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\begin{document}

\title{Climate oscillation and the invasion of alien species influence~the
oceanic distribution of seabirds~~~~}



\author[1,2,3]{Julian Perez-Correa}%
\author[1,4]{Peter Carr}%
\author[5]{Jessica Meeuwig}%
\author[4,6]{Heather Koldewey}%
\author[1,5]{Tom Letessier}%
\affil[1]{Institute of Zoology, Zoological Society of London, Regent’s Park, London, UK }%
\affil[2]{Escuela de Ciencias Ambientales, Facultad de Ingeniería, Universidad Espíritu Santo, Samborondón, Ecuador}%
\affil[3]{Imperial College London, London, United Kingdom }%
\affil[4]{Centre for Ecology and Conservation, University of Exeter, Penryn Campus, Cornwall, UK}%
\affil[5]{Centre for Marine Futures, Oceans Institute and School of Animal Biology, The University of Western Australia, Crawley, WA, Australia }%
\affil[6]{Conservation and Policy, Zoological Society of London, Regent’s Park, London, UK}%


\vspace{-1em}



  
  \date{June 02, 2024}


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\selectlanguage{english}
\begin{abstract}
Spatial and temporal distribution of seabird transiting and foraging at
sea is an important consideration for marine conservation planning.
Using at-sea observations of seabirds (n = 317), collected during the
breeding season from 2012 to 2016, we built boosted regression tree
(BRT) models to identify relationships between numerically dominant
seabird species (red-footed booby, brown noddy, white tern and
wedge-tailed shearwater), geomorphology, oceanographic variability, and
climate oscillation in the Chagos Archipelago. We documented positive
relationships between red-footed booby and wedge-tailed shearwater
abundance with the strength in the Indian Ocean Dipole, as represented
by the Dipole Mode Index (6.7\% and 23.7\% contribution respectively).
The abundance of red-footed boobies, brown noddies and white terns
declined abruptly with greater distance to island (17.6\%, 34.1\% and
41.1\% contribution respectively). We further quantified the effects of
proximity to rat-free and rat-invaded islands on seabird distribution at
sea, and identify breaking point distribution thresholds. We identified
areas of increased abundance at sea and habitat use-age under a scenario
where rats are eradicated from invaded nearby islands and recolonised by
seabirds. Following rat eradication, abundance at sea of red-footed
booby, brown noddy, and white terns increased by 14\%, 17\% and 3\%
respectively, with no important increase detected for shearwaters. Our
results have implication for seabird conservation and island
restoration. Climate oscillations may cause shifts in seabird
distribution, possibly through changes in regional productivity and prey
distribution. Invasive species eradications and subsequent island
recolonization can lead to greater access for seabirds to areas at-sea,
due to increased foraging or transiting through, potentially leading to
distribution gains and increased competition. Our approach predicting
distribution after successful eradications enables anticipatory
threat-mitigation in these areas, minimising competition between
colonies and thereby maximising the risk of success and the conservation
impact of eradication programmes. ~%
\end{abstract}%



\sloppy


\section*{}

{\label{introduction}}

\textbf{\emph{Keywords}:} Chagos Archipelago, British Indian Ocean
Territory, marine protected areas, ship rat, island invasive species,
\emph{Rattus rattus}, boosted regression tree.

~

\section*{1. INTRODUCTION}\label{introduction}

Seabirds are apex predators in marine ecosystems (Estes, Crooks \& Holt,
2001; Ballance, Pitman \& Fiedler, 2006) and play important roles in
ecosystem functionality (Loder III, Ganning \& Love, 1996; Schmidt,
Dennison, Moss \& Stewart, 2004; Fukami et al., 2006; Graham et al.,
2018). Many seabirds, such as boobies (Sulidae), shearwaters, and
petrels (Procellariidae), complete their life cycle primarily at sea and
feed predominantly on pelagic forage species (e.g. anchovies, flying
fish, euphausiids; Votier \& Sherley, 2017; Brooke, 2004). Multiple
threats to seabirds occur throughout their distribution, which is
impacted by climatic variability and invasive species (Dias et al.,
2019; Croxall et al., 2012). A recent assessment revealed that 69.7\%
of global seabird populations, including the families Laridae, Sulidae
and Procellariidae, are declining (Paleczny, Hammill, Karpouzi, and
Pauly 2015).

By linking terrestrial and oceanic trophic webs, seabirds can be
sensitive indicators of long-term and large-scale changes in both
environmental conditions and human activities (Piatt, Sydeman \& Wiese,
2007; Einoder, 2009). At sea, seabird distribution is closely linked to
that of their prey. Oceanographic conditions (e.g. sea surface
temperature, chlorophyll-a) and geomorphic characteristics (e.g. slope,
depth), are therefore commonly used as a proxy of food availability (Fox
et al., 2017; Mannocci, Catalogna, et al., 2014; Mannocci, Laran, et
al., 2014; Maxwell \& Morgan, 2013; Hyrenbach, Veit, Weimerskirch,
Metzl, \& Hunt Jr, 2007; Vilchis, Balance \& Fiedler, 2006).~

In the Indian Ocean (IO), several studies have explored linkages between
oceanographic and geomorphic conditions and seabird distribution derived
from single year surveys (e.g. Mendez et al, 2017; Kappes, Weimerskirch,
Pinaud, Le Corre, 2011; Weimerskirch, Le Corre, Jaquemet, Marsac, 2005).
Hyrenbach et al. (2007) explored drivers of seabird distribution in the
southern IO, identifying the influence of sea surface temperature (SST)
and proximity to sub-Antarctic Islands. Mannocci, Laran, et al. (2014),
found that their distribution in the Southwest IO was closely related to
persistent oceanographic conditions, and that time-averaged values over
the long term (\textgreater{} 7 years) were more predictive of
distribution than those averaged over the short-term (1 week).

