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Decline of the plastochrone interval in a Zostera marina meadow: 18 years of data

Published online by Cambridge University Press:  30 May 2025

Elena Solana-Arellano Open the ORCID record for Elena Solana-Arellano [Opens in a new window] ,
Olga Flores-Uzeta ,
Carlos Eduardo Cabrera-Ramos  and
J. Adán Avilés-Chávez Open the ORCID record for J. Adán Avilés-Chávez [Opens in a new window]
Elena Solana-Arellano*
Affiliation:
Department of Marine Ecology, Centro de Investigación Científica y de Educación Superior de Ensenada, Ensenada, Baja California, México
Olga Flores-Uzeta
Affiliation:
Department of Marine Ecology, Centro de Investigación Científica y de Educación Superior de Ensenada, Ensenada, Baja California, México
Carlos Eduardo Cabrera-Ramos
Affiliation:
Department of Marine Ecology, Centro de Investigación Científica y de Educación Superior de Ensenada, Ensenada, Baja California, México
J. Adán Avilés-Chávez
Affiliation:
Department of Marine Ecology, Centro de Investigación Científica y de Educación Superior de Ensenada, Ensenada, Baja California, México
*
Corresponding author: Elena Solana-Arellano; Email: esolana@cicese.mx
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Abstract

Climate change profoundly affects plant phenology. An important parameter in research on plant dynamics is the plastochrone interval (PI), which is define as the time interval between the formation of successive leaves. The PI has been used to evaluate seagrass demography and as a direct measure of shoot growth and age. Variations in PI determine the growth rates, maintenance, and success of seagrass beds. Global warming could affect the PI dynamics of Zostera marina and, consequently, alter the dynamics of seagrass beds. Using Bayesian linear regression with a time series composed of 316 biweekly sampling dates from 1998 to 2018, we evaluated PI dynamics in the Punta Banda Estuary in Baja California, Mexico. We found that the tendency of the series was linear with parameter values of β0 = 1.65 (SD ±0.19) and β1 = −0.012 (SD ±0. 001). The Bayesian analysis of variance showed strong evidence of differences in the PI among years, given probabilities from 3.2 to 1.88 × 106 times higher of differences than no differences. The largest differences were detected between cold and hot years. The climatology of the time series PI values showed changes in seasonality over time. Summer and autumn were found to be the most perturbed seasons. Finally, by linking the PI estimates with the sea surface temperature anomalies for the complete series, a good inverse correspondence was observed between hot years and high PI, as well as cold years and low PI values, suggesting that climate change has affected PI among years and seasons.

Keywords

Bayesian statisticsclimate changeONIplastochrone intervalsea surface temperatureseagrass

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Type
Research Article
Information
Journal of the Marine Biological Association of the United Kingdom , Volume 105 , 2025 , e54
Copyright
Copyright © The Author(s), 2025. Published by Cambridge University Press on behalf of Marine Biological Association of the United Kingdom

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