Cotonou city and studied neighborhoods
Cotonou is located in the southern part of the Republic of Benin, between 6°20' and 6°24' North latitude and 2°20' and 2°30' East longitude. Before the colonial period, the current Cotonou area was probably home to a few isolated villages of Toffinou fishermen (Ciavollela and Choplin 2018). Its location, favorable to the slave trade and then to the maritime trade of various commodities, is at the origin of the first European settlements (Ciavollela and Choplin 2018). Its territory was ceded to France in 1868 (Brasseur-Marion 1953; Houngnikpo and Decalo 2013) and the construction of a wharf as early as 1900 in order to facilitate the transboarding of merchandises and people fueled the intensification of its role as an exchange hub (Janin 1964) and the development of the associated urban space. Though initially slow, Cotonou growth increased following the extension of the wharf in 1952 and the commissioning of a deep-water seaport in 1960 (Ciavolella and Choplin 2018). From there, its demography rapidly accelerated to reach 57,000 inhabitants in 1980, i.e. three times more than in 1945 (Capo 2008). Between 2002 and 2020, it grew from 665,000 to 1,190,000 inhabitants (INSAE 2018).
The city is compressed between Lake Nokoué at North, and by the Atlantic Ocean at South, thus undergoing spatial and demographic expansion to the East (towards Sémè-Kpodji, Porto Novo then Nigeria), to the West (towards Godomey, Cococodji, Cocotomey, Ouidah then Togo) and to the North on the West bank of Lake Nokoué (Abomey-Calavi). The sprawl of Cotonou thus participates to the formation of a wide urban area that occupies most of coastal Benin (INSAE 2017) and is part of the even larger, coastal so-called "Abidjan-Lagos corridor" that is in the process of giving rise to one of the largest African megacities expected to house up to 40 million inhabitants by 2050 (URBACOT 2017; Sun et al. 2020a). The core city has essentially sprawled into a low level sandy plain associated with a dense hydrographic network. Together with 1,200 mm of average annual rainfall, this results in repeated and sometimes long-lasting flooding events during most rainy seasons (Okou 1989).
In 2017, three model neighborhoods in Cotonou were investigated (Supplementary Information 1) as part of a broader program on small mammal-associated (essentially rodent-associated) zoonotic risks in urban and peri-urban settings of southern Benin. They were selected from previously established socioeconomic typologies (Dansou 2006) as well as our own observations to be representative of major neighborhoods profiles of the city. In Cotonou, flooding is an aggravating factor of households’ precariousness and poor socio-environmental condition. Each year, they regularly force the poorest inhabitants to abandon their homes for several days when not weeks (PCUG3C 2012). The three neighborhoods studied here are also representative of the major flooding regimes that characterize the different areas of Cotonou.
Agla is a neighborhood in the process of rapid human densification and formalization. Some areas lack basic services (e.g. access to drinking water and electricity supply) and are home to economically poor households who inhabit very precarious housing structures. These zones correspond to large shallows that are humid all year round, but which sometimes overflow, from the beginning of the main rainy season onwards, due to the accumulation of rainwater. (NB: Agla has been the subject of many infrastructure developments since our work).
Ladji is a poor and densely populated informal settlement bordering and extending over Lake Nokoué (i.e., on-stilt cabans). Formal basic services are rare and housing range from hard-built to very precarious housing. Like almost all of the neighborhoods bordering the lake, it floods partially by overflowing of the lake several weeks/months a year, especially at the end of the rainy season. Waste produced throughout the Cotonou city is partly dumped in vast unmanaged dumpsites, or sometimes used to fill-in the shallows and banks of the lake.
Saint-Jean is an old colonial-type neighborhood, formally subdivided and equipped with drinking water and electricity networks; however, hygiene conditions remain poor in many homes. In this neighborhood, rainwater accumulates in large puddles that sometimes persist during a few days, but the area does not undergo lasting floods per se.
