Biodiversity monitoring at large spatial and temporal scales is essential for informing conservation policies. The International Waterbird Census (IWC) is one of the longest-running global citizen science monitoring schemes, providing critical information to several international agreements. However, analyzing IWC count data poses statistical challenges, including zero inflation, overdispersion, spatial autocorrelation, and missing data. While various modeling approaches have been used to estimate waterbird population size and trends, their ability to handle these issues and the implications for trend estimates remain unassessed. Using IWC count data from five species in the East Atlantic Flyway, we compared four modeling approaches: TRIM (TRends and Indices for Monitoring data), LORI (Low-Rank Interactions), and two generalized linear mixed models (GLMMs) with simple or optimized parametrizations. We benchmarked their performance in addressing zero inflation, overdispersion, and spatial autocorrelation across different realistic sampling designs (i.e., alternative dataset configurations). Our results highlight significant limitations in commonly used methods. Simple GLMMs, TRIM, and LORI generally failed to mitigate both zero inflation and overdispersion. In contrast, optimized GLMMs improved model convergence and better addressed these issues by selecting appropriate probability distributions. However, no single distribution performed consistently well across species and sampling designs. Spatial structures were effective in reducing spatial autocorrelation in most cases. We recommend a careful species-specific selection of statistical methods when analyzing count data, as inadequate models may misrepresent population trends and thus misguide conservation efforts. Future research should explore the integration of advanced hierarchical and spatio-temporal models to improve inference from large-scale citizen science datasets.
Artisanal and small-scale gold mining (ASGM) represents a crucial socio-economic activity in many low- and middle-income countries yet poses significant environmental and public health threats due to unregulated practices. This study assessed water contamination and associated health risks in the ASGM areas of Siguiri, Guinea. We collected 36 water samples (12 boreholes, 24 river points) from three major mining sites during dry (April) and rainy (August) seasons. Samples were analyzed for eight heavy metals (mercury, lead, aluminium, cadmium, nickel, manganese, copper, iron) using UV-visible spectrophotometry. Health risk assessment followed USEPA methodology, including hazard identification, exposure, toxicity assessment, and risk characterization. We computed hazard indices and carcinogenic risk for adults and children. Multivariate statistical analyses identified contamination patterns and vulnerable populations. Heavy metal concentrations exceeded WHO guidelines, particularly during rains. River water showed higher contamination than borehole water, Nickel and manganese exceeded limits by 10-20 fold, while mercury and lead surpassed limits by >50-fold at some sites. Water hazard index peaked at 56.3. Non-carcinogenic ingestion risks for children reached alarming values of 476 (dry season) and 409 (rainy season) far exceeding the acceptable risk threshold of 1. Dermal exposure posed negligible risks compared to ingestion (HIderm < 0.01). Carcinogenic risks exceeded EPA thresholds (>10⁻4) for nickel, lead, and cadmium, with children being 4-10 times more vulnerable than adults. Multivariate analysis identified three contamination clusters: "Low Toxicity (Dry Season)," "Moderate Toxicity (Rainy Season)," and "High Toxicity (Oudoula)." Our findings demonstrate significant health risks, particularly for children, necessitating urgent interventions. We recommend installation of low-cost water treatment systems, buffer zones around mining sites, tailings containment structures and phytoremediation using local metal-accumulating plants. These measures align with SDGs 3, 6, and 12, supporting Guinea's national development agenda while protecting vulnerable communities from mining-related health hazards.
Background Water quality degradation in artisanal gold mining areas is a major public health concern because of the co-occurrence of chemical and microbiological contamination. This study assessed the microbiological indicators in borehole and river water from the artisanal mining area of Doko, Siguiri, Guinea. Methods An analytical cross-sectional study was conducted on 36 water samples, including 12 borehole and 24 river samples, collected during the dry and rainy seasons in three localities. Five microbiological indicators were enumerated by colony counting on culture media and expressed as colony-forming units per source-specific volume. Comparisons were performed using Mann-Whitney tests and seasonal analyses stratified by source; associations were assessed using Spearman correlations with Benjamini-Hochberg adjustment, followed by exploratory multivariable linear models for total coliforms, fecal coliforms, and total mesophilic flora. Results River water was significantly more contaminated than borehole water for all five microbiological indicators (all p < 0.001). Total coliforms were non-compliant in 100% of river samples. Turbidity showed the strongest positive associations with microbial contamination (total coliforms: rho = 0.81; total mesophilic flora: rho = 0.75; fecal coliforms: rho = 0.66). In the main exploratory model, river water was associated with approximately 56-fold higher total coliform concentrations than borehole water. Conclusion Water sources in the Siguiri mining area showed substantial microbiological contamination, particularly river water. Turbidity may serve as a simple operational warning indicator but should not replace microbiological testing. These findings support integrated water quality monitoring, point-of-use water treatment and improved sanitation around water sources.
