Northeast Fisheries Science Center Reference Document 01-15
Bycatch
estimates of coastal bottlenose dolphin (Tursiops truncatus)
in U.S. mid-Atlantic gillnet fisheries for 1996 to 2000
by Debra
Palka and Marjorie Rossman
National Marine Fisheries Service, 166 Water Street, Woods Hole
MA 02543
Print
publication date November 2001;
web version posted November 23,
2001
Citation: Palka, D.L.; Rossman, M.C. 2001. Bycatch estimates of coastal bottlenose dolphin (Tursiops truncatus) in U.S.
Mid-Atlantic gillnet fisheries for 1996 to 2000. Northeast Fish. Sci. Cent. Ref. Doc. 01-15; 77 p.
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Executive
Summary
To assess the status of coastal bottlenose dolphins (Tursiops
truncatus), estimates of population size and human-caused
mortalities are required. The purpose of this manuscript is to
estimate human-caused mortalities of coastal bottlenose dolphins
due to bycatch in mid-Atlantic gillnet fisheries operating during
1996 to 2000 in oceanic waters (outside of bays) adjacent to New
Jersey to North Carolina.
During summer (May to October), three coastal bottlenose
dolphin management units reside in these waters: the Northern migratory,
Northern NC, and Southern NC management units, and during winter (November
to April) there is one management unit: the mixed stock management
unit. This management unit was divided into two sub-units, one off
North Carolina and one off Virginia. This is because, during winter
most coastal bottlenose dolphins are off North Carolina, however, a
small percentage are off Virginia; thus, bycatch rates off these two
states differ. The management units in waters north of Cape Hatteras
extend12 km offshore, and those south of Cape Hatteras extend 27 km
offshore.
Total bycatch was defined as the product of the bycatch rate, takes
per unit effort, estimated from a sample of the fishery, and the total
effort from the fishery. Due to practical reasons, bycatch rates were
defined as the ratio of observed dead coastal bottlenose dolphins to
observed metric tons of fish landed. Consequently, total effort was
total commercial gillnet landings.
Bycatch rates were estimated from a sample of fishing trips observed
by the National Marine Fisheries Service (NMFS) Northeast Fisheries
Science Center (NEFSC) fisheries sampling program. These rates were
estimated using a generalized linear model (GLM) that quantified the
relationship between the number of observed takes and several variables:
observed landings, seasonal management unit, body of water (state or
federal waters), and mesh size category (small (5 inches), medium (> 5
to < 7 inches) or large (7 inches)). These variables were chosen
out of 14 variables using a stepwise selection method. The GLM model
is essentially a way to, in one step, estimate separate bycatch rates
within each seasonal management unit for sub-fisheries represented
by their mesh size and where they fish. Two variables not included
in the bycatch model for practical reasons were a finer definition
of distance from shore, and the presence or absence of an anchor. These
two variables did not significantly improve the model's fit.
Total commercial metric tons of fish landed by the mid-Atlantic gillnet
fishing fleet were calculated using landings recorded in the NMFS Northeast
Region (NER) dealer reported commercial landings database and the North
Carolina Division of Marine Fisheries (NCDMF) trip ticket program.
These two databases contain the amount of fish landed by species. To
generate bycatch estimates, landings were needed for each level of
the factors selected by the GLM. Because data from NER and NCDMF lacked
information on haul locations, landings of specific fish species (to
account for the GLM factor mesh size category) from either the NER
vessel trip reports (VTR) or NEFSC fisheries sampling databases were
used to prorate total landings from the NER and NCDMF databases to
water bodies within seasonal management units (to account for the GLM
factors seasonal management and water body). Ideally, VTR data would
be the best to use for the prorations, because these data are suppose
to be a census of the fishing fleet. However, the fisheries sampling
observer data from North Carolina were more representative than the
North Carolina VTR data with respect to species composition and relative
quantities of fish landed. Thus, fishery sampling observer data were
used to prorate landings from North Carolina, and VTR data were used
for the other states.
A total of 12 coastal bottlenose dolphins were observed taken within
the mid-Atlantic gillnet fisheries during 1996 to 2000. One was released
alive during the summer of 1999 on a trip in the Southern NC management
unit. This uninjured animal was not considered in this analysis. Seven
animals were taken in winter, and ten were taken from state waters.
Bycatch rates in state waters were 4 - 11 times higher than rates
in federal waters. Rates in large mesh fisheries were 10 - 30 times
higher than small mesh fisheries, and 4 - 9 times higher than medium
mesh fisheries. Highest rates were in the summer Northern NC (0.0801
takes per mt landed; CV = 61.2%) and summer Northern migratory (0.0211;
CV = 48.1%) management units. The lowest rate was in the Southern NC
management unit (0). CV's of the bycatch rates when stratified to seasonal
management unit, body of water, and mesh size category were large,
mostly over 60%. However, when averaged to seasonal management unit,
CV's improved to 48 - 84%.
Observer coverage was fairly high (generally 3 - 6%) in federal waters.
Because of this level of coverage and the large number of observed
hauls, power analyses indicate more takes should have been observed
if the true bycatch was large. In conclusion, bycatch in federal waters
was probably low. Observer coverage in state waters, where most of
the takes were, was generally low, often below 1%, generally below
2.5%. Even with this low observer coverage, because there were so many
trips observed during winter in North Carolina, the area with the most
takes, there was a very good chance of observing a take when there
were truly more than 150 takes per season. A disadvantage of low observer
coverage is sub-fisheries may be missed. This is probably not the case
in North Carolina in winter because the number of observed fish species
and the level of landings in the observer data resemble the general
patterns from the entire fishery. However, low observer coverage was
a problem in small fisheries, such as those in the summer Northern
and Southern NC management units. Thus, it is likely bycatch estimates
for the summer Northern and Southern NC management units are biased
low.
Total estimated bycatch was highest in the winter mixed stock management
unit (146 in 2000 to 211 in 1997 per year), with most off North Carolina
and few from Virginia. Estimated takes in Virginia during winter increased
annually, from 11 in 1996 to 53 in 2000, though the differences were
not significant. The 2000 bycatch estimate in North Carolina in the
winter (93) was about half that of the highest year in that time-area
(187), though the difference was not significant.
Average summer takes (53) were about one-third average winter takes
(180). Over all areas, the annual average was 233 (CV = 16%; 95% CI
= 171-318) coastal bottlenose dolphins. Total annual bycatch estimates
for 2000 were the lowest (202), though it was not significantly different
than other years. Average annual bycatch estimates for 1996 to 2000
were 30 (CV = 21.9%) for the summer Northern migratory, 23 (CV = 28.7%)
for the summer Northern NC, 0 for the summer Southern NC, and180 (CV
= 20.9%) for the winter mixed stock management units.
INTRODUCTION
In accordance to the 1994 amendments to the Marine Mammal Protection
Act (MMPA), the status of all marine mammal stocks within the US EEZ
are assessed by comparing the population size, and resulting potential
biological removal (PBR) level, to the annual human-caused mortalities
levels. The purpose of this manuscript is to estimate for seasonal
coastal bottlenose dolphin management units, the human-caused mortalities
during 1996 to 2000 that were due to bycatch in mid-Atlantic gillnet
fisheries. The mid-Atlantic gillnet fisheries being considered take
place in oceanic waters adjacent to New Jersey to North Carolina.
