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\begin{document}

\title{Refining seabird marine protected areas by predicting habitat inside
foraging range - a case study from the global tropics}



\author[1]{Mark Miller}%
\author[2]{Graham Hemson}%
\author[2]{Julie Du Toit}%
\author[2]{Andrew McDougall}%
\author[3]{Peter Miller}%
\author[4]{Akira Mizutani}%
\author[5]{Alice Trevail}%
\author[6]{Alison Small}%
\author[7]{Andreas Ravache}%
\author[8]{Annalea Beard}%
\author[9]{Ashley Bunce}%
\author[10]{Caroline Poli}%
\author[11]{Chris Surman}%
\author[12]{Diego Gonzalez-Zamora}%
\author[13]{Elizabeth Clingham}%
\author[7]{Eric Vidal}%
\author[14]{Fiona McDuie}%
\author[15]{Gabriel Machovsky-Capuska}%
\author[16]{Graeme Cumming}%
\author[17]{Grant  Humphries}%
\author[18]{Henri Weimerskirch}%
\author[19]{Judy Shamoun-Baranes}%
\author[13]{Leeann Henry}%
\author[20]{Hannah Wood}%
\author[21]{Hillary Young}%
\author[4]{Hiroyoshi Kohno}%
\author[22]{Jacob Gonzalez-Sol�s}%
\author[23]{Jacopo Cecere}%
\author[24]{Jan Veen}%
\author[25]{Jessica Neumann}%
\author[26]{Jill SHEPHARD}%
\author[27]{Jonathan Green}%
\author[28]{José Castillo-Guerrero}%
\author[29]{Julia Sommerfeld}%
\author[30]{Justine Dossa}%
\author[31]{Karen Bourgeois}%
\author[32]{Ken Yoda}%
\author[33]{Lachlan McLeay}%
\author[34]{Licia Calabrese}%
\author[35]{Loriane Mendez}%
\author[27]{Louise Soanes}%
\author[36]{Malcolm Nicoll}%
\author[37]{Mia Derhé}%
\author[38]{Morgan Gilmour}%
\author[39]{Ngone Diop}%
\author[40]{Nicholas James}%
\author[36]{Pete Carr}%
\author[41]{Rhiannon Austin}%
\author[36]{Robin Freeman}%
\author[1]{Rohan Clarke}%
\author[1]{Rowan Mott}%
\author[42]{Sarah Maxwell}%
\author[22]{Sarah Saldanha}%
\author[43]{Scott Shaffer}%
\author[44]{S. Oppel}%
\author[45]{Stephen Votier}%
\author[46]{Takashi Yamamoto}%
\author[22]{Teresa Militão}%
\author[47]{Maria Beger}%
\author[40]{Bradley Congdon}%
\affil[1]{Monash University}%
\affil[2]{Queensland Government}%
\affil[3]{Plymouth Marine Laboratory}%
\affil[4]{Island Ecosystem Research}%
\affil[5]{University of Exeter}%
\affil[6]{NatureSpace Partnership}%
\affil[7]{IRD, Univ Réunion, CNRS, IFREMER, Univ Nouvelle Calédonie}%
\affil[8]{Cardiff University}%
\affil[9]{The University of Queensland School of Biological Sciences}%
\affil[10]{University of Florida}%
\affil[11]{Halfmoon Biosciences}%
\affil[12]{Centro de Investigación Científica y de Educación Superior de Ensenada}%
\affil[13]{St Helena Government}%
\affil[14]{U.S. Geological Survey Western Ecological Research Center}%
\affil[15]{Nutrilens}%
\affil[16]{The University of Western Australia}%
\affil[17]{Black Bawks Data Science}%
\affil[18]{CEBC}%
\affil[19]{Universiteit van Amsterdam}%
\affil[20]{Zoological Society of London Institute of Zoology}%
\affil[21]{University of California Santa Barbara Department of Ecology Evolution and Marine Biology}%
\affil[22]{Universitat de Barcelona Departament de Biologia Evolutiva Ecologia i Ciències Ambientals}%
\affil[23]{Area Avifauna Migratrice, Istituto Superiore per la Protezione e la Ricerca Ambientale (ISPRA)}%
\affil[24]{VEDA Consultancy}%
\affil[25]{University of Reading}%
\affil[26]{Murdoch University}%
\affil[27]{University of Liverpool School of Environmental Sciences}%
\affil[28]{Centro de Investigación en Alimentación y Desarrollo}%
\affil[29]{Büro für faunistische Fachfragen}%
\affil[30]{Birdlife International}%
\affil[31]{IMBE, Aix Marseille Université, CNRS, IRD, Avignon Université, Centre IRD Nouméa}%
\affil[32]{Nagoya University}%
\affil[33]{South Australian Research and Development Institute}%
\affil[34]{University of Seychelles}%
\affil[35]{Mediterranean Science Commission (CIESM)}%
\affil[36]{Institute of Zoology of the Zoological Society of London}%
\affil[37]{Bumblebee Conservation Trust}%
\affil[38]{University of California Santa Cruz Department of Ocean Sciences}%
\affil[39]{Cheikh Anta Diop University of Dakar}%
\affil[40]{James Cook University College of Science and Engineering}%
\affil[41]{University of Liverpool}%
\affil[42]{University of Washington}%
\affil[43]{San Jose State University}%
\affil[44]{RSPB}%
\affil[45]{Heriot-Watt University}%
\affil[46]{Azabu University Graduate School of Veterinary Medicine School of Veterinary Medicine}%
\affil[47]{University of Leeds Faculty of Biological Sciences}%


\vspace{-1em}



  
  \date{May 18, 2024}


\begingroup
\let\center\flushleft
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\selectlanguage{english}
\begin{abstract}
Conservation of breeding seabirds typically requires detailed data on
where they feed at sea. Ecological niche models (ENMs) can fill data
gaps, but rarely perform well when transferred to new regions.
Alternatively, the foraging radius approach simply encircles the sea
surrounding a breeding seabird colony (a foraging circle), but
overestimates foraging habitat. Here, we investigate whether ENMs can
transfer (predict) foraging niches of breeding tropical seabirds between
global colonies, and whether ENMs can refine foraging circles. We
collate a large global dataset of tropical seabird tracks (12000 trips,
16 species, 60 colonies) to build a comprehensive summary of tropical
seabird foraging ranges and to train ENMs. We interrogate ENM
transferability and assess the confidence with which unsuitable habitat
predicted by ENMs can be excluded from within foraging circles. We apply
this refinement framework to the Great Barrier Reef (GBR), Australia to
identify a network of candidate marine protected areas (MPAs) for
seabirds. We found little ability to generalise and transfer breeding
tropical seabird foraging niches across all colonies for any species
(mean AUC: 0.56, range 0.4-0.82). Low global transferability was
partially explained by colony clusters that predicted well internally
but other colony clusters poorly. After refinement with ENMs, foraging
circles still contained 89\% of known foraging areas from tracking data,
providing confidence that important foraging habitat was not erroneously
excluded by greater refinement from high transferability ENMs nor minor
refinement from low transferability ENMs. Foraging radii estimated the
total foraging area of the GBR breeding seabird community as 2,941,000
km2, which was refined by excluding between 197,000 km2 and 1,826,000
km2 of unsuitable foraging habitat. ENMs trained on local GBR tracking
achieved superior refinement over globally trained models, demonstrating
the value of local tracking. Our framework demonstrates an effective
method to delineate candidate MPAs for breeding seabirds in data-poor
regions.%
\end{abstract}%



\sloppy


\section*{Title}

{\label{title}}

Refining seabird marine protected areas by predicting habitat inside
foraging range - a case study from the global tropics

\section*{Abstract}

{\label{abstract}}

Conservation of breeding seabirds typically requires detailed data on
where they feed at sea. Ecological niche models (ENMs) can fill data
gaps, but rarely perform well when transferred to new regions.
Alternatively, the foraging radius approach simply encircles the sea
surrounding a breeding seabird colony (a foraging circle), but
overestimates foraging habitat.

Here, we investigate whether ENMs can transfer (predict) foraging niches
of breeding tropical seabirds between global colonies, and whether ENMs
can refine foraging circles. We collate a large global dataset of
tropical seabird tracks (12000 trips, 16 species, 60 colonies) to build
a comprehensive summary of tropical seabird foraging ranges and to train
ENMs. We interrogate ENM transferability and assess the confidence with
which unsuitable habitat predicted by ENMs can be excluded from within
foraging circles. We apply this refinement framework to the Great
Barrier Reef (GBR), Australia to identify a network of candidate marine
protected areas (MPAs) for seabirds.

We found little ability to generalise and transfer breeding tropical
seabird foraging niches across all colonies for any species (mean AUC:
0.56, range 0.4-0.82). Low global transferability was partially
explained by colony clusters that predicted well internally but other
colony clusters poorly. After refinement with ENMs, foraging circles
still contained 89\% of known foraging areas from tracking data,
providing confidence that important foraging habitat was not erroneously
excluded by greater refinement from high transferability ENMs nor minor
refinement from low transferability ENMs.

Foraging radii estimated the total foraging area of the GBR breeding
seabird community as 2,941,000 km\textsuperscript{2}, which was refined
by excluding between 197,000 km\textsuperscript{2} and 1,826,000
km\textsuperscript{2} of unsuitable foraging habitat. ENMs trained on
local GBR tracking achieved superior refinement over globally trained
models, demonstrating the value of local tracking. Our framework
demonstrates an effective method to delineate candidate MPAs for
breeding seabirds in data-poor regions.

