SAGE: A sampling-aware global evaluation benchmark for species distribution modeling

Emilia Arens; Nina van Tiel; Robin Zbinden; Damien Robert; Lukas Drees; Chiara Vanalli; Benjamin Kellenberger; Niklaus E. Zimmermann

cs.LGSep 25, 2026

SAGE: A sampling-aware global evaluation benchmark for species distribution modeling

Authors: Emilia Arens, Nina van Tiel, Robin Zbinden, Damien Robert, Lukas Drees, Chiara Vanalli, Benjamin Kellenberger, Niklaus E. Zimmermann,

Organizations: EcoVision Lab, Department of Mathematical Modeling and Machine Learning, University of Zurich, Zurich, Switzerland · ECEO, École Polytechnique Fédérale de Lausanne, Sion, Switzerland · Swiss Federal Institute for Forest, Snow and Landscape Research, WSL Birmensdorf, Switzerland · Ecosystems and Landscape Evolution, Institute of Terrestrial Ecosystems, Department of Environmental Systems Science, ETH Zürich, Zürich, Switzerland ·

Abstract

Knowing where species occur is fundamental for biodiversity research and conservation. Species distribution models (SDMs) link species observations to environmental conditions to estimate their spatial distribution. However, accuracy varies with the underlying data and models, making it essential to know for which species models can be trusted. Deep-learning-based SDMs ("DeepSDMs") now jointly model thousands of species, drawing on hundreds of millions of community-science records. At this scale, averaging performance hides substantial species-level variability, particularly for rare species, often of greatest conservation concern. Records are also strongly biased, making occurrence counts misleading. Accounting for these factors is essential for a reliable and informative evaluation of multi-species SDMs. Here, we introduce a Sampling-Aware Global Evaluation (SAGE) benchmark, combining GBIF records for training with sPlotOpen vegetation plots for presence-absence evaluation across 5771 plant species. We propose an evaluation framework that groups species based on two properties, sampling effort and relative prevalence, which describe how densely a species' range is sampled and how frequently the species is recorded. Evaluating single-species SDMs and multi-species DeepSDMs, we find that Random Forests and DeepSDMs perform best overall, but neither dominates: DeepSDMs outperform single-species SDMs for infrequently recorded species while offering no consistent advantage for well-sampled ones. Crucially, this advantage emerges only when established bias-correction practices, such as spatial thinning and reweighting, are carried over to the deep-learning setting. SAGE helps identify the species and data conditions for which a given approach is beneficial, thereby supporting the development of more transparent and ecologically credible SDMs. Data and code: https://earens.github.io/sage/

Figures & tables

Figure 1: Overview of SAGE. Dataset: $89\,768\,872$ presence-only (PO) occurrences from GBIF paired with $53\,336$ expert-curated presence-absence (PA) vegetation plots from sPlotOpen ( Sabatini et al.,, 2021 ) , covering $5771$ non-anonymized plant species. Evaluation framework: each species is characterized by two properties, sampling effort and relative prevalence, defining a two-dimensional space partitioned into sparse versus dense sampling effort and infrequently versus frequently recorded species. Model comparison: models are compared across this space, with performance differences reported per quadrant rather than as a single average.

Benchmark	Geographic Scope	# Species	Species Identity	Evaluation Data
Elith et al., (2020)	Regional	$223$	No	Plot data
Cole et al., (2023)	Global	$47\,375$ †	Yes	Expert ranges
SatBird ( Teng et al.,, 2023 )	Regional	$670$	Yes	Community science
GeoPlant ( Picek et al.,, 2024 )	Europe	$5016$	No	Plot data
SAGE (ours)	Global	$5771$	Yes	Plot data

Table 1: Comparison of existing SDM benchmark datasets. Color indicators reflect the degree to which each criterion supports transparent, species-resolved evaluation: fully, partially, not fulfilled. † Training data only; evaluation covers up to

2418

species.

