Whale fall chemosymbiotic communities in a southwest Australian submarine canyon fill a distributional gap

A whale fall community of chemosymbiotic invertebrates living on cetacean bones has been identified off southwestern Australia during a Remotely Operated Vehicle (ROV) survey at bathyal depths within the Bremer Marine Park, which is part of important marine mammal areas (IMMA) of the Albany Canyon Region. Cetacean bones on the seafloor of the Hood Canyon, consisted of isolated skulls of three species of beaked whales (family Ziphiidae): Mesoplodon cf. layardii, M. grayi, and M. hectori, a few vertebrae, and lower jaws. One of the beaked whale skulls (Mesoplodon cf. layardii) was sampled and found to be intensely colonised by hundreds of specimens of a bathymodilinae mussel ("Adipicola" s.l.). Live polychaetes (Phyllochaetopterus?), skeneimorph gastropods, and amphipods (Seba, Leptamphopus) colonised the skull bone, which represent a later stage (sulfophilic) of carcass decomposition. The reducing sediment below the skull was inhabited by lucinid (Lucinoma) and vesicomyid (Calyptogena) chemosymbiotic bivalves. Additionally, the sediment thanatocoenosis comprised shells of various other chemosymbiotic bivalves, such as Acharax, thyasirids, lucinids, vesicomyids, and limpets, representing the complex ecological turnover phases through time in this whale fall chemosynthetic habitat. With one exception, all bones recovered were colonized by bathymodiolin mussels. This is the first documented case of a chemosynthetic community and associated chemosymbiotic fauna relating to beaked whales, and the first fully documented record of a whale fall community within the Australian Southern Ocean region.

A real-time data assimilative forecasting system for animal tracking

Monitoring technologies now provide real‐time animal location information, which opens up the possibility of developing forecasting systems to fuse these data with movement models to predict future trajectories. State‐space modeling approaches are well established for retrospective location estimation and behavioral inference through state and parameter estimation. Here we use a state‐space model within a comprehensive data assimilative framework for probabilistic animal movement forecasting. Real‐time location information is combined with stochastic movement model predictions to provide forecasts of future animal locations and trajectories, as well as estimates of key behavioral parameters. Implementation uses ensemble‐based sequential Monte Carlo methods (a particle filter). We first apply the framework to an idealized case using a nondimensional animal movement model based on a continuous‐time random walk process. A set of numerical forecasting experiments demonstrates the workflow and key features, such as the online estimation of behavioral parameters using state augmentation, the use of potential functions for habitat preference, and the role of observation error and sampling frequency on forecast skill. For a realistic demonstration, we adapt the framework to short‐term forecasting of the endangered southern resident killer whale (SRKW) in the Salish Sea using visual sighting information wherein the potential function reflects historical habitat utilization of SRKW. We successfully estimate whale locations up to 2.5 h in advance with a moderate prediction error (<5 km), providing reasonable lead‐in time to mitigate vessel–whale interactions. It is argued that this forecasting framework can be used to synthesize diverse data types and improve animal movement models and behavioral understanding and has the potential to lead to important advances in movement ecology.