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Taylor’s law predicts unprecedented pulses of forest disturbance under global change

  • Cornelius Senf*
  • , Rupert Seidl
  • , Thomas Knoke
  • , Tommaso Jucker
  • *Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)peer-review

10 Citations (Scopus)

Abstract

Climate extremes are causing increasingly large pulses of forest disturbance across biomes, raising concerns that forests are pushed beyond their safe operating space. However, predicting future disturbance pulses remains a major challenge, as these events are stochastic and driven by complex ecological and socio-economic processes. Here, we provide a tractable solution to this problem using Taylor’s law, which predicts changes in variability (and thus the frequency of extremes) from changes in the mean. We empirically test the hypothesis that forest disturbance dynamics can be described through Taylor’s law using high-resolution annual disturbance maps of Europe’s forests going back 35 years. We find strong evidence for a power law relationship between mean disturbance rates and their temporal variability, indicating that increasing mean disturbance rates – as observed for Europe and many other parts of the globe – significantly amplify the probability of large disturbance pulses. The power law relationship was consistent across natural disturbance agents, spatial grains, and biomes, and applied also to human-driven disturbances. Our findings challenge the assumption that extreme disturbance pulses are inherently unpredictable, providing a data-driven framework for their integration into forest policy and management.
Original languageEnglish
Article number6133
JournalNature Communications
Volume16
Issue number1
Early online date3 Jul 2025
DOIs
Publication statusE-pub ahead of print - 3 Jul 2025

Bibliographical note

Publisher Copyright:
© The Author(s) 2025.

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 13 - Climate Action
    SDG 13 Climate Action

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