An Assessment of the Risk of Shipping Traffic on Seabirds in the Aleutian Archipelago

262 Pages·2014·35.81 MB·English

An Assessment of the Risk of Shipping Traffic on
Seabirds in the Aleutian Archipelago
Martin Renner 1 2 Kathy J. Kuletz 3
November 24, 2014
1Tern Again Consulting, 388 E. Bayview Ave, Homer, AK 99603, USA
2corresponding author, email: [email protected]
3U.S. FWS, Migratory Bird Mgmt., 1011 E. Tudor Rd., Anchorage, AK 99503, USA

Executive Summary
A major international shipping lane intersects the Aleutian Archipelago, an area with some
of the greatest concentrations of seabirds in the Northern Hemisphere. Seabirds using
the area include local breeders, some of which are migratory, migrants from around the
Pacific which come to feed in the area, and species that breed farther north that come
to the Aleutians Archipelago chiefly in winter. Here we identify areas where seabirds are
particularly at risk from shipping incidences, especially large oil spills.
Pursuant of this goal, we built a stochastic model for each seabird species to predict
the expected densities for any given location and time within 100km of land throughout
the Aleutian Archipelago. The model was based on at-sea seabird surveys collected in all
seasons within a 500km buffer around the study area, a suite of real-time and static en-
vironmental predictors, location and size of colonies, and month as a seasonal component
We fit the model using Random Forests. For each month, we applied ordinary kriging to
the residuals of the current and adjacent months. Summing these models, over all species,
we produced seasonal maps of densities of all seabird species combined. The most com-
mon seabird species were short-tailed shearwater (Puffinus tenuirostris), northern fulmar
(Fulmarus glacialis), least auklet (emphAethia pusilla), and crested auklet (A. cristatella).
Highest densities of all species combined were along the shelf edge in the eastern and central
Aleutian Archipelago during summer, especially to the south of the islands. During winter,
seabird concentrations were highest around Unimak Pass and in coastal areas close to the
islands. Seabird densities peaked in August, when large numbers of short-tailed shearwater
aggregated in the Aleutian Archipelago.
Shipping data comes from the hourly to daily position reports of the Voluntary Observ-
ing Ships (VOS) weather reporting program (http://www.vos.noaa.gov/vos_scheme.
shtml). We use all data available online, spanning the years 2001–2013 and interpolate
the position data by months using a kernel density estimator.
Risk is here defined as the product of likelihood of an incident (for which we used
shipping density as a proxy) times the expected severity of an incident (for which we use
bird densities). To give both components equal weight, we rescale each by dividing by their
respective standard deviations, giving them equal variance. Calculating the risk product,
we find that the highest areas of risk are year-round in Unimak Pass and approaches to
the pass. In an alternative approach, we identify areas of above- and below-average seabird
densities combined with above- and below-average shipping densities. Areas of above-
average shipping and bird densities include Unimak Pass, Buldir Pass, Samalga Pass, and
the shelf-break zone (200m isobath) to the south of the Alaska Peninsula. Especially
Unimak Pass is at high risk throughout the year, whereas Buldir Pass is in this category
only during the summer months.
The results of this report, especially all model outputs of predicted monthly seabird
densities by species will be available on the Alaska Ocean Observing System website
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(http://www.aoos.org/). Potential applications for this website are numerous, includ-
ing outreach programs, information for the general public. Managers and biologists will
be able to quickly check what species to expect at what densities a particular time and
location. This knowledge could, e. g. help direct a response and management action in
the event of an oil spill, or be used in planing preventative measures, or be used in spatial
planning, like the delineation of marine reserves.
Survey data is sparse in offshore areas, and in winter, especially in the western Aleutian
Archipelago. Nevertheless, a bootstrap analysis shows that the greatest model uncertainty
is during summer along the Aleutian Trench. Future survey efforts should endeavor to
cover these areas of high model uncertainty. In fact, so little is known about the ecology of
the Aleutian Trench, that a comprehensive ecological program investigating the Aleutian
Trench would be most beneficial.
