Low species turnover of upland Amazonian birds in the absence of physical barriers

Aim

One of the oldest and most powerful ways for ecologists to explain distinct biological communities is to invoke underlying environmental differences. But in hyper-diverse systems, which often display high species richness and low species abundance, these sorts of community comparisons are especially challenging. The classic view for Amazonian birds posits that riverine barriers and habitat specialization determine local and regional community composition. We test the tacit, complementary assumption that similar bird communities should therefore permeate uniform habitat between major rivers, regardless of distance.

Location

Upland (terra firme) rainforests of central Amazonia.

Methods

We conducted intensive whole-community surveys of birds in three pairs of 100-ha plots, separated by 40–60 km. We then used dissimilarity indices, cluster analysis, and ordination to characterize differences among the six avian communities.

Results

In all, we detected 244 forest-dependent birds, with an average of 190 species (78%) per plot. Species turnover was negligible, no unique indicator species were found among plot pairs, and all documented species were already known from a complete inventory at one of the three sites.

Main Conclusions

Our study corroborates the classic biogeographical pattern and suggests that turnover contributes little to regional avian diversity within upland forests. Using a grain size of 100 ha, this implies that upland birds perceive the environment as uniform, at least over distances of ~60 km. Therefore, to maximize both local species richness and population persistence, our findings support the conservation of very large tracts of upland rainforest. Our analyses also revealed that the avifauna at Reserva Ducke, encroached by urban sprawl from the city of Manaus, shows the hallmarks of a disturbed community, with fewer vulnerable insectivores. This defaunation signals that even an enormous preserve (10 × 10 km) in lowland Amazonia is not insulated from anthropogenic degradation within the surrounding landscape.     

Causal inference and large-scale expert validation shed light on the drivers of SDM accuracy and variance

Aim

To develop a causal understanding of the drivers of Species distribution model (SDM) performance.

Location

United Kingdom (UK).

Methods

We measured the accuracy and variance of SDMs fitted for 518 species of invertebrate and plant in the UK. Our measure of variance reflects variation among replicate model fits, and taxon experts assessed model accuracy. Using directed acyclic graphs, we developed a causal model depicting plausible effects of explanatory variables (e.g. species' prevalence, sample size) on SDM accuracy and variance and quantified those effects using a multilevel piecewise path model.

Results

According to our model, sample size and niche completeness (proportion of a species' niche covered by sampling) directly affect SDM accuracy and variance. Prevalence and range completeness have indirect effects mediated by sample size. Challenging conventional wisdom, we found that the effect of prevalence on SDM accuracy is positive. This reflects the facts that sample size has a positive effect on accuracy and larger sample sizes are possible for widespread species. It is possible, however, that the omission of an unobserved confounder biased this effect. Previous studies, which reported negative correlations between prevalence and SDM accuracy, conditioned on sample size.

Main conclusions

Our model explicates the causal basis of previously reported correlations between SDM performance and species/data characteristics. It also suggests that niche completeness has similarly large effects on SDM accuracy and variance as sample size. Analysts should consider niche completeness, or proxies thereof, in addition to sample size when deciding whether modelling is worthwhile.     

Three-dimensional species distribution modelling reveals the realized spatial niche for coral recruitment on contemporary Caribbean reefs

The three-dimensional structure of habitats is a critical component of species' niches driving coexistence in species-rich ecosystems. However, its influence on structuring and partitioning recruitment niches has not been widely addressed. We developed a new method to combine species distribution modelling and structure from motion, and characterized three-dimensional recruitment niches of two ecosystem engineers on Caribbean coral reefs, scleractinian corals and gorgonians. Fine-scale roughness was the most important predictor of suitable habitat for both taxa, and their niches largely overlapped, primarily due to scleractinians' broader niche breadth. Crevices and holes at mm scales on calcareous rock with low coral cover were more suitable for octocorals than for scleractinian recruits, suggesting that the decline in scleractinian corals is facilitating the recruitment of octocorals on contemporary Caribbean reefs. However, the relative abundances of the taxa were independent of the amount of suitable habitat on the reef, emphasizing that niche processes alone do not predict recruitment rates.     

