scholarly journals DNA diet profiles with high‐resolution animal tracking data reveal levels of prey selection relative to habitat choice in a crepuscular insectivorous bird

2020 ◽  
Vol 10 (23) ◽  
pp. 13044-13056
Author(s):  
Ruben Evens ◽  
Greg Conway ◽  
Kirsty Franklin ◽  
Ian Henderson ◽  
Jennifer Stockdale ◽  
...  
Keyword(s):  
High Resolution ◽  
Prey Selection ◽  
Habitat Choice ◽  
Tracking Data ◽  
Animal Tracking ◽  
Insectivorous Bird

scholarly journals BonZeb: open-source, modular software tools for high-resolution zebrafish tracking and analysis

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Nicholas C. Guilbeault ◽  
Jordan Guerguiev ◽  
Michael Martin ◽  
Isabelle Tate ◽  
Tod R. Thiele
Keyword(s):  
High Resolution ◽  
Open Loop ◽  
Ease Of Use ◽  
Neural Mechanisms ◽  
Animal Tracking ◽  
Larval Zebrafish ◽  
Optogenetic Stimulation ◽  
Modular Software ◽  
Behavioral Tracking ◽  
Experimental Protocols

AbstractWe present BonZeb—a suite of modular Bonsai packages which allow high-resolution zebrafish tracking with dynamic visual feedback. Bonsai is an increasingly popular software platform that is accelerating the standardization of experimental protocols within the neurosciences due to its speed, flexibility, and minimal programming overhead. BonZeb can be implemented into novel and existing Bonsai workflows for online behavioral tracking and offline tracking with batch processing. We demonstrate that BonZeb can run a variety of experimental configurations used for gaining insights into the neural mechanisms of zebrafish behavior. BonZeb supports head-fixed closed-loop and free-swimming virtual open-loop assays as well as multi-animal tracking, optogenetic stimulation, and calcium imaging during behavior. The combined performance, ease of use and versatility of BonZeb opens new experimental avenues for researchers seeking high-resolution behavioral tracking of larval zebrafish.

scholarly journals Analysis of local habitat selection and large-scale attraction/avoidance based on animal tracking data: is there a single best method?

2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Moritz Mercker ◽  
Philipp Schwemmer ◽  
Verena Peschko ◽  
Leonie Enners ◽  
Stefan Garthe
Keyword(s):  
Habitat Selection ◽  
Large Scale ◽  
Error Rates ◽  
Process Models ◽  
Statistical Hypothesis ◽  
Type I ◽  
Tracking Data ◽  
Selection Models ◽  
Animal Tracking ◽  
Step Selection

Abstract Background New wildlife telemetry and tracking technologies have become available in the last decade, leading to a large increase in the volume and resolution of animal tracking data. These technical developments have been accompanied by various statistical tools aimed at analysing the data obtained by these methods. Methods We used simulated habitat and tracking data to compare some of the different statistical methods frequently used to infer local resource selection and large-scale attraction/avoidance from tracking data. Notably, we compared spatial logistic regression models (SLRMs), spatio-temporal point process models (ST-PPMs), step selection models (SSMs), and integrated step selection models (iSSMs) and their interplay with habitat and animal movement properties in terms of statistical hypothesis testing. Results We demonstrated that only iSSMs and ST-PPMs showed nominal type I error rates in all studied cases, whereas SSMs may slightly and SLRMs may frequently and strongly exceed these levels. iSSMs appeared to have on average a more robust and higher statistical power than ST-PPMs. Conclusions Based on our results, we recommend the use of iSSMs to infer habitat selection or large-scale attraction/avoidance from animal tracking data. Further advantages over other approaches include short computation times, predictive capacity, and the possibility of deriving mechanistic movement models.

scholarly journals Translating Marine Animal Tracking Data into Conservation Policy and Management

2019 ◽  
Vol 34 (5) ◽  
pp. 459-473 ◽  
Author(s):  
Graeme C. Hays ◽  
Helen Bailey ◽  
Steven J. Bograd ◽  
W. Don Bowen ◽  
Claudio Campagna ◽  
...  
Keyword(s):  
Conservation Policy ◽  
Marine Animal ◽  
Tracking Data ◽  
Animal Tracking

Data fusion in buildings: Synthesis of high-resolution IEQ and occupant tracking data

2021 ◽  
pp. 146047
Author(s):  
Brett Pollard ◽  
Fabian Held ◽  
Lina Engelen ◽  
Lauren Powell ◽  
Richard de Dear
Keyword(s):  
High Resolution ◽  
Data Fusion ◽  
Tracking Data

scholarly journals Moderating Argos location errors in animal tracking data

2012 ◽  
Vol 3 (6) ◽  
pp. 999-1007 ◽  
Author(s):  
David C. Douglas ◽  
Rolf Weinzierl ◽  
Sarah C. Davidson ◽  
Roland Kays ◽  
Martin Wikelski ◽  
...  
Keyword(s):  
Tracking Data ◽  
Animal Tracking ◽  
Location Errors

scholarly journals BonZeb: Open-source, modular software tools for high-resolution zebrafish tracking and analysis