The combined results of genetic, stable isotope, telemetric, and photo
identification analyses were used to define the spatial ranges of mid-Atlantic
bottlenose dolphin (Tursiops truncatus) seasonal management
units (SEFSC, in review). These results revealed that the coastal form
of bottlenose dolphins inhabit waters out to 12 kilometers from shore
when off the coasts of New Jersey to Cape Hatteras, NC (35 13.8' N),
and out to 27 kilometers when in waters from Cape Hatteras, NC to Jacksonville,
FL. These results also revealed the coastal bottlenose dolphins move
between two seasonal habitats, where summer was defined as May to October
and winter as November to April. Thus, the seasonal management units
of the coastal bottlenose dolphin that will be considered here include
(Figure 1):
During summer (May to October):
1) Northern Migratory Management Unit,
located between New Jersey (40 30' N) and the North Carolina/Virginia border
(36 31.8' N);
2) Northern NC Management Unit,
located between the North Carolina/Virginia border (36 31.8' N) and Cape
Lookout, NC (34 37.8' N);
3) Southern NC Management Unit,
located between Cape Lookout, NC (34 37.8' N) and Murrell's Inlet, SC (33
31.2' N);
During winter (November to April):
4) Mixed Stock Management Unit,
located mainly north of Murrell's Inlet, SC (33 31.2' N) to the North Carolina/Virginia
border (36 31.8' N), however, few are within Virginia waters, thus there
are two sub-units:
Total bycatch of dolphins from these seasonal management units was
defined as the product of the: 1) bycatch rate, takes per unit effort,
estimated from a sample of the fishery, and 2) total effort from the
entire fishery. Due to practical reasons, the bycatch rate was defined
as the ratio of the observed number of dead coastal bottlenose dolphin
takes to observed metric tons of fish landed. Consequently, total effort
was defined as total commercial gillnet landings (in metric tons).
Bycatch rates were estimated from a sample of fishing trips observed
by the National Marine Fisheries Service (NMFS) Northeast Fisheries
Science Center (NEFSC) fisheries sampling program. This program, initiated
during mid-1994 in mid-Atlantic waters, was designed to monitor the
incidental take of marine mammals in selected fisheries. The total
commercial metric tons of fish landed by the mid-Atlantic gillnet fishing
fleet were calculated using landings recorded in the NMFS Northeast
Region (NER) dealer reported commercial landings database and the North
Carolina Division of Marine Fisheries (NCDMF) trip ticket program.
These two databases contain the amount of fish landed by species for
different types of fishing gear.
In the past, the NEFSC used the ratio method (Cochran 1977) to estimate
total bycatch of marine mammals in fisheries (e.g., Rossman and Merrick
1999). However, other analytical methods were necessary in this case
because the 1996 to 2000 data set of observed coastal bottlenose dolphin
bycatch was binomially distributed (contains either zero or one bottlenose
dolphin per fishing haul) with a very large proportion of zeros (13
bottlenose dolphin takes in over 7000 observed hauls, Waring et
al. 2000). Two methods that have been used to estimate bycatch
from data sets with such properties include the delta method (for example
for marine mammals and turtles bycaugth in the pelagic long line fishery;
Johnson et al. 1999), and the generalized linear model (GLM)
method (for example, for fish bycatch in US shrimp trawls; Ortiz et
al. 2000).
After preliminary analysis and review of these three statistical methods,
the GLM method was determined to be the most appropriate analytical
method for the coastal bottlenose dolphin bycatch data. This is because
(Table 1):
- Half of the assumptions for the ratio method were violated,
- All the assumptions for the GLM method were valid; and
- Though the two primary assumptions from the delta-distribution
method were not violated, conditions that indicated the presence
of a positive bias and inefficiency could be demonstrated.
Thus, the GLM method will yield the most accurate and least biased
bycatch rates and mortality estimates.
Section 1 of this manuscript describes the fishery sampling observer
data and statistical methods used to estimate bycatch rates. The bycatch
rates were estimated from a GLM that used gear characteristics and
fishing practices to predict the number of coastal bottlenose dolphin
takes that were observed in the fishery sampling observer program.
Section 2 describes the landing databases and analytical methods used
to estimate total commercial gillnet landings in oceanic waters (excluding
bays and sounds) for the factors selected by the bycatch rate model.
Section 3 reports observer coverage of each dolphin seasonal management
unit. And finally, section 4 reports annual estimates of bycatch from
the mid-Atlantic gillnet fisheries for each coastal bottlenose dolphin
seasonal management unit for 1996 to 2000.
METHODS
1.
Bycatch Rates
1.A.
Data
Bottlenose dolphins were observed taken in mid-Atlantic gillnet fisheries
in both the coastal bottlenose dolphin habitat ( 0 to 12 (or 27) km
from shore - as defined above) and offshore bottlenose dolphin habitat
(outside of the coastal bottlenose dolphin habitat). This paper focuses
only on coastal bottlenose dolphin takes.
To estimate coastal bottlenose dolphin bycatch rates, the NMFS/NEFSC
fisheries sampling observer data collected during 1996-2000 were used.
The fishery observer program collects data on characteristics of the
trip, haul, gear, economic factors, fish species caught, and incidental
takes. Trip characteristics include the vessel name and number, date
sailed, date landed, home port, port fish landed, steam time, and number
of crew. Economic factors related to the trip include the tons of ice
used, fuel used, damage costs, and price of water, food, oil, and bait.
Characteristics of a haul include weather conditions, wind speed and
direction, wave height, depth range, time string soaked for, direction
string set, latitude and longitude of the set and haul locations, date
of the set and haul, time set and time hauled, fish species captain
targeted, presence and quantity of fish caught to be landed and to
be discarded, presence of incidental takes of marine mammals, seals,
turtles, or birds, and presence and number of active and passive deterrent
devices. Gear characteristics include number of nets, length and height
of a net, hanging ratio, vertical mesh count, mesh size range, twine
size, number of strands in the net, net material, net color, use of
a float line, length of float line, material of float line, number
of floats, length of lead line, number and width of spaces between
nets, presence of and length of tie downs, and weight of anchor, if
used. When an incidental take occurs, the following are also recorded:
species identification, number of each species, net animal caught in,
condition of the body, body length, sex, tag number (if body returned
to sea), types of samples taken (body parts or whole animal), and frame
number of roll of film if pictures were taken.
The GLM of bycatch rates used data from those gillnet hauls observed
within the oceanic portion of the coastal bottlenose dolphin habitat
(excluded bays and sounds), and within the time period November 1995
to November 2000. This time period covers winter 1996 (November 1995
through April 1996) to summer 2000 (May through October 2000). The
data include all types of gillnets; those that were anchored and not
anchored, and those on the bottom, in the water column, and at the
surface. Because it was not possible to distinguish between these different
types, all types were included in this analysis. Only dead bottlenose
dolphin were considered to in the bycatch estimate.
To investigate bycatch rates in the winter mixed stock management
unit, only trips that landed in North Carolina and Virginia were used.
This was done because field observations indicate that during winter,
most coastal bottlenose dolphins are found in waters off North Carolina,
with very few dolphins off Virginia, and even fewer, if any, north
of Virginia (SEFSC in review). Thus, bycatch from the winter mixed
stock management unit was the sum of the bycatch from the winter VA
mixed stock sub-management unit and from the winter NC mixed stock
sub-management unit. To investigate bycatch rates in the Northern migratory
management unit during summer, trips that landed in New Jersey, Delaware,
Maryland, and Virginia were used. For bycatch rates in the Southern
NC management unit, only trips from North Carolina were used. Although
this management unit extends into South Carolina, only very limited
gillnetting is allowed in South Carolina, so it was assumed there was
no bycatch in the waters of South Carolina.
1.B.
Development of a GLM Bycatch Model
The aim of the GLM was to use the fishery observer program data to
describe the relationship between bycatch rates of coastal bottlenose
dolphins (number of bottlenose dolphin takes per metric tons of landings
of fish) and important gear characteristics and fishing practices.
A way to interpret a GLM like this is, in one step, estimate separate
bycatch rates within each seasonal management unit for sub-fisheries
that are represented by some combination of gear characteristics and
fishing practices.
Examples of gear characteristics include twine size, string length,
and soak duration. Examples of fishing practices include season, year,
location, and management unit. All variables that were investigated
were defined in Table 2. Assuming the observed
trips were representative of the entire fishery, the GLM was then used
to predict bycatch rates that explicitly account for differences in
gear characteristics and fishing practices. This assumption is investigated
in the proration section below.
The steps to develop the GLM between the bycatch rate of coastal bottlenose
dolphins and potential explanatory variables (gear characteristics
and fishing practices) were: 1) determine the general formula for the
model, 2) determine most appropriate statistical distribution to model
the relationship, 3) select a set of variables that best describes
the relationship, 4) investigate if other variables should be included,
and then 5) check the fit of the model.
