Evaluating the performance of neutrality tests of a local community using a niche‐structured simulation model

Understanding the processes that underlie species diversity and abundance in a community is a fundamental issue in community ecology. While the species abundance distributions (SADs) of various natural communities may be well explained by Hubbell's neutral model, it has been repeatedly pointed out that Hubbell's SAD‐fitting approach lacks the ability to detect the effects of non‐neutral factors such as niche differentiation; however, our understanding of its quantitative effect is limited. Herein, we conducted extensive simulations to quantitatively evaluate the performance of the SAD‐fitting method and other recently developed tests. For simulations, we developed a niche model that incorporates the random stochastic demography of individuals and the nonrandom replacements of those individuals, i.e. niche differentiation. It therefore allows us to explore situations with various degrees of niche differentiation. We found that niche differentiation has strong effects on SADs and the number of species in the community under this model. We then examined the performance of these neutrality tests, including Hubbell's SAD‐fitting method, using extensive simulations. It was demonstrated that all these tests have relatively poor performance except for the cases with very strong niche structure, which is in accordance with previous studies. This is likely because two important parameters in Hubbell's model are usually unknown and are commonly estimated from the data to be tested. To demonstrate this point, we showed that the precise estimation of the two parameters substantially improved the performance of these neutrality tests, indicating that poor performance can be owed to overfitting Hubbell's neutral model with unrealistic parameters. Our results therefore emphasize the importance of accurate parameter estimation, which should be obtained from data independent of the local community to be tested.     

Universal scaling of species‐abundance distributions across multiple scales

The scale‐dependent species abundance distribution (SAD) is fundamental in ecology, but few spatially explicit models of this pattern have thus far been studied. Here we show spatially explicit neutral model predictions for SADs over a wide range of spatial scales, which appear to match empirical patterns qualitatively. We find that the assumption of a log‐series SAD in the metacommunity made by spatially implicit neutral models can be justified with a spatially explicit model in the large area limit. Furthermore, our model predicts that SADs on multiple scales are characterized by a single, compound parameter that represents the ratio of the survey area to the species’ average biogeographic range (which is in turn set by the speciation rate and the dispersal distance). This intriguing prediction is in line with recent empirical evidence for a universal scaling of the species‐area curve. Hence we hypothesize that empirical SAD patterns will show a similar universal scaling for many different taxa and across multiple spatial scales.     

Neutral theory and the species abundance distribution: recent developments and prospects for unifying niche and neutral perspectives

Published in 2001, The Unified Neutral Theory of Biodiversity and Biogeography (UNTB) emphasizes the importance of stochastic processes in ecological community structure, and has challenged the traditional niche‐based view of ecology. While neutral models have since been applied to a broad range of ecological and macroecological phenomena, the majority of research relating to neutral theory has focused exclusively on the species abundance distribution (SAD). Here, we synthesize the large body of work on neutral theory in the context of the species abundance distribution, with a particular focus on integrating ideas from neutral theory with traditional niche theory. First, we summarize the basic tenets of neutral theory; both in general and in the context of SADs. Second, we explore the issues associated with neutral theory and the SAD, such as complications with fitting and model comparison, the underlying assumptions of neutral models, and the difficultly of linking pattern to process. Third, we highlight the advances in understanding of SADs that have resulted from neutral theory and models. Finally, we focus consideration on recent developments aimed at unifying neutral‐ and niche‐based approaches to ecology, with a particular emphasis on what this means for SAD theory, embracing, for instance, ideas of emergent neutrality and stochastic niche theory. We put forward the argument that the prospect of the unification of niche and neutral perspectives represents one of the most promising future avenues of neutral theory research.     