2021 ◽  
Author(s):  
Nicholas C. Guilbeault ◽  
Jordan Guerguiev ◽  
Michael Martin ◽  
Isabelle Tate ◽  
Tod R. Thiele
Keyword(s):  
High Resolution ◽  
Open Loop ◽  
Ease Of Use ◽  
Neural Mechanisms ◽  
Animal Tracking ◽  
Larval Zebrafish ◽  
Optogenetic Stimulation ◽  
Modular Software ◽  
Behavioral Tracking ◽  
Experimental Protocols

AbstractWe present BonZeb – a suite of modular Bonsai packages which allow high-resolution zebrafish tracking with dynamic visual feedback. Bonsai is an increasingly popular software platform that is accelerating the standardization of experimental protocols within the neurosciences due to its speed, flexibility, and minimal programming overhead. BonZeb can be implemented into novel and existing Bonsai workflows for online behavioral tracking and offline tracking with batch processing. We demonstrate that BonZeb can run a variety of experimental configurations used for gaining insights into the neural mechanisms of zebrafish behavior. BonZeb supports head-fixed closed-loop and free-swimming virtual open-loop assays as well as multi-animal tracking, optogenetic stimulation, and calcium imaging during behavior. The combined performance, ease of use and versatility of BonZeb opens new experimental avenues for researchers seeking high-resolution behavioral tracking of larval zebrafish.

scholarly journals Movebank

2018 ◽  
pp. 24-27 ◽  
Author(s):  
Tomasz Mrozewski
Keyword(s):  
Data Management ◽  
Management Tool ◽  
Special Focus ◽  
Tracking Data ◽  
Web Based ◽  
Data Archiving ◽  
Animal Tracking

Movebank is a web-based data management tool and data archiving repository for animal tracking data. Its special focus enables specialized search and discovery tools for users searching for animal tracking data. However, there are some idiosyncrasies with the site and researcher update is not universal.

scholarly journals Scale-insensitive estimation of speed and distance traveled from animal tracking data

2019 ◽  
Vol 7 (1) ◽  
Author(s):  
Michael J. Noonan ◽  
Christen H. Fleming ◽  
Thomas S. Akre ◽  
Jonathan Drescher-Lehman ◽  
Eliezer Gurarie ◽  
...  
Keyword(s):  
Measurement Error ◽  
Confidence Intervals ◽  
Continuous Time ◽  
Estimation Method ◽  
Sampling Frequency ◽  
Measurement Scale ◽  
Tracking Data ◽  
Animal Tracking ◽  
Time Movement ◽  
Sampling Schedule

Abstract Background Speed and distance traveled provide quantifiable links between behavior and energetics, and are among the metrics most routinely estimated from animal tracking data. Researchers typically sum over the straight-line displacements (SLDs) between sampled locations to quantify distance traveled, while speed is estimated by dividing these displacements by time. Problematically, this approach is highly sensitive to the measurement scale, with biases subject to the sampling frequency, the tortuosity of the animal’s movement, and the amount of measurement error. Compounding the issue of scale-sensitivity, SLD estimates do not come equipped with confidence intervals to quantify their uncertainty. Methods To overcome the limitations of SLD estimation, we outline a continuous-time speed and distance (CTSD) estimation method. An inherent property of working in continuous-time is the ability to separate the underlying continuous-time movement process from the discrete-time sampling process, making these models less sensitive to the sampling schedule when estimating parameters. The first step of CTSD is to estimate the device’s error parameters to calibrate the measurement error. Once the errors have been calibrated, model selection techniques are employed to identify the best fit continuous-time movement model for the data. A simulation-based approach is then employed to sample from the distribution of trajectories conditional on the data, from which the mean speed estimate and its confidence intervals can be extracted. Results Using simulated data, we demonstrate how CTSD provides accurate, scale-insensitive estimates with reliable confidence intervals. When applied to empirical GPS data, we found that SLD estimates varied substantially with sampling frequency, whereas CTSD provided relatively consistent estimates, with often dramatic improvements over SLD. Conclusions The methods described in this study allow for the computationally efficient, scale-insensitive estimation of speed and distance traveled, without biases due to the sampling frequency, the tortuosity of the animal’s movement, or the amount of measurement error. In addition to being robust to the sampling schedule, the point estimates come equipped with confidence intervals, permitting formal statistical inference. All the methods developed in this study are now freely available in the package or the point-and-click web based graphical user interface.

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