Step 1: determine best general formula for the model
The bycatch rate of bottlenose dolphins is a ratio of two quantities:
count of observed bottlenose dolphin takes, and metric tons of observed
fish landings. A GLM of the bycatch rate is:
 |
(1) |
which can be re-written as:
 |
(2) |
where the b's are parameter coefficients and x's
are the gear characteristic and fishing practice variables. In words,
Equation 2 means the number of takes is modeled by a set of explanatory
variables (b0 + b1 x1 + b2 x2 +
...) and an offset variable (log(landings)). Equation 2 becomes
the general formula for the GLM used in this analysis.
Step 2: determine most appropriate distribution to model the relationship
The question is: What type of distribution best models the relationship
between the response variable (number of bottlenose dolphin takes)
and potential explanatory variables (management unit, landings kept,
gear characteristics and fishing practices)? In this case, the number
of dolphin takes were counts that ranged from zero to one. The Poisson
distribution is commonly used with count data. The Binomial distribution
is used with binary data, that is, data represented by the presence
or absence of a factor (zero or one take per haul). In the case of
bottlenose dolphin takes both the Poisson and Binomial distributions
are applicable because the dolphin takes are counts that range from
zero to one. There is no a prior reason that the count of
dolphins caught in a gillnet are limited to just zero and one (thus,
leading to a Poisson distribution), however, in our sample, the counts
have been limited to zero and one (thus, leading to a Binomial distribution).
When the response variable is a binary response, and the other variables
in the model are explanatory variables, using the Binomial distribution
is equivalent to the Poisson distribution. McCullagh and Nelder (1991)
recommends that when there is a single response variable that is binomially
distributed, it is simpler and more natural to use the Binomial distribution,
though the Poisson distribution is also appropriate.
Step 3: select a set of variables that best describes the relationship
The backward-forward stepwise selection method was used to determine
the best fitting model. This method starts with a complex model and
then, in steps, successively simplifies the model so that the fit improves
at each step. In this analysis, the complex model contained variables
that were essential for the purpose of estimating the total bycatch,
as well as other potential explanatory variables. The two essential
variables are landings kept and management unit. The latter is essential
because the model must estimate the total bycatch rate for each management
unit. So, for example, even if management unit was not one of the best
fitting variables in the bycatch model, it was necessary to include
it in the model. Potential explanatory variables included in the model
have two requirements: 1) be related to the bycatch rate of coastal
bottlenose dolphins, and 2) be structured such that both observed and
total landings kept can be calculated for each level of the variable.
It was necessary to pool levels of some variables which have many
levels, were continuous, or had to be redefined to calculate total
landings for each level. This was necessary because commercial landings
were known for specific fish species, but not known for other variables,
such as mesh size, twine size, soak duration, etc. So, to determine
the best way to pool like-levels and account for the fish species landed,
the classification TREE method was used (Venables and Ripley, 1999).
For example, to determine the best definition of categories of mesh
sizes, the classification TREE method was used to divide mesh sizes
into groups of mesh sizes that best predicted the mesh size used for
each target species. The classification TREE model was fit using binary
recursive partitioning whereby data were successively split along coordinate
axes of the predictor variables so that at any node, the split maximally
distinguished the response variable in the left and right branches.
Splitting continued until nodes were pure or data were too sparse.
The new definition of levels of the mesh size were then put into the
GLM to determine how the new classification was related to the bycatch
rate.
The final model selected was the combination of variables that best
fit the data, i.e., had the smallest Akaike Information Criterion (AIC),
which is defined as:
 |
(3) |
The AIC is similar to an adjusted R2 in regular
regression in that it is a measure of the level of parsimony. A parsimonious
model is a model that fits the data well and includes as few parameters
as possible.
Step 4: investigate if other variables should be included
Because not all variables were included in the complex model used
in the stepwise selection method, it was of interest to investigate
if including other variables could improve the fit of the model. This
was done by adding additional variables into the model that resulted
from the stepwise selection method and evaluating the AIC statistic.
If the AIC decreased, then the additional variable improved
the fit of the model. A Chi-square test between the models with and
without the new variable was used to determine if the improvement was
significant. The additional variables also had to be evaluated to determine
the practicality in calculating total landings for the fleet for each
level.
Step 5: check the fit of the model
A model provides an accurate description and inference for a data
set only if it fits that data set well. Tests of each parameter, summary
goodness-of-fit statistics, and investigation of residuals were used
to determine the adequacy of the model fit, where a residual is the
difference between the observed value and the value predicted by the
model. For a model that fits perfectly, each residual equals zero;
that is, the observed value equals the predicted value.
To test the significance of each variable in the model, an analysis
of deviance for the sequential addition of each variable was conducted.
This test indicates the significance of a variable given the variables
already in the model. If the p-value for the Chi-square test is less
than 0.05 then that variable is important and adds new information,
even after adjusting for the previous variables.
The purpose of goodness-of-fit tests are to test whether the chosen
model fits the data. This was done by performing a Fisher Exact test
and a linear regression on the predicted and actual number of takes
within each management unit. Within each management unit, the sum of
predicted takes, and sum of observed takes were computed. Using these
sums, the following null hypothesis (H0) was tested:
H0: number of predicted takes = number
of actual takes.
The Fisher Exact test is similar to the Pearson's Chi-square test,
but the Fisher Exact test does not require large sample sizes within
a cell, as the Chi-square test does. Thus, the Fisher Exact test is
more appropriate in this case. If the p-value for this test is less
than 0.05 then the null hypothesis is rejected, implying that the number
of predicted takes does not equal the actual takes and so the model
does not fit well.
A linear regression between the number of predicted takes and the
number of observed takes visually displays how well the model fits,
and the R2 value of the regression indicates how well the
model predicted the actual data. R2 ranges from 0 to 1,
where 0 indicates no association between the actual and predicted values
(the model does not fit) and 1 indicates perfect association (the model
fits perfectly).
Goodness-of-fit statistics broadly summarize how well the model fits
the data. To obtain further insight into details of the fit of the
model, the structure of the residuals (difference between observed
and predicted values) were investigated. Examining a plot of residuals
against predicted values can reveal unexplained structure in the residuals
of, say, only a part of the data. In a model that fits well there is
no structure in the residuals; the pattern appears as random noise.
Two sets of diagnostic plots were investigated. The first set of diagnostic
plots come from a diagnostic model that was between the predicted number
of takes from the GLM model and the observed number of takes. The diagnostic
plots included: 1) a plot of the residuals from the diagnostic model
versus the predicted values from the GLM model, and 2) a plot of the
observed number of takes versus the predicted number of takes from
the diagnostic model. The second set of diagnostic plots were plots
of the relative contribution of each level of each explanatory variable.
In a model that fits well, residuals are spread evenly around the mean
of each level, and standard errors (SE) are tight around the mean.
1.C.
Bycatch Rate Estimates
Parameters estimates from the best fitting GLM were used to predict
the average bycatch rate for each management unit, accounting for important
gear characteristics and fishing practices selected by the model.
The coefficient of variation (CV) of a bycatch rate was estimated
using bootstrap re-sampling techniques. The re-sampling unit was a
haul and was sampled with replacement. One thousand bootstrap samples
were drawn. The CV of the bycatch rate was defined as the standard
error (SE) of the bycatch rates from the 1000 bootstrap samples divided
by the bycatch rate estimated from the original data.
2.
Commercial Gillnet Landings
2.A.
Data
Two data sources used to compile total gillnet landings during the
time period winter 1996 (November 1995 through April 1996) to summer
2000 (May to October 2000) were: 1) the NMFS NER dealer reported commercial
landings database; and 2) the NCDMF trip ticket program database.
