Spatiotemporal dynamics and geo-environmental factors influencing mangrove gross primary productivity during 2000-2020 in Gaoqiao Mangrove Reserve, China

Background: Mangrove forests are a significant contributor to the global carbon cycle, and the accurate estimation of their gross primary productivity (GPP) is essential for understanding the carbon budget within blue carbon ecosystems. Little attention has been given to the investigation of spatiotemporal patterns and ecological variations within mangrove ecosystems, as well as the quantitative analysis of the influence of geo-environmental factors on time-series estimations of mangrove GPP.Methods: This study explored the spatiotemporal dynamics of mangrove GPP from 2000 to 2020 in Gaoqiao Mangrove Reserve, China. A leaf area index (LAI)-based light-use efficiency (LUE) model was combined with Landsat data on Google Earth Engine (GEE) to reveal the variations in mangrove GPP using the Mann-Kendall (MK) test and Theil-Sen median trend. Moreover, the spatiotemporal patterns and ecological variations in mangrove ecosystems across regions were explored using four landscape indicators. Furthermore, the effects of six geo-environmental factors (species distribution, offshore distance, elevation, slope, planar curvature and profile curvature) on GPP were investigated using Geodetector and multi-scale geo-weighted regression (MGWR).Results: The results showed that the mangrove forest in the study area experienced an area loss from 766.26 ha in 2000 to 718.29 ha in 2020, mainly due to the conversion to farming, terrestrial forest and aquaculture zones. Landscape patterns indicated high levels of vegetation aggregation near water bodies and aquaculture zones, and low levels of aggregation but high species diversity and distribution density near building zone. The mean value of mangrove GPP continuously increased from 6.35 g C<middle dot>m(-2)<middle dot>d(-1) in 2000 to 8.33 g C<middle dot>m(-2)<middle dot>d(-1) in 2020, with 23.21% of areas showing a highly and significantly increasing trend (trend value > 0.50). The Geodetector and MGWR analyses showed that species distribution, offshore distance and elevation contributed most to the GPP variations.Conclusions: These results provide guidelines for selecting GPP products, and the combination of Geodetector and MGWR based on multiple geo-environmental factors could quantitatively capture the mode, direction, pathway and intensity of the influencing factors on mangrove GPP variation. The findings provide a foundation for understanding the spatiotemporal dynamics of mangrove GPP at the landscape or regional scale.