasitic sea lamprey cohort that caused them. Schneider et al. (1996) found a significant correlation between A1 marks in September and the number of lamprey-induced lake trout deaths during the same year in Lake Ontario. They also found that the number of A3 marks observed in fall surveys in Lake Ontario correlated with the number of A1 marks the previous year, but not the number of A1 marks in the current year, and concluded that the majority of A3 marks observed in the fall were caused by the previous cohort of sea lampreys. The only published information we found describing healing times based on the King and Edsall (1979) and King (1980) mark classification system was in figure legends or example pictures in King (1980). Our primary interest is in healing of type-A marks to stage A2 and stage A4, because current approaches generally use A1 marks in the fall and A1 A3 marks in the spring to estimate sea lamprey-induced mortality of lake trout (Eshenroder and Koonce 1984, Sitar et al. 1999). The objectives of this paper were to evaluate the consistency of classifying sea lamprey marks among individuals and field crews, to evaluate the

Classifying Sea Lamprey Marks effect of inter-agency differences at classifying marks on reported marking rates, to evaluate the effects of healing time on interpretation of marks at various seasonal times of data collection, and to recommend ways to standardize the classification and reporting of sea lamprey marks in the Great Lakes. Observer agreement at classifying sea lamprey marks and a statistical summary of healing time for sea lamprey marks has not yet been presented in the scientific literature. METHODS Classifying Marks Workshops were held in 1997 and 1998 to evaluate the consistency of personnel classifying sea lamprey marks. Technicians, biologists, and researchers from 16 agencies, representing 20 different field crews directly responsible for classifying sea lamprey marks in the field, or for compiling and analyzing the marking statistics, participated in the workshops. Workshop I was held in 1997 at the USGS. Hammond Bay Biological Station and was attended by staff that primarily worked on Lakes Michigan and Huron, while workshop II in 1998 was held at the U. S. Fish and Wildlife Service (USFWS) Marquette Biological Station and attended by agencies that worked only on Lake Superior. The levels of experience of individual observers at classifying sea lamprey marks at both workshops ranged from novice to 23 years and averaged 8 years. Each workshop began by having each participant record the number of sea lamprey marks of each type and stage on 12 or 13 lake trout, and identify one labeled mark on each fish (trial I). Lake trout used in the workshops were caught with gill nets in the North Channel of Lake Huron directly behind Drummond Island in February and March of 1997 and 1998 and then subsequently frozen. The fish were chosen based on the number, type, and severity of marks on each fish. At the end of the workshop participants again classified sea lamprey marks on a different group of 12 or 13 lake trout and were again asked to identify one labeled mark on each fish (trial II). Kappa coefficients (Cohen 1960, Fleiss 1981, Mullet and Bergstedt 2003) were estimated for trials I and II and used to compare agreement among observers classifying sea lamprey marks at the beginning and end of each workshop. A Kappa coefficient of 0.4 was considered the minimum satisfactory level of agreement (Fleiss 1981). At each workshop we attempted to increase con-

285

sistency at classifying marks between trials I and II through a 1-day, interactive process that included presentations and group discussions. Presentations were made concerning the King (1980) classification system, the use of sea lamprey marking data, mortality of lake trout due to sea lamprey predation in the wild (Bergstedt and Schneider 1988, Schneider et al. 1996), and laboratory observations of sea lamprey-induced mortality and healing of marks (Swink and Hanson 1986). A person from each agency described their agency’s methods and protocols for classifying, recording, and maintaining files of sea lamprey marks. Group discussions included reviewing photographs of marks from trial I, distinguishing A1 to A3 marks from other marks, classifying “sliding marks” (Fig. 3; “Interpreting and Recording Marks” below), recording multiple marks that appear to result from a single sea lamprey, and reviewing results that summarized the frequency and distribution of sea lamprey marks classified in trial I of the workshop. Healing Times Between Mark Classes King (1980) allowed individual sea lampreys to attack lake trout in the laboratory and then made detailed observations on those marks over time. The lake trout were held at different temperatures, including 10°C, 4°C, and 1°C. Here his data are analyzed on the number of days from the sea lamprey attack to each transition to a new mark classification (stage). We define an observation as the observed time period during which a mark remained in a stage or defined range of stages. This time period ended either because the mark healed to the next stage, or because the fish either died or the study ended, and the mark had not yet healed to the next stage. In the following text we use the term “censored” to refer to observations for which the period of observation ended for reasons other than healing to the next stage (i.e., fish died or study ended). Of interest are type-A marks and especially healing times from the initial attack to stage 2 or 4. Any mark that was classified as a type A prior to healing to stage 2 was treated as an A mark (some marks that were initially type B became type A before healing to later stages). Due to limited data, especially of type-A marks at low temperatures, healing of type-B marks was also examined with the goal to better understand how healing time responded to temperature. Also, due to limited numbers of type

286

Ebener et al.

