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Transferability of trait-based species distribution models
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  • Peter Vesk,
  • William Morris,
  • William Neal,
  • Karel Mokany,
  • Laura Pollock
Peter Vesk
University of Melbourne
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William Morris
The University of Melbourne
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William Neal
University of Melbourne
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Karel Mokany
CSIRO
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Laura Pollock
Laboratoire d'Ecologie Alpine
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Abstract

Trait-based species distribution models (trait-SDMs) enable prediction to new species and situations based on traits. However, predictive transferability is unknown. We fit trait-SDMs with specific leaf area (SLA), maximum height and seed mass as species level predictors in generalised linear mixed models with four environmental predictors for 20 species of eucalypt trees in an outlying reference region. Trait-environment interactions included heavy-seeded species increasing in rugged areas and high-SLA species increasing in areas receiving runoff. We predicted occurrences using traits for 82 species across 18 target regions over >100,000 km2 in south-eastern Australia. Median predictive performance for new species in target regions was 0.65 (area under the receiver operating curve) and 1.24 times that of random (area under the precision recall curve). Prediction in target regions did not worsen across geographic, environmental or compositional space. This work provides a path for first-order models of species distribution using traits.