Sandy coast erosion threatens vital ecosystem services

Sandy coast erosion threatens vital ecosystem services | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Sandy coast erosion threatens vital ecosystem services Dominik Paprotny, Michalis I. Vousdoukas, Panagiotis Athanasiou, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6716780/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Coastal zones provide services that are essential for human well-being, but they are under increasing threat from coastal erosion with accelerating sea level rise. Here, we value ecosystem services at risk of sandy coastline erosion worldwide in view of climate change. We find that 2.7-4.5% of the services provided by sandy coasts could be lost by 2150, depending on the warming scenario, but 13-21% of specifically coastal ecosystems. Particularly endangered are the prevention of soil degradation, moderation of extreme events, and tourism. The Caribbean, Central America and Western Asia would lose the highest share of their services, particularly Small Island Developing States (SIDS). Inland migration of sandy coasts, where possible, could reduce losses by 26-32%, but most coasts have limited retreat space due to anthropogenic or topographical barriers. We show that current ambient coastline change trends could substantially exacerbate the impacts, unless they are reversed by effective coastal management practices. Earth and environmental sciences/Hydrology Earth and environmental sciences/Climate sciences/Climate change/Climate-change impacts Earth and environmental sciences/Environmental social sciences/Climate-change impacts Earth and environmental sciences/Environmental social sciences/Environmental impact Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Nature directly and indirectly benefits humans by providing a variety of functions commonly referred to as ecosystem services (Constanza 1997). Quantitative evaluations have shown that coastal ecosystems - especially tidal marshes, deltas, estuaries, beaches, and dunes - are particularly valuable per unit area compared to other marine or terrestrial ecosystems (de Groot et al. 2012, Mehvar et al. 2018, Brander et al. 2024). The high added value of sustained services provided by coastal ecosystems has been especially highlighted in terms of food provisioning, protection against extreme events, economic benefits from tourism and cultural relevance (Liquete et al. 2013, Arkema et al. 2015, De Valck et al. 2023). Globally, this translates into implicit benefits for humanity, worth trillions of US dollars per year (Costanza et al. 2014). Coastal ecosystems are under increasing threat from human activity, including climate change-induced sea level rise (SLR) (Schuerch et al. 2018, Saintilan et al. 2023) that increases coastal erosion (Mentaschi et al. 2018, Vousdoukas et al. 2020). Even if global warming is constrained to 1.5°C above pre-industrial level, sea level is projected to increase by 44 cm until 2100 relative to average 1995-2014 level (IPCC 2021). At 4°C warming, sea level could increase by 70 cm. Sandy coastlines are at particular risk as they are highly dynamic in time and space (Luijendijk et al. 2018) and SLR could result in extensive losses of coastline width (Vousdoukas et al. 2020). So far, few studies have quantified the consequences of erosion on future coastal zone ecosystem services (Alves et al. 2009, Roebeling et al. 2013, Paprotny et al. 2021, Vousdoukas et al. 2024) and none gave a global perspective. A study for the coastlines of Europe estimated that around 5% of ecosystem services could be lost by 2100 (Paprotny et al. 2021, IPCC 2022). The inland migration of coastal ecosystems may help conserve some of them (Kirwan et al. 2016, Cooper et al. 2020, Saintilan et al. 2023), but this process is constrained by what is known as “coastal squeeze.” This phenomenon refers to the limited availability of natural land behind coastal ecosystems into which they could retreat—primarily because human developments, such as buildings and infrastructure, create artificial barriers near the shoreline (Silva et al. 2020, Lansu et al. 2024, Nawarat et al. 2024). Difficult to erode, high terrain (topographical barriers) can also limit coastal ecosystem migration (Paprotny et al. 2021). On the other hand, unobstructed retreat could result in the loss of ecosystem services from valuable inland biomes, e.g. forests. Erosion could be further increased or decreased by ambient shoreline change driven by long-term hydrodynamic, geological and anthropic factors, including coastal management practices (Mentaschi et al. 2018). Here, we analyse how SLR-induced erosion could reduce sandy coast ecosystem services (SCES) worldwide. We build upon recent data advances describing coastal characteristics (Athanasiou et al. 2024), projecting erosion extent (Vousdoukas et al. 2020), and valuing ecosystem services (Brander et al. 2024). Firstly, we quantify the present value of SCES along 250,338 transects with a sandy shoreline, constituting 34% of the global ice-free coastline (Athanasiou et al. 2024). The transects are spaced 1 km of each other and extend up to 4 km inland and provide detailed information on the topography and land cover in the coastal zone (see Methods). Then, we calculate SCES losses from SLR for global warming scenarios of 1.5, 2, 3 and 4°C until 2150. We hereby assume that ‘coastal systems’ (beaches, dunes, cliffs, inlets, deltas and tidal marshes) will migrate inland as long as there is available space to do so (i.e. in the absence of artificial and topographical barriers), and inland ecosystems are lost first before any loss of the coastal ecosystem occurs. We quantify how much this coastal ecosystem migration reduces future SCES losses by comparing it with a scenario without backshore migration. Finally, we quantify the potential impact of ambient shoreline dynamics on SCES. SCES are valued in US dollars (USD) at 2023 prices and purchasing power parities (PPP) and compared with gross domestic product (GDP) of affected countries in 2023. Existing value of sandy coast ecosystem services The global sandy coastal zone spans nearly 960,000 km 2 within 4 km of the shoreline (Supplementary Table 1). More than half is barely or sparsely vegetated, or covered by low vegetation (Supplementary Fig. S1a), while 26% are forests and only 2% are specifically coastal systems, including beaches, dunes, cliffs, inlets, deltas and tidal marshes. We estimate that SCES are presently worth 901 billion USD per year, corresponding to 0.64% of global GDP in 2023. Most value is provided by forests (26%), various types of low or sparse vegetation (25%, of which 11% by croplands), permanent water bodies (16%), and inland wetlands (11%). Despite their limited spatial extent, coastal ecosystems provide 8% of the total SCES (Supplementary Fig. S1b). The largest share of SCES relates to regulating services (43%), which includes particularly the moderation of extreme events as well as pollination and maintenance of soil fertility (Supplementary Fig. S2), in addition to regulation of air quality, climate and water flows, waste treatment and soil erosion prevention. Cultural services are second-most important (30%), especially the existence and bequest value of ecosystems as well as the opportunities for recreation and tourism they provide. Provisioning services (23%) relate to goods that can be consumed or used by people, including food, fresh water, raw materials and other resources. Finally, habitat services (4%) are responsible for the maintenance of life cycles and genetic diversity. The highest absolute value of SCES exists in Canada, where it amounts to 90 billion USD, followed by Australia (76 billion USD) and Russia (50 billion USD). Other countries with high absolute value (35-42 billion USD) are the United States, Chile, China, Norway and Mexico. Relative to GDP, the distribution of SCES varies considerably. In Western Europe, Southern Asia and Eastern Asia, they are worth about 0.2% of GDP, in Northern America 0.5%, but 2.7% in Eastern Africa, 3.5% in the Caribbean, 5.1% in Micronesia, and 5.3% in Australia and New Zealand (Fig. 1). These values depend on the coastal length and the share of sandy coasts therein (Supplementary Fig. S3), as well as on the economic development level. In 20 countries and territories, SCES are worth more than 10% of GDP (Supplementary Fig. S4). Countries with particularly high absolute and relative values are Mozambique (16.8 billion USD, or 30% of GDP), the Bahamas (11.3 billion USD, or 82% of GDP), and Madagascar (9.8 billion USD, or 21% of GDP). This is because in those countries the majority of coasts are sandy, something not common worldwide. For instance, in the Philippines, which has one of the longest coastline lengths worldwide, only 3% of coasts is sandy, resulting in SCES of only 2 billion USD or 0.2% of its GDP. Erosion-induced losses of sandy coast ecosystem services globally By mid-century, 5400 to 6300 km 2 of sandy coastal zone is expected to be eroded for warming scenarios between 1.5 and 4°C. This increases to 10,700 km 2 for a 1.5°C scenario and 15,800 km 2 for 4°C by the end of the century, and 12,600 to 22,600 km 