Predictions of 27 Arctic pelagic seabird distributions using public environmental variables, assessed with colony data: a first digital IPY and GBIF open access synthesis platform

Abstract

We present a first compilation, quantification and summary of 27 seabird species presence data for north of the Arctic circle (>66 degrees latitude North) and the ice-free period (summer). For species names, we use several taxonomically valid online databases [Integrated Taxonomic Information System (ITIS), AviBase, 4 letter species codes of the American Ornithological Union (AOU), The British List 2000, taxonomic serial numbers TSNs, World Register of Marine Species (WORMS) and APHIA ID] allowing for a compatible taxonomic species cross-walk, and subsequent applications, e.g., phylogenies. Based on the data mining and machine learning RandomForest algorithm, and 26 environmental publicly available Geographic Information Systems (GIS) layers, we built 27 predictive seabird models based on public open access data archives such as the Global Biodiversity Information Facility (GBIF), North Pacific Pelagic Seabird Database (NPPSD) and PIROP database (in OBIS-Seamap). Model-prediction scenarios using pseudo-absence and expert-derived absence were run; aspatial and spatial model assessment metrics were applied. Further, we used an additional species model performance metric based on the best publicly available Arctic seabird colony location datasets compiled by the authors using digital and literature sources. The obtained models perform reasonably: from poor (only a few coastal species with low samples) to very high (many pelagic species). In compliance with data policies of the International Polar Year (IPY) and similar initiatives, data and models are documented with FGDC NBII metadata and publicly available online for further improvement, sustainability applications, synergy, and intellectual explorations in times of a global biodiversity, ocean and Arctic crisis.

Hotspots in cold seas: The composition, distribution, and abundance of marine birds in the North American Arctic

“…Using environmental and seabird data, Huettmann et al . [] ran predictive seabird models to predict the summer occurrence of many pelagic seabirds in the North American Arctic. The distributions of some species, such as dovekies, black‐legged kittiwakes, and thick‐billed murres, in our current study was similar to that predicted by Huettmann et al .…”

“…The distributions of some species, such as dovekies, black‐legged kittiwakes, and thick‐billed murres, in our current study was similar to that predicted by Huettmann et al . [].…”

How much does climate change threaten European forest tree species distributions?

“…The benefit from using GBIF data is derived from additional sampling of areas usually not covered by forest inventories and too large for reliable sampling by researchers. This information may supplement the existing knowledge about species distributions (Ashcroft, Gollan, & Batley, ; Huettmann, Artukhin, Gilg, & Humphries, ). Furthermore, conclusions from recently published papers containing predictions of tree responses to climate change are equivocal.…”

Melting barriers to faunal exchange across ocean basins

“…Recent seabird records also suggest movements through the Northwest Passages, as predicted by habitat models (Huettmann et al 2011). The northern gannet (Morus bassanus, Sulidae) has a distribution limited to the North Atlantic Ocean.…”

“…To date, the vast majority of these efforts have focused on terrestrial ecosystems where barriers to dispersal are much less dramatic than Arctic sea ice. Species distribution models have immediate applications to projecting the movements of marine mammals and birds as they colonize new ranges in the Pacific and Atlantic basins (Huettmann et al 2011, Kaschner et al 2011. Recent work estimating the suitable habitat range of gray whales projects highly suitable habitat for this species across the northeastern US and Canada (Kaschner et al 2011(Kaschner et al , www.aquamaps.org 2013.…”