An Overview of the Annual Cost Survey 

Protocol and Results 

in the Northeast (2007 to 2009)

US DEPARTMENT OF COMMERCE 

National Oceanic and Atmospheric Administration 

National Marine Fisheries Service 

Northeast Fisheries Science Center 

Woods Hole, Massachusetts 

November 2013

NOAA Technical Memorandum NMFS-NE-226 
An Overview of the Annual Cost Survey 

Protocol and Results 

in the Northeast (2007 to 2009) 

Chhandita Das 

NOAA Fisheries, Northeast Fisheries Science Center/Integrated Statistics, 

13 Church Street, Woods Hole, MA 02543 

 

US DEPARTMENT OF COMMERCE 

Penny Pritzker, Secretary 

National Oceanic and Atmospheric Administration 

Kathryn Sullivan, Acting NOAA Administrator 

National Marine Fisheries Service 

Samuel D. Rauch III, Acting Assistant Administrator for Fisheries 

Northeast Fisheries Science Center 

Woods Hole, Massachusetts 

November 2013

NOAA Technical Memorandum NMFS-NE-226 

This series represents a secondary level of scientific publishing. All issues employ thorough internal 

scientific review; some issues employ external scientific review. Reviews are transparent collegial 

reviews, not anonymous peer reviews. All issues may be cited in formal scientific communications. 
Editorial Notes 

Information Quality Act Compliance: In accordance with section 515 of Public Law 106-554, the 

Northeast Fisheries Science Center completed both technical and policy reviews for this report. 

These predissemination reviews are on file at the NEFSC Editorial Office. 

Species Names: The NEFSC Editorial Office’s policy on the use of species names in all 

technical communications is generally to follow the American Fisheries Society’s lists of 

scientific and common names for fishes, mollusks, and decapod crustaceans and to follow the 

Society for Marine Mammalogy's guidance on scientific and common names for marine 

mammals. Exceptions to this policy occur when there are subsequent compelling revisions in the 

classifications of species, resulting in changes in the names of species. 

Statistical Terms: The NEFSC Editorial Office’s policy on the use of statistical terms in all 

technical communications is generally to follow the International Standards Organization’s 

handbook of statistical methods. 

Internet Availability: This issue of the NOAA Technical Memorandum NMFS-NE series is 

being as a paper and Web document in HTML (and thus searchable) and PDF formats and can be 

accessed at: http://www.nefsc.noaa.gov/nefsc/publications/. 
ii 

TABLE OF CONTENTS 

Executive Summary ........................................................................................................................ 1 

1 Introduction .................................................................................................................................. 2 

2 Overview of the Cost Survey ....................................................................................................... 2 

2.1 Objective ................................................................................................................................ 2 

2.2 Survey Methodology .............................................................................................................. 2 

2.2.1 Survey Design and Fielding ............................................................................................ 2

2.2.2 Population and Sample .................................................................................................... 2 

2.3 Response Rates ....................................................................................................................... 3 

2.4 Nonresponse ........................................................................................................................... 4 

2.4.1 Item Nonresponse ............................................................................................................ 4 

2.4.2 Unit Nonresponse ............................................................................................................ 4 

2.5 Nonresponse Bias Test ........................................................................................................... 5 

3 Data .............................................................................................................................................. 6 

3.1 Data Auditing ......................................................................................................................... 6 

3.2 Data Summary ........................................................................................................................ 7 

3.3 Crew Share System ................................................................................................................ 8 

3.4 Data Weighting Procedure ..................................................................................................... 8 

4 The Modeling Framework ........................................................................................................... 9 

4.1 Repair/Maintenance/Improvement (RMI) Cost ................................................................... 10 

4.2 Other Annual Cost ................................................................................................................ 10 

4.3 Crew Cost ............................................................................................................................. 10 

4.4 Predicted Cost Summary ...................................................................................................... 11 

5 Conclusions ................................................................................................................................ 12 

Acknowledgements ....................................................................................................................... 12 

References Cited ........................................................................................................................... 13 

Appendix ....................................................................................................................................... 35
1 

EXECUTIVE SUMMARY 

Fisheries vessel cost data are used in virtually every framework, amendment and fishery 

management plan that requires economic analyses. This document summarizes the results of the 

annual commercial vessel cost data collection program conducted by the staff of the Social 

Sciences Branch of the NMFS Northeast Fisheries Science Center in 2007, 2008, and 2009. 

Information is presented on data collection methodology, data coverage, and data quality. 

Summary statistics are provided by vessel characteristics. Cost estimation and prediction 

methodologies are also discussed. The analyses and findings from the 2007-2009 programs are 

expected to improve future data collection efforts, and to enhance future analyses and evaluations 

of the economic status of commercial fisheries in the Northeast. 
2 

1 INTRODUCTION 

The Social Sciences Branch (SSB) of the NMFS Northeast Fisheries Science Center 

(NEFSC) has collected annual cost information from commercial fishing vessel owners for many 

years through several initiatives. The latest effort was undertaken in 2007 when cost information 

was requested from vessel owners via a voluntary mail-in survey. This effort continued for two 

more years through 2009. This document describes the survey and presents information on the 

data, analyses, and initial findings from the data collection effort. 

2 OVERVIEW OF THE COST SURVEY 

2.1 Objective 

Economic data on the costs of operating commercial fishing businesses are needed to 

meet the legislative requirements of the Magnuson-Stevens Fishery Conservation and 

Management Act, the National Environmental Policy Act, Executive Order 12866, and the 

Regulatory Flexibility Act. Fisheries cost data are used in the economic analyses in many 

frameworks and amendments to fishery management plans. Examples include regulatory impact 

analyses, economic profitability profiles, fleet efficiency and productivity measures estimations, 

and economic impact evaluations of proposed management measures and regulations. An 

accurate understanding of the financial costs incurred by commercial fishing businesses is critical 

for these analyses. The objective of this study was to obtain reliable, timely, and updated 

information on commercial fishing vessel costs. 

2.2 Survey Methodology 

2.2.1 Survey Design and Fielding 

The Social Sciences Branch at the Northeast Fisheries Science Center has been collecting 

fishing cost information for many years, using both formal and informal methods. In 2007, SSB 

initiated a formal data collection process that continued through 2009 in which cost information 

was acquired via voluntary mail-in surveys. The survey requested vessel owners to report their 

annual costs for the year preceding the survey year. For example, the survey in year 2007 

requested vessel costs incurred during 2006. Reference to survey years in this report pertains to 

the years in which the costs were incurred (i.e., survey years 2006, 2007 and 2008). A copy of the 

2008 survey is presented in the appendix. 

