Journal of Experimental Agriculture International

Aims: This study aimed to identify the physical and environmental determinants of the spatial distribution of Naja melanoleuca in southern Bénin and to evaluate how this spatial structure can inform sustainable aquaculture development. Specifically, the study examined whether cobra distribution patterns and hotspot clusters correlate with climatic variables and whether predictive mapping can support risk-based aquaculture planning.

Study Design: A cross-sectional ecological study integrating spatial statistics, environmental modeling, and geospatial prediction.

Place and Duration of Study: The research was conducted across 39 communes of southern Bénin between January and December 2024. Spatial analyses and modeling were performed at the Laboratory of Animals and Fisheries Sciences (LaSAH), National University of Agriculture, Bénin.

Methodology: Field records of N. melanoleuca encounters were compiled and georeferenced. Spatial structure was assessed using Global Moran’s I and the Getis-Ord Gi* statistic. Climatic variables (rainfall, vapour pressure, visibility, wind speed) were extracted from national datasets and integrated into a multiple regression model. Residual diagnostics (Shapiro–Wilk W = .98, p = .73) validated model assumptions. The final regression equation was projected in a GIS to generate a predictive abundance map. Model accuracy was evaluated by correlating predictions with independent field observations.

Results: Global Moran’s I showed spatial independence (I = .01, p > .05), but local Gi* analysis revealed significant clusters ranging from −2 to 1.95, with higher Z-scores concentrated in the southeast. A directional gradient was observed from northwest to southeast, matching climatic gradients. The predictive model explained a significant portion of spatial variability (Adjusted R² = 0.78) and correlated strongly with independent observations (r = .77). Predicted abundance declined from the southeast toward the northwest, identifying the southeastern region as a high-risk zone for cobra predation in aquaculture. Rainfall was identified as the primary environmental determinant, showing a significant negative relationship with cobra presence.

Conclusion: Cobra distribution in southern Bénin is not random but environmentally structured. Predictive mapping offers a practical tool for minimizing predation risks, guiding site selection, and strengthening sustainable aquaculture development.