一株C3形态多重耐药大肠杆菌噬菌体生物学特性和基因组分析

【目的】多重耐药菌的出现对公共卫生安全构成严重威胁,本研究分离多重耐药大肠杆菌噬菌体,研究其生物学特性和基因组特征,为耐药菌的噬菌体疗法提供理论依据。【方法】使用双层平板法从污水样本中分离纯化大肠杆菌噬菌体;磷钨酸染色后通过透射电镜观察形态;测定其宿主范围,测定温度和pH稳定性、一步生长曲线和体外抑菌效果等生物学特性;体内抑菌试验评估噬菌体对多重耐药大肠杆菌N1203-1Af感染的大蜡螟幼虫的保护作用;基于全基因组测序对其基因组特点进行分析。【结果】本研究分离共得到5株大肠杆菌噬菌体,分别命名为pEC-S163-2.1、pEC-S163-2.2、pEC-M1167-5Ar.1、pEC-m1291-2Dr.1和pEC-N1203-2Af.1;电镜结果显示噬菌体pEC-N1203-2Af.1属于短尾噬菌体中罕见的C3形态型,头部较长,长是宽的2–3倍;pEC-N1203-2Af.1可裂解受试15株大肠杆菌中的3株;感染10 min后进入指数增长期,–20-50℃、pH值为4.0–10.0的环境下均能够保持稳定活性;大蜡螟幼虫感染大肠杆菌N1203-2Af后噬菌体pEC-N1203-2Af....     

Comparison of conformational characteristics in structurally similar protein pairs.

Although it is known that three-dimensional structure is well conserved during the evolutionary development of proteins, there have been few studies that consider other parameters apart from divergence of the main-chain coordinates. In this study, we align the structures of 90 pairs of homologous proteins having sequence identities ranging from 5 to 100%. Their structures are compared as a function of sequence identity, including not only consideration of C alpha coordinates but also accessibility, Ooi numbers, secondary structure, and side-chain angles. We discuss how these properties change as the sequences become less similar. This will be of practical use in homology modeling, especially for modeling very distantly related or analogous proteins. We also consider how the average size and number of insertions and deletions vary as sequences diverge. This study presents further quantitative evidence that structure is remarkably well conserved in detail, as well as at the topological level, even when the sequences do not show similarity that is significant statistically.     

Structures of randomly generated mutants of T4 lysozyme show that protein stability can be enhanced by relaxation of strain and by improved hydrogen bonding via bound solvent.

The structures of three mutants of bacteriophage T4 lysozyme selected using a screen designed to identify thermostable variants are described. Each of the mutants has a substitution involving threonine. Two of the variants, Thr 26-->Ser (T26S) and Thr 151-->Ser (T151S), have increased reversible melting temperatures with respect to the wild-type protein. The third, Ala 93-->Thr (A93T), has essentially the same stability as wild type. Thr 26 is in the wall of the active-site cleft. Its replacement with serine results in the rearrangement of nearby residues, most notably Tyr 18, suggesting that the increase in stability may result from the removal of strain. Thr 151 in the wild-type structure is far from the active site and appears to sterically prevent the access of solvent to a preformed binding site. In the mutant, the removal of the methyl group allows access to the solvent binding site and, in addition, the Ser 151 hydroxyl rotates to a new position so that it also contributes to solvent binding. Residue 93 is in a highly exposed site on the surface of the molecule, and presumably is equally solvent exposed in the unfolded protein. It is, therefore, not surprising that the substitution Ala 93-->Thr does not change stability. The mutant structures show how chemically similar mutations can have different effects on both the structure and stability of the protein, depending on the structural context. The results also illustrate the power of random mutagenesis in obtaining variants with a desired phenotype.(ABSTRACT TRUNCATED AT 250 WORDS)     

Cross-trait prediction accuracy of summary statistics in genome-wide association studies

In the era of big data, univariate models have widely been used as a workhorse tool for quickly producing marginal estimators; and this is true even when in a high-dimensional dense setting, in which many features are “true,” but weak signals. Genome-wide association studies (GWAS) epitomize this type of setting. Although the GWAS marginal estimator is popular, it has long been criticized for ignoring the correlation structure of genetic variants (i.e., the linkage disequilibrium [LD] pattern). In this paper, we study the effects of LD pattern on the GWAS marginal estimator and investigate whether or not additionally accounting for the LD can improve the prediction accuracy of complex traits. We consider a general high-dimensional dense setting for GWAS and study a class of ridge-type estimators, including the popular marginal estimator and the best linear unbiased prediction (BLUP) estimator as two special cases. We show that the performance of GWAS marginal estimator depends on the LD pattern through the first three moments of its eigenvalue distribution. Furthermore, we uncover that the relative performance of GWAS marginal and BLUP estimators highly depends on the ratio of GWAS sample size over the number of genetic variants. Particularly, our finding reveals that the marginal estimator can easily become near-optimal within this class when the sample size is relatively small, even though it ignores the LD pattern. On the other hand, BLUP estimator has substantially better performance than the marginal estimator as the sample size increases toward the number of genetic variants, which is typically in millions. Therefore, adjusting for the LD (such as in the BLUP) is most needed when GWAS sample size is large. We illustrate the importance of our results by using the simulated data and real GWAS.     