Figure 4: Examples of species representing the four quadrants of the property space , with their sampling effort (SE in blue) and relative prevalence (RP in orange), and their corresponding percentile ranking (P): (a) Chryselium gnaphalioides sparse and infrequent; (b) Castilleja fissifolia sparse and frequent; (c) Aspidistra attenuata dense and infrequent; (d) Staphylea formosana dense and frequent. Species photos: Chryselium gnaphalioides © Fabien Anthelme, Pl@ntNet, CC BY-SA 2.0 (observed April 3, 2019), Castilleja fissifolia © Fabien Anthelme, Pl@ntNet, CC BY-SA 2.0 (observed November 25, 2021), Aspidistra attenuata © Jacy Chen, iNaturalist, CC BY 4.0 (observed December 9, 2021), Staphylea formosana © Cheng-Te Hsu, iNaturalist, CC BY 4.0 (observed March 31, 2026).

	AUROC (%) $\uparrow$
		Sparse		Dense
Model	Overall	Infrequent	Frequent	Infrequent	Frequent	Time (min)
Single-species SDMs
GLM	84.0 ± 0.0	84.5	81.9	86.3	83.2	1667 ± 25
BRT	85.2 ± 0.0	85.5	83.5	87.2	84.6	1152 ± 29
GAM	83.8 ± 0.1	83.9	82.0	85.9	83.4	2073 ± 49

Table 2: Models performance comparison on SAGE . Overall and group-level AUROC (

\%

) are reported as the mean ± standard deviation over 5 random seeds. Training times are given in minutes (see Section 2.3.3 for details). For the corresponding AUPRG performance, see Appendix D.2 .

Figure 5: Performance differences ( $\Delta$ AUROC, %) across species groups in the data-property space , with sampling effort on the horizontal axis and relative prevalence on the vertical axis. The thin dashed L-strips along the bottom and left edges isolate the tails of the distribution (bottom: the 10% of species with the most sparsely sampled ranges; left: the 10% of species most infrequently observed). (a–c) Pairwise model comparisons: (a) Random Forest – MaxEnt, (b) Optimized DeepSDM – RF, (c) Optimized DeepSDM – Baseline DeepSDM. Positive values (green) indicate that the model named first in each panel title outperforms the one named second; negative values (purple) indicate the opposite. (d–g) Incremental effect of each design choice with respect to the previous configuration considered for the DeepSDM setup: (d) aggregation, (e) subsampling, (f) architecture tuning, (g) loss function. Positive values (green) indicate that the added design choice outperforms the previous configuration, while negative values (purple) indicate the reverse.

(a) Subsampling		(b) Architecture				(c) Loss
Threshold $N$	AUROC	Model	$d$	$B$	AUROC	Pseudo-absence	Weighting	AUROC
None (full)	84.0	MLP	512	4	84.3	Both	None	84.5
$100\,000$	84.0	ResNet	512	4	84.4	Random	None	80.1
$10\,000$	84.1	ResNet	512	8	84.5	Target-group	None	85.5
$1000$	84.3	ResNet	1024	8	84.5	Target-group	Tuned	85.7
$100$	83.7	Transf.	512	4	84.5	Target-group	Freq.	85.7

Table 3: Sensitivity analyses of the design choices. Average AUROC (

\%

) across all species for different (a) subsampling thresholds, (b) model architectures, and (c) loss configurations. Here,

d

denotes the hidden dimension,

B

the number of blocks, and the loss weighting can be based on the frequency of species presences and/or fine-tuned via its hyperparameters.

Figure 6: Prediction maps for four species representing the four quadrants of the property space , comparing Random Forest (b) and the optimized DeepSDM (c) . Predicted probabilities are rescaled for each species and model through min-max normalization; colorbars are therefore not comparable. Training and test data are shown in the top row for reference. Species photos: Quercus ilex © Guillaume Martin, iNaturalist, CC BY-NC 4.0 (observed February 7, 2026), Azorella pedunculata © David Torres, Pl@ntNet, CC BY-NC 4.0 (observed January 18, 2026), Aspidistra attenuata © Jacy Chen, iNaturalist, CC BY 4.0 (observed December 9, 2021), Artemisia mesatlantica © Abdelmonaim Homrani Bakali, teline.fr (Biodiversité végétale du sud-ouest marocain), CC BY-NC 4.0 (observed October 2018).