Multiyear studies can provide managers with useful information needed
to anticipate how inter-annual climate oscillations such as El Ni\selectlanguage{ngerman}ño
Southern Oscillation (ENSO; Sprogis, Christiansen, Wandres, \& Bejder,
2018), and the IO Dipole (Saji, Goswami, Vinayachandran \& Yamagata,
1999) may impact seabird distribution. Interannual and climate
variability in the tropical IO is to a large degree characterised by
oscillations in SST gradient between the eastern and western basin,
referred to as the IO Dipole (Saji et al., 1999). This gradient is
represented by the Dipole Mode Index (DMI), where positive values
correspond to cooler waters in the eastern basin and warmer in the west,
whereas negative values correspond to warmer waters in eastern basin and
colder in the west. Although the influence of climate oscillations and
seabird dynamics has been the subject of a vast body of work (reviewed
in Oro, 2014), the effects of the dipole on higher trophic levels remain
poorly understood. A few studies have explored linkages between the IO
Dipole and seabird population dynamics and behaviour (Tryjanowski,
Stenseth \& Matysioková, 2013; Rivalan, Barbraud, Inchausti \&
Weimerskirch, 2010), due in part to its relatively recent discovery
(Ashok, Guan \& Yamagat, 2003). Recent research on land birds has shown
a positive correlation between the IO Dipole and bird community
composition (Mehta \& Wilby, 2018). However, to our knowledge, the
influence of climate variability on seabird distribution in the central
Indian Ocean has not been studied.

Whilst seabird distribution at sea may fluctuate as a function of
climatic variability, such as that reflected by the IO Dipole (Dias et
al., 2019), distribution at sea is likely also impacted by invasive
species on nearby islands. Island invasion by rodents, such as the ship
rat \emph{Rattus rattus}, is one of the greatest threats to seabird
populations (Dias et al., 2019; Jones et al., 2008; King, 1985).
Seabirds are central place foragers and thus sensitivity to rats may
restricts their distributions in the water adjacent to invaded islands.
Seabirds require islands to rest and breed, and both survival and
breeding success rates are highest on islands with limited disturbance
(King, 1985). Rats successfully invade islands by quickly adapting to
new habitats, in part because of their omnivorous diet. Rats prey on
both chicks and adults, causing population declines which can lead to
extirpation (Jones et al., 2008; Major, Jones, Charette \& Diamond,
2007; King, 1985; Fleet, 1972). The impact of rat invasion on seabirds
is species-dependent and depends upon a combination of biological
traits, such as breeding strategy, body weight and life history. For
example, small seabirds nesting in burrows such as storm-petrels are
particularly vulnerable to rat predation (Jones et al., 2008; Woodward,
1972). Rat eradication programmes are considered a major component of
successful island restoration and seabird population recovery (Borrelle,
Boersch-Supan, Gaskin \& Towns, 2018; Russell \& Holmes, 2015;~~ Le
Corre et al., 2012; Towns et al., 2009; Hutton, Parkes, \& Sinclair,
2007).

The IO has 27 archipelagos that are considered hotspots of marine
biodiversity (Danckwerts et al., 2014; Le Corre et al., 2012), most of
which are particularly important to seabirds (Le Corre \& Jaquemet,
2005). In the central IO, the Chagos Archipelago, encompassed within the
British Indian Ocean Territory (BIOT), is comprised of 55 tropical
islands, and was designated a no-take marine protected area (MPA) in
2010. The majority of the archipelago has been closed to human
activities since 1971, and is therefore relatively undisturbed
(Everaarts et al., 1999; Readman et al., 2013; Sheppard \& Sheppard,
2019). The archipelago is considered of great importance for seabird
conservation, harbouring eighteen species of resident breeders, and ten
designated and two proposed `Important Birds Areas' (Hilton \& Cuthbert,
2010; McGowan, Broderick \& Godley, 2008; Carr, 2006). Activities
surrounding the historical coconut plantations, dating back to the turn
of the 18\textsuperscript{th} century, led to invasions of ship rats and
other invasive mammals (i.e. feral cats \emph{Felis catus}; Wenban-Smith
\& Carter, 2016) on 26 islands (95.3\% of the island area), negatively
affecting the seabird populations (Harper, Carr \& Pitman, 2019; Harper
\& Bunbury, 2015; Hilton \& Cuthbert, 2010). Seabird densities on
rat-free islands are up to 760 times greater than on invaded islands,
leading to nutrients subsidies and increased productivity on adjacent
coral reefs (Graham et al. 2018). Notably, these subsidised reefs may
recover faster following coral bleaching (Benkwitt, Wilson \& Graham,
2019), primarily enhanced by biodiversity richness and ecosystem
functionality (Benkwitt, Wilson \& Graham, 2020). As productivity near
rat-free islands is enriched, it is therefore conceivable that seabirds
on these islands have greater opportunities to feed in proximity to
their colonies. After the successful eradication of rats from Île Vache
Marine in 2017 (Harper et al., 2019), further rat eradication has been
designated a priority target within the conservation framework of the
draft BIOT management plan (BIOT Management Conservation Framework in
prep. 2019).

Here, we expand on previous work done in the IO (i.e. Mannocci, Laran,
et al., 2014; Hyrenbach et al., 2007) by using a multiyear seabird
survey (from 2012 to 2016) within the BIOT MPA to identify drivers of
distribution. First, we modelled seabird distribution using
oceanographic variables, and distance to the nearest island, in order to
make general seabird distribution predictions, and establish the
influence of oceanography and interannual variability. Then, having
established that the at-sea distribution of these species are in fact
sensitive to the nearest island, we built new models considering
distance to closest rat-free island or to closest rat-invaded island.
Finally, predictions based on the rat-invaded island model were
subtracted from those of the rat-free model to infer the spatial effect
of rats on seabird distribution at sea during transiting or foraging.
This approach enables us to estimate potential suitable marine habitats
(i.e. distribution gain) in a scenario of a successful archipelago-wide
rat eradication programme, and possible factors relevant to island
restoration priorities.

