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.

Quantifying habitat quality is dependent on measuring a site’s relative contribution to population growth rate. This is challenging for studies of waterbirds, whose high mobility can decouple demographic rates from local habitat conditions and make sustained monitoring of individuals near-impossible. To overcome these challenges, biologists have used many direct and indirect proxies of waterbird habitat quality. However, consensus on what methods are most appropriate for a given scenario is lacking. We undertook a structured literature review of the methods used to quantify waterbird habitat quality, and provide a synthesis of the context-dependent strengths and limitations of those methods. Our structured search of the Web of Science database returned a sample of 398 studies, upon which our review was based. The reviewed studies assessed habitat quality by either measuring habitat attributes (e.g., food abundance, water quality, vegetation structure), or measuring attributes of the waterbirds themselves (e.g., demographic parameters, body condition, behaviour, distribution). Measuring habitat attributes, although they are only indirectly related to demographic rates, has the advantage of being unaffected by waterbird behavioural stochasticity. Conversely, waterbird-derived measures (e.g., body condition, peck rates) may be more directly related to demographic rates than habitat variables, but may be subject to greater stochastic variation (e.g., behavioural change due to presence of conspecifics). Therefore, caution is needed to ensure that the measured variable does influence waterbird demographic rates. This assumption was usually based on ecological theory rather than empirical evidence. Our review highlighted that there is no single best, universally applicable method to quantify waterbird habitat quality. Individual project specifics (e.g., time frame, spatial scale, funding) will influence the choice of variables measured. Where possible, practitioners should measure variables most directly related to demographic rates. Generally, measuring multiple variables yields a better chance of accurately capturing the relationship between habitat characteristics and demographic rates.