Negative Plant–Soil Feedback and Positive Species Interaction in a Herbaceous Plant Community

Increasing evidence shows that facilitative interaction and negative plant–soil feedback are driving factors of plant population dynamics and community processes. We studied the intensity and the relative impact of negative feedback on clonal growth and seed germination of Scirpus holoschoenus, a ‘ring’ forming sedge dominant in grazed grassland, and the consequences for species coexistence. The structure of aboveground tussocks was described. A Lithium tracer assessed belowground distribution of functional roots. Seed rain and seedling emergence were compared for different positions in relation to Scirpus tussocks. Soil bioassays were used to compare growth on soil taken from inside and outside Scirpus tussocks of four coexisting species (Mentha acquatica, Pulicaria dysenterica, Scirpus holoschoenus and Dittrichia viscosa). We also compared plant performance of dominant plant species inside and outside Scirpus tussocks in the field. The ‘ring’ shaped tussocks of S. holoschoenus were generated by centrifugal rhizome development. Roots were functional and abundant under the tillers and extending outside the tussocks. The large roots mats that were present in the inner tussock zone were almost all dead. Seedling emergence and growth both showed a strong negative feedback of Scirpus in the inner tussock zone. Scirpus clonal development strongly reduced grass biomass. In the degenerated tussock zone, Pulicaria and Mentha mortality was lower, and biomass of individual plants and seed production were higher. This positive indirect interaction could be related to species-specific affinity to soil conditions generated by Scirpus, and interspecific competitive release in the degenerated tussock zone. We conclude that Scirpus negative feedback affects its seedling emergence and growth contributing to the development of the degenerated inner tussock zone. Moreover, this enhances species coexistence through facilitative interaction because the colonization of the inner tussock zone is highly species-specific.     

Nonparametric Bayesian Variable Selection With Applications to Multiple Quantitative Trait Loci Mapping With Epistasis and Gene–Environment Interaction

