Global mangrove loss footprint mappings across space and time

Global mangrove loss footprint mappings across space and time | 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 Global mangrove loss footprint mappings across space and time Shen Qu, Mimi Gong, Guoqiang Wang, Yinglan A, Baolin Xue, Shiqi Tao, and 10 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5778596/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted You are reading this latest preprint version Abstract Trade-related declines in mangrove forest cover have raised global concern for decades, given the numerous ecosystem services mangrove forests can provide. However, there has yet to be a comprehensive evaluation of the relationship between mangrove loss and global supply chains. This study presents an assessment of mangrove loss footprint, defined as fine-scale mappings of mangrove loss associated with international trade. Mangrove loss footprint is calculated by tracing 30m*30m mangrove loss on the ground to final consumption embodied in international trade through a multi-region input-output model and quantifying their spatiotemporal changes from 2000 to 2016. Moreover, the study adopts the metacoupling framework to understand how global consumption across space (domestic, adjacent, and distant) drives focal mangrove forest losses. Results indicate that influential economies, especially those with limited mangrove forests, have driven mangrove losses beyond their borders. The top 10 countries that drove mangrove loss in other countries are countries such as the USA, China, Japan, and South Korea, and outsourced to distant countries. These countries had a decreasing trend in outsourcing mangrove loss beyond borders from 2000 to 2016. China had the slowest decline rate and became the largest importer of mangrove loss in 2011–2016, and 98% of its mangrove loss footprint lies in twelve Southeast countries. Indonesia, Myanmar, and Vietnam are the top 3 exporters whose mangrove forests are used for other countries’ consumption. Although our study didn’t consider nations’ restoration efforts, the results emphasize the need to use footprint mapping approaches to create mangrove loss footprint base maps. These maps can be dynamically updated to monitor and assess mangrove depletion, enhance supply chain transparency, and foster stronger international collaboration. Earth and environmental sciences/Environmental social sciences/Sustainability Earth and environmental sciences/Ecology/Forestry mangrove loss footprint spatially explicit Input-output analysis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction Mangrove ecosystems thrive within intertidal zones of tropical and subtropical coastlines. They nurture marine life, bolster local economies, mitigate poverty, sequester carbon, and participate in global climate regulation 1 . Although mangrove loss rates have come down to < 1% in recent years, mangrove deforestation has been as high as 35% during the 1980s-1990s and has been at an annual rate of 1–8% between 1980 and 2012 1–3 . Global mangrove ecosystem loss has been driven by escalating population growth, particularly in coastal regions, exacerbating recent net deforestation trends. 4 – 10 However, identifying the underlying drivers of mangrove forests has been challenging, particularly given the variation caused by the dynamic flux of land uses, climate change, and the erosive dynamism of extreme events 11 – 13 . Moreover, mangrove ecosystems display substantial heterogeneity driven by abiotic contexts, forest configurations, species richness, and regional disparities 54 . Hence, while numerous case studies delve into the intricacies of localized mangrove loss drivers and their interplay 14 , 15 , a conspicuous research gap persists concerning a comprehensive global exploration of these drivers and their interactions. Land change science has quantified mangrove forest dynamics across spatial scales, with analytical improvements propelled by the availability of high-resolution satellite imagery. Illustratively, Hamilton and Casey (2016) 10 mapped the planetary ebb and flow of mangrove extents, leveraging pre-existing datasets, such as the comprehensive mangrove map crafted by Giri et al. 13 , the global forest change atlases devised by Hansen et al. 16 , and the terrestrial ecosystems database enveloping the world's biomes 17 . Hamilton and Casey converged these datasets into an intersected mangrove map, enabling the quantification of global mangrove cover change from 2000 to 2014 at the granularity of 30 meters. Thomas et al. (2017) 7 created an alternative dataset, harnessing radar datasets systematically collected across the planet, with particular emphasis on data from the Japanese L-land sensors. Their comprehensive dataset, hosted on the Global Mangrove Watch platform, encompasses mangrove coverage during two distinct epochs: 1995–1997 and 2007–2010. By combining resources from multiple time-series radar composite imagery, this dataset provides a nuanced depiction of mangrove dynamics. Notably, this dataset has undergone updates, with the latest iteration, as of 2022, attributed to Bunting et al. 8 , 9 . This update extends the dataset's reach, documenting global mangrove forest changes and gains from 1996 to 2020, enriching our understanding of long-term trends. The most recent insights into mangrove loss and its underlying drivers have been expounded upon by Goldberg et al. (2022). Their seminal work underscores the significant human-induced mangrove losses, with a staggering 62% of global losses between 2000 and 2016 attributable to land-use changes, primarily stemming from conversions to aquaculture and agriculture. These insights provide a critical foundation for comprehending the complex dynamics of global mangrove ecosystems and their vulnerability to anthropogenic influences. The global transition of mangrove forests is typically studied through the synthesis of localized case studies, and the discourse surrounding this topic has unearthed an array of drivers influencing mangrove losses across diverse spatial scales 15 , 18 – 20 . Interestingly, these independent inquiries have a common finding: trade plays a pivotal role in the degradation and deforestation of mangrove ecosystems. Friess et al. 2 traced this relationship over millennia to its nascent origins in the Middle East approximately 6500 years ago and found that the evolutionary trajectory of mangrove resource usage witnessed a transformative phase with industrial practices; that is, the voracious demand for mangrove timber, driven by the imperatives of shipbuilding, was discernible under the auspices of Spanish and German colonial administrations in the 1750s. Furthermore, the comprehensive synthesis conducted by Bhowmik et al. (2022) 14 , which scrutinized two hundred published papers from 1980 to 2021, aimed to unravel the intricate tapestry of social-ecological drivers underpinning the global deforestation of mangroves. These studies employ divergent methodological approaches but converge on recognizing commodities, notably aquaculture and agriculture, as the predominant catalysts driving global mangrove deforestation, impacting regions worldwide 21 . In response to the far-reaching influence of economic globalization over the last two decades, a growing body of research has underscored the harmful ramifications of international trade on local environments. A compelling instance lies in studies identifying a symbiotic relationship between China's economic surge and shifting dietary preferences toward meat products. This dietary transformation, primarily to satiate the appetite for pork, has triggered a rise in soybean imports from Brazil, resulting in Amazon deforestation, including mangrove ecosystems 22 – 25 . Another study has explored the economic beneficiaries of the commercialized products of large agribusiness enterprises in Thailand. It underscored that a mere 17% of the workforce employed in mangrove concessions hails from residents of mangrove forests, while the majority of beneficiaries are outsiders 26 . These studies have pointed out that examining deforestation at only the domestic level can lead to misleading interpretations of its drivers. Notably, consumer demand in other countries may instigate the drivers of forest loss in one region. Moreover, to compensate for the environmental pressures caused by international trade, countries such as the USA, Japan, France, China, and India have embarked on mangrove restoration initiatives, resulting in forest cover gains. Nonetheless, the complex entanglement between these gains and the continued imports that entail 'embodied' mangrove loss in distant regions remains an enigmatic problem, underscoring the need for a nuanced scientific exploration of the intricate dynamics of international trade vis-à-vis global-scale mangrove deforestation. The metacoupling framework (MCF) provides a holistic understanding of the interdependencies between locally coupled human-nature systems and their external actors (adjacent and distant) across space qualitatively 27 – 29 . MCF explores the dynamics, impacts, mechanisms, and structure of internal and external human-nature interactions and has been used to explain many environmental challenges such as biodiversity loss 30 – 33 , climate change 29 , food security 34 – 38 , and deforestation 39 – 41 . However, our understanding of how these interactions influence the mangrove ecosystem is limited. This paper is the first to investigate global mangrove loss embodied in international trade across space and time. It provides the first spatially explicit map that traces the mangrove loss on the ground to final demand and understand mangrove dynamics and its spatial patterns from consumption perspective. Using high-resolution mangrove loss data at 30m*30m, a spatial classification of mangrove loss drivers, and a detailed global supply chain model, we visualize the map-based comprehensive synthesis of how international trade drove the spatiotemporal changes in global mangrove loss from 2000 to 2016. Our research answers three questions: (1) How is mangrove loss on the ground linked with global consumption at pixel and country-levels? (2) Which countries are the top exporters and importers of mangrove loss footprint? (3) How is mangrove loss footprint distributed across geographic factors (domestic, adjacent vs distant)? 2. Results Our analysis establishes a nuanced linkage between global mangrove loss data from 2000 to 2016 and the intricate worldwide supply chain model 5,6,42 . We create detailed, high-resolution global mangrove loss footprint maps and pinpoint potential deforestation footprints at a granular pixel level (30m*30m). This analysis enables the dynamic tracking of spatiotemporal changes and facilitates a comprehensive understanding of global-scale mangrove forest dynamics from 2000 to 2016 embedded in international trade. 2.1. Mangrove-holding countries’ consumption drives domestic mangrove loss the most, while mangrove losses driven by distant influential countries are second. By comparing the flows between consumption and sourcing patterns in 2000–2005 (a) and 2011–2016 (b), the main trading partners in mangrove deforestation include many Southeast Asian countries and some Latin and African countries (Fig. 1), such as Indonesia, Myanmar, Vietnam, Malaysia, Nigeria, Guinea, Madagascar, and Brazil. These mangrove-holding countries export mangrove-risk commodities (such as shrimp, rice, coffee, palm oil, and timber) to satisfy their consumption and consumption in OECD countries (for example, Japan, USA, South Korea, Mexico, Germany), China, and India. Indonesia is the largest mangrove exporter, with 34% of its loss driven by consumption from external countries such as the USA, Japan, South Korea, China, and India. Moreover, 33.2% of Indonesia’s mangrove loss is identified to be driven by geographically distant consumer countries (countries without political shared boundaries) (Fig. 2c). Similarly, the top 20 and all mangrove-holding countries’ mangrove loss is also significantly driven by distant consumptions with percentages as 26.5% and 26.2% respectively. (Fig. 2c and SI-table 3). The country-to-country loss flow from 2000–2016 is depicted in SI-Figure 1. Of the top 20 consumer countries that drive mangrove loss, sixteen are mangrove-holding countries, except for Japan, South Korea, the United Kingdom, and Germany. (Fig. 2d). Most of these countries are low—and lower-middle-income countries, whose mangrove loss is driven by domestic consumption. Exceptions include China, Thailand, and India, which have much mangrove loss driven by consumption from adjacent countries such as Myanmar, Vietnam, and Bangladesh (SI-table 2). Countries such as Japan, the United States, Korea, Singapore, the United Kingdom, and Germany have driven mangrove loss in distant countries such as Southeast Asian countries. These high-income countries transfer their consumption through international trade to mangrove-holding countries. 