Antarctic petrels ‘on the ice rocks’: wintering strategy of an Antarctic seabird

There is a paucity of information on the foraging ecology, especially individual use of sea-ice features and icebergs, over the non-breeding season in many seabird species. Using geolocators and stable isotopes, we defined the movements, distribution and diet of adult Antarctic petrels Thalassoica antarctica from the largest known breeding colony, the inland Svarthamaren, Antarctica. More specifically, we examined how sea-ice concentration and free-drifting icebergs affect the distribution of Antarctic petrels. After breeding, birds moved north to the marginal ice zone (MIZ) in the Weddell sector of the Southern Ocean, following its northward extension during freeze-up in April, and they wintered there in April–August. There, the birds stayed predominantly out of the water (60–80% of the time) suggesting they use icebergs as platforms to stand on and/or to rest. Feather δ 15 N values encompassed one full trophic level, indicating that birds fed on various proportions of crustaceans and fish/squid, most likely Antarctic krill Euphausia superba and the myctophid fish Electrona antarctica and/or the squid Psychroteuthis glacialis . Birds showed strong affinity for the open waters of the northern boundary of the MIZ, an important iceberg transit area, which offers roosting opportunities and rich prey fields. The strong association of Antarctic petrels with sea-ice cycle and icebergs suggests the species can serve, year-round, as a sentinel of environmental changes for this remote region.

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Adélie penguins foraging consistency and site fidelity are conditioned by breeding status and environmental conditions

PLoS ONE ◽

10.1371/journal.pone.0244298 ◽

2021 ◽

Vol 16 (1) ◽

pp. e0244298

Author(s):

Candice Michelot ◽

Akiko Kato ◽

Thierry Raclot ◽

Yan Ropert-Coudert

Keyword(s):

Reproductive Success ◽

Sea Ice ◽

Site Fidelity ◽

Environmental Changes ◽

Foraging Activity ◽

Sea Ice Concentration ◽

Ice Concentration ◽

Foraging Site ◽

Ice Conditions ◽

Adélie Penguins

There is a growing interest in studying consistency and site fidelity of individuals to assess, respectively, how individual behaviour shapes the population response to environmental changes, and to highlight the critical habitats needed by species. In Antarctica, the foraging activity of central place foragers like Adélie penguins (Pygoscelis adeliae) is constrained by the sea-ice cover during the breeding season. We estimated the population-level repeatability in foraging trip parameters and sea-ice conditions encountered by birds across successive trips over several years, and we examined their foraging site fidelity linked to sea-ice concentrations throughout the chick-rearing season. Penguins’ foraging activity was repeatable despite varying annual sea-ice conditions. Birds’ site fidelity is constrained by both sea-ice conditions around the colony that limit movements and resources availability, and also behavioural repeatability of individuals driven by phenological constraints. Adélie penguins favoured sea-ice concentrations between 20–30%, as these facilitate access to open water while opening multiple patches for exploration in restricted areas in case of prey depletion. When the sea-ice concentration became greater than 30%, foraging site fidelity decreased and showed higher variability, while it increased again after 60%. Between two trips, the foraging site fidelity remained high when sea-ice concentration changed by ± 10% but showed greater variability when sea-ice concentrations differed on a larger range. In summary, Adélie penguins specialize their foraging behaviour during chick-rearing according to sea-ice conditions to enhance their reproductive success. The balance between being consistent under favourable environmental conditions vs. being flexible under more challenging conditions may be key to improving foraging efficiency and reproductive success to face fast environmental changes.

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Lagged response of Adélie penguin population dynamics to environmental variability in the Ross Sea, Antarctica

10.5194/egusphere-egu2020-21001 ◽

2020 ◽

Author(s):

Xintong Chen ◽

Xianglan Li

Keyword(s):

