Enhancing the use of argos satellite data for home range and long distance migration studies of marine animals

63Citations
Citations of this article
206Readers
Mendeley users who have this article in their library.

Abstract

Accurately quantifying animals' spatial utilisation is critical for conservation, but has long remained an elusive goal due to technological impediments. The Argos telemetry system has been extensively used to remotely track marine animals, however location estimates are characterised by substantial spatial error. State-space models (SSM) constitute a robust statistical approach to refine Argos tracking data by accounting for observation errors and stochasticity in animal movement. Despite their wide use in ecology, few studies have thoroughly quantified the error associated with SSM predicted locations and no research has assessed their validity for describing animal movement behaviour. We compared home ranges and migratory pathways of seven hawksbill sea turtles (Eretmochelys imbricata) estimated from (a) highly accurate Fastloc GPS data and (b) locations computed using common Argos data analytical approaches. Argos 68th percentile error was <1 km for LC 1, 2, and 3 while markedly less accurate (>4 km) for LC ≤0. Argos error structure was highly longitudinally skewed and was, for all LC, adequately modelled by a Student's t distribution. Both habitat use and migration routes were best recreated using SSM locations post-processed by re-adding good Argos positions (LC 1, 2 and 3) and filtering terrestrial points (mean distance to migratory tracks ± SD = 2.2±2.4 km; mean home range overlap and error ratio = 92.2% and 285.6 respectively). This parsimonious and objective statistical procedure however still markedly overestimated true home range sizes, especially for animals exhibiting restricted movements. Post-processing SSM locations nonetheless constitutes the best analytical technique for remotely sensed Argos tracking data and we therefore recommend using this approach to rework historical Argos datasets for better estimation of animal spatial utilisation for research and evidence-based conservation purposes. © 2012 Hoenner et al.

Figures

  • Figure 1. Flow diagram showing the different approaches used to obtain our five Argos-derived datasets. Data transformation procedures are indicated within dashed line boxes, datasets are indicated within solid line boxes. doi:10.1371/journal.pone.0040713.g001
  • Figure 2. Home Range Accuracy (HRA) index as a function of the overlaying percentage (OP) and error ratio (ER). The top panel represents the evolution of the HRA index for ERs comprised between 0 and 10 000. The bottom panel highlights the smooth join for ER = 1. doi:10.1371/journal.pone.0040713.g002
  • Figure 3. Joint log-likelihood surface plots for t distribution parameters t and n, for the longitude and latitude components of error. Argos location classes are indicated in the lower right corner of each panel. Maximum likelihood estimates are represented by filled circles. The 95% confidence region on each panel is indicated in gray and delimitated by a thick black line. The contour interval is -1 with log-likelihood values decreasing from the maximum likelihood point estimate. doi:10.1371/journal.pone.0040713.g003
  • Table 1. Comparison of the 68th percentile spatial error associated with each Argos location class from different studies (in km).
  • Table 2. Maximum likelihood estimates and standard errors for t distribution parameters t and n for longitudinal and latitudinal components of Argos error.
  • Figure 4. Combined 50% and 95% utilisation distribution (UD) contour polygon calculated for each Argos-based approach compared to GPS estimates. The core GPS area (50% UD) and overall home range (95% UD) are delimitated by a red and orange line respectively. Argos-based 50 and 95% UD polygons are coloured in dark and light blue respectively. (A) Argos, (B) filtered Argos, (C) SSM, (D) post-processed SSM, (E) filtered SSM. doi:10.1371/journal.pone.0040713.g004
  • Table 3. Mean HRA index (error ratio/overlaying percentage) associated to individual and combined inter-nesting home range estimates using Argos-derived locations.
  • Figure 6. Logarithmic relationship between post-processed SSM and GPS home range sizes. The red solid line represents the best fit of a linear generalised linear model (a = 1.352, b =22.287, adjusted r2 = 0.36), which had the most support (wAICc = 0.66) amongst other polynomial candidate models. Red dashed lines represent the 2.5 and 97.5% confidence intervals. doi:10.1371/journal.pone.0040713.g006

References Powered by Scopus

The package "adehabitat" for the R software: A tool for the analysis of space and habitat use by animals

3282Citations
N/AReaders
Get full text

Kernel methods for estimating the utilization distribution in home- range studies

3270Citations
N/AReaders
Get full text

The need for evidence-based conservation

1426Citations
N/AReaders
Get full text

Cited by Powered by Scopus

The accuracy of Fastloc-GPS locations and implications for animal tracking

144Citations
N/AReaders
Get full text

Evidence-based marine protected area planning for a highly mobile endangered marine vertebrate

120Citations
N/AReaders
Get full text

Demographic and genetic approaches to study dispersal in wild animal populations: A methodological review

109Citations
N/AReaders
Get full text

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Cite

CITATION STYLE

APA

Hoenner, X., Whiting, S. D., Hindell, M. A., & McMahon, C. R. (2012). Enhancing the use of argos satellite data for home range and long distance migration studies of marine animals. PLoS ONE, 7(7). https://doi.org/10.1371/journal.pone.0040713

Readers over time

‘12‘13‘14‘15‘16‘17‘18‘19‘20‘21‘22‘23‘24‘2507142128

Readers' Seniority

Tooltip

PhD / Post grad / Masters / Doc 87

61%

Researcher 44

31%

Professor / Associate Prof. 9

6%

Lecturer / Post doc 2

1%

Readers' Discipline

Tooltip

Agricultural and Biological Sciences 114

74%

Environmental Science 32

21%

Earth and Planetary Sciences 7

5%

Medicine and Dentistry 2

1%

Save time finding and organizing research with Mendeley

Sign up for free
0