长白山不同演替阶段针阔混交林群落物种多样性及其影响因子

基于长白山3个5.2hm²针阔混交林永久监测样地的野外调查数据,以胸径(DBH)≥1cm的乔木树种为对象,运用R4.0.3软件计算物种多样性指数以及进行物种多样性相关性分析和多元回归分析,以探讨生物和非生物因素对不同演替阶段针阔混交林物种多样性的影响。结果表明：(1)3个典型针阔混交林的4个物种多样性指数均随演替的正向进行而下降,其中物种丰富度、Shannon-Wiener指数和Simpson指数在3个演替阶段存在显著性差异,而Pielou均匀度指数在次生杨桦林与次生针阔混交林阶段差异不显著。(2)胸径结构变量是影响物种多样性变化的主要驱动因子,随着演替的进行,林分密度对物种多样性产生的影响越来越大。(3)所有土壤因子在针阔混交林阶段对物种多样性指数均表现出显著的影响,而在次生杨桦林只有土壤全氮与物种多样性指数存在显著相关关系,土壤全磷在原始椴树红松林阶段是影响物种多样性的主要因素。综上,研究认为在各演替阶段,生物和非生物因素均影响着森林群落的物种多样性,胸径结构和林分密度是影响群落物种多样性的主要因素,林分中的土壤因子受到不同群落特征的影响产生不同变化,从而对群落的物种多样性产生影响也有所不同。     

Fixed or random? On the reliability of mixed‐effects models for a small number of levels in grouping variables

Biological data are often intrinsically hierarchical (e.g., species from different genera, plants within different mountain regions), which made mixed‐effects models a common analysis tool in ecology and evolution because they can account for the non‐independence. Many questions around their practical applications are solved but one is still debated: Should we treat a grouping variable with a low number of levels as a random or fixed effect? In such situations, the variance estimate of the random effect can be imprecise, but it is unknown if this affects statistical power and type I error rates of the fixed effects of interest. Here, we analyzed the consequences of treating a grouping variable with 2–8 levels as fixed or random effect in correctly specified and alternative models (under‐ or overparametrized models). We calculated type I error rates and statistical power for all‐model specifications and quantified the influences of study design on these quantities. We found no influence of model choice on type I error rate and power on the population‐level effect (slope) for random intercept‐only models. However, with varying intercepts and slopes in the data‐generating process, using a random slope and intercept model, and switching to a fixed‐effects model, in case of a singular fit, avoids overconfidence in the results. Additionally, the number and difference between levels strongly influences power and type I error. We conclude that inferring the correct random‐effect structure is of great importance to obtain correct type I error rates. We encourage to start with a mixed‐effects model independent of the number of levels in the grouping variable and switch to a fixed‐effects model only in case of a singular fit. With these recommendations, we allow for more informative choices about study design and data analysis and make ecological inference with mixed‐effects models more robust for small number of levels.     

50年来山东塔山植被与物种多样性的变化

为分析塔山植被与物种多样性50a自然演替和动态变化,2009-2010年,采用系统勘踏法和典型取样法进行了调查。当前塔山主要植被类型为\"黑松林-赤松林-栓皮栎林\"。50a间,该区针叶林从黑松(Pinus thunbergii)林演替为黑松林、赤松(P. densiflora)林和油松林(P. tabulaeformis)为主的混合针叶林,但针叶林的整体优势度下降,以栓皮栎(Quercus variabilis)和麻栎(Q. acutissima)为建群种的阔叶林面积明显增大,由针叶林向阔叶林的演替趋势明朗。物种丰富度为草本层>灌木层>乔木层,Shannon-wiener指数和Simpson指数为灌木层>草本层>乔木层,物种多样性较低,处于森林演替初期。     

Can vegetation be discretely classified in species‐poor environments? Testing plant community concepts for vegetation monitoring on sub‐Antarctic Marion Island

