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Table 4. Linear regression statistics
Model: Seabird density = Constant + B,(Nekton Density)

Species B F P (2 Tail) R
COMU* ~ 0.002 14.991 0.001 0.454
RHAU 0.001 10.945 0.004 0.366
GWGL 0.002 8.401 0.009 0.307
SOSH -0.004 0.549 0.468 0.028
Table 5. Results of Monte Carlo analysis
Statistic COMU* GWGL RHAU

Mean 0.503 0.497 0.503

Standard Error 0.009 0.009 0.009

Median 0.507 0.485 0.508

Standard Deviation 0.292 0.278 0.272

Sample Variance 0.085 0.078 0.074

Kurtosis -1.213 -1.086 -0.995

Skewness -0.018 0.042 -0.03.

Range 0.999 0.999 0.999

Minimum 0.000002 0.000002 0.00003

Maximum 0.999 0.999 0.999

Sum 503.229 496.691 503.037

Count 1000.000 1000.000 1000.000

Confidence Level (95.0%) 0.018 0.017 0.017

Actual P Value 0.001 0.009 0.004

% of Values Less than the Actual Value 0.70% 0.80% 0.70%

Z-statistic -1.725 -1.775 -1.845

Significance Level (P value) on the 0.0427 0.0384 0.0329

Actual P Value in this Distribution

*Residuals lagged one kilometer.



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Hamner, W. M., & Hauri, I. 1981. Long-distance horizontal migrations of zooplankton
(Scyphomedusae:Mastigias). Limnol. Oceanogr. 26:414-423.

Hay, D. E., Healey, M. C., Ware, D. M., & Wilimovsky, N. J. 1992. Distribution,
abundance, and habitat of prey fish on the west coast of Vancouver Island. In: K.
Vermeer, R. W. Butler, & K. H. Morgan (eds.) The Ecology, Status, and
Conservation of Marine and Shoreline Birds in the West Coast of Vancouver Island.
Can. Wildl. Serv. Spec. Publ. pp:22-29.

Heinemann, D., Hunt, G., & Everson, I. 1989. Relationships between the distributions of
marine avian predators and their prey, Euphausia superba, in Bransfield Strait and
southern Drake Passage, Antarctica. Mar. Ecol. Prog. Ser. 58:3-16.

Johannesson, K. A., & Mitson, R. B. 1983. Fisheries Acoustics. A Practical Manual for
Aquatic Biomass Estimation. FAO Fish. Tech. Pap. #240.

Kaiser, G. W., & Forbes, L. S. 1992. Climatic and oceanographic influences on island
use in four burrow-nesting alcids. Ornis Scand. 23:1-6.

Lemberg, N. A. 1978. Hydroacoustic assessment of Puget Sound herring, 1972-78. WA
Dept. Fish. T-ch. Rept. #41.

Monaghan, P., Uttley, J. D., Burns, M. D., Thane, C., & Blackwood, J. 1989. The
relationship between food supply, reproductive effort and breeding success in arctic
tems Sterna paradisaea. J. Anim. Ecol. 58:261-274.

NMFS 1995. Preliminary Cruise Results NOAA Ship Miller Freeman Cruise #95-10. Echo
integration-trawl survey of Pacific Whiting along the wast coast of the United States
and Canada.

Obst, B. S. 1985. Densities of Antarctic seabirds at sea and the presence of the krill
Euphausia superba. Auk 102:540-549,

Parrish, J. K. 1996. Continued monitoring of Comon Murres on Tatoosh Island. Unpubl.
Draft Final Rept. to the Tenyo Maru Trustees Council, January 1996.

Piatt, J. F. 1990. The aggregative repsonse of Common Murres and Atlantic Puffins to
schools of capelin. Stud. Avian Biol. 14:36-51.

Piatt, J.F. and P.J. Anderson 1995. Response of Common Murres to the Exxon Valdez oil
spill and long-term changes in the Gulf of Alaska marine ecosystem. In: Rice, S.D.,
R.B. Spies, D.A. Wolfe, and B.A. Wright (eds.) Exxon Valdez Oil Spill Symposium
Proceedings. American Fisheries Society. In press.

Safina, C., & Burger, J. 1985. Common Tern foraging: Seasonal trends in prey fish
densities and competition with bluefish. Ecology 66:1457-1463.

Schneider, D. C., & Hunt, G. L. Jr. 1984. A comparison of seabird diets and foraging
distribution around the Pribilof Islands, Alaska. In: D. N. Nettleship, G. A. Sanger, &

16


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standardized entries (see below) using a set of function keys to minimize time spent typing.

Observations were recorded as:

1.) date; automatically recorded by the program

2.) time; automatically recorded by the program

3.) species

4.) number = number of birds counted per sighting

5.) behavior = flying; sitting; feeding; following boat; rafting (used for group sizes greater
than 5 and all mixed species rafts - see comments below); feeding aggregation (used for
group sizes greater than 5); flying with fish; sleeping; other (see comments below)

6.) direction = clock direction relative to the axis of transit, with the bow of the boat at
1200; only applicable if the behavior code was flying.

