Wetland fragmentation associated with large populations across Africa

Domain: earth_science
Published: 2025-05-31
Editorial outcome: major_revision

Abstract
Wetlands provide essential ecosystem services in Africa, yet their extent and fragmentation remain poorly understood. Here we classify African wetlands at 10 m resolution, using seasonal composite imagery and a random forest algorithm. We estimate a total wetland area of 947,750 km² (10% of global wetlands), comprising 46% marshes, 25% swamps, 22% peatlands, 5% seasonal wetlands, and 2% mangroves. Wetland fragmentation is strongly associated with high population densities in countries such as Nigeria, Liberia, Guinea, Egypt, Algeria, and Kenya. African wetlands store an estimated 54 ± 11 Gt of carbon, surpassing Europe’s 12–31 Gt. If drained, they could release 260 Mt C yr−¹, nearly ten times the carbon sequestration of pristine wetlands (27 Mt C yr−¹). These findings provide a crucial foundation for sustainable wetland management and policy development.

Introduction
African wetlands are among the most productive ecosystems in the world1, providing a wide range of services that contribute to human wellbeing, such as the provision of water, food, dry season grazing, and fuelwood. They can support a wide range of flora and fauna and serve as an important carbon pool, sequestrating large amounts of carbon from the atmosphere, thereby regulating climate2. Depending on topographic context, wetlands can also play a significant function in flood attenuation and shoreline protection3–5 and also play a key role in the hydrological cycle6.

Wetlands are dynamic ecosystems that can be categorized based on their hydrology, soil composition, and vegetation types, each supporting unique ecological functions and biodiversity. Marshes, for instance, are wetlands dominated by herbaceous (non-woody) plants, characterized by periodic or continuous flooding. They can be found in both freshwater and saline environments, where nutrient-rich soils foster diverse flora and fauna. Swamps are wetlands with mineral soils (although some classifications also distinguish organic soil peat swamps), dominated by woody vegetation such as trees and shrubs. These ecosystems experience seasonal or permanent flooding and include coastal mangrove swamps, which are crucial for coastal protection, carbon sequestration, and biodiversity conservation.

Peatlands represent a distinct wetland category, defined by the accumulation of partially decomposed organic matter (peat) due to water saturation. Peatlands are further classified into bogs and fens. Bogs are rain-fed (ombrotrophic) systems that are typically acidic and nutrient-poor, often supporting mosses, shrubs, and sometimes trees. Fens, by contrast, are groundwater-fed (minerotrophic) and more nutrient-rich, allowing for a mix of grasses, sedges, and woody vegetation. Seasonal wetlands, another important type, experience periodic inundation during specific times of the year, followed by dry conditions. These include natural systems like ephemeral ponds and human-made systems such as rice paddies, which harbor species adapted to fluctuating water levels.

Amongst these wetland types, marshes are especially susceptible to anthropogenic pressures because of their accessibility, fertile soils, and proximity to densely populated regions. High demand for agricultural and urban development, combined with inadequate protection measures, makes these wetlands highly vulnerable and often heavily exploited.

In this study, wetlands are broadly classified into five types: swamps (mineral soil dominated), marshes including seasonal marshes are classified under the broader category of marshes, emphasizing vegetation type as the primary distinguishing factor, peatlands (encompassing both bogs and fens), seasonal wetlands (including human-made systems and lakes), and mangroves (coastal wetlands dominated by salt-tolerant woody species). This classification framework emphasizes the hydrological, vegetative, and soil characteristics of each wetland type, facilitating effective differentiation and mapping. By recognizing these distinctions, the study captures the ecological and functional diversity of wetlands across Africa, enabling better conservation and management strategies.

Wetlands in Africa are experiencing immense pressure from human activities, the most important being direct drainage and conversion to farmland, diversion of water away from wetlands for agricultural irrigation, population growth and urban expansion into wetland areas, pollution, overgrazing, and hydropower development; there has often been excessive exploitation by local communities7–9. A large number of African wetlands are thought to have been heavily modified by overexploitation (e.g., the Yala swamp and Kingwal wetland in Kenya and Nakivubo swamps in Uganda)2,10 and upstream developments altering the quality and flow of water feeding wetlands (e.g., Hadejia Jam’are floodplain in Nigeria9). Many African wetlands have been lost due to agricultural conversion, such as the Ga-mampa swamp in South Africa11. However, the current extent of wetland across Africa, at high resolution, is not known, and most continental datasets are very coarse estimates (e.g., 250 m to 1 km resolution)3,10,12–14. Small-scale wetlands may have been omitted or overestimated in previous continental mapping studies due to coarse resolution datasets, lack of ground control points and validation15–19. It is therefore not known whether the cumulative coverage of small wetlands is important to diverse ecological, climatic, and hydrological functions, and there is a need to ensure appropriate representation of African wetlands for sustainable management and for modeling climate mitigation and biogeochemical cycles. The lack of high-resolution data hinders our estimates of the total amount of carbon stored by these wetlands and estimates of the potential for net carbon uptake or loss from African wetlands at a continental scale. Much wetland carbon is below ground, yet potentially fragile and susceptible to rapid loss with wetland degradation20. Wetlands can become divided or separated into smaller, isolated patches or fragments due to both human activities and natural processes, including urbanization, agriculture, infrastructure development, and changes in hydrology. In addition to topographic reasons for the occurrence of small wetland patches in a landscape, formerly larger wetlands can become divided or separated into smaller patches due to natural processes. Wetland fragmentation poses a serious threat to the health and functionality of wetland ecosystems, highlighting the need for conservation efforts focused on preserving and restoring these valuable habitats. Wetland degradation refers to a decline in the ecological integrity and functionality of wetlands, characterized by reduced biodiversity, compromised water quality, and diminished carbon sequestration potential. It occurs due to drivers such as pollution, drainage, excessive water extraction, encroachment by invasive species, and the impacts of climate change. Unlike fragmentation, which involves the physical separation of wetland areas into smaller patches, degradation focuses on the deterioration of the ecosystem’s health and its ability to provide essential services, irrespective of spatial continuity. Climate change further exacerbates fragmentation through altered hydrological regimes and extreme weather events.

This study combines spatial mapping, fragmentation analysis, and carbon stock estimation to develop a comprehensive understanding of wetland dynamics across Africa. The primary objectives of this study are to: (1) systematically map the spatial distribution of wetlands across Africa by categorizing them into five distinct types—marsh, mangrove, swamp, peatland, and seasonal wetland—based on ecological and hydrological characteristics; (2) analyze wetland patchiness and assess its relationship with population density, testing the hypothesis that wetlands in highly populated areas are more fragmented, and (3) estimate the total carbon stocks for each wetland type and calculate potential carbon emissions under pristine and drained conditions across various climate zones.

To classify our wetland types, we compile control points for climate zones. The control points are grouped into five wetland types, including marsh (2202), mangrove (1477), swamps (1891), peatland (1580), and seasonal wetland (1054). Here we classify swamps as mineral soil wetlands, while peatlands include fen and bog systems with or without trees (these include what are sometimes referred to as peat swamps). These classes capture critical differences in wetland vegetation, soils and water levels (see Supplementary Tables S2–S6) and importantly are separable using optical and radar-derived indices from freely available satellite datasets21. We analyze the relationship between wetland patchiness derived from our map and population data from the Gridded Population of the World database (GPWV4) and test the hypothesis that highly fragmented wetlands are associated with large populations. We use a 10 km grid for fragmentation analysis based on our previous studies that suggested that average continuous wetland patches cover an area of 10–11 km221.

We calculate total carbon stocks for each wetland type by multiplying the total area of the wetland with typical values of carbon stock per hectare estimated by previous studies22–28. We then estimate the carbon emissions from different wetland types for two wetland degradation states (pristine and drained conditions) in each climate zone.

Results
The current extent of the African wetland
Our high-resolution continental study reveals that wetlands cover ~947,750 km2 of Africa (excluding deep water bodies), which constitutes ~3% of the total land area. Marshes, covering 436,743 km² (46% of total wetlands), are more extensive than swamps, which account for 231,776 km2 (24%). Peatlands cover 208,842 km2 (22%), while seasonal wetlands (5%) and mangroves (3%) have the least coverage. Most of these wetlands are concentrated in western and central parts of Africa (Fig. 1a), where there is a high amount of rainfall throughout the year. However, some important wetland complexes (regions where there are two or more wetland types clustered together) are situated in North Africa, such as in the Nile region. The largest wetland complex is located in the Congo region of central Africa covering about 278,450 km2, which contains the most extensive peatland area (165,250 km2) in the entire continent (Fig. 1e). Other important wetland complexes are situated in southern Sudan (the Sudd) (67,150 km2) (Fig. 1d), the Zambia (43,170 km2), Angola (46,072 km2) and Nigeria (47,130 km2).

Distribution of wetland across African climate zones
We present the spatial distribution of different wetland types based on high resolution image processing, according to the five main climatic regions in Africa based on the Köppen–Geiger classification29: Tropical wet (TW), tropical wet and dry (TWD), semi-arid (SARD), arid or desert (ARD) and mediterranean subtropical climate (MED). The overall accuracy of the trained algorithm compared to validation ground control data is higher for wetlands in the TWD region (89%), with mangrove and marsh well distinguished from other wetland classes with producer’s accuracies exceeding 80% (Supplementary Table S2). TW has the most extensive wetland, hosting 57% (~448,210 km2) (Fig. 2) of the total wetland area in Africa. Peatlands (37%) and swamps (34%) are the most dominant wetland types of TW, which cover 165,950 km2 and 153,580 km2, respectively. Mangroves (2%) and seasonal wetlands (0.5%) are the least common wetland types in TW, covering only about 14,000 km2. The largest climate region is the TWD, covering up to 38% of the total area of Africa. Wetlands in this region constitute only 3.2% (~362,980 km2) (Supplementary Table S1) of the total area with 52% being marshes (Fig. 2a). This region has a distinct climatic feature with alternating wet and dry periods throughout the year, which plays a significant role in the formation of different wetland conditions and variability across the season. Thus, TWD has a higher amount of seasonal wetland cover relative to other climate zones (Fig. 2b). SARD is characterized by 250–500 mm of rainfall throughout the year, covering 6,700,000 km2 (22%) of Africa. Only about 1.4% of SARD is covered by wetland, of which seasonal wetlands are the dominant type. ARD is the second largest climatic region in Africa, extending up to about 9,000,000 km2, and has the lowest wetland coverage (0.4%).

