Responses of Epilithic Diatom Communities to Environmental Gradients in Some Finnish Rivers

Responses of the epilithic diatom communities to environmental gradients were analysed at 135 river stations in Finland. In addition, the biological quality of the water was evaluated using diatoms in five rivers having different types of human pressures. According to CCA, conductivity, total P, pH and humus content had the most significant effect on the diatom community structure. The ratio of the constrained axis (λ₁) and first unconstrained axis (λ₂) was highest for conductivity (1.21), total P (0.83) and pH (0.67). Physical factors had somewhat weaker effects. Five different diatom community types were separated in chemically different rivers using ordination. The effects of purified sewage on diatom‐inferred water quality estimates were strongest in late summer. The effects of cattle‐farming and cultivation were difficult to separate.     

Sequence clustering threshold has little effect on the recovery of microbial community structure

Analysis of microbial community structure by multivariate ordination methods, using data obtained by high‐throughput sequencing of amplified markers (i.e., DNA metabarcoding), often requires clustering of DNA sequences into operational taxonomic units (OTUs). Parameters for the clustering procedure tend not to be justified but are set by tradition rather than being based on explicit knowledge. In this study, we explore the extent to which ordination results are affected by variation in parameter settings for the clustering procedure. Amplicon sequence data from nine microbial community studies, representing different sampling designs, spatial scales and ecosystems, were subjected to clustering into OTUs at seven different similarity thresholds (clustering thresholds) ranging from 87% to 99% sequence similarity. The 63 data sets thus obtained were subjected to parallel DCA and GNMDS ordinations. The resulting community structures were highly similar across all clustering thresholds. We explain this pattern by the existence of strong ecological structuring gradients and phylogenetically diverse sets of abundant OTUs that are highly stable across clustering thresholds. Removing low‐abundance, rare OTUs had negligible effects on community patterns. Our results indicate that microbial data sets with a clear gradient structure are highly robust to choice of sequence clustering threshold.     

Metacommunity analysis of Mexican bats: environmentally mediated structure in an area of high geographic and environmental complexity

Aim We tested the hypothesis that distributions of Mexican bats are defined by shared responses to environmental gradients for the entire Mexican bat metacommunity and for each of four metaensembles (frugivores, nectarivores, gleaning insectivores, and aerial insectivores). Further, we identified the main environmental factors to which bats respond for multiple spatial extents. Location Mexico. Methods Using bat presence–absence data, as well as vegetation composition for each of 31 sites, we analysed metacommunity structure via a comprehensive, hierarchical approach that uses reciprocal averaging (RA) to detect latent environmental gradients corresponding to each metacommunity structure (e.g. Clementsian, Gleasonian, nested, random). Canonical correspondence analysis (CCA) was used to relate such gradients to variation in vegetation composition. Results For all bat species and for each ensemble, the primary gradient of ordination from RA, which is based on species data only, recovered an axis of humidity that matched that obtained for the first axis of the CCA ordination, which is based both on vegetation attributes and on species composition of sites. For the complete assemblage as well as for aerial and gleaning insectivores, analyses revealed Clementsian or quasi‐Clementsian structures with discrete compartments (distinctive groups of species along portions of an environmental gradient) coincident with the humidity gradient and with the Nearctic–Neotropical divide. Within‐compartment analysis further revealed Clementsian or quasi‐Clementsian structures corresponding to a gradient of elevational complexity that matched the second ordination axis in CCA. Frugivores had quasi‐nested structure, whereas nectarivores had Gleasonian structure. Main conclusions Our hierarchical approach to metacommunity analysis detected complex metacommunity structures associated with multiple environmental gradients at different spatial extents. More importantly, the resulting structures and their extent along environmental gradients are determined by ensemble‐specific characteristics and not by arbitrarily circumscribed study areas. This property renders compartment‐level analyses particularly useful for large‐scale ecological analyses in areas where more than one gradient may exist and species sorting may occur at multiple scales.     

Do community‐level models describe community variation effectively?

