Common pesticides disrupt critical ecological interactions

Critical ecological interactions can be disrupted by pesticides, leading to serious ecosystem and economic harm. For the most part, however, the extent and magnitude of these impacts are unknown. We argue for increased investigation of ecosystem impacts of common pesticides by scientists and scrutiny by regulators.     

distantia: an open-source toolset to quantify dissimilarity between multivariate ecological time-series

There is a large array of methods to extract knowledge and perform ecological forecasting from ecological time-series. However, in spite of its importance for data-mining, pattern-matching and ecological synthesis, methods to assess their similarity are scarce. We introduce distantia (v1.0.1), an R package providing general toolset to quantify dissimilarity between ecological time-series, independently of their regularity and number of samples. The functions in distantia provide the means to compute dissimilarity scores by time and by shape and assess their significance, evaluate the partial contribution of each variable to dissimilarity, and align or combine sequences by similarity. We evaluate the sensitivity of the dissimilarity metrics implemented in distantia, describe its structure and functionality, and showcase its applications with two examples. Particularly, we evaluate how geographic factors drive the dissimilarity between nine pollen sequences dated to the Last Interglacial, and compare the temporal dynamics of climate and enhanced vegetation index of three stands across the range of the European beech. We expect this package may enhance the capabilities of researchers from different fields to explore dissimilarity patterns between multivariate ecological time-series, and aid in generating and testing new hypotheses on why the temporal dynamics of complex-systems changes over space and time.     

Detecting causality from short time-series data based on prediction of topologically equivalent attractors

Background

Detecting causality for short time-series data such as gene regulation data is quite important but it is usually very difficult. This can be used in many fields especially in biological systems. Recently, several powerful methods have been set up to solve this problem. However, it usually needs very long time-series data or much more samples for the existing methods to detect causality among the given or observed data. In our real applications, such as for biological systems, the obtained data or samples are short or small. Since the data or samples are highly depended on experiment or limited resource.

Results

In order to overcome these limitations, here we propose a new method called topologically equivalent position method which can detect causality for very short time-series data or small samples. This method is mainly based on attractor embedding theory in nonlinear dynamical systems. By comparing with inner composition alignment, we use theoretical models and real gene expression data to show the effectiveness of our method.

Conclusions

As a result, it shows our method can be effectively used in biological systems. We hope our method can be useful in many other fields in near future such as complex networks, ecological systems and so on.

     

A general framework for analyzing data from two short time-series microarray experiments

We propose a general theoretical framework for analyzing differentially expressed genes and behavior patterns from two homogenous short time-course data. The framework generalizes the recently proposed Hilbert-Schmidt Independence Criterion (HSIC)-based framework adapting it to the time-series scenario by utilizing tensor analysis for data transformation. The proposed framework is effective in yielding criteria that can identify both the differentially expressed genes and time-course patterns of interest between two time-series experiments without requiring to explicitly cluster the data. The results, obtained by applying the proposed framework with a linear kernel formulation, on various data sets are found to be both biologically meaningful and consistent with published studies.     

Extending nonlinear analysis to short ecological time series

Nonlinearity is important and ubiquitous in ecology. Though detectable in principle, nonlinear behavior is often difficult to characterize, analyze, and incorporate mechanistically into models of ecosystem function. One obvious reason is that quantitative nonlinear analysis tools are data intensive (require long time series), and time series in ecology are generally short. Here we demonstrate a useful method that circumvents data limitation and reduces sampling error by combining ecologically similar multispecies time series into one long time series. With this technique, individual ecological time series containing as few as 20 data points can be mined for such important information as (1) significantly improved forecast ability, (2) the presence and location of nonlinearity, and (3) the effective dimensionality (the number of relevant variables) of an ecological system.     

Quantifying population structure on short timescales

Quantifying the contribution of the various processes that influence population genetic structure is important, but difficult. One of the reasons is that no single measure appropriately quantifies all aspects of genetic structure. An increasing number of studies is analysing population structure using the statistic D, which measures genetic differentiation, next to G_ST, which quantifies the standardized variance in allele frequencies among populations. Few studies have evaluated which statistic is most appropriate in particular situations. In this study, we evaluated which index is more suitable in quantifying postglacial divergence between three‐spined stickleback (Gasterosteus aculeatus) populations from Western Europe. Population structure on this short timescale (10 000 generations) is probably shaped by colonization history, followed by migration and drift. Using microsatellite markers and anticipating that D and G_ST might have different capacities to reveal these processes, we evaluated population structure at two levels: (i) between lowland and upland populations, aiming to infer historical processes; and (ii) among upland populations, aiming to quantify contemporary processes. In the first case, only D revealed clear clusters of populations, putatively indicative of population ancestry. In the second case, only G_ST was indicative for the balance between migration and drift. Simulations of colonization and subsequent divergence in a hierarchical stepping stone model confirmed this discrepancy, which becomes particularly strong for markers with moderate to high mutation rates. We conclude that on short timescales, and across strong clines in population size and connectivity, D is useful to infer colonization history, whereas G_ST is sensitive to more recent demographic events.     