Wetland fragmentation and human population
We develop a wetland fragmentation and population index (WFPI) by overlaying the gridded population layer with the gridded wetland fragmentation layer using the fuzzy overlay method (see “Method”). The fragmentation index is an indicator of regions with a high number of wetland patches (based on the 10 m resolution data) per 10 km grid, and the population index is a count of persons per kilometer grid downscaled into a 10 km grid, indicating areas of high concentrations of population. Our WFPI shows areas where fragmentation is coincident with humans (Fig. 3) using 10 km grid cells across Africa.

We identify nine regions with a WFPI value indicating highly fragmented grid cells (80–226 wetland fragments per 10 km2) related to large population size (40,000–300,000 persons per 10 km2). Six of these regions are in West Africa (Nigeria, Liberia, Gabon, Guinea, and Cameroon), two in North Africa (Egypt and Algeria), and one in East Africa (Kenya). In West Africa, areas such as Rivers State and Lagos in Nigeria, and Monrovia in Liberia, have the highest WFPIs of 0.89, 0.76, and 0.83, respectively (Table 1). These areas are characterized by high population growth associated with urban expansion, thereby increasing pressure on nearby wetlands, mainly coastal mangroves and swamps. Other areas with high WFPI include Conakry in Guinea (0.68), Alexandria in Egypt (0.66), Algiers in Algeria (0.61), and Murang’a in Kenya (0.59) often associated with agriculture encroaching on wetlands in these regions30–32. Our index indicates that a total of 13,021 km2 of wetlands may be heavily threatened by human activity within Africa (WFPI > 0.5), and about 28,724 km2 of wetland occur in populated areas, which suggests a moderate risk of human interactions (WFPI 0.3–0.5). However, large wetland areas with a high concentration of fragments (for example the Congo basin wetlands) that are far away from settlements or sparsely populated show little or no relation between fragmentation and human populations (Fig. 3). The high concentration of fragments in the Congo basin are thought to be geomorphologically and climatologically controlled rather than driven by human activities25, though these peatlands could be highly sensitive to human-induced fragmentation in the future. The low fragmentation in the Great African Lakes region is likely due to the hydrological stability and the presence of large, continuous wetland systems. These wetlands are naturally resistant to fragmentation, while human activities like agriculture and urbanization are primarily confined to upland areas.

Carbon stock in African wetlands
Healthy wetlands can store large amounts of carbon, but the quantity of carbon stored varies among different wetland types20,22. Among these wetland types, peatlands are thought to have the highest carbon stock followed by mangroves, swamps and marshes22–24,33. We use the IPCC mean carbon stock for each wetland type, according to different climate zones, to estimate the total carbon stored in the four wetland types of Africa. Our continental map indicates that African wetlands contain 54 ± 11 Gt of carbon, which is around 5% to 9% of wetland soil carbon stored globally (520–710 Gt C)20, and higher than that of European wetlands (12–31 Gt)34 based on differences in wetland area. Peatlands store about 41% of this African wetland carbon, while 28% is stored in marshes, 27% in swamps, and 3% in mangroves (Fig. 2d).

Net carbon uptake or loss from African wetlands
We estimate the contribution of African wetlands to the global carbon budget across each climatic region using empirically derived emission rates for selected wetland types for which data are available (see Method). We use two approaches, First, we use the default emission factor from the IPCC emission factor database to calculate total carbon emissions from wetlands under two conditions (drained or natural). Using our high resolution map of wetlands in Africa, we calculate that drained peatland, mangrove and marsh are capable of emitting 260 Mt C yr−1 (936 Mt CO2 equivalents yr−1) which is equivalent to 2.4% of global net annual CO2 emissions35 and almost ten times the mean net annual uptake under natural conditions of 27 Mt C yr−1 (98 Mt CO2 equivalents yr−1) by these wetlands. Wetlands within high WFPI areas, under drained conditions, could release 10.3 Mt C yr−1 (37 Mt CO2 equivalents yr−1).

The net wetland carbon flux varies according to water level36,37. Therefore, in our second approach, we use the emission factor for different wetland types at various water levels obtained from Zou et al.37 to estimate the carbon flux for peatlands, marshes, and mineral soil swamps. The six categories of water level range from −3 to 2 (WTL-3 ≤ −70 cm; −70 cm < WTL-2 ≤ −50 cm; −50 cm < WTL-1 ≤ −30 cm; −30 cm < WTL0 ≤ −5 cm; −5 cm < WTL1 ≤ 40 cm; and 40 cm < WTL2), where negative values indicate depth below the surface, while positive values indicate ponding. At water level −3 we estimated that African wetlands will have a net release of 310 Mt CO2 equivalent yr−1, while for water level −2 they will emit 115 Mt CO2 yr−1 and 46 Mt CO2 yr−1 for water level −1, while 91 Mt CO2 yr−1 will be taken up by African wetlands when the water level is at level 1.

Methods
Datasets
Ground control points. We collated data on the location and characteristics of wetlands across Africa from reliable sources, including the Food and Agriculture Organization (FAO) global dryland assessment database, Global Peatland Database (GPD), journal papers, academic reports, and NGO reports. We verified each data point and screened them to exclude any coordinates that were inaccurate, mislabeled, or inconsistent by using visual interpretation of very high spatial resolution digital globe images (>1 m pixel) made available for visualization through Google Earth. Our final dataset used 8204 control points for different wetland types in Africa. These control points were sorted based on the climate zones in Africa and assigned to either training or validation points (Supplementary Fig. S4). Thus, the control points were grouped into an equal number of training and testing points to ensure robust accuracy assessment16,44.

Satellite data. Sentinel-1 and -2 satellite images covering the entire study area for the period of January 2021 to December 2021 were available through the Google Earth Engine platform (GEE) at 10 m resolution. We used the Ground Range Detected interferometric wide-swath with a pixel spacing of 10 m Sentinel-1 images acquired in dual-polarization (VV/VH) and pre-processed as a Level-1 data product, with an average acquisition interval of 12 days. A total of 5728 Sentinel-1 Sentinel-1 images in ascending order were collected for the study area. Sentinel-2A and 2B Top of Atmosphere reflectance data with 13 spectral bands were obtained through the GEE. We used blue (0.496 µm, band 2), green (0.560 µm, band 3), red (0.665 µm, band 4), near infrared (NIR, 0.835 µm, band 8), and short-wave infrared 2 (SWIR2 2.202 µm, band 12) bands. Sentinel-2 images with cloud cover of <20% were selected from January 2020 to January 2021 which resulted in a total of 13596 images45–49.

Population data. We obtained information about population distribution from the Gridded Population of the World database (GPW V4) provided by Center for International Earth Science Information Network (https://sedac.ciesin.columbia.edu). The GPW dataset has an approximate resolution of 30 arcsec, equivalent to 1 km at the Equator, that contains global population counts, density, urban/rural status, age and gender structures with more than 12,500,000 input units maintained by NASA’s Socio-Economic Data and Applications Center (SEDAC). The population input data were collected at the finest resolution available from the ‘2010’ round of censuses, which occurred between 2005 and 2014. The data were used to produce population estimates for the years 2000, 2005, 2010, 2015, and 2020 (https://earthdata.nasa.gov/data/catalog?keyword=gpw-v4/methods). We selected the population estimates for 2020 for our analysis.

Mapping of wetland extent
To accurately delineate the wetlands of Africa we classified the continent into different major zones according to the climatic and ecological features. These zones include tropical wet (TW), tropical wet and dry (TWD), mediterranean subtropical (MED), semi-arid (SARD), and desert or arid (ARD). We grouped the control points for each wetland type based on these climate zones. TWD consists of 3120 control points, TW includes 2550, SARD has 1144, ARD contains 848, and MED comprises 536. We processed the images collected from Sentinel-1 and -2 images for the period of 2020–2021 to develop optical and radar indices for each climate zone. The optical variables used includes spectral bands 2 (blue), 3 (green), 4 (red), 8 (NIR), 11 and 12 (SWIR), the normalized difference vegetation index (NDVI), normalized difference water index (NDWI), modified normalized differential water Indices (MNDWI), and tasseled cap wetness index (TCWI). SAR variables included vertically transmitted, vertically received SAR backscattering coefficient (σ0VV), vertically transmitted, horizontally received SAR backscattering coefficient (σ0VH), and the normalized difference (NdiffVH−VV/VH+VV) and ratio indices (Nratio=VV/VH+VV) for the wet and dry season. We then undertook a variable importance analysis50–57 for each climate zone to select the most important variables to input into the final classification. The highest importance is typically placed on the variables that contribute the most to reducing the model’s impurity or error. We identified these variables by computing variable importance in an RF algorithm using the mean decrease impurity (MDI), which measures how much each variable improves the model’s predictive performance. We ran the variable importance algorithm five times before finally selecting the variables with higher reduction in impurity considered as higher importance. For all images in the arid and semi-arid region, we extracted the maximum pixel values, while a median value was used for other regions to enhance identification58. Finally, we applied a Random Forest (RF)59,60 algorithm to classify and validate wetland types in each climate zone.

To produce the forest tree in RF we needed to identify the two important parameters: the number of decision trees to be generated (Ntree); and the number of variables to be selected and tested for the best split when growing the trees (Mtry). The parameter Ntree was assessed for the values of 100–600: a value of 500 was selected as error rates for all classification models were constant beyond this point. In this study, we used the combined SAR and Optical indices as input variables.

Classification map accuracy and uncertainties
We undertook the classification of wetlands according to each climate zone using the RF classifier. The control points for different wetland types were compiled for each climate region separately to classify the input variables developed for each region. To accurately classify the wetland types based on their distinctive features in a particular region, the input variables were extracted from composite images constructed from different pixel values over a particular period of the year. In ARD, seasonality is often a key property of wetlands. We therefore used the variables extracted from seasonal composites of maximum pixel value to train the RF classifier (Table 2). Seasonal wetlands were also identified using the maximum value from our seasonal composites of wet and dry seasons in each climate zone. For the TWD and MED zones, the variables constructed for the wet and dry season from the mean pixel value composites were used to train the RF classifier (Table 2). The accuracy of RF classifications for each climate zone was assessed using cross-validation by splitting the control points into two halves (50% training and 50% testing points) (Supplementary Fig. S4), spatially selected for each climate zone from each class on a random basis. Our accuracy estimation matrix included the overall accuracy (OA), Kappa coefficient, producer accuracy, and user accuracy. We selected the Kappa coefficient in this analysis due to its widespread application and interpretability in evaluating agreement between observed and predicted classes, making a reliable metric for wetland classification studies. Overall accuracy determines how well the classification algorithm performed, which can be measured by dividing the total number of correctly identified sample point by the total number of the testing points (Supplementary Tables S2–S6). We evaluated our uncertainties by comparing classifications made using the entire control point dataset with those produced using only a subset of control points selected at random for each wetland class in each climate zone. The uncertainties were associated with our classification accuracy, high confusion between wetland classes (e.g., swamps and peatlands), and limitations in the number of control points. A common issue with the gridded population data is a misallocation of population to areas outside urban areas. These errors were minimized by down-sampling the 1 km gridded population and taking the average population within 10 km grids.