Aim The aim of community‐level modelling is to improve the performance of species distributional models by taking patterns of co‐occurrence among species into account. Here, we test this expectation by examining how well three community‐level modelling strategies (‘assemble first, predict later’, ‘predict first, assemble later’, and ‘assemble and predict together’) spatially project the observed composition of species assemblages. Location Europe. Methods Variation in the composition of European tree assemblages and its spatial and environmental correlates were examined with cluster analysis and constrained analysis of principal coordinates. Results were used to benchmark spatial projections from three community‐based strategies: (1) assemble first, predict later (cluster analysis first, then generalized linear models, GLMs); (2) predict first, assemble later (GLMs first, then cluster analysis); and (3) assemble and predict together (constrained quadratic ordination). Results None of the community‐level modelling strategies was able to accurately model the observed distribution of tree assemblages in Europe. Uncertainty was particularly high in southern Europe, where modelled assemblages were markedly different from observed ones. Assembling first and predicting later led to distribution models with the simultaneous occurrence of several types of assemblages in southern Europe that do not co‐occur, and the remaining strategies yielded models with the presence of non‐analogue assemblages that presently do not exist and that are much more strongly correlated with environmental gradients than with the real assemblages. Main conclusions Community‐level models were unable to characterize the distribution of European tree assemblages effectively. Models accounting for co‐occurrence patterns along environmental gradients did not outperform methods that assume individual responses of species to climate. Unrealistic assemblages were generated because of the models’ inability to capture fundamental processes causing patterns of covariation among species. The usefulness of these forms of community‐based models thus remains uncertain and further research is required to demonstrate their utility.     

Quantifying soil organic carbon in complex landscapes: an example of grassland undergoing encroachment of woody plants

The invasion of woody plants into grass‐dominated ecosystems has occurred worldwide during the past century with potentially significant impacts on soil organic carbon (SOC) storage, ecosystem carbon sequestration and global climate warming. To date, most studies of tree and shrub encroachment impacts on SOC have been conducted at small scales and results are equivocal. To quantify the effects of woody plant proliferation on SOC at broad spatial scales and to potentially resolve inconsistencies reported from studies conducted at fine spatial scales, information regarding spatial variability and uncertainty of SOC is essential. We used sequential indicator simulation (SIS) to quantify spatial uncertainty of SOC in a grassland undergoing shrub encroachment in the Southern Great Plains, USA. Results showed that both SOC pool size and its spatial uncertainty increased with the development of woody communities in grasslands. Higher uncertainty of SOC in new shrub‐dominated communities may be the result of their relatively recent development, their more complex above‐ and belowground architecture, stronger within‐community gradients, and a greater degree of faunal disturbance. Simulations of alternative sampling designs demonstrated the effects of spatial uncertainty on the accuracy of SOC estimates and enabled us to evaluate the efficiency of sampling strategies aimed at quantifying landscape‐scale SOC pools. An approach combining stratified random sampling with unequal point densities and transect sampling of landscape elements exhibiting strong internal gradients yielded the best estimates. Complete random sampling was less effective and required much higher sampling densities. Results provide novel insights into spatial uncertainty of SOC and its effects on estimates of carbon sequestration in terrestrial ecosystem and suggest effective protocol for the estimating of soil attributes in landscapes with complex vegetation patterns.     

微生物生态研究中BIOLOG方法数据分析及R语言实现

微生物生态研究中,对微生物群落结构、群落特征以及其与环境因素的关系的揭示,一直受到广泛关注;适当的数据分析方法有助于更清晰地刻画微生物群落结构特征,明确其与环境因素的关系。结合实例,对微生物生态研究中基于BIOLOG微平板技术的数据分析方法进行梳理,分别介绍数据读取整理、特征指数计算、非限制性排序、限制性排序、聚类分析、环境向量拟合、蒙特尔检验等常用数据操作及生态分析方法;针对不同方法结论,结合研究目标和生态理论给出具有统计学意义的解释,并评价不同方法特点及适用场景;分析过程以R语言实现,并提供全部代码。结果表明,BIOLOG方法产生数据能从多个角度表征微生物群落功能特征,并结合环境指标梯度进行分析;但BIOLOG数据可能不满足正态性分布,在基于正态分布的分析前应提前进行检验;排序分析时应慎用主成分分析,可优先采用其他基于距离矩阵的排序方法;R语言能够简化BIOLOG数据读取及操作,易于完成各类统计分析。本研究能够对微生物生态研究者科学选择应用统计分析方法、提高数据处理效率提供直接参考。     

