Tracking data processing
Tracking datasets were speed filtered (removal of points >
90 km/h; (Mendez et al. 2017), and linearly interpolated using theAdehabitatLT (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 thetrack2kba (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).
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.
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 (~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 Péron 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.