Taxon:
Loxodonta africana

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Scientific Name
Loxodonta africana
Common Name
African Bush Elephant
African elephant
African savannah elephant
Taxa Group
Elephantidae
Environment
Move Mode

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2014 - 20172
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Subject: African elephant ×

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    Data from: Suite of simple metrics reveals common movement syndromes across vertebrate taxa
    (2017-06-01) Abrahms, Briana
    Background: Because empirical studies of animal movement are most-often site- and species-specific, we lack understanding of the level of consistency in movement patterns across diverse taxa, as well as a framework for quantitatively classifying movement patterns. We aim to address this gap by determining the extent to which statistical signatures of animal movement patterns recur across ecological systems. We assessed a suite of movement metrics derived from GPS trajectories of thirteen marine and terrestrial vertebrate species spanning three taxonomic classes, orders of magnitude in body size, and modes of movement (swimming, flying, walking). Using these metrics, we performed a principal components analysis and cluster analysis to determine if individuals organized into statistically distinct clusters. Finally, to identify and interpret commonalities within clusters, we compared them to computer-simulated idealized movement syndromes representing suites of correlated movement traits observed across taxa (migration, nomadism, territoriality, and central place foraging). Results: Two principal components explained 70% of the variance among the movement metrics we evaluated across the thirteen species, and were used for the cluster analysis. The resulting analysis revealed four statistically distinct clusters. All simulated individuals of each idealized movement syndrome organized into separate clusters, suggesting that the four clusters are explained by common movement syndrome. Conclusions: Our results offer early indication of widespread recurrent patterns in movement ecology that have consistent statistical signatures, regardless of taxon, body size, mode of movement, or environment. We further show that a simple set of metrics can be used to classify broad-scale movement patterns in disparate vertebrate taxa. Our comparative approach provides a general framework for quantifying and classifying animal movements, and facilitates new inquiries into relationships between movement syndromes and other ecological processes.
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    Data package
    Data from: Elliptical Time-Density model to estimate wildlife utilization distributions
    (2014-08-01) Wall, Jake; Wittemyer, George; LeMay, Valerie; Douglas-Hamilton, Iain; Klinkenberg, Brian
    We present a new animal space-use model (Elliptical Time-Density - ETD) that uses discrete-time tracking data collected in wildlife movement studies. The ETD model provides a trajectory-based, non-parametric approach to estimate the utilization distribution (UD) of an animal, using model parameters derived directly from the movement behavior of the species. The model builds on the theory of ‘time-geography’ whereby elliptical constraining regions are established between temporally-adjacent recorded locations. Using a Weibull speed distribution fitted for an animal's movement data, a time-density value (i.e., time per unit landscape) is determined from the expectation of all elliptical regions equal to, or greater-than, the minimum bounding ellipse for a given landscape point. We tested the ETD model using a tracking dataset for an African elephant (Loxodonta africana) and compared the resulting UDs for regularly sampled, frequently recorded locations, as well as irregular random time intervals between locations and also infrequent temporal-sampling regimes, providing insight to the method's performance with different resolution data. We compared the performance of the ETD model, the Brownian Bridge Movement Model (BBMM), the Time-Geography Density Estimator (TGDE) and the Kernel Density Estimator (KDE) by calculating omission/commission errors from the predicted space-use distribution of each model relative to the true known UD of our elephant test data. The comparison was made for the 10% to 99% percentile UD model areas. The ETD90 model (i.e., ETD model parameterized using the 90% percentile value of the Weibull speed distribution) resulted in the fewest errors of commission and omission with regards to locating the true movement path at the 99% percentile UD area. The ETD model provides an improved approach for estimating animal UDs since: i) parameters are derived directly from the tracking data rather than assumed; ii) parameter values are biologically interpretable; iii) the Weibull speed distribution is adaptable to various temporal-sampling regimes; and iv) the ETD model handles the case of degenerate ellipses thus preserving landscape connectivity in the UD. Software (freeware) for calculating the ETD and a Bayesian framework for estimating the Weibull distribution speed parameters are also introduced in the paper.