Scholarlink

<p>Aquaculture effluent threatens aquatic ecosystem health, yet source tracking remains challenging due to the lack of specific microbial fingerprints and result priority. Here, we developed a microbial fingerprints-based machine learning approach for hierarchically tracking aquaculture effluent sources. Through high-throughput sequencing of 386 source samples (aquaculture effluent, domestic sewage effluent, cropland and orchard runoff), we screened four microbial taxa (<i>g_ML602J-51</i>, <i>g_Silicimonas</i>, <i>g_Lewinella</i> and <i>f_Balneolaceae</i>) as fingerprints for aquaculture effluent, exhibiting high sensitivity (0.512–0.649) and specificity (0.804–0.974). Artificial Neural Network and Support Vector Machine-radial were optimal models using fingerprint relative abundance and presence data, with accuracies of 0.8335 ± 0.0090 and 0.8221 ± 0.0047, respectively. The models’ ensemble improved accuracy to 0.8706 ± 0.0175, outperforming individual classifiers by 4.44%–5.90% and fingerprint matching by 17.29%. The predicted uncertainty was stratified into five-credibility tier for tracking primary aquaculture effluent sources. Application across three coastal regions of China demonstrated the generalizability of the approach.</p><p></p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Hierarchically tracking aquaculture effluent in waters by microbial fingerprints-driven machine learning ensemble

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月5日

查看全文
  • Pengcheng Li,
  • Mengzhu Xue,
  • Guohui Xia,
  • Lu Dong,
  • Kening Wang,
  • Xin Zhang,
  • Peng Liu,
  • Liping Li,
  • Cheng Zhang,
  • Baoshan Cui,
  • Junhong Bai,
  • Xinhui Liu

摘要

Aquaculture effluent threatens aquatic ecosystem health, yet source tracking remains challenging due to the lack of specific microbial fingerprints and result priority. Here, we developed a microbial fingerprints-based machine learning approach for hierarchically tracking aquaculture effluent sources. Through high-throughput sequencing of 386 source samples (aquaculture effluent, domestic sewage effluent, cropland and orchard runoff), we screened four microbial taxa (g_ML602J-51, g_Silicimonas, g_Lewinella and f_Balneolaceae) as fingerprints for aquaculture effluent, exhibiting high sensitivity (0.512–0.649) and specificity (0.804–0.974). Artificial Neural Network and Support Vector Machine-radial were optimal models using fingerprint relative abundance and presence data, with accuracies of 0.8335 ± 0.0090 and 0.8221 ± 0.0047, respectively. The models’ ensemble improved accuracy to 0.8706 ± 0.0175, outperforming individual classifiers by 4.44%–5.90% and fingerprint matching by 17.29%. The predicted uncertainty was stratified into five-credibility tier for tracking primary aquaculture effluent sources. Application across three coastal regions of China demonstrated the generalizability of the approach.