Wetland fragmentation and population index (WFPI)
We compared the distribution of wetland fragments and population at the same cell size across 10 km × 10 km grid areas. For our analysis, we used only the count of wetland fragments estimated at a resolution of 10 m to allow comparison to the gridded population data at 10 km resolution. This resolution was selected because it was found to be the mean dimension of wetland fragments from an earlier study in southern Nigeria21.

We sought to identify the association of wetland fragmentation with human population. We use a fuzzy logic approach to create a membership rank for the fragmentation grid and population grid (ranging from 0 to 1), with 0 representing the lowest membership and 1 the highest membership in increasing order. Lower membership indicated grid cells with less fragments or which are sparsely populated, while grid cells with a large number of wetland fragments or which are densely populated were assigned to a higher membership group. Finally, we overlaid the gridded fragmentation membership layer with the gridded population membership layer to quantify the coincidence of wetland fragments and human population. Higher WFPI indicated interaction of dense population with wetlands, resulting in patchiness within the grid cells.

Population grid. The population grid was created by transforming 1 km resolution population data obtained from the GPW V4 data using 10 km grid reference cells across the continent of Africa. We classified the cells in different class ranges from lowest to highest based on the population count in each grid cell (Supplementary Fig. S1). Most of the grids with dense population were located near major city centers or close to river networks. We used this grid as an input for the fuzzy membership transformation.

Fragmentation grid. To create the fragmentation grid, we converted the classified wetland raster to polygons using the conversion tool in ArcGIS Pro. We used an algorithm similar to spatial aggregation by overlaying a 10 km × 10 km grid on the original 10 m resolution fragmentation map. For every 10 m cell within a 10 km grid, the number of unique wetland fragments was calculated. We then identified and labeled distinct fragments within each grid. The fragment count within each 10 km grid cell was computed to derive a metric of the total number of fragments. The total number of fragments per grid cell was used to group the cells into eight groups from low to highly fragmented (Supplementary Fig. S2). The total fragment in each cell was calculated by:
Frag grid = Σn grid i (1)
where Frag grid is the fragmentation grid (10 km), n is the number of fragments in grid cell i, and i is the code of the grid cell.

Fuzzy membership. We transformed the population and fragmentation grid into a fuzzy membership layer scaled from 0 to 1. 0 indicated grid cells that are not members of any set while 1 was assigned to grid cells with full membership. We used the fuzzy linear membership function to transform the input values linearly on the 0 to 1 scale, with 0 being assigned to the lowest input value and 1 to the largest input value (Supplementary Figs. S3 and S4). All of the values in between receive some membership value based on a linear scale, with the larger input values.

Fuzzy overlay. The Fuzzy Overlay tool is used to analyze the possibility of a phenomenon belonging to multiple sets in a multicriteria overlay. It determines whether a phenomenon is a possible member of a particular set and analyzes the relationships between the membership of multiple sets. We used the “fuzzy And” function to find the relationship between the population and fragmentation membership layer. We overlaid the population grid with the fragmentation grid using a fuzzy overlay tool61. This allowed us to analyze the relationship between the multiple members set from each grid layer. Stronger relationships are found between higher membership sets while lower membership sets show weak relationships. Coincidence of a dense population grid (higher population membership grid) with a highly fragmented grid (higher fragmentation membership grid) resulted in the highest WFPI region.

Carbon loss estimation
We used the CO2 emission factor provided by the IPCC Wetland Supplement guidance28 to estimate the amount of carbon loss from each wetland type for different climate zones in Africa. An emission/removal factor is a coefficient that quantifies the emissions or removals of a gas per unit area. It is calculated based on a sample of measurement data, averaged to develop a representative rate of emission for a given activity level under a given set of operating conditions. We multiplied the total area of each wetland type with its corresponding emission/removal factor across the different climate zones for two assumptions: (1) the wetlands are in pristine condition; (2) wetlands are drained. The CO2 equivalent emission was calculated by:
CO2 emission i,c,y = (EF * Area) (2)
where CO2 emission is the annual net carbon emission/uptake from a wetland type in tonnes CO2 yr−1, area is the land area of drained organic soils in a land-use category in climate domain c, in ha and EF is the emission factor for drained organic soils, by climate domain c, in tonnes C ha−1 yr−1.

We also adopted the empirical function of Zou et al.37 to estimate the wetland carbon flux using water level as a function of carbon emission. The equation below was used to calculate the carbon flux from selected wetland types under different moisture regimes in different climate zones:
Emission = Σ Efs ijk * Area ijy (3)
where Efs is the emission factors, i is the climate zone, j is the water-table level (coded −3 to 2), y is the year, and k is the wetland type.

Carbon stock estimation
The total amount of carbon stored by each wetland type in Africa was evaluated using the acquired data of carbon stock per hectare from the studies by refs. 22–24, 26–28. To calculate the total amount of carbon stored by each wetland, we multiplied the total area of the wetland by the value of carbon stock per hectare: Wetland carbon = total wetland area (hectare) * carbon stock (t C ha−I).