2 by 2150. This shows that the benefits of climate mitigation grow in time, but also that even when global warming stabilizes, SLR-driven erosion will continue due to the delayed effects of SLR to global warming (Mengel et al., 2018). The corresponding total annual SCES loss due to erosion would amount to 11.2 billion USD for a 1.5°C scenario and 12.8 billion USD for a 4°C scenario by 2050, which further rises to 20.8-29.2 billion USD by 2100 and 24.6-40.1 billion USD by 2150. This represents a loss of 1.2-1.4% of their current value by 2050, 2.3-3.2% by 2100 and 2.7-4.5% by 2150. Almost a quarter (19-24%, depending on scenario) of the area lost by erosion is land covered by coastal systems (beaches, dunes, cliffs, inlets, deltas and tidal marshes). Given that they constitute only 2% of ecosystems in the sandy coastal zone, this suggests that in many places, essential coastal ecosystems will be unable to migrate landward due to human-made or natural barriers, leading to their loss. Due to their large value per unit area, they account for 37-41% of the total SCES losses, amounting to 5-6 billion USD per year by 2050 and 12-18 billion USD by 2150 (Fig. 2a). For other coastal ecosystems, such as beaches, dunes, cliffs, inlets, deltas and tidal marshes, present services would be at least 6% lowered by 2050 and between 13 to 21% by 2150 (Fig. 2b). Most of the remaining value is lost in services from mangroves, inland wetlands and water bodies, which sum up to 6-7 billion USD per year by 2050 and 14-23 billion USD by 2150, or a loss of 4-6% of their present value by 2150. Other land cover classes, mainly grasslands, would contribute to 12-14% of the losses, amounting to 4-7 billion USD per year by 2150. Despite their extensive spatial coverage in sandy coastal zones, service losses by forests other than mangroves remain limited, especially in temperate and boreal climates, while in tropical climates 1.6-2.5% of present value could be lost by 2150. Reduced regulating services constitute 39% of total SCES losses, especially through the lowered moderation of extreme events (Fig. 2c), with annual losses estimated at 2-3 billion USD in 2050 and 5-7 billion USD in 2150. Regulating services lost relating to soil fertility, waste treatment and preventing soil erosion remain below 4, 2 and 1 billion USD, respectively, in 2150 even under very high warming. Relative to their present value, losses of regulating services are in the range of 4 to 8% depending on the scenario and timing (Fig. 2d). Nearly a third of SCES losses relate to cultural services, mainly through reduced opportunities for recreation and tourism (4-6 billion USD in 2150, 4-7% of present value) and lowered existence and bequest value (3-5 billion USD in 2150, 3-4% of present value). Coastal erosion further reduces the provisioning of water, food and other goods with 7-11 billion USD in 2150, a loss of 3-4% of the present value, while habitat and biodiversity services would be lowered by 2-3 billion USD in 2150, or 4-6y% of the present value. Erosion-induced losses of sandy coast ecosystem services by region The largest absolute SCES losses are projected for Australia and New Zealand, amounting to about 3 billion USD per year by 2050 and 6-8 billion USD by 2150. This corresponds to a loss of 4% of their SCES by 2050 and 6-9% by 2150, depending on the warming level, much higher than the global average (Fig. 3). Similarly, impact relative to GDP would be one of the highest (up to 0.5% of GDP by 2150). The highest relative impacts are projected in the Caribbean, with 7-8% of services lost by 2050, 12-15% by 2100 and 14-19% by 2150. Other regions with high projected relative losses (up to 6-9% of existing value by 2150) are Central America, Eastern Africa, and Western Asia. On the other end of the scale, Northern Europe and Polynesia would lose no more than 0.8% and 1.5% of SCES, respectively. Relative to GDP, the lowest impacts are projected in Southern Asia, Western Europe, and Eastern Asia (Fig. 3). Small Island Developing States (SIDS) are expected to be particularly affected by sea level rise (Vousdoukas et al. 2023), and their SCES are no exception. SIDS are projected to be many times more affected than other countries (Supplementary Fig. S5). 6-7% of the services would be lost by 2050, and 13-17% by 2150. By contrast, other countries are projected to lose on average 1% of services by 2050 and 2-4% by 2150. Relative to GDP, the impacts would be 13-16 times higher, depending on the timestep and warming scenario. Most of the losses affecting SIDS would occur in the Caribbean region, which was already highlighted above due to the projected magnitude of impacts. Losses are further linked to income levels ((Supplementary Fig. S5). In the long-term, high income countries (as classified by the World Bank) would encounter lowest losses relative to existing value of services, not exceeding 4%. Impacts relative to GDP would be 4-7 times lower in high income countries compared to high-income ones, though it would be nearly twice higher compared to middle income countries. Benefits of backshore migration The coastline has always been dynamic and here we used the optimistic assumption that coastal ecosystems will be preserved by landward retreat when space is available. Fig. 4 shows the benefits by region of backshore migration in terms of reduced SCES losses compared to a scenario in which coastal ecosystems are not allowed to migrate inland. Globally, around a third of SCES losses would be avoided by the retreat of beaches, dunes and other coastal systems. Benefits of migration are limited in regions where natural barriers or anthropogenic structures and actions prevent the natural landward migration of coastal ecosystems, known as coastal squeeze. This effect is most severe along highly developed Caribbean coastlines, which partly explains the large magnitude of SCES losses shown in Figure 3. Here, only 6-7% of SCES losses could be prevented through retreat, which is also the case for SIDS countries as a whole. Other regions where coastal squeeze is more than the global average are Central and Northern America, Australia/New Zealand and Eastern Europe, where retreat could prevent at maximum around 20% of SCES losses in 2150. On the other hand, unrestricted inland migration in the absence of human-made or natural barriers along large extents of coastlines in Africa and Micronesia lowers SCES potentially lost without migration by up to 50%. The benefits of inland migration lower with increasing erosion extent as time proceeds and for higher warming levels. Effects of ambient shoreline dynamics Results discussed above consider only SLR-induced erosion, while ambient shoreline changes caused by long-term hydrodynamic, geological and anthropic factors (Vousdoukas et al. 2020) could exacerbate or counter-balance the projected erosion trends. In case the current ambient dynamics (1984-2015) (Mentaschi et al. 2018) would sustain in the future, they could have a larger effect than SLR-driven retreat under warming below 2°C in both 2100 and 2150, and even at 3°C by 2150. If the SLR is contained by strong greenhouse gas mitigation (1.5°C warning), ambient shoreline change could progressively become a greater challenge for preserving SCES. At regional level (Fig. 5), ambient change maintaining historical rates would increase losses by about 50% or more in most parts of the world, especially Polynesia and Southern Asia. Similar effects are less prominent along Northern America, Middle Africa and Australia/New Zealand regions, not exceeding 20%. Such ambient change contributions are often related to human interventions (Mentaschi et al. 2018), which implies that they could be mitigated or even reversed by adopting more sustainable coastal zone management practices. Discussion In this study we have mapped globally sandy coast ecosystem services (SCES) and how they are threatened by SLR-driven erosion. While acknowledging the assumptions and uncertainties inherent to the scale of application, our findings show that climate mitigation can avoid at maximum around 30% of the projected SCES losses by 2100 and 40% by 2150. This is because most sandy coastlines are already eroding and sea levels will continue to rise, even if we achieve the Paris Agreement warming goals (Mengel et al. 2018), and affect the already fragile ecosystems. The actual magnitude of the loss will depend on the local interplay between sea levels, coastal morphology, ambient coastal changes, urban development and coastal squeeze. It is important to highlight that even though the term ‘coastal squeeze’ typically refers to anthropogenic barriers to inland migration, in the context of our study natural topographic barriers to shoreline retreat will have the same effect on ecosystem viability as hard, artificial surfaces. Given the increasing human occupation of the world’s coastal zones (Neumann et al., 2015), our study underlines the importance of coastal resilience planning for preserving SCES to prevent the worst-case scenarios discussed in our manuscript. An example of the latter are set-back zones, which are already part of policy initiatives, e.g. EU Floods Directive (European Union 2007), and by