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.
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, Péron 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.
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.
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 (>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.