The relationship between habitat and behaviour provides important information for species management.For large,free roaming,marine animals satellite tags provide high resolution information on movement,but such datase...The relationship between habitat and behaviour provides important information for species management.For large,free roaming,marine animals satellite tags provide high resolution information on movement,but such datasets are restricted due to cost.Extracting additional biologically important information from these data would increase utilisation and value.Several modelling approaches have been developed to identify behavioural states in tracking data.The objective of this study was to evaluate a behavioural state prediction model for blue shark(Prionace glauca)ARGOS surface location-only data.The novel nature of the six SPLASH satellite tags used enabled behavioural events to be identified in blue shark dive data and accurately mapped spatio-temporally along respective surface location-only tracks.Behavioural states modelled along the six surface location-only tracks were then tested against observed behavioural events to evaluate the model’s accuracy.Results showed that the Behavioural Change Point Analysis(BCPA)model augmented with K means clustering analysis performed well for predicting foraging behaviour(correct 86%of the time).Prediction accuracy was lower for searching(52%)and travelling(63%)behaviour,likely related to the numerical dominance of foraging events in dive data.The model’s validation for predicting foraging behaviour justified its application to nine additional surface location-only(SPOT tag)tracks,substantially increasing the utilisation of expensive and rare data.Results enabled the critical behavioural state of foraging,to be mapped throughout the entire home range of blue sharks,allowing drivers of critical habitat to be investigated.This validation strengthens the use of such modelling to interpret historic and future datasets,for blue sharks but also other species,contributing to conservational management.展开更多
The structure of the cerebellar cortex is remarkably similar across vertebrate phylogeny. It is well developed in basaljawed fishes, such as sharks and rays with many of the same cell types and organizational features...The structure of the cerebellar cortex is remarkably similar across vertebrate phylogeny. It is well developed in basaljawed fishes, such as sharks and rays with many of the same cell types and organizational features found in other vertebrategroups, including mammals. In particular, the lattice-like organization of cerebellar cortex (with a molecular layer of parallel fibres,interneurons, spiny Purkinje cell dendrites, and climbing fires) is a common defining characteristic. In addition to the cerebellarcortex, fishes and aquatic amphibians have a variety of cerebellum-like structures in the dorso-lateral wall of the hindbrain.These structures are adjacent to, and in part, contiguous with, the cerebellum. They derive their cerebellum-like name from thepresence of a molecular layer of parallel fibers and inhibitory interneurons, which has striking organizational similarities to themolecular layer of the cerebellar cortex. However, these structures also have characteristics which differ from the cerebellum. Forexample, cerebellum-like structures do not have climbing fibres, and they are clearly sensory. They receive direct afferent inputfrom peripheral sensory receptors and relay their outputs to midbrain sensory areas. As a consequence of this close sensory association,and the ability to characterise their signal processing in a behaviourally relevant context, good progress has been made indetermining the fundamental processing algorithm in cerebellar-like structures. In particular, we have come to understand thecontribution to signal processing made by the molecular layer, which provides an adaptive filter to cancel self-generated noise inelectrosensory and lateral line systems. Given the fundamental similarities of the molecular layer across these structures, coupledwith evidence that cerebellum-like structures may have been the evolutionary antecedent of the cerebellum, we address the question:do both share the same functional algorithm? [Current Zoology 56 (3): 277-284, 2010].展开更多
文摘The relationship between habitat and behaviour provides important information for species management.For large,free roaming,marine animals satellite tags provide high resolution information on movement,but such datasets are restricted due to cost.Extracting additional biologically important information from these data would increase utilisation and value.Several modelling approaches have been developed to identify behavioural states in tracking data.The objective of this study was to evaluate a behavioural state prediction model for blue shark(Prionace glauca)ARGOS surface location-only data.The novel nature of the six SPLASH satellite tags used enabled behavioural events to be identified in blue shark dive data and accurately mapped spatio-temporally along respective surface location-only tracks.Behavioural states modelled along the six surface location-only tracks were then tested against observed behavioural events to evaluate the model’s accuracy.Results showed that the Behavioural Change Point Analysis(BCPA)model augmented with K means clustering analysis performed well for predicting foraging behaviour(correct 86%of the time).Prediction accuracy was lower for searching(52%)and travelling(63%)behaviour,likely related to the numerical dominance of foraging events in dive data.The model’s validation for predicting foraging behaviour justified its application to nine additional surface location-only(SPOT tag)tracks,substantially increasing the utilisation of expensive and rare data.Results enabled the critical behavioural state of foraging,to be mapped throughout the entire home range of blue sharks,allowing drivers of critical habitat to be investigated.This validation strengthens the use of such modelling to interpret historic and future datasets,for blue sharks but also other species,contributing to conservational management.
文摘The structure of the cerebellar cortex is remarkably similar across vertebrate phylogeny. It is well developed in basaljawed fishes, such as sharks and rays with many of the same cell types and organizational features found in other vertebrategroups, including mammals. In particular, the lattice-like organization of cerebellar cortex (with a molecular layer of parallel fibres,interneurons, spiny Purkinje cell dendrites, and climbing fires) is a common defining characteristic. In addition to the cerebellarcortex, fishes and aquatic amphibians have a variety of cerebellum-like structures in the dorso-lateral wall of the hindbrain.These structures are adjacent to, and in part, contiguous with, the cerebellum. They derive their cerebellum-like name from thepresence of a molecular layer of parallel fibers and inhibitory interneurons, which has striking organizational similarities to themolecular layer of the cerebellar cortex. However, these structures also have characteristics which differ from the cerebellum. Forexample, cerebellum-like structures do not have climbing fibres, and they are clearly sensory. They receive direct afferent inputfrom peripheral sensory receptors and relay their outputs to midbrain sensory areas. As a consequence of this close sensory association,and the ability to characterise their signal processing in a behaviourally relevant context, good progress has been made indetermining the fundamental processing algorithm in cerebellar-like structures. In particular, we have come to understand thecontribution to signal processing made by the molecular layer, which provides an adaptive filter to cancel self-generated noise inelectrosensory and lateral line systems. Given the fundamental similarities of the molecular layer across these structures, coupledwith evidence that cerebellum-like structures may have been the evolutionary antecedent of the cerebellum, we address the question:do both share the same functional algorithm? [Current Zoology 56 (3): 277-284, 2010].
