## 3 Gross Primary Production  
There has been a dramatic increase in the number of observations of mangrove gross primary production due to advances in remote sensing technology and modelling techniques and the use of eddy covariance methods. Four methods have been used most frequently: (1) eddy covariance, (2) light attenuation, (3) combined litterfall and gas exchange, and (4) MODIS (Moderate Resolution Imaging Spectroradiometer), which is a satellite-based sensor used for earth and climate measurements. No significant differences were found among the five methods (one-way ANOVA on ranks, H = 6.65, df = 3, \(p\) = 0.08), so all data were subsequently combined. The mangrove GPP averaged 5.4 g C m\({}^{-2}\) d\({}^{-1}\) (2.9\(\pm\) 1 SD) with a median of 5.1 g C m\({}^{-2}\) d\({}^{-1}\), a range of 0.3-17.3 g C m\({}^{-2}\) d\({}^{-1}\), and 25% and 75% confidence intervals of 3.7 and 6.3 g C m\({}^{-2}\) d\({}^{-1}\), respectively (Figure 1). The partial correlation analysis indicated that the mangrove gross primary production was only positively correlated with rainfall (\(\mathrm{P}_{\mathrm{GPPRainTemp}}\) = 0.404, \(p\) < 0.001), after controlling for the positive relationship between rainfall and temperature (\(\mathrm{P}_{\mathrm{RainTemp}}\) = 0.326, \(p\) < 0.001). The correlation underscores a decline in GPP away from the wet tropics and a reliance on freshwater for regulating GPP. Other drivers of the mangrove GPP likely include variables in combination, such as the porewater salinity, light intensity, species composition, sediment temperature, pH, redox potential, nutrient availability, frequency of tidal inundation, disturbance, and geomorphological setting. For example, in ar  
Figure 1: Rates of mangrove gross primary production (g C m\({}^{-2}\) d\({}^{-1}\)) based on combined data (GPP) using 5 methods: litterfall + gas exchange (LGE), MODIS (MOD), eddy covariance (ECV), light attenuation (LATT), and remote sensing/modelling (RSM). Horizontal line in each box: median; lower and upper hinges: 25 th and 75 th percentiles; error bars: 95% confidence intervals. Values above median line are means. Original data from references: [8; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36; 37; 38; 39; 40; 41; 42; 43; 44; 45; 46; 47; 48]. Gulf of California, Mexico, the GPP was limited by the lack of rainfall during the pre-monsoon and high tides, as an air temperature and vapor pressure deficit strongly drove GPP during the monsoon season [46]. The global mangrove GPP and its drivers were explored using global mangrove eddy covariance data and an applied Gaussian Process Regression (GPR) for the 1996-2020 period (Figure 2). The globally averaged mangrove GPP was estimated at 6.0 g C m\({}^{-2}\) d\({}^{-1}\), which is slightly greater than my calculated estimate and comparable to that of evergreen broad-leaf forests, exceeding the GPP of most other plant types [47; 48]. Production hotspots near the equator (Indonesia, Papua New Guinea, Solomon Islands, north shore of the Gulf of Guinea, and parts of the Caribbean Sea) exceeded 8 g C m\({}^{-2}\) d\({}^{-1}\), but the analysis revealed a decline in the global mangrove GPP during 1996-2020 of \(\sim\)0.9 Tg C y\({}^{-1}\), coinciding with a strengthening seasonal cycle and increasing variability [47]. A temporal pattern was discovered displaying a bimodal seasonal cycle with peak rates in February and November and a lower GPP in May, coinciding with the opposing seasonal cycles between the northern (April-May) and southern (November-February) hemispheres. The most important variable in estimating the GPP was the fraction of absorbed photosynthetically active radiation (fAPAR), with the salinity, sea surface temperatures, vapor pressure difference, air temperature, and precipitation as secondary factors.