2.2 Although consumer countries imported distant mangrove forests rather than consuming their forests, their import amount and percentage decreased from 2000 to 2016. As shown in Fig. 1, high-income and OECD countries such as Japan, the USA, South Korea, China, India, and Singapore mainly imported mangrove loss from other countries rather than consuming their mangrove forests. This phenomenon is reflected in the red flow lines in the graph, which show that countries with higher-income consumption drive lower-income countries’ mangrove loss. For example, the USA imported 72 and 11.76 km2 of mangrove loss globally, while its domestic loss was 1.89 and 0.018 km2 in 2000–2005 and 2011–2016, respectively. In addition, the total amount and percentage of mangrove loss exported to consumer countries decreased between both epochs. The decrease fits into the overall decreasing trend of mangrove loss on the ground due to the worldwide rise of public awareness of the ecosystem services that mangrove forests can provide. In Fig. 1, we notice that the domestic consumption percentage increased in 2011–2016 compared to 2000–2005 in representative mangrove-loss countries such as Indonesia, Myanmar, Vietnam, Malaysia, Madagascar, Guinea, and Cuba. For example, domestic consumption in Myanmar took 13% in 2000–2005 and 41.3% in 2011–2016. This indicates a decreasing trend of mangrove forest consumption in external countries from 2000 to 2016. Moreover, the top 10 countries with the highest differences between mangrove loss footprint and mangrove losses also have decreasing values from 2000 to 2016 (Fig. 2a and b). It indicates the countries whose consumption drives the most losses in external mangrove-holding countries than their domestic mangroves (‘outsourced’), and the top 10 countries whose losses are attributed to external consumption (‘leaked’) have a decreasing trend in their losses from 2000 to 2016. 2.3 The impact of external consumers on local mangrove loss varies, and case-by-case discussions are more appropriate. In Fig. 2a and b, we compare the differences between actual loss and mangrove loss footprint and select the top 10 countries' ‘leaked’ and ‘outsourced’ mangrove loss in three periods. Southeast Asia countries, such as Indonesia, Myanmar, and Vietnam, are the central mangrove resource-providing countries, i.e., the top leakage countries. The differences between countries varied in different epochs. For example, Indonesia and Myanmar alternate as the countries with the most leaked mangrove loss. Certain emerging countries, like Malaysia, may face more significant losses in the future due to a widening gap between their mangrove loss and footprint. Moreover, small countries in Latin America and Africa, such as Guinea, Suriname, Nigeria, and Madagascar, should be prioritized for international conservation policies because they are within the top 10 countries with mangrove loss driven by external consumer countries and relatively small total areas. The mangrove ecosystem in these countries vulnerable to high economic development pressures and low local conservation awareness. International collaborations are needed to improve these countries’ mangrove forests and benefit the local environment with efficiency. Japan, the USA, and China are the main contributors that transfer their mangrove loss to other countries through international trade. Their differences decreased as the years passed, indicating a decrease in the total loss embodied in these countries’ trade consumption. However, China’s contribution increased to the top in 2010–2016, becoming the country with the most extensive mangrove loss difference between its actual loss and trade-adjusted loss. The countries that outsourced their consumption related to mangrove loss are high-income and OCED countries. Moreover, these consumer countries outsourced their consumption mainly to distant countries (Fig. 2d). The exception lies in China, Thailand, and India. China and Thailand primarily drove the loss of mangroves in adjacent countries, while India relied on its own mangrove forests and adjacent countries’ resources. We list the adjacent countries of these three countries in the supplement table as a reference (SI-table 2). Therefore, the impact of external consumers on local mangrove loss varies, and case-by-case discussions are more appropriate. We selected the United States and China to create their spatially explicit maps at pixel and country levels because they are the most influential economies in the world and are among the top 3 consumer countries that outsource their consumption for other countries’ mangrove loss (Fig. 3 and Fig. 4). The spatial explicit maps can help understand how their consumption drives the mangrove loss worldwide. 2.4 Spatially explicit mapping indicates that US consumption has an intense and aggregated impact on closer areas, such as Latin America, due to commodity production and a lighter but wider effect on distant regions, such as Southeast Asia, through non-productive conversion and human settlement. As the world's largest economy, the United States substantially influenced global mangrove loss from 2000 to 2016. The intensity of its impact diminishes with increasing geographic distance (Fig. 3b, c, and d), such that the regions closer have more intense pixel-level mangrove loss footprints driven by US consumption embodied in international trade. Regions with more significant US footprints (depicted in red) are primarily situated in Latin America—encompassing nations like Belize, Nicaragua, El Salvador, and Honduras—and sporadically extend to African countries such as Nigeria and Gabon. The larger footprint values stem from commodities that replaced mangrove forests in these countries and are directly imported or indirectly consumed by the USA through the supply chain. Conversely, Southeast Asian countries predominantly exhibit low values (pixels in blue and yellow), mainly driven by non-productive conversions and human settlement, such as household settlement, non-profit services for education, health or conservation, road construction, etc., due to the consumption from the USA. However, Southeast Asia countries take the most significant amount (74km2) and percentage (82%) of mangrove loss driven by US consumption (SI-Table 6) from 2000–2016, although most countries worldwide with noticeable mangrove losses driven by US consumption in 2000–2005 experienced fewer impacts in 2011–2016 (SI-Figure2). Exceptions include Indonesia and Madagascar, which maintained their mangrove loss trends in both periods, and Honduras, which has an increased mangrove loss footprint. The escalating trend in Honduras is substantiated by remote sensing evidence, revealing extensive mangrove deforestation across the mangrove delta over the past three decades 43,44 . SI-Figure 2a, c, and d compare the footprint expansion driven by US consumption in these three epochs by applying natural break classifications to categorize mangrove loss footprints in each epoch. US-driven consumption has varied impacts on other countries, and no clear expansion patterns have been identified. For example, US consumption’s impact on Latin America and most African countries decreased. However, some African countries experience more mangrove loss driven by US consumption, such as Madagascar, whose mangrove loss footprint increased from 2000 to 2016. 2 .5 China was the largest consumer country of mangrove loss from 2011 to 2016, and Southeast Asian countries are key outsourced areas, although the overall loss decreased from 2000 to 2016. We compare the mangrove loss footprints’ changes in 65 mangrove-holding countries driven by Chinese consumption in 2000–2005 and 2011–2016, shown in Fig. 2.4. In Fig. 2.4a and b under natural break classification, the total amount of mangrove loss driven from Chinese demand has decreased over the years, with the highest mangrove loss footprint as 25.7 in 2000–2005, decreasing to 7.03 in 2011–2016. Southeast Asia is the critical area where China’s consumption has been outsourced, and Thailand and Indonesia are the top two countries. Mangrove losses more significant than 0.249 km2 driven by China’s consumption are all in Southeast East Asia except for Suriname, and twelve countries in Southeast Asia have accounted for 97.8% of mangrove loss from 2000–2016 compared to other areas worldwide (SI-table 5). These twelve countries are listed in SI-table 4. Meanwhile, China has expanded its impact to countries outside of Southeast Asia, and the percentage of these areas increased from 2–3.5% from 2000 to 2016 (SI-table 5). Looking closer at China’s expansion in Latin America and Africa, as shown in Fig. 4c and d, countries such as Ecuador, Suriname, and Nigeria have less mangrove loss from China’s consumption from 2000 to 2016. Instead, Cuba, Peru, Gabon, and Angola are four countries that leaked more from China's consumption in 2011–2016 than from 2000 to 2005. For example, due to China's consumption, Angola lost 0.164 km2 in 2000–2005; the number increased to 0.457 in 2011–2016 d. 3. Discussion This paper is the first to calculate mangrove loss embodied in international trade (mangrove loss footprint). It provides fine-scale spatiotemporal maps of the mangrove loss footprint stemming from the demands of different nations. A similar method has been applied to understand the biodiversity loss and deforestation embodied in international trade, and these studies identified a noticeable amount of loss occurring outside the territorial boundaries of developed economies 42 , 45 , 46 . Our analysis echoes these studies’ conclusions and indicates that China, India, and OECD economies have expanded their external mangrove loss footprints to countries where mangrove forests prevail. These consumers, such as Japan, the United States, Korea, Singapore, the United Kingdom, and Germany, mainly drive mangrove losses in distant countries. China and Thailand’s consumption significantly drives losses in adjacent mangrove forests. In contrast, mangrove-holding countries identified as top exporters embodied in international trade are also biodiversity hotspots, such as Indonesia, Myanmar, Vietnam, Malaysia, Cuba, Nigeria, and Madagascar, leading to significant conservation concerns. Although the international drivers of loss tended to decrease from 2000 to 2016, mangrove deforestation activities remain, and their impacts vary across space through international trade. The United States has driven different sectors in producer countries depending on the geographic distances (Fig. 3 ). China has expanded its mangrove loss footprint from nearby countries in Southeast Asia to some distant countries in Latin America and Africa (Fig. 4 ). Because the ecosystem service values of mangrove forests are irreplaceable, a priority for mangrove conservation is to monitor mangrove deforestation induced by international consumption according to their geographic distances. Despite these important findings, it should be noted that this analysis has limitations associated with the Eora database's classification and spatial resolution of mangrove deforestation drivers. Firstly, this analysis discusses mangrove loss embodied in the global supply chain, so only mangrove loss directly associated with anthropogenic drivers in Goldberg’s dataset is addressed, and natural drivers such as erosion and climate change are excluded from the study because these two drivers have no direct human activity related to industry sectors in the Eora database. A similar exclusion was applied to the deforestation footprint measurement. Moreover, due to the resolution of Goldberg’s mangrove loss driver maps, we cannot distinguish specific commodities’ contribution, such as long-term/shifting agriculture and aquaculture on the ground; Goldberg’s driver merges various types of commodities and represents multiple sectors in the Eora database including agriculture, aquaculture, wood, and paper, mining and energy infrastructure, and other commodity-related sector that drove loss. Accordingly, we may overestimate mangrove deforestation for the commodities sector when we link mangrove forest loss to the global supply