scholarly journals Satellite Tracking and Analysis Tool (STAT): an integrated system for archiving, analyzing and mapping animal tracking data

2005 ◽  
Vol 301 ◽  
pp. 1-7 ◽  
Author(s):  
MS Coyne ◽  
BJ Godley
Keyword(s):  
Satellite Tracking ◽  
Integrated System ◽  
Analysis Tool ◽  
Tracking Data ◽  
Animal Tracking

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

A method to determine regional lunar gravity fields from earth-based satellite tracking data

2005 ◽  
Vol 53 (13) ◽  
pp. 1331-1340 ◽  
Author(s):  
S. Goossens ◽  
P.N.A.M. Visser ◽  
B.A.C. Ambrosius
Keyword(s):  
Satellite Tracking ◽  
Tracking Data ◽  
Lunar Gravity ◽  
Gravity Fields

Reliable methods for identifying animal deaths in GPS ‐ and satellite‐tracking data: Review, testing, and calibration

2018 ◽  
Vol 56 (3) ◽  
pp. 562-572 ◽  
Author(s):  
Fabrizio Sergio ◽  
Alessandro Tanferna ◽  
Julio Blas ◽  
Guillermo Blanco ◽  
Fernando Hiraldo
Keyword(s):  
Satellite Tracking ◽  
Tracking Data ◽  
Reliable Methods

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

Earth's Gravity Field Derived from Grace Satellite Tracking Data

2006 ◽  
Vol 49 (3) ◽  
pp. 651-656 ◽  
Author(s):  
Xu-Hua ZHOU ◽  
Houtse HSU ◽  
Bin WU ◽  
Bi-Bo PENG ◽  
Yang LU
Keyword(s):  
Gravity Field ◽  
Satellite Tracking ◽  
Tracking Data ◽  
Earth’S Gravity Field ◽  
Grace Satellite
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