金华北山南坡主要植被类型的群落特征

浙江省金华北山地处中亚热带北部, 在植被区划上属于中亚热带常绿阔叶林北部亚地带。该区植被是遭砍伐后恢复的次生林, 目前正处于快速的正向演替进程中, 这为研究常绿阔叶林植物群落动态演替机制及受损生态系统的恢复提供了平台。为进一步了解金华北山地区主要植被类型的群落特征, 该文以国际上通用的大样地调查方法, 采用固定样方对其南坡主要植物群落进行了调查, 其中森林样方面积30 m × 30 m, 灌丛样方面积为20 m × 10 m、30 m × 10 m。调查群落物种组成及其数量特征, 记录群落生境信息, 计算木本植物重要值, 分析群落的类型及特征, 并制作每个样方主要木本植物的空间分布图。论文提供了24组详细的群落样方数据(包括21个森林样方和3个灌丛样方), 含有11个群系。     

Defining a Target Map of Native Species Assemblages for Restoration

Vegetation restoration is usually based on predefined species assemblages from large‐scale maps of potential vegetation. However, most restoration plans apply to smaller spatial scales, so a homogeneous species assemblage is usually assigned to the target site. We propose defining species assemblages for restoration by modeling the distribution of individual target species. The example presented here is about postfire restoration, but it can be used in other types of disturbed areas. We surveyed 212 plots in well‐preserved vegetation around the burned area to obtain a list of target species and physical parameters of the plots. The burned area was divided in a grid of 723 squares, 1 ha each, and then characterized according to the same physical parameters. From these data, we modeled the distribution of 23 target species. A target map of predicted species assemblages was built combining species maps. This map largely resembles the native vegetation in terms of species richness per plot, environmental gradients in α‐diversity, spatial variation in β‐diversity, and frequency of species occurrence. Comparison between the target map and the current vegetation (recovery status) indicated that, on average, only half of the potential set of species is already present in each plot. Analysis of the recovery status suggested that both rock outcrops and areas at lower altitude, with gentle slope and deeper soil, recover faster. This illustrates the utility of target maps to outline plots in more need of restoration.     

Environmental constraints at the edge of a species distribution, the Eurasian badger (Meles meles L.): a biogeographic approach

Aim This paper describes the distribution pattern (occurrence) of badgers (Meles meles L. 1758) in a Mediterranean mountain area of central Spain, in relation to landscape characteristics and climatic data. We test the fit of the badger occurrence pattern to the niche hypothesis predictions. Location We sampled twenty-four survey plots randomly distributed in the mountains of central Spain (Sierra de Guadarrama, Madrid): ten in the north and fourteen in the south slopes. All habitat types and climatic conditions typical of central Spain can be found. Methods The ocurrence of badgers in the plots were recorded through an exhaustive search of setts and latrines in four different (and evenly spaced) points in each plot. The index of occurrence was made by dividing the number of positive points (badger presence) by the total number of points sampled (four in all plots). Landscape patterns (% type of habitat in each plot) were obtained from detailed maps and climate data from meteorological stations placed in the plot. We analyse the contribution of landscape variables and climate data to explain the differences in badger occurrence through Pearson correlations and fixed one-way ANCOVA with climatic data as fixed factor, vegetation cover as covariates and badger occurrence as response variable. Additionally, we use an one-way ANOVA to check the differences in occurrence between north and south plots with the location of each plot as fixed factor. Results The results indicate that the badger is more abundant in rainy areas of the mountains, and in open landscapes vegetated by ash-tree forests than in closed landscapes vegetated by holm oak forests. In addition, the species is more abundant in the northern plots than in the southern ones. Northern plots were homogeneous areas characterized by their open landscape and wet climate, while southern plots were characterized by their dry climate and closed landscapes. In addition, climate (measured as summer rain) is more determinant than habitat type (holm oak cover) to explain the pattern of badger occurrence. Main conclusions Overall, we consider that the typical Mediterranean landscapes are poor habitats for badgers due to changes in the environmental conditions associated with concomitant changes in food resources. These data support the niche hypothesis to explain the changes in abundance or occurrence close to the edge of the distribution of species, and in particular, in Palearctic species in the Mediterranean area.     

