Published December 16, 2025 | Version v1
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Warming effects on dynamic interactions of three duckweed species

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Description

Plant interactions critically govern ecosystem structure and stability, with climate warming altering interaction dynamics. This study investigates how these warming-induced shifts manifest in duckweed communities by examining temperature effects (25°C vs. 35°C) on three species: Spirodela polyrhiza, Lemna minor, and Wolffia arrhiza. Drawing upon the stress gradient hypothesis, we quantify the temporal shifts in intra- and interspecific interaction types and strengths using empirical dynamic models to test hypotheses regarding competitive and facilitative responses to elevated temperatures. Results showed that facilitation dominated for intra- and interspecific interactions at both temperature conditions. Warming did, however, reconfigure certain interactions. Specifically,  intraspecific interactions in W. arrhiza shifted from competition to facilitation in biculture with S. polyrhiza; in the triculture, warming shifted S. polyrhizaW. arrhiza interactions from neutral to facilitative, and W. arrhizaL. minor interactions from facilitation to competitiona shift that was also observed in the L. minor and W. arrhiza biculture. Facilitation strength in tri-mixtures increased at 35°C, while interspecific interactions weakened across time. Notably, population stability of S. polyrhiza and community stability increased with stronger interspecific interactions, whereas no such pattern was observed for L. minor or W. arrhiza. Our findings highlight that warming restructures duckweed interactions networks via varying dynamic balance between facilitation and competition. The observed widespread facilitation, even under greater warming, suggests a strong inherent capacity for positive interactions within this system. Identifying temperature thresholds triggering transitions between facilitative and competitive interaction regimes will be critical for anticipating and managing aquatic ecosystem resilience under projected climate warming scenarios.

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Programming language
R