Use of hidden Markov capture–recapture models to estimate abundance in the presence of uncertainty: Application to the estimation of prevalence of hybrids in animal populations

Estimating the relative abundance (prevalence) of different population segments is a key step in addressing fundamental research questions in ecology, evolution, and conservation. The raw percentage of individuals in the sample (naive prevalence) is generally used for this purpose, but it is likely to be subject to two main sources of bias. First, the detectability of individuals is ignored; second, classification errors may occur due to some inherent limits of the diagnostic methods. We developed a hidden Markov (also known as multievent) capture–recapture model to estimate prevalence in free‐ranging populations accounting for imperfect detectability and uncertainty in individual's classification. We carried out a simulation study to compare naive and model‐based estimates of prevalence and assess the performance of our model under different sampling scenarios. We then illustrate our method with a real‐world case study of estimating the prevalence of wolf (Canis lupus) and dog (Canis lupus familiaris) hybrids in a wolf population in northern Italy. We showed that the prevalence of hybrids could be estimated while accounting for both detectability and classification uncertainty. Model‐based prevalence consistently had better performance than naive prevalence in the presence of differential detectability and assignment probability and was unbiased for sampling scenarios with high detectability. We also showed that ignoring detectability and uncertainty in the wolf case study would lead to underestimating the prevalence of hybrids. Our results underline the importance of a model‐based approach to obtain unbiased estimates of prevalence of different population segments. Our model can be adapted to any taxa, and it can be used to estimate absolute abundance and prevalence in a variety of cases involving imperfect detection and uncertainty in classification of individuals (e.g., sex ratio, proportion of breeders, and prevalence of infected individuals).     

Reconstructing community relationships: the impact of sampling error, ordination approach, and gradient length

Effectively summarizing complex community relationships is an important feature in studies such as biodiversity, global change, and invasion ecology. The reliability of such community summaries depends on the degree of sampling variability that is present in the data, the structure of the data, and the choice of ordination method, but the relative importance of these factors is not understood. We compared the validity of results from different ordination methods by applying five levels of sampling error to a simulated coenoplane model at two gradient lengths using two types of data (abundance and presence–absence). The multivariate methods we compared were correspondence analysis (CA), detrended correspondence analysis (DCA), non-metric multidimensional scaling (NMDS), principal component analysis (PCA) and principal coordinates analysis (PCoA). Our results showed CA and PCA using presence–absence data were the most successful methods regardless of sampling error and gradient length, closely followed by the other methods using presence–absence data. With abundance data, PCA and CA were the most successful approaches with the short and long gradients, respectively. Approaches based on PCoA and NMDS using abundance data did not perform well regardless of the choice of distance measure used in the analysis. Both of these methods, along with the PCA using abundance data, were strongly affected by the longer gradient, leading to more distorted results.     

Intraspecific variation in epiphyte functional traits reveals limited effects of microclimate on community assembly in temperate deciduous oak canopies

