Important marine habitat off east Antarctica revealed by two decades of multi‐species predator tracking

Abstract

International audienceSatellite telemetry data are a key source of animal distribution information for marine ecosystem management and conservation activities. We used two decades of telemetry data from the East Antarctic sector of the Southern Ocean. Habitat utilization models for the spring/summer period were developed for six highly abundant, wide-ranging meso- and top-predator species: Adélie Pygoscelisadeliae and emperor Aptenodytes forsteri penguins, light-mantled albatross Phoebetria palpebrata , Antarctic fur seals Arctocephalus gazella , southern elephant seals Mirounga leonina , and Weddell seals Leptony-chotes weddellii . The regional predictions from these models were combined to identify areas utilized by multiple species, and therefore likely to be of particular ecological significance. These areas were distributed across the longitudinal breadth of the East Antarctic sector, and were characterized by proximity to breeding colonies, both on the Antarctic continent and on subantarctic islands to the north, and by sea-ice dynamics, particularly locations of winter polynyas. These areas of important habitat were also congruent with many of the areas reported to be showing the strongest regional trends in sea ice seasonality. Th e results emphasize the importance of on-shore and sea-ice processes to Antarctic marine ecosystems. Our study provides ocean-basin-scale predictions of predator habitat utilization, an assessment of contemporary habitat use against which future changes can be assessed, and is of direct relevance to current conservation planning and spatial management efforts

Influence of sea ice phenology on the movement ecology of ringed seals across their latitudinal range

“…These results have implications on regional and global management and conservation strategies for endemic Arctic marine mammals, many of whom are data poor with varying sensitivities to climate change (Laidre et al 2008(Laidre et al , 2015. Using large and extensive telemetry datasets of marine megafauna, researchers can shed new light on ecosystem trophodynamics and important marine habitats (Raymond et al 2015, Hindell et al 2016, Lascelles et al 2016) to inform stakeholders with conservation planning efforts in response to a changing climate. Muir DCG, Fisk AT (2016a) …”

Key Questions in Marine Megafauna Movement Ecology

“…For example, the last few years have shown the immense value of collaborations among ecologists, mathematicians, physicists, oceanographers, engineers and information technologists to identify general patterns in animal movement [8,42,72,73]. Additionally, increasing engagement with policy makers will help translate tracking data into real world conservation benefits.…”

“…Two approaches might overcome these problems. Firstly, collaborative studies that combine efforts to increase sample sizes to create synoptic views of individual and multispecies movement patterns [42,73,99]. Secondly, the development of global, freely available databases to facilitate data sharing in animal movement ecology [1,2,100].…”

Individual-level Variation and Higher-level Interpretations of Space Use in Wide-ranging Species: An Albatross Case Study of Sampling Effects

“…Grid cell methods offer a simple alternative, where the cumulative time spent within cells of a predefined grid size is used to identify the extent of a group's range and areas of most intense use (e.g., Soanes et al, 2013). Other methods take a habitat preference modeling approach, which takes into account environmental factors that shape patterns of space use (e.g., Aarts et al, 2008;Wakefield et al, 2011;Raymond et al, 2015). There have been a number of recent advances in approaches for estimating space use at the individual level which incorporate both the spatial and temporal nature of tracking data to estimate distribution contours, but most have not been expanded to generate group-level estimates of space use (e.g., Time Local Convex Hull, Lyons et al, 2013;Baker et al, 2015;movement-based KDE, Benhamou, 2011).…”