Description of the study area
The study covers urban, peri-urban and rural wetlands in the Bamenda Municipality of the North West Region of Cameroon that have evolved concomitantly with different stages of urbanization (Fig. 1). In this study, urbanisation is considered a loose term that is aimed at giving a geographical expression to the variation in the economic, social and cultural practices in the milieu. The central town with many economic activities is termed the urban, the fringe area with sprawls is termed peri-urban while the rural has typical peasant activities and make-shift structures. From the variation of human activities in the three sub-zones, a variety of chemical substances are discharged into drains, playing a substantial role in soil quality, and therefore plant macrophyte diversity. The Plants studied were ubiquitous in the area and verification of their IUCN conservation status in the red data book of plants of Cameroon confirmed their abundance14. Information on protected sites in Cameroon does not place the study area under conservation status. In line with that, permits are not required to undertake academic and research studies as well as do a responsible collection of plants in the study area. The urbanization rate of Bamenda is 42%, and the population grew from 48,111 inhabitants in 1976 to 488,883 inhabitants in 201015, with 150–200 inhabitants/km2.
Relief Map of Bamenda showing the Bamenda escarpment, topography and the location for quadrat sites.
The study area is part of the Bamenda escarpment that is located between latitudes 5° 55″ N and 6° 30″ N and longitudes 10° 25″ E and 10° 67″ E. The town shows an altitudinal range of 1200–1700 m and is divided into two parts by escarpments—a low-lying and gently undulating part with altitudes ranging from 1200 to 1400 m, with many flat areas that are usually inundated for most parts of the year, and an elevated part that range from 1400 to 1700 m altitude. Most of the streams take their rise from this elevated part (Fig. 1).
This area experiences two seasons—a rainy season (mid-March to mid-October) and a short dry season (mid-October to mid-March). The thermic and hyperthermic temperature regimes dominate in the area. The mean annual temperature stands at 19.9 °C. January and February are the hottest months with mean monthly temperatures of 29.1 and 29.7 °C, respectively. This area is dominated by the Ustic and Udic moisture regimes with the Udic extending to the south9. Annual rainfall ranges from 1300 to 3000 mm16. The area has a rich hydrographical network with intense human activities and a dense population along different water courses in the watershed. The area is bounded on the West, North and East by the Cameroon Volcanic Line (made up of basalts, trachytes, rhyolites and numerous salt springs). The geologic history of this area originates from the Precambrian era when there was a vast formation of geosynclinal complexes, which became filled with clay-calcareous, and sandstone sediments9. These materials, crossed by intrusions of crystalline rocks, were folded in a generally NE-SW direction and underwent variable metamorphism9. The Rocks in the area are thus of igneous (granitic and volcanic) and metamorphic (migmatites) origin17, which gives rise to ferralitic soils18.
Agriculture is the principal human activity in and around this region18. The area equally harbours the commercial center that has factories ranging from soap production, and mechanic workshops to metallurgy, which may be potential sources of pollutants that can influence wetland Geochemistry. Raffia farinifera bush, which is largely limited to the wetlands, is an important vegetation type in this area. R. farinifera provides raffia wine, a vital economic resource to the inhabitants who are fighting against the cultivation of these wetlands by vegetable farmers.
Methods of the study
Macrophyte diversity study
The plant diversity of the wetlands was evaluated using quadrats in the dry season for accessibility reasons. For each of the three wetlands (the urban, peri-urban and rural areas), three transects were established on which representative quadrats, each measuring 10 m × 10 m, were mapped out in uncultivated areas for the determination of plant species cover-abundance and diversity. It is perceived that the different zones receive different mixtures of chemical substances and thus influence macrophyte diversity differently.
According to a publication by14 on the vascular plants of Cameroon and a taxonomic checklist with IUCN assessment, the plants of the area are placed under the Least Concern Category(LC), and therefore not in the risky category. Diversity studies involved the identification of a specific area called “relevé” by progressively increasing test quadrat size and sampling for specific diversity until the smallest area with adequate species representation was reached. The relevé size determined here was 1 m2, making a total of 300 sub-quadrats (relevé) in the entire study ie. 100 in each main quadrat). For each site (main quadrat), 10 representative relevés were sampled and all plant species were enumerated. Most plant species in each of them were identified in the field by the Botanist, Dr Ndam Lawrence Monah using visual observation of the morphology of the leaves and flowers, a self-made field guide, the Flora of West Africa and the Flora of Cameroon. 10 unidentified plants were appropriately collected where there were in abundance, placed onto a conventional plant press and dried in the field. Voucher specimens were tagged and transported to the Limbe Botanic Gardens (SCA: Southern Cameroon, the code of the Limbe Botanic Gardens Herbarium) for identification. Mr Elias Ndive, the Taxonomist of the Limbe Botanic Gardens compared unidentified specimens with authentic herbarium specimens and other taxonomic guides and finally identified them. Voucher specimens of the 10 plants were given identification numbers and deposited in the Herbarium of the Limbe Botanic Gardens.
