Ontogenetic change in body shape for white sharks, Carcharodon carcharias, in Australian waters.

The analysis of how biological shape changes across ontogeny can provide us with valuable information on how species adapt behaviorally, physiologically, and ecologically. The white shark Carcharodon carcharias is one of the largest and most widely distributed apex predators globally, yet an understanding of ontogenetic changes in body shape and relative scaling of length and weight measures is limited, especially in relation to foraging ecology. Through analysis of a suite of shape-related metrics, we identified ontogenetic patterns of scaling throughout development.

Rapid assessment of adult abundance and demographic connectivity from juvenile kin pairs in a critically endangered species.

The viability of spatially structured populations depends on the abundance and connectivity between subpopulations of breeding adults. Yet, for many species, both are extremely difficult to assess. The speartooth shark is a critically endangered elasmobranch inhabiting tropical rivers with only three adults ever recorded in Australia.

Diving into the vertical dimension of elasmobranch movement ecology.

Knowledge of the three-dimensional movement patterns of elasmobranchs is vital to understand their ecological roles and exposure to anthropogenic pressures. To date, comparative studies among species at global scales have mostly focused on horizontal movements. Our study addresses the knowledge gap of vertical movements by compiling the first global synthesis of vertical habitat use by elasmobranchs from data obtained by deployment of 989 biotelemetry tags on 38 elasmobranch species.

Uncovering ecological state dynamics with hidden Markov models.

Ecological systems can often be characterised by changes among a finite set of underlying states pertaining to individuals, populations, communities or entire ecosystems through time. Owing to the inherent difficulty of empirical field studies, ecological state dynamics operating at any level of this hierarchy can often be unobservable or 'hidden'. Ecologists must therefore often contend with incomplete or indirect observations that are somehow related to these underlying processes.

A continuous-time state-space model for rapid quality control of argos locations from animal-borne tags.

Background: State-space models are important tools for quality control and analysis of error-prone animal movement data. The near real-time (within 24 h) capability of the Argos satellite system can aid dynamic ocean management of human activities by informing when animals enter wind farms, shipping lanes, and other intensive use zones. This capability also facilitates the use of ocean observations from animal-borne sensors in operational ocean forecasting models.

Spatiotemporal distribution patterns of immature Australasian white sharks (Carcharodon carcharias).

In Australian and New Zealand waters, current knowledge on white shark (Carcharodon carcharias) movement ecology is based on individual tracking studies using relatively small numbers of tags. These studies describe a species that occupies highly variable and complex habitats. However, uncertainty remains as to whether the proposed movement patterns are representative of the wider population.

Global spatial risk assessment of sharks under the footprint of fisheries.

Effective ocean management and the conservation of highly migratory species depend on resolving the overlap between animal movements and distributions, and fishing effort. However, this information is lacking at a global scale. Here we show, using a big-data approach that combines satellite-tracked movements of pelagic sharks and global fishing fleets, that 24% of the mean monthly space used by sharks falls under the footprint of pelagic longline fisheries.

Scaling marine fish movement behavior from individuals to populations.

Understanding how, where, and when animals move is a central problem in marine ecology and conservation. Key to improving our knowledge about what drives animal movement is the rising deployment of telemetry devices on a range of free-roaming species. An increasingly popular way of gaining meaningful inference from an animal's recorded movements is the application of hidden Markov models (HMMs), which allow for the identification of latent behavioral states in the movement paths of individuals.

Sea surface temperature predicts the movements of an Arctic cetacean: the bowhead whale.

The effects of climate change constitute a major concern in Arctic waters due to the rapid decline of sea ice, which may strongly alter the movements and habitat availability of Arctic marine mammals. We tracked 98 bowhead whales by satellite over an 11-year period (2001-2011) in Baffin Bay - West Greenland to investigate the environmental drivers (specifically sea surface temperature and sea ice) involved in bowhead whale's movements. Movement patterns differed according to season, with aggregations of whales found at higher latitudes during spring and summer likely in response to sea-ice retreat and increasing sea temperature (SST) facilitated by the warm West Greenland Current.