MOHYSE, short for MOd & egrave;le HYdrologique Simplifi & eacute; & agrave; l'Extr & ecirc;me (French for Hydrological model simplified to the extreme), is a straightforward lumped hydrological model initially designed to teach hydrological modelling to undergraduate and graduate students nearly two decades ago. Due to its simplicity and relatively good performance in simulating streamflow across various watersheds, it has become a popular choice for research studies. Although some studies directly use MOHYSE for generating streamflow results, it is primarily utilized in comparative studies and multi-model applications. Given its widespread use within the scientific community, it has become essential to provide a clear reference detailing its structure, which this paper addresses. The paper also presents a comparison of MOHYSE with three others lumped hydrological models using a selection of 3,255 North American watersheds to establish a benchmark for MOHYSE's performance. MOHYSE, abr & eacute;viation de MOd & egrave;le HYdrologique Simplifi & eacute; & agrave; l'Extr & ecirc;me, est un mod & egrave;le hydrologique global simple con & ccedil;u & agrave; l'origine pour enseigner la mod & eacute;lisation hydrologique aux & eacute;tudiants de premier et deuxi & egrave;me cycle il y a pr & egrave;s de deux d & eacute;cennies. En raison de sa simplicit & eacute; et de ses performances relativement bonnes dans la simulation du d & eacute;bit sur diff & eacute;rents bassins versants, il est devenu un choix populaire pour les projets de recherche. Bien que certaines & eacute;tudes utilisent directement MOHYSE pour g & eacute;n & eacute;rer des r & eacute;sultats de d & eacute;bits, il est principalement utilis & eacute; dans des & eacute;tudes comparatives et des applications multi-mod & egrave;les. & Eacute;tant largement utilis & eacute; au sein de la communaut & eacute; scientifique, il est essentiel de fournir une r & eacute;f & eacute;rence claire d & eacute;taillant sa structure, ce que traite cet article. L'article pr & eacute;sente aussi une comparaison de MOHYSE avec trois autres mod & egrave;les hydrologiques globaux en utilisant une s & eacute;lection de 3,255 bassins versants nord-am & eacute;ricains pour & eacute;tablir une r & eacute;f & eacute;rence de performance pour MOHYSE.
In the context of climate change, which is leading to more erratic rainfall, it is crucial to identify solutions that enhance crop resistance to water stress. This study evaluates the impact of mycorrhizal inoculation on the growth and yield of three tomato cultivars: Akikon (V1), Tounvi (V2), and Anita F1 (V3), under water stress conditions. A factorial block design was employed, combining uninoculated (I0) and inoculated (I1) plants with Terea inoculum, and subjected to either conditions without water stress (S0) or conditions with water stress, achieved by watering every 12 days (S1). Various growth, phenology, and yield parameters were measured. The results revealed highly significant differences among the cultivars for most of the measured parameters (p < 0.001). Mycorrhizal inoculation was shown to positively influence plant growth. Additionally, water stress significantly reduced leaf length (p < 0.01) and increased the number of burnt leaves (p < 0.05). Notably, the inoculated cultivars maintained relatively stable yields despite experiencing water stress. Among the tested tomato cultivars, Akikon (V1) exhibited increased plant height (94.89 ± 1.69 cm) and collar diameter (6.37 ± 0.17 cm) following mycorrhizal inoculation, even under stress conditions. The cultivar Tounvi (V2) demonstrated the best vegetative growth response to water deficit. In contrast, Anita F1 (V3) achieved the highest fruit weight yield, with inoculated plants showing a significant increase (1.9 t/ha). In conclusion, mycorrhizal inoculation enhances water stress tolerance and improves tomato yields, with responses varying by cultivar. Among the tested cultivars, Akikon and Tounvi showed improved growth, while Anita F1 excelled in yield and resilience. Thus, mycorrhizal inoculation represents a promising strategy to strengthen agricultural resilience and sustainability in Benin amid climatic challenges.