\section*{Keywords}

{\label{keywords}}

Foraging Radius, Foraging niche, Great Barrier Reef, Ecological Niche
Model, Marine Protected Area, Transferability

\section*{Introduction}

{\label{introduction}}

To counter the biodiversity crisis, there is an urgent need to protect
important habitats to ensure the stability of global ecosystems. In
response, the International Union for Conservation of Nature (IUCN) has
called for 30\% of the Earth's overall land and sea area to be protected
by 2030. However, as of April 2023 only 2.9\% of the ocean was highly
protected (www.mpatlas.org; Morgan et al. 2018). There is also concern
that protected areas could be situated in locations where increased
protection offers marginal benefit for biodiversity (Devillers et al.
2015, Woodley et al. 2021). One barrier to protected area implementation
is that limited knowledge of animal distributions and abundance hampers
the identification of the most critical locations. In the marine realm,
data collection is logistically and financially challenging; however,
there are numerous, pressing threats to biodiversity that include
fishing, climate change, pollution, shipping and energy generation
(Halpern et al. 2008).

Seabirds are a highly threatened animal group (Croxall et al. 2012) with
a marine foraging niche (they depend upon the sea for food). Their
relative detectability and accessibility (i.e. above water, colonial,
terrestrial breeding) compared to other marine species has provided
sufficient data to merit global analyses of conservation priority (Dias
et al. 2019a) and designate marine protected areas (MPAs) (e.g. North
Atlantic Current and Evlanov Sea-basin MPA; Davies et al. 2021). During
the breeding season, seabird distribution is focussed around colonies,
as birds regularly return to perform parental duties such as incubating
eggs and feeding chicks, and area-based conservation measures, such as
MPAs, are more feasible than during more dispersive migratory and
non-breeding seasons (Oppel et al. 2018). To identify MPAs for seabirds,
a spatial representation of their marine foraging niche is required.
This can come directly from bird-borne tracking devices or at-sea
surveys, which locate hotspots of occurrence or abundance to delineate
candidate MPAs (Lascelles et al. 2016). Alternatively, such data can be
entered into Ecological Niche Models (ENMs), also known as species
distribution models, to make predictions beyond surveyed areas. ENMs
build statistical relationships between species space use and
remotely-sensed environmental variables, and then predict these
relationships over broad areas sampled by remote sensors (e.g. satellite
imagery). For seabirds, these environmental variables describe important
marine habitat and bio-physical processes such as seamounts, frontal
systems and productivity blooms, which can characterise their foraging
niches, for example the foraging niche of a seabird population could be
described as specialising on upwellings at pelagic seamounts. The
ability of ENMs to predict seabird foraging niches beyond surveyed
areas, has seen them regularly used for marine spatial planning and
identification of priority conservation areas (Nur et al. 2011, \selectlanguage{polish}Ž\selectlanguage{english}ydelis
et al. 2011, Lavers et al. 2014, Dias et al. 2019b).

However, a key hurdle for ENMs is to extrapolate predictions beyond the
geographical and temporal range of training data, known as model
`transferability' (Randin et al. 2006, Yates et al. 2018). A growing
body of literature suggests limited ENM transferability between
different regions (Redfern et al. 2017, Mannocci et al. 2020). In
seabirds, Warwick-Evans et al. (2018) found that the foraging niche of
chinstrap penguins\emph{Pygoscelis antarcticus} was transferable between
colonies at \textless{}100 km, while P\selectlanguage{ngerman}éron et al. (2018) found that
foraging niche of Scopoli's shearwater\emph{Calonectris diomedea} was
transferable locally but not regionally (\textgreater{}200 km).
Transferability at large scales (\textgreater{}1000's of km) does not
apply in grey petrel\emph{Procellaria cinerea} wintering distributions
(Torres et al. 2015), nor red-billed tropicbird \emph{Phaethon
aethereus}foraging niches (Diop et al. 2018). These studies support
contrasting regional marine habitats and differing associations with
habitat by different populations (local adaptation) as major barriers to
transferability. Local adaptation is driven by strong philopatry in
seabirds, many of which have evolved population-specific foraging
behaviours suited to local biotic and oceanographic conditions (Peck \&
Congdon 2005, Mendez et al. 2017, Gilmour et al. 2018). To improve
transferability, ENMs can be trained with data from different regions to
extract commonalities in local adaptation and better generalise a
species' niche (Matthiopoulos et al. 2011). With the growing
availability of multi-colony seabird tracking datasets (e.g.
Ropert-Coudert et al. 2020; www.seabirdtracking.org), it is now feasible
to train such ENMs and conduct a comprehensive evaluation of the
transferability of breeding seabird foraging niches. This assessment is
particularly warranted in tropical regions, which have received
significantly less effort from seabird tracking studies relative to
higher latitudes (Bernard et al. 2021), and ENMs could be particularly
important for filling gaps in knowledge on seabird distributions.

A simple, pragmatic and generally effective alternative to ENMs for
defining important areas of seascape for breeding seabirds is the
foraging radius approach (Birdlife International 2010, Thaxter et al.
2012, Soanes et al. 2016, Critchley et al. 2020). At-sea observations
and/or tracking data are used to calculate the distance breeding
seabirds forage from their colony (hereafter a ``foraging radius''),
this is mapped with a circle centred on the colony to represent the sea
area within which the breeding seabird population feeds (hereafter a
``foraging circle''). Averaging foraging radii from multiple colonies
can generalise a species' foraging radius which can then be applied to
colonies lacking information on the at-sea distribution of breeding
residents (Thaxter et al. 2012). With a representative sample of
colonies, the process can be applied globally (e.g.
https://maps.birdlife.org/marineibas). However, the foraging radius
approach overestimates foraging habitat, as not all areas within the
foraging circle will be used. A foraging circle can be refined with
information relating to habitat preference, such as bathymetry (Birdlife
International 2010, Soanes et al. 2016) or prey availability (Grecian et
al. 2012), where unsuitable habitat is `cut out' from the circle.
Extending this approach by using a range of remotely-sensed variables
commonly used in ENMs allows a more holistic approximation of unsuitable
habitat, which could improve the accuracy of foraging circle refinement.
As such, this new approach better meets a key goal of systematic
conservation planning, minimising the cost (in terms of size) of
protected areas while maximising the confidence they contain core
habitat (Margules \& Pressey 2000).

The Great Barrier Reef (GBR) Marine Park, Australia is arguably the most
thoroughly managed large MPA in the tropics (Fernandes et al. 2005). In
addition to the mega-diverse coral reef community, for which the park
was created, the GBR's cays support numerous globally and regionally
significant breeding populations of tropical seabirds (King 1993).
However, numerous breeding GBR seabird populations are in decline
(Heatwole et al. 1996, Batianoff \& Cornelius 2005, Hemson 2015,
Woodworth et al. 2020). Despite this knowledge of seabird population
change, the ability of managers to understand the causes of these
changes and respond to them is limited by the lack of information on
where they forage. Limited tracking studies underpin much of what we
know (Fig. 1), with no standardised boat or aerial-based seabird survey
data existing (but see CSIRO (2020) for a survey dataset from the
adjacent Coral Sea). Consequently, seabird foraging resources have not
been considered in the designation of marine park zoning that dictates
the location of permitted activities within the GBR Marine Park.
Overcoming these data gaps could inform future amendments to marine park
zoning and guide other management interventions, and potentially
mitigate seabird population declines.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image1/image1}

\end{center}
\end{figure}

Fig. 1 Extent of published seabird tracking data on the Great Barrier
Reef. Displayed GBR seabird breeding sites qualify under globally or
regionally significant seabird breeding areas.

Here, we collate the most comprehensive tropical seabird tracking
dataset from across the globe to train ENMs, describing foraging niche
and to estimate tropical seabird foraging radii. Firstly, we determine
whether breeding seabird foraging niches can be transferred (predicted)
between global colonies using ENMs. We investigate the influence of
local adaptation on transferability by testing whether nearby or
oceanographically similar colonies show better transferability, and
whether this can be overcome by training ENMs on multiple colonies to
generalise a `global' foraging niche. Secondly, we present a framework
combining ENMs with the foraging radius approach to refine foraging
circles by excluding areas of predicted unsuitable habitat. We validate
our framework's robustness by testing the ability of unrefined and
refined foraging circles to include known foraging areas of colonies
from our global tracking dataset. Finally, we demonstrate how global
models can inform local planning by applying our framework to the
breeding seabird community of the Great Barrier Reef to identify a
network of candidate MPAs for seabirds, and explore the trade-off
between their size and the confidence in their prediction.

\section*{Methods}

{\label{methods}}

\subsection*{Tracking data}

{\label{tracking-data}}

The validity of our modelling approach hinges on constructing ENMs from
multiple colonies. We thus compiled the most comprehensive database of
tropical seabird tracking data to date, featuring 16 species
representing 60 colonies around the globe and totalling
\textasciitilde{}12000 individual trips (Table 1; Supporting
information). Species that had more than 10 tracking studies were
modelled individually. Tropicbird \emph{Phaethon} and
frigatebird\emph{Fregata} species were grouped respectively (Table 1) to
boost sample size and make family level models, acknowledging that
foraging differences between species within these families have been
observed (e.g. Mott et al. 2016). Noddy \emph{Anous} species were
modelled as a family group (following evidence of foraging niche
overlap; Surman \& Wooller 2003, Shephard et al. 2018), and large tern
\emph{Thalasseus}and \emph{Hydroprogne} species were aggregated into a
meta-family group for modelling. The exceptions were sooty terns, which
were modelled as an individual species (despite few tracking studies)
because of their unique, highly pelagic foraging niche among terns
(Surman \& Wooller 2003). Wedge-tailed shearwaters were also modelled
separately for short trips and long trips because we had good a-priori
knowledge of dual-foraging behaviours during breeding on the GBR
(Congdon et al. 2005, McDuie et al. 2015) and in New Caledonia
(Weimerskirch et al. 2020), and sufficient tracking studies to do so.
Both individual species (e.g. brown booby BRBO) and family groups (e.g.
large terns TERN) are hereafter referred to as ``modelled species''.