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "Thank you for considering me as an reviewer for this study on the large-scale fragmentation of wetlands across the African continent. The work presents significant content on the theme of wetland protection. Overall, this study provides important results, with well-written paragraphs and a logical flow. However, in my humble opinion, the new information provided still requires significant changes and additions. This raises questions about its suitability for publication in a journal like Nature Communications. Therefore, I propose major revisions to the article, with the possibility of submission to another Nature journal, such as Scientific Reports, for example. You will found my comment in the attached file. Kind regards", "reviewer_2": "Overview:\nThis paper addresses the lack of high-resolution data on Africa’s wetlands - the extent, the intactness of the wetlands and carbon storage in the wetlands. There is an abundance of information in this paper.\nUsing this wetland map, and the GPW V4 data used to map of human densities, the authors have derived a measure of wetland intactness/fragmentation and the volumes of carbon stored in the wetlands of Africa.\nIn our opinion, the highlight of this paper is the production of a 10m resolution map of Africa’s wetlands. This is a valuable output which is likely to improve on previous maps with lower resolutions that cannot pick up all the smaller wetlands.\nGeneral comments/questions:\no The paper does not have a specific question to be answered or hypothesis to be tested.\no It is a multi-disciplinary study – involving computer techniques, cartography, quantification of carbon etc. However, the disciplines do not link seamlessly. It seems as if there are different degrees of rigor in assessing accuracy and precision at different levels in this study. For instance, the wetlands are mapped at a 10 m resolution, but the human distribution patterns at a 10 km resolution. The issues in bringing together these resolutions are not adequately dealt with. There needs to be some understanding of where humans settle. Empirically their distribution is a narrow linear pattern along rivers and in floodplains which is difficult to evaluate when using a 10 km grid for human distribution. It would be good to give some analysis of this.\no We also have a concern that there may be a compounding of errors – from population censuses, to allocation of densities in 10 km grids, and errors relating to overlapping these on wetlands mapped at a small scale. This is something that does need to be addressed.\no Overall, the study would benefit by having a discussion on the processes involved – in wetland fragmentation and human settlement patterns. And then, what are the global impacts of such fragmentation.\n.\nMapping of wetlands\no With the mapping of wetlands, we question the classification of the wetlands into the categories the authors have used. There needs to be a clear definition of each wetland type. What is the difference between swamp and marsh? Swamp is usually used to describe a wetland with trees while marsh is shorter vegetation. Papyrus wetlands are a feature of large areas in Africa – are they swamp or marsh? And what distinguishes wooded wetland from seasonal wetland? And peatlands from marsh?\nHow is a ‘seasonal wetland’ determined? The term ‘wetland complex’ is used – what is meant by this?\no Verification: How does the wetland extent compare to other wetland maps generated for specific regions in Africa with smaller geographical extents e.g. Lourenco et al. 2022 mapped the Angolan highlands peatlands. How does this (or other similar maps) compare with the mapping produced for this study? For this study the verification is at a density of one point per 200 km2. Is this adequate for such a fine-scale resolution map covering such variable conditions as the whole of Africa?\nMapping of people\no People do live where there is water and wetlands. This is especially the case in regions where water is not abundant. So, one would expect a correlation between human numbers and wetlands. Has there been any change in this over time? Or is it just that, as human numbers increase, so this effect is more pronounced. This can only be shown by having a time series – to detect if this human distribution pattern stays the same or changes over time, and is there a corresponding change in wetland fragmentation?\nFragmentation\no There are causes of wetland fragmentation that do not relate to human population density that need to be discussed? These include the downstream impacts of dams, the impacts of lowering of groundwater table, the impacts of mining and large-scale agriculture.\no To separate natural fragmentation patterns (e.g. due to the nature of topography) from human-caused changes it is necessary to show the relationship over time between numbers of people and a change in fragmentation/transformation. Sentinel 2 was fully commissioned by 2017 – is it within the scope of this study to compare wetland extent between 2017 and 2020? Or at a lower spatial resolution but a greater temporal scale the European Space Agency (ESA) Climate Change Initiative (CCI) land cover classification (Copernicus Climate Change Service, Climate data store) 300m resolution maps could be used to estimate of the land cover change from 1992 to 2020.\nRecommendation\no Our recommendation is that this study be split into three or four publications:\ni. Mapping of wetlands in Africa (and possibly relate these to topography or position within the basin);\nii. Techniques used to differentiate wetlands from drylands and to distinguish between different wetland types (the classes used need to be defined);\niii. Human settlement patterns relative to wetlands – and possibly some subsets to show trends as human populations increase;\niv. Carbon storage and balance.", "reviewer_3": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts."}, "author_response": "Response to review comments\nReviewer #1 (Remarks to the Author):\nThank you for considering me as an reviewer for this study on the large-scale fragmentation of wetlands\nacross the African continent. The work presents significant content on the theme of wetland protection.\nOverall, this study provides important results, with well-written paragraphs and a logical flow.\nHowever, in my humble opinion, the new information provided still requires significant changes and\nadditions. This raises questions about its suitability for publication in a journal like Nature\nCommunications. Therefore, I propose major revisions to the article, with the possibility of submission\nto another Nature journal, such as Scientific Reports, for example. You will found my comment in the\nattached file.\nResponse: Thank you for your positive comments and useful feedback\n1) Line 11-12: I am not sure that high-resolution map of Africa is lacking (Zhang et al., 2024, Yan and\nNiu, 2009; Anzhen Li et al., 2022, etc. have studied that question).\nResponse: Our study uses Sentinel-2 imagery as our primary data source, which has major advantages\nover Landsat/Modis used in the studies mentioned by the reviewer. The spatial resolution of Sentinel-\n2 imagery is 10m, relative to 30m for LandSAT and 250m for MODIS. Sentinel-2 (2015-2022) imagery\nalso provides the most up to date snapshot of wetland extent. In addition to the generation of this\nimproved high-resolution map, our study also goes on to use it to study wetland fragmentation across\nthe continent and make estimates of carbon stores and fluxes.\n2) Line 15: Could you please add one or two sentences on the methodological approach used in the\npaper?\nResponse: Sentences about the methodological approach have been added in Line 15 (Page 1), as\nfollows; “We use indices derived from seasonal composite images and a random forest classification\nalgorithm to classify wetlands in Africa. We use the resulting 10m wetland map and datasets of human\npopulation to investigate the relationship between wetland fragmentation and population”.\n3) Line 20: modify the sentence please\nResponse: The sentence has been modified in Line 23 page 1 as follows “We estimated the total carbon\nstock of African wetlands to be 54 ± 11 Gt, larger than that of Europe (12-31 Gt)”.\n4) Line 21: how did you find and write the value of C carbon for European wetlands? (12-31 Gt)\nResponse: The value for European wetland carbon was sourced from reference 34 in line 180-183 (Page\n8), as follows:“Our new continental map indicates that African wetland contains 54 ±11 Gt of carbon\nwhich is around 5% to 9% of wetland soil carbon stored globally (520 - 710 Gt C) 20, and higher than\nthat of European wetlands (12-31 Gt) 34”.\n5) Line 39: more other activities are mentioned In African wetlands as highlighted in some papers,\ncould you please revise this please\nResponse: More human activities that impact wetlands in Africa have been added (line 40 - 44) page 2\nas follows; “Wetlands in Africa are experiencing immense pressure from human activities, the most\nimportant being direct drainage and conversion to farmland, diversion of water away from wetlands for\nagricultural irrigation, population growth and urban expansion into wetland areas, pollution,\novergrazing, and hydropower development”.\n6) Line 57: missing information on why studying wetland fragmentation? Wetland fragmentation is a\nsignificant threat to the health and functioning of wetland ecosystems and highlights the importance\nof conservation efforts aimed at preserving and restoring these valuable habitats. Wetlands become\ndivided or separated into smaller, isolated patches or fragments. This fragmentation can occur due to\nvarious human activities and natural processes, such as urbanization, agriculture, infrastructure\ndevelopment, and changes in hydrology. Did you consider that? OR highlight that in the introduction\nplease\nResponse: Information on wetland fragmentation has been added in Line 60-65 (Page 2) as follows:\n“Wetlands can become divided or separated into smaller, isolated patches or fragments due to both\nhuman activities and natural processes, including urbanization, agriculture, infrastructure development,\nand changes in hydrology. Wetland fragmentation is a significant threat to the health and functioning\nof wetland ecosystems and highlights the importance of conservation efforts aimed at preserving and\nrestoring these valuable habitats”.\n7) Line 60-63: To be revised\nResponse: We have revised the sentence in line 81-84 (Page 3) as follows; “We analyse the relationship\nbetween wetland patchiness derived from our map and population data from Gridded Population of the\nWorld database (GPW V4) and test the hypothesis that highly fragmented wetlands are associated with\nlarge populations”.\n8) Line 82: Nothing in the south? South Africa, Zambia, Zimbabwe, Botswana, Tanzania, Tchad,\nMorocco, etc. What are you calling wetland complexes?\nResponse: There are wetlands in these areas and the high-resolution map shows them. To highlight this\nfurther in our article additional plates have been added to Figure 1 to show zoomed in locations of\nwetlands in southern Africa, Chad and Morocco. We refer to a wetland complex as an area consisting\nof two or more wetland types. We have now added this explanation to the paper at line 100 (Page 4), as\nfollows “…wetland complexes (regions where there are two or more wetland types clustered\ntogether) …”.\n9) Line 58: please specify the number of ground control for each wetland type and distribution.\nSpecify, the source of the data map of climatic zones, the process used to estimate the carbon stock,\nResponse: We added the number of ground control points for each wetland (Line 75-76, page 3),\n“Control points were grouped into five wetland types including marsh (2202), mangrove (1477),\nswamps (1891), peatland (1580) and seasonal wetland (1054)”. The data source for climatic zone\nclassification is reference 29 (Line 116, page 5), “We present the spatial distribution of different wetland\ntypes based on our high resolution mapping, according to the five main climatic regions in Africa 29”,\nand the process of carbon stock estimation has been explained (Line 87-89, page 3), “We calculate total\ncarbon stocks for each wetland type by multiplying the total area of the wetland with typical values of\ncarbon stock per hectare estimated by previous studies22–28”.\n10) Line 139-140: please specify and be clearer please. All the values from 156 to 177 have to be\njustified and sourced.\nResponse: The revised statement is in current line 167-173(Page 8), “However, large wetland areas with\na high concentration of fragments (for example the Congo basin wetlands) that are far away from\nsettlements or sparsely populated show little or no relation between fragmentation and human\npopulations (Figure 3). The high concentration of fragments in the Congo basin are thought to be\ngeomorphologically and climatologically controlled rather than driven by human activities25, though\nthese peatlands could be highly sensitive to human-induced fragmentation in the future”. Here, we point\nout that our model shows a strong relationship between high population density and wetland\nfragmentation, though some highly fragmented wetland areas (Figure S2) do not coincide with high\npopulation. All values are calculated using the emission factor and carbon stock value for each wetland\ntype obtained from the reference cited in line 87 page 3.\n11) There still more things to add in this discussion please.\nResponse\nWe have added more details to the discussion section at\n- Line 229 (page 10), “Our study shows close similarities with smaller geographical scale studies, such as\nthe peatland map of Angolan highlands constructed by Lourenco et al. 2022 (see supplementary figure\nS6)”.