Genetic and ecological structure of Hafnia alvei in Australia

Multilocus enzyme electrophoresis of 161 Hafnia alvei isolates from 158 hosts and 3 water column samples collected in Australia revealed that this species consists of two genetically distinct groups. The two groups of H. alvei differed significantly in their genetic structure and host distribution. The taxonomic class of the host but not geographic locality explained a significant proportion of the observed genetic and biochemical variation among strains within each genetic group.     

Higher order structure in a short repeat length chromatin

Polynucleosomes from calf brain cortical neurone nuclei have an average repeat length of less than 168 base pairs. The ability of this material to adopt higher order structure has been assessed by various physical techniques. Although containing on average less DNA per nucleosome than is required to form a chromatosome, this short repeat length chromatin folded in an H1 dependent manner to a structure with properties similar to those observed for longer repeat length chromatins such as that of chicken erythrocyte (McGhee, J.D., D.C. Rau, E. Charney, and G. Felsenfeld, 1980, Cell, 22:87-96). These observations are discussed in the context of H1 location in the higher order chromatin fiber.     

Trophic structure and dynamical complexity in simple ecological models

We study the dynamical complexity of five non-linear deterministic predator–prey model systems. These simple systems were selected to represent a diversity of trophic structures and ecological interactions in the real world while still preserving reasonable tractability. We find that these systems can dramatically change attractor types, and the switching among different attractors is dependent on system parameters. While dynamical complexity depends on the nature (e.g., inter-specific competition versus predation) and degree (e.g., number of interacting components) of trophic structure present in the system, these systems all evolve principally on intrinsically noisy limit cycles. Our results support the common observation of cycling and rare observation of chaos in natural populations. Our study also allows us to speculate on the functional role of specialist versus generalist predators in food web modeling.     

A call for applying trophic structure in ecological restoration

Ecological restoration projects have traditionally focused on vegetation as both a means (seeding, planting, and substrate amendments) and ends (success based upon primary productivity and vegetation diversity). This vegetation‐centric approach to ecological restoration stems from an historic emphasis on esthetics and cost but provides a limited measure of total ecosystem functioning and overlooks alternative ways to achieve current and future restoration targets. We advocate a shift to planning beyond the plant community and toward the physical and biological components necessary to initiate autogenic recovery, then guiding this process through the timely introduction of top predators and environmental modifications such as soil amendments and physical structures for animal nesting and refugia.     

Spatial structure of ecological opportunity drives adaptation in a bacterium

Abundant ecological opportunity is thought to drive adaptation and diversification. The presence of multiple opportunities leads to divergent selection, which can slow adaptation when niche-specific beneficial mutations have antagonistically pleiotropic effects. Alternately, competition for multiple opportunities can generate divergent selection, which leads to high rates of adaptive differentiation. Which outcome occurs may depend on the spatial structure of those ecological opportunities. In a mixture of resources, competition for multiple opportunities can drive divergent selection; however, if each resource is available in a spatially distinct patch, simultaneous competition for multiple opportunities cannot occur. We report the effects of the extent and spatial structure of ecological opportunity on the evolutionary dynamics of populations of Pseudomonas fluorescens over 1,000 generations. We varied the extent of ecological opportunity by varying the number of sugar resources (mannose, glucose, and xylose), and we varied spatial structure by providing resources in either mixtures or spatially distinct patches. We saw that a particularly novel resource (xylose) drove the rate of adaptation when provided in a mixture but had no effect on diversity. Instead, we saw the evolution of a single adaptive strategy that differed with respect to phenotype and degree of specialization, depending on both the extent and the spatial structure of ecological opportunity.     