A metacommunity-scale comparison of species-abundance distribution models for plant communities of eastern Australia

Various models of the species-abundance distribution (SAD) have been proposed to fit empirically derived data however there is no general consensus as to which model provides the best fit. Further, the zero-sum multinomial SAD model (ZSM) was proposed as a metacommunity model, yet it has not previously been fitted at the metacommunity scale. We note that SAD models based on compound lognormal distributions (such as the Poisson-lognormal, PLN, and the negative binomial-lognormal models, NBLN) can also be thought of as metacommunity models, and we compare these with the ZSM when fitted as metacommunity models to SADs of related communities.
We collected five datasets in the Sydney Basin, eastern Australia, representing five different types of subtropical/temperate plant communities ranging from closed warm-temperate rainforest to open wet sclerophyll forest to dry sclerophyll woodland. For each type of plant community, five local communities were identified across the Sydney Basin, and SAD data collected in five randomly placed 0.2-ha quadrats at each local community. Analysis was performed at two levels: all abundance data from each local community were pooled across each metacommunity and analysed as a single pooled community; and a metacommunity model was fitted to all five local communities of a community type, simultaneously. For the pooled data, we considered the negative-binomial (NB) and the log-series (LS) models in addition to ZSM, PLN and NBLN. All five models performed similarly, however the LS had a better fit to three pooled communities and the ZSM and PLN to the remaining two communities. By contrast, the ZSM performed statistically better against the PLN and NBLN when considered as a metacommunity model. We conclude that the ZSM generally provides a more reliable null model for metacommunity abundance data than the lognormal model.     

天童常绿阔叶林中常绿与落叶物种的物种多度分布格局

物种多度分布是对群落内不同物种多度情况的数量描述, 作为理解群落性质的基石, 其形成机制受到广泛关注。常绿与落叶物种是两类有着不同物候性状与生长策略的物种集合, 它们普遍共存于常绿阔叶林中。在天童20 ha常绿阔叶林动态监测样地内, 虽然常绿物种在物种多度和胸高断面积等指标上占有绝对优势, 但其在物种丰富度上却不及落叶物种。分析两者在常绿阔叶林中的物种多度分布特征, 能够为理解常绿阔叶林内物种多样性的维持机制提供一个全新的视角。为此, 我们基于天童样地的植被调查数据, 一方面利用累积经验分布函数对两类生活型植物的物种多度分布进行描述, 使用Kolmogorov-Smirnov检验(K-S检验)判断其差异性; 另一方面, 采用纯统计模型、生态位模型和中性理论模型对二者的物种多度分布曲线进行拟合, 并基于K-S检验的结果以及AIC值进行最优模型的筛选。结果显示: (1)常绿与落叶物种的物种多度分布曲线间并无显著差异。(2)在选用的3类模型中, 中性理论模型对于两类物种多度分布曲线的拟合效果都最好, 而生态位模型的拟合效果则一般。从上述结果可以看出, 尽管常绿与落叶物种在物种数量和多度等方面均存在差异, 但它们却有着近似的物种多度分布格局以及相近的多样性维持机制。然而, 鉴于模型拟合的结果只能作为理解群落多样性构建机制的必要非充分条件, 故而只能初步判定中性过程对于常绿与落叶物种的物种多样性格局影响更大, 却不能排除或衡量诸如生态位分化等其他过程在两类生活型多样性格局形成中的贡献。     

The continuous smooth hockey stick: a newly proposed spawner‐recruitment model

Spawner‐recruit relationships are important components of fisheries management. The two most widely used models have been criticized for unsatisfactory fits and biologically unreasonable extrapolations. A simple hockey stick model has been shown to provide more robust predictions, however, this model is not widely used, possibly because the abrupt change from density‐dependence to density‐independence is unrealistic and the piecewise model is difficult to fit. Here I present a continuous two‐parameter model that resembles a smoothed hockey stick and provides parameter estimates similar to the piecewise hockey stick. The new model is easily parameterized with regular curve‐fitting routines.     