allowing the coastal zone to act as a buffer between the ocean and the land (Toimil et al. 2023). Our analysis involves several sources of uncertainty (Supplementary Text S1 and Supplementary Fig. S6). A key source of uncertainty in the estimated absolute SCES losses relates to the valuation of ecosystem services. We use mean values of services per unit area based on harmonized local studies (Brander et al. 2024), but individual reported estimates vary widely. We also assume that the value of services per unit area remains constant in time, but this will depend on socio-economic dynamics that are difficult to predict in the long term (Christensen et al. 2018). For example, SLR also leads to higher coastal flood hazard (Vousdoukas et al. 2018), hence the coastal ecosystem value to moderate coastal extremes will grow in many coastal regions around the world. Another major driver of uncertainty are the erosion projections, for both SLR due to the underlying use of several models (Vousdoukas et al. 2020) and the extrapolation of ambient dynamics into the future (Mentaschi et al. 2018). The landward migration of coastal systems might reduce some SCES losses from erosion (see Supplementary Fig. S6b and Figure 4). However, this relies on coastal habitats adapting and migrating inland quickly enough to offset erosion rates. This scenario presents challenges, as it involves adapting to environmental change rates that have not been previously experienced (Moore and Schindler 2022). Methods Coastal transect-level data. We used the global coastal transects from the Global Coastal Characteristics (GCC) dataset (Athanasiou et al. 2024) with an alongshore resolution of 1 km, based on the Open Street Map (OSM) coastline. Two different elevation profiles were available for each transect based on Copernicus DEM (European Space Agency and Airbus, 2022) and DeltaDTM (Pronk et al. 2024), at a 25 m cross-shore resolution. Moreover, a land-cover profile at 10 m cross-shore resolution was available for each transect based on the ESA WorldCover dataset (Zanaga et al. 2021). In order to constrain the analysis to sandy transects, the “sandy” label from the indicator “Coastal type” of the GCC dataset was used, which is based on Hulskamp et al. (2023). In the original GCC dataset the transects overlap due to the complexity of the coastline. Here, we made a nearest-neighbourhood analysis of the points where transects intersect the OSM coastline and uniquely assigned a section of a 4-km buffer around the coastline to each transect. Transects were truncated where necessary to fit within the 4-km buffer uniquely assigned to each transect. Erosion projections. Shoreline change projections are produced based on the approach of Vousdoukas et al. (2020) and include two components. Firstly, SLR-driven retreat was estimated by applying a modified version of the Bruun rule, based on the latest IPCC AR6 SLR projections for global warming scenarios ranging between 1.5 and 4°C (Garner et al. 2021, Kopp et al. 2023) and the nearshore beach slope of the GCC dataset (Athanasiou et al. 2024). The SLR and ambient change components of coastal erosion were combined using Monte Carlo sampling with sufficient ensemble size to ensure convergence.The SLR retreat estimates include several sources of uncertainty, such as accuracy of nearshore slope data, SLR magnitude at given warming level or exact beach location (Athanasiou et al. 2020, Thiéblemont et al. 2021), dynamics of nearshore wave conditions and other sources of epistemic uncertainty. Secondly, ambient change was assessed based on global satellite observations covering years 1984-2015. Two datasets produced by Mentaschi et al. (2018) and Luijendijk et al. (2018) were combined in a probabilistic framework to generate an ensemble of 10,000 realizations of future ambient change. The two datasets have undergone various improvements and quality control since they were first published and the latest versions were used. As the only feasible projection of ambient change is an extrapolation of the historical pattern. As ambient change is often related to coastal management practices, we do not include this factor in the main results and only as additional analysis (Fig. 5). Ecosystem service values. We used data from the Ecosystem Services Valuation Database (ESVD) (Brander et al. 2024) for 12 biomes. We assigned the biomes to WorldCover land cover classes (Supplementary Table 1). In some cases, ESVD biomes and WorldCover classes could be directly linked (e.g. mangroves, wetlands and croplands). Polar-alpine biome was assigned to three WorldCover classes with minimal or no vegetation (bare/sparse vegetation; snow and ice; moss and lichen). The ‘tree cover’ class was allocated using data on Köppen-Geiger climate zones for 1991-2020 (Beck et al. 2023). If the transect was within the equatorial climate, tree cover was assumed to correspond to ‘tropical and subtropical forests’ biome from ESVD. In case of an arid climate, it was assigned to ‘shrubland and shrubby woodland’, in temperate climate - ‘temperate forest and woodland’, and in cold climate - ‘boreal and montane forests & woodland’. Finally, an additional land cover class ‘coastal systems’ was created to correspond to the same ESVD biome. This biome includes shorelines, dunes, cliffs, inlets, deltas and tidal marshes. It was assumed that ‘bare / sparse vegetation’ (which also represents areas covered by sand), ‘permanent water bodies’ and ‘herbaceous wetland’ corresponded to ‘coastal systems’ as long as they are located between the shoreline and the first elevation peak identified in the GCC dataset, which approximates the division between backshore and hinterland. As GCC provides two estimates based on different DEMs, we use the one occurring closer to the shoreline. ESVD provides values in 2020 USD, but was converted here to 2023 USD using a deflator for the United States from the International Monetary Fund (2024) for consistency with GDP data (see below). Local estimates of the value of services are not available worldwide, as obtaining such information would be practically impossible. However, our assumption of equal value everywhere for each type of service is very likely to introduce epistemic uncertainty. For example, the benefit of the service of moderating extreme events will strongly depend on the level of risk at a given location, particularly exposure of population and assets. Quality of services could be also lower than expected due to local conditions and practices, like pollution, overexploitation, salinization, infrastructure building or landscape fragmentation (Barbier et al. 2011, Lu et al. 2018, He et al. 2019, Biswas et al. 2023). Certain services are also more comprehensively quantified than others. In the absence of local estimates of ecosystem service values across the globe, we used for each type of ecosystem service a mean estimate based on studies available in ESVD for that service. We quantified uncertainty by assuming a normal distribution centred on this value with a standard deviation computed from the standard deviations in available studies per biome and type of ecosystem service. Quantifying coastal squeeze. We constrained possible erosion extent, and thus coastal ecosystem retreat, with two types of barriers. The ‘artificial barrier’ represents permanently built-up areas which most likely would be protected to avoid negative consequences to population and the economy, if necessary by means of hard protection (Nordstrom 2014, Nunn et al. 2021). Here, we assumed that the WorldCover class ‘Built-up’ forms an impenetrable barrier to erosion. Second, erosion was confined by topographical features that act as a ‘natural barrier’. The exact elevation threshold is difficult to define due to the diversity of the coastal morphology and geology. Here, we assumed an elevation of 10 meters above sea level, which is commonly used to delineate the “low-elevation coastal zones” in analyses on SLR impacts (McGranahan et al. 2007, Neumann et al. 2015, Magnan et al. 2022). It should be noted that the detection of artificial barriers, coastal systems and other relevant land covers could be affected by the accuracy of ESA WorldCover used here. The overall accuracy of this dataset is 77%, but it is noticeably lower for certain biomes, particularly wetlands (Tsendbazar et al. 2022). Calculating ecosystem service value loss. Projected erosion extents were superimposed on each transect independently. If the transect does not contain any ‘coastal system’ land cover (Supplementary Fig. S7a), erosion starts at the shoreline and continues until it reaches the erosion extent specific to each transect and scenario (warming level, timestep, uncertainty percentile). Though all transects considered should include a beach based on Hulskamp et al. (2023), the ESA WorldCover data may not always similarly detect them, either because the beach is too narrow or either dataset is incorrect (e.g. mangrove-covered coasts shouldn’t be considered sandy). If there are any artificial or topographic barriers along the transect, the erosion extent is truncated at that barrier (Supplementary Fig. S7a). On the other hand, if the transect contains a coastal