Historical and ecological determinants of genetic structure in arctic canids

Wolves (Canis lupus) and arctic foxes (Alopex lagopus) are the only canid species found throughout the mainland tundra and arctic islands of North America. Contrasting evolutionary histories, and the contemporary ecology of each species, have combined to produce their divergent population genetic characteristics. Arctic foxes are more variable than wolves, and both island and mainland fox populations possess similarly high microsatellite variation. These differences result from larger effective population sizes in arctic foxes, and the fact that, unlike wolves, foxes were not isolated in discrete refugia during the Pleistocene. Despite the large physical distances and distinct ecotypes represented, a single, panmictic population of arctic foxes was found which spans the Svalbard Archipelago and the North American range of the species. This pattern likely reflects both the absence of historical population bottlenecks and current, high levels of gene flow following frequent long-distance foraging movements. In contrast, genetic structure in wolves correlates strongly to transitions in habitat type, and is probably determined by natal habitat-biased dispersal. Nonrandom dispersal may be cued by relative levels of vegetation cover between tundra and forest habitats, but especially by wolf prey specialization on ungulate species of familiar type and behaviour (sedentary or migratory). Results presented here suggest that, through its influence on sea ice, vegetation, prey dynamics and distribution, continued arctic climate change may have effects as dramatic as those of the Pleistocene on the genetic structure of arctic canid species.     

Temporally auto-correlated predator attacks structure ecological communities

For species primarily regulated by a common predator, the P* rule of Holt & Lawton (Holt & Lawton, 1993. Am. Nat. 142, 623–645. (doi:10.1086/285561)) predicts that the prey species that supports the highest mean predator density (P*) excludes the other prey species. This prediction is re-examined in the presence of temporal fluctuations in the vital rates of the interacting species including predator attack rates. When the fluctuations in predator attack rates are temporally uncorrelated, the P* rule still holds even when the other vital rates are temporally auto-correlated. However, when temporal auto-correlations in attack rates are positive but not too strong, the prey species can coexist due to the emergence of a positive covariance between predator density and prey vulnerability. This coexistence mechanism is similar to the storage effect for species regulated by a common resource. Negative or strongly positive auto-correlations in attack rates generate a negative covariance between predator density and prey vulnerability and a stochastic priority effect can emerge: with non-zero probability either prey species is excluded. These results highlight how temporally auto-correlated species’ interaction rates impact the structure and dynamics of ecological communities.     

Salt-bridging effects on short amphiphilic helical structure and introducing sequence-based short beta-turn motifs

Determining the minimal sequence necessary to induce protein folding is beneficial in understanding the role of protein–protein interactions in biological systems, as their three-dimensional structures often dictate their activity. Proteins are generally comprised of discrete secondary structures, from α-helices to β-turns and larger β-sheets, each of which is influenced by its primary structure. Manipulating the sequence of short, moderately helical peptides can help elucidate the influences on folding. We created two new scaffolds based on a modestly helical eight-residue peptide, PT3, we previously published. Using circular dichroism (CD) spectroscopy and changing the possible salt-bridging residues to new combinations of Lys, Arg, Glu, and Asp, we found that our most helical improvements came from the Arg–Glu combination, whereas the Lys–Asp was not significantly different from the Lys–Glu of the parent scaffold, PT3. The marked 3₁₀-helical contributions in PT3 were lessened in the Arg–Glu-containing peptide with the beginning of cooperative unfolding seen through a thermal denaturation. However, a unique and unexpected signature was seen for the denaturation of the Lys–Asp peptide which could help elucidate the stages of folding between the 3₁₀ and α-helix. In addition, we developed a short six-residue peptide with β-turn/sheet CD signature, again to help study minimal sequences needed for folding. Overall, the results indicate that improvements made to short peptide scaffolds by fine-tuning the salt-bridging residues can enhance scaffold structure. Likewise, with the results from the new, short β-turn motif, these can help impact future peptidomimetic designs in creating biologically useful, short, structured β-sheet-forming peptides.     

Evidence for polyproline II helical structure in short polyglutamine tracts

Nine neurodegenerative diseases, including Huntington's disease, are associated with the aggregation of proteins containing expanded polyglutamine sequences. The end result of polyglutamine aggregation is a beta-sheet-rich deposit. There exists evidence that an important intermediate in the aggregation process involves intramolecular beta-hairpin structures. However, little is known about the starting state, monomeric polyglutamine. Most experimental studies of monomeric polyglutamine have concluded that the backbone is completely disordered. However, such studies are hampered by the inherent tendency for polyglutamine to aggregate. A recent computational study suggested that the glutamine residues in polyglutamine tracts have a significant propensity to adopt the left-handed polyproline II (P(II)) helical conformation. In this work, we use NMR spectroscopy to demonstrate that glutamine residues possess a high propensity to adopt the P(II) conformation. We present circular dichroism spectra that indicate the presence of significant amounts of P(II) helical structure in short glutamine tracts. These data demonstrate that the propensity to adopt the P(II) structure is retained for glutamine repeats of up to at least 15 residues. Although other structures, such as alpha-helices and beta-sheets, become possible at greater lengths, our data indicate that glutamine residues in monomeric polyglutamine have a significant propensity to adopt the P(II) structure, although not necessarily in long contiguous helical stretches. We note that we have no evidence to suggest that the observed P(II) helical structure is a precursor to polyglutamine aggregation. Nonetheless, increased understanding of monomeric polyglutamine structures will aid our understanding of the aggregation process.     