中国亚热带4个森林动态监测样地无人机可见光遥感影像数据集

常绿阔叶林是我国亚热带地区的地带性植被类型。由于亚热带森林植物群落垂直结构复杂、林冠郁闭度高, 对常绿阔叶林冠层的研究尚缺乏高质量的监测数据。本数据集包含浙江天童山、浙江百山祖、广东车八岭、广东鼎湖山4个大于20 ha的森林动态监测样地2014年8月或2016年9月采集的无人机可见光遥感影像。本数据集是通过将无人机影像、地面控制点和地面调查数据相结合而获得的。每个样地的数据集包括4个文件: ~5 cm空间分辨率的正射影像图和数字表面模型、1 m空间分辨率的森林冠层高度数据和正射影像质量报告。本数据集可为常绿阔叶林的林冠生态学、生物多样性、生态系统功能等研究提供数据支撑。     

木论喀斯特峰丛洼地森林群落空间格局及环境解释

基于广西壮族自治区木论国家级自然保护区典型峰丛洼地景观尺度内不同微生境条件和植物群落类型50个样地 (20 m × 20 m)的系统取样调查, 用二元物种指示方法(TWINSPAN)对样地内胸径(DBH) ≥ 1 cm的木本植物进行分类, 选择10个土壤环境因子和5个空间因子, 利用除趋势典范对应分析(DCCA)研究了森林群落分布的土壤环境与空间格局, 并给予定量化的合理解释。结果如下: 1) TWINSPAN将森林群落划分为11组, 在三级水平上分为4类生态群落类型。2) DCCA第一排序轴集中了排序的大部分信息, 突出反映了各森林群落所在的坡向和土壤主要养分梯度, 沿第一轴从左到右, 坡向由阴转阳, 岩石裸露率越来越高, 土壤主要养分逐渐降低, 森林群落分别出现了由原生性和耐阴性强逐步向阳性先锋树种为主的次生林和人工林变化的格局。3)因子分离分析结果表明, 土壤环境因子对森林群落分布格局的解释能力为39.16%, 其中21.02%单纯由土壤环境因子所引起, 空间因子的解释能力为31.34%, 其中13.16%独立于土壤环境的变化, 18.15%是土壤环境和空间因子相互耦合作用的结果, 不可解释部分达47.66%, 表明喀斯特峰丛洼地森林群落的物种共存受生态位分化理论和中性理论双重控制。     

Availability of hollow‐bearing trees and their utilization by small animals in Cawston ranch,Zimbabwe

The objective of this study was to characterize hollows found in selected tree species (in Cawston Ranch in Zimbabwe) as a potential habitat for small mammals because cavities are important as resources for wildlife: for foraging, shelter, roosting and nesting purposes. Four vegetation types were chosen for analysis: Baikiaea plurijuga, Colophospermum mopane, Combretum molle and Commiphora mollis according to abundance and dominance in the plots they occurred. Transect sampling was done to collect vegetation data. The total number of living trees and stags were recorded in each plot. Trees in a plot were thoroughly inspected for hollows. The following dimensions were measured on cavity trees: hollow size (diameter), hollow location and diameter at breast height. The results indicate that the rate of hollow formation and utilization is significantly dependent on tree species. There was a significant difference in the provision of hollows in stem and crown of a tree in each of the vegetation classes analysed. B. plurijuga hollows had the highest utilization whilst C. molle had the least utilization. Hollows are utilized irrespective of their location as long as the animal finds a niche, and stags at advanced stages of decay are not important in providing habitats for animals.     