The survey was administered by the Permit Office within NOAA’s Northeast Regional 

Office (NERO). Each year during the 3-year study period, SSB provided the NERO Permit 

Office with a list of hull numbers of the vessels selected to be surveyed. The Permit Office then 

mailed the surveys to the prospective vessel owners along with their permit renewal forms. A 

stamped self-addressed return envelope was also included in the survey packet, which contained 

a cover letter that clearly stated (a) the objective of the survey and (b) that participation in the 

survey was voluntary. 

2.2.2 Population and Sample 

The unit of observation of the survey was the fishing vessel, and the survey population 

comprised all active commercial fishing vessels in each of the three years. In each survey year, an 
3 

active fishing vessel was defined as one that held at least one Northeast Federal Fishing Permit 

and reported landings of at least one pound of fish through the Northeast Seafood Dealer 

Reporting System. These criteria resulted in a population of 3,055 vessels in 2006, 2,597 vessels 

in 2007, and 2,879 vessels in 2008. Each year, surveys were sent to all the vessels in the 

population for that year. 

2.3 Response Rates 

For year 2006, 630 completed surveys were returned for a response rate of 20.6%. The 

response rate declined to 16.5% (430 responses) for 2007 and to 8% (232 responses) for 2008. 

With survey data, analysts are often concerned about obtaining a sufficient response rate in order 

for the information collected to be scientifically representative and precise. There are different 

hypotheses in the literature regarding this response rate. For example, Dillman et al. (2009) 

provides the following formula to determine the sample size needed to make population estimates 

within a selected level of confidence interval: 

N

s

= 

(N

p

)(p)(1−p)

(N

p

−1)(B/C)

2

+(p)(1−p)

 (1) 

Where: N

s

 = the completed sample size needed for the desired level of precision. 

N

p

 = the size of the population 

p = a measure of population heterogeneity 

B = margin of error; (i.e., half of the desired confidence interval width), 0.03=±3% 

C= Z score associated with the confidence level (1.96 corresponds to the 95% 

confidence interval). 

 

Based on this formula, and assuming maximum heterogeneity in the population (p=0.5 

implying 50/50 split in population) and a confidence interval of 95%, the number of completed 

responses needed to be able to estimate the characteristic of the population within ±3% points 

95% of the time, was 341 for 2006, 335 in 2007 and 339 for 2008. Hence, the completed survey 

responses for 2006 and 2007 exceeded the minimum baselines in these years, but fell short in 

2008. 

Table 11

 lists the overall survey population sizes and response rates, as well as by gear 

category, region, vessel length category, and revenue category. Vessel characteristics were 

obtained from the permit data base, the vessel logbook data base and the dealer data base. The 

principal gear (and landing region) by a vessel was determined by the gear (or landing state) that 

accounted for the maximum revenue share for that vessel. Six principal gear categories were 

 

1

 Note, the population sizes in Table 1 are different from the population sizes reported under subsection “2.2.2 

Population and Sample." This difference is because responses were received from vessel owners who were not 

included in the original sample. This irregularity might be because some vessel owners received the surveys through 

their business partner or via a shared address and chose to respond. These additional responses were considered to be 

part of the population, resulting in a slightly higher population size in Table 1 than in subsection “2.2.2 Population 

and Sample."
4 

used: Dredge, Gillnet, Handgear, Longline, Pot/Trap, Trawls, and Others. Two principal landing 

regions were defined: Mid-Atlantic and New England. The Mid-Atlantic region includes the states 

of New Jersey, Maryland, Virginia, New York, Delaware, and North Carolina. The New England

region includes Massachusetts, Rhode Island, Maine, New Hampshire, and Connecticut. Three 

vessel length categories were established: Large (greater than 80 ft), Medium (40- 80 ft), and 

Small (less than 40 ft). Total vessel revenues were grouped into five categories: less than $25 

thousand, $25 thousand to $100 thousand, $100 to $500 thousand, $500 to $1 million, and over 

$1 million. The unclassified category consists of vessels that either had missing information, or 

lacked a match with the vessel logbook or the dealer data - and therefore characteristic variables 

could not be defined for these vessels. 

A returned survey was regarded complete if it had at least one cost-related question 

answered. Overall, response rates declined from 2006 through 2008 and across all categories. For 

example, in 2006, the response rate was 16% for Dredge vessels, whereas in 2008, the response 

rate for Dredge vessels was only 8%. A similar pattern in declining annual response rates 

occurred in every category except for the Others principal gear category, where response rates 

increased from 10% in 2006 to 18% in 2007 but declined in 2008 to 7%. 

The rate of response differs largely across categories, which may lead to nonresponse 

bias. As nonresponse bias may lead to biased inferences, it is extremely important to test for the 

existence of nonresponse bias and correct for it. 

2.4 Nonresponse 

The previous section showed high and varied rates of nonresponse in the surveys. The 

focus of this section is to understand the nature and degree of this nonresponse to improve future 

data collection efforts. There are two types of nonresponse: one is unit nonresponse, where no 

response is received from a surveyed vessel owner; the other is item nonresponse, where a vessel 

owner responds with some−but not all−information (Lohr 2010). Each type of nonresponse is 

discussed below. 

2.4.1 Item Nonresponse 

In the cost survey, receiving an incomplete survey is not unexpected as it is quite possible 

for a respondent to not have any costs in some expense categories included in the survey. In 

2007, only 17 surveys were received in which replies were provided for all of the survey 

questions; in 2006 and 2008, all returned surveys had some missing information. Although the 

survey allowed respondents to indicate a zero expense in a category by choosing the option “NA" 

(Not Applicable), only a few respondents chose to respond in this manner. That is, several 

respondents neither reported a cost nor chose “NA.” For these responses, it is not clear whether 

the missing information was due to nonresponse or because no cost was incurred in that 

particular category. In the absence of further information to make this distinction, no action was 

taken to correct for these item nonresponses. 

2.4.2 Unit Nonresponse 

Unit nonresponse can arise from several reasons: (a) the respondent may not be willing to 

provide the information; (b) the respondent may be unable to provide the information; and (c) 

survey fatigue may have occurred. From an analytical perspective, it is not possible to test for the 

first two reasons. However, some insight can be gained regarding the third reason by examining 
5 

the pattern of responses over time. Because the entire population of vessels was surveyed in each 

of the three years of the cost collection effort, it was possible for a vessel owner to receive the 

survey multiple times. Tables 2 and 3 examine if these repetitive survey attempts might have led 

to survey fatigue and thus nonresponse. 