亚热带喀斯特地区不同地形植物群落物种多度分布格局

为探究亚热带喀斯特地区不同地形下植物群落物种多度分布格局,揭示不同地形下群落的物种多度格局形成的作用机制,丰富该地区植物群落构建理论,该文以贵州茂兰喀斯特地区山脊、槽谷、鞍部、洼地4种典型地形下植物群落的乔木层与灌木层为对象,统计物种多度,采用累计经验分布曲线(ECDF)表征多度分布格局,采取Wilcoxon秩和检验探究不同地形之间物种多度的差异性。采用不同生态学模型进行多度拟合,利用Kolmogorov-Smirnov(K-S)检验与赤池信息量准则(AIC)检验模型接受与拟合优度。结果表明:(1)不同地形下植物群落的个体数量与物种数存在差异,鞍部个体数最多,洼地的物种数最多,山脊的个体数、物种数均最少。(2)不同地形下植物群落的乔木层物种多度格局无显著差异,灌木层之间出现显著差异,山脊与鞍部、洼部,鞍部与槽谷、洼部都存在显著差异。(3)不同地形下乔木层物种多度对中性模型接受较好,其中山脊拟合最优,对生态位模型接受较差,仅山脊与鞍部通过两种生态位模型,拟合优度不及中性模型。灌木层对中性模型接受也较好,鞍部拟合最优,对生态位模型接受较差,仅洼地通过断棍模型。整体而言,乔木层比灌木层能更好地接受两种生态学模型,可能乔木层物种多度格局有更明显生态过程的印记,但不同地形下灌木层拟合优度差异更大,可能与灌木层物种对环境变化更剧烈有关。不同地形会引起群落构建不同程度的生态学过程,物种多度分布格局会逐渐适应地形。     

The community ecology of pathogens: coinfection,coexistence and community composition

Disease and community ecology share conceptual and theoretical lineages, and there has been a resurgence of interest in strengthening links between these fields. Building on recent syntheses focused on the effects of host community composition on single pathogen systems, we examine pathogen (microparasite) communities using a stochastic metacommunity model as a starting point to bridge community and disease ecology perspectives. Such models incorporate the effects of core community processes, such as ecological drift, selection and dispersal, but have not been extended to incorporate host–pathogen interactions, such as immunosuppression or synergistic mortality, that are central to disease ecology. We use a two‐pathogen susceptible‐infected (SI) model to fill these gaps in the metacommunity approach; however, SI models can be intractable for examining species‐diverse, spatially structured systems. By placing disease into a framework developed for community ecology, our synthesis highlights areas ripe for progress, including a theoretical framework that incorporates host dynamics, spatial structuring and evolutionary processes, as well as the data needed to test the predictions of such a model. Our synthesis points the way for this framework and demonstrates that a deeper understanding of pathogen community dynamics will emerge from approaches working at the interface of disease and community ecology.     

Community versus single‐species distribution models for British plants

Aim Species distribution models are increasingly used to predict the impacts of global change on whole ecological communities by modelling the individualistic niche responses of large numbers of species. However, it is not clear whether this single‐species ensemble approach is preferable to community‐wide strategies that represent interspecific associations or shared responses to environmental gradients. Here, we test the performance of two multi‐species modelling approaches against equivalent single‐species models. Location Great Britain. Methods Single‐ and multi‐species distribution models were fitted for 701 native British plant species at a 10‐km grid scale. Two machine learning methods were used – classification and regression trees (CARTs) and artificial neural networks (ANNs). The single‐species versions are widely used in ecology but their multivariate extensions are less well known and have not previously been evaluated against one another. We compared their abilities to predict species distributions, community compositions and species richness in an independent geographical region reserved from model‐fitting. Results The single‐ and multi‐species models performed similarly, although the community models gave slightly poorer predictive accuracy by all measures. However, from the point of view of the whole community they were much simpler than the array of single‐species models, involving orders of magnitude fewer parameters. Multi‐species approaches also left greater residual spatial autocorrelation than the individualistic models and, contrary to expectation, were relatively less accurate for rarer species. However, the fitted multi‐species response curves had lower tendency for pronounced discontinuities that are unlikely to be a feature of realized niche responses. Main conclusions Although community distribution models were slightly less accurate than single‐species models, they offered a highly simplified way of modelling spatial patterns in British plant diversity. Moreover, an advantage of the multi‐species approach was that the modelling of shared environmental responses resolved more realistic response curves. However, there was a slight tendency for community models to predict rare species less accurately, which is potentially disadvantageous for conservation applications. We conclude that multi‐species distribution models may have potential for understanding and predicting the structure of ecological communities, but were slightly inferior to single‐species ensembles for our data.     

