国家标本资源共享平台(NSII)支撑生物多样性科学研究的成效分析

随着生物多样性信息学的迅速发展,越来越多开放的生物数据可供科研人员使用。以一个公开数据平台为例分析我国生物多样性领域的研究热点与发展趋势,有助于生物多样性工作者和决策者及时了解我国生物研究的现状及动向,为生物多样性建设提供决策支持。该文以“国家标本资源共享平台(NSII)”及相关词为检索对象,对中国知网和谷歌学术上2013—2023年间的文献进行全文检索,共检索出1 070篇NSII支撑的文献,包括期刊论文(822篇)、学位论文(233篇)、科普文章(5篇)、会议文章(6篇)和报道(4篇)。基于NSII支撑的822篇期刊论文,通过文献计量学的手段和方法,从发文情况、研究主题与热点、研究机构等方面探究NSII支撑的生物多样性研究现状、热点与态势。关键词共现网络图谱分析结果显示,基于数据平台的生物多样性研究热点集中在物种分布分析和建模、气候变化、分类学、生物多样性研究、研究平台建设五个方面。当前我国生物多样性信息学领域发展较快,未来仍需从数据源建设、资源整合、共享能力、业务能力和国际合作等方面努力提升,持续推动生物多样性科学研究的发展。     

Parallel declines in survival of adult Northern Fulmars Fulmarus glacialis at colonies in Scotland and Ireland

Assessing broad‐scale changes in seabird populations across the North Atlantic requires an integration of available datasets to understand the spatial extent of potential drivers and demographic change. Here, we compared survival of Northern Fulmars Fulmarus glacialis from a Scottish and an Irish colony from 1974 to 2009. Despite lower recapture probabilities of monel‐ringed Irish birds compared with colour‐ringed Scottish birds, survival probability decreased at both colonies. The extent to which the decline in survival is related to density‐dependent processes or other external drivers remains uncertain, but our results suggest that these changes in survival are possibly indicative of larger‐scale processes and are not confined to local colony dynamics.     

Protein structure prediction assisted with sparse NMR data in CASP13

CASP13 has investigated the impact of sparse NMR data on the accuracy of protein structure prediction. NOESY and ¹⁵N-¹H residual dipolar coupling data, typical of that obtained for ¹⁵N,¹³C-enriched, perdeuterated proteins up to about 40 kDa, were simulated for 11 CASP13 targets ranging in size from 80 to 326 residues. For several targets, two prediction groups generated models that are more accurate than those produced using baseline methods. Real NMR data collected for a de novo designed protein were also provided to predictors, including one data set in which only backbone resonance assignments were available. Some NMR-assisted prediction groups also did very well with these data. CASP13 also assessed whether incorporation of sparse NMR data improves the accuracy of protein structure prediction relative to nonassisted regular methods. In most cases, incorporation of sparse, noisy NMR data results in models with higher accuracy. The best NMR-assisted models were also compared with the best regular predictions of any CASP13 group for the same target. For six of 13 targets, the most accurate model provided by any NMR-assisted prediction group was more accurate than the most accurate model provided by any regular prediction group; however, for the remaining seven targets, one or more regular prediction method provided a more accurate model than even the best NMR-assisted model. These results suggest a novel approach for protein structure determination, in which advanced prediction methods are first used to generate structural models, and sparse NMR data is then used to validate and/or refine these models.     

Different climate response of three tree ring proxies of Pinus sylvestris from the Eastern Carpathians,Romania

The aim of this study was to compare the climatic responses of three tree rings proxies: tree ring width (TRW), maximum latewood density (MXD), and blue intensity (BI). For this study, 20 cores of Pinus sylvestris covering the period 1886–2015 were extracted from living non-damaged trees from the Eastern Carpathian Mountains (Romania). Each chronology was compared to monthly and daily climate data. All tree ring proxies had a stronger correlation with the daily climate data compared to monthly data. The highest correlation coefficient was obtained between the MXD chronology and daily maximum temperature over the period beginning with the end of July and ending in the middle of September (r = 0.64). The optimal intervals for the temperature signature were 01 Aug – 24 Sept for the MXD chronology, 05 Aug – 25 Aug for the BI chronology, and both 16 Nov of the previous year – 16 March of the current year and 15 Apr – 05 May for the TRW chronology. The results from our study indicate that MXD can be used as a proxy indicator for summer maximum temperature, while TRW can be used as a proxy indicator for just March maximum temperature. The weak and unstable relationship between BI and maximum temperature indicates that BI is not a good proxy indicator for climate reconstructions over the analysed region.     

