Literature DB >> 16354784

Interpolation of animal tracking data in a fluid environment.

Yann Tremblay1, Scott A Shaffer, Shannon L Fowler, Carey E Kuhn, Birgitte I McDonald, Michael J Weise, Charle-André Bost, Henri Weimerskirch, Daniel E Crocker, Michael E Goebel, Daniel P Costa.   

Abstract

Interpolation of geolocation or Argos tracking data is a necessity for habitat use analyses of marine vertebrates. In a fluid marine environment, characterized by curvilinear structures, linearly interpolated track data are not realistic. Based on these two facts, we interpolated tracking data from albatrosses, penguins, boobies, sea lions, fur seals and elephant seals using six mathematical algorithms. Given their popularity in mathematical computing, we chose Bézier, hermite and cubic splines, in addition to a commonly used linear algorithm to interpolate data. Performance of interpolation methods was compared with different temporal resolutions representative of the less-precise geolocation and the more-precise Argos tracking techniques. Parameters from interpolated sub-sampled tracks were compared with those obtained from intact tracks. Average accuracy of the interpolated location was not affected by the interpolation method and was always within the precision of the tracking technique used. However, depending on the species tested, some curvilinear interpolation algorithms produced greater occurrences of more accurate locations, compared with the linear interpolation method. Total track lengths were consistently underestimated but were always more accurate using curvilinear interpolation than linear interpolation. Curvilinear algorithms are safe to use because accuracy, shape and length of the tracks are either not different or are slightly enhanced and because analyses always remain conservative. The choice of the curvilinear algorithm does not affect the resulting track dramatically so it should not preclude their use. We thus recommend using curvilinear interpolation techniques because of the more realistic fluid movements of animals. We also provide some guidelines for choosing an algorithm that is most likely to maximize track quality for different types of marine vertebrates.

Entities:  

Mesh:

Year:  2006        PMID: 16354784     DOI: 10.1242/jeb.01970

Source DB:  PubMed          Journal:  J Exp Biol        ISSN: 0022-0949            Impact factor:   3.312


  20 in total

1.  Tracking leatherback turtles from the world's largest rookery: assessing threats across the South Atlantic.

Authors:  Matthew J Witt; Eric Augowet Bonguno; Annette C Broderick; Michael S Coyne; Angela Formia; Alain Gibudi; Gil Avery Mounguengui Mounguengui; Carine Moussounda; Monique NSafou; Solange Nougessono; Richard J Parnell; Guy-Philippe Sounguet; Sebastian Verhage; Brendan J Godley
Journal:  Proc Biol Sci       Date:  2011-01-05       Impact factor: 5.349

2.  Dynamic habitat models: using telemetry data to project fisheries bycatch.

Authors:  Ramūnas Zydelis; Rebecca L Lewison; Scott A Shaffer; Jeffrey E Moore; Andre M Boustany; Jason J Roberts; Michelle Sims; Daniel C Dunn; Benjamin D Best; Yann Tremblay; Michelle A Kappes; Patrick N Halpin; Daniel P Costa; Larry B Crowder
Journal:  Proc Biol Sci       Date:  2011-03-23       Impact factor: 5.349

3.  Behavioural mapping of a pelagic seabird: combining multiple sensors and a hidden Markov model reveals the distribution of at-sea behaviour.

Authors:  Ben Dean
Journal:  J R Soc Interface       Date:  2012-11-08       Impact factor: 4.118

4.  Migratory shearwaters integrate oceanic resources across the Pacific Ocean in an endless summer.

Authors:  Scott A Shaffer; Yann Tremblay; Henri Weimerskirch; Darren Scott; David R Thompson; Paul M Sagar; Henrik Moller; Graeme A Taylor; David G Foley; Barbara A Block; Daniel P Costa
Journal:  Proc Natl Acad Sci U S A       Date:  2006-08-14       Impact factor: 11.205

5.  Differences in foraging ecology align with genetically divergent ecotypes of a highly mobile marine top predator.

Authors:  Jana W E Jeglinski; Jochen B W Wolf; Christiane Werner; Daniel P Costa; Fritz Trillmich
Journal:  Oecologia       Date:  2015-08-26       Impact factor: 3.225

6.  Young frigatebirds learn how to compensate for wind drift.

Authors:  Joe Wynn; Julien Collet; Aurélien Prudor; Alexandre Corbeau; Oliver Padget; Tim Guilford; Henri Weimerskirch
Journal:  Proc Biol Sci       Date:  2020-10-21       Impact factor: 5.349

7.  Latitudinal range influences the seasonal variation in the foraging behavior of marine top predators.

Authors:  Stella Villegas-Amtmann; Samantha E Simmons; Carey E Kuhn; Luis A Huckstadt; Daniel P Costa
Journal:  PLoS One       Date:  2011-08-10       Impact factor: 3.240

8.  Bayesian estimation of animal movement from archival and satellite tags.

Authors:  Michael D Sumner; Simon J Wotherspoon; Mark A Hindell
Journal:  PLoS One       Date:  2009-10-13       Impact factor: 3.240

9.  Accuracy of ARGOS locations of Pinnipeds at-sea estimated using Fastloc GPS.

Authors:  Daniel P Costa; Patrick W Robinson; John P Y Arnould; Autumn-Lynn Harrison; Samantha E Simmons; Jason L Hassrick; Andrew J Hoskins; Stephen P Kirkman; Herman Oosthuizen; Stella Villegas-Amtmann; Daniel E Crocker
Journal:  PLoS One       Date:  2010-01-15       Impact factor: 3.240

10.  A comparison of spatial and movement patterns between sympatric predators: bull sharks (Carcharhinus leucas) and Atlantic tarpon (Megalops atlanticus).

Authors:  Neil Hammerschlag; Jiangang Luo; Duncan J Irschick; Jerald S Ault
Journal:  PLoS One       Date:  2012-09-26       Impact factor: 3.240

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.