Department of Food Science and Technology, Virginia Tech, Blacksburg, VA 24061, USA.
Department of Food Science and Technology, Virginia Tech, Blacksburg, VA 24061, USA; Department of Biostatistics and Computational Biology, University of Rochester Medical Center, Rochester, NY USA.
J Food Prot. 2023 Mar;86(3):100045. doi: 10.1016/j.jfp.2023.100045. Epub 2023 Jan 24.
Surface water environments are inherently heterogenous, and little is known about variation in microbial water quality between locations. This study sought to understand how microbial water quality differs within and between Virginia ponds. Grab samples were collected twice per week from 30 sampling sites across nine Virginia ponds (n = 600). Samples (100 mL) were enumerated for total coliform (TC) and Escherichia coli (EC) levels, and physicochemical, weather, and environmental data were collected. Bayesian models of coregionalization were used to quantify the variance in TC and EC levels attributable to spatial (e.g., site, pond) versus nonspatial (e.g., date, pH) sources. Mixed-effects Bayesian regressions and conditional inference trees were used to characterize relationships between data and TC or EC levels. Analyses were performed separately for each pond with ≥3 sampling sites (5 intrapond) while one interpond model was developed using data from all sampling sites and all ponds. More variance in TC levels were attributable to spatial opposed to nonspatial sources for the interpond model (variance ratio [VR] = 1.55) while intrapond models were pond dependent (VR: 0.65-18.89). For EC levels, more variance was attributable to spatial sources in the interpond model (VR = 1.62), compared to all intrapond models (VR < 1.0) suggesting that more variance is attributable to nonspatial factors within individual ponds and spatial factors when multiple ponds are considered. Within each pond, TC and EC levels were spatially independent for sites 56-87 m apart, indicating that different sites within the same pond represent different water quality for risk management. Rainfall was positively and pH negatively associated with TC and EC levels in both inter- and intrapond models. For all other factors, the direction and strength of associations varied. Factors driving microbial dynamics in ponds appear to be pond-specific and differ depending on the spatial scale considered.
地表水环境本质上是不均匀的,对于不同地点之间微生物水质的变化知之甚少。本研究旨在了解弗吉尼亚池塘内和池塘间的微生物水质差异。从弗吉尼亚州的 9 个池塘的 30 个采样点每周采集两次抓样(n=600)。对总大肠菌群(TC)和大肠杆菌(EC)水平进行计数,并采集理化、气象和环境数据。采用协变量区域化模型来量化归因于空间(例如,地点、池塘)和非空间(例如,日期、pH)来源的 TC 和 EC 水平的方差。采用混合效应贝叶斯回归和条件推断树来描述数据与 TC 或 EC 水平之间的关系。对每个具有≥3 个采样点的池塘(5 个内部池塘)分别进行分析,同时使用所有采样点和所有池塘的数据开发一个池塘间模型。对于池塘间模型,归因于空间来源的 TC 水平的方差大于非空间来源(方差比 [VR] = 1.55),而内部池塘模型则取决于池塘(VR:0.65-18.89)。对于 EC 水平,在池塘间模型中,归因于空间来源的方差大于所有内部池塘模型(VR=1.62),这表明在单个池塘内,更多的方差归因于非空间因素,而在考虑多个池塘时,更多的方差归因于空间因素。在每个池塘内,相距 56-87 m 的站点之间的 TC 和 EC 水平是空间独立的,这表明同一池塘内的不同站点代表不同的水质以进行风险管理。降雨与 TC 和 EC 水平在池塘间和内部池塘模型中均呈正相关,而 pH 则呈负相关。对于所有其他因素,关联的方向和强度都有所不同。影响池塘中微生物动态的因素似乎是特定于池塘的,并且取决于所考虑的空间尺度而有所不同。