Table 2 the number of vessels owners who received the survey in multiple years. In 2007, 

surveys were sent to 2,597 vessel owners, of which 2,150 had received the survey in 2006. In 

2008, surveys were sent to 2,879 vessel owners, of which 150 had received the survey also in 

2006, 323 had received the survey in 2007 as well, and 1,791 had received the survey in both 

2006 and 2007.2

 Table 3 reports a summary of the order and frequency at which a respondent 

received the survey and subsequently responded. Of the 1,491 vessel owners (36%) that received 

the survey only once during the three years, 107 (7%) responded and 1,384 (93%) did not. Of the 

1,791 vessel owners (43%) who received the survey in all three years, only 65 (4%) responded in 

all three years, 205 (11%) responded in two of the three years, and 336 (19%) responded only 

once. 

The order of responses helps to understand if a respondent’s willingness to return the 

survey depended on whether the owner had received the survey in the previous year or not. Of 

the 832 vessel owners who received the survey twice, 147 responded only once (Table 3). Of 

these 147 respondents, 113 responded during the first year in which they received the survey and 

34 of them responded in the second year. That is, overall, 158 (113+45) responded in the first 

year, and 79 (34+45) responded in the second year. Of the 1,791 vessel owners who received the 

survey in all three years, 336 responded only once. Of these, 209 responded in the first year only, 

73 responded in the second year only, and 54 responded in the third year. Among the 205 vessels 

who responded twice, 157 responded in the 2006 and 2007, 15 responded in both 2007 and 2008, 

and 33 responded in 2006 and 2008. Overall, this indicates that 464 owners (209+157+33+65) 

responded in the first year, 310 (157+73+15+65) responded in the second year, and 167 

(54+15+33+65) responded in the third year. Response rate did increase with the number of times 

a vessel owner received the survey suggesting that multiple annual attempts succeeded to some 

degree in generating new responses. However, the number of new responses gained in 

subsequent years by multiple attempts was outweighed by the number of responses lost. This 

might be because vessel owners developed fatigue by receiving multiple surveys and accordingly 

decided not to respond in subsequent years. The next section specifically tests for existence of 

nonresponse biases. 

2.5 Nonresponse Bias Test 

In the previous sections, it was noted that the survey response rate declined in 2007 and 

2008, and was below 10% in 2008. This section specifically tests for nonresponse bias in the 

data. Because an ample amount of information exists on the characteristics of the vessels in the 

survey populations, it is possible to compare survey non-respondents with survey respondents to 

determine if there are significant differences between the two groups. To accomplish this, two 

sample t-tests were conducted in each year comparing the two groups based on vessel length, 

vessel horsepower, vessel gross tonnage, and total gross revenue. Significant differences were 

detected between respondents and non-respondents with respect to almost all four vessel 

characteristics, and this was consistent in all three years except for vessel horse power in 2008 

and total gross revenue in 2007 (Table 4). These results suggest that nonresponse is an issue in 

 

2

 2,150 from cell (1,2) in table 2 includes the figure 1,791 from cell (2,4) 
6 

the survey data, and hence, if possible, it would be desirable to adjust the survey responses for 

this bias. 

3 DATA 

The survey asked cost-related questions under four broad headings. The cost-related 

questions included expenses on quota or days-at-sea leasing, vessel haul-out, repair-maintenance, 

vessel mooring or dockage, business vehicle use, business travel, office, communication, 

business taxes, permit fees, professional fees, association fees, interest and principal loan 

payments, catch handling, and non-crew labor services. Crew expenses consisted of crew 

payments and benefits. Respondents were also allowed to report their total Improvement Costs 

under six categories and to provide descriptions of the improvement on their fishing vessels. If 

respondents incurred costs that did not fit under any of the questions asked in the survey, they 

could report these costs under Other Costs and also describe these costs. 

Before using the survey data for analysis, it was necessary to audit the data for possible 

data entry errors, logical errors, and outliers. The first step of the data auditing process involved a 

careful review of the data and eliminating any data inconsistencies, mislabeling of answers, and 

any double counting. In the next step, each individual cost element was plotted to identify 

outliers, which were subsequently removed from the final analysis. The following section 

elaborates on the first step in the data auditing process. 

3.1 Data Auditing 

The data auditing process was conducted after several rules were developed. The rules 

were crafted after carefully examining each and every response, and were guided by the 

descriptive responses from the survey. This assessment was then used to revise the existing data 

to facilitate better analysis and inference. The initial auditing process and rules are explained 

below: 

1. Improvement cost-related responses were examined first. The survey questionnaire allowed 

respondents to enter up to six types of improvement costs and provide descriptions 

associated with these costs. Inspection of the descriptions revealed that many respondents 

were confused between improvement costs and repair maintenance costs, and thus 

described a repair or maintenance cost as an improvement cost. To correct these 

inconsistencies, the costs were moved to the correct category. However, if the respondents 

entered the exact same cost under each category, the entries were considered to be 

duplicates (i.e., double counting). In such cases, the correctly labeled cost was kept and the 

other entry deleted. 

2. Some respondents also reported costs under “improvement costs" which were asked 

somewhere else in the survey. In these cases, the costs were moved from “improvement 

costs" to the correct category. Again, if there was sufficient evidence that double counting 

occurred (based on the values and description), the extra entry was deleted. If the cost 

descriptions were not clear enough to make such judgments, no changes were made. 

3. A similar approach was taken to edit the “other costs" category. Respondents were allowed 

to report and describe any fishing expenses that were not specifically covered in the survey 
7 

under the label “Other Cost.” Examination of the data revealed that a large number of 

respondents entered trip costs as “other costs.” As trip-related costs collected by the 

observer program are not considered part of the annual costs, these were excluded from the 

analyses. To identify double counting, the approach used for “improvement costs" was 

followed. 

4. If a cost reported under “Other Costs" was not generally considered to be part of the annual 

costs (such as aquaculture costs), these were also removed from the data set. 

3.2 Data Summary 

This section provides an overview of the revised data. This revised final data set 

comprises 1,153 responses: 569 responses from 2006, 383 from 2007, and 201 from 2008. To 

enable comparisons across years, the 2007 and 2008 cost data were converted into 2006 dollars.3

Next, the data were weighted to correct for statistically significant nonresponse bias (as discussed 

in section 2.5). Because the response rates across gear vessel categories varied widely in all three 

years (i.e., in 2006, Dredge gear responses were under-represented, while in 2008 Trawl gear 

responses were over-represented), the data were weighted by gear category and summary 

statistics calculated based on the weighted data. The weighting procedure is explained in the 

appendix.4 

For analytical purposes, individual cost items were grouped in one of three major cost 

categories: (a) Repair, Maintenance, and Improvement Cost, together denoted as RMI (Lian 

2010); (b) Crew Cost; and (c) Other Annual Cost. Crew Cost is the sum of crew payments and 

crew benefits, while Other Annual Cost is the sum of the individual cost items not included in 

Crew Cost and RMI Cost. The Total Annual Cost represents the sum of all individual cost items. 

For each survey year, average (per vessel) costs incurred in each of the three major cost 

categories are listed in Table 5. Also listed is the average total revenue in each year. 