The Braun–Banquet method was used19 for the assessment of species cover abundance. Relative abundance and abundance ratings were determined using the Braun–Banquet rating scheme (Table 1).
Simpson’s diversity index
Species richness was evaluated using Simpson’s diversity index (D), which takes into account both species richness and the Braun-Blanquet rating scheme for vegetation cover abundance and evenness of abundance among the species present. In essence, D measures the probability that two individuals that are randomly selected from an area will belong to the same species. The formula for calculating D is presented as:
$${text{D}} = frac{{sum {{text{n}}_{i} left( {{text{n}}_{i} – 1} right)} }}{{{text{N}}({text{N}} – 1)}}$$
where ni = the total number of each species; N = the total number of individuals of all species.
The value of D ranges from 0 to 1. With this index, 0 represents infinite diversity and 1 represents no diversity. That is, the larger the value the lower the diversity.
Alternatively, Simpson’s Diversity Index, = 1–D,
1-D was used as a measure of diversity because it is more logical and less likely to cause confusion. The scale then gives a score from 0 to 1 with higher scores showing higher diversity (instead of being associated with low scores).
The Simpson index is a dominance index because it gives more weight to common or dominant species. In this case, a few rare species with only a few representatives will not affect the diversity.
Soil sampling and analysis
Soil sampling was done in and around the three quadrats laid in the urban, peri-urban and rural wetlands for macrophytes sampling. Twenty-one (21) composite samples (0–25 cm) were randomly collected (Fig. 2) and taken to the laboratory in black plastic bags. Each composite sample was a collection of 5 dried core soil samples. Due to the observed greater heterogeneity in the urban sector, the sampling density was intensified. The soil samples were air-dried and screened through a 2-mm sieve. They were analyzed in duplicate for their physicochemical properties in the Environmental and Analytical Chemistry Laboratory of the University of Dschang, Cameroon. Particle size distribution, cation exchange capacity (CEC), exchangeable bases, electrical conductivity (EC) and pH were determined by standard procedures20. Soil pH was measured both in water and KCl (1:2.5 soil/water mixture) using a glass electrode pH meter. Part of the soil was ball-milled for organic carbon (Walkley–Black method) and total nitrogen (Macro-Kjeldahl method) as largely described by20. Available phosphorus (P) was determined by Bray I method. Exchangeable cations were extracted using 1 N ammonium acetate at pH 7. Potassium (K) and sodium (Na) in the extract were determined using a flame photometer and magnesium (Mg) and calcium (Ca) were determined by complexiometric titration. Exchange acidity was extracted with 1 M KCl followed by quantification of Al and H by titration20. Effective cation exchange capacity (ECEC) was determined as the sum of bases and exchanged acidity.
Adapted from the 1980 land use map of the Bamenda City Area showing soil sampling points: Source Bamenda City Council.
Map of the study area in freshwater wetlands of Bamenda Municipality.
Apparent CEC (CEC at pH 7) was determined directly as outlined by20. Based on critical values of nutrients established for vegetables, nutrients were declared sufficient or deficient.
Statistical analysis
The data were subjected to statistical analysis using Microsoft Excel 2007 and SPSS statistical package 20.0. Soil properties were assessed for their variability using the coefficient of variation (CV) and compared with variability classes (Table 2).
$$CV=frac{Sd}{X}X 100$$
where: Sd = standard deviation; = X arithmetic mean of soil properties.
The hierarchical cluster analysis (HCA) was used to group the area under managing units. The main goal of the hierarchical agglomerative cluster analysis is to spontaneously classify the data into groups of similarity (clusters). This is done by searching objects in the n-dimensional space that is located in the closest neighborhood and separating a stable cluster from other clusters. The sampling sites were considered objects for classification. Each object was determined by a set of variables (chemical concentrations of the soils in this case).
Source: Ecology - nature.com