A marks that healed to stage 4 at lower temperatures healing to stage 3 was also examined. A Weibull distribution was fitted to the time required for healing to each mark classification of interest and at each temperature. The Weibull distribution is widely used to flexibly model the distribution of “survival” times such as stage duration, where the likelihood of transition from one stage to the next changes with time in the stage. The parameters of the Weibull distribution were estimated by maximum likelihood. The usual maximum likelihood estimators for the parameters of a Weibull distribution (e.g., Law and Kelton 1982, Table 5.1) cannot be used because some observations were censored. As is often required for censored distributions (Klein and Moeschberger 1997) numerical methods were used to maximizing likelihood. From the definition of likelihood (Larsen and Marx 1981) and assumed independence among trials the likelihood of the data given the parameters is given by: i=c

L = ∏ f ( xi α , β ) i =1

i=k

∫ (1 − F( xi α , β )) i = c +1

(1)

Where α and β are the parameters of the Weibull distribution, f(x |α,β) and F(x|α,β) are the probability density and cumulative distribution functions of the Weibull distribution, respectively (as given by Law and Kelton 1982, Table 5.1), observations 1, . . . , c are complete (not censored), observations c+1, . . . , k are censored, and x is the duration in a stage (or group of stages) before transition to the next stage. In our application these times are from attachment to the defined termination stage (2, 3, or 4) or end of observations. To improve numerical stability values of ln(α) and ln(β) were searched to identify those that maximized ln(L). This was done using the quasi-newton method with central differencing as implemented in the SOLVER module of EXCEL. The mean and CV (for among individual variation in healing times) were calculated from the parameter estimates according to relationships given by Law and Kelton (1982, Table 5.1). The analysis was restricted to subsets of the data for which there were at least five total observations and for which at least three were not censored. These are admittedly ad hoc criteria, however, it was felt that without at least five observations characterization of the coefficient of variation would be problematic. In addition, three completed observations were required because these types of observa-

tions are the ones that provide information about the right hand tail of the distribution. RESULTS Classifying Marks There was considerable variation among individual observers at classifying sea lamprey marks during both workshops. Observers reported more type-A marks on the lake trout than type-B marks during all trials (Fig. 1). Over all four trials, an average of 14 type-A and six type-B marks were reported by individual observers per trial (based on examining 12 fish during an individual trial). The number of type-A marks reported by individual participants varied from nine to 16 in trial I and from 11 to 25 in trial II of workshop I. During workshop II the number of type-A marks varied from five to 17 in trial I and from seven to 16 in trial II. Similarly large variation was seen among individuals in the number of type-B marks reported, with the range in number of reported marks being zero to 15, one to 13, three to 15, and one to eight for trials I and II of workshop I and trials I and II of workshop II, respectively. An average of eleven A1–A3 marks were observed by individual observers in each trial. The number of A1–A3 marks reported by individual participants varied from six to 14 in trial I and from seven to 21 in trial II of workshop I. In workshop II these numbers varied from five to 11 and from six to 15 in trials I and II, respectively. The mean number of A1–A3 sea lamprey marks per lake trout observed by individual participants at both workshops ranged from 0.4 to 1.8 marks per fish and averaged 0.9 marks per fish. The number of A1–A3 marks per fish observed by individual participants ranged from 0.5 to 1.2 and 0.6 to 1.8 marks per fish in trial I and trial II of workshop I, respectively, and 0.4 to 0.9 and 0.5 to 1.3 marks per fish in trial I and trial II in workshop II, respectively. In nearly all trials the Kappa coefficient was ≤ 0.40 for each of the eight classifications of sea lamprey marks (Fig. 2). In trial I at both workshops the Kappa coefficient was generally greater for type-A marks than for type-B marks, and typically greater for A1, A2, and A3 marks than for other marks. The degree of observer agreement in trial I at both workshops was best for A1 and A2 marks and produced Kappa coefficients of 0.37–0.40. Observer agreement on A3 marks was good at workshop II as the Kappa coefficient was 0.45, but observer agreement on A3 marks in trial I of work-

