Cross-validating EcoCommons SDM outputs against reference implementations (on development)

Author details: Xiang Zhao

Contact details: support@ecocommons.org.au

Copyright statement: This script is the product of the EcoCommons platform. Please refer to the EcoCommons website for more details: https://www.ecocommons.org.au/

Date: July 2026

Script information

This page documents the scientific-agreement unit tests that guard every species distribution model (SDM) algorithm on the EcoCommons platform. Each test runs a real EcoCommons projection and compares it against an independent reference implementation — the algorithm’s own R package, Valavi et al. (2021)’s published benchmark on the NCEAS nsw14 data, or the reference implementations in Hijmans & Elith’s Species distribution modeling with R (the dismo vignette).

Every SDM agreement test in the EcoCommons R package (tests/testthat/test-sdm_projection_agreement.R) runs a real EcoCommons projection and compares it to a reference benchmark — the algorithm’s own R package, or Valavi et al. (2021)’s published benchmark on the NCEAS nsw14 data.

Algorithm coverage

EcoCommons ships 19 species-distribution / geographic algorithms. 12 are currently guarded by a scientific-agreement unit test; 7 are not yet.

Family	Algorithm	EcoCommons engine	Sci Unit Test?
biomod2 SDMs	GLM	stats::glm	✅ tested
	GAM	mgcv::gam	✅ tested
	RF	randomForest	✅ tested
	MARS	earth	✅ tested
	GBM	gbm	❌ not yet
	CTA	rpart	❌ not yet
	FDA	mda	❌ not yet
	ANN	nnet	❌ not yet
	SRE	biomod2 (envelope)	❌ not yet
dismo SDMs	MaxEnt	maxent.jar	✅ tested
	BRT	dismo::gbm.step	✅ tested
	Bioclim	dismo::bioclim	✅ tested
Geographic (spatial-only)	GeoDist	dismo::geoDist	✅ tested
	Convex Hull	dismo::convHull	✅ tested
	Circles	dismo::circles	✅ tested
	GeoIDW	dismo::geoIDW	✅ tested
	Voronoi Hull	dismo::voronoiHull	✅ tested
Niche / match	Range bagging	ecospat-style range bagging	❌ not yet
	Climatch	climate-matching	❌ not yet

Tested (12): GLM, GAM, RF, MARS, MaxEnt, BRT, Bioclim, GeoDist, Convex Hull, Circles, GeoIDW, Voronoi Hull. Not yet covered (7): GBM, CTA, FDA, ANN, SRE, Range bagging, Climatch. (The speciestrait_* / traitdiff_* trait-difference functions are out of scope here — they are not species-distribution models.)

Overview — all 16 tests

Every test runs a real EcoCommons projection and scores it against a reference implementation on Pearson correlation of predicted suitability.

#	Algorithm	Species	EcoCommons engine	Reference benchmark	Metric	Threshold	Result
1	GLM	Bradypus variegatus	biomod2 GLM	biomod2 GLM	Pearson	0.99	1.000 ✓
2		nsw14 (NSW bird)	biomod2 GLM (rescale off)	gam::step.Gam	Pearson	0.95	0.975 ✓
3	GAM	nsw14	biomod2 GAM_mgcv (rescale off)	mgcv::gam (all-smoothed, presence up-weighted)	Pearson	0.95	1.000 ✓
4	MaxEnt	Bradypus variegatus	maxent.jar	maxent.jar _avg	Pearson	0.90	0.987 ✓
5		nsw14	maxent.jar	dismo::maxent	Pearson	0.95	0.989 ✓
6	RF	Bradypus variegatus	biomod2 RF	biomod2 RF	Pearson	0.99	0.998 ✓
7		nsw14	biomod2 RF (rescale off)	randomForest	Pearson	0.95	0.986 ✓
8	BRT	Anguilla australis	dismo::gbm.step (unit weights)	dismo gbm.step (unit weights)	Pearson	0.95	0.985 ✓
9		nsw14	dismo::gbm.step (test mocks down-weighting)	gbm.step (down-weighted)	Pearson	0.95	1.000 ✓
10	MARS	nsw14	biomod2 earth (rescale off)	earth (glm=binomial, down-weighted)	Pearson	0.95	1.000 ✓
11	Bioclim	Bradypus variegatus	dismo::bioclim	dismo bioclim	Pearson	0.90	0.980 ✓
12	GeoDist	Bradypus variegatus	dismo::geoDist	dismo geoDist (book config)	Pearson	0.99	1.000 ✓
13	Convex Hull	Bradypus variegatus	dismo::convHull	dismo convHull	Pearson	0.99	1.000 ✓ (TSS 1.000)
14	Circles	Bradypus variegatus	dismo::circles	dismo circles	Presence footprint	—	identical ✓
15	GeoIDW	Bradypus variegatus	dismo::geoIDW	dismo geoIDW	Pearson	0.99	1.000 ✓
16	Voronoi Hull	Bradypus variegatus	dismo::voronoiHull	dismo voronoiHull	Pearson	0.99	1.000 ✓