Multikink quantile regression for longitudinal data with application to progesterone data analysis

Motivated by investigating the relationship between progesterone and the days in a menstrual cycle in a longitudinal study, we propose a multikink quantile regression model for longitudinal data analysis. It relaxes the linearity condition and assumes different regression forms in different regions of the domain of the threshold covariate. In this paper, we first propose a multikink quantile regression for longitudinal data. Two estimation procedures are proposed to estimate the regression coefficients and the kink points locations: one is a computationally efficient profile estimator under the working independence framework while the other one considers the within-subject correlations by using the unbiased generalized estimation equation approach. The selection consistency of the number of kink points and the asymptotic normality of two proposed estimators are established. Second, we construct a rank score test based on partial subgradients for the existence of the kink effect in longitudinal studies. Both the null distribution and the local alternative distribution of the test statistic have been derived. Simulation studies show that the proposed methods have excellent finite sample performance. In the application to the longitudinal progesterone data, we identify two kink points in the progesterone curves over different quantiles and observe that the progesterone level remains stable before the day of ovulation, then increases quickly in 5 to 6 days after ovulation and then changes to stable again or drops slightly.     

The Conserved Yeast Protein Knr4 Involved in Cell Wall Integrity Is a Multi-domain Intrinsically Disordered Protein

Knr4/Smi1 proteins are specific to the fungal kingdom and their deletion in the model yeast Saccharomyces cerevisiae and the human pathogen Candida albicans results in hypersensitivity to specific antifungal agents and a wide range of parietal stresses. In S. cerevisiae, Knr4 is located at the crossroads of several signalling pathways, including the conserved cell wall integrity and calcineurin pathways. Knr4 interacts genetically and physically with several protein members of those pathways. Its sequence suggests that it contains large intrinsically disordered regions. Here, a combination of small-angle X-ray scattering (SAXS) and crystallographic analysis led to a comprehensive structural view of Knr4. This experimental work unambiguously showed that Knr4 comprises two large intrinsically disordered regions flanking a central globular domain whose structure has been established. The structured domain is itself interrupted by a disordered loop. Using the CRISPR/Cas9 genome editing technique, strains expressing KNR4 genes deleted from different domains were constructed. The N-terminal domain and the loop are essential for optimal resistance to cell wall-binding stressors. The C-terminal disordered domain, on the other hand, acts as a negative regulator of this function of Knr4. The identification of molecular recognition features, the possible presence of secondary structure in these disordered domains and the functional importance of the disordered domains revealed here designate these domains as putative interacting spots with partners in either pathway. Targeting these interacting regions is a promising route to the discovery of inhibitory molecules that could increase the susceptibility of pathogens to the antifungals currently in clinical use.     

Enhancing estimation methods for integrating probability and nonprobability survey samples with machine-learning techniques. An application to a Survey on the impact of the COVID-19 pandemic in Spain

Web surveys have replaced Face-to-Face and computer assisted telephone interviewing (CATI) as the main mode of data collection in most countries. This trend was reinforced as a consequence of COVID-19 pandemic-related restrictions. However, this mode still faces significant limitations in obtaining probability-based samples of the general population. For this reason, most web surveys rely on nonprobability survey designs. Whereas probability-based designs continue to be the gold standard in survey sampling, nonprobability web surveys may still prove useful in some situations. For instance, when small subpopulations are the group under study and probability sampling is unlikely to meet sample size requirements, complementing a small probability sample with a larger nonprobability one may improve the efficiency of the estimates. Nonprobability samples may also be designed as a mean for compensating for known biases in probability-based web survey samples by purposely targeting respondent profiles that tend to be underrepresented in these surveys. This is the case in the Survey on the impact of the COVID-19 pandemic in Spain (ESPACOV) that motivates this paper. In this paper, we propose a methodology for combining probability and nonprobability web-based survey samples with the help of machine-learning techniques. We then assess the efficiency of the resulting estimates by comparing them with other strategies that have been used before. Our simulation study and the application of the proposed estimation method to the second wave of the ESPACOV Survey allow us to conclude that this is the best option for reducing the biases observed in our data.