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.