@prefix this: . @prefix sub: . @prefix np: . @prefix rdf: . @prefix prov: . @prefix npx: . @prefix dc: . @prefix xsd: . sub:Head { this: a np:Nanopublication; np:hasAssertion sub:assertion; np:hasProvenance sub:provenance; np:hasPublicationInfo sub:pubinfo . } sub:assertion { sub:phillips-table2-maxent-reproduction a , ; "Reproduction of Phillips et al. 2009 Table 2 — MaxEnt random vs target-group background AUC"; , , ; """(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."""; ; "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."; "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."; . } sub:provenance { sub:assertion prov:wasAttributedTo . } sub:pubinfo { "Anne Fouilloux" . this: dc:created "2026-05-31T08:08:45.811Z"^^xsd:dateTime; dc:creator ; dc:license ; npx:introduces sub:phillips-table2-maxent-reproduction; npx:wasCreatedAt ; "Reproduction of Phillips et al. 2009 Table 2 — MaxEnt random vs target-group background AUC"; . sub:sig npx:hasAlgorithm "RSA"; npx:hasPublicKey "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"; npx:hasSignature "BtMFYI1flRZ9+kwy+CTdoIjew3ANC8UgdPi5FBZLxXle+mhoAMt3zZ5qyJ8shaSr3o49pVOmRiO67HrGNt+iKDF42JlaPPV6uSTQdC0qQoZPyCUe4iDehyYfCSbkrHGstjGseByxLEPT/KfuUKYbnkO8beT+b+m/wZK9j7rnBzFtbeeORbp9Rou8DnruWrNi+O9bDSJ9oXZR81BLjVVx0/NV9L9al9AQS/8fJtB3dmiWeGtHhSSFAksWDDQT4wZkN5y38ok2kFYJnjV17+uaEHRetzxTyx+erl4fHNfl0PZBHiE6Y66kXiI6Rcolk6tTpN2Xnzo92widIg4tkjgGaFs5awPacxpNZPTDzqThEBcFfBPVtTYCXQuKFDRpHIeBzErosqb7sMweWlG+5+yWC7s1/EKGacxmJXqvCJoF/Qd7BjVpbdrvZ1UfGMqO8BweSidKDuTLGPxfHvEKEsLS5FKOyNhnQVNwr/HC5pTDosuxUEUp2eSkonqu+NPklDPGgMC7sTlXeRiBKRiBmWBZHdhWZm7/HxHs9c2WKtDHaX449k8wfe8ljzN5U2jHFuC/ZyZjvLLFlxxn1k8RgW9MrWyAK5q9ccSGMYQiv2vRBUVfZxpmvWy39aOYyUgUTu487HPwGdI19dRoSo1jl9C3RSBrn9j65AsmFLf2l8KgJJg="; npx:hasSignatureTarget this:; npx:signedBy . }