Discussion
Wind regimes are changing, in terms of the mean strength, and the
frequency of extreme weather events35,36. Yet research
on how wind affects seabirds has focused on their at-sea behaviour
(though see37,34). Through our novel application of
CFD, we demonstrate that airflows are also critical in the selection of
breeding habitat. Areas of coastline vary predictably in their exposure
to prevailing wind conditions, and we show this is an important driver
of habitat preference in cliff-nesting auks. Interestingly, while colony
location was predicted by low exposure to the prevailing wind, it was
not predicted by low wind speed. This apparent contradiction is
explained by the fact that windward cliffs can block the oncoming flow,
with the blocking effect increasing with cliff height and slope,
producing low wind speeds and high pressures over large parts of
windward cliffs (bar the top, where flow is accelerated, Fig 3a,b).
Areas of low wind speed therefore occur on both windward and leeward
cliffs (Supplementary Fig. 3), but guillemots select the latter. This
suggests habitat selection is driven as much by the need to shelter
young from the impact of rain or wave action (both of which should
increase with exposure), as it is to shelter from high wind speeds,
which can affect wind chill38 and flight capacity
(either of the guillemots or their aerial
predators31,38). Nonetheless, flight capacity may be
more critical for species such as large albatrosses, which require
relatively high winds to take-off and therefore may be constrained to
nest in exposed areas, despite the intuitive benefits of shelter for
chicks across species.
Our models confirm the role of slope angle in colony selection, with the
densest and largest colonies on Skomer being associated with the
steepest cliffs. Steep slopes offer the possibility of breeding in high
densities with better protection from predators39, as
well as easier access to the sea when chicks jump from their
nests40,41. Yet here we show that steep cliffs with a
south-westerly orientation are avoided on Skomer, even though they are
widely available. This trend was not significant in a previous
assessment of whether colonies varied in aspect31,
confirming our prediction that cliff aspect alone is not a good proxy
for exposure. Furthermore, the fact that slope angle had a lower
contribution in our models than pressure and turbulence, suggests that
colonies are better tuned to wind rather than topographical features.
While guillemots preferentially breed in areas that are not exposed to
the prevailing wind, they cannot shelter from all wind directions. Winds
diametrically opposed to the prevailing direction (here NE winds) will
be problematic for any species breeding in sheltered sites. The penalty
of exposure to NE winds for the 10 largest colonies on Skomer, was a
~10% increase in mean wind speed compared to the same
at-sea wind speeds from the SW. How this might impact birds will depend
on the factors driving the need for shelter and the magnitude of the
wind when it comes from a different direction. Nonetheless, our results
highlight that colonies experience increased exposure from changes in
wind direction, independent of rising wind speeds. Increases in wind
speed, as already observed in the North Atlantic and other
areas35,36, are also likely to be most detrimental to
birds at the nest when accompanied by a change in wind
direction37.
A further challenge potentially facing birds on Skomer in NE and NW
winds is increased turbulence. The absolute levels of turbulence that
birds experience in SW winds are low because the wind speeds themselves
are low. However, in NE winds of the same magnitude, birds experience
both stronger winds and increased turbulence. Wind speed has been shown
to reduce the probability of guillemots landing successfully at their
breeding cliffs31 and turbulence is likely to present
further difficulties for flight control in stronger
winds42,43.
The fact that our models performed better in correctly predicting the
densest colonies, compared to the presence of any breeding birds,
suggests that they work best in predicting high quality habitat.
Previous studies have shown that breeding success increases with the
density of breeding pairs7,44. Appropriate areas that
can support larger numbers are therefore of higher quality. Such areas
have previously been described in terms of the number of walls, slope
and width of the ledge where the egg was incubated, and distance from
the top of the cliff6,7. The ability to predict high
quality breeding habitat without such fine-scale topographical
information is advantageous, as it allows habitat quality to be
predicted in remote and inaccessible sites.
Models of absence should be interpreted with more caution than models of
presence, as cliffs that are unoccupied now may have been occupied in
the past. Indeed, photographs of the breeding cliffs on Skomer from the
1930s provide evidence that numbers were much higher
historically45, and whole island counts undertaken
since 1963 demonstrate that numbers have been increasing since
then46. The relative abundance of common guillemots
makes this less of an issue than for many species where current breeding
activity occurs in a small fraction of the former range. In cases where
populations are increasing, our approach could be extended to see
whether airflow characteristics can predict colony growth rates, or
which areas most likely to be expanded into.
Overall, the fact that 90% of the densest colonies on Skomer could be
predicted solely from variation in pressure values i.e. without the need
for slope angle, is testament to the predictive power of our approach.
CFD is particularly well-suited to modelling habitat selection in
seabirds, as marine and coastal environments experience some of the most
extreme wind conditions47, and wind fields also tend
to be reasonably laminar ahead of islands. A key future challenge will
be to test this approach over larger areas. Combining airflow modelling
with data on rainfall and breeding success will also provide new
mechanistic insight into the basis for habitat selection and how global
change may impact birds at their nesting sites.
Methods
Our approach centres around the estimation of airflow parameters around
Skomer Island (51° 44.271’N, 5° 17.668’W) and the use of these
parameters, in combination with slope angle, from a highly resolved
LiDAR digital elevation model, to predict the distribution of breeding
guillemots on Skomer and then on the neighbouring island of Skokholm.
The 2015 Skomer guillemot breeding bird survey48 was
digitized in ArcMap 10.5.1 (ESRI, Redlands, California) and used to
delineate sections on the island’s cliffs that were occupied by breeding
birds. It was also used to identify the 10 largest colonies (count ≥ 592
individuals) and 11 densest colonies (density ≥ 0.835, birds per sq. m),
with thresholds being selected by visually identifying clear
breakpoints.
A “digital elevation model” (DEM) (50 cm resolution retrieved from Lle
Geo-Portal http://lle.gov.wales) was used to identify cliff habitat by
selecting slopes ≥ 20o (initial trials showed this
value performed well in isolating cliff habitat). The resultant area was
divided into sections according to those used in the breeding survey.
These same sections (71 in total) were used in all further analyses, 38
of which were occupied by breeding birds (Fig. 2 a). The minimum height
of each section was taken as 10 m to account for variation in tide
height (maximum tide height ~ 5 m on the day the DEM was
produced), the maximum wave height (taken to be 3 m), as well as a
minimum distance above water that birds tend to nest, taken to be 2
metres7. The maximum height of each section was
reduced using a minimum distance of 15 m from the top of the cliff. This
distance was the mean proximity of nests from the top of the cliffs for
three major colonies, based on highly resolved theodolite
measurements49.
A similar approach was taken to digitize the distribution of breeding
guillemots on Skokholm from the 2018 breeding bird
survey50 (Supplementary Fig. 1a). However, because the
elevation of Skokholm’s cliffs is much lower, the minimum distance from
the top of the cliffs, was set at 7 m (this value was arrived at in
consultation with the wardens). The small proportion of occupied cliffs
that did not satisfy this threshold were not mapped. In the cases where
estimates of bird numbers were given in relation to a single point on
the map, we used a minimum section length of 30 m of coastline, unless
ascribing this width to adjacent colonies would have resulted in
unoccupied sections of < 30 m, in which case we assigned a
section of 30-50 m in length. This approach resulted in 35 colonised
areas from a total of 91 (Supplementary Fig. 1b).