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

Lindström T, Phillips BL, Brown GP, Shine R - Mov Ecol (2015)

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Department of Physics, Biology and Chemistry, Linköping University, 58183 Linköping, Sweden.

ABSTRACT

Background: 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.

Results: 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.

Conclusion: 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.

No MeSH data available.


Related in: MedlinePlus

Difference in DIC (ΔDIC) for models describing the data as synchronous for frequencies f ≤ φ. Each frequency corresponds to a time scale of L/f and the lowest value is found for φ = 3, corresponding to 21 days.
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Fig2: Difference in DIC (ΔDIC) for models describing the data as synchronous for frequencies f ≤ φ. Each frequency corresponds to a time scale of L/f and the lowest value is found for φ = 3, corresponding to 21 days.

Mentions: Figure 2 plots ΔDIC, defined as the difference between model DIC and DICmin, where the latter is the DIC of the model with the lowest score. This was found for φ = 3, which corresponds to a time scale of 21 days (given by 64/3). Spiegelhalter et al. [20] suggest that a difference in DIC of more than three constitute a model with considerably less support than the preferred model. Figure 2 shows that the jump between φ = 3 and φ = 4 is greater than this cutoff, suggesting that the best model describes the data as being synchronous only for frequencies equal to and below this cutoff. Consequently, we conclude that for these data, movement is synchronous at time scales above three weeks but not at shorter time scales, indicating that factors that cause synchronization only act at these larger time scales, whereas daily fluctuations of movement are regulated by factors at the individual level (such as internal drivers).Figure 2


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

Lindström T, Phillips BL, Brown GP, Shine R - Mov Ecol (2015)

Difference in DIC (ΔDIC) for models describing the data as synchronous for frequencies f ≤ φ. Each frequency corresponds to a time scale of L/f and the lowest value is found for φ = 3, corresponding to 21 days.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4418100&req=5

Fig2: Difference in DIC (ΔDIC) for models describing the data as synchronous for frequencies f ≤ φ. Each frequency corresponds to a time scale of L/f and the lowest value is found for φ = 3, corresponding to 21 days.
Mentions: Figure 2 plots ΔDIC, defined as the difference between model DIC and DICmin, where the latter is the DIC of the model with the lowest score. This was found for φ = 3, which corresponds to a time scale of 21 days (given by 64/3). Spiegelhalter et al. [20] suggest that a difference in DIC of more than three constitute a model with considerably less support than the preferred model. Figure 2 shows that the jump between φ = 3 and φ = 4 is greater than this cutoff, suggesting that the best model describes the data as being synchronous only for frequencies equal to and below this cutoff. Consequently, we conclude that for these data, movement is synchronous at time scales above three weeks but not at shorter time scales, indicating that factors that cause synchronization only act at these larger time scales, whereas daily fluctuations of movement are regulated by factors at the individual level (such as internal drivers).Figure 2

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Department of Physics, Biology and Chemistry, Linköping University, 58183 Linköping, Sweden.

ABSTRACT

Background: 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.

Results: 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.

Conclusion: 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.

No MeSH data available.


Related in: MedlinePlus