五种海桑属红树植物叶片的结构及其生态适应

对海桑属五种红树植物的叶片进行解剖学的观察和研究。结果表明:海桑属红树植物的叶片上、下均有栅栏组织,栅栏组织与叶片厚的比值在0.3375～0.4349之间,属等面叶;表皮的角质膜厚1.88～7.63μm;输导组织发达;多数种类的栅栏组织中有分泌腔,而海绵组织中有单宁异细胞分布。说明了海桑属红树植物叶片的结构具有很强的耐旱及抗腐蚀能力。     

Dynamical structure of the short multifunctional peptide BP100 in membranes

BP100 is a multifunctional membrane-active peptide of only 11 amino acids, with a high antimicrobial activity, an efficient cell-penetrating ability, and low hemolytic side-effects. It forms an amphiphilic α-helix, similar to other antimicrobial peptides like magainin. However, BP100 is very short and thus unlikely to form membrane-spanning pores as proposed for longer peptides as a mechanism of action. We thus studied the conformation, membrane alignment and dynamical behavior of BP100 in lipid bilayers (DMPC/DMPG), using oriented circular dichroism (OCD) and solid-state ¹⁹F and ¹⁵N NMR. According to OCD and ¹⁵N NMR, the BP100 helix is oriented roughly parallel to the membrane surface, but these methods yield no information on the azimuthal alignment angle or the dynamics of the molecule. To address these questions, a systematic ¹⁹F NMR analysis was performed, which was not straightforward for this short peptide. Only a limited number of positions could be ¹⁹F-labeled, all of which are located on one face of the helix, which was found to lead to artifacts in the data analysis. It was nevertheless possible to reconcile the ¹⁹F NMR data with the OCD and ¹⁵N NMR data by using an advanced dynamical model, in which peptide mobility is described by fluctuating tilt and azimuthal angles with Gaussian distributions. ¹⁹F NMR thus confirmed the regular α-helical conformation of BP100, revealed its azimuthal angle, and described its high mobility in the membrane. Furthermore, the very sensitive ¹⁹F NMR experiments showed that the alignment of BP100 does not vary with peptide concentration over a peptide-to-lipid molar ratio from 1:10 to 1:3000.     

运城盐湖细菌群落结构及生态多样性分析

运城盐湖作为一个人类活动深入参与的高盐环境,其中的细菌群落结构及生态多样性既有盐湖环境的共性,又有自身的特殊性。【目的】运城盐湖湖水颜色丰富,蕴含着大量嗜盐及耐盐微生物资源。为了深入探究运城盐湖细菌资源分布规律,对不同水域中细菌多样性和群落结构进行研究,探讨运城盐湖不同水域中细菌群落结构的变化规律。【方法】基于16S rRNA基因的扩增子高通量测序,对运城盐湖不同水域的细菌群落结构进行分析,同时对微生物的潜在代谢功能进行预测。【结果】运城盐湖不同水域中的优势细菌类群有所差异,在盐湖中部,假单胞菌门(Pseudomonadota)、放线菌门(Actinobacteriota)和拟杆菌门(Bacteroidota)是优势类群;而在运城盐湖东部,芽孢杆菌门(Bacillota)则是主要类群;在运城盐湖西部,髌骨菌门(Patescibacteria)类群较为丰富。对运城盐湖不同区域的细菌多样性进行分析,数据显示盐湖中部浅黄色湖水中微生物多样性显著高于盐湖东部和西部区域,但盐湖中部红色湖水区域的微生物多样性较低。另外,在盐湖中部,湖水颜色不同的区域细菌物种分布也具有较大的差异。对运城盐湖细菌代谢功能进行预测分析发现,在盐湖不同区域的微生物参与的代谢通路活性各不相同,表现出较强的区域分布性,盐湖东部和西部的微生物代谢比盐湖中部更具有活性。【结论】运城盐湖微生物多样性丰富,不同水域的细菌多样性具有显著差异,盐湖不同水域的环境对细菌群落结构具有一定影响。本研究为运城盐湖细菌资源多样性的保育及开发利用提供了重要的理论基础。