Resilience of South Asian mangroves to weather extremes and anthropogenic water pollution

doi:10.21203/rs.3.rs-2754392/v1

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Resilience of South Asian mangroves to weather extremes and anthropogenic water pollution

https://doi.org/10.21203/rs.3.rs-2754392/v1

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Sundarbans in the coastal South Asia, the largest contiguous mangrove forest in the world, faces an intensifying compound stress of climate extremes and anthropogenically influenced water pollution. However, our knowledge about the responses of mangroves to these stressors with the recovery mechanism is largely limited. We address this research gap by delineating causal networks between in-situ observations of soil-water chemistry, carbon fluxes, and hydro-meteorological variables from Sundarbans mangroves. Our results show that mangroves recover from physiological stresses caused by weather extremes quickly, within one to two weeks and maintain stable productivity despite steeply declining nutrient composition due to human-induced water pollution. We demonstrate that mangroves maintain this stable productivity during the stress period by increasing link strength and memory with the hydro-meteorological variables of the region. Our findings highlight the resilience of South Asian mangroves to natural and anthropogenic stressors and the importance of estimating thresholds of their critical transitions.

Mangroves are the most valuable coastal ecosystem, covering 15 million hectares across 118 countries^1,2. They are the cornerstones of coastal ecology³, the first line of defence of coastal communities against floods^4–7, and a source of livelihood and blue economy for millions of people across the globe^1,8,9. In addition, mangroves have the potential to contribute to mitigating climate change due to their high carbon densities and sequestration rates^10–13. Due to these reasons, they are an essential component of the global carbon budget¹⁴ and are often called ‘Blue Forests’ or ‘Wetland Ecosystem Engineers’^15–17. However, increasing human activities and resulting stressors threaten these ecosystems and might have even brought them to their tolerable limits¹⁸.

In the last five decades, around one-third of global mangrove ecosystems have degraded due to deforestation driven by agriculture, aquaculture, and urbanization^{5, 19–22}. The ambitious inter-basin water transfer projects of various countries, where water is transferred from surplus to deficit basin to meet the growing water demand, have also added to the stress on mangroves^23–25. Water pollution also harms mangroves by either changing the nutrient availability, soil chemistry, or microbiome^26–31. The sixth assessment report (AR6) of the Inter-governmental Panel on Climate Change (IPCC)³² highlighted the importance of mangrove restoration as a method for carbon dioxide removal (CDR) from the atmosphere. IPCC AR6 has also warned about increasing marine heatwaves, which can threaten coastal carbon stocks in mangroves. The unprecedented degradation of mangroves has called for global action, with many mangrove forests now being classified as ‘endangered’ under the Red List of Ecosystems (RLE)³³ — a global standard for assessing the risk of ecosystem collapse by the International Union for the Conservation of Nature (IUCN)³⁴. The UN Global Forest Goals have also laid sufficient emphasis on the resonation of these ecosystems. The Decade of Ecosystem Restoration (2021–2031), adopted as a resolution in the United Nations General Assembly (UNGA) on 1 March 2019, contains restoring mangrove ecosystems as a crucial goal³⁵. The United Nations Sustainable Development Goal (SDG) 14, “Conserve and sustainably use the oceans, seas, and marine resources,” acknowledges the enormous benefit of mangroves to local communities and the significance of restoring and maintaining mangroves. Additionally, restoring mangrove forests also aids in the accomplishment of many other SDGs, such as SDGs 1 and 2 on eradicating poverty and hunger (a large share of the poor population live in the vicinity of mangroves), SDG 8 on ensuring livelihoods and economic growth, SDG 13 on addressing the impacts of climate change, and SDG 15 on restoration and halting degradation and biodiversity loss. Hence, preserving mangroves is vital for creating a climate change resilient socio-economy.