Forest canopies are heterogeneous environments where changes in microclimate over short distances create an opportunity for niche‐based filtering of canopy‐dwelling species assemblages. This environmental filtering may not occur if species' physiological capacities are flexible or if rapid dispersal alleviates compositional differences. I assess the role of humidity, light and temperature gradients in structuring epiphyte communities in temperate deciduous oak (Quercus) canopies and determine whether gradients filter species with fixed traits or whether environmental constraints act primarily to alter individual phenotypes. I measured environmental conditions and seven functional traits related to water and light acquisition on individual macrolichens at 60 sample locations in northern red oaks Quercus rubra in two Piedmont forests in North Carolina, USA. The effects of environmental variables on individual‐level traits and community composition were evaluated using linear mixed models and constrained ordination (RDA). In general, traits and community composition responded weakly to environmental variables and trait variation within taxa was high. Cortex thickness exhibited the strongest response, such that individuals with thicker cortices were found in samples experiencing lower humidity and higher light levels. Overall, gradients of humidity, light and temperature were not strong environmental filters that caused large changes in community composition. This was probably due to phenotypic variability within taxa that enabled species to persist across the full range of environmental conditions measured. Thus, humidity affected the phenotype of individuals, but did not limit species distributions or alter community composition at the scale of branches within trees. Community and trait responses were primarily associated with site‐level differences in humidity, suggesting that in these forests landscape‐scale climatic gradients may be stronger drivers of epiphyte community assembly than intra‐canopy environmental gradients.     

Mapping gradual landscape-scale floristic changes in Amazonian primary rain forests by combining ordination and remote sensing

Aim We present a new method to economically map gradual changes in plant species composition in lowland rain forests using field data and satellite images. Such a method will be a useful tool in planning the sustainable use and conservation of Amazonian rain forests. Location The study covered an area of c. 700 km² of primary rain forest in Amazonian Ecuador. Methods We field inventoried the species composition of pteridophytes and Melastomataceae in 340 inventory plots (5 m × 50 m), described the prevailing topography and analysed soil cation concentration and texture. We used non‐metric multidimensional scaling (NMDS) to summarize the floristic variation among the inventory plots in three ordination dimensions. The scores of the three ordination axes were predicted to non‐visited places using a Landsat TM (thematic mapper) satellite image and the k nearest neighbours (knn) estimation method. To avoid extrapolation, we excluded from the analysis those pixel windows whose spectral values were not represented in the areas covered by field sampling. The accuracy of the predictions was evaluated by cross‐validation and by comparing the predictions based on spectrally nearest neighbours to the predictions based on random neighbours. Results The floristic gradients presented by NMDS ordination were interpretable in terms of topography, drainage and soil cation content. Thirteen percent of the cloud‐free pixels were excluded from the knn analysis to avoid extrapolation. The estimates of the floristic ordination scores based on spectrally nearest neighbours were always more accurate than estimates based on random neighbours. Main conclusions The presented method needs a relatively small input of work and resources, is mechanistic and produces maps that give relevant information on floristic variation over forest areas that are traditionally considered essentially homogeneous. Therefore, the method appears to have a great potential for use in mapping large areas of Amazonian rain forests.     

Uncovering the drivers of host‐associated microbiota with joint species distribution modelling

In addition to the processes structuring free‐living communities, host‐associated microbiota are directly or indirectly shaped by the host. Therefore, microbiota data have a hierarchical structure where samples are nested under one or several variables representing host‐specific factors, often spanning multiple levels of biological organization. Current statistical methods do not accommodate this hierarchical data structure and therefore cannot explicitly account for the effect of the host in structuring the microbiota. We introduce a novel extension of joint species distribution models (JSDMs) which can straightforwardly accommodate and discern between effects such as host phylogeny and traits, recorded covariates such as diet and collection site, among other ecological processes. Our proposed methodology includes powerful yet familiar outputs seen in community ecology overall, including (a) model‐based ordination to visualize and quantify the main patterns in the data; (b) variance partitioning to assess how influential the included host‐specific factors are in structuring the microbiota; and (c) co‐occurrence networks to visualize microbe‐to‐microbe associations.     

Integrating correlation between traits improves spatial predictions of plant functional composition

