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\begin{document}

\title{Airflow modelling predicts seabird breeding habitat across islands}



\author[1]{Emmanouil Lempidakis}%
\author[2]{Andrew N Ross}%
\author[3]{Luca Börger}%
\author[1]{Emily L C Shepard}%
\affil[1]{Department of Biosciences, Swansea University}%
\affil[2]{School of Earth and Environment, University of Leeds}%
\affil[3]{Affiliation not available}%


\vspace{-1em}



  
  \date{May 25, 2024}


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\selectlanguage{english}
\begin{abstract}
Wind is a fundamental driver of the distribution and energy expenditure
of birds at sea. Wind can also influence mortality at the nest. Yet
airflows have never been fully integrated into models of breeding
habitat selection. We use computational fluid dynamics to provide the
first assessment of whether and how airflows predict the distribution of
seabird colonies, taking common guillemots (\emph{Uria aalge}) breeding
on Skomer island as our study system. We reveal that air pressure
predicts occupancy, demonstrating the importance of exposure (rather
than wind speed) in habitat selection. Our simple model with pressure
and slope correctly identified 80\% of the largest colonies and 93\% of
avoided sites. While previous approaches have not predicted space use in
novel sites, our model predicted 73\% of the largest colonies on a
neighbouring island. This suggests generality in the mechanisms linking
airflows and breeding distributions, and highlights a novel route by
which seabirds may be affected by global change.~ ~ ~ ~%
\end{abstract}%



\sloppy


\textbf{Introduction}

For animals operating in aerial and aquatic environments, movement costs
and capacities are profoundly affected by air and water currents
respectively\textsuperscript{1}. This drives widespread and large-scale
patterns of animal movement; with birds selecting tailwinds or updrafts
as they migrate across ocean basins or between land
masses\textsuperscript{2}, and fish minimising counter-flows as they
migrate upstream\textsuperscript{3}. In fish, current selection is also
a key driver of habitat use outside periods of
travel\textsuperscript{4}. Indeed, there is a rich history of research
on fish space-use in relation to flow characteristics in rivers dating
back to the 1960s\textsuperscript{5}, which has demonstrated that water
velocity, and in some cases velocity shear, determine abundance, habitat
use (both within and between species), and interspecific competition in
some species\textsuperscript{4}. In contrast, there is little to no
research on how local airflows affect the distribution of birds outside
travelling, including the distribution of breeding colonies.

Reproductive success is closely linked to the physical characteristics
of breeding sites in many taxa\textsuperscript{6,7} which can feed into
broader species distribution models\textsuperscript{8}. In colonial
animals, breeding sites can represent the nexus of reproductive activity
for tens of thousands of individuals\textsuperscript{9}. There is
therefore a clear need to establish what drives colony location in these
taxa, in order to identify the availability of breeding habitat, and
predict how areas differ in quality, now and in the
future\textsuperscript{10-16}. Over 95\% of seabirds are colonial
breeders\textsuperscript{17}. Seabirds are also more at risk than other
comparable groups of birds, with widespread decline in populations due
to commercial fisheries, pollution, habitat change and the introduction
of invasive predators\textsuperscript{18}. In some cases this has led to
entire breeding colonies being lost\textsuperscript{19,20}. Here,
conservation practitioners need to know where to focus restoration
efforts e.g. by decoy deployment and acoustic attraction to re-seed
breeding activity\textsuperscript{21}. This is crucial given that there
will always be a fitness cost associated with breeding in sub-optimal
habitat\textsuperscript{22}. However, while a wide range of studies have
analysed breeding site characteristics in
seabirds\textsuperscript{6,7,9,23}, and compared them with available
habitat\textsuperscript{24}, we are unaware of any that have
successfully applied predictions from one site to another (\emph{cf}
.\textsuperscript{25}).

