Ecology

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Chemicals and reagentsEOs of basil (Ocimum basilicum L.), bergamot (Citrus bergamia Risso & Poit), camphor [Cinnamomum camphora (L.) J. Presl.], cinnamon (Cinnamomum zeylanicum Blume), citronella [Cymbopogon nardus (L.) Rendle], clove (Eugenia caryophyllus Wight), eucalyptus (Eucalyptus globulus Labill.), jasmine (Jasminum officinale L.), lavender (Lavandula angustifolia Mill.), lemon grass [Cymbopogan citratus (DC.) Stapf], mentha (Mentha piperita L.), rosemary (Rosmarinus officinalis L.), patchouli (Pogostemon patchouli Benth), and wild turmeric (Curcuma aromatica Salisb.) were procured from Talent Technologies (Talent Technologies, Kanpur, India). Acetylcholinesterase (AChE) activity assay kit, Anti-OBP2A antibody, ELISA kits, 1,1-diphenyl-2-picrylhydrazyl (DPPH), radioimmunoprecipitation (RIPA) buffer and phosphate buffer saline (PBS) were purchased from Sigma Aldrich (Sigma Aldrich Chemical Co., St. Luis, USA). TRPV1 antibody was purchased from Santa Cruz (Santa Cruz, California, USA). 1-chloro-2,4-dinitrobenzene (CDNB) was purchased from Cayman (Cayman Chemical Company, Michigan, USA). Human normal lung cell line (L-132) was obtained from the National Centre for Cell Sciences (NCCS), Pune, India. High performance liquid chromatography (HPLC) grade acetone was purchased from Merck (Merck Pvt. Ltd., Mumbai, India). All other chemicals used were of the highest analytical grade available.Test insects5–7 days old adult female Ae. albopictus mosquitoes were housed at the laboratory insectary, Division of Pharmaceutical Technology, Defence Research Laboratory, Tezpur, Assam, India. Mosquitoes were reared by maintaining temperature at 27 ± 2 °C, relative humidity: 75 ± 5% RH and 14L:10D h of light–dark alternative cycles in standard-sized wooden cages (75 cm × 60 cm × 60 cm) with a sleeve opening on one side as described previously63. 10% sucrose solution ad libitum were provided for nourishment. Before testing, the mosquitoes were starved for 24 h.Screening of EOsDose response study was performed to evaluate the best oils among the fourteen EOs. This study was approved (approval number: 032/2021TMCH, 28/08/2018) by the Institutional Human Ethical Committee (IHEC), of the Tezpur Medical College & Hospital (TMCH), Tezpur, Assam, India, and all experiments were performed in accordance with relevant guidelines and regulations. Five volunteers are chosen, not allergic to mosquito bite and all volunteers provided written informed consent. A volunteer’s thigh was marked according to the door opening hole of the K&D module as described by Klun and Debboun64. It is made of Plexiglas and the base of the rectangular cage (26 cm × 5 cm × 5 cm) has six holes, each with rectangular 3 × 4 cm holes that are opened and closed by a sliding door (Supplementary Fig. S8: Provide the photograph of K&D module). The flexor region of the forearms of a human volunteer was outlined with four rectangular (3 cm × 4 cm) test areas. A volume of 25 µL of each concentration of the EOs in soybean oil (40, 4 and 0.4 µg/cm2) and 25 µL of the soybean oil (diluent) as control was applied to the marked areas. After air drying for 5 min, a K&D module with matching cut outs in its floor was placed over the treated areas, containing five nulliparous 5–7 days old female mosquitoes in each hole. The doors of the cells were opened and the number of mosquitoes biting in each cell was recorded within a 2 min exposure, after which the doors were closed. After completion of each observation, mosquitoes were freed by opening cells of the K&D module in a sleeved screened cage. For each test, fresh sets of mosquitoes are used. Five replications for each test were carried out. The efficacy of EOs were determined by the percentage repellency against mosquitoes, using the formula or Eq. (2) described by WHO46.$$% ;{text{repellency}} = frac{C – T}{C} times 100$$
(2)
where, C is the number of mosquitoes landing, or biting at the control area; T is the number of mosquitoes landing or biting at the treated area.Fourier transform-infra red spectroscopy (FT-IR)Study of chemical compatibility for each formulation ingredients are necessary. All formulation ingredients possess specific value of vibrational frequency and have varied functional groups in their chemical structures. For compatibility study, each EOs, excipients to be used in cream formulation, and their physical mixture was placed one by one over the sample plate of the FT-IR instrument (Bruker, ALPHA, Billerica, MA, USA). The covering probe was placed over the sample and IR spectra was obtained over a wavelength of 2.5–25 μm at room temperature. Functional groups possessed by each individual ingredient should be identical in their physical