Nonparametric spatial covariance functions: Estimation and testing

By OTTAR N. Bjørnstad, W. Falck. Environmental and Ecological Statistics, 2001

504 citations recorded by the source corpus.

Subfields: Environmental Engineering, Genetics, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/6dfdd4e65e18f7a853317bb8eb96a3981adce8c7

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Citations in the corpus

SPATIAL SYNCHRONY IN FOREST INSECT OUTBREAKS: ROLES OF REGIONAL STOCHASTICITY AND DISPERSAL (2002, methodology): “First, we tested for significant spatial variation in the autoregressive parameters using spline correlograms (the univariate analogue of the nonparametric covariance function (Bjørnstad and Falck 2001)).”
Recruitment and survival of Northeast Pacific Ocean fish stocks: temporal trends, covariation, and regime shifts (2007, methodology): “Average correlations at a given separation distance were estimated by fitting a nonparametric covariance function ( Bjornstad and Falck 2000 ) to the observed correlations as a function of distance.”
Across-Species Comparisons of Spatial Scales of Environmental Effects on Survival Rates of Northeast Pacific Salmon (2005, methodology): “To quantify these relationships, we fit a nonparametric covariance function ( Bjornstad and Falck 2000 ) to pairwise correlations as a function of the great-circle distance between the points of ocean entry of each pair of stocks.”
Learning from a benign neglect strategy in a national park: Response of saproxylic beetles to dead wood accumulation (2010, methodology): “, 2007), we used cross-correlograms provided by the add-on package ncf (Bjørnstad and Falck, 2001) in R (R Development Core Team, 2008).”
Accounting for spatial pattern when modeling organism- environment interactions (2002, unknown): “The spatial correlation function is estimated using a spline correlogram with 10 degrees of freedom (Bjørnstad and Falck 2001). gesting that voles avoid dryer areas.”
Resource dynamic plays a key role in regional fluctuations of the spruce bark beetles Ips typographus (2004, background): “The nonparametric spatial covariance function (Bjørnstad & Falck, 2001) revealed a regional level of synchrony of 0.92 (Økland, unpublished data).”
Masting and trophic cascades: interplay between rowan trees, apple fruit moth, and their parasitoid in southern Norway (2004, methodology): “The spatial correlation function (SCF) in berry production decreased with distance (Fig.”
Spatial Synchrony in Population Dynamics (2004, methodology): “Bootstrap procedures are, again, useful for testing the significance of nuances in these spatial correlation functions, such as the maximum distance over which spatial synchrony extends (Koenig & Knops 1998b, Bjørnstad et al. 1999b, Bjørnstad & Falck 2001).”
Kernel lot distribution assessment (KeLDA): a study on the distribution of GMO in large soybean shipments (2006, methodology): “Specifically, Bjørnstad and Falck [35] propose a dedicated bootstrap algorithm to generate confidence intervals for non-parametric covariance functions (see also [36] for exhaustive discussion).”
Methods to account for spatial autocorrelation in the analysis of species distributional data : a review (2007, methodology): “2005), with packages gee (Carey 2002), geepack (Yan 2002, 2004), spdep (Bivand 2005), ncf (Bjørnstad and Falck 2000) and MASS (Venables and Ripley 2002).”
Circumpolar variation in periodicity and synchrony among gypsy moth populations (2005, methodology): “As a third measure of spatial synchrony, a spatial spline correlogram (Bjørnstad & Falck 2001) was constructed from pairwise comparisons of changes in intensity of gypsy moth defoliation [ log 10 ( N t +1 + 1) − log 10 ( N t + 1)].”
Mixed Effects Models and Extensions in Ecology with R (2009, methodology): “The spline correlogram that we use is essentially a correlogram that is smoothed using a spline function (Bjørnstad and Falck, 2001).”
Differences of floral resource use between honey bees and wild bees in an intensive farming system (2013, methodology): “Spline correlogram uncertainty was assessed by Monte Carlo simulations (Bjornstad and Falck, 2001) with 1000 simulated random data sets, using the nfc package (Bjornstad, 2009) in R version 2.”