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Comprehensively evaluating the performance of species distribution models across clades and resolutions: choosing the right tool for the job

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Abstract

Context

Species distribution modeling (SDM) is an integral tool for conservation, biogeography, and climate change biology. However, practioners have to choose from increasingly numerous SDM algorithms performing well under different conditions, including clade and resolution.

Objectives

To identify the most suitable SDM algorithms for trees, birds, mammals, and insects, uncover the driving factors of predictive performance, and examine how resolution affects performance and variable importance.

Methods

We use 27 SDM implementations, including random forests (RF), boosted regression trees (BRT), and Mahalanobis distance (MAH), and a comprehensive dataset of 49 species in Europe (trees, birds, mammals, and insects) to fit a total of 19,845 models, at 20 km, 10 km, and 5 km resolution. For selected species, we also compare the mapped predictions of 3 algorithms, and assess how variable importance changes with resolution for BRT.

Results

RF and BRT outperformed in terms of model performance (AUC = 0.938) for all clades (but not species), whereas decision trees, MaxLike, and Lasso overall underperformed (AUC = 0.848). The performance majorly depended on both clade (F = 101.4) and its interaction with resolution (F = 133.2), and displayed a general decline with resolution, while variable importance exhibited complex shifts in response to resolution.

Conclusions

RF and BRT are highly recommended but may require bias correction methods, whereas decision trees appeared unfavorable—particularly at higher resolutions. Given the complicated picture at the species level, varying tendencies to overfit, and resolution effects on both model performance and variable importance, we urge to routinely explore a range of algorithms, parametrizations, and resolutions.

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Abbreviations

AUC:

Area under the curve (of the receiver-operating characteristic)

BRT:

Boosted regression trees

BRTt:

Tuned BRT

CART:

Classification and regression trees

CCR:

Correct classification rate

FDA:

Flexible discriminant analysis

GAM:

Generalized additive models

GBIF:

Global biodiversity information facility

GLM:

Generalized linear models

GLMP:

GLM using orthogonal polynomials

Lasso:

GLM with Lasso regularization

MAH:

Mahalanobis distance

MARS:

Multivariate adaptive regression splines

MDA:

Mixture discriminant analysis

MLP:

Multi-layer perceptron

MXE:

MaxEnt (implemented in maxnet R package)

MXElh:

MXE with linear & hinge features

MXElq:

MXE with linear & quadratic features

MXL:

Maximum Likelihood

PHI:

Yule’s Phi (also known as Matthew’s correlation coefficient)

RBF:

Radial basis function neural network

RF:

Random forest

RFt:

Tuned RF

RNGR:

Ranger (fast implementation of RF)

RPRT:

Recursive partitioning and regression trees

SDM:

Species distribution modeling

SEN:

Sensitivity

SPE:

Specificity

SVM:

Support vector machines

SVMt:

Tuned SVM

TSS:

True skill statistic

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Acknowledgements

The council of agriculture of Taiwan supported this study (Project No: 109-AS-4.2.2-ST-a1 and 109-2621-M-002 -008 -MY3).

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Authors and Affiliations

  1. Department of Bioenvironmental Systems Engineering, National Taiwan University, No.1, Sec.4, Roosevelt Rd., Taipei, 10617, Taiwan

    Rainer Ferdinand Wunderlich, Hussnain Mukhtar & Yu-Pin Lin

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  1. Rainer Ferdinand Wunderlich
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  2. Hussnain Mukhtar
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  3. Yu-Pin Lin
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Contributions

Manuscript (first draft)— RFW; Conceptualization—RFW, HM, & YPL; Data collection—RFW; Coding & Analysis—RFW; Visualization—RFW & HM; Manuscript (revision)—HM & YPL; Supervision—HM & YPL.

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Correspondence to Yu-Pin Lin.

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Wunderlich, R.F., Mukhtar, H. & Lin, YP. Comprehensively evaluating the performance of species distribution models across clades and resolutions: choosing the right tool for the job. Landsc Ecol 37, 2045–2063 (2022). https://doi.org/10.1007/s10980-022-01465-1

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  • DOI: https://doi.org/10.1007/s10980-022-01465-1

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