mixture which confirms their compatibility37.Thermogravimetric analysis (TGA)The thermal behaviour of citronella oil, clove oil, lemon grass oil, their mixture and EO-MRC were evaluated using a thermal analyser (TG 209 F1 Libra®, NETZSCH-Gerätebau GmbH, 95100 Selb, Germany). Approximately about 10 mg sample weight was placed in the crucible each time. Nitrogen was used as a shielding gas. Heating program was fixed as 30–600 °C at a rate of 10 °C/min.Formulation development and optimizationFor optimization, a 17-run, 3-factor, 3-level Box-Behnken design (BBD) was utilized. A second order polynomial model was constructed by quadratic response surface methodology (RSM) using Design-Expert software (Version 6.0.8, Stat-Ease Inc., USA). Total seventeen formulations were obtained using EO concentrations as dependent variables against complete protection time (CPT) as independent variable or response variable. Analysis of variance (ANOVA) was performed using the same software to obtain the most effective formulation.Preparation of creamPhase inversion temperature method was applied for the preparation of EO-based mosquito repellent cream (EO-MRC). About 50 g cream sample was prepared in order to get enough for performing the various qualitative and quantitative assay. The oil phase (phase B) was prepared by dissolving the oil soluble excipients, except phase A (mosquito repellent active ingredients) under mild heating at 200 rpm in a hot magnetic plate stirrer (Magnetic Stirrer IKA RCT basic) and heated to 65 °C. The aqueous phase was prepared by mixing various aqueous soluble ingredients (phase C) under gentle heating and stirring. Temperature of the aqueous phase was raised to 65 °C. Phase A was gently added to the oil phase at a stirring speed of 200 rpm and 55 ± 2 °C. The mixture was then emulsified by adding phase C slowly and kept for 1 h at a stirring rate of 800 rpm and 60 ± 2 °C. The formulated EO-MRC was then kept for natural cooling.Efficacy assessmentCPT of the developed cream (EO-MRC) formulation was carried out by arm in cage bioassay. 1 mL EO-MRC was applied to ≈ 600 cm2 area of the forearm skin between the wrist and elbow and 1 mL of the 12% N, N-di ethyl benzamide (DEBA) based marketed cream (DBMC) was compared on the other arm. Two mosquito cages (size: 40 × 40 × 40 cm) each containing 200–250 non-blood-fed female Ae. Albopictus were used. One cage is designated for testing the EO-MRC and the other for the positive control (DBMC). During testing, hands were protected by surgical gloves for which the mosquitoes cannot bite while the volunteer avoids movement of the arm. EO-MRC and DBMC treated arms were exposed for 3 min at 30 min intervals to determine landing and/or probing activity. A single landing or probing of mosquito within a 3 min test interval concludes the test. CPT was calculated as the time (min) required for the first mosquito landing or probing after repellent application to the treated area. The median CPT and confidence intervals were estimated from the Kaplan–Meier Survival Function46.Efficacy was correlated with DEBA based marketed cream (DBMC). The inclusion of the specific commercial product DBMC is for comparison and does not constitute any recommendations.CharacterizationGas chromatography-mass spectroscopy (GC–MS)Qualitative studyDifferent chemical components in fourteen EOs and the selected blend were identified by a GC–MS system of Agilent Technologies (5301 Stevens Creek Blvd. Santa Clara, CA 95051, United States). Test sample concentration of 500 μg/mL was prepared in GC grade acetone. A sample volume of 1 μL was introduced into the injector held at 250 °C. Oven temperature of 40–300 °C was programmed at 20 °C/min. Helium was used as carrier gas at flow rate 1 mL/min. The injector and detector temperature were set at 250 °C and 230 °C (quad) and 150 °C (core) respectively37. Standard C7–C30 saturated alkanes were purchased from Sigma Aldrich Chemicals Co., St. Louis, USA. Retention indices (RI) of the identified components were determined for identification of the detected components.