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.
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
0th 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 90th 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.
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 0th percentile (unrefined foraging
circle) to the 90th 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 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 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.
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 km2, until all
refined foraging circles fell below 100,000 km2(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 (<100,000
km2). 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.