Prediction of occurrence of vascular plants in deciduous forests of South Sweden by means of Ellenberg indicator values
Prediction of occurrence of vascular plants in deciduous forests of South Sweden by means of Ellenberg indicator values
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DOI：
10.2307/1479092
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发表时间：
1998-05-01
影响因子：
2.8
通讯作者：
Diekmann, Martin
中科院分区：
文献类型：
--
作者：
Dupre, Cecilia;Diekmann, Martin
． 在这项研究中，我们提出了一种利用瑞典南部落叶林的数据预测物种发生的新方法。编制了101个林分和林内代表性样地维管植物的完整种表。采集各林分土壤样品进行pH和氮矿化测定。采用线性（多元）Logistic回归（LLR）和高斯（多元）Logistic回归（GLR）对土壤湿度、土壤反应（pH）、土壤氮和光照4个环境变量进行拟合。首先，通过计算Ellenberg指标值的加权平均值来估计这些值。其次，用pH值和矿化NH4+的实际测量值代替了反应和氮的估计值，保持了Ellenberg对光和湿度的估计。通过独立的测试数据集对模型进行了验证。总的来说，这些模型具有较高的预测能力。GLR对林分物种发生的拟合效果优于LLR，但对林分物种发生的预测精度较低。使用土壤测量值代替Ellenberg指标值并没有提高模型的预测能力。利用样地的物种数据，成功地估计了试验台的环境条件。当使用林分物种表代替样地数据时，预测能力略好。然而，地块数据的收集更容易，也更省时。预测的准确性在不同物种之间差别很大。
. In this study we present a new method for predicting the occurrences of species using data from deciduous forests in South Sweden. Complete species lists of vascular plants were compiled from 101 stands and from representative sample plots inside the stands. Soil samples from each stand were collected for determination of pH and nitrogen mineralization. Presence-absence data for species were fitted to the values of four environmental variables - soil moisture, soil reaction (pH), soil nitrogen and light - by means of Linear (Multiple) Logistic Regression (LLR), and Gaussian (Multiple) Logistic Regression (GLR). First, these values were estimated by calculating the weighted averages of Ellenberg indicator values. Second, the estimates for reaction and nitrogen were substituted by the real measurements of pH and mineralized NH4+, keeping the Ellenberg estimates for light and moisture. The models were validated by an independent test data set. In general, the models had high predictive abilities. GLR fitted the species occurrences better to the environmental variables than LLR, but had a lower accuracy of prediction of species occurrence in the stands. The use of soil measurements instead of Ellenberg indicator values did not improve the predictive abilities of the models. The environmental conditions in the stand test set were successfully estimated by using species data from the plots. When using the species lists of the stands instead of plot data, a slightly better predictive ability was obtained. The collection of plot data, however, is easier and less time-consuming. The accuracy of prediction differed considerably between species.