system, it is assumed that it will migrate landward as a response to erosion (Supplementary Fig. S7b). Depending on the location of the barriers, three outcomes are possible in this case: If there is adequate migration space, i.e. the distance between the coastal system boundary and a barrier is greater than the estimated erosion extent, the land cover in the migration space is considered lost and the whole coastal system moves landward. If there is some migration space, but it is smaller than the erosion extent, the land cover in the migration space is considered lost, and the coastal system loses the remaining erosion extent, starting from landward. If there is no migration space because of a barrier, the coastal system shrinks by the erosion extent, starting from its landward boundary. Transects are divided into 10-meter segments, therefore the remainder of the erosion extent divided by 10 is used as a fraction of the land cover eroded in the corresponding segment. As the transects correspond to coastal sectors (nearest neighbourhood of each transect within the 4-km landward buffer of the coast) of various shapes, each 10-meter segment of the transect corresponds to a different width, deviating from the default 1 km separating transects where they intersect the coastline. To reduce the complexity of the analysis, we assume that the sectors are either triangular or trapezoidal in shape, depending on the total area of a sector divided by the transect length. This enables assigning a width to each 10-m segment and computing the area eroded in each segment, which is then multiplied by ecosystem service value per unit area. This assumes that the transect is representative of the land cover and elevation in the corresponding zone of a 1 km width. Aggregation of ecosystem service value loss. Absolute loss was computed by summing losses per transect per considered geographical area, while relative loss was calculated by dividing absolute loss by existing value of services. Existing value corresponds to the sum of value per each 10-meter segment created by multiplying area corresponding to each segment by ecosystem service value per unit area. The results were aggregated to countries and groups of countries, such as United Nations geographical subregions, SIDS status (United Nations Statistical Division 2024), and World Bank income groups (World Bank 2024a). Losses relative to the size of the economy were computed based on GDP per country in 2023 valued at Purchasing Power Parities in 2017 USD (converted to 2023 to match ecosystem values), compiled from the United Nations (2025), International Monetary Fund (2024), World Bank (2024b) and data from national statistical institutes. Declarations Author contributions D.P. and M.V. conceived and designed the study. L.F. and P.A. provided additional contribution to the study design. D.P. implemented the methods and analysed the results. M.V., P.A. and L.M. contributed datasets and methods to the analysis. J.S. and P.T. visualized the results. All authors contributed to the writing of the manuscript. Data and code availability The code used for the calculation, input data and results are publicly available on Zenodo (https://doi.org/10.5281/zenodo.15194956), with the exception of detailed land use and elevation along transects, which can be either obtained from Panagiotis Athanasiou upon request, or calculated using publicly available data cited in the Methods. References Costanza, R. et al. The value of the world’s ecosystem services and natural capital. Nature 387 , 253–260 (1997). Brander, L. M. et al. Economic values for ecosystem services: A global synthesis and way forward. Ecosyst. Serv. 66 , 101606 (2024). de Groot, R. et al. 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Ecol. Inform. 77 , 102283 (2023), Nordstrom, K. F. Living with shore protection structures: a review. Estuar. Coast. Shelf Sci. 150 , 11–23 (2014). Nunn, P. D., Klöck, C. & Duvat, V., Seawalls as maladaptations along island coasts. Ocean Coast. Manag. 205 , 105554 (2021). McGranahan, G., Balk, D. & Anderson, B. The rising tide: Assessing the risks of climate change and human settlements in low elevation coastal zones. Environ. Urban. 19 , 17–37 (2007). Neumann, B., Vafeidis, A. T., Zimmerman, J. & Nicholls, R. J. Future coastal population growth and exposure to sea level rise and coastal flooding—A global assessment. PLoS One 10 , e0118571 (2015). Magnan, A. K. et al. Sea level rise risks and societal adaptation benefits in low-lying coastal areas. Sci. Rep. 12 , 10677 (2022). Tsendbazar, N. et al. WorldCover Product Validation Report D12-PVR. https://worldcover2021.esa.int/data/docs/WorldCover_PVR_V2.0.pdf (last accessed 1-4-2025). United Nations Statistics Division. Standard country or area codes for statistical use (M49). https://unstats.un.org/unsd/methodology/m49/ (last accessed 1-4-2025). World Bank. World Bank Country and Lending Groups. https://datahelpdesk.worldbank.org/knowledgebase/articles/906519-world-bank-country-and-lending-groups (last accessed 1-4-2025). World Bank. World Development Indicators. https://databank.worldbank.org/source/world-development-indicators (last accessed 1-4-2025). United Nations. National Accounts - Analysis of Main Aggregates (AMA). https://unstats.un.org/unsd/snaama/ (last accessed 1-4-2025). Additional Declarations There is NO Competing Interest. Supplementary Files SupplementaryInformation.docx Supplementary Information Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6716780","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":470425952,"identity":"0544bf6e-6465-4fed-84c6-cc9753d39531","order_by":0,"name":"Dominik Paprotny","email":"data:image/png;base64,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","orcid":"https://orcid.org/0000-0001-5090-8402","institution":"Potsdam Institute for Climate Impact Research (PIK)","correspondingAuthor":true,"prefix":"","firstName":"Dominik","middleName":"","lastName":"Paprotny","suffix":""},{"id":470425953,"identity":"9e82eae9-c8a9-4170-977c-32f8cf3df49a","order_by":1,"name":"Michalis I. Vousdoukas","email":"","orcid":"https://orcid.org/0000-0003-2655-6181","institution":"University of the Aegean","correspondingAuthor":false,"prefix":"","firstName":"Michalis","middleName":"I.","lastName":"Vousdoukas","suffix":""},{"id":470425954,"identity":"d48007a7-ee5f-4a4a-94fa-9d3d497595a4","order_by":2,"name":"Panagiotis Athanasiou","email":"","orcid":"https://orcid.org/0000-0002-1937-7794","institution":"Deltares","correspondingAuthor":false,"prefix":"","firstName":"Panagiotis","middleName":"","lastName":"Athanasiou","suffix":""},{"id":470425955,"identity":"9bbd7c15-6a06-4fdb-b50d-ba8144a70ecb","order_by":3,"name":"Lorenzo Mentaschi","email":"","orcid":"","institution":"University of Bologna","correspondingAuthor":false,"prefix":"","firstName":"Lorenzo","middleName":"","lastName":"Mentaschi","suffix":""},{"id":470425956,"identity":"5781be46-a1a2-43ef-a7bb-6232c3c112d2","order_by":4,"name":"Jakub Śledziowski","email":"","orcid":"","institution":"University of Szczecin","correspondingAuthor":false,"prefix":"","firstName":"Jakub","middleName":"","lastName":"Śledziowski","suffix":""},{"id":470425957,"identity":"07aded1a-4185-4437-a1e1-a6bbe72bd968","order_by":5,"name":"Paweł Terefenko","email":"","orcid":"","institution":"University of Szczecin","correspondingAuthor":false,"prefix":"","firstName":"Paweł","middleName":"","lastName":"Terefenko","suffix":""},{"id":470425958,"identity":"c0b7367f-80ed-45f3-8a60-cbda1cd0c4e4","order_by":6,"name":"Luc Feyen","email":"","orcid":"https://orcid.org/0000-0003-4225-2962","institution":"Joint Research Centre","correspondingAuthor":false,"prefix":"","firstName":"Luc","middleName":"","lastName":"Feyen","suffix":""}],"badges":[],"createdAt":"2025-05-21 13:06:34","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6716780/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6716780/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":84558993,"identity":"1dd40e64-5905-4db4-b093-5c737e44370f","added_by":"auto","created_at":"2025-06-13 12:28:23","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":165447,"visible":true,"origin":"","legend":"\u003cp\u003eExisting sandy coast ecosystem services (SCES) by United Nations subregions. Bar graphs indicate their present value in billion USD (red) and as % of regional gross domestic product (dark blue).\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6716780/v1/ca27f7db224afed845185253.png"},{"id":84558994,"identity":"5d697728-fd5f-4dad-bcf0-2be6185324d6","added_by":"auto","created_at":"2025-06-13 12:28:23","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":80946,"visible":true,"origin":"","legend":"\u003cp\u003eLoss of SCES globally, by land cover \u003cstrong\u003e(a,c)\u003c/strong\u003e and type of ecosystem service \u003cstrong\u003e(b,d)\u003c/strong\u003e, in absolute terms in billion USD \u003cstrong\u003e(a,b)\u003c/strong\u003e and relative to existing value in % \u003cstrong\u003e(c,d)\u003c/strong\u003e. Colors indicate different warming scenarios and the shading indicates timesteps of the analysis (2050, 2100, 2150).