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Chris Field - One of the best experts on this subject based on the ideXlab platform.
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hierarchical state space estimation of leatherback turtle navigation ability
PLOS ONE, 2010Co-Authors: Joanna Mills Flemming, Ian D. Jonsen, Ransom A Myers, Chris FieldAbstract: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.
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how well can animals navigate estimating the Circle of Confusion from tracking data
Environmetrics, 2006Co-Authors: Mills J Flemming, Ian D. Jonsen, Chris Field, Michael C James, Ransom A MyersAbstract:State-space models have recently been shown to effectively model animal movement. In this paper we illustrate how such models can be used to improve our knowledge of animal navigation ability, something which is poorly understood. This work is of great interest when modeling the behavior of animals that are migrating, often over tremendously large distances. We use the term Circle of Confusion, first proposed by Kendall (1974), to describe the general inability of an animal to know its location precisely. Our modeling strategy enables us to statistically describe the Circle of Confusion associated with any animal movements where departure and destination points are known. For illustration, we use ARGOS satellite telemetry of leatherback turtles migrating over a distance of approximately 4000 km in the Atlantic Ocean. Robust features of the model enable one to deal with outlying observations, highly characteristic of these types of data. Although specifically designed for data obtained using satellite telemetry, our approach is generalizable to other common kinds of movement data such as archival tag data. Copyright © 2005 John Wiley & Sons, Ltd.
Ian D. Jonsen - One of the best experts on this subject based on the ideXlab platform.
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Hierarchical State-Space Estimation of Leatherback Turtle Navigation Ability
2016Co-Authors: Ian D. Jonsen, Ransom A Myers, Joanna Mills Flemming, Christopher A. FieldAbstract: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 tha
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hierarchical state space estimation of leatherback turtle navigation ability
PLOS ONE, 2010Co-Authors: Joanna Mills Flemming, Ian D. Jonsen, Ransom A Myers, Chris FieldAbstract: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.
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how well can animals navigate estimating the Circle of Confusion from tracking data
Environmetrics, 2006Co-Authors: Mills J Flemming, Ian D. Jonsen, Chris Field, Michael C James, Ransom A MyersAbstract:State-space models have recently been shown to effectively model animal movement. In this paper we illustrate how such models can be used to improve our knowledge of animal navigation ability, something which is poorly understood. This work is of great interest when modeling the behavior of animals that are migrating, often over tremendously large distances. We use the term Circle of Confusion, first proposed by Kendall (1974), to describe the general inability of an animal to know its location precisely. Our modeling strategy enables us to statistically describe the Circle of Confusion associated with any animal movements where departure and destination points are known. For illustration, we use ARGOS satellite telemetry of leatherback turtles migrating over a distance of approximately 4000 km in the Atlantic Ocean. Robust features of the model enable one to deal with outlying observations, highly characteristic of these types of data. Although specifically designed for data obtained using satellite telemetry, our approach is generalizable to other common kinds of movement data such as archival tag data. Copyright © 2005 John Wiley & Sons, Ltd.
Joanna Mills Flemming - One of the best experts on this subject based on the ideXlab platform.
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Hierarchical State-Space Estimation of Leatherback Turtle Navigation Ability
2016Co-Authors: Ian D. Jonsen, Ransom A Myers, Joanna Mills Flemming, Christopher A. FieldAbstract: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 tha
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hierarchical state space estimation of leatherback turtle navigation ability
PLOS ONE, 2010Co-Authors: Joanna Mills Flemming, Ian D. Jonsen, Ransom A Myers, Chris FieldAbstract: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.
Ransom A Myers - One of the best experts on this subject based on the ideXlab platform.
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Hierarchical State-Space Estimation of Leatherback Turtle Navigation Ability
2016Co-Authors: Ian D. Jonsen, Ransom A Myers, Joanna Mills Flemming, Christopher A. FieldAbstract: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 tha
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hierarchical state space estimation of leatherback turtle navigation ability
PLOS ONE, 2010Co-Authors: Joanna Mills Flemming, Ian D. Jonsen, Ransom A Myers, Chris FieldAbstract: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.
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how well can animals navigate estimating the Circle of Confusion from tracking data
Environmetrics, 2006Co-Authors: Mills J Flemming, Ian D. Jonsen, Chris Field, Michael C James, Ransom A MyersAbstract:State-space models have recently been shown to effectively model animal movement. In this paper we illustrate how such models can be used to improve our knowledge of animal navigation ability, something which is poorly understood. This work is of great interest when modeling the behavior of animals that are migrating, often over tremendously large distances. We use the term Circle of Confusion, first proposed by Kendall (1974), to describe the general inability of an animal to know its location precisely. Our modeling strategy enables us to statistically describe the Circle of Confusion associated with any animal movements where departure and destination points are known. For illustration, we use ARGOS satellite telemetry of leatherback turtles migrating over a distance of approximately 4000 km in the Atlantic Ocean. Robust features of the model enable one to deal with outlying observations, highly characteristic of these types of data. Although specifically designed for data obtained using satellite telemetry, our approach is generalizable to other common kinds of movement data such as archival tag data. Copyright © 2005 John Wiley & Sons, Ltd.
Ivo Ihrke - One of the best experts on this subject based on the ideXlab platform.
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BlurTags: spatially varying PSF estimation with out-of-focus patterns
2012Co-Authors: Alexander Reuter, Hans-peter Seidel, Ivo IhrkeAbstract:Current research is targeting the estimation and correction of lens imperfections. often modeled as a set of spatially varying point spread functions (PSFs). One way to measure these PSFs is their calibration with checkerboard patterns. Previous work, however, does not fully exploit all benefits of using a checkerboard. In particular, we show in this paper that the pose of the checkerboard with respect to the camera can be exploited to yield information on the Circle of Confusion, and thus the image blur of an ideal camera. By removing this expected blur, we can estimate residual PSFs that are due to the deviation of the optical system from a thin-lens model. The residual PSFs can then be used to sharpen images at comparable lens settings. Practical side effects of our method are the design of a self-identifying pattern that can be robustly detected even in the case of image blur, and a corresponding algorithm for its detection.