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Quantifying effects of tracking data bias on species distribution models

Quantifying effects of tracking data bias on species distribution models
Quantifying effects of tracking data bias on species distribution models
Telemetry datasets are becoming increasingly large and covering a wider range of species using different technologies (GPS, Argos, light‐based geolocation). Together, such datasets hold tremendous potential to understand species' space use at broad spatial scale, through the development of species distribution or habitat suitability models (SDMs) to predict environmental dependencies of species across space and time. However, tracking datasets can be heavily biased and an assessment of how such biases affect SDM predictions, and therefore, our interpretation of animal distributions is lacking.
We generated simulated tracks based on predetermined environmental values for a random predator and a central place forager, and then sampled positions from those tracks based on a combination of five common biases in tracking datasets: (a) tagging location; (b) tracking device; (c) data gaps within tracks; (d) premature tag detachment (or failure) and (e) different processing methods. We then used 240 combinations of the resulting biased simulated datasets to develop binomial generalised linear (GLM) and additive (GAM) models to estimate habitat suitability in different environmental sets (cool deep, cool coastal, warm deep and warm coastal environments).
Our results show that tagging location and length of tracks have the largest effects in decreasing model performance, but that these biases can be overcome by adding a small percentage of additional, relatively less biased tracks to the dataset. In comparison, the effects from all other biases were almost negligible, including for low resolution tracking datasets for which sufficient tracks are available. We also highlight the need for a cautionary approach when using processing methods that can introduce other biases (e.g. interpolated locations). Similar trends were obtained for the random predator and the central place forager, but with relatively lower model performance for the latter.
We provide evidence that even non‐GPS tracking datasets can be readily used to improve the knowledge of large‐scale space use by species without the need for detailed processing and tracking reconstruction. This is especially relevant in the current context of rapid increase in data acquisition and the urgent need to address the large spatial scale ecological consequences of global change.
Global Positioning System, big data, geolocation, global scale, habitat suitability models, marine megafauna, tracking
2041-210X
170-181
O'Toole, Malcolm
7716487c-cfaa-465f-aa95-85167b6f50b6
Queiroz, Nuno
1b1b741e-a2ee-49c2-bbcc-2864044ba8e3
Humphries, Nicolas E.
7eb196e4-95ec-4878-a26e-e96abd8accd6
Sims, David W.
7234b444-25e2-4bd5-8348-a1c142d0cf81
Sequeira, Ana M. M.
64479575-3ab1-45f0-bf23-6b0d39f07523
Freckleton, Robert
67864017-6932-416e-a090-ce551918e217
O'Toole, Malcolm
7716487c-cfaa-465f-aa95-85167b6f50b6
Queiroz, Nuno
1b1b741e-a2ee-49c2-bbcc-2864044ba8e3
Humphries, Nicolas E.
7eb196e4-95ec-4878-a26e-e96abd8accd6
Sims, David W.
7234b444-25e2-4bd5-8348-a1c142d0cf81
Sequeira, Ana M. M.
64479575-3ab1-45f0-bf23-6b0d39f07523
Freckleton, Robert
67864017-6932-416e-a090-ce551918e217

O'Toole, Malcolm, Queiroz, Nuno, Humphries, Nicolas E., Sims, David W., Sequeira, Ana M. M. and Freckleton, Robert (2021) Quantifying effects of tracking data bias on species distribution models. Methods in Ecology and Evolution, 12 (1), 170-181. (doi:10.1111/2041-210X.13507).

Record type: Article

Abstract

Telemetry datasets are becoming increasingly large and covering a wider range of species using different technologies (GPS, Argos, light‐based geolocation). Together, such datasets hold tremendous potential to understand species' space use at broad spatial scale, through the development of species distribution or habitat suitability models (SDMs) to predict environmental dependencies of species across space and time. However, tracking datasets can be heavily biased and an assessment of how such biases affect SDM predictions, and therefore, our interpretation of animal distributions is lacking.
We generated simulated tracks based on predetermined environmental values for a random predator and a central place forager, and then sampled positions from those tracks based on a combination of five common biases in tracking datasets: (a) tagging location; (b) tracking device; (c) data gaps within tracks; (d) premature tag detachment (or failure) and (e) different processing methods. We then used 240 combinations of the resulting biased simulated datasets to develop binomial generalised linear (GLM) and additive (GAM) models to estimate habitat suitability in different environmental sets (cool deep, cool coastal, warm deep and warm coastal environments).
Our results show that tagging location and length of tracks have the largest effects in decreasing model performance, but that these biases can be overcome by adding a small percentage of additional, relatively less biased tracks to the dataset. In comparison, the effects from all other biases were almost negligible, including for low resolution tracking datasets for which sufficient tracks are available. We also highlight the need for a cautionary approach when using processing methods that can introduce other biases (e.g. interpolated locations). Similar trends were obtained for the random predator and the central place forager, but with relatively lower model performance for the latter.
We provide evidence that even non‐GPS tracking datasets can be readily used to improve the knowledge of large‐scale space use by species without the need for detailed processing and tracking reconstruction. This is especially relevant in the current context of rapid increase in data acquisition and the urgent need to address the large spatial scale ecological consequences of global change.

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More information

Accepted/In Press date: 2 September 2020
e-pub ahead of print date: 20 October 2020
Published date: January 2021
Keywords: Global Positioning System, big data, geolocation, global scale, habitat suitability models, marine megafauna, tracking

Identifiers

Local EPrints ID: 449930
URI: http://eprints.soton.ac.uk/id/eprint/449930
ISSN: 2041-210X
PURE UUID: 66bd774c-78c8-43fd-ba16-0fe921869a79

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Date deposited: 25 Jun 2021 16:32
Last modified: 25 Jun 2021 16:33

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Contributors

Author: Malcolm O'Toole
Author: Nuno Queiroz
Author: Nicolas E. Humphries
Author: David W. Sims
Author: Ana M. M. Sequeira
Author: Robert Freckleton

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