The Sundarban forest located on the delta of Ganga-Brahmaputra-Meghna (GBM) is the largest contiguous mangrove forest in the world¹, with around 1 million hectares of mangroves spanning the coasts of India and Bangladesh (box, Fig. 1(a,b)). The Sundarbans in India extend along the estuarine coastline in the southern region of West Bengal and is home to rich biodiversity including existence of endemic species. The Sundarbans were designated a World Heritage Site by UNESCO in 1997 and have recently been declared a Ramsar site of International Importance³⁶. Unfortunately, the Sundarbans mangroves have experienced spatially varying degradation, from 11–98% in some areas, in the last few decades due to human activities, such as rapid urbanization, rampant coastal aquaculture, and agricultural practices. They have been classified as ‘endangered’ under RLE³⁷. India has a National Coastal Mission Programme on ‘Conservation and Management of Mangroves and Coral Reefs’, under which action plans on mangrove conservation are implemented in coastal regions and are reviewed annually³⁸. These schemes have increased mangrove cover in the country by around 1700 hectares from 2019 to 2021. While there has been some progress in reducing mangrove deforestation^22,37, they still face a potent threat from increasing urbanisation and water pollution^{27,30, 39–42}, rising temperatures, and extreme events due to global warming^{32, 43–45}. Mangroves are highly productive ecosystems known to recover from physical perturbations⁴⁶. Walcker et al., 2019⁴⁷, showed that some species of mangroves damaged by an extremely severe cyclonic storm could recover in three months. However, recovery times and mechanisms of recovery from stressors like pollution, cyclones, small spells of droughts, nutrient limitations, and change in salinity will be different because these stressors don’t cause any physical damage to the plants but impact their physiological processes^{30, 39–41}. Scientific knowledge on the recovery of mangroves from climate extremes and anthropogenic pollutants-induced changing water chemistry is needed to plan appropriate interventions for meaningful ecological restoration.

Nutrient availability is essential for the productivity of any vegetation. Carbon to Nitrogen to Phosphorous (the Redfield ratio) is critical for coastal vegetation productivity and has an optimum value of 106:16:1^48,49. Hence, the ratio N/P is also significantly important and should have an optimum value of 16:1. Any deviations from these ideal proportions can limit photosynthesis and affect vegetation productivity^40,50,51. Rapid urbanization and changes in land use land cover led to the increasing discharge of pollutants into rivers, changing the nutrient availability for mangroves. Sarker et al., 2016³⁶ show that the mangrove species in Sundarbans have varying physiological responses to changing concentrations of nutrients, with some species’ productivity increasing with an increase of Nitrogen and phosphorous and some showing the opposite behaviour. Many mangroves around the world are phosphorous-limited. Lovelock et al., 2006⁴⁰ showed that phosphorous limitation causes more productivity loss in mangroves than nitrogen limitation. Nutrient availability is projected to deteriorate in the future augmenting anthropogenic stress on mangroves; however, the physiological mechanism of mangrove’s resistance to extreme events and changing nutrient supply is poorly understood^5,32. The present study fills this gap by advancing our understanding of the dynamics of Sundarbans mangroves in South Asia under climate extremes and changing nutrient composition. Using observed meteorological data from a flux tower, in-situ soil-water chemistry measurements, and a causal network learning algorithm, we examine the behaviour of mangrove vegetation productivity under rapidly changing nutrients and climate extremes.

Figure 1(c) shows the climatology of vegetation productivity of mangroves. Vegetation productivity in this region follows a bell-shaped curve, with maximum productivity during the summer monsoon (June to September, known as the Indian Summer Monsoon or ISM), when the Indian subcontinent receives 80% of its annual rainfall^52–55. The study area also receives some precipitation due to local thunderstorms called ‘Norwester events’⁵⁶ in pre-monsoon. This is reflected as a slight jump in vegetation productivity in April-May in Fig. 1(c). These events bring intense rain and strong winds and can last a few hours. The period from December to March generally has low vegetation productivity as there is limited freshwater due to minimal precipitation. Supplementary figure S1(a-e) shows the anomalies (diurnal cycle removed) of eco-hydro-meteorological variables from January 2013 to December 2015 averaged to a weekly time scale. The figure shows all variables plotted with NEE as the second axis to visualize their variations with carbon fluxes.

Winters (figure S1) are characterised by lower TA and P, leading to lower LE and high VPD. During this period, WS is also low. During pre-monsoon, LE rises due to increased evapotranspiration under high TA. P during pre-monsoon is scarce and mainly occurs in the form of thunderstorms. During pre-monsoon, WS is high, and VPD starts reducing as air holds more moisture because of more land evaporation and moisture brought by hot air from the Bay of Bengal. During monsoon, LE drops as TA is reduced due to high P and continuous cloud cover. Winds during monsoon are strong, and VPD is very low because of the high moisture content of the air and continuous rainfall. The high LE in 2014 (figure S1(a)) is because of the strong El-Nino of 2014-15, which reduced P and high TA during ISM^57,58. The presence of El Nino years in our three-year study period will change the location and scale parameters of the distributions; however, due to the limited availability of data, we decided to choose this study period.