山东省滨海湿地柽柳种群的空间分布格局及其关联性

柽柳(Tamarix chinensis)是暖温带滨海盐碱湿地的先锋灌木物种, 在滨海湿地植物群落演替和防止沿海地区海水入侵中发挥着重要的作用。研究柽柳种群的空间分布格局和不同径级柽柳个体之间的空间关联性, 揭示种群发展规律, 可以为盐碱地柽柳种群的保护提供指导, 并为滨海湿地生态系统的演替和生态管理提供依据。该研究在昌邑国家海洋生态特别保护区核心区内沿平行海岸线方向设置两条间隔800 m左右的样带, 每条样带上设置3个50 m × 50 m的样地, 共设置6块样地进行每木调查, 绘制柽柳种群空间位置分布图, 并将调查的柽柳按照其基径大小分为≤4 cm、4-8 cm、>8 cm 3个不同径级。利用Programita软件对柽柳种群的分布格局以及不同径级间的空间关联性进行分析。结果显示: (1) 6块样地共调查柽柳个体374株; (2)不同样地间柽柳植株密度差别较大, 说明柽柳在区域尺度上的分布并不均匀; (3)柽柳种群在小尺度(小于5 m)上表现为聚集分布, 在大尺度(大于15 m)上表现为随机分布, 总体表现为随空间尺度的增大柽柳种群呈现由聚集分布过渡到随机分布的趋势; (4) 3个径级两两之间在小尺度上表现为正关联, 在大尺度上表现为无关联, 但在15 m尺度上径级II与径级III因为竞争而呈空间负相关关系。     

Spatial Structure of Above-Ground Biomass Limits Accuracy of Carbon Mapping in Rainforest but Large Scale Forest Inventories Can Help to Overcome

Precise mapping of above-ground biomass (AGB) is a major challenge for the success of REDD+ processes in tropical rainforest. The usual mapping methods are based on two hypotheses: a large and long-ranged spatial autocorrelation and a strong environment influence at the regional scale. However, there are no studies of the spatial structure of AGB at the landscapes scale to support these assumptions. We studied spatial variation in AGB at various scales using two large forest inventories conducted in French Guiana. The dataset comprised 2507 plots (0.4 to 0.5 ha) of undisturbed rainforest distributed over the whole region. After checking the uncertainties of estimates obtained from these data, we used half of the dataset to develop explicit predictive models including spatial and environmental effects and tested the accuracy of the resulting maps according to their resolution using the rest of the data. Forest inventories provided accurate AGB estimates at the plot scale, for a mean of 325 Mg.ha^-1. They revealed high local variability combined with a weak autocorrelation up to distances of no more than10 km. Environmental variables accounted for a minor part of spatial variation. Accuracy of the best model including spatial effects was 90 Mg.ha^-1 at plot scale but coarse graining up to 2-km resolution allowed mapping AGB with accuracy lower than 50 Mg.ha^-1. Whatever the resolution, no agreement was found with available pan-tropical reference maps at all resolutions. We concluded that the combined weak autocorrelation and weak environmental effect limit AGB maps accuracy in rainforest, and that a trade-off has to be found between spatial resolution and effective accuracy until adequate “wall-to-wall” remote sensing signals provide reliable AGB predictions. Waiting for this, using large forest inventories with low sampling rate (<0.5%) may be an efficient way to increase the global coverage of AGB maps with acceptable accuracy at kilometric resolution.     

Processes of community assembly in an environmentally heterogeneous,high biodiversity region

Despite decades of study, the relative importance of niche‐based versus neutral processes in community assembly remains largely ambiguous. Recent work suggests niche‐based processes are more easily detectable at coarser spatial scales, while neutrality dominates at finer scales. Analyses of functional traits with multi‐year multi‐site biodiversity inventories may provide deeper insights into assembly processes and the effects of spatial scale. We examined associations between community composition, species functional traits, and environmental conditions for plant communities in the Kouga‐Baviaanskloof region, an area within South Africa's Cape Floristic Region (CFR) containing high α and β diversity. This region contains strong climatic gradients and topographic heterogeneity, and is comprised of distinct vegetation classes with varying fire histories, making it an ideal location to assess the role of niche‐based environmental filtering on community composition by examining how traits vary with environment. We combined functional trait measurements for over 300 species with observations from vegetation surveys carried out in 1991/1992 and repeated in 2011/2012. We applied redundancy analysis, quantile regression, and null model tests to examine trends in species turnover and functional traits along environmental gradients in space and through time. Functional trait values were weakly associated with most spatial environmental gradients and only showed trends with respect to vegetation class and time since fire. However, survey plots showed greater compositional and functional stability through time than expected based on null models. Taken together, we found clear evidence for functional distinctions between vegetation classes, suggesting strong environmental filtering at this scale, most likely driven by fire dynamics. In contrast, there was little evidence of filtering effects along environmental gradients within vegetation classes, suggesting that assembly processes are largely neutral at this scale, likely the result of very high functional redundancy among species in the regional species pool.     