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Contents
1 Introduction 1
2 Seasonal Model 5
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
Study area . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
Seabird data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
Independent variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
Statistical methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
Software . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
Model performance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
Variable importance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
Individual species models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
Combining all species . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
Management implications . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
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CONTENTS CONTENTS
3 Risk Assessment 34
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
Seabird vulnerability index . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
Shipping traffic data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
Risk Calculation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
Study Area . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
Software . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
Distribution of seabird vulnerability . . . . . . . . . . . . . . . . . . . . . . . 42
Seasonal distribution of shipping traffic . . . . . . . . . . . . . . . . . . . . . 42
Spatial distribution of Oil Spill Risk to seabirds . . . . . . . . . . . . . . . . 44
Seasonal patterns of oil spill risk . . . . . . . . . . . . . . . . . . . . . . . . . 46
Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48
Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53
Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53
4 Next steps 54
Model improvement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55
Distribution of fishing effort . . . . . . . . . . . . . . . . . . . . . . . . . . . 55
Data gaps . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56
Expanding geographic range . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57
Web interface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57
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CONTENTS CONTENTS
A Four-letter species codes 68
B Changes in species assignments and pro-ratings 71
Codes changed by assignment . . . . . . . . . . . . . . . . . . . . . . . . . . 71
Pro-ratings of unidentified individuals . . . . . . . . . . . . . . . . . . . . . . 72
C Seabird colonies 73
D Modeled and observed seasonal patterns 82
E Oil spill vulnerability index 87
F Model output for each species and season 90
G Modeling code 166
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List of Figures
1.1 Wreck of the soybean freighter M/V Selendang Ayu in December 2004 off
Unalaska Island, in the eastern Aleutian Archipelago, and one of the crested
auklets (Aethia cristatella) oiled by the spill. Photos courtesy of U.S. Fish
and Wildlife Service. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
1.2 Known shipwrecks within the Aleutian Archipelago from 1950–2013. Wrecks
associated with World War II are therefore excluded. Shipwreck data cour-
tesy of Eric Nelson (Alaska Maritime National Wildlife Refuge). . . . . . . . 3
2.1 Location of the study area (white), and geographic features mentioned in the
text. Also shown is the 200m isobaths, marking the edge of the continental
shelf. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
2.2 Study area (green), model domain (gray), and survey effort by season. We
used the data within the study area to build the habitat models, but made
predictions and applied residual kriging to all data within the model domain,
being a 500km buffer around the study area. Final predictions were then
subset back to the study area. Each blue dot represents a typical 10-min or
3-km section of survey transect. . . . . . . . . . . . . . . . . . . . . . . . . 9
2.3 Seasonal distribution of sampling effort by month. While there was some
sampling in every month, winter months (November to February) had low
coverage. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
2.4 Distribution of depth in the study area and in the sample of surveys. Shown
are violin plots (Hintze and Nelson, 1998), a standard box-plot over a kernel
density plot. Samples are strongly biased towards coastal shallow waters. . . 11
2.5 Static seascape variables used to model seabird densities. To show the
spatial patterns better, logBathySlope, prod, M2tideV, M2tidalshear, and
M2tideHstresswerelog+1-transformedforthisfigure. Inaddition,M2tidalshear
and M2tideHstress were capped at mean+1 standard deviation, prior to log-
transformation. See Methods for other variables used in the models. . . . . 15
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LIST OF FIGURES LIST OF FIGURES
2.6 Seasonal variation in sea surface temperature [°C] from satellite remote sens-
ing(ReynoldsandSmith,1994). Shownaremonthlyclimatologies(long-term
averages) calculated from the years 2001–2010. The climatologies are also
