“Location matters”: incorporating spatial autocorrelation into modeling the drivers of harmful cyanobacteria in tropical reservoirs
Year: 2025
Type: preprint
Abstract: Spatial autocorrelation (SAC) is a critical yet often overlooked component of ecological analyses, which can significantly influence the accuracy and interpretation of statistical models. Therefore, this study aimed to assess the spatial distribution of cyanobacterial biomass in tropical reservoirs and identify the main environmental predictors associated with this distribution, explicitly incorporating SAC into regression models. Specifically, we investigated whether the inclusion of spatial structure improves model fit, modifies regression coefficient estimates, and alters associated statistical inferences. The analysis was conducted using data from 32 tropical reservoirs in Northeastern Brazil, using non-spatial (Ordinary Least Squares – OLS) and spatial (Spatial Error Model – SEM; Spatial Lag Model – SLM) regression models. SAC was assessed using Moran’s Index and Local Indicators of Spatial Association (LISA). The results showed a significant positive SAC for total cyanobacterial biomass, especially at the watershed scale, although spatial effects were not significant across all basins. “High-High” and “Low-Low” clustering patterns were observed, indicating that geographically close reservoirs share similar environmental and anthropogenic conditions that either favor or inhibit cyanobacterial growth. Furthermore, the manifestation of SAC was species-specific, highlighting that spatial patterns are not generalizable and depend on the ecological and biogeographic context of each species. Predictor variables included rainfall, water volume, water temperature, dissolved oxygen, total phosphorus, ammonia nitrogen, and total alkalinity. The spatial models, especially SEM, outperformed the non-spatial model, with higher R² and lower AIC. They also altered the magnitude of the coefficients and, in some cases, reversed the direction of the predictor effects and increased the number of significant variables. These results reinforce the importance of incorporating SAC into ecological models to improve predictive accuracy and guide management strategies for cyanobacterial blooms in tropical reservoirs.
Author: Juliana Severiano
Institution: Universidade Estadual da Paraíba
Language: English
Subfield: Environmental Chemistry
Field: Environmental Science
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W4413875155