% Assay by GC–MS studyCalibration samples of eugenol and citronellol were prepared by dissolving an appropriate amount in GC grade acetone to get concentrations of 62.5 μg/mL, 125 μg/mL, 250 μg/mL and 500 μg/mL. Test samples of EO-MRC, clove oil and citronella oil were prepared by dissolving a required amount in acetone to quantify the EO components in the final formulation. A sample volume of 1 μL was introduced into the injector as described in ‘Qualitative study’ section.Physicochemical parametersPhysical parameters of the EO-MRC and placebo formulations were determined in order to establish aesthetic compliance and consumer acceptability. To determine the viscosity, a programmable viscometer was used (Model: DV2T, Ametek Brookfield, Middleboro, MA, USA); combined with software Rheo3000, version 1.2.2019.1 [R]. Sample volume was fixed at 30 g and viscosities were determined at 10 rpm for 40 s at room temperature using a T-Bar spindle (B-92) (Helipath spindle set, Brookfield Engineering Labs. Inc). Density was determined by using a pycnometer. pH of EO-MRC was checked by using digital pH meter (Labman Scientific instruments, Tamil Nadu, India).Spread ability of EO-MRC was determined as per the method reported earlier by Sabale65. In brief, 1 g of EO-MRC was placed on 1 cm2 pre-marked circular area on the glass slide (7.5 cm × 2.5 cm). EO-MRC was compressed using another glass slide placed from edge to centre of primary slide. 200 g of commercial weight was placed on the set up and allowed the gel to spread for the period of 1 min. The spread diameter was calculated with the aid of graph paper and spread ability was evaluated using formula expressed as Eq. (3):$$mathrm{Spread, ability}=mathrm{m}times frac{mathrm{l}}{mathrm{t}}$$
(3)
where, m is the commercial weight placed on the setup; l is the length of cream spread; and t is the time.Safety assessmentCytotoxicity by MTT assayThe reduction of tetrazolium salts is now widely accepted as a reliable way to examine cell proliferation. The yellow tetrazolium MTT (3-(4,5-dimethylthiazolyl-2)-2,5-diphenyltetrazolium bromide) is reduced by metabolically active cells, in part by the action of dehydrogenase enzymes, to generate reducing equivalents such as NADH and NADPH. With the help of spectrophotometric means, the resulting intracellular purple formazan can be quantified. The assay measures the cell proliferation rate and conversely, when metabolic events cause apoptosis or necrosis, the reduction in cell viability66.Cells cultured in T-25 flasks were trypsinized and aspirated into a 5 mL centrifuge tube. Cell pellet was obtained by centrifugation at 3000 rpm. The cell count was adjusted, using DMEM HG medium, such that 200 μL of suspension contained approximately 10,000 cells. To each well of the 96 well microtiter plate, 200 μL of the cell suspension was added and the plate was incubated at 37 ℃ and 5% CO2 atmosphere for 24 h. After 24 h, the spent medium was aspirated. 200 μL of different test concentrations viz. 62 µg/mL, 125 µg/mL, 250 µg/mL, 500 µg/mL, and 1000 µg/mL, of EO-MRC were added to the respective wells. The plate was then incubated at 37 °C and 5% CO2 atmosphere for 24 h. The plate was removed from the incubator and the drug containing media was aspirated. 200 μL of medium containing10% MTT reagent was then added to each well to get a final concentration of 0.5 mg/mL and the plate was incubated at 37 ℃ and 5% CO2 atmosphere for 3 h. Without disturbing the crystals formed in the wells, culture medium was completely removed. 100 μL of solubilisation solution (DMSO) was added to each well and the plate was then gently shake in a rocking shaker (ROCKYMAX™, Tarsons, Kolkata, India) to solubilize the formed formazan. The absorbance was measured at a wavelength of 570 nm and also at 630 nm using a microplate reader. The percentage growth inhibition was calculated and concentration of EO-MRC needed to inhibit cell growth by 50% (IC50) was generated from the dose–response curve for the cell line.Animals and ethics statementAll experimenting protocols using animal were performed according to the “Principles of Laboratory Animal care” (NIH publication 85–23, revised 1985) and approved by the Institutional Animal Ethical Committee (IAEC) of Defence Research Laboratory (DRL), Tezpur, Assam, India (approval no. CPCSEA/DRL/Protocol no. 3, 20/06/2018). All studies involving animals are reported in accordance with the ARRIVE guidelines for reporting experiments involving animals67. All efforts were made during the study period to minimize the suffering of animals and to reduce the number of animals used.5–8 weeks old, about 210–250 g of male healthy adult Wistar rats (Rattus norvegicus) and young and healthy New Zealand albino rabbits (Oryctolagus cuniculus) were obtained from the institutional animal housing facility and allowed to acclimatize for 7 days prior to the study. Standard food and purified water ad libitum were provided in clean and hygienic condition at 22–25 ℃, 40–70% RH with 12 h light–dark cycles.Acute dermal irritation studyAcute dermal irritation study was conducted on healthy New Zealand albino rabbits following the OECD test guidelines 40468. Approximately 24 h before the test, fur was removed from the dorsal area of the trunk. 0.5 g EO-MRC, was directly applied to the skin and after 4 h exposure period, residual EO-MRC was removed by using water without disturbing the integrity of the epidermis and examined for signs of erythema and oedema, at 60 min, and then at 24 h, 48 h and 72 h after EO-MRC removal. Dermal reactions are graded and recorded according to the grades in the Table 8. As per the method described by Banerjee et al.69; primary irritation index (PII) was calculated. Further, we have followed the Draize method of classification for PII scoring as non-irritant (if PII  More

