Download Appendix S1. Details of survey methods, datasets and analyses

Appendix S1. Details of survey methods, datasets and analyses
Aerial HD methods
Monthly surveys were conducted from January 2010 to June 2012. Data from 2009 was
available but not included, as corresponding boat data were not available prior to January
2010. For each monthly survey, planes flew at an altitude of approximately 600 m. Transects
were aligned north-south (see Figure S1) and were approximately 3 km apart. The whole
zone was covered over the course of a day, with two planes flying concurrently. Alternate
transects set equidistant between the first set of transects were covered on the following
day or another subsequent day when weather prevented surveying the following day. High
definition video images were recorded on four cameras with a resolution of 1-3 cm (at the
sea surface). The width of the transects were typically around 200 m wide. However, this
width varied throughout the course of the study dependent on the resolution of the
cameras, which increased over time, and the height the plane flew at. Camera resolutions
were usually the same across all four cameras but during experimental periods, resolutions
varied. The ground coverage widths of each camera were therefore either 50 m, 75 m, or
125 m. This variation in transect width was allowed for in the analysis when using monthly
survey effort area as an offset variable in the habitat association modelling.
The digital images were viewed by trained operators who detected and identified individual
objects for further analysis. For quality assurance of detected objects, a blind review of 20%
of the raw data was carried out to ensure that 90% agreement was reached between
ornithologists. Where 90% agreement was not met, the data were re-examined for objects,
with additional training given to improve reviewer performance. Images marked as requiring
further analysis were then passed to specialist ornithologists at WWT Consulting, who aimed
to identify the objects to species level with the aid of video enhancement software. A
randomly selected 20% sample of the images were also examined (blind) by trained
ornithologists and compared to the original identification. If there was less than 90%
agreement or any significant discrepancies arose, the images were re-reviewed by an
additional ornithologist who acted as an adjudicator to make a decision on the correct
observations. Birds were assigned to one of 19 species ID groups (see Table S1) and where
possible, birds were also identified to species level. Identification to species ID groups (and
species) for each individual bird were categorised by the confidence in the assignment,
either ’certain’ (as certain of identification as it is reasonably possible to be), ‘probable’
(between 50% and 95% towards certainty of identification over any other species) or
‘possible’ (between 25% and 50% towards certainty of identification over any other species).
In January 2011 camera pixel resolution was increased from 3 cm to 2 cm and to 1 cm from
one of the four cameras for the months of April and May 2011. A transect spacing of 3 km
was retained across the zone, however in tranche A, an area within the zone, the transect
spacing was reduced to 1 km, following recommendations from Austin & Ross-Smith (2011).
Boat-based methods
From January 2010, boat based surveys were undertaken on a monthly basis within the zone
on board the survey vessels MV Sea Profiler (January-September 2010) and MV Vigilant
(October 2010 onwards). The survey area was covered by a series of 21 parallel transects
located 8 km apart. During months in which weather permitted further surveys, up to an
additional 20 transect lines were surveyed. These were also 8 km apart, and set equidistant
between the first set of transect lines, so all lines were separated by 4 km. The number of
days taken to survey the zone varied and whilst primary transects were completed in all
months, poor weather prevented complete coverage of secondary transects in some
months.
Following Camphuysen et al. (2004), observers on the boats collected data from a 90° area
between the front and the bow of the vessel. Birds recorded on the sea surface, on one side
of the boat, were allocated to distance bands perpendicular to the transect line (0-50 m, 50100 m, 100-200 m, 200-300 m, >300 m) at one minute intervals. For flying birds, observers
used the snapshot method (Tasker et al. 1984; Camphuysen et al. 2004; Maclean et al. 2009),
whereby birds flying within an area 300 m ahead (300 m) and perpendicular to the observer
(90,000 m2) were counted at a defined time interval, according to the speed of the vessel.
For example, at 9.7 knots the snapshot was taken every minute. These “snapshots” to
provide a single count of birds within the transect, up to 300 m from the boat on one side of
the boat, at that time. Three observers were present during the boat surveys; one to collect
the data perpendicular to the boat, one to collect ’snapshot’ counts of flying birds, whilst a
third was resting. Roles were rotated during the surveys, and the surveyor resting
communicated with the bridge and informed the surveyors when to cease and commence
observations. Observers were equipped with 8x or 10x binoculars, hand-held Global
Positioning System (GPS) devices, Dictaphones and a rangefinder tool.