Population Dynamics ◽

Environmental Factors ◽

Sea Ice ◽

Environmental Changes ◽

Environmental Variability ◽

Ross Sea ◽

Chlorophyll Concentration ◽

Pygoscelis Adeliae ◽

Sea Ice Concentration ◽

Ice Concentration

<p>Ad&#233;lie penguin (Pygoscelis adeliae) are known as an eco-indicator species of the Southern Ocean marine ecosystem. Environmental variability drives penguin population dynamics through its effects on vital rates (e.g., survival, dispersal, or breeding success). Recent studies suggested that inter-annual variation strongly affected Ad&#233;lie penguin abundance in individual colonies, and aggregating abundance across space provided more reliable trends of population dynamics. By considering the similarity of the regional sea-ice concentration, we divided the Ross Sea region into six areas to investigate the effects of environmental changes on penguin population dynamics from 1982&#8211;2013. Time lagged analysis of 0&#8211;6 years between penguin abundance and environmental factors were conducted in our study. We found that penguin abundance was significantly correlated with environmental factors at different lag times (p <0.05). In the western Ross Sea region, penguin abundance was positively correlated with temperature over the past 5 years (p <0.05), and positively correlated with sea-ice concentration at a lag of 4&#8211;6 years (p <0.05). In the northernmost region of the Ross Sea, penguin abundance was significantly correlated with chlorophyll concentration 4 years earlier (p <0.01). Generalized additive model (GAM) results showed that in mid-Victoria Land, the relationship between sea-ice concentration and penguin abundance was quadratic. Penguin abundance peaked when sea-ice concentration was approximately 40%. On Franklin Island, temperature positively affected penguin abundance when temperature was lower than -3&#176;C, and the contribution decreased considerably when temperature was higher than -2.5&#176;C. Optimal ranges of environmental factors for Ad&#233;lie penguin population might exist and differ spatially in the Ross Sea. Our study highlighted the lagged response of penguin population to environmental factors to further understand the effects of climate changes on the Antarctic biosphere.</p>

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Comparing the Satellite Microwave and Visual Shipborne Sea Ice Concentration

Исследования Земли из космоса ◽

10.31857/s020596140003369-6 ◽

2018 ◽

pp. 65-76

Author(s):

T. Alekseeva ◽

◽

E. Sharkov ◽

V. Tikhonov ◽

S. Frolov ◽

...

Keyword(s):

Sea Ice ◽

Sea Ice Concentration ◽

Ice Concentration ◽

Satellite Microwave

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DASSO: a data assimilation system for the Southern Ocean that utilizes both sea-ice concentration and thickness observations

Journal of Glaciology ◽

10.1017/jog.2021.57 ◽

2021 ◽

pp. 1-6

Author(s):

Hao Luo ◽

Qinghua Yang ◽

Longjiang Mu ◽

Xiangshan Tian-Kunze ◽

Lars Nerger ◽

...

Keyword(s):

Data Assimilation ◽

Southern Ocean ◽

Sea Ice ◽

Coupled Model ◽

Sea Ice Concentration ◽

Model Free ◽

Antarctic Sea Ice ◽

Ice Concentration ◽

Data Assimilation System ◽

Assimilation System

Abstract To improve Antarctic sea-ice simulations and estimations, an ensemble-based Data Assimilation System for the Southern Ocean (DASSO) was developed based on a regional sea ice–ocean coupled model, which assimilates sea-ice thickness (SIT) together with sea-ice concentration (SIC) derived from satellites. To validate the performance of DASSO, experiments were conducted from 15 April to 14 October 2016. Generally, assimilating SIC and SIT can suppress the overestimation of sea ice in the model-free run. Besides considering uncertainties in the operational atmospheric forcing data, a covariance inflation procedure in data assimilation further improves the simulation of Antarctic sea ice, especially SIT. The results demonstrate the effectiveness of assimilating sea-ice observations in reconstructing the state of Antarctic sea ice, but also highlight the necessity of more reasonable error estimation for the background as well as the observation.

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Sea Ice Concentration and Sea Ice Extent Mapping with L-Band Microwave Radiometry and GNSS-R Data from the FFSCat Mission Using Neural Networks

Remote Sensing ◽

10.3390/rs13061139 ◽

2021 ◽

Vol 13 (6) ◽

pp. 1139

Author(s):

David Llaveria ◽

Juan Francesc Munoz-Martin ◽

Christoph Herbert ◽

Miriam Pablos ◽

Hyuk Park ◽

...