~

\section*{2. METHODS}\label{methods}

\subsection*{2.1 Study Area}\label{study-area}

The Chagos Archipelago is located in the central IO at 6° S and 72° E at
the southern limit of the Chagos-Laccadive ridge, and is over 1,500 km
from the nearest continental land mass (Carr, 2012). Fifty-five islands
are clustered within the atolls of Diego Garcia, Peros Banhos, Salomon,
Egmont, and on the Great Chagos Bank (Fig. 1a) and constitute combined
approximately 60 km\textsuperscript{2} of land area. The territory
encompasses approximately 60,000 km\textsuperscript{2} of shallow photic
reefs, and 580,000 km\textsuperscript{2} of primarily oceanic habitat,
with a maximum depth over 6,000 meters (Carr, 2011; Dumbraveanu \&
Sheppard, 1999). The climate is tropical, characterised by oceanic
conditions and the seasonal reversal monsoon (Sheppard, 1999). Situated
in the inter-tropical convergence zone (ITCZ), the archipelago has
moderate winds generally from the north-west (October to April) and the
south-east (May to September). Sea surface temperature has an
approximately bimodal distribution with maxima in December--January and
March--April with a yearly mean of 28°C (Pfeiffer, Dullo, Zinke \&
Garbe-Schönberg, 2008) oscillating between 24.8 °C and 30.5 °C.

~

\subsection*{2.2 Seabird observations}\label{seabird-observations}

In order to identify the influence of oceanographic conditions and
island on seabird distribution, we conducted a multiyear survey of the
archipelago of seabirds at sea. The survey ran from 2012 to 2016 between
November and April, to overlap with the moderate phase of the monsoon.
This period generally coincides with peak seabird breeding activity in
the Chagos Archipelago (Carr, et al., 2019; Carr, 2011; Carr, 2015).
During the months of sampling, the BIOT MPA and the IO experienced two
seasons of modestly positive IO Dipole (2012-2013), which was followed
by three neutral IO Dipole events (2014-2016; NOAA Earth System Research
Laboratory {[}NOAA ESRL{]}, 2017).

Seabird transects (n = 317) were conducted from the BIOT patrol vessel.
Our sampling was primarily designed to target focal sites that were
typical pelagic habitats within the MPA, such as shallow seamounts
(\textless{}70 m), deep banks (ca. 400 m), deep seamounts
(\textless{}900 m), and deep basins (\textless{}2,000 m). Transects were
generated by adapting the method of Tasker, Jones, Dixon \& Blaker,
(1984). Each transect had a duration of 30 minutes, during which the
vessel typically steamed at 12 knots and travelled ca. 11 km. All
transects were generated within a 180° arc forward of the ship, out to
approximately 300 meters (Table 1, Fig. 1). Each year during the survey,
sampling effort was a trade-off between partial replacement of the
previous years to ensure a time series, and the addition of new
locations, to increase the diversity of habitats surveyed. This meant
that in certain years sampling was more evenly spread throughout the
archipelago (2012 and 2016), and more clustered in others (2013, 2014,
2015, Fig. 2).

All seabird observations were led by Pete Carr, an expert on seabirds
within the archipelago (e.g. Carr, 2011; Carr, 2012; Carr, 2015),
supported by 1-2 field assistants. This consistency in the same lead
observer with multiple field assistants and the use of transects with
limited strip (i.e. 300 m) reduced potential sources of bias (Spear,
Ainley, Hardesty, Howell \& Webb, 2004).

~

\subsection*{2.3 Oceanic habitat
modelling}\label{oceanic-habitat-modelling}

\subsubsection*{\texorpdfstring{\emph{2.3.1 Variables
selection}}{2.3.1 Variables selection}}\label{variables-selection}

We retained the most frequent and abundant seabird species (total sum of
observations \textgreater{} 100) in the BIOT MPA in order to model
oceanic distributions. These distributions were modelled based on
geomorphic and oceanographic variables using Boosted Regression Tree
models (BRT), an advanced form of regression (Friedman, Hastie \&
Tibshirani, 2000) that use boosting to combine and adapt large numbers
of relatively simple tree models, enabling ~model performance
optimization (Elith, Leathwick \& Hastie, 2008).

~

The BRTs were fitted using individual species count per sample (a proxy
for abundance) as the response variables, against explanatory variables
that previously have been shown to contribute to seabird distribution
(Fox et al., 2017: Mannocci, Catalogna, et al., 2014; Mannocci, Laran,
et al., 2014; Vilchis, Balance \& Fiedler, 2006; Yen, Sydeman \&
Hyrenbach, 2004). Slope and distance to nearest island were calculated
using QGIS version 3.8. Slope was derived from seabed depth values from
a GEBCO 30 arc seconds bathymetry grid (Becker et al., 2009), whereas
distance to coast was obtained in function to the nearest island from
each transect. Oceanographic variables were sea surface temperature
(SST), chlorophyll-a (CHL) concentration, and sea-level anomaly (SLA).
SST and CHL were obtained from aqua-MODIS sensor at 4 km spatial
resolution from Ocean Color Web (NASA Ocean Biology Processing Group,
2015). The SLA values were obtained from E.U. Copernicus Marine Service
Information (2018). For each oceanographic covariate, we used the
long-term average for the month of sampling, computed over the last 15
years (2002 -- 2017) following recommendations by Mannocci et al. (2017)
for modelling mesoscale distributions of highly mobile animals, such as
seabirds. Time-averaged oceanographic variables have been shown to be
more predictive of apex predators than short-term values (Suryan,
Santora, \& Sydeman, 2012; Mannocci et al., 2017) and are more directly
related to habitat consistency and thus ecologically relevant (Mannocci,
Laran, et al., 2014). We also included an index of climatic variability,
as represented by the Dipole Mode Index {[}DMI{]} and oceanic Niño index
{[}ONI{]}. ONI and DMI were downloaded from NOAA (2020) repository.