Detectability of birds on the sea surface
For birds on the sea surface, we extended the definition of detection to also include
identification, hence both detectability and probability of identification decrease over
distance from the transect line. Within 50 m from the transect line 98% of individuals on the
sea surface were identified to species, and therefore it is likely this assumption will hold.
Therefore, the removal of observations of birds on the sea surface not identified to species
will not bias the results as long as all birds on the transect line are identified. Approximately
two thirds of the observations not identified to species were of birds seen in flight, and their
omission may lead to a small bias in the analysis.
For some species there were fewer observations within the first distance band compared to
the second distance band. This indicated a violation of one (or more) of the assumptions of
distance sampling (Buckland et al. 2004). The likely reasons for this pattern were assessed by
considering each of species ID groups separately. Decisions were then made on whether to
pool observations from the first two distance bands, or use left-truncation in the analysis.
Auks on the sea surface were often observed by observer looking ahead of the boat to swim
away from the boat’s trajectory towards further distance bands. Movements of birds beyond
100 m from the boat were thought to be rare however. Therefore the auks were assumed to
be displaced from the first to the second distance band, and consequently the first two
distance bands were pooled for the analysis. Large gulls and northern gannets were mostly
seen flying, rather than on the sea surface. Those seen on the sea surface are normally seen
from a distance, at which the distance bands are hard to define. They had normally flown
away by the time the boat was nearby. It was unlikely any were missed, as they are large and
easily detectable, but the difficulty of assigning birds to distance bands, led to the first two
distance bands being pooled. There were also fewer detections of black-legged kittiwakes
and northern fulmars in the first distance band compared to the second. These species, along
with northern gannets and some gull species, were noted to associate with the vessel (and
observations were flagged up when birds were closely associated with the boat). This
association behaviour may have led to black-legged kittiwakes, northern fulmars as well as
gulls and northern gannet being detected within band B more than band A, hence the first
two distance bands were also pooled for these species.
Details of covariates in the spatial abundance model
i.
Seasonal definitions
A variable of ‘season’ was included in this analysis (breeding versus non breeding), and was
allowed to interact with environmental covariates to represent differences in relationships
due to seasonal constraints. All analyses were restricted to two-way interactions to avoid
over-parameterisation of models. Based on a combination of observations in the Dogger
Bank Zone, species knowledge, and the definitions provided by Kober et al. (2010, 2012),
breeding periods were defined as: northern fulmar, March-September; northern gannet,
April-September; lesser black-backed gull, May-August; great black-backed gull, April-August;
black-legged kittiwake, April-September; common guillemot, May-July; razorbill, May-July;
Atlantic puffin, April-August. Little auks were recorded in the area only on during nonbreeding seasons.
ii.
Sea depth
The links between the spatial abundance of seabirds and sea depth have been widely
observed. For example, for common guillemot, Yen et al. (2004) found a negative
relationship between abundance and sea depth, and Schneider (1997) noted that auks were
associated with inshore shallow water masses and northern fulmars offshore deeper water
masses. Here, we use the depth of ocean across the zone (‘depth’) as a proxy for general
vertical prey availability (e.g. Kissling et al. 2007; Amorim et al. 2009). Sea depth data were
obtained from a high-resolution modelled dataset (TCarta 2012). In this study, sea depth
varied between 18 to 59 m and was greatest in the north of zone. Therefore, the range of
sea depths was small compared to these other studies (e.g. Yen et al. 2004), and therefore
the same relationships with sea depth seen in other studies were not necessarily expected.
In addition, the diet and habitat use of adult birds may vary across the year; therefore, an
interaction between sea depth and season was also included in abundance models.
iii. Sandeel abundance
A fixed measure of the density of Danish fishing vessels of over 15 m in length was also
included (‘sandeels’), obtained from the Danish Vessel Monitoring System (see, for example,
Fock, 2008). This variable effectively represented the sandeel fishery, providing a useful
proxy for spatial sandeel abundance, which is the most important food resource in the North
Sea for a number of the species considered in this analysis (Monaghan 1992; Oro & Furness
2002; Daunt et al. 2008; Cook et al. 2014). As such we would expect a positive relationship
between sandeel and bird abundance for these species Outside of the breeding season,
these species can utilise different habitats and different prey species (e.g. Linnebjerg et al.
2013; Thaxter et al. 2013) and therefore an interaction between season and sandeels was
also included in the models.
iv.
