On the Overdispersed Molecular Clock

Rates of molecular evolution at some loci are more irregular than described by simple Poisson processes. Three situations under which molecular evolution would not follow simple Poisson processes are reevaluated from the viewpoint of the neutrality hypothesis: concomitant or multiple substitutions in a gene, fluctuating substitution rates in time caused by coupled effects of deleterious mutations and bottlenecks, and changes in the degree of selective constraints against a gene (neutral space) caused by successive substitutions. The common underlying assumption that these causes are lineage nonspecific excludes the case where mutation rates themselves change systematically among lineages or taxonomic groups, and severely limits the extent of variation in the number of substitutions among lineages. Even under this stringent condition, however, the third hypothesis, the fluctuating neutral space model, can generate fairly large variation. This is described by a time-dependent renewal process, which does not exhibit any episodic nature of molecular evolution. It is argued that the observed elevated variances in the number of nucleotide or amino acid substitutions do not immediately call for positive Darwinian selection in molecular evolution.     

Competitive interactions change the pattern of species co‐occurrences under neutral dispersal

Non‐random patterns of species segregation and aggregation within ecological communities are often interpreted as evidence for interspecific interactions. However, it is unclear whether theoretical models can predict such patterns and how environmental factors may modify the effects of species interactions on species co‐occurrence. Here we extend a spatially explicit neutral model by including competitive effects on birth and death probabilities to assess whether competition alone is able to produce non‐random patterns of species co‐occurrence. We show that transitive and intransitive competitive hierarchies alone (in the absence of environmental heterogeneity) are indeed able to generate non‐random patterns with commonly used metrics and null models. Moreover, even weak levels of intransitive competition can increase local species richness. However, there is no simple rule or consistent directional change towards aggregation or segregation caused by competitive interactions. Instead, the spatial pattern depends on both the type of species interaction and the strength of dispersal. We conclude that co‐occurrence analysis alone may not able to identify the underlying processes that generate the patterns.     

Are potential natural vegetation maps a meaningful alternative to neutral landscape models?

Abstract. In this paper, we present a short overview of neutral landscape models traditionally adopted in the landscape ecological literature to differentiate landscape patterns that are the result of simple random processes from patterns that are generated from more complex ecological processes. Then, we present another family of models based on Tuxen’ s definition of potential natural vegetation that play an important role, especially in Europe, for landscape planning and management. While neutral landscape models by their very nature do not take into account vegetation dynamics, nor abiotic constraints to vegetation distribution, the concept of potential natural vegetation includes the effects of vegetation dynamics in a spatially explicit manner. Therefore, we believe that distribution maps of potential natural vegetation may represent an ecological meaningful alternative to neutral landscape models for evaluating the effects of landscape structure on ecological processes.     

Thinking outside the barrier: neutral and adaptive divergence in Indo-Pacific coral reef faunas

Population divergence in marine species frequently occurs where there is no conspicuous geographic feature to mark a population boundary. In the absence of a discernible barrier to gene flow, strong conclusions about the processes that drive population divergence in the ocean are often elusive. Because our knowledge of patterns far outpaces our understanding of processes, there is a need to expand hypotheses regarding population divergence in marine species. Here, I posit that certain population-level processes (range expansion, spatial diffusion constraints and metapopulation dynamics) are primary causes of spatial structuring in marine populations rather than auxiliary factors that aid divergence across an incomplete physical barrier to gene flow. In particular, I use examples from Indo-Pacific coral reef organisms to illustrate this point. In connection, spatial patterns of adaptive divergence are unlikely to be correlated with patterns of selectively neutral divergence in many cases, as the processes affecting adaptive and neutral genetic markers are different.     

Generative models versus underlying symmetries to explain biological pattern

Mathematical models play an increasingly important role in the interpretation of biological experiments. Studies often present a model that generates the observations, connecting hypothesized process to an observed pattern. Such generative models confirm the plausibility of an explanation and make testable hypotheses for further experiments. However, studies rarely consider the broad family of alternative models that match the same observed pattern. The symmetries that define the broad class of matching models are in fact the only aspects of information truly revealed by observed pattern. Commonly observed patterns derive from simple underlying symmetries. This article illustrates the problem by showing the symmetry associated with the observed rate of increase in fitness in a constant environment. That underlying symmetry reveals how each particular generative model defines a single example within the broad class of matching models. Further progress on the relation between pattern and process requires deeper consideration of the underlying symmetries.     

