estimates are a common product of animal tracking data, as each range
informs on the area needed by a given individual. Population-level
inference on home-range areas—where multiple individual home-ranges are
considered to be sampled from a population—is also important to evaluate
changes over time, space, or covariates, such as habitat quality or
fragmentation, and for comparative analyses of species averages.
Population-level home-range parameters have traditionally been estimated
by first assuming that the input tracking data were sampled
independently when calculating home ranges via conventional kernel
density estimation (KDE) or minimal convex polygon (MCP) methods, and
then assuming that those individual home ranges were measured exactly
when calculating the population-level estimates. This conventional
approach does not account for the temporal autocorrelation that is
inherent in modern tracking data, nor for the uncertainties of each
individual home-range estimate, which are often large and heterogeneous.
we introduce a statistically and computationally efficient framework
for the population-level analysis of home-range areas, based on
autocorrelated kernel density estimation (AKDE), that can account for
variable temporal autocorrelation and estimation uncertainty.
We apply our method to empirical examples on lowland tapir (Tapirus terrestris), kinkajou (Potos flavus), white-nosed coati (Nasua narica), white-faced capuchin monkey (Cebus capucinus), and spider monkey (Ateles geoffroyi), and quantify differences between species, environments, and sexes.
approach allows researchers to more accurately compare different
populations with different movement behaviors or sampling schedules,
while retaining statistical precision and power when individual
home-range uncertainties vary. Finally, we emphasize the estimation of
effect sizes when comparing populations, rather than mere significance
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 GPS location error can often be ignored in coarsely sampled data, fine-scale data require more care, and tools to do this have not kept pace. 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. In some cases these approaches can provide a level of correction, but they have known limitations, and in some cases they can be worse than doing nothing. Here, we provide a general framework to account for location error in the analysis of triangulated and trilateralized animal tracking data, which includes GPS, Argos Doppler-shift, triangulated VHF, trilateralized acoustic and cellular location data. We apply our error-model-selection framework to 190 GPS, cellular, and acoustic devices representing 27 models from 14 manufacturers. Collectively, these devices were used to track a wide range of taxa comprising birds, fish, reptiles, and mammals of different sizes and with different behaviors, in urban, suburban, and wild settings. In almost half of the tested device models, error-model selection was necessary to obtain the best performing error model, and in almost a quarter of tested device models, the reported DOP values were actually misinformative. Then, using empirical tracking data 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. Because error-induced biases depend on many factors—sampling schedule, movement characteristics, tracking device, habitat, etc.—differential bias can easily confound biological inference and lead researchers to draw false conclusions. We demonstrate how error-informed analyses on calibrated tracking data can provide more accurate estimates are that are insensitive to location error, and allow researchers to use all of their data.