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The improved performance of the mangrove LUE model considering coastal environments in this study was mainly attributed to the determination of environmental scalars. Parameters determining environmental stressors (e.g., Topt, Tmin, Tmax, VPDmin, and VPDmax) were set based on the general characteristics of mangroves worldwide. It may not be as accurate for the mangroves in our study sites, but it generally reflects the response of mangroves to environmental changes. Furthermore, as can be seen in Fig. S1, it is applicable to our study sites. Despite the specific characteristics of each mangrove ecosystem at different sites being preferred, this study first offers the possibility to estimate mangrove productivity at a larger scale to track GPP, thus emphasizing the role of mangrove ecosystems nationally or worldwide.The validation results showed that the LUE values of the mangrove model agreed well with those estimated by EC method (Fig. 3) and indicated improved performance (slope = 0.8218–1.0108, intercept = -0.0006–0.0052, R2 = 0.54–0.64, RMSE = 0.0051–0.0068, Pearson’s r = 0.73–1), compared to the MOD17 model (slope = 0.4993–0.5566, intercept = 0.0311–0.0313, R2 = 0.24–0.45, RMSE = 0.0217–0.0220, Pearson’s r = 0.45–0.49). Firstly, the RS-based LUE model for terrestrial ecosystems (MOD17) considers only the environmental stressors of Tair and VPD. The photosynthesis in mangrove forests is influenced by other unique environmental factors caused by tidal inundation. According to Fig. S3, PAR caused the most significant effect on LUE, which is consistent with previous studies14,30,32. The impact of SST has not been quantitatively assessed, however, SST is a critical control that determines the upper latitudinal range of mangrove ecosystems12,33. In our study, the effects of SST and salinity on the mangrove LUE were quantified and helped improve LUE modeling.Secondly, LUEmax was typically defined for different land covers, however, there were no specific values for mangrove forests. In this study, the LUEmax of mangroves was first determined. It is worth noting that daytime NEE responses to PAR vary depending on the Tair23,30,34 so that LUEmax was determined separately at high, optimal, and low temperatures. The results showed that LUEmax reached a maximum when Tair was within the optimal range for mangroves, which represents the high productivity of mangrove ecosystems. Furthermore, the estimated LUEmax of mangrove forests (0.057) was larger than most terrestrial forests35,36,37, which could contribute to the high production and carbon sequestration in mangrove forests.Lastly, the relatively low stomatal conductance of mangroves leads to low LSP compared with terrestrial forests, which could result in the high-irradiance stress for photosynthesis38,39. Mangrove LSP ranges from about 0.2–1.2 mmol/m2/s, depending on the species and environments40,41,42. LUE was relatively low in April and May when seasonal PAR was high, as photosynthesis is more likely to reach saturation. Therefore, we assumed the LUE of mangroves decreased with increasing PAR. In addition, we found that the downscaling effect of PAR on LUE was not constant, but varied with increasing PAR. As follows, different PAR scalars were set for mangroves according to different PAR values. This is a first attempt at refining PARscalar considering different solar radiation, which represents a significant departure from the assumption of a constant downscaling effect of PAR in RS-driven models14,43. The accuracy of the LUE model was improved by refining the PARscalar with different downscaling slopes, especially in periods of high PAR values.Compared with the results obtained from flux-tower measurements, the modeled GPP was basically within the confidence interval of the measured results. The annual averages of GPP in Zhangjiang were 1729 g C/m2/year and 1924 g C/m2/year, in 2012 and 2016, and the annual mean value of GPP in Zhanjiang was 1434 g C/m2/year in 2015. The previous study showed that the GPP in Zhangjiang ranged from 1763 to 1919 g C/m2/year with a mean value of 1871 g C/m2/year32,44,45, which is in good agreement with the estimated values obtained in this study. Liu and Lai46 reported that the GPP of the Mai Po mangrove reserve was 2827 g C/m2/year. Rodda, et al.20 found a GPP value of 1271 g C/m2/year for