Keyword(s):

Sea Ice ◽

Microwave Radiometer ◽

Cost Effective ◽

The Arctic ◽

Sea Ice Concentration ◽

Sea Ice Extent ◽

Neural Network Approach ◽

Ice Concentration ◽

L Band ◽

Moderate Cost

CubeSat-based Earth Observation missions have emerged in recent times, achieving scientifically valuable data at a moderate cost. FSSCat is a two 6U CubeSats mission, winner of the ESA S3 challenge and overall winner of the 2017 Copernicus Masters Competition, that was launched in September 2020. The first satellite, 3Cat-5/A, carries the FMPL-2 instrument, an L-band microwave radiometer and a GNSS-Reflectometer. This work presents a neural network approach for retrieving sea ice concentration and sea ice extent maps on the Arctic and the Antarctic oceans using FMPL-2 data. The results from the first months of operations are presented and analyzed, and the quality of the retrieved maps is assessed by comparing them with other existing sea ice concentration maps. As compared to OSI SAF products, the overall accuracy for the sea ice extent maps is greater than 97% using MWR data, and up to 99% when using combined GNSS-R and MWR data. In the case of Sea ice concentration, the absolute errors are lower than 5%, with MWR and lower than 3% combining it with the GNSS-R. The total extent area computed using this methodology is close, with 2.5% difference, to those computed by other well consolidated algorithms, such as OSI SAF or NSIDC. The approach presented for estimating sea ice extent and concentration maps is a cost-effective alternative, and using a constellation of CubeSats, it can be further improved.

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Retrieval of Summer Sea Ice Concentration in the Pacific Arctic Ocean from AMSR2 Observations and Numerical Weather Data Using Random Forest Regression

Remote Sensing ◽

10.3390/rs13122283 ◽

2021 ◽

Vol 13 (12) ◽

pp. 2283

Author(s):

Hyangsun Han ◽

Sungjae Lee ◽

Hyun-Cheol Kim ◽

Miae Kim

Keyword(s):

Random Forest ◽

Sea Ice ◽

Arctic Ocean ◽

Open Water ◽

The Arctic ◽

Atmospheric Effects ◽

Sea Ice Concentration ◽

Air Temperatures ◽

The Pacific ◽

Ice Concentration

The Arctic sea ice concentration (SIC) in summer is a key indicator of global climate change and important information for the development of a more economically valuable Northern Sea Route. Passive microwave (PM) sensors have provided information on the SIC since the 1970s by observing the brightness temperature (TB) of sea ice and open water. However, the SIC in the Arctic estimated by operational algorithms for PM observations is very inaccurate in summer because the TB values of sea ice and open water become similar due to atmospheric effects. In this study, we developed a summer SIC retrieval model for the Pacific Arctic Ocean using Advanced Microwave Scanning Radiometer 2 (AMSR2) observations and European Reanalysis Agency-5 (ERA-5) reanalysis fields based on Random Forest (RF) regression. SIC values computed from the ice/water maps generated from the Korean Multi-purpose Satellite-5 synthetic aperture radar images from July to September in 2015–2017 were used as a reference dataset. A total of 24 features including the TB values of AMSR2 channels, the ratios of TB values (the polarization ratio and the spectral gradient ratio (GR)), total columnar water vapor (TCWV), wind speed, air temperature at 2 m and 925 hPa, and the 30-day average of the air temperatures from the ERA-5 were used as the input variables for the RF model. The RF model showed greatly superior performance in retrieving summer SIC values in the Pacific Arctic Ocean to the Bootstrap (BT) and Arctic Radiation and Turbulence Interaction STudy (ARTIST) Sea Ice (ASI) algorithms under various atmospheric conditions. The root mean square error (RMSE) of the RF SIC values was 7.89% compared to the reference SIC values. The BT and ASI SIC values had three times greater values of RMSE (20.19% and 21.39%, respectively) than the RF SIC values. The air temperatures at 2 m and 925 hPa and their 30-day averages, which indicate the ice surface melting conditions, as well as the GR using the vertically polarized channels at 23 GHz and 18 GHz (GR(23V18V)), TCWV, and GR(36V18V), which accounts for atmospheric water content, were identified as the variables that contributed greatly to the RF model. These important variables allowed the RF model to retrieve unbiased and accurate SIC values by taking into account the changes in TB values of sea ice and open water caused by atmospheric effects.

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