7.) age = juvenile or adult; only applicable if the species code was Common Murre

8.) comments = encoded miscellaneous information which could be typed in as text, and
used to flag species observations where birds were part of a mixed species raft (MR),
flock (MF), or feeding aggregation (MFAGG). The comment flag could be used at any
point in the sequence of data entry.

Mistakes in data entry or observer identification which were caught during data collection

were flagged in the comments column and corrected in the subsequent entry. In cases

where boat presence altered bird behavior, as in a diving escape response (usually Alcids)

or a seated raft which took flight (usually shearwaters), we noted the prior behavior.

Occasionally seated birds avoided the boat by flying forward along the axis of transit and

reseating within the strip transect. In these cases, birds were only recorded once and

behavior was classified as the first (i.e. pre-boat) behavior observed. Data entries were

stored as ASCII files where each column represented a standardized entry (see above) and

each row an observation.

Hydroacoustic: Nekton was used as a surrogate for forage fish, and was assessed using
the echo integration and target strength analyses techniques described by Johannesson et.
al. (1983). Acoustic data were acquired with a 120Khz Simrad EK500 echosounding
system coupled to a 7.4 degree split beam transducer. The transducer was suspended one
meter below the surface and transmitted energy vertically through the water column at a one
second ping rate.. As a consequence of the transducer position, the upper 5m were not
adequately sampled. Acoustic data were collected and output at one minute time intervals
directly to computer (ASCII format) as well as to hard copy color echogram. Data were
displayed as density and number of acoustic targets, over ten continuous depth intervals of
15m each.


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The pattern of density of all three island breeders could be modeled with the nonlinear
diffusion function; SOSH densities could not (Figure 6A-D). In essence, the island can be
thought of as an attraction source for breeding species, while land per se is probably a
repellant for shearwaters. Residual densities from the nonlinear model, averaged for each
annulus, were regressed against average nekton density (Figure 8A-D). Murre densities
were lagged one kilometer (that is, km 2 becomes kim 1) to account for the hyperdensity
created by rafts adjacent to the island. For the three breeders, nekton density was a
significant predictor of residual seabird density (Table 4). In all cases higher nekton
densities had higher seabird densities, although the relationships were driven by only a few
points (Figure 8A-C). Thus, it is possible that the relationship is spurious.

To account for these results, we ran a Monte Carlo simulation, which holds nekton density
constant and reshuffles seabird density iteratively. In each run of the simulation, a linear
regression is performed and a T statistic and associated p-value calculated. At the end of
the simulation, the actual p-value is compared to the generated population of p-values, to
assess the probability that the actual regression reflects a chance occurrence. In all three
cases, less than 1% of the generated p-values fell below the actual value, a highly
significant result (Table 5).

A second line of evidence suggesting an association between seabirds and their prey is the
relationship between type of multispecies aggregation and prey density (measured as Sa) in
the localized area (Figure 9). In general, there is no apparent relationship between either
raft size and Sa, or feeding aggregation size and Sa, as the highest prey densities are
associated with smaller raft sizes. This pattern may be due to small sample sizes, the
possibility that smaller aggregations would have become larger later, or the possibility that
raft size is not determined by food alone. However, it is noteworthy that on average,
feeding aggregations occurred in areas of higher prey density than rafts.

Discussion

Our data indicate that common seabirds using the nearshore and shelf environments (sensu
Wahl 1984) in the area surrounding Tatoosh Island and the mouth of the Strait of Juan de
Fuca are distributed according to both breeding location and food. In fact, the relationship
between nekton and seabird density is strong enough that minimal pilot data (that is,
without adequate sample sizes) binned into average values as a function of distance from
the breeding island (despite the fact that there is no reason for densities of either seabirds or
nekton to be uniform within annulus) is still significant. The similarities in pattern of

12


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non-zero cells distributed over the sampling area. Therefore, the remaining analysis was
conducted on these four species.

Seabird and hydroacoustic data were stored as two separate Excel files, where each row of
data contained a date and time stamp. Because hydroacoustic data were collected
continuously and recorded every minute while seabird data were collected opportunistically
(i.e. whenever birds appeared), data files were not initially integratable. Furthermore, we
wished to analyze the data as a function of space, not time, such that data from repeated
transects could be compared and/or averaged. To surmount these problems, all data were
binned into minutes of latitudinal space. For example, all seabird observations occurring
within the boundaries 124° 40° W and 124° 41’ W on a given transect were summed to
produce density per space minute, by species. Sa values were similarly treated. The
resulting data sets were combined into a single file where each row represented binned data
by space minute on a known transect. This data file was used to address the following

questions: Where were the birds; and was there an association between the birds and the
nekton?