The joint action of multiple genes is an important source of variation for complex traits and human diseases. However, mapping genes with epistatic effects and gene–environment interactions is a difficult problem because of relatively small sample sizes and very large parameter spaces for quantitative trait locus models that include such interactions. Here we present a nonparametric Bayesian method to map multiple quantitative trait loci (QTL) by considering epistatic and gene–environment interactions. The proposed method is not restricted to pairwise interactions among genes, as is typically done in parametric QTL analysis. Rather than modeling each main and interaction term explicitly, our nonparametric Bayesian method measures the importance of each QTL, irrespective of whether it is mostly due to a main effect or due to some interaction effect(s), via an unspecified function of the genotypes at all candidate QTL. A Gaussian process prior is assigned to this unknown function. In addition to the candidate QTL, nongenetic factors and covariates, such as age, gender, and environmental conditions, can also be included in the unspecified function. The importance of each genetic factor (QTL) and each nongenetic factor/covariate included in the function is estimated by a single hyperparameter, which enters the covariance function and captures any main or interaction effect associated with a given factor/covariate. An initial evaluation of the performance of the proposed method is obtained via analysis of simulated and real data.TRAITS showing continuous variation are called quantitative traits and are typically controlled by multiple genetic and nongenetic factors, which tend to have relatively small effects individually. Crosses between inbred lines produce suitable populations for quantitative trait locus (QTL) mapping and are available for agricultural plants and for animal (e.g., mouse) models of human diseases. Such crosses are often used to detect QTL. For these inbred line crosses, uniform genetic backgrounds, controlled breeding schemes, and controlled environment ensure that there is little or no confounding of uncontrolled sources of variability with genetic effects. The potential for such confounding complicates and limits the analysis and interpretation of human data. Because of the homology between humans and rodents, rodent models can be extremely useful in advancing our understanding of certain human diseases. In the past 2 decades, various statistical approaches have been developed to identify QTL in inbred line crosses (see, for example, Doerge et al. 1997 for review). To perform QTL mapping (identification), a large number of candidate positions (candidate QTL) along the genome are selected. These candidate QTL may all be located at genetic markers (positions of sequence variants in the genome where the genotypes of all individuals in a mapping population can be measured) or also in between markers if the marker density is not high. QTL mapping may then be performed by considering one candidate QTL at a time or multiple candidate QTL simultaneously. For inbred line crosses with low marker density and considering a single candidate QTL at a time, the interval-mapping method was proposed by Lander and Botstein (1989). However, these authors showed that interval mapping tends to identify a “ghost” QTL located in between two actual linked QTL if two or more closely linked QTL exist. This problem can be reduced or eliminated in two ways: (1) by using composite-interval mapping (Jansen and Stam 1994; Zeng 1994) which still performs a one-dimensional QTL search but conditional on the genotypes at a pair of markers flanking the marker interval containing the current QTL, to absorb the effects of background (nontarget QTL) outside of the target interval; or (2) by performing multiple QTL mapping, where two or more QTL are mapped simultaneously. Furthermore, if several QTL affect a quantitative trait mostly through their interactions (epistasis) while having nonexistent or weak main effects, then interval mapping or single-marker analysis will fail to detect such QTL. QTL interactions may not be limited to pairwise interactions. Marchini et al. (2005) have shown by simulation that searching for three loci jointly in the presence of a three-way interaction is more powerful than searching for a single or a pair of QTL. There are various different implementations of multiple QTL mapping. Most methods still perform only pairwise searches, with and without epistasis. The most recent methods are based on Bayesian variable selection and consider a group of candidate QTL or all candidate QTL in the genome simultaneously (e.g., Yi et al. 2007). These methods are typically still limited to pairwise interactions among QTL and do not consider gene–environment interactions.The identification of QTL can be viewed as a very large variable selection problem: for p candidate QTL, with p typically in the hundreds or thousands and sample size in the low hundreds, there are 2^p possible main-effect models, possible two-way interactions, and possible higher-order (k > 2) interactions. For inbred line crosses, where multiple-QTL mapping models can be represented as multiple linear regression models, classical variable selection methods such as forward and stepwise selection (Broman and Speed 2002) have been used in searching for main and two-way interaction effects. Bayesian analysis implemented by Markov chain Monte Carlo (MCMC) and based on the composite model space framework (Godsill 2001, 2003) has been introduced to genetic mapping (Yi 2004). Well-known Bayesian variable selection methods such as reversible jump MCMC (Green 1995) and stochastic search variable selection (SSVS) (George and McCulloch 1993) are special