Distance decay of similarity,effects of environmental noise and ecological heterogeneity among species in the spatio‐temporal dynamics of a dispersal‐limited community

The joint spatial and temporal fluctuations in community structure may be due to dispersal, variation in environmental conditions, ecological heterogeneity among species and demographic stochasticity. These factors are not mutually exclusive, and their relative contribution towards shaping species abundance distributions and in causing species fluctuations have been hard to disentangle. To better understand community dynamics when the exchange of individuals between localities is very low, we studied the dynamics of the freshwater zooplankton communities in 17 lakes located in independent catchment areas, sampled at end of summer from 2002 to 2008 in Norway. We analysed the joint spatial and temporal fluctuations in the community structure by fitting the two‐dimensional Poisson lognormal model under a two‐stage sampling scheme. We partitioned the variance of the distribution of log abundance for a random species at a random time and location into components of demographic stochasticity, ecological heterogeneity among species, and independent environmental noise components for the different species. Non‐neutral mechanisms such as ecological heterogeneity among species (20%) and spatiotemporal variation in the environment (75%) explained the majority of the variance in log abundances. Overdispersion relative to Poisson sampling and demographic stochasticity had a small contribution to the variance (5%). Among a set of environmental variables, lake acidity was the environmental variable that was most strongly related to decay of community similarity in space and time.     

Assessment of species diversity from species abundance distributions at different localities

We show how the spatial structure of species diversity can be analyzed using the correlation between the log abundances of the species in the communities, assuming that two communities at different localities can be described by a bivariate lognormal species abundance distribution. A useful property of this approach is that the log abundances of the species at two localities can be considered as samples from a bivariate normal distribution defined by only five parameters. The variances and the correlation can be estimated by maximum likelihood methods even if there is no information about the sampling intensity and the number of unobserved species. This method also enables estimation of over-dispersion in the sampling relative to a Poisson distribution that allows sampling adjustment of the estimate of β-diversity. Furthermore, we also obtain a partitioning of species diversity into additive components of α-, β- and γ-diversity. For instance, if the correlation between the log abundances of the species is close to one, the same species will be common and rare in the two communities and the β-diversity will be low. We illustrate this approach by analysing similarities of communities of rare and endangered species of oak-living beetles in south-eastern Norway. The number of recorded species was estimated to be only 48.1% of the total number of species actually present in these communities. The correlations among communities dropped rather quickly with distance with a scaling of order 200 km. This illustrates large spatial heterogeneity in species composition, which should be accounted for in the design of schemes of such devices for assessing species diversity in these habitat-types.     

Heterogeneous communities with lognormal species abundance distribution: Species-area curves and sustainability

Heterogeneous species abundance models are models in which the dynamics differ between species, described by variation among parameters defining the dynamics. Using a dynamic and heterogeneous species abundance model generating the lognormal species abundance distribution it is first shown that different degrees of heterogeneity may result in equivalent species abundance distributions. An alternative to Preston's canonical lognormal model is defined by assuming that reduction in resources, for example reduction in available area, increases the density regulation of each species. This leads to species-individual curves and species-area curves that are approximately linear in a double logarithmic plot. Preston's canonical parameter gamma varies little along these curves and takes values in the neighborhood of one. Quite remarkably, the curves, which define the sensitivity of the community to area reductions, are independent of the heterogeneity among species for this model. As a consequence, the curves can be estimated from a single sample from the community using the Poisson lognormal distribution. It is shown how to perform sensitivity analysis with respect to over-dispersion in sampling relative to the Poisson distribution as well as sampling intensity, that is, the fraction of the community sampled. The method is exemplified by analyzing three simulated data sets.     

Factors controlling community structure in heterogeneous metacommunities

1.  Modern theories of species coexistence recognize the importance of environmental heterogeneity.
2.  Despite the existence of many observational studies, few experimental studies have evaluated the extent to which, and mechanisms by which, fixed spatial heterogeneity increases community diversity and alters community structure.
3  In experimental protist communities, we found that non-spatial mechanisms unrelated to heterogeneity were responsible for a large component of baseline diversity. Above this baseline, fixed spatial heterogeneity produced small but predictable increases in metacommunity diversity through species sorting, while heterogeneity and dispersal together altered local community structure (composition and relative abundance) through mass effects.
4.  Our study illustrates that heterogeneity is not always the strongest driver of diversity, while experimentally demonstrating mechanisms by which heterogeneity alters community structure.     