Seabirds in Space: The average density of each of the four main species was calculated per
unique space minute for the area covered by the short transects (48° 22’ N to 48° 25’ N,

“and 124° 41’ W to 124° 47’ W) and again for the long transects (see Figure 1 for transect

position and sample size). Data are presented as density by space minute by species.

Seabirds as a Function of Nekton: Because the transects were centered on a known
breeding island, and three of the four main species bred there (Common Murres,
Rhinoceros Auklets, and Glaucous-winged Gulls), seabird densities should be influenced
by distance from land as well as other factors such as food. Therefore, seabird-nekton

associations were assessed after removing the effect of distance from the data, according to
the following protocol. .

Data were averaged within unique space minute (see Seabirds in Space), such that all space
minutes were equally weighted regardless of original sample size (range 1 to 5; see Figure
1). All space minutes were then categorized as a function of distance from land, in
kilometers, to the center of the space minute. Initially, two distance functions were
explored, distance from the mainland (including Vancouver Island) and distance from
Tatoosh. Results of the mainland analysis were not as clean as those relative to the island,
so the former was dropped. Essentially the data were binned into a set of concentric


To calculate nekton density, the integration process quantified the amount of transmitted
acoustic energy which was backscattered by targets (e.g. fish) to the transducer. This
quantity was measured as 10log (Sv), where Sv is the volume backscattering measurement

over a vertical layer r, to r,, Averaged outputs were expressed as Sa (m*/nm‘) according to
the equation:

Sa=4nr’e neo | sur| © (1852m/nm)?
2

(Simrad Subsea 1993). Occasionally the equipment failed to detect the bottom, and/or

produced Sa values which were obviously incorrect (always orders of magnitude too

large). In these cases, values were first checked against the color echograms to verify
authenticity, and then deleted if necessary.

Nekton Groundtruthing. Concentrations of nekton observed by the hydroacoustic
equipment is normally identified by net sampling (Lemberg 1978). Attempts were made to
groundtruth the acoustic data using a second vessel outfitted for trawl net sampling. The
RV Kittiwake was chartered to tow a single wire, 3m wide x 7m deep x 15m long trawl
net, modified with a removable fine meshed codend to enumerate larval and minnow size
fishes, as well as invertebrates that may constitute nekton sign fed upon by seabirds. A 3m
x 3m x 10m Kvichak trawl net was also deployed. Both nets were calibrated at the site for
wire out to depth using a Bathykymograph.

Net sampling was hindered by a prodigous quantity of jellyfish: Cyanea capillata and
Aurelia aurita, that encompassed the sampling region. All attemptes to sample aroundthese
organisms by area, water depth and time of sampling resulted in the trawl nets plugged and
coasted with jellyfish. Eventually, cod ends of both nets gave way. Identity of the nekton
concentrations observed by the hydroacoustic equipment was therefore unobtainable at the
time of this pilot study.

Data Analysis

All seabird observations were summed to produce a sighting index by species. Although
16 known species were sighted (Figure 2A & B; Table 2), a few species predominated the
sightings (76%) as well as the overall count (72%): Sooty Shearwaters, Common Murres,
Rhinoceros Auklets, and Glaucous-winged Gulls. In determining the relationship between
spatial arrangement of seabirds and their prey, it is important to have an adequate sample of


1000

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11%

27%

TUPU

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RHAU CAGU SOSH COMU
Species

GWGU


the generated population. In other words, we asked the question: what percentage of the
generated p-values were equal to or less than our actual value?

Results

Density of each of the four main species (COMU, RHAU, SOSH, GWGL), averaged
within each space minute sampled is shown in Figure 4 (data are from short transects and
relevant long transect excerpts) and Figure 5A-D (data are from long transects only). Data
are presented separately to minimize the visual concentration created by superimposing the
short transect data on the long transect area. In general, it is apparent that island breeders
occur much closer to Tatoosh and appear to concentrate immediately north of the island.
Rhinoceros Auklets had the most restricted distribution. However, this species accounted
for only 8.6% of all birds seen (compare to the three other species, Table 2) and is the
smallest of the three, meaning we may have missed individuals and/or not had a sampling
rate (in time or space) adequate to represent the distribution. Sooty Shearwaters, on the
other hand, appeared to actively avoid the nearshore environment (Figure 4). Although
Figure 5D appears to si 2w a relationship between distance from shore and latitude, this
may simply be an artifact of sampling (see Figure 1).

When the data are presented as a function of distance from Tatoosh, the breeder versus
nonbreeder pattern is more strikingly apparent (Figure 6A-D; note unit differences on the
ordinate axis). The densities of all three breeding species: COMU, RHAU, and GWGL
drop off precipitously. Within the first 7km, 75%, 96%, and 78% of cumulative density of
each species is accounted for, respectively. By contrast, only 13% of cumulative SOSH
density is found within 7km of Tatoosh. Several additional elements are worth noting.
First, murre density is highest within the first kilometer of the island, in contrast to the
other two breeders. This may be the murre rafts to the north of the island, commonly
found off North and Pole Islands (USF&WS rock #’s 022 and 023; Parrish unpub. obs.).
Neither of the other two breeders aggregate to the same degree, although GWGL are
regularly seen in feeding aggregations (Parrish unpub. obs.). Second, close to the island,
RHAU and GWGL have similar patterns of density as a function of distance, a pattern
mirrored by COMU lagged one kilometer. Nekton density (measured as average Sa per
space minute within annulus) shared this pattern - densities were highest close to the island,
showed the three-step pattern to a maximum at 3km, and decreased sharply thereafter
(Figure 7).

