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Establishing size-weight relationships for heavily targeted coral species is an important first step towards informing sustainable harvest limits19. Placing coral harvests into an ecological context is a core requirement for implementing a defensible stock assessment strategy, and this need is particularly critical given escalating disturbances and widespread reports of coral loss7,17,25. Using these relationships, managers can now easily sample and calculate biomass per unit area. It is important to point out that all sites sampled in our study represent fished locations, and there is no information available to test whether standing biomass has declined due to sustained coral harvesting at these locations. While these data may now provide a critical baseline for assessing the future effects of ongoing fishing, it is also important to sample at comparable locations where fishing is not permitted or has not occurred (where possible), to test for potential effects of recent and historical harvesting.Biomass per unit area data presented herein highlights the highly patchy abundance and biomass of targeted coral species14, which is evident based on the often vastly different mean and median values (Table 2). Examining biomass per unit area estimates for C. jardinei for example, which returned some of the highest biomass estimates, the 33.75 kg·m−2 maximum estimate from a transect stands as an extreme outlier, with 12 of the 16 other transects being below 0.2 kg·m−2. This indicates the challenges of managing species that occur in patchily distributed concentrations, particularly in a management area the size of the QCF. It is also important to note, these estimates are generated only on transects where the target species occurred, and therefore, should technically not be considered as an overall estimate of standing biomass. While the estimation of size-weight relationships is a step towards a standing biomass estimate, many challenges remain in terms of sampling or reliably predicting the occurrence of these patchily distributed species. Bruckner et al.14 attempted to overcome this management challenge in a major coral fishery region of Indonesia by categorising and sampling corals (in terms of coral numbers) in defined habitat types, and then extrapolating to estimated habitat area based on visual surveys and available data. This approach, utilising size-weight relationship derived biomass per unit area estimates (instead of coral numbers), may be a viable method for the QCF, however much more information is needed to understand the habitat associations (e.g., nearshore to offshore), and environmental gradients that influence the size and abundance of individual corals. Fundamentally, it is also clear that much more data is required to effectively assess the standing biomass of aquarium corals in the very large area of operation available to Australian coral fisheries.These corals are found in a range of environments, and it is important to consider available information on life history if attempting to use coral size-weight relationships to inform management strategies via standing biomass estimation. All corals in this study can be found as free living corals (at least post-settlement) in soft-sediment, inter-reefal habitats, from which they are typically harvested by commercial collectors19. However, only four of the 6 species are colonial (C. jardinei, D. axifuga, E. glabrescens, M. lordhowensis) while the remaining two species (H. cf. australis and T. geoffroyi) are more typically monostomatous or solitary. As indicated in previous work24, if larger colonial corals were to be fragmented during harvesting instead of removed entirely, fishery impacts would likely be lessened24. Given the power relationship between coral maximum diameter and weight, larger corals contribute disproportionately to the total available biomass of each species in a given area. The potential environmental benefit of leaving larger colonies (at least partially) intact is not limited to impacts on standing biomass, as this practice would likely be demographically beneficial given the greater reproductive potential (i.e., fecundity) of larger colonies, which also do not need to overcome barriers to replenishment of populations associated with new recruits (i.e., high mortality during and post-settlement26). This conclusion was drawn largely from data on branching taxa (e.g., Acropora), which are relatively resilient to fragmentation and commonly undergo fragmentation as a result of natural processes27,28,29. D. axifuga can be considered to exhibit a relatively similar branching growth form, however, the growth form of E. glabrescens and C. jardinei changes with size, moving from small discrete polyps to large phaceloid and flabello-meandroid colonies, respectively19. While larger colonies of E. glabrescens and C. jardinei may be relatively resilient to harvesting via fragmentation, the same may not be true for smaller colonies, or species with massive growth forms such as M. lordhowensis. Typically, for each species, the average reported weight was quite low, coinciding with the lower end of the sampled maximum diameter range. For colonial species, the harvested smaller maximum diameters (if fragments) are ideal from an ecological perspective as this will have the least impact possible on standing biomass, and may also leave a potentially mature breeding colony intact. Ultimately, in light of these considerations, the development of uniform and standardised industry-wide harvest guidelines to balance economic and ecological outcomes may be necessary. The development of these guidelines would require consultation with commercial harvesters, as well as considerable additional work in measuring ecological impacts and better understanding the cost of these impacts from an economic perspective. Conversely, if whole colonies are collected, which is necessarily the case for solitary species such as H. cf. australis and T. geoffroyi (and potentially smaller colonies of other species such as E. glabrescens and C. jardinei); smaller colonies may be collected before they reach sexual maturity, hindering their ability to contribute to population replenishment. Therefore, collection of small fragments should be encouraged for colonial species; while for monostomatous species where this is not possible, introduction of a minimum harvest size based on sexual maturity should be considered.Additionally, the need for further consideration of the selectivity of ornamental coral harvest fisheries3,4,30 when assessing standing biomass is evident. Due to various desirable traits, the majority of available biomass may not be targeted by collectors. As emphasised in this study, the focus on smaller corals is indicative of the trend towards collection of most of these species at the lower portion of their size range, at least compared to some of the maximum sizes recorded on transects (e.g., see Tables 1 and 2, section b). However, it is also important to consider that transects were conducted in areas subject to commercial collection and are likely to skew results and prevent clear conclusions relating to size selectivity. Sampling of unfished populations (i.e., any residing outside of permitted fishing zones) and/or spatial and temporal matching of catch data and