The NER dealer reported commercial landings database contains data
on commercial landings of marine species harvested from both state
(0 to 4.8km (0 to 3 nautical miles (nmi)) from shore) and federal waters
(4.8 to 320km (3 to 200 nmi) from shore). Data from the states of Maine
through North Carolina are contained in this database. Data on species
harvested exclusively under state jurisdiction are reported to NMFS
by the state of Virginia. In contrast, data on species harvested under
both state and federal jurisdictions combined are reported by the states
of Maryland, Delaware and New Jersey. Data from all states are collected
from federally permitted seafood dealers and include species, market
category, pounds landed, gear type, water body (when available), date,
port, and county where species were landed. However, data on individual
trip location or gear characteristics are not recorded in this database
(Wigley et al. 1998).
The NCDMF data from the trip ticket program include commercial landings
from North Carolina only. These data are collected by federally permitted
seafood dealers and include species harvested from both state and federal
waters. Similar to the NER dealer database, the NCDMF include data
on species, pounds landed, gear type, water body, date, and county
where species were landed, but no data on individual trip location
or gear characteristics.
When we requested the NCDMF data from the state of North Carolina,
the data from 2000 were considered by the state of North Carolina as
preliminary. These data will have to be requested again and bycatch
estimates will have to be re-calculated. Therefore, it is possible
landings from North Carolina during 2000 will change, and so may the
2000 bycatch estimates for management units off North Carolina.
2.B. Prorating
Commercial Gillnet Landings
To generate estimates of total bycatch, landings were needed for each
level of the factors selected by the GLM (seasonal management unit,
mesh size category, and water body). Because data in the NER and NCDMF
databases lacked information on haul locations, data from the NEFSC
fishery sampling observer data or NER vessel trip reports (VTR) data,
which do include locations of fishing trips, were used to prorate the
landings to water body. The VTR data have also been used to prorate
NER landings to water body for bycatch estimates from the Northeast
multispecies gillnet fisheries in the Gulf of Maine region (Rossman
and Merrick 1999). The proration factor was the percentage of landings
of specific fish species (to account for the GLM factor mesh size category)
that were within each water body and seasonal management unit (to account
for the GLM factors seasonal management and water body). There were
landings from three water bodies included in the landing databases:
1) state waters within the coastal dolphin habitat; 2) federal waters
within the coastal dolphin habitat; and 3) federal waters outside the
coastal dolphin habitat (within the offshore dolphin habitat). Only
the first two water bodies were needed to estimate bycatch of coastal
bottlenose dolphins.
For NER landings data from several mid-Atlantic states, landings from
state oceanic waters were explicitly defined, and so proration of landings
within the state coastal habitat was not necessary. While for data
from other states, landings from state oceanic waters were combined
with federal oceanic waters and so proration was necessary to determine
the landings within each water body.
Because quality of location data in the VTR and fishery sampling observer
databases varied by state, prorations of landings from a state were
calculated from the database with the highest quality data for that
state. Ideally, the VTR data is the best database to use for the prorations,
because these data are suppose to be a census of the fishing industry.
In contrast, the fishery sampling observer data was designed to be
a sample of the fishery. However, in North Carolina, the VTR data were
missing trips from most of the counties and many of the smaller sub-fisheries.
Thus, landings from North Carolina were prorated using the fishery
sampling observer data, while landings from the other states were prorated
using the VTR data. Details of the proration methods for each management
unit are described below.
2.B.1.
Management Units adjacent to New Jersey through Virginia
VTR data were used to prorate commercial gillnet landings from the
NER dealer landings database to the water bodies within the seasonal
management units adjacent to Virginia, Maryland, Delaware, and New
Jersey.
For each season, winter 1996 to summer 2000, total effort for a management
unit and water body (Hysumw) was defined as metric
tons of fish landed (H) by year (y), season
(s), management unit (u), mesh
category (m), and water body (w). This was
estimated by first, prorating the individual state's (t) total
landings (Htysum) by the percentage of fish landed
within each water body, as reported in the VTR database (tysumw),
then summing over the states (t):
 |
(4) |
where
 |
(5) |
Gillnet landings from Virginia state waters were explicitly defined
and so did not have to be prorated. Landings from Virginia federal
waters were prorated to the federal coastal habitat and the federal
offshore habitat water bodies. In contrast, gillnet landings from state
waters of Maryland and New Jersey were not explicitly defined and so
landings for these states were prorated to all three water bodies.
Ocean gillnet landings from Delaware came entirely from within the
state coastal habitat water body and hence did not have to be prorated.
2.B.2.
North Carolina Management Units
To determine which data set contained the most representative sample
of North Carolina's fisheries, landings from the VTR database were
compared to landings from the fisheries sampling observer and the NCDMF
databases. It was found that the VTR's were only available from Dare
county. Therefore, fishery harvests south of Cape Hatteras were not
reported in the VTR data. Although the number of VTR's submitted by
North Carolina have consistently increased since 1996, the proportion
of VTR's with missing or unknown locations has also increased. On the
other hand, the NEFSC sampling fisheries observer database included
information on trip's locations and contained samples of gillnet trips
from all counties, and samples from trips that landed many species,
even rare species (Appendix A). An interpretation
of this is, in order to capture the rare species and also capture the
same peaks as in the NCDMF landings data, the fishery sampling observer
program must be tracking the spatial and temporal patterns of the different
sub-fisheries fairly well. In conclusion, the fisheries sampling observer
data from North Carolina are likely a more representative sample than
the NC VTR data. Therefore, the NEFSC fisheries sampling observer data
were used to prorate commercial landings from the NCDMF database to
water bodies within the winter NC mixed stock, summer Northern NC,
and summer Southern NC management units.
Total effort within the North Carolina seasonal management units,
water bodies, and mesh categories were defined using Equations 4 and
5, where the state (t) was always North Carolina and the proration
() was estimated from observer data.
Gillnet landings from North Carolina state waters were explicitly
defined and so did not have to be prorated. Therefore, only landings
from North Carolina federal waters were prorated to the federal coastal
habitat and federal offshore habitat water bodies.
3.
Observer Coverage
Observer coverage of the mid-Atlantic gillnet fisheries was defined
as the percent of observed metric tons of fished landed from the fishery
sampling observer data to the total metric tons of fish landed. Observer
coverage was calculated for each year, seasonal management unit, and
water body combination. Years included 1996 to 2000; seasons included
summer (May to October) and winter (November to April); water bodies
included state waters within the coastal dolphin habitat, federal waters
within the coastal dolphin habitat, and federal waters outside the
coastal dolphin habitat. The pro-rated metric tons of landings to water
body were used as the measure of effort for each strata.
4.
Total Bycatch
Total bycatch within a year for a seasonal management unit (Cysu)
was the sum of bycatch estimates from all water bodies and mesh size
category combinations within that seasonal management unit and year
combination. Each bycatch estimate was the product of the bycatch rate
(R) as estimated by the GLM and landings (H):
Total bycatch for the entire winter mixed stock management unit was
the sum of bycatch from the winter NC mixed stock sub-management unit
(covering waters off of North Carolina) and the winter VA mixed stock
sub-management unit (coverings waters from Virginia).
The amount of commercial landings was assumed to be known and so its
CV = 0. Thus, the CV of total bycatch was equal to the CV of the bycatch
rate.
RESULTS
1.
Bycatch Rates
1.A.
Data
A total of 12 coastal bottlenose dolphins were observed taken within
the mid-Atlantic gillnet fisheries during 1996 to 2000. Seven of the
animals were taken in winter (November through April) and five were
taken in summer (May through October). One of the 12 animals was caught
and released alive from a gillnet fished during the summer of 1999
on a trip observed off of North Carolina in waters in the Southern
NC management unit. Thus, this animal is not considered a lethal take,
and was not included in the bycatch estimate. Of the 12 animals observed,
2, 0, 2, 5, and 3 were taken in 1996, 1997, 1998, 1999 and 2000, respectively
(Table 3).
Of the observed coastal bottlenose dolphins, 83% (10 out of 12) were
within the state coastal habitat, or within 4.8 km (3 nmi) from shore.
The remaining 17% (2 out of 12) were within the federal coastal habitat,
or between 4.8 and 27 km from shore (Table 3; Figure
2). The winter NC mixed stock sub-management unit had the highest
observed number of takes of coastal bottlenose dolphins (n = 6; Table
4C), while the summer Northern migratory management unit had the
second highest number (n = 3; Table 4B).