11


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Seabird-Prey-base Interaction
_ Final Report to the Tenyo Maru Trustees Council
February 1998

Julia K. Parrish

Principal Investigators:
Julia K. Parrish - University of Washington
Greg Bargman - WA Dept. Fisheries and Wildlife

"Project Personnel:
Norm Lemberg - Research Scientist, WDFW
Steve Burton - Research Biologist, WOFW
Dick Mueller - Fish Biologist, WDFW
Karen Jensen - Research Technician, UW
Laura South-Oryshchyn - Undergraduate Research Assistant, UW
Neuee Vitalis - Undergraduate Research Assistant, UW; Makah Tribe Student Intem


study area 16-17 August 1995) indicates a strong whiting signal in the area surveyed by
our study (see Figure 4 in NMFS 1995). It is also apparent from these data that strong
signals appear to be absent frm the remainder of the Washington coast. One major caveat
to these results is that the acoustic frequency used to detect adult whiting was 38Khz, well
below the 120Khz used for forage fish in our study. However, it is also probably true that
highly productive areas will sustain elevated levels of both seabird and fish predators.

Is food, or can food be, limiting in this system? Woodby (1984), studying associations
between murres and their prey in the Bering Sea between the Pribilof Islands and the
mainland, found no obvious relationships and suggested that in areas where food is
abundant or superabundant predators need not necessarily forage only in areas of highest
prey density (but see krill patch size arguments in Heinemann et. al. 1989, Veit et. al.
1993). If this hypothesis is true, one might expect to find seabird-prey associations in
systems or years where/when food is limiting, or potentially in systems where food is
superabundant extremely close to the breeding colony. If food was limiting in this system,
we vould expect breeding seabirds to experience lowered reproductive success (e.g.
Monaghan et. al. 1989, Aebischer et. al. 1990). In 1995, murres breeding on monitored
crevice subcolonies on Tatoosh had higher reproductive success than in previous years
(1991-1994; Parrish 1996) suggesting that food was not limiting in that year. Given a
relatively high level of reproductive success combined with indications from this pilot study
as well as other fisheries surveys (e.g. NMFS 1995 whiting cruise), it seems likely that
seabird-forage fish association in the mouth of the Strait of Juan de Fuca system is

probably a response to persistent, nearshore, abundant sources of food.

Future Ri en

Of the five study goals outlined in the Introduction, four were met. We were not able to
groundtruth the hydroacoustic data (goal #3). We feel it is imperative to adequately
groundtruth the hydroacoustic data as well as try to determine forage fish species occuring
undermeath feeding aggregations. We suggest that any future studies explore the use of
larger vessels able to trawl with larger nets, as well as other gear types, potentially
including purse seines. Groundtruthing would also allow accurate target strength analysis
as a function of known forage fish species and length/frequency distribution. Although the
hydroacoustic equipment was highly sensitive to acoustic targets, it did not provide a total
picture of the water column, specifically underestimating surface-associated nekton (to
5m). As many seabirds feed within this depth range, future studies with increased effort
might include sidescan sonar, albeit at increased cost. Basic oceanographic data, including

14


breeding seabird and nekton density (i.e. the three-step to a maximum followed by a
precipitous drop; Figures 6A-C & 7) is striking. In short, it appears that these birds do not
need to go far from their breeding grounds to obtain food, even though they may be
physically able to do so (see distances in Schneider and Hunt 1984, Ainley and
Boekelheide 1990). The fact that Sooty Shearwater distribution did not match nekton
distribution may be explicable in terms of the nearshore distribution of maximum nekton
density (i.e. within the repellant zone of land). Vermeer et. al. (1989) found a similar,
albeit coarser pattern (distance from shore categories were lumped into 15km bins) of
seabird occurrence off the west coast of Vancouver Island for Common Murres, adult
Glaucous-winged Gulls and Sooty Shearwaters during the months of September and
October (data from 1986 and 1987; Figure 10).

Schneider et. al. (1990) suggest that foraging seabirds should balance both the cost and
expected gain to maximize net gain, where costs are proportional to distance traveled
(Figure 11). Assuming the cost function is static, net gain is a function of the changing
distribution of gross gain, as prey resvurces move within the range boundaries of the
seabirds. Because seabird prey is mobile (i.e. schooling) and may also not be present

_ within the foraging range of the colony throughout the breeding period (see Hay et. al.

