2,887 research outputs found
Providence College Faculty Author Series 2012-2013: Dr. Adrian Weimer
No full textDr. Adrian Weimer (History, Providence College) discusses her new book Martyrs\u27 Mirror: Persecution and Holiness in Early New England and the cultural importance of martyrdom within Colonial America
Providence College Faculty Author Series 2012-2013: Dr. Adrian Weimer
No full textDr. Adrian Weimer (History, Providence College) discusses her new book Martyrs\u27 Mirror: Persecution and Holiness in Early New England and the cultural importance of martyrdom within Colonial America
Adrian Matejka, 34th Annual ODU Literary Festival
No full textAdrian Matejka is the author of The Devil’s Garden and Mixology, which was a winner of the 2008 National Poetry Series. He is the recipient of two Illinois Arts Council Literary Awards and fellowships from Cave Canem and the Lannan Foundation. His work has been featured in American Poetry Review, The Best American Poetry 2010, and Ploughshares, among other journals and anthologies. He teaches at Southern Illinois University Edwardsville
Evaluating the constraints governing activity patterns of a coastal marine top predator
No full textByrnes EE, Daly R, Leos-Barajas V, Langrock R, Gleiss AC. Evaluating the constraints governing activity patterns of a coastal marine top predator. Marine Biology. 2021;168(1): 11
Making overall dynamic body acceleration work: on the theory of acceleration as a proxy for energy expenditure
No full textGleiss, A., Wilson, R. P., Shepard, E. L. C. (2011). Making dynamic body acceleration work: on the theory of acceleration as a proxy for energy expenditure. Methods in Ecol. Evol. 2; 23-33
The physiological and ecological effects of temperature and oxygen on an estuarine fish
No full textTemperature and oxygen are the primary abiotic variables controlling and limiting the metabolic capacity of fishes. This has been attributed to the strong influence of each variable on aerobic scope; the capacity of organisms to distribute energy across physiological functions. This study incorporates laboratory quantification of aerobic scope with field acoustic accelerometry to determine the relative importance of both temperature and oxygen to an estuarine teleost, the black bream (Acanthopagrus butcheri). In respirometry experiments, A. butcheri were found to display remarkably high thermal tolerance, maintaining stable aerobic scope across a 9°C thermal window. However, their aerobic scope was heavily reduced with reductions in oxygen availability, reaching a critical oxygen level at ~30% DO. The ecological importance of this was quantified in wild fishes as, whilst temperature displayed little effect on movement dynamics, the presence of hypoxia resulted in significant habitat compression, with bream restricted to shallow, oxygenated microhabitats. Under such compression, bream are likely to be at increased risk of predation and the negative effects of increased density, including competition and disease. These results provide empirical evidence for the hypothesis that hypoxia is a key driver of A. butcheri growth rates within the Swan River Estuary. It also highlights the population’s vulnerability to hypoxic episodes, which are expected to increase in both extent and frequency as a result of anthropogenically-enhanced eutrophication and climate change
Multi-Channel Data-Logging: Towards Determination of Behaviour and Metabolic Rate in Free-Swimming Sharks
No full textGleiss, A. C., Gruber, S. H., Wilson, R. P. (in press). Multi-channel data-logging; Towards determination of behaviour and metabolic rate in free-swimming sharks. In; Reviews: Methods and technologies in Fish Biology and Fisheries. (eds Nielsen, J. et al.). (In press)
Behavioural and ecological energetics of elasmobranchs
No full textUnderstanding the capacity of animals to behaviourally and physiologically cope with environmental change is becoming crucial for developing process-based approaches to wildlife management. Energy is one of the most basic resources of animals, and the need to acquire energy to fuel daily activities, growth, and reproduction drives many aspects of ecology. Decades of comparative physiology work have shown the metabolic rates of animals systematically vary based on a suite of organismal traits and environmental factors. However, it is poorly understood how such metabolic variation influences animal’s behaviours in the wild, and as such, their capacity to adapt to environmental change. Recently, novel technologies have allowed physiological research to be conducted in the field, facilitating investigations of how animal physiology drives the behaviours of animals in natural settings. In this thesis, I present a number of studies that investigate how the three primary factors governing the physiology of ectotherms, namely body size, temperature, and habitat shape an animals’ capacity to cope with anthropogenic threats and environmental change. Using a combination of respirometry experiments, on-animal motion-sensing, biotelemetry tracking tools, and sophisticated behavioural modelling, I examined how changes in metabolism drive the behavioural and spatial ecology of sharks. First, I demonstrate how local environmental pressures place constraints on a species ability to expand home ranges to escape local resource limitations. Second, investigations of the drivers of behavioural and activity patterns of sharks revealed that within these spatial constraints, sharks’ ability to adjust their foraging and resting patterns are relatively limited. While metabolic changes associated with increased environmental temperatures pressure sharks to forage more often, functional traits limit their ability to forage outside short temporal windows. Taken together, these results indicate that physiological requirements place substantial constraints on the behavioural flexibility of sharks, which will likely have serious impacts on their life history, fitness, and survival in the face of continued environmental change. Lastly, I discuss the implications of physiological and behavioural constraints for the management of threatened shark population