The tendency of seabirds to breed on offshore islands and/ or coastal
cliffs has been attributed to the need to reduce exposure to terrestrial
predators and be close to feeding areas\textsuperscript{9,26}.
Nonetheless, for cliff nesting species, it is clear that not all cliffs
are equal, as colonies tend to be clumped, with great swathes of cliff
habitat left empty\textsuperscript{7}. Indeed, cliffs should vary in
their accessibility to terrestrial predators (primarily through
variation in slope angle), as well as the availability of suitable
breeding ledges, with species varying in their need for different ledge
characteristics according to their body size and nest building
habit\textsuperscript{6,27}.

There are also compelling reasons why flow characteristics should affect
breeding habitat preferences, particularly for groups such as seabirds,
which are exposed to strong flows. Wind can affect the risk of eggs/
birds being displaced from the nest\textsuperscript{28}, as well as
influencing exposure to rain (particularly in cliff nesting species) and
heat stress (through evaporative heat loss), both of which can cause
mortality \textsuperscript{28,29,30}. Wind also has a strong influence
on flight capacity. In common guillemots (\emph{Uria aalge} ) and
razorbills (\emph{Alca torda} ), 60\% of attempts to land at their cliff
nests were found to fail in a strong breeze\textsuperscript{31},
suggesting there are advantages to breeding on more sheltered cliffs.
Indeed, frigatebirds (\emph{Fregata magnificens} ) nest in relatively
wind still areas, despite the associated reduction in ability of birds
to lose heat, which may reflect the difficulties that adults would
experience in remaining on the nest and operating close to it in high
winds due to their low weight and wing loading\textsuperscript{32}. The
importance of being able to maintain flight control close to the nest
suggests that habitat selection could be influenced by several airflow
characteristics, including the strength of the horizontal and vertical
components, as well as the turbulence.

Despite the potential importance of airflows for these animals that
breed in exposed locations, there is a complete lack of information on
the flow characteristics associated with colony presence and absence
(though see\textsuperscript{33}). This is likely due to the difficulties
of quantifying wind over complex, often steep terrain. It may also
reflect our inability to see flow characteristics, in contrast to rivers
where this can be evident from surface characteristics. Where the impact
of wind has been assessed by proxy, wind fetch was found to have
contrasting effects in the presence/absence of colonies of three
Pygoscelis penguin species\textsuperscript{34}, while colony aspect was
not significant for species of the auks family\textsuperscript{23,31}.
Nonetheless, aspect may be a poor proxy for the precise wind conditions
experienced at colonies, as airflow characteristics will be modified by
the particular topography of the surrounding area. As a result, two
cliffs with the same aspect and prevailing wind conditions can
experience very different flow regimes\textsuperscript{31}.

We use computational fluid dynamics (CFD) to provide the first
assessment of whether and how local airflow conditions predicts the
distribution of seabirds, taking colonies of common guillemots
(\emph{Uria aalge} ) breeding on Skomer island, UK, as our study system.
We estimate a number of airflow characteristics, including the magnitude
of the wind, the horizontal and vertical wind components, air pressure
(as a predictor of exposure) and finally gustiness and turbulence, which
may affect flight control close to the breeding
cliffs\textsuperscript{31}. Our specific objectives were to: (i) assess
whether airflows associated with the prevailing wind direction predict
breeding site selection (patterns of presence and absence), and habitat
quality (colony density), (ii) quantify the airflow conditions that
birds will be exposed to with changes in wind direction and (iii) test
our model of habitat selection by predicting colony presence and absence
on a neighbouring island. This test that is considerably stronger than
standard cross-validation, but rarely performed\textsuperscript{25}.
Overall, our approach should provide insight into the conditions birds
select and avoid in the prevailing wind, and the ``penalty'' they suffer
in terms of the adverse conditions they are exposed to if the wind
direction changes, either over the short-term, or as part of larger
scale climatic shifts\textsuperscript{35,36}.

\textbf{Results}

\textbf{Prevailing environmental conditions}

Winds around Skomer are predominantly from the SW, although there is
some variation with wind strength, as light winds (\textless{} 6 m
s\textsuperscript{-1}) are equally likely to be from the SW or NW
(Supplementary Fig. 2a,b). Overall, easterly winds are the least
frequent (Supplementary Table 1).