\n-Line 250 (page 11) “Our future work will concentrate on gathering more and better-quality ground\ncontrol data to support future timeseries analyses”.\nLine 252 (page 11) “We explored the possible impact of African wetlands on global climate through net\ncarbon uptake/loss under natural and a range of drained conditions. We found that the three selected\nwetland types (peatland, mangrove and marsh) under drained conditions could contribute up to 3% of\nglobal net annual carbon loss, a value that might be much higher if data for emissions from other\nwetlands become available and included in the estimation. Human activities have been widely reported\nto be a key driver of wetland degradation”\n12) Still confusion on how you certified the value used. You may have chosen a date (2015),\nfor example, because before this date, some older databases may include wetland areas that are no\nlonger considered as such (many marshes, for example, have disappeared). Other wetland areas are so\nsmall and insignificant that they are not represented. Additionally, in some conflict-affected regions where it\nis difficult to obtain field data, there are limitations in these areas.\nResponse: We have added sentences to make the text clearer at Line 68-74 (page 2) “We verify the\nclassification of each wetland control point by visual interpretation of Digital Globe very high spatial\nresolution images (< 1 m pixels) through Google Earth and spanning 2018 -2021. Our independent\nsources of data for control points for wetland types include papers, reports, and academic theses from\nvarious dates spanning 2015 - 2019, so we consider verification with the more recent Digital Globe\nimagery (2018 - 2021) a necessary step for classification of the 2020-2021 Sentinel 1 and 2 imageries”.\n13) Line 246: Please specify how you find 16 spectral bands of 10m resolution for Sentinel 2. Specify\nalso which type of Sentinel you used: 2A or 2B, etc.\nResponse: Sentinel 2 has 13 spectral bands - we have rectified the typo from 16 to 13 spectral bands in\nLine 294 (page 12). We use sentinel 2A and 2B data, as stated in the article at line 294.\n14) Line 265: Here, I process…….\nResponse: We have rephrased the sentence in line 313 (page 13) as follows; “Here we process the\nimages collected from Sentinel-1 and -2 images for the period of 2020-2021 to develop optical and radar\nindices for each climate zone”.\n15) Globally the method is not clear, how you integerated and maintained the 10m resolution, how you\ncombined the population and sentinel images with different resolutions.\nResponse: Sentinel 1 GRD (Interferometric Wide swath mode (IW) has a pixel spacing of 10m and\nSentinel 2 imagery has a 10 m resolution (Line 290-294). However, for our analysis we use only the\ncount of wetland fragments estimated at a resolution of 10 km to allow comparison to the gridded\npopulation data at 10 km resolution. This resolution was selected because it was found to be the mean\ndimension of wetland fragments from our earlier study (Garba et al., 2023) (Line 384-388).\n16) Overall, this approach could not identify the inland valleys, floodplains, ponds, etc. wetlands.\nThe seasonal wetlands also cannot be identified.\nResponse: This is not true. We categorize these features based on the type of wetland cover as identified\nfrom optical and radar imagery. For example, the floodplains in the Niger delta of Nigeria are covered\nby different wetland types such as swamps and marshes. The zoomed areas in Figure 1 display some\nareas covered by seasonal wetlands, for example seasonal wetlands in Morocco (Figure 1f) identified\nby using composites showing maximum wetness.\n17) Since wetlands are defined based on topography, hydrology, vegetation cover, etc. Why did you\nselect only vegetation cover? Using Optical data from sentinel? Why did not use the DTM, DEM\nimages with topographic indices such as slope, curvature, TWI, TPI, etc.\nResponse: We use 8204 ground control points with detailed information about wetland type and\nlocation, giving us confidence that our classification of optical and radar imagery is acceptably accurate\neven in the absence of topographic data. We also limit our classification to wetland classes that we are\nconfident can be distinguished from optical and radar imagery alone. However, we intend to further\nrefine the models in a future paper by including topographic variables. This will require choices about\ndata resolution, since there is currently no freely available, uniform, global DEM at as a high a resolution\nas Sentinels1 and 2.\n18) Please justify also why choosing the RF model that others. Why did not you use other accuracy\ncoefficients such as AUROC, TSS, DeLong Test, etc.\nResponse: This has been added to line 329 – 333 (Page 13) “RF is more robust compared to other\nclassification algorithms, solving the problems of over-fitting with other decision trees. The RF is\nparticularly suitable for handling variation within land cover classes and reducing noise in the data. It\ndoes not require prior knowledge of the data distribution compared to other classifiers”.\n19) Other factors such as distance to villages, city, road, etc. should be used to better highlight the\npopulation activities that are drivers of change in wetlands.\nResponse: We choose to use gridded population data to ease comparison with wetland fragmentation.\nThis approach has the advantage of using a relatively uniform dataset instead of relying on population\nindicators (like distances to villages, city, road) that vary from country to country. Deriving a novel set\nof meaningful population indicators for comparison would be a substantial additional piece of work,\nand will be a focus of future efforts.\n20) Line 365 to 400: Not clear, need to be as clear as possible.\nResponse: We have added sentences to make the text clearer in line 438-442 (page 18) “An\nemission/removal factor is a coefficient that quantifies the emissions or removals of a gas per unit area.\nIt is calculated based on measurement of samples, averaged to develop a representative rate of emission\nfor a given activity level under a given set of operating conditions”.\n21) Some sentence still need grammar check.\nResponse: We have reviewed all grammar and spelling mistakes\nReviewer #2 (Remarks to the Author):\nThis paper addresses the lack of high-resolution data on Africa’s wetlands - the extent, the intactness of\nthe wetlands and carbon storage in the wetlands. There is an abundance of information in this paper.\nUsing this wetland map, and the GPW V4 data used to map of human densities, the authors have derived\na measure of wetland intactness/fragmentation and the volumes of carbon stored in the\nWetlands of Africa.\nIn our opinion, the highlight of this paper is the production of a 10m resolution map of Africa’s wetlands.\nThis is a valuable output which is likely to improve on previous maps with lower resolutions that cannot\npick up all the smaller wetlands.\nResponse: Thank you for your positive comments and useful feedback.\nGeneral comments/questions:\n1) The paper does not have a specific question to be answered or hypothesis to be tested.\nResponse: We state a hypothesis that highly fragmented wetlands are associated with large populations\nin Line 82 – 83 (Page 3).\n2) It is a multi-disciplinary study – involving computer techniques, cartography, quantification of carbon\netc. However, the disciplines do not link seamlessly. It seems as if there are different degrees of rigor in\nassessing accuracy and precision at different levels in this study. For instance, the wetlands are mapped\nat a 10 m resolution, but the human distribution patterns at a 10 km resolution. The issues in bringing\ntogether these resolutions are not adequately dealt with. There needs to be some understanding of where\nhumans settle. Empirically their distribution is a narrow linear pattern along rivers and in floodplains\nwhich is difficult to evaluate when using a 10 km grid for human distribution. It would be good to give\nsome analysis of this.\nResponse: We used a 10 km grid for fragmentation analysis based on our previous studies that suggested\nthat average continuous wetland patches cover an area of 10 -11km2 (Garba et al., 2023) (Line 83-85,\npage 3). The wetland fragmentation uses the total count of wetland fragments, using the 10 m data,\nwithin a 10 km grid used as input in the model. The gridded population data is also down sampled from\n1km to 10km cells to be able to be accurately overlayed with the wetland patches. We have clarified\nthese points in the text and revised the caption for Figure 3 to help avoid confusion in Line 151-153(page\n7) “Data for wetland cover uses our 10 m gridded dataset to determine fragmentation within each 10\nkm cell. Data for population density uses 1 km data reprofiled to 10 km”. A common issue with the\ngridded population data is misallocation of population to areas outside the urban areas. However, the\ngridded data does allow a fair, first order comparison to datasets derived from EO data.\n3) We also have a concern that there may be a compounding of errors – from population censuses, to\nallocation of densities in 10 km grids, and errors relating to overlapping these on wetlands mapped at a\nsmall scale. This is something that does need to be addressed.\nResponse: We have clarified in the manuscript that we use the full resolution EO data to make\nestimations of fragmentation at a lower resolution and compared that to population data downsampled\nto the same cell size at Line 151-153 “Data for wetland cover uses our 10 m gridded dataset to determine\nfragmentation within each 10 km cell. Data for population density uses 1 km data reprofiled to 10 km”\nLine 384 “We compared the distribution of wetland fragments and population at the same cell size\nacross 10 km grid areas”. This approach minimises errors associated with comparing at differing\nresolutions, although of course it limits the spatial scale over which we are able to make conclusions.\nWe down sampled the 1 km grid population taking the average population within 10 km grid minimizing\nerrors from small scale grid comparison (Line 379-381, page 16). We have larger uncertainties in our\nfragmentation (see line 376-378, page 16) compared to the population data, although we acknowledge\nthat the uncertainty in populations datasets will vary geographically.\n4) Overall, the study would benefit by having a discussion on the processes involved – in wetland\nfragmentation and human settlement patterns. And then, what are the global impacts of such\nfragmentation.\nResponse: A discussion on wetland fragmentation has been added in line 60-65 (Page 2) “Wetlands can\nbecome divided or separated into smaller, isolated patches or fragments due to both human activities\nand natural processes, including urbanization, agriculture, infrastructure development, and changes in\nhydrology. Wetland fragmentation is a significant threat to the health and functioning of wetland\necosystems and highlights the importance of conservation efforts aimed at preserving and restoring\nthese valuable habitats”.\nMapping of wetlands\n5) With the mapping of wetlands, we question the classification of the wetlands into the categories the\nauthors have used. There needs to be a clear definition of each wetland type. What is the difference\nbetween swamp and marsh? Swamp is usually used to describe a wetland with trees while marsh is\nshorter vegetation. Papyrus wetlands are a feature of large areas in Africa – are they swamp or marsh?\nAnd what distinguishes wooded wetland from seasonal wetland? And peatlands from marsh?\nHow is a ‘seasonal wetland’ determined? The term ‘wetland complex’ is used – what is meant by this?\nResponse: We have added a clear description of each wetland type that we have used in supplementary\nTable S2. These classes capture critical differences in wetland vegetation, soils and water levels (see\nSupplementary Table S3-S7) and are separable using optical and radar-derived indices from freely\navailable satellite datasets. Seasonal wetlands were identified using the maximum value from our\nseasonal composites (line 351-352, page 14). We refer to a wetland complex as an area consisting\nof two or more wetland types in Line 100 (page 4) “regions where there are two or more wetland types\nclustered together”.\nTable S2 Description of each wetland type used in this study.\nWetland class Soil systems Water sources Typical settings and Plant species\nfeatures\nMarsh Mineral Direct flow from lakes, Edges of lakes and Herbaceous\nstreams, precipitation streams, Coastal zone\n(salt/tidal marshes)\nSwamp Mineral or Organic Precipitation, Along large rivers or on Woody, forested\ngroundwater, freshwater the shores of large lakes\nflooding from rivers or\nlakes\nMangrove Organic Precipitation, Coastal zone mostly Trees and shrubs\ngroundwater and tidal grows in sheltered low\nflow. lying coasts estuaries,\nand lagoons\nPeatland Organic Groundwater inflow or Standing water of lakes Herbaceous plants,\nprecipitation or margins of slow Shrubs, small trees.\nflowing rivers,\nSeasonal wetland Organic or mineral Precipitation Low lying areas and Herbaceous\nopen fields.\n6) Verification: How does the wetland extent compare to other wetland maps generated for specific\nregions in Africa with smaller geographical extents e.g. Lourenco et al. 2022 mapped the Angolan\nhighlands peatlands. How does this (or other similar maps) compare with the mapping produced for this\nstudy? For this study the verification is at a density of one point per 200 km2. Is this adequate for such\na fine-scale resolution map covering such variable conditions as the whole of Africa?