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, Žydelis 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 penguinsPygoscelis antarcticus was transferable between colonies at
<100 km, while
Péron
et al. (2018) found that foraging niche of Scopoli’s shearwaterCalonectris diomedea was transferable locally but not regionally
(>200 km). Transferability at large scales
(>1000’s of km) does not apply in grey petrelProcellaria cinerea wintering distributions
(Torres
et al. 2015), nor red-billed tropicbird Phaethon aethereusforaging 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.