Table 1 Summary of quantity and coverage of tracking data analysed for
each species, and the grouping of species with less data.\selectlanguage{english}
\begin{longtable}[]{@{}lllll@{}}
\toprule
Species common name & Latin name & Number of colonies & Number of trips
& Modelled species (acronym)\tabularnewline
\midrule
\endhead
Brown booby & \emph{Sula leucogaster} & 16 & 2926 & brown booby
(BRBO)\tabularnewline
Masked booby & \emph{Sula dactylactra} & 12 & 2255 & masked booby
(MABO)\tabularnewline
Red-footed booby & \emph{Sula sula} & 13 & 733 & red-footed booby
(RFBO)\tabularnewline
Great frigatebird & \emph{Fregata minor} & 3 & 1195 & frigatebirds group
(FRBD)\tabularnewline
Lesser frigatebird & \emph{Fregata ariel} & 2 & 732 & frigatebirds group
(FRBD)\tabularnewline
Magnificent frigatebird & \emph{Fregata magnificens} & 4 & 97 &
frigatebirds group (FRBD)\tabularnewline
Red-billed tropicbird & \emph{Phaethon aethereus} & 10 & 1100 &
tropicbirds group (TRBD)\tabularnewline
Red-tailed tropicbird & \emph{Phaethon rubricauda} & 1 & 61 &
tropicbirds group (TRBD)\tabularnewline
Wedge-tailed shearwater & \emph{Ardenna pacifica} & 8 & 313 &
wedge-tailed shearwater short trip (WTST)\tabularnewline
Wedge-tailed shearwater & \emph{Ardenna pacifica} & 7 & 125 &
wedge-tailed shearwater long trip (WTLG)\tabularnewline
Sooty tern & \emph{Onychoprion fuscatus} & 3 & 19 & sooty tern
(SOTE)\tabularnewline
Black noddy & \emph{Anous minutus} & 1 & 7 & noddies group
(NODD)\tabularnewline
Lesser noddy & \emph{Anous tenuirostris} & 1 & 54 & noddies group
(NODD)\tabularnewline
Brown noddy & \emph{Anous stolidus} & 3 & 181 & noddies group
(NODD)\tabularnewline
Caspian tern & \emph{Hydroprogne caspia} & 1 & 313 & large terns group
(TERN)\tabularnewline
Greater crested tern & \emph{Thalasseus bergii} & 1 & 14 & large terns
group (TERN)\tabularnewline
Royal tern & \emph{Thalasseus maximus} & 1 & 253 & large terns group
(TERN)\tabularnewline
\bottomrule
\end{longtable}

\subsection*{Tracking data processing}

{\label{tracking-data-processing}}

Tracking datasets were speed filtered (removal of points \textgreater{}
90 km/h; (Mendez et al. 2017), and linearly interpolated using
the\emph{AdehabitatLT} (0.3.24) R package (Calenge 2011). Due to
differences in temporal resolution of different datasets we interpolated
each dataset to either 1, 2, 3, 5, 10, 15, or 20 minute resolutions. We
split individual foraging trips from multi-day tracks using
the\emph{track2kba} (1.0.0) R package (Beal et al. 2021), removing small
foraging trips within 4 km of the colony and under 1 hr in duration. We
also manually removed trips that spent too much time away from the
colony, indicating breeding failure. Upper trip duration limits were set
at five days for boobies (Mendez et al. 2017), 12 days for frigatebirds
(Mott et al. 2016) and tropicbirds, 14 days for wedge-tailed shearwater
long trips (McDuie et al. 2015) and sooty terns (Neumann et al. 2018),
three days for wedge-tailed shearwater short trips (Weimerskirch et al.
2020) and two days for noddies and terns (from inspection of data).

\subsection*{Foraging radii}

{\label{foraging-radii}}

We estimated radii and mapped foraging circles for each modelled species
to predict likely foraging range around untracked colonies (Birdlife
International 2010; Thaxter et al. 2012). We first obtained the maximum
distance from the colony observed across all the foraging trips made by
birds from each tracked colony. We then took the average of these
colony-specific maximum distances to generate a `mean maximum foraging
radius' for each modelled species (Thaxter et al. 2012). To provide
lower and upper extremes for each modelled species, we also present the
minimum and maximum of colony-specific maximum distances observed.

\subsection*{Ecological niche
modelling}

{\label{ecological-niche-modelling}}

To model the foraging niche of each modelled species, we assumed a
binomial response comparing the oceanographic covariates of known
foraging areas (1) against the oceanographic covariates of accessible
habitat (0). To identify known foraging areas for each dataset we
performed location-based kernel density analyses with a 1 km grid on a
subset of tracking datapoints identified as representing foraging
behaviour (Miller et al. 2017; see supporting information for further
details), and treated all grid points within the 50\% utilization
distribution (UD) as `presence' points in the model. The accessible
habitat for each colony was defined as the convex hull containing all
tracking locations (all behaviours included) and curtailed to marine
regions. Areas inside convex hulls were sampled using pseudo-absence
datapoints distributed randomly but weighted by inverse distance to the
colony (to constrain access to foraging habitat by central-place
foraging seabirds). Pseudo-absences were created at a rate of 3:1 to
presences (Wakefield et al. 2011) and given the same timestamp as their
respective presences for dynamic covariate extraction. To account for
the potential influence of random sampling of pseudo-absences on model
stability (Barbet-Massin et al. 2012), we repeated the random selection
of pseudo-absences five times, generating five replicate presence and
pseudo-absence datasets per original dataset. Each of the five
replicates was modelled separately and then averaged together for model
validation and prediction.

Although tropical seabird prey opportunities are patchily distributed
and ephemeral in nature (Weimerskirch 2007), their location and
availability are governed by physical ocean processes at broader spatial
scales (10-100kms; Wakefield et al. 2009). Ocean covariates for
modelling were created to capture broad-scale ocean features
representing attractive and/or reliable locations for tropical seabird
foraging (Table 2). For dynamic covariates, chlorophyll concentration,
sea surface temperature and frontal activity, we created a long-term
(\textasciitilde{}10 year) mean for each month of the year to model
whether birds target specific covariate values, and also created the
long-term standard deviation over 12 months of the year to describe
whether birds target areas that are temporally dynamic/homogenous. The
month in which each tracking dataset commenced was used to select the
monthly dynamic covariate layer to extract values from. The use of
long-term averages in models means that the oceanographic conditions
fitted against known foraging areas of tracked birds do not reflect the
concurrent oceanography. Rather, they characterise persistent
oceanographic features in the seascape. The advantage of this approach
is that a global layer for each oceanographic variable can be created,
meaning subsequent predictions of seabird foraging at each colony are
all built on the same, standardised, comparable oceanography. This is
particularly important for assessing model transferability between
colonies. Models also included static covariates of bathymetry and
seabed slope to allow them to describe the importance of geographical
features such as reefs, shelves and seamounts for foraging. Following
the seabird foraging niche transferability study of P\selectlanguage{ngerman}éron et al. (2018),
we did not include a distance-to-colony variable because it is
explicitly linked to colony-specific demographic information, such as
population size (Lewis et al. 2001), which we lacked for training and
test colonies. All data handling and statistical analyses were performed
in the statistical software environment R version 3.5.1 (R Core Team
2020). For more detail on tracking and oceanographic data processing see
supporting information.

Table 2 Oceanographic data sourced for modelling.\selectlanguage{english}
\begin{longtable}[]{@{}llll@{}}
\toprule
\begin{minipage}[b]{0.22\columnwidth}\raggedright\strut
Covariate (units)\strut
\end{minipage} & \begin{minipage}[b]{0.22\columnwidth}\raggedright\strut
Temporal resolution\strut
\end{minipage} & \begin{minipage}[b]{0.22\columnwidth}\raggedright\strut
Spatial resolution\strut
\end{minipage} & \begin{minipage}[b]{0.22\columnwidth}\raggedright\strut
Data source (provider)\strut
\end{minipage}\tabularnewline
\midrule
\endhead
\begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
Log chlorophyll-a concentration (mg C m\textsuperscript{-3})\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
Monthly mean 2000-2019\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
4.4 km\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
MODIS-Terra (https://oceancolor.gsfc.nasa.gov)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
Sea surface temperature (\selectlanguage{ngerman}°C)\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
Monthly mean 2000-2019\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
4.4 km\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
MODIS-Terra (https://oceancolor.gsfc.nasa.gov)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
Front persistence (probability of front at pixel 0-1)\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
Monthly mean 2006-2016\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
5.5 km\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
SST-CCI v2.1 daily SST (https://climate.esa.int/; Miller 2009)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
Mean front strength (\selectlanguage{ngerman}°C pixel\textsuperscript{-1})\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
Monthly mean 2006-2016\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
5.5 km\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
SST-CCI v2.1 daily SST (https://climate.esa.int/; Miller 2009)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
Seasonal chlorophyll-a concentration variability (mg
m\textsuperscript{-3})\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
Std Dev. across 12 months (2000-2019)\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
4.4 km\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
MODIS-Terra (https://oceancolor.gsfc.nasa.gov)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
Seasonal sea surface temperature variability (\selectlanguage{ngerman}°C)\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
Std Dev. across 12 months (2000-2019)\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
4.4 km\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
MODIS-Terra (https://oceancolor.gsfc.nasa.gov)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
Seasonal front persistence variability ([?] monthly front probability
0-1)\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
Std Dev. across 12 months (2006-2016)\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
5.5 km\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
SST-CCI v2.1 daily SST (https://climate.esa.int/; Miller 2009)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
Seasonal mean front strength variability (\selectlanguage{ngerman}°C
pixel\textsuperscript{-1})\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
Std Dev. across 12 months (2006-2016)\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
5.5 km\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
SST-CCI v2.1 daily SST (https://climate.esa.int/; Miller 2009)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
Bathymetry (m)\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
Static\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
0.46 km\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
GEBCO (www.gebco.net)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
Seabed slope angle (\selectlanguage{ngerman}°)\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
Static\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
0.46 km\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
Derived from GEBCO (www.gebco.net)\strut
\end{minipage}\tabularnewline
\bottomrule
\end{longtable}

We used random forests to model seabird foraging niches, parameterising
each model with the 10 oceanographic variables shown in Table 2. Random
forests are robust to both challenges present in spatial data (e.g.
autocorrelation) and in hierarchically structured data (e.g. global
trends but local variation) (Evans et al. 2011, Doherty et al. 2016). We
used two approaches for each modelled species: 1) a multi-colony model,
where datasets from multiple colonies were combined in a single model;
and 2) colony-specific models, where each colony was modelled
separately. The multi-colony model was designed to learn from the
foraging niches of each colony and generalise a global foraging niche to
predict foraging habitat suitability. Colony-specific models were
designed to test the ability of local foraging niche to predict local
foraging habitat suitability, investigate local adaptation across
species ranges, and to make accurate models with GBR tracking where
available (Fig. 2).

