Oil Spills and Marine Mammals in British Columbia, Canada: Development and Application of a Risk-Based Conceptual Framework

Marine mammals are inherently vulnerable to oil spills. We developed a conceptual framework to evaluate the impacts of potential oil exposure on marine mammals and applied it to 21 species inhabiting coastal British Columbia (BC), Canada. Oil spill vulnerability was determined by examining both the likelihood of species-specific (individual) oil exposure and the consequent likelihood of population-level effects. Oil exposure pathways, ecology, and physiological characteristics were first used to assign species-specific vulnerability rankings. Baleen whales were found to be highly vulnerable due to blowhole breathing, surface filter feeding, and invertebrate prey. Sea otters (Enhydra lutris) were ranked as highly vulnerable due to their time spent at the ocean surface, dense pelage, and benthic feeding techniques. Species-specific vulnerabilities were considered to estimate the likelihood of population-level effects occurring after oil exposure. Killer whale (Orcinus orca) populations were deemed at highest risk due to small population sizes, complex social structure, long lives, slow reproductive turnover, and dietary specialization. Finally, we related the species-specific and population-level vulnerabilities. In BC, vulnerability was deemed highest for Northern and Southern Resident killer whales and sea otters, followed by Bigg's killer whales and Steller sea lions (Eumetopias jubatus). Our findings challenge the typical "indicator species" approach routinely used and underscore the need to examine marine mammals at a species and population level for risk-based oil spill predictions. This conceptual framework can be combined with spill probabilities and volumes to develop more robust risk assessments and may be applied elsewhere to identify vulnerability themes for marine mammals.

Correlated velocity models as a fundamental unit of animal movement: synthesis and applications

BACKGROUND

Continuous time movement models resolve many of the problems with scaling, sampling, and interpretation that affect discrete movement models. They can, however, be challenging to estimate, have been presented in inconsistent ways, and are not widely used.

METHODS

We review the literature on integrated Ornstein-Uhlenbeck velocity models and propose four fundamental correlated velocity movement models (CVM's): random, advective, rotational, and rotational-advective. The models are defined in terms of biologically meaningful speeds and time scales of autocorrelation. We summarize several approaches to estimating the models, and apply these tools for the higher order task of behavioral partitioning via change point analysis.

RESULTS

An array of simulation illustrate the precision and accuracy of the estimation tools. An analysis of a swimming track of a bowhead whale (Balaena mysticetus) illustrates their robustness to irregular and sparse sampling and identifies switches between slower and faster, and directed vs. random movements. An analysis of a short flight of a lesser kestrel (Falco naumanni) identifies exact moments when switches occur between loopy, thermal soaring and directed flapping or gliding flights.

CONCLUSIONS

We provide tools to estimate parameters and perform change point analyses in continuous time movement models as an R package (smoove). These resources, together with the synthesis, should facilitate the wider application and development of correlated velocity models among movement ecologists.

Maximum-entropy description of animal movement

We introduce a class of maximum-entropy states that naturally includes within it all of the major continuous-time stochastic processes that have been applied to animal movement, including Brownian motion, Ornstein-Uhlenbeck motion, integrated Ornstein-Uhlenbeck motion, a recently discovered hybrid of the previous models, and a new model that describes central-place foraging. We are also able to predict a further hierarchy of new models that will emerge as data quality improves to better resolve the underlying continuity of animal movement. Finally, we also show that Langevin equations must obey a fluctuation-dissipation theorem to generate processes that fall from this class of maximum-entropy distributions when the constraints are purely kinematic.

Investigating behaviour and population dynamics of striped marlin (Kajikia audax) from the southwest Pacific Ocean with satellite tags