~

\subsubsection*{\texorpdfstring{\emph{2.3.2 Species distribution
model}}{2.3.2 Species distribution model}}\label{species-distribution-model}

We first constructed full BRT models that included all explanatory
variables (seven variables) for each species. Secondly, we observed the
individual contribution of each variable and rebuilt the models
selecting the variables with contributions greater than 5\%. We then
used the total explained deviance (TED) to evaluate the explanatory
power of the models. TED was calculated dividing the residual deviance
by the null deviance resulting from each selected species models. BRT
models were fitted using the package \emph{gbm} on R (R Development Core
Team 2018 version R version 3.5.2) with code modifications provided by
Elith et al. (2008). As recommended by Elith et al. (2008) and D'agata
et al. (2014), we did an analytical exploration of BRT models in order
to find a ``trade-off'' between numbers of trees (nt; number of
interactions), learning rate (lr; the shrinkage parameter), and tree
complexity (tc; depth of interactions between factors of each tree).
This approach required investigation of the bag fraction term (bg) that
controls overfitting via the introduction of stochasticity to the models
(Friedman, 2002). Model parameters were chosen while considering the
goodness-of-fit, as determined via cross validation (CV, Table S2).
Finally, we used the dependence plots resulted from the BRT models to
understand the shape of the influence of every variable in every
species.~~

~

\subsubsection*{\texorpdfstring{\emph{2.3.2 Predictions of seabird
distribution}}{2.3.2 Predictions of seabird distribution}}\label{predictions-of-seabird-distribution}

Spatial predictions in unsampled areas were limited to the convex hull
defined within the BIOT MPA and restricted by the range values of the
variables used to build each model and the max recorded value of
distance from coast (\textasciitilde{}137 km). This constraint ensured
that predictions were only made in areas with similar environmental
conditions (see Fig. S1 and Table S1). Using this approach, we avoided
extrapolating beyond the range of the model, whilst generating
meaningful predictions beyond our sampled area (Yates et al., 2018).
Whenever ONI or DMI were retained in the model, we rendered predictions
based on the values for the last year of sampling, 2016. We rendered
predictions on a 0.4 x 0.4 decimal degree resolved grid. This resolution
was considered a reasonable trade-off in order to capture distribution
for species with uncertain range sizes (Seo, Thorne, Hannah, \&
Thuiller, 2008).

~

\subsection*{2.4 Modelling the effect of rat
invasion}\label{modelling-the-effect-of-rat-invasion}

We hypothesise that the presence of rat-invade islands will influence
the distribution of seabirds at sea. We modelled the effect of rat
invasion on seabird distribution at sea by modifying our BRTs. We first
exchanged the variable distance to coast from each transect with either
the distance to the closest rat-free island or the closest rat-invaded
island. This resulted in two additional models. The model that included
`distance to the closest rat-free islands (km)' was considered to
represent bird distribution at its theorised maximum abundance, in the
absence of any rat invasion. The model that included `distance to the
closest rat-invaded islands (km)' was considering to represent bird
distribution assuming total invasion.

These two models were then used to identify thresholds based on a
broken-line regression analysis of the effect of rat invasion or
absence. This analysis gave us the chance to quantify the degree to
which seabird distribution is influenced by the distance to rat-free or
rat-invaded islands using a Davies' test (Davies, 2002). Davies' test
enabled us to find the inflexion point of the partial dependence plots
by testing the difference in the slopes. The test was done using the R
package \emph{segmented} (Muggeo, 2008). This analysis has been
previously used to find thresholds on the response to explanatory
variables (e.g. Clausen, Christensen, Gundersen, \& Madsen, 2017; Isles,
Xu, Stockwell \& Schroth, 2017; Picard, Rutishauser, Ploton, \&
Ngomanda, 2015; D'agatha et al, 2014). As a result, the test provided
with break-points (BP) of the dependence plots with a range of the 95\%
confidence intervals (CI). We contrasted the BP and the CI of the
rat-free model, the rat-invaded model and the original distribution
model.

The final objective of our analysis was to identify possible
distribution shifts, after a rat eradication scenario. In order to
determine the potential net gain in distribution following a scenario of
an archipelago wide rat eradication programme, we subtracted the
predictions of the rat-invaded models from the predictions of the
rat-free models. The predictions were mapped only where the nearest
island was rat-invaded since we assume that no new islands will be
invaded, showing net gain and net loss in seabird abundance and habitat
suitability. An eradication programme will not increase a seabird
population immediately, as islands may first need to be recolonised, and
only after several years of high reproductive output can substantial
population increases be expected (e.g. Jones, 2010). Depending on
whether an island is actually occupied by a species or not, the initial
recolonisation may also spill over the abundance elsewhere (as the
colonising birds must come from somewhere). As such these predictions
should be considered as potential only.

~

\section*{3. RESULTS}\label{results}

\subsection*{3.1 Seabird sightings}\label{seabird-sightings}

In total, 7,008 seabirds were observed during the five expeditions
(Table 2). Seven families were recorded: Laridae (noddies and terns),
Sulidae (boobies), Procellariidae (shearwaters and petrels),
Phaethontidae (tropicbirds), Fregatidae (frigatebirds), Hydrobatidae
(northern storm-petrels) and Oceanitidae (southern storm-petrels). The
most abundant species were red-footed booby (\emph{Sula sula}, 1,712
individuals and 255 observations), brown noddy (\emph{Anous stolidus},
3,027 individuals and 171 observations), white tern (\emph{Gygis alba},
546 individuals and 154 observations), and wedge-tailed shearwater
(\emph{Ardenna pacifica;} 562 individuals and 113 observations). These
species were retained for further distribution modelling (Fig. 3).