: 1992), seabirds should adopt foraging strategies which maximize the likelihood of finding
mobile food. Such strategies might include use of ephemeral physical oceanographic
structures such as fronts and eddies (e.g. Woodby 1984, Schneider et. al. 1990) and
forage-area copying resulting in both mono and heterospecific feeding aggregations (e.g.
Piatt 1990, Veit 1994). Our finding that feeding aggregations occurred over higher prey
densities than rafting birds supports this latter strategy.

At the level of the population as well as the colony (i.e. seabird community), persistent
areas of positive net gain (e.g. Figure 11) should result in stable colonies the size of which
may be regulated by total food availability (Kaiser and Forbes 1992). The mouth of the
Strait of Juan de Fuca may be such an area. This area is subject to extremes of tidal flow in
and out of the Strait. Seabird feeding aggregations are often observed along the slick
created by the moving tidal front immediately south of Tatoosh Island (J. Parrish pers.
obs.). The area also experiences localized upwelling associated with the Juan de Fuca
canyon (Freeman 1992) which begins in the Strait and arcs west and south, curving around
Tatoosh Island. The highest seabird densities observed in this study (i.e. Figures 4 and 5)
were north and northwest of Tatoosh, close to the canyon. The preliminary report of the
1995 whiting, Merluccius productus, cruise on the NOAA ship Miller Freeman (in pilot

13


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annuli, centered on Tatoosh (Figure 3). Annulus width was set at 2 km (except for the first
and last categories, see Figure 3) for the initial analysis, but later reduced to 1 km to
increase resolution.

Because annuli were of constant width but increasing radius, the number of space minutes
encompassed by each ring should theoretically grow. This relationship is somewhat
subverted by the U. S. mainland, which cuts off approximately 25% of each ring, as well
as the boundaries of our survey (Figure 3; Table 3). Conversely, the percent of units we
sampled should decrease, in part because of the hypersampling within the area bounded by
the short transects, and in part because we are imposing a circular structure on data
collected rectilinearly. This problem is largely unaddressed in this pilot project, as the
number of times we were able to repeat any transect was small enough to make adequate
measures of variance unreliable. However, we did attempt to take sampling differences
into account: density as a function of distance analysis was cut off for annuli with fewer
than four unique space minutes sampled for either seabird or hydroacoustic data as the

limiting factor. Data are presented as the average seabird density (per space minute) by
distance category, in km.

In order to factor out the effect of Tatoosh on the pattern of seabird density, by species,
data were fit to the following simplistic diffusion function:

Density = 8, + B,(Distance from Tatoosh)*

where “Density” refers to seabird density within each unique space minute categorized by
annulus from | to 22 km distant from the island. The square of the distance was used
because the birds could diffuse anywhere within the area of a circle of radius equal to
“Distance from Tatoosh.” For each of the four main species, residuals from these
functions (observed density - expected density) were averaged within annulus (i.e. sample
size becomes 22) and linearly regressed against average Sa within each annulus to test the
association between nekton and seabird density unrelated to island effects.

For regressions in which the slope was a significant predictor of the relationship between
birds and nekton, a Monte Carlo simulation was used to assess the probability that the
significance level of the relationship was a non-random event: seabird residuals were re-
shuffled and then linearly regressed against nekton 1000 times. For each regression, a
significance level (p-value) was calculated. Thus, we created a “population” of significance
levels given 1001 different combinations of the data, 1 actual and 1000 manufactured. A Z
statistic was used to determine the probability that the actual significance value was part of

10


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Table 2. Numbers of sightings and birds counted

#Sightings[#Birds Counted 2) 3% ‘of Sightings:|=% of Count |
10 13 0.36% 0.08%
10 10 0.36% 0.06%
121 1786 4.31% 11.04%
850 4034 30.30% 24.94%
6 7 0.21% 0.04%
929 4394] 33.12% 27.16%
9 238 0.32% 1.47%
4 3 0.14% 0.02%
135 332 4.81% 2.05%
33 38 1.18% 0.23%
338 1211 12.05% 7.49%
306 3962 10.91% 24.49%
5 55 0.18% 0.34%
45 87 1.60% 0.54%
4 7 0.14% 0.04%
TOTALS 2805) 16177| |



Table 3. Actual and potential space minutes sampled at different distances from Tatoosh Island

0-3 10 310.00%
3-5 31 222.58%
5-7 26 126.92%
79 44 54.55%
9-11 35 37.14%
11-13 46 47.83%
13-15 42 52.38%
15-17 37 54.05%
17-19 26 46.15%
19-21 ; 32 50.00%
21-23 27 51.85%
23-25 24 50.00%
25-28 4 7 57.14%
Totals 292 | 387 I 75.45%



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colony most probably affected by the Tenyo Maru oil spill - Tatoosh Island. The specific
goals of the study were to:
1.) Describe the distribution and abundance of seabirds on the water around Tatoosh Island
a.) during the same time of year as the original spill (22 July 1991), and
b.) during the period when chicks were present on the colony (late July through
August), thus maximizing the probability of observing seabird-preybase
interactions.
2.) Concurrently sample forage fish using hydroacoustic surveys.
3.) Sample fish directly to groundtruth hydroacoustic data.
4.) Determine the degree to which seabird distribution and abundance was associated with
forage fish densities versus other non-food factors.
5.) Determine the efficacy of survey techniques for potential future use in ongoing
restoration efforts.

