Cutrim Marco Valério Jansen, Sá Ana Karoline Duarte Dos Santos, Cruz Quedyane Silva da, Azevedo-Cutrim Andrea Christina Gomes de, Santos Ricardo Luvizotto, Dias Francisco José da Silva, Jorge Marianna Basso, Cavalcanti-Lima Lisana Furtado
Environmental Science and Technology - PPGC&Tamb - Federal University of Maranhão, Cidade Universitária Dom Delgado, Portugueses Road, N°1966, 65080-805, São Luís, Maranhão, Brazil; Water Resources Management and Regulation - ProfÁgua UEMA - State University of Maranhão, Brazil.
Department of Education of Paço do Lumiar, Av. 13, Quadra 145, nº 5, 65130-000, Paço do Lumiar, Maranhão, Brazil.
Environ Pollut. 2025 Mar 1;368:125680. doi: 10.1016/j.envpol.2025.125680. Epub 2025 Jan 10.
Dredging in estuarine systems significantly impacts phytoplankton communities, with suspended particulate matter (SPM) and dissolved aluminum (Al) serving as indicators of disturbance intensity. This study assessed the effects of dredging in the São Marcos Estuarine Complex (SMEC), Brazil, over three distinct events (2015, 2017, 2020), involving varying sediment volumes and climatic influences. Prolonged dredging operations and increased sediment volumes led to a pronounced 43.81% reduction in species diversity, with diatoms and dinoflagellates being the most affected. Climatic variability, particularly El Niño events, exacerbated environmental dispersion, amplifying the complexity of ecosystem responses. Despite these losses, certain centric diatoms persisted, reflecting resilience mechanisms within this tropical macrotidal estuary. Machine learning approaches, specifically Random Forest (RF) models, revealed SPM and dissolved Al as critical stressors influencing species diversity. Additionally, river discharge and salinity were identified as key predictors of phytoplankton biomass. Generalized Additive Models (GAMs) confirmed that chlorophyll-a concentrations responded negatively to elevated SPM and Al levels but were less sensitive to dredging than diversity metrics. This study provides novel insights into the compounded effects of dredging and climatic variability, emphasizing the utility of RF and GAM models for predicting ecosystem responses and guiding management strategies. Recommendations include optimizing operations to reduce biodiversity impacts, minimizing sediment resuspension, and integrating predictive tools to mitigate long-term disturbances. These findings offer a data-driven framework for sustainable dredging in sensitive estuarine ecosystems.
河口系统的疏浚对浮游植物群落有显著影响，悬浮颗粒物（SPM）和溶解态铝（Al）可作为干扰强度的指标。本研究评估了巴西圣马科斯河口复合体（SMEC）在三个不同事件（2015年、2017年、2020年）期间的疏浚影响，这些事件涉及不同的沉积物量和气候影响。长期的疏浚作业和增加的沉积物量导致物种多样性显著降低了43.81%，其中硅藻和甲藻受影响最大。气候变异性，特别是厄尔尼诺事件，加剧了环境扩散，放大了生态系统响应的复杂性。尽管有这些损失，但某些中心硅藻仍然存在，反映了这个热带大潮河口的恢复机制。机器学习方法，特别是随机森林（RF）模型，揭示了SPM和溶解态Al是影响物种多样性的关键压力源。此外，河流流量和盐度被确定为浮游植物生物量的关键预测因子。广义相加模型（GAMs）证实，叶绿素a浓度对升高的SPM和Al水平呈负响应，但比对多样性指标对疏浚的敏感性更低。本研究为疏浚和气候变异性的复合影响提供了新的见解，强调了RF和GAM模型在预测生态系统响应和指导管理策略方面的实用性。建议包括优化作业以减少对生物多样性的影响，尽量减少沉积物再悬浮，并整合预测工具以减轻长期干扰。这些发现为敏感河口生态系统的可持续疏浚提供了一个数据驱动的框架。
