Background: A fundamental understanding of the spatial distribution and ecology of mosquito larvae is essential for effective vector control intervention strategies. In this study, data-driven decision tree models, generalized linear models and ordination analysis were used to identify the most important biotic and abiotic factors that affect the occurrence and abundance of mosquito larvae in Southwest Ethiopia. Methods: In total, 220 samples were taken at 180 sampling locations during the years 2010 and 2012. Sampling sites were characterized based on physical, chemical and biological attributes. The predictive performance of decision tree models was evaluated based on correctly classified instances (CCI), Cohen’s kappa statistic (κ) and the determination coefficient (R²). A conditional analysis was performed on the regression tree models to test the relation between key environmental and biological parameters and the abundance of mosquito larvae. Results: The decision tree model developed for anopheline larvae showed a good model performance (CCI = 84 ± 2%, and κ = 0.66 ± 0.04), indicating that the genus has clear habitat requirements. Anopheline mosquito larvae showed a widespread distribution and especially occurred in small human-made aquatic habitats. Water temperature, canopy cover, emergent vegetation cover, and presence of predators and competitors were found to be the main variables determining the abundance and distribution of anopheline larvae. In contrast, anopheline mosquito larvae were found to be less prominently present in permanent larval habitats. This could be attributed to the high abundance and diversity of natural predators and competitors suppressing the mosquito population densities. Conclusions: The findings of this study suggest that targeting smaller human-made aquatic habitats could result in effective larval control of anopheline mosquitoes in the study area. Controlling the occurrence of mosquito larvae via drainage of permanent wetlands may not be a good management strategy as it negatively affects the occurrence and abundance of mosquito predators and competitors and promotes an increase in anopheline population densities.

The combination of rapid urbanization, industrialization, population growth, and low environmental awareness poses a major threat to worldwide valuable freshwater resources, which provide important ecosystem services to humans. There is an urgent need to monitor and assess these resources, as this information is indispensable for sustainable decision-making and management. In this context, we analyzed the chemical and ecological water quality of the riverine environment of a fast growing city in Southwest Ethiopia for which we proposed possible remediation options that were evaluated with an empirical model. The chemical and ecological water quality was assessed at 53 sampling locations using the oxygen Prati index and the ETHbios, which is a biotic index based on macroinvertebrates. In addition, a microbiological analysis was performed to estimate the degree of fecal contamination. Finally, we analyzed the relationship between the oxygen content and the organic pollution to simulate the effect of organics removal from waste streams on the chemical water quality. Our results showed that the average values for dissolved oxygen (4.2 mg DO·L⁻¹) and nutrients (0.9 mg oPO₄³⁻·L⁻¹ and 12.8 mg TAN·L⁻¹) exceeded international standards. Moreover, high turbidity levels revealed that land erosion is a severe problem in the region. Along the rivers, a significant increase in oxygen consumption and in nutrient concentrations was observed, indicating organic pollution originating from different diffuse and point sources of pollution. The lack of proper sanitation also led to exceedingly high abundances of fecal coliforms in the surface water (>320 MPN·mL⁻¹). However, fecal contamination was strongly reduced (>92%) after the polluted river water passed Boye wetland, indicating the purification potential of natural wetlands and the importance of conserving and protecting those ecosystems. The simulation results of the model showed that water quality could be substantially improved if municipal, industrial, and institutional wastewater was efficiently collected and transported to a treatment facility. Waste stabilization ponds and constructed wetlands are highly promising techniques, as they provide a cheap, effective, reliable, and sustainable way to purify wastewater. It is advised that the environmental awareness of the people via sensitization, education, and law enforcement is increased, as this is essential for sustainable development.

Patches of riparian woody vegetation potentially help mitigate environmental impacts of agriculture and safeguard biodiversity. We investigated the effects of riparian forest on invertebrate diversity in coupled stream-riparian networks using a case study in the Zwalm river basin (Flanders, Belgium). Agriculture is one of the main pressures in the basin and riparian forest is limited to a number of isolated patches. Our 32 study sites comprised nine unshaded “unbuffered” sites which were paired with nine shaded “buffered” sites on the same stream reach, along with five ‘least-disturbed’ sites and nine downstream sites. We sampled water chemistry, habitat characteristics and stream and riparian invertebrates (carabid beetles and spiders) at each site. Three methods were used to quantify riparian attributes at different spatial scales: a visually-assessed qualitative index, quantitative estimates of habitat categories in six rectangular plots (10 × 5 m) and geographic information system (GIS)-derived land cover data. We investigated relationships between invertebrates and riparian attributes at different scales with linear regression and redundancy analyses. Spiders and carabids were most associated with local riparian attributes. In contrast, aquatic macroinvertebrates were strongly influenced by the extent of riparian vegetation in a riparian band upstream (100–300 m). These findings demonstrate the value of quantifying GIS-based metrics of riparian cover over larger spatial scales into assessments of the efficacy of riparian management as a complement to more detailed local scale riparian assessments in situ. Our findings highlight the value of even small patches of riparian vegetation in an otherwise extensively disturbed landscape in supporting biodiversity of both terrestrial and freshwater invertebrates and emphasize the need to consider multiple spatial scales in riparian management strategies which aim to mitigate human impacts on biodiversity in stream-riparian networks.

We critically analyzed a set of ecological models that are used to assess the impact of hydropower dams on water quality and habitat suitability for biological communities. After a literature search, we developed an integrated conceptual model that illustrates the linkages between the main input variables, model approaches, the output variables and biotic-abiotic interactions in the ecosystems related to hydropower dams. We found that variations in water flow and water depth coupled with increased nutrient availability are major variables that contribute to structural and functional ecosystem changes. We also found that ecological models are an important tool to assess the impact of hydropower dams. For instance, model simulation of different scenarios (e.g., with and without the dam, different operation methods) can analyze and predict the related ecosystem shifts. However, one of the remaining shortcomings of these models is the limited capacity to separate dam-related impacts from other anthropogenic influences (e.g., agriculture, urbanization). Moreover, collecting sufficient high-quality data to increase the statistical power remains a challenge. The severely altered conditions (e.g., generation of very deep lakes) also lead to difficulties for standardized data collection. We see future opportunities in the integration of models to improve the understanding of the different processes affected by hydropower dam development and operation, as well as the use of remote sensing methods for data collection.