\textbf{Environmental parameters associated with colony distribution on
Skomer island}

Wilcoxon rank sum tests for all colony classifications revealed
statistically different slope angles between colonies and non-colonies
on Skomer Island (taking any positive count of birds as colony presence,
W = 611724994, p-value \textless{} 2.2e-16). Unoccupied sections were
generally less steep than occupied sections (median slope angles
45\textsuperscript{o} and 51\textsuperscript{o} respectively) and the
densest and largest colonies were associated with the steepest cliffs
(Fig. 1). Slope angle varied with cliff orientation, with mean slope
angle being lowest for cliffs with S and SE orientations. Interestingly,
cliffs facing SW have relatively high mean slope angles (Fig. 1a).
Despite this, most occupied sections were orientated away from the SW
(Fig. 2a). Both the largest, as well as the densest colonies, were
mainly orientated towards the NW and E.

Models of wind and slope angle significantly predicted colony presence
and absence on Skomer, with the model correctly identifying 80\% of the
colonies and 93\% of avoided sites for a SW wind, where presence was
taken as the 10 largest colonies (Table 1). In fact, models of colony
location performed well across all modelled wind directions except for
SE winds, indicating that colonies are characterised by particular sets
of flow characteristics in most scenarios. Indeed, in each wind
direction, one colony classification could be predicted by airflow
parameters alone, without slope angle. The lowest model performance was
seen with SE winds where only one colony classification yielded a
reasonable model fit (McFadden\textsuperscript{41} R\textsuperscript{2}[?]
0.2, table 1). In this model, colony presence was predicted by slope
angle alone (Table 1).

Across the different colony classifications, the pseudo
R\textsuperscript{2} was lowest overall in models of any occupancy
(1\textsuperscript{st} classification SW 0.28; NW 0.24, SE 0.12, NE
0.38) compared to those predicting the 10 largest colonies
(2\textsuperscript{nd} classification SW 0.59; NW 0.58,SE 0.32, NE 0.74)
or the 11 densest (3\textsuperscript{rd} classification SW 0.17; NW
0.40, SE 0.15, NE 0.67) (Table 1). The overall accuracy and true skill
statistics followed the same general trend, being highest for the 10
largest colonies (Table 1). The sensitivity tended to be somewhat lower
than specificity in the 1\textsuperscript{st} classification
(sensitivity was: SW 0.63; NW 1.00, SE 0.21, NE 0.60 and specificity
was: SW 0.69; NW 0.21, SE 0.96, NE 0.84), but increased in the
2\textsuperscript{nd} and 3\textsuperscript{rd} classifications
indicating a better ability to predict true presence compared to true
absence for the largest and densest colonies.

While mean slope angle was included in two of three models for SW, NW
and NE wind directions, airflow parameters always had a higher effect
size (SI Table 2). A narrow set of airflow parameters was identified as
significant in predicting colony presence across wind directions (Table
1). Furthermore, there was broad agreement between the airflow
characteristics identified within each wind direction, irrespective of
the way colonies were classified. Pressure statistics were included in
all SW models, while TKE and horizontal wind speed parameters were
included in all NW and NE models, respectively.

Unexpectedly, wind speed (in terms of median or skewness) was not
included in the simplest top models of SW winds, instead colonies were
associated with a lower median pressure and pressure gradient (Table 1),
both of which are linked to lower exposure (Fig. 2, Fig. 3).

Colonies were associated with higher turbulence compared to unoccupied
sites, particularly in the case of NW and NE winds (Table 1). In NE
winds, colonies experienced both higher wind speeds (Fig. 4) (positive
horizontal median, 1\textsuperscript{st} classification and negative
horizontal skewness, 2\textsuperscript{nd} and
3\textsuperscript{rd}classifications) and higher turbulence (here TKE
estimates in the 1\textsuperscript{st} and 3\textsuperscript{rd}
classifications).

\textbf{Predicting colony distribution on Skokholm}

The model predicting the largest colonies on Skomer under SW winds also
performed well when applied to the island of Skokholm (Fig. 5),
correctly predicting the distribution of \textasciitilde{} 73\% of the
largest colonies (eight out of eleven), and \textasciitilde{} 63\% of
unoccupied cliff sections (35 out of 56), which corresponds to
\textasciitilde{} 80\% of the total unoccupied area (60,863
m\textsuperscript{2} of 76,259 m\textsuperscript{2}). Model performance,
although lower than the models on Skomer, was satisfactory with an
overall accuracy of 0.64 and TSS 0.35.

