Evaluation of the dry weight rank method for botanical analysis of grassland by means of simulation

J. Neuteboom, E. Lantinga, P. Struik
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引用次数: 12

Abstract

With the Dry Weight Rank (DWR) method of 't Mannetje and Haydock [see Journal of British Grassland Society (1963) 18, 268-275] for botanical analysis in pastures, the dry weight proportions of species are estimated from their first, second and third ranks in dry weight in single quadrats. The yield correction of Haydock and Shaw [see Australian Journal of Experimental Agriculture and Animal Husbandry (1975) 15, 663-670] is used additionally to solve the problem of the respective under- and overestimates of the dry weight proportions of high and low yielding species when these grow in patches. In this paper the DWR method is evaluated by means of computer simulation. Main element of the simulation model is a computer sampling program with which a fictitious vegetation can be sampled with a circular quadrat. The output shows that the DWR method works well using relatively small sampling quadrats with, on average, only a few plants per quadrat, irrespective of the horizontal vegetation structure. In vegetations where species grow patchwise, satisfactory results are also obtained using large quadrats with much more plants (i.e. tens) per quadrat. The reason is that in these cases also minor species can compete successfully for first, second and third ranks. However, it appeared that only a certain degree of patchiness is necessary, and with the usually applied quadrat sizes up to 25 dmsuperscript 2, probably in most vegetations this condition is fulfilled. Care should be taken in applying the DWR method for estimating species composition in recently sown grasslands where species usually occur more or less at random. In those cases, in principle a very small sampling quadrat (smaller than 1 dmsuperscript 2) could be used. However, this has practical limitations since the quadrat size should not be too small for realistic yield estimations, needed for the Haydock & Shaw yield correction. The simulations revealed that one condition (i.e., that the sampling quadrat should be at least as large that it usually contains three or more species) is not necessary because of the almost always perfect functioning of the correction for missing ranks. Generally speaking, a sampling quadrat should be chosen not larger than is strictly necessary from the viewpoint of horizontal vegetation structure and from the viewpoint of realistic yield estimations. Multipliers calculated from simulation data could satisfactorily mimic the original multipliers of DWR given by 't Mannetje & Haydock. It is postulated that the DWR method is well suited for studying vegetation changes in old, floristically diverse grasslands with dominant species often in moderate dry weight proportions and species usually growing in patches.
草地植物分析干重等级法的模拟评价
用't Mannetje和Haydock的干重等级(DWR)方法(参见Journal of British草地学会(1963)18,268 -275)对牧草进行植物分析,从单样方的干重第一、第二和第三级来估计物种的干重比例。Haydock和Shaw的产量修正[参见澳大利亚实验农业和畜牧业杂志(1975)15,663 -670]还用于解决高、低产品种在成片种植时各自低估和高估干重比例的问题。本文通过计算机仿真对DWR方法进行了评价。模拟模型的主要元素是一个计算机采样程序,该程序可以用圆形样方对虚构的植被进行采样。输出结果表明,DWR方法使用相对较小的采样样方时效果良好,平均每个样方只有少量植物,无论水平植被结构如何。在物种斑块生长的植被中,使用更大的样方(即每样方数十株)也可以获得令人满意的结果。原因是在这种情况下,小物种也可以成功地竞争第一、第二和第三名。然而,似乎只有一定程度的斑块是必要的,并且通常应用的样方大小高达25 dm_上标2,可能在大多数植被中都满足了这个条件。应用DWR方法估算新播种草原的物种组成时应注意,因为新播种草原的物种通常或多或少随机出现。在这些情况下,原则上可以使用非常小的抽样样方(小于1 dm_上标2)。然而,这有实际的局限性,因为对于Haydock & Shaw产量校正所需的实际产量估计,样方大小不应该太小。模拟显示,一个条件(即,抽样样方至少应该包含三个或更多物种)是不必要的,因为对缺失秩的校正几乎总是完美的。一般来说,从水平植被结构和实际产量估计的角度来看,抽样样方的选择不应超过严格需要的范围。根据仿真数据计算的乘数可以很好地模拟t Mannetje & Haydock给出的DWR的原始乘数。研究结果表明,DWR方法适合于研究植物区系多样化、优势种通常为中等干重比例、物种通常为斑块型生长的古老草原的植被变化。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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