This study aimed to investigate the seasonal variation of zooplankton diversity and abundance in the Nokoué Lagoon in southern Benin. Through extensive sampling, a total of 109 zooplanktonic taxa were identified and quantified. The average zooplankton abundance was found to be 60 individuals per liter, with copepods and rotifers being the most dominant groups, comprising 68.1% and 29.1% of the total abundance, respectively. The key factor identified as driving the structure of the zooplanktonic assemblages was salinity, which showed significant seasonal variation. The results revealed that during the high water period, when the lagoon was filled with fresh water, rotifers were dominant, zooplanktonic diversity was highest, and abundances were quite high. Conversely, during the low water period, when the lagoon was characterized by brackish water, diversity was minimal, and abundance decreased slightly. The study also found that some areas of the lagoon showed high abundances independent of salinity levels, suggesting that other factors such as riverine inputs or the presence of acadjas (home-made brush parks used as fish traps) may also have notable effects on the zooplankton community. Overall, the findings of this study provide valuable insights into the functioning of one of the most biologically productive lagoons in West Africa.

This study investigates the effects of tidal cycles on the zooplankton community within the Cotonou Channel, an important waterway connecting the large Nokoué Lagoon to the Atlantic Ocean in Benin. From the determination of zooplankton composition from 25-hour samples collected in July 2020, alpha diversity indices and abundance were assessed, while relationships between biotic and abiotic parameters were analyzed through Pearson correlation, analysis of variance, and principal component analysis. A total of 66 zooplankton taxa were identified, with rotifers exhibiting the highest species richness (35 taxa), while copepods dominated in abundance (71%). Zooplankton abundance varied significantly, ranging from 2 to 95 ind L⁻¹ depending on the tidal phase. A negative correlation was found between species richness (r = −0.51, p < 0.01) and increasing salinity (3–37), indicating that higher salinity reduced diversity (r = 0.06, p > 0.05). Resilient species like Synchaeta bicornis persisted despite salinity changes. The tidal cycle structurally altered the zooplankton community, with abundance and diversity peaking at different phases, notably higher at high tide (15 ind L⁻¹.) These initial findings underscore the complex interactions between tidal dynamics and estuarine biodiversity, suggesting the need for further research across different tidal and seasonal conditions to inform effective management and conservation efforts.

Global climate change significantly alters environmental and health dynamics, posing a major 21st-century public health threat. Diseases commonly transmitted through water and/or food (diarrhea, cholera, dysentery) are particularly sensitive to these changes. While numerous studies have investigated the climate impacts on waterborne diseases, a critical assessment of the applied methodologies and the consistencies of their outcomes is lacking. This systematic review synthesizes the impact of climate change on waterborne diseases, focusing on analytical methods and predictive models used to explain disease presence or abundance. We further evaluate the strengths and limitations of each approach, identifying key gaps for future research. We analyzed 78 indexed research articles published between 1996 and 2023. Our findings demonstrate that diverse climate-related extreme events, including precipitation, flooding, and drought, substantially impact waterborne disease incidence. Additionally, temperature, salinity, humidity, sunshine duration, wind patterns, and specific water quality parameters play significant roles, with regional variations observed. Sensitivity analysis methods and mathematical models are employed to assess sensitivities, quantify correlations, examine impacts, and predict risks. Based on these findings, we urge future research to prioritize elucidating mechanisms and developing models that leverage climate data to predict disease presence or abundance accurately and quantitatively.