\textbf{Discussion}

Wind regimes are changing, in terms of the mean strength, and the
frequency of extreme weather events\textsuperscript{35,36}. Yet research
on how wind affects seabirds has focused on their at-sea behaviour
(though see\textsuperscript{37,34}). 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 chill\textsuperscript{38} and flight capacity
(either of the guillemots or their aerial
predators\textsuperscript{31,38}). 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 predators\textsuperscript{39}, as
well as easier access to the sea when chicks jump from their
nests\textsuperscript{40,41}. 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 aspect\textsuperscript{31},
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
\textasciitilde{}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
areas\textsuperscript{35,36}, are also likely to be most detrimental to
birds at the nest when accompanied by a change in wind
direction\textsuperscript{37}.

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 cliffs\textsuperscript{31} and turbulence is likely to present
further difficulties for flight control in stronger
winds\textsuperscript{42,43}.

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 pairs\textsuperscript{7,44}. 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 cliff\textsuperscript{6,7}. 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
historically\textsuperscript{45}, and whole island counts undertaken
since 1963 demonstrate that numbers have been increasing since
then\textsuperscript{46}. 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 conditions\textsuperscript{47}, 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.

\textbf{Methods}

Our approach centres around the estimation of airflow parameters around
Skomer Island (51\selectlanguage{ngerman}° 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 survey\textsuperscript{48} 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 \selectlanguage{english}[?] 592
individuals) and 11 densest colonies (density \selectlanguage{english}[?] 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 \selectlanguage{english}[?] 20\textsuperscript{o} (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 \textasciitilde{} 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
metres\textsuperscript{7}. 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
measurements\textsuperscript{49}.

A similar approach was taken to digitize the distribution of breeding
guillemots on Skokholm from the 2018 breeding bird
survey\textsuperscript{50} (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 \textless{} 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).

\textbf{Computational fluid dynamics modelling of wind conditions}

In order to assess whether breeding distributions are affected by
airflow characteristics, wind conditions around the cliffs of Skomer
were simulated using the computational fluid dynamics (CFD) model
OpenFoam (openfoam.org version 5.x). OpenFoam is widely used for
modelling atmospheric boundary layer flows (e.g. in the wind energy
industry) and has been extensively validated over a similarly steep
island\textsuperscript{51}.

The initial coarse model domain was 5300 x 5000 x 1000 m, with a
horizontal resolution of 20 m and a vertical resolution of 10 m. The
bottom boundary represented the surface of the island which was taken
from a DEM of Skomer with 2 m resolution (Lle Geo-Portal
http://lle.gov.wales). A simulation was also run using the DEM with 1 m
resolution, however, the outputs were not significantly different. After
establishing the initial mesh, the tool snappyHexMesh in OpenFoam was
used to incorporate the DEM into the modelling process, refining initial
mesh cells close to the surface up to 3 times. This resulted in a finer
resolution close to surface of 2.5 m in the horizontal and 1.25 m in the
vertical. Simulations were completed when convergence was achieved using
a steady-state incompressible solver with a k-\selectlanguage{greek}ε \selectlanguage{english}turbulence closure
scheme.

Wind simulations were run for NW, NE, SE and SW wind directions to cover
a representative sample of wind directions, including the prevailing SW
direction (see Results). This allowed us to test whether birds selected
sites that were predicted by a specific set of wind parameters across
wind directions. The initial wind speed was set to 10 m
s\textsuperscript{-1} at 20 m height. The following airflow
characteristics were extracted from the model output at 2 m normal to
the ground surface (this height was selected in order to estimate the
airflow conditions that birds would be exposed to close to their
breeding cliffs): The two horizontal and vertical wind vectors (U\_0,
U\_1 and U\_2 respectively), mean wind speed (MeanU), turbulent kinetic
energy (TKE), pressure (P), kinematic viscosity (Nut) of the air medium
and turbulence dissipation rate (\selectlanguage{greek}ε\selectlanguage{english}). These outputs were further used to
estimate horizontal wind speed; gustiness and turbulence intensity (TI)
following\textsuperscript{31}.