Empirical realised niche models for British higher and lower plants – development and preliminary testing

Question: Can useful realised niche models be constructed for British plant species using climate, canopy height and mean Ellenberg indices as explanatory variables? Location: Great Britain. Methods: Generalised linear models were constructed using occurrence data covering all major natural and semi‐natural vegetation types (n=40 683 quadrat samples). Paired species and soil records were only available for 4% of the training data (n=1033) so modelling was carried out in two stages. First, multiple regression was used to express mean Ellenberg values for moisture, pH and fertility, in terms of direct soil measurements. Next, species presence/absence was modelled using mean indicator scores, cover‐weighted canopy height, three climate variables and interactions between these factors, but correcting for the presence of each target species in training plots to avoid circularity. Results: Eight hundred and three higher plants and 327 bryophytes were modelled. Thirteen per cent of the niche models for higher plants were tested against an independent survey dataset not used to build the models. Models performed better when predictions were based only on indices derived from the species composition of each plot rather than measured soil variables. This reflects the high variation in vegetation indices that was not explained by the measured soil variables. Conclusions: The models should be used to estimate expected habitat suitability rather than to predict species presence. Least uncertainty also attaches to their use as risk assessment and monitoring tools on nature reserves because they can be solved using mean environmental indicators calculated from the existing species composition, with or without climate data.     

Limits to convergence of vegetation during early primary succession

Questions: Primary succession, measured by changes in species composition, is slow, usually forcing a chronose‐quence approach. A unique data set is used to explore spatial and temporal changes in vegetation structure after a 1980 volcanic eruption. On the basis of data from a transect of 20 permanent plots with an altitudinal range of 250 m sampled through 2005, two questions are asked: Do changes along the transect recapitulate succession? Do plots converge to similar composition over time? Location: A ridge between 1218 and 1468 m on Mount St. Helens, Washington, USA. Methods: Repeat sampling of plots for species cover along a 1‐km transect. Floristic changes were characterized by techniques including DCA, clustering and similarity. Results: Species richness and cover increased with time at rates that decreased with increasing elevation. The establishment of Lupinus lepidus accelerated the rate of succession and may control its trajectory. Diversity (H) at first increased with richness, then declined as dominance hierarchies developed. Primary succession was characterized by overlapping phases of species assembly (richness), vegetation maturation (diversity peaks, cover expands) and inhibition (diversity declines). Each plot passed through several community classes, but by 2005, only four classes persisted. Succession trajectories (measured by DCA) became shorter with elevation. Similarity between groups of plots defined by their classification in 2005 did not increase with time. Similarity within plot groups converged slightly at the lower elevations. Despite similarities between temporal and spatial trends in composition, trajectories of higher plots do not recapitulate those of lower plots, apparently because Lupinus was not an early colonist. Any vegetation convergence has been limited to plots that are in close proximity.     

Effects of plot size on the ordination of vegetation samples

Questions: Do ordination patterns differ when based on vegetation samples recorded in plots of different size? If so, how large is the effect of plot size relative to the effects of data set heterogeneity and of using presence/absence or cover‐abundance data? Can we combine plots of different size in a single ordination? Methods: Two homogeneous and two heterogeneous data sets were sampled in Czech forests and grasslands. Cover‐abundances of plant species were recorded in series of five or six nested quadrats of increasing size (forest 49‐961 m2; grassland 1‐49 m²). Separate ordinations were computed for plots of each size for each data set, using either species presences/absences or cover‐abundances recorded on an ordinal scale. Ordination patterns were compared with Procrustean analysis. Also, ordinations of data sets jointly containing plots of different size were calculated; effects of plot size were evaluated using a Monte Carlo test in constrained ordination. Results: The results were consistent between forest and grassland data sets. In homogeneous data sets, the effect of presence/absence vs. cover‐abundance was similar to, or larger than, the effect of plot size; for presence/absence data the differences between ordinations of differently sized plots were smaller than for cover‐abundance data. In heterogeneous data sets, the effect of plot size was larger than the effect of presence‐absence vs. cover‐abundance. The plots of smaller size (= 100 m2 in forests, = 4 m² in grasslands) yielded the most deviating ordination patterns. Joint ordinations of differently sized plots mostly did not yield patterns that would be artifacts of different plot size, except for plots from the homogeneous data sets that differed in size by a factor of four or higher. Conclusions: Variation in plot size does influence ordination patterns. Smaller plots tend to produce less stable ordination patterns, especially in data sets with low ß‐diversity and species cover‐abundances. Data sets containing samples from plots of different sizes can be used for ordination if they represent vegetation with large ß‐diversity. However, if data sets are homogeneous, i.e. with low ß‐diversity, the differences in plot sizes should not be very large, in order to avoid the danger of plot size differences distorting the real vegetation differentiation in ordination patterns.     