Stability against changing hydro-meteorological variables

Figure 2(a-d) shows standardized deviations of daily averaged flux tower data from respective monthly means plotted as three-day moving averages for NEE, P, WS, and TA, respectively. For example, the mean value of January month (90 values) is subtracted from all values of January in the daily time series, and thus generated anomalies for January days are divided by the standard deviation of January month (90 values). We show three-day moving averages instead of daily anomalies to smoothen the signal for better visibility. Dotted lines in the plot represent thresholds of data being one standard deviation away from the mean. While hydro-meteorological variables show significant variation throughout the observation period, generating values greater than one standard deviation, NEE remains mostly stable. For measuring this stability, we defined the Mangrove stress period as the period for which the Mangroves have NEE continuously greater than one standard deviation from the mean value. We fitted the beta distribution to the duration of this stress period (using daily data, histogram shown in Fig. 2f, and best-fit distribution Fig. 2e) and found that the average duration lies between 1–2 days with 95% confidence (statistically significant using the sample of size 91). The low mean value of the distribution shows a rapid recovery of NEE from perturbations with an average recovery period of between 1 and 2 days.

Stability against deteriorating nutrient supply

Figure 3 shows boxplots of NEE (Fig. 3(a)) from the flux tower during the study period (2013–2015), along with the observed monthly in-situ measurements of nutrients. Figure 3(b-d) shows Nitrate (Ni) to Phosphate (Ph) ratio (N/P), silicate (Si), and Ammonium (Am), respectively. Coastal areas have an ideal ratio of Si to Ni to Ph as 106:16:1, called the Redfield ratio^49,59, which is essential for ensuring optimal vegetation productivity. We found that the N/P ratio has constantly increased from being close to the ideal value of around 16:1 in 2013 to an average value of around 75:1 in 2015. The increase is roughly five-fold, with a few months showing around 10-fold increases from 2013 to 2015 (Fig. 3b). During this period, the amount of Ni increased, and Ph decreased continuously from 2013 to 2015. A decrease in P indicates the phosphorous limitation on vegetation productivity, which has previously been reported in the literature⁴⁰. Si and Am have also shown an increase of around 3 times (from 20 µM to around 60 µM average) and 7 times (from 1 µM to around 7 µM average), respectively. We found that while the nutrient composition has deteriorated significantly during the study period (Fig. 3(b-d)), NEE has remained stable (Fig. 3(a)). No apparent change in mangrove NEE is an interesting observation as it indicates that mangroves have so far been able to resist anthropogenic stressors which are known to reduce vegetation productivity by impacting plant physiology across short-term and long-term levels. The stability of mangroves becomes even more surprising because the changes in nutrients were also accompanied by a prolonged El Nino event, which is known to reduce the vegetation productivity and cause droughts across South Asia^57,60.

Response mechanism through varying process connectivity

The aforementioned findings raise the following query: how do mangroves stabilize their productivity under varying natural and anthropogenic stressors? To answer this, we examine the changes in process connectivity of mangrove NEE with hydro-meteorological variables through weekly and monthly causal networks generated using PCMCI (see methods). Since PCMCI can delineate non-linear causal associations, the temporal evolution of causal networks thus generated should reveal the evolving dynamics of mangroves under anthropogenic stressors. Figure 4 (a) shows the mean incoming link strength to NEE in the weekly networks. Vertical lines indicate the beginning and end monsoons (uniformly taken as 1st June to 30th September) of three years. We find that the incoming link strength from other variables to NEE has increased from 2013 to 2015 (the statistically significant increasing trend at 95% confidence using 158 data points). The increase in the connection strength is also accompanied by an increase in lags (Fig. 4(b), statistically significant increasing trend at 90% confidence using 158 data points) which indicates that physiological processes of mangroves are becoming more memory-driven under anthropogenic stressors. As an example, we show the networks of December month generated using 3-hour data (248 values) for two years, 2013 and 2015 (Fig. 4 (c) and (d), respectively). For December, incoming link strength to NEE from TA and P became stronger, and one new link from VPD was established, while links from LE and WS were lost. More figures for each month of each year are presented in supplementary figure S2. The increase in the average incoming link strength from 2013 to 2015 is clearly visible for most of the months; however, the increases cannot be narrowed down to a single source variable. The connections are dynamic and vary from month to month. While some links disappeared from 2013 to 2015, new links appeared with stronger connection strengths and larger lags. Hence, the mangroves are able to resist and adapt to increasing anthropogenic stressors by reorienting the strength and memory of their dynamic process connectivity with hydro-meteorological variables.

