Nitrate and ammonium levels of some water bodies and their interaction with some selected properties of soils in Douala metropolis, Cameroon

The present study examined the nitrate (NO3-) and ammonium (NH4+) levels of Rivers Wouri and Dibamba and some streams that feed them. The interaction of NO3- and NH4+ with some soil properties was also investigated. It was necessitated by the usage of these rivers for livelihood, despite the deposition of discharges into these streams. Twenty eight (28) surface water and four soil samples were collected from various sites within the Douala metropolis. The water was analysed for pH, electrical conductivity (EC), NO3-, NH4+ while the soil was analysed for particle size and cation exchange capacity (CEC). In both cases, standard methods were used. The NO3- and NH4+ levels were higher than the WHO threshold levels in some sites. Nitrate and NH4+ levels of 341.6 and 632.8 mg/l, respectively, were observed in some sites in Dibamba River despite the high level of clay in this area. The values in the Wouri River were low, contrary to the low level of clay in this area. This trend was also reflected in the streams that feed both rivers. The NH4+/NO3- molar ratio was low in areas proxy to the industries reflecting industrial source of  pollution. The continuous use of water from Rivers Wouri and Dibamba for domestic purposes is variably unsafe and needs attention.Key words: Nitrate, ammonium, water bodies/quality, soils, Douala metropolis

Contribution of some water bodies and the role of soils in the physicochemical enrichment of the Douala-Edea mangrove ecosystem

The effect of enrichment of water bodies could be of serious crises to the mangrove ecosystem. Changes in physicochemical properties of some water bodies in the Douala-Edea mangrove ecosystem was investigated alongside the potential role of soils in controlling these parameters. Water and soil samples within the Douala industrial zones were collected in February 2010 and analysed using standard methods. The concentrations of cations and chlorides (Cl-) in the rivers increased from upstream to downstream and with depth. These parameters were not distinct with other anions which showed higher fluctuations around confluences. Many anomalies were obtained in streams and wells at vicinity of the industries. Mean Cl- concentrations in streams and wells around River Wouri (135.1 and 57.9 mg/l, respectively) were higher than those around River Dibamba (59.3 and 38,2 mg/l, respectively). A low retention capacity of the soils was observed by the non significant (P > 0.05) relationship between the clay fraction and cation exchange capacity (CEC). This makes the mangrove  ecosystem vulnerable to the increase nutrient from anthropogenic activities as indicated by the occurrence of Nypa Palms (Nypa fructicans) and Water Hyacinths (Echhornia cassipes). It is therefore imminent that the Douala-Edea Mangrove Ecosystem is being degraded.Key words: Soils, water, physicochemical properties, mangrove ecosystem

The C:N:P:S stoichiometry of soil organic matter

The formation and turnover of soil organic matter (SOM) includes the biogeochemical processing of the macronutrient elements nitrogen (N), phosphorus (P) and sulphur (S), which alters their stoichiometric relationships to carbon (C) and to each other. We sought patterns among soil organic C, N, P and S in data for c. 2000 globally distributed soil samples, covering all soil horizons. For non-peat soils, strong negative correlations (p < 0.001) were found between N:C, P:C and S:C ratios and % organic carbon (OC), showing that SOM of soils with low OC concentrations (high in mineral matter) is rich in N, P and S. The results can be described approximately with a simple mixing model in which nutrient-poor SOM (NPSOM) has N:C, P:C and S:C ratios of 0.039, 0.0011 and 0.0054, while nutrient-rich SOM (NRSOM) has corresponding ratios of 0.12, 0.016 and 0.016, so that P is especially enriched in NRSOM compared to NPSOM. The trends hold across a range of ecosystems, for topsoils, including O horizons, and subsoils, and across different soil classes. The major exception is that tropical soils tend to have low P:C ratios especially at low N:C. We suggest that NRSOM comprises compounds selected by their strong adsorption to mineral matter. The stoichiometric patterns established here offer a new quantitative framework for SOM classification and characterisation, and provide important constraints to dynamic soil and ecosystem models of carbon turnover and nutrient dynamics

