Uncertainty in the detection of disturbance spatial patterns in temperate forests

The use of individual-based models in the study of the spatial patterns of disturbances has opened new horizons in forest ecosystem research. However, no studies so far have addressed (i) the uncertainty in geostatistical modelling of the spatial relationships in dendrochronological data, (ii) the number of increment cores necessary to study disturbance spatial patterns, and (iii) the choice of an appropriate geostatistical model in relation to disturbance regime. In addressing these issues, we hope to contribute to advances in research methodology as well as to improve interpretations and generalizations from case studies.We used data from the beech-dominated Žofínský Prales forest reserve (Czech Republic), where we cored 3020 trees on 74 ha. Block bootstrap and geostatistics were applied to the data, which covered five decades with highly different disturbance histories. This allowed us to assess the general behavior of various mathematical models. Uncertainty in the spatial patterns and stability of the models was measured as the length of the 95% confidence interval (CI) of model parameters.According to Akaike Information Criterion (AIC), the spherical model fitted best at the range of ca. 20 m, while the exponential model was best at the range of ca. 60 m. However, the best fitting models were not always the most stable. The stability of models grew significantly with sample size. At <500 cores the spherical model was the most stable, while the Gaussian model was very unstable at <300 cores. The pure nugget model produced the most precise nugget estimate. The choice of model should thus be based on the expected spatial relations of the forest ecosystem under study. Sill was the most stable parameter, with an error of ±6–20% for ≥1110 core series. By contrast, practical range was the most sensitive, with an error of at least ±59%. The estimation of the spatial pattern of severe disturbances was more precise than that of fine-scale disturbances.The results suggest that with a sample size of 1000–1400 cores and a properly chosen model, one reaches a certain precision in estimation that does not increase significantly with growing sample size. It appears that in temperate old-growth forests controlled by fine-scale disturbances, it is necessary to have at least 500 cores to estimate sill, nugget and relative nugget, while to estimate practical range at least 1000 cores are needed. When choosing the best model, the stability of the model should be considered together with the value of AIC. Our results indicate the general limits of disturbance spatial pattern studies using dendrochronological and geostatistical methods, which can be only partially overcome by sample size or sampling design.     

用地统计学方法对落叶松人工纯林表层细根生物量的估计

 采用地统计学的变异函数分析方法定量研究了落叶松（Larix olgensis）纯林表层（0～10 cm）细根的空 间异质性特征,利用地统计学的克里格内插法结合定积分,对落叶松纯林表层细根（<2 mm）的生物量进 行了估测。结果表明：1）6种林龄（14～40 年）的落叶松人工纯林表层细根的变异函数曲线理论模型均 为球状模型,空间变异主要是由结构性因素引起,且空间自相关程度均属中等以上（空间结构比>25%）。 14、19、22、26、32、40年生的落叶松纯林表层细根的空间变异尺度分别为1.76、3.40、1.02、4.12、 3.37和5.58 m。在所研究的林龄范围内,随林龄的增长,落叶松纯林表层细根的空间变异尺度近似呈直线 增长（p =0.074 4）。2）非参数统计的成对样本符号检验结果表明,变异函数分析结果基础上的克里格 内插法适用于落叶松纯林表层细根生物量的估计。利用此估计值,拟合其与位置坐标值之间的多元回归关 系均为二元十次余弦级数多项式。利用此多项式,通过定积分的方法（积分区间为整块样地的大小）,估 计出14、19、22、26、32、40年生的落叶松纯林表层细根生物量分别为1.097 3、1.434 0、1.185 4、 0.974 3、1.682 6、1.255 6 Mg• hm-2。3）在本次调查的林龄范围内（14～40年）,落叶松纯林表层细 根的现存量近似相等（α=0.037 3）,土壤表层单株细根生物量与林龄之间呈极显著的指数增长关系（α =0.002）。4）采用地统计学的克里格空间插值,结合多元回归和定积分的方法,可以实现落叶松人工林 表层细根生物量的准确估计。     