Classifying Sea Lamprey Marks

287

FIG. 1. Frequency distribution of the number of type-A and type-B sea lamprey marks observed by each individual on 12 lake trout during trials I and II at the marking workshops held in 1997 and 1998. shop I was poor and yielded a Kappa coefficient of only 0.07. Observer agreement on all other marks was generally poor in trial I at both workshops. Variation in observer classification of individual marks was high, but primarily for both type-A and type-B marks that showed some signs of healing (stages 2–4). Of the 49 labeled marks in the four trials combined, 53% were classified as both type A and type B. Ten of the 13 individual marks labeled for identification in trial II of workshop I were classified as both type A and type B (Table 1). Observers classified 37% of the individual marks solely as type A and only 2% solely as type B. When individual marks were clearly fresh open wounds (marks 2, 8, 11, 14, 17, 29, 35, 37, 39, 40) observer agreement was good with individual marks being recognized by 50–89% of the workshop participants. Before the group discussions and presentations, observer agreement of 50% or more

occurred on type-B or A4 marks only twice (marks 4 and 33). Discussions held at the workshops were somewhat successful at increasing the level of agreement at classifying marks, but not across all types and stages of marks. Training at the workshops was successful in helping observers distinguish between type-A and type-B marks as there was little overlap in the distribution of type-A and type-B marks identified by the individual observers in trial II at both workshops. Observer agreement increased from trial I to trial II at workshop I for A2, A3, A4, B1, and B2 marks with Kappa coefficients ranging from 0.23–0.54 (Fig. 2). Kappa coefficients increased from trial I to trial II at workshop II only for A2, A4, and B2 marks. The level of agreement on A1 marks in trial I in workshop II was disheartening as the Kappa value declined from 0.40 to 0.15. The level of agreement at identifying B2

288

Ebener et al.

FIG. 2. Kappa coefficients for determining the level of observer agreement at classifying sea lamprey marks on lake trout in workshops held in 1997 and 1998.

marks (0.23–0.33), B3 marks (0.07–0.11), and B4 marks (0.01–0.03) was poor even after the group discussions and presentations. Grouping individual observers into their respective field crews slightly reduced the variability in marking rates and illustrated agency bias. Agency specific marking rates varied from 0.5 to 1.6 marks per fish during all trials and the lowest variability in marking was associated with trial I of workshop II where marking varied only from 0.5 to 0.7 marks per fish among agencies (Table 2). Unfortunately, variability in marking rates increased between trials at workshop II. Among agencies attending workshop I, the MIDNR-Huron staff classified the most marks as A4 in trial I, but not trial II, so their ability to distinguish A3 and A4 marks did improve from trial I to trial II.

Interpreting and Recording Marks During the group discussions and presentations it became apparent that “sliding” and “multiple” marks were contributing to the variability in classifying marks. Sliding marks are made by sea lampreys that initially attach to one site on a fish, but while staying attached, the sea lamprey moves along the body of the fish to another site. This type of attack typically produces a 7–15 cm long mark where the scales and skin are missing, and there may or may not be several identifiable sites where the sea lamprey remained for longer periods (Fig. 3). These sliding marks may also move from one side of the fish to the other, further increasing the level of uncertainty in classifying marks. Many observers would record more than two types or stages of marks for a sliding mark, which may have been caused by only one sea lamprey. Sliding marks where the skin sloughed off after the attachment, but the sea lamprey did not penetrate the muscle, were routinely classified as type A when these are actually a type B. Multiple marks adjacent to each other, which may have been made by the same sea lamprey without sign of “sliding,” were interpreted as being two distinct marks by many of the observers at the workshops. During presentations on agency protocols, it was discovered that another cause of the variability in marking rates was that not all agencies were reporting the sum of A1–A3 marks, as suggested by Eshenroder and Koonce (1984). The MIDNR-Huron staff at the first workshop and Minnesota Dept. of Natural Resources (MNDNR) staff at workshop II reported that they did not routinely record A3 marks as part of their sampling protocol, instead, they recorded A1 and A2 marks as fresh marks, and grouped the A3 marks with the A4 marks as healed marks. Hence these agencies had not adopted the King (1980) classification system. Field staff from both these agencies were taught by individuals who learned to classify marks before the King (1980) system was developed and standardized reporting categories were suggested by Eshenroder and Koonce (1984). Agencies exhibited a diversity of methods for recording and storing information on sea lamprey marks. MNDNR and Ontario Ministry of Natural Resources (OMNR-Superior) recorded the number of marks as fresh or old wounds and both agencies defined fresh wounds as being red or being easy to feel with the skin broken, whereas scars/old wounds were defined as healing or have healed and were

Classifying Sea Lamprey Marks

289

TABLE 1. Frequency distribution of observer identification of 49 specific sea lamprey marks on 48 lake trout used in trials I and II at the marking workshops held in 1997 and 1998. Wound Number 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49

A1 0.083 0.143

A2 0.057 0.889 0.314

0.861 0.056

0.139 0.417

Type (A or B) and stage (1–4) of mark A3 A4 B1 B2 0.114 0.257 0.114 0.200 0.028 0.286 0.029 0.143 0.057 0.257 0.543 0.029 0.143 0.250