Functional trait composition is increasingly recognized as key to better understand and predict community responses to environmental gradients. Predictive approaches traditionally model the weighted mean trait values of communities (CWMs) as a function of environmental gradients. However, most approaches treat traits as independent regardless of known tradeoffs between them, which could lead to spurious predictions. To address this issue, we suggest jointly modeling a suit of functional traits along environmental gradients while accounting for relationships between traits. We use generalized additive mixed effect models to predict the functional composition of alpine grasslands in the Guisane Valley (France). We demonstrate that, compared to traditional approaches, joint trait models explain considerable amounts of variation in CWMs, yield less uncertainty in trait CWM predictions and provide more realistic spatial projections when extrapolating to novel environmental conditions. Modeling traits and their co‐variation jointly is an alternative and superior approach to predicting traits independently. Additionally, compared to a ‘predict first, assemble later’ approach that estimates trait CWMs post hoc based on stacked species distribution models, our ‘assemble first, predict later’ approach directly models trait‐responses along environmental gradients, and does not require data and models on species’ distributions, but only mean functional trait values per community plot. This highlights the great potential of joint trait modeling approaches in large‐scale mapping applications, such as spatial projections of the functional composition of vegetation and associated ecosystem services as a response to contemporary global change.     

Integrating over uncertainty in spatial scale of response within multispecies occupancy models yields more accurate assessments of community composition

Species abundance and community composition are affected not only by the local environment, but also by broader landscape and regional context. Yet, determining the spatial scales at which landscapes affect species remains a persistent challenge, hindering our ability to understand how environmental gradients shape communities. This problem is amplified by rare species and imperfect species detection. Here, we present a Bayesian framework that allows uncertainty surrounding the ‘true’ spatial scale of species’ responses (i.e. changes in presence/absence) to be integrated directly into a community hierarchical model. This scale‐selecting multispecies occupancy model (ssMSOM) estimates the scale of response, and shows high accuracy and correct levels of uncertainty in parameter estimates across a broad range of simulation conditions. An ssMSOM can be run in a matter of minutes, as opposed to the many hours required to run normal multispecies occupancy models at all queried spatial scales, and then conduct model selection – a problem that up to now has prohibited scale of response from being rigorously evaluated in an occupancy framework. Alternatives to the ssMSOM, such as GLM‐based approaches frequently fail to detect the correct spatial scale and magnitude of response, and are often falsely confident by favoring the incorrect parameter estimates, especially as species’ detection probabilities deviate from perfect. We further show how trait information can be leveraged to understand how individual species’ scales of response vary within communities. Integrating spatial scale selection directly into hierarchical community models provides a means of formally testing hypotheses regarding spatial scales of response, and more accurately determining the environmental drivers that shape communities.     

Random effects in censored ordinal regression: latent structure and Bayesian approach

This paper discusses random effects in censored ordinal regression and presents a Gibbs sampling approach to fit the regression model. A latent structure and its corresponding Bayesian formulation are introduced to effectively deal with heterogeneous and censored ordinal observations. This work is motivated by the need to analyze interval-censored ordinal data from multiple studies in toxicological risk assessment. Application of our methodology to the data offers further support to the conclusions developed earlier using GEE methods yet provides additional insight into the uncertainty levels of the risk estimates.     

A comparison of detrended correspondence analysis and principal co-ordinates analysis using four sets of Tasmanian vegetation data

Indirect gradient analysis, which entails the elucidation of relationships between trends in community composition and underlying environmental or successional gradients, is a major objective of ordination in plant ecology. Two ordination techniques, detrended correspondence analysis (DCA) and principal co-ordinates analysis (PCOA), were compared using three sets of Tasmanian vegetation data having known gradients and one set where the vegetation was expected to respond to diverse environmental variables. In every case, the results obtained by DCA were considered superior to, or at least as good as, those of PCOA. Hence, DCA appears to be the more suitable of the two methods for indirect gradient analysis.     

Environmental,Spatial and Structural Components in the Composition of Mountain Forest in the Bavarian Alps