cases. SSVS and similar methods employ mixture priors for the regression coefficients, which specify different distributions for the coefficients under the null (effect negligible) and alternative (effect nonnegligible) hypotheses. The marginal posterior probabilities of the alternative hypotheses can be used to identify a subset of important parameters on the basis of Bayesian multiple comparison rules, including the median probability model (with a threshold of 0.5) and Bayesian false discovery rate control (e.g., Müller et al. 2006).An alternative to variable selection with mixture priors is classical and Bayesian shrinkage- or penalty-based inference. For the classical approach of penalized regression, while an L₂-based shrinkage method (ridge regression) cannot perform variable selection, other methods, in particular the L₁-based lasso of Tibshirani (1996) and later lasso extensions, are capable of performing variable selection by reducing the effects of unimportant variables effectively to zero. The lasso has been applied to parametric, regression-based QTL mapping (Yi and Xu 2008). The penalized regression methods can be interpreted as Bayesian regression models with particular sparsity priors imposed on the regression coefficients (Park and Casella 2008).Regression methods are also used for association mapping in human populations. Recently, Kwee et al. (2008) proposed a semiparametric regression-based approach for candidate regions in human association mapping, where a quantitative trait is regressed on a nonparametric function of the tagSNP genotypes within a region. They analyzed a (small) subset of the genome and tested for the joint significance of the subset. Their method potentially can be used to model interactions among SNPs and covariates. However, Kwee et al. (2008) fit their model using least-squares kernel machines, a dimension-reducing technique that is identical to an analysis based on a specific linear mixed model. Model selection for different types of kernels and different sets of variables is performed using criteria such as Akaike''s information criteria (Akaike 1974) and Bayesian information criteria (Schwarz 1978), which may not be appropriate or feasible in large-scale, sparse variable selection situations.We (Huang et al. 2010) recently developed a Bayesian semiparametric QTL mapping method, where nongenetic covariate effects are modeled nonparametrically. This method was implemented via MCMC, and a Gaussian process prior (O''Hagan 1978; Neal 1996, 1997) was placed on the unknown covariate function. The Gaussian process is particularly well suited for curve estimation due to its flexible sample path shapes. This method allows one or more nongenetic covariates to have an arbitrary (nonlinear) relationship with the phenotype. Another strong advantage of the Gaussian process is its ability to deal with high-dimensional data compared to other nonparametric techniques such as spline regression (Wahba 1984; Heckman 1986; Chen 1988; Speckman 1988; Cuzick 1992; Hastie and Loader 1993). There has been a growing interest in using Gaussian processes as a unifying framework for studying multivariate regression (Rasmussen 1996), pattern classification (Williams and Barber 1998), and hierarchical modeling (Menzefricke 2000). In this article, we build on this work and propose a nonparametric Bayesian method for multiple QTL mapping by including not only nongenetic covariates but also all candidate QTL in the unknown function. A Gaussian process prior (GPP) is again placed on the unknown function, and a variable selection approach is implemented for the hyperparameters of the GPP (one for each QTL and nongenetic covariate). Here, we rely on mixture priors and MCMC implementation, and we focus on linkage mapping in inbred line crosses, while in ongoing and future work we are considering shrinkage priors, deterministic algorithms, and association mapping. Our application of the GPP differs from “standard” applications in that the QTL covariates included in the unknown function are discrete, not continuous, with a small number (two or three) of possible values (the genotype codes). The goal of using a GPP here is not curve or response surface modeling but rather high-dimensional variable selection (QTL and nongenetic covariates) with a method requiring only a single parameter for each variable while accounting for any multiway interactions among the candidate variables.To improve current methods for linkage mapping in inbred line crosses and for association analysis of human populations, we need to be able to detect QTL irrespective of whether they act mostly through main effects, interactions with other QTL, or interactions with environment. Fitting a parametric model including all these potential effects for a genome-wide search would substantially increase the multiple-testing problem, in addition to being computationally extremely demanding. Here we offer an alternative. We show that our nonparametric Bayesian method can identify QTL irrespective of whether they act through main effects, through interactions with other QTL, or with environmental factors. This method cannot identify the source(s) of a QTL''s importance (main or interaction effects involving this QTL). Therefore, once a small number of important QTL have been identified in a genome-wide scan, then these QTL can be further analyzed with detailed parametric models to determine the source(s) of their importance.The remainder of the article is organized as follows. We first present the nonparametric multiple-QTL model and outline the MCMC sampler in the next section. Simulation results and the analysis of a real data set are presented in the section following that. And we end the article with a discussion and conclusions.     