Estimation and simulation of foraging trips in land-based marine predators.

The behavior of colony-based marine predators is the focus of much research globally. Large telemetry and tracking data sets have been collected for this group of animals, and are accompanied by many empirical studies that seek to segment tracks in some useful way, as well as theoretical studies of optimal foraging strategies. However, relatively few studies have detailed statistical methods for inferring behaviors in central place foraging trips.

Dynamic optimal foraging theory explains vertical migrations of Bigeye tuna.

Bigeye tuna are known for remarkable daytime vertical migrations between deep water, where food is abundant but the water is cold, and the surface, where water is warm but food is relatively scarce. Here we investigate if these dive patterns can be explained by dynamic optimal foraging theory, where the tuna maximizes its energy harvest rate. We assume that foraging efficiency increases with body temperature, so that the vertical migrations are thermoregulatory.

Objective classification of latent behavioral states in bio-logging data using multivariate-normal hidden Markov models.

Analysis of complex time-series data from ecological system study requires quantitative tools for objective description and classification. These tools must take into account largely ignored problems of bias in manual classification, autocorrelation, and noise. Here we describe a method using existing estimation techniques for multivariate-normal hidden Markov models (HMMs) to develop such a classification.

Multi year observations reveal variability in residence of a tropical Demersal Fish, Lethrinus nebulosus: implications for spatial management.

Off the Ningaloo coast of North West Western Australia, Spangled Emperor Lethrinus nebulosus are among the most highly targeted recreational fish species. The Ningaloo Reef Marine Park comprises an area of 4,566 km2 of which 34% is protected from fishing by 18 no-take sanctuary zones ranging in size from 0.08-44.

Accounting for location error in Kalman filters: integrating animal borne sensor data into assimilation schemes.

Data assimilation is a crucial aspect of modern oceanography. It allows the future forecasting and backward smoothing of ocean state from the noisy observations. Statistical methods are employed to perform these tasks and are often based on or related to the Kalman filter.

Spatial variation in foraging behaviour of a marine top predator (Phoca vitulina) determined by a large-scale satellite tagging program.

The harbour seal (Phoca vitulina) is a widespread marine predator in Northern Hemisphere waters. British populations have been subject to rapid declines in recent years. Food supply or inter-specific competition may be implicated but basic ecological data are lacking and there are few studies of harbour seal foraging distribution and habits.

Reproductive schedules in southern bluefin tuna: are current assumptions appropriate?

Southern bluefin tuna (SBT) appear to comprise a single stock that is assumed to be both mixed across its distribution and having reproductive adults that are obligate, annual spawners. The putative annual migration cycle of mature SBT consists of dispersed foraging at temperate latitudes with migration to a single spawning ground in the tropical eastern Indian Ocean. Spawning migrations have been assumed to target two peaks in spawning activity; one in September-October and a second in February-March.

Predicting feeding success in a migratory predator: integrating telemetry, environment, and modeling techniques.

Foraging theory predicts that mobile predators should target high profitability areas with plentiful resources and minimize time spent moving between these areas. This has led to a focus in recent literature on the identification of "hotspots" important for migratory marine predators, i.e.

Classifying movement behaviour in relation to environmental conditions using hidden Markov models.

1. Linking the movement and behaviour of animals to their environment is a central problem in ecology. Through the use of electronic tagging and tracking (ETT), collection of in situ data from free-roaming animals is now commonplace, yet statistical approaches enabling direct relation of movement observations to environmental conditions are still in development.

Feeding ecology of wild migratory tunas revealed by archival tag records of visceral warming.

1. Seasonal long-distance migrations are often expected to be related to resource distribution, and foraging theory predicts that animals should spend more time in areas with relatively richer resources. Yet for highly migratory marine species, data on feeding success are difficult to obtain.

State-space models of individual animal movement.

Detailed observation of the movement of individual animals offers the potential to understand spatial population processes as the ultimate consequence of individual behaviour, physiological constraints and fine-scale environmental influences. However, movement data from individuals are intrinsically stochastic and often subject to severe observation error. Linking such complex data to dynamical models of movement is a major challenge for animal ecology.