Figure 7: Geographic distribution of model performance and data properties. Values are obtained by averaging over all species whose ranges overlap each country polygon, or finer administrative units for larger countries. Differences are computed between the optimized DeepSDM and the baseline DeepSDM (a) , and between the optimized DeepSDM and the Random Forest (b) . The two species data properties, sampling effort (c) and relative prevalence (d) , are computed over the same regions. Note : the color scale differs between the two performance panels.

Appendix figures & tables13 assets

Supplementary material from the paper’s appendix.

Appendix

Figure 8: Geographic distribution of occurrence records across the two other GBIF training dataset variants : aggregated to a 1 km grid ( $57\,084\,622$ records; $4\,100\,969$ samples), and subsampled with $N=1000$ occurrences per species ( $43\,071\,245$ records; $1\,627\,928$ samples). Color indicates observation density per H3 hexagonal cell (resolution 3) on a logarithmic scale. All variants retain the same $5771$ species.

Figure 9: Distribution of per-species occurrence counts across dataset variants. Top row : full, aggregated (1 km), and subsampled ( $N=1000$ ). Bottom row : additional subsampling thresholds ( $N\in\{10,100,10000,100000\}$ ). Each panel shows the number of species as a function of occurrence count.

		Sparse		Dense
Statistic	Overall	Infreq.	Freq.	Infreq.	Freq.
# species	$5771$	$1421$	$1464$	$1464$	$1422$
Presence-only (training)
# occurrences (full)	$89\,768\,872$	$2\,647\,373$	$20\,489\,219$	$5\,902\,674$	$60\,729\,606$
# occurrences (1 km)	$57\,084\,622$	$1\,927\,774$	$13\,293\,634$	$3\,916\,522$	$37\,946\,692$
avg. / species	$9892$	$1357$	$9080$	$2675$	$26\,685$

Appendix

Table 4: Detailed dataset statistics for SAGE , overall and stratified by the four species groups defined by the median of sampling effort (sparse/dense) and relative prevalence (infrequent/frequent). PO occurrence counts are reported both for the full training set and the 1 km-aggregated variant used to compute the species properties; PA presences are recorded across

53\,336

sPlotOpen plots. Per-species averages and medians are computed over the species in each group.

Acronym	Name	Description	Unit
bio1	Annual mean temperature	Mean annual near-surface air temperature calculated as the average of mean monthly temperatures over the year	∘ C
bio2	Mean diurnal range	Mean diurnal near-surface air temperature range computed as the average of monthly (tasmax $-$ tasmin)	∘ C
bio3	Isothermality	Isothermality: $100\times(\text{bio2}/\text{bio7})$ ; compares day–night variability to annual temperature range	unitless
bio4	Temperature seasonality	Standard deviation of mean monthly temperatures	∘ C $/100$
bio5	Max temp. warmest month	Highest monthly mean of daily maximum temperatures across the year	∘ C
bio6	Min temp. coldest month	Lowest monthly mean of daily minimum temperatures across the year	∘ C

Appendix

Table 5: Bioclimatic predictors derived from CHELSA v2.1 at 1 km spatial resolution ( Brun et al.,, 2022 ) .

Acronym	Name	Description	Unit
ORCDRC	Soil organic carbon	Organic carbon content of the fine earth fraction	g kg -1
PHIHOX	Soil pH (H 2 O)	Soil pH measured in water (stored as pH $\times 10$ )	pH $\times 10$
SNDPPT	Sand content	Sand fraction of the fine earth fraction	w%
SLTPPT	Silt content	Silt fraction of the fine earth fraction	w%
CLYPPT	Clay content	Clay fraction of the fine earth fraction	w%
BLDFIE	Bulk density	Bulk density of the fine earth fraction	kg m -3

Appendix

Table 6: Soil property predictors derived from SoilGrids at 250 m spatial resolution ( Hengl et al.,, 2017 ) .