The winter VA mixed stock sub-management unit (Table
4A), summer Northern NC, and Southern NC management units (Tables
4D and 4E) each had one observed coastal
bottlenose dolphin take.
1.B.
Development of a GLM Bycatch Model
Step 1: determine best general formula for the model
The general formula of the GLM was the number of takes modeled
by a set of explanatory variables (b0 + b1 x1 + b2 x2 +
...) and an offset variable (log(landings)), as expressed
in Equation 2 and discussed in the Methods section.
Step 2: determine most appropriate distribution to model the relationship
The Binomial distribution was chosen (see Methods discussion).
Step 3: select a set of variables that best describes the relationship
The full model (a large, complex model) had the essential variables
(management unit and the offset of landings) and many other potential
explanatory variables:
log(num.bodo) ~ MU + offset(log(land.kept.mton)) + state.or.fed
+ year +
target.species + soak.duration + km.hr + season + string.length +
escape.panel.used + twine.size + mesh.size. |
(7) |
All variables investigated, their definitions, and abbreviations are
listed in Table 2, The full model was used
to start the stepwise model selection.
Using the backward-forward stepwise model selection method, the model:
| log(num.bodo) ~ MU + state.or.fed + mesh.size + offset(log(land.kept.mton)) |
(8) |
was chosen because it fit the data the best (i.e., it had the lowest
AIC; Table 5).
The variable mesh.size had values that range from 1.3 to 13 inches.
To use Equation 8 to estimate total bycatch, the bycatch rate for each
mesh size must be multiplied by the total landings for each mesh size.
Due to the lack of mesh size data in the commercial landings databases,
it was not possible to determine total landings for each mesh size
directly. So a mesh size proxy was generated by creating three categories
of mesh sizes (small, medium, and large), where each target species
was assigned to a mesh size category.
The best definition of mesh size categories was defined using a classification
TREE method that used mesh size, management unit, and year to predict
the target species. Using this model, it was possible to correctly
predict which target species the captain was fishing for 74% of the
time. This TREE model indicated the appropriate cut points for the
mesh size categories were 5.1 and 6.85 inches (Figure
3). This resulted in three mesh size categories: small: 5.0 inches,
medium: > 5.0 to < 7.0 inches, and large: 7.0 inches. These categories
represent different groups of target species quite well (Table
6). Using this definition of mesh size categories, the NEFSC fishery
sampling observer data (Appendix B) and
total commercial landings were divided into seasonal management units,
water bodies, years, and mesh size categories.
Using mesh size categories (mesh.cats) in the GLM improved the AIC
to174.3 from 175.38, which was for the model that used the exact mesh
size (mesh.size). In other words, the model that used the mesh size
categories fit the data slightly better. In conclusion, the best fitting
model was:
| log(num.bodo) ~ MU + state.or.fed + mesh.cats + offset(log(land.kept.mton)) |
(9) |
Step 4: determine if other variables should be included
Because not all potential variables were included in the complex,
full model, other variables were added to the model to determine if
the fit could be improved. Eight additional variables were investigated,
and it was found that they did not significantly improve the fit (Table
7). The AIC was lower (i.e., the model fit slightly, but
not significantly, better) when two types of variables were added.
The first type of variable that improved the fit was finer resolution
of the distance from shore (dist2shore and dist2shoreA). The model
in Equation 9 included the variable state.or.fed, which indicated if
the haul was in state or federal waters. But when an even finer resolution
of distance from shore was included in the model, the new model fit
slightly better. The new model indicated the bycatch rate was highest
for observed hauls within 3 km of shore. A Chi-square test comparing
the model with and without dist2shoreA indicated the model with dist2shoreA
was not a significantly better model (2 test; p=1.0). In addition,
for the purposes of estimating the total bycatch, the variable dist2shoreA
was problematic in that it required a further very fine-scale proration
of the landings data. That is, it would of been necessary to estimate
total landings that used a specific mesh size category and that were
caught between the shore and 3 km, between 3 and 4.8 km, and between
4.8 km and the outside of the coastal bottlenose dolphin habitat (12
or 27 km). Because of this complexity and lack of significance, this
variable was not used in the bycatch model.
The second variable type that improved the fit of the model was the
presence or absence of an anchor (anchor.used). As with the distance
to shore variables, adding anchor.used did not significantly improve
the model (2 test; p=0.81). In addition, it was not practical to use
it to estimate total landings because it was not possible to predictable
divide the fish species caught (a reliable variable in the landings
databases) into those caught in strings with or without an anchor.
For example, 54% of the observed hauls that targeted Atlantic croaker
did not use an anchor, and 42% of the observed hauls that targeted
spiny dogfish did not use an anchor.
Step 5: check the fit of the model
An analysis of deviance for the sequential addition of each variable
was conducted to test the variable's significance. The results (Table
8) indicated that the variable management unit (MU) was not as
important as the other variables variable. However, it was necessary
to include it in the model to produce a bycatch rate for each management
unit . The other two variables, state.or.fed and mesh.size.catagory,
were significant and therefore, useful in modeling the bycatch rate.
This analysis also indicated that the state.or.fed variable was more
highly correlated with bycatch than the mesh.cat variable was.
The tests for goodness-of-fit investigate if the chosen model fits
the data, i.e., the predicted number of takes equals the observed number
of takes. Within a management unit, this hypothesis could not be rejected
using the Fisher Exact test (Table 9); thus,
indicating the model fits the data. The linear regression results indicated
the same thing. Fits between observed number of takes and predicted
number of takes for a management unit were quite good (R2 greater
than 0.5) for all management units, except the summer Northern Migratory
management unit, where the R2 was 0.34 (Table
9; Figure 4A and Figure 4B).
For this management unit, the model slightly under-estimated the number
of takes.
Two sets of diagnostic plots displayed the structure of residuals
of a model between the predicted and observed number of takes in a
seasonal management unit. The first set of diagnostic plots indicated
that there were no obvious outliers, but there still was some unexplained
structure in the residuals (Figure 5A and Figure 5B).
In a model that fits perfectly, the residuals would be scattered about
zero with no pattern. The R2 for this model was 0.75 (CV=0.12)
and the slope was 0.92. If there was a perfect fit, the R2 and
slope would both be 1.0. The second set of diagnostic plots (Figure
6) showed the SE bars were fairly tightly clustered around the
mean, especially for levels of variables with many observed hauls,
and the spread of the residuals were what is excepted when the number
of takes are restricted to the values of zero and one. In a model that
fits perfectly, the residuals would be spread evenly around the mean
of each level of the explanatory variable, and the SE bars would be
tight around the mean. Overall, the fit was good.
1.C.
Bycatch Rate Estimates
The best fitting and practical model was Equation 9, where the AIC
was 174.3. The variables in that model were water body, mesh size category,
and seasonal management unit (Table 10).
The coefficients for this model were:
| Intercept |
state or
federal |
small
mesh |
medium
mesh |
NC mix
Winter
sub-MU |
VA mix
Winter
sub-MU |
NNC
Summer
MU |
Nmigratory
Summer
MU |
| -4.214 |
1.214 |
0.595 |
0.929 |
0.768 |
0.066 |
-0.508 |
-0.724 |
where MU is management unit. The coefficient for the last level of
each variable (federal waters, large mesh size, and Southern NC management)
is by definition zero because the base line level is defined as federal
waters, large mesh size, and in Southern NC management unit, so, all
other levels are relative to this base line level.
Within the coastal dolphin habitat, estimated bycatch rates for hauls
in state waters were higher than rates in federal waters (Table
11A). Rates in large mesh fisheries were highest, and rates in
small mesh fisheries were lowest. Rates in the summer Northern NC and
summer Northern migratory management units were highest (Table
12), and rates in the Southern NC management unit were the lowest
(no observed dead animals, though one animal was released alive and
uninjuried). Even though there were no observed dead takes in the Southern
NC management unit, the model predicted a very small bycatch rate.
