they require additional scientific (causal) models The reasons for a statistical analysis are not found in the data themselves, but rather in the causes of the data The causes of the data cannot be extracted from the data alone. No causes in; no causes out.
1 0 1 0 1 0 1 1 i 1 islands per D species 0 2 i 2 Fig. 3. Example of a simple presence/absence matrix with a checker- board distribution: four islands and two species islands and are absent from species-rich islands (see Fig. 2 right, which has a supertramp as species e but has the same row sums and grand total as Fig. 2 left). Now compare the rearrangeability of the two matrices of Fig. 2. Recall that the rearrangement algorithm by which Connor and Simberloff generate simulated matrices seeks 2 by 2 subma- trices (not necessarily in adjacent rows or columns) of the form 10) 1)o (0, (?0 and changes them to the opposite form. This manipulation alters neither row nor column sums and hence maintains the con- Islands 00101011110100001110 Ii010100001011110001 00101111001001001110 11010000110110110001 10110000000011111011 01001111111100000100 10011101011001001100 01100010100110110011 00101011100100110110 Species ii010100011011001001 11010111100001011000 00101000011110100111 i0000111100011001110 01111000011100110001 11001100100111000110 00110011011000111001 00010101101011101010 11101010010100010101 01101101010001010101 10010010101110101010 Conor & Simberloff 1979, Diamond & Gilpin 1982 Species Locations No null ecology
data points must not change under permutations. In node- . CC-BY-NC-ND 4.0 International license available under a was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint (which this version posted June 7, 2021. ; https://doi.org/10.1101/2021.06.04.447124 doi: bioRxiv preprint Hart et al 2021 Network permutation methods: low power, high false positives