An improved neutral landscape model for recreating real landscapes and generating landscape series for spatial ecological simulations

Many studies have assessed the effect of landscape patterns on spatial ecological processes by simulating these processes in computer‐generated landscapes with varying composition and configuration. To generate such landscapes, various neutral landscape models have been developed. However, the limited set of landscape‐level pattern variables included in these models is often inadequate to generate landscapes that reflect real landscapes. In order to achieve more flexibility and variability in the generated landscapes patterns, a more complete set of class‐ and patch‐level pattern variables should be implemented in these models. These enhancements have been implemented in Landscape Generator (LG), which is a software that uses optimization algorithms to generate landscapes that match user‐defined target values. Developed for participatory spatial planning at small scale, we enhanced the usability of LG and demonstrated how it can be used for larger scale ecological studies. First, we used LG to recreate landscape patterns from a real landscape (i.e., a mountainous region in Switzerland). Second, we generated landscape series with incrementally changing pattern variables, which could be used in ecological simulation studies. We found that LG was able to recreate landscape patterns that approximate those of real landscapes. Furthermore, we successfully generated landscape series that would not have been possible with traditional neutral landscape models. LG is a promising novel approach for generating neutral landscapes and enables testing of new hypotheses regarding the influence of landscape patterns on ecological processes. LG is freely available online.     

Can environmental constraints determine random patterns of plant species co-occurrence?

Plant community ecologists use the null model approach to infer assembly processes from observed patterns of species co‐occurrence. In about a third of published studies, the null hypothesis of random assembly cannot be rejected. When this occurs, plant ecologists interpret that the observed random pattern is not environmentally constrained – but probably generated by stochastic processes. The null model approach (using the C‐score and the discrepancy index) was used to test for random assembly under two simulation algorithms. Logistic regression, distance‐based redundancy analysis, and constrained ordination were used to test for environmental determinism (species segregation along environmental gradients or turnover and species aggregation). This article introduces an environmentally determined community of alpine hydrophytes that presents itself as randomly assembled. The pathway through which the random pattern arises in this community is suggested to be as follows: Two simultaneous environmental processes, one leading to species aggregation and the other leading to species segregation, concurrently generate the observed pattern, which results to be neither aggregated nor segregated – but random. A simulation study supports this suggestion. Although apparently simple, the null model approach seems to assume that a single ecological factor prevails or that if several factors decisively influence the community, then they all exert their influence in the same direction, generating either aggregation or segregation. As these assumptions are unlikely to hold in most cases and assembly processes cannot be inferred from random patterns, we would like to propose plant ecologists to investigate specifically the ecological processes responsible for observed random patterns, instead of trying to infer processes from patterns.     

Sexual dimorphism in the size and shape of the os coxae and the effects of microevolutionary processes

Sexual dimorphism in the human pelvis has been studied widely for forensic purposes, but it is still unclear to what extent it varies among human populations. There is evidence that microevolutionary processes, both neutral (i.e., population history) and selective (e.g., thermoregulatory adaptation and size‐related obstetrical constraints) contribute to explain pelvic variation among populations, but the extent to which these factors affect pelvic sexual dimorphism is unknown. In this study, I analyze sexual dimorphism of the os coxae in 20 globally distributed human populations, using 3D morphometric data to separate the size and shape components of sexual differences. After evaluating population differences in the degree and pattern of sexual dimorphism, I test for the effect of population history, climate, and body size in shaping global diversity. The results show that size and shape dimorphism follow different patterns. Coxal size dimorphism is generally quite consistent through populations, with males bigger than females, but it appears to be reduced in small‐bodied populations, possibly in relation to obstetrically‐related selective pressures for a spacious birth canal. Beyond a general species‐wide pattern of shape dimorphism, commonly used for forensic sex determination, other aspects of sexual differences in coxal shape vary among human populations, reflecting the effects of neutral demographic processes and climatic adaptation. Am J Phys Anthropol 153:167–177, 2014. © 2013 Wiley Periodicals, Inc.     