Using functional traits to predict species growth trajectories,and cross‐validation to evaluate these models for ecological prediction

Modeling plant growth using functional traits is important for understanding the mechanisms that underpin growth and for predicting new situations. We use three data sets on plant height over time and two validation methods—in‐sample model fit and leave‐one‐species‐out cross‐validation—to evaluate non‐linear growth model predictive performance based on functional traits. In‐sample measures of model fit differed substantially from out‐of‐sample model predictive performance; the best fitting models were rarely the best predictive models. Careful selection of predictor variables reduced the bias in parameter estimates, and there was no single best model across our three data sets. Testing and comparing multiple model forms is important. We developed an R package with a formula interface for straightforward fitting and validation of hierarchical, non‐linear growth models. Our intent is to encourage thorough testing of multiple growth model forms and an increased emphasis on assessing model fit relative to a model's purpose.     

A Probit Latent Class Model with General Correlation Structures for Evaluating Accuracy of Diagnostic Tests

Summary Traditional latent class modeling has been widely applied to assess the accuracy of dichotomous diagnostic tests. These models, however, assume that the tests are independent conditional on the true disease status, which is rarely valid in practice. Alternative models using probit analysis have been proposed to incorporate dependence among tests, but these models consider restricted correlation structures. In this article, we propose a probit latent class model that allows a general correlation structure. When combined with some helpful diagnostics, this model provides a more flexible framework from which to evaluate the correlation structure and model fit. Our model encompasses several other PLC models but uses a parameter‐expanded Monte Carlo EM algorithm to obtain the maximum‐likelihood estimates. The parameter‐expanded EM algorithm was designed to accelerate the convergence rate of the EM algorithm by expanding the complete‐data model to include a larger set of parameters and it ensures a simple solution in fitting the PLC model. We demonstrate our estimation and model selection methods using a simulation study and two published medical studies.     

Among‐species overlap in rodent body size distributions predicts species richness along a temperature gradient

Temperature is widely regarded as a major driver of species richness, but the mechanisms are debated. Niche theory suggests temperature may affect richness by filtering traits and species in colder habitats while promoting specialization in warmer ones. However, tests of this theory are rare because niche dimensions are challenging to quantify along broad thermal gradients. Here, we use individual‐level trait data from a long‐term monitoring network spanning a large geographic extent to test niche‐based theory of community assembly in small mammals. We examined variation in body size among 23 communities of North American rodents sampled across the National Ecological Observatory Network (NEON), ranging from northern hardwood forests to subtropical deserts. We quantified body size similarity among species using a metric of overlap that accounts for individual variation, and fit a structural equation model to disentangle the relationships between temperature, productivity, body size overlap, and species richness. We document a latitudinal gradient of declining similarity in body size among species towards the tropics and overall increase in the dimensions of community‐wide trait space in warmer habitats. Neither environmental temperature nor net primary productivity directly affect rodent species richness. Instead, temperature determines the community‐wide niche space that species can occupy, which in turn alters richness. We suggest a latitudinal gradient of trait space expansion towards the tropics may be widespread and underlie gradients in species diversity.     

A novel genealogical approach to neutral biodiversity theory

Current neutral theory in community ecology views local biodiversity as a result of the interplay between speciation, extinction and immigration. Simulations and a mean‐field approximation have been used to study this neutral theory. As simulations have limitations of convergence and the mean‐field approximation ignores dependencies between species’ abundances when applied to species‐abundance data, there is still no final conclusion whether the neutral theory or the traditional lognormal model describes community structure best. We present a novel analytical framework, based on the genealogy of individuals in the local community, to overcome the problems of previous approaches, and show, using Bayesian statistics, that the lognormal model provides a slightly better fit to the species‐abundance distribution of a much‐discussed tropical tree community. A key feature of our approach is that it shows the tight link between genetic and species diversity, which creates important perspectives to future integration of evolutionary and community ecological theory.     