Methods

All data were collected during daylight cruises from 24 to 27 July 1995 (Table 1). Seabird
and hydroacoustic data were collected aboard the 20m WDFW vessel G. H. Corliss.
Vessel speed ranged from 7 to 10 knots. All transects were conducted along a rectilinear
grid centered on Tatoosh Island (48° 24’N, 124° 44’ W), ranging from 48° 18’N to 48°
32’N and 124° 37’ W to 124° 59’ W. Data were collected on latitudinal legs only.
Transects were divided into “long” and “short,” operationally defined by length, position
relative to the island, and latitudinal separation (Figure 1). On long transects hydroacoustic
data were collected continuously and seabird data were collected in 15 minute sampling
periods spaced by intervals of 5 minutes. On short transects, all data were collected
continuously. Vessel position was calculated by GPS and recorded at one minute intervals.
Forage fish groundtruthing was attempted on the 15m Research Vessel Kittiwake
(Biomarine Enterprises).

Data Collection

Seabirds: Seabird distribution and abundance were collected following strip transect
methods outlined in Gould and Forsell (1989). One or two observers identified all seabirds
south of the vessel within a 300 m wide strip defined by the axis of transit. Only birds
lateral to and foreward of the observer were identified. All observations were immediately
entered into a TurboBasic program using an HP palmtop computer, which stamped each
entry with the date and time. The program prompted the data recorder to type in


P. F. Springer (eds.) Marine Birds: Their Feedings Ecology and Commercial Fisheries
Relationships. Can. Wildl. Ser. Spec. Publ.

Schneider, D. C., Harrison, N. M., & Hunt, G. L. Jr. 1990. Seabird diet at a front near
the Pribilof Islands, Alaska. Stud. Avian Biol. 14:61-66.

Schneider, D. C., & Piatt, J. F. 1986. Scale-dependent correlation of seabirds with
schooling fish in a coastal ecosystem. Mar. Ecol. Prog. Ser. 32:237-246.

Simrad Subsea AS. 1993. Simrad EK 500 Scientific Echo Sounder. Simrad Subsea AS
Documentation Department. Operator Manual P2170E.

Speich, S. M., & Wahl, T. R. 1989. Catalog of Washington Seabird Colonies. OCS Study
MMS 89-0054, Biological Rept. 88(6).

Springer, A. M., Roseneau, D. G., Lloyd, D. S., McRoy, C. P., & Murphy, E. C. 1986.
Seabird responses to fluctuating prey availability in the eastern Bering Sea. Mar. Ecol.
Prog. Ser. 32:1-12.

Veit, R. 1994. Pelagic communities of seabirds in the South Atlantic Ocean. Ibis 137:1-10.

Veit, R. R., Silverman, E. D., & Everson, I. 1993. Aggregation patterns of pelagic
predators and their principal prey, Antractic krill, near South Georgia. J. Anim. Ecol.
62:551-564.

Vermeer, K., Morgan, K. H., John Smith, G. E., & Hay, R. 1989. Fall distribution of
pelagic birds over the shelf off SW Vancouver Island. Colonial Waterbirds 12:207-
214.

Wahl, T. R. 1984. Distribution and abundance of seabirds over the continental shelf off
Washington. WA Dept. of Ecology.

Warheit, K. I. 1995. NOAA Ship McArthur Research Cruise - Olympic Coast Marine
Sanctuary, Preliminary Cruise Summary: Seabirds.

Wolanski, E. & Hamner, W. M. 1988. Topographically controlled fronts in the ocean and
their biological influence. Science. 241:177-181.

Woodby, D. A. 1984. The April distribution of murres and prey patches in the
southeastern Bering Sea. Limnol. Oceanogr. 29:181-188.

17


October 1986-87. NOFU - Northern Fulmar; A - adult; all other abbreviations as in
Figure 2. Note that the most abundant species, CAGU, is not represented on this
graph. Data originally from Table 3, Vermeer et. al. 1989.

Figure 11. Theoretical relationship between the cost of seabird foraging (a linear function
of distance traveled) and the gain (a function of the quantity and quality of food).
Because net gain decreases as distance increases, seabirds should forage close to their
breeding colonies even if there are higher gross gains within their range. After Figure
5, Schneider et. al. 1990.