transect data across a larger sample of operators will be required to properly address industry size selectivity trends. For instance, only 17.5% of C. jardinei corals measured on transects fell within the diameter range represented by data obtained from collectors, with 81.9% of corals measured on transects exceeding this range. If it is viable to collect fragments from larger colonies (which does appear to be the case for some corals such as C. jardinei), then a larger proportion of standing biomass outside of this size range could be targeted by fishers. As an additional consideration, only desirable colour morphs of these corals will be harvested, and due to lack of appropriate data, the prevalence of these morphs remains unclear. H. cf. australis and M. lordhowensis for example often occur in brown colour morphs, which are far less popular in markets where certain aesthetic qualities (e.g., specific, eye-catching colours or combinations of colours) are desired, such as the ornamental aquarium industry. Even without delving into further considerations such as heritability of phenotypic traits, management conclusions drawn from standing biomass estimates may be ineffective in the absence of efforts to account for selectivity in this fishery.The relationship between size and weight was found to differ between all corals, with the exception of C. jardinei and E. glabrescens. There can be some moderate similarity in skeletal structure between these two species, particularly between small colonies, reflecting the similar maximum diameter range of sampling in the current study. Subsequently, inherent physiological constraints may be imposed on corals that prevent the maintenance of growth rates between corals of smaller and larger sizes, for example, as the surface area to volume ratio declines with growth31. In the current study, all corals, with the exception of C. jardinei, showed evidence of allometric growth, as exhibited by an estimated exponent value different to 3. Sample size for C. jardinei was greatly limited, as this species typically forms extensive beds, and are rarely brought to facilities as whole colonies. Therefore, the lack of evidence for allometric growth may reflect higher error for the species coefficient parameter due to the comparatively small sample size for this species. This suggests that mass would not increase consistently with changes in colony size in 3 dimensions31, which seems likely considering the change in exhibited form described for E. glabrescens and C. jardinei previously. In the current context, this indicates that the estimated ‘a’ and ‘b’ constants are likely to vary as the sample range increases, reflecting the changes in the size-weight relationship between smaller and larger samples of these species. Therefore, ideally, these models should incorporate data that reflect the maximum diameter range of the species in the region of application to allow increased accuracy of biomass estimation. To achieve this will require additional fishery-independent sampling, as large colonies are rarely collected whole, though may be collected as fragments depending on the species. Sampling may be challenging for some species given the difficulty of physically collecting and replacing large whole colonies, particularly for inter-reefal species such as M. lordhowensis, which can occur in deep, soft sediment habitat, subject to strong currents. Importantly, obtaining ex situ or in situ growth rate data should be considered a priority for the management of heavily targeted species. This data is likely to be another necessary component (in conjunction with size-weight relationships) of any stock assessment model developed for LPS corals, and may also eliminate the need to collect large sample colonies to improve estimated size-weight relationships.The disproportionate focus on smaller corals (i.e., corals in the current study averaged between 4.28 and 11.48 cm in maximum diameter) is likely to lead to an underestimation of weight in corals at greater diameters when used as inputs for size-weight models. This may explain the apparent minor underestimation observed in some species (e.g., M. micromussa, T. geoffroyi). In the current context, this represents an added level of conservatism with estimates obtained from these equations. While the relationship between size and weight was particularly strong for some species, (mainly D. axifuga and T. geoffroyi), for other species, such as M. lordhowensis, growth curves tended towards underestimation at larger diameter values. As the mass of a coral is reflective of the amount of carbonate skeleton that has been deposited32, the coral skeleton may increase disproportionately to coral diameter if or when corals start growing vertically. For example, in massive corals such as M. lordhowensis, vertical growth (i.e., skeletal thickening) is often very negligible among smaller colonies, with thickening of the coral skeleton only becoming apparent once the coral has reached a threshold size in terms of horizontal planar area. Additional fisheries-independent sampling outside of the relatively narrow size range of harvested colonies will be required to address this source of error in future applications. Ecological context in the form of fishery independent data on stock size and structure is essential for effective management, especially in ensuring that exploitation levels are sustainable and appropriate limits are in place. Coral harvest fisheries offer managers an ecologically and biologically unique challenge, as the implementation of standard fisheries management techniques and frameworks is hampered by their coloniality and unique biology, as well as a general lack of relevant data for assessing standing biomass and population turnover, not to mention the evolving taxonomy of scleractinian corals33. Similarly, fishery-related management challenges such as the extreme selectivity in terms of targeted size-ranges and colour-morphs, plus the potentially vast difference in the impact of various collection strategies (i.e., whole colony collection vs fragmentation during collection) also complicates the application of typical fisheries stock assessment frameworks. The relationships and equations established in the current work offer an important first step for coral fisheries globally by laying the groundwork for a defensible, ecologically sound management strategy through estimation of standing biomass, thus bridging the gap between weight-based quotas and potential environmental impacts of ongoing harvesting. It is important to note that the species selected for the current work do not represent the extent of heavily targeted LPS corals. For example, Fimbriaphyllia ancora (Veron & Pichon, 1980), Fimbriaphyllia paraancora (Veron, 1990), Cycloseris cyclolites (Lamark, 1815), and Acanthophyllia deshayesiana (Michelin, 1850) are examples of other heavily targeted corals of potential environmental concern19, and management would also benefit from the estimation of size-weight relationships for these species. Moving forward, the next challenge for the coral harvest fisheries will be to comprehensively document and track the standing biomass of heavily targeted and highly vulnerable coral stocks, explicitly accounting for fisheries effects and also non-fisheries threats, especially global climate change. More