Patterns of species richness hotspots and estimates of their protection are sensitive to spatial resolution

Aim

Species richness is a measure of biodiversity often used in spatial conservation assessments and mapped by summing species distribution maps. Commission errors inherent those maps influence richness patterns and conservation assessments. We sought to further the understanding of the sensitivity of hotspot delineation methods and conservation assessments to commission errors, and choice of threshold for hotspot delineation.

Location

United States.

Methods

We created range maps and 30‐m and 1‐km resolution habitat maps for terrestrial vertebrates in the United States and generated species richness maps with each dataset. With the richness maps and the GAP Protected Areas Dataset, we created species richness hotspot maps and calculated the proportion of hotspots within protected areas; calculating protection under a range of thresholds for defining hotspots. Our method allowed us to identify the influence of commission errors by comparing hotspot maps.

Results

Commission errors from coarse spatial grain data and lack of porosity in the range data inflated richness estimates and altered their spatial patterns. Coincidence of hotspots from different data types was low. The 30‐m hotspots were spatially dispersed, and some were very long distances from the hotspots mapped with coarser data. Estimates of protection were low for each of the taxa. The relationship between estimates of hotspot protection and threshold choice was nonlinear and inconsistent among data types (habitat and range) and grain size (30‐m and 1‐km).

Main conclusions

Coarse mapping methods and grain sizes can introduce commission errors into species distribution data that could result in misidentifications of the regions where hotspots occur and affect estimates of hotspot protection. Hotspot conservation assessments are also sensitive to choice of threshold for hotspot delineation. There is value in developing species distribution maps with high resolution and low rates of commission error for conservation assessments.     

Shape matters in sampling plant diversity: Evidence from the field

The identification of shape and size of sampling units that maximises the number of plant species recorded in multiscale sampling designs has major implications in conservation planning and monitoring actions. In this paper we tested the effect of three sampling shapes (rectangles, squared, and randomly shaped sampling units) on the number of recorded species. We used a large dataset derived from the network of protected areas in the Siena Province, Italy. This dataset is composed of plant species occurrence data recorded from 604 plots (10 m × 10 m), each divided in a grid of 16 contiguous subplot units (2.5 m × 2.5 m). Moreover, we evaluated the effect of plot orientation along the main environmental gradient, to examine how the selection of plot orientation (when elongated plots are used) influences the number of species collected. In total, 1041 plant species were recorded from the study plots. A significantly higher species richness was recorded by the random arrangement of 4 subplots within each plot in comparison to the ‘rectangle’ and ‘square’ shapes. Although the rectangular shape captured a significant larger number of species than squared ones, plot orientation along the main environmental gradient did not show a systematic effect on the number of recorded species. We concluded that the choice of whether or not using elongated (rectangular) versus squared plots should dependent upon the objectives of the specific survey with squared plots being more suitable for assessing species composition of more homogeneous vegetation units and rectangular plots being more suited for recording more species in the pooled sample of a large area.     