Sunderbans mangroves in India. Gnanamoorthy, et al.47 estimated a GPP of 2305 g C/m2/year for Pichavaram mangroves. Variations in these estimates across sites were possibly caused by different climate-hydrological conditions, mangrove species, and ages. Differences in the same location may be due to different time scales and different methods of data gap filling and flux partitioning.In a similar way to the GPP model for terrestrial ecosystems48, the effect of the mangrove GPP model on the accuracy of GPP estimates can vary considerably under different environmental conditions. However, in comparison with the accuracy of models built for other vegetation types, the GPP model in this study performed substantially in two sites with RMSE of 2.54–3.41 g C/m2/day. Wang et al.49 adopted different models to estimate GPP for global vegetation and validation results showed the RMSE ranged from 1.79 to 2.33 g C/m2/day. Xiao, et al.50 demonstrated that the deviation between observed and predicted GPP was about 35–282 g C/m2 in an evergreen needleleaf forest. Also, the absolute GPP errors were 7.94–20.92% and 9.97–13.70% for maize cropland and degraded grassland36. Despite the discrepancy, our results were generally consistent with previous studies and were verified by field observations near the flux towers.The comparison of MODIS GPP and EC-estimated GPP showed that the MODIS GPP had a large fluctuation and weakly reflected productivity, being overestimated in 2012 and underestimated in 2015. Different meteorological inputs, different environmental scalars and fraction of absorbed photosynthetic active radiation (fAPAR) products in MODIS GPP and our mangrove GPP model can explain their different results. However, the improvements in our GPP model may help to obtain more accurate GPP estimates. The response of mangrove productivity to Tair has not been well-calibrated in the MODIS GPP product, which may partly account for the poor correlation between the MODIS GPP and EC estimates. Besides, MODIS GPP product was developed based on the International Geosphere-Biosphere Programme (IGBP) land cover map, which doesn’t include mangroves as a specific land cover37. Therefore, LUEmax and environmental parameters were not defined for mangroves, which varied with different environments. This may lead to uncertainty in MODIS GPP product for mangrove forests14. However, the GPP model generated in our study showed similar trends to the field measurements, capturing seasonal variations. The increase in the difference between MODIS GPP and EC estimates may be due to the assumption that the increase in GPP is linear with respect to PAR. In our model, the response of GPP to PAR was suppressed, resulting in seasonal changes in GPP that better match the observations. In addition, the GPP derived from this study was in higher agreement with measured values compared with GPP estimated from the vegetation photosynthesis model (VPM), as shown in Fig. S4. The improvement of this model was more obvious in winter (December to February), which may be due to the environmental stress of SST and PAR. The VPM without considering SSTscalar and PARscalar overestimated GPP in winter. It is indicated that the performance of the mangrove GPP model in this study varied with season. It is recommended to improve the estimation of GPP in the future by considering the seasonal variation of mangrove forests when determining environmental variables.Most studies provide EC-based estimates of GPP that are measurements from a limited footprint. It is possible to extrapolate results across similar vegetation types and geographic settings, but not to areas of heterogeneous vegetation. The RS-based GPP model offers spatial-scale estimates that can be directly incorporated into ecosystem-type models. PAR, SST, and salinity are the key environmental parameters of this RS-based mangrove GPP model. SST and salinity data were derived from the satellite images, while PAR was generated from the reconstructed PAR data, since it is more accurate than the existing RS data and has historical year data. However, PAR products from Hamawari-8, MERIS, and SeaWiFS are available now, which provide an opportunity to obtain large-scale PAR data using RS in the future. In addition to this, GPP of two mangrove forests was assessed and validated with three-year measurements. Validation at different sites and years showed similar results, which indicated the model has similar performance across mangrove forests. Nonetheless, these estimates need to be corroborated with EC databases, which are relatively accurate and provide many additional variables that are currently beyond the scope of higher spatial-resolution RS estimates. The proposed GPP model considering coastal environments