植被恢复对黄土区煤矿排土场土壤团聚体特征的影响

目前关于植被恢复对排土场土壤团聚性的影响还不清楚,以植被恢复下黄土区露天煤矿排土场为研究对象,采用湿筛法测定了排土场土壤水稳性团聚体组成,研究了植被恢复类型(草地、灌木)和排土场地形(平台、边坡)对土壤团聚体特征的影响。结果表明:植被恢复促进了排土场水稳性团聚体的形成,平台0—20 cm土层水稳性大团聚体数量(R_(0.25))、平均重量直径(MWD)和几何平均直径(GMD)分别达到31.1%,0.70 mm和0.26 mm,边坡分别达到13.3%,0.37 mm和0.17 mm,均显著高于裸地,分形维数(D)在平台和边坡分别为2.91和2.96,均显著低于裸地;平台土壤团聚性要好于边坡,草地对于平台土壤团聚结构改良效果较好,而灌木对于边坡改良效果较好;排土场土壤有机碳和粘粒含量均与土壤团聚体指标有显著相关性。植被恢复提高了排土场土壤团聚性,植被恢复类型和地形对排土场土壤团聚体特征有显著影响。     

云雾山自然保护区优势植物种群分布格局的分形特征

对宁夏云雾山草原自然保护区植物种群分布格局的分形计盒维数进行了研究。结果表明：本氏针茅、铁杆蒿和百里香是该区的优势种。3种优势种的计盒维数在1．7左右,表明它们具有较大的空间占据能力。但是它们的计盒维数最大值出现的时间不同,本氏针茅、百里香计盒维数最大值出现存封育20a的样地中,铁杆蒿出现在封育25a的样地中。     

群落均匀度分形分析

修正了Frontier和Ricotta等关于有效物种丰富度指数A与物种丰富度指数S之间幂律关系的定义．探讨了A与S之间分形关系的生态学意义．认为分形维数D是群落均匀度测度值在物种数S不断增加的过程中．向其逼近的一个理论值;提出了利用双对数坐标上建立的A与S拟合直线的方程．对群落均匀度的4种变化趋势进行描述的方法。以广东黑石顶自然保护区森林演替系列为例．研究了针阔叶混交林和常绿阔叶林样带上．随着样带观察长度的逐渐增加群落均匀度的变化情况。结果表明．230m长的混交林样带只存在一个线性无标度区间．群落均匀度随样带长度的不断增加而逐渐降低．向分形维数D=0．810趋近。170m长的常绿阔叶林样带存在两个线性无标度区问．在0～25m的尺度域内．随着样带长度的逐渐增加均匀度不断降低．向分形维数D=0．525逼近;在30～170m的尺度域内．随着样带观察长度的增加．群落均匀度也逐渐增加．向分形维数D=0．920趋近。     

Multi-scale trajectory analysis:powerful conceptual tool for understanding ecological change

The model at the basis of trajectory analysis is conceptually simple.When applied to time series vegetation data,the projectile becomes a surrogate for vegetation state,the trajectory for the evolving vegetation process,and the properties of the trajectory for the true process characteristics.Notwithstanding its simplicity,the model is well-defined under natural circumstances and easily adapted to serial vegetation data,irrespective of source.As a major advantage,compared to other models that isolate the elementary processes and probe vegetation dynamics for informative regularities on the elementary level,the trajectory model allows us to probe for regularities on the level of the highest process integrity.Theories and a data analytical methodology developed around the trajectory model are outlined,including many numerical examples.A rich list of key references and volumes of supplementary information supplied in the Web Only Appendices rounds out the presentation.     

Evaluating scaling models in biology using hierarchical Bayesian approaches

Theoretical models for allometric relationships between organismal form and function are typically tested by comparing a single predicted relationship with empirical data. Several prominent models, however, predict more than one allometric relationship, and comparisons among alternative models have not taken this into account. Here we evaluate several different scaling models of plant morphology within a hierarchical Bayesian framework that simultaneously fits multiple scaling relationships to three large allometric datasets. The scaling models include: inflexible universal models derived from biophysical assumptions (e.g. elastic similarity or fractal networks), a flexible variation of a fractal network model, and a highly flexible model constrained only by basic algebraic relationships. We demonstrate that variation in intraspecific allometric scaling exponents is inconsistent with the universal models, and that more flexible approaches that allow for biological variability at the species level outperform universal models, even when accounting for relative increases in model complexity.