Process connectivity during weather extremes

To delineate the process changes in mangroves during weather extremes, we analyzed weekly causal networks around the cyclone Viyaru, which crossed the study area from 10th – 14th May, 2013⁶¹, and a norwester event in April 2013. We present our results in Figures S3 and S4, respectively. For cyclone (Figure S3), we showed five weekly networks starting from 29th April 2013, which include the cyclone at the end of the second week. From the number of incoming links to NEE in the networks, we clearly see that the incoming links to NEE drop immediately after the cyclone (figure S3(c)) and quickly recover in the next week (figure S3(d)) and a further increase in the following week (figure S3(e)). We found a similar case for the norwester event, as shown in figure S4. We show the number of incoming and outgoing links with node NEE for five weeks with a ‘norwester’ event lying at the third week (figure S4(a)) to include two preceding and two succeeding weeks in the analysis. We found that incoming links to NEE drop during the week of the thunderstorm but quickly recover in the next week. Outgoing links from NEE do not show the same signature as they represent the feedback from vegetation to the atmosphere, which might depend on other factors and is still poorly understood at short time scales. Overall, we found that mangrove NEE is stable to short spells of extreme weather events like thunderstorms. It shows that dynamic processes involving NEE in mangroves are briefly disturbed during weather extremes but are restored within one to two weeks.

Mangroves serve as a straddle between ocean and land ecosystems and are vital for coastal communities. Sundarbans, the largest contiguous mangrove in the world, can aid in the continual socio-economic development by ensuring the sustainability of the blue economy of the coastal areas of India and Bangladesh if they are optimally managed. While mangrove restoration is essential for building resilience against global warming, it is also vital for the sustainable economic growth of coastal communities. In this regard, understanding the damage that human activities are causing to mangroves lies at the forefront of restoration efforts. Our study addresses this issue by improving our understanding of the resilience of mangrove vegetation to natural and anthropogenic stressors, which will be vital for undertaking ecological restoration.

Using half-hourly observed data from the flux tower and monthly in-situ observations of nutrients, we showed that the Sundarbans mangroves are resilient to short-lived weather extremes and have so far been able to resist changing water chemistry originating from anthropogenic pollutants. We found that the productivity of mangroves drops in the presence of weather extremes but can recover quickly, with an average recovery time of one to two weeks. We also showed that while the water chemistry around mangroves has changed rapidly between 2013 and 2015, in the form of a manyfold increase in N/P ratio, silicate, and ammonium, NEE from these mangroves has remained stable. This shows that in addition to mangroves showing resilience against hydro-meteorological stressors, they are also resistant to physiological stress caused by human-induced water pollution. To understand their mechanism of recovery and resistance, we generated weekly and monthly causal networks of NEE with hydro-meteorological variables using flux tower data. We found that mangroves resist external stress by reorganizing their interaction with hydro-meteorological variables. This reorganization happens in the form of increasing strength of connection as well as an increase in the memory of the interaction.

Our study has two limitations. First, we were constrained by limited data availability. A long-term flux tower data together with in-situ nutrient observations, will contain more events like cyclones to test the dynamic responses of mangroves to extreme events and long-term changes in water chemistry. Second, we are limited by the effectiveness of the causal delineation technique in detecting and removing spurious causalities. While PCMCI is claimed to be one of the most robust methods available in the literature^62,63, it does not reduce spurious causalities to zero, and hence, improvements in the field of causal delineation can lead to new insights on mangrove dynamics.

Ecosystems can undergo an abrupt critical transition or a slow change depending on the complexity and heterogeneity of its components⁶⁴. Highly connected ecosystems resist external stressors by repairs provided locally which can lead to critical transitions above certain thresholds of stressors. This is in contrast to modular ecosystems which show adaptive capacity and gradual change⁶⁴. The mangrove resistance to natural and anthropogenic stressors observed reflects the resistance provided by highly connected networks, however, the limits of this resistance and the nature of associated critical transitions remain unknown. Our study advances the understanding of the dynamics of mangroves and calls for examining and quantifying the limits of mangrove resistance to anthropogenic stressors. The generated information will be particularly important for undertaking scientifically driven nature-based solutions (NbS) interventions for the restoration of mangroves in South Asia.

