Considerations for ecological reconstruction of historic vegetation: Analysis of the spatial uncertainties in the California Vegetation Type Map dataset

Historical ecological data are valuable for reconstructing early environmental and vegetation community conditions and examining change to vegetation communities and disturbance regimes over decadal and longer temporal scales, but these data are not free from error. We examine the spatial uncertainties associated with 18,000 vegetation plots in the decades-old California Vegetation Type Mapping (VTM) dataset that has been digitized for use in modern ecological analysis. We examine the relationship between plot location error and basemap year, basemap scale, plot elevation, plot slope, and general plot habitat type. Bivariate plots and classification and regression tree analysis (CART) confirm that basemap scale and age are the strongest explanation of total error. Total error in spatial location for all plots ranged from 126.9 m to 462.3 m; plots drawn on 15-min (1:62,500-scale) basemaps had total error ranging from 126 m to 199.7 m, and plots drawn on coarser-scale basemaps (1:125,000-scale) had total errors ranging from 241 m to 461.2 m. Relocation of individual VTM plots is considerably easier for plots originally marked on 1:62,500-scale maps produced after 1904, and more difficult for plots originally marked on 1:125,000-scale maps produced before 1898. Biogeographical analyses that rely less on relocating individual plots, such as environmental niche modeling or multivariate analyses can alleviate some of these concerns, but all researchers using these kinds of data need to consider errors in spatial location of plots. The paper also discusses ways in which the differing spatial error might be reported and visualized by those using the dataset, and how the data might be used in modern environmental niche models.     

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.     

Secondary Succession in Two South-eastern Ontario Old-fields

Secondary succession was documented throughout a long-term study of two abandoned hay fields in south-eastern Ontario. In one field (Field 1), eighteen plots (measuring 100 m²) were established. Nine of these plots were ploughed to bare ground and nine remained in their original abandoned state. A third set of nine plots were established in a second field (Field 2), which had been abandoned 5 years earlier. Detailed surveys of the plant flora were conducted twice yearly, insect sweep data were collected bi-monthly, from 1976 – 1988, 1995 and 1998. Soil and climate data were collected and community patterns analyzed against these variables and field treatment (ploughing). While the vegetation in all plots began predominantly as a grass mixture, only a few exhibited the typical secondary succession pathway for this region. The remaining plots persist at various stages of succession, with some still in the early stages. Succession stage, as quantified by a succession ratio, varied across these plots largely by soil moisture, and to a lesser degree by field treatment (ploughing). When grouped by soil moisture, wet plots had lower ratios (characterized by later species and trees), than dry plots (characterized by early or grass species). Species richness was highest in plots that were wet, regardless of field treatment. Partial CC analysis using CANOCO of plant species versus field treatment, soil moisture and insect outbreak determined their contribution to the plant variation observed. It is generally accepted that plots over time, regardless of initial composition, will converge to become similar. Convergence of our plots, grouped according to soil moisture, did not occur. Analyses of insect outbreaks against changes in the succession ratio over time provide some evidence that herbivory can influence the likelihood of plots to converge.     

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.     

A microarray analysis for differential gene expression in the soybean genome using Bioconductor and R

This article describes specific procedures for conducting quality assessment of Affymetrix GeneChip(R) soybean genome data and for performing analyses to determine differential gene expression using the open-source R programming environment in conjunction with the open-source Bioconductor software. We describe procedures for extracting those Affymetrix probe set IDs related specifically to the soybean genome on the Affymetrix soybean chip and demonstrate the use of exploratory plots including images of raw probe-level data, boxplots, density plots and M versus A plots. RNA degradation and recommended procedures from Affymetrix for quality control are discussed. An appropriate probe-level model provides an excellent quality assessment tool. To demonstrate this, we discuss and display chip pseudo-images of weights, residuals and signed residuals and additional probe-level modeling plots that may be used to identify aberrant chips. The Robust Multichip Averaging (RMA) procedure was used for background correction, normalization and summarization of the AffyBatch probe-level data to obtain expression level data and to discover differentially expressed genes. Examples of boxplots and MA plots are presented for the expression level data. Volcano plots and heatmaps are used to demonstrate the use of (log) fold changes in conjunction with ordinary and moderated t-statistics for determining interesting genes. We show, with real data, how implementation of functions in R and Bioconductor successfully identified differentially expressed genes that may play a role in soybean resistance to a fungal pathogen, Phakopsora pachyrhizi. Complete source code for performing all quality assessment and statistical procedures may be downloaded from our web source: http://css.ncifcrf.gov/services/download/MicroarraySoybean.zip.     

