David Slip

High sea surface temperatures driven by a strengthening current reduce foraging success by penguins

The world’s oceans are undergoing rapid, regionally specific warming. Strengthening western boundary currents play a role in this phenomenon, with sea surface temperatures (SST) in their paths rising faster than the global average. To understand how dynamic oceanography influences food availability in these ocean warming “hotspots”, we use a novel prey capture signature derived from accelerometry to understand how the warm East Australian Current shapes foraging success by a meso-predator, the little penguin. This seabird feeds on low trophic level species that are sensitive to environmental change. We found that in 2012, prey capture success by penguins was high when SST was low relative to the long-term mean. In 2013 prey capture success was low, coincident with an unusually strong penetration of warm water. Overall there was an optimal temperature range for prey capture around 19–21 °C, with lower success at both lower and higher temperatures, mirroring published relationships between commercial sardine catch and SST. Spatially, higher SSTs corresponded to a lower probability of penguins using an area, and lower prey capture success. These links between high SST and reduced prey capture success by penguins suggest negative implications for future resource availability in a system dominated by a strengthening western boundary current.

Super machine learning: improving accuracy and reducing variance of behaviour classification from accelerometry

BackgroundSemi-automating the analyses of accelerometry data makes it possible to synthesize large data sets. However, when constructing activity budgets from accelerometry data, there are many methods to extract, analyse and report data and results. For instance, machine learning is a robust approach to classifying data. We used a new method, super learning, that combines base learners (different machine learning methods) in an optimal manner to achieve overall improved accuracy. Other facets of super learning include the number of behavioural categories to predict, the number of epochs (sample window size) used to split data for training and testing and the parameters on which to train the models.ResultsThe super learner accurately classified behaviour categories with higher accuracy and lower variance than comparative models. For all models tested, using four behaviours, in comparison with six, achieved higher rates of accuracy. The number of epochs chosen also affected the accuracy with smaller epochs (7 and 13) performing better than longer epochs (25 and 75).ConclusionsCorrect model selection, training and testing are imperative to creating reliable and valid classification models. To do so means model fitting must use a wide array of selection criteria. We evaluated a number of these including model, number of behaviours to classify and epoch length and then used a parameter grid search to implement the models. We found that all criteria tested contributed to the models’ overall accuracies. Fewer behaviour categories and shorter epoch length improved the performance of all models tested. The super learner classified behaviours with higher accuracy and lower variance than other models tested. However, when using this model, users need to consider the additional human and computational time required for implementation. Machine learning is a powerful method for classifying the behaviour of animals from accelerometers. Care and consideration of the modelling parameters evaluated in this study are essential when using this type of statistical analysis.

Seeing It All: Evaluating Supervised Machine Learning Methods for the Classification of Diverse Otariid Behaviours

Constructing activity budgets for marine animals when they are at sea and cannot be directly observed is challenging, but recent advances in bio-logging technology offer solutions to this problem. Accelerometers can potentially identify a wide range of behaviours for animals based on unique patterns of acceleration. However, when analysing data derived from accelerometers, there are many statistical techniques available which when applied to different data sets produce different classification accuracies. We investigated a selection of supervised machine learning methods for interpreting behavioural data from captive otariids (fur seals and sea lions). We conducted controlled experiments with 12 seals, where their behaviours were filmed while they were wearing 3-axis accelerometers. From video we identified 26 behaviours that could be grouped into one of four categories (foraging, resting, travelling and grooming) representing key behaviour states for wild seals. We used data from 10 seals to train four predictive classification models: stochastic gradient boosting (GBM), random forests, support vector machine using four different kernels and a baseline model: penalised logistic regression. We then took the best parameters from each model and cross-validated the results on the two seals unseen so far. We also investigated the influence of feature statistics (describing some characteristic of the seal), testing the models both with and without these. Cross-validation accuracies were lower than training accuracy, but the SVM with a polynomial kernel was still able to classify seal behaviour with high accuracy (>70%). Adding feature statistics improved accuracies across all models tested. Most categories of behaviour -resting, grooming and feeding—were all predicted with reasonable accuracy (52–81%) by the SVM while travelling was poorly categorised (31–41%). These results show that model selection is important when classifying behaviour and that by using animal characteristics we can strengthen the overall accuracy.

Swimming metabolic rates vary by sex and development stage, but not by species, in three species of Australian otariid seals

Physiology may limit the ability for marine mammals to adapt to changing environments. Depth and duration of foraging dives are a function of total available oxygen stores, which theoretically increase as animals grow, and metabolic costs. To evaluate how physiology may influence the travelling costs for seals to foraging patches in the wild, we measured metabolic rates of a cross-section of New Zealand fur seals, Australian fur seals and Australian sea lions representing different foraging strategies, development stages, sexes and sizes. We report values for standard metabolic rate, active metabolic rate (obtained from submerged swimming), along with estimates of cost of transport (COT), measured via respirometry. We found a decline in mass-specific metabolic rate with increased duration of submerged swimming. For most seals mass-specific metabolic rate increased with speed and for all seals mass-specific COT decreased with speed. Mass-specific metabolic rate was higher for subadult than adult fur seals and sea lions, corresponding to an overall higher minimum COT. Some sex differences were also apparent, such that female Australian fur seals and Australian sea lions had higher mass-specific metabolic rates than males. There were no species differences in standard or active metabolic rates for adult males or females. The seals in our study appear to operate at their physiological optimum during submerged swimming. However, the higher metabolic rates of young and female fur seals and sea lions may limit their scope for increasing foraging effort during times of resource limitation.

Intrinsic and extrinsic influences on standard metabolic rates of three species of Australian otariid

Abstract This paper investigates intrinsic and extrinsic influences on otariid energetics. We conducted metabolic experiments with three species of Australian otariid and found that sex and water temperature, but not species, significantly influenced metabolic rates. These findings have implications for understanding the physiological capability of otariids to adapt to changing environments.

Proxies of energy expenditure for marine mammals: an experimental test of "the time trap"

Direct measures of energy expenditure are difficult to obtain in marine mammals, and accelerometry may be a useful proxy. Recently its utility has been questioned as some analyses derived their measure of activity level by calculating the sum of accelerometry-based values and then comparing this summation to summed (total) energy expenditure (the so-called “time trap”). To test this hypothesis, we measured oxygen consumption of captive fur seals and sea lions wearing accelerometers during submerged swimming and calculated total and rate of energy expenditure. We compared these values with two potential proxies of energy expenditure derived from accelerometry data: flipper strokes and dynamic body acceleration (DBA). Total number of strokes, total DBA, and submergence time all predicted total oxygen consumption

Understanding meta-population trends of the Australian fur seal, with insights for adaptive monitoring

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Recent prey capture experience and dynamic habitat quality mediate short-term foraging site fidelity in a seabird

(sVO2 ml kg−1). However, both total DBA and total number of strokes were correlated with submergence time. Neither stroke rate nor mean DBA could predict the rate of oxygen consumption (