The CV's of the bycatch rates when stratified to seasonal management
unit, body of water, and mesh size are large, mostly over 60% (Table
11B). However, when averaged to seasonal management unit, the level
we are most interested in, the CV's improved to 48 - 84% (Table
12).
2.
Commercial Gillnet Landings
2.B.
Prorating Commercial Gillnet Landings
2.B.1.
Management Units adjacent to New Jersey through Virginia
Winter Virginia mixed stock Sub-Management Unit (Virginia only):
All water bodies: Most of the gillnet landings from ports
that fished in the winter VA mixed stock sub-management unit were
from federal waters (both inside and outside the coastal dolphin
habitat), and were from the medium mesh category which was dominated
by the dogfish fishery (Figure 7). The
medium mesh category fisheries landings (dominated by dogfish and
American shad) from state waters were significantly less than that
from federal water. The majority of landings from the small mesh
category originated from state waters and were dominated by croaker,
weakfish, bluefish and Atlantic mackerel fisheries. Most of the landings
from the large mesh category were attributed to the striped bass
fishery within state waters, and to the monkfish (anglerfish) fishery
in federal waters.
Coastal Habitat water bodies: The majority of gillnet landings
from the coastal dolphin habitat originated from state waters (Table
13A). Most of the landings from federal waters in the coastal dolphin
habitat were from the medium mesh category, while most landings from
state coastal waters were from the small mesh category. As an aside,
most of the landings from federal waters came from areas outside of
the coastal dolphin habitat.
Winter Northern Migratory Management Unit (Maryland, Delaware, and
New Jersey only):
Winter landings from Maryland (Table 13B), Delaware
(Table 13C), and New Jersey (Table
13D) were prorated into the three water bodies. However, these landings
were not used in the bycatch estimate because it was assumed that there were
very few, if any, coastal bottlenose dolphins in these waters during the winter;
thus, it was assumed the bycatch estimate for these waters was zero.
Summer Northern Migratory Management Unit:
Virginia
All water bodies: The largest landings within a year-mesh
size category occurred in1999 in the federal offshore habitat waters
within the large mesh monkfish category (Figure
8). Other than this exception, most gillnet landings from Virginia
ports that fished in the summer Northern migratory management unit
habitat originated from state waters within the small mesh category,
and were dominated by the croaker and spot fisheries. Fisheries within
the medium mesh category were primarily harvested from federal waters
and were dominated by dogfish. Most landings from the large mesh category
were for black drum within state waters, and for monkfish in federal
waters.
Coastal Habitat water bodies: Most gillnet landings from
the coastal dolphin habitat of the Virginia portion of the summer Northern
migratory management unit were from state waters and the small mesh
category (Table 13E). Again, most of the
fish caught in federal waters were caught outside of the coastal dolphin
habitat.
Maryland
All water bodies: Most gillnet landings from the ports in
Maryland that were derived from the summer Northern migratory management
unit habitat were from the large and medium mesh categories, dominated
by monkfish and dogfish, respectively (Figure
9). The small mesh category was dominated by several species: croaker,
spot, bluefish, weakfish and menhaden.
Coastal Habitat water bodies: Most of the landings within
the coastal habitat came from federal waters (Table
13F). Landings from the federal coastal habitat came from the small
mesh category, except in 1999 when medium mesh landings were at a high.
Delaware
All water bodies: Because there were no reported landings
in federal waters for the Delaware portion of the summer Northern migratory
management unit, landings were not prorated, and so all landings were
attributed to the state coastal habitat (Figure
10). The majority of landings were from the small mesh category,
which was dominated by the croaker, bluefish and weakfish fisheries.
In 1996, less than 1 mt was landed within the medium mesh category,
which was dominated by shad. There were no landings from the large
mesh category.
Coastal Habitat water bodies: Within state waters, there
were no landings reported from the large mesh category during any year,
and from the medium mesh category during 1997 to 2000 (Table
13G). Nearly all landings were from the state coastal dolphin habitat
small mesh category, where the landings were small, less than 6.5 mt
per year.
New Jersey
All water bodies: Landings from New Jersey ports that fished
within the summer Northern migratory management unit habitat were similar
across all mesh categories (Figure 11).
Bluefish and weakfish dominated the small mesh category landings, dogfish
dominated the medium mesh category landings, and monkfish dominated
the large mesh category landings.
Coastal Habitat water bodies: Coastal habitat landings from
the small mesh category originated mainly from state waters, while
the majority of landings from the medium and large mesh categories
originated from federal coastal waters (Table
13H). Again, landings from federal waters outside of coastal dolphin
habitat made up most of the New Jersey landings.
2.B.2.
North Carolina Management Units
Winter North Carolina Mixed Stock Sub-Management Unit:
All water bodies: Reported landings from federal waters
were highest for fish species in the medium mesh category (dominated
by dogfish), second highest in the small mesh category (dominated
by Atlantic croaker, bluefish and weakfish), and lowest in the large
mesh category (dominated by monkfish; Figure
12). Within each mesh category, landings from state waters were
nearly similar to landings from federal waters, in terms of species
composition and quantities landed. The exception is in the large
mesh category, where striped bass dominated landings in state waters,
and monkfish dominated landings in federal waters.
Coastal Habitat water bodies:. Most of the gillnet landings
within the winter NC mixed stock sub-management unit habitat came from
small mesh fisheries with catches in state waters typically exceeding
those from federal coastal waters (Table 13I).
There appears to have been a significant shift in the pattern of landings
in federal waters during 2000; prior to 2000 most of the small mesh
catches came from federal coastal waters, but in 2000 most came from
federal offshore waters, outside the coastal dolphin habitat.
Summer Northern North Carolina Management Unit:
All water bodies: Most gillnet landings from ports that
fished during summer within the Northern NC management unit habitat
originated in state waters (Figure 13).
In particular, most were within the small mesh category (dominated
by spanish mackerel, spot and bluefish). Landings from state waters
in the medium mesh category were dominated by sharks, king mackerel
and dogfish. The large mesh category had the least amount of landings
from both state and federal waters. All landings from federal waters
were from the small and medium mesh categories, with the exception
of 1996, when there was fishing for monkfish.
Coastal Habitat water bodies:. The majority of gillnet landings
within this management unit was from small mesh fisheries within state
waters (Table 13J).
Summer Southern North Carolina Management Unit:
All water bodies: Nearly all gillnet landings from ports
that fished during summer within the Southern NC management unit
habitat originated from state waters within the small mesh category
and was dominated by the spot fishery (Figure
14). Landings from state waters within the medium mesh category
were dominated by sharks, fluke and king mackerel. All landings from
federal waters were from the small mesh category, and was dominated
by the spot fishery.
Coastal Habitat water bodies:. Nearly all the gillnet landings
within the coastal dolphin habitat in this management unit were from
the small mesh fisheries in state waters (Table
13K).
3.
Observer Coverage
3.A.
Management Units adjacent to New Jersey through Virginia
Winter Virginia mixed sub-management unit (Virginia only):
Annual number of trips observed and percent coverage of gillnet landings within
this seasonal sub-management unit ranged from a low of 35 trips (7.50% coverage)
in 1996 to a high of 92 trips (3.27% coverage) in 2000 (Table
4A). Although coverage of the gillnet fisheries was highest in the federal
coastal and offshore habitats there was considerable variability, ranging
from 8.20%-100% and 0.25%-57.51%, respectively. Landings reported in 1996
for federal water bodies were very low compared to other years. Coverage
was lowest in the state coastal habitat, ranging from 0.50%-1.48%.
Summer Northern migratory management unit (Virginia through New Jersey):
Annual number of trips observed and percent coverage of gillnet landings within
these seasonal management unit ranged from a low of 110 trips (1.50% coverage)
in 1999 to a high of 162 trips (2.94% coverage) in 2000 (Table
4B). Coverage of gillnet fisheries was highest in the federal coastal
and offshore habitats, ranging from 3.43%-16.60% and 1.24%-3.50%, respectively.
Coverage was lowest in the state coastal habitat, ranging from 0.58%-2.98%.