Land vulnerability of an area is directly related to the natural as well as anthropogenic activities involved in the geomorphological unit. Being one of the most vulnerable ecosystems, the estuaries and estuarine islands are delicately affected by both ecological processes of the sea and land and have pressures from multiple anthropogenic stressors and global climate change42,43,44. Ecological vulnerability and ecological sensitivity are similar and both originated from the concept of ecotone10,45. The geomorphologic concept of landscape sensitivity was first proposed by Brunsden and Thornes, who argued that the sensitivity indicated the propensity to change and the capacity to absorb the effects of disturbances10,46,47. Landscape sensitivity is studied by many researchers such as Allison and Thomas, Miles et al., Harvey, Knox, Usher, Haara et al., Thomas, Jennings and Yuan Chi8,47,48,49,50,51,52,53,54, through different case studies. Based on their findings Yuan Chi summarized the important characteristics of the landscape sensitivity are: a, the change of the landscape ecosystem; it involves the change likelihood, ratio, and component, as well as the resistance and susceptibility to the change, b, the temporal and spatial scales; which determine the occurrence, degree, and distribution of the change, c, the external disturbances that cause the change; the disturbances included natural and anthropogenic origins with different categories and intensities, and d, the threshold of the landscape sensitivity; it refers to the point of transition for the landscape ecosystem8. The environmental vulnerability of the Munroe Island has been studied based on the characterization of the geomorphological and sociocultural dynamics of the region based on the above characteristics.Bathymetric surveys in Ashtamudi lake and the Kallada riverThe present study shows that the geomorphic processes occurring on the Munroe Island are affected by anthropogenic disturbances in the morpho-dynamics of the Kallada river, Ashtamudi backwaters and associated fluvio-tidal interactions. A detailed bathymetric survey of both water bodies up to the tidal-influenced upper limit of the Kallada river27 was conducted with 200 m spaced grid references (Fig. 5). Bathymetry shows that the deepest point of the Ashtamudi backwater system is in Vellimon lake (13.45 m), the SE extension of Ashtamudi lake. The eastern side of Ashtamudi lake is deeper than the western side of this backwater system. The depth of the backwater decreases towards the estuary, and most parts of the lakebed are exposed here at the mouth of the inlet during the low tide. Compared to Ashtamudi lake, the Kallada river is deeper, and the riverbed area is recorded as the average depth is greater than 13 m. The deepest part of 14.9 m is recorded near Kunnathoor bridge, which is 12 km upstream from Munroe Island. Except for a few spots of hard (resistant) rocks, the river fairly and consistently follows a higher depth throughout its course.Figure 5Bathymetric profile of Ashtamudi lake and adjoining Kallada river (Figure was generated by Arc GIS 10.6).Full size imageOnce the Kallada river supplied very fertile alluvium during its flooding seasons (monsoon/rainy season), and most of this alluvium is deposited in the floodplains of the Munroe Island and the Ashtamudi lake. With a vast river catchment area from elevated lands of Western Ghats and a shorter course of 121 km33,55 and a higher elevation gradient of 12.6 m/km56, the Kallada river has a higher transporting capacity. The eroded surface and mined river/lakebeds at lower courses were replaced by the sediment load supplied by the Kallada river during each flood season until dam construction. During the focus group discussions with residents of the Island, they had described that they were crossing the Kallada river on foot in the 1990s or even earlier during the dry seasons. The construction of the Thenmala reservoir dam in 1980s across the river drastically choked the sediment supply of the Kallada river. In addition, excessive commercial sand mining without any regulation from the riverbeds of Kallada and Ashtamudi waterbodies accelerated the deepening of waterbodies. It increased the erosion of surface and subsurface soils through fluvial and hydraulic action. This, in turn, drastically reduced the deposition of fertile alluvium over the low-lying Munroe Island. The current bathymetry shows that the river channel has deepened its course to 14 m compared to 5–6 m of 1980s. When comparing the bathymetric data of 200127, it is interesting to note that no considerable changes occurred in the bathymetry of Ashtamudi lake over the last two decades.Dams indeed alter aquatic ecology and river hydrology, upstream and downstream, affecting water quality, quantity, breeding grounds and habitation22. The other significant impact of the damming of the Kallada river is the saline water intrusion towards upstream of Ashtamudi lake and the Kallada river. The freshwater discharge is regulated after the construction of the Thenmala reservoir, and the water is being diverted to the reservoir and associated canals. There is a decline in sedimentation over the floodplains and catchment area as a result of the increased tidal effects and associated running water dynamics, which may accelerate the erosion trend of the nearby places.Lithological characterization of the Munroe IslandThe Munroe Island is a riverine delta formation by the Kallada river at the conjunction of river and backwater systems. To understand the micro-geomorphological processes of the study area, the near-surface geology of the Munroe Island had been studied in detail with the help of resistivity meter surveys and borehole datalogs from different locations. As per the current resistivity survey, it is evident that the Munroe Island is formed by recent unconsolidated loose sediments more than 120 m thick succession below ground level (Figs. 6 and 7). The electrical resistivity tomography of identified locations within the deltaic region shows a meagre resistance value to its maximum penetration (Fig. 6), which proves that the sedimentary column with intercalations of sand and carbonaceous clays of varying thickness extends to a depth of 120 m, in turn indicating the process of enormous sedimentation happened during the recent geological period. Loose wet soils of saline nature records a lower resistance value for an electric circuit. The layers formed in the diagram (Fig. 6) represent the seasonal deposition of unconsolidated soils as thin sequence. The Mulachanthara station of the resistivity meter tomography, which is situated at a more stable location of the Island, has a higher resistivity value than the West Pattamthuruth location, which is located at the exact alluvial flood plain.Figure 6Electrical resistivity profiles of Munroe Island.Full size imageFigure 