Description Details Author(s) References Examples
Methods for computing spatial, temporal, and spatiotemporal statistics as described in Gouhier and Guichard (2014) <doi:10.1111/2041-210X.12188>. These methods include empirical univariate, bivariate and multivariate variograms; fitting variogram models; phase locking and synchrony analysis; generating autocorrelated and cross-correlated matrices.
Package: | synchrony |
Type: | Package |
Version: | 0.3.8 |
Date: | 2019-12-05 |
License: | GPL (>=2) |
URL: | https://github.com/tgouhier/synchrony |
LazyLoad: | yes |
Tarik C. Gouhier (tarik.gouhier@gmail.com)
Maintainer: Tarik C. Gouhier (tarik.gouhier@gmail.com)
Bjornstad, O. N., and W. Falck. 2001. Nonparametric spatial covariance functions: Estimation and testing. Environmental and Ecological Statistics 8:53-70.
Bjornstad, O. N., R. A. Ims, and X. Lambin. 1999. Spatial population dynamics: analyzing patterns and processes of population synchrony. Trends in Ecology & Evolution 14:427-432.
Buonaccorsi, J. P., J. S. Elkinton, S. R. Evans, and A. M. Liebhold. 2001. Measuring and testing for spatial synchrony. Ecology 82:1668-1679.
Cazelles, B., and L. Stone. 2003. Detection of imperfect population synchrony in an uncertain world. Journal of Animal Ecology 72:953-968.
Fortin, M. J., and M. R. T. Dale. 2005. Spatial Analysis: A Guide for Ecologists. Cambridge University Press.
Gouhier, T. C., and F. Guichard. 2007. Local disturbance cycles and the maintenance of spatial heterogeneity across scales in marine metapopulations. Ecology 88:647-657.
Gouhier, T. C., F. Guichard, and A. Gonzalez. 2010. Synchrony and stability of food webs in metacommunities. The American Naturalist 175:E16-E34.
Gouhier, T. C., F. Guichard, and B. A. Menge. 2010. Ecological processes can synchronize marine population dynamics over continental scales. Proceedings of the National Academy of Sciences 107:8281-8286.
Loreau, M., and C. de Mazancourt. 2008. Species synchrony and its drivers: Neutral and nonneutral community dynamics in fluctuating environments. The American Naturalist 172:E48-E66.
Purves, D. W., and R. Law. 2002. Fine-scale spatial structure in a grassland community: quantifying the plant's eye view. Journal of Ecology 90:121-129.
Vasseur, D. A. 2007. Environmental colour intensifies the Moran effect when population dynamics are spatially heterogeneous. Oikos 116:1726-1736.
Zar, J. H. 1999. Biostatistical Analysis, Fourth edition. Prentice-Hall, Inc., Upper Saddle River, NJ.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 | # Compute phase synchrony
t1=runif(100)
t2=runif(100)
sync=phase.sync(t1, t2)
# Distribution of phase difference
hist(sync$deltaphase$mod_phase_diff_2pi)
# Compute concordant peaks
p=peaks(t1, t2, nrands=100)
# Find proportion of time steps where both time series peak together
p$peaks
# Plot (null) distribution of proportion of time steps where both time
# series peak together
hist(p$rand)
# p-value of observed value
p$pval
# Compute Kendall's W
data(bird.traits)
(w=kendall.w(bird.traits))
# Community matrix for 20 species undergoing random fluctuations
comm.rand=matrix(runif(100), nrow=5, ncol=20)
community.sync(comm.rand, nrands=10)
# Community matrix for 20 species undergoing synchronized fluctuations
comm.corr=matrix(rep(comm.rand[,1], 20), nrow=5, ncol=20)
community.sync(comm.corr, nrands=10)
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synchrony 0.2.3 loaded.
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|======================================================================| 100%NULL
[1] 0.6336634
Kendall's W (uncorrected for ties): 0.9134
Kendall's W (corrected for ties): 0.9241
Spearman's ranked correlation: 0.8862
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|======================================================================| 100%Community synchrony: 0.06859
Mean pairwise correlation: 0.0105
Community synchrony p-value (one-tailed test [greater]): 0.7273
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|======================================================================| 100%Community synchrony: 1
Mean pairwise correlation: 1
Community synchrony p-value (one-tailed test [greater]): 0.09091
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