To assess model predictive performance, we used the
threshold-independent measure, area under the receiver operating
characteristic curve (AUC; Fielding \& Bell 1997). AUC values of 0.5 are
equivalent to random prediction, 0.6-0.7 indicate poor performance,
0.7-0.8 moderate performance, and \textgreater{}0.8 indicate good
performance. We validated models by assessing performance (AUC) when
predicting to different colonies, which we use as our measure of model
transferability. For multi-colony models, we used leave-group-out cross
validation: iterating through \emph{n} colonies, we trained the model
on\emph{n} -1 colonies and predicted to the remaining colony. For
colony-specific models we predicted to all other colonies. For both
model types, we calculated global transferability (mean predictive
performance across all other colonies). To assess internal model
performance (colony-specific models predicting to their training colony)
we performed internal spatial cross validation. We used the 4-fold clock
method, where each dataset was split spatially into quarters (by k-means
clustering of data coordinates), and models iteratively trained on three
quarters of the data with predictive performance assessed on the
remaining quarter (Guillaumot et al. 2019).

For both multi-colony and colony-specific random forest models,
hyperparameters were tuned to optimise predictive performance between
colonies. Model parameters, mtry (number of environmental covariates
randomly chosen per node: limits tree strength and correlation) and
minimum node size (number of datapoints per tree node: limits tree depth
and thus model complexity) were tuned across values 2, 3, 4, 5, 6 and 5,
10, 20, 50, respectively. Models were firstly tuned for optimal
predictive performance on GBR test data (for available species), and
secondarily to generalise across all other colonies. When assessing
model predictive performance locally, the same hyperparameters were
tuned to optimise predictive performance on the training colony during
internal spatial cross validation. Random forest models were constructed
using the \emph{ranger} (0.11.2) R package (Wright \& Ziegler 2017) and
tuned using the \emph{caret} (6.0-82) R package (Kuhn 2008).

To investigate local adaptation in each modelled species we first
described inter-colony transferability by summing the two
colony-specific model AUC values of each colony pair
(AUC\textsubscript{colonyA - colonyB} + AUC\textsubscript{colonyB -
colonyA}), before entering pairwise colony sums into a distance matrix.
We then tested whether geographically closer colonies were more
transferable by correlating the inter-colony transferability matrix with
a matrix of pairwise colony geographical distance, and whether colonies
with more similar oceanographic habitat were more transferable by
correlating the colony transferability matrix with a matrix of pairwise
colony oceanographic distance (matrix of Euclidean distances between
colonies from mean oceanographic variable values within their accessible
habitat). We tested significance of correlations with Mantel tests. To
help visualise groups of colonies that were transferable to each other
we performed hierarchical clustering (using the average method) on the
inter-colony transferability matrix.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image2/image2}

\end{center}
\end{figure}

Fig. 2 Schematic of analytical framework and study glossary. Steps A-C
show how a refined foraging circle (green) can be predicted for a
seabird colony (yellow triangle). If tracking data originates from the
colony then this is used to train a colony-specific model to predict
habitat suitability and to inform a colony foraging circle; if not,
habitat suitability is predicted by a multi-colony model and a global
foraging circle is applied. Methods of refinement apply to both colony
foraging circles and global foraging circles.

\subsection*{A framework integrating the foraging radius approach with
ecological niche
models}

{\label{a-framework-integrating-the-foraging-radius-approach-with-ecological-niche-models}}

We integrated models of seabird foraging niche with the foraging radius
approach by excluding predicted unsuitable foraging habitat within
foraging radius circles to produce ``refined foraging circles''. Taking
a precautionary approach to minimise erroneously excluding good foraging
habitat from within foraging circles, we specified that the area of
unsuitable foraging habitat excluded within circles was dependent upon
ENM transferability, which we term ``transferability-supported
refinement''. This allowed greater foraging circle refinement with more
transferable models. The first step of our framework is to predict
foraging habitat suitability and clip it to within the foraging circle
of a colony of interest, limiting the foraging habitat available to the
population. This allows us to derive percentiles of habitat suitability
within the foraging circle which are mapped as contours. The second step
scales ENM transferability values onto the percentiles of predicted
foraging habitat suitability, delineating a refined foraging circle from
habitat suitability values equal or above to the selected percentile. We
scale transferability to percentiles ((AUC-0.5)/(0.9-0.5))*(0.9-0), such
that AUC [?] 0.5 (prediction no better than random) takes the
0\textsuperscript{th} percentile of foraging habitat suitability values,
thus defaulting to the unrefined foraging circle. Higher AUC values take
higher percentiles up until AUC [?] 0.9, which is set to an upper limit of
the 90\textsuperscript{th} percentile (a threshold proposed for
translating seabird ENM predictions into marine Important Bird Areas by
Dias et al. (2019b), thus allowing models with excellent transferability
(AUC [?] 0.9) to delineate refined foraging circles with the top 10\% of
foraging habitat suitability values.

Transferability-supported refinement simply offers a suggested
percentile of habitat suitability with which to refine foraging circles,
but planners may wish to select a different percentile to create a
smaller or larger refined foraging circle. We term this ``area-limited
refinement'' and anticipate that it may be necessary to use when
transferability-supported refinement produces refined foraging circles
that are still considered too large for area-based management.

\subsection*{Framework validation and attributing refinement
confidence}

{\label{framework-validation-and-attributing-refinement-confidence}}

To validate our framework, we simulated increasing foraging circle
refinement at each colony in our global tracking dataset to estimate
``refinement confidence'': the probability of known foraging areas (50\%
UDs) being included in refined foraging circles. The results allowed us
to estimate how well unrefined global foraging circles capture known
foraging areas and model the rate this inclusion declines when refining
for different transferability ENMs. For each modelled species and colony
(Table 1), we predicted foraging habitat suitability using the
corresponding multi-colony model, trained on all colonies except the
colony being predicted to. We predicted models to each colony using the
same oceanographic variables and same month used in model training,
assessed transferability using the multi-colony leave group out cross
validation AUC value, and mapped foraging habitat suitability
predictions onto a raster with two km cell size. At each colony, we
refined the global foraging circle using the percentile selected by the
model transferability AUC value, and also simulated refinement
increasing from the 0\textsuperscript{th} percentile (unrefined foraging
circle) to the 90\textsuperscript{th} percentile at five percentile
intervals. Refined foraging circle polygons were obtained by binarizing
foraging habitat suitability rasters with the specified percentile
value, and tidied with R package \emph{smoothr} (0.1.1) (Strimas-Mackey
2021) to remove small `crumbs' and holes in polygons. In each iteration,
we calculated the percentage of known foraging areas tracked from the
colony that were included in the refined foraging circle. We fitted
logistic regression models with percentage of known foraging area
inclusion as the response variable and foraging habitat suitability
percentile and its interaction with model transferability as explanatory
variables, allowing the slope to vary within a species-colony random
effect. Logistic regression models were fitted for each modelled species
separately, and all combined, using R package \emph{lme4} (1.1-21)
(Bates et al. 2015); random effects were dropped from TERN and SOTE
regression models due to too few colonies.

The intercept and slopes of logistic regression models allowed
refinement confidence to be predicted given model transferability and
the percentile of habitat suitability selected for refined foraging
circle delineation. This firstly allowed us to validate whether the
AUC-percentile scaling was appropriate, under transferability-supported
refinement we would expect to see high refinement confidence for all
refined foraging circles, regardless of transferability (poor models
refine a little, excellent models refine a lot; all retain known
foraging areas). Secondly, logistic regression model coefficients
allowed us to explore the trade-off between refined foraging circle size
and refinement confidence as coefficients predict refinement confidence
for any given size of refined foraging circle. When refining a foraging
circle for species x at untracked colony y, its' size can be suggested
from transferability-supported refinement (using the multi-colony global
transferability value) or specified by area-limited refinement. Having
the associated refinement confidence predicted from logistic regression
model coefficients for refined foraging circles of different sizes
allowed us to judge the amount of refinement most appropriate during our
Great Barrier Reef case study.

