Data and analysis supporting: Quantifying aquatic plant commonness and cooccurrence across scales to support ecological understanding and management

These data and R statistical code support the publication, "Quantifying aquatic plant commonness and cooccurrence across scales to support ecological understanding and management," in Journal of Ecology. We analyzed aquatic plant surveys from 1,658 lakes across Minnesota and Wisconsin, USA, collected over two decades (2000-2022) and encompassing nearly one million sampling points. These data were collected by agency staff, consultants, researchers, and others who performed the thousands of aquatic plant surveys that enabled this work. For 106 focal taxa, we quantified commonness as occupancy (at regional and local scales) and cooccurrence as diversity fields (the mean species richness of lakes or sampling locations where each focal species occurred). We used statistical models that incorporated environmental, spatial, and temporal covariates to correct for biased sampling and isolate community processes from other influential factors, and leveraged the temporal span of the data to investigate interannual variability in commonness and cooccurrence.

本数据集与R语言统计代码支撑发表于《Journal of Ecology》（《生态学杂志》）的研究论文《跨尺度量化水生植物常见性与共现性以助力生态学认知与管理》，原英文标题为"Quantifying aquatic plant commonness and cooccurrence across scales to support ecological understanding and management"。本研究分析了美国明尼苏达州与威斯康星州境内1658个湖泊的水生植物调查数据，该数据集采集周期跨越二十年（2000年至2022年），覆盖近百万个采样点位。上述数据由各机构工作人员、顾问、研究人员及其他相关人员采集，他们完成了数千项水生植物调查工作，为本研究提供了核心数据支撑。针对106个目标类群（focal taxa），本研究以占据率（区域与局地尺度）量化常见性，以多样性场（即各目标物种出现的湖泊或采样点位的平均物种丰富度）量化共现性。本研究采用纳入环境、空间与时间协变量的统计模型，以校正采样偏差，并将群落过程与其他影响因素区分开来；同时依托数据集的时间跨度，探究常见性与共现性的年际变化规律。