~

\subsection*{3.2 Predictive Modelling}\label{predictive-modelling}

\subsubsection*{\texorpdfstring{\emph{3.2.1 Oceanic drivers of
distribution and spatial
patterns}}{3.2.1 Oceanic drivers of distribution and spatial patterns}}\label{oceanic-drivers-of-distribution-and-spatial-patterns}

Total deviance explained for each BRT was 80\% for red-footed booby,
89\% for brown noddy, 88\% for white tern and, 99\% for wedge-tailed
shearwater. Distance to coast was an important variable for all species,
explaining between 17.6 and 41.1\% of the deviance (Fig.~ (Fig. 4.q).
Slope was particularly important variable influencing for red-footed
booby (22.3\% contribution, Fig. 4.a) whereas sea surface temperature
was the most important for wedge-tailed shearwater (29.4\%).
Chlorophyll-a concentration explained between 6.4\% and 23.5\% for all
species. DMI was retained for red-footed booby (6.7\%, Fig. 5a) and
wedge-tailed shearwater (23.7\%, Fig. 5b), with both species showing
increasing abundance with positive values of the DMI.

Spatial predictions for red-footed booby, brown noddy and white tern
revealed a strong coast signature (Fig. 6a - c), while wedge-tailed
shearwater distribution was more uniform with higher abundance levels
near high slope areas and towards the northeast of the Archipelago (Fig.
6d). Brown noddy and white tern abundance was pronounced over shallow
seabeds (\textless{} 1000 m) in proximity to islands and atolls (Fig. 6b
and 6c). Red-footed booby abundance was more pronounced in pelagic and
deeper areas, and in areas with intermediate slope (ca. 15º, Fig. 6a).

~

\subsubsection*{\texorpdfstring{\emph{3.2.2 Response to rat
invasion}}{3.2.2 Response to rat invasion}}\label{response-to-rat-invasion}

The rat-invaded BRT models outperformed the rat-free models for the
red-footed booby (86\% vs 82\%, total deviance explained), the brown
noddy (90\% vs 85\%) and the wedge-tailed (97\% vs 94\%). The
contribution of distance to rat-free island was higher than distance to
rat-invaded island for red-footed booby (21.7\% vs 16.2\%), and
wedge-tailed shearwater (11.1\% vs 0.8\%) and to a lesser degree for
brown noddy (39.7\% vs 37.9\%) ~Conversely, for white tern distance to
rat-free island explained less deviance (28.7\%) than distance to
rat-invaded island (36.6\%; Fig. 7). ~

Breaking points (BP) indicated the threshold to which the nearest
island, whether invaded or not, influenced the distribution of seabirds.
Thresholds BP in the effect of islands differ among all species (Fig.
8). The BP for red-footed booby was 37.8 km for rat-free model {[}CI
39.4, 40.7{]} and at 60.0 km for rat-invaded model {[}CI 55.2, 65.0{]}.
The BP for brown noddy was at 48.6 km {[}CI 46.6, 50.6{]} for rat-free
model and 56.6 km for rat-invaded model {[}CI 51.9, 61.3{]}. The BP for
white tern was 54.5 km for rat-free model {[}CI 50.9, 58.0{]} and 24.4
km {[}CI 23.4, 25.4{]} for rat-invaded model. ~The effect on
wedge-tailed shearwater showed a bimodality with different response from
lesser and greater distance. At lesser distance the BP was 21.2 km for
rat-free model {[}CI 18.9, 23.5{]} and 32.1 km {[}CI 30.4, 33.8{]} for
rat-invaded model (Table 3).

The presence of rats on nearby islands reduced the suitable habitat of
seabirds (Fig. 9). Following rat eradication, abundance at sea of
red-footed booby and brown noddy, white tern and wedge-tailed shearwater
increased by 14\%, 17\%, 3\% and 4\% respectively. However, the models
for wedge-tailed shearwater distribution were weak and the effect where
negligent. Hence, not reported in figure.

~

\section*{4. DISCUSSION}\label{discussion}

Using our at-sea observations in the Chagos Archipelago over a 5 year
period, we have identified spatio-temporal trends in seabird
distribution. Our multiyear survey spanned a broad range of
environmental conditions and has enabled us to identify how geomorphic
and oceanographic variability drive seabird distribution, including a
possible association between regional abundance and the IO Dipole
oscillation through the DMI.~Furthermore, we have modelled the spatially
explicit impact of islands with and without rats on seabird
distribution, and have identified areas of net increase in abundance
under an archipelago-wide rat eradication scenario, adding to a mounting
body of research on the considerations for rat eradications priorities,
both globally (Buxton, Jones, Moller and Towns, 2014), and in BIOT
(Dawson et al, 2015). Information on seabird hotspots, sensitivity to
climate oscillation, and how eradication can result in distribution
shifts has critical implications for tropical seabird conservation and
for island restoration strategies.

~

\subsection*{4.1 Drivers of seabird distribution within the Chagos
Archipelago}\label{drivers-of-seabird-distribution-within-the-chagos-archipelago}

The red-footed booby showed a primarily oceanic distribution, with
pronounced hotspots east of Diego Garcia, northwest of Peros Banhos and
south of Salomon. Red-footed booby distribution has traditionally been
thought to be positively associated with areas of high productivity and
elevated chlorophyll-a concentration (\textgreater{} 0.16 mg.
m\textsuperscript{-3}chl-a) (Ballance, Pitman, \& Reilly, 1997;
Jaquemet, Le Corre, Marsac, Potier, \& Weimerskirch, 2005; Weimerskirch,
Le Corre, Jaquemet, \& Marsac, 2005). In contrast, Mendez et al. (2017)
identified a negative correlation between red-footed boobies and
chlorophyll-a, by tracking foraging behaviour of red-footed boobies in
colonies along the equator (Galapagos Islands, Mozambique Channel, New
Caledonia and the IO) which permitted a more robust understanding of
factors that determines distribution. Mendez et al. (2017) concluded
that across the pantropical range of the red-footed booby, distribution
is closely driven by intra and interspecific competition for prey. As
chlorophyll-a concentration was negatively correlated with red-footed
boobies here, our results appear consistent with Mendez et al. (2017),
with competition being an important driver of distribution. Red-footed
booby were sensitive to rats, aggregating in greater abundance near
rat-free islands.