7Geomorphological map showing litho-log of north (Kannamkadu); middle (Konnayil Kadavu); and south (Perumon bridge) locations of Munroe Island (borehole data source: PWD, Govt of Kerala) (Software used: Arc GIS 10.6).Full size imageThe Public Works Department (PWD), Kerala State carried out soil profile studies through Soil Penetrating Test (SPT) borehole drilling method as part of constructing bridges at three different locations up to a depth of 62 m, i.e., one across the Kallada river (north side)57, one across Ashtamudi lake in southern Munroe Island58 and one at the central part of Munroe Island (across a canal)59 (Fig. 7). The hard rock is found only on the southern side of the lake at a depth of 45 m. The litho-log shows that unconsolidated loose sediments of significantly higher thickness occur in the entire Munroe Island (Fig. 7). Anidas Khan et al.60 studied the shear strength and compressibility characteristics of Munroe Island’s soil for two different locations with disturbed and undisturbed samples. They classified the soil of Mundrothuruth into medium compressibility clay (CI) and high compressibility clay (CH) with natural moisture contents of 44.5% and 74%, respectively. The unconfined compressive strengths of the undisturbed and remolded samples for the first location are 34.5 kN/m2 and 22.1 kN/m2, respectively, while they are 13 kN/m2 and 9 kN/m2 respectively for the second location60. Such compressive strength indicates that the soils of Munroe Island are soft or very soft in nature.Land degradation: a morphological analysisTo decrease the impact of the monsoon floods and to distribute the alluvium to the southern part of the island, Canol Munroe, the then Diwan of the Thiruvithamkoor Dynasty, made an artificial man-made canal during the 1820s connecting the Kallada river with the eastern extension of Ashtamudi lake, and this river is known as “Puthanar” (meaning a new river). During the last few decades, (after 1980s) the estuarine island ecosystem of Munroe Island has faced several structural deformities. The natural sedimentation and flooding happening in the Islands were very limited and hence, the normal events happened over the past several decades disturbed and significantly affected the land neutrality. These islands, once known as the region’s rice bowl, now devoid of any paddy cultivation mainly because of the increased soil salinity. According to the Cadastral map prepared by the revenue department (1960s) there were many paddy fields, locally named as Mathirampalli Vayal (Vayal is the local name for paddy field), Thekke Kothapppalam Vayal, Mattil Vayal, Kottuvayal, pallaykattu Vayal, Konnayil Vayal, Vadakke Kundara Vayal, Thachan Vayal, Thekke Kundara Vayal, Kizhakke Oveli Vayal, Thekke Oveli Vayal, Odiyil Vettukattu Vayal, Nedumala Vayal, Madathil Vayal, Karichal Vayal, Moonumukkil Vayal, Arupara Vayal, Kaniyampalli Vayal, Manakkadavu Vayal, Panampu Vayal, Pattamthuruth Vayal etc. The recent satellite images shows that no paddy cultivation exist now, which is further confirmed by the field observations conducted through our study. The annual report published by Gramapanchayat39 indicate that the paddy field of region was reduced from 227 to 8 acres (from 1950 to 1995) and now about in 2 acres only (2018). Most of the paddy fields of northern and northwestern regions are severely affected by land degradation due to erosion, saline water intrusion and flooding and are entirely or partially buried under the backwater system. Figure 8 depicts the morphological degradation of the severely affected areas of Munroe Island from 1989 to 2021 through different satellite images. Some paddy fields are converted into filtration ponds to take the benefit of frequent tidal flooding. The coconut plantations were later introduced in place of paddy fields, and they eventually replaced the paddy fields. However, during the last decades, it has been observed that these coconut plantations are also under threat mainly because of degradation of the soil fertility, which directly bears the quality and quantity of production (Fig. 9).Figure 8Morphological changes in the study area from the satellite images (a) 1989 (aerial photograph); (b) 2000 (Landsat); (c) 2011 (World View—II); (d) 2021 (Sentinel) (the modified maps of (a) is obtained from National remote Sensing Centre (NRSC), Hyderabad, (b) is downloaded from https://earthexplorer.usgs.gov/ (c) is obtained from Digital Globe through NRSC and (d) is downloaded from https://scihub.copernicus.eu/. Figures were generated using Arc GIS 10.6).Full size imageFigure 9Threatened coconut plantations indicating the low productive regime. Photographs taken by Rafeeque MK.Full size imageOver the study area the most affected alluvial plain of the Peringalam and Cheriyakadavu island are taken separately to study the morphological changes over the decades. This area is named Puthan Yekkalpuram (which means new alluvium land), and the north side of the Kallada river (the northward extension in the Mundrothuruth GP) is demarcated as old alluvium land (Pazhaya Yekkalpuram) as per the revenue department’s cadastral map. The study shows that total 38.73 acres of land has lost from the Peringalam and Cheriyakadavu Islands during the last 32 years, which is equivalent to 11.78% and 46.95% of the total geographical area of the Peringalam and Cheriyakadavu Islands, respectively. The land degradation details over the last three decades are given in the Table 2. Many other locations, such as Nenmeni and West Pattamthuruth, are also severely affected by land degradation. However, these areas are landlocked and less affected by running water or floods. Hence, the land degradation experienced is the settling of the topsoil and subsidence of structures such as houses and bridges. The sinking of basements of many houses and even the subsidence of railway platforms are well observed during field visits, indicating the alarming land degradation issues (Figs. 1 and 10) to be addressed its deserving importance. There are also clear indications of the gradual formation of new waterlogged areas in the islands, which may further deteriorate and forms the part of the backwater system which eventually affects total land area of the Munroe Island.Table 2 Land degradation of Peringalam and Cheriyakadavu region for the past 32 years.Full size tableFigure 10Various environmental degradations in Munroe Island. Photographs taken by Rafeeque MK.Full size imageThe island population also shows a negative growth over the years. According to the census report of 201138, the total population of Gramapanchayat has decreased to 9440 person/km2 in 2011 from 10,013 person/km2 of 2001 and 10,010 person/km2 of 1991 census reports. Frequent flooding (especially tidal flooding), the lack of drinking water, and migration in search of a better livelihood are the main reasons for the observed population