Refining seabird marine protected areas by predicting habitat inside
foraging range - a case study from the global tropics
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.