\subsection*{Foraging circle refinement on the Great Barrier
Reef}

{\label{foraging-circle-refinement-on-the-great-barrier-reef}}

To demonstrate the application of our framework, we refine foraging
circles for the breeding seabird community of the Great Barrier Reef.
From these we identify three networks of candidate MPAs (unrefined
foraging circles, transferability-supported refined foraging circles,
and area-limited refined foraging circles) to demonstrate the trade-off
between total area and refinement confidence. For species without
tracking data on the GBR, we predicted habitat suitability using
multi-colony models. The remaining species had tracking data from one or
two breeding sites on the GBR (Fig. 1). For these species, habitat
suitability was predicted by colony-specific models to corresponding
tracked colonies and neighbouring colonies within the same designated
breeding site (see supporting information), and multi-colony models to
the remaining area. We predicted models upon annual averages of monthly
dynamic oceanographic variables, as the modelled species breed
year-round across the GBR (but most show seasonal breeding peaks), with
the exception of wedge-tailed shearwaters for which oceanographic
variables were averaged over their distinct breeding season
(December-April). Predictions were mapped across the GBR and Coral Sea
using a raster with two km cell size. We placed foraging circles around
breeding sites from significant seabird areas on the GBR. Significant
seabird areas were either designated as internationally-recognised Key
Biodiversity Areas (KBAs) or possessed regionally significant breeding
populations of one or more species (see supporting information). For the
seabird species listed at each breeding site we applied the
corresponding global foraging circle, unless the breeding site was part
of a significant seabird area with a tracked colony, in which case the
colony foraging circle was applied.

Following our framework, we conducted transferability-supported
refinement of global foraging circles using multi-colony model
inter-colony transferability values and colony foraging circles using
colony-specific model local transferability values. To investigate the
effect of further refinement, we conducted area-limited refinement on
any refined foraging circle produced by transferability-supported
refinement that was over 100,000 km\textsuperscript{2}, until all
refined foraging circles fell below 100,000
km\textsuperscript{2}(representing a maximum layer size in a
hypothetical conservation planning exercise). The refinement confidence
in colony-specific models was obtained using the inclusion of known
foraging areas (50\% UD) from tracked GBR breeding sites within refined
foraging circles while the refinement confidence in multi-colony models
was determined using the logistic regression model coefficients from our
global framework validation exercise. To compare differences between
different refinement approaches, we created three networks of candidate
MPAs from unrefined foraging circles, transferability-supported foraging
circle refinement, and area-limited foraging circle refinement
(\textless{}100,000 km\textsuperscript{2}). We summarised differences
between networks by comparing the total foraging area required for the
GBR breeding community, dissolving overlapping refined foraging circles
shared by multiple breeding sites, and by comparing refinement
confidence under each approach. Finally, we selected the most
appropriate refinement approach for each species and merged results to
create the most suitable network of candidate MPAs for seabirds on the
GBR.

\section*{Results}

{\label{results}}

\subsection*{Foraging niche
transferability}

{\label{foraging-niche-transferability}}

Multi-colony models were not able to successfully generalise the
foraging niche of modelled species across all colonies, showing
generally poor global transferability (mean AUC: 0.56, range 0.4-0.82;
Table 3). However, leave-group-out cross validation showed that
multi-colony models could predict suitable foraging habitat at some
individual colonies (Fig 3).

Exploration of inter-colony transferability revealed that
colony-specific models were transferable to one, or sometimes several,
other colonies, but not all colonies (Fig. 3). For many modelled species
this issue manifested as clustering in colony transferability: groups of
colonies that could predict to one another, but poorly to other groups
of colonies (Fig. 3-J), and partially explains low global
transferability of colony-specific models (Table 3). Inter-colony
transferability was not explained by geographical distance or
oceanographic similarity between colonies (19 of 20 Mantel tests p
\textgreater{} 0.05; supporting information). However, with the
exception of noddies, all multi-colony models had slightly better global
transferability than the colony-specific model average (Table 3),
indicating multi-colony models generalised information from disparate
colony clusters, boosting transferability.

We found that multi-colony models frequently (species mean: 41\%; Table
3) matched or outperformed colony-specific models at local prediction
(multi-colony leave-group-out cross validation vs colony-specific
spatial cross validation; multi-colony row values vs diagonal values in
Fig. 3). This finding indicates that the global foraging niche was able
to predict suitable foraging habitat at approximately 40\% of colonies,
with comparable or better accuracy than models of the local foraging
niche, although this was strongly species dependent (0\% for noddies,
66\% for frigatebirds and terns; Table 3). However, all of the
colony-specific models from GBR tracking were superior to their
multi-colony model equivalent (brown booby from Raine Island and Swain
Reefs, Fig. 3A; masked booby from Swain Reefs, Fig. 3E; wedge-tailed
shearwaters from Heron Island on short trips, Fig. 3I and long trips,
Fig. 3G; and noddies from Heron Island, Fig. 3C).

Despite poor overall global transferability, we observed differences
between modelled species (Table 3). Booby species had similar
multi-colony global transferability (AUC: 0.53-0.55) and showed
clustering of inter-colony predictive performance (Fig. 3A, E, F). Brown
and masked booby colony-specific models had a greater range of
transferability than red-footed booby but the same overall average (AUC:
0.51). Frigatebirds and wedge-tailed shearwater short trip multi-colony
models (AUC: 0.61 and 0.58, respectively) showed better global
transferability than boobies, and the greatest transferability increase
over the colony-specific model average. Both of these modelled species
showed clustering of inter-colony predictive performance, the former
demonstrating examples of good inter-species and inter-colony
transferability (Fig. 3G, I). Tropicbirds and wedge-tailed shearwater
long trip multi-colony models had poor global transferability,
equivalent to that of boobies (AUC: 0.56 and 0.54 respectively). Sooty
terns and noddies showed the poorest global transferability (AUC: 0.48
and 0.40 respectively), with the latter's multi-colony model being
outperformed by the colony-specific model average. Terns showed the best
global transferability between colonies (AUC: 0.82) but were limited by
sample size (Fig. 3B).

Table 3 Summary of model transferability and foraging radii. Global
transferability is the predictive performance of a model averaged over
all colonies (MEAN column; Fig. 3). Local prediction gives the
percentage of colonies where the multi-colony model matched or
outperformed self-prediction by the colony-specific model. Foraging
ranges are summarised across tracked colonies (for colony breakdown see
supporting information), and inclusion of known foraging areas in global
foraging circles are averaged across all tracked colonies.\selectlanguage{english}
\begin{longtable}[]{@{}llllllll@{}}
\toprule
\begin{minipage}[b]{0.12\columnwidth}\raggedright\strut
Modelled species (n training colonies)\strut
\end{minipage} & \begin{minipage}[b]{0.12\columnwidth}\raggedright\strut
Colony-specific global transferability (mean AUC \selectlanguage{ngerman}\emph{±} sd)\strut
\end{minipage} & \begin{minipage}[b]{0.12\columnwidth}\raggedright\strut
Multi-colony model global transferability (AUC)\strut
\end{minipage} & \begin{minipage}[b]{0.12\columnwidth}\raggedright\strut
Local prediction (\% of colonies where multi-colony AUC [?]
colony-specific AUC)\strut
\end{minipage} & \begin{minipage}[b]{0.12\columnwidth}\raggedright\strut
Global minimum foraging radius (km)\strut
\end{minipage} & \begin{minipage}[b]{0.12\columnwidth}\raggedright\strut
Global maximum foraging radius (km)\strut
\end{minipage} & \begin{minipage}[b]{0.12\columnwidth}\raggedright\strut
Global (mean- maximum) foraging radius (km \selectlanguage{ngerman}± sd)\strut
\end{minipage} & \begin{minipage}[b]{0.12\columnwidth}\raggedright\strut
Probability of known foraging area (\selectlanguage{ngerman}±95 CI) inside global foraging
circle\strut
\end{minipage}\tabularnewline
\midrule
\endhead
\begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
BRBO (16)\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
0.51\selectlanguage{ngerman}±0.05\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
0.55\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
25\%\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
45.3\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
190.3\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
111 \selectlanguage{ngerman}± 35\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
0.99 (0.98,1)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
MABO (12)\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
0.51\selectlanguage{ngerman}±0.06\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
0.53\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
8\%\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
68.8\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
290.6\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
171 \selectlanguage{ngerman}± 65\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
0.99 (0.97,1)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
RFBO (13)\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
0.51\selectlanguage{ngerman}±0.02\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
0.54\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
54\%\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
96.9\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
473.1\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
273 \selectlanguage{ngerman}± 125\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
0.99 (0.97,1)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
FRBD (9)\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
0.53\selectlanguage{ngerman}±0.06\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
0.61\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
66\%\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
28.4\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
1081.6\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
556 \selectlanguage{ngerman}± 435\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
1 (0.98,1)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
TRBD (11)\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
0.52\selectlanguage{ngerman}±0.05\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
0.56\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
54\%\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
344.3\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
801.5\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
531 \selectlanguage{ngerman}± 174\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
0.99 (0.96,1)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
WTST (8)\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
0.53\selectlanguage{ngerman}±0.06\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
0.58\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
62\%\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
185.5\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
298.2\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
254 \selectlanguage{ngerman}± 42\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
0.98 (0.96,0.99)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
WTLG (7)\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
0.52\selectlanguage{ngerman}±0.03\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
0.54\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
43\%\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
509.3\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
1166.2\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
755 \selectlanguage{ngerman}± 286\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
0.99 (0.93,1)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
SOTE (3)\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
0.41\selectlanguage{ngerman}±0.07\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
0.48\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
33\%\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
198.4\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
895.3\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
535 \selectlanguage{ngerman}± 349\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
0.89 (0.76,0.95)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
NODD (5)\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
0.49\selectlanguage{ngerman}±0.05\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
0.40\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
0\%\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
54.8\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
269.1\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
144 \selectlanguage{ngerman}± 78\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
0.98 (0.87,1)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
TERN (3)\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
0.76\selectlanguage{ngerman}±0.01\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
0.82\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
66\%\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
38.6\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
159.7\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
109 \selectlanguage{ngerman}± 63\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
0.98 (0.98,0.99)\strut
\end{minipage}\tabularnewline
\bottomrule
\end{longtable}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image3/image3}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image4/image4}