reduction as revealed through the survey. The high intrusion of saline water into the cultivated land through tidal flooding and the lack of flushing of surface saline soils by monsoon floods (freshwater) decreased agricultural productivity of the area, and hence, now people are more dependent on fishing and backwater activities for their livelihood. Lack of proper transportation to the nearby markets limits their fishing activities to a daily subsistence level. Due to the flooding caused by subsidence/tidal surges and land degradation during the last few decades, more than 500 households have vacated their houses38,39.Tidal Flooding and Estuarine ProcessesIn Mundrothuruth, the major environmental degradation problems where occurring due to tidal flooding and saline water intrusion into the freshwater ecosystem. Mathew et al. studied the tidal and current mechanisms of the Ashtamudi backwater in 200161. They reported that the Kallada river plays a vital role in determining the eastern lake’s circulation pattern. In addition, the increased discharge from the north Chavara canal and the south Kollam canal also influences the local circulation of the Ashtamudi backwater. The current velocity reaches up to 100 cm/s at the estuary entrance, but it rapidly diminishes in the eastern parts, where the speed is generally less than 30 cm/s. One of the critical observations made during the field study, which corroborates with the acquaintance of local people as well, is that the flooding on Munroe Island is not related to the spring tide of the open ocean. The disappearance of the semidiurnal tide in the central lakes occurs due to frictional resistance and the time lags for the tide to travel across the estuary61. At the shorter semidiurnal period of approximately 12 h, the tide is more dissipated than the more extended constituents of 24-h duration. The survey conducted with the island inhabitants also reiterates these views.As per the experience of local inhabitants, tidal flooding in Munroe Island was not frequent in earlier times. The comparison of the bathymetry data collected during 200058 and 2017 (Fig. 5) in and around the regions of Munro Islands shows that there is not much change in bathymetry during the period. Hence, changes in basin geometry are not having a significant role in tidal dynamics in imparting the variations as observed. In addition to the bathymetric survey, the data on tide measurements at four locations corresponding to three seasons were also collected. The tide data measured during the pre-monsoon period is shown in Fig. 11a. The figure shows that the tidal range in the inland area is almost the same even during the spring and neap tides. As discussed earlier, the tidal flooding in Munro Island is not related to spring tide in the ocean, and there may be the influence of specific complicated dynamics in the basin for this flooding that needs to be studied more profoundly. Further the data pertaining to tidal dynamics were inadequate; we established three tide gauges in selected locations in and around Munro Island. From the analysis of tide gauge data, it is found that the signature of anomalous variability in water column height, which is not at all linked to the tidal dynamics.Figure 11(a) Salinity variation of bottom water at selected locations in Kallada river during monsoon and post monsoon. (b) Observed tide during pre-monsoon months.Full size imageThe water quality analysis for three time periods, during the year of the cyclonic storm, Okhi (2017), was conducted to understand river run-up impact on salinity in and around Munroe Island (Fig. 11). The riverbed is lowered below the baseline of erosion, and dense saline water is trapped in the deeps during high tide. This has been confirmed during the bathymetric survey of the Kallada river and Ashtamudi backwaters, which showed a significant increase in water depth, particularly within the river channel. The high-density saline water is trapped in the basins and trenches created in the river channel due to uncontrolled sand mining, which leads to the degradation of the quality of sediments and groundwater in the region. Nevertheless, the samples collected immediately after Okhi (when the dam’s shutter was opened due to heavy rainfall in the catchment area) show that the high runoff replaced the trapped saline water with fresh water. After ten days of the first sampling, the water became saline nature after the closure of the dam’s shutter. This proves that because of dam construction, the river runoff in the Kallada river was reduced significantly, and extensive human interactions especially sand mining activities increased the riverbed deepening and formation of pools beyond the base level of running water.Conservations and management strategiesConsidering the facts discussed above, the Munroe Island may continue to be badly affected unless suitable sustainable management strategies are not evolved. Construction and associated activities, such as the damming of reservoirs, sand mining and landfilling, are indispensable for any nation’s economic and social development. United Nations’s member states have formulated 17-point Sustainable Developmental Goals (SDGs) to better the world sustainably. Local and national governments pertaining to the Munroe Island need to develop a sustainable management plan to protect this Ramsar-listed wetland. The environmental issues of Mundrothuruth can be controlled, and land degradation may be monitored through a well-drafted working plan. All aspects of earth and social sciences may be integrated to draft such a management plan of reverse landscaping. The reverse landscaping (i.e., recalling the degrading landscape to its geomorphic isostatic state) method is a must-considered sustainable solution for land degradation and other environmental issues.The deep courses of Kallada river must be upwarped through a well-planned artificial sedimentation to eradicate the saline banks of deep basins. The sediments deposited in the Thenmala reservoir and the sediments removed through the digging of boat channels may be utilized in a periodic monitoring method. Sand mining from Ashtamudi lake and the Kallada river may be strictly controlled, and the minimum freshwater flow should be ensured. The construction methods practiced in Mundrothuruth are outdated and technically nonexistent. Well-studied engineering methods suitable for an environmentally fragile area must be implemented with a proper understanding of the soil characteristics, such as shear strength and compressibility rate, and hydrodynamics, such as tidal and fluvial actions. Soil fertility must be increased by supplying additional fertile soil and freshwater, at least for a minimum period. The inhabitants’ socioeconomic well-being is strengthened by advancing technology and providing easy access to the market and other social amenities. More

