Ontogenetic trait variation and metacommunity effects influence species relative abundances during tree community assembly

Predicting species abundance is one of the most fundamental pursuits of ecology. Combining the information encoded in functional traits and metacommunities provides a new perspective to predict the abundance of species in communities. We applied a community assembly via trait selection model to predict quadrat-scale species abundances using functional trait variation on ontogenetic stages and metacommunity information for over 490 plant species in a subtropical forest and a lowland tropical forest in Yunnan, China. The relative importance of trait-based selection, mass effects, and stochasticity in shaping local species abundances is evaluated using different null models. We found both mass effects and trait selection contribute to local abundance patterns. Trait selection was detectable at all studied spatial scales (0.04–1 ha), with its strength stronger at larger scales and in the subtropical forest. In contrast, the importance of stochasticity decreased with spatial scale. A significant mass effect of the metacommunity was observed at small spatial scales. Our results indicate that tree community assembly is primarily driven by ontogenetic traits and metacommunity effects. Our findings also demonstrate that including ontogenetic trait variation into predictive frameworks allows ecologists to infer ecological mechanisms operating in community assembly at the individual level.     

Phylogenetic community structure metrics and null models: a review with new methods and software

Competitive exclusion and habitat filtering influence community assembly, but ecologists and evolutionary biologists have not reached consensus on how to quantify patterns that would reveal the action of these processes. Currently, at least 22 α‐diversity and 10 β‐diversity metrics of community phylogenetic structure can be combined with nine null models (eight for β‐diversity metrics), providing 278 potentially distinct approaches to test for phylogenetic clustering and overdispersion. Selecting the appropriate approach for a study is daunting. First, we describe similarities among metrics and null models across variance in phylogeny size and shape, species abundance, and species richness. Second, we develop spatially explicit, individual‐based simulations of neutral, competitive exclusion, or habitat filtering community assembly, and quantify the performance (type I and II error rates) of all 278 metric and null model combinations against each assembly process. Many α‐diversity metrics and null models are at least functionally equivalent, reducing the number of truly unique metrics to 12 and the number of unique metric + null model combinations to 72. An even smaller subset of metric and null model combinations showed robust statistical performance. For α‐diversity metrics, phylogenetic diversity and mean nearest taxon distance were best able to detect habitat filtering, while mean pairwise phylogenetic distance‐based metrics were best able to detect competitive exclusion. Overall, β‐diversity metrics tended to have greater power to detect habitat filtering and competitive exclusion than α‐diversity metrics, but had higher type 1 error in some cases. Across both α‐ and β‐diversity metrics, null model selection affected type I error rates more than metric selection. A null model that maintained species richness, and approximately maintained species occurrence frequency and abundance across sites, exhibited low type I and II error rates. This regional null model simulates neutral dispersal of individuals into local communities by sampling from a regional species pool. We introduce a flexible new R package, metricTester, to facilitate robust analyses of method performance.     

Importance sampling method of correction for multiple testing in affected sib-pair linkage analysis

Using the Genetic Analysis Workshop 13 simulated data set, we compared the technique of importance sampling to several other methods designed to adjust p-values for multiple testing: the Bonferroni correction, the method proposed by Feingold et al., and na?ve Monte Carlo simulation. We performed affected sib-pair linkage analysis for each of the 100 replicates for each of five binary traits and adjusted the derived p-values using each of the correction methods. The type I error rates for each correction method and the ability of each of the methods to detect loci known to influence trait values were compared. All of the methods considered were conservative with respect to type I error, especially the Bonferroni method. The ability of these methods to detect trait loci was also low. However, this may be partially due to a limitation inherent in our binary trait definitions.     