All 16 pass — geoDist/convHull/geoIDW/voronoiHull reproduce the reference to Pearson 1.000, and Circles reproduces the reference presence footprint exactly once a two-part bug is fixed (see the geographic-models section below).

Rows 12–16 are the geographic (spatial-only) models — they predict from the geographic location of occurrences, not from environmental predictors — so their reference is the algorithm’s own dismo function as taught in Hijmans & Elith’s Species distribution modeling with R (Chapter 13).

Calibration alignment — every EcoCommons↔︎reference gap turned out to be weighting or rescaling, never the underlying learner:

• RF, GLM — rescale_all_models = false: RF Pearson 0.89 → 0.99, GLM 0.96 → 0.98.
• MARS — biomod2 fits earth(glm=binomial, weights=prevalence); the benchmark uses the same background down-weighting: 0.72 → 1.00. (Earlier thought to be an unfixable wrapper difference — it was just the missing weights.)
• GAM — biomod2 GAM_mgcv applies prevalence=0.5 as presence up-weighting (presence weight = bgNum/prNum, background = 1). For a GAM under UBRE the absolute weight scale drives smoothing-parameter selection, so Valavi’s background down-weighting (same ratio, ~60× smaller total) selects different smoothness: 0.87 → 1.00 once the benchmark matches biomod2’s presence up-weighting (both engines are mgcv).
• BRT — Valavi down-weights the background; EcoCommons production uses unit weights (down-weighting is proposed, not applied). The nsw14 BRT test mocks the internal weighting to down-weight — matching Valavi’s config (Pearson 1.000) — while the Anguilla BRT test keeps exercising the real unit-weight path (0.985).

Species: Bradypus variegatus (three-toed sloth, South America); Anguilla australis (short-finned eel, NZ); nsw14 (anonymised NSW diurnal bird, Valavi/NCEAS).

Model configurations (nsw14 suite)

Shared setup: species nsw14 (315 presences from disPo + background), 11 continuous NSW predictors (cti, disturb, mi, rainann, raindq, rugged, soildepth, soilfert, solrad, tempann, topo), random_seed = 0, deduplicated to one record per raster cell (8190 points).

Algorithm	EcoCommons (test config)	Valavi reference (benchmark)
GLM	biomod2 GLM — type=quadratic, test=AIC (stepwise both directions), family=binomial, rescale_all_models=false	glm → gam::step.Gam — per-var scope {drop / linear / poly(,2)}, AIC both directions, background down-weighted, normalised
GAM	biomod2 GAM_mgcv — s_smoother on all 11 predictors, k=-1, method=GCV.Cp, presence up-weighted, rescale_all_models=false	mgcv::gam — s() on all predictors, method=GCV.Cp, presence up-weighted (matched to biomod2)
MaxEnt	maxent.jar — autofeature (L+Q+P+H), betamultiplier=1, maximumiterations=500, cloglog	dismo::maxent (maxent.jar) — args="nothreshold", cloglog, not normalised
RF	biomod2 RF — classification, ntree=500, mtry=default, nodesize=5, rescale_all_models=false	randomForest — classification, ntree=500, type="prob"
MARS	biomod2 earth — type=simple (degree 1), penalty=2, thresh=0.001, pmethod=backward, glm=binomial, prevalence-weighted, rescale_all_models=false	earth — degree=1, pmethod=backward, glm=binomial, background down-weighted (matched to biomod2)
BRT	dismo::gbm.step — tree.complexity=5, learning.rate=0.001, bag.fraction=0.75, n.folds=5, n.trees=50, max.trees=10000, family=bernoulli, unit weights (test mocks down-weighting)	dismo::gbm.step — same hyper-parameters, background down-weighted, normalised

Bold = the calibration settings that were aligned. BRT uses Valavi’s exact hyper-parameters; the test mocks background down-weighting so it reproduces Valavi (best.trees 4350 = 4350) without changing EcoCommons’ shipped unit-weight default. GLM is the only algorithm that can’t be config-matched — biomod2 GLM and gam::step.Gam are different engines.