19


along a broad swath of marine habitat, usually encompassing the entire outer coast from
nearshore to offshore environments. Occasionally surveys have been run repeatedly, such
that estimates of error could be made. Wah! (1984) and Briggs et. al. (1992) sampled the
outer coast using both boat and aerial surveys, and Warheit (1995) used boat only. In
general, all studies concluded that shearwaters, gulls, and alcids were the most abundant
groups of seabirds in the nearshore and shelf environment during July and August. Wahl
(1984) estimated these populations at: shearwaters - 740,000 (62% of total sample), larids -
150,000 (13%), and alcids - 195,000 (16%), respectively. Briggs et. al. (1992) estimated
maximum seabird abundance in the shelf environment in July of each year sampled (1989
and 1990), and maximum abundance in July of 1990 for the slope environment. Warheit
(1995), in a preliminary report of the NOAA ship McArthur (21-27 July 1995) presents
data indicating the three most abundant species on-transect were Sooty Shearwaters
(SOSH), Puffinus griseus (56% of total sample), Fork-tailed Storm-petrels, Oceanodroma
Surcata (21%), and Common Murres (COMU), Uria aalge (13%). Larids, in total,
accounted for only 2% of the total sample. Vermeer et. al. (1989) surveyed the distribution
and abundance of seabirds off the southwest coast of Vancouver Island during September
and October 1986-87. The lower end of the survey area overlapped with the survey area

_ for this pilot study. In contrast to the Washington coast surveys, Vermeer et. al. (1989)

" found that California Gulls (CAGU), Larus californicus were the most abundant species,
followed in rank order by Sooty Shearwaters, Cassin’s Auklets, Northern Fulmars,
Fulmar glacialis, and Common Murres. Glaucous-winged Gulls (GWGU), Larus
glaucescens, and Rhinoceros Auklets were not well represented in the data. Vermeer et. al.
(1989) noted that CAGU abundance, specifically large feeding aggregations, were typically
found in conjunction with fishing trawlers.

Although these surveys do provide a clear and consistent picture of seabird diversity along
the outer coast of Washington, they are not detailed enough to provide information on
seabird distribution and abundance around single known breeding colonies. Furthermore,
none of these studies have concurrently sampled forage fish.

System Background

In 1991, the Tenyo Maru oil spill resulted in the recovery of 4,300 bird carcasses, and the
death of an unknown additional number. Ten species representing 88% of this count breed
on Tatoosh Island, the closest breeding colony to the spill and one of the largest seabird
colonies on the outer coast of Washington State (Speich and Wah! 1989). The vast
majority (73%) of the carcasses were Common Murres. Following the oil spill, murre


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Although it is obvious that there should be a correlation between the distribution, and
perhaps the abundance, of seabirds and their prey this association is “noisy” for several
Teasons: Predators would not necessarily be expected to find all patches of prey,
particularly when food is not limiting. Thus, while some aggregations of seabirds may be
associated with concentrations of prey, other prey concentrations will remain “untouched.”
Prey patch size may also play a role in the tendency for seabirds to aggregate. Heinemann
et. al. (1989) has suggested that krill predators may forego small patches of known location
in favor of continued searching for large patches. Seabirds may distribute themselves
according to other factors than food - gregarious species are often found in large rafts in the
immediate vicinity of the breeding colony (Parrish pers. obs.). Thus, some aggregations
of seabirds may not be associated with concentrations of prey. Taken together, this means
that although seabirds and fish may co-occur, both predator and prey would also be

expected to be found separately. Veit et. al. (1993) found just such a relationship between
” several species of Antarctic seabird and the principal prey item, krill: “...of the 962 data

points on each graph in Figure 8, fewer than 10 represent the coincidence of large numbers
of both predators and prey.” (pg. 561).

Attempts to find correlations between seabird distribution and abundance, and that of their
prey have been varied. Several studies have found that seabird density was an indicator of
the presence of prey (Obst 1985, Safina and Burger 1985, Heinemann et. al. 1989), at least
one study has found no relationship between seabird and prey density (Woodby 1984),
while still others have found significant relationships between seabird and prey abundance
(Veit et. al. 1993, Schneider and Piatt 1986, Piatt 1990). Several generalizations can be
drawn from these studies. First, the amount of variance in predator abundance explained
by prey abundance is usually small, albeit significant (see for instance Veit et. al. 1993).
Second, there appears to be a “threshold” in prey density below which there is no apparent
correlation between predators and prey (Piatt 1990, Veit et. al. 1993). Third, significant
relationships were found at “coarse” scales, usually greater than one nautical mile, often
tens of miles (see for instance “event” and “block” scale of Heinemann et. al. 1989; 8 and
16 minute frame size of Piatt 1990). Fourth, at larger scales elevated seabird densities are
ultimately related to colony size. This last point brings one full circle: large, stable colonies
are found in areas with predictable food supplies.

Few systematic studies of the distribution and abundance of seabirds off the Washington
Coast have been performed. Most studies have attempted to describe seabird diversity


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bathymetry, sea surface temperature, conductivity and sea state might also improve our
ability to link seabird and forage fish density to underlying physical structuring processes.
Finally, we strongly recommend all future data be collected relative to both direct output of
clocktime and space (using DGPS).