Disentangling community patterns of nestedness and species co-occurrence

Two opposing patterns of meta‐community organization are nestedness and negative species co‐occurrence. Both patterns can be quantified with metrics that are applied to presence‐absence matrices and tested with null model analysis. Previous meta‐analyses have given conflicting results, with the same set of matrices apparently showing high nestedness (Wright et al. 1998) and negative species co‐occurrence (Gotelli and McCabe 2002). We clarified the relationship between nestedness and co‐occurrence by creating random matrices, altering them systematically to increase or decrease the degree of nestedness or co‐occurrence, and then testing the resulting patterns with null models. Species co‐occurrence is related to the degree of nestedness, but the sign of the relationship depends on how the test matrices were created. Low‐fill matrices created by simple, uniform sampling generate negative correlations between nestedness and co‐occurrence: negative species co‐occurrence is associated with disordered matrices. However, high‐fill matrices created by passive sampling generate the opposite pattern: negative species co‐occurrence is associated with highly nested matrices. The patterns depend on which index of species co‐occurrence is used, and they are not symmetric: systematic changes in the co‐occurrence structure of a matrix are only weakly associated with changes in the pattern of nestedness. In all analyses, the fixed‐fixed null model that preserves matrix row and column totals has lower type I and type II error probabilities than an equiprobable null model that relaxes row and column totals. The latter model is part of the popular nestedness temperature calculator, which detects nestedness too frequently in random matrices (type I statistical error). When compared to a valid null model, a matrix with negative species co‐occurrence may be either highly nested or disordered, depending on the biological processes that determine row totals (number of species occurrences) and column totals (number of species per site).     

Statistical challenges in null model analysis

This review identifies several important challenges in null model testing in ecology: 1) developing randomization algorithms that generate appropriate patterns for a specified null hypothesis; these randomization algorithms stake out a middle ground between formal Pearson–Neyman tests (which require a fully‐specified null distribution) and specific process‐based models (which require parameter values that cannot be easily and independently estimated); 2) developing metrics that specify a particular pattern in a matrix, but ideally exclude other, related patterns; 3) avoiding classification schemes based on idealized matrix patterns that may prove to be inconsistent or contradictory when tested with empirical matrices that do not have the idealized pattern; 4) testing the performance of proposed null models and metrics with artificial test matrices that contain specified levels of pattern and randomness; 5) moving beyond simple presence–absence matrices to incorporate species‐level traits (such as abundance) and site‐level traits (such as habitat suitability) into null model analysis; 6) creating null models that perform well with many sites, many species pairs, and varying degrees of spatial autocorrelation in species occurrence data. In spite of these challenges, the development and application of null models has continued to provide valuable insights in ecology, evolution, and biogeography for over 80 years.     

Can traits predict species' vulnerability? A test with farmland passerines in two continents

Species' traits have been used both to explain and, increasingly, to predict species' vulnerability. Trait-based comparative analyses allow mechanisms causing vulnerability to be inferred and, ideally, conservation effort to be focused efficiently and effectively. However, empirical evidence of the predictive ability of trait-based approaches is largely wanting. I tested the predictive power of trait-based analyses on geographically replicated datasets of farmland bird population trends. I related the traits of farmland passerines with their long-term trends in abundance (an assessment of their response to agricultural intensification) in eight regions in two continents. These analyses successfully identified explanatory relationships in the regions, specifically: species faring badly tended to be medium-sized, had relatively short incubation and fledging periods, were longer distant migrants, had small relative brain sizes and were farmland specialists. Despite this, the models had poor ability to predict species' vulnerability in one region from trait-population trend relationships from a different region. In many cases, the explained variation was low (median R(2) = 8%). The low predictive ability of trait-based analyses must therefore be considered if such trait-based models are used to inform conservation priorities.     

Changing assembly rules during secondary succession: evidence for non-random patterns

Describing the rules of community assembly is a central topic of ecology. Studying successional processes through a trait-based null model approach can help to better understand the rules of community assembly.According to theoretical considerations, at the beginning of succession - after getting over the dispersal limitation stage - community composition is primarily shaped by environmental filters (generating functional convergence), while in later stages limiting similarity (generating functional divergence) will be dominant. However, empirical evidence does not clearly support theoretical expectations.Our aim was to detect the presence and changes of trait-based assembly processes during old-field succession based on twelve traits. Changes in vegetation composition were evaluated by a combination of time series and space-for-time substitution: conducting three resurveys of permanent plots on four old-field age-groups. The individual dispersion of traits was transformed into effect size (i.e. departure from null model expectation). The impact of time since abandonment on effect sizes was tested by generalized additive mixed effect models.We detected a non-random pattern for each trait in at least some part of the succession. Departure from randomness did not change significantly over time for six traits: seed mass, lateral spread and pollination type were divergent, while leaf size, generative height and length of flowering were convergent. Six traits had changing patterns along the succession. Four of them showed increasing divergence (e.g. dispersal type, LDMC), which supports our hypothesis. While two (SLA, life form) displayed increasing convergence, contrary to expectations.We confirmed the general hypothesis that convergence is predominant initially and that divergence can be detected later in succession for four traits. However, the large variation found in trait dispersion indicates that complex processes operate during succession.     