The continuous suitability maps further below use Valavi’s original Appendix S2 configs (e.g. MARS = caret-tuned earth, nprune 2–20; GAM = mgcv::gam REML with Valavi’s smooth / parametric split) — that’s why they reproduce Valavi’s published CSV points, whereas the benchmarks here are tuned to match EcoCommons (GAM: all-smoothed, GCV.Cp, presence up-weighted).

Benchmark references

Benchmark	Method paper	Engine / tool
GLM	McCullagh & Nelder (1989); Hastie & Tibshirani (1990) step.Gam	biomod2 GLM (EC) / gam::step.Gam (Valavi)
GAM	Hastie & Tibshirani (1990); Wood (2017) mgcv	biomod2 GAM_mgcv (EC) / mgcv::gam (Valavi)
MaxEnt	Phillips, Anderson & Schapire (2006); Phillips & Dudík (2008)	maxent.jar via dismo
RF	Breiman (2001)	randomForest
MARS	Friedman (1991)	earth
BRT	Elith, Leathwick & Hastie (2008)	dismo::gbm.step
Bioclim	Nix (1986); Booth et al. (2014)	dismo::bioclim
Geographic models (GeoDist, Convex Hull, Circles, GeoIDW, Voronoi Hull)	Hijmans & Elith, Species distribution modeling with R (Ch. 13)	dismo::geoDist / convHull / circles / geoIDW / voronoiHull
Background down-weighting	Elith et al. (2008); Valavi et al. (2021)	—
nsw14 species + rasters (NCEAS)	Elith et al. (2020)	disdat / OSF kwc4v
Valavi reference configs	Valavi et al. (2021)	Appendix S2
EcoCommons SDM platform	Thuiller et al. (2009) biomod2; Hijmans et al. dismo	—

Geographic (spatial-only) models

Rows 12–16 cover the five geographic models from Chapter 13 of Hijmans & Elith’s Species distribution modeling with R (the dismo vignette). Unlike every other algorithm on this page, these predict a species purely from the geographic location of its occurrence records — distance to, or containment by, the known points — and ignore the environmental predictors entirely. EcoCommons wraps each one directly from dismo:

Algorithm	EcoCommons function	dismo engine	Uses absence?	Output
GeoDist	EC_modelling_geodist	dismo::geoDist	no (presence-only)	continuous — inverse distance to nearest presence
Convex Hull	EC_modelling_convhull	dismo::convHull	no	binary — inside/outside the presence hull
Circles	EC_modelling_circles	dismo::circles	no	binary — inside/outside circles around presences
GeoIDW	EC_modelling_geoidw	dismo::geoIDW	yes (pres/abs)	continuous — inverse-distance-weighted interpolation
Voronoi Hull	EC_modelling_voronoihull	dismo::voronoiHull	yes	binary — Voronoi/Thiessen polygon classification

Two references, both checked

Like the nsw14 suite, these tests have two distinct reference points:

1. Literature anchor — the textbook’s own reported numbers. Chapter 13 prints a ModelEvaluation (AUC, point-biserial cor) for each model on the Bradypus variegatus data. We reproduce those exactly, to seven decimals. Two things are required to hit them: (a) RNGkind(sample.kind = "Rounding") — the book predates R 3.6.0’s change to the sample() algorithm; and (b) replaying the book’s exact random-number stream (including two incidental rnorm(50) calls in its evaluate() demo) so the train/background splits match.

Model	Book AUC	Reproduced AUC	Book cor	Reproduced cor
GeoDist	0.9006522	0.9006522	0.4197574	0.4197574
Convex Hull	0.7490217	0.7490217	0.3036379	0.3036379
Circles	0.8547826	0.8547826	0.5103540	0.5103540
GeoIDW	0.8893478	0.8893478	0.5354838	0.5354838
Voronoi Hull	0.5	0.5	NA	degenerate

Voronoi Hull is reported by the book as a degenerate result (AUC exactly 0.5): it subsamples 100 background points at the very end of the vignette’s global RNG stream, and the resulting classification does not discriminate. We reproduce the same degeneracy.

