Antarktis-bibliografi er en database over den norske Antarktis-litteraturen.
Hensikten med bibliografien er å synliggjøre norsk antarktisforskning og annen virksomhet/historie i det ekstreme sør. Bibliografien er ikke komplett, spesielt ikke for nyere forskning, men den blir oppdatert.
Norsk er her definert som minst én norsk forfatter, publikasjonssted Norge eller publikasjon som har utspring i norsk forskningsprosjekt.
Antarktis er her definert som alt sør for 60 grader. I tillegg har vi tatt med Bouvetøya.
Det er ingen avgrensing på språk (men det meste av innholdet er på norsk eller engelsk). Eldre norske antarktispublikasjoner (den eldste er fra 1894) er dominert av kvalfangst og ekspedisjoner. I nyere tid er det den internasjonale polarforskninga som dominerer. Bibliografien er tverrfaglig; den dekker både naturvitenskapene, politikk, historie osv. Skjønnlitteratur er også inkludert, men ikke avisartikler eller upublisert materiale.
Til høyre finner du en «HELP-knapp» for informasjon om søkemulighetene i databasen. Mange referanser har lett synlige lenker til fulltekstversjon av det aktuelle dokumentet. For de fleste tidsskriftartiklene er det også lagt inn sammendrag.
Bibliografien er produsert ved Norsk Polarinstitutts bibliotek.
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The dive profiles of pursuit-diving marine predators are often used to infer foraging behaviour, including potential indicators of prey consumption. ‘Wiggles’ are undulations in dive profiles that relate to foraging activity in a variety of marine predators. In penguins, wiggles are sometimes used as a proxy for prey consumption (e.g., catch per unit effort, CPUE), but this relationship remains poorly validated and likely varies with diet. We deployed animal-borne video cameras and depth recorders on chinstrap penguins (Pygoscelis antarcticus; n = 37) and identified over 17,000 euphausiid prey captures - mainly Antarctic krill (Euphausia superba) - during dives deeper than 3 m (n = 2458 dives). Using the video-observed prey captures as a reference, we tested how well various wiggle metrics derived from 1 Hz depth data predicted krill consumption by the penguins. Wiggle metrics generally showed a positive but noisy and highly variable relationship with the number of krill captured per dive, with association strength varying among metrics. While it is tempting to infer detailed foraging behaviours from dive wiggles (including ‘bottom distance’ generated by the R package diveMove), our results show: (1) notable rates of foraging – non-foraging dive misclassification; (2) only moderate agreement between CPUE estimated from wiggle counts and video observations; and (3) imprecise predictive models of actual prey consumption. While wiggle analyses offer some insight into prey consumption of krill-feeding penguins, our results suggest that alternative methods (e.g., acceleration-based indices) are needed to obtain more robust quantitative estimates of prey consumption.
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Marine predators are integral to the functioning of marine ecosystems, and their consumption requirements should be integrated into ecosystem-based management policies. However, estimating prey consumption in diving marine predators requires innovative methods as predator-prey interactions are rarely observable. We developed a novel method, validated by animal-borne video, that uses tri-axial acceleration and depth data to quantify prey capture rates in chinstrap penguins (Pygoscelis antarctica). These penguins are important consumers of Antarctic krill (Euphausia superba), a commercially harvested crustacean central to the Southern Ocean food web. We collected a large data set (n = 41 individuals) comprising overlapping video, accelerometer and depth data from foraging penguins. Prey captures were manually identified in videos, and those observations were used in supervised training of two deep learning neural networks (convolutional neural network (CNN) and V-Net). Although the CNN and V-Net architectures and input data pipelines differed, both trained models were able to predict prey captures from new acceleration and depth data (linear regression slope of predictions against video-observed prey captures = 1.13; R2 approximate to 0.86). Our results illustrate that deep learning algorithms offer a means to process the large quantities of data generated by contemporary bio-logging sensors to robustly estimate prey capture events in diving marine predators.
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Topic
- Sørishavet
- biologging (2)
- krill (1)
- marin biologi (2)
- marin zoologi (1)
- marine økosystemer (1)
- økologi (1)
- pingviner (2)
- plankton (1)
- sjøfugler (1)
Resource type
- Journal Article (2)
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Online resource
- yes (2)