\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6716780/v1/8e679f53393219ce2aaf2674.png"},{"id":84558996,"identity":"1a1a5c83-4908-47f6-9849-185c629930aa","added_by":"auto","created_at":"2025-06-13 12:28:23","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":174711,"visible":true,"origin":"","legend":"\u003cp\u003eProjected losses to SCES relative to present value. by United Nations subregions. Bar graphs indicate losses (% of present value) under 1.5°C warming and three timesteps (green). The red colour in the bar graphs indicates the increase (percentage points) in losses under 4°C scenario.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6716780/v1/af8626468c08d48a8c441c7d.png"},{"id":84559182,"identity":"36b01902-df45-4f67-8da7-0fd524fba55b","added_by":"auto","created_at":"2025-06-13 12:36:24","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":179927,"visible":true,"origin":"","legend":"\u003cp\u003ePotential reduction in SCES loss due to beach migration. Bar graphs indicate the reduction of losses (%) due to beach migration under 4°C warming and three timesteps (green). The red colour in the bar graphs indicates the difference (percentage points) from the 1.5°C scenario.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6716780/v1/0a7a94c030422e7d022f25bb.png"},{"id":84558997,"identity":"2ac324d0-2c22-4399-abe5-c625dc47cb05","added_by":"auto","created_at":"2025-06-13 12:28:24","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":179650,"visible":true,"origin":"","legend":"\u003cp\u003ePossible increase in SCES loss due to ambient shoreline change. Bar graphs indicate the increase in losses (%) due to ambient change under 4°C warming and three timesteps (green). The red colour in the bar graphs indicates the difference (percentage points) from the 1.5°C scenario.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-6716780/v1/1bcfe35187f4827efe29ecb4.png"},{"id":89148226,"identity":"88414961-a1ea-4ff6-ab60-81d49c1f5504","added_by":"auto","created_at":"2025-08-15 11:48:52","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1356856,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6716780/v1/3f12d61b-d578-42af-bafe-996d869c0de5.pdf"},{"id":84558995,"identity":"7ef6747c-05ce-43cb-8cd8-b2f3d19f11b5","added_by":"auto","created_at":"2025-06-13 12:28:23","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":951483,"visible":true,"origin":"","legend":"Supplementary Information","description":"","filename":"SupplementaryInformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-6716780/v1/4cdbc5ebe18ee94c469d808d.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Sandy coast erosion threatens vital ecosystem services","fulltext":[{"header":"Introduction","content":"\u003cp\u003eNature directly and indirectly benefits humans by providing a variety of functions commonly referred to as ecosystem services (Constanza 1997). Quantitative evaluations have shown that coastal ecosystems - especially tidal marshes, deltas, estuaries, beaches, and dunes - are particularly valuable per unit area compared to other marine or terrestrial ecosystems (de Groot et al. 2012, Mehvar et al. 2018, Brander et al. 2024). The high added value of sustained services provided by coastal ecosystems has been especially highlighted in terms of food provisioning, protection against extreme events, economic benefits from tourism and cultural relevance (Liquete et al. 2013, Arkema et al. 2015, De Valck et al. 2023). Globally, this translates into implicit benefits for humanity, worth trillions of US dollars per year (Costanza et al. 2014).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCoastal ecosystems are under increasing threat from human activity, including climate change-induced sea level rise (SLR) (Schuerch et al. 2018, Saintilan et al. 2023) that increases coastal erosion (Mentaschi et al. 2018, Vousdoukas et al. 2020). Even if global warming is constrained to 1.5°C above pre-industrial level, sea level is projected to increase by 44 cm until 2100 relative to average 1995-2014 level (IPCC 2021). At 4°C warming, sea level could increase by 70 cm. Sandy coastlines are at particular risk as they are highly dynamic in time and space (Luijendijk et al. 2018) and SLR could result in extensive losses of coastline width (Vousdoukas et al. 2020). So far, few studies have quantified the consequences of erosion on future coastal zone ecosystem services (Alves et al. 2009, Roebeling et al. 2013, Paprotny et al. 2021, Vousdoukas et al. 2024) and none gave a global perspective. A study for the coastlines of Europe estimated that around 5% of ecosystem services could be lost by 2100 (Paprotny et al. 2021, IPCC 2022).\u003c/p\u003e\n\u003cp\u003eThe inland migration of coastal ecosystems may help conserve some of them (Kirwan et al. 2016, Cooper et al. 2020, Saintilan et al. 2023), but this process is constrained by what is known as “coastal squeeze.” This phenomenon refers to the limited availability of natural land behind coastal ecosystems into which they could retreat—primarily because human developments, such as buildings and infrastructure, create artificial barriers near the shoreline (Silva et al. 2020, Lansu et al. 2024, Nawarat et al. 2024). Difficult to erode, high terrain (topographical barriers) can also limit coastal ecosystem migration (Paprotny et al. 2021). On the other hand, unobstructed retreat could result in the loss of ecosystem services from valuable inland biomes, e.g. forests. Erosion could be further increased or decreased by ambient shoreline change driven by long-term hydrodynamic, geological and anthropic factors, including coastal management practices (Mentaschi et al. 2018).\u003c/p\u003e\n\u003cp\u003eHere, we analyse how SLR-induced erosion could reduce sandy coast ecosystem services (SCES) worldwide. We build upon recent data advances describing coastal characteristics (Athanasiou et al. 2024), projecting erosion extent (Vousdoukas et al. 2020), and valuing ecosystem services (Brander et al. 2024). Firstly, we quantify the present value of SCES along 250,338 transects with a sandy shoreline, constituting 34% of the global ice-free coastline (Athanasiou et al. 2024). The transects are spaced 1 km of each other and extend up to 4 km inland and provide detailed information on the topography and land cover in the coastal zone (see Methods). Then, we calculate SCES losses from SLR for global warming scenarios of 1.5, 2, 3 and 4°C until 2150. We hereby assume that ‘coastal systems’ (beaches, dunes, cliffs, inlets, deltas and tidal marshes) will migrate inland as long as there is available space to do so (i.e. in the absence of artificial and topographical barriers), and inland ecosystems are lost first before any loss of the coastal ecosystem occurs. We quantify how much this coastal ecosystem migration reduces future SCES losses by comparing it with a scenario without backshore migration. Finally, we quantify the potential impact of ambient shoreline dynamics on SCES. SCES are valued in US dollars (USD) at 2023 prices and purchasing power parities (PPP) and compared with gross domestic product (GDP) of affected countries in 2023.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eExisting value of sandy coast ecosystem services\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe global sandy coastal zone spans nearly 960,000 km\u003csup\u003e2\u003c/sup\u003e within 4 km of the shoreline (Supplementary Table 1). More than half is barely or sparsely vegetated, or covered by low vegetation (Supplementary Fig. S1a), while 26% are forests and only 2% are specifically coastal systems, including beaches, dunes, cliffs, inlets, deltas and tidal marshes. We estimate that SCES are presently worth 901 billion USD per year, corresponding to 0.64% of global GDP in 2023. Most value is provided by forests (26%), various types of low or sparse vegetation (25%, of which 11% by croplands), permanent water bodies (16%), and inland wetlands (11%). \u0026nbsp;Despite their limited spatial extent, coastal ecosystems provide 8% of the total SCES (Supplementary Fig. S1b). The largest share of SCES relates to regulating services (43%), which includes particularly the moderation of extreme events as well as pollination and maintenance of soil fertility (Supplementary Fig. S2), in addition to regulation of air quality, climate and water flows, waste treatment and soil erosion prevention. Cultural services are second-most important (30%), especially the existence and bequest value of ecosystems as well as the opportunities for recreation and tourism they provide. Provisioning services (23%) relate to goods that can be consumed or used by people, including food, fresh water, raw materials and other resources. Finally, habitat services (4%) are responsible for the maintenance of life cycles and genetic diversity.