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Identifying the time scale of synchronous movement: a study on tropical snakes

Overview of attention for article published in Movement Ecology, May 2015
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  • Above-average Attention Score compared to outputs of the same age (57th percentile)

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Identifying the time scale of synchronous movement: a study on tropical snakes
Published in
Movement Ecology, May 2015
DOI 10.1186/s40462-015-0038-5
Pubmed ID

Tom Lindström, Benjamin L Phillips, Gregory P Brown, Richard Shine


Individual movement is critical to organismal fitness and also influences broader population processes such as demographic stochasticity and gene flow. Climatic change and habitat fragmentation render the drivers of individual movement especially critical to understand. Rates of movement of free-ranging animals through the landscape are influenced both by intrinsic attributes of an organism (e.g., size, body condition, age), and by external forces (e.g., weather, predation risk). Statistical modelling can clarify the relative importance of those processes, because externally-imposed pressures should generate synchronous displacements among individuals within a population, whereas intrinsic factors should generate consistency through time within each individual. External and intrinsic factors may vary in importance at different time scales. In this study we focused on daily displacement of an ambush-foraging snake from tropical Australia (the Northern Death Adder Acanthophis praelongus), based on a radiotelemetric study. We used a mixture of spectral representation and Bayesian inference to study synchrony in snake displacement by phase shift analysis. We further studied autocorrelation in fluctuations of displacement distances as "one over f noise". Displacement distances were positively autocorrelated with all considered noise colour parameters estimated as >0. We show how the methodology can reveal time scales of particular interest for synchrony and found that for the analysed data, synchrony was only present at time scales above approximately three weeks. We conclude that the spectral representation combined with Bayesian inference is a promising approach for analysis of movement data. Applying the framework to telemetry data of A. praelongus, we were able to identify a cut-off time scale above which we found support for synchrony, thus revealing a time scale where global external drivers have a larger impact on the movement behaviour. Our results suggest that for the considered study period, movement at shorter time scales was primarily driven by factors at the individual level; daily fluctuations in weather conditions had little effect on snake movement.

Twitter Demographics

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Mendeley readers

The data shown below were compiled from readership statistics for 24 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 24 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 6 25%
Researcher 6 25%
Student > Bachelor 4 17%
Student > Master 3 13%
Professor > Associate Professor 2 8%
Other 2 8%
Unknown 1 4%
Readers by discipline Count As %
Agricultural and Biological Sciences 11 46%
Environmental Science 6 25%
Social Sciences 2 8%
Unspecified 1 4%
Medicine and Dentistry 1 4%
Other 1 4%
Unknown 2 8%

Attention Score in Context

This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 05 June 2015.
All research outputs
of 9,175,276 outputs
Outputs from Movement Ecology
of 110 outputs
Outputs of similar age
of 210,884 outputs
Outputs of similar age from Movement Ecology
of 2 outputs
Altmetric has tracked 9,175,276 research outputs across all sources so far. This one has received more attention than most of these and is in the 54th percentile.
So far Altmetric has tracked 110 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 16.2. This one is in the 32nd percentile – i.e., 32% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 210,884 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 57% of its contemporaries.
We're also able to compare this research output to 2 others from the same source and published within six weeks on either side of this one.