In all three years, Crew Cost accounts for half or more of the Total Annual Cost, followed 

by Other Annual Cost and RMI Cost. In Table 6, the averages of these different types of annual 

costs are depicted for each of the three vessel length categories. In all cost categories and years, 

average costs are highest for the Large vessels and lowest for the Small vessels. 

In Figures 1 to 4, graphical representations of the major costs by vessel length category 

are depicted with boxplots. Boxplots are a convenient way of illustrating the spread of the data 

and any observations that might be considered outliers. The spacing between different parts of 

the box helps indicate the degree of dispersion in the data. The bottom and top of the box are 25th 

and 75th percentiles, respectively, and the band in the middle is the 50th percentile. The bottom 

and top whiskers are within 1.5 times the inter-quartile range from the lower and upper quartile, 

respectively. The figures show that for each cost type, the costs are more dispersed for large 

vessels than for smaller vessels. As well, the dispersion of Crew Cost distribution is higher than 

Other Annual Cost and RMI Cost distributions. Possible outliers exist mostly for Medium and 

Small vessels, as indicated by those observations lying beyond the top and bottom whiskers. 

 

3

 Producer Price Index for unprocessed finfish is used to make this conversion. 

4

 The Unclassified geargroup was not used for calculating the weights or included in the summary statistics. 
8 

Individual cost components within each of the three general cost categories are listed in 

Tables 7 and 8.5 These tables summarize individual cost items by vessel length and survey year. 

Average expenses in all three years are highest for crew payments, improvements, repairmaintenance, and insurance. Mean expenses on crew benefits, interest payments, lease costs, and 

permit fees vary considerably within years. In 2008, vessels spent a large amount on interest 

compared to other years.6

 

3.3 Crew Share System 

The crew share system is an important element in the commercial fishing business. The 

2006 survey contained questions on total crew payments and crew benefits, but did not request 

vessel owners to provide details on their crew share systems. Questions on the crew share system 

(also called the lay system) were added in the 2007 and 2008 surveys. In 2007, 88% of 

respondents answered the questions on the lay system, and 91% responded in 2008 (Table 9). Of 

the 2007 respondents, 73% reported their lay system to be either “Broken Lay” (gross earning is 

split between boat and crew; then trip expenses are deducted from the crew’s share) or “Clear 

Lay” (trip expenses are deducted from the gross earning; then split between boat and crew). In 

2008, this percentage was 77%. Although most vessels owners used “broken lay" to pay their 

crew, there are a variety of methods for compensating hired crews. 

In 2007, 81% of the respondents to the question on Captain Status indicated that they 

were the owner-operator of their vessel (Table 9). In 2008, only 71% indicated they were owneroperators. On a percentage basis, the average boat share was higher when a vessel was owneroperated than when it was operated by a hired captain (Table 10). The average percentage boat 

share for an owner-operated vessel was 52% in 2007 and 60% in 2008. For vessels operated by a 

hired captain, the mean percentage crew share was higher than the boat share in both 2007 and 

2008 (i.e., ≈51% in each year). In Table10, the percentage values listed in the aggregate row also 

include those vessels for which no information was provided on captain status but other crew pay 

related answers were provided. 

Total crew costs of a vessel obviously depend on the number of hired crew. In 2007, 97% 

of respondents reported crew size information, and 94% of respondents in 2008 provided such 

information. The maximum reported crew size was 8 in 2007 and 7 in 2008. The mean crew size 

in both years was between 2 and 3. 

3.4 Data Weighting Procedure 

The data summaries presented in this document are weighted by gear category to correct 

for under-representation and over-representation of certain gear categories. The weight factor 

(whi) for respondent i in gear category h is the reciprocal of its inclusion probabilities (πi

), i.e.; 

whi=1/πhi. The inclusion probability of respondent i is calculated as nh

/Nh, where nh is the 

number of respondents and Nh

 is the size of the population in gear category h (Lohr 2010). 

 

5

 Note, since not all respondents entered all cost information, the number of observations are different for each cost 

type in the detail data summary presented in Tables 7 and 8. 

6

 In 2008, the survey added questions on principal payments and catch handling costs. Since these costs were not 

asked in 2006 and 2007 surveys, they were excluded in this analysis. 
9 

4 THE MODELING FRAMEWORK 

Although economic information is key to several analyses relevant to commercial 

fisheries management, it is extremely challenging to obtain this information. Surveys are a useful 

tool to meet this gap regarding the need for economic data. However, it is often not feasible to 

survey the entire population of active fishing vessel owners. In rare cases, even if the whole 

population is surveyed, one cannot guarantee a 100% response rate. Therefore, it is extremely 

important to build robust statistical models which can estimate and predict costs for unsurveyed 

vessels with reasonable accuracy. This section discusses the modeling framework for estimating 

and predicting costs for fishing vessels. Separate models were estimated for Other Annual Cost, 

RMI Cost and Crew Cost. These separate models allow for more detailed inferences about 

fisheries cost structure and profitability. The use of separate models also provides more flexibility 

in addressing specific research questions. 

Typically, an ordinary least square method (OLS) is used to estimate fisheries costs. 

However, using OLS with cost data often leads to negative cost predictions. Therefore 

researchers use OLS with the log of the dependent variable, and predict cost via exponentiating 

the predicted cost values in log scales. This retransformation, though frequently used, causes bias 

(Manning 1998; Jia and Rathi 2008; Manning and Mullahy 2001). The bias is worse if there is 

heteroscedasticity (nonconstant variance) in the log-transformed model. To correctly predict cost 

when using the log-transformed linear model estimation, proper adjustment should be applied 

with anti-log-transformation. However, this adjustment process for unbiasedness can be 

computationally expensive. An alternative method is to use a generalized linear model (GLM). 

A GLM can be viewed as a differentially weighted nonlinear least square estimation 

method. The advantages in using a GLM approach are that: (a) there is no retransformation bias; 

(b) no adjustment is needed for anti-log transformation; and (c) GLM does not assume constant 

variance. GLM is also a preferred method for analyzing skewed data as often encountered with 

cost and expenditure data. These data are typically characterized by nonnegative measurements 

of the outcomes, and a positively skewed empirical distribution of nonzero realizations (Manning 

and Mullahy 2001; Moran et al. 2007). GLMs have been widely applied in analyzing human 

health expense data where skewness of the distribution is common (Knerer et al. 2005; Wu et al. 

2007; Moran et al. 2007). 

Fisheries cost data share the same characteristics as health expense data. The skewness of 

the distributions in each of the major fishery vessel cost categories is evident in Figures 5 and 6. 