0.111 0.387

0.333 0.306 0.114 0.444 0.353

0.583 0.361 0.029 0.778 0.528 0.029 0.647

0.083 0.222 0.086 0.222 0.028 0.647

0.176 0.029 0.500

0.794 0.029 0.350

0.029 0.059

0.088

0.647

0.265 0.529 0.353 0.321 0.406

0.100 0.409 0.818 0.273 0.333 0.409 0.045 0.045 0.273 0.045

0.545 0.500 0.500 0.045

0.409 0.136 0.591

0.353

0.088

0.265 0.029

0.029 0.647

0.029 0.118

0.029 0.118

0.029

0.500 0.150 0.176

0.294

0.059

0.412

0.235

0.773

0.091 0.143 0.286 0.333

0.045 0.143 0.095 0.048 0.600

0.182 0.409 0.727 0.136 0.591

0.273

0.682

0.088

0.029 0.029 0.036 0.031

0.029 0.036

0.091

0.091

0.273

0.381 0.095 0.143 0.200

0.045 0.333 0.190 0.143 0.050

0.048 0.333 0.050

0.091

0.091

0.125

0.045 0.125

0.182

0.091

0.091

0.591 0.136 0.045

0.045 0.045

0.318 0.500

0.273

0.057

0.471

0.182

0.045 0.773 0.045 0.136

0.194

0.059

0.318

0.545 0.136 0.136 0.591 0.667 0.591

0.387 0.086

0.045 0.318 0.136

0.238

0.167 0.032 0.056 0.171

0.036 0.063

0.048

0.029

0.056 0.371

0.441 0.588 0.571 0.500

B4 0.029

0.029

0.086

0.176

0.136 0.364 0.500

B3 0.229

0.682 0.091 0.045

0.091

0.250 0.045 0.545

Number observers 35 36 35 35 36 36 31 36 36 35 36 36 34 34 34 34 34 34 40 34 34 34 34 28 32 22 22 22 22 22 21 21 21 20 22 22 22 22 21 22 22 22 22 22 22 16 22 22 22

290

Ebener et al.

TABLE 2. Number of each type and stage of sea lamprey mark, and mean marking rate of lake trout estimated for each agencies field crews at the marking workshops held in 1997 and 1998.

Trial I-I

I-II

Number Sampling agency observers BMIC 2 CORA 5 GTB 4 KBIC 2 Illinois Dept. of Natural Resource 1 MIDNR – Huron 3 MIDNR-Superior 2 MIDNR-Michigan 2 NYDEC 1 OMNR-Superior 1 OMNR-Huron 3 WIDNR-Michigan 1 USGS-Huron 4 USGS-Michigan 3 USGS-Hammond Bay 2 USFWS 2 Great Lakes Fishery Comm. 1 Michigan State University 1 BMIC CORA GTB KBIC Illinois Dept. of Natural Resources MIDNR – Huron MIDNR-Superior MIDNR-Michigan OMNR-Superior OMNR-Huron WIDNR-Michigan USGS-Huron USGS-Michigan USGS-Hammond Bay USFWS Great Lakes Fishery Comm. Michigan State University

Type (A or B) and stage (1-4) of marks A3 A4 B1 B2 B3 3 4 1 4 5 12 6 5 5 6 12 11 7 8 3 8 8 0 0 0