地统计学方法在长白落叶松人工林凋落物现存量估测中的应用

采用地统计学的变异函数分析方法定量研究了15年生落叶松(Larix olgensis)人工纯林凋落物层重量的空间异质性特征,利用地统计学的克里格内插法结合定积分,对凋落物层的重量进行了估测.结果表明1)不同分解层次的凋落物重量的变异函数曲线理论模型均为球状模型,空间变异主要是由结构性因素引起,且空间自相关程度均属中等以上(空间结构比＞25%).2)非参数统计的成对样本符号检验结果表明,变异函数分析结果基础上的克里格内插法适用于凋落物重量的估计.利用此估计值,拟合其与位置坐标值之间的多元回归关系均为二元十次余弦级数多项式.利用此多项式,通过定积分的方法(积分区间为整块样地的大小),估计出15年生落叶松人工纯林中凋落物层的未分解、半分解、完全分解和腐殖质层的重量分别为4.7753、5.4129、8.0742、10.9269t hm-2.3)采用地统计学的克里格空间插值,结合多元回归和定积分的方法,可以实现落叶松人工林凋落物层重量的准确估计.     

木焦油污染土壤中微生物特性的空间变异性研究

运用地统计学方法研究了木焦油污染土壤中微生物量、微生物群落结构、微生物活性等的空间变异特征.分别采用26种主要磷脂类脂肪酸(PLFA)的总含量(totPLFA)、PLFA的第一主成分和第二主成分(PLFA PC1和PC2)以及土壤培养过程中CO₂-C的累积释放量(Cre)来表征土壤中的微生物量、微生物群落结构以及微生物活性.结果表明,多数微生物特性指标均存在不同程度的空间自相关性,其半变异函数曲线可用带块金效应的球状模型进行拟合.变量的空间相关距离在2.50～16.60 m之间.PLFA PC1、totPLFA和Cre均具有较强的空间依赖性,其相对结构变差（由结构性因素引起的空间变异）分别为82.3%、79.6%和64.7%,而PLFA PC2 不存在明显的空间依赖性.克立格空间插值图表明,样地中存在几处微生物相对密集分布且代谢活性较高的区域,其中优势微生物菌群是由PLFAs 16:1ω7t,cy17:0,18:1ω7 和cy19:0所表征的革兰氏阴性细菌.土壤中主要污染物多环芳烃含量和空间分布是影响微生物特性空间分布格局的重要因素之一.     

地理统计学表达的麦二叉蚜及蚜茧蜂空间格局特征

应用地理统计学的原理和方法研究了不同时期麦二叉蚜及蚜茧峰种群的空间结构的空间相关性。结果表明,不同时期麦二叉蚜种群的半变异函数曲线皆为球型,其空间格局为聚集型,变程在21-61cm之间;蚜茧蜂种群的拟合半变民间函数曲线也表现为球型,呈聚集空间格局,空间变程在36-55cm之间,并与麦二叉蚜种群在数量和空间上有较强的追随关系,说明蚜茧蜂种群是麦二叉蚜种群的优势种天敌。     

Mapping habitat indices across river networks using spatial statistical modelling of River Habitat Survey data

Freshwater ecosystems are declining faster than their terrestrial and marine counterparts because of physical pressures on habitats. European legislation requires member states to achieve ecological targets through the effective management of freshwater habitats. Maps of habitats across river networks would help diagnose environmental problems and plan for the delivery of improvement work. Existing habitat mapping methods are generally time consuming, require experts and are expensive to implement. Surveys based on sampling are cheaper but provide patchy representations of habitat distribution. In this study, we present a method for mapping habitat indices across networks using semi-quantitative data and a geostatistical technique called regression kriging. The method consists of the derivation of habitat indices using multivariate statistical techniques that are regressed on map-based covariates such as altitude, slope and geology. Regression kriging combines the Generalised Least Squares (GLS) regression technique with a spatial analysis of model residuals. Predictions from the GLS model are ‘corrected’ using weighted averages of model residuals following an analysis of spatial correlation. The method was applied to channel substrate data from the River Habitat Survey in Great Britain. A Channel Substrate Index (CSI) was derived using Correspondence Analysis and predicted using regression kriging. The model explained 74% of the main sample variability and 64% in a test sample. The model was applied to the English and Welsh river network and a map of CSI was produced. The proposed approach demonstrates how existing national monitoring data and geostatistical techniques can be used to produce continuous maps of habitat indices at the national scale.