Microscale nonrandomness revisited: An overlooked predictor of ecological change

Abstract

Abrupt ecological transitions have driven the development of early warning signal metrics that track how ecological systems respond as external conditions gradually change and systems approach transitions between states. However, their generality and applicability remain uncertain, particularly for short-term ecological dynamics that can occur without directional external change. To address this gap, we propose a general framework based on the hypothesis that microscale nonrandomness, quantified from frequency distributions or spatial patterns, predicts subsequent short-term rates of change in populations and interactions, as well as current macroscale ecosystem states. We applied the framework to four datasets: time series from an incubation experiment of phytoplankton-parasitic chytrid interactions; spatiotemporal field observations of leaf beetles on willow trees and aphids in a crop system; and spatial snapshot data linking soil surface characteristics with aboveground and soil ecosystem properties in a forest vegetations-soil system. We quantified microscale nonrandomness using Morisita I_{delta} from frequency distributions and Moran's I and SADIE from spatial patterns. Across all systems, models incorporating microscale nonrandomness metrics consistently showed greater cross-validated predictive performance than those based on macroscale variables or spatial averages alone. Although their relative performance varied among systems, frequency-based metrics performed as well as or better than spatially explicit metrics in some cases. The consistent improvement across contrasting systems supports the broad applicability of this framework, including under limited data conditions. This framework broadens ecological forecasting to short-term, continuous dynamics beyond those associated with directional environmental change.

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BibTeX RIS

BibTeX

@misc{miki2026microscale,
  title = {{Microscale nonrandomness revisited: An overlooked predictor of ecological change}},
  author = {T. Miki and K. Yoneya and A. Katayama and S. H. Han and H. Nakamura and S. Takeda and H. Miyawaki and T. Kume and K. Shirotsuka and S. Kaneko and K. Seto and S. Van den Wyngaert and M. Kagami},
  howpublished = {bioRxiv},
  year = {2026},
  month = nov,
  doi = {10.64898/2026.11.05.764087},
  url = {https://www.biorxiv.org/content/10.64898/2026.11.05.764087v1},
  note = {CC BY-NC-ND},
}

RIS

TY  - UNPB
TI  - Microscale nonrandomness revisited: An overlooked predictor of ecological change
AU  - T. Miki
AU  - K. Yoneya
AU  - A. Katayama
AU  - S. H. Han
AU  - H. Nakamura
AU  - S. Takeda
AU  - H. Miyawaki
AU  - T. Kume
AU  - K. Shirotsuka
AU  - S. Kaneko
AU  - K. Seto
AU  - S. Van den Wyngaert
AU  - M. Kagami
PY  - 2026
DA  - 2026/11/05
PB  - bioRxiv
DO  - 10.64898/2026.11.05.764087
UR  - https://www.biorxiv.org/content/10.64898/2026.11.05.764087v1
AB  - Abrupt ecological transitions have driven the development of early warning signal metrics that track how ecological systems respond as external conditions gradually change and systems approach transitions between states. However, their generality and applicability remain uncertain, particularly for short-term ecological dynamics that can occur without directional external change. To address this gap, we propose a general framework based on the hypothesis that microscale nonrandomness, quantified from frequency distributions or spatial patterns, predicts subsequent short-term rates of change in populations and interactions, as well as current macroscale ecosystem states. We applied the framework to four datasets: time series from an incubation experiment of phytoplankton-parasitic chytrid interactions; spatiotemporal field observations of leaf beetles on willow trees and aphids in a crop system; and spatial snapshot data linking soil surface characteristics with aboveground and soil ecosystem properties in a forest vegetations-soil system. We quantified microscale nonrandomness using Morisita I_{delta} from frequency distributions and Moran's I and SADIE from spatial patterns. Across all systems, models incorporating microscale nonrandomness metrics consistently showed greater cross-validated predictive performance than those based on macroscale variables or spatial averages alone. Although their relative performance varied among systems, frequency-based metrics performed as well as or better than spatially explicit metrics in some cases. The consistent improvement across contrasting systems supports the broad applicability of this framework, including under limited data conditions. This framework broadens ecological forecasting to short-term, continuous dynamics beyond those associated with directional environmental change.
ER  -

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