Research frontiers in null model analysis

Null models are pattern‐generating models that deliberately exclude a mechanism of interest, and allow for randomization tests of ecological and biogeographic data. Although they have had a controversial history, null models are widely used as statistical tools by ecologists and biogeographers. Three active research fronts in null model analysis include biodiversity measures, species co‐occurrence patterns, and macroecology. In the analysis of biodiversity, ecologists have used random sampling procedures such as rarefaction to adjust for differences in abundance and sampling effort. In the analysis of species co‐occurrence and assembly rules, null models have been used to detect the signature of species interactions. However, controversy persists over the details of computer algorithms used for randomizing presence–absence matrices. Finally, in the newly emerging discipline of macroecology, null models can be used to identify constraining boundaries in bivariate scatterplots of variables such as body size, range size, and population density. Null models provide specificity and flexibility in data analysis that is often not possible with conventional statistical tests.     

Inappropriate Fiddling with Statistical Analyses to Obtain a Desirable P-value: Tests to Detect its Presence in Published Literature

Much has been written regarding p-values below certain thresholds (most notably 0.05) denoting statistical significance and the tendency of such p-values to be more readily publishable in peer-reviewed journals. Intuition suggests that there may be a tendency to manipulate statistical analyses to push a “near significant p-value” to a level that is considered significant. This article presents a method for detecting the presence of such manipulation (herein called “fiddling”) in a distribution of p-values from independent studies. Simulations are used to illustrate the properties of the method. The results suggest that the method has low type I error and that power approaches acceptable levels as the number of p-values being studied approaches 1000.     

Trait‐based approach confirms the importance of propagule limitation and assembly rules in old‐field restoration

Community assembly theory is suggested as a guiding principle for ecological restoration to help understand the mechanisms that structure biological communities and identify where restoration interventions are needed. We studied three hypotheses related to propagule limitation, stress‐dominance, and limiting similarity concepts in community assembly in a restoration field experiment with a trait‐based null model approach. The experiment aimed to assist the recovery of sand grassland on former arable land in the Kiskunság, Pannonian biogeographic region, Europe. Treatments included initial seeding of five grassland species, carbon amendment, low‐intensity mowing, and combinations in 1 m by 1 m plots in three old fields from 2003 to 2008. The distribution of 10 individual plant traits was compared to the null model and the effect of time and treatments were tested with linear mixed effect models. Initial seeding had the most visible impact on species and trait composition confirming propagule limitation in grassland recovery. Reducing nutrient availability through carbon amendment strengthened trait convergence for length of flowering as expected based on the stress‐dominance hypothesis. Mowing changed trait divergence to convergence for plant height with a strengthening impact with time, supporting our hypothesis of increasing dominance of limiting similarity with time. Our results support the idea that community assembly is simultaneously influenced by propagule limitation and multiple trait‐based processes that act through different traits. The limited impact of manipulating environmental filtering and limiting similarity compared to seeding, however, supports the view that only targeting the dispersal and environmental filters in parallel would improve restoration outcome.     