Brown noddy and white tern distributions were strongly related to
distance to coast, concentrating around islands, and over shallow water
(\textless{}1000 m). Furthermore, brown noddy distribution was
associated with high sea surface temperature (\textgreater{}29.5 °C),
while white terns were strongly influenced by low chlorophyll-a
concentration (\textless{} 0.2 mg. m\textsuperscript{-3} chl-a). Our
results are in line with previous observations of brown noddies leaving
and returning to islands during the same day in the breeding season,
suggesting they are not long-distance or multiday foragers (Surman,
Nicholson, \& Ayling, 2017; Jaquemet, Le Corre, \& Weimerskirch, 2004).
The two species appeared to have different responses to rat invasion.
The reasons for this difference in response remain unknown, but could be
linked to the fact that both species breed on rat-invaded islands (Carr,
pers. obs.), thus confounding any signal related to invasion status.

~

Wedge-tailed shearwater were restricted to less productive waters
(\textless{}0.20 mg. m\textsuperscript{-3} chl-a) with high slope
(\textgreater{} 20°) and were more attracted to colder water
(\textless{} 29 °C) across the BIOT MPA. Our observations were
consistent with those of Mannocci, Laran, et al. (2014) in the western
IO, reporting lower numbers in productive areas (\textgreater{} 0.37 mg
m\textsuperscript{-3}). The wedge-tailed shearwater were the least
sensitive species to distance to coast and, in contrast to the other
species appeared to increase in abundance with increasing distance. A
potential reason for this is that shearwaters typically have a wider
foraging range from nesting colonies (ca. 480 km; King 1974) than
red-footed booby (ca. 67.5 km; Young et al. 2010) and brown noddy (ca.
80 km; Harrison \& Stone-Burner, 1981; King, 1974). Therefore, this
response is likely an artefact of the observed birds commuting to
foraging areas, rather than actually foraging in the relatively
near-coast areas. Wedge-tailed shearwater return to burrows only at
night, so that their distribution appears independent from islands may
be due to a predominantly scattered and remote distribution during the
day (Dias, Alho, Granadeiro \& Catry, 2015). Although we found no
significant effect of rat invasion on wedge-tailed shearwater
distribution, many shearwater species and allies are particularly
vulnerable to invasive land predators (Dias et al., 2019; Smith, 2002),
because they nest in ground burrows. The pelagic behaviour and large
foraging range which our sampling range failed to capture may mask any
distribution shift related to rat invasion and the other environmental
variables, as indicated by the weaker models.

Constant competition over prey is expected to lead to a prey depredation
zones around colonies, otherwise known as Ashmole's Halo (Ashmole,
1963). Halos vary as a function of colony, size, and bird foraging range
(Birt, Birt, Goulet, Cairns \& Montevecchi, 1987). Within the Chagos
Archipelago, many islands are less than 100 km apart and are clustered
close together (\textless{} 20 km between islands) within atolls. The
range to which the abundant red-footed booby, brown noddy and white tern
distributions radiate out from islands (i.e. 263 km, 136 km and 133 km
respectively), makes it therefore very likely that neighbouring colonies
compete, either by overlapping in distribution or by expressing
behaviour to minimise foraging overlap (Wakefield et al. 2013; Mendez et
al. 2017). The wider distribution range and lower abundance of the
wedge-tailed shearwater makes competition between colonies less likely
than for other seabirds (Gaston, Ydenberg \& Smith, 2007).

~

\subsection*{4.2 Influence of Climate
Oscillation}\label{influence-of-climate-oscillation}

Our multiyear time series enabled us to document effect of climatic
oscillations at the inter-annual scale on distribution. We observed
similar abundance trends for red-footed booby and wedge-tailed
shearwater during the five years of sampling, with both species
abundance positively correlating with the DMI. We detected no
correspondence between seabird abundance and the ONI, a proxy for the
ENSO index. These observations are consistent with current understanding
regarding the influence of the dipole of higher trophic levels in the
Indian Ocean, adding to a limited but growing body of research on the
importance of the Dipole on IO megafauna. For example, Kumar, Pillai \&
Ushadevi (2016), identified a positive association between IO tuna
productivity and the Dipole Index. In the southern IO, albatross
breeding success has also been positively correlated with the Dipole
Index (Rivaland et al., 2010). The knowledge of mechanisms driving these
patterns is at present limited and any explanation must remain
speculative at this stage. There is evidence that equatorial upwellings
in the IO are more pronounced and that westerly winds decrease in
intensity during positive Dipole events (Du \& Zhang, 2015). In the
Chagos Archipelago, it is thus possible that negative Dipole events
result in weakened regional upwelling and therefore require seabirds to
forage further afield, leading to a drop in regional abundance. This
would be consistent with present understanding regarding other climate
oscillations such as ENSO, which is known to influence forage species
productivity (Lehodey, Bertignac, Hampton, Lewis, \& Picaut, 1997), with
implications for higher trophic levels. For example, common bottlenose
dolphin (\emph{Tursiuops trunctatus}) migrate offshore during strong
ENSO years, possibly due to a lack of inshore prey (Sprogis et al.
2018). Use of telemetry and satellite tracking is currently being
deployed on red-footed boobies in BIOT (Carr, pers. obs.), which will
enable mechanisms to be explored in more detail. The greater sensitivity
of wedge-tailed shearwater to both oceanographic variables and to the
Dipole suggests this family may be the most vulnerable to global
environmental change.