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Our findings highlight the critical role of polyP in Sodalinema stali-formed cyanobacterial mats, as it was dynamically accumulated and recycled during acclimation to P fluctuations.Cellular response to progressive P starvationAnalogous to planktonic cyanobacteria, growth under low P availability could be sustained by recycling polyP, which acted as a primary P source (Fig. 2a) [16, 23, 24]. We further attribute the rapid reduction of easily dispensable cellular P-containing compounds to the substitution of cellular phospholipids with S- or N-containing membrane lipids to maintain growth at the onset of P stress (Fig. 2a) [15, 23]. However, the exhaustion of this easily dispensable P pool limited proliferation in Phase 2, and the metabolic strategy switched from a focus on growth towards maintenance (Fig. 5). The interpretation of prevailing cellular processes based on our results is graphically summarized and explained in detail below (Fig. 5).Fig. 5: Schematic interpretation of cellular phosphorus (P) cycling in a cyanobacterial mat, based on significant changes of the monitored parameters (arbitrary units).a At low P availability, initially contained polyphosphate (polyP) was recycled simultaneously with phosphate uptake to sustain growth at constant C:N:P ratios. Further proliferation at the onset of P stress in Phase 1 was sustained by mobilization of cellular P, e.g. phospholipids, which led to rapidly increasing C:N:P ratios. Severe P stress in Phase 2, indicated by increasing APase activity, prevented proliferation and photosynthesis, indicated by a loss of green chlorophyll pigments. PolyP accumulation by deficiency response occurs with severely increasing P stress, whereby globular DNA accumulation indicates the allocation of P contained in DNA into polyP. P re-addition to the P-stressed cells in Phase 3 triggered overplus uptake and narrow C:N:P ratios, transitioning to luxury uptake at higher C:N:P ratios following polyP recycling. b At high P availability, polyP in Phase 1 was accumulated by overplus uptake at narrow C:N:P ratios, transitioning to luxury uptake at higher C:N:P ratios during polyP recycling in Phase 2. P-deprivation in Phase 3 did not affect the cells, which we attributed to a sufficient amount of phosphate in the residual medium or within the biofilm matrix. Arrows indicate phosphorus transformation processes, whereby arrows pointing towards DNA represent cell growth. Yellow granules = polyP, blue granules = globular DNA spheres, P = phospholipids, S = substitute lipids.Full size imageSevere P stress in Phase 2 was indicated by the colour change from green towards yellow-green (Fig. S1) and increasing APase activity (Fig. 2a). The colour change suggested the loss of photosynthetic pigments [40], but we could not clarify whether this occurred through active cellular pigment reduction or degradation of available chlorophyll e.g., by oxidation. The increasing APase activity (Fig. 2a) suggested that Sodalinema stali is capable of hydrolysing organic P [14]. Even though APase expression did not trigger proliferation, it likely hydrolysed a potentially available organic P pool, as increasing DIC, NH4 and decreasing pH indicated progressive decay and remineralisation of organic matter (Fig. 1a). This suggests that in analogous oligotrophic environments with often fluctuating conditions, the strategy has to be maximizing the utilization of external P sources contained in organic and inorganic sediment particles that get trapped in the EPS [41]. The sediment can contain large amounts of organic P [42] and the fluctuating physico-chemical gradients in the EPS matrix due to high daytime pH and low oxygen conditions at night, facilitate P desorption from metal oxides, leading to higher dissolved phosphate concentrations within the mat, compared to the overlying water body [3]. However, alternating redox conditions at the SWI could also trigger polyP release from benthic microorganisms to the sediments, where it could act as a P source for the benthic food-chain, or ultimately trigger the formation of mineral P phases [32], to sustainably remove P from the aquatic cycle. Either way, we suggest that polyP-containing cyanobacterial mats critically impact P fluxes at the SWI.With persisting severe P stress and increasing APase activity in Phase 2, polyP accumulation as a deficiency response was observed (Fig. 2a), which has been reported from planktonic cyanobacteria of different habitats [24, 29, 23], as well as stream periphyton [28]. However, the reasons causing this deficiency response remain unresolved. In marine phytoplankton of the oligotrophic Sargasso Sea, Martin et al. [23] excluded that polyP-rich cells were in a perpetual overplus state with ‘undetectable’ pulses driving this state and suggested that polyP accumulation occurred as a cellular stress response. In other studies, reduced biosynthesis of P-rich rRNA coincided with deficiency responses [26, 28] and led to the suggestion that polyP accumulation at P concentrations below a certain threshold required for growth occurs because of P allocation changes away from growth and towards storage. Further, APase can hydrolytically cleave phosphate groups from nucleic acids and convert DNA-lipid-P to DNA-lipids, which were shown to self-assemble into globular lipid-based DNA micelles [43]. These preferentially anchor on cell membranes [44], and indeed, such DNA spheres were found to accumulate at the cell’s polar membranes in our experiments adjacent to polyP during deficiency response (Fig. 4a: Phase 2,c). Therefore, we suggest that intracellular P recovery by cleavage from P-rich DNA and reallocation to polyP, and potentially reduced rRNA synthesis [31], is also a strategy in benthic mats of Sodalinema stali as a response to severe P stress when P availability is too low to sustain growth. This supports the theory of a reallocation of resources away from growth towards flexibly available P and energy storage. Such direct intracellular P cycling could be beneficial to help retain P within the cyanobacterial population; while external P moieties such as dissolved organic P within the matrix can act as an additional P source, they are also likely to be subject to nutrient competition between cyanobacteria and other organisms inhabiting the matrix.Such effects of potential interactions in terms of nutrient competition or provision between cyanobacteria and mutualistic microorganisms contained within the same EPS matrix are difficult to assess and we cannot exclude some potential effects on our results. However, mutualistic microorganisms that are naturally contained in many cyanobacterial or algal cultures are often critical for metacommunity functioning and hence, working with axenic mat-forming strains may even further falsify any obtained results. Furthermore, microscopic analyses revealed that Sodalinema always dominated the biomass and hence, it is here considered reasonable to work with a non-axenic culture.Cellular response to a simulated P pulseIn P-deficient cells, the affinity of the P uptake system is typically increased to maximize P uptake for future pulses [13, 45]. The simulated P pulse to the P-stressed cells in Phase 3 led to a rapid increase of the cellular P content by 1260% relative to C within 3 days (Fig. 2a), whereby P was accumulated to a significant part as polyP, which is characteristic for overplus uptake [25]. Many different types of oligotrophic aquatic habitats experience only temporal P pulses, e.g., from redox changes at the benthic interface leading to P release from the sediment [32], storm run-off [28], upwelling [46], or excretions of aquatic animals [47]. The capability of microorganisms to immediately take up, store, and efficiently re-use this P by overplus uptake is hence of critical importance for a population to sustain a potential subsequent period of low P availability. Overplus uptake is typically accompanied by the overall slow growth of the population and cellular recovery from P starvation, including ultrastructural organization and recovery of the photosynthetic apparatus [48]. This took one week after re-feeding of P-starved Nostoc sp. PCC 7118 cells [48]—a timeframe very similar to the delayed onset of photosynthesis observed in our study, indicated by the elevated pH at day 9 (Fig. 1a). Regarding overplus-triggering mechanisms following P pulses, Solovochenko et al. [48] suggested that overplus uptake occurs due to a delayed down-regulation of high-affinity Pi transporters, which are active during P starvation, and emphasized the simultaneous advantage of osmotically inert polyP accumulation as a response to dramatically high phosphate concentrations in the cells. Even though APase levels declined following our experimental P re-addition, they were significantly elevated for at least 9 days (Fig. 2a). As our experimental design involved replacing the medium with APase-free, BG11 + medium after Phase 2, we assume that the APase detected in Phase 3 was actively produced, and we conclude that previously relevant, low-P response mechanisms are slowly disengaged with some sort of lag, even when ambient P is repleted. Following cellular recovery, Sodalinema now recycled stored polyP instead of further accumulating it during the transition from overplus-to luxury uptake, which was reflected in the increasing C:N:P molar ratios and decreasing polyP levels without significant additional phosphate uptake (Figs. 1a, 2a, 5).Qualitative observations on polyP distributionMost methods applied to analyse polyP in microorganisms are quantitative and do not contain information on its spatial distribution within a population. The here observed variable distribution of polyP between the cells during luxury uptake and deficiency response, as well as the retention of polyP in few individual filaments during polyP recycling in Phase 1 of the low P experiment (Fig. 4) suggests strategies of either slow growth with a retention of polyP, or of high growth with polyP recycling. This was also suggested for cells of a unicellular Synechocystis sp. PCC 6803 population during overplus uptake [47]. In contrast, polyP in our experiment was distributed homogeneously between all cyanobacterial cells during overplus uptake (Fig. 4a: Phase 3, Fig. 4b: Phase 1). Yet, we are unaware of any polyP distribution study in multicellular or mat-forming cyanobacteria and hence, further mechanisms of interactions, e.g., cell-to-cell communication [49, 50], might also contribute to purposeful differentiation of cells or filaments within a common matrix.In summary, our study shows that the mat-forming Sodalinema stali (1) is capable of luxury uptake, overplus uptake and deficiency response with a heterogenous polyP distribution during polyP recycling, luxury uptake and deficiency response, while (2) dynamically adjusting cellular P content to changing phosphate concentrations. (3) Proliferation is sustained under the expense of polyP, followed by P acquisition from other easily dispensable cellular P-containing compounds under the onset of P stress. (4) Further, biosynthetic allocation changes away from growth towards maintenance with relative polyP accumulation at the expense of P-rich DNA are conducted under severe P stress. Our findings demonstrate the extraordinary capabilities of mat-forming cyanobacteria to adapt their P acquisition strategies to strong P fluctuations. While lasting proliferation under P limitation requires the mobilization of additional P sources through regeneration of P from particulate matter, the transition to net P accumulation under excess ambient P is rapid and effective. Since current projections of climate and land use change include intensified pulses of P load to aquatic ecosystems [50], e.g., through external input from surplus of agriculture fertilizer, inefficient wastewater treatment plants, and internal loads via the mobilization of legacy P, these P ‘bioaccumulators’ could form an important component in P remediation by temporarily accumulating P within the mat, and synthesizing polyP that could ultimately stimulate the formation of mineral P phases to sustainably remove P from the aquatic cycle. More