A combined systematic and stratified sampling design was conducted in mountain forests of the Bavarian Alps to find the principal dimensions of compositional variation of vegetation and their environmental drivers. In 1,505 plots species composition, forest types and soil profiles were recorded. Data from 14 climate stations were included. As we hypothesized that the tree layer is more influenced by management than the understorey and that the former modifies the habitat of the latter, the two matrices were analysed separately and the species composition of the tree layer was used as a structural predictor variable for the understorey. We applied constrained ordination to reveal the main gradients in floristic composition and variance partitioning to examine the portions of climatic, edaphic, spatial and structural components. Ellenberg indicator values and a generalized linear model were used to test whether a significant spatial gradient exists from east to west, the main spatial extent of the investigation area. Forest types were used as an overlay to assess the underlying environmental factors. It turned out that explained variance of the tree layer was considerably lower than in the understorey. Tree layer composition was more influenced by climatic variables than by soil. In the understorey, edaphic and climatic variables contributed almost equally to explained variance, but the tree layer had an additional explanatory power. No continentality gradient could be detected within the investigation area. Plant communities were well separated along gradients of acidity, moisture, nutrients and climate, which broadly confirms the known gradients for montane and subalpine zonal forests in the region. The study provides a quantitative synthesis of the knowledge on a diverse set of community types, which has so far been subject to disparate and sectorial treatment in the Bavarian Alps.     

Forest community analysis and the point-centered quarter method

The point-centered-quarter (PCQ) method has been applied in community analysis since the publication of the method nearly 50 years ago. This and other distance methods offer increased sampling efficiency over fixed-area plots (FAP), but have long been known to produce biased density estimates when plant distribution deviates from random spatial patterns. Spatial indices have been developed to quantify the direction of this bias when plant distributions are aggregated or evenly distributed. Its continued use, especially in community analysis, requires additional scrutiny in measurements of community structure. We measured 14 forest stands of varying age, elevation and disturbance regime using FAP and PCQ methods. Density estimates were biased, with the point-centered quarter method lower than fixed-area plot estimates when stems were aggregated and higher when stems were evenly spaced. In general the PCQ method underestimated species richness. The efficiency of the PCQ method makes it popular for ordination studies, although comparison of community structure varied from 18% to 90% similarity between the measurements of species basal area in the same stands using the two different methods. The bias observed in calculations of stem density, species abundance and community similarity indicate that use of the PCQ method should be approached with caution when used in community level analysis.     

内蒙古草原植物的生态替代及其对全球变化下草原动态的指示（英）

在内蒙古草原区,从荒漠草原带到森林草原带,沿气候干燥度变化的方向调查了119个草原样地的植被与环境特征,依此定量分析了植被—环境关系。 根据表述环境因子取样质量的环境因子墒值及环境因子与出现频率最高的50个植物种间的平均相互信息,分析了所调查30个环境因子在决定植被组成中的作用优势度。结果表明,气候因子的影响是占绝对优势的,而地形和管理因子的作用是次一级的,或是地区性的。群落的物种组成与其第一优势种关联极好,因而依群落第—优势种识别的植物群落类型是组成和结构相对稳定的群落单位。基于植物种在降水和气温梯度上最适区域的计算及依此在植物降水-气温平面上的直接排序,揭示出广泛的草原植物在气候梯度上的生态替代。这种替代,尤其是包括针茅属(Stipa)植物在内的草原优势植物的替代,导致了针茅草原的生态替代。本研究也定量刻划了内蒙古主要草原植物群落的植被特征,及其在气候、土壤和人为影响梯度上的分布幅度和生态适宜区域。草原植物种和植物群落在气候和放牧梯度上的直接排序提供了气候和土地利用变化后草原植被动态的可能图景。     

卧龙自然保护区亚高山草甸的数量分类与排序

在野外植被调查的基础上,采用植被数量分析方法对岷江流域卧龙自然保护区亚高山草甸进行TWINSPAN分类和DCA排序,研究了植物种、植物群落与环境之间的关系.结果表明,该地区亚高山草甸共有植物139种,隶属于31科88属.应用数量分类方法将114个样方分为12个群落类型.在分析不同环境因子间的关系基础上,从定量的角度揭示了影响群落分布的主要因素是海拔梯度和土壤含水量.DCA排序图反映出排序轴的生态意义,第1轴反映了各群落类型所在环境的海拔梯度,从上到下,随着海拔的升高,植物群落或植物种的耐寒性越来越强;第2轴基本上反映了各群落类型所在环境的土壤含水量.排序结果与分类结果比较吻合,反映出植物群落类型和物种分布随环境因子梯度变化的趋势.