chain. Lastly, because the Eora database is at the national level, the mangrove loss footprints are the mean value of the mangrove loss area by each driver in the consumer country over the entire production country; we do not localize the embodied mangrove loss for each driver at the sub-national level. For instance, the mangrove loss footprint mapping cannot distinguish a farmed shrimp production in Indonesia driven by the food consumption in Michigan, the United States, because the map pixels can only represent the mangrove loss driven by commodities on the ground in Indonesia is driven by food consumption in the United States. Although limitations exist and the mapping method has been applied to deforestation and biodiversity loss footprints, this study depicts the mangrove loss footprint at a global scale with high-resolution spatially explicit mappings for the first time. Understanding mangrove loss embodied in international trade can complement existing biodiversity threat and deforestation footprint maps by providing detailed, mangrove-specific insights to inform decision-making in conservation policies. By overlaying a spatially explicit mangrove loss footprint map with biodiversity threat footprint maps, decision-makers can receive comprehensive details about why certain areas are considered high-risk and how to strategize their conservation efforts. For example, countries such as Guinea, Suriname, Madagascar, and Nigeria have been listed as the top 10 countries whose mangrove losses are outsourced for other countries’ consumption and these countries are also species threat hotspots. These countries could be prioritized for potential case studies through international mangrove restoration policies. Moreover, unlike generic deforestation footprints, which may misattribute mangrove loss due to the intertidal nature of mangrove forests, mangrove loss footprint map pinpoints the loss and its drivers on the ground and connects them to global consumption patterns. We also conducted a brief literature review to compare mangrove loss footprint with other footprints to clarify its advantages and drawbacks (SI-Table 7). Moreover, spatially explicit mangrove loss footprint mappings can improve public awareness of mangrove forest loss embodied in the global supply chain to clarify consumers’ environmental responsibilities. The global supply chain has traceability and transparency issues due to the complex, interconnected nature of their operations, where unintended or hidden externalities, inefficiencies, or regulatory gaps can create leakage and damage the environment. Mangrove loss could be caused by indirect suppliers, unexpected products, or unadopted areas and existing strong regulating policies for private enterprises cannot fully solve the problem because the supply chain may leak losses to areas with illegal activities or poor forest governance with public ownership. Mangrove loss footprint calculation and mapping provide a spatial and temporal base map on how mangrove loss is associated with producer and consumer countries through the supply chain and also offer policy-makers science-based knowledge in mangrove conservation. It can encourage more responsible consumption and corporate practices with better data transparency. Moreover, this map can be dynamically updated with new data on mangrove coverage and threats, increasing its utility over time and helping nations improve their mangrove-related pledges. Future Improvements This study is the first to conduct mangrove loss footprint and mapping, and the limitations are depicted in the discussion section. Several directions can be addressed in future analysis to improve these findings. Firstly, the ‘commodities’ driver from Goldberg’s data adopted in this paper is coarse, and many new remote sensing techniques can be applied to enhance its resolution in the future. For example, the aquaculture land cover data from Clark Labs ( https://clarklabs.org/aquaculture/landcover-data/ ) covers ten southeast countries in 1999–2014, 2014–2018, and 1999–2018 for 10km grid resolution and is developing a global aquaculture dataset 47 . This global dataset could be added to distinguish the aquaculture driver from the generic ‘commodities’ driver in Goldberg’s dataset to enhance the accuracy of mangrove loss footprints with higher resolution in identifying mangrove loss drivers. Moreover, this study used the Eora 26 to calculate the mangrove footprint by connecting the mangrove drivers on the ground with 26 sectors of Eora Input-output tables (See method Step 2). In contrast, the full Eora dataset would offer a more precise calculation. It can be applied to five countries, Vietnam, Thailand, Venezuela, Singapore, and Australia, by incorporating their drivers into more sophisticated categories of sectors. In addition, other multi-regional input-output table (MRIO) datasets with more comprehensive data on mangrove-holding countries and more sophisticated categories of commodities products can enhance the resolution of the current mangrove loss footprint analysis. For example, the Carbon Accounts & Datasets (CEADs) website just published a new MRIO table (EMERGING) for 2010, 2015, 2016,2017,2018, and 2019 with 246 countries and 135 sectors covered 48 , which covers all mangrove-holding countries in Goldberg’s mangrove loss datasets and has a broader coverage of commodities sectors compared to the Eora database. ( https://ceads.net/user/index.php?id=1274&lang=en ). It can be a good addition for further analysis to improve the resolution of this study. Second, the mangrove loss footprint calculation considers loss only, which does not account for the countries' restoration efforts. Therefore, a consumer country that outsources its consumption to other countries may be able to compensate for these losses via successful mangrove restoration. Incorporating mangrove restoration efforts in future research requires remote sensing data to precisely underline the drivers of loss and gain on the ground before footprint estimates at a global scale. Lastly, better visualization of mangrove loss footprint can be applied to examine differences by land cover type. For example, to distinguish the values of mangrove ecosystem services or restoration potential, we can disaggregate the mangrove deforestation footprints into four domains: delta, estuary, lagoon, and open coast, based on their geomorphic and sedimentary settings, such as their proximity to coastal features. In the new mangrove typology maps by Worthington et al. (2020) 49 , all deltaic and estuarine units are classed as terrigenous (i.e., dominated by minerogenic sedimentation from terrestrial sources), and lagoonal or open coast patches could be classed as terrigenous or carbonate (i.e., dominated by calcareous sedimentation). This distinction can help prioritize the values of the mangrove forests we lost and link them with their potential drivers through a global supply chain to compare their costs and benefits, which can bring about more precise policy recommendations. 4. Methods The calculation of the mangrove loss footprint involves three main steps: 1) summarizing mangrove forest loss per country per driver per epoch, 2) constructing bilateral trade flows per driver per epoch, and 3) calculating mangrove deforestation embodied in trade. The flowchart below (Fig. 2.5) visualizes the steps with details. Step 1: Calculate mangrove loss per country per epoch per driver. We use Goldberg et al.'s (2022) data for a GIS analysis to calculate mangrove loss per country per epoch per driver. The dataset is classified using high-resolution Google Earth images, random forest machine learning techniques, and a series of decision trees for several global-scale land use datasets. The final products include spatially explicit raster data with 30m*30m resolution per mangrove driver in three periods (2000–2005, 2005–2010, and 2010–2016) in 39 mangrove-holding countries. It groups mangrove loss drivers into five categories: commodity production (agriculture, aquaculture), settlement, erosion, extreme climatic events, and non-productive conversion. We use this product to create spatially explicit mangrove loss footprint maps and use its country-level statistics to understand their spatiotemporal dynamics. We use its country statistics directly from Goldberg et al. (2022), which contains 72 countries. In addition, we aggregate the country-level loss using the spatial explicit raster data for 39 countries to validate its mangrove loss per epoch per driver in each country. We batch GIS analysis for 39 country layers to combine mangrove loss data in each epoch and each driver and conduct the reprojection to tabulate the areas in each category. Moreover, the raster datasets for each country are mosaiced for a cross-country map, and we use them for global spatially explicit mangrove loss footprint mappings. Step 2: Identify mangrove drivers and their linkages with Eora. We then follow the Hoang et al. (2021) method to link the drivers of mangrove loss with the Eora global multi-region input-output (MRIO) database. The complete Eora database covers 15,000 industry sectors in 190 countries, but only 34 out of 39 countries are included in Goldberg’s mangrove loss spatial explicit map, and 65 out of 72 are included in its country-level statistics. Each country has its coverage of sectors ranging from 26 to 345. Most of the mangrove-holding countries have 26 industries covered in Eora, and only five countries (Vietnam, Thailand, Venezuela, Singapore, and Australia) have entire sectors covered. Therefore, we used Eora with 26 industries for this cross-country analysis. We then link the MRIO sectors with three drivers of mangrove loss to align with the Goldberg et al dataset: commodities, human settlement, and non-productive conversion. We only examined anthropogenic drivers of loss, given our primary objective of linking mangrove loss to global trade. The aggregation of human settlement includes building infrastructure, such as roads, ports, and shipping channels, to support transport, shipping, etc. The non-productive conversion of mangrove loss is derived from Goldberg et al. (2022), which uses remote sensing images to overlap the human influence mask layer with the low density of the productive industry layer. It includes private household settlements for daily life support, recreational, cultural, or other service activities for non-commercial purposes, such as education, health, and social work. The commodities-driven mangrove loss covers a broad range of sectors, including long-term agriculture, aquaculture from shrimp farms, forestry, mining, and energy infrastructure, following Hoang et al (2021)’s definition of commodity driver in the deforestation footprint analysis. We also verify the distinct commodity drivers by comparing the commodity-driven mangrove loss from Goldberg et al. (2022) with the drivers of deforestation map by Curtis et al. (2018), to confirm that the commodities from Goldberg’s datasets include various commodity types from Curtis such as agriculture, aquaculture, forestry, and other commodities. Step 3: Mapping mangrove loss footprint To map the mangrove loss footprint, which is the consumption-based mangrove loss, we follow the logic as Eq. 2.1 . $$\:MLF=M+I-E$$ 2.1 Mangrove forest loss footprint (MLF) is expressed in Eq. (1) and shows that M is the mangrove forest cover loss in the production country, E is the mangrove loss embodied in exports, and I the loss embodied in imports. Mangrove loss (M) is measured and calculated through Goldberg’s driver datasets; the area of five drivers is in units as m 2 . Specifically, we first calculate the adjusted consumption based on Hoang's (2021) analysis. AC \(\:=\sum\:_{tj}{L}_{ij}^{rt}{y}_{j}^{ts}+\:\sum\:_{t\ne\:s,j}{L}_{ij}^{rt}{y}_{j}^{ts}-\sum\:_{t\ne\:s,j}{L}_{ij}^{rs}{y}_{j}^{st}\) (2.2) Adjusted consumption (AC) = total consumption + imports - exports. L is the Leontief inverse, measuring the transformation between i sectors of origin in r export countries and j sectors of destination in s import countries. (r*i,s*j), y is the final demand of s import countries with j destination sectors. (s*j). t is both the last sale in the consumption and import term, and the country of final consumption in the export term. We then decompose the total deforestation into the embodied deforestation in country s using the Eora MRIO model. $$\:{F}^{s}\left(Production\right)=\sum\:_{ri}{f}_{i}AC$$ 2.3 Where F s (Production) is the total mangrove deforestation embodied in international trade driven by country s, f is mangrove