Consistent patterns of common species across tropical tree communities

Trees structure the Earth’s most biodiverse ecosystem, tropical forests. The vast number of tree species presents a formidable challenge to understanding these forests, including their response to environmental change, as very little is known about most tropical tree species. A focus on the common species may circumvent this challenge. Here we investigate abundance patterns of common tree species using inventory data on 1,003,805 trees with trunk diameters of at least 10 cm across 1,568 locations1,2,3,4,5,6 in closed-canopy, structurally intact old-growth tropical forests in Africa, Amazonia and Southeast Asia. We estimate that 2.2%, 2.2% and 2.3% of species comprise 50% of the tropical trees in these regions, respectively. Extrapolating across all closed-canopy tropical forests, we estimate that just 1,053 species comprise half of Earth’s 800 billion tropical trees with trunk diameters of at least 10 cm. Despite differing biogeographic, climatic and anthropogenic histories7, we find notably consistent patterns of common species and species abundance distributions across the continents. This suggests that fundamental mechanisms of tree community assembly may apply to all tropical forests. Resampling analyses show that the most common species are likely to belong to a manageable list of known species, enabling targeted efforts to understand their ecology. Although they do not detract from the importance of rare species, our results open new opportunities to understand the world’s most diverse forests, including modelling their response to environmental change, by focusing on the common species that constitute the majority of their trees

Consistent patterns of common species across tropical tree communities

Trees structure the Earth's most biodiverse ecosystem, tropical forests. The vast number of tree species presents a formidable challenge to understanding these forests, including their response to environmental change, as very little is known about most tropical tree species. A focus on the common species may circumvent this challenge. Here we investigate abundance patterns of common tree species using inventory data on 1,003,805 trees with trunk diameters of at least 10 cm across 1,568 locations1-6 in closed-canopy, structurally intact old-growth tropical forests in Africa, Amazonia and Southeast Asia. We estimate that 2.2%, 2.2% and 2.3% of species comprise 50% of the tropical trees in these regions, respectively. Extrapolating across all closed-canopy tropical forests, we estimate that just 1,053 species comprise half of Earth's 800 billion tropical trees with trunk diameters of at least 10 cm. Despite differing biogeographic, climatic and anthropogenic histories7, we find notably consistent patterns of common species and species abundance distributions across the continents. This suggests that fundamental mechanisms of tree community assembly may apply to all tropical forests. Resampling analyses show that the most common species are likely to belong to a manageable list of known species, enabling targeted efforts to understand their ecology. Although they do not detract from the importance of rare species, our results open new opportunities to understand the world's most diverse forests, including modelling their response to environmental change, by focusing on the common species that constitute the majority of their trees

Buttress form of the central African rain forest tree Microberlinia bisulcata, and its possible role in nutrient acquisition

Buttressing is a trait special to tropical trees but explanations for its occurrence remain inconclusive. The two main hypotheses are that they provide structural support and/or promote nutrient acquisition. Studies of the first are common but the second has received much less attention. Architectural measurements were made on adult and juvenile trees of the ectomycorrhizal species Microberlinia bisulcata, in Korup (Cameroon). Buttressing on this species is highly distinctive with strong lateral extension of surface roots of the juveniles leading to a mature buttress system of a shallow spreading form on adults. This contrasts with more vertical buttresses, closer to the stem, found on many other tropical tree species. No clear relationship between main buttress and large branch distribution was found. Whilst this does not argue against the essential structural role of buttresses for these very large tropical trees, the form on M. bisulcata does suggest a likely second role, that of aiding nutrient acquisition. At the Korup site, with its deep sandy soils of very low phosphorus status, and where most nutrient cycling takes place in a thin surface layer of fine roots and mycorrhizas, it appears that buttress form could develop from soil-surface root exploration for nutrients by juvenile trees. It may accordingly allow M. bisulcata to attain the higher greater competitive ability, faster growth rate, and maximum tree size that it does compared with other co-occurring tree species. For sites across the tropics in general, the degree of shallowness and spatial extension of buttresses of the dominant species is hypothesized to increase with decreasing nutrient availability

