关于鼠尾藻群体数量分布的三种统计模型比较

鼠尾藻是潮间带海藻床的主要构建者,但何种统计模型更适合鼠尾藻的数量分布研究目前尚不清楚。选取山东荣成内遮岛15个25m2区域进行了调查和数量统计,比较了算数平均模型、反距离权重模型及普通克里金模型的精度差异,分析了群体密度、丛生指数及盖度等因素对模型统计精度的影响。结果表明,反距离权重模型表现较为稳定、平均误差最低(平均绝对误差39.1株,均方根误差53.3株,偏差率13.0%),而算术平均模型的精度波动最大、平均误差最高(平均绝对误差53.8株,均方根误差65.3株,偏差率14.6%)。群体密度和盖度因素对模型精度无明显影响(P0.05),但丛生指数能显著影响3种模型的平均绝对误差和均方根误差(P0.05)。研究表明,3种模型精度差异并不明显,模型精度在一些指标上受丛生指数影响。总体来看,反距离权重模型和普通克里金模型稳定性较好,误差均值较小,且均能够反映鼠尾藻群体的空间分布,因而在鼠尾藻群体数量分布计算中具有一定优势。

Comparison of three statistical models for the quantitative distribution of Sargassum thunbergii populations

Sargassum thunbergii is the main component of intertidal algal beds. However, which statistical model best describes the Sargassum thunbergii quantitative distribution is poorly understood. In this study, 15 plots with an area of 25 m² were selected on Neizhe Island, Rongcheng, Shandong Province, China, for vegetation surveys and quantitative statistical analysis. A comparison of the arithmetic average, inverse distance weighted, and ordinary kriging models enabled their accuracy to be ascertained. The factors that might affect model accuracy were also tested, including population density, clumping index, and vegetation fractional coverage. The results showed that the performance of the inverse distance weighted model was the most stable and the average errors were the lowest (mean absolute error 39.1 individual, root mean square error 53.3 individual, rate of deviation 13.0%), while the accuracy of the arithmetic average model varied considerably and the average errors were the highest (mean absolute error 53.8 individual, root mean square error 65.3 individual, rate of deviation 14.6%). It was verified that population density and vegetation fractional coverage had no obvious influence on model precision (P > 0.05), but the clumping index clearly affected the mean absolute error and the root mean square error in the three models (P < 0.05). The results showed that there was no significant difference in the accuracy of the three models, and that the clumping index could influence model accuracy for some indicators. Generally, both the inverse distance weighted model and the ordinary kriging model had certain advantages due to a more stable performance, the lower mean error, and their ability to reveal the spatial distribution of the Sargassum thunbergii population.