\end{center}
\end{figure}

Fig. 3 Model validation and transferability for modelled species (A-J).
Matrix values describe predictive accuracy via area under the receiver
operator characteristic curve (AUC); AUC values (0-1) have been
multiplied by 100 to save space. Reading by row shows how well each
model trained on colony-specific data predicts foraging habitat
suitability at all other colonies, reading by column shows how well
foraging habitat suitability at each colony is predicted by models
trained with data from all other colonies. Row names denote each
colony-specific model and the multi-colony model. The `MEAN' column
gives global transferability of each colony-specific model and the
multi-colony model by averaging the predictive ability across all
colonies in the row (excluding the self-prediction value in
colony-specific models). Local prediction is compared between
colony-specific model self-prediction (diagonals) and multi-colony
models (bottom row) by colour of AUC value: green shows model is better
than the other; red shows model is worse than the other; black shows
models are comparable (AUC values within 0.05 of each other). AUC value
background colour denotes performance: grey (no better than chance/very
poor); yellow (poor); light orange (moderate); dark orange (good); red
(excellent). Dendrograms show clustering of pairwise colony AUC values;
closely clustered colonies predict one another better. Family group
models (B, C \& G) have species codes added to colony names: CRTE =
crested tern, CATE = caspian tern, ROTE = royal tern, LENO = lesser
noddy, BLNO = black noddy, BRNO = brown noddy, GRFR = great frigatebird,
LEFR = lesser frigatebird, MAFR = magnificent frigatebird. All colonies
in the family group model for tropicbirds (H) represent red-billed
tropicbird, with the exception of the Ashmore Reef (Australia) colony,
which is red-tailed tropicbird (denoted with *).

\subsection*{Tropical seabird foraging
radii}

{\label{tropical-seabird-foraging-radii}}

When considering all species and colonies, foraging radii ranged from
28.4 to 1166.2 km (Table 3). By increasing foraging range, modelled
species were generally ordered: terns, brown booby, noddies, masked
booby, wedge-tailed shearwater short trip, red-footed booby,
tropicbirds, sooty tern, frigatebirds, wedge-tailed shearwater long
trip. We found that the global foraging circle for each species
contained a high percentage (89-100\%) of known foraging areas from each
tracked colony (Table 3).

\subsection*{Validating foraging circle
refinement}

{\label{validating-foraging-circle-refinement}}

We found an average of 89\% of known foraging areas were included within
refined foraging circles when using transferability-supported refinement
of global foraging circles around each colony from our tracking dataset
(Fig. 4). Our AUC-percentile scaling was effective at limiting the
exclusion of erroneously predicted foraging habitat in poorer
transferability models giving 80-99\% known foraging area inclusion when
the 45\textsuperscript{th} percentile of habitat suitability or below,
was used to delimit refined foraging circles. For refined foraging
circles delimited with habitat suitability above the
45\textsuperscript{th} percentile, our scaling maintained known foraging
area inclusion at \textasciitilde{}80\%, showing that greater foraging
circle refinement was effectively offset by the more accurate prediction
of foraging habitat by higher transferability models.

Simulating increasing refinement of global foraging circles around each
colony from our tracking dataset demonstrated that in the absence of
transferability-supported refinement, refined foraging circles delimited
by less transferable models always had a lower probability of including
known foraging areas than those delimited by more transferable models
(Interaction between percentile of foraging habitat suitability and
model transferability: \selectlanguage{greek}χ\selectlanguage{english}\textsuperscript{2}\textsubscript{1} = 100.1, p
\textless{} 0.001; Fig. 4). Simulation results for individual modelled
species demonstrated the same pattern but there were differences between
species due to ecology and sampling effort (Table 4, visualised in
supporting information). Inclusion of known foraging areas in global
foraging circles set the baseline probability for inclusion prior to
refinement and differed between species (e.g. high in frigatebirds, low
in sooty terns -- despite similar foraging ranges; Tables 3 \& 4). The
steepest declines in known foraging area inclusion with foraging circle
refinement were seen in masked and brown boobies and tropicbirds,
whereas terns retained the highest inclusion of known foraging areas
under refinement because their multi-colony model had good global
transferability. Improving model transferability gave the greatest boost
to inclusion of known foraging areas within refined foraging circles in
tropicbirds, brown booby, sooty tern and masked booby, whereas
frigatebirds known foraging area inclusion was least affected by model
transferability (Table 4). The noddies model did not include the
interaction term with transferability as all multi-colony model
predictions were below 0.5 AUC.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image5/image5}

\end{center}
\end{figure}

Fig. 4 Probability of known foraging area inclusion within refined
foraging circles refined by multi-colony models with different
transferability (AUC). Coloured lines (\selectlanguage{ngerman}± 95\% CI) represent the
interaction between inclusion of known foraging areas in refined
foraging circles, when simulating refinement over all possible
percentiles of predicted foraging habitat suitability, and multi-colony
model transferability (five transferability values selected for
demonstration; regression equation provided to predict on log odds
scale). Black rings show the known foraging area inclusion observed at
tracked colonies when refined foraging circles were refined using the
percentile of habitat suitability selected by transferability-supported
refinement. The black line (± 95\% CI) displays the trend through these
rings (from a generalised additive model), showing overall high known
foraging area inclusion and effective offset of greater refinement by
more accurate models.

Table 4. Species-specific logistic regression model coefficients for
predicting probability of known foraging area inclusion within refined
foraging circles. Intercepts give the probability of known foraging area
inclusion within unrefined global foraging circles. Coefficients are
presented on the log odds scale and can predict probability of known
foraging area inclusion for a desired percentile of habitat suitability
value (\emph{p} ) from a multi-colony model with transferability
(\emph{t} ) using the equation:
exp(\selectlanguage{greek}β\selectlanguage{english}\textsubscript{0}+(\selectlanguage{greek}β\selectlanguage{english}\textsubscript{1}*\emph{p}
)+(\selectlanguage{greek}β\selectlanguage{english}\textsubscript{2}*\emph{p*t} ))/(1+
exp(\selectlanguage{greek}β\selectlanguage{english}\textsubscript{0}+(\selectlanguage{greek}β\selectlanguage{english}\textsubscript{1}*\emph{p}
)+(\selectlanguage{greek}β\selectlanguage{english}\textsubscript{2}*\emph{p*t} ))). For example, the probability of
masked booby (MABO) known foraging areas being included in a refined
foraging circle refined with the 30\textsuperscript{th} percentile (0.3)
of foraging habitat suitability and predicted from a poor
transferability model (e.g. AUC=0.64) is:
exp(4.39+(-15.42*0.3)+(13.1*0.3*0.64))/(1+exp(4.39+(-15.42*0.3)+(13.1*0.3*0.64)))=
90\%.\selectlanguage{english}
\begin{longtable}[]{@{}lllll@{}}
\toprule
Species & \selectlanguage{greek}β\selectlanguage{english}\textsubscript{0} Intercept & \selectlanguage{greek}β\selectlanguage{english}\textsubscript{1} foraging
habitat suitability percentile & \selectlanguage{greek}β\selectlanguage{english}\textsubscript{2} interaction between
foraging habitat suitability percentile and multi-colony model
transferability & Foraging habitat suitability percentile training
range\tabularnewline
\midrule
\endhead
\textbf{BRBO} & 4.84\selectlanguage{ngerman}±0.45 & -18.83\selectlanguage{ngerman}±2.02 & 18.44\selectlanguage{ngerman}±3.28 &
0-0.66\tabularnewline
\textbf{MABO} & 4.39\selectlanguage{ngerman}±0.54 & -15.42\selectlanguage{ngerman}±1.84 & 13.1\selectlanguage{ngerman}±3.04 &
0-0.53\tabularnewline
\textbf{RFBO} & 4.58\selectlanguage{ngerman}±0.57 & -11.58\selectlanguage{ngerman}±2.21 & 8.23\selectlanguage{ngerman}±3.84 &
0-0.57\tabularnewline
\textbf{FRBD} & 6.4\selectlanguage{ngerman}±1.37 & -11.13\selectlanguage{ngerman}±2.98 & 4.51\selectlanguage{ngerman}±3.48 &
0-0.84\tabularnewline
\textbf{TRBD} & 4.8\selectlanguage{ngerman}±0.8 & -19.96\selectlanguage{ngerman}±1.88 & 21.4\selectlanguage{ngerman}±2.71 &
0-0.45\tabularnewline
\textbf{WTST} & 3.97\selectlanguage{ngerman}±0.43 & -11.28\selectlanguage{ngerman}±2.47 & 7.58\selectlanguage{ngerman}±4.08 &
0-0.56\tabularnewline
\textbf{WTLG} & 4.22\selectlanguage{ngerman}±0.8 & -12.49\selectlanguage{ngerman}±1.68 & 8.64\selectlanguage{ngerman}±1.76 &
0-0.33\tabularnewline
\textbf{SOTE} & 2.09\selectlanguage{ngerman}±0.47 & -12.18\selectlanguage{ngerman}±4.13 & 16.9\selectlanguage{ngerman}±8.02 &
0-0.14\tabularnewline
\textbf{NODD} & 3.76\selectlanguage{ngerman}±0.95 & -9.38\selectlanguage{ngerman}±1.42 & NA & 0-0\tabularnewline
\textbf{TERN} & 4.12\selectlanguage{ngerman}±0.18 & -9.18\selectlanguage{ngerman}±0.57 & 7\selectlanguage{ngerman}±0.65 &
0.28-0.9\tabularnewline
\bottomrule
\end{longtable}

\subsection*{Total foraging area of the Great Barrier Reef breeding
seabird
community}

{\label{total-foraging-area-of-the-great-barrier-reef-breeding-seabird-community}}