deforestation intensity for each sector i (mangrove forest loss area from Goldenberg’s data divided by gross output). AC represents adjusted consumption, which is measured in Eq. 2.2. Finally, the spatial footprint analysis method calculates mangrove loss footprint M in country r driven by country s. $$\:{M}^{S}=\:\sum\:_{ℎr}{D}_{r}^{ℎ}\frac{{\sum\:}_{i}{f}_{ℎi}{\sum\:}_{tj}{L}_{ij}^{rt}{y}_{j}^{ts}}{{\sum\:}_{i}{m}_{ℎi}^{r}}$$ 2.4 M s represents spatial explicit mangrove forest loss map driven by country s (30m*30m). Mangrove forest loss map (D) by mangrove-holding countries r by driver h and embodied mangrove loss (numerator in Eq. 2.3 (fLy)) are in absolute values, and the embodied mangrove loss (fLy) is normalized by total mangrove loss m by each driver h in mangrove-holding countries r for each industrial sector i to downscale values from nations to pixels. The mangrove loss maps (D) are prepared in step 1 with the shapefile layers for each driver h in each mangrove-holding country r and mangrove deforestation intensity f for each production sector i. Concept clarifications between mangrove loss and mangrove loss footprint is in SI-Table 8. Declarations Data and Code Availability We combine three map datasets with the Eora global MRIO database to trace mangrove forest loss footprints. The main map is Goldberg's (2022) loss and driver maps, which contains two series of raster datasets of 39 countries in measuring the extent of mangrove loss (https://daac.ornl.gov/CMS/guides/CMS_Global_Mangrove_Loss.html) 5,6 . One depicts the mangrove loss raster with five drivers: commodity production (agriculture, aquaculture), settlement, erosion, extreme climatic events, and non-productive conversion; the other depicts the loss raster in three epochs: 2000–2005, 2005–2010, and 2010–2016. We also used Curtis (2018) to verify the commodity driver of mangrove loss layers (https://data.globalforestwatch.org/documents/gfw::tree-cover-loss-by-dominant-driver-2022/about), which should contain various commodities, including agriculture and aquaculture, and forestry identified in Curtis deforestation driver map 50 . This driver map identified the dominant drivers of tree cover loss in a 10km grid cell from 2001 to 2022. The dominant drivers are commodity-driven, shifting agriculture, forestry, wildfire, and urbanization. The last dataset is Eora Database, which is one of the most well-developed global multiregional input-output databases. This database describes the world economy in terms of the annual production, trade, intermediate consumption, and final consumption of 26 homogeneous sectors between and within 189 countries from 2000 to 2022 51,52 . The code for the mangrove loss footprint was developed in Matlab and Python to process and visualize. The primary datasets and code for visualization can be found on the GitHub repository, accessible at lwt852/mangrove footprint (github.com). Matlab code for mangrove loss footprint calculation in this study will be made available upon request. References Friess, D. A. Ecosystem Services and Disservices of Mangrove Forests: Insights from Historical Colonial Observations. Forests 7, 183 (2016). Friess, D. A. et al. 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Metacoupled Tourism and Wildlife Translocations Affect Synergies and Trade-Offs among Sustainable Development Goals across Spillover Systems. Sustainability 12, 7677 (2020). Barbieri, P., MacDonald, G. K., Bernard de Raymond, A. & Nesme, T. Food system resilience to phosphorus shortages on a telecoupled planet. Nat Sustain 5, 114–122 (2022). Chung, M. G. Complex Interactions among Ecosystem Services, Human Well-Being, and Their Linkages to Telecoupling Processes. (2020). Chung, M. G. & Liu, J. International food trade benefits biodiversity and food security in low-income countries. Nat Food 3, 349–355 (2022). Dou, Y. et al. Understanding How Smallholders Integrated into Pericoupled and Telecoupled Systems. Sustainability 12, 1596 (2020). da Silva, R. F. B. et al. Socioeconomic and environmental effects of soybean production in metacoupled systems. Sci Rep 11, 18662 (2021). Liu, J. An Integrated Framework for Achieving Sustainable Development Goals Around the World. Ecol. Econ. Soc. 1, (2018). Liu, J. Forest Sustainability in China and Implications for a Telecoupled World: China and a Telecoupled World. Asia & the Pacific Policy Studies 1, 230–250 (2014). Liu, J. et al. Nexus approaches to global sustainable development. Nature Sustainability 1, 466–476 (2018). Hoang, N. T. & Kanemoto, K. Mapping the deforestation footprint of nations reveals growing threat to tropical forests. Nature Ecology & Evolution 1–9 (2021) doi:10.1038/s41559-021-01417-z. Chen, C.-F. et al. Multi-Decadal Mangrove Forest Change Detection and Prediction in Honduras, Central America, with Landsat Imagery and a Markov Chain Model. Remote Sensing 5, 6408–6426 (2013). UNEP. Mangrove forest cover fading fast. Thematic focus: Ecosystem management, Disasters and conflicts, Climate change https://na.unep.net/geas/getUNEPPageWithArticleIDScript.php?article_id=103 (2013). Moran, D. & Kanemoto, K. Identifying species threat hotspots from global supply chains. Nature Ecology & Evolution 1, 0023 (2017). Wiedmann, T. & Lenzen, M. Environmental and social footprints of international trade. Nature Geoscience 11, 314–321 (2018). Eastman, J. R., Toledano, J., Crema, S. & Singh, R. Phase 3 Extension to the Mapping of Tropical Pond Aquaculture, Mangroves and Coastal Wetlands. (2020). Huo, J., Meng, J., Zheng, H., Parikh, P. & Guan, D. Achieving decent living standards in emerging economies challenges national mitigation goals for CO2 emissions. Nat Commun 14, 6342 (2023). Worthington, T. A. et al. A global biophysical typology of mangroves and its relevance for ecosystem structure and deforestation. Sci Rep 10, 14652 (2020). Curtis, P. G., Slay, C. M., Harris, N. L., Tyukavina, A. & Hansen, M. C. Classifying drivers of global forest loss. Science 361, 1108–1111 (2018). Lenzen, M. et al. International trade drives biodiversity threats in developing nations. Nature 486, 109–112 (2012). Lenzen, M., Moran, D., Kanemoto, K. & Geschke, A. Building Eora: A Global Multi-Region Input–Output Database at High Country and Sector Resolution. Economic Systems Research 25, 20–49 (2013). Additional Declarations There is NO Competing Interest. Supplementary Files SImangrovelossfootprint112024.docx Cite Share Download PDF Status: Under Review 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-5778596","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":406729371,"identity":"b9fdbf13-e9c4-48c9-857f-2ba82db7d9ea","order_by":0,"name":"Shen Qu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA80lEQVRIiWNgGAWjYDACCSjNxsx+4MDHBhCTsfEAUVr42HsSH85sAPEZG4jTIsdzwNiYtwHCx6tFfnbzsQcMZYcZ2CQS0qRtd9jU6bYfBtpSYxONS4vBnWPpBgznQFoSj0nnnkmTMDuTCNRyLC23AZcWiRwzCcY2qC25bYclzA4AtTA2HMapRX5G/jeYFjNpS5CW8w/xa2G4kcMG0QLyPiNIyw0CthjcSDOTSDiXzsMGCuTetjTJbTeAtiTg8Yv8jORnEh/KrOXkm4FR+bPNht/sfPrDBx9qbHA7DAQS2Bh40ETwKQcDNoIqRsEoGAWjYCQDALZoXJI9w8tXAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0002-8526-3680","institution":"Beijing Institute of Technology","correspondingAuthor":true,"prefix":"","firstName":"Shen","middleName":"","lastName":"Qu","suffix":""},{"id":406729372,"identity":"18ee29c7-7122-4df4-b03b-15366276cf55","order_by":1,"name":"Mimi Gong","email":"","orcid":"","institution":"Beijing Normal University","correspondingAuthor":false,"prefix":"","firstName":"Mimi","middleName":"","lastName":"Gong","suffix":""},{"id":406729373,"identity":"840b657e-88ed-4729-8f9d-7e4a4fa38aa1","order_by":2,"name":"Guoqiang Wang","email":"","orcid":"","institution":"Beijing Normal University","correspondingAuthor":false,"prefix":"","firstName":"Guoqiang","middleName":"","lastName":"Wang","suffix":""},{"id":406729374,"identity":"b088087c-e682-4133-890b-e3a51c32712c","order_by":3,"name":"Yinglan A","email":"","orcid":"","institution":"Beijing Normal University","correspondingAuthor":false,"prefix":"","firstName":"Yinglan","middleName":"","lastName":"A","suffix":""},{"id":406729375,"identity":"e84247fc-46c1-4501-ba97-1772243708fe","order_by":4,"name":"Baolin Xue","email":"","orcid":"https://orcid.org/0000-0002-4479-7889","institution":"Beijing Normal University","correspondingAuthor":false,"prefix":"","firstName":"Baolin","middleName":"","lastName":"Xue","suffix":""},{"id":406729376,"identity":"f6ef1ff5-af2b-4633-949d-7561f08f7864","order_by":5,"name":"Shiqi Tao","email":"","orcid":"","institution":"Clark university","correspondingAuthor":false,"prefix":"","firstName":"Shiqi","middleName":"","lastName":"Tao","suffix":""},{"id":406729377,"identity":"b7168e96-73a1-4e9b-a413-d9ce3cd9c02a","order_by":6,"name":"Heran Zheng","email":"","orcid":"https://orcid.org/0000-0003-0818-7933","institution":"University College London","correspondingAuthor":false,"prefix":"","firstName":"Heran","middleName":"","lastName":"Zheng","suffix":""},{"id":406729378,"identity":"b1a373bf-4f7e-41ac-9e3a-fb9ed3ae6cdb","order_by":7,"name":"Elizabeth Golebie","email":"","orcid":"","institution":"University of Illinois","correspondingAuthor":false,"prefix":"","firstName":"Elizabeth","middleName":"","lastName":"Golebie","suffix":""},{"id":406729379,"identity":"e41f8e9b-812a-408f-94de-d43d84dd3739","order_by":8,"name":"Jacob J. 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The arc length of the circle indicates the sum of consumption exported and imported between the mangrove-holding and consumer countries. The arc color of the circle indicates the region of countries, which is ordered by their geographic location. The red color of mangrove flows suggests that the consumption of higher-income countries drives lower-income countries’ mangrove loss, and the green color is the opposite.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5778596/v1/3b338d59c0073a88c4fd00cd.png"},{"id":78155870,"identity":"1e0ba2c9-04b9-433a-a134-7280dfae1973","added_by":"auto","created_at":"2025-03-10 12:35:18","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":373306,"visible":true,"origin":"","legend":"\u003cp\u003eTop countries of interest related to mangrove loss footprint. a-b: Top 10 countries (a) ‘leaked’ and (b) ‘outsourced’ mangrove loss (km2) beyond their mangrove coverage in three periods: 2000-2005, 2006-2010, and 2011-2016. c: Top 20 countries with the highest mangrove loss and their ‘leaked’ countries’ geographic information. d: Top 20 countries with the highest mangrove loss footprint with their ‘outsourced’ countries’ geographic information. Adjacent countries are defined by whether two countries have overlapping political boundaries. Fig.2a and b are calculated by measuring the difference between the loss on the ground and the mangrove loss footprint. They are the top 10 countries whose losses are attributed to external consumption (‘leaked’) and the top 10 countries whose consumption drives the most losses in external mangrove-holding countries than their domestic mangroves (‘outsourced). The actual mangrove loss was derived from Goldberg’s mangrove loss per country in 65 mangrove-holding countries. All mangrove-holding countries are listed in SI-Table 1. The mangrove loss footprint is equivalent to the mangrove loss minus the mangrove loss embodied in exports plus the mangrove loss embodied in imports. The study only considers mangrove loss, and mangrove restoration efforts, shown as mangrove gain, are not considered due to data discrepancy.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5778596/v1/7efaa56bf44abc71b6666c0f.png"},{"id":78154588,"identity":"8c300d86-1d76-4407-a084-4354e53c37ab","added_by":"auto","created_at":"2025-03-10 12:19:18","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":272796,"visible":true,"origin":"","legend":"\u003cp\u003eGlobal spatially explicit mangrove loss footprint maps at 30*30m resolution driven by USA consumption from 2000 to 2016. It includes the worldwide map indicating pixel-level mangrove loss driven by the USA’s consumption (a) and zoom-in maps for areas in Latin America (b), Africa (c), and Southeast Asia (d). Mappings of spatially explicit mangrove loss footprints can illustrate the extent and magnitude of consumption in the USA. The area of mangrove loss shows the pixel coverage with values, while the magnitude is indicated by a color code, with blue representing low intensity and red representing high intensity.