Fungal diversity in ectomycorrhizal communities: sampling effort and species detection

A number of recent review articles on ectomycorrhizal (ECM) fungal community diversity have highlighted the unprecedented increase in the number of publications on this ecologically important but neglected area. The general features of these species-rich, highly dynamic and complex communities have been comprehensively covered but one aspect crucial to our assessment of diversity, namely the sampling of ECM communities has received less attention. This is a complex issue with two principal components, the physical sampling strategy employed and the life cycle traits of the ECM fungi being examined. Combined, these two components provide the image that we perceive as ECM diversity. This contribution will focus primarily on the former of these components using a recent study from a pine forest in central Sweden to highlight some sampling problems and also to discuss some features common to ECM communities. The two commonly used elements of diversity, species richness and community evenness, present rather different problems in the assessment of ECM diversity. The applicability of using current measures of abundance (number or percentage of root tips colonised) to determine community evenness is discussed in relation to our lack of knowledge on the size of individual genets of ECM fungi. The inherent structure of most ECM communities, with a few common species and a large number of rare species, severely limits our ability to accurately assess species richness. A discussion of theoretical detection limits is included that demonstrates the importance of the sampling effort (no. of samples or tips) involved in assessing species richness. Species area abundance plots are also discussed in this context. It is suggested that sampling strategy (bulk samples versus multiple collections of single tips) may have important consequences when sampling from communities where root tip densities differ. Finally, the need for studies of the spatial distribution of ECM on roots in relation to small-scale soil heterogeneity and of the temporal aspects of ECM community dynamics is raised.     

Spatial scaling of arbuscular mycorrhizal fungal diversity is affected by farming practice

Evidence suggests that microbial communities show patterns of spatial scaling which can be driven by geographical distance and environmental heterogeneity. Here we demonstrate that human management can have a major impact on microbial distribution patterns at both the local and landscape scale. Mycorrhizal fungi are vital components of terrestrial ecosystems, forming a mutualistic symbiosis with plant roots which has a major impact on above ground ecology and productivity. We used contrasting agricultural systems to investigate the spatial scaling of the most widespread mycorrhizal fungus group, the arbuscular mycorrhizal fungi (AMF). Using multiple sampling sites with a maximum separation of 250 km we describe for the first time the roles which land management, environmental heterogeneity and geographical distance play in determining spatial patterns of microbial distribution. Analysis of AMF taxa–area relationships at each sampling site revealed that AMF diversity and spatial turnover was greater under organic relative to conventional farm management. At the regional scale (250 km) distance–decay analyses showed that there was significant change in AMF community composition with distance, and that this was greater under organic relative to conventional management. Environmental heterogeneity was found to be the major factor determining turnover of AMF taxa at the landscape scale. Overall we demonstrate that human management can play a key role in determining the turnover of microbial communities at both the local and regional scales.     

Analysis of Grassland Vegetation of the Southwest Heilongjiang Steppe （China） Using the Power Law

In 1997, we conducted a vegetation survey in three semi-arid natural grasslands （steppes） with different livestock grazing intensities in Southwest Heilongjiang Province, China, The dominant grassland species was the grass Stipa baicalensis Roshev. Grasslands with light, intermediate, and heavy grazing intensities were located 10, 5, and 2 km from a village, respectively. Villagers use the steppe to raise cattle, horses, sheep, and goats. Each of the three grasslands was surveyed by placing 100 quadrats （50 cm×50 cm） along a 50 m line transect. Each quadrat was divided into four equal areas （25 cm×25 cm; S-quadrats） and all plant species occurring in each of these smaller areas were identified and recorded. These data were summarized into frequency distributions and the percentage of S-quadrats containing a given species and the variance of each species were estimated. The power law was applied to these estimates. The power law was used to evaluate the spatial heterogeneity and frequency of occurrence for each species in the grassland community. The lightly grazed grassland exhibited high spatial heterogeneity （caused by large plant size）, the highest species diversity, and a high occurrence of S. baicalensis. In contrast, the heavily grazed grassland exhibited high spatial heterogeneity （caused by patchy populations of small plant size）, low species diversity, and a low occurrence of S. baicalensis. We judged that the heavily grazed grassland was overgrazed and exclusion of livestock from the degraded areas is necessary for recovery.     