The updating and rethinking of vegetation classifications is important for ecosystem monitoring in a rapidly changing world, where the distribution of vegetation is changing. The general assumption that discrete and persistent plant communities exist that can be monitored efficiently, is rarely tested before undertaking a classification. Marion Island (MI) is comprised of species‐poor vegetation undergoing rapid environmental change. It presents a unique opportunity to test the ability to discretely classify species‐poor vegetation with recently developed objective classification techniques and relate it to previous classifications. We classified vascular species data of 476 plots sampled across MI, using Ward hierarchical clustering, divisive analysis clustering, non‐hierarchical kmeans and partitioning around medoids. Internal cluster validation was performed using silhouette widths, Dunn index, connectivity of clusters and gap statistic. Indicator species analyses were also conducted on the best performing clustering methods. We evaluated the outputs against previously classified units. Ward clustering performed the best, with the highest average silhouette width and Dunn index, as well as the lowest connectivity. The number of clusters differed amongst the clustering methods, but most validation measures, including for Ward clustering, indicated that two and three clusters are the best fit for the data. However, all classification methods produced weakly separated, highly connected clusters with low compactness and low fidelity and specificity to clusters. There was no particularly robust and effective classification outcome that could group plots into previously suggested vegetation units based on species composition alone. The relatively recent age (c. 450,000 years B.P.), glaciation history (last glacial maximum 34,500 years B.P.) and isolation of the sub‐Antarctic islands may have hindered the development of strong vascular plant species assemblages with discrete boundaries. Discrete classification at the community‐level using species composition may not be suitable in such species‐poor environments. Species‐level, rather than community‐level, monitoring may thus be more appropriate in species‐poor environments, aligning with continuum theory rather than community theory.     

Rates of change in tree communities of secondary Neotropical forests following major disturbances

Rates of change in tree communities following major disturbances are determined by a complex set of interactions between local site factors, landscape history and structure, regional species pools and species life histories. Our analysis focuses on vegetation change following abandonment of agricultural fields or pastures, as this is the most extensive form of major disturbance in Neotropical forests. We consider five tree community attributes: stem density, basal area, species density, species richness and species composition. We describe two case studies, in northeastern Costa Rica and Chiapas, Mexico, where both chronosequence and annual tree dynamics studies are being applied. These case studies show that the rates of change in tree communities often deviate from chronosequence trends. With respect to tree species composition, sites of different ages differ more than a single site followed over time through the same age range. Dynamic changes in basal area within stands, on the other hand, generally followed chronosequence trends. Basal area accumulation was more linked with tree growth rates than with net changes in tree density due to recruitment and mortality. Stem turnover rates were poor predictors of species turnover rates, particularly at longer time-intervals. Effects of the surrounding landscape on tree community dynamics within individual plots are poorly understood, but are likely to be important determinants of species accumulation rates and relative abundance patterns.     

林火干扰对北方针叶林林下植被的影响

林下植被在北方针叶林植被群落中的物种多样性最高, 且具有较高的生物量周转率和地上部分净初级生产力, 对北方针叶林生态系统功能起着重要作用。火干扰是决定北方针叶林林下植被结构与功能的一个重要景观过程。该文综述了火干扰是如何通过与地形、火前林冠组成的交互作用而影响环境资源和林下植被的。最近的研究表明: 林下植被能够影响火后树木更新苗的定植、重建速率及森林演替轨迹; 林下植被还会通过影响元素的生物地球化学过程(凋落物降解和养分循环)影响林下环境资源的数量与异质性。因此, 研究火后初期北方针叶林林下植被的动态变化, 对于物种多样性保护和森林管理具有重要意义。     

川西米亚罗地区暗针叶林采伐迹地早期植被演替过程的研究

 A secondary succession of vegetation on clearcut sites was studied in Western Sichuan. The coverage and biomass of trees, shrubs and herbs at different stages of succession have been surveyed. The result showed that the coverage and biomass of trees and shrubs have changed greatly in the succession. After the forest was cut, raspberry’s (Rubus daeus) coverage was the greatest during the first 0–15 years, Red birch’s coverage was the greatest during 16–29 years, while the total coverage of other shrubs was greater than those of raspberry and red birch during 10–20 years.The following empirical formulas with good fitness have been used:Ctree=0.803+2.347t1/2 (r=0.88 p<0.01) Cshrub=l0.481 l0.392T+0.373T2 (r=0.87 p<0.01)Cherb=0.489+87.001/T (r=0.93 p<0.01) C: biomass, kilogram/ha T: time period, year According to the curves, the succession of vegetation can be divided into four stages: herb stage. 0—3 years; raspberry stage: 4—10 year; shrub and small broad-leaved forest stage: 11—20 years, and small broad–leaved forest stage: 21—29 years.