Neutral community dynamics, the mid-domain effect and spatial patterns in species richness

The mid‐domain effect (MDE) aims to explain spatial patterns in species richness invoking only stochasticity and geometrical constraints. In this paper, we used simulations to show that its main qualitative prediction, a hump‐shaped pattern in species richness, converges to the expectation of a spatially bounded neutral model when communities are linked by short‐distance migration. As these two models can be linked under specific situations, neutral theory may provide a mechanistic population level basis for MDE. This link also allows establishing in which situations MDE patterns are more likely to be found. Also, in this situation, MDE models could be used as a first approximation to understand the role of both stochastic (ecological drift and migration) and deterministic (adaptation to environmental conditions) processes driving the spatial structure of species richness.     

Neutral processes forming large clones during colonization of new areas

In species reproducing both sexually and asexually clones are often more common in recently established populations. Earlier studies have suggested that this pattern arises due to natural selection favouring generally or locally successful genotypes in new environments. Alternatively, as we show here, this pattern may result from neutral processes during species’ range expansions. We model a dioecious species expanding into a new area in which all individuals are capable of both sexual and asexual reproduction, and all individuals have equal survival rates and dispersal distances. Even under conditions that favour sexual recruitment in the long run, colonization starts with an asexual wave. After colonization is completed, a sexual wave erodes clonal dominance. If individuals reproduce more than one season, and with only local dispersal, a few large clones typically dominate for thousands of reproductive seasons. Adding occasional long‐distance dispersal, more dominant clones emerge, but they persist for a shorter period of time. The general mechanism involved is simple: edge effects at the expansion front favour asexual (uniparental) recruitment where potential mates are rare. Specifically, our model shows that neutral processes (with respect to genotype fitness) during the population expansion, such as random dispersal and demographic stochasticity, produce genotype patterns that differ from the patterns arising in a selection model. The comparison with empirical data from a post‐glacially established seaweed species (Fucus radicans) shows that in this case, a neutral mechanism is strongly supported.     

Is regional species diversity bounded or unbounded?

Two conflicting hypotheses have been proposed to explain large‐scale species diversity patterns and dynamics. The unbounded hypothesis proposes that regional diversity depends only on time and diversification rate and increases without limit. The bounded hypothesis proposes that ecological constraints place upper limits on regional diversity and that diversity is usually close to its limit. Recent evidence from the fossil record, phylogenetic analysis, biogeography, and phenotypic disparity during lineage diversification suggests that diversity is constrained by ecological processes but that it is rarely asymptotic. Niche space is often unfilled or can be more finely subdivided and still permit coexistence, and new niche space is often created before ecological limits are reached. Damped increases in diversity over time are the prevalent pattern, suggesting the need for a new ‘damped increase hypothesis'. The damped increase hypothesis predicts that diversity generally increases through time but that its rate of increase is often slowed by ecological constraints. However, slowing due to niche limitation must be distinguished from other possible mechanisms creating similar patterns. These include sampling artifacts, the inability to detect extinctions or declines in clade diversity with some methods, the distorting effects of correlated speciation‐extinction dynamics, the likelihood that opportunities for allopatric speciation will vary in space and time, and the role of undetected natural enemies in reducing host ranges and thus slowing speciation rates. The taxonomic scope of regional diversity studies must be broadened to include all ecologically similar species so that ecological constraints may be accurately inferred. The damped increase hypothesis suggests that information on evolutionary processes such as time‐for‐speciation and intrinsic diversification rates as well as ecological factors will be required to explain why regional diversity varies among times, places and taxa.     

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).     

Informational Landscapes in Art, Science, and Evolution

An informational landscape refers to an array of information related to a particular theme or function. The Internet is an example of an informational landscape designed by humans for purposes of communication. Once it exists, however, any informational landscape may be exploited to serve a new purpose. Listening Post is the name of a dynamic multimedia work of art that exploits the informational landscape of the Internet to produce a visual and auditory environment. Here, I use Listening Post as a prototypic example for considering the creative role of informational landscapes in the processes that beget evolution and science.     