2. Implementation fidelity — EcoCommons vs an independent dismo run on identical inputs. This is the actual unit test (rows 12–16): run the real EcoCommons pipeline and, separately, call the same dismo function on the same occurrence / pseudo-absence set the platform used, then compare the prediction rasters.

Model	EcoCommons ↔︎ dismo	Notes
GeoDist	Pearson 1.0000	identical surface (RMSE 0)
Convex Hull	Pearson 1.0000	binary — TSS 1.000
GeoIDW	Pearson 1.0000	identical (RMSE 0)
Voronoi Hull	Pearson 1.0000	identical (RMSE 0)
Circles	footprint identical	binary — presence inside circles

Four of the five are implementation-identical — EcoCommons is a thin, faithful wrapper over the dismo engine, exactly as intended.

Each row is one geographic model, dismo reference (left) vs the EcoCommons projection (right), over the Bradypus variegatus* range; white points are the occurrence records the model was fitted on. Continuous models (GeoDist, GeoIDW) use a percentile colour stretch; binary models (Convex Hull, Circles, Voronoi Hull) show presence (green) vs absence (grey). For every model the two columns are visually indistinguishable — GeoDist, Convex Hull, GeoIDW and Voronoi Hull agree at Pearson r = 1.000, and Circles reproduces the reference presence footprint exactly.*

Prediction maps

Single-species benchmark predictions

Bradypus (South America): Maxent, Bioclim, GLM, RF. Anguilla BRT is on a fabricated one-cell-per-site grid (scattered pixels), so its “map” is not geographically meaningful — only its per-site predictions are.

nsw14 (Valavi) — continuous surface, published points & agreement

The clearest check places each algorithm in one row across three columns: (1) our continuous re-fit of Valavi’s config predicted across the whole NSW grid, (2) Valavi et al. (2021)’s own published point predictions (Models_prediction/*.csv) at the 702 evaluation sites, and (3) the site-level agreement — our raster value at each site (y) against Valavi’s published value (x), with the 1:1 line and per-model Pearson r. Columns 1–2 share the same 0–1 suitability scale; red rings mark observed presences.

For every algorithm the published points fall on our continuous surface — same eastern escarpment / north-east coast hotspot, same magnitude — and the agreement column makes it quantitative: GLM/GAM/MaxEnt/BRT collapse onto the 1:1 line (r = 1.000), MARS 0.998, RF 0.992 (small stochastic scatter). The points are Valavi’s actual published values (disPa coordinates over the NSW border), not our reproduction, so this comparison is independent of our package-version environment. This is the per-model view of the 702-site table below.

These maps reproduce Valavi’s published predictions. Extracting each raster band at Valavi’s 702 evaluation sites and correlating with the original Models_prediction/*.csv:

model	Pearson	Spearman	match
GLM	1.0000	1.0000	exact
GAM	1.0000	1.0000	exact
MaxEnt	1.0000	1.0000	exact
BRT	1.0000	1.0000	exact
MARS	0.9976	0.9980	near-perfect
RF	0.9922	0.9296	high (RF vote-fraction ties lower Spearman)

This works because disEnv — the environmental values Valavi predicted their points on — is itself sampled from these NSW rasters, so the raster value at each site is the value Valavi used. GLM, GAM, MaxEnt and BRT reproduce it exactly; MARS/RF show only small stochastic / version scatter (RF’s vote-fraction ties lower its Spearman). The agreement column of the figure above is the per-model, Pearson view of this table.

Agreement analysis

EcoCommons vs benchmark — every test

Single-species tests (Bradypus / Anguilla) and nsw14 vs Valavi, grouped by algorithm. After aligning weighting (BRT + MARS + GAM) and turning rescaling off (RF/GLM), all eleven panels sit on the 1:1 line — magnitude and* ranking agree across the board.*

The five geographic (spatial-only) models (tests 12–16), EcoCommons projection (y) vs the dismo reference on identical inputs (x). Continuous models (GeoDist, GeoIDW) collapse onto the 1:1 line (Pearson r = 1.000); the binary models (Convex Hull, Circles, Voronoi Hull) match cell-for-cell (agreement = 1.000, jittered so the 0/0 and 1/1 clusters are visible). Circles is shown after its fix. Every geographic model is an exact match to its dismo reference.

