AutoCorrelations_README.txt
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Date: 19 December 2019

Dataset creator and contact person:
Roeland C. van de Vijsel
roeland.van.de.vijsel[at]nioz.nl
NIOZ Royal Netherlands Institute for Sea Research (www.nioz.nl)
Yerseke, The Netherlands

This data belongs to the dataset that can be downloaded from the NIOZ Data Repository (https://dataverse.nioz.nl) and/or can be found as DOI: 10.25850/nioz/7b.b.m (https://doi.org/10.25850/nioz/7b.b.m). 

This dataset is part of the following publication:
van de Vijsel, R.C., van Belzen, J., Bouma, T.J., van der Wal, D., Cusseddu, V., Purkis, S.J., Rietkerk, M. & van de Koppel, J. (2019). Estuarine biofilm patterns: Modern analogues for Precambrian self-organization. Earth Surface Processes and Landforms. DOI: 10.1002/esp.4783 (https://doi.org/10.1002/esp.4783).

Whenever any of these data (scripts, figures, raw and processed data) are used elsewhere, please refer correctly to this publication and its authors and please cite the dataset. Options for citing the dataset can be found on https://dataverse.nioz.nl. Dataset and publication published under the Creative Commons Attribution License.

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The folder “~/AutoCorrelations” (where “~” denotes the “main” folder) should contain the contents of the following zipped folders (to be downloaded from the NIOZ Data Repository):

AutoCorrelations_README_and_scripts.zip
AutoCorrelations_output_part1of3.zip
AutoCorrelations_output_part2of3.zip
AutoCorrelations_output_part3of3.zip

For instructions on how to restore the correct folder-subfolder structure, please see section B.2 in “README.txt” (to be downloaded from the NIOZ Data Repository).

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Below follows an explanation of all the files in “~/AutoCorrelations”:

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“AutoCorrelations_README.txt”
The current README-file.

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“PermutationTest_Pearson_AutoCrossCorr.m” 
A Matlab script that computes:
-Pearson’s r (linear correlation coefficients)
-Spatial auto- and cross-correlation matrices
and applies this to the following data:
-Laserscan-derived Digital Elevation Models (DEM) and drone-derived algal cover (VDVI), for the years 2015, 2016 and 2017
-GoogleEarth image-derived algal cover (VDVI) for the years 2012, 2013, 2015 and 2017
-X-radiographs of vertical sediment profiles

Since these data are either don’t have normally distributed residuals (necessary for Pearson’s r), or statistical significance cannot be readily computed (auto/cross-corr.), another method is here used to test statistical significane. This is based on a large number (100 or 1000) of random permutations of the original data. The fraction of permuted datasets that gains a more extreme statistical outcome than the real data, then gives the p-value. 

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“~/AutoCorrelations/backup”
A subfolder containing a zipped backup of “PermutationTest_Pearson_AutoCrossCorr.m”.

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“~/AutoCorrelations/output”
A subfolder containing all output that was computed with the script “PermutationTest_Pearson_AutoCrossCorr.m”.

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