Mapping the landscape: land covers and associated social uses
Land covers were defined a priori on the basis of preliminary prospections in the field and our own knowledge of Cotonou (Table 1; Supplementary Information 1). Spaces constituted of a unique continuous land cover were considered as homogeneous landscape units. The collect of landscape units were performed by foot, alley by alley, over a surface area of 529,931 m2 in Agla, 223,873 m2 in Ladji and 256,030 m2 in Saint-Jean. To do so, we used an Android smartphone on which tools developed within the Open Street Map community (OSM, i.e. a collaborative project aiming to provide free open access mapping data) were installed: the KoBoCollect APK v1.23.3k suite of the KoBoToolbox (Harvard Humanitarian Initiative), ODK Collect 1.7.1 and OSM Tracker 0.6.11.
Table 1
Land covers that were collected, with their abbreviations in brackets
Land covers
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Hard-built houses (Hard): closed enclosure buildings, made of permanent material (i.e. breeze blocks) and often cement / breeze block roofs.
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Precarious houses (Preca): closed enclosure made of precarious material (raffia, bamboo, steel sheet, plastic).
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Precarious spaces and roofed (Psr): spaces covered by steel sheets or raffia, but open on at least two sides.
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Bush (Bush): dense, bushy vegetation cover.
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Grassy-cover (Gras): herbaceous vegetation cover.
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Sanitation (San): modern or traditional sanitation infrastructure (sewers, water-collector, culverts).
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Wild dumps (Wdum): informal dumping sites.
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Lake Nokoué (Nok): lake.
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Cemented soils (Csoil): asphalt roads, paved roads, cemented terraces.
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Bare soil (Bsoil): bare soils outside the buildings.
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In addition to land uses, nine social uses associated with buildings were defined from field observations and further categorized according to the presence or absence of food stocks and/or foodstuffs, the presence or absence of humans at night as well as the presence of traded or manufactured non-food products (Table 2). During the mapping process in the field, each building (i.e., both hard-built and precarious houses) was systematically characterized for its social uses. Exceptionally, some buildings were characterized by two or more social uses (up to four uses for a single building), thus representing 7.4%, 3.3% and 11.6% of buildings of Agla, Ladji and Saint-Jean, respectively. In such cases, we relied on an a priori presumed attractiveness of social uses for small mammals (the focus of the wider program), particularly rodents, in order to retain only the most pertinent one. This led us to consider the following hierarchy, by decreasing attractiveness for rodents: presence of grain and other foodstocks = food shops > night dwelling > restaurant = bar > artisanal craft > non-food shops > services and offices. As an example, a building showing both the “dwelling” and “presence of grain and other foodstocks” social uses was finally associated with “presence of grain and other foodstocks”. In doing so, we were able to assign a unique social use to each building.
Table 2
Social uses associated with buildings, with their abbreviations in brackets. See text for details
Social uses associated with buildings
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Services and office (Serv): buildings that do not contain food storage, are not shops neither occupied at night (e.g. offices, schools, religious buildings).
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Dwelling (Dwel): buildings that are occupied at night (e.g. houses, hotels).
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Food storage and cooking space (FsCs): food storage, cooking and/or processing spaces, non inhabited at night (e.g. fish roasting sites, doughnut cooking, restaurants, food shops, butcher shops, grain and condiment mills, street kiosks/cafeterias, bars).
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Manufacturing space (Manu): spaces dedicated for trade, manufacturing or storage of non-food materials (e.g. craft mechanical workshops and woodworking shops), hardware stores, printing works, sawmill).
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Shop (Shop): non-food items store (ex. clothes, shoes, bags, thrift store).
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Breeding-pens (Bpen): livestock infrastructures (livestock pens, hen houses).
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Uninhabited (Uhab): uninhabited, neglected and/or empty buildings (i.e., houses under construction, abandoned houses).
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Toilet (Toil): hard-built permanent buildings used as a public toilet.
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Cars station (CarS): bus station, parking lots.