Dynamical Transitions in a Pollination–Herbivory Interaction: A Conflict between Mutualism and Antagonism

Plant-pollinator associations are often seen as purely mutualistic, while in reality they can be more complex. Indeed they may also display a diverse array of antagonistic interactions, such as competition and victim–exploiter interactions. In some cases mutualistic and antagonistic interactions are carried-out by the same species but at different life-stages. As a consequence, population structure affects the balance of inter-specific associations, a topic that is receiving increased attention. In this paper, we developed a model that captures the basic features of the interaction between a flowering plant and an insect with a larval stage that feeds on the plant’s vegetative tissues (e.g. leaves) and an adult pollinator stage. Our model is able to display a rich set of dynamics, the most remarkable of which involves victim–exploiter oscillations that allow plants to attain abundances above their carrying capacities and the periodic alternation between states dominated by mutualism or antagonism. Our study indicates that changes in the insect’s life cycle can modify the balance between mutualism and antagonism, causing important qualitative changes in the interaction dynamics. These changes in the life cycle could be caused by a variety of external drivers, such as temperature, plant nutrients, pesticides and changes in the diet of adult pollinators.     

Outfoxing a Rash: Clinical Example of Human–Wildlife Interaction

Increasing human–wildlife contact can manifest in a variety of clinical conditions that may be overlooked by both human health and veterinary professionals. We report on an outbreak of scabies infection in a community, affecting both animals and humans, and representing the effects of an emerging infectious disease in a wildlife population. These cases underscore the potential importance of animal sentinel events for human, animal, and ecosystem health. The treatment given to the human cases of infection ranged from aggressive therapy to watchful waiting, with similar outcomes. There is a need for further collaborative, evidence-based research by human and veterinary health professionals into the optimal treatment and prevention of infections resulting from cross-species transmission.     

Plant–insect–pathogen interactions: a naturally complex ménage à trois

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Plant–environment interactions through a functional traits perspective: a review of Italian studies

Abstract

Italy is among the European countries with the greatest plant diversity due to both a great environmental heterogeneity and a long history of man–environment interactions. Trait-based approaches to ecological studies have developed greatly over recent decades worldwide, although several issues concerning the relationships between plant functional traits and the environment still lack sufficient empirical evaluation. To draw insights on the association between plant functional traits and direct and indirect human and natural pressures on the environmental drivers, this article summarizes the existing knowledge on this topic by reviewing the results of studies performed in Italy adopting a functional trait approach on vascular plants, bryophytes and lichens. Although we recorded trait measurements for 1418 taxa, our review highlighted some major gaps in plant traits knowledge: Mediterranean ecosystems are poorly represented; traits related to belowground organs are still overlooked; traits measurements for bryophytes and lichens are lacking. Finally, intraspecific variation has been little studied at community level so far. We conclude by highlighting the need for approaches evaluating trait–environment relationship at large spatial and temporal scales and the need of a more effective contribution to online databases to tie more firmly Italian researchers to international scientific networks on plant traits.     

R. A. Fisher, Lancelot Hogben, and the Origin(s) of Genotype–Environment Interaction

This essay examines the origin(s) of genotype–environment interaction, or G × E. “Origin(s)” and not “the origin” because the thesis is that there were actually two distinct concepts of G × E at this beginning: a biometric concept, or G × E_B, and a developmental concept, or G × E_D. R. A. Fisher, one of the founders of population genetics and the creator of the statistical analysis of variance, introduced the biometric concept as he attempted to resolve one of the main problems in the biometric tradition of biology – partitioning the relative contributions of nature and nurture responsible for variation in a population. Lancelot Hogben, an experimental embryologist and also a statistician, introduced the developmental concept as he attempted to resolve one of the main problems in the developmental tradition of biology – determining the role that developmental relationships between genotype and environment played in the generation of variation. To argue for this thesis, I outline Fisher and Hogben’s separate routes to their respective concepts of G × E; then these separate interpretations of G × E are drawn on to explicate a debate between Fisher and Hogben over the importance of G × E, the first installment of a persistent controversy. Finally, Fisher’s G × E_B and Hogben’s G × E_D are traced beyond their own work into mid-20th century population and developmental genetics, and then into the infamous IQ Controversy of the 1970s.     

Plant–soil feedbacks of exotic plant species across life forms: a meta-analysis

Invasive exotic plant species effects on soil biota and processes in their new range can promote or counteract invasions via changed plant–soil feedback interactions to themselves or to native plant species. Recent meta-analyses reveale that soil influenced by native and exotic plant species is affecting growth and performance of natives more strongly than exotics. However, the question is how uniform these responses are across contrasting life forms. Here, we test the hypothesis that life form matters for effects on soil and plant–soil feedback. In a meta-analysis we show that exotics enhanced C cycling, numbers of meso-invertebrates and nematodes, while having variable effects on other soil biota and processes. Plant effects on soil biota and processes were not dependent on life form, but patterns in feedback effects of natives and exotics were dependent on life form. Native grasses and forbs caused changes in soil that subsequently negatively affected their biomass, whereas native trees caused changes in soil that subsequently positively affected their biomass. Most exotics had neutral feedback effects, although exotic forbs had positive feedback effects. Effects of exotics on natives differed among plant life forms. Native trees were inhibited in soils conditioned by exotics, whereas native grasses were positively influenced in soil conditioned by exotics. We conclude that plant life form matters when comparing plant–soil feedback effects both within and between natives and exotics. We propose that impact analyses of exotic plant species on the performance of native plant species can be improved by comparing responses within plant life form.     