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-5778596/v1/e1b66f92dcb0890721593f51.png"},{"id":78154264,"identity":"5875695b-4763-4ba6-84d2-940840b4ae68","added_by":"auto","created_at":"2025-03-10 12:11:18","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":572887,"visible":true,"origin":"","legend":"\u003cp\u003eMaps of mangrove loss footprints (km2) driven by Chinese consumption in 65 mangrove-holding countries. (a) and (b) indicate mangrove loss footprint values (km2) in 2000-2005 and 2011-2016 using the natural break classificationcompare the overall changes; (c) and (d) expand the categories of mangrove loss footprint in range 0 to 0.249 to visualize the changes of Chinese demand to distant countries (Latin America and Africa) at the country level.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-5778596/v1/f1ed0ff3f9a7b941c36fc4f4.png"},{"id":78154595,"identity":"5cdf3b49-2d27-413f-8c64-c1fc9d4c3bf1","added_by":"auto","created_at":"2025-03-10 12:19:19","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":373999,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of mangrove loss footprint mapping.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-5778596/v1/0dfaa4cdfde43f1bfd51d335.png"},{"id":78154291,"identity":"83e94230-3d0b-43d4-8977-9d0c61f0548d","added_by":"auto","created_at":"2025-03-10 12:11:20","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":971039,"visible":true,"origin":"","legend":"","description":"","filename":"SImangrovelossfootprint112024.docx","url":"https://assets-eu.researchsquare.com/files/rs-5778596/v1/307ea373f53ad56e5fafdf1d.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Global mangrove loss footprint mappings across space and time","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eMangrove ecosystems thrive within intertidal zones of tropical and subtropical coastlines. They nurture marine life, bolster local economies, mitigate poverty, sequester carbon, and participate in global climate regulation\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Although mangrove loss rates have come down to \u0026lt;\u0026thinsp;1% in recent years, mangrove deforestation has been as high as 35% during the 1980s-1990s and has been at an annual rate of 1\u0026ndash;8% between 1980 and 2012\u003csup\u003e1\u0026ndash;3\u003c/sup\u003e. Global mangrove ecosystem loss has been driven by escalating population growth, particularly in coastal regions, exacerbating recent net deforestation trends.\u003csup\u003e\u003cspan additionalcitationids=\"CR5 CR6 CR7 CR8 CR9\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e However, identifying the underlying drivers of mangrove forests has been challenging, particularly given the variation caused by the dynamic flux of land uses, climate change, and the erosive dynamism of extreme events\u003csup\u003e\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. Moreover, mangrove ecosystems display substantial heterogeneity driven by abiotic contexts, forest configurations, species richness, and regional disparities\u003csup\u003e54\u003c/sup\u003e. Hence, while numerous case studies delve into the intricacies of localized mangrove loss drivers and their interplay\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e, a conspicuous research gap persists concerning a comprehensive global exploration of these drivers and their interactions.\u003c/p\u003e \u003cp\u003eLand change science has quantified mangrove forest dynamics across spatial scales, with analytical improvements propelled by the availability of high-resolution satellite imagery. Illustratively, Hamilton and Casey (2016)\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e mapped the planetary ebb and flow of mangrove extents, leveraging pre-existing datasets, such as the comprehensive mangrove map crafted by Giri et al.\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e, the global forest change atlases devised by Hansen et al.\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e, and the terrestrial ecosystems database enveloping the world's biomes\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. Hamilton and Casey converged these datasets into an intersected mangrove map, enabling the quantification of global mangrove cover change from 2000 to 2014 at the granularity of 30 meters. Thomas et al. (2017)\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e created an alternative dataset, harnessing radar datasets systematically collected across the planet, with particular emphasis on data from the Japanese L-land sensors. Their comprehensive dataset, hosted on the Global Mangrove Watch platform, encompasses mangrove coverage during two distinct epochs: 1995\u0026ndash;1997 and 2007\u0026ndash;2010. By combining resources from multiple time-series radar composite imagery, this dataset provides a nuanced depiction of mangrove dynamics. Notably, this dataset has undergone updates, with the latest iteration, as of 2022, attributed to Bunting et al.\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. This update extends the dataset's reach, documenting global mangrove forest changes and gains from 1996 to 2020, enriching our understanding of long-term trends. The most recent insights into mangrove loss and its underlying drivers have been expounded upon by Goldberg et al. (2022). Their seminal work underscores the significant human-induced mangrove losses, with a staggering 62% of global losses between 2000 and 2016 attributable to land-use changes, primarily stemming from conversions to aquaculture and agriculture. These insights provide a critical foundation for comprehending the complex dynamics of global mangrove ecosystems and their vulnerability to anthropogenic influences.\u003c/p\u003e \u003cp\u003eThe global transition of mangrove forests is typically studied through the synthesis of localized case studies, and the discourse surrounding this topic has unearthed an array of drivers influencing mangrove losses across diverse spatial scales\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan additionalcitationids=\"CR19\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. Interestingly, these independent inquiries have a common finding: trade plays a pivotal role in the degradation and deforestation of mangrove ecosystems. Friess et al.\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e traced this relationship over millennia to its nascent origins in the Middle East approximately 6500 years ago and found that the evolutionary trajectory of mangrove resource usage witnessed a transformative phase with industrial practices; that is, the voracious demand for mangrove timber, driven by the imperatives of shipbuilding, was discernible under the auspices of Spanish and German colonial administrations in the 1750s. Furthermore, the comprehensive synthesis conducted by Bhowmik et al. (2022)\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e, which scrutinized two hundred published papers from 1980 to 2021, aimed to unravel the intricate tapestry of social-ecological drivers underpinning the global deforestation of mangroves. These studies employ divergent methodological approaches but converge on recognizing commodities, notably aquaculture and agriculture, as the predominant catalysts driving global mangrove deforestation, impacting regions worldwide\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn response to the far-reaching influence of economic globalization over the last two decades, a growing body of research has underscored the harmful ramifications of international trade on local environments. A compelling instance lies in studies identifying a symbiotic relationship between China's economic surge and shifting dietary preferences toward meat products. This dietary transformation, primarily to satiate the appetite for pork, has triggered a rise in soybean imports from Brazil, resulting in Amazon deforestation, including mangrove ecosystems\u003csup\u003e\u003cspan additionalcitationids=\"CR23 CR24\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. Another study has explored the economic beneficiaries of the commercialized products of large agribusiness enterprises in Thailand. It underscored that a mere 17% of the workforce employed in mangrove concessions hails from residents of mangrove forests, while the majority of beneficiaries are outsiders\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. These studies have pointed out that examining deforestation at only the domestic level can lead to misleading interpretations of its drivers. Notably, consumer demand in other countries may instigate the drivers of forest loss in one region. Moreover, to compensate for the environmental pressures caused by international trade, countries such as the USA, Japan, France, China, and India have embarked on mangrove restoration initiatives, resulting in forest cover gains. Nonetheless, the complex entanglement between these gains and the continued imports that entail 'embodied' mangrove loss in distant regions remains an enigmatic problem, underscoring the need for a nuanced scientific exploration of the intricate dynamics of international trade vis-\u0026agrave;-vis global-scale mangrove deforestation.\u003c/p\u003e \u003cp\u003eThe metacoupling framework (MCF) provides a holistic understanding of the interdependencies between locally coupled human-nature systems and their external actors (adjacent and distant) across space qualitatively\u003csup\u003e\u003cspan additionalcitationids=\"CR28\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. MCF explores the dynamics, impacts, mechanisms, and structure of internal and external human-nature interactions and has been used to explain many environmental challenges such as biodiversity loss\u003csup\u003e\u003cspan additionalcitationids=\"CR31 CR32\" citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e, climate change\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e, food security\u003csup\u003e\u003cspan additionalcitationids=\"CR35 CR36 CR37\" citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e, and deforestation\u003csup\u003e\u003cspan additionalcitationids=\"CR40\" citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. However, our understanding of how these interactions influence the mangrove ecosystem is limited. This paper is the first to investigate global mangrove loss embodied in international trade across space and time. It provides the first spatially explicit map that traces the mangrove loss on the ground to final demand and understand mangrove dynamics and its spatial patterns from consumption perspective. Using high-resolution mangrove loss data at 30m*30m, a spatial classification of mangrove loss drivers, and a detailed global supply chain model, we visualize the map-based comprehensive synthesis of how international trade drove the spatiotemporal changes in global mangrove loss from 2000 to 2016. Our research answers three questions: (1) How is mangrove loss on the ground linked with global consumption at pixel and country-levels? (2) Which countries are the top exporters and importers of mangrove loss footprint? (3) How is mangrove loss footprint distributed across geographic factors (domestic, adjacent vs distant)?\u003c/p\u003e"},{"header":"2. Results","content":"\u003cp\u003eOur analysis establishes a nuanced linkage between global mangrove loss data from 2000 to 2016 and the intricate worldwide supply chain model\u003csup\u003e5,6,42\u003c/sup\u003e. We create detailed, high-resolution global mangrove loss footprint maps and pinpoint potential deforestation footprints at a granular pixel level (30m*30m). This analysis enables the dynamic tracking of spatiotemporal changes and facilitates a comprehensive understanding of global-scale mangrove forest dynamics from 2000 to 2016 embedded in international trade.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e2.1. Mangrove-holding countries’ consumption drives domestic mangrove loss the most, while mangrove losses driven by distant influential countries are second.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eBy comparing the flows between consumption and sourcing patterns in 2000–2005 (a) and 2011–2016 (b), the main trading partners in mangrove deforestation include many Southeast Asian countries and some Latin and African countries (Fig. 1), such as Indonesia, Myanmar, Vietnam, Malaysia, Nigeria, Guinea, Madagascar, and Brazil. These mangrove-holding countries export mangrove-risk commodities (such as shrimp, rice, coffee, palm oil, and timber) to satisfy their consumption and consumption in OECD countries (for example, Japan, USA, South Korea, Mexico, Germany), China, and India. Indonesia is the largest mangrove exporter, with 34% of its loss driven by consumption from external countries such as the USA, Japan, South Korea, China, and India. Moreover, 33.2% of Indonesia’s mangrove loss is identified to be driven by geographically distant consumer countries (countries without political shared boundaries) (Fig.