\nResponse: We have added a figure comparing our study with Lourenco et al. 2022 in the supplementary\ndocument (Figure S6). Line 2290 (page 10), “Our study shows close similarities with smaller\ngeographical scale studies, such as the peatland map of Angolan highlands constructed by Lourenco et\nal. 2022 (see supplementary figure S6). We mention the issues about future research benefitting from\nmore ground control data and better carbon characterization for African wetlands in line 251, “Our\nfuture work will be concentrated on gathering more and better-quality ground control data to support\nsome future timeseries analyses”.\nMapping of people\n7) People do live where there is water and wetlands. This is especially the case in regions where water\nis not abundant. So, one would expect a correlation between human numbers and wetlands. Has there\nbeen any change in this over time? Or is it just that, as human numbers increase, so this effect is more\npronounced. This can only be shown by having a time series – to detect if this human distribution pattern\nstays the same or changes over time, and is there a corresponding change in wetland fragmentation?\nResponse: Our comparison shows that there is high correlation between human numbers and wetlands.\nWe will explore time series changes of wetland fragmentation/ population in further studies. However,\na high spatial resolution time series analysis of wetlands and wetland change is out of scope for the\ncurrent paper and would be a significant new body of work.\nFragmentation\n8) There are causes of wetland fragmentation that do not relate to human population density that need\nto be discussed? These include the downstream impacts of dams, the impacts of lowering of\ngroundwater table, the impacts of mining and large-scale agriculture.\nResponse: We have added these points in Line 60-65 “Wetlands can become divided or separated into\nsmaller, isolated patches or fragments due to both human activities and natural processes, including\nurbanization, agriculture, infrastructure development, and changes in hydrology”.\nWe also highlight an example from the results of our study in Line 167-173 “However, large wetland\nareas with a high concentration of fragments (for example the Congo basin wetlands) that are far away\nfrom settlements or sparsely populated show little or no relation between fragmentation and human\npopulations (Figure 3). The high concentration of fragments in the Congo basin are thought to be\ngeomorphologically and climatologically controlled rather than driven by human activities25, though\nthese peatlands could be highly sensitive to human-induced fragmentation in the future”.\n9) To separate natural fragmentation patterns (e.g. due to the nature of topography) from human-caused\nchanges it is necessary to show the relationship over time between numbers of people and a change in\nfragmentation/transformation. Sentinel 2 was fully commissioned by 2017 – is it within the scope of\nthis study to compare wetland extent between 2017 and 2020? Or at a lower spatial resolution but a\ngreater temporal scale the European Space Agency (ESA) Climate Change Initiative (CCI) land cover\nclassification (Copernicus Climate Change Service, Climate data store) 300m resolution maps could be\nused to estimate of the land cover change from 1992 to 2020.\nResponse: It would be a significant new undertaking, and essentially an entirely new paper, to explore\na 10 m resolution timeseries analysis (e.g. requiring timeseries of ground truth data). We aim to explore\ntemporal changes of wetland fragmentation/ population in our future papers.\nRecommendation\n10) Our recommendation is that this study be split into three or four publications:\ni. Mapping of wetlands in Africa (and possibly relate these to topography or position within the basin);\nii. Techniques used to differentiate wetlands from drylands and to distinguish between different wetland\ntypes (the classes used need to be defined);\niii. Human settlement patterns relative to wetlands – and possibly some subsets to show trends as human\npopulations increase;\niv. Carbon storage and balance.\nResponse: Thank you for the recommendations. We think that our current paper is a significant new\nbody of work which incorporates most of these elements already as one powerful and impactful study.\nWe therefore strongly prefer to keep the paper as one body of work rather than split it into smaller\npapers. Future papers will aim to incorporate time series analysis and topographic variables.\nReviewer #3 (Remarks to the Author):\nI co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of\nthe Nature Communications initiative to facilitate training in peer review and to provide appropriate\nrecognition for Early Career Researchers who co-review manuscripts.\nResponse: That’s great – we very much appreciate your feedback."}, {"round": 2, "reviewer_comments": {"reviewer_1": "Thank you once again for considering my remarks to enhance this study.\nWhile the authors have adequately addressed the proposed comments, I believe there is still room for improvement in terms of results and identifying a clear research question. The issue of coupling fragmentation and wetland typology persists.\nThere is a diversity within the fragmentation of classes in the observed changes. How did you isolate natural changes from those induced by the population?\nAnother question is whether population density alone can explain these changes. Are there not areas with low density where significant effects are observed, independent of population?\nIt would be beneficial to include in the discussion or conclusion the limitations of the approach used and potential avenues for future research. I maintain that the study could be divided into two themes, as the two parts investigated are not fully interconnected.\nIntegrating images at 10m resolution does not inherently make the study more important or useful than others using lower resolutions. The value lies in the accessibility of these images and the potential for similar approaches to be tested with 30m and 100m resolution data, likely yielding comparable results.\nRegarding the separability of wetland classes or types, which are more susceptible to anthropogenic pressure?\nGiven my familiarity with the densely populated Great African Lakes region, what explains the low fragmentation observed in these areas based on your results? The same can be asked for the Nile delta and the Ngiri Ntuma peatland region (between DRC and R Congo), where a very low population density exists. Would the results differ if a polygon-based approach were used instead of a pixel-based one? Please confirm whether African mangroves are typically organic, organo-mineral, or more mineral-dominant.\nWhy was elevation not considered, as high peatlands and low peatlands exhibit distinct behaviors? Similar considerations apply to other wetland types like Indonesian valleys and plains. Justify the inclusion of the \"deep water\" class and why others were categorized as marshes.\nTopographic indices could provide additional insights. Would you consider creating a new index based on the difference between the NDVI of wet and dry periods?\nUltimately, I believe the article is not suitable for publication in its current form but could be divided into two separate articles with distinct themes.\nOther comments are in the pdf file.\nKind regards", "reviewer_4": "The manuscript deals with important topics around extent, fragmentation and degradation, and carbon storage in African wetlands and includes an impressive amount of work. The study is remote sensing based and aims at a continental scale, while considering different climatic zones in the continent. The study is without doubt useful and necessary due to the importance of wetlands for ecosystem functions, including the global carbon cycle. The newly applied/modified methods aim at providing more accurate estimates of wetland extent than previous works, due to a finer scale that includes smaller wetlands. Also, the relations between wetland fragmentation and population density are a major aspect of the work, by suggesting a Wetland Fragmentation Population Index, which may help to assess, understand, monitor, and predict wetland fragmentation / degradation on the African continent, as well as resulting greenhouse gas emissions.\nHowever, in the current version, I see both needs and potentials for improving the manuscript before publication. Some aspects remain unclear, and while I am sure the authors thought about them, they need to be better clarified and discussed in the presented work.\nMain comments / suggestions:\nThe Objectives: While the main objectives are mentioned in the introduction, I would recommend the authors to clearly state all the objectives of the work at the end of the introduction in a more concise way for an easier understanding, e.g. as listing them as opposed to running text. The authors might as well consider reducing the number of objectives and focusing on key objectives / or potentially make two different publications.\nThe introduction currently has some details on the methods which might not be necessary in the introduction (rather in the method section) (66 ff and 74 ff).\nOn the other hand, the text should be clearer on the definitions of wetland fragmentation and wetland degradation concerning the differences and overlaps between these terms / concepts.\nWetland fragmentation, as the authors state themselves with reference to the Congo Basin, is not necessarily caused by human activity, but can be simply attributed to topography, even in densely populated areas. Wetland degradation on the other hand, does not necessarily mean wetland fragmentation. Wetlands can be intensively used for (dry season) cropping, which can strongly reduce their ecosystem service provision and functions, but still be a wetland. Wetland fragmentation as a process would imply to me a loss of the wetland status, by altering the hydrology (drainage, dams, etc.).\nA better definition / explanation of the wetland types would also be helpful. While it is included\nin the supplementary material, it would also be helpful to get more information in the text.\nThe aspect of land use does not play a big role in the manuscript, even though many wetlands are agriculturally used, and agriculture is described as a main driver for wetland degradation. The method section on Carbon loss estimations speaks of land use categories (line 447) that are not mentioned elsewhere in the document. If these categories have been assessed, it would be valuable to indicate their extent / proportions in the result section, as well as to explain which land use classes were used at all in the methods. If land use was not assessed in the work, i recommend this to be stated as a limitation in the discussion.\nThe language and formal aspects need a thorough check and revision. E.g., some parts of the methods are written in present tense and others in past tense, without an obvious reason for the use of different tenses. Sometimes a space between number and unit is missing, and between numbers and hyphens, eg line 19, 24,70,85 …\nSpecific comments / suggestions:\nLine 32: “some of” not necessary\nLine 34: “endemic” not necessary here, many wetland species are cosmopolitan or pantropic species\nLine 43: what kind of development activities? Aren’t they included in the previously mentioned pressures?\nLine 54: significant in which sense?\nLine 63: this sentence should be rephrased.\nLine 95 ff: The numbers and percentages do not seem to match, or it is unclear to me, what the reference is. 436.000 km2\nare more then 33% of 947.000 km²..\nLine 116ff: The classification is based on Köppen-Geiger, according to the reference. This should be mentioned in the text.\nLine 127: it should be TWD instead of TW. Again, what does significant mean here? Higher? Or is it a result of a statistical test?\nFigure 2 b): in greyscale printing, only 4 classes are visible. The colors / shadings should be adapted.\nLine 170 ff: this is an important aspect. Please also mention it in the discussion\nLine 178: How specific are these values? Are they specific for climatic zones in Africa? Please be clearer about this.\nTable 1: Please add a reference year for the population.\nLine 209: it should be WFPI\nLine 232ff. This should be part of the results as well.\nLine 306: What is the purpose to get the population data for all these years?\nLine 311ff: according to line 116, the classification is only climate based, here it includes ecological aspects. Please clarify.\nLine 326: Introduce RF as an abbreviation here.\nLine 328: I wonder if these details are relevant for this publication, a reference for the method might justify shortening this paragraph.\nLine 353: Abbreviation TWD missing.\nTable 2: Why was a mean value used in TWD? Shouldn’t there be season specific values?"}, "author_response": "Response to review comments\nReviewer #1:\nWe thank the reviewer for their helpful guidance and feedback\n1) There is a diversity within the fragmentation of classes in the observed changes. How did you\nisolate natural changes from those induced by the population?\nResponse: We assess the relationship between wetland fragmentation and population density using\na fuzzy membership approach. While wetlands exhibiting high fragmentation and located near\ndensely populated areas may indicate pressure from human activities, this correlation does not\nprove causality. Processes that can cause fragmentation of wetlands include urbanization,\nagricultural expansion, water management activities such as flow diversions and channelization,\ndeforestation, and pollution. We discussed these at line 97. The intersection of fragmentation\nand high population density is also indicative of increased future vulnerability to human activities,\nas discussed at line 293 “The WFPI analysis generally identifies sites where the impact of human\nactivities results in patchiness of the surrounding wetlands. The overlap between fragmentation\nand high population density indicates an increased susceptibility to future human impacts”.