Any linkage between the Dipole and mobile megafauna is likely mediated
by multiple trophic links (Oro, 2014). As foragers commensal with
subsurface predators, seabirds could be impacted by the Dipole both
directly, for example, by a reduction in forage species abundance, and
indirectly, by an increase in tuna abundance (Maxwell \& Morgan, 2013;
Kumar et al., 2016). It is beyond our scope to distinguish these
processes here, however we are currently expanding our analysis of
seabird distribution to include data on subsurface prey and predator
abundance collected simultaneously to the seabird observations, using
midwater baited videography (Letessier, Bouchet \& Meeuwig, 2017;
Letessier et al., 2019).~

~

\subsection*{4.3 Implication for rat eradication
programmes}\label{implication-for-rat-eradication-programmes}

Past rat eradication efforts in the Chagos Archipelago include a failed
attempt on Eagle Island (Meier, 2006) and successful attempts on Îles
Vache Marine, du Sel and Jacobin (Harper et al., 2019). The latter
attempts were focussed on small islands to test the feasibility of
eradication and appropriate methodologies on a small scale.~ Island
rodent eradication is increasingly recognized as a powerful strategy for
the preservation and recovery of avian populations (Brooke, et al.,
2018; Jones et al., 2016; Lavers, Wilcox, \& Donlan, 2010). However,
eradication is technically challenging and expensive (Warren, 2018),
requiring the application of toxic rodenticide posing a risk to humans,
livestock, pets, and wildlife (Pickrell, 2019; Van den Brink, Elliott,
Shore \& Rattner, 2017). Eradication is more likely to fail in the
tropics, with high mean annual temperatures and constant precipitation
(Russell \& Holmes, 2015), and in the presence of land crabs and coconut
palms (Holmes, et al., 2015), making a programme in the Chagos
Archipelago challenging. Eradication on the largest island of Diego
Garcia is likely to be particularly complex and expensive as it is
inhabited (Harper \& Carr, 2015).

Our analysis has revealed potential increases in habitat usage following
rat eradication and that these habitats are spatially and
species-specific. ~On the basis of our study, we propose that
eradication should be prioritised on Île Manoel, Île Yeye and, Île de la
Passe in Peros Banhos Atoll and on Eagle Island in the western Great
Chagos Bank. In addition to minimising overlap between distributions of
the recovering colonies, these islands fulfil all the criteria
identified by Buxon, et al. (2014), such as proximity to healthy
metapopulation and seabird diversity. Our recommendations are consistent
with those of Dawson et al (2015), which rank Île de la Passe in the top
25 islands for invasive vertebrate eradication in the UK overseas
territories. We note that these recommendations are on the basis of
factors explored in this study only, and that there are other factors
that dictate the feasibility, success and approach to rodent
eradication. Our results here aim to form part of a far wider set of
considerations, with the ultimate aim of eradicating rats from all
islands in the archipelago, in order to achieve full conservation
impact.

~

\subsection*{4.4 Concluding remarks}\label{concluding-remarks}

Seabird abundance and distribution at sea in BIOT is driven by
geomorphology and oceanographic conditions. Our distribution predictions
complements previous efforts elsewhere in the IO and our time-series has
enabled us to identify potential interannual variability related to
climate oscillation. Seabird populations are vulnerable to both climatic
variability and human activities (Dias et al., 2019; Paleczny et al.,
2015). Environmental variability is predicted to increase globally under
climate change scenarios (Allen et al., 2014), and evidence suggest that
global warming variability may decouple the Dipole from upwelling in the
western IO (Watanabe, Watanabe, Yamazaki, Pfeiffer \& Claereboudt,
2019). Identifying how inter-annual processes like the IO Dipole drives
seabird distribution where human activities are limited, is valuable for
identifying long-term strategies for seabird protection. For example,
our predictions will enable responses to predicted extreme climatic
event to be anticipated and thus mitigated in spatial management
regimes.~

To our knowledge, this is the first attempt at predicting the potential
response of seabird distribution by predicting potential shifts in
habitats usage following a rat-eradication scenario. We have
demonstrated areas of potential distribution gain and have predicted new
hotspots at sea following a rat-eradication programme. There is
considerable impetus for eradicating invasive species on islands (Holmes
et al., 2019; Brooke et al., 2018; Jones et al., 2016; Dawson et al.,
2015; Lavers et al., 2010), further supported by our research here and
other related research in the Chagos Archipelago (Benkwitt et al., 2019;
Benkwitt et al., 2020; Graham et al., 2019; Harper et al., 2019). In
addition to practical considerations such as cost and probability of
success, eradication programmes should identify where eradication can
have the greatest conservation potential and ecological impact. This is
particularly important for seabirds, whose niche extends beyond
terrestrial breeding colonies.

~

\section*{5. ACKNOWLEDGEMENTS}\label{acknowledgements}

We acknowledge Dr. David Curnick for helping us with the variable
extractions and the BIOT Administration for support and access to the
BIOT patrol vessel. We want to thank all field assistants for their
help, and Nicholas Dunn for valuable comments on the manuscript. We are
gratefully to the Master, Chief engineer, and crew of the BIOT patrol
vessels for their hard work and assistance. T.B.L. was supported by the
Marine Biodiversity Hub through the Australian Government's National
Environmental Research Program (NERP). We would like to acknowledge the
support of the Bertarelli Foundation for funding field expeditions in
2015 and 2016 and for supporting P.C., H.K and T.B.L., This work
contributes to the Bertarelli Programme in Marine Science.

~

\section*{BIOSKETCH}\label{biosketch}

\textbf{Julian Perez-Correa} is an ecologist interested in describing
and modelling biodiversity patterns focused on birds.

\textbf{Tom B. Letessier is} a~marine biologist with an interest in both
tropical and pelagic ecosystems. His research primarily focuses on the
spatial ecology of predators and their prey, to inform marine
conservation and management strategies.

~

\textbf{AUTHORS' CONTRIBUTIONS}

P.C. conducted the seabird observations. J.P.C. and T.B.L conceive the
ideas and led the analysis and writings, assisted by J.J.M., H.K., and
P.C. All authors contributed to the ecological interpretation of the
analysis.