\u0026nbsp;2c). Similarly, the top 20 and all mangrove-holding countries’ mangrove loss is also significantly driven by distant consumptions with percentages as 26.5% and 26.2% respectively. (Fig.\u0026nbsp;2c and SI-table 3). The country-to-country loss flow from 2000–2016 is depicted in SI-Figure 1.\u003c/p\u003e\n\u003cp\u003eOf the top 20 consumer countries that drive mangrove loss, sixteen are mangrove-holding countries, except for Japan, South Korea, the United Kingdom, and Germany. (Fig.\u0026nbsp;2d). Most of these countries are low—and lower-middle-income countries, whose mangrove loss is driven by domestic consumption. Exceptions include China, Thailand, and India, which have much mangrove loss driven by consumption from adjacent countries such as Myanmar, Vietnam, and Bangladesh (SI-table 2). Countries such as Japan, the United States, Korea, Singapore, the United Kingdom, and Germany have driven mangrove loss in distant countries such as Southeast Asian countries. These high-income countries transfer their consumption through international trade to mangrove-holding countries.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e2.2 Although consumer countries imported distant mangrove forests rather than consuming their forests, their import amount and percentage decreased from 2000 to 2016.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAs shown in Fig. 1, high-income and OECD countries such as Japan, the USA, South Korea, China, India, and Singapore mainly imported mangrove loss from other countries rather than consuming their mangrove forests. This phenomenon is reflected in the red flow lines in the graph, which show that countries with higher-income consumption drive lower-income countries’ mangrove loss. For example, the USA imported 72 and 11.76 km2 of mangrove loss globally, while its domestic loss was 1.89 and 0.018 km2 in 2000–2005 and 2011–2016, respectively.\u003c/p\u003e\n\u003cp\u003eIn addition, the total amount and percentage of mangrove loss exported to consumer countries decreased between both epochs. The decrease fits into the overall decreasing trend of mangrove loss on the ground due to the worldwide rise of public awareness of the ecosystem services that mangrove forests can provide. In Fig. 1, we notice that the domestic consumption percentage increased in 2011–2016 compared to 2000–2005 in representative mangrove-loss countries such as Indonesia, Myanmar, Vietnam, Malaysia, Madagascar, Guinea, and Cuba. For example, domestic consumption in Myanmar took 13% in 2000–2005 and 41.3% in 2011–2016. This indicates a decreasing trend of mangrove forest consumption in external countries from 2000 to 2016. Moreover, the top 10 countries with the highest differences between mangrove loss footprint and mangrove losses also have decreasing values from 2000 to 2016 (Fig.\u0026nbsp;2a and b). It indicates the countries whose consumption drives the most losses in external mangrove-holding countries than their domestic mangroves (‘outsourced’), and the top 10 countries whose losses are attributed to external consumption (‘leaked’) have a decreasing trend in their losses from 2000 to 2016.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e2.3 The impact of external consumers on local mangrove loss varies, and case-by-case discussions are more appropriate.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eIn Fig.\u0026nbsp;2a and b, we compare the differences between actual loss and mangrove loss footprint and select the top 10 countries' ‘leaked’ and ‘outsourced’ mangrove loss in three periods. Southeast Asia countries, such as Indonesia, Myanmar, and Vietnam, are the central mangrove resource-providing countries, i.e., the top leakage countries. The differences between countries varied in different epochs. For example, Indonesia and Myanmar alternate as the countries with the most leaked mangrove loss. Certain emerging countries, like Malaysia, may face more significant losses in the future due to a widening gap between their mangrove loss and footprint. Moreover, small countries in Latin America and Africa, such as Guinea, Suriname, Nigeria, and Madagascar, should be prioritized for international conservation policies because they are within the top 10 countries with mangrove loss driven by external consumer countries and relatively small total areas. The mangrove ecosystem in these countries vulnerable to high economic development pressures and low local conservation awareness. International collaborations are needed to improve these countries’ mangrove forests and benefit the local environment with efficiency.\u003c/p\u003e\n\u003cp\u003eJapan, the USA, and China are the main contributors that transfer their mangrove loss to other countries through international trade. Their differences decreased as the years passed, indicating a decrease in the total loss embodied in these countries’ trade consumption. However, China’s contribution increased to the top in 2010–2016, becoming the country with the most extensive mangrove loss difference between its actual loss and trade-adjusted loss. The countries that outsourced their consumption related to mangrove loss are high-income and OCED countries. Moreover, these consumer countries outsourced their consumption mainly to distant countries (Fig. 2d). The exception lies in China, Thailand, and India. China and Thailand primarily drove the loss of mangroves in adjacent countries, while India relied on its own mangrove forests and adjacent countries’ resources. We list the adjacent countries of these three countries in the supplement table as a reference (SI-table 2). Therefore, the impact of external consumers on local mangrove loss varies, and case-by-case discussions are more appropriate. We selected the United States and China to create their spatially explicit maps at pixel and country levels because they are the most influential economies in the world and are among the top 3 consumer countries that outsource their consumption for other countries’ mangrove loss (Fig. 3 and Fig. 4). The spatial explicit maps can help understand how their consumption drives the mangrove loss worldwide.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e2.4 Spatially explicit mapping indicates that US consumption has an intense and aggregated impact on closer areas, such as Latin America, due to commodity production and a lighter but wider effect on distant regions, such as Southeast Asia, through non-productive conversion and human settlement.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAs the world's largest economy, the United States substantially influenced global mangrove loss from 2000 to 2016. The intensity of its impact diminishes with increasing geographic distance (Fig. 3b, c, and d), such that the regions closer have more intense pixel-level mangrove loss footprints driven by US consumption embodied in international trade. Regions with more significant US footprints (depicted in red) are primarily situated in Latin America—encompassing nations like Belize, Nicaragua, El Salvador, and Honduras—and sporadically extend to African countries such as Nigeria and Gabon. The larger footprint values stem from commodities that replaced mangrove forests in these countries and are directly imported or indirectly consumed by the USA through the supply chain. Conversely, Southeast Asian countries predominantly exhibit low values (pixels in blue and yellow), mainly driven by non-productive conversions and human settlement, such as household settlement, non-profit services for education, health or conservation, road construction, etc., due to the consumption from the USA.\u003c/p\u003e\n\u003cp\u003eHowever, Southeast Asia countries take the most significant amount (74km2) and percentage (82%) of mangrove loss driven by US consumption (SI-Table\u0026nbsp;6) from 2000–2016, although most countries worldwide with noticeable mangrove losses driven by US consumption in 2000–2005 experienced fewer impacts in 2011–2016 (SI-Figure2). Exceptions include Indonesia and Madagascar, which maintained their mangrove loss trends in both periods, and Honduras, which has an increased mangrove loss footprint. The escalating trend in Honduras is substantiated by remote sensing evidence, revealing extensive mangrove deforestation across the mangrove delta over the past three decades\u003csup\u003e43,44\u003c/sup\u003e. SI-Figure 2a, c, and d compare the footprint expansion driven by US consumption in these three epochs by applying natural break classifications to categorize mangrove loss footprints in each epoch. US-driven consumption has varied impacts on other countries, and no clear expansion patterns have been identified. For example, US consumption’s impact on Latin America and most African countries decreased. However, some African countries experience more mangrove loss driven by US consumption, such as Madagascar, whose mangrove loss footprint increased from 2000 to 2016.\u003c/p\u003e\n\u003cp\u003e2\u003cem\u003e.5 China was the largest consumer country of mangrove loss from 2011 to 2016, and Southeast Asian countries are key outsourced areas, although the overall loss decreased from 2000 to 2016.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eWe compare the mangrove loss footprints’ changes in 65 mangrove-holding countries driven by Chinese consumption in 2000–2005 and 2011–2016, shown in Fig.\u0026nbsp;2.4. In Fig.\u0026nbsp;2.4a and b under natural break classification, the total amount of mangrove loss driven from Chinese demand has decreased over the years, with the highest mangrove loss footprint as 25.7 in 2000–2005, decreasing to 7.03 in 2011–2016. Southeast Asia is the critical area where China’s consumption has been outsourced, and Thailand and Indonesia are the top two countries. Mangrove losses more significant than 0.249 km2 driven by China’s consumption are all in Southeast East Asia except for Suriname, and twelve countries in Southeast Asia have accounted for 97.8% of mangrove loss from 2000–2016 compared to other areas worldwide (SI-table 5). These twelve countries are listed in SI-table 4. Meanwhile, China has expanded its impact to countries outside of Southeast Asia, and the percentage of these areas increased from 2–3.5% from 2000 to 2016 (SI-table 5).\u003c/p\u003e\n\u003cp\u003eLooking closer at China’s expansion in Latin America and Africa, as shown in Fig. 4c and d, countries such as Ecuador, Suriname, and Nigeria have less mangrove loss from China’s consumption from 2000 to 2016. Instead, Cuba, Peru, Gabon, and Angola are four countries that leaked more from China's consumption in 2011–2016 than from 2000 to 2005. For example, due to China's consumption, Angola lost 0.164 km2 in 2000–2005; the number increased to 0.457 in 2011–2016 d.\u003c/p\u003e"},{"header":"3. Discussion","content":"\u003cp\u003eThis paper is the first to calculate mangrove loss embodied in international trade (mangrove loss footprint). It provides fine-scale spatiotemporal maps of the mangrove loss footprint stemming from the demands of different nations. A similar method has been applied to understand the biodiversity loss and deforestation embodied in international trade, and these studies identified a noticeable amount of loss occurring outside the territorial boundaries of developed economies\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e,\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e,\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e. Our analysis echoes these studies\u0026rsquo; conclusions and indicates that China, India, and OECD economies have expanded their external mangrove loss footprints to countries where mangrove forests prevail. These consumers, such as Japan, the United States, Korea, Singapore, the United Kingdom, and Germany, mainly drive mangrove losses in distant countries. China and Thailand\u0026rsquo;s consumption significantly drives losses in adjacent mangrove forests. In contrast, mangrove-holding countries identified as top exporters embodied in international trade are also biodiversity hotspots, such as Indonesia, Myanmar, Vietnam, Malaysia, Cuba, Nigeria, and Madagascar, leading to significant conservation concerns. Although the international drivers of loss tended to decrease from 2000 to 2016, mangrove deforestation activities remain, and their impacts vary across space through international trade. The United States has driven different sectors in producer countries depending on the geographic distances (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e). China has expanded its mangrove loss footprint from nearby countries in Southeast Asia to some distant countries in Latin America and Africa (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Because the ecosystem service values of mangrove forests are irreplaceable, a priority for mangrove conservation is to monitor mangrove deforestation induced by international consumption according to their geographic distances.