A Unified Data-Driven Framework for Ecological Dynamics: Derivation and Cross-System Validation

A Unified Data-Driven Framework for Ecological Dynamics: Derivation and Cross-System Validation | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 29 September 2025 V1 Latest version Share on A Unified Data-Driven Framework for Ecological Dynamics: Derivation and Cross-System Validation Authors : Dongqi Liu 0009-0006-4018-9292 and shifa liu 0009-0003-6570-2812 [email protected] Authors Info & Affiliations https://doi.org/10.22541/au.175915671.19902575/v1 255 views 141 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Traditional ecological models are often constrained by pre-defined the oretical structures that may not capture the full complexity of real-world systems. This paper presents a purely data-driven approach to derive a unified ecological dynamical equation from large-scale, heterogeneous datasets. We employed Sparse Identification of Nonlinear Dynamics (SINDy) on 12 distinct public datasets encompassing terrestrial, aquatic, and mi crobial ecosystems. The identified parsimonious core equation, 1 Xi dXi dt =αi−βiXi+ j̸=i γijXj + k δikEk, encapsulates intrinsic growth (αi), density-dependence (βi), species interactions (γij), and environmental forcing (δik). This unified model was rigorously validated on 10 additional independent datasets, achieving an average predictive accuracy (R2) of 0.953±0.003, significantly outper forming traditional models such as Lotka-Volterra and Logistic growth models. This work demonstrates the power of data-driven methods to discover fundamental, cross-system ecological principles, offering a pow erful new tool for ecological forecasting. Supplementary Material File (eco1.pdf) Download 332.76 KB Information & Authors Information Version history V1 Version 1 29 September 2025 Copyright This work is licensed under a Creative Commons Attribution 4.0 International License Keywords cross-system validation data-driven modeling ecological forecasting machine learning sindy unified ecological theory Authors Affiliations Dongqi Liu 0009-0006-4018-9292 View all articles by this author shifa liu 0009-0003-6570-2812 [email protected] View all articles by this author Metrics & Citations Metrics Article Usage 255 views 141 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Dongqi Liu, shifa liu. A Unified Data-Driven Framework for Ecological Dynamics: Derivation and Cross-System Validation. Authorea . 29 September 2025. DOI: https://doi.org/10.22541/au.175915671.19902575/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. 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