Application of multivariate analysis toward biotech processes: case study of a cell-culture unit operation

This paper examines the feasibility of using multivariate data analysis (MVDA) for supporting some of the key activities that are required for successful manufacturing of biopharmaceutical products. These activities include scale-up, process comparability, process characterization, and fault diagnosis. Multivariate data analysis and modeling were performed using representative data from small-scale (2 L) and large-scale (2000 L) batches of a cell-culture process. Several input parameters (pCO2, pO2, glucose, pH, lactate, ammonium ions) and output parameters (purity, viable cell density, viability, osmolality) were evaluated in this analysis. Score plots, loadings plots, and VIP plots were utilized for assessing scale-up and comparability of the cell-culture process. Batch control charts were found to be useful for fault diagnosis during routine manufacturing. Finally, observations made from reviewing VIP plots were found to be in agreement with conclusions from process characterization studies demonstrating the effectiveness of MVDA as a tool for extracting process knowledge.     

Detecting spatial patterns in species composition with multiple plot similarity coefficients and singularity measures

Recently, several multiple plot similarity indices have been presented that cure some of the problems associated with the approaches for the calculation of compositional similarity for groups of plots by averaging pairwise similarities. These new indices calculate the similarity between more than two plots whilst considering the species composition on all compared plots. The resulting similarity value is true for the whole group of plots considered (called neighborhood in the following). Here, we review the possibilities for multiple plot similarity calculation and additionally explore coefficients that examine multiple plot similarity between a reference plot (named focal plot in the following) and any number of surrounding plots. The latter represent measures of singularity. Further, we establish a framework for applying these two kinds of multiple plot measures to gridded data including an algorithm for testing the significance of calculated values against random expectations. The capability of multiple plot measures for detecting species compositional gradients and local/regional hotspots within this framework is tested. For this purpose, several artificial data sets with known gradients in species composition (random, gradient, central hotspot, hotspot bottom right) are constructed on the basis of a real data set from a Tundra ecosystem in northern Sweden (Abisko). The coefficients that best reflect the positions of the plots on the realized gradients in species composition are considered as performing best with regard to pattern detection. The tested measures of multiple plot similarity and singularity produced considerably different results when applied to one real and 4 artificial data sets. The newly proposed symmetric singularity coefficient has the best overall performance which makes it suitable for local/regional hotspot detection and for incorporating local to regional similarity analyses in reserve selection procedures.     

Estimating species richness at large spatial scales using data from small discrete plots

Estimating species richness in large biomes is a central challenge in ecology and conservation biology. However, accurate census data is often available only from small discrete plots distributed within the biome. Using tree species richness data collected from 48 plots (0.25 ha each) widely distributed through 60 000 km² in the rainforests of the Western Ghats of southern India, we test the application of a proposed method for estimating species richness at large scales from measured species commonalities between pairs of censused plots. We show that the method allows extrapolation of species richness from a scale of 0.25 ha plots to that of the entire biome, or 10⁵ km².     

