Quantifying key indicators of essential biodiversity variables for mangrove species in response to hydro-meteorological factors

Abstract

<p>Mangroves are critical for climate mitigation and biodiversity conservation, yet their spatiotemporal dynamics and physiological responses to hydrometeorological drivers remain poorly understood. This study extracted three essential biodiversity variables (area distribution, phenology, and physiological traits) and further revealed their dependencies on hydrometeorological conditions. We developed a continuous time-series monitoring method (CTSM) to enhance the Detect-Monitor-Predict detection framework for accurately tracking mangrove spatial succession in the Beibu Gulf from 2000 to 2021. We combined Continuous Change Detection and Classification with Harmonic Analysis of Time Series (HANTS) methods to capture the seasonal changes of physiological traits of dominant mangrove species. This study utilized HANTS-PLSR (partial least squares regression) response models and structural equation models to explore the seasonal responses of physiological trait to hydro-meteorological factors. The results indicated that (1) the improved detect component delineated fine-scale expansion patterns of mangroves, with area-hydrometeorology coupling evolving from uncoordinated to highly coordination during 2000–2021. (2) The start, peak and end of the growing season for mangroves are in March-April, June-September and January-February of the following year, respectively. The mangroves in different regions exhibit relatively delayed growth periods. (3) Aegiceras corniculatum exhibited bimodal phenological trajectories, contrasting with unimodal patterns in three co-occurring species. (4) The physiological traits displayed a positive correlation with water/air temperature and sunshine duration. The phenological changes of four mangrove species are driven by the interaction between hydrological and meteorological variables, with meteorological factors dominating (path coefficient > 0.50, p < 0.001). The findings provide insights into mangrove conservation and biodiversity monitoring.</p>