\u003c/p\u003e\n\u003cp\u003eThe highest absolute value of SCES exists in Canada, where it amounts to 90 billion USD, followed by Australia (76 billion USD) and Russia (50 billion USD). Other countries with high absolute value (35-42 billion USD) are the United States, Chile, China, Norway and Mexico. Relative to GDP, the distribution of SCES varies considerably. In Western Europe, Southern Asia and Eastern Asia, they are worth about 0.2% of GDP, in Northern America 0.5%, but 2.7% in Eastern Africa, 3.5% in the Caribbean, 5.1% in Micronesia, and 5.3% in Australia and New Zealand (Fig. 1). These values depend on the coastal length and the share of sandy coasts therein (Supplementary Fig. S3), as well as on the economic development level. In 20 countries and territories, SCES are worth more than 10% of GDP (Supplementary Fig. S4). Countries with particularly high absolute and relative values are Mozambique (16.8 billion USD, or 30% of GDP), the Bahamas (11.3 billion USD, or 82% of GDP), and Madagascar (9.8 billion USD, or 21% of GDP). This is because in those countries the majority of coasts are sandy, something not common worldwide. For instance, in the Philippines, which has one of the longest coastline lengths worldwide, only 3% of coasts is sandy, resulting in SCES of only 2 billion USD or 0.2% of its GDP.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eErosion-induced losses of sandy coast ecosystem services globally\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBy mid-century, 5400 to 6300 km\u003csup\u003e2\u003c/sup\u003e of sandy coastal zone is expected to be eroded for warming scenarios between 1.5 and 4°C. This increases to 10,700 km\u003csup\u003e2\u003c/sup\u003e for a 1.5°C scenario and 15,800 km\u003csup\u003e2\u003c/sup\u003e for 4°C by the end of the century, and 12,600 to 22,600 km\u003csup\u003e2\u003c/sup\u003e by 2150. This shows that the benefits of climate mitigation grow in time, but also that even when global warming stabilizes, SLR-driven erosion will continue due to the delayed effects of SLR to global warming (Mengel et al., 2018). The corresponding total annual SCES loss due to erosion would amount to 11.2 billion USD for a 1.5°C scenario and 12.8 billion USD for a 4°C scenario by 2050, which further rises to 20.8-29.2 billion USD by 2100 and 24.6-40.1 billion USD by 2150. This represents a loss of 1.2-1.4% of their current value by 2050, 2.3-3.2% by 2100 and 2.7-4.5% by 2150.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAlmost a quarter (19-24%, depending on scenario) of the area lost by erosion is land covered by coastal systems (beaches, dunes, cliffs, inlets, deltas and tidal marshes). Given that they constitute only 2% of ecosystems in the sandy coastal zone, this suggests that in many places, essential coastal ecosystems will be unable to migrate landward due to human-made or natural barriers, leading to their loss. Due to their large value per unit area, they account for 37-41% of the total SCES losses, amounting to 5-6 billion USD per year by 2050 and 12-18 billion USD by 2150 (Fig. 2a). For other coastal ecosystems, such as beaches, dunes, cliffs, inlets, deltas and tidal marshes, present services would be at least 6% lowered by 2050 and between 13 to 21% by 2150 (Fig. 2b). Most of the remaining value is lost in services from mangroves, inland wetlands and water bodies, which sum up to 6-7 billion USD per year by 2050 and 14-23 billion USD by 2150, or a loss of 4-6% of their present value by 2150. Other land cover classes, mainly grasslands, would contribute to 12-14% of the losses, amounting to 4-7 billion USD per year by 2150. Despite their extensive spatial coverage in sandy coastal zones, service losses by forests other than mangroves remain limited, especially in temperate and boreal climates, while in tropical climates 1.6-2.5% of present value could be lost by 2150.\u003c/p\u003e\n\u003cp\u003eReduced regulating services constitute 39% of total SCES losses, especially through the lowered moderation of extreme events (Fig. 2c), with annual losses estimated at 2-3 billion USD in 2050 and 5-7 billion USD in 2150. Regulating services lost relating to soil fertility, waste treatment and preventing soil erosion remain below 4, 2 and 1 billion USD, respectively, in 2150 even under very high warming. Relative to their present value, losses of regulating services are in the range of 4 to 8% depending on the scenario and timing (Fig. 2d). Nearly a third of SCES losses relate to cultural services, mainly through reduced opportunities for recreation and tourism (4-6 billion USD in 2150, 4-7% of present value) and lowered existence and bequest value (3-5 billion USD in 2150, 3-4% of present value). Coastal erosion further reduces the provisioning of water, food and other goods with 7-11 billion USD in 2150, a loss of 3-4% of the present value, while habitat and biodiversity services would be lowered by 2-3 billion USD in 2150, or 4-6y% of the present value. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eErosion-induced losses of sandy coast ecosystem services by region\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe largest absolute SCES losses are projected for Australia and New Zealand, amounting to about 3 billion USD per year by 2050 and 6-8 billion USD by 2150. This corresponds to a loss of 4% of their SCES by 2050 and 6-9% by 2150, depending on the warming level, much higher than the global average (Fig. 3). Similarly, impact relative to GDP would be one of the highest (up to 0.5% of GDP by 2150). The highest relative impacts are projected in the Caribbean, with 7-8% of services lost by 2050, 12-15% by 2100 and 14-19% by 2150. Other regions with high projected relative losses (up to 6-9% of existing value by 2150) are Central America, Eastern Africa, and Western Asia. On the other end of the scale, Northern Europe and Polynesia would lose no more than 0.8% and 1.5% of SCES, respectively. Relative to GDP, the lowest impacts are projected in Southern Asia, Western Europe, and Eastern Asia (Fig. 3).\u003c/p\u003e\n\u003cp\u003eSmall Island Developing States (SIDS) are expected to be particularly affected by sea level rise (Vousdoukas et al. 2023), and their SCES are no exception. SIDS are projected to be many times more affected than other countries (Supplementary Fig. S5). 6-7% of the services would be lost by 2050, and 13-17% by 2150. By contrast, other countries are projected to lose on average 1% of services by 2050 and 2-4% by 2150. Relative to GDP, the impacts would be 13-16 times higher, depending on the timestep and warming scenario. Most of the losses affecting SIDS would occur in the Caribbean region, which was already highlighted above due to the projected magnitude of impacts. Losses are further linked to income levels ((Supplementary Fig. S5). In the long-term, high income countries (as classified by the World Bank) would encounter lowest losses relative to existing value of services, not exceeding 4%. Impacts relative to GDP would be 4-7 times lower in high income countries compared to high-income ones, though it would be nearly twice higher compared to middle income countries.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBenefits of backshore migration\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe coastline has always been dynamic and here we used the optimistic assumption that coastal ecosystems will be preserved by landward retreat when space is available. Fig. 4 shows the benefits by region of backshore migration in terms of reduced SCES losses compared to a scenario in which coastal ecosystems are not allowed to migrate inland. Globally, around a third of SCES losses would be avoided by the retreat of beaches, dunes and other coastal systems. \u0026nbsp;Benefits of migration are limited in regions where natural barriers or anthropogenic structures and actions prevent the natural landward migration of coastal ecosystems, known as coastal squeeze.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis effect is most severe along highly developed Caribbean coastlines, which partly explains the large magnitude of SCES losses shown in Figure 3. Here, only 6-7% of SCES losses could be prevented through retreat, which is also the case for SIDS countries as a whole. Other regions where coastal squeeze is more than the global average are Central and Northern America, Australia/New Zealand and Eastern Europe, where retreat could prevent at maximum around 20% of SCES losses in 2150. On the other hand, unrestricted inland migration in the absence of human-made or natural barriers along large extents of coastlines in Africa and Micronesia lowers SCES potentially lost without migration by up to 50%. The benefits of inland migration lower with increasing erosion extent as time proceeds and for higher warming levels.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEffects of ambient shoreline dynamics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eResults discussed above consider only SLR-induced erosion, while ambient shoreline changes caused by long-term hydrodynamic, geological and anthropic factors (Vousdoukas et al. 2020) could exacerbate or counter-balance the projected erosion trends. In case the current ambient dynamics (1984-2015) (Mentaschi et al. 2018) would sustain in the future, they could have a larger effect than SLR-driven retreat under warming below 2°C in both 2100 and 2150, and even at 3°C by 2150. If the SLR is contained by strong greenhouse gas mitigation (1.5°C warning), ambient shoreline change could progressively become a greater challenge for preserving SCES.