\textbf{Statistical analysis}

Each of the four OpenFoam simulations (i.e. for NW, NE, SE and SW wind
directions) resulted in 76,913 data points across all cliff sections.
For each wind direction, the modelled airflow parameters within each
occupied/ unoccupied section were reduced to the following summary
statistics; median, interquartile range (IQR) and skewness. Median
statistics were used to identify the strength of each wind parameter
within a section. The IQR values identify the variability or gradient of
each wind parameter within a section. High skewness statistics for
horizontal wind speed (skewed right) correspond to shelter.

The complete set of 27 airflow parameters was tested for collinearity by
producing a correlation hierarchy table for each initial wind condition.
Highly correlated terms (Pearson correlation coefficient [?] 0.7) were
removed from the analysis, leading to the inclusion of 15 wind
parameters. The mean slope steepness per section was added to the total
set and parameters were standardized using the MuMin
package\textsuperscript{52} version 1.43.17.

We considered that it would be stretching the data to model the density
or number of breeding birds as a continuous variable, particularly as
the extent of each section was determined by the need to survey cliffs,
and did not accord with the beginning or end of occupied/ unoccupied
areas. We therefore ran separate models, with colony presence defined as
(i) the presence of any breeding birds, (ii) the largest (iii) the
densest colonies. For the latter two categories, areas with breeding
birds that did not fall into either the largest or densest categories
were excluded from the modelling process instead of defining them as
absence. The excluded areas represented 23.5\% and 27.7\% of all
breeding birds respectively. This resulted in trained datasets of 43 and
44 sections for the largest and densest datasets.

A two-step approach was used to build the global model and identify the
final, best-fitting models. First, to reduce the large number of
covariates, a random forest classifier was fitted, using the package
randomForest\textsuperscript{53} version 4.6.14. The 10 most important
terms were selected to build the global logistic regression model. This
parameter set was further simplified using the dredge function (MuMin
package\textsuperscript{52}), to perform stepwise Bayesian information
criterion (BIC) selection, penalising for model size. In the case of the
models for largest and densest colonies in a NW wind, the number of
terms in dredge had to be gradually reduced to eight and seven
respectively, to prevent fitting of models with probabilities of zero
and one. The simplest model among those with a difference in BIC [?] 2 was
selected as the best final model.

The final model was assessed for goodness of fit using the
McFadden\textsuperscript{54} pseudo R\textsuperscript{2}. Values between
0.2 -- 0.4 were considered as very satisfactory\textsuperscript{42}.
Model performance was also evaluated in terms of overall accuracy (OA);
true skill statistic (TSS), sensitivity and
specificity\textsuperscript{55,56}. The effect size of each predictor
included in the final model was determined by computing the odds ratio.

The previous steps were repeated for all four initial wind directions,
with three logistic regression models of colony presence/absence
implemented per direction, in order to identify links between wind and
slope that were robust to different classifications of breeding colony.
All statistical analyses were conducted in in
R\textsuperscript{57}version 3.6.3 and RStudio\textsuperscript{58}
version~1.1.463.

\textbf{Acknowledgements}

We are grateful to the Wildlife Trust of South and West Wales, Skomer
wardens Bee B\selectlanguage{ngerman}üche and Eddie Stubbings, and Skokholm wardens Giselle
Eagle and Richard Brown, for providing the survey maps used in this
study, and to Dr Ben Evans for discussions on interpreting pressure
fields. We thank Natural Resources Wales for providing the wind data
from the Coastguard lookout station at Wooltack point. EL and ELCS are
supported by the European Research Council under the European Union's
Horizon 2020 research and innovation program Grant 715874 (to ELCS).

\textbf{Competing Interests}

The authors declare no competing interests.