The total foraging area of the Great Barrier Reef breeding seabird
community was estimated at 2,941,000 km\textsuperscript{2} by global
foraging circles, of which 197,000 km\textsuperscript{2} was predicted
unsuitable foraging habitat and excluded following
transferability-supported refinement (Table 5). There were large
differences in the total area of unsuitable habitat excluded from
foraging circles between species due to radius size (Table 3), proximity
of GBR colonies (overlap of foraging circles; Fig. 5a) and model
transferability (Fig. 5b). We saw the greatest refinement in species
with large foraging radii (e.g. wedge-tailed shearwater long trips,
frigatebirds, tropicbirds) and species which had better model
transferability (e.g. terns, Fig. 5b). The better local prediction of
GBR colony-specific models over multi-colony models afforded locally
tracked species greater transferability-supported refinement (Fig. 5).
This led to greater total unsuitable habitat exclusion when locally
tracked sites were well represented and transferability was higher (e.g.
wedge-tailed shearwater short trips compared to brown booby and masked
booby; Fig 5; Table 5). By contrast, noddies had the highest number of
breeding colonies but unsuitable foraging habitat could only be excluded
at one site with local tracking because their multi-colony model
transferability was too poor (AUC\textless{}0.5) for foraging circle
refinement. Sooty terns were the only species for which we were unable
to use transferability-supported refinement for any foraging circles due
to their poor multi-colony model transferability and absence of GBR
tracking data. Refined foraging circles on the GBR produced through
transferability-refined refinement had high refinement confidence, with
a 96\% average probability of including known foraging areas (Table 5).
This was higher than the 89\% average from our global validation
exercise (Fig. 4) due to the contribution of locally tracked GBR
colonies, which all had 100\% inclusion of known foraging areas in
refined foraging circles.

After transferability-supported refinement had been completed, six
modelled species still contained some colonies with refined foraging
circles that exceeded 100,000 km\textsuperscript{2} (Fig. 5b). Using
area-limited refinement to drop these refined foraging circles below
100,000 km\textsuperscript{2} excluded a further 1,629,000
km\textsuperscript{2} of predicted unsuitable foraging habitat from the
community (Table 5). However, this came at a cost of reducing refinement
confidence, with the average inclusion of known foraging areas in
refined foraging circles dropping from 97\% to 55\% in these species.
Sooty tern, tropicbirds and frigatebirds all required large area-limited
refinement to meet the 100,000 km\textsuperscript{2} limit, greatly
reducing their refinement confidence (16-40\% inclusion; Table 5).
Wedge-tailed shearwater long trips required the greatest refinement to
meet the area limit and highlighted the power of tracking data from the
GBR; refined foraging circles delineated by the
90\textsuperscript{th}percentile of habitat suitability dropped
predicted inclusion of known foraging areas to 6\% in multi-colony
models but only to 87\% in colony-specific models (Table 5).
Area-limited refinement of wedge-tailed shearwater short trips and
red-footed booby foraging circles to meet the 100,000
km\textsuperscript{2} limit was achieved with minor reduction in known
foraging area inclusion (77-95\%). Applying the most appropriate
foraging circle refinement approach to each modelled species (none for
sooty tern, area-limited for wedge-tailed shearwater and red-footed
booby, and transferability-supported for the remainder) created a
network of refined foraging circles for the breeding seabird community
of the GBR that balanced size against refinement confidence (Fig. 6).\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image6/image6}

\end{center}
\end{figure}

Fig. 5 Foraging circle refinement on the Great Barrier Reef, Australia.
Panel a) shows foraging circles and refined foraging circles (over more
suitable predicted habitat), mapped for each species using their GBR
breeding sites. Panel b) plots increasing simulated foraging circle
refinement at each GBR breeding site, and where the thresholds used for
refined foraging circles mapped in a) lie in relation to the trade-off
between size and refinement confidence. If GBR colonies have local
tracking data then colony foraging circles are refined using GBR
colony-specific models (yellow); otherwise global foraging circles are
refined using multi-colony models (blue). Unrefined foraging circles are
mapped and their areas shown at habitat suitability percentiles of 0.
Refined foraging circles from transferability-supported refinement are
shown in green. Refined foraging circles from area-limited refinement
are shown in red for colonies that need further refinement to get below
a 100,000 km\textsuperscript{2} limit (purple dotted line). Greater
foraging circle refinement reduces the confidence that refined foraging
areas include known foraging areas, as demonstrated for a red-footed
booby (RFBO) colony (see supporting information for breakdown of
predicted known foraging area inclusion at all GBR breeding sites).

Table 5 Summary of foraging area of the Great Barrier Reef breeding
seabird community. Total foraging area is estimated from unrefined
global foraging circles and refined foraging circles from
transferability-supported refinement and area-limited refinement
(\textless{}100,000km\textsuperscript{2}). The foraging areas generated
by both refinement methods are supported by associated refinement
confidence (predicted probability that refined foraging circles contain
known foraging areas). All species totals represent total foraging area
of all colonies, with overlapping areas dissolved. For a breakdown of
results per colony see supporting information.\selectlanguage{english}
\begin{longtable}[]{@{}lllllllll@{}}
\toprule
Species & n breeding sites (n with tracking data) & n breeding colonies
& Area of global foraging circles (km\textsuperscript{2}) & Area of
refined foraging circles after transferability-supported refinement
(km\textsuperscript{2}) & Mean (min-max) probability of known foraging
area inclusion after transferability -supported refinements & n colonies
with refined foraging circles \textgreater{} 100,000
km\textsuperscript{2} after transferability -supported refinement & Area
of refined foraging circles after area-limited refinement
(km\textsuperscript{2}) & Mean (min-max) probability of known foraging
area inclusion after area-limited refinements\tabularnewline
\midrule
\endhead
BRBO & 6 (2) & 19 & 181,000 & 147,000 & 0.99 (0.98-1) & 0 & 147,000 &
0.99 (0.98-1)\tabularnewline
MABO & 3 (1) & 10 & 239,000 & 195,000 & 0.99 (0.98-1) & 0 & 195,000 &
0.99 (0.98-1)\tabularnewline
RFBO & 3 & 4 & 269,000 & 252,000 & 0.98 (0.98-0.98) & 4 & 197,000 & 0.86
(0.8-0.95)\tabularnewline
FRBD & 3 & 3 & 1,231,000 & 951,000 & 0.99 (0.99-0.99) & 3 & 218,000 &
0.38 (0.32-0.4)\tabularnewline
TRBD & 2 & 2 & 988,000 & 868,000 & 0.98 (0.98-0.98) & 2 & 193,000 & 0.19
(0.16-0.21)\tabularnewline
WTST & 4 (1) & 14 & 455,000 & 318,000 & 0.99 (0.94-1) & 2 & 281,000 &
0.97 (0.77-1)\tabularnewline
WTLG & 4 (1) & 14 & 2,936,000 & 2,265,000 & 0.99 (0.97-1) & 14 & 347,000
& 0.7 (0.06-0.87)\tabularnewline
SOTE & 6 & 9 & 1,420,000 & 1,420,000 & 0.89 (0.89-0.89) & 9 & 332,000 &
0.23 (0.2-0.26)\tabularnewline
NODD & 7 (1) & 25 & 328,000 & 313,000 & 0.99 (0.98-1) & 0 & 313,000 &
0.99 (0.98-1)\tabularnewline
TERN & 9 & 22 & 277,000 & 85,000 & 0.84 (0.84-0.84) & 0 & 85,000 & 0.84
(0.84-0.84)\tabularnewline
All species & 11 & 37 & 2,941,000 & 2,744,000 & 0.96 (0.84-1) & 34 &
1,115,000 & 0.71 (0.06-1)\tabularnewline
\bottomrule
\end{longtable}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image7/image7}

\end{center}
\end{figure}

Fig. 6 Network of candidate MPAs for the breeding seabird community of
the GBR, that balances size of protected area against refinement
confidence (probability of including known foraging areas). Refined
foraging areas are delineated for each modelled species and overlapped
to show richness hotspots, see supporting information where
species-specific boundaries are mapped. Breeding sites are omitted that
exclusively contained seabird species we could not model (e.g. small
terns, see supporting information)

\section*{Discussion}

{\label{discussion}}

Relying on the most comprehensive tropical seabird tracking dataset to
date, we investigated whether ENMs can transfer (predict) foraging
niches of breeding tropical seabirds between global colonies, and
whether ENMs can be combined with the foraging radius approach (Thaxter
et al. 2012) to refine foraging circles around breeding colonies. We
found little ability to generalise and transfer ENM predictions across
all colonies for any tropical seabird species. However, we frequently
observed clusters of colonies that predicted well to one another, but
poorly to other colony clusters. Despite the limited ability of ENMs to
predict foraging niche in new regions, they were able to refine foraging
circles by excluding predicted areas of unsuitable foraging habitat. We
found inclusion of known foraging areas was almost certain in unrefined
foraging circles, and remained high when foraging circle refinement was
specified by model transferability, where neither greater refinement
from high transferability models nor minor refinement from poor
transferability models erroneously excluded important foraging habitat.
When applied to the Great Barrier Reef, this framework was able to
reduce, with confidence, the area required to protect foraging resources
of the breeding seabird community.

\subsection*{ENM transferability}

{\label{enm-transferability}}

Limited ENM transferability can be caused by differences in the range
and/or combinations of environmental variables between training and test
datasets (extrapolation); poor description of underlying processes by
explanatory variables in models (misspecification); and differences in
pressures (e.g. competition, predation, local marine productivity etc)
between training and test populations (local adaptation) (Randin et al.
2006, M. McPherson \& Jetz 2007, Torres et al. 2015, P\selectlanguage{ngerman}éron et al. 2018).
Extrapolation likely impacted transferability of colony-specific models,
as they were more likely to encounter novel environmental values when
predicting to test colonies. By contrast, training of multi-colony
models across numerous colony-specific environmental ranges reduced the
likelihood of extrapolation when predicting to test colonies, and could
explain their slightly better global transferability compared to
colony-specific models (Table 3).