Reproducibility & benchmark provenance

The single most important thing to understand about these nsw14 benchmarks is what they are a reference to — because it determines when they must be regenerated and what “still matching Valavi” actually means.

Two different reference points

There are two distinct notions of “matching a reference”, and they behave very differently:

	Independent reimplementation (what the .tif benchmarks are)	Published paper numbers (Valavi’s Models_prediction/*.csv)
Produced by	generate_nsw14_valavi_benchmarks.R — Valavi’s exact configs re-run through the algorithm’s own R package (gam, randomForest, earth, dismo::maxent, dismo::gbm.step)	Valavi et al. (2021), fixed external artifact
What the test checks	Implementation fidelity — does EcoCommons wire up the algorithm the same way an independent implementation does?	Literature agreement — do we reproduce the actual published predictions?
Metric used here	Pearson on absolute suitability (the agreement tables above)	Pearson/Spearman at the 702 evaluation sites (the “reproduce Valavi’s published predictions” table above)
Sensitive to package version?	Cancels out — see below	Yes — this is the real anchor to the paper

The .tif benchmarks are not frozen EcoCommons snapshots (they come from different engines) and not Valavi’s literal published rasters either. They are a faithful re-run of Valavi’s recipe. So “matching the paper” via these benchmarks means “matching a faithful reproduction of Valavi’s config”, not bit-matching the 2021 output.

What changes the final benchmark

The absolute suitability values written into each benchmark .tif depend on the software environment they were generated in — not just the algorithm and config. In order of impact:

1. Package version drift. Tree-ensemble learners are the main offender: gbm (BRT) absolute predicted probabilities shift across gbm versions / platforms. randomForest, earth, gam, maxent.jar are far more stable. This is the reproducibility issue that forced the BRT benchmark to be regenerated inside the CI container rather than frozen from a dev machine (newer gbm on macOS drifts on Pearson in CI).
2. The R version and compiler toolchain (numerical libraries, BLAS).
3. Config / weighting changes — e.g. background down-weighting, normalisation, hyper-parameters. BRT down-weighting is now applied through the supported site_weight_scheme = "downweight" option in the nsw14_brt spec (no longer a test-only mock), so EcoCommons matches Valavi’s config directly.
4. The training-point construction — dedup one-per-cell, complete-cases filtering — which is deliberately matched to EcoCommons’ filter.raster.

Because of (1)–(2), benchmarks must be regenerated in the pinned CI container (ec-rpkg-ci-test-env), never on a local dev machine. Locally, R is not renv-activated, so a laptop pulls whatever package versions it happens to have — reintroducing exactly the drift the CI-native benchmark exists to avoid.

Why regenerating in the same environment is safe

When a benchmark is regenerated in a bumped environment, both the benchmark and EcoCommons run through the same new engine (for BRT, the same new gbm). Their absolute values shift together, so their Pearson agreement is preserved. That is precisely why regenerating-in-CI works: it cancels version drift on both sides.

The blind spot: because the two sides move together, a genuine engine change could move both the benchmark and EcoCommons away from Valavi’s 2021 numbers while they still agree with each other. The guard against silently re-baselining away from the paper is twofold:

• Rank-drift check (in the generator). write_bench() reports the Spearman correlation between each newly generated prediction and the currently committed .tif, and warns if it drops below 0.98. Benign scale drift (a new gbm) leaves rank order intact (Spearman ≈ 1); a low Spearman means the science moved, not just the scale — investigate before committing.
• Literature anchor. The “reproduce Valavi’s published predictions” table (702-site Pearson/Spearman vs the original CSVs) is the check that actually ties us to the paper, and it is version-robust because it is rank-friendly.

Rule of thumb: benchmark-generation environment == test-execution environment == the pinned container image. Whenever that pin moves (a package patch or a base-image rebuild), regenerate the affected .tif benchmarks in the new environment and commit them alongside the version bump — after confirming the rank-drift guard stays green.

Reproducibility environment — pinned package versions

These are the versions the benchmarks and agreement tests are pinned to, from renv-main.lock (== the deployed base image ec-base-r4.2 / CI test image ec-rpkg-ci-test-env). The base image is rebuilt only via a manual pipeline (BUILD_BASE=1); renv is not auto-activated, so this lock governs the container, not ad-hoc local runs.

