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Table 1 AIC-based calculations for the various pseudo-absence strategies.

From: Do pseudo-absence selection strategies influence species distribution models and their predictions? An information-theoretic approach based on simulated data

Pseudo-absence Selection strategy Ranking of true model out of 726 candidate models based on Akaike weights (w i ) Delta AIC (ΔAIC) of the true model Akaike weight (w i ) of the true model Akaike weight (w i ) of the top ranking model
ENFA     
100 9 3.604723 0.023729 0.143891
90 42 6.920832 0.004007 0.127529
80 16 3.882684 0.018746 0.130621
70 14 3.845144 0.021403 0.146365
60 13 3.863541 0.021315 0.14711
50 12 3.737772 0.025063843 0.162440578
Bioclim     
100 55 9.80294298 0.001365089 0.183588
90 22 3.599437 0.013378 0.080906
80 33 3.027241 0.009334 0.042406
70 38 3.581413 0.007646 0.045825144
60 26 3.146754 0.0108 0.052088
50 33 3.708884 0.008264 0.052791
Random 7 1.474885235 0.034985305 0.073139658
True absences 2 1.130481 0.104319 0.183588
  1. For each pseudo-absence strategy we fit 726 candidate logistic regression models using 131 presence records and 10,000 pseudo-absences. (ΔAIC) values less than 2 indicate considerable support, > 2 but < 10 indicate substantially less, and > 10 indicate essentially no support by the data. Akaike weights (w i ) can be interpreted as the percent of times we would expect a candidate model to be the one most strongly supported by the data if the experiment were repeated on a different sample.