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Inherent high correlation of individual motility enhances population dispersal in a heterotrophic, planktonic protist.

Menden-Deuer S - PLoS Comput. Biol. (2010)

Bottom Line: These minute-scale estimates are considerably greater than previous estimates of second-scale correlation times.Considerable cell-to-cell variation and behavioral heterogeneity were critical to these results.Strongly correlated random walkers were predicted to have significantly greater dispersal distances and more rapid encounters with remote targets (e.g. resource patches, predators) than weakly correlated random walkers.

View Article: PubMed Central - PubMed

Affiliation: University of Rhode Island, Graduate School of Oceanography, Narragansett, Rhode Island, USA. smenden@gso.uri.edu

ABSTRACT
Quantitative linkages between individual organism movements and the resulting population distributions are fundamental to understanding a wide range of ecological processes, including rates of reproduction, consumption, and mortality, as well as the spread of diseases and invasions. Typically, quantitative data are collected on either movement behaviors or population distributions, rarely both. This study combines empirical observations and model simulations to gain a mechanistic understanding and predictive ability of the linkages between both individual movement behaviors and population distributions of a single-celled planktonic herbivore. In the laboratory, microscopic 3D movements and macroscopic population distributions were simultaneously quantified in a 1L tank, using automated video- and image-analysis routines. The vertical velocity component of cell movements was extracted from the empirical data and used to motivate a series of correlated random walk models that predicted population distributions. Validation of the model predictions with empirical data was essential to distinguish amongst a number of theoretically plausible model formulations. All model predictions captured the essence of the population redistribution (mean upward drift) but only models assuming long correlation times (minute), captured the variance in population distribution. Models assuming correlation times of 8 minutes predicted the least deviation from the empirical observations. Autocorrelation analysis of the empirical data failed to identify a de-correlation time in the up to 30-second-long swimming trajectories. These minute-scale estimates are considerably greater than previous estimates of second-scale correlation times. Considerable cell-to-cell variation and behavioral heterogeneity were critical to these results. Strongly correlated random walkers were predicted to have significantly greater dispersal distances and more rapid encounters with remote targets (e.g. resource patches, predators) than weakly correlated random walkers. The tendency to disperse rapidly in the absence of aggregative stimuli has important ramifications for the ecology and biogeography of planktonic organisms that perform this kind of random walk.

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Root mean square error (RMSE) of triplicate, model predicted distributions decreased significantly with increasing correlation time  relative to the empirically measured distribution.Error bars are three standard deviations of the mean. RMSE was scaled to the maximum RMSE estimate at 0.25 seconds. Empirical and simulation data were sampled with identical order and frequency. Model predictions for 500 were statistically indistinguishable from one another and deviated the least from the empirical data.
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pcbi-1000942-g007: Root mean square error (RMSE) of triplicate, model predicted distributions decreased significantly with increasing correlation time relative to the empirically measured distribution.Error bars are three standard deviations of the mean. RMSE was scaled to the maximum RMSE estimate at 0.25 seconds. Empirical and simulation data were sampled with identical order and frequency. Model predictions for 500 were statistically indistinguishable from one another and deviated the least from the empirical data.

Mentions: Root mean square error (RMSE) of model predictions compared to the empirical distribution data decreased significantly with increasing correlation time (Fig. 7). Model predictions differed most from empirical observations when assumed correlation was weak. Abundance predictions from highly correlated random walk models with 500 seconds differed least from the empirical data. RMSE was highest and statistically significantly different among models assuming  = 1 to 300 seconds. RMSE estimates for 500 were lowest and statistically indistinguishable from one another, suggesting a minimum correlation time of 8 minutes. Further refinement or an upper limit of the correlation time was not identifiable based on this comparison of empirical and predicted population distributions.


Inherent high correlation of individual motility enhances population dispersal in a heterotrophic, planktonic protist.

Menden-Deuer S - PLoS Comput. Biol. (2010)

Root mean square error (RMSE) of triplicate, model predicted distributions decreased significantly with increasing correlation time  relative to the empirically measured distribution.Error bars are three standard deviations of the mean. RMSE was scaled to the maximum RMSE estimate at 0.25 seconds. Empirical and simulation data were sampled with identical order and frequency. Model predictions for 500 were statistically indistinguishable from one another and deviated the least from the empirical data.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC2958803&req=5

pcbi-1000942-g007: Root mean square error (RMSE) of triplicate, model predicted distributions decreased significantly with increasing correlation time relative to the empirically measured distribution.Error bars are three standard deviations of the mean. RMSE was scaled to the maximum RMSE estimate at 0.25 seconds. Empirical and simulation data were sampled with identical order and frequency. Model predictions for 500 were statistically indistinguishable from one another and deviated the least from the empirical data.
Mentions: Root mean square error (RMSE) of model predictions compared to the empirical distribution data decreased significantly with increasing correlation time (Fig. 7). Model predictions differed most from empirical observations when assumed correlation was weak. Abundance predictions from highly correlated random walk models with 500 seconds differed least from the empirical data. RMSE was highest and statistically significantly different among models assuming  = 1 to 300 seconds. RMSE estimates for 500 were lowest and statistically indistinguishable from one another, suggesting a minimum correlation time of 8 minutes. Further refinement or an upper limit of the correlation time was not identifiable based on this comparison of empirical and predicted population distributions.

Bottom Line: These minute-scale estimates are considerably greater than previous estimates of second-scale correlation times.Considerable cell-to-cell variation and behavioral heterogeneity were critical to these results.Strongly correlated random walkers were predicted to have significantly greater dispersal distances and more rapid encounters with remote targets (e.g. resource patches, predators) than weakly correlated random walkers.

View Article: PubMed Central - PubMed

Affiliation: University of Rhode Island, Graduate School of Oceanography, Narragansett, Rhode Island, USA. smenden@gso.uri.edu

ABSTRACT
Quantitative linkages between individual organism movements and the resulting population distributions are fundamental to understanding a wide range of ecological processes, including rates of reproduction, consumption, and mortality, as well as the spread of diseases and invasions. Typically, quantitative data are collected on either movement behaviors or population distributions, rarely both. This study combines empirical observations and model simulations to gain a mechanistic understanding and predictive ability of the linkages between both individual movement behaviors and population distributions of a single-celled planktonic herbivore. In the laboratory, microscopic 3D movements and macroscopic population distributions were simultaneously quantified in a 1L tank, using automated video- and image-analysis routines. The vertical velocity component of cell movements was extracted from the empirical data and used to motivate a series of correlated random walk models that predicted population distributions. Validation of the model predictions with empirical data was essential to distinguish amongst a number of theoretically plausible model formulations. All model predictions captured the essence of the population redistribution (mean upward drift) but only models assuming long correlation times (minute), captured the variance in population distribution. Models assuming correlation times of 8 minutes predicted the least deviation from the empirical observations. Autocorrelation analysis of the empirical data failed to identify a de-correlation time in the up to 30-second-long swimming trajectories. These minute-scale estimates are considerably greater than previous estimates of second-scale correlation times. Considerable cell-to-cell variation and behavioral heterogeneity were critical to these results. Strongly correlated random walkers were predicted to have significantly greater dispersal distances and more rapid encounters with remote targets (e.g. resource patches, predators) than weakly correlated random walkers. The tendency to disperse rapidly in the absence of aggregative stimuli has important ramifications for the ecology and biogeography of planktonic organisms that perform this kind of random walk.

Show MeSH
Related in: MedlinePlus