Vegetation-environment relationships of old-growth spruce forest vegetation in Østmarka Nature Reserve, SE Norway, and comparison of three ordination methods

The relationship between vegetation and environmental variables has been studied in 100 sample plots, each 0.25 m², in old-growth spruce forest at Høgkollen, ØOstmarka Nature Reserve, SE Norway. Each sample plot was supplied with measurements of 13 environmental and 5 biotic variables. Parallel application of three ordination techniques, PCA, DCA and LNMDS, resulted in different sample plot configurations. PCA performed poorest due to strong influence of outliers and circumstantial evidence indicated better performance of LNMDS than DCA. Statistical analyses of the relationships between vegetation and ecological data revealed a parallel gradient in soil moisture (decreasing) and canopy closure (increasing) as the most important for differentiation of the vegetation. Species number and field layer cover decreased, while bottom layer cover increased, due to increasing cover of Dicranum majus , with decreasing soil moisture and increasing canopy closure. Constrained canonical correspondence analysis (CCA) was used to partition the variation of the species-sample plot matrix into spatial, environmental and unexplained variation, and combinations. The fraction of unexplained variation was high (80.9 %), most likely due to small sample plot size and short gradient lengths. Most of the explained variation was attributable to environmental factors alone (54.5%). Only 6.3% was shared between environmental and spatial variation, which indicated minor importance of broad-scale and geographically structured environmental variation. Strictly spatial variation constituted 39.3%. However, the spatially structured environmental variation was low, so the causes of spatial variation were likely not to be found among the measured environmental variables.     

The effect of positional error on fine scale species distribution models increases for specialist species

Species occurrences inherently include positional error. Such error can be problematic for species distribution models (SDMs), especially those based on fine-resolution environmental data. It has been suggested that there could be a link between the influence of positional error and the width of the species ecological niche. Although positional errors in species occurrence data may imply serious limitations, especially for modelling species with narrow ecological niche, it has never been thoroughly explored. We used a virtual species approach to assess the effects of the positional error on fine-scale SDMs for species with environmental niches of different widths. We simulated three virtual species with varying niche breadth, from specialist to generalist. The true distribution of these virtual species was then altered by introducing different levels of positional error (from 5 to 500 m). We built generalized linear models and MaxEnt models using the distribution of the three virtual species (unaltered and altered) and a combination of environmental data at 5 m resolution. The models’ performance and niche overlap were compared to assess the effect of positional error with varying niche breadth in the geographical and environmental space. The positional error negatively impacted performance and niche overlap metrics. The amplitude of the influence of positional error depended on the species niche, with models for specialist species being more affected than those for generalist species. The positional error had the same effect on both modelling techniques. Finally, increasing sample size did not mitigate the negative influence of positional error. We showed that fine-scale SDMs are considerably affected by positional error, even when such error is low. Therefore, where new surveys are undertaken, we recommend paying attention to data collection techniques to minimize the positional error in occurrence data and thus to avoid its negative effect on SDMs, especially when studying specialist species.     

Randomvs non-random sampling: Effects on patterns of species abundance, species richness and vegetation-environment relationships

From a strictly statistical perspective, most of the commonly used statistical tests cannot be performed on vegetation data obtained using a non-random sampling design. Despite this, non-randomly sampled plots such as phytosociological relevés still make sense: because they may focus on objectives not appropriately addressed by random sampling, such as the study of rare plant communities or species; and because random sampling is often more time-demanding and expensive. Considering the huge body of phytosociological data available, an interesting question arises: if we compare randomly and non-randomly sampled data sets, to what extent do the results of our analyses differ with respect to various species and vegetation parameters? We present an attempt to tackle this question by comparing two data sets collected in a 25 km² area close to the city of Bremen, northwestern Germany: the first data set consisted of 30 subjectively (non-randomly) placed, homogeneous plots across different plant communities, each of which was laid out in a nested design including 9 sizes from 0.5 m² to 1,000 m². The second data set consisted of 30 (again nested) plots randomly selected and located with a GPS device; plots were rejected only if they for some reason were inaccessible. The data collection was the same for both data sets: presence-absence of all vascular plants was recorded for the different plot sizes, and soil samples were collected for the determination of the values of some important environmental variables. For the comparison of the two data sets, we used either the complete data sets or sub-sets of those plots located in woodlands. The main results included the following: (1) Species abundance patterns: Random sampling resulted in a larger number of common and a smaller number of rare species than non-random sampling. (2) Species richness at different spatial scales: For the small plot sizes, the number of species in the non-randomly placed plots was higher than in the randomly placed plots, while the differences were less pronounced at larger spatial scales. As a consequence, also the parameters of species-area curves differed between the data sets, especially in the sub-set including woodland plots. (3) Vegetation differentiation: In random sampling, there was considerable redundancy, i.e., there were several plots with high floristic similarity. (4) Vegetation-environment relationships: The ordination scores of the non-randomly placed plots showed a larger number of significant correlations to soil parameters than the scores of randomly placed plots. The results suggest that conclusions drawn from the analysis of non-randomly placed plots such as phytosociological relevés may be biased, especially regarding estimates of species abundance and species richness patterns.     