\u003c/p\u003e\n\u003cp\u003eAt regional level (Fig. 5), ambient change maintaining historical rates would increase losses by about 50% or more in most parts of the world, especially Polynesia and Southern Asia. Similar effects are less prominent along Northern America, Middle Africa and Australia/New Zealand regions, not exceeding 20%. Such ambient change contributions are often related to human interventions (Mentaschi et al. 2018), which implies that they could be mitigated or even reversed by adopting more sustainable coastal zone management practices.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study we have mapped globally sandy coast ecosystem services (SCES) and how they are threatened by SLR-driven erosion. While acknowledging the assumptions and uncertainties inherent to the scale of application, our findings show that climate mitigation can avoid at maximum around 30% of the projected SCES losses by 2100 and 40% by 2150. This is because most sandy coastlines are already eroding and sea levels will continue to rise, even if we achieve the Paris Agreement warming goals (Mengel et al. 2018), and affect the already fragile ecosystems. The actual magnitude of the loss will depend on the local interplay between sea levels, coastal morphology, ambient coastal changes, urban development and coastal squeeze. It is important to highlight that even though the term ‘coastal squeeze’ typically refers to anthropogenic barriers to inland migration, in the context of our study natural topographic barriers to shoreline retreat will have the same effect on ecosystem viability as hard, artificial surfaces. Given the increasing human occupation of the world’s coastal zones (Neumann et al., 2015), our study underlines the importance of coastal resilience planning for preserving SCES to prevent the worst-case scenarios discussed in our manuscript. An example of the latter are set-back zones, which are already part of policy initiatives, e.g. EU Floods Directive (European Union 2007), and by allowing the coastal zone to act as a buffer between the ocean and the land (Toimil et al. 2023).\u003c/p\u003e\n\u003cp\u003eOur analysis involves several sources of uncertainty (Supplementary Text S1 and Supplementary Fig. S6). A key source of uncertainty in the estimated absolute SCES losses relates to the valuation of ecosystem services. We use mean values of services per unit area based on harmonized local studies (Brander et al. 2024), but individual reported estimates vary widely. \u0026nbsp;We also assume that the value of services per unit area remains constant in time, but this will depend on socio-economic dynamics that are difficult to predict in the long term (Christensen et al. 2018). For example, SLR also leads to higher coastal flood hazard (Vousdoukas et al. 2018), hence the coastal ecosystem value to moderate coastal extremes will grow in many coastal regions around the world. Another major driver of uncertainty are the erosion projections, for both SLR due to the underlying use of several models (Vousdoukas et al. 2020) and the extrapolation of ambient dynamics into the future (Mentaschi et al. 2018). The landward migration of coastal systems might reduce some SCES losses from erosion (see Supplementary Fig. S6b and Figure 4). However, this relies on coastal habitats adapting and migrating inland quickly enough to offset erosion rates. This scenario presents challenges, as it involves adapting to environmental change rates that have not been previously experienced (Moore and Schindler 2022). \u0026nbsp;\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eCoastal transect-level data.\u0026nbsp;\u003c/strong\u003eWe used the global coastal transects from the Global Coastal Characteristics (GCC) dataset (Athanasiou et al. 2024) with an alongshore resolution of 1 km, based on the Open Street Map (OSM) coastline. Two different elevation profiles were available for each transect based on Copernicus DEM (European Space Agency and Airbus, 2022) and DeltaDTM (Pronk et al. 2024), at a 25 m cross-shore resolution. Moreover, a land-cover profile at 10 m cross-shore resolution was available for each transect based on the ESA WorldCover dataset (Zanaga et al. 2021). In order to constrain the analysis to sandy transects, the “sandy” label from the indicator “Coastal type” of the GCC dataset was used, which is based on Hulskamp et al. (2023). In the original GCC dataset the transects overlap due to the complexity of the coastline. Here, we made a nearest-neighbourhood analysis of the points where transects intersect the OSM coastline and uniquely assigned a section of a 4-km buffer around the coastline to each transect. Transects were truncated where necessary to fit within the 4-km buffer uniquely assigned to each transect.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eErosion projections.\u0026nbsp;\u003c/strong\u003eShoreline change projections are produced based on the approach of Vousdoukas et al. (2020) and include two components. Firstly, SLR-driven retreat was estimated by applying a modified version of the Bruun rule, based on the latest IPCC AR6 SLR projections for global warming scenarios ranging between 1.5 and 4°C (Garner et al. 2021, Kopp et al. 2023) and the nearshore beach slope of the GCC dataset (Athanasiou et al. 2024). The SLR and ambient change components of coastal erosion were combined using Monte Carlo sampling with sufficient ensemble size to ensure convergence.The SLR retreat estimates include several sources of uncertainty, such as accuracy of nearshore slope data, SLR magnitude at given warming level or exact beach location (Athanasiou et al. 2020, Thiéblemont et al. 2021), dynamics of nearshore wave conditions and other sources of epistemic uncertainty.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSecondly, ambient change was assessed based on global satellite observations covering years 1984-2015. Two datasets produced by Mentaschi et al. (2018) and Luijendijk et al. (2018) were combined in a probabilistic framework to generate an ensemble of 10,000 realizations of future ambient change. The two datasets have undergone various improvements and quality control since they were first published and the latest versions were used. As the only feasible projection of ambient change is an extrapolation of the historical pattern. As ambient change is often related to coastal management practices, we do not include this factor in the main results and only as additional analysis (Fig. 5).\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003cstrong\u003eEcosystem service values.\u0026nbsp;\u003c/strong\u003eWe used data from the Ecosystem Services Valuation Database (ESVD) (Brander et al. 2024) for 12 biomes. We assigned the biomes to WorldCover land cover classes (Supplementary Table 1). In some cases, ESVD biomes and WorldCover classes could be directly linked (e.g. mangroves, wetlands and croplands). Polar-alpine biome was assigned to three WorldCover classes with minimal or no vegetation (bare/sparse vegetation; snow and ice; moss and lichen). The ‘tree cover’ class was allocated using data on Köppen-Geiger climate zones for 1991-2020 (Beck et al. 2023). If the transect was within the equatorial climate, tree cover was assumed to correspond to ‘tropical and subtropical forests’ biome from ESVD. In case of an arid climate, it was assigned to ‘shrubland and shrubby woodland’, in temperate climate - ‘temperate forest and woodland’, and in cold climate - ‘boreal and montane forests \u0026amp; woodland’. Finally, an additional land cover class ‘coastal systems’ was created to correspond to the same ESVD biome. This biome includes shorelines, dunes, cliffs, inlets, deltas and tidal marshes. It was assumed that ‘bare / sparse vegetation’ (which also represents areas covered by sand), ‘permanent water bodies’ and ‘herbaceous wetland’ corresponded to ‘coastal systems’ as long as they are located between the shoreline and the first elevation peak identified in the GCC dataset, which approximates the division between backshore and hinterland. As GCC provides two estimates based on different DEMs, we use the one occurring closer to the shoreline. ESVD provides values in 2020 USD, but was converted here to 2023 USD using a deflator for the United States from the International Monetary Fund (2024) for consistency with GDP data (see below).\u003c/p\u003e\n\u003cp\u003eLocal estimates of the value of services are not available worldwide, as obtaining such information would be practically impossible. However, our assumption of equal value everywhere for each type of service is very likely to introduce epistemic uncertainty. For example, the benefit of the service of moderating extreme events will strongly depend on the level of risk at a given location, particularly exposure of population and assets. Quality of services could be also lower than expected due to local conditions and practices, like pollution, overexploitation, salinization, infrastructure building or landscape fragmentation (Barbier et al. 2011, Lu et al. 2018, He et al. 2019, Biswas et al. 2023). Certain services are also more comprehensively quantified than others. In the absence of local estimates of ecosystem service values across the globe, we used for each type of ecosystem service a mean estimate based on studies available in ESVD for that service. We quantified uncertainty by assuming a normal distribution centred on this value with a standard deviation computed from the standard deviations in available studies per biome and type of ecosystem service.