Predicting alpha diversity of African rain forests: models based on climate and satellite-derived data do not perform better than a purely spatial model

Aim Our aim was to evaluate the extent to which we can predict and map tree alpha diversity across broad spatial scales either by using climate and remote sensing data or by exploiting spatial autocorrelation patterns. Location Tropical rain forest, West Africa and Atlantic Central Africa. Methods Alpha diversity estimates were compiled for trees with diameter at breast height ≥10cm in 573 inventory plots. Linear regression (ordinary least squares, OLS) and random forest (RF) statistical techniques were used to project alpha diversity estimates at unsampled locations using climate data and remote sensing data [Moderate Resolution Imaging Spectroradiometer (MODIS), normalized difference vegetation index (NDVI), Quick Scatterometer (QSCAT), tree cover, elevation]. The prediction reliabilities of OLS and RF models were evaluated using a novel approach and compared to that of a kriging model based on geographic location alone. Results The predictive power of the kriging model was comparable to that of OLS and RF models based on climatic and remote sensing data. The three models provided congruent predictions of alpha diversity in well-sampled areas but not in poorly inventoried locations. The reliability of the predictions of all three models declined markedly with distance from points with inventory data, becoming very low at distances &gt;50km. According to inventory data, Atlantic Central African forests display a higher mean alpha diversity than do West African forests. Main conclusions The lower tree alpha diversity in West Africa than in Atlantic Central Africa may reflect a richer regional species pool in the latter. Our results emphasize and illustrate the need to test model predictions in a spatially explicit manner. Good OLS or RF model predictions from inventory data at short distance largely result from the strong spatial autocorrelation displayed by both the alpha diversity and the predictive variables rather than necessarily from causal relationships. Our results suggest that alpha diversity is driven by history rather than by the contemporary environment. Given the low predictive power of models, we call for a major effort to broaden the geographical extent and intensity of forest assessments to expand our knowledge of African rain forest diversity. © 2011 Blackwell Publishing Ltd

Predicting alpha diversity of African rain forests: models based on climate and satellite-derived data do not perform better than a purely spatial model

Aim: Our aim was to evaluate the extent to which we can predict and map tree alpha diversity across broad spatial scales either by using climate and remote sensing data or by exploiting spatial autocorrelation patterns.Location: Tropical rain forest, West Africa and Atlantic Central Africa.Methods: Alpha diversity estimates were compiled for trees with diameter at breast height ≥ 10 cm in 573 inventory plots. Linear regression (ordinary least squares, OLS) and random forest (RF) statistical techniques were used to project alpha diversity estimates at unsampled locations using climate data and remote sensing data [Moderate Resolution Imaging Spectroradiometer (MODIS), normalized difference vegetation index (NDVI), Quick Scatterometer (QSCAT), tree cover, elevation]. The prediction reliabilities of OLS and RF models were evaluated using a novel approach and compared to that of a kriging model based on geographic location alone.Results: The predictive power of the kriging model was comparable to that of OLS and RF models based on climatic and remote sensing data. The three models provided congruent predictions of alpha diversity in well-sampled areas but not in poorly inventoried locations. The reliability of the predictions of all three models declined markedly with distance from points with inventory data, becoming very low at distances ≥ 50 km. According to inventory data, Atlantic Central African forests display a higher mean alpha diversity than do West African forests.Main conclusions: The lower tree alpha diversity in West Africa than in Atlantic Central Africa may reflect a richer regional species pool in the latter. Our results emphasize and illustrate the need to test model predictions in a spatially explicit manner. Good OLS or RF model predictions from inventory data at short distance largely result from the strong spatial autocorrelation displayed by both the alpha diversity and the predictive variables rather than necessarily from causal relationships. Our results suggest that alpha diversity is driven by history rather than by the contemporary environment. Given the low predictive power of models, we call for a major effort to broaden the geographical extent and intensity of forest assessments to expand our knowledge of African rain forest diversity.</p