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.     

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.     

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.     

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.     

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.     

Random ecological systems with structure: Stability-complexity relationship

We have numerically examined more than one million Large Complex Systems (LCS) of interacting variables (interpretable as interacting populations) governed by Generalized Lotka-Volterra Equations (GLV), with self-regulation term. The scope was to have some insight on the stability-complexity relationship. We considered systems of prey-predator type, and we gave appropriate rules for constructing the model systems, rules that specify the behaviour of model systems in order to put them near the biological reality. The results show, among other things, a strict correlation between the stability and the prey-predator ratio (which, in our model, uniquely determines the connectedness of the system).     

Enemy free space and the structure of ecological communities

We define 'enemy free space' as ways of living that reduce or eliminate a species' vulnerability to one or more species of natural enemies. Many aspects of species' niches, in ecological and evolutionary time have apparently been moulded by interactions with natural enemies for enemy free space. We review a large number of examples. Yet many ecologists continue to think and write as though classical resource based competition for food or space is the primary determinant of species' niches. Often it is not. The recognition that the struggle for enemy free space is an important component of many species' ecologies may have important consequences for studies of community convergence, limits to species packing, and the ratio of predator species to prey species in natural communities.     

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.     

Identifying the density-dependent structure underlying ecological time series

A central problem in ecology is explaining the causes of population fluctuations, and an important step in the solution is determining the structure of the negative feedback (density dependent) process regulating population dynamics. The conventional way to determine the dimension or order of density dependence in a time series is to calculate the partial autorcorrelation function (PACF). We maintain, however, that PACF is not designed with biological populations in mind and has the wrong null model for detecting the structure of density dependence. We suggest an alternative diagnostic, the partial rate correlation function (PRCF), which is specifically designed for biological populations and has an appropriate null model for detecting their density dependent structures. Tests with simulated data show PRCF to be superior to PACF in detecting the underlying density dependent structure of two simple mathematical models.     

Parasites, disease and the structure of ecological communities

Pathogens and parasites are fascinating to epidemiologists and ecologists alike; as well as causing disease in individual species, they can perturb the normal functioning of a community and thus give insights into the way that the community 'functions' Several recent studies on diseases in animal populations have confirmed the importance of pathogens and parasites as components of ecological systems, while also revealing the underlying structure of complex multispecies communities.     

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.     

Spatial structure leads to ecological breakdown and loss of diversity

Spatial structure has been identified as a major contributor to the maintenance of diversity. Here, we show that the impact of spatial structure on diversity is strongly affected by the ecological mechanisms maintaining diversity. In well-mixed, unstructured environments, microbial populations can diversify by production of metabolites during growth, providing additional resources for novel specialists. By contrast, spatially structured environments potentially limit such facilitation due to reduced metabolite diffusion. Using replicate microcosms containing the bacterium Escherichia coli, we predicted the loss of diversity during an environmental shift from a spatially unstructured environment to spatially structured conditions. Although spatial structure is frequently observed to be a major promoter of diversity, our results indicate that it can also have negative impacts on diversity.     

Estimating density dependence in time-series of age-structured populations

For a life history with age at maturity alpha, and stochasticity and density dependence in adult recruitment and mortality, we derive a linearized autoregressive equation with time-lags of from 1 to alpha years. Contrary to current interpretations, the coefficients for different time-lags in the autoregressive dynamics do not simply measure delayed density dependence, but also depend on life-history parameters. We define a new measure of total density dependence in a life history, D, as the negative elasticity of population growth rate per generation with respect to change in population size, D = - partial differential lnlambda(T)/partial differential lnN, where lambda is the asymptotic multiplicative growth rate per year, T is the generation time and N is adult population size. We show that D can be estimated from the sum of the autoregression coefficients. We estimated D in populations of six avian species for which life-history data and unusually long time-series of complete population censuses were available. Estimates of D were in the order of 1 or higher, indicating strong, statistically significant density dependence in four of the six species.     

PAS domains. Common structure and common flexibility

PAS (PER-ARNT-SIM) domains are a family of sensor protein domains involved in signal transduction in a wide range of organisms. Recent structural studies have revealed that these domains contain a structurally conserved alpha/beta-fold, whereas almost no conservation is observed at the amino acid sequence level. The photoactive yellow protein, a bacterial light sensor, has been proposed as the PAS structural prototype yet contains an N-terminal helix-turn-helix motif not found in other PAS domains. Here we describe the atomic resolution structure of a photoactive yellow protein deletion mutant lacking this motif, revealing that the PAS domain is indeed able to fold independently and is not affected by the removal of these residues. Computer simulations of currently known PAS domain structures reveal that these domains are not only structurally conserved but are also similar in their conformational flexibilities. The observed motions point to a possible common mechanism for communicating ligand binding/activation to downstream transducer proteins.