B4 2 0 3 0

A1-A3 marks per fish mean range 0.8 0.9 0.7-1.0 0.9 0.7-1.1 0.9 0.8-1.0

A1 3 15 11 5

A2 12 25 20 9

2 13 2 8 4 5 7 0 11 8 2 8

6 12 14 10 5 8 13 9 14 14 6 15

3 5 8 7 1 0 7 3 14 7 0 4

5 17 4 1 4 1 6 0 9 13 3 4

1 6 3 4 1 2 5 2 5 12 1 0

0 2 6 5 5 2 3 1 4 8 1 4

0 0 1 3 1 4 11 2 3 16 1 1

0 1 0 6 0 1 1 2 2 5 1 3

0.9 0.8 1.0 1.0 0.9 1.1 0.8 1.0 0.8 0.8 0.7 1.1

1

5

0

3

3

2

1

0

0.5

2

3

3

3

3

0

5

4

0.7

2 4 4 2

14 18 9 5

10 31 25 15

2 26 15 8

8 6 24 12

10 15 10 2

2 6 7 1

4 1 7 1

3 0 1 3

1.1 1.6 1.0 1.2

1 3 2 2 3 3 1 4 3 2 2

2 8 3 13 14 13 3 14 11 13 13

12 17 19 10 0 16 7 24 17 8 8

5 8 17 9 3 20 6 8 8 10 11

4 17 4 10 6 4 0 18 20 6 8

1 4 11 6 2 6 2 12 7 11 3

2 13 3 4 1 10 5 7 5 1 3

1 3 1 2 4 1 0 5 2 0 1

0 2 1 0 0 0 0 0 0 0 1

1.6 0.9 1.6 1.3 1.4 1.4 1.3 1.0 1.0 1.3 1.3

1.1-1.5 1.2-1.5

2

8

17

5

8

3

7

2

3

1.3

0.8-1.7

1

2

6

5

5

1

3

3

1

1.1

0.8-0.9 0.9-1.1 1.0-1.1

0.7-0.9 0.8-1.0 0.8-0.9 1.1-1.2

0.9-1.3 1.3-1.6 0.9-1.1 1.0-1.3

0.9-1.0 1.5-1.8 1.3-1.4 1.2-1.5 0.6-1.3

Classifying Sea Lamprey Marks TABLE 2.

Trial II-I

II-II

291

Continued.

Number Sampling agency observers A1 CORA 5 16 GLIFWC 1 4 KBIC 2 2 RCFD 2 5 BMIC 2 3 MIDNR-Superior 2 8 WIDNR-Superior 3 8 MNDNR 2 1 USFWS 1 1 OMNR-Superior 1 4

A2 13 0 5 4 7 7 6 6 4 2

CORA GLIFWC KBIC RCFD BMIC MIDNR-Superior WIDNR-Superior MNDNR USFWS OMNR-Superior

25 3 5 11 10 16 15 10 3 6

5 1 2 2 2 2 3 2 1 1

11 2 5 5 1 4 6 3 1 0

Type (A or B) and stage (1-4) of marks A3 A4 B1 B2 B3 12 4 11 18 9 2 1 0 0 0 7 3 10 2 2 3 1 3 0 5 4 0 4 3 3 9 9 6 8 2 11 6 11 4 3 6 15 4 3 1 2 4 0 2 9 1 4 2 0 1 15 1 6 2 4 9 11 6 4 3

smooth to the touch. MNDNR further defined small sea lampreys marks as those smaller than a nickel, and type-B marks were defined as riding marks with scales roughed up, but the skin was not broken. The New York State Dept. of Environmental Conservation (NYDEC) recorded the number of A1 through A4 marks and grouped all type-B marks. Staff from the Great Lakes Indian Fish and Wildlife Commission (GLIFWC) and the Keweenaw Bay Indian Community (KBIC) did not regularly record type-B marks. The numbers of A1–A4 and B1–B4 marks observed on each fish were routinely recorded by staff from Red Cliff Fisheries Dept. (RCFD), WIDNR-Superior, MIDNR-Superior and MIDNR-Michigan, CORA, Bay Mills Indian Community (BMIC), and USGS. CORA, BMIC, and USGS biological databases specifically contain fields for each type and stage of mark. Healing Times for Marks Figure 4 illustrates the method with an example of a fitted distribution along with the associated

12 3 5 4 5 11 9 14 4 3

7 1 3 2 1 4 5 2 0 0

8 1 0 1 3 2 2 0 2 2

5 0 1 5 2 2 2 2 0 0

B4 4 9 3 5 7 1 3 0 4 3 6 2 4 3 1 2 7 1 0 3

A1-A3 marks per fish mean range 0.7 0.6-0.9 0.5 0.6 0.5 0.4-0.6 0.6 0.7 0.6-0.8 0.7 0.6-0.8 0.5 0.5-0.6 0.6 0.6 0.9 0.5 0.7 0.8 0.6 0.8 0.9 0.8 0.7 0.8

0.7-1.1 0.6-0.8 0.5-1.0 0.6-0.7 0.7-1.3 0.7-0.9

data. For each case we report the estimated mean healing time and, to characterize the variability among marks in their healing time, the estimated coefficient of variation for the among-individual variation in healing times (Table 3). The King (1980) data are not extensive since sample sizes were often small, but do provide some insight (Table 3). Two overriding results are that healing times are highly variable and that low temperature can substantially increase the healing time. Mean healing time to an A2 was 13 days at 10°C and this increased to 47 days at 4°C. Mean healing time to an A4 at 10°C was 94 days, but unfortunately not enough data were available for estimates at lower temperatures. The relative length of healing time to an A4 versus an A2 and A3, combined with the apparent effects of temperature on healing for other type-A and type-B marks, also suggests that marks entering the winter as A1 or A2 marks could reasonably be still in the A1–A3 category in the spring. However the variability in the data also indicates that some marks might heal before spring.

292

Ebener et al.