宜昌百里荒草山草坡群落物种分布的空间趋势分析

通过基于CCA的趋势面分析和空间插值方法,研究了宜昌百里荒山地草场的群落结构空间变化,以及群落结构空间趋势与主要环境因子的相关性。结果表明,该群落物种空间中的群落结构面和物理空间中的空间趋势面可以很好地吻合,说明该群落的结构由一种具有强烈空间结构化特征的机制控制。对群落结构和空间趋势影响最显著的环境因素是土壤有效磷。     

Estimating similarity of communities: a parametric approach to spatio‐temporal analysis of species diversity

Several stochastic models with environmental noise generate spatio‐temporal Gaussian fields of log densities for the species in a community. Combinations of such models for many species often lead to lognormal species abundance distributions. In spatio‐temporal analysis it is often realistic to assume that the same species are expected to occur at different times and/or locations because extinctions are rare events. Spatial and temporal β‐diversity can then be analyzed by studying pairs of communities at different times or locations defined by a bivariate lognormal species abundance model in which a single correlation occurs. This correlation, which is a measure of similarity between two communities, can be estimated from samples even if the sampling intensities vary and are unknown, using the bivariate Poisson lognormal distribution. The estimators are approximately unbiased, although each specific correlation may be rather uncertain when the sampling effort is low with only a small fraction of the species represented in the samples. An important characteristic of this community correlation is that it relates to the classical Jaccard‐ or the Sørensen‐indices of similarity based on the number of species present or absent in two communities. However, these indices calculated from samples of species in a community do not necessarily reflect similarity of the communities because the observed number of species depends strongly on the sampling intensities. Thus, we propose that our community correlation should be considered as an alternative to these indices when comparing similarity of communities. We illustrate the application of the correlation method by computing the similarity between temperate bird communities.     

A conceptual framework for the spatial analysis of functional trait diversity

Functional trait diversity is a popular tool in modern ecology, mainly used to infer assembly processes and ecosystem functioning. Patterns of functional trait diversity are shaped by ecological processes such as environmental filtering, species interactions and dispersal that are inherently spatial, and different processes may operate at different spatial scales. Adding a spatial dimension to the analysis of functional trait diversity may thus increase our ability to infer community assembly processes and to predict change in assembly processes following disturbance or land‐use change. Richness, evenness and divergence of functional traits are commonly used indices of functional trait diversity that are known to respond differently to large‐scale filters related to environmental heterogeneity and dispersal and fine‐scale filters related to species interactions (competition). Recent developments in spatial statistics make it possible to separately quantify large‐scale patterns (variation in local means) and fine‐scale patterns (variation around local means) by decomposing overall spatial autocorrelation quantified by Moran's coefficient into its positive and negative components using Moran eigenvector maps (MEM). We thus propose to identify the spatial signature of multiple ecological processes that are potentially acting at different spatial scales by contrasting positive and negative components of spatial autocorrelation for each of the three indices of functional trait diversity. We illustrate this approach with a case study from riparian plant communities, where we test the effects of disturbance on spatial patterns of functional trait diversity. The fine‐scale pattern of all three indices was increased in the disturbed versus control habitat, suggesting an increase in local scale competition and an overall increase in unexplained variance in the post‐disturbance versus control community. Further research using simulation modeling should focus on establishing the proposed link between community assembly rules and spatial patterns of functional trait diversity to maximize our ability to infer multiple processes from spatial community structure.     

