Advancing single species abundance models by leveraging multi-species data to reveal lakespecific patterns for fisheries predictions

Predicting species abundance is critical for understanding ecological dynamics and guiding conservation and management strategies. Traditional species abundance models (SAMs) rely on environmental variables and the presence or absence of key species, but often overlook community context and unmeasured environmental variation. Community composition can serve as a proxy for both unobserved environmental variables and biotic interactions influencing focal species. Here, we tested whether incorporating community composition via latent variables improves abundance predictions of sport fishing using a large-scale dataset. We assessed how latent variables selection and lake characteristics influences model accuracy across species. Our results show that low-abundance species were better predicted by models based solely on environment, while high-abundance species benefited from latent variables. Lake contribution to accuracy were correlated among species with similar occurrence, but unrelated t..., , This readme file was generated on 2026-01-08 by Dr. Stahl \# GENERAL INFORMATION Title of Dataset: Advancing single species abundance models by leveraging multi-species data to reveal lakespecific patterns for fisheries predictions. Author Name: Stahl, Aliénor ORCID: 0000-0002-2297-7379 Institution: Concordia University, Montreal, Canada Email: [[e-mail]](mailto:[e-mail]) Author Name: Eric Pedersen ORCID: 0000-0003-1016-540X Institution: Concordia University, Montreal, Canada Email: [[e-mail]](mailto:[e-mail]) Author Name: Pedro Peres-Neto ORCID: 0000-0002-5629-8067 Institution: Concordia University, Montreal, Canada Email: [[e-mail]](mailto:[e-mail]) SHARING/ACCESS INFORMATION Links to publications that cite or use the data: Data and code for: Stahl, A., Pedersen, E., Peres-Neto, P.. Advancing single species abundance models by leveraging multi-species data to re...,物种丰度预测对于理解生态动态、指导保护与管理策略至关重要。传统物种丰度模型（SAMs）依赖环境变量以及关键物种的有无，但往往忽略群落背景与未观测的环境变异。群落组成（community composition）可作为未观测环境变量与影响目标物种的生物间相互作用（biotic interactions）的替代指标。本研究基于大规模数据集，验证了通过潜在变量（latent variables）整合群落组成是否能够提升休闲渔业（sport fishing）物种丰度的预测效果。我们评估了潜在变量选择与湖泊特征如何影响不同物种的模型准确率。结果显示，仅基于环境变量的模型可更好地预测低丰度物种，而高丰度物种则能从潜在变量的使用中获益。湖泊对模型准确率的贡献在发生模式相似的物种间存在相关性，但与……无关。本自述文件于2026年1月8日由Stahl博士生成。 # 通用信息 数据集标题：通过多物种数据改进单物种丰度模型以揭示渔业预测的湖泊特异性模式 作者 姓名：Stahl, Aliénor ORCID标识符（ORCID）：0000-0002-2297-7379 所属机构：加拿大蒙特利尔康考迪亚大学 电子邮箱：[e-mail] 作者 姓名：Eric Pedersen ORCID标识符（ORCID）：0000-0003-1016-540X 所属机构：加拿大蒙特利尔康考迪亚大学 电子邮箱：[e-mail] 作者 姓名：Pedro Peres-Neto ORCID标识符（ORCID）：0000-0002-5629-8067 所属机构：加拿大蒙特利尔康考迪亚大学 电子邮箱：[e-mail] 共享与获取信息 引用或使用本数据集的出版物链接： 配套数据与代码： Stahl, A., Pedersen, E., Peres-Neto, P.. Advancing single species abundance models by leveraging multi-species data to re...,