We used 4-day resolution (2001–2020) contiguous Solar-Induced Chlorophyll Fluorescence (CSIF) data from Zhang et al., 2018⁶⁵ which represents vegetation productivity. We also used half-hourly eddy covariance (EC) flux tower observations of eco-hydro-meteorological variables precipitation (P), air temperature (TA), wind speed (WS), latent heat flux (LE), vapor pressure deficit (VPD), and net ecosystem exchange (NEE) for three years 2013–2015 from a flux tower located in Sundarbans mangrove forest^55,66.

The EC flux tower of 15 meters in height is located on an uninhabited island with a land area of around 11 km² in Sundarban mangrove forests (Location SFT, Fig. 1b), at 21º49ʹ N, 88 º37ʹE. The tower is located on a near-flat marshy terrain and is surrounded by mangroves with an average canopy height of about 5 meters. The complete description of the tower infrastructure and instrumentation is detailed in Rodda et al., 2016^55,66. In brief, the tower is equipped with a high-frequency Infra-red Gas Analyser (IRGA; LI7500A; LI-COR USA) and Sonic anemometer (CSAT-3; Campbell Scientific USA), which measure continuous ecosystem-scale Carbon (NEE) and energy (LE) fluxes at a height 10 meters (above ground) at a frequency of 30 minutes. The vapour pressure deficit (VPD) is a derived parameter from the EC flux data processing and was available at 30-minute intervals. In addition, other meteorological parameters (viz., TA, P, WS) are measured at varying heights with a frequency of 10 minutes (measurements at 2m height are used in this study). The continuous multi-year dataset from April 2012 to March 2016 has been processed by employing both standard and site-specific protocols to yield a consistent and good quality dataset. For the current study, the EC flux tower data from 03 full calendar years (i.e. January 2013 to December 2015) was used by aggregating meteorological and fluxes to a 30-minute interval.

We also use monthly in-situ measurements of ammonium (Am), nitrate (Ni), phosphate (o-phosphate expressed as Ph) and silicates (Si), in surface water during the same period (2013–2015) from two stations (St1 and St2 in Fig. 1) a long-term monthly dataset called ‘Sundarbans Biological Observatory Time Series’ (SBOTS)⁴⁸ available from 2010. Samples in SBOTS (Stations St1 and St2 in this study are called Stn1 and Stn3 in the parent dataset, see Choudhury et al. 2015⁴⁸) for nutrient analysis are mostly taken from surface water of both stations on board a motorised boat during the periods of high tide and subsequently dissolved nutrients were measured using published methods as detailed earlier. Since St1 is located relatively upstream than St2, it has lesser tidal influence. Both of these stations are shallow, have dense mangroves surrounding them and also there are exchanges between soil and water layers due to the shallow depth. Together with flux tower measurements these observations form one of the very few extensive datasets available for mangroves across the globe. The two stations are close to the flux tower^48,67 (Locations St1 and St2 in Fig. 1b, at 21º40ʹ44.4ʹʹN, 88 º08ʹ49.5ʹʹE, and 21º40ʹ40.6ʹʹN, 88 º09ʹ19.2ʹʹE respectively) and represent the water chemistry around Sundarbans mangroves at SFT. Hence, any variation in upstream industrial and agriculture/aquaculture activities should alter the vegetation productivity of Sundarbans mangroves at location SFT and reflect as changes in observed carbon fluxes.

PCMCI

Peter Clark’s Momentary Conditional Independence (PCMCI)⁶² is a class of causal network learning algorithms that generate causal graphs by recursively removing links from a fully connected graph based on conditional independence. PCMCI is superior to classic causal inference methods like granger causality⁶⁸ and information-theoretic transfer entropy^69,70 as it captures non-linear causal associations even in high dimensional datasets while controlling for false causal discovery^{62, 71–73}. A brief summary of PCMCI is as follows. It has a two-stage process. The first stage, called the PC step, recursively performs conditional independence tests to generate the best possible conditioning set (called ‘Parents’), reducing the data’s dimensionality. From a set of all variables \({\underset{\_}{X}}_{t}\) we first initializes a preliminary set of parents \(\underset{\_}{P}\left({X}_{t}^{j}\right) = \left({\underset{\_}{X}}_{t-1},{\underset{\_}{X}}_{t-2},\dots ,{\underset{\_}{X}}_{t-{\tau }_{max}}\right)\) and then for all variables \({\underset{\_}{X}}_{t}^{j} \in {\underset{\_}{X}}_{t}\) and a subset \(S\) of \(\underset{\_}{P}\left({X}_{t}^{j}\right)\setminus \left\{{X}_{t-\tau }^{i}\right\}\)with cardinality \(p\), we test the null hypothesis