\textbf{Manuscript format:} Nature

\textbf{Authorship statement:} The study was conceived by ELCS and AR.
EL undertook the data analysis with input from all authors. The
manuscript was first drafted by EL and ELCS with further contributions
from all authors.

\textbf{Data statement:} ~All data generated or analysed during this
study are included in the submission of this article (and its
supplementary information files) and will be available with publication.



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\par\null

\textbf{Figure 1.} Slope angles selected by breeding birds in relation
to those available on Skomer. a) Mean slope angle (\textsuperscript{o})
according to orientation (number of points per orientation bin; 2911-
8718). b) Slope angle in relation to (A) unoccupied sections (n=33,
median= \textasciitilde{}45.1\textsuperscript{o}), (B) occupied but not
classified as either largest or densest sections (n=31, median=
\textasciitilde{}47.5\textsuperscript{o}) and (C) sections classified as
both largest and densest (n=7, median= 68.5\textsuperscript{o}).\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.55\columnwidth]{figures/image1/image1}

\end{center}
\end{figure}

\textbf{Figure 2.} The guillemot survey sections and modelled air
pressure on the cliffs of Skomer. a) The distribution of breeding
guillemots. Areas classified as both densest and largest are indicated
in red (n = 7 sections). Areas that were identified as either among the
largest (n = 3) or densest (n = 4) are indicated in orange respectively.
Residual occupied areas are indicated in yellow. b) Modelled air
pressure at 2 m normal to the surface around Skomer for the SW wind
direction (indicated with a black arrow). High pressure regimes are a
feature of exposed cliffs, while pressure is lower in sheltered areas
and over the main island.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.57\columnwidth]{figures/image2/image2}

\end{center}
\end{figure}

\par\null

\textbf{Figure 3.} The OpenFoam model output of a) wind speed and b)
pressure, over a windward cliff (``Skomer head'') with SW wind (denoted
with black arrows). In the lower parts of the cliffs, the wind is
blocked, resulting in high pressure regions where flow is decelerated.
Closer to the top (55 - 60 m ASL), the flow is accelerated, generating
areas of low pressure. This generates a wider range of pressures on
windward cliffs, compared to leeward cliffs, where flow separation
occurs, viscous forces take over, and areas of consistently low pressure
are generated that are not associated with high wind speeds.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.55\columnwidth]{figures/image3/image3}

\end{center}
\end{figure}

\textbf{Figure 4} . Modelled horizontal wind speeds on the cliffs of
Skomer. Modelled wind speeds under a) a SW and b) a NE wind direction.
Mean wind speeds were reduced on leeward cliffs, increased on windward
cliffs and reached their highest estimates at the crests, as expected.
Winds were modelled 2 m normal to the surface and the mapped area was
constrained by the 4 m and 40 m elevation contours.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image4/image4}

\end{center}
\end{figure}

\textbf{Figure 5.} Predicted distribution of guillemot colonies on
Skokholm. The distribution was predicted using the model of the largest
colonies on Skomer and the wind field on Skokholm as predicted under the
prevailing SW wind.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image5/image5}

\end{center}
\end{figure}

\textbf{Table 1} . The outputs of logistic regression models predicting
colony presence for four different initial wind directions. Three
summary statistics were modelled for each section and wind property:
``skewness'' (Skew); ``median'' (Med) and ``interquartile range'' (IQR).
In addition, three colony classifications were tested, where colony
presence was taken as i) any positive count, ii) the 10 largest colonies
and iii) the 11 densest colonies. Shelter from higher wind speeds is
associated with low pressure (PMed) and reduced pressure range (PIQR).
Exposure to higher wind speeds is highlighted by negative and decreased
skewness in horizontal wind speed. Higher turbulence is indicated by
increased TKE median and low TKE skewness. TI terms follow the same
pattern. Significance is indicated according to p-value: p\textless{}
0.001 (***), p\textless{} 0.01 (**), p\textless{} 0.005 (*), and model
predictors are listed in order of descending effect size (Supplementary
Table 2).

\par\null\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=1.00\columnwidth]{figures/Table1/Table1}

\end{center}
\end{figure}

\selectlanguage{english}
\FloatBarrier
\end{document}