FIG. 3. Examples of sliding and multiple sea lamprey marks on a 482 mm total length and 750 g lake trout caught in northern Lake Huron on 1 April 1997 and used in trial I of workshop I in 1997.

Indeed, the two marks observed to heal to an A4 at 4°C did so in 4 to 5 months. However, recent photographs of fish tagged and photographed in the fall and recaptured the next summer also indicate that some wounds can persist without healing to an A4. DISCUSSION Classifying Marks It is obvious that the variability in sea lamprey marking statistics may be as influenced by how individuals and agencies classify and record marks as by abundance of sea lampreys and their prey. Observer-specific marking rates varied four- to five-

fold even when all observers were looking at the same fish, and up to a three-fold difference was seen for the A1–A3 combination, which is the established reporting standard. When the marking data from individual observers were grouped together the variability declined somewhat, but there was still a nearly two-fold difference in marking rate among agencies. It was disheartening to learn that although a Great Lakes basin-wide protocol for classifying sea lamprey marks had been established in the mid 1980, not all agencies had implemented the protocol. To assist in classifying sea lamprey marks it is recommended that a simple dichotomous key be

Classifying Sea Lamprey Marks

3a. Muscle exposed and reddish, membrane covering pit ...................................................A2 3b. Muscle slightly or not exposed and not reddish, some pigmentation...........................................4 4a. Muscle somewhat exposed, limited pigmentation .................................................A3 4b. No exposed muscle, complete pigmentation .A4 5a. Skin broken and rough to the touch, scales missing..........................................................B1 5b. Skin smooth to the touch, may or may not be broken, some healing .......................................6 6a. Skin broken ...................................................B2 6b. Skin not broken................................................7 7a. Dark pigment, no scales ................................B3 7b. Regenerated scales, pigmentation complete..B4

FIG. 4. Fitted Weibull probability distribution for one example combination of mark type and temperature. Shown is the distribution for times to healing of an A2 mark at 10°C.

Healing Times for Marks Uncertainty regarding the time required for a new mark to heal to each successive stage still complicates the interpretation of marking data and may account for the scarcity of published accounts directly relating marking statistics to damage by sea lampreys. The mean time for an A1 mark to heal to an A3 mark at 10°C was 94 days—long enough to enable many A1 marks made in late May or early June by the outgoing parasitic cohort to persist as A3 marks in late summer or early fall. The substantial increases in healing time associated with cooler

adopted by each agency. The key separates marks based on the presence or absence of a pit and the degree of healing. The key is: 1a. 1b. 2a. 2b.

293

Pit ....................................................................2 No pit...............................................................5 Skin rough to touch and white, no healing....A1 Skin not rough to touch and white, some sign of healing.........................................................3

TABLE 3. Estimated mean healing times (days) from time of detachment to healing to specified stage (mark classification) and associated coefficient of variation (CV) among individuals in estimated healing times. Shown also are sample sizes of complete and incomplete observations associated with each estimate. Estimates based on fitting Weibull distributions to healing time data obtained from King (1980). Mark Variable classification Healing time A-2 A-3 A-4 B-2 B-3 B-4 Sample size

A-2 A-3 A-4 B-2 B-3 B-4

Temperature Regime in °C 10 13.4 (0.47) 38.0 (0.36) 93.7 (0.57) 51.9 (1.25) — —

4 46.6 (0.50) 94.2 (0.33) — 50.4 (0.32) 142.3 (0.30) —

0–1 — — — 74.7 (0.62) 176.0 (0.29) 212.6 (0.30)

Complete + incomplete observation 10+0 4+2 — 7+3 3+2 — 8+0 — — 5+0 6+1 6+0 — 3+4 4+2 — — 3+3

294

Ebener et al.

temperatures suggests that many marks from attacks occurring in late summer or fall might be classified as A1–A3 marks the next spring. However, the high variability in healing suggests that some of these attacks will have healed to an A4. Furthermore, the possibility that an A3 mark persisting into the fall from the previous cohort might still appear as an A3 the next spring cannot be ruled out. Recording of Marking Data Agencies should continue to follow the standard reporting format (Eshenroder and Koonce 1984) to provide for long-term continuity. However, recording only the sum of A1–A3 marks severely limits our ability to use marking statistics, and in seasons where A1–A3 might not be attributable to a single cohort may make the statistic unusable. With the computer capabilities now at our disposal, there is no reason not to record detailed data. Marking statistics could then be presented by the reporting standard and by other combinations that seem reasonable. Maintenance of the detailed databases will allow future researchers to use the data in ways that might not be apparent now. There might be an additional use of marking records in monitoring the success of sea lamprey management if data were reported with a second type of index. The convention recommended by Eshenroder and Koonce (1984) was to report the number of A1–A3 marks per 100 fish. Reporting the marks per 100 fish acknowledges that marking, as a rate, varies with both the number of sea lampreys and the number of hosts. If statistics are from a catch where the underlying effort is not known, as is the often case for sportfishing samples or random samples from commercial fish houses, then a marking rate is all that can be reported. Marks per 100 fish have the added appeal of directly reflecting sea lampre-induced mortality in a host population. For example, if the host population either doubled or the numbers of sea lampreys were decreased by half, the marking rate would be reduced by half and the annual mortality rate due to sea lamprey predation would also be reduced by a similar amount if other factors were constant. Therefore the marking rate, as now reported, reflects the current relative effect of sea lampreys on a host population, but does not necessarily reflect sea lamprey numbers. However, where effort is constant or known (as in an annual agency field survey) there is the opportunity to use the catch-per-effort of the marks them-