\u003c/p\u003e \u003cp\u003eDespite these important findings, it should be noted that this analysis has limitations associated with the Eora database's classification and spatial resolution of mangrove deforestation drivers. Firstly, this analysis discusses mangrove loss embodied in the global supply chain, so only mangrove loss directly associated with anthropogenic drivers in Goldberg\u0026rsquo;s dataset is addressed, and natural drivers such as erosion and climate change are excluded from the study because these two drivers have no direct human activity related to industry sectors in the Eora database. A similar exclusion was applied to the deforestation footprint measurement. Moreover, due to the resolution of Goldberg\u0026rsquo;s mangrove loss driver maps, we cannot distinguish specific commodities\u0026rsquo; contribution, such as long-term/shifting agriculture and aquaculture on the ground; Goldberg\u0026rsquo;s driver merges various types of commodities and represents multiple sectors in the Eora database including agriculture, aquaculture, wood, and paper, mining and energy infrastructure, and other commodity-related sector that drove loss. Accordingly, we may overestimate mangrove deforestation for the commodities sector when we link mangrove forest loss to the global supply chain. Lastly, because the Eora database is at the national level, the mangrove loss footprints are the mean value of the mangrove loss area by each driver in the consumer country over the entire production country; we do not localize the embodied mangrove loss for each driver at the sub-national level. For instance, the mangrove loss footprint mapping cannot distinguish a farmed shrimp production in Indonesia driven by the food consumption in Michigan, the United States, because the map pixels can only represent the mangrove loss driven by commodities on the ground in Indonesia is driven by food consumption in the United States.\u003c/p\u003e \u003cp\u003eAlthough limitations exist and the mapping method has been applied to deforestation and biodiversity loss footprints, this study depicts the mangrove loss footprint at a global scale with high-resolution spatially explicit mappings for the first time. Understanding mangrove loss embodied in international trade can complement existing biodiversity threat and deforestation footprint maps by providing detailed, mangrove-specific insights to inform decision-making in conservation policies. By overlaying a spatially explicit mangrove loss footprint map with biodiversity threat footprint maps, decision-makers can receive comprehensive details about why certain areas are considered high-risk and how to strategize their conservation efforts. For example, countries such as Guinea, Suriname, Madagascar, and Nigeria have been listed as the top 10 countries whose mangrove losses are outsourced for other countries\u0026rsquo; consumption and these countries are also species threat hotspots. These countries could be prioritized for potential case studies through international mangrove restoration policies. Moreover, unlike generic deforestation footprints, which may misattribute mangrove loss due to the intertidal nature of mangrove forests, mangrove loss footprint map pinpoints the loss and its drivers on the ground and connects them to global consumption patterns. We also conducted a brief literature review to compare mangrove loss footprint with other footprints to clarify its advantages and drawbacks (SI-Table\u0026nbsp;7).\u003c/p\u003e \u003cp\u003eMoreover, spatially explicit mangrove loss footprint mappings can improve public awareness of mangrove forest loss embodied in the global supply chain to clarify consumers\u0026rsquo; environmental responsibilities. The global supply chain has traceability and transparency issues due to the complex, interconnected nature of their operations, where unintended or hidden externalities, inefficiencies, or regulatory gaps can create leakage and damage the environment. Mangrove loss could be caused by indirect suppliers, unexpected products, or unadopted areas and existing strong regulating policies for private enterprises cannot fully solve the problem because the supply chain may leak losses to areas with illegal activities or poor forest governance with public ownership. Mangrove loss footprint calculation and mapping provide a spatial and temporal base map on how mangrove loss is associated with producer and consumer countries through the supply chain and also offer policy-makers science-based knowledge in mangrove conservation. It can encourage more responsible consumption and corporate practices with better data transparency. Moreover, this map can be dynamically updated with new data on mangrove coverage and threats, increasing its utility over time and helping nations improve their mangrove-related pledges.\u003c/p\u003e \u003cp\u003e \u003cem\u003eFuture Improvements\u003c/em\u003e \u003c/p\u003e \u003cp\u003eThis study is the first to conduct mangrove loss footprint and mapping, and the limitations are depicted in the discussion section. Several directions can be addressed in future analysis to improve these findings. Firstly, the \u0026lsquo;commodities\u0026rsquo; driver from Goldberg\u0026rsquo;s data adopted in this paper is coarse, and many new remote sensing techniques can be applied to enhance its resolution in the future. For example, the aquaculture land cover data from Clark Labs (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://clarklabs.org/aquaculture/landcover-data/\u003c/span\u003e\u003cspan address=\"https://clarklabs.org/aquaculture/landcover-data/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) covers ten southeast countries in 1999\u0026ndash;2014, 2014\u0026ndash;2018, and 1999\u0026ndash;2018 for 10km grid resolution and is developing a global aquaculture dataset\u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e. This global dataset could be added to distinguish the aquaculture driver from the generic \u0026lsquo;commodities\u0026rsquo; driver in Goldberg\u0026rsquo;s dataset to enhance the accuracy of mangrove loss footprints with higher resolution in identifying mangrove loss drivers. Moreover, this study used the Eora 26 to calculate the mangrove footprint by connecting the mangrove drivers on the ground with 26 sectors of Eora Input-output tables (See method Step 2). In contrast, the full Eora dataset would offer a more precise calculation. It can be applied to five countries, Vietnam, Thailand, Venezuela, Singapore, and Australia, by incorporating their drivers into more sophisticated categories of sectors. In addition, other multi-regional input-output table (MRIO) datasets with more comprehensive data on mangrove-holding countries and more sophisticated categories of commodities products can enhance the resolution of the current mangrove loss footprint analysis. For example, the Carbon Accounts \u0026amp; Datasets (CEADs) website just published a new MRIO table (EMERGING) for 2010, 2015, 2016,2017,2018, and 2019 with 246 countries and 135 sectors covered\u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e, which covers all mangrove-holding countries in Goldberg\u0026rsquo;s mangrove loss datasets and has a broader coverage of commodities sectors compared to the Eora database. (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://ceads.net/user/index.php?id=1274\u0026amp;lang=en\u003c/span\u003e\u003cspan address=\"https://ceads.net/user/index.php?id=1274\u0026amp;lang=en\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). It can be a good addition for further analysis to improve the resolution of this study.\u003c/p\u003e \u003cp\u003eSecond, the mangrove loss footprint calculation considers loss only, which does not account for the countries' restoration efforts. Therefore, a consumer country that outsources its consumption to other countries may be able to compensate for these losses via successful mangrove restoration. Incorporating mangrove restoration efforts in future research requires remote sensing data to precisely underline the drivers of loss and gain on the ground before footprint estimates at a global scale.\u003c/p\u003e \u003cp\u003eLastly, better visualization of mangrove loss footprint can be applied to examine differences by land cover type. For example, to distinguish the values of mangrove ecosystem services or restoration potential, we can disaggregate the mangrove deforestation footprints into four domains: delta, estuary, lagoon, and open coast, based on their geomorphic and sedimentary settings, such as their proximity to coastal features. In the new mangrove typology maps by Worthington et al. (2020)\u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e, all deltaic and estuarine units are classed as terrigenous (i.e., dominated by minerogenic sedimentation from terrestrial sources), and lagoonal or open coast patches could be classed as terrigenous or carbonate (i.e., dominated by calcareous sedimentation). This distinction can help prioritize the values of the mangrove forests we lost and link them with their potential drivers through a global supply chain to compare their costs and benefits, which can bring about more precise policy recommendations.\u003c/p\u003e"},{"header":"4. Methods","content":"\u003cp\u003eThe calculation of the mangrove loss footprint involves three main steps: 1) summarizing mangrove forest loss per country per driver per epoch, 2) constructing bilateral trade flows per driver per epoch, and 3) calculating mangrove deforestation embodied in trade. The flowchart below (Fig.\u0026nbsp;2.5) visualizes the steps with details.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eStep 1: Calculate mangrove loss per country per epoch per driver.\u003c/p\u003e \u003cp\u003eWe use Goldberg et al.'s (2022) data for a GIS analysis to calculate mangrove loss per country per epoch per driver. The dataset is classified using high-resolution Google Earth images, random forest machine learning techniques, and a series of decision trees for several global-scale land use datasets. The final products include spatially explicit raster data with 30m*30m resolution per mangrove driver in three periods (2000\u0026ndash;2005, 2005\u0026ndash;2010, and 2010\u0026ndash;2016) in 39 mangrove-holding countries. It groups mangrove loss drivers into five categories: commodity production (agriculture, aquaculture), settlement, erosion, extreme climatic events, and non-productive conversion. We use this product to create spatially explicit mangrove loss footprint maps and use its country-level statistics to understand their spatiotemporal dynamics.\u003c/p\u003e \u003cp\u003eWe use its country statistics directly from Goldberg et al. (2022), which contains 72 countries. In addition, we aggregate the country-level loss using the spatial explicit raster data for 39 countries to validate its mangrove loss per epoch per driver in each country. We batch GIS analysis for 39 country layers to combine mangrove loss data in each epoch and each driver and conduct the reprojection to tabulate the areas in each category. Moreover, the raster datasets for each country are mosaiced for a cross-country map, and we use them for global spatially explicit mangrove loss footprint mappings.\u003c/p\u003e \u003cp\u003eStep 2: Identify mangrove drivers and their linkages with Eora.\u003c/p\u003e \u003cp\u003eWe then follow the Hoang et al. (2021) method to link the drivers of mangrove loss with the Eora global multi-region input-output (MRIO) database. The complete Eora database covers 15,000 industry sectors in 190 countries, but only 34 out of 39 countries are included in Goldberg\u0026rsquo;s mangrove loss spatial explicit map, and 65 out of 72 are included in its country-level statistics. Each country has its coverage of sectors ranging from 26 to 345. Most of the mangrove-holding countries have 26 industries covered in Eora, and only five countries (Vietnam, Thailand, Venezuela, Singapore, and Australia) have entire sectors covered. Therefore, we used Eora with 26 industries for this cross-country analysis.