Spatial autocorrelation analysis allows disentangling the balance between neutral and niche processes in metacommunities

One of the most popular approaches for investigating the roles of niche and neutral processes driving metacommunity patterns consists of partitioning variation in species data into environmental and spatial components. The logic is that the distance decay of similarity in communities is expected under neutral models. However, because environmental variation is often spatially structured, the decay could also be attributed to environmental factors that are missing from the analysis. Here, we use a spatial autocorrelation analysis protocol, previously developed to detect isolation‐by‐distance in allele frequencies, to evaluate patterns of species abundances under neutral dynamics. We show that this protocol can be linked with variation partitioning analyses. Moreover, in an attempt to test the neutral model, we derive three predictions to be applied both to original species abundances and to abundances predicted by a pure spatial model species abundances will be uncorrelated; Moran's I correlograms will reveal similar short‐distance autocorrelation patterns; an increasing degree of non‐neutrality will tend to generate patterns of correlation among abundances within groups of species with similar correlograms (i.e. within species with neutral and non‐neutral dynamics). We illustrate our protocol by analyzing spatial patterns in abundance of 28 terrestrially breeding anuran species from Central Amazonia. We recommend that researchers should investigate spatial autocorrelation patterns of abundances predicted by pure spatial models to identify similar patterns of spatial autocorrelation at short distances and lack of correlation between species abundances. Therefore, the hypothesis that spatial patterns in abundances are primarily due to pure neutral dynamics (rather than to missing spatiallystructured environmental factors) can be confirmed after taking environmental variables into account.     

Dealing with virtual aggregation – a new index for analysing heterogeneous point patterns

Point pattern analyses such as the estimation of Ripley's K‐function or the pair‐correlation function g are commonly used in ecology to characterise ecological patterns in space. However, a major disadvantage of these methods is their missing ability to deal with spatial heterogeneity. A heterogeneous intensity of points causes a systematic bias in estimates of the K‐ and g‐functions, a phenomenon termed “virtual aggregation” in the recent literature. To address this problem, we derive a new index, called K2‐index, as an extension of existing point pattern characteristics. The K2‐index has a heuristic interpretation as an approximation to the first derivative of the g‐function. We estimate the K‐, g‐ and K2‐functions for six different types of simulated point patterns and show that the K2‐index may provide important information on point patterns that the other methods fail to detect. The results indicate that particularly the small‐scale distributions of points are better represented by the K2‐index. This might be important for testing hypotheses on ecological processes, because most of these processes, such as direct neighbour interactions, occur very locally. When applied to empirical patterns of molehill distribution, the results of the K2‐analysis show regularity up to distances between 0.1 and 0.4 m in most of the study areas, and aggregation of molehills up to distances between 0.2 and 1.1 m. The type and scale of these deviations from randomness agree with a priori expectations on the hill‐building behaviour of moles. In contrast, the estimated g‐functions merely indicate aggregation at the full range from 0 to 7 m (or even above). Considering the advantages and disadvantages of the different methods, we suggest that the K2‐index should be used as a complement to existing approaches, particularly for point patterns generated by processes that act on more than one scale.     

Neutral mutations and neutral substitutions in bacterial genomes

Molecular evolutionary biologists usually assess the underlying spectrum of mutations within a bacterial genome by examining substitutions that occur at sites believed to be under no selective constraints. Alternatively, bacterial mutation rates can also be estimated in a variety of experimental systems. The two classes of changes occurring in DNA sequences-i.e., mutations and neutral substitutions-are, in theory, identical; however, the rates and patterns of mutations in bacteria, as inferred from sequence comparisons, often differ significantly from those derived experimentally. These differences have resulted in conflicting interpretations of the nonselective forces that affect mutation rates.     