Behaviour and distribution of striped marlin within the southwest Pacific Ocean were investigated using electronic tagging data collected from 2005-2008. A continuous-time correlated random-walk Kalman filter was used to integrate double-tagging data exhibiting variable error structures into movement trajectories composed of regular time-steps. This state-space trajectory integration approach improved longitude and latitude error distributions by 38.5 km and 22.2 km respectively. Using these trajectories as inputs, a behavioural classification model was developed to infer when, and where, 'transiting' and 'area-restricted' (ARB) pseudo-behavioural states occurred. ARB tended to occur at shallower depths (108 ± 49 m) than did transiting behaviours (127 ± 57 m). A 16 day post-release period of diminished ARB activity suggests that patterns of behaviour were affected by the capture and/or tagging events, implying that tagged animals may exhibit atypical behaviour upon release. The striped marlin in this study dove deeper and spent greater time at ≥ 200 m depth than those in the central and eastern Pacific Ocean. As marlin reached tropical latitudes (20-21 °S) they consistently reversed directions, increased swimming speed and shifted to transiting behaviour. Reversals in the tropics also coincided with increases in swimming depth, including increased time ≥ 250 m. Our research provides enhanced understanding of the behavioural ecology of striped marlin. This has implications for the effectiveness of spatially explicit population models and we demonstrate the need to consider geographic variation when standardizing CPUE by depth, and provide data to inform natural and recreational fishing mortality parameters.

Hierarchical state-space estimation of leatherback turtle navigation ability

Remotely sensed tracking technology has revealed remarkable migration patterns that were previously unknown; however, models to optimally use such data have developed more slowly. Here, we present a hierarchical Bayes state-space framework that allows us to combine tracking data from a collection of animals and make inferences at both individual and broader levels. We formulate models that allow the navigation ability of animals to be estimated and demonstrate how information can be combined over many animals to allow improved estimation. We also show how formal hypothesis testing regarding navigation ability can easily be accomplished in this framework. Using Argos satellite tracking data from 14 leatherback turtles, 7 males and 7 females, during their southward migration from Nova Scotia, Canada, we find that the circle of confusion (the radius around an animal's location within which it is unable to determine its location precisely) is approximately 96 km. This estimate suggests that the turtles' navigation does not need to be highly accurate, especially if they are able to use more reliable cues as they near their destination. Moreover, for the 14 turtles examined, there is little evidence to suggest that male and female navigation abilities differ. Because of the minimal assumptions made about the movement process, our approach can be used to estimate and compare navigation ability for many migratory species that are able to carry electronic tracking devices.

Continuous-time correlated random walk model for animal telemetry data

We propose a continuous-time version of the correlated random walk model for animal telemetry data. The continuous-time formulation allows data that have been nonuniformly collected over time to be modeled without subsampling, interpolation, or aggregation to obtain a set of locations uniformly spaced in time. The model is derived from a continuous-time Ornstein-Uhlenbeck velocity process that is integrated to form a location process. The continuous-time model was placed into a state-space framework to allow parameter estimation and location predictions from observed animal locations. Two previously unpublished marine mammal telemetry data sets were analyzed to illustrate use of the model, by-products available from the analysis, and different modifications which are possible. A harbor seal data set was analyzed with a model that incorporates the proportion of each hour spent on land. Also, a northern fur seal pup data set was analyzed with a random drift component to account for directed travel and ocean currents.

Inferring resource distributions from Atlantic bluefin tuna movements: an analysis based on net displacement and length of track

We use observed movement tracks of Atlantic bluefin tuna in the Gulf of Maine and mathematical modeling of this movement to identify possible resource patches. We infer bounds on the overall sizes and distribution of such patches, even though they are difficult to quantify by direct observation in situ. To do so, we segment individual fish tracks into intervals of distinct motion types based on the ratio of net displacement to length of track (DeltaD/DeltaL) over a time window Deltat. To find the best segmentation, we optimize the fit of a random-walk movement model to each motion type. We compare results from two distinct movement models: biased turning and biased speed, to check the model-dependence of our inferences, and find that uncertainty in choice of movement model dominates the uncertainties of our conclusions. We find that our data are best described using two motion types: "localized" (DeltaD/DeltaL small) and "long-ranged" (DeltaD/DeltaL large). The biased turning model leads to significantly better resolution of localized movement intervals than the biased speed model. We hypothesize that localized movement corresponds to exploitation of resource patches. Comparison with visual behavior observations made during tracking suggests that many inferred intervals of localized motion do indeed correspond to feeding activity. From our analysis, we estimate that, on average, bluefin tuna in the Gulf of Maine encounter a resource patch every 2h, that those patches have an average radius of 0.7-1.2 km, and that, overall, there are at most 5-9 such patches per 100 km(2) in the region studied.