A comprehensive framework for handling location error in animal tracking data

Abstract

Animal tracking data are being collected more frequently, in greater detail, and on smaller taxa than ever before. These data hold the promise to increase the relevance of animal movement for understanding ecological processes, but this potential will only be fully realized if their accompanying location error is properly addressed. Historically, coarsely-sampled movement data have proved invaluable for understanding large scale processes (e.g., home range, habitat selection, etc.), but modern fine-scale data promise to unlock far more ecological information. While location error can often be ignored in coarsely sampled data, fine-scale data require much more care, and tools to do this have been lacking. Current approaches to dealing with location error largely fall into two categories—either discarding the least accurate location estimates prior to analysis or simultaneously fitting movement and error parameters in a hidden-state model. Unfortunately, both of these approaches have serious flaws. Here, we provide a general framework to account for location error in the analysis of animal tracking data, so that their potential can be unlocked. We apply our error-model-selection framework to 190 GPS, cellular, and acoustic devices representing 27 models from 14 manufacturers. Collectively, these devices are used to track a wide range of animal species comprising birds, fish, reptiles, and mammals of different sizes and with different behaviors, in urban, suburban, and wild settings. Then, using empirical data on tracked individuals from multiple species, we provide an overview of modern, error-informed movement analyses, including continuous-time path reconstruction, home-range distribution, home-range overlap, speed and distance estimation. Adding to these techniques, we introduce new error-informed estimators for outlier detection and autocorrelation visualization. We furthermore demonstrate how error-informed analyses on calibrated tracking data can be necessary to ensure that estimates are accurate and insensitive to location error, and allow researchers to use all of their data. Because error-induced biases depend on so many factors—sampling schedule, movement characteristics, tracking device, habitat, etc.—differential bias can easily confound biological inference and lead researchers to draw false conclusions.

A Guide to Pre-Processing High-Throughput Animal Tracking Data

“…Yet cleaning tracking data even partially before modelling location error is faster than error-modelling on the full data, and the removal of large location errors may improve model fits. Thus we see our pipeline as complementary to these approaches (Fleming et al, 2014, 2020). Finally, we recognise that the diversity and complexity of animal movement and data collection techniques often requires system-specific, even bespoke, pre-processing solutions.…”

“…For instance, smoothing breaks the relationship between a coordinate and the location error estimate around it (VARX, VARY, and SD in ATLAS systems). This makes subsequent filtering o n c ovariates o f d ata quality unreliable, and smoothed data are unsuitable for use with methods that model location uncertainty (Calabrese et al, 2016; Fleming et al, 2014, 2020; Noonan et al, 2019). Thus, when applying location error modelling methods, users should be sure that the error measure bears a mechanistic relationship with the location estimate (see Fleming et al, 2020; Noonan et al, 2019, for more details).…”

“…The aggregation method is less sensitive to selecting point outliers by chance than subsampling. However, when users want to account for location error with methods such as state-space models (Johnson et al, 2008; Jonsen et al, 2005, 2003), or continuous time movement models (Calabrese et al, 2016; Fleming et al, 2014, 2020; Gurarie et al, 2017; Noonan et al, 2019), correctly propagating the location error is important, and subsampling directly propagates these errors without further processing. In the aggregation method, the location error around each coordinate provided by either GPS or reverse-GPS systems can be propagated to the averaged position as the sum of errors divided by the square of the number of observations contributing to each average ( N ): Similarly, the overall location error estimate for the average of N positions in a time-interval can be calculated by treating it as a variance.…”

Roads as ecological traps for giant anteaters

“…For each location estimate, the GPS trackers recorded a unitless Horizontal Dilution of Precision (HDOP), value which is a measure of the accuracy of each positional fix. We converted the HDOP values into calibrated error circles by estimating an equivalent range error from 6,948 calibration data points where a tag had been left in a fixed location (Fleming et al 2020). For each individual dataset, we then removed outliers based on error-informed distance from the median location, and the minimum speed required to explain each location's displacement.…”

A pilot study on the home range and movement patterns of the Andean Fox Lycalopex culpaeus (Molina, 1782) in Cotopaxi National Park, Ecuador

“…However, these stationary GPS positions taken following Mashca's death were used as opportunistic calibration data to calculate the location error for the collar (Fleming et al 2020).…”