Regional variation in Caribbean dry forest tree species composition

How does tree species composition vary in relation to geographical and environmental gradients in a globally rare tropical/subtropical broadleaf dry forest community in the Caribbean? We analyzed data from 153 Forest Inventory and Analysis (FIA) plots from Puerto Rico and the U.S. Virgin Islands (USVI), along with 42 plots that we sampled in the Bahamian Archipelago (on Abaco and Eleuthera Islands). FIA data were collected using published protocols. In the Bahamian Archipelago, we recorded terrain and landscape variables, and identified to species and measured the diameter of all stems ≥5 cm at 1.3 m height in 10 m radius plots. All data were analyzed using clustering, ordination, and indicator species analysis at regional and local scales. Regionally, the largest cluster group included over half of all plots and comprised plots from all three island groups. Indicator species were native Bursera simaruba (Burseraceae) and Metopium toxiferum (Anacardiaceae). Species composition was similar to dry forests throughout the region based on published studies. Other groups we identified at the regional scale consisted of many Puerto Rico and USVI plots that were dominated by non-native species, documenting the widespread nature of novel ecosystems. At the local scale the Bahamian data clustered into two main groups corresponding largely to the two islands sampled, a pattern consistent with the latitudinal aridity gradient. Bahamian dry forests share previously undocumented compositional similarity with native-dominated dry forests found throughout the Caribbean, but they lack extensive post-disturbance novel dry forests dominated by non-native trees found in the Greater Antilles.     

Power analysis to determine sample size for monitoring vegetation change in salt marsh habitats

Numerous initiatives are underway throughout New England and elsewhere to quantify salt marsh vegetation change, mostly in response to habitat restoration, sea level rise, and nutrient enrichment. To detect temporal changes in vegetation at a marsh or to compare vegetation among different marshes with a degree of statistical certainty an adequate sample size is required. Based on sampling 1 m² vegetation plots from 11 New England salt marsh data sets, we conducted a power analysis to determine the minimum number of samples that were necessary to detect change between vegetation communities. Statistical power was determined for sample sizes of 5, 10, 15, and 20 vegetation plots at an alpha level of 0.05. Detection of subtle differences between vegetation data sets (e.g., comparing vegetation in the same marsh over two consecutive years) can be accomplished using a sample size of 20 plots with a reasonable probability of detecting a difference when one truly exists. With a lower sample size, and thus lower power, there is an increased probability of not detecting a difference when one exists (e.g., Type II error). However, if investigators expect to detect major changes in vegetation (e.g., such as those between an un-impacted and a highly impacted marsh) then a sample size of 5, 10, or 15 plots may be appropriate while still maintaining adequate power. Due to the relative ease of collecting vegetation data, we suggest a minimum sample size of 20 randomly located 1 m² plots when developing monitoring designs to detect vegetation community change of salt marshes. The sample size of 20 plots per New England salt marsh is appropriate regardless of marsh size or permanency (permanent or non-permanent) of the plots.     

How do trees respond to species mixing in experimental compared to observational studies?

For decades, ecologists have investigated the effects of tree species diversity on tree productivity at different scales and with different approaches ranging from observational to experimental study designs. Using data from five European national forest inventories (16,773 plots), six tree species diversity experiments (584 plots), and six networks of comparative plots (169 plots), we tested whether tree species growth responses to species mixing are consistent and therefore transferrable between those different research approaches. Our results confirm the general positive effect of tree species mixing on species growth (16% on average) but we found no consistency in species‐specific responses to mixing between any of the three approaches, even after restricting comparisons to only those plots that shared similar mixtures compositions and forest types. These findings highlight the necessity to consider results from different research approaches when selecting species mixtures that should maximize positive forest biodiversity and functioning relationships.     

Timing of white-tailed deer browsing affects wetland plant communities

White-tailed deer (Odocoileus virginianus Zimm.) have the potential to alter plant community composition and successional trajectory by browsing differentially on forb, graminoid, and woody species. The objective of this study was to determine if seasonal elimination of deer browsing changed wetland plant community composition and structure. We established 66 deer exclosure plots in two wetland vegetation communities in Canaan Valley, West Virginia, USA. Plots were established in April 2005 and monitoring was conducted in June and October, 2005–2007 to obtain data on both early and late species. Composition differed between control and treatment plots in Solidago spp.–Rubus hispidus L. communities in late-protected plots (enclosed July–October) when data were gathered in October. Community composition also varied in early-protected plots (enclosed April–July) when data were gathered in June. Forb cover increased in treatment plots in Solidago spp.–Rubus hispidus communities. Composition differed in Populus tremuloides Michx. communities in late-protected and continuously protected plots. There was no increase in cover by any wetland indicator status categories after 2 years of protection. Timing of browse played an influential role in the effect that white-tailed deer have on wetland plant communities. Our results suggest that reducing browsing pressure seasonally can increase forb species cover.     

