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. 2022 Oct 6;12:16680. doi: 10.1038/s41598-022-20370-w

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© The Author(s) 2022

Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

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Variance partitioning (a) and canonical correspondence analyses (b–d) for the first investigation. A key to the CCA figure labels is provided in Supplementary Table S2. The variance partitioning shows model choice (X4) to be the major factor affecting predictive accuracy, more than twice the strength of the effects of spatial complexity of the resistance surface (X2), which in turn is roughly triple that of the mechanism (X1) and the remaining parameters - autocorrelation, scale and scaling function - combined (X3). In (b) we see that predictions by resistant kernels overall give the highest correlation and overlap, and those by Circuitscape result in the least error, whereas factorial least-cost paths is seen to be the least accurate with all three statistics. (c) shows that higher levels of spatial complexity result in great model error and overlap, with little effect on correlation. (d) demonstrates model performance to be much lower when using only the attraction mechanism, without any inclusion of energetic cost or mortality risk.