3.B.
North Carolina Management Units
In general, the NEFSC fisheries sampling observer data appear to be
fairly representative of the North Carolina gillnet fishery, especially
in the winter when most of the fishing occurs. In North Carolina, the
diversity of fish species landed in the observed trips and the relative
pattern of quantities of landings resemble that from the NCDMF landings
(Appendix A).
Winter North Carolina mixed stock sub-management unit:
Annual number of trips observed and percent coverage of gillnet landings within
this seasonal sub-management unit ranged from a low of 104 trips (1.69% coverage)
in 1997 to a high of 173 trips (1.97% coverage) in 1999 (Table
4C). Coverage of the gillnet fisheries was highest in federal coastal
and offshore habitats, ranging from 2.45%-4.51% and 2.76%-5.65%, respectively.
Coverage was lowest in the state coastal habitat, ranging from 0.41%-1.49%.
Summer Northern North Carolina management unit (Dare and Hyde counties):
Annual number of trips observed and percent coverage of gillnet landings within
this seasonal management unit ranged from a low of 1 trip (0.20% coverage)
in 1997 to a high of 35 trips (3.28% coverage) in 2000 (Table
4D). Although coverage of gillnet fisheries was highest in the federal
coastal and offshore habitats there was considerable variability, ranging
from 0.00%-18.07% and 0.00%-24.18%, respectively. Coverage was lowest in
the state coastal habitat, ranging from 0.00%-2.53%.
Summer Southern North Carolina management unit (Carteret, Onslow
, Pender, New Hanover, Brunswick counties:
Annual number of trips observed and percent coverage of gillnet landings within
this seasonal management unit ranged from a low of 0 trips (0.00% coverage)
in 1996 and 1997, to a high of 44 trips (3.4% coverage) in 2000 (Table
4E). Beginning in 1998, coverage improved; in the federal coastal and federal
offshore habitats coverage ranged from 3.12%-100% and 2.32%-100%, respectively,
and coverage in the state coastal habitat ranged from 0.96%-2.70%.
4.
Total Bycatch
Total estimated bycatch was highest in the winter mixed stock management
unit (146 in 2000 to 211 in 1997), with most of the takes coming from
waters off North Carolina and only a few from off Virginia (Table
14 and Table 15). The annual differences in
this management unit were not statistically different.
Estimated takes in Virginia during winter increased, from a low of
11 in 1996 to a high of 53 in 2000, though the differences were not
significant (Table 15). The increase was
due to increased landings in the large mesh fisheries in state waters
(in particular striped bass), from 29 mt in 1996 to 248 mt in 2000
(Table 14A).
The 2000 bycatch estimate in North Carolina during winter (93) was
about half that from the year with the highest bycatch in the same
area and season (187 in 1997), though the difference was not significant
(Table 15) . This difference was due to a
decrease in landings from all mesh size fisheries in state waters and
the small mesh fisheries in federal waters; however, the landings in
the large mesh fisheries in federal waters inside the coastal dolphin
habitat increased from 59 in 1999 to 283 in 2000 (Table
14C).
Average estimated summer takes (53) were about one-third of the average
winter takes (180). In all waters between New Jersey and North Carolina,
during 1996 to 2000, an average of 233 (CV = 16%; 95% CI = 171-318)
coastal bottlenose dolphins were taken during a year (Table
15). Total annual bycatch estimates for 2000 were the lowest in
the series (202), though the decrease was not significant.
The level of uncertainties (%CV) about the annual estimates were fairly
high, ranging from 48% to 84% (Table 15).
The uncertainties about the 5-year averages were lower (22% to 42%).
Five-year average annual bycatch estimates for the management units
were 30 (CV = 21.9%) for summer Northern migratory, 23 (CV = 28.7%)
for summer Northern NC, 0 for summer Southern NC, and180 (CV = 20.9%)
for the winter mixed stock management units (Table
15).
DISCUSSION
1.
Bycatch Rates
Two factors that had the highest correlation with the bycatch rate
were distance from shore and mesh size. The bycatch rate was highest
for hauls that were within state waters, particularly within 3 km of
shore. This could be because there are more dolphins closer to the
shore, as seen during aerial surveys conducted off New Jersey to North
Carolina (Garrison and Yeung, in review). The GLM also detected bycatch
rates were highest for hauls that used large mesh sizes (7 inches),
intermediate for medium mesh sizes (>5 to <7 inches), and lowest
for hauls that used small mesh sizes (5 inches). One interpretation
of the mesh size categories is these categories represent different
sub-fisheries within the mid-Atlantic that have different bycatch rates.
Two variables that were not included in the bycatch model for practical
reasons, but could possibly be useful were a finer definition of distance
from shore (dist2shoreA) and the presence or absence of an anchor (anchor.used).
The effect of adding either of these two variables into the bycatch
model was not significant; that is, they were not as important as the
variables already in the model. The model already included a variable
that was a measure of distance from shore (state.or.fed). So it is
reasonable to believe that there may be a pattern in the bycatch rate
of hauls within state waters. Especially because there appears to be
a density gradient of coastal bottlenose dolphins relative to distance
from shore, where there are more coastal bottlenose dolphins near the
beach, and less offshore.
The other variable that slightly improved the bycatch model was anchor.used.
However, the observation that strings with anchors had a higher bycatch
rate than strings without anchors may only be a statistical artifact.
That is, it is known that strings with large mesh sizes have a higher
bycatch rate than strings with small mesh sizes. Of the observed strings
that used large mesh sizes, 73% used an anchor, while only 46% that
used small mesh sizes used an anchor. Thus, even if there was no true
relationship between bycatch and the presence of an anchor, it is more
likely that a take would be observed in a string with an anchor, because
strings with anchors tend to use larger mesh sizes. In statistical
terms, the variables mesh.cats and anchor.used are aliased, that is,
they are correlated and so represent similar bycatch rates. Still another
way to look at the data is 10 of the 11 dead bottlenose dolphins were
taken in nets that had an anchor. Thus, it is possible that even within
each mesh size category, nets with an anchor have a higher chance of
taking a bottlenose dolphin. This could be investigated further.
The bycatch rate model was used to estimate the average bycatch rate
within a seasonal management unit. It is important not to over-interpret
these estimated bycatch rates. That is, the model is valid when estimating
rates within the times and areas sampled, but they are not necessarily
valid for the future or in areas not sampled. To make the inference
to other times and areas, two assumptions must be made: 1) gear characteristics
and fishing practices must somehow predict the probability of a bycatch;
that is, there is a cause-and-effect relationship; and 2) fishing practices
and combination of gear characteristics that were observed in the data
will be the same as in other times and areas. Neither of these assumptions
have been shown to be valid or invalid. Consequently, caution should
be exercised when applying modeled results to times and areas other
than that used to develop the model.
Most biological systems demonstrate inter-annual variability. Yet
the stepwise selection method did not choose the variable year. This
could be because there is little or no inter-annual variability, or
there were insufficient data to accurately distinguish inter-annual
variability in the bycatch rates within seasonal management unit, water
body, and mesh size category. It is more likely that the latter is
true. In the future, it is possible that fishery management actions
or other events will cause fishing practices to alter sufficiently
to change the bycatch rate. If this is thought to be happening or one
wants to investigate inter-annual changes, then in the future when
there are more data, the model can to be modified to explicitly account
for effects of year, and yet still maintain the same bycatch rate estimates
for 1996 to 2000.
Landings were used as the unit of effort in the bycatch rate because
no other data were available for the measure of effort in the entire
fishery. This unit of effort may not be the ideal measure because landings
data from both the NER dealer and NCDMF databases may under-estimate
fishing effort because, for example, not all landings are recorded
into these databases, and some fishing effort may result in no landings.
Thus, the total amount of fishing effort, the bycatch rate, and total
bycatch is likely to be negatively biased. The magnitude of this bias
is unknown. Other measures of effort should be explored and evaluated.
2.