\u003c/p\u003e \u003cp\u003eWe then link the MRIO sectors with three drivers of mangrove loss to align with the Goldberg et al dataset: commodities, human settlement, and non-productive conversion. We only examined anthropogenic drivers of loss, given our primary objective of linking mangrove loss to global trade. The aggregation of human settlement includes building infrastructure, such as roads, ports, and shipping channels, to support transport, shipping, etc. The non-productive conversion of mangrove loss is derived from Goldberg et al. (2022), which uses remote sensing images to overlap the human influence mask layer with the low density of the productive industry layer. It includes private household settlements for daily life support, recreational, cultural, or other service activities for non-commercial purposes, such as education, health, and social work. The commodities-driven mangrove loss covers a broad range of sectors, including long-term agriculture, aquaculture from shrimp farms, forestry, mining, and energy infrastructure, following Hoang et al (2021)\u0026rsquo;s definition of commodity driver in the deforestation footprint analysis. We also verify the distinct commodity drivers by comparing the commodity-driven mangrove loss from Goldberg et al. (2022) with the drivers of deforestation map by Curtis et al. (2018), to confirm that the commodities from Goldberg\u0026rsquo;s datasets include various commodity types from Curtis such as agriculture, aquaculture, forestry, and other commodities.\u003c/p\u003e \u003cp\u003eStep 3: Mapping mangrove loss footprint\u003c/p\u003e \u003cp\u003eTo map the mangrove loss footprint, which is the consumption-based mangrove loss, we follow the logic as Eq.\u0026nbsp;\u003cspan refid=\"Equ1\" class=\"InternalRef\"\u003e2.1\u003c/span\u003e.\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\:MLF=M+I-E$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2.1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eMangrove forest loss footprint (MLF) is expressed in Eq.\u0026nbsp;(1) and shows that M is the mangrove forest cover loss in the production country, E is the mangrove loss embodied in exports, and I the loss embodied in imports. Mangrove loss (M) is measured and calculated through Goldberg\u0026rsquo;s driver datasets; the area of five drivers is in units as m\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eSpecifically, we first calculate the adjusted consumption based on Hoang's (2021) analysis.\u003c/p\u003e \u003cp\u003e \u003cem\u003eAC\u003c/em\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:=\\sum\\:_{tj}{L}_{ij}^{rt}{y}_{j}^{ts}+\\:\\sum\\:_{t\\ne\\:s,j}{L}_{ij}^{rt}{y}_{j}^{ts}-\\sum\\:_{t\\ne\\:s,j}{L}_{ij}^{rs}{y}_{j}^{st}\\)\u003c/span\u003e \u003c/span\u003e (2.2)\u003c/p\u003e \u003cp\u003eAdjusted consumption (AC)\u0026thinsp;=\u0026thinsp;total consumption\u0026thinsp;+\u0026thinsp;imports - exports. L is the Leontief inverse, measuring the transformation between i sectors of origin in r export countries and j sectors of destination in s import countries. (r*i,s*j), y is the final demand of s import countries with j destination sectors. (s*j). t is both the last sale in the consumption and import term, and the country of final consumption in the export term.\u003c/p\u003e \u003cp\u003eWe then decompose the total deforestation into the embodied deforestation in country s using the Eora MRIO model.\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$$\\:{F}^{s}\\left(Production\\right)=\\sum\\:_{ri}{f}_{i}AC$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2.3\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere F\u003csup\u003es\u003c/sup\u003e (Production) is the total mangrove deforestation embodied in international trade driven by country s, f is mangrove deforestation intensity for each sector i (mangrove forest loss area from Goldenberg\u0026rsquo;s data divided by gross output). AC represents adjusted consumption, which is measured in Eq.\u0026nbsp;2.2.\u003c/p\u003e \u003cp\u003eFinally, the spatial footprint analysis method calculates mangrove loss footprint M in country r driven by country s.\u003cdiv id=\"Equ3\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ3\" name=\"EquationSource\"\u003e\n$$\\:{M}^{S}=\\:\\sum\\:_{ℎr}{D}_{r}^{ℎ}\\frac{{\\sum\\:}_{i}{f}_{ℎi}{\\sum\\:}_{tj}{L}_{ij}^{rt}{y}_{j}^{ts}}{{\\sum\\:}_{i}{m}_{ℎi}^{r}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2.4\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eM\u003csup\u003es\u003c/sup\u003e represents spatial explicit mangrove forest loss map driven by country s (30m*30m). Mangrove forest loss map (D) by mangrove-holding countries r by driver h and embodied mangrove loss (numerator in Eq.\u0026nbsp;\u003cspan refid=\"Equ2\" class=\"InternalRef\"\u003e2.3\u003c/span\u003e (fLy)) are in absolute values, and the embodied mangrove loss (fLy) is normalized by total mangrove loss m by each driver h in mangrove-holding countries r for each industrial sector i to downscale values from nations to pixels. The mangrove loss maps (D) are prepared in step 1 with the shapefile layers for each driver h in each mangrove-holding country r and mangrove deforestation intensity f for each production sector i. Concept clarifications between mangrove loss and mangrove loss footprint is in SI-Table\u0026nbsp;8.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData and Code Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe combine three map datasets with the Eora global MRIO database to trace mangrove forest loss footprints. The main map is Goldberg\u0026apos;s (2022) loss and driver maps, which contains two series of raster datasets of 39 countries in measuring the extent of mangrove loss (https://daac.ornl.gov/CMS/guides/CMS_Global_Mangrove_Loss.html)\u003csup\u003e5,6\u003c/sup\u003e. One depicts the mangrove loss raster with five drivers: commodity production (agriculture, aquaculture), settlement, erosion, extreme climatic events, and non-productive conversion; the other depicts the loss raster in three epochs: 2000\u0026ndash;2005, 2005\u0026ndash;2010, and 2010\u0026ndash;2016. We also used Curtis (2018) to verify the commodity driver of mangrove loss layers (https://data.globalforestwatch.org/documents/gfw::tree-cover-loss-by-dominant-driver-2022/about), which should contain various commodities, including agriculture and aquaculture, and forestry identified in Curtis deforestation driver map\u003csup\u003e50\u003c/sup\u003e. This driver map identified the dominant drivers of tree cover loss in a 10km grid cell from 2001 to 2022. The dominant drivers are commodity-driven, shifting agriculture, forestry, wildfire, and urbanization. The last dataset is\u0026nbsp;Eora Database, which is one of the most well-developed global multiregional input-output databases. This database describes the world economy in terms of the annual production, trade, intermediate consumption, and final consumption of 26 homogeneous sectors between and within 189 countries from 2000 to 2022\u003csup\u003e51,52\u003c/sup\u003e. The code for the mangrove loss footprint was developed in Matlab and Python to process and visualize. The primary datasets and code for visualization can be found on the GitHub repository, accessible at lwt852/mangrove footprint (github.com). Matlab code for mangrove loss footprint calculation in this study will be made available upon request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eFriess, D. A. Ecosystem Services and Disservices of Mangrove Forests: Insights from Historical Colonial Observations. Forests 7, 183 (2016).\u003c/li\u003e\n\u003cli\u003eFriess, D. A. et al. The State of the World\u0026rsquo;s Mangrove Forests: Past, Present, and Future. Annual Review of Environment and Resources 44, (2019).\u003c/li\u003e\n\u003cli\u003eAdame, M. F. et al. Future carbon emissions from global mangrove forest loss. Global Change Biology 27, 2856\u0026ndash;2866 (2021).\u003c/li\u003e\n\u003cli\u003eAlban, J. D. T. D., Jamaludin, J., Wen, D. W. de, Than, M. M. \u0026amp; Webb, E. L. Improved estimates of mangrove cover and change reveal catastrophic deforestation in Myanmar. Environ. Res. 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Nat Commun 14, 6342 (2023).\u003c/li\u003e\n\u003cli\u003eWorthington, T. A. et al. A global biophysical typology of mangroves and its relevance for ecosystem structure and deforestation. Sci Rep 10, 14652 (2020).\u003c/li\u003e\n\u003cli\u003eCurtis, P. G., Slay, C. M., Harris, N. L., Tyukavina, A. \u0026amp; Hansen, M. C. Classifying drivers of global forest loss. Science 361, 1108\u0026ndash;1111 (2018).\u003c/li\u003e\n\u003cli\u003eLenzen, M. et al. International trade drives biodiversity threats in developing nations. Nature 486, 109\u0026ndash;112 (2012).\u003c/li\u003e\n\u003cli\u003eLenzen, M., Moran, D., Kanemoto, K. \u0026amp; Geschke, A. Building Eora: A Global Multi-Region Input\u0026ndash;Output Database at High Country and Sector Resolution. Economic Systems Research 25, 20\u0026ndash;49 (2013).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":false,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"mangrove loss footprint, spatially explicit, Input-output analysis","lastPublishedDoi":"10.21203/rs.3.rs-5778596/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5778596/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eTrade-related declines in mangrove forest cover have raised global concern for decades, given the numerous ecosystem services mangrove forests can provide. However, there has yet to be a comprehensive evaluation of the relationship between mangrove loss and global supply chains. This study presents an assessment of mangrove loss footprint, defined as fine-scale mappings of mangrove loss associated with international trade. Mangrove loss footprint is calculated by tracing 30m*30m mangrove loss on the ground to final consumption embodied in international trade through a multi-region input-output model and quantifying their spatiotemporal changes from 2000 to 2016. Moreover, the study adopts the metacoupling framework to understand how global consumption across space (domestic, adjacent, and distant) drives focal mangrove forest losses. Results indicate that influential economies, especially those with limited mangrove forests, have driven mangrove losses beyond their borders. The top 10 countries that drove mangrove loss in other countries are countries such as the USA, China, Japan, and South Korea, and outsourced to distant countries. These countries had a decreasing trend in outsourcing mangrove loss beyond borders from 2000 to 2016. China had the slowest decline rate and became the largest importer of mangrove loss in 2011\u0026ndash;2016, and 98% of its mangrove loss footprint lies in twelve Southeast countries. Indonesia, Myanmar, and Vietnam are the top 3 exporters whose mangrove forests are used for other countries\u0026rsquo; consumption. Although our study didn\u0026rsquo;t consider nations\u0026rsquo; restoration efforts, the results emphasize the need to use footprint mapping approaches to create mangrove loss footprint base maps. These maps can be dynamically updated to monitor and assess mangrove depletion, enhance supply chain transparency, and foster stronger international collaboration.\u003c/p\u003e","manuscriptTitle":"Global mangrove loss footprint mappings across space and time","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-10 12:11:13","doi":"10.21203/rs.3.rs-5778596/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"nature-communications","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"NCOMMS","sideBox":"Learn more about [Nature Communications](http://www.nature.com/ncomms/)","snPcode":"","submissionUrl":"https://mts-ncomms.nature.com/","title":"Nature Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature Communications","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"4f9409b3-0e85-454b-b89d-cbfae78c4cd2","owner":[],"postedDate":"March 10th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":43375938,"name":"Earth and environmental sciences/Environmental social sciences/Sustainability"},{"id":43375939,"name":"Earth and environmental sciences/Ecology/Forestry"}],"tags":[],"updatedAt":"2025-03-10T12:11:14+00:00","versionOfRecord":[],"versionCreatedAt":"2025-03-10 12:11:13","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5778596","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5778596","identity":"rs-5778596","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}