Because of this, all three cost models were estimated via GLM. The estimation method was 

carried out by specifying a gamma distribution function for the error term and a log-link function 

for cost using the GENMOD procedure in SAS.7

 For estimation purposes, the data from all three 

years were combined. As a result, the estimation sample consisted of multiple observations per 

vessel, as some vessels were surveyed in multiple years and also responded in multiple years. 

Therefore, it was possible for the responses from the same vessel to be correlated. These withinvessel correlations were taken into account via the repeated option within GENMOD. 

To identify the best predictors, several continuous and categorical variables were 

constructed based on the information available from the permit database, the vessel logbook 

database, and the dealer database. All dollar values were converted to 2006 dollars in this 

 

7

 A gamma distribution is often a preferred distribution choice for the error term in cost analysis with GLM (Moran 

et al. 2007; Knerer et al. 2005) 
10 

analysis. These variables used in the model are described in Tables 11 and 12. For this part of the 

analysis, principal gear categories were further grouped under three major gear types: Static, 

Mobile, and Other (Table 12). The Mobile gear group included Dredge and Trawl, whereas the 

Static gear group included Gillnet, Longline, Pot/Trap and Handgear. All remaining gear 

categories were included in the Other gear group. 

Although there were 1,153 vessels in the estimation set, not all characteristics information 

could be calculated for all these vessels. On average, the vessels included in the estimation 

sample were 47 ft long, 22 years old, weighed 43 gross tons, and were equipped with 394 horse 

power (Table 11). In addition, the average vessel had 3 crew members and had an average annual 

revenue of $212,000. Most of the vessels used Static gear (61%), were constructed of fiber glass 

(60%), and landed their catch primarily in the New England region (68%) (Table 12).8

The final model specification was chosen based on the quasi-likelihood values, Mean 

Absolute Error (MAE) and Root Mean Squared Error (RMSE). RMSE was calculated by (a) 

taking the square of the difference between the predicted and known costs; (b) then taking the 

mean of these squared differences; and (c) then taking the square root of the mean. A low RMSE 

indicates a better fit (Moran et al. 2007). The following sections elaborate on the three different 

cost models. 

4.1 Repair/Maintenance/Improvement (RMI) Cost 

The GLM coefficients of the RMI Cost model are listed in Table 13. The positive 

coefficient for vessel length implies that costs increase with length, but the negative coefficient 

for length-square indicates that RMI Cost eventually decreases as vessel length increases. The 

results indicate that newer vessels have higher repair, maintenance, and improvement costs as do 

vessels constructed of steel rather than other types of construction. Variable gtons has a negative 

coefficient implying lower RMI Cost for heavier vessels, but this effect is not significant. 

4.2 Other Annual Cost 

Table 14 reports the estimated GLM coefficients of the Other Annual Cost model. All the 

variables included in the final model are significant. The results indicate that vessels with a 

higher age have lower Other Annual Cost. The positive coefficient on length implies higher 

Other Annual Cost for larger vessels. The year coefficients for 2006 and 2007 are both negative 

and statistically significant implying that the Other Annual Costs are lower in these years than in 

2008. Positive fglass and gtons coefficients imply higher costs for vessels with fiber glass 

construction and for vessels with higher weights. 

4.3 Crew Cost 

Table 15 lists the GLM coefficients from the Crew Cost model. Crew Cost increases as 

vessel length increases. Vessels which primarily use Static gear to fish are likely to have a higher 

Crew Costs than do vessels that use mobile or other gear types. Crew Costs were also higher for 

vessels with higher total revenues. 

 

8

 Although vessel’s holding capacity could have been an important predictor, it has not been considered for 

estimation because measures on this variable were missing for about 17% vessels in the estimation set. 
11 

4.4 Predicted Cost Summary 

The estimated coefficients from the models were used to predict costs for all of the 

vessels that were not included in the estimation sample. Summary statistics for the continuous 

variables used in the prediction data set are listed in Table 16, with the frequency distribution of 

the categorical variables in this data set shown in Table 17. Vessels having a total revenue below 

$100 were excluded from the prediction dataset, as were a few other vessels which had invalid 

values for some variables. This resulted in 6,765 vessels in the cost prediction data set. However, 

because not all variables could be defined for all vessels, it was not possible to predict costs for 

each of the 6,765 vessels. On average, the vessels included in the prediction data set were 22 

years old, 51 feet long, weighed 53 gross tons, had 455 horse power, and earned $308,510 in total 

annual revenue (Table 16). More than half of the vessels in the prediction data set fished with 

Static gear (54%), were constructed of fiber glass materials (58%), and landed primarily in the 

New England region (72%) (Table 17). 

Table 18 presents the summary statistics of the predicted costs based on the prediction 

sample, along with the known costs from the estimation sample. To calculate the predicted Total 

Annual Cost, the Other Annual Cost and RMI Cost for each vessel were predicted separately 

based on the model coefficients in Table 14 and 13. The predicted Crew Costs were based on 

Crew Cost model estimates in Table 15. These cost components were then summed to derive the 

Total Annual Cost for each vessel. 

The greatest discrepancy between the known costs and predicted costs occurred for Crew 

Cost (Table18), and this affected the disparity in Total Annual Cost as Crew Cost enters in the 

calculation of the Total Annual Cost. The predicted mean Crew Cost was $148,176, nearly 

double the known mean value of $76,917. The higher standard deviations of the predicted costs 

indicate a larger dispersion in their distributions as well. Because Crew Cost increases with 

vessel revenue, the predicted Crew Costs are extremely large for vessels with higher revenue 

values compared to vessels with lower total revenues. On the other hand, known and predicted 

average values for RMI and Other Annual Costs are comparable. The predicted mean values of 

RMI and Other Annual Cost were $37,535 and $52,283 compared to the known values of 

$32,337 and $41,904, respectively. 

Table 19 compares known and predicted costs by cost category and vessel length 

category; the corresponding boxplots are depicted in Figures 7 and 10. Overall, absolute 

differences between predicted and known values in all cost categories become larger as vessel 

length increases. These differences among large size vessels are most pronounced for Crew Cost

and hence Total Annual Cost. As noted earlier, these differences are caused by Crew Cost, which 

increases with total revenues and total revenues are higher for the larger vessels resulting in very 

high predicted Crew Cost for the large vessels. The high standard deviations for Large vessels 

also indicate high disparity in predicted Crew Costs and Total Annual Costs for large vessels. The 

boxplots for these two cost categories (Figures 9 and 10) also show more potential outliers for 

Large vessels. On the other hand, for the Medium vessels, RMI and Other Annual Costs are 

slightly more dispersed than the Large and Small vessels as can be seen in the boxplots as well 

(Figures 7 and 8). 