\n2) Another question is whether population density alone can explain these changes. Are there\nnot areas with low density where significant effects are observed, independent of population\nResponse: We prioritized the assessment of human-induced wetland fragmentation because\nhuman activities are the leading cause of rapid and extensive ecosystem disruption, particularly in\ndensely populated or rapidly developing areas (Vitousek et al., 1997; Foley et al., 2005; Pörtner et\nal., 2022). In contrast, natural fragmentation processes, such as geomorphological patterns or\nhydrological dynamics (e.g., those observed in the Congo Basin wetlands as already discussed in\nthe manuscript at Line 210), typically occur over extended timescales and are less directly\nimpacted by short-term policy or management measures.\n3) Integrating images at 10m resolution does not inherently make the study more important or\nuseful than others using lower resolutions. The value lies in the accessibility of these images and\nthe potential for similar approaches to be tested with 30m and 100m resolution data, likely yielding\ncomparable results\nResponse: Lower-resolution data (e.g., 30m Landsat or 100m MODIS) may yield similar results\nfor larger wetlands or general trends but may fail to capture fine-scale variability and smaller\nfragments. The lower the resolution, the greater the impact of 'mixed pixels' where wetland features\nare averaged with non-wetland features. This can obscure the presence of small wetlands, leading\nto underrepresentation or misclassification. Integrating 10m resolution data inherently adds value\nfor studies like ours, focused on identifying small, fragmented wetlands and updating estimates of\nthe significance of African wetlands for global climate. We discuss the specific value of the higher\nresolution wetland maps to our study at lines 273.\n4) Regarding the separability of wetland classes or types, which are more susceptible to\nanthropogenic pressure?\nResponse: We have discussed this in line 61 “Amongst these wetland types, marshes are especially\nsusceptible to anthropogenic pressures because of their accessibility, fertile soils, and proximity to\ndensely populated regions. High demand for agricultural and urban development, combined with\ninadequate protection measures, makes these wetlands highly vulnerable and often heavily\nexploited.”\n5) Given my familiarity with the densely populated Great African Lakes region, what explains the\nlow fragmentation observed in these areas based on your results? The same can be asked for the\nNile delta and the Ngiri Ntuma peatland region (between DRC and R Congo), where a very low\npopulation density exists. Would the results differ if a polygon-based approach were used instead\nof a pixel-based one? Please confirm whether African mangroves are typically organic, organo-mineral, or more mineral-dominant.\nResponse: We have discussed this in line 228 “The low fragmentation in the Great African Lakes\nregion is likely due to the hydrological stability and the presence of large, continuous wetland\nsystems. These wetlands are naturally resistant to fragmentation, while human activities like\nagriculture and urbanization are primarily confined to upland areas”.\nFor areas like the Nile delta and Ngiri Ntuma peatlands (between DRC and R Congo), the high\nfragmentation despite low population is linked to natural factors such as hydrology and\ngeomorphology as we highlighted in line 295 “It should be noted that extensive wetland regions\nwith a high density of fragments, such as those in the Congo Basin, generally show minimal\ncorrelation between fragmentation and human populations, especially in areas distant from\nsettlements or with sparse human activity. The fragmentation in these wetlands is largely attributed\nto geomorphological and climatological processes rather than anthropogenic influences. However,\nthese peatlands remain susceptible to potential future fragmentation driven by human activities,\nemphasizing the need for future studies that examine wetland change and detect change in\nfragmentation to support vigilant conservation and management strategies.”.\nAfrican mangroves are generally classified as organo-mineral systems (Spalding et al., 2010;\nBunting et al., 2018).\n6) Why was elevation not considered, as high peatlands and low peatlands exhibit distinct\nbehaviors? Similar considerations apply to other wetland types like Indonesian valleys and plains.\nJustify the inclusion of the \"deep water\" class and why others were categorized as marshes.\nResponse: We have discussed this in line 308 “We have used detailed ground control points\nspecifying wetland types and locations, ensuring that incorporating topographic indices would not\nsubstantially affect classification accuracy. However, we aim to refine the models further in future\nresearch by integrating topographic variables to enhance their precision and applicability”.\n7) Topographic indices could provide additional insights. Would you consider creating a new\nindex based on the difference between the NDVI of wet and dry periods?\nResponse: We have used both wet and dry periods for classification of some the climate zones as\ndiscussed in line 425 “Seasonal wetlands were also identified using the maximum value from our\nseasonal composites of wet and dry season in each climate zone. For the Tropical Wet and Dry\nand Mediterranean subtropical climate (MED) zones, the variables constructed for the wet and dry\nseason from the mean pixel value composites were used to train the RF classifier”\n8) Ultimately, I believe the article is not suitable for publication in its current form but could be\ndivided into two separate articles with distinct themes.\nResponse: Thank you for the suggestions. We believe our current paper represents a substantial\nand cohesive body of work, integrating many of these elements into a single, impactful study. As\nsuch, we prefer to maintain it as one comprehensive piece rather than divide it into smaller\npublications.\n9) Please confirm that area occupied by marshes is larger than swamps\nResponse: Yes, the area of marshes is greater than swamps as discussed in line 145 “Marshes,\ncovering 436,743 km² (46% of total wetlands), are more extensive than swamps, which account\nfor 231,776 km² (24%).”\n10) Some marshes in South and central Africa are seasonal\nResponse: We classify seasonal marshes in this region under the broader category of marshes,\nemphasizing vegetation type as the primary distinguishing factor, as stated in line 67.\n11) Is the difference based on the fragmentation or covered areas? I think it's normal to have high\ntotal carbon stock in Africa than Europe (surface, types, etc)\nResponse: The higher total carbon stock in Africa compared to Europe is primarily based on\ndifferences in wetland area, as stated at line 238, “ Our new continental map indicates that African\nwetland contains 54 ±11 Gt of carbon which is around 5% to 9% of wetland soil carbon stored\nglobally (520 - 710 Gt C) 20, and higher than that of European wetlands (12-31 Gt) 34 based on\ndifferences in wetland area”.\n12) Please add a source here. As globally 70*30 is mostly used.\nResponse: We have added the source in line 348 “Thus, the control points were grouped into an\nequal number of training and testing points to ensure robust accuracy assessment (Mahdianpari et\nal., 2018; Zhang et al., 2023)”.\n13) I think you did not used all the based; please specify the one you used.\nResponse: We have specified the bands used in lines 361 “We use blue (0.496 µm, band 2), green\n(0.560 µm band 3), red (0.665 µm, band 4), and near infrared (NIR, 0.835 µm, band 8), and short-\nwave infra- red 2 (SWIR2 2.202 µm, band 12) bands”.\n14) Why did you therefore select the images of the same year to reduice error\nResponse: We discuss our selection of imagery for training and classification in line 343. Given\nthat the Sentinels were only launched in 2015/16, using the most recent imagery was prioritized to\nensure that the analysis reflects current wetland dynamics and conditions. Wetland ecosystems are\nhighly dynamic, and recent changes driven by human activities or climate impacts are better\ncaptured with up-to-date satellite imagery.\n15) Please give more details how this was made\nResponse: The details of how the population was estimated for the target years is available\ndocumentation for the Gridded Population of the World, Version 4 (GPWv4) accessed through:\nhttp://sedac.ciesin.columbia.edu/data/collection/gpw-v4/methods/method1. This is referenced in\nour article at line 363.\n16) I don't remember seeing those numbers for each climate zone\nResponse: The number for each climate zone has been given in supplementary material Table S3\n– S7. We refer to this in the main text at line 385 “TWD consists of 3,120 control points, TW\nincludes 2,550, SARD has 1,144, ARD contains 848, and MED comprises 536”.\n17) Results from this can be added as Supplementary materials\nResponse: We have added the result of variable importance to supplementary materials in figure\nS7.\n18) Results will depend on the tableau of number of controled points for each wetland type.\nHow did you manage training points with confusion of wetland types\nResponse: To manage confusion in training points across wetland types, a systematic approach\nwas implemented. High-resolution imagery verification ensured accuracy in control point labeling.\nClass-specific spectral and radar features were used to distinguish overlapping wetland types, and\nperformance was evaluated by calculating overall accuracy, user’s and producer’s accuracy, and\nthe kappa coefficient based on a confusion matrix derived from validation data as discussed at line\n440.\n19) I suppose that the variables varied from on wetland type to another. So how did you manage\nthat?\nResponse: This has been clarified in line 401. We compute the variable importance five times\nbefore selecting the variables that display highest performance for all of the wetland classes.\n20) I do not understand how seasonality was analyzed; As it varied from one climate region to\nanother, from one agroecological zone to another\nPLEASE CLARIFY THIS\nI think this can be done if images where selected again based on seasonality in each region\nResponse: We selected seasonal images to construct our seasonal composite as we stated in line\n419, “To accurately classify the wetland types based on their distinctive features in a particular\nregion, the input variables were extracted from composite images constructed from different pixel\nvalues over a particular period of the year. In the arid and semi-arid region, seasonality is often a\nkey property of wetlands. We therefore use the variables extracted from seasonal composites of\nmaximum pixel value to train the RF classifier (Table 2). Seasonal wetlands were also identified\nusing the maximum value from our seasonal composites of wet and dry season in each climate\nzone.”.\nImages were selected depending on the distinct seasonal patterns for each climate region. There is\nconsiderable variation in the timing and intensity of wet and dry seasons between climate zones,\nand so the selection of images was tailored to reflect local conditions. For instance, in the TWD\nregion, where there is distinct rainfall and a dry period within the year, wet season images\ncorrespond to peak rainfall months, while dry season images reflect periods of minimal\nprecipitation.\n21) What value for you be identified as HIGHEST IMPORTANCE?????\nResponse: We have discussed this in line 397 “The highest importance is typically placed on the\nvariables that contribute the most to reducing the model's impurity or error. We identify these\nvariables by computing variable importance in an RF algorithm using the Mean Decrease Impurity\n(MDI) which measures how much each variable improves the model’s predictive performance.\nWe run the variable importance algorithm multiple times before finally selecting the variables with\nhigher reduction in impurity considered as higher importance”.\n22) WHY DID NOT YOU USED OTHER ACCURACY COEFFICIENT?????\nResponse: We have explained this in line 444 “We selected kappa coefficient in this analysis due\nto its widespread application and interpretability in evaluating agreement between observed and\npredicted classes, making it a reliable metric for wetland classification studies”.\n23) As most of population are located in or around cities and few in rural areas. This looks like\nyou are assessing the effect on only wetlands located in the vicinity of cities. So why you didn't\ntake access to road, distance from wetland to city as also factors. As mentioned in most articles as\nfragmentation elements\nResponse: We considered all wetlands across the study area, encompassing urban, peri-urban, and\nrural regions, rather than focusing solely on wetlands near cities. While it might appear that the\nanalysis primarily reflects urban impacts due to population concentration near cities, the study\ncaptures a broader picture of wetland fragmentation and population distribution.\n24) This was made at 10m, so please specify the algorithm used to scale these grid areas please\nResponse: This is explained at line 487 “We use an algorithm similar to spatial aggregation by\noverlaying 10 km x 10 km grid on the original 10m resolution fragmentation map. For every 10m\ncell within a 10 km grid, the number of unique wetland fragments is calculated. We then identify\nand label distinct fragments within each grid. The fragment count within each 10 km grid cell is\ncomputed to derive a metrics of the total number of fragments”.