Establishing best practice for the classification of shark behaviour from bio-logging data
No full textUnderstanding the behaviours of free-ranging animals over biologically meaningful time scales (e.g. diel, tidal, lunar, seasonal, annual) gives important insights into their ecology. Bio-logging tools allow the remote study of elusive or inaccessible animals by recording high resolution multi-channel movement data, however archival device recording duration is limited to relatively short temporal-scales by memory and battery capacity. Machine learning (ML) is becoming common for automatic classification of behaviours from large data sets. This thesis develops a framework for the programming of bio-loggers for the classification of shark behaviour through the optimisation of sampling frequency (Chapter 2) and the choice of movement sensor (Chapter 3).
The effects of sampling frequency on behavioural classification were assessed using data published in a previous study collected from accelerometer equipped juvenile lemon sharks (Negaprion brevirostris) during captive trials in Bimini, Bahamas. The impacts of different combinations of movement sensors (accelerometer, magnetometer and gyroscope) were assessed using data collected from sub adult sicklefin lemon sharks (Negaprion acutidens). Sharks were equipped with multi-sensor devices recording acceleration, angular rotation and angular velocity during captive trials at St Joseph Atoll, Seychelles. Catalogues of discrete classes of behaviours (ethograms) were developed by observing sharks during captive trials.
Behaviours (swim, rest, burst, chafe, headshake) were classified using a random forest ML algorithm with predictor variables extracted from the ground-truthed data. A range of sampling frequencies (30, 15, 10, 5, 3 and 1 Hz) and combinations of movement sensors were tested. For each dataset, a confusion matrix was determined from model predictions for calculation and comparison of evaluation metrics. Classifier performance was best described by the class or macro F- score, a measure of model performance, one indicating perfect classification and zero indicating no classification.
As sampling frequency decreased, classifier performance decreased. Best overall classification was achieved at 30 Hz (F- score >0.790), although 5 Hz was appropriate for classification of swim and rest (>0.964). Behaviours characterised by complex movements (headshake, burst, chafe) were best classified at 30 Hz (0.535- 0.846). Classification of behaviours was best with a tri-sensor combination (0.597), although incorporating an additional sensor (magnetometer or gyroscope) resulted in little increase in classifier performance compared to using an accelerometer alone (0.590 compared to 0.535 respectively).
These results demonstrate the ideal sampling frequencies and movement sensors for best-practice programming of bio-logging devices for classifying shark behaviour over extended durations. This thesis will inform future studies incorporating behaviour classification, enabling improved classifier performance and extending recording duration of bio-logging devices
Testing the efficacy of unsupervised machine learning techniques to infer shark behaviour from accelerometry data
No full textBiologging is becoming a powerful tool in the study of free-ranging animal behaviour. Accelerometers play an important role particularly for cryptic aquatic species by facilitating the measurement of animal body movement and thus, behaviour. However, our ability to collect large and complex data sets is surpassing our ability to analyse them, prompting a need to develop methodologies for automated behavioural classification. Unsupervised machine learning is particularly useful for behavioural classification where direct observations to link patterns of acceleration to animal behaviour are not always attainable. We tested the ability of unsupervised machine learning to classify shark behaviour by applying two common unsupervised approaches, K-means clustering and Hidden Markov models (HMM), to ground-truthed accelerometry data collected from captive juvenile lemon sharks (Negaprion brevirostris). Although K-means clustering demonstrated low classification performance, the HMM performed well in distinguishing broad categories in behaviour (resting vs swimming), but generally had poor performance in rare and more complex behaviours (e.g. prey handling or burst swimming). This study is one of the first to validate the use of common unsupervised machine learning algorithms and lends further support to their use in the study of behaviour in free-ranging animals, while also showing limitations in their ability to discern complex behaviours
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