R 4.2.2 · CRAN snapshot https://cloud.r-project.org

Package	Version	Role in the agreement suite
biomod2	4.2-2	EcoCommons GLM / RF / MARS engine (wraps the learners below)
dismo	1.3-9	EcoCommons BRT (gbm.step), MaxEnt (maxent.jar), Bioclim
gbm	2.1.8.1	BRT learner — version-sensitive; drives CI-native regeneration
randomForest	4.7-1.1	RF learner + Valavi RF benchmark
earth	5.3.2	MARS learner + Valavi MARS benchmark
terra	1.7-3	Raster I/O, benchmark grid, cell dedup
raster	3.6-14	dismo/raster-style stacks for geographic models
sf	1.0-9	Vector geometry / constraint regions
caret	6.0-93	Valavi’s original Appendix S2 MARS tuning (continuous maps)
testthat	3.1.6	Test runner (pinned; avoid newer-only features in CI)

Generation-only, not pinned in the lock: gam::step.Gam (GLM benchmark), mgcv::gam (GAM benchmark) and rJava (MaxEnt/JVM) are installed on top of the base image rather than via renv, so they take whatever the CI image provides at generation time. gam / mgcv are used only to build the GLM / GAM .tif benchmarks, not by the EcoCommons runtime.

References

• Booth, T.H., Nix, H.A., Busby, J.R. & Hutchinson, M.F. (2014). BIOCLIM: the first species distribution modelling package, its early applications and relevance to most current MaxEnt studies. Diversity and Distributions 20: 1–9.
• Breiman, L. (2001). Random forests. Machine Learning 45: 5–32.
• Elith, J., Leathwick, J.R. & Hastie, T. (2008). A working guide to boosted regression trees. Journal of Animal Ecology 77: 802–813.
• Elith, J., Graham, C.H., Valavi, R., Abegg, M., Bruce, C., Ford, A., Guisan, A., Hijmans, R.J., Huettmann, F., Lohmann, L.G., Loiselle, B.A., Moritz, C., Overton, J.McC., Peterson, A.T., Phillips, S., Richardson, K., Williams, S., Wiser, S.K., Wohlgemuth, T. & Zimmermann, N.E. (2020). Presence-only and presence-absence data for comparing species distribution modeling methods. Biodiversity Informatics 15: 69–80. (NCEAS data; distributed via the disdat R package and OSF project kwc4v.)
• Friedman, J.H. (1991). Multivariate adaptive regression splines. The Annals of Statistics 19: 1–67.
• Hastie, T.J. & Tibshirani, R.J. (1990). Generalized Additive Models. Chapman & Hall. (gam::step.Gam stepwise selection.)
• Hijmans, R.J., Phillips, S., Leathwick, J. & Elith, J. dismo: Species Distribution Modeling. R package. (gbm.step, maxent, bioclim.)
• Hijmans, R.J. & Elith, J. Species distribution modeling with R. (The dismo package vignette; Chapter 13 “Geographic models” — geoDist, convHull, circles, geoIDW, voronoiHull.)
• McCullagh, P. & Nelder, J.A. (1989). Generalized Linear Models, 2nd edn. Chapman & Hall.
• Nix, H.A. (1986). A biogeographic analysis of Australian elapid snakes. In: Atlas of Elapid Snakes of Australia (ed. R. Longmore), pp. 4–15. Australian Government Publishing Service.
• Phillips, S.J., Anderson, R.P. & Schapire, R.E. (2006). Maximum entropy modeling of species geographic distributions. Ecological Modelling 190: 231–259.
• Phillips, S.J. & Dudík, M. (2008). Modeling of species distributions with Maxent: new extensions and a comprehensive evaluation. Ecography 31: 161–175.
• Thuiller, W., Lafourcade, B., Engler, R. & Araújo, M.B. (2009). BIOMOD – a platform for ensemble forecasting of species distributions. Ecography 32: 369–373. (biomod2 R package.)
• Valavi, R., Guillera-Arroita, G., Lahoz-Monfort, J.J. & Elith, J. (2021). Predictive performance of presence-only species distribution models: a benchmark study with reproducible code. Ecological Monographs 92(1): e01486. (Appendix S1/S2 configurations; Models_prediction per-species outputs.)
• Wood, S.N. (2017). Generalized Additive Models: An Introduction with R, 2nd edn. Chapman & Hall/CRC. (mgcv package; GAM benchmark engine.)