Arctic-breeding seabirds’ hotspots in space and time - A methodological framework for year-round modelling of environmental niche and abundance using light-logger data

• Main Authors: Fauchald, Per, Tarroux, Arnaud, Bråthen, Vegard Sandøy, Descamps, Sebastien, Ekker, Morten, Helgason, Hálfdán Helgi, Merkel, Benjamin, Moe, Børge, Åström, Jens, Strøm, Hallvard
• Format: Report
• Language: English
• Published: Norsk institutt for naturforskning (NINA) 2019
• Subjects: NINA Rapport; NINA Report; Northern fulmar; Black-legged kittiwake; Little auk; Common guillemot; Brünnich’s guillemot; Atlantic puffin; Alcids Geolocation; Light-loggers; Habitat modelling; abundance estimation; movement modelling; Migration; Non-breeding season; VDP::Matematikk og Naturvitenskap: 400::Zoologiske og botaniske fag: 480; Arctic; Barents Sea; Faroe Islands; Fulmar; Jan Mayen; Moe; Norway; Norwegian Sea; Svalbard; Black-legged Kittiwake; common guillemot; Iceland; little auk; Northeast Atlantic; Northern Fulmar
• Online Access: http://hdl.handle.net/11250/2595504

Fauchald, P., Tarroux, A., Bråthen, V. S., Descamps, S., Ekker, M., Helgason, H. H., Merkel, B., Moe, B., Åström, J., Strøm, H. 2019. Arctic-breeding seabirds’ hotspots in space and time -a methodological framework for year-round modelling of abundance and environmen-tal niche using light-logger data. NINA Report 1657. Norwegian Institute for Nature Re-search. By positioning a large number of seabirds throughout the year using miniaturized geoloca-tors (GLS), the SEATRACK program provides a unique dataset on the seasonal distribution of seabirds from colonies in Russia (Barents and White Seas), Norway (incl. Svalbard and Jan Mayen), Iceland, Faroe Islands and the British Isles. Combining this extensive dataset with data on population sizes has for the first time made it possible to develop seasonal estimates of the spatial distribution of Northeast Atlantic seabirds. In this report, we document the workflow and methods used to develop monthly estimates of the distribution of seabirds from colonies covered by the SEATRACK design. The work-flow presented here consists of three steps, starting from pre-processed GLS data. First, because the position data from the loggers represent “presence-only” data, it is vital to re-move sampling biases before using the data to make interpretations of the spatial distribu-tion. Therefore, in step 1 we developed a tailored algorithm, IRMA (Informed Random Move-ment Algorithm), to reduce biases and fill gaps in the dataset due to various factors such as polar day/night, equinox and positions over land. IRMA uses available information and data to triangulate new positions and does ultimately provide a dataset where sampling biases has been reduced to a minimum. In the next step, we combined the position dataset with environmental data to model the habitat of each SEATRACK colony throughout the year. Environmental variables included remote sensing data of oceanography and primary pro-duction, and data on bathymetry. We used standard Species Distribution Models (SDM) on ...