Benthic macrofauna in Tasmanian estuaries: scales of distribution and relationships with environmental variables

Information on the distribution of species richness, faunal density, biomass and estimated productivity of benthic invertebrates in Tasmanian estuaries was quantified at a variety of spatial and temporal scales to assess general hypothesis relating community metrics to such environmental variables as salinity, seagrass biomass and sediment particle size. An associated aim was to assess appropriate scales of investigation for soft-sediment biota distributed in estuaries, including whether patterns identified at individual sites, estuaries, tidal levels or times are likely to have more general relevance. Faunal biomass and productivity varied principally at between-estuary (10 to 1000 km) and replicate-sample (1 m) scales, indicating that these two community metrics were largely responding to estuary-wide effects, such as nutrient loading, and to microhabitat features, rather than to locality characteristics at intermediate scales such as salinity, anoxia or sediment particle size. By contrast, faunal density showed greater response to tidal height (1 to 100 m) and to factors distributed at the locality scale within estuary (10 km) than to factors between estuary. Both faunal density and species richness in estuaries declined over three- and fivefold ranges down the shore from high water mark to the shallow sublittoral, while estimated productivity and biomass showed highest overall levels at low water mark. The greatest component of variance in species richness was associated with tidal height, with variance then distributed approximately evenly between other spatial scales examined. At the low-tide and shallow subtidal levels, species richness, faunal biomass and estimated productivity were all highly correlated with salinity and biomass of macrophytes, whereas faunal density was highly correlated with biomass of macrophytes only. Relationships between environmental and biological variables examined were poorly defined at high tidal levels. Seasonal plus interannual variance was much lower than spatial variance—a clear indication that sampling effort in studies would generally be better directed across a range of localities than for a single locality to be repeatedly investigated over time.     

Local and Regional Effects on Community Structure of Dung Beetles in a Mainland-Island Scenario

Understanding the ecological mechanisms driving beta diversity is a major goal of community ecology. Metacommunity theory brings new ways of thinking about the structure of local communities, including processes occurring at different spatial scales. In addition to new theories, new methods have been developed which allow the partitioning of individual and shared contributions of environmental and spatial effects, as well as identification of species and sites that have importance in the generation of beta diversity along ecological gradients. We analyzed the spatial distribution of dung beetle communities in areas of Atlantic Forest in a mainland-island scenario in southern Brazil, with the objective of identifying the mechanisms driving composition, abundance and biomass at three spatial scales (mainland-island, areas and sites). We sampled 20 sites across four large areas, two on the mainland and two on the island. The distribution of our sampling sites was hierarchical and areas are isolated. We used standardized protocols to assess environmental heterogeneity and sample dung beetles. We used spatial eigenfunctions analysis to generate the spatial patterns of sampling points. Environmental heterogeneity showed strong variation among sites and a mild increase with increasing spatial scale. The analysis of diversity partitioning showed an increase in beta diversity with increasing spatial scale. Variation partitioning based on environmental and spatial variables suggests that environmental heterogeneity is the most important driver of beta diversity at the local scale. The spatial effects were significant only at larger spatial scales. Our study presents a case where environmental heterogeneity seems to be the main factor structuring communities at smaller scales, while spatial effects are more important at larger scales. The increase in beta diversity that occurs at larger scales seems to be the result of limitation in species dispersal ability due to habitat fragmentation and the presence of geographical barriers.     

Geostatistical modelling of regional bird species richness: exploring environmental proxies for conservation purpose

Identifying spatial patterns in species diversity represents an essential task to be accounted for when establishing conservation strategies or monitoring programs. Predicting patterns of species richness by a model-based approach has recently been recognised as a significant component of conservation planning. Finding those environmental predictors which are related to these patterns is crucial since they may represent surrogates of biodiversity, indicating in a fast and cheap way the spatial location of biodiversity hotspots and, consequently, where conservation efforts should be addressed. Predictive models based on classical multiple linear regression or generalised linear models crowded the recent ecological literature. However, very often, problems related with spatial autocorrelation in observed data were not adequately considered. Here, a spatially-explicit data-set on birds presence and distribution across the whole Tuscany region was analysed. Species richness was calculated within 1 × 1 km grid cells and 10 environmental predictors (e.g. altitude, habitat diversity and satellite-derived landscape heterogeneity indices) were included in the analysis. Integrating spatial components of variation with predictive ecological factors, i.e. using geostatistical models, a general model of bird species richness was developed and used to obtain predictive regional maps of bird diversity hotspots. A meaningful subset of environmental predictors, namely habitat productivity, habitat heterogeneity, combined with topographic and geographic information, were included in the final geostatistical model. Conservation strategies based on the predicted hotspots as well as directions for increasing sampling effort efficiency could be extrapolated by the proposed model.     