带状间伐对巴山木竹林复壮更新及大熊猫取食的影响

间伐是竹林复壮更新的有效手段之一.以陕西佛坪国家自然保护区为研究区域,对大熊猫(Ailuropoda melanoleuca)冬季栖息地非主要采食区域竹林老龄化严重且密度过大的巴山木竹(Bashania fargesii)林进行为期3年(2017至2019年)的带状间伐样方监测,研究间伐对竹林复壮更新以及大熊猫觅食选择...     

Similar taxonomic richness but different communities of ectomycorrhizas in native forests and non-native plantation forests

This investigation sought to examine if there was a difference between the ectomycorrhizal (ECM) communities in plots of native oak and introduced Scots pine and Sitka spruce forest. The ECM communities in four plots of each forest type were described, from five soil cores collected in each plot, by morphotyping, internal transcribed spacer (ITS)-restriction fragment length polymorphism matching of mycorrhizas and sporocarps and ITS sequencing. Fifty-one distinct taxa were distinguished; 25 were identified to species level, 11 to genus and 15 remained unidentified. Seventy-one ECM species were recorded as sporocarps from the forest plots; most (43 species) were found in the Sitka spruce plots. The below-ground ECM communities of the different forest types did not differ significantly with respect to species richness of taxa on roots, but differed in species composition. Multivariate analysis produced a clear separation of the communities of the different forest types using below-ground data, but the above-ground sporocarp data did not separate the forest types. Moreover, results of a Mantel test found no relationship between the above- and below-ground similarity matrices. The oak plots had the most distinctive ECM community, with Laccaria amethystina and Elaphomyces granulatus being frequent. The Sitka spruce plots showed the lowest intra-forest type similarity and were often dominated by "nursery type" ectomycorrhizas. There was only 10% similarity between the above- and below-ground ECM species in these plots, different colonisation methods of ectomycorrhizal taxa and insufficient below-ground sampling being possible reasons for this disparity. Our results indicate that plantations of non-native Sitka spruce can support similar levels of ECM diversity as native forests.     

利用TM数据提取粤西地区的森林生物量

通过样方调查获取森林材积 ,借助于 GPS技术为调查样方准确定位。通过研究针叶林和阔叶林材积与 Landsat TM数据各波段及 NDVI和 RVI等指数的相关性 ,筛选出估算针叶林和阔叶林材积的光谱因子。根据 TM数据 7个波段信息及其线形与非线形组合 ,应用逐步回归技术分别建立估算针叶林和阔叶林材积的最优光谱模型。进而研究了粤西及附近地区的森林生物量和森林覆盖。结果表明 :若不计少量云层及地形影响 ,粤西及附近地区的森林覆盖率约为 47.8%。西江干流以北地区的森林覆盖率明显高于西江以南 ,阔叶林主要分布在西江以北 ,西江以南主要为针叶林。粤西及附近地区的森林生物量多介于 2 3～ 45 1 t· hm- 2之间 ;在约 1 90 5 0 km2范围内 ,森林生物量共计 9.2 2× 1 0 7t左右。西江以北地区的森林生物量较高 ,西江以南的森林生物量较低。生物量 >40 0 t· hm- 2的森林主要分布在黑石顶自然保护区及附近、鼎湖山及附近、德庆东北部和广宁东部。     

Spatial analysis of two-species interactions

Summary In this paper, I present and discuss some methods for the analysis of univariate and bivariate spatial point pattern data. Examples of such data in ecology include x-y coordinates of organisms in mapped field plots. I illustrate the methods with analyses of data from mapped field plots on Mount St. Helens, Washington state, USA. The statistical methods I emphasize are graphical methods that rely on analysis of distances between organisms. Hypothesis testing for methods like these is easily done using Monte Carlo methods, which I also discuss. For both univariate and bivariate analyses, I find that second-order methods such as K-function plots are often preferable to first-order methods (i.e., QQ-plots). However, for multivariate analyses, these second-order methods are more sensitive to small sample sizes than first-order analyses.     

