TL;DR: In this article, an experimental longline fishery for hake Merluccius spp. commenced in the shelf waters off South Africa, and participants were required to record any birds caught, and these data were supplemented by ship-based observers on several vessels.
TL;DR: The approach to overcome this limitation is to combine data from the different species into a single discriminant that results in 84 to 97% correct classifications and can be applied to other populations of fulmarine petrels without requiring samples of birds of known sex.
Abstract: ABsTRAcr.-Discriminant analysis can use morphometric differences between known male and female birds to predict the sex of unknown individuals in field studies. Geographic variation in size and shape often limits the predictive value of a discriminant function to the population from which it was derived. Specific discriminant functions for populations of five species of fulmarine petrels (Northern Fulmar, Fulmarus glacialis; Southern Fulmar, F. glacialoides; Antarctic Petrel, Thalassoica antarctica; Cape Petrel, Daption capense; and Snow Petrel, Pagodroma nivea) assigned 81 to 98% of birds in the samples to the correct sex, but the validity of each discriminant applied to alternative populations remained questionable. Our approach to overcome this limitation is to combine data from the different species into a single discriminant. Adequate performance of this generalized discriminant in samples of different species shows its validity for use in other populations of any of these species. The generalized function calculates the discriminant scores for individual fulmarine petrels as: Y = HL + 2.38BD + 0.41TL 0.21CL, where HL is head length, BD is bill depth, TL is tarsus length and CL is bill length (measurements in millimeters). The cut point to split sexes is different in each sample and may be calculated directly from discriminant scores, without reference to sexed birds, by using a maximum-likelihood method. Depending on species, the generalized method results in 84 to 97% correct classifications and can be applied to other populations of fulmarine petrels without requiring samples of birds of known sex. Received 19 November 1991, accepted 20 November 1992.
TL;DR: The distribution of seabirds and pinnipeds and their relationship to physical oceanographic variables were investigated as part of the US Southern Ocean Global Ocean Ecosystem Dynamics field program along a study grid centered around Marguerite Bay on the west Antarctic Peninsula during late fall (April-May) and winter (July-August), 2001 as mentioned in this paper.
Abstract: The distribution of seabirds and pinnipeds and their relationship to physical oceanographic variables were investigated as part of the US Southern Ocean Global Ocean Ecosystem Dynamics field program along a study grid centered around Marguerite Bay on the west Antarctic Peninsula during late fall (April–May) and winter (July–August), 2001. Sea-ice conditions during the cruises provided an opportunity to compare the relationship among physical oceanographic variables and species distributions before and after the development of pack ice. During the fall cruise before pack ice development, both sea-ice-affiliated species and open-water-affiliated were observed in the area. The most common ice-affiliated species observed at this time were snow petrel ( Pagodroma nivea , 0.7 individuals km −2 ) and Antarctic petrel ( Thalassoica antarctica , 0.2 individuals km −2 ) and the most common open-water-affiliated species were blue petrel ( Halobaena caerulea , 0.4 individuals km −2 ), cape petrel ( Daption capense , 0.2 individuals km −2 ), and southern fulmar ( Fulmarus glacialoides , 0.1 individuals km −2 ). In addition, Antarctic fur seals ( Arctocephalus gazella , 0.1 individuals km −2 ) and crabeater seals ( Lobodon carcinophagus , 0.4 individuals km −2 ) were observed in low numbers. Akaike's information criterion was used to assess competing models that predicted predator distributions based on physical oceanographic variables proposed to structure predator distribution in previous research. These analyses indicated that predator distributions were primarily associated with water-mass structure and variability in bottom depth during the fall cruise. Crabeater seal, snow petrel, Antarctic petrel, and southern fulmar had higher densities in Inner Shelf Water, particularly near Alexander Island where a coastal current was present. Blue petrel, kelp gull ( Larus dominicanus ), and southern giant petrel ( Macronectes giganteus ) were positively associated with variability in bottom depth in April–May, suggesting that hydrographic processes influenced by bathymetry may have been important in structuring bird distributions. After the development of pack ice, during July and August, only sea-ice-affiliated species, including snow petrel (1.0 individuals km −2 ), Antarctic petrel (0.1 individuals km −2 ), Adelie penguin ( Pygoscelis adeliae , 0.4 individuals km −2 ), and crabeater seal (0.3 individuals km −2 ), were observed. Seabirds were primarily associated with sea-ice characteristics (e.g. sea-ice concentration, sea-ice type) rather than the water-column environment later in the winter. Results from this study suggest that the timing and extent of sea-ice development in the fall may influence over-winter predation by seabirds and pinnipeds on zooplankton and fish on the western Antarctic Peninsula. Delays in sea-ice development may allow seabirds and pinnipeds access to biologically important areas such as the Inner Shelf Water for a longer period of time thereby increasing predation on zooplankton and fish.
TL;DR: KARINE DELORD,* PATRICK PINET, DAVID PINAUD, CHRISTOPHE BARBRAUD, SOPHIE de GRISSAC, AGNES LEWDEN, YVES CHEREL & HENRI WEIMERSKIRCH
Abstract: KARINE DELORD,* PATRICK PINET, DAVID PINAUD, CHRISTOPHE BARBRAUD, SOPHIE DE GRISSAC, AGNES LEWDEN, YVES CHEREL & HENRI WEIMERSKIRCH Centre d’ Etudes Biologiques de Chiz e, UMR 7372 du CNRS-Universit e de La Rochelle, 79360 Villiers-en-Bois, France beGraX auto entreprise, La R eunion, France Universit e de Strasbourg, IPHC, 23 rue Becquerel, 67087 Strasbourg, France CNRS, UMR7178, 67087 Strasbourg, France