Purpose of Review Biodiversity remains an immense source of benefit to mankind and a major contributor to his well-being. Severe loss of biodiversity necessitated the conservation of a part of the biodiversity hotspots as protected areas. Despite this initiative, a sizable part of the conserved hotspots was still lost to anthropogenic factors among which is urban development. Therefore, we assessed the current status (2014–2021) of the biodiversity hotspots in the African region by estimating landmass loss in \({\mathrm{km}}^{2}\) per biodiversity hotspot, identifying the direct-and-indirect impacts of urban development on biodiversity, while also giving recommendations for further research. Recent Findings Africa has the fifth-highest urban-population presently. Thirteen countries on the continent will become heavily urbanized within the next three decades (2016 to 2050). Perennial urban problems remain a structural constraint to Africa’s urban development; hence, African biodiversity hotspots suffer both direct-and-indirect consequences of urban development. Only 13% (86,859 \({\mathrm{km}}^{2}\)) of the protected areas of the biodiversity hotspots remains from a total of 665,845 \({\mathrm{km}}^{2}\). Furthermore, two out of the eight hotspots in Africa (Eastern Afromontane and Guinean forests of West Africa) are among the three global hotspots predicted to experience intense (about ten times) urban encroachment and possible loss of all their endemic species. Summary We implore future research to focus on regional studies (aside from global studies) because regional studies would provide more in-depth-analysis of the hotspots, provide mapping data and reveal their current status in terms of landmass and the surviving endemic species, among others. Furthermore, the issue of climate change in the urban development biodiversity mix needs more research. Finally, there is an urgent need for sustainability and policy development studies that would advise on the appropriate management modalities for the hotspots in the face of continuous urban development.

Peri-urban land use is diverse and rapidly expanding in sub-Saharan Africa (SSA). It is characterised by haphazard development and unplanned land, which has become a significant global preoccupation due to environmental challenges. The issue of PUAs in sub-Saharan Africa has dramatically concerned many scholars and stakeholders, including city authorities and professionals in urban planning and management. This study evaluates the differences and similarities in the trends and consequences of the spread of SSA peri-urban areas (PUAs), focusing on Nigeria and Ghana. The urban–rural linkages and flows provided the theoretical background for the study. The study solely relied on secondary data through the desktop methodology to obtain specific data on development, sustainable development, peri-urban areas (PUAs), urbanisation, and urban expansion in sub-Saharan Africa, particularly Nigeria and Ghana, from databases such as Google, Google Scholar, ResearchGate, and Mendeley. Other sources consulted intermittently for necessary information were relevant books, conferences, working papers, and reports from global organisations. The study found that the expansion of PUAs in Nigeria and Ghana is not at the same level due to different population sizes. The study also found that the reasons for PUA expansion in Nigeria are not similar to those in Ghana. To enhance sustainable growth, the study recommended that the government or city authorities take over the lands on the urban fringes and design a plan that includes the road networks and other specified purposes for the land. This will guide against the spread of disorganised and unplanned neighbourhoods currently experienced in both countries.

Variety approaches are being used to interpret the concealed variables that determine the variance of observed water quality of various source points. A considerable proportion of these approaches are statistical methods, multivariate statistical techniques in particular. The use of multivariate statistical technique(s) is/are required when the number of variables is large and greater than two for easy and robust evaluation. By means of multivariate statistics of principal components analysis (PCA) and cluster analysis (CA), this study attempted to determine major factors responsible for the variations in the quality of 30 surface ponds used for domestic purposes in six (6) selected communities of Akoko Northeast LGA, Ondo State, Nigeria. The samples’ locations were classified into mutually exclusive unknown groups that share similar characteristics/properties. The laboratory results of 20 parameters comprising 6 physicals, 8 chemicals, 4 heavy metals and 2 microbial from the sampled springs were subjected to PCA and CA for further interpretation. The result shows that 5 components account for 97.52% of total variance of the surface spring quality while 2 cluster groups were identified for the locations. Based on the parameters concentrations and the land uses impacts, it was concluded that domestic and agricultural waste strongly influenced the variation and the quality of ponds in the area. Key words: Multivariate statistics, surface water, quality, interpretation.

Understanding the relative impact of land use, land cover (LULC) and climate change (CC) on basin runoff is necessary in assessing basin water stress, for which long-term observed rainfall time series and LULC spatial data are required. However, there are challenges with the availability of spatio-temporal data, particularly the limited range of available historical hydro-meteorological measurements. The study used long-term (1961–2007) rainfall data to drive the Pitman monthly rainfall–runoff model to assess changes to the water resources of three basins in Nigeria—Asa, Ogun and Owena. Three CGCMs—CSIRO Mark3.5, MIROC3.2-medres and UKMO-HadCM3—dynamically downscaled to a 60 km by 60 km grid using the Conformal-Cubic Atmospheric Model (C-CAM) are used to simulate impacts of future climate changes on water resources. These three models were found suitable for simulating rainfall–runoff based on the insignificant differences of the modelled mean with mean of observed rainfall and temperature for pre-2010 data compared to other downscaled C-CAM models (GFDL-CM2.0, GFDL-CM2.1 and ECHAM5/MPI-Ocean model). The model results show increases in the runoff coefficient with decreases in forest cover between 1981 and 2007, with average runoff coefficients of 5.3%, 12.0% and 6.4% for Asa, Ogun and Owena basins respectively. Based on annual reduction in rainfall trend projected by CSIRO, MIROC and UKMO, the future scenarios revealed a low runoff coefficient for the three basins—Asa (CSIRO 6.0%, MIROC 6.0% and UKMO 5.9%), Ogun (CSIRO14.6%, MIROC 14.6% and UKMO 14.4%) and Owena (CSIRO 8.5%, MIROC 8.7% and UKMO 8.9%). In all scenarios, Asa basin has a lower runoff coefficient when compared to Ogun and Owena basins, indicating that future water stress in Asa basin would be much greater.