Permutation tests for phylogenetic comparative analyses of high‐dimensional shape data: What you shuffle matters

Evaluating statistical trends in high‐dimensional phenotypes poses challenges for comparative biologists, because the high‐dimensionality of the trait data relative to the number of species can prohibit parametric tests from being computed. Recently, two comparative methods were proposed to circumvent this difficulty. One obtains phylogenetic independent contrasts for all variables, and statistically evaluates the linear model by permuting the phylogenetically independent contrasts (PICs) of the response data. The other uses a distance‐based approach to obtain coefficients for generalized least squares models (D‐PGLS), and subsequently permutes the original data to evaluate the model effects. Here, we show that permuting PICs is not equivalent to permuting the data prior to the analyses as in D‐PGLS. We further explain why PICs are not the correct exchangeable units under the null hypothesis, and demonstrate that this misspecification of permutable units leads to inflated type I error rates of statistical tests. We then show that simply shuffling the original data and recalculating the independent contrasts with each iteration yields significance levels that correspond to those found using D‐PGLS. Thus, while summary statistics from methods based on PICs and PGLS are the same, permuting PICs can lead to strikingly different inferential outcomes with respect to statistical and biological inferences.     

Impact of ‘Stretch’ Targets for Cardiovascular Disease Management within a Local Pay-for-Performance Programme

Pay-for-performance programs are often aimed to improve the management of chronic diseases. We evaluate the impact of a local pay for performance programme (QOF+), which rewarded financially more ambitious quality targets (‘stretch targets’) than those used nationally in the Quality and Outcomes Framework (QOF). We focus on targets for intermediate outcomes in patients with cardiovascular disease and diabetes. A difference-in-difference approach is used to compare practice level achievements before and after the introduction of the local pay for performance program. In addition, we analysed patient-level data on exception reporting and intermediate outcomes utilizing an interrupted time series analysis. The local pay for performance program led to significantly higher target achievements (hypertension: p-value <0.001, coronary heart disease: p-values <0.001, diabetes: p-values <0.061, stroke: p-values <0.003). However, the increase was driven by higher rates of exception reporting (hypertension: p-value <0.001, coronary heart disease: p-values <0.03, diabetes: p-values <0.05) in patients with all conditions except for stroke. Exception reporting allows practitioners to exclude patients from target calculations if certain criteria are met, e.g. informed dissent of the patient for treatment. There were no statistically significant improvements in mean blood pressure, cholesterol or HbA1c levels. Thus, achievement of higher payment thresholds in the local pay for performance scheme was mainly attributed to increased exception reporting by practices with no discernable improvements in overall clinical quality. Hence, active monitoring of exception reporting should be considered when setting more ambitious quality targets. More generally, the study suggests a trade-off between additional incentive for better care and monitoring costs.     

On testing predictions of species relative abundance from maximum entropy optimisation

A randomisation test is described for assessing relative abundance predictions from the maximum entropy approach to biodiversity. The null model underlying the test randomly allocates observed abundances to species, but retains key aspects of the structure of the observed communities; site richness, species composition, and trait covariance. Three test statistics are used to explore different characteristics of the predictions. Two are based on pairwise comparisons between observed and predicted species abundances (RMSE, RMSE_Sqrt). The third statistic is novel and is based on community‐level abundance patterns, using an index calculated from the observed and predicted community entropies (E_Diff). Validation of the test to quantify type I and type II error rates showed no evidence of bias or circularity, confirming the dependencies quantified by Roxburgh and Mokany (2007) and Shipley (2007) have been fully accounted for within the null model. Application of the test to the vineyard data of Shipley et al. (2006) and to an Australian grassland dataset indicated significant departures from the null model, suggesting the integration of species trait information within the maximum entropy framework can successfully predict species abundance patterns. The paper concludes with some general comments on the use of maximum entropy in ecology, including a discussion of the mathematics underlying the Maxent optimisation algorithm and its implementation, the role of absent species in generating biased predictions, and some comments on determining the most appropriate level of data aggregation for Maxent analysis.     