Bias in vegetation databases? A comparison of stratified‐random and preferential sampling

Aim: Vegetation plots collected since the early 20th century and stored in large vegetation databases are an important source of ecological information. These databases are used for analyses of vegetation diversity and estimation of vegetation parameters, however such analyses can be biased due to preferential sampling of the original data. In contrast, modern vegetation survey increasingly uses stratified‐random instead of preferential sampling. To explore how these two sampling schemes affect vegetation analyses, we compare parameters of vegetation diversity based on preferentially sampled plots from a large vegetation database with those based on stratified‐random sampling. Location: Moravian Karst and Silesia, Czech Republic. Methods: We compared two parallel analyses of forest vegetation, one based on preferentially sampled plots taken from a national vegetation database and the other on plots sampled in the field according to a stratified‐random design. We repeated this comparison for two different regions in the Czech Republic. We focussed on vegetation properties commonly analysed using data from large vegetation databases, including alpha (within‐plot) diversity, cover and participation of different species groups, such as endangered and alien species within plots, total species richness of data sets, beta diversity and ordination patterns. Results: The preferentially sampled data sets obtained from the database contained more endangered species and had higher beta diversity, whereas estimates of alpha diversity and representation of alien species were not consistently different between preferentially and stratified‐randomly sampled data sets. In ordinations, plots from the preferential samples tended to be more common at margins of plot scatters. Conclusions: Vegetation data stored in large databases are influenced by researcher subjectivity in plot positioning, but we demonstrated that not all of their properties necessarily differ from data sets obtained by stratified‐random sampling. This indicates the value of vegetation databases for use in biodiversity studies; however, some analyses based on these databases are clearly biased and their results must be interpreted with caution.     

Local recruitment of great and blue tits (Parus major, P. caevuleus) in relation to study plot size and degree of isolation

Few studies are available that analyse variation in dispersal rates between populations. Here we present data on the degree of local recruitment (LR) of great and blue tits ( Parus major. P. caeruleus ) in a large number of nest-box plots in northern Belgium that vary in size, degree of isolation and population density. These plots have been studied for varying lengths of time over the past 40 yr. As expected. LR was higher among male than female birds, and this difference was most pronounced in blue tits. Regardless of species and sex. more local recruits were found in larger plots but also in plots with a higher population density. Thus, LR increased with population size (number of pairs) but levelled off in the largest populations at ca 50% for male birds. LR was higher in forest fragments compared to plots inside continuous forest, suggesting that fragmentation reduces exchange among local populations. However, LR was not related to the degree of isolation of individual fragments. We also found a weak but significant increase in LR with productivity (number of nestlings) of plots, but no relationship with other demographic variables.     

Historical review of termite activity at forest service termiticide test sites from 1971 to 2004

The U.S. Forest Service has a long history of providing termiticide efficacy data used for product registration and labeling. Four primary test sites (Arizona and Florida, Mississippi, and South Carolina [hereafter southeast]) have been used for this purpose. Various parameters of termite attack at water-only control plots were examined in this study to assess the relative pressures of termites at each site. Termiticide studies installed between 1971 and 2001 by using ground board (GB) and concrete slab (CS) test methods were included. GB control plots were attacked 85% of the time in the southeast, about twice the rate observed in Arizona (43%). CS plots were attacked 59-70% of the time in the southeast, significantly higher than in Arizona (43%). Termites were slower to initiate attack at control plots in Arizona compared with the southeast, and they were up to twice as slow at GB controls. Once initial attack began, GB plots were reattacked at higher percentages in the southeast (89-90%) than in Arizona (67%). Reattack at CS plots ranged from 65% in Arizona and South Carolina to 76% in Mississippi. Termites caused less damage to wooden blocks in control plots in Arizona than the southeast. Attack rates at controls generally declined during the 1990s, but these rates have rebounded since 2000, except at CS plots in Arizona and South Carolina. Statistical analysis of attacks at plots treated with chlorpyrifos, cypermethrin, fenvalerate, and permethrin also was undertaken. Time to initial termite attack (failure) of the organophosphate chlorpyrifos was generally shorter in Arizona than in the southeast, whereas time to initial attack in plots treated with one of three pyrethroids (cypermethrin, fenvalerate, and permethrin) was generally longer in Arizona.