β-Diversity and Species Accumulation in Antarctic Coastal Benthos: Influence of Habitat,Distance and Productivity on Ecological Connectivity

High Antarctic coastal marine environments are comparatively pristine with strong environmental gradients, which make them important places to investigate biodiversity relationships. Defining how different environmental features contribute to shifts in β-diversity is especially important as these shifts reflect both spatio-temporal variations in species richness and the degree of ecological separation between local and regional species pools. We used complementary techniques (species accumulation models, multivariate variance partitioning and generalized linear models) to assess how the roles of productivity, bio-physical habitat heterogeneity and connectivity change with spatial scales from metres to 100''s of km. Our results demonstrated that the relative importance of specific processes influencing species accumulation and β–diversity changed with increasing spatial scale, and that patterns were never driven by only one factor. Bio-physical habitat heterogeneity had a strong influence on β-diversity at scales <290 km, while the effects of productivity were low and significant only at scales >40 km. Our analysis supports the emphasis on the analysis of diversity relationships across multiple spatial scales and highlights the unequal connectivity of individual sites to the regional species pool. This has important implications for resilience to habitat loss and community homogenisation, especially for Antarctic benthic communities where rates of recovery from disturbance are slow, there is a high ratio of poor-dispersing and brooding species, and high biogenic habitat heterogeneity and spatio-temporal variability in primary production make the system vulnerable to disturbance. Consequently, large areas need to be included within marine protected areas for effective management and conservation of these special ecosystems in the face of increasing anthropogenic disturbance.     

Heterogeneity in dynamic species abundance models: the selective effect of extinction processes

There is often large variation in traits across the species of a community. In particular, variation in life history traits affecting population dynamics is likely to affect the species abundance distribution. Applying a dynamic and heterogeneous species abundance model we study how differences in extinction time for species in a community act as a force changing the distribution of dynamic parameters across species. This process may generate communities that are more heterogeneous then the heterogeneity measured as the species enter the community. Analytical results for some versions of the lognormal and gamma species abundance model are given as exemplifications of this process, together with stochastic simulations demonstrating the temporal changes in number of species and community heterogeneity through time.     

A comparative analysis of alternative approaches to fitting species-abundance models

Aims Fits of species-abundance distributions to empirical data are increasingly used to evaluate models of diversity maintenance and community structure and to infer properties of communities, such as species richness. Two distributions predicted by several models are the Poisson lognormal (PLN) and the negative binomial (NB) distribution; however, at least three different ways to parameterize the PLN have been proposed, which differ in whether unobserved species contribute to the likelihood and in whether the likelihood is conditional upon the total number of individuals in the sample. Each of these has an analogue for the NB. Here, we propose a new formulation of the PLN and NB that includes the number of unobserved species as one of the estimated parameters. We investigate the performance of parameter estimates obtained from this reformulation, as well as the existing alternatives, for drawing inferences about the shape of species abundance distributions and estimation of species richness.Methods We simulate the random sampling of a fixed number of individuals from lognormal and gamma community relative abundance distributions, using a previously developed 'individual-based' bootstrap algorithm. We use a range of sample sizes, community species richness levels and shape parameters for the species abundance distributions that span much of the realistic range for empirical data, generating 1?000 simulated data sets for each parameter combination. We then fit each of the alternative likelihoods to each of the simulated data sets, and we assess the bias, sampling variance and estimation error for each method.Important findings Parameter estimates behave reasonably well for most parameter values, exhibiting modest levels of median error. However, for the NB, median error becomes extremely large as the NB approaches either of two limiting cases. For both the NB and PLN,>90% of the variation in the error in model parameters across parameter sets is explained by three quantities that corresponded to the proportion of species not observed in the sample, the expected number of species observed in the sample and the discrepancy between the true NB or PLN distribution and a Poisson distribution with the same mean. There are relatively few systematic differences between the four alternative likelihoods. In particular, failing to condition the likelihood on the total sample sizes does not appear to systematically increase the bias in parameter estimates. Indeed, overall, the classical likelihood performs slightly better than the alternatives. However, our reparameterized likelihood, for which species richness is a fitted parameter, has important advantages over existing approaches for estimating species richness from fitted species-abundance models.     