was well suited to extend the study area by incorporating RS information and meteorological data. Currently, there are still few mangrove carbon flux towers worldwide. The LUE and GPP models proposed in this study are difficult to validate with measurements from flux towers in other countries. However, local measurements are available in many countries with large mangrove forests, such as Thailand, Vietnam, India, and Bangladesh. Therefore, it is expected that comparisons with measurements from previous studies can be conducted to show the consistency and applicability.The LUE model considering the effects of SST, salinity, and PAR performed well, however, the GPP estimated from the LUE, fAPAR, and PAR showed discrepancies and were generally lower than the measured values. Although the results are better than MODIS GPP products, limitations exist still.Firstly, the effects of salinity and SST on mangrove productivity were directly related to tidal activities. The soil pore water and surface water salinity could affect the osmotic pressure of mangroves especially for the submerged parts which would control the stomatal conductance. In the same way, SST could influence the temperature of mangrove root systems and soil sediments which has impacts on mangrove roots’ respiration and transpiration. Although, theoretically, salinity and SST should be considered as environmental variables affecting mangrove LUE, our results (Fig. S3) indicated that salinity and SST have little influence on mangrove productivity51. To date, the quantitative impact of SST has not been comprehensively unfolded, but it is a global control that determines the upper limit of the latitudinal range of mangroves12,33. The weak relationships between salinity, SST, and mangrove GPP could be due to the uncertainty caused by tidal inundation. Tide duration, tide height, and tide cycle would determine the effect of salinity and SST on the mangrove LUE and GPP. However, quantifying the influence from the tidal cycle remains a challenging task, which could result in the relatively poor performance of Salinityscalar and SSTscalar as shown in Fig. S3. Quantifying the soil temperature and surface water salinity considering the tidal cycle will contribute to model the LUE and GPP of mangrove forests.Secondly, mangroves of different species and ages exhibit diverse structural and physical conditions, resulting in different LUEmax, and optimal growing conditions such as Topt and VPDmin. The environmental settings would also vary from region to region. Liu and Lai46 found that LUE increased slightly with the increasing salinity below 15 ppt (R2 = 0.16). However, it was noted that photosynthetic activity of mangroves would be inhibited when the surface water salinity was high30,51,52,53. Probably, the mutual relationship between LUE and salinity depends on the salinity level and mangrove species. However, we have not specified the variables for different mangrove species, ages and locations which could be improved in the future. Besides, there are multicollinearities between different environmental variables. For example, Tair may have effects on SST and VPD, but as shown in Fig. S5, they are all important for mangrove photosynthesis. However, the correlations between them are not clear and need to be quantified in the future.Thirdly, the relatively low spatial and temporal resolution of the environmental data from RS would influence the accuracy of the model. The datasets have a relatively coarse resolution (usually 500 m–1 km and daily) and are thereby less suitable for smaller nature reserves, especially in the narrow patches of mangrove areas that are rapidly being exploited in coastal China. Moreover, the variability in LUE decreases with increasing temporal scale54. In our study, we determined the PARscalar based on the response of LUE to hourly-scale PAR and found the different down-regulation effects with increasing PAR. However, this phenomenon is not obvious in previous studies. Most RS-based LUE models were developed at a daily or 8-day temporal scale6,50,55,56,57. In terrestrial forests, the light saturated effect caused by increasing PAR was neglectable with coarse temporal scale because the average PAR was usually lower than the LSP. However, as the time scale increases, the effect of light saturation on LUE becomes more pronounced32,58,59. More importantly, this effect is more obvious in mangroves due to their lower LSP18,38, which makes it important in mangrove LUE modeling. The results in Fig. 3 show similar performances of LUE model on hourly and daily scale. Thus, we suggested that our model can be adopted in hourly and daily temporal resolution. However, the PARscalar developed in this study was based on the mangrove forests in one