Commercial Gillnet Landings
Because the bycatch rate model accounted for sub-fisheries (mesh size
categories and water body), some of the commercial landings had to
be prorated using data that were collected from only a sample of the
total landings (VTR and NEFSC fisheries sampling observer data). In
the past, the amount of landings have been assumed to be known with
certainty and so its CV equaled zero. However, when prorating the landings
using data from a sample there is some uncertainty. Therefore, the
CV should not equal zero. In the present analysis this component of
the CV had not been estimated and so the presented CV's of the total
bycatch estimates are biased low by some unknown amount. One reasonable
way to obtain a CV of the prorated landings is by using bootstrap re-sampling
techniques, because it is not possible to derive a theoretical CV.
While the CV of landings when prorated using the VTR data is probably
small because, for many states, the VTR data is a large sample of the
total landings, the exact amount is presently unknown. This could be
investigated further.
In the NCDMF database, when the number of vessels that report landings
from a water body/county combination was equal to or less than three,
their landings were considered confidential and so were not provided
to us. This means that during summer when there were few vessels fishing
from some North Carolina counties in some water bodies, these landings
would be considered confidential. In these cases, the landings we reported
were biased low and observer coverage biased high. Though, since there
were only three or less vessels fishing, it was unlikely that the level
of effort was not large, the bycatch estimate likely small, especially
compared to the winter in NC, and so the level of bias also likely
small.
Winter landings reported from 1996 for federal water bodies landed
in Virginia were very low compared to other years. If these landings
were erroneous, the reported levels of landings, bycatch estimate,
as well as observer coverage would also be erroneous. This requires
further investigation.
3.
Observer Coverage
Observer coverage in federal waters, both inside and outside of the
coastal bottlenose dolphin habitat, was generally 3 - 6%. In federal
waters outside of coastal dolphin habitat no coastal dolphin takes
were observed. Because of the relatively high level of coverage in
federal waters outside of the coastal dolphin habitat and the large
number of observed hauls, more observed takes should have been observed
if the true bycatch was high. For example, assuming a binomial distribution
of bycatch rates, for the total landings in federal waters outside
the winter NC mixed stock sub-management unit during 1996 to 2000 (6238
mt) (annual estimates in Table 4C), and an
average observer coverage of 4% (annual estimates in Table
4C), there was a 64% chance of observing one or more takes sometime
during 1996 to 2000 if there were truly 25 dolphins taken in total
during all five years, 87% chance if there were truly 50 dolphins taken,
and a 98% chance if there were truly 100 takes during all five years.
But a take was not observed. In conclusion, it is highly likely that
the bycatch is very small in federal waters outside the coastal dolphin
habitat, especially outside the winter NC mixed stock sub-management
unit.
In federal waters inside the coastal bottlenose dolphin habitat, two
bottlenose dolphins were observed taken (Table
2), and the observer coverage was generally between 2.5 and 6%,
with some times and places higher (> 17% in winter VA mixed stock
sub-management unit during 1996 to 1998), and other times and places
lower (0% in the Southern NC management unit in the summers of 1996
to 1997). The only take observed in federal waters inside the coastal
bottlenose dolphin habitat in the winter NC mixed stock sub-management
unit was during 1998, the year with the highest observer coverage (4.51%).
One interpretation of this is that the true bycatch in federal coastal
waters was low because we needed a fairly high coverage of 4.5% to
see one take. That is, assuming a binomial distribution of bycatch
rates, for the 1,991 mt of landings in the federal coastal waters of
the winter NC mixed stock sub-management unit (Table
4C), with an average observer coverage of 4.51%, there was a 90%
chance of observing one or more takes if there were truly 50 dolphins
taken in this time and area, and a 99% chance if there were truly 100
takes. In contrast to the year with the lowest coverage, 2.45% coverage
in 2000, there was a 80% chance of observing one or more takes if there
were truly 50 dolphins taken in this seasonal management unit, and
a 91% chance if there were truly 100 takes. It was estimated that there
were on average eight animals per year taken in this management unit
(annual estimates in Table 14C). If it is
true that the observer coverage (average of 2.9%) was too low in most
years to have observed a take, then the estimated average bycatch of
eight animals per season may be negatively biased perhaps up to 64%
(2.9/4.5). If this level of bias were true, the estimated average bycatch
for the federal coastal waters in the winter NC mixed stock sub-management
unit would increase to only 13 animals. In conclusion, the bycatch
in federal waters inside the coastal dolphin habitat was probably low,
though not as low as outside the dolphin habitat.
Observer coverage in state waters was generally low, often below 1%,
generally below 2.5%. However, even though the observer coverage was
low, there were nine observed takes in state waters, five of which
were in the winter NC mixed stock sub-management unit. In this management
unit, average observer coverage in state waters was 0.8% with an average
of 3876 mt of landings in the winter season (annual estimates in Table
4C). Using these facts and assuming a binomially distributed bycatch
rate, if there were 50, 100, 150, 200, or 250 animals that were truly
taken in the winter season, then there was a 33%, 56%, 71%, 81% and
92% chance of observing one take in the winter season, respectively.
The landings in these state waters have been declining, but the observer
coverage has been increasing. So in the most recent year, using the
facts from 2000 (2246 mt landed and a 1.49% observer coverage), there
was a 90% chance of observing a take if there were truly 150 animals
taken in the winter season. In other words, even with the low observer
coverage, because there were so many trips observed (i.e., tons of
landings observed), there was a good chance of observing a take in
the winter NC waters when the true number of takes were 150 or more
animals per season.
Low observer coverage was a problem in small fisheries (few mt landed),
such as those in the summer Northern and Southern NC management units.
In the Southern NC management unit, there was no coverage during 1996
and 1997, and a maximum of 2.7% observer coverage in 1999, when there
were only 128 mt of landings from this seasonal management unit. The
only observed take was during 1999, the year with the most coverage.
Note, this animal was released alive from a net that soaked for a half
hour, so it was not included in the bycatch estimate. Assuming takes
were binomially distributed, there was a 72% chance of observing a
take if the true number of takes were 45 animals per season, and a
91% chance if there were truly 70 animals taken. Thus, due to low coverage
of a small fishery, it is likely the bycatch estimates for the summer
Northern and Southern NC management units are biased low, by an unknown
amount.
4.
Total Bycatch
The CV's around the 5-year mean bycatch estimates ranged from 22 to
42% (Table 15). The most uncertainty was
around the winter VA mixed stock sub-management unit, where the 95%
confidence interval was16 to 74 animals per season. This may be due
to low observer coverage (average of 1.1%) in state coastal habitat
waters (annual estimates in Table 4A) or
to the fact that coastal bottlenose dolphins may not always be present
in waters off of Virginia during the winter.
In the NC winter mixed stock sub-management unit, the bycatch for
2000 (93) was about 40% lower than the average from the previous years
(159 during1996 to1999). This change was due to a decline in landings
in state waters, particularly that from the large mesh fisheries, and
not due to a change in the bycatch rate. The reason for the decreased
landings may be fishery management actions, natural variability in
the distribution of dolphins and/or fishers, or something else. It
does not appear that the change in landings during the winter 2000
season (November 1999 through April 2000) was due to the monkfish fishery
management plan implemented on 1 May 2000, or a closure for turtles
from 12 May to 12 June 2000. If these or other fishery management plans
do influence future distributions and quantities of landings, then
future bycatch estimates of coastal bottlenose dolphins will probably
vary from that seen in this paper. In general it is expected that if
landings decline so will the bycatch estimate.
Bycatch rates were highest for large mesh fisheries that target monkfish,
striped bass, and black drum. So changes in these fisheries will have
large influences on the bycatch estimate. The monkfish fishery was
relatively small in coastal dolphin habitat and more intense in waters
offshore the coastal dolphin habitat, while the striped bass and black
drum fisheries were more intense in state waters. Striped bass landings
have been increasing, while monkfish landings started decreasing in
2000 and is suppose to continue decreasing. If both of these patterns
continue to hold true for the future, and the bycatch rates of monkfish
and striped bass hauls continue to be similar to each other, then it
is possible that bycatch of coastal bottlenose dolphins will not decrease,
even though fishing for monkfish declines drastically.
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