Although the results of this pilot study shed light on the importance of the interaction
between seabirds and fish, limited data collections, both in space and time, prevented an
accurate portrayal of either seabird or nekton distribution and abundance, especially as
regards variance estimation. As food limitation is a product of total food availability at any
one time, as well as the variance inherent in the system, both types of data need to be
collected. We recommend repeating transects to increase sample size within a sampling
period, and more than one sampling period. Sampling periods should be relative to the
breeding phenology on colonies of interest (e.g. Tatoosh Island): pre-egg/egg stage (mid-
late June), chick stage (late July), fledging/migratory stage (late August).

References
Aebischer, N. J., Coulson, J. C., & Colebrook, J. M. 1990. Parallel long-term trends
~ across four marine trophic levels and weather. Nature 347:753-755.

Ainley, D. G., & Boekelheide, R. J. 1990. Seabirds of the Farallon Islands. Stanford
University Press, Stanford CA.

Bayer, R. D., Lowe, R. S., & Loeffel, R. E. 1991. Persistent summer mortalities of
Common Murres along the Oregon central coast. Condor 93:516-525.

Briggs, K. T., Varoujean, D. H., Williams, W. W., Ford, R. G., Bonnell, M. L., &
Casey, J. L. 1992. Seabirds of the Oregon and Washington OCS, 1989-1990. In: J. J.
Brueggeman (ed.) Oregon and Washington Marine Mammal and Seabird Surveys,
Final Report to MMS, May 1992.

Brodeur, R. D., & Pearcy, W. G. 1986. Distribution and relative abundance of pelagic
nonsalmonid nekton off Oregon and Washington, 1979-84. NOAA Tech. Rept. NMFS
46.

Freeland, H. J. 1992. The physical oceanography of the west coast of Vancouver Island.
In: K. Vermeer, R. W. Butler, & K. H. Morgan (eds.) The Ecology, Status, and
Conservation of Marine and Shoreline Birds in the West Coast of Vancouver Island.
Can. Wildl. Serv. Spec. Publ. pp:10-14.

Furness, R. W., & Monaghan, P. 1987. Seabird Ecology. Blackie, London.

Gould, P. J., & Forsell, D. J. 1989. Techniques for shipboard surveys of marine birds.
U. S. Fish. & Wildl. Tech. Rept. 25. USDOI, F&WS, Los Angeles CA.

15


Figure Legends

Figure 1. Map of the pilot study area, with transects outlined by date. Hydroacoustic and
seabird data were collected from the same vessel, WDFW vessel G. H. Corliss. See
table 1 for relevant inforraation.

Figure 2A & B. Number of (A) observations (sightings) and (B) individuals, by species.
Data from all transects. PIGU - Pigeon Guillemot, TUPU - Tufted Puffin, CAGU -
California Gull, PECO - Pelagic Cormorant, SOSH - Sooty Shearwater, THAU -
Rhinoceros Auklet, COMU - Common Murre, GWGU - Glaucous-winged Gull (also
included are Western Gulls and Glaucous-winged Western hybrids), HEGU -
Hermann’s Gull. See Table 2 for relevant statistics.

Figure 3. Graphic representation of distance categories, relative to all space minutes within
the sample area. In the final analysis, distance categories were 1 km in width and
ranged from 1 to 22, excluding 20 due to small sample sizes. See Table 3 for relevant
Statistics.

Figure 4. Average densities of the four main species within the short transect sample area.
Data include both short an¢ long transects. Sample size ranges from 2 to 5.

Figure SA-D. Average densities of the four main species for the long transects only.
Sample size ranges from 1 to 2.

Figure 6A-D. Average densities of the four main species as a function of distance from
Tatoosh, in 1 km categories. Data were averaged within unique space minutes, and
then within distance category (i.e. all unique space minutes were equally weighted
regardless of initial sample size). Open bars are actual data, lines are modeled values
using a simplistic diffusion function (see text for details). Dashed lines are 5 and 95%
confidence about the expected value.

Figure 7. Average nekton density (measured as Sa) as a function of distance from Tatoosh,
in 1 km categories. Data prepared as in Figure 6.

Figure 8A-D. Averaged residual densities of the four main species as a function of
averaged nekton density (measured as Sa), one sample per distance category: 1 to 22
km, except for km 20. See Tables 4 and 5 for relevant statistics.

Figure 9A & B. Nekton density (measured as Sa) as a function of size of observed seabird
rafts (A; indicated by large, sometimes multispecies aggregations of resting birds) and
feeding aggregations (B). Nekton density within the same space minute (see Figure 1)
as well as a 3 minute range surrounding the relevant “raft” minute are plotted. The
dotted line in B represents the maximum value on the Y-axis of A.

Figure 10. Seabirds per km of transect as a function of distance from shore from surveys
off SW Vancouver Island (including the sample area in this study) in September and

18