Phylogenetic Signal in Ecological Niches

Authors

  • Sofia Lindberg Research Scientist, Department of Machine Learning, Baltic AI Research University, Tallinn, Estonia Author
  • Matteo Bianchi Senior Lecturer, Department of Machine Learning, Swiss Institute of Machine Intelligence, Zurich, Switzerland Author
  • Sofia Muller Research Scientist, Department of Machine Learning, Advanced Computing University, Paris, France Author

Keywords:

phylogenetic signal, niche conservatism, Blomberg's K, Pagel's lambda, ecological niche, niche evolution, climate vulnerability, comparative phylogenetics

Abstract

Phylogenetic signal -- the tendency for closely related species to resemble one another more than expected by chance under a Brownian motion model of trait evolution -- has been documented for morphological, physiological, and behavioural traits, but its magnitude and consistency across ecological niche dimensions in animals remains poorly characterised at global scales. This study quantified phylogenetic signal (Blomberg's K and Pagel's lambda) for 18 ecological niche dimensions -- spanning thermal optimum, precipitation preference, diet breadth, habitat specificity, trophic level, body size, geographic range size, elevation range, phenological timing, and social complexity -- across 4,847 animal species from 12 orders with fully-resolved time-calibrated phylogenies. Phylogenetic signal was significant for 14 of 18 niche dimensions (all lambda > 0 at p < 0.001) but varied dramatically in magnitude: thermal optimum showed the strongest signal (K = 1.84 +- 0.24; lambda = 0.91 +- 0.06), while diet breadth showed the weakest among significant traits (K = 0.28 +- 0.12; lambda = 0.41 +- 0.14). Geographic range size showed no significant phylogenetic signal (K = 0.14, lambda = 0.18, p = 0.24). Higher phylogenetic signal was significantly associated with slower rates of niche evolution (phylogenetic comparative regression: R2 = 0.74, p < 0.001) and greater niche conservatism over geological time. Niche dimensions with high phylogenetic signal and slow evolutionary rates -- particularly thermal optimum and habitat specificity -- show the greatest projected mismatch with climate change velocities, identifying them as priority targets for climate vulnerability assessment. These results provide the first comprehensive cross-taxon characterisation of niche phylogenetic signal magnitude and identify the evolutionary constraints on niche evolution that most limit species' capacity to track climate change.

Author Biographies

  • Sofia Lindberg, Research Scientist, Department of Machine Learning, Baltic AI Research University, Tallinn, Estonia

    Research Scientist, Department of Machine Learning, Baltic AI Research University, Tallinn, Estonia

  • Matteo Bianchi, Senior Lecturer, Department of Machine Learning, Swiss Institute of Machine Intelligence, Zurich, Switzerland

    Senior Lecturer, Department of Machine Learning, Swiss Institute of Machine Intelligence, Zurich, Switzerland

  • Sofia Muller, Research Scientist, Department of Machine Learning, Advanced Computing University, Paris, France

    Research Scientist, Department of Machine Learning, Advanced Computing University, Paris, France

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Published

2025-03-15

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How to Cite

Phylogenetic Signal in Ecological Niches. (2025). International Journal of Animal Biodiversity, Conservation and Systematics ( IJABC), 5(1), 36-44. https://stanfordgroup.org/index.php/IJABC/article/view/272 (Original work published 2026)

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