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To calculate total root biomass C colonized by AM and EcM fungi, we developed a workflow that combines multiple publicly available datasets to ultimately link fine root stocks to mycorrhizal colonization estimates (Fig. 1). These estimates were individually derived for 881 different spatial units that were constructed by combining 28 different ecoregions, 15 land cover types and six continents. In a given spatial unit, the relationship between the proportion of AM- and EcM-plants aboveground biomass and the proportion of AM- and EcM-associated root biomass depends on the prevalence of distinct growth forms. Therefore, to increase the accuracy of our estimates, calculations were made separately for woody and herbaceous vegetation and combined in the final step and subsequently mapped. Below we detail the specific methodologies we followed within the workflow and the main assumptions and uncertainties associated.Fig. 1Workflow used to create maps of mycorrhizal fine root biomass carbon. The workflow consists of two main steps: (1) Estimation of total fine root stock capable to form mycorrhizal associations with AM and EcM fungi and (2) estimation of the proportion of fine roots colonized by AM and EcM fungi.Full size imageDefinition of spatial unitsAs a basis for mapping mycorrhizal root abundances at a global scale, we defined spatial units based on a coarse division of Bailey’s ecoregions23 After removing regions of permanent ice and water bodies, we included 28 ecoregions defined according to differences in climatic regimes and elevation (deposited at Dryad-Table S1). A map of Bailey’s ecoregions was provided by the Oak Ridge National Laboratory Distributed Active Archive Center24 at 10 arcmin spatial resolution. Due to potential considerable differences in plant species identities, ecoregions that extended across multiple continents were split for each continent. The continent division was based upon the FAO Global Administrative Unit Layers (http://www.fao.org/geonetwork/srv/en/). Finally, each ecoregion-continent combination was further divided according to differences in land cover types using the 2015 Land Cover Initiative map developed by the European Space Agency at 300 m spatial resolution (https://www.esa-landcover-cci.org/). To ensure reliability, non-natural areas (croplands and urban areas), bare areas and water bodies were discarded (Table 1). In summary, a combination of 28 ecoregions, 15 land cover types and six continents were combined to define a total of 881 different spatial units (deposited at Dryad-Table S2). The use of ecoregion/land cover/continent combination provided a much greater resolution than using a traditional biome classification and allowed to account for human-driven transformations of vegetation, the latter based on the land cover data.Table 1 List of land cover categories within the ESA CCI Land Cover dataset, used to assemble maps of mycorrhizal root biomass.Full size tableMycorrhizal fine root stocksTotal root C stocksEstimation of the total root C stock in each of the spatial units was obtained from the harmonized belowground biomass C density maps of Spawn et al.20. These maps are based on continental-to-global scale remote sensing data of aboveground biomass C density and land cover-specific root-to-shoot relationships to generate matching belowground biomass C maps. This product is the best up-to-date estimation of live root stock available. For subsequent steps in our workflow, we distinguished woody and herbaceous belowground biomass C as provided by Spawn et al.20. As the tundra belowground biomass C map was provided without growth form distinction, it was assessed following a slightly different workflow (see Section 2.2.3 for more details). To match the resolution of other input maps in the workflow, all three belowground biomass C maps were scaled up from the original spatial resolution of 10-arc seconds (approximately 300 m at the equator) to 10 arc‐minutes resolution (approximately 18.5 km at the equator) using the mean location of the raster cells as aggregation criterion.As the root biomass C maps do not distinguish between fine and coarse roots and mycorrhizal fungi colonize only the fine fractions of the roots, we considered the fine root fraction to be 88,5% and 14,1% of the total root biomass for herbaceous and woody plants, respectively. These constants represent the mean value of coarse/fine root mass ratios of herbaceous and woody plants provided by the Fine-Root Ecology Database (FRED) (https://roots.ornl.gov/)25 (deposited at Dryad-Table S3). Due to the non-normality of coarse/fine root mass ratios, mean values were obtained from log-transformed data and then back-transformed for inclusion into the workflow.Finally, the belowground biomass C maps consider the whole root system, but mycorrhizal colonization occurs mainly in the upper 30 cm of the soil18. Therefore, we estimated the total fine root stocks in the upper 30 cm by applying the asymptotic equation of vertical root distribution developed by Gale & Grigal26:$$y=1-{beta }^{d}$$where y is the cumulative root fraction from the soil surface to depth d (cm), and β is the fitted coefficient of extension. β values of trees (β = 0.970), shrubs (β = 0.978) and herbs (β = 0.952) were obtained from Jackson et al.27. A mean value was then calculated for trees and shrubs to obtain a woody vegetation β value of 0.974. As a result, we estimated that 54.6% of the total live root of woody vegetation and 77.1% of herbaceous vegetation is stored in the upper 30 cm of the soil. In combination, this allowed deriving fine root C stocks in the upper 30 cm of woody and herbaceous vegetation.The proportion of root stocks colonized by AM and EcMThe proportion of root stock that forms associations with AM or EcM fungi was obtained from the global maps of aboveground biomass distribution of dominant mycorrhizal types published by Soudzilovskaia et al.14. These maps provide the relative abundance of EcM and AM plants based on information about the biomass of grass, shrub and tree vegetation at 10arcmin resolution. To match with belowground root woody plants biomass data, proportions of AM trees and shrubs underlying the maps of Soudzilovskaia et al.14 were summed up to obtain the proportion of AM woody vegetation. The same was done for EcM trees and shrubs.Our calculations are subjected to the main assumption that, within each growth form, the proportion of aboveground biomass associated with AM and EcM fungi reflects the proportional association of AM and EM fungi to belowground biomass. We tested whether root:shoot ratios were significantly different between AM and EcM woody plants (the number of EcM herbaceous plants is extremely small17). Genera were linked to growth form based on the TRY database (https://www.try-db.org/)19 and the mycorrhizal type association based on the FungalRoots database17. Subsequently, it was tested whether root:shoot ratios of genera from the TRY database (https://www.try-db.org/)19 were significantly different for AM vs EcM woody plants. No statistically significant differences (ANOVA-tests p-value = 0.595) were found (Fig. 2).Fig. 2Mean and standar error of root to shoot ratios of AM and EcM woody plant species.Full size imageEstimation of mycorrhizal fine root stocksWe calculated the total biomass C of fine roots that can potentially be colonized by AM or EcM fungi by multiplying the total woody and herbaceous fine root C biomass in the upper 30 cm of the soil by the proportion of AM and EcM of woody and herbaceous vegetation. In the case of tundra vegetation, fine root C stocks were multiplied by the relative abundance of AM and EcM vegetation without distinction of growth forms (for simplicity, this path was not included in Fig. 1, but can be seen in Fig. 3. As tundra vegetation consists mainly of herbs and small shrubs, the distinction between woody and herbaceous vegetation is not essential in this case.Fig. 3Workflow used to create mycorrhizal fine root biomass C maps specific for tundra areas.Full size imageFinally, we obtained the mean value of mycorrhiza growth form fine root C stocks in each of the defined spatial units. These resulted in six independent estimations: AM woody, AM herbaceous, EcM woody, EcM herbaceous, AM tundra and EcM tundra total fine root biomass C (Fig. 4).Fig. 4Fine root biomass stocks capable to form association with AM (a) and EcM (b) fungi for woody, herbaceous and tundra vegetation. Final AM and EcM stock result from the sum of the growth form individual maps. There were no records of fine root biomass of EcM herbaceous vegetation.Full size imageThe intensity of root colonization by mycorrhizal fungiColonization databaseThe FungalRoot database is the largest up-to-date compilation of intensity of root colonization data, providing 36303 species observations for 14870 plant species. Colonization data was filtered to remove occurrences from non-natural conditions (i.e., from plantations, nurseries, greenhouses, pots, etc.) and data collected outside growing seasons. Records without explicit information about habitat naturalness and growing season were maintained as colonization intensity is generally recorded in the growing season of natural habitats. When the intensity of colonization occurrences was expressed in categorical levels, they were converted to percentages following the transformation methods stated in the original publications. Finally, plant species were distinguished between woody and herbaceous species using the publicly available data from TRY (https://www.trydb.org/)19. As a result, 9905 AM colonization observations of 4494 species and 521 EcM colonization observations of 201 species were used for the final calculations (Fig. 5).Fig. 5Number of AM (a) and EcM (b) herbaceous and woody plant species and total observations obtained from FungalRoot database.Full size imageThe use of the mean of mycorrhizal colonization intensity per plant species is based on two main assumptions:

1)

The intensity of root colonization is a plant trait: It is known that the intensity of mycorrhizal infections of a given plant species varies under different climatic and soil conditions28,29, plant age30 and the identity of colonizing fungal species31. However, Soudzilovskaia et al.9 showed that under natural growth conditions the intraspecific variation of root mycorrhizal colonization is lower than interspecific variation, and is within the range of variations in other plant eco-physiological traits. Moreover, recent literature reported a positive correlation between root morphological traits and mycorrhizal colonization, with a strong phylogenetic signature of these correlations32,33. These findings provide support for the use of mycorrhizal root colonization of plants grown in natural conditions as a species-specific trait.

2)

The percentage of root length or root tips colonized can be translated to the percentage of biomass colonized: intensity of root colonization is generally expressed as the proportion of root length colonized by AM fungi or proportion of root tips colonized by EcM fungi (as EcM infection is restricted to fine root tips). Coupling this data with total root biomass C stocks requires assuming that the proportion of root length or proportion of root tips colonized is equivalent to the proportion of root biomass colonized. While for AM colonization this equivalence can be straightforward, EcM colonization can be more problematic as the number of root tips varies between tree species. However, given that root tips represent the terminal ends of a root network34, the proportion of root tips colonized by EcM fungi can be seen as a measurement of mycorrhizal infection of the root system and translated to biomass independently of the number of root tips of each individual. Yet, it is important to stress that estimations of fine root biomass colonized by AM and EcM as provided in this paper might not be directly comparable.

sPlot databaseThe sPlotOpen database21 holds information about the relative abundance of vascular plant species in 95104 different vegetation plots spanning 114 countries. In addition, sPlotOpen provides three partially overlapping resampled subset of 50000 plots each that has been geographically and environmentally balanced to cover the highest plant species variability while avoiding rare communities. From these three available subsets, we selected the one that maximizes the number of spatial units that have at least one vegetation plot. We further checked if any empty spatial unit could be filled by including sPlot data from other resampling subsets.Plant species in the selected subset were classified as AM and EcM according to genus-based mycorrhizal types assignments, provided in the FungalRoot database17. Plant species that could not be assigned to any mycorrhizal type were excluded. Facultative AM species were not distinguished from obligated AM species, and all were considered AM species. The relative abundance of species with dual colonization was treated as 50% AM and 50% ECM. Plant species were further classified into woody and herbaceous species using the TRY database.Estimation of the intensity of mycorrhizal colonizationThe percentage of AM and EcM root biomass colonized per plant species was spatially upscaled by inferring the relative abundance of AM and EcM plant species in each plot. For each mycorrhizal-growth form and each vegetation plot, the relative abundance of plant species was determined to include only the plant species for which information on the intensity of root colonization was available. Then, a weighted mean intensity of colonization per mycorrhizal-growth form was calculated according to the relative abundance of the species featuring that mycorrhizal-growth form in the vegetation plot. Lastly, the final intensity of colonization per spatial unit was calculated by taking the mean value of colonization across all plots within that spatial unit. These calculations are based on 38127 vegetation plots that hold colonization information, spanning 384 spatial units.The use of vegetation plots as the main entity to estimate the relative abundance of AM and EcM plant species in each spatial unit assumes that the plant species occurrences and their relative abundances in the selected plots are representative of the total spatial unit. This is likely to be true for spatial units that are represented by a high number of plots. However, in those spatial units where the number of plots is low, certain vegetation types or plant species may be misrepresented. We addressed this issue in our uncertainty analysis. Details are provided in the Quality index maps section.Final calculation and maps assemblyThe fraction of total fine root C stocks that is colonized by AM and EcM fungi was estimated by multiplying fine root C stocks by the mean root colonization intensity in each spatial unit. This calculation was made separately for tundra, woody and herbaceous vegetation.To generate raster maps based on the resulting AM and EcM fine root biomass C data, we first created a 10 arcmin raster map of the spatial units. To do this, we overlaid the raster map of Bailey ecoregions (10 arcmin resolution)24, the raster of ESA CCI land cover data at 300 m resolution aggregated to 10 arcmin using a nearest neighbour approach (https://www.esa-landcover-cci.org/) and the FAO polygon map of continents (http://www.fao.org/geonetwork/srv/en/), rasterized at 10 arcmin. Finally, we assigned to each pixel the corresponding biomass of fine root colonized by mycorrhiza, considering the prevailing spatial unit. Those spatial units that remained empty due to lack of vegetation plots or colonization data were filled with the mean value of the ecoregion x continent combination.Quality index mapsAs our workflow comprises many different data sources and the extracted data acts in distinct hierarchical levels (i.e plant species, plots or spatial unit level), providing a unified uncertainty estimation for our maps is particularly challenging. Estimates of mycorrhizal fine root C stocks are related mainly to belowground biomass C density maps and mycorrhizal aboveground biomass maps, which have associated uncertainties maps provided by the original publications. In contrast, estimates of the intensity of root colonization in each spatial unit have been associated with three main sources of uncertainties:

1)

The number of observations in the FungalRoot database. The mean species-level intensity of mycorrhizal colonization in the vegetation plots has been associated with a number of independent observations of root colonization for each plant species. We calculated the mean number of observations of each plant species for each of the vegetation plots and, subsequently the mean number of observations (per plant species) from all vegetation plots in each spatial unit. These spatial unit averaged number of observations ranged from 1 to 14 in AM and from 1 to 26 in EcM. A higher number of observations would indicate that the intraspecific variation in the intensity of colonization is better captured and, therefore, the species-specific colonization estimates are more robust.

2)

The relative plant coverage that was associated with colonization data. From the selected vegetation plots, only a certain proportion of plant species could be associated with the intensity of colonization data in FungalRoot database. The relative abundance of the plant species with colonization data was summed up in each vegetation plot. Then, we calculated the average values for each spatial unit. Mean abundance values ranged from 0.3 to 100% in both AM and EcM spatial units. A high number indicates that the dominant plant species of the vegetation plots have colonization data associated and, consequently, the community-averaged intensity of colonization estimates are more robust.

3)

The number of vegetation plots in each spatial unit. Each of the spatial units differs in the number of plots used to calculate the mean intensity of colonization, ranging from 1 to 1583 and from 1 to 768 plots in AM and EcM estimations, respectively. A higher number of plots is associated with a better representation of the vegetation variability in the spatial units, although this will ultimately depend on plot size and intrinsic heterogeneity (i.e., a big but homogeneous spatial unit may need fewer vegetation plots for a good representation than a small but very heterogeneous spatial unit).

We provide independent quality index maps of the spatial unit average of these three sources of uncertainty. These quality index maps can be used to locate areas where our estimates have higher or lower robustness. More

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