Misspecification of models could have contributed to limited global
transferability, as we tried to predict seabird foraging with long-term
averages of oceanographic variables. Our goal here was to characterise
seabird foraging associations with persistent oceanographic features in
the seascape rather than ephemeral ocean phenomena, as predictive maps
representative across space and time are likely to be the most pertinent
for conservation scientists and managers (Guisan et al. 2013). The
downside of this approach is that foraging areas selected by GPS-tracked
birds do not reflect the concurrent oceanography in our models, which
could miss foraging association with fine scale dynamic features (e.g.
intra-seasonal upwellings), known to attract tropical seabirds (Kai et
al. 2009, Miller et al. 2018). Model misspecification is supported by a
general inability of colony-specific models to predict their own
foraging niche (diagonal values in Fig. 3) and attributed to using
long-term oceanographic variables because similar studies using
high-resolution oceanographic variables produced good self-prediction
(Péron et al. 2018). Future marine transferability studies should
consider their study objective (Yates et al. 2018) (e.g. informing
management or ecological understanding) and the dynamism of their ocean
region (e.g. temperate shelf break vs pelagic frontal system) when
selecting spatial and temporal scale of ocean covariates to use in ENMs.

We consider local adaptation a major cause of limited transferability in
this study because multi-colony models did not provide a great
improvement in transferability over the colony-specific model average.
This finding suggests a limited ability of multi-colony models to
generalise patterns in local adaptation of foraging niche from multiple
colonies (Gilmour et al. 2018), and boost their transferability
(Matthiopoulos et al. 2011). This could be explained by local adaptation
causing homogenisation: when models fitted at large spatial scales
average the responses of populations from contrasting habitats, and fail
to capture and predict local extremes (hotspots and coldspots) (Paton \&
Matthiopoulos 2016). Model homogenisation likely affected all species
known for local adaptation: frigatebirds (Mott et al. 2016); tropicbirds
(Diop et al. 2018); wedge-tailed shearwater (Weimerskirch et al. 2020);
and in particular boobies, known for their extreme foraging plasticity
(Mendez et al. 2017, Gilmour et al. 2018).

We failed to explain inter-colony transferability with two factors
describing local adaptation (geographic distance and oceanographic
similarity; Redfern et al. 2017, Gilmour et al. 2018), suggesting colony
demographic information could be an important missing factor (Paton \&
Matthiopoulos 2016). Breeding colony size is known to be a major driver
of local adaptation in seabird foraging behaviours, where density
dependent competition forces greater foraging ranges at larger colonies
(Ashmole 1963, Lewis et al. 2001). Competition pressure may also come
from neighbouring breeders, such that populations segregate foraging
areas (Bolton et al. 2018), and from different species (Oppel et al.
2015, Mendez et al. 2017). However, including demographic covariates
within ENMs requires complex modelling (Wakefield et al. 2017), which
given their interaction in driving local adaptation may extrapolate
poorly beyond the training region.

We modelled seabird foraging niches excluding a distance-to-colony
variable (Péron et al. 2018), which is commonly included in seabird
ENMs. Distance-to-colony is often the most important explanatory
variable (Oppel et al. 2017, Miller et al. 2019) in seabird ENMs but
encodes local adaptation as it is driven by colony-specific demographic
information (Lewis et al. 2001). It is difficult to see how ENMs
dominated by distance-to-colony could accurately predict foraging areas
at unknown colonies unless training and test colonies had similar
demographic pressures. Furthermore, if modelling multiple colonies
together, model homogenisation would cause an average distance-to-colony
to be predicted, potentially yielding the same result as the foraging
radius approach (predicting a colony buffer). Our results show that
modelling seabird foraging niches using long-term ocean variables alone
yields generally poor transferability, and further study is needed to
assess whether including distance-to-colony boosts transferability and
generates spatial predictions useful for informing management.

\subsection*{Refining foraging circles}

{\label{refining-foraging-circles}}

Minimising the cost of protected areas while maximising the confidence
that they appropriately serve a species (e.g. contain core habitat) is a
key goal of systematic conservation planning (Margules \& Pressey 2000),
and the refinement of seabird foraging circles presents a good example
of this challenge. We demonstrate high confidence that known foraging
areas are included within presented global foraging radii estimates
(Table 3). However, foraging circles from these radii are too large to
implement practical conservation measures across, particularly for wider
ranging seabird species (Soanes et al. 2016, McGowan et al. 2017), for
which an area-based conservation approach may not be efficient or
desirable (Oppel et al. 2018). Although we acknowledge that cost and
area are not analogous within conservation planning, smaller protected
areas for seabirds reduce likelihood of conflict with other marine users
(e.g. fishers) and require lower monitoring/policing effort (work hours,
fuel etc) relative to larger areas. Using predicted habitat suitability
to refine foraging circles provides a way of reducing protected area
size, but the confidence in predictions must be considered so that
conservation efforts are not allocated to the wrong areas. Our framework
accounts for confidence by making the level of foraging circle
refinement dependent upon ENM transferability. As we found that
inclusion of known foraging areas remained high when foraging circles
were refined using transferability-supported levels, we can be confident
that neither greater refinement from high transferability models nor
minor refinement from poor transferability models erroneously exclude
important foraging habitat.

Even minor refinement of foraging circles adds information to marine
spatial planning when multiple breeding seabirds are considered
together. Overlaying unrefined foraging circles from multiple species
just shows increasing overlap of concentric circles towards colonies,
informing planners only that areas of sea surrounding the most seabird
species-rich islands are the most important. These can be improved by
weighting foraging circles with their breeding populations and
distributing birds over an accessibility surface (inverse colony
distance) within the circle (Critchley et al. 2018), but this still
lacks ecological realism. The refinement of foraging circles with ENMs
integrates habitat preferences into the planning process. For GBR
seabirds, this reveals that areas offshore from colonies, particularly
open sea adjacent to outer reefs, are likely foraging hotspots for
multiple species (Fig. 6).

Our study of foraging circle refinement presents several tools for
tropical seabird conservation, whose use we advocate in a hierarchical
manner based on local data availability. Firstly, we present the most
comprehensive collation of tropical seabird foraging radii to date. The
estimates presented for each species can be used to represent foraging
ranges for any population in the world without local tracking, and the
colony-specific foraging ranges presented in the supporting information
form a valuable resource for users interested in specific regions.
Foraging radii alone have important applications, particularly the
mean-maximum foraging range, such as assessing seabird population
connectivity with planned offshore energy generation projects (Woodward
et al. 2019). Secondly, where a candidate seabird protected area is
required for a tracked colony, our framework can be followed to generate
a colony foraging circle and refine it using a colony-specific model
trained with the tracking data. Thirdly, where candidate seabird
protected areas are required for untracked colonies and tracking data
exist from several colonies in the same region, a global foraging circle
can be estimated and refined with a multi-colony model trained on the
regional tracking data. For species without any local tracking
(frigatebirds, tropicbirds, red-footed booby, sooty tern and terns in
our GBR study), we advise cautious application of a global multi-colony
model. The generally poor transferability of global ENMs would only
prescribe minor refinement of global foraging ranges, but we nonetheless
advise local expert opinion or distribution data (e.g. at-sea surveys)
should be used to verify that predicted `unsuitable' habitat is indeed
unsuitable. Refined foraging circles can be considered candidate MPAs
for their respective seabird population. As distinct polygons they can
be considered individually or overlapped, to identify multi-species
foraging hotspots, in higher level marine spatial planning exercises, to
ensure seabird representation in multi-taxa MPA delineation. It should
be noted that our foraging circle refinement framework is ENM neutral,
and users should select environmental covariates and model algorithms of
their preference.

\subsection*{Advancing regional knowledge of seabird foraging
areas}

{\label{advancing-regional-knowledge-of-seabird-foraging-areas}}

ENMs trained on GBR tracking allowed better refinement than globally
trained models, demonstrating the value of local tracking data. A key
recommendation from our study is collection of more seabird tracking
data at regional level. If there was a representative (good coverage of
species and sites) GBR database of seabird tracking information, there
would be no need to apply model predictions from across the world to the
GBR as we have done here. Coordination of a systematic regional seabird
tracking campaign offers the most efficient solution to credibly
identify known foraging areas for the GBR seabird community, as
demonstrated by projects in the UK (FAME and STAR; Wakefield et al.
2017). Key breeding populations should be prioritized for tracking, but
it is essential that colonies from the same species are tracked in
different areas of the GBR. Our observation that foraging habitat
suitable for brown boobies from Swain Reefs could not predict that of
conspecifics from Raine Island (and vice versa; Fig. 6A) highlights the
limits of model transferability within the same region. Furthermore, it
may be necessary to investigate whether models of foraging niche are
transferable between neighbouring colonies, in particular when
partitioning of foraging areas between colonies is observed, as shown
for wedge-tailed shearwaters breeding in New Caledonia (Weimerskirch et
al. 2020). Selected colonies should be tracked during years of typical
ocean conditions with a good sample of birds (\textgreater{}30; Soanes
et al. 2013, Lascelles et al. 2016) to ensure the observed foraging
niche is representative of the populations' true foraging niche. A
systematic seabird tracking campaign would dramatically reduce
uncertainty in where seabirds forage on the GBR, enabling better focused
management actions and inclusion of seabird foraging areas in higher
level planning such as zoning of MPAs within the GBR. Nonetheless, the
globally-informed predictions of habitat suitability and foraging radii
presented here form the best working hypotheses of where seabirds forage
on the GBR, and are a valuable starting point for management and
protection of seabird foraging resources.

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