Distance to colony
During the breeding season, species are constrained in foraging range from their colonies,
which may influence habitat use and therefore the abundance of species in a given area. An
inverse-distance squared weighted species-specific variable (‘dist_colony’) was included in
models (e.g. Garthe 1997; Hyrenbach et al. 2007) to describe the accessibility of each grid
square (i) to birds from breeding colonies (k) of varying sizes, with greater weight given to
those closer colonies:
𝑛
Distance to colonies𝑖 = ∑ (pop𝑘 ∗ (
𝑘=1
1
dist 𝑘𝑖 2
))
Where popk is the population size of colony k and distki is the distance from square i to colony
k. Squares with higher values were therefore more ‘accessible’ to birds, either due to being
very close to a small colony, or reasonably close to a number of larger colonies. Distance to
colony was expected to be negatively correlated with abundance for most species (e.g.
Garthe 1997). Since here we include this variable as a weighted reciprocal measure, we
expected a positive relationship to be shown. Colony size data were taken from Mitchell et
al. (2004), and calculations were performed in ArcInfo GIS (ESRI 2010). Seabirds are not
constrained to their breeding colonies outside the breeding period, so a season*dist_colony
interaction was also specified to test different regression slopes depending on season.
v.
Distance to coast
Species may differ in whether they forage in near-shore, offshore or pelagic areas, and,
therefore, their abundance may simply be linked to distance from the nearest shore. For
example, negative relationships with distance from coast have been recorded in terns and
small gull species (Garthe 1997, Amorim et al. 2009), but positive relationships have been
shown for lesser black-backed gull (Garthe 1997). In this study, the distance to the nearest
European coastline (‘dist_coast’) was calculated for each grid square in GIS ArcInfo (ESRI
2010). However, given that habitat use is likely to vary through the year, an interaction with
season was also included.
vi. Sea surface temperature
Numerous studies have found that seabird distribution at sea may be driven though sea
surface temperature For example, a negative relationship has been recorded for northern
fulmar (Ainley et al. 2005) and common guillemot (Oedekoven et al., 2001), but positive
correlations have been recorded for some other species such as terns (Amorim et al. 2009).
In this study sea surface temperature (SST) data were taken from the GHRSST Level 4 OSTIA
Global Foundation Sea Surface Temperature Analysis (Stark et al. 2007), available on a daily
basis. This dataset is a blended High Resolution dataset, produced by the UK Met Office using
optimal interpolation (OI) on a global 0.054 degree grid (Cylindrical Lat-Lon, WGS 84
projection). The SST data were matched to the modelling grid. The range of values for SST in
this study were relatively small, between 3.4 and 17.3°C, hence relationships with
abundance of species were expected to be less apparent than other studies (e.g Ainley et al.
2005). SST varies itself on a daily basis and was therefore not included in any interactions.
vii.
Spatial location
Within initial habitat association models, a non-mechanistic location (x,y) variable was
originally examined (e.g. see Mackenzie et al. 2013). However, there was co-linearity
between individual latitude and longitude centroids of the analysis squares and other
predictor variables, the latter of which were of greater interest ecologically to assess
relationships. The longitude showed a high Pearson’s R correlation (> 0.7) with distance to
coast (R = 0.94) and for most species there was a high correlation with the weighted inverse
distance to nearest colonies variable (common guillemot, R = 0.97, razorbill, R = 0.96,
Atlantic puffin, R = 0.96, black-legged kittiwake, R = 0.94, great black-backed gull, R = 0.76,
lesser black-backed gull, R = 0.92, and northern gannet, R = 0.98). The latitude variable
showed high (> 0.7) correlation with sea depth (R = 0.85), due to an apparent north-south
gradient in sea depth across the zone, as well as distance to coast (R = 0.81). Such nonmechanistic variation is, therefore, explained by our predictor variables, which we deemed
more meaningful than assessment through spatial coordinates.
Model Selection for habitat association models
In this study, to produce final estimates of species abundance, habitat association models
were fitted to 199 realisations (199 separate models for each species), each time starting
with a fully saturated model (see main article for the model). Thereafter, model selection
was conducted through backwards stepwise removal of least significant terms until a
minimum adequate model was reached, using z-tests and chi-squared tests as diagnostic
criteria for variable inclusion. For all 199 realisations, the number of occasions that each
variable was retained in the final minimum adequate models is presented in Table 1 in the
main article. Too many individual models were tested to sufficiently reproduce each one
with its respective diagnostic criteria. However, as supplementary information to support
Table 1, Table S3 is presented to show the final minimum adequate models selected for all
199 models per species. Table S3 also presents the number of occasions that individual
models were selected.
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