Mapping forest vegetation patterns in an Atlantic-Mediterranean transitional area by integration of ordination and geostatistical techniques

Aims Forest vegetation variability may be explained by the complex interplay among several spatial structuring factors, including climate and topography. We modelled the spatial variability of forest vegetation assemblages and significant environmental variables along a complex environmental gradient or coenocline to produce a detailed cartographic database portraying the distribution of forests along it.Methods We combined an analysis of ordination coenoclines with kriging over 772 field data plots from the third Spanish National Forest Inventory in an Atlantic–Mediterranean transitional area (northern Spain).Important findings The best fitted empirical semivariogram revealed a strong spatial structure of forest species composition along the complex environmental gradient considered (the climatic–topographic gradient from north to south). The steady and gradual increase of semivariance with a marked lag distance indicates a gradual turnover of forest assemblages according to the climatic–topographic variations (regional or local). Two changes in the slope of the semivariogram suggest the existence of two different scales of spatial variation. The interpolation map by Kriging of forest vegetation assemblages along the main coenocline shows a clear spatial distribution pattern of trees and shrubs in accordance with the spatial variation of significant environmental variables. We concluded that the multivariate geostatistical approach is a suitable technique for spatial analysis of forest systems employing data from national forest inventories based on a regular network of field plots. The development of an assortment of maps describing changes in vegetation assemblages and variation in environmental variables is expected to be a suitable tool for an integrated forest management and planning.     

Accounting for tree spatial distribution in a comparison of plot sizes and shapes in dense forest and woodland in Benin (West Africa)

The study examined simultaneously, the effect of tree spatial distribution, inventory plot size and shape on the estimation error of basal area in two contrasting environments. Twenty and fifteen square plots of 1 ha each (divided into 100 quadrats of 0.01 ha) were randomly set in dense forest and woodland, respectively. Thirteen subplots of various shapes and sizes were obtained from the association of adjacent quadrats. Estimation error was calculated using residual mean square of one‐way ANOVA, based on replications of subplot within 1 ha plots. Tree spatial distribution was measured using Green index. Weighted linear regression and mixed effect models were applied to Box & Cox transformed data. In general, the estimation error of basal area decreased with increase in subplot size. However, the effects of tree spatial distribution and plot shape varied with the vegetation type. Where trees tended to be aggregated, estimation error increased with degree of aggregation, and rectangular plots of 0.24 ha produced an acceptable precision. It was concluded that 0.24 ha rectangular plots can be used in tropical environments where the target parameters vary constantly according to one direction, while square plots of the same size are optimal for reliable analysis in case of randomness.     

Spatial dynamics of forest decline: the role of neighbouring trees

Abstract. Changes in horizontal structure and the influence of neighbours on tree vitality were studied in a spruce forest under air pollution stress. Five permanent plots along an altitudinal gradient in the Krkono?e Mts., Czech Republic, were monitored for 18 years. Digitized maps of each plot were used for the analysis: the health of each tree, expressed by the defoliation degree was recorded each year, the biometrical characteristics were measured at five-year intervals. Various indices of neighbourhood competition were used to evaluate the interference with neighbours. The results show that the suppressed trees are most susceptible to other environmental stresses, particularly to the air pollution stress. Similarly, tree damage is more severe in plots near the natural timberline, where the trees are close to their natural environmental limits. The spatial pattern of surviving trees changes towards regularity at a scale of 2–5 m (K-function analysis) when the suppressed trees, usually those with close neighbours, die.