黄土丘陵沟壑区不同坡向撂荒草地植物群落种群空间格局

本研究以黄土丘陵沟壑区4种不同坡向的草地植物群落为研究对象,探讨其物种组成和多样性特征,并运用幂乘方法则分析了群落的空间异质性和种群空间格局.结果表明: 铁杆蒿在不同坡向均有分布,为群落优势种;其他物种在不同坡向群落中的地位及对群落空间格局的贡献不同.不同坡向植物群落的空间分布格局均为集群分布,且聚集程度大小为阳坡＞半阳坡＞半阴坡＞阴坡.群落空间异质性指数与Simpson多样性指数呈显著正相关,与Margalef丰富度指数、Shannon多样性指数和Pielou均匀度指数呈显著负相关.优势种铁杆蒿、亚优势种达乌里胡枝子和长芒草,以及少数伴生种如猪毛蒿、臭草的相对空间异质性指数大于群落整体空间异质性指数,使群落趋于集群分布;多数伴生种如芦苇、苦荬菜、白羊草的相对空间异质性指数与群落整体空间异质性指数相近,它们与群落自身分布状态一致;刺儿菜、米口袋、茭蒿等偶见种的相对空间异质性指数小于群落整体空间异质性指数,降低了群落的聚集程度.综上,群落的聚集程度主要由优势种、亚优势种和偶见种共同决定,其中优势种、亚优势种和部分伴生种促使群落呈聚集分布,偶见种则降低了群落的集群分布.     

Quantitative estimation of insect diversity inhabiting Calotropis procera in industrial and nonindustrial areas of West Bengal, India

This paper compares the species richnesses, Bray–Curtis similarities, species diversities, evennesses, and the results of various analyses (multivariate, logarithmic series, and lognormal distribution analyses) of insects from four orders (viz., Hemiptera, Orthoptera, aculeate Hymenoptera, and Coleoptera) inhabiting a medicinal plant, Calotropis procera among eight sites within industrial and nonindustrial zones in the Purba Medinipur district (erstwhile Midnapore), West Bengal, India. Both cluster analysis and principal component analysis indicate an effect of industrialization on the diversity of the four insect orders. The species community at each site exhibits an almost pure lognormal distribution. Bootstrap estimation of species diversity also points to the effect of industrialization. A comparison of the values of x and N/S of the logarithmic series indicates a typical relationship between the species community and its diversity. The number of unique species among the eight study sites is 6 according to jackknife estimation. Beta-diversity analysis of the samples shows the impact of industrialization on the abundances and species richness of the insects. Altogether, 16 types of analytical procedure are applied for this purpose, and their suitabilities for this kind of data set are assessed. Disturbance, probably due to industrial emissions, has changed the compositions and structures of the insect communities out to a distance of at least 6 km from the core industrial area.     

Upward shift of alpine plants increases floristic similarity of mountain summits

Question: Does the upward shift of species and accompanied increase in species richness, induced by climate change, lead to homogenization of Alpine summit vegetation? Location: Bernina region of the Swiss Alps. Methods: Based on a data set from previous literature we expand the analysis from species richness to beta‐diversity and spatial heterogeneity. Species compositions of mountain summits are compared using a two‐component heterogeneity concept including the mean and the variance of Sørensen similarities calculated between the summits. Non‐metric multidimensional scaling is applied to explore developments of single summits in detail. Results: Both heterogeneity components (mean dissimilarity and variance) decrease over time, indicating a trend towards more homogeneous vegetation among Alpine summits. However, the development on single summits is not strictly unidirectional. Conclusions: The upward shift of plant species leads to homogenization of alpine summit regions. Thus, increasing alpha‐diversity is accompanied by decreasing beta‐diversity. Beta‐diversity demands higher recognition by scientists as well as nature conservationists as it detects changes which cannot be described using species richness alone.