Arms Race Coevolution: The Local and Geographical Structure of a Host–Parasite Interaction

Consideration of complex geographic patterns of reciprocal adaptation has provided insight into new features of the coevolutionary process. In this paper, we provide ecological, historical, and geographical evidence for coevolution under complex temporal and spatial scenarios that include intermittent selection, species turnover across localities, and a range of trait match/mismatch across populations. Our study focuses on a plant host–parasitic plant interaction endemic to arid and semiarid regions of Chile. The long spines of Chilean cacti have been suggested to evolve under parasite-mediated selection as a first line of defense against the mistletoe Tristerix aphyllus. The mistletoe, in turn, has evolved an extremely long morphological structure that emerges from the seed endosperm (radicle) to reach the host cuticle. When spine length was traced along cactus phylogenies, a significant association between spine length and parasitism was detected, indicating that defensive traits evolved in high correspondence with the presence or absence of parasitism in two cactus lineages. Assessment of spine-radicle matching across populations revealed a potential for coevolution in 50% of interaction pairs. Interestingly, hot spots for coevolution did not distribute at random across sites. On the contrary, interaction pairs showing high matching values occur mostly in the northern distribution of the interaction, suggesting a geographical structure for coevolution in this system. Only three sampled interaction pairs were so mismatched that reciprocal selection could not occur given current trait distributions. Overall, different lines of evidence indicate that arms-race coevolution is an ongoing phenomenon that occurs in the global system of interconnected populations.     

7 The Molecular Biology of Tryptophan Synthase: A Model for Protein–Protein Interaction

Abstract

The use of plastic produced from non-renewable resources constitutes a major environmental problem of the modern society. Polylactide polymers (PLA) have recently gained enormous attention as one possible substitution of petroleum derived polymers. A prerequisite for high quality PLA production is the provision of optically pure lactic acid, which cannot be obtained by chemical synthesis in an economical way. Microbial fermentation is therefore the commercial option to obtain lactic acid as monomer for PLA production. However, one major economic hurdle for commercial lactic acid production as basis for PLA is the costly separation procedure, which is needed to recover and purify the product from the fermentation broth. Yeasts, such as Saccharomyces cerevisiae (bakers yeast) offer themselves as production organisms because they can tolerate low pH and grow on mineral media what eases the purification of the acid. However, naturally yeasts do not produce lactic acid. By metabolic engineering, ethanol was exchanged with lactic acid as end product of fermentation. A vast amount of effort has been invested into the development of yeasts for lactic acid production since the first paper on this topic by Dequin and process insight. If pH stress is used as basis for DNA microarray analyses, in order to improve the host, what exactly is addressed? Growth? Or productivity? They might be connected, but can be negatively correlated. A better growing strain might not be a better producer. So if the question was growth, the answer might not be what was initially intended (productivity).

A major task for the future is to learn to ask the right questions – a lot of studies intended to lead to better productivity, did lead to interesting results, but NOT to better production strains.

Taking together what we learned from lactic acid production with yeasts, we see a bright future for bulk and fine chemical production with these versatile hosts.     

Ecosystem Development After Mangrove Wetland Creation: Plant–Soil Change Across a 20-Year Chronosequence

Mangrove wetland restoration and creation efforts are increasingly proposed as mechanisms to compensate for mangrove wetland losses. However, ecosystem development and functional equivalence in restored and created mangrove wetlands are poorly understood. We compared a 20-year chronosequence of created tidal wetland sites in Tampa Bay, Florida (USA) to natural reference mangrove wetlands. Across the chronosequence, our sites represent the succession from salt marsh to mangrove forest communities. Our results identify important soil and plant structural differences between the created and natural reference wetland sites; however, they also depict a positive developmental trajectory for the created wetland sites that reflects tightly coupled plant-soil development. Because upland soils and/or dredge spoils were used to create the new mangrove habitats, the soils at younger created sites and at lower depths (10–30?cm) had higher bulk densities, higher sand content, lower soil organic matter (SOM), lower total carbon (TC), and lower total nitrogen (TN) than did natural reference wetland soils. However, in the upper soil layer (0–10?cm), SOM, TC, and TN increased with created wetland site age simultaneously with mangrove forest growth. The rate of created wetland soil C accumulation was comparable to literature values for natural mangrove wetlands. Notably, the time to equivalence for the upper soil layer of created mangrove wetlands appears to be faster than for many other wetland ecosystem types. Collectively, our findings characterize the rate and trajectory of above- and below-ground changes associated with ecosystem development in created mangrove wetlands; this is valuable information for environmental managers planning to sustain existing mangrove wetlands or mitigate for mangrove wetland losses.     