selves as an index of sea lamprey abundance. If it is assumed that the probability of catching any fish in a population is proportional to effort expended, then the probability of capturing any mark made on a fish by a sea lamprey would also be proportional to sea lamprey abundance. The number of marks observed per-unit- effort, and not the marks per 100 fish, would be expected to be proportional to total sea lamprey feeding activity and to the actual loss of fish to sea lamprey predation. There is already some evidence that reporting a catch-per-effort of marks would be useful. Schneider et al. (1996) related the number of A1 marks observed in an annual gillnet assessment with fixed effort to an estimate of the number of dead, lamprey-killed lake trout per ha on the lake bottom. A reporting statistic that indexes sea lamprey abundance and that is not confounded by changes in fish abundance would be useful for tracking changes in control effectiveness and could be useful in methods that combine multiple data sources to estimate sea lamprey abundance (Young et al. 2003). Recommendations To reduce variability in classifying marks and to assist in reporting marking statistics the following are recommended, some of which restates recommendations from Eshenroder and Koonce (1984). 1. All agencies should record the number of each type (A or B) and stage (1–4) of mark, and maintain the resulting data in documented databases so that the actual stages recorded can be retrieved in the future. Summaries can then be generated combining any range of type and stage. 2. The most recent (i.e., earliest stage) attachment on a sliding mark should be the mark classified and recorded. 3. The most severe (i.e., type A over type B and within type earliest stage) single mark among a group of multiple marks made by the same sea lamprey should be the mark recorded; Do not record more than one mark per individual sea lamprey attack. 4. In order to separate marks made by different sea lamprey cohorts, only large marks, those equal to or larger than a quarter, should be included in computation of marking statistics for standard late summer, fall, or spring data collections of marking data.

Classifying Sea Lamprey Marks 5. An ongoing program of workshops needs to be established with a goal of improving and checking on consistency of marking statistics. 6. Statistics should be reported both as the marks per 100 fish and as the absolute number observed per-unit-of-effort. 7. Additional research needs to be conducted to better define the time required for healing between stages under realistic seasonal temperature regimes. ACKNOWLEDGMENTS We thank the Great Lakes Fishery Commission (GLFC) for providing financial support for the workshops, and Gavin Christie for encouraging those efforts. We are grateful to the many field staff and biologists who participated in the workshops. Chuck Madenjian and Henry Quinlan provided thoughtful reviews that improved the manuscript. Jim Bence’s contributions to this paper were supported with funding from the GLFC and from the Michigan DNR. We are grateful to Louis King for his dedication and effort in observing healing of marked lake trout and for providing us the resulting data. This article is Contribution 1176 of the U.S. Geological Survey, Great Lakes Science Center. REFERENCES Bence, J.R., Bergstedt, R.A., Christie, G.C., Cochran, P.A., Ebener, M.P., Koonce, J.F., Rutter, M.A., and Swink, W.P. 2003. Sea lamprey (Petromyzon marinus) parasite-host interactions in the Great Lakes. J. Great Lakes Res. 29 (Suppl. 1):253–282. Bergstedt, R.A., and Schneider, C.P. 1988. Assessment of sea lamprey (Petromyzon marinus) predation by recovery of dead lake trout (Salvelinus namaycush) from Lake Ontario, 1982–85. Can. J. Fish. Aquat. Sci. 45:1406–1410. Berst, A.H., and Spangler, G.R. 1970. Population dynamics of F1 splake (Salvelinus fontinalis x S. namaycush) in Lake Huron. J. Fish. Res. Board Can. 27:1017–1032. ———, and Wainio, A.A. 1967. Lamprey parasitism of rainbow trout in southern Georgian Bay. J. Fish. Res. Board Can. 24:2539–2548. Christie, W.J., and Kolenosky, D.P. 1980. Parasitic phase of the sea lamprey (Petromyzon marinus) in Lake Ontario. Can. J. Fish. Aquat. Sci. 37:2021–2038. Cohen, J. 1960. A coefficient of agreement for nominal scales. Educ. Psychol. Meas. 20:37–46. Eshenroder, R.L., and Koonce, J.F. 1984. Recommendations for standardizing the reporting of sea lamprey

