Macroinvertebrate/environmental data and R script: Using taxa accumulation curves to evaluate macroinvertebrate sampling effectiveness of two handnet and a driftnet methods across water body types

These data files here contain the enviromental variables, fielddata, and the macroinvertebrate data for the standard, subsampling and driftnet method used in the paper. Also attached here is a script for calculating average models for taxa accumulation curves based onWeibull, Logarithmic, Negative Exponential, Asymptotic, and Monod models. We used the ‘vegan’ package (v2.6-8; Oksanen et al. 2024) under R Studio version 4.4.2 (R Core Team 2023).

Abstract:
Aquatic macroinvertebrates are key indicators of freshwater ecosystem health, yet effectively sampling their diversity remains challenging due to habitat heterogeneity and method limitations. This study evaluated three sampling approaches - standard handnet sampling, habitat-based subsampling, and driftnet collection - across various waterbody types. Using taxa accumulation curves (TACs), we compared the effectiveness of each method in capturing macroinvertebrate richness and examined whether richness estimates varied by waterbody.
Habitat-based subsampling consistently captured the highest richness (70–90% of estimated total), followed by standard handnet sampling (50–70%) and driftnets (60–70%), although driftnets showed greater variability due to lower organism counts. TACs proved useful for assessing sampling efficiency and richness capture across methods.
While less comprehensive, driftnets provided complementary data by detecting drifting taxa missed by handnet methods and offering insights into diel activity and potential recolonization processes. A combined sampling strategy therefore offers the most complete biodiversity assessment.
If standard methods are used alone, a preliminary habitat assessment is advised to ensure representative sampling. Subsampling strategies that distribute effort across multiple smaller samples were found effective in heterogeneous, but also visually homogeneous waterbodies. Relying on single large samples may misrepresent biodiversity, underscoring the need for tailored approaches to support water management and conservation.

本数据集包含本研究论文中所采用的标准手网采样、基于生境的分样采样以及拖网采样三种方法对应的环境变量、野外实测数据与大型底栖无脊椎动物（macroinvertebrate）数据。附件还附带了基于威布尔（Weibull）、对数（Logarithmic）、负指数（Negative Exponential）、渐近（Asymptotic）与莫诺（Monod）模型，计算类群累积曲线平均拟合模型的脚本。本研究依托R Studio 4.4.2版本（R开发团队，2023），使用vegan包（v2.6-8；Oksanen等，2024）完成数据分析。

摘要：
水生大型底栖无脊椎动物（aquatic macroinvertebrates）是淡水生态系统健康的关键指示生物，但受生境异质性与采样方法局限影响，高效获取其物种多样性仍存在较大挑战。本研究针对多种水体类型，评估了三种采样方案：标准手网采样、基于生境的分样采样与拖网采样。借助类群累积曲线（taxa accumulation curves, TACs），本研究对比了各方法对大型底栖无脊椎动物物种丰富度的捕获效果，并探究了物种丰富度估算值是否因水体类型而异。

基于生境的分样采样始终能获取最高的物种丰富度（可达估算总丰富度的70%~90%），其次为标准手网采样（50%~70%）与拖网采样（60%~70%）；不过由于个体采集量较低，拖网采样的结果变异度更高。类群累积曲线可有效用于评估不同采样方法的采样效率与物种丰富度捕获能力。

尽管拖网采样的覆盖范围相对有限，但其可捕获手网采样遗漏的漂流类群，为昼夜活动模式与潜在再定植过程的研究提供补充数据。因此，采用组合采样策略可实现最全面的生物多样性评估。

若仅单独使用标准采样方法，建议先开展初步生境评估以确保采样具有代表性。研究发现，将采样工作量分散至多个小样本的分样策略，在异质性甚至视觉上均一的水体中均能取得良好效果。仅依赖单个大样本采样可能会歪曲生物多样性的真实情况，这凸显了定制化采样方案对水管理与保护工作的支撑必要性。