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The Steller's sea cow - Hydrodamalis gigas (Dugongidae: Sirenia) - is an extinct herbivorous marine mammal which inhabited the North Pacific Ocean during the Pleistocene and Holocene. H. gigas was the largest member of the Sirenia order and disappeared in the middle of the 18th century. Here, we present the complete sequence of the mitochondrial genome of this extinct animal. The Steller's sea cow mitochondrial DNA (mtDNA) is 16,872 base pairs (bp) in length and contains a set of mitochondrial genes typical for mammals. Phylogenetic analysis based on complete mitochondrial genomes of the sirenian species allows accurate assessment of the degree of their mitogenomic diversification during millions of years of evolution., (Copyright © 2018 Elsevier Inc. All rights reserved.)

The coastal seagrass meadows in the Townsville region of the Great Barrier Reef are crucial seagrass foraging habitat for endangered dugong populations. Deteriorating coastal water quality and in situ light levels reduce the extent of these meadows, particularly in years with significant terrestrial runoff from the nearby Burdekin River catchment. However, uncertainty surrounds the impact of variable seagrass abundance on dugong carrying capacity. Here, I demonstrate that a power-law relationship with exponent value of -1 (R 2 ~0.87) links mortality data with predicted changes in annual above ground seagrass biomass. This relationship indicates that the dugong carrying capacity of the region is tightly coupled to the biomass of seagrass available for metabolism. Thus, mortality rates increase precipitously following large flood events with a response lag of <12-months. The management implications of this result are discussed in terms of climate scenarios that indicate an increased future likelihood of extreme flood events., (Copyright © 2016 Elsevier Ltd. All rights reserved.)

Knowledge of the relationships between environmental forcing and demographic parameters is important for predicting responses from climatic changes and to manage populations effectively. We explore the relationships between the proportion of sea cows (Dugong dugon) classified as calves and four climatic drivers (rainfall anomaly, Southern Oscillation El Niño Index [SOI], NINO 3.4 sea surface temperature index, and number of tropical cyclones) at a range of spatially distinct locations in Queensland, Australia, a region with relatively high dugong density. Dugong and calf data were obtained from standardized aerial surveys conducted along the study region. A range of lagged versions of each of the focal climatic drivers (1 to 4 years) were included in a global model containing the proportion of calves in each population crossed with each of the lagged versions of the climatic drivers to explore relationships. The relative influence of each predictor was estimated via Gibbs variable selection. The relationships between the proportion of dependent calves and the climatic drivers varied spatially and temporally, with climatic drivers influencing calf counts at sub-regional scales. Thus we recommend that the assessment of and management response to indirect climatic threats on dugongs should also occur at sub-regional scales.

Understanding environmental and climatic drivers of natural mortality of marine mammals is critical for managing populations effectively and for predicting responses to climate change. Here we use a 17-year dataset to demonstrate a clear relationship between environmental forcing and natural mortality of inshore marine mammals across a subtropical-tropical coastline spanning a latitudinal gradient of 13° (>2000 km of coastline). Peak mortality of inshore dolphins and dugongs followed sustained periods of elevated freshwater discharge (9 months) and low air temperature (3 months). At a regional scale, these results translated into a strong relationship between annual mortality and an index of El Niño-Southern Oscillation. The number of cyclones crossing the coastline had a comparatively weak effect on inshore marine mammal mortality, and only in the tropics. Natural mortality of offshore/migratory cetaceans was not predicted by freshwater discharge, but was related to lagged air temperature. These results represent the first quantitative link between environmental forcing and marine mammal mortality in the tropics, and form the basis of a predictive tool for managers to prepare responses to periods of elevated marine mammal mortality.

Background: Conservation planning and the design of marine protected areas (MPAs) requires spatially explicit information on the distribution of ecological features. Most species of marine mammals range over large areas and across multiple planning regions. The spatial distributions of marine mammals are difficult to predict using habitat modelling at ecological scales because of insufficient understanding of their habitat needs, however, relevant information may be available from surveys conducted to inform mandatory stock assessments., Methodology and Results: We use a 20-year time series of systematic aerial surveys of dugong (Dugong dugong) abundance to create spatially-explicit models of dugong distribution and relative density at the scale of the coastal waters of northeast Australia (∼136,000 km(2)). We interpolated the corrected data at the scale of 2 km * 2 km planning units using geostatistics. Planning units were classified as low, medium, high and very high dugong density on the basis of the relative density of dugongs estimated from the models and a frequency analysis. Torres Strait was identified as the most significant dugong habitat in northeast Australia and the most globally significant habitat known for any member of the Order Sirenia. The models are used by local, State and Federal agencies to inform management decisions related to the Indigenous harvest of dugongs, gill-net fisheries and Australia's National Representative System of Marine Protected Areas., Conclusion/significance: In this paper we demonstrate that spatially-explicit population models add value to data collected for stock assessments, provide a robust alternative to predictive habitat distribution models, and inform species conservation at multiple scales.