295

marking data. Great Lakes Fish. Comm. Spec. Pub. 84–1. Fleiss, J.L. 1981. Statistical Methods for Rates and Proportions. Second Edition. New York, New York: John Wiley & Son. Fry, F.E.J. 1953. The 1944 year class of lake trout in South Bay, Lake Huron. Trans. Am. Fish. Soc. 82: 178–192. Henderson, B.A. 1986. Effect of sea lamprey (Petromyzon marinus) parasitism on the abundance of white suckers (Catostomus commersoni) in South Bay, Lake Huron. J. Applied Ecology 23:381–389. King, E.L., Jr. 1980. Classification of sea lamprey (Petromyzon marinus) attack marks on Great Lakes lake trout (Salvelinus namaycush). Can. J. Fish. Aquat. Sci. 37:1989–2006. ———, and Edsall, T.A. 1979. Illustrated field guide for the classification of sea lamprey attack marks on Great Lakes lake trout. Great Lakes Fish. Comm. Spec. Pub. 79-1. Klein, J.P., and Moeschberger, M.L. 1997. Survival analysis: techniques for censored and truncated data. New York, New York: Springer. Larsen, R.J., and Marx, M.L. 1981. An introduction to mathematical statistics and its applications. Englewood Cliffs, New Jersey: Prentice-Hall. Larson, G.L., Christie, G.C., Johnson D.A., Koonce, J.F., Mullett, K.M., and Sullivan, W.P. 2003. The history of sea lamprey control in Lake Ontario and updated estimates of suppression targets. J. Great Lakes Res. 29 (Suppl. 1):637–654. Law, A.M., and Kelton, W.D. 1982. Simulation modeling and analysis. New York, New York: McGrawHill. Lupi, F., Hoehn, J.P., and Christie, G.C. 2003. Using an economic model of recreational fishing to evaluate the benefits of sea lamprey (Petromyron marinus) control on the St. Marys River. J. Great Lakes Res. 29 (Suppl. 1):742–754. Mullet, K.M., and Bergstedt, R.A. 2003. Agreement among observers in classifying larval sea lamprey (Petromyzon marinus) habitat. J. Great Lakes Res. 29 (Suppl. 1):183–189. Pycha, R.L. 1980. Changes in mortality of lake trout (Salvelinus namaycush) in Michigan waters of Lake Superior in relation to sea lamprey (Petromyzon marinus) predation. Can. J. Fish. Aquat. Sci. 37: 2063–2073. Rutter, M.A., and Bence, J.R. 2003. An improved method to estimate sea lamprey wounding rate on hosts with application to lake trout in Lake Huron. J. Great Lakes Res. 29 (Suppl. 1):320–331. Schneider, C.P., Owens, R.W., Bergstedt, R.A., and O’Gorman, R. 1996. Predation by sea lamprey (Petromyzon marinus) on lake trout (Salvelinus namaycush) in southern Lake Ontario, 1982–1992. Can. J. Fish. Aquat. Sci. 53:1921–1932.

296

Ebener et al.

Sitar, S.P., Bence, J.R., Taylor, W.W., and Johnson, J.E. 1997. Sea lamprey wounding rates on lake trout in Lake Huron, 1984–1994. Michigan Academician XXIX:21–37. ———, Bence, J.R., Johnson, J.E., Ebener, M.P., and Taylor, W.W. 1999. Lake trout mortality and abundance in southern Lake Huron. N. Am. J. Fish. Manage. 19:881–900. Spangler, G.R., Robson, D.S., and Regier, H.A. 1980. Estimates of lamprey-induced mortality in whitefish, Coregonus clupeaformis. Can. J. Fish. Aquat. Sci. 37:2146–2150. Swink, W.D., and Hanson, L.H. 1986. Survival from sea

lamprey (Petromyzon marinus) predation by two strains of lake trout (Salvelinus namaycush). Can. J. Fish. Aquat. Sci. 43:2528–2531. Young, R.J., Jones, M.L., Bence, J.R., McDonald, R.B., Mullett, K.M., and Bergstedt, R.A. 2003. Estimating parasitic sea lamprey abundance in Lake Huron from heterogeneous data sources. J. Great Lakes Res. 29 (Suppl. 1):214–225. Submitted: 12 December 2000 Accepted: 24 March 2002 Editorial handling: Gerald T. Klar