Reproduction of Phillips et al. 2009 Table 2 — MaxEnt random vs target-group background AUC
created: 2026-05-31T08:08:45.811Z
creator: Anne Fouilloux
license: https://creativecommons.org/licenses/by/4.0/
introduces: Reproduction of Phillips et al. 2009 Table 2 — MaxEnt random vs target-group background AUC
was created at: platform.sciencelive4all.org
type: Nanopublication
has assertion: assertion
has provenance: provenance
has publication info: pubinfo

sig
has algorithm: RSA
signed by: Anne Fouilloux

Reproduction of Phillips et al. 2009 Table 2 — MaxEnt random vs target-group background AUC
type: FORRT-Replication-Study
type: Reproduction-Study
related: Q122175981
related: Q2725298
related: Q327120
has deviation description: (1) MaxEnt engine: the open-source elapid/maxnet implementation is used rather than Phillips' original Java Maxent, so exact AUC decimals are expected to differ even where direction and magnitude agree. (2) Only MaxEnt is run — Phillips' broader Table 2 also covered BRT, MARS, GAM and other methods, which are not reproduced here. (3) One species fewer is modelled than Phillips' 226 (225 here) because a species whose presence-absence evaluation column has no presence/absence variation gives an undefined AUC and is dropped. (4) A minimum-presence threshold of 5 occurrences is applied before a species is fit.
has discipline: Q7150
has methodology description: The Elith et al. 2006 NCEAS presence-only / presence-absence benchmark — the same data Phillips used — is obtained from the rspatial/disdat R data package (data paper doi 10.17161/bi.v15i2.13384) by downloading its .rds tables and reading them in Python with pyreadr. For each species across the six regions (AWT, CAN, NSW, NZ, SA, SWI), a MaxEnt model (elapid engine, linear + quadratic + hinge features) is fit twice: once against the region's random background sites supplied by disdat, and once against a target-group background formed from the pooled presence localities of all species in the same biological target group. Both models predict at the independent presence-absence evaluation sites and AUC is computed with scikit-learn. Per-species AUC for the two background types is aggregated to region, group and overall means, and the paired difference is tested with a Wilcoxon signed-rank test. 225 species across 6 regions are modelled.
has scope description: This study reproduces the Maxent row of Phillips et al. 2009 Table 2: the comparison of mean predictive AUC for presence-only species distribution models trained with random background versus target-group background. In scope: the direction and magnitude of the AUC gain from target-group background, aggregated across species, and the stronger gain in regions with greater sampling bias. Out of scope: the other modelling methods Phillips also tested (BRT, MARS, GAM and others) and the absolute predicted-distribution maps — only the MaxEnt AUC comparison on this benchmark is tested here.
targets claim: target-group-background-improves-auc