TATES: Efficient Multivariate Genotype-Phenotype Analysis for Genome-Wide Association Studies

To date, the genome-wide association study (GWAS) is the primary tool to identify genetic variants that cause phenotypic variation. As GWAS analyses are generally univariate in nature, multivariate phenotypic information is usually reduced to a single composite score. This practice often results in loss of statistical power to detect causal variants. Multivariate genotype–phenotype methods do exist but attain maximal power only in special circumstances. Here, we present a new multivariate method that we refer to as TATES (Trait-based Association Test that uses Extended Simes procedure), inspired by the GATES procedure proposed by Li et al (2011). For each component of a multivariate trait, TATES combines p-values obtained in standard univariate GWAS to acquire one trait-based p-value, while correcting for correlations between components. Extensive simulations, probing a wide variety of genotype–phenotype models, show that TATES''s false positive rate is correct, and that TATES''s statistical power to detect causal variants explaining 0.5% of the variance can be 2.5–9 times higher than the power of univariate tests based on composite scores and 1.5–2 times higher than the power of the standard MANOVA. Unlike other multivariate methods, TATES detects both genetic variants that are common to multiple phenotypes and genetic variants that are specific to a single phenotype, i.e. TATES provides a more complete view of the genetic architecture of complex traits. As the actual causal genotype–phenotype model is usually unknown and probably phenotypically and genetically complex, TATES, available as an open source program, constitutes a powerful new multivariate strategy that allows researchers to identify novel causal variants, while the complexity of traits is no longer a limiting factor.     

Demography and Population Dynamics of Massive Coral Communities in Adjacent High Latitude Regions (United Arab Emirates)

Individual massive coral colonies, primarily faviids and poritids, from three distinct assemblages within the southeastern Arabian Gulf and northwestern Gulf of Oman (United Arab Emirates) were studied from 2006–2009. Annual photographic censuses of approximately 2000 colonies were used to describe the demographics (size class frequencies, abundance, area cover) and population dynamics under “normal” environmental conditions. Size class transitions included growth, which occurred in 10–20% of the colonies, followed in decending order by partial mortality (3–16%), colony fission (<5%) and ramet fusion (<3%). Recruitment and whole colony mortality rates were low (<0.7 colonies/m²) with minimal interannual variation. Transition matrices indicated that the Arabian Gulf assemblages have declining growth rates (λ<1) whereas the massive coral population is stable (λ = 1) in the Gulf of Oman. Projection models indicated that (i) the Arabian Gulf population and area cover declines would be exacerbated under 10-year and 16-year disturbance scenarios as the vital rates do not allow for recovery to pre-disturbance levels during these timeframes, and (ii) the Gulf of Oman assemblage could return to its pre-disturbance area cover but its overall population size would not fully recover under the same scenarios.     

Microbial response to simulated global change is phylogenetically conserved and linked with functional potential

The high diversity of microbial communities hampers predictions about their responses to global change. Here we investigate the potential for using a phylogenetic, trait-based framework to capture the response of bacteria and fungi to global change manipulations. Replicated grassland plots were subjected to 3+ years of drought and nitrogen fertilization. The responses of leaf litter bacteria and fungi to these simulated changes were significantly phylogenetically conserved. Proportional changes in abundance were highly correlated among related organisms, such that relatives with approximately 5% ribosomal DNA genetic distance showed similar responses to the treatments. A microbe''s change in relative abundance was significantly correlated between the treatments, suggesting a compromise between numerical abundance in undisturbed environments and resistance to change in general, independent of disturbance type. Lineages in which at least 90% of the microbes shared the same response were circumscribed at a modest phylogenetic depth (τ_D 0.014–0.021), but significantly larger than randomized simulations predict. In several clades, phylogenetic depth of trait consensus was higher. Fungal response to drought was more conserved than was response to nitrogen fertilization, whereas bacteria responded equally to both treatments. Finally, we show that a bacterium''s response to the manipulations is correlated with its potential functional traits (measured here as the number of glycoside hydrolase genes encoding the capacity to degrade different types of carbohydrates). Together, these results suggest that a phylogenetic, trait-based framework may be useful for predicting shifts in microbial composition and functioning in the face of global change.     