The implicit assumption of symmetry and the species abundance distribution

Species abundance distributions (SADs) have played a historical role in the development of community ecology. They summarize information about the number and the relative abundance of the species encountered in a sample from a given community. For years ecologists have developed theory to characterize species abundance patterns, and the study of these patterns has received special attention in recent years. In particular, ecologists have developed statistical sampling theories to predict the SAD expected in a sample taken from a region. Here, we emphasize an important limitation of all current sampling theories: they ignore species identity. We present an alternative formulation of statistical sampling theory that incorporates species asymmetries in sampling and dynamics, and relate, in a general way, the community-level SAD to the distribution of population abundances of the species integrating the community. We illustrate the theory on a stochastic community model that can accommodate species asymmetry. Finally, we discuss the potentially important role of species asymmetries in shaping recently observed multi-humped SADs and in comparisons of the relative success of niche and neutral theories at predicting SADs.     

sars: an R package for fitting,evaluating and comparing species–area relationship models

The species–area relationship (SAR) constitutes one of the most general ecological patterns globally. A number of different SAR models have been proposed. Recent work has shown that no single model universally provides the best fit to empirical SAR datasets: multiple models may be of practical and theoretical interest. However, there are no software packages available that a) allow users to fit the full range of published SAR models, or b) provide functions to undertake a range of additional SAR‐related analyses. To address these needs, we have developed the R package ‘sars’ that provides a wide variety of SAR‐related functionality. The package provides functions to: a) fit 20 SAR models using non‐linear and linear regression, b) calculate multi‐model averaged curves using various information criteria, and c) generate confidence intervals using bootstrapping. Plotting functions allow users to depict and scrutinize the fits of individual models and multi‐model averaged curves. The package also provides additional SAR functionality, including functions to fit, plot and evaluate the random placement model using a species–sites abundance matrix, and to fit the general dynamic model of oceanic island biogeography. The ‘sars’ R package will aid future SAR research by providing a comprehensive set of simple to use tools that enable in‐depth exploration of SARs and SAR‐related patterns. The package has been designed to allow other researchers to add new functions and models in the future and thus the package represents a resource for future SAR work that can be built on and expanded by workers in the field.     

The contribution of common and rare species to species abundance patterns in alpine meadows: The effect of elevation gradients

Ecological niche modeling uses environmental variables associated with species distribution points to simulate species distribution and its importance in biodiversity conservation. This study aimed to quantify plant community composition and species abundance distribution (SAD) in alpine meadows at different elevations and to assess the contribution of rare and common species to SAD. We established a permanent study plot of 210 hm² in Gannan Tibetan Autonomous Prefecture, China, surveyed 315 sample squares (0.5 m × 0.5 m), and calculated the Hill numbers. The results showed that (1) a total of 72 species were surveyed at different altitudes, with Kobresia humilis and Kobresia macrantha as the main dominant species; (2) the SADs of overall and common species fit the ecological niche model (GSM (Geometric Sequence Model)), indicating that ecological niche differentiation is the main factor influencing SAD. The fitted model for rare species SAD varied with elevation, suggesting that various ecological processes influence rare species SAD. (3) Hill numbers showed a “single peak” pattern with increasing elevation. The number of rare species was higher than that of common species. Still, the distribution frequency of common species was significantly higher than rare species. The correlation between common-rare species sequences and cumulative species distribution was high. This indicates that common species dominate the species diversity pattern of the community, are the main contributors to the SAD pattern, and should be protected first. Rare species are also important carriers of community function and include much spatial information. Rare and common species work together in different ways to influence and maintain the species diversity patterns of alpine meadow plant communities.     

