2,005 research outputs found
Optimal methods for fitting probability distributions to propagule retention time in studies of zoochorous dispersal
Background: Propagule retention time is a key factor in determining propagule dispersal distance and the shape of
“seed shadows”. Propagules dispersed by animal vectors are either ingested and retained in the gut until defecation or
attached externally to the body until detachment. Retention time is a continuous variable, but it is commonly measured
at discrete time points, according to pre-established sampling time-intervals. Although parametric continuous
distributions have been widely fitted to these interval-censored data, the performance of different fitting methods
has not been evaluated. To investigate the performance of five different fitting methods, we fitted parametric probability
distributions to typical discretized retention-time data with known distribution using as data-points either the
lower, mid or upper bounds of sampling intervals, as well as the cumulative distribution of observed values (using
either maximum likelihood or non-linear least squares for parameter estimation); then compared the estimated and
original distributions to assess the accuracy of each method. We also assessed the robustness of these methods to
variations in the sampling procedure (sample size and length of sampling time-intervals).
Results: Fittings to the cumulative distribution performed better for all types of parametric distributions (lognormal,
gamma and Weibull distributions) and were more robust to variations in sample size and sampling time-intervals.
These estimated distributions had negligible deviations of up to 0.045 in cumulative probability of retention times
(according to the Kolmogorov–Smirnov statistic) in relation to original distributions from which propagule retention
time was simulated, supporting the overall accuracy of this fitting method. In contrast, fitting the sampling-interval
bounds resulted in greater deviations that ranged from 0.058 to 0.273 in cumulative probability of retention times,
which may introduce considerable biases in parameter estimates.
Conclusions: We recommend the use of cumulative probability to fit parametric probability distributions to propagule
retention time, specifically using maximum likelihood for parameter estimation. Furthermore, the experimental
design for an optimal characterization of unimodal propagule retention time should contemplate at least 500 recovered
propagules and sampling time-intervals not larger than the time peak of propagule retrieval, except in the tail of
the distribution where broader sampling time-intervals may also produce accurate fitsPeer reviewe
Migratory Birds as Global Dispersal Vectors
Propagule dispersal beyond local scales has been considered rare and unpredictable. However, for many plants, invertebrates, and microbes dispersed by birds, long-distance dispersal (LDD) might be regularly achieved when mediated by migratory movements. Because LDD operates over spatial extents spanning hundreds to thousands of kilometers, it can promote rapid range shifts and determine species distributions. We review evidence supporting this widespread LDD service and propose a conceptual framework for estimating LDD by migratory birds. Although further research and validation efforts are still needed, we show that current knowledge can be used to make more realistic estimations of LDD mediated by regular bird migrations, thus refining current predictions of its ecological and evolutionary consequences.Peer reviewe
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