Fixed or random? On the reliability of mixed‐effects models for a small number of levels in grouping variables

Biological data are often intrinsically hierarchical (e.g., species from different genera, plants within different mountain regions), which made mixed‐effects models a common analysis tool in ecology and evolution because they can account for the non‐independence. Many questions around their practical applications are solved but one is still debated: Should we treat a grouping variable with a low number of levels as a random or fixed effect? In such situations, the variance estimate of the random effect can be imprecise, but it is unknown if this affects statistical power and type I error rates of the fixed effects of interest. Here, we analyzed the consequences of treating a grouping variable with 2–8 levels as fixed or random effect in correctly specified and alternative models (under‐ or overparametrized models). We calculated type I error rates and statistical power for all‐model specifications and quantified the influences of study design on these quantities. We found no influence of model choice on type I error rate and power on the population‐level effect (slope) for random intercept‐only models. However, with varying intercepts and slopes in the data‐generating process, using a random slope and intercept model, and switching to a fixed‐effects model, in case of a singular fit, avoids overconfidence in the results. Additionally, the number and difference between levels strongly influences power and type I error. We conclude that inferring the correct random‐effect structure is of great importance to obtain correct type I error rates. We encourage to start with a mixed‐effects model independent of the number of levels in the grouping variable and switch to a fixed‐effects model only in case of a singular fit. With these recommendations, we allow for more informative choices about study design and data analysis and make ecological inference with mixed‐effects models more robust for small number of levels.     

Species–topography association in a species-rich subtropical forest of China

Habitat specialization has been considered as a primary factor in determining the distribution of species. In this study, we investigated species–habitat associations while controlling for spatial neighbourhood effects in a large-scale (20 ha) stem-mapping plot in a species-rich subtropical forest of China. Habitat specialization was measured by topographic variation and its effects on species distributions were modelled at three different spatial scales (10×10, 20×20 and 25×25 m²) using log-linear regression models and randomization tests. Our results showed: (1) 83% of the species were related to at least one or more topographic variables. Among them, 66%, 60%, 65% and 70% were closely dependent on slope, aspect, elevation and convexity, respectively. (2) Topographic variables have much stronger non-linear (quadratic and cubic) effects on species distributions than linear effects. (3) The effects of topographic heterogeneity on the distribution of shrubs species are smaller than on the distribution of canopy species, and smaller effects were also found in less abundant species. (4) There was a strong neighbourhood effect on species distribution: In 85% of the species, abundance in a focal quadrat was significantly correlated with abundance in the neighbour quadrats. We conclude that habitat specialization plays an important role in maintaining the diversity of this species-rich subtropical forest.     

ENMTools: a toolbox for comparative studies of environmental niche models

We present software that facilitates quantitative comparisons of environmental niche models (ENMs). Our software quantifies similarity of ENMs generated using the program Maxent and uses randomization tests to compare observed similarity to that expected under different null hypotheses. ENMTools is available online free of charge from < http://purl.oclc.org/enmtools >.     

The effects of fertilization on the trait-abundance relationships in a Tibetan alpine meadow community

Aims Comparisons of the trait–abundance relationships from various habitat types are critical for community ecology, which can offer us insights about the mechanisms underlying the local community assembly, such as the relative role of neutral vs. niche processes in shaping community structure. Here, we explored the responses of trait–abundance relationships to nitrogen (N), phosphorus (P) and potassium (K) fertilization in an alpine meadow.Methods Five fertilization treatments (an unfertilized control and additions of N, P, K and NPK respectively) were implemented using randomized block design in an alpine Tibetan meadow. Species relative abundance (SRA), plant above-ground biomass and species richness were measured in each plot. For 24 common species, we measured species functional traits: saturated height, specific leaf area (SLA) and leaf dry matter content (LDMC) in each treatment but seed size only in the unfertilized control. Standard major axis (SMA) regression and phylogenetically independent contrasts (PICs) analysis were used to analyse species trait–abundance relationships in response to different fertilization treatments.Important findings Positive correlations between SRA and saturated height were raised following N, P and NPK fertilizations, which indicated an increase in light competition in these plots. In P fertilized plots, SRA was also positively correlated with LDMC because tall grasses with a nutrients conservation strategy often have a relative competitive advantage in capturing limited light and soil nutrients. In K fertilized plots, neither the trait–abundance relationships nor above-ground biomass or species richness significantly differed from that in the control, which suggests that K was not a limiting resource in our study site. These significant correlations between species traits and relative abundance in fertilized treatment suggest that trait-based selection plays an important role in determining species abundance within local communities in alpine meadows.