The impact of rate heterogeneity on inference of phylogenetic models of trait evolution

Rates of trait evolution are known to vary across phylogenies; however, standard evolutionary models assume a homogeneous process of trait change. These simple methods are widely applied in small‐scale phylogenetic studies, whereas models of rate heterogeneity are not, so the prevalence and patterns of potential rate variation in groups up to hundreds of species remain unclear. The extent to which trait evolution is modelled accurately on a given phylogeny is also largely unknown because studies typically lack absolute model fit tests. We investigated these issues by applying both rate‐static and variable‐rates methods on (i) body mass data for 88 avian clades of 10–318 species, and (ii) data simulated under a range of rate‐heterogeneity scenarios. Our results show that rate heterogeneity is present across small‐scaled avian clades, and consequently applying only standard single‐process models prompts inaccurate inferences about the generating evolutionary process. Specifically, these approaches underestimate rate variation, and systematically mislabel temporal trends in trait evolution. Conversely, variable‐rates approaches have superior relative fit (they are the best model) and absolute fit (they describe the data well). We show that rate changes such as single internal branch variations, rate decreases and early bursts are hard to detect, even by variable‐rates models. We also use recently developed absolute adequacy tests to highlight misleading conclusions based on relative fit alone (e.g. a consistent preference for constrained evolution when isolated terminal branch rate increases are present). This work highlights the potential for robust inferences about trait evolution when fitting flexible models in conjunction with tests for absolute model fit.     

An evaluation of the state of spatial point pattern analysis in ecology

Over the last two decades spatial point pattern analysis (SPPA) has become increasingly popular in ecological research. To direct future work in this area we review studies using SPPA techniques in ecology and related disciplines. We first summarize the key elements of SPPA in ecology (i.e. data types, summary statistics and their estimation, null models, comparison of data and models, and consideration of heterogeneity); second, we review how ecologists have used these key elements; and finally, we identify practical difficulties that are still commonly encountered and point to new methods that allow current key questions in ecology to be effectively addressed. Our review of 308 articles published over the period 1992–2012 reveals that a standard canon of SPPA techniques in ecology has been largely identified and that most of the earlier technical issues that occupied ecologists, such as edge correction, have been solved. However, the majority of studies underused the methodological potential offered by modern SPPA. More advanced techniques of SPPA offer the potential to address a variety of highly relevant ecological questions. For example, inhomogeneous summary statistics can quantify the impact of heterogeneous environments, mark correlation functions can include trait and phylogenetic information in the analysis of multivariate spatial patterns, and more refined point process models can be used to realistically characterize the structure of a wide range of patterns. Additionally, recent advances in fitting spatially‐explicit simulation models of community dynamics to point pattern summary statistics hold the promise for solving the longstanding problem of linking pattern to process. All these newer developments allow ecologists to keep up with the increasing availability of spatial data sets provided by newer technologies, which allow point patterns and environmental variables to be mapped over large spatial extents at increasingly higher image resolutions.     

Evidence from GC-TRFLP that bacterial communities in soil are lognormally distributed

The Species Abundance Distribution (SAD) is a fundamental property of ecological communities and the form and formation of SADs have been examined for a wide range of communities including those of microorganisms. Progress in understanding microbial SADs, however, has been limited by the remarkable diversity and vast size of microbial communities. As a result, few microbial systems have been sampled with sufficient depth to generate reliable estimates of the community SAD. We have used a novel approach to characterize the SAD of bacterial communities by coupling genomic DNA fractionation with analysis of terminal restriction fragment length polymorphisms (GC-TRFLP). Examination of a soil microbial community through GC-TRFLP revealed 731 bacterial operational taxonomic units (OTUs) that followed a lognormal distribution. To recover the same 731 OTUs through analysis of DNA sequence data is estimated to require analysis of 86,264 16S rRNA sequences. The approach is examined and validated through construction and analysis of simulated microbial communities in silico. Additional simulations performed to assess the potential effects of PCR bias show that biased amplification can cause a community whose distribution follows a power-law function to appear lognormally distributed. We also show that TRFLP analysis, in contrast to GC-TRFLP, is not able to effectively distinguish between competing SAD models. Our analysis supports use of the lognormal as the null distribution for studying the SAD of bacterial communities as for plant and animal communities.