The predicted cost summaries show that the model coefficients are successful in 

generating predictions for RMI and Other Annual Costs that are generally consistent with known 

values. However, model based predictions for Crew Costs yield average values which are highly 

divergent from the known mean values, and the distribution of the predicted values is highly 

dispersed as well. Crew Costs are expected to vary largely among vessel owners depending on 
12 

the crew payment system they use. Better understanding and data on these crew payment systems 

should potentially improve the Crew Cost models. However, in the absence of such additional 

information, the predicted Crew Costs should be carefully interpreted. 

5 CONCLUSIONS 

This document explains the cost data collection effort that the Social Sciences Branch of 

the Northeast Science Center had undertaken from years 2006 - 2008. A detailed description of 

the survey methodology, the data, and the data auditing process is presented here. Several 

summary statistics of the revised data are also presented. In order to make cost predictions for 

vessels for which this information is not available, several modeling approaches are discussed. 

Three different cost models estimated are, Repair/Maintenance/Improvement (RMI) Cost, Other 

Annual Cost, and Crew Cost. Summary of predicted costs based on model-coefficients in 

comparison to known costs are also given. 

This analysis will facilitate the application of cost data within SSB and among 

collaborating partners. The cost models will allow analysts to predict vessel level cost estimates 

rather than using average values. These models will enhance analyses that require cost estimates, 

such as break-even analyses, profitability profiles and economic performance indicator 

calculations. 

The NEFSC Social Sciences Branch is currently engaged in relaunching the vessel annual 

cost data collection effort. The new effort will build on the findings and lessons from the 2007-

2009 study to improve survey coverage and data quality and to enhance future analyses and 

evaluations of the economic status of commercial fisheries in the Northeast. 

ACKNOWLEDGEMENTS 

The author thanks numerous individuals who contributed to this data collection effort. 

The author especially thanks Andrew Kitts for his efforts in the survey design and 

implementation of this project. The assistance of the Northeast Regional Permit office is duly 

appreciated for administering the survey. Thanks are also due to thousands of commercial vessel 

owners who responded to the survey. 
13 

REFERENCES CITED 

Dillman DA, Smyth JD, Christian LM. 2009. Internet, mail and mixed-mode surveys: The 

tailored design method. 3rd ed. John Wiley & Sons, Inc. 512p. 

Jia S, Rathi S. 2008, On predicting log-transformed linear models with heteroscedasticity. SAS 

Publication. 

Knerer G, Byford S, Johnson T, Seivewright H, Tyrer P. 2005. The Nottingham study of neurotic 

disorder: Predictors of 12 year costs. Acta Psychiatrica Scandinavica 112(3):224–232. 

Lian CE. 2010. West Coast limited entry groundfish trawl cost earning survey: Protocols and 

results for 2004. U.S. Department of Commerce., NOAA Technical Memorandum, 

NMFS-NWFSC-107, 35p. 

Lohr SL. 2010. Sampling: design and analysis, 2nd Ed, Cengage Learning, Wadsworth 

Publishing, 608p. 

Manning WG. 1998. The logged dependent variable, heteroscedasticity, and the retransformation 

problem. Journal of Health Economics 17(3):283–295. 

Manning WG, Mullahy J. 2001. Estimating log models: To transform or not to transform? 

Journal of Health Economics 20(4):461–494. 

Moran JL, Solomon PJ, Peisach AR, Martin J. 2007. New models for old questions: Generalized 

linear models for cost prediction. Journal of Evaluation in Clinical Practice 13(3):381–

389. 

Wu E Q, Birnbaum HG, Zhang HF, Ivanova JI, Yang E, Mallet D. 2007, Health care costs of 

adults treated for attention-deficit/hyperactivity disorder who received alternative drug 

therapies. Journal of Managed Care Pharmacy 13(7):561-569. 
14 

Table 1. Survey response by vessel type, revenue, and landing states. 
15 

Table 2. Frequency of multiple survey receipts and responses over three years. 

Table 3. Order in which surveys received and returned over the three years. 
16 

Table 4. Nonresponse bias test. 
17 

Table 5. Summary statistics of weighted annual costs by major cost categories and of total revenue by survey yeaTable 6. Summary statistics of weighted annual costs by major cost categories and of total revenue by survey yeacategories. 
18 

Table 7. Summary statistics of weighted individual cost items by survey years and length categories. 
19 

Table 8. Summary statistics of weighted individual cost items by survey years and length categories, continued fro
20 

Table 9. Frequency and percentages of responses on lay system and captain status. 

Table 10. Average boat and crew share by captain status. 

 
21 

Table 11. Summary statistics of the continuous independent variables. 

Table 12. Frequency distribution of the independent categorical variables. 

Table 13. Generalized Linear Model Estimates of the RMI cost model. 

 
22 

Table 14. Generalized Linear Model Estimates of the other annual cost model 

Table 15. Generalized Linear Model Estimates of the crew cost model. 

Table 16. Summary statistics of the continuous variables in the prediction dataset. 

 
23 

Table 17. Frequency distribution of the categorical variables in the prediction dataset. 

Table 18. Summary statistics of the known and predicted costs. 

 
24 

Table 19. Summary statistics of the known and predicted costs by length categories. 
25 

Figure 1. Distributions of RMI costs by year and length categories. 

 
26 

Figure 2. Distributions of other annual costs by year and length categories. 
27 

Figure 3. Distributions of crew costs by year and length categories. 
28 

Figure 4. Distributions of total annual costs by year and length categories. 
29 

Figure 5. Histograms of RMI costs and other annual costs for three years. 

 
30 

Figure 6. Histograms of crew costs and total annual costs for three years. 

 
31 

Figure 7. Distributions of known vs predicted RMI costs by length categories. 
32 

Figure 8. Distributions of know vs. predicted other annual costs by length categories. 
33 

Figure 9. Distributions of known vs. predicted crew costs by length categories. 

 
34 

Figure 10. Distributions of known vs. predicted total annual costs by length categories. 
35 

APPENDIX 
NMFS Permit Number: 123456 Hull Number: 12345678 

OMB Control No. 0648-0369 

Expires: 07/31/2009 

Northeast Fishing Vessel Annual Cost Survey 

United State Department of Commerce 

National Oceanic & Atmospheric Administration 

National Marine Fisheries Service 

Permit Office 

55 Great Republic Drive 

Gloucester, MA 01930-2276 

Tel: (978) 281-9370 

Instructions: Please record the annual costs associated with only the vessel identified below. If you own 

more than one vessel, certain costs may need to be divided among vessels (for example, divide office expenses 

by the number of vessels owned). Record the combined annual cost for all fisheries you may have participated 

in this fiscal year. This survey does not have questions about trip costs such as fuel, ice, bait, and supplies. 

This type of information is collected by observers at sea. IMPORTANT: if you do not know the cost of a 

particular item (but an expense was incurred), please leave the question blank. If this expense does not apply

to your vessel, please check the “not applicable” box. 