\(PC: {X}_{t-\tau }^{i} ⫫ {X}_{t}^{j} \mid S\) for all variables \({\underset{\_}{X}}_{t-\tau }^{i}\) from \(\underset{\_}{P}\left({X}_{t}^{j}\right)\). We test the hypothesis with a less stringent type I error rate of \(\alpha =0.2\) so as not to loose true links. If we fail to reject the null hypothesis, the link is removed from \(P\). In every iteration, \(p\) is increased and parents are sorted according to their decreasing strength of association with \({X}_{t}^{j}\). Every consecutive iteration starts with the strongest parent. The second stage, called the MCI step, establishes causal relationships at various time delays \(\tau =\left\{\text{1,2},\dots ,{\tau }_{max}\right\}\) for each pair \({X}_{t-\tau }^{i}\to {X}_{t}^{j}\)of the parents generated in PC stage and tests the following null hypothesis \(MCI: {X}_{t-\tau }^{i} ⫫ {X}_{t}^{j} \mid \underset{\_}{P}\left({X}_{t}^{j} \right) \setminus \left\{ {X}_{t-\tau }^{i} \right\},\underset{\_}{P}\left({X}_{t-\tau }^{i} \right) \forall {X}_{t-\tau }^{i}\in {X}_{t}^{-}\), at \(\alpha =0.05\). Here \({X}_{t}^{-} = \left({X}_{t-1},{X}_{t-1},\dots ,{X}_{t-{\tau }_{max}}\right)\) and \(\underset{\_}{P}\left({X}_{t-\tau }^{i}\right)\) is the conditioning set generated in the above step. Detailed algorithm for PCMCI can be found in Runge et al., 2019^62,72. For this study, we used k-nearest neighbour variant of conditional mutual information (CMI-knn)⁷⁴ as our choice of test statistic as it is best suited for detecting non-linear dependencies. To delineate the interactions of NEE with meteorological variables, we generate non overlapping monthly and weekly causal networks from 1st January 2013 to 31st December 2015 using PCMCI.

Weekly networks reveal the dynamics of sub-daily scale interactions while monthly networks show the inter-annual evolution of process connectivity of NEE. In this study, weekly networks are generated using a half hourly dataset (336 values, diurnal cycle removed) and monthly networks are generated using a 3-h dataset (approximately 248 values, aggregated to 3-h temporal resolution from half hourly anomalies). The choice of 3-hour aggregation is constrained by sample size. As recommended by Runge et al., 2018⁷⁴, two parameters of CMI-knn are set as k_perm = 10 and k_CMI = 0.2n where n is the number of data points. For weekly networks, we used maximum lag as 22-time steps (11 hours) to delineate sub-daily interactions; for monthly networks, we used a maximum lag of 16-time steps (48 hours) to delineate interactions up to two days. Higher lags exponentially increase the dimensionality of the system, leading to increased spurious links and computation time; hence, for monthly dataset, we have kept a maximum lag of two days.

Acknowledgements

TC acknowledges support of the Prime Minister’s Research Fellowship (PMRF), Ministry of Education, Government of India. The work is financially supported by Department of Science and Technology Swarnajayanti Fellowship Scheme, through project nos. DST/SJF/E&ASA-01/2017-18, DST/ SJF/ E&ASA-01/2018-19; SB/SJF/2019-20/11, and Strategic Programs, Large Initiatives and Coordinated Action Enabler (SPLICE) and Climate Change Program through project no. DST/CCP/CoE/140/2018. National Remote Sensing Centre (NRSC), Indian Space Research Organisation (ISRO) is acknowledged for the flux tower data set.

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Resilience of South Asian mangroves to weather extremes and anthropogenic water pollution

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Stability against changing hydro-meteorological variables

Stability against deteriorating nutrient supply

Response mechanism through varying process connectivity

Process connectivity during weather extremes

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