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eQuantifying coastal squeeze.\u0026nbsp;\u003c/strong\u003eWe constrained possible erosion extent, and thus coastal ecosystem retreat, with two types of barriers. The ‘artificial barrier’ represents permanently built-up areas which most likely would be protected to avoid negative consequences to population and the economy, if necessary by means of hard protection (Nordstrom 2014, Nunn et al. 2021). Here, we assumed that the WorldCover class ‘Built-up’ forms an impenetrable barrier to erosion. Second, erosion was confined by topographical features that act as a ‘natural barrier’. The exact elevation threshold is difficult to define due to the diversity of the coastal morphology and geology. Here, we assumed an elevation of 10 meters above sea level, which is commonly used to delineate the “low-elevation coastal zones” in analyses on SLR impacts (McGranahan et al. 2007, Neumann et al. 2015, Magnan et al. 2022).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIt should be noted that the detection of artificial barriers, coastal systems and other relevant land covers could be affected by the accuracy of ESA WorldCover used here. The overall accuracy of this dataset is 77%, but it is noticeably lower for certain biomes, particularly wetlands (Tsendbazar et al. 2022).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCalculating ecosystem service value loss.\u003c/strong\u003e Projected erosion extents were superimposed on each transect independently. If the transect does not contain any ‘coastal system’ land cover (Supplementary Fig. S7a), erosion starts at the shoreline and continues until it reaches the erosion extent specific to each transect and scenario (warming level, timestep, uncertainty percentile). Though all transects considered should include a beach based on Hulskamp et al. (2023), the ESA WorldCover data may not always similarly detect them, either because the beach is too narrow or either dataset is incorrect (e.g. mangrove-covered coasts shouldn’t be considered sandy). If there are any artificial or topographic barriers along the transect, the erosion extent is truncated at that barrier (Supplementary Fig. S7a). On the other hand, if the transect contains a coastal system, it is assumed that it will migrate landward as a response to erosion (Supplementary Fig. S7b). Depending on the location of the barriers, three outcomes are possible in this case:\u003c/p\u003e\n\u003col\u003e\n \u003cli\u003eIf there is adequate migration space, i.e. the distance between the coastal system boundary and a barrier is greater than the estimated erosion extent, the land cover in the migration space is considered lost and the whole coastal system moves landward.\u003c/li\u003e\n \u003cli\u003eIf there is some migration space, but it is smaller than the erosion extent, the land cover in the migration space is considered lost, and the coastal system loses the remaining erosion extent, starting from landward.\u003c/li\u003e\n \u003cli\u003eIf there is no migration space because of a barrier, the coastal system shrinks by the erosion extent, starting from its landward boundary.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eTransects are divided into 10-meter segments, therefore the remainder of the erosion extent divided by 10 is used as a fraction of the land cover eroded in the corresponding segment. As the transects correspond to coastal sectors (nearest neighbourhood of each transect within the 4-km landward buffer of the coast) of various shapes, each 10-meter segment of the transect corresponds to a different width, deviating from the default 1 km separating transects where they intersect the coastline. To reduce the complexity of the analysis, we assume that the sectors are either triangular or trapezoidal in shape, depending on the total area of a sector divided by the transect length. This enables assigning a width to each 10-m segment and computing the area eroded in each segment, which is then multiplied by ecosystem service value per unit area. This assumes that the transect is representative of the land cover and elevation in the corresponding zone of a 1 km width.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAggregation of ecosystem service value loss.\u0026nbsp;\u003c/strong\u003eAbsolute loss was computed by summing losses per transect per considered geographical area, while relative loss was calculated by dividing absolute loss by existing value of services. Existing value corresponds to the sum of value per each 10-meter segment created by multiplying area corresponding to each segment by ecosystem service value per unit area. The results were aggregated to countries and groups of countries, such as United Nations geographical subregions, SIDS status (United Nations Statistical Division 2024), and World Bank income groups (World Bank 2024a). Losses relative to the size of the economy were computed based on GDP per country in 2023 valued at Purchasing Power Parities in 2017 USD (converted to 2023 to match ecosystem values), compiled from the United Nations (2025), International Monetary Fund (2024), World Bank (2024b) and data from national statistical institutes.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eD.P. and M.V. conceived and designed the study. L.F. and P.A. provided additional contribution to the study design. D.P. implemented the methods and analysed the results. M.V., P.A. and L.M. contributed datasets and methods to the analysis. J.S. and P.T. visualized the results. All authors contributed to the writing of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData and code availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe code used for the calculation, input data and results are publicly available on Zenodo (https://doi.org/10.5281/zenodo.15194956), with the exception of detailed land use and elevation along transects, which can be either obtained from Panagiotis Athanasiou upon request, or calculated using publicly available data cited in the Methods.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eCostanza, R. et al. The value of the world\u0026rsquo;s ecosystem services and natural capital. \u003cem\u003eNature\u003c/em\u003e\u003cstrong\u003e387\u003c/strong\u003e, 253\u0026ndash;260 (1997).\u003c/li\u003e\n\u003cli\u003eBrander, L. M. et al. Economic values for ecosystem services: A global synthesis and way forward. \u003cem\u003eEcosyst. 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National Accounts - Analysis of Main Aggregates (AMA). https://unstats.un.org/unsd/snaama/\u003cu\u003e \u003c/u\u003e(last accessed 1-4-2025).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-6716780/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6716780/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eCoastal zones provide services that are essential for human\u003cstrong\u003e \u003c/strong\u003ewell-being, but they are under increasing threat from coastal erosion with accelerating sea level rise. Here, we value ecosystem services at risk of sandy coastline erosion worldwide in view of climate change. We find that 2.7-4.5% of the services provided by sandy coasts could be lost by 2150, depending on the warming scenario, but 13-21% of specifically coastal ecosystems. Particularly endangered are the prevention of soil degradation, moderation of extreme events, and tourism. The Caribbean, Central America and Western Asia would lose the highest share of their services, particularly Small Island Developing States (SIDS). Inland migration of sandy coasts, where possible, could reduce losses by 26-32%, but most coasts have limited retreat space due to anthropogenic or topographical barriers. We show that current ambient coastline change trends could substantially exacerbate the impacts, unless they are reversed by effective coastal management practices.\u003c/p\u003e","manuscriptTitle":"Sandy coast erosion threatens vital ecosystem services","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-13 12:28:19","doi":"10.21203/rs.3.rs-6716780/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f4bd80e2-cb87-466e-b86f-deb4a4205c0a","owner":[],"postedDate":"June 13th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":49955869,"name":"Earth and environmental sciences/Hydrology"},{"id":49955870,"name":"Earth and environmental sciences/Climate sciences/Climate change/Climate-change impacts"},{"id":49955871,"name":"Earth and environmental sciences/Environmental social sciences/Climate-change impacts"},{"id":49955872,"name":"Earth and environmental sciences/Environmental social sciences/Environmental impact"}],"tags":[],"updatedAt":"2026-03-11T18:06:44+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-13 12:28:19","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6716780","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6716780","identity":"rs-6716780","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}