Please return completed surveys to the Permit Office 

 

SECTION A - Vessel Information 

Coast Guard Documentation or State Registration Number: 12345678 (one survey per vessel)

Fiscal year that corresponds with the annual costs you will provide below (use the most recent year for which 

you have complete records). Please provide information for one year only. Format: (mm/dd/yyyy) 

׀__׀__׀__׀__׀ / ׀__׀__׀ / ׀__׀__׀ :date End ׀__׀__׀__׀__׀ / ׀__׀__׀ / ׀__׀__׀ :date Start

Vessel Ownership Type (check one): 

A. Sole proprietorship 

B. General partnership 

C. Limited partnership 

D. Corporation 

׀__׀__׀__׀__׀__׀ Other .E

If you checked “D” (Corporation), please check which 

type: 

C corporation 

S corporation 

Limited Liability Corporation 

Please list the number of owners: ׀__׀__׀

Was the vessel purchased from a previous owner or was it bought new? Previous owner New 

In what calendar year did you acquire the vessel? ׀__׀__׀__׀__׀

Please estimate the market value of your vessel (including all equipment, fishing gear, permits, and fishing 

history). 

׀__׀__׀__׀ , ׀__׀__׀__׀ , ׀__׀__׀ $ 

┌ ┐

└ ┘
NMFS Permit Number: 123456 Hull Number: 12345678 

SECTION B - 

Improvements, Quota Transfer/Lease, Repair/Maintenance, Crew Compensation 

What improvements (new or replacement gear, equipment, electronics, etc.) were made to the vessel this fiscal 

year? Please use the table to list the improvements. Also in this table, please include the cost of buying 

PERMANENT quota (surf clam/ocean quahog ITQ shares, for example). If you leased quota or days-at-sea, please 

provide those costs in the next question.

Description of improvement or quota transfer Cost of improvement or quota transfer 

׀__׀__׀__׀ , ׀__׀__׀__׀ , ׀__׀ $ ׀__׀__׀__׀__׀__׀__׀__׀__׀__׀__׀__׀__׀__׀

׀__׀__׀__׀ , ׀__׀__׀__׀ , ׀__׀ $ ׀__׀__׀__׀__׀__׀__׀__׀__׀__׀__׀__׀__׀__׀

׀__׀__׀__׀ , ׀__׀__׀__׀ , ׀__׀ $ ׀__׀__׀__׀__׀__׀__׀__׀__׀__׀__׀__׀__׀__׀

׀__׀__׀__׀ , ׀__׀__׀__׀ , ׀__׀ $ ׀__׀__׀__׀__׀__׀__׀__׀__׀__׀__׀__׀__׀__׀

׀__׀__׀__׀ , ׀__׀__׀__׀ , ׀__׀ $ ׀__׀__׀__׀__׀__׀__׀__׀__׀__׀__׀__׀__׀__׀

׀__׀__׀__׀ , ׀__׀__׀__׀ , ׀__׀ $ ׀__׀__׀__׀__׀__׀__׀__׀__׀__׀__׀__׀__׀__׀

What was the cost of LEASING quota or days-at-sea for use this fiscal year (include one-year leasing of surf 

clam/ocean quahog ITQ, days-at-sea in the multispecies fishery, or sector quota)? 

 $ ׀__׀__׀__׀ , ׀__׀__׀__׀ not applicable – did not lease quota or days-at-sea this fiscal year

Was the vessel hauled out this fiscal year? Yes No 

If yes, what was the cost of the haul-out (not including the cost of vessel improvements listed above)? 

 $ ׀__׀__׀__׀ , ׀__׀__׀__׀ not applicable – vessel was not hauled out this fiscal year

What is the typical number of years between haul-outs for this vessel? ׀__׀__׀

What was the cost of all other repair/maintenance for this fiscal year (not including haul-out and improvement 

costs)? $ ׀__׀__׀__׀ , ׀__׀__׀__׀ not applicable – no repair/maintenance costs this fiscal year 

Please record the total payments to crew for the fiscal year (include hired captain): 

 

year fiscal this payments crew no - applicable not ׀__׀__׀__׀ , ׀__׀__׀__׀ , ׀__׀__׀ $

Please record the annual cost of the benefits you provided for your crew (e.g., retirement benefits; your portion 

of health, life, or disability insurance premiums): 

 $ ׀__׀__׀__׀ , ׀__׀__׀__׀ not applicable – no benefits were provided for the crew this fiscal year 

┌ ┐

└ ┘
NMFS Permit Number: 123456 Hull Number: 12345678 

SECTION C – Fishing Business Related Costs 

Please record the total annual cost of these following items: 

Mooring/ 

dockage fee 

 insurance Vessel ׀__׀__׀__׀ , ׀__׀__׀__׀ $

׀__׀__׀__׀ , ׀__׀__׀__׀ $ (premium(

׀__׀__׀ :insured months of #

not applicable – no mooring/dockage fees not applicable – vessel not insured 

Use of business 

׀__׀__׀__׀ , ׀__׀__׀__׀ $ vehicle

Cell phone and 

׀__׀__׀__׀ , ׀__׀__׀__׀ $ costs VMS

not applicable – no vehicle expense not applicable – no cell phone or VMS costs 

Business travel 

costs (not 

including vehicle 

costs)

׀__׀__׀__׀ , ׀__׀__׀__׀ $׀__׀__׀__׀ , ׀__׀__׀__׀ $ taxes Business

not applicable – no travel costs not applicable – no business related taxes 

Professional 

fees (settlement 

fees, accounting, 

legal, etc) 

׀__׀__׀__׀ , ׀__׀__׀__׀ $

not applicable – no prof. fees 

Catch handling 

costs (auction fees, 

lumping, grading, 

transportation)

׀__׀__׀__׀ , ׀__׀__׀__׀ $

not applicable – no handling fees 

Association fees 

(cooperative, 

fishing 

organization, etc)

׀__׀__׀__׀ , ׀__׀__׀__׀ $

not applicable – no assoc. fees 

Non-crew labor 

services

(Night watchman, 

etc. Do not include 

repair/maint costs)

׀__׀__׀__׀ , ׀__׀__׀__׀ $

not applicable – no service costs 

׀__׀__׀__׀ , ׀__׀__׀__׀ $ expenses Office

not applicable – no office expenses 

Permit and/or 

license fees 

׀__׀__׀__׀ , ׀__׀__׀__׀ $

not applicable – no perm./lic. fees 

Principal paid

on business 

loans 

׀__׀__׀__׀ , ׀__׀__׀__׀ $

not applicable – no loans 

Interest paid on 

׀__׀__׀__׀ , ׀__׀__׀__׀ $ loans business

not applicable – no loans

SECTION D - 

Other Annual Costs not Listed in Sections B or C 

(please do not record trip costs such as fuel, oil, ice, supplies, etc. - observers collect this information at sea) 