study site which may be influenced by the mangrove species with different LSP and light conditions. What’s more, VPD was on a monthly scale, which cannot reflect environmental dynamics. However, the hourly and daily VPD data are currently not available for coastal areas in China. Therefore, we used monthly averages to represent daily VPD, which may lead to uncertainty in the derived GPP estimates (Figs. 6 and 7). Besides, porewater salinity is controlled by sea surface salinity, precipitation, and river discharge. However, currently, pore water salinity was expressed in terms of sea surface salinity, which may lead to an underestimation of Salinityscalar. More systematic study is necessary to make it more applicable and accurate on a large scale, of which modeling the LUE for different mangrove species and locations is inevitable. However, serving as a fundamental and preliminary step, our study aims to provide a framework for RS-based mangrove GPP modeling. Recently, with the advancement of satellite imagery, hourly-scale RS data for PAR, temperature and SST are available. It can be expected that our current work could be further improved by investigating the light saturation effects in different mangrove forests and adopt higher temporal resolution RS products such as Himawari-8 and GCOM-C in the future.Lastly, the overall underestimation of GPP was mainly caused by the underestimation of fAPAR. Even though the fAPAR computed from Sentinel-2 had higher resolution and accuracy than MODIS fAPAR products, future improvements are still needed. Sentinel-2 fAPAR products (fAPAR-S2) was calculated as the instantaneous fAPAR obtained at 10:00 local solar time which only roughly represented the daily average but was not accurate. Besides, RS-derived fAPAR only considers the absorptions by living green vegetation elements, whereas the ground measured fAPAR refers to the contributions from all absorbing components60. The lower fAPAR-S2 values in mangrove forests may be due to the exposed-to-air root systems which absorb the radiation. Moreover, the spatial distribution of PAR was determined by Co-Kriging interpolation. The elevation was taken as the covariate to estimate spatial PAR. There are many other variables affecting the incoming PAR (e.g., slope and clearness)61. A more comprehensive set of variables needs to be included in the Co-kriging interpolation to improve the PAR estimation.The spatial and seasonal variations of the mangrove GPP were related to environmental changes along the shoreline. The low summer GPP was explained by the lower fAPAR in summer compared with other seasons, which was principally due to the underestimation of fAPAR in summer. Furthermore, PARscalar took a mean value of LSP as 1 mmol/m2/s, however, LSP varied with different species and environmental conditions. In summer, mangroves are more likely to obtain light saturation, and thus PARscalar may lead to an underestimation of LUE and thus GPP. On the contrary, PAR values in winter were relatively low but increased slightly with decreasing latitude. Thus, the inhibitory effect of PAR on LUE was not significant, and GPP increased with decreasing latitude. Salinity and VPD were more stable across years and locations and had no noticeable effect on the mangrove LUE and GPP. The seasonal latitudinal patterns and effects on mangrove productivity were similar for Tair and SST. Tair and SST were lower in winter, especially at high latitudes where mangroves were more sensitive to cold weather. Therefore, the GPP of mangroves at high latitudes in winter was the lowest throughout the year. However, hot weather in summer also limited the photosynthesis in mangroves, especially at low latitudes, where Tair and SST were higher. Nevertheless, there were some correlations among these environmental constants. For example, the Tair affects the vapor pressure and SST. There was a positive correlation between PAR and Tair. The multicollinearity among these variables and the various conditions of mangroves may affect the performance of the model and show variations along the coastline, which would be improved in future studies.Additionally, the GPP of mangroves increased from 2007 to 2018, which was mainly due to the expansion of mangrove forests in the coastal areas. As mangroves grow, canopy size and tree density increase, which may lead to higher LUE and less underestimation of fAPAR, thus contributing to high productivity. However, Zhejiang province (27° 02′ N–31° 11′ N) experienced extremely cold weather in January 2016 caused by the East Asia cold wave62,63, and large areas of mangrove forests died or became sick, leading to a decline in the mangrove GPP at high latitudes in 2018. More

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