Acronym	Name	Description	Unit
elevation	Elevation	Terrain elevation	m
roughness	Roughness	Largest local elevation difference	m
tri	Terrain Ruggedness Index	Mean local elevation difference	m
tpi	Topographic Position Index	Elevation relative to local neighbourhood	m
vrm	Vector Ruggedness Measure	Local dispersion of surface-normal vectors	unitless
aspectcosine	Aspect cosine	Cosine-transformed aspect	unitless

Appendix

Table 7: Topographic predictors derived from global terrain products at 1 km spatial resolution ( Amatulli et al.,, 2018 ) .

Acronym	Name	Pressure Description	Unit
Built2009	Built environments	Urban and built-up areas	score
Popdensity2010	Population density	Human population density	score
Lights2009	Night-time lights	Night-time light emissions	score
Croplands2005	Croplands	Intensive cropland agriculture	score
Pasture2009	Pasturelands	Grazing and pasture lands	score
Roads	Major roadways	Roads and associated access	score

Appendix

Table 8: Human footprint predictors describing anthropogenic pressures. Variables are derived following the Human Footprint methodology ( Venter et al.,, 2016 ) at 1 km spatial resolution.

Figure 10: Distribution of species in the two-dimensional property space , defined by sampling effort (SE) and relative prevalence (RP). (a) Joint distribution shown as a 2D hexbin plot on the original scale, with marginal histograms along each axis. (b) Same distribution shown in percentile-rank space, with percentile thresholds ( $P_{10}$ , $P_{50}$ , $P_{100}$ ) indicated. (c) The resulting species grouping scheme: four quadrants defined by the median of each property and the lowest-decile groups.

Table 9: Hyperparameter grids tested for each standard SDM , along with the resulting overall test AUROC (%; single reference run, seed 0).

Figure 11: Fine-grained performance differences ( $\Delta$ AUROC, %) across the property space. Same panel layout as Figure 5 , but computed on a smoothed rank grid (window size 2000, step 50) rather than eight discrete regions. Panels (a–c) show pairwise model comparisons; panels (d–g) show the incremental effect of each design choice.

	AUPRG ( $\uparrow$ )
		Sparse		Dense
Model / Configuration	Overall	Infrequent	Frequent	Infrequent	Frequent	Time (min)
Non-DL models
GLM	79.3 ± 0.2	76.6	77.6	82.0	80.3	1667 ± 25
BRT	84.5 ± 0.2	83.9	82.1	87.7	84.0	1152 ± 29
GAM	81.0 ± 0.2	78.4	78.8	84.2	81.7	2073 ± 49

Appendix

Table 10: Models performance comparison on SAGE. Overall and group-level AUPRG (

\%

) are reported as median ± standard deviation over 5 random seeds.

Figure 12: Performance differences ( $\Delta$ AUPRG, %) across species groups defined by sampling effort and relative prevalence , mirroring Figure 5 with AUPRG in place of AUROC.

	Unweighted		$\sqrt{\text{Area}}$ -weighted		Area-weighted
Model	AUROC	Rank	AUROC	Rank	AUROC	Rank
Single-species SDMs
GLM	84.0	5	83.8	5	83.8	5
BRT	85.3	3	85.1	3	85.1	3
GAM	83.7	6	83.5	6	83.5	6
MaxEnt	84.5	4	84.3	4	84.4	4

Appendix

Table 11: Model performance under area-weighted evaluation. AUROC (%) averaged over all species, with each evaluation cell weighted uniformly, by the square root of its surveyed area, or proportionally to it. Cells without a recorded area are assigned the mean recorded area (

530

m 2 ). Scores are computed from the seed-0 run of each model. Down-weighting small plots lowers every score slightly and leaves the ranking unchanged.

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