Cost Description of other annual costs 

׀__׀__׀__׀__׀__׀__׀__׀__׀__׀__׀__׀__׀__׀__׀__׀__׀__׀__׀ ׀__׀__׀__׀ , ׀__׀__׀__׀ $

׀__׀__׀__׀__׀__׀__׀__׀__׀__׀__׀__׀__׀__׀__׀__׀__׀__׀__׀ ׀__׀__׀__׀ , ׀__׀__׀__׀ $

׀__׀__׀__׀__׀__׀__׀__׀__׀__׀__׀__׀__׀__׀__׀__׀__׀__׀__׀ ׀__׀__׀__׀ , ׀__׀__׀__׀ $

┌ ┐

└ ┘
NMFS Permit Number: 123456 Hull Number: 12345678 

SECTION E - Typical Lay System 

What was your primary fishery (based on revenue) this fiscal year? Please list only one (e.g., groundfish, scallops, etc.) 

׀__׀__׀__׀__׀__׀__׀__׀__׀__׀__׀__׀__׀__׀__׀__׀__׀__׀__׀__׀__׀__׀__׀__׀__׀__׀

For the primary fishery you listed above, which best describes how the crew (including the captain) is paid: 

 Clear lay (gross stock is split between boat and crew; then trip expenses are deducted from the crew’s share)

 Broken lay (trip expenses are deducted from the gross stock; then split between boat and crew)

 Per-trip or hourly wage Other -- please describe in the comments section below 

For clear or broken lay systems, what is the percentage share to the boat and crew? (should add to 100%)

 ׀__׀__׀ % Boat (owner) share ׀__׀__׀ % Crew share (include hired captain’s share)

For clear or broken lay systems, which trip expenses are normally deducted? (check all that apply) 

 Fuel Water Oil/lubrication Lost/damaged gear Fishing quota or days-at-sea 

 Food Bait Unloading fees Settlement fees Other: ׀__׀__׀__׀__׀__׀

 Ice Electronics Cell phone General fishing supplies (hooks, bags, totes, gloves, etc.) 

 

For the primary fishery you listed above, do you hire a captain? Owner operated Hired captain 

How many years of experience does the captain have in the primary fishery you listed above? ׀__׀__׀ years 

What is the size of the crew in the primary fishery you listed above? ׀__׀__׀ crew members 

(include the captain)

Please use this space to provide additional information or comments 

׀__׀__׀__׀__׀__׀__׀__׀__׀__׀__׀__׀__׀__׀__׀__׀__׀__׀__׀__׀__׀__׀__׀__׀__׀__׀__׀

׀__׀__׀__׀__׀__׀__׀__׀__׀__׀__׀__׀__׀__׀__׀__׀__׀__׀__׀__׀__׀__׀__׀__׀__׀__׀__׀

׀__׀__׀__׀__׀__׀__׀__׀__׀__׀__׀__׀__׀__׀__׀__׀__׀__׀__׀__׀__׀__׀__׀__׀__׀__׀__׀

׀__׀__׀__׀__׀__׀__׀__׀__׀__׀__׀__׀__׀__׀__׀__׀__׀__׀__׀__׀__׀__׀__׀__׀__׀__׀__׀

׀__׀__׀__׀__׀__׀__׀__׀__׀__׀__׀__׀__׀__׀__׀__׀__׀__׀__׀__׀__׀__׀__׀__׀__׀__׀__׀

׀__׀__׀__׀__׀__׀__׀__׀__׀__׀__׀__׀__׀__׀__׀__׀__׀__׀__׀__׀__׀__׀__׀__׀__׀__׀__׀

┌ ┐

└ ┘
Publishing in NOAA Technical Memorandum NMFS-NE

Manuscript Qualification

This series represents a secondary level of scientific

publishing in the National Marine Fisheries Service (NMFS).

For all issues, the series employs thorough internal scientific

review, but not necessarily external scientific review. For most

issues, the series employs rigorous technical and copy editing.

Manuscripts that may warrant a primary level of scientific

publishing should be initially submitted to one of NMFS's

primary series (i.e., Fishery Bulletin, NOAA Professional Paper

NMFS, or Marine Fisheries Review).

Identical, or fundamentally identical, manuscripts should

not be concurrently submitted to this and any other publication

series. Manuscripts which have been rejected by any primary

series strictly because of geographic or temporal limitations

may be submitted to this series.

Manuscripts by Northeast Fisheries Science Center

(NEFSC) authors will be published in this series upon approval

by the NEFSC's Deputy Science & Research Director. Manuscripts by non-NEFSC authors may be published in this series

if: 1) the manuscript serves the NEFSC's mission; 2) the

manuscript meets the Deputy Science & Research Director's

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The ethics of scientific research and scientific publishing

are a serious matter. All manuscripts submitted to this series

are expected to adhere -- at a minimum -- to the ethical guidelines

contained in Chapter 2 ("Publication Policies and Practices") of

the Scientific Style and Format: the CSE Manual for Authors,

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manuscripts. NEFSC publication and report series rely more on the

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For in-text citations, use the name-date system. A special

effort should be made to ensure that the list of cited works contains

all necessary bibliographic information. For abbreviating serial

titles in such lists, use the guidance of the International Standards

Organization; such guidance is easily accessed through the various

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For spelling of scientific and common names of fishes, mollusks, and decapod crustaceans from the United States and Canada,

use Special Publications No. 29 (fishes), 26 (mollusks), and 17

(decapod crustaceans) of the American Fisheries Society (Bethesda

MD). For spelling of scientific and common names of marine

mammals, use Special Publication No. 4 of the Society for Marine

Mammalogy (Lawrence KS). For spelling in general, use the most

recent edition of Webster’s Third New International Dictionary of

the English Language Unabridged (Springfield MA: G. & C.

Merriam).

Typing text, tables, and figure captions: Text, tables, and

figure captions should be converted to Word. In general, keep text

simple (e.g., do not switch fonts and type sizes, do not use hard

returns within paragraphs, do not indent except to begin paragraphs). Also, do not use an automatic footnoting function; all notes

should be indicated in the text by simple numerical superscripts, and

listed together in an "Endnotes" section prior to the "References

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Tables should be prepared with a table formatting function.

Each figure should be supplied in digital format (preferably GIF or

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Area," "Methods & Materials," "Results," "Discussion" and/

or "Conclusions," "Acknowledgments," and "References Cited."

Style: All NEFSC publication and report series are

obligated to conform to the style contained in the most recent

Northeast Fisheries Science Center

Operations, Management & Information Division

Research Communications Branch

Editorial Office

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Send all materials and address all correspondence to: Jarita A.

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