What causes conspecific plant aggregation? Disentangling the role of dispersal,habitat heterogeneity and plant–plant interactions

Spatial patterns of plant species are determined by an array of ecologica factors including biotic and abiotic environmental constraints and intrinsic species traits. Thus, an observed aggregated pattern may be the result of short‐distance dispersal, the presence of habitat heterogeneity, plant–plant interactions or a combination of the above. Here, we studied the spatial pattern of Mediterranean alpine plant Silene ciliata (Caryophyllaceae) in five populations and assessed the contribution of dispersal, habitat heterogeneity and conspecific plant interactions to observed patterns. For this purpose, we used spatial point pattern analysis combined with specific a priori hypotheses linked to spatial pattern creation. The spatial pattern of S. ciliata recruits was not homogeneous and showed small‐scale aggregation. This is consistent with the species’ short‐distance seed dispersal and the heterogeneous distribution of suitable sites for germination and establishment. Furthermore, the spatial pattern of recruits was independent of the spatial pattern of adults. This suggests a low relevance of adult‐recruits interactions in the spatial pattern creation. The difference in aggregation between recruits and adults suggests that once established, recruits are subjected to self‐thinning. However, seedling mortality did not erase the spatial pattern generated by seed dispersal, as S. ciliata adults were still aggregated. Thus, the spatial aggregation of adults is probably due to seed dispersal limitation and the heterogeneous distribution of suitable sites at seedling establishment rather than the presence of positive plant–plant interactions at the adult stage. In fact, a negative density‐dependent effect of the conspecific neighbourhood was found on adult reproductive performance. Overall, results provide empirical evidence of the lack of a simple and direct relationship between the spatial structure of plant populations and the sign of plant–plant interactions and outline the importance of considering dispersal and habitat heterogeneity when performing spatial analysis assessments.     

A probabilistic model for secondary structure prediction from protein chemical shifts

Protein chemical shifts encode detailed structural information that is difficult and computationally costly to describe at a fundamental level. Statistical and machine learning approaches have been used to infer correlations between chemical shifts and secondary structure from experimental chemical shifts. These methods range from simple statistics such as the chemical shift index to complex methods using neural networks. Notwithstanding their higher accuracy, more complex approaches tend to obscure the relationship between secondary structure and chemical shift and often involve many parameters that need to be trained. We present hidden Markov models (HMMs) with Gaussian emission probabilities to model the dependence between protein chemical shifts and secondary structure. The continuous emission probabilities are modeled as conditional probabilities for a given amino acid and secondary structure type. Using these distributions as outputs of first‐ and second‐order HMMs, we achieve a prediction accuracy of 82.3%, which is competitive with existing methods for predicting secondary structure from protein chemical shifts. Incorporation of sequence‐based secondary structure prediction into our HMM improves the prediction accuracy to 84.0%. Our findings suggest that an HMM with correlated Gaussian distributions conditioned on the secondary structure provides an adequate generative model of chemical shifts. Proteins 2013; © 2012 Wiley Periodicals, Inc.     

Pattern detection in null model analysis

Synthesis The identification of distinctive patterns in species x site presence‐absence matrices is important for understanding meta‐community organisation. We compared the performance of a suite of null models and metrics that have been proposed to measure patterns of segregation, aggregation, nestedness, coherence, and species turnover. We found that any matrix with segregated species pairs can be re‐ordered to highlight aggregated pairs, indicating that these seemingly opposite patterns are closely related. Recently proposed classification schemes failed to correctly classify realistic matrices that included multiple co‐occurrence structures. We propose using a combination of metrics and decomposing matrix‐wide patterns into those of individual pairs of species and sites to pinpoint sources of non‐randomness. Null model analysis has been a popular tool for detecting pattern in binary presence–absence matrices, and previous tests have identified algorithms and metrics that have good statistical properties. However, the behavior of different metrics is often correlated, making it difficult to distinguish different patterns. We compared the performance of a suite of null models and metrics that have been proposed to measure patterns of segregation, aggregation, nestedness, coherence, and species turnover. We found that any matrix with segregated species pairs can be re‐ordered to highlight aggregated pairs. As a consequence, the same null model can identify a single matrix as being simultaneously aggregated, segregated or nested. These results cast doubt on previous conclusions of matrix‐wide species segregation based on the C‐score and the fixed‐fixed algorithm. Similarly, we found that recently proposed classification schemes based on patterns of coherence, nestedness, and segregation and aggregation cannot be uniquely distinguished using proposed metrics and null model algorithms. It may be necessary to use a combination of different metrics and to decompose matrix‐wide patterns into those of individual pairs of species or pairs of sites to pinpoint the sources of non‐randomness.