~

\section*{DATA AVAILABILITY STATEMENT}\label{data-availability-statement}

R code and data used to conduct this research are available for public
access in https://github.com/juperez/SeabirdChagos

~

\section*{ORCID}\label{orcid}

Julian Perez-Correa \url{https://orcid.org/0000-0002-9205-5396}

Jessica J. Meeuwig https://orcid.org/0000-0003-1169-8785

Tom B. Letessier \url{https://orcid.org/0000-0003-4011-0207}



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\par\null

\textbf{Table 1.} Number of transects made by month and year within the
BIOT MPA.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Table1/Table1}

\end{center}
\end{figure}

\par\null

\textbf{Table 2.} Number of observations and individual counts during
the survey (2012-2016), separated by family and species. (*) Represent
the species with the greatest \textbf{} counts used for modelling.

\par\null\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Table2/Table2}

\end{center}
\end{figure}

\textbf{Table 3.} Results of the broken-stick regression for the boosted
regression trees models considering the original distribution model, a
rat-free model (where we exchanged the variable distance to coast with
distant to the nearest ret-free island) and the rat-invaded model (where
we replaced distance to coast by distance to nearest rat-invaded
island). The broken stick regression is represented by the breaking
point which is the inflection point of the partial dependence plot from
each model. Lower and upper confidence interval at 95\% are reported in
each break point.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Table3/Table3}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure-1/Figure-1}
\caption{\selectlanguage{ngerman}{\textbf{} Seabird sampling effort within the Chagos Archipelago and the
British Indian Ocean Territory. Boundaries of the BIOT MPA (a), and
locations within the Indian Ocean (inset). Individual atolls and islands
(b-g). Black dots represent the location of individual bird transects.
Rat-invaded islands are coloured in red and rat-free islands in blue.
Six islands have been reported with uncertain rat status (Île Verte, Île
Manon, Île Finon, Île de la Passe and a small Unnamed Island in Peros
Banhos) and are for representation purposes shown as rat-invaded.
\textbf{}
{\label{882151}}%
}}
\end{center}
\end{figure}\selectlanguage{ngerman}

\textbf{}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure-2/Figure-2}
\caption{{Year-to-year sampling effort in the Chagos Archipelago. Each 300 m
transect is represented by a black dot as the spatial resolution does
not allowed us to perfectly draw lines.
{\label{674359}}%
}}
\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure-3/Figure-3}
\caption{{Number of individual seabirds by family summed across year (a) and for
the four most abundant species red-footed booby (\emph{Sula sula}),
brown noddy (\emph{Anous stolidus}), white tern (\emph{Gygis alba}) and
wedge-tailed shearwater (\emph{Ardenna pacifica}) standardised by the
number of samples for each year (b).
{\label{400175}}%
}}
\end{center}
\end{figure}

\par\null

~\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure-4/Figure-4}
\caption{{Partial dependence plots of each variable modelled in the BRTs for all
four species. The green-solid line in each graph represents the response
of the species to the variables. The relative contribution of the model
is showed from the greatest to the least. Plots manifest that
non-monotonic responses were found.
{\label{409989}}%
}}
\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure-51/Figure-51}
\caption{{Partial dependence plots of the response of red-footed booby (a) and
wedge-tailed shearwater (b) to Dipole Mode Index. Both species show a
positive correlation with increasing positive DMI. Positive DMI means
cooler temperature in eastern Indian Ocean and warmer temperature in
western Indian Ocean.
{\label{965208}}%
}}
\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure-6/Figure-6}
\caption{{Predicted distribution of red-footed booby, brown noddy, white tern and
wedge --tailed shearwater as a function the BRT models. Colour scales
represent the seabird abundance response, ranging from highest predicted
value (red) to zero (blue). Scales differ between species as a function
of difference in abundance. Predictions were run within the convex hull
of the variables. ~
{\label{999012}}%
}}
\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure-7/Figure-7}
\caption{{Contribution of the distance to coast on the distribution model,
rat-free model and rat-invaded model for red-footed booby (RFT), brown
noddy (BN), white tern (WT), and wedge-tailed shearwater (WTS).
Contribution of distance to a near rat-free island were greater in
red-footed booby and brown noddy. White tern showed that rat-invaded
island has a greater contribution. Wedge-tailed shearwater showed
difference in contribution between rat-free and rat-invaded island but
the contribution of distance to coast in the distribution model was
greater.
{\label{358939}}%
}}
\end{center}
\end{figure}

\textbf{~}

~\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure-8/Figure-8}
\caption{{Sensitivity of seabird distribution to presence or absence of rats on
nearby islands. Partial dependence plot (a, c, e, g) of the effects of
rat-free (blue lines) and rat-invaded (red lines) islands contrasted
with the original distribution model (green lines). Stippled line
denotes location of the breaking point that denotes a threshold in
distribution. Comparison of the break point regression using the 95\%
confident intervals calculated in the Davies' test is shown (b, d, f, h)
{\label{574665}}%
}}
\end{center}
\end{figure}

\par\null

\textbf{}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure-9/Figure-9}
\caption{{Predicted areas of increase in seabird abundance under a scenario of
rat-eradication. Rat-free distribution predicted from BRT model using a
rat-free scenario (a-c), and a rat-invaded scenario (d-f), where each
island is set as either `rat-free' or `rat-invated' respectively. These
models represent the distribution predictions under complete
eradications of rats and a full invasion of rats respectively. Hence, an
approach to understand the variations of distribution is calculated by
subtracting the rat-invaded minus the rat-free models. This yielded to
the net gain in bird abundance under a scenario of rat eradication
(g-i). The upper 95\% quantile increase of the net gain is also showed
(j-l). White shaded areas indicate areas nearby currently rat-free
islands in which a rat eradication programme would not have any effect
since they are already at the theorised maximum abundance.
{\label{205628}}%
}}
\end{center}
\end{figure}

\textbf{}

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