A Nonlinear Stability Analysis of Vegetative Turing Pattern Formation for an Interaction–Diffusion Plant-Surface Water Model System in an Arid Flat Environment

The development of spontaneous stationary vegetative patterns in an arid flat environment is investigated by means of a weakly nonlinear diffusive instability analysis applied to the appropriate model system for this phenomenon. In particular, that process can be modeled by a partial differential interaction–diffusion equation system for the plant biomass density and the surface water content defined on an unbounded flat spatial domain. The main results of this analysis can be represented by closed-form plots in the rate of precipitation versus the specific rate of plant density loss parameter space. From these plots, regions corresponding to bare ground and vegetative patterns consisting of parallel stripes, labyrinth-like mazes, hexagonal arrays of gaps, irregular mosaics, and homogeneous distributions of vegetation, respectively, may be identified in this parameter space. Then those theoretical predictions are compared with both relevant observational evidence involving tiger and pearled bush patterns and existing numerical simulations of similar model systems as well as placed in the context of the results from some recent nonlinear vegetative pattern formation studies.     

Plant–plant interactions,environmental gradients and plant diversity: A global synthesis of community-level studies

Previous syntheses on the effects of environmental conditions on the outcome of plant–plant interactions summarize results from pairwise studies. However, the upscaling to the community-level of such studies is problematic because of the existence of multiple species assemblages and species-specific responses to both the environmental conditions and the presence of neighbors. We conducted the first global synthesis of community-level studies from harsh environments, which included data from 71 alpine and 137 dryland communities to: (i) test how important are facilitative interactions as a driver of community structure, (ii) evaluate whether we can predict the frequency of positive plant–plant interactions across differing environmental conditions and habitats, and (iii) assess whether thresholds in the response of plant–plant interactions to environmental gradients exists between “moderate” and “extreme” environments. We also used those community-level studies performed across gradients of at least three points to evaluate how the average environmental conditions, the length of the gradient studied, and the number of points sampled across such gradient affect the form and strength of the facilitation-environmental conditions relationship. Over 25% of the species present were more spatially associated to nurse plants than expected by chance in both alpine and dryland areas, illustrating the high importance of positive plant–plant interactions for the maintenance of plant diversity in these environments. Facilitative interactions were more frequent, and more related to environmental conditions, in alpine than in dryland areas, perhaps because drylands are generally characterized by a larger variety of environmental stress factors and plant functional traits. The frequency of facilitative interactions in alpine communities peaked at 1000 mm of annual rainfall, and globally decreased with elevation. The frequency of positive interactions in dryland communities decreased globally with water scarcity or temperature annual range. Positive facilitation-drought stress relationships are more likely in shorter regional gradients, but these relationships are obscured in regions with a greater species turnover or with complex environmental gradients. By showing the different climatic drivers and behaviors of plant–plant interactions in dryland and alpine areas, our results will improve predictions regarding the effect of facilitation on the assembly of plant communities and their response to changes in environmental conditions.     

PHISDetector: A Tool to Detect Diverse In Silico Phage–host Interaction Signals for Virome Studies

Phage–microbe interactions are appealing systems to study coevolution, and have also been increasingly emphasized due to their roles in human health, disease, and the development of novel therapeutics. Phage–microbe interactions leave diverse signals in bacterial and phage genomic sequences, defined as phage–host interaction signals(PHISs), which include clustered regularly interspaced short palindromic repeats(CRISPR) targeting, prophage, and protein–protein interaction signals. In the present ...