used to make predictions of seabird densities. The models are fit using the
actual daily values for the location of each observation. . . . . . . . . . . . 16
2.7 Colony effect by species, being the expected at-sea distribution if seabirds
were to disperse evenly from their colonies. For this visualization, we log-
transformed and rescaled the variable for each species to make spatial distri-
butions comparable (comparing absolute values between species is therefore
not meaningful here). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
2.8 Cumulative distribution of sampling effort over time. Satellite-based sea
surface temperature records are available from 1982 onwards (dashed line,
83% of the data) and were imputed for the remaining earlier years. . . . . . 18
2.9 Analysis pathways used to make seasonal predictions of seabird densities. . . 20
2.10 Modeled densities, being the average of monthly predictions and the uncor-
rected mean observed densities per species. The line represents the 1:1 (slope
= 1) relationship, rather than a regression line. Note that both axes are on
a log-scale. Note that the true density cannot be known. . . . . . . . . . . . 26
2.11 Predicted seasonal distribution of crested auklets (Aethia cristatella, a local
breeder and short-distance migrant in the Aleutian Archipelago. Note how
the model reflects both changes in distribution and changes in abundance
over the year. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
2.12 Variable importance by species in Random Forest model, measured by mean
squared error (MSE) and by node impurity. The size of the circles is pro-
portional to the importance of a particular variable x in the Random Forest
model of species y. Importance measures were standardized within species,
so absolute values are not comparable between species. Variables are sorted
by importance rank average across species (averages given at top of each
figure). See Appendix A for species codes. . . . . . . . . . . . . . . . . . . . 28
2.13 Seasonal changes of predicted average at-sea density for six of the most com-
mon seabird species in the Aleutian Archipelago and densities of all species
combined. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
2.14 Seasonaldistributionofthesumofallseabirdspeciescombined. Specieswere
modeled individually and then summed. Densities were log -transformed to
10
better illustrate large-scale patterns. The contour line represents the 200m
isobath. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
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LIST OF FIGURES LIST OF FIGURES
3.1 Flowchart diagram of this risk analysis. Seasonal seabird distributions were
taken from the results of the analysis described in Chapter 2. . . . . . . . . 38
3.2 Study area (green, a 100km buffer around the Aleutian Islands), and ship-
ping data (interpolated and raw positions from the Voluntary Observer Sys-
tem). We used all available data within a 500km buffer around the study
area to avoid edge effects from the kernel-density model within the study area. 39
3.3 We expressed risk, the combination of the likelihood and severity of an inci-
dent in a given area, in two ways: 1. as the continuous product of likelihood
and severity (left) and 2. defining categories based on the respective means
and mean+1 SD (right). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
3.4 Study area and major geographic features mentioned in the text. Also shown
is the 200m isobath, marking the edge of the continental shelf. . . . . . . . 41
3.5 Seabird vulnerability, calculated from scaling the predicted densities of all
species occurring within the study area (Chapter 2), after scaling them with
an Oil spill Vunerability Index (see Appendix E). . . . . . . . . . . . . . . . 43
3.6 Shippingdensity, basedonakernel-densityestimatorofVoluntaryObserving
System vessel positions; all months combined, on a log-scale. . . . . . . . . . 44
3.7 Spatial distribution of shipping density by month on a log scale. . . . . . . 45
10
3.8 Average relative shipping density in the study area by month. Shown are
the observed monthly densities (solid line) and a cyclical GAM smoother
(dashed line). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46
3.9 Risk of shipping to seabirds (all months combined), expressed as the product
of the seabird and shipping densities, plotted on a linear scale (top) and log
scale (bottom). These plots display the same data, but the linear scale puts
the emphasis on the absolute highest risk (top), whereas the bottom plot
gives better resolution of the lower and intermediate risk areas. . . . . . . . . 47
3.10 Risk of shipping to seabirds (all months combined), by categories. Areas
of elevated risk have above-mean (indicated by captials) shipping or bird
densities, or both. In the highest risk category, shipping and bird densities
are > standard deviation above the mean. Categories were calculated after
averaging monthly shipping densities and seabird vulnerability over the year. 48
3.11 Risk of shipping to seabirds, expressed in five categories, by seasons. . . . . . 49
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