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All land cover and associated social uses were digitized on a 2016 Spot 7 (multispectral) satellite image base, 6m resolution. UTM projection to WGS 84 was implemented in QGIS v3.16.2 software. This digitizing process resulted in shapefiles (Supplementary Information 1) which were then converted to 0.5 m resolution raster files (Supplementary Information 2) where each pixel is characterized by a specific value corresponding to landscape unit.
Computation of landscape metrics
Rasters were processed under R (R Core Team 2020) in order to allow for the extraction of local mini-landscapes following a moving window strategy (McGarigal et al. 2012). Mini-landscapes were represented by circular buffers which were preferred to square ones due to equidistance between the center and all edge points. In Cotonou, mapping of the above-mentioned landscape units was carried out on a perimeter covering an area whose edges were at least 100 meters away from the sampling sites (the most distant) – the latter being selected as part of a broader program on zoonotic risks in urban and peri-urban areas in southern Benin. On a square grid of 5 m mesh size, a series of 30m radius buffers (mini-landscapes) were extracted from 20,398 points in Agla, 8,213 points in Ladji and 9,766 points in Saint-Jean. In order to avoid edge effects (i.e., the influence of “incompleteness” of edge-located buffers on the metrics calculation), all buffers that were crossed by the edges of the neighborhoods right-of-way were removed.
Landscape metrics (Table 3) were computed using the R package ''landscapemetrics'' (Hesselbarth et al. 2019) on all individual buffers for the 10 landscape units and the 9 social uses associated with buildings described here above. The class-level landscape proportion (PLAND) was calculated for each landscape unit in order to quantify the composition of the landscape through the proportion of area covered by each land cover type. The Modified Simpson’s Evenness Index (MSIEI) and the Edge Density Index (ED) were measured for both land covers and social uses in order to obtain synthetic indices of landscape diversity and complexity, respectively, thus reflecting of the intermingling of the different patches (Gerbeaud Maulin and Long 2008). Altogether, the landscape was thus described by 23 quantitative metrics.
Table 3
Landscape metrics used to describe the urban neighborhoods landscape within Cotonou (adapted from McGarigal et al., 2012). Level indicates if the metrics are computed for land-cover classes or for landscape taken as a whole. See text for details
Names (acronyms)
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Levels
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Descriptions
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Units
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Extents
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Percentage of landscape (PLAND)
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Class
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Measures the proportional
abundance of each landscape unit type in the landscape: landscape composition
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Percentages
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0 < PLAND ≤ 100
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Modified Simpson’s Evenness Index (MSIEI)
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Landscape
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Measures the level of diversity achieved in
the landscape
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None
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0 < MSIEI ≤ 1
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Edge density (ED)
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Landscape
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Measures the length of the contours of all landscape
units per unit area
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Meters per hectare
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ED ≥ 0, with no upper limit
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Principal Component Analysis (PCA) of landscape metrics
The Principal Component Analysis allows one to explore the relationships between a large set of descriptors (here, the metrics used as variables) as well as to identify and quantify the sources of landscape variability (see Rossi and Dobigny 2019, for an urban example).
A first PCA analysis (PCA-Cot) was conducted at the scale of the Cotonou city by pooling the three studied neighborhoods, namely Agla, Ladji and Saint-Jean datasets. Three other analyses were conducted at the neighborhood scale, i.e. on each neighborhood considered independently (PCA-Agl for Agla; PCA-Lad for Ladji; and PCA-Jea for Saint-Jean).
Mapping of scores on principal components
The coordinates (scores) of each mini-landscape along the principal components (PC) were used to map the landscape characteristics as identified by the PCA (Supplementary Information 2). To do this, each pixel of the map was associated with its score along one of the different PCs. A colored gradient was used to reflect the range of scores, and allowed us to visualize the spatial variability of the PCA scores across the urban space (Rossi and Dobigny 2019). Such a spatial projection of the PCA scores was used to visualize the landscape variability within each of the three neighborhoods, either when analyzed together (PCA-Cot) or separately (PCA-Agl, PCA-Lad and PCA-Jea).