\n25) Is it 1km or 10km? in the methodology it is said to be 1km. Please confirm this\nResponse: We use 10 km grids.\n26) Why two??? and not three?? Low, medium and high\nResponse: The value is a range from 0 to 1, as referred to in supplementary figure S3.\nReviewer #4 (Remarks to the Author):\nThe manuscript deals with important topics around extent, fragmentation and degradation, and\ncarbon storage in African wetlands and includes an impressive amount of work. The study is\nremote sensing based and aims at a continental scale, while considering different climatic zones in\nthe continent. The study is without doubt useful and necessary due to the importance of wetlands\nfor ecosystem functions, including the global carbon cycle. The newly applied/modified methods\naim at providing more accurate estimates of wetland extent than previous works, due to a finer\nscale that includes smaller wetlands. Also, the relations between wetland fragmentation and\npopulation density are a major aspect of the work, by suggesting a Wetland Fragmentation\nPopulation Index, which may help to assess, understand, monitor, and predict wetland\nfragmentation / degradation on the African continent, as well as resulting greenhouse gas\nemissions. However, in the current version, I see both needs and potentials for improving the\nmanuscript before publication. Some aspects remain unclear, and while I am sure the authors\nthought about them, they need to be better clarified and discussed in the presented work.\nResponse: Thank you for your positive comments and useful feedback\nMain comments / suggestions:\n1) The Objectives: While the main objectives are mentioned in the introduction, I would\nrecommend the authors to clearly state all the objectives of the work at the end of the introduction\nin a more concise way for an easier understanding, e.g. as listing them as opposed to running text.\nThe authors might as well consider reducing the number of objectives and focusing on key\nobjectives / or potentially make two different publications.\nResponse: We have listed the objectives in lines 114 “The primary objectives of this study are to:\n1) systematically map the spatial distribution of wetlands across Africa by categorizing them into\nfive distinct types—marsh, mangrove, swamp, peatland, and seasonal wetland—based on\necological and hydrological characteristics, 2) analyze wetland patchiness and assess its\nrelationship with population density, testing the hypothesis that wetlands in highly populated areas\nare more fragmented, 3) estimate the total carbon stocks for each wetland type and calculate\npotential carbon emissions under pristine and drained conditions across various climate zones”.\n2) The introduction currently has some details on the methods which might not be necessary in the\nintroduction (rather in the method section) (66 ff and 74 ff). On the other hand, the text should be\nclearer on the definitions of wetland fragmentation and wetland degradation concerning the\ndifferences and overlaps between these terms / concepts.\nResponse: We have clarified the definition of wetland degradation and fragmentation in lines 102\n“Wetland degradation refers to a decline in the ecological integrity and functionality of wetlands,\ncharacterized by reduced biodiversity, compromised water quality, and diminished carbon\nsequestration potential. It occurs due to drivers such as pollution, excessive water extraction,\nencroachment by invasive species, and the impacts of climate change. Unlike fragmentation, which\ninvolves the physical separation of wetland areas into smaller patches, degradation refers to the\ndeterioration of the ecosystem’s health and its ability to provide essential services, irrespective of\nspatial continuity”.\n3) Wetland fragmentation, as the authors state themselves with reference to the Congo Basin, is\nnot necessarily caused by human activity, but can be simply attributed to topography, even in\ndensely populated areas. Wetland degradation on the other hand, does not necessarily mean\nwetland fragmentation. Wetlands can be intensively used for (dry season) cropping, which can\nstrongly reduce their ecosystem service provision and functions, but still be a wetland. Wetland\nfragmentation as a process would imply to me a loss of the wetland status, by altering the\nhydrology (drainage, dams, etc.).\nResponse: We have clarified this in lines 97, “Wetlands can become divided or separated into\nsmaller, isolated patches or fragments due to both human activities and natural processes, including\nurbanization, agriculture, infrastructure development, and changes in hydrology. Wetland\nfragmentation poses a serious threat to the health and functionality of wetland ecosystems,\nhighlighting the need for conservation efforts focused on preserving and restoring these valuable\nhabitats. Wetland degradation refers to a decline in the ecological integrity and functionality of\nwetlands, characterized by reduced biodiversity, compromised water quality, and diminished\ncarbon sequestration potential. It occurs due to drivers such as pollution, excessive water\nextraction, encroachment by invasive species, and the impacts of climate change. Unlike\nfragmentation, which involves the physical separation of wetland areas into smaller patches,\ndegradation focuses on the deterioration of the ecosystem’s health and its ability to provide\nessential services, irrespective of spatial continuity”.\n4) A better definition / explanation of the wetland types would also be helpful. While it is included\nin the supplementary material, it would also be helpful to get more information in the text\nResponse: We have included a better definition / explanation of the wetland types in lines 40,\n“Wetlands are dynamic ecosystems that can be categorized based on their hydrology, soil\ncomposition, and vegetation types, each supporting unique ecological functions and biodiversity.\nMarshes, for instance, are wetlands dominated by herbaceous (non-woody) plants, characterized\nby periodic or continuous flooding. They can be found in both freshwater and saline environments,\nwhere nutrient-rich soils foster diverse flora and fauna. Swamps are wetlands with mineral soils\n(although some classifications also distinguish organic soil peat swamps), dominated by woody\nvegetation such as trees and shrubs. These ecosystems experience seasonal or permanent flooding\nand include coastal mangrove swamps, which are crucial for coastal protection, carbon\nsequestration, and biodiversity conservation.\nPeatlands represent a distinct wetland category, defined by the accumulation of partially\ndecomposed organic matter (peat) due to water saturation. Peatlands are further classified into\nbogs and fens. Bogs are rain-fed (ombrotrophic) systems that are typically acidic and nutrient-\npoor, often supporting mosses, shrubs, and sometimes trees. Fens, by contrast, are groundwater-\nfed (minerotrophic) and more nutrient-rich, allowing for a mix of grasses, sedges, and woody\nvegetation. Seasonal wetlands, another important type, experience periodic inundation during\nspecific times of the year, followed by dry conditions. These include natural systems like\nephemeral ponds and human-made systems such as rice paddies, which harbour species adapted\nto fluctuating water levels”.\n5) The aspect of land use does not play a big role in the manuscript, even though many wetlands\nare agriculturally used, and agriculture is described as a main driver for wetland degradation. The\nmethod section on Carbon loss estimations speaks of land use categories (line 447) that are not\nmentioned elsewhere in the document. If these categories have been assessed, it would be valuable\nto indicate their extent / proportions in the result section, as well as to explain which land use\nclasses were used at all in the methods. If land use was not assessed in the work, i recommend this\nto be stated as a limitation in the discussion.\nResponse: We have added this as limitation in line 328 “Although land use was indirectly\nconsidered in the carbon loss estimations, a comprehensive evaluation of its specific impacts was\noutside the scope of this study. Future investigations should integrate detailed land use data to\nbetter understand and quantify its contribution to wetland fragmentation and degradation”.\nSpecific comments / suggestions:\n6) Line 32: “some of” not necessary\nResponse: We have removed the words “some of”.\n7) Line 34: “endemic” not necessary here, many wetland species are cosmopolitan or\npantropic species\nResponse: We have addressed this by removing the word “endemic”.\n8) Line 43: what kind of development activities? Aren’t they included in the previously\nmentioned pressures?\nResponse: We have removed the “development activities” as the directly or indirectly included in\npreviously mentioned pressures.\n9) Line 54: significant in which sense?\nResponse: We refer to the importance of cumulative coverage of small wetlands to diverse\necological, climatic, and hydrological functions. We have added more text to clarify this in current\nline 86 “It is therefore not known whether the cumulative coverage of small wetlands is important\nto diverse ecological, climatic, and hydrological functions and there is a need to ensure appropriate\nrepresentation of African wetlands for sustainable management and for modelling climate\nmitigation and biogeochemical cycles”.\n10) Line 63: this sentence should be rephrased.\nResponse: The sentence has been rephrased in lines 96 “Wetland fragmentation poses a serious\nthreat to the health and functionality of wetland ecosystems, highlighting the need for conservation\nefforts focused on preserving and restoring these valuable habitats”.\n11) Line 95 ff: The numbers and percentages do not seem to match, or it is unclear to me, what\nthe reference is. 436.000 km2 are more then 33% of 947.000 km².\nResponse: We have corrected this error in lines 145 “Marshes and swamps are the most dominant\nwetland covering 436,743 km2 (46% of total wetlands) and 231,776 km2 (24%) respectively.\nPeatlands cover 208,842 km2 (22%), while seasonal wetlands (5%) and mangroves (3%) have the\nleast coverage”.\n12) Line 116ff: The classification is based on Köppen-Geiger, according to the reference. This\nshould be mentioned in the text.\nResponse: We have added this in the text at lines 168.\n13) Line 127: it should be TWD instead of TW. Again, what does significant mean here?\nHigher? Or is it a result of a statistical test?\nResponse: We have corrected the typo from “TW” to “TWD” in current line 182. Significant has\nbeen replaced with Higher for clarification “Thus, TWD has a higher amount of seasonal wetland\ncover relative to other climate zones (Figure 2b)”.\n14) Figure 2 b): in greyscale printing, only 4 classes are visible. The colors / shadings should\nbe adapted.\nResponse: The journal publish all materials in full colour so we have not modified the colours.\n15) Line 170 ff: this is an important aspect. Please also mention it in the discussion.\nResponse: We have added this in lines 295 “It should be noted that extensive wetland regions with\na high density of fragments, such as those in the Congo Basin, generally show minimal correlation\nbetween fragmentation and human populations, especially in areas distant from settlements or with\nsparse human activity. The fragmentation in these wetlands is largely attributed to\ngeomorphological and climatological processes rather than anthropogenic influences. However,\nthese peatlands remain susceptible to potential future fragmentation driven by human activities,\nemphasizing the need for vigilant conservation and management strategies”.\n16) Line 178: How specific are these values? Are they specific for climatic zones in Africa?\nPlease be clearer about this.\nResponse: The values are according to climate zones mentioned in current line 237.\n17) Table 1: Please add a reference year for the population.\nResponse: The reference year for the population has been added to Table 1\n18) Line 209: it should be WFPI\nResponse: The typo “WPFI” has been corrected to WFPI in current line 259.\n19) Line 232ff. This should be part of the results as well.\nResponse: The sentence in line 232 has been moved to result section in line 162.\n20) Line 306: What is the purpose to get the population data for all these years?\nResponse: We use the population for 2020. We added text to clarify that in line 378.\n21) Line 311ff: according to line 116, the classification is only climate based, here it includes\necological aspects. Please clarify.\nResponse: Climate zones in Africa differ significantly in both climate and ecological features,\nresulting in diverse ecosystems across the continent.\n22) Line 326: Introduce RF as an abbreviation here.\nResponse: We have added the abbreviation of RF in current line 399\n23) Line 328: I wonder if these details are relevant for this publication, a reference for the\nmethod might justify shortening this paragraph.\nResponse: We have added references to the RF method to shorten this paragraph in current line\n405.\n24) Line 353: Abbreviation TWD missing.\nResponse: We have added the abbreviation of TWD at current line 383\n25) Table 2: Why was a mean value used in TWD? Shouldn’t there be season specific values?\nResponse: The reason we use a mean value to construct image composites for the TWD was to\nensure that the analysis is not biased by short-term fluctuations or extreme seasonal variations,\nproviding a stable representation of wetland conditions over time. Furthermore, there is presence\nof high cloud cover which limits the availability of season-specific images, making a mean\ncomposite the most practical alternative."}]

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