Integration of ground survey and remote sensing derived data: Producing robust indicators of habitat extent and condition

The availability of suitable habitat is a key predictor of the changing status of biodiversity. Quantifying habitat availability over large spatial scales is, however, challenging. Although remote sensing techniques have high spatial coverage, there is uncertainty associated with these estimates due to errors in classification. Alternatively, the extent of habitats can be estimated from ground‐based field survey. Financial and logistical constraints mean that on‐the‐ground surveys have much lower coverage, but they can produce much higher quality estimates of habitat extent in the areas that are surveyed. Here, we demonstrate a new combined model which uses both types of data to produce unified national estimates of the extent of four key habitats across Great Britain based on Countryside Survey and Land Cover Map. This approach considers that the true proportion of habitat per km² (Z_i) is unobserved, but both ground survey and remote sensing can be used to estimate Z_i. The model allows the relationship between remote sensing data and Z_i to be spatially biased while ground survey is assumed to be unbiased. Taking a statistical model‐based approach to integrating field survey and remote sensing data allows for information on bias and precision to be captured and propagated such that estimates produced and parameters estimated are robust and interpretable. A simulation study shows that the combined model should perform best when error in the ground survey data is low. We use repeat surveys to parameterize the variance of ground survey data and demonstrate that error in this data source is small. The model produced revised national estimates of broadleaved woodland, arable land, bog, and fen, marsh and swamp extent across Britain in 2007.     

Observations below multiple lower limits of quantification: How to estimate the mean and variance

Multiple lower limits of quantification (MLOQs) result if various laboratories are involved in the analysis of concentration data and some observations are too low to be quantified. For normally distributed data under MLOQs there exists only the multiple regression method of Helsel to estimate the mean and variance. We propose a simple imputation method and two new maximum likelihood estimation methods: the multiple truncated sample method and the multiple censored sample method. A simulation study is conducted to compare the performances of the newly introduced methods to Helsel's via the criteria root mean squared error (RMSE) and bias of the parameter estimates. Two and four lower limits of quantification (LLOQs), various amounts of unquantifiable observations and two sample sizes are studied. Furthermore, the robustness is investigated under model misspecification. The methods perform with decreasing accuracy for increasing rates of unquantified observations. Increasing sample sizes lead to smaller bias. There is almost no change in the performance between two and four LLOQs. The magnitude of the variance impairs the performance of all methods. For a smaller variance, the multiple censored sample method leads to superior estimates regarding the RMSE and bias, whereas Helsel's method is superior regarding the bias for a larger variance. Under model misspecification, Helsel's method was inferior to the other methods. Estimating the mean, the multiple censored sample method performed better, whereas the multiple truncated sample method performs best in estimating the variance. Summarizing, for a large sample size and normally distributed data we recommend to use Helsel's method. Otherwise, the multiple censored sample method should be used to obtain estimates of the mean and variance of data including MLOQs.     

A BC200-derived element and Z-DNA as structural markers in annexin I genes: Relevance to Alu evolution and annexin tetrad formation

We have identified two types of structural elements in genomic DNA for annexin I that provide physical evidence of genetic events leading to conserved changes in gene structure. The sequence upstream of the transcribed region in human annexin I contained a rare, Alu-like repetitive element with flanking direct repeats, probably derived from the active BC200 gene via germline retroposition. Nucleotide substitutions in this BC200 insert relative to the 7SL gene and its absence in rodent annexins I identified it as a recent primate pseudogene. Phylogenetic analysis showed that the BC200 gene represents a new clade of primate Alu evolution that branched near the time of appearance of the progenitor to the free left Alu monomer, FLAM-C. Separate analysis identified a Z-DNA motif in pigeon annexin I intron 7 that may represent the vestigial recombination site involved in primordial assembly of the annexin tetrad. These distinct structural features in annexin I genes provide insight into the evolution of Alu repeats and the mechanism of annexin tetrad formation.     

神农架川金丝猴栖息地优势乔木树种遥感识别及其分布特征

针对神农架川金丝猴生境基础研究中乔木树种大范围分布数据难以获取问题,尝试利用多源多时相遥感数据结合专家知识分层次实现树种识别。首先采用冬季Landsat8/OLI数据根据物侯特性分层提取常绿、落叶林的地域范围;进而依据夏季Worldview-2高分遥感影像的实地乔木样本的光谱特征分层次完成常绿树种(巴山冷杉、华山松、青$、刺叶栎)和落叶树种(红桦、日本落叶松、米心水青冈、漆树、锐齿槲栎、椅杨)的识别;并通过实地植被样方及专家知识通过高程数据完成分类结果的修正;最后结合GIS对主要优势树种的地形及地域分布特征进行了空间分析。实验精度表明常绿林中巴山冷杉、华山松、刺叶栎、虫害华山松整体精度较高,落叶林中红桦、漆树等识别精度相对较高,部分树种如椅杨、锐齿槲栎识别精度较低;总体上常绿树种的精度要优于落叶树种。从植物地理学、遥感、GIS三者相结合的角度,将多源、多时相遥感数据与物种物候特性、专家知识进行有效整合,提出了一种乔木树种识别的方法(1)提供了复杂山地环境的主要乔木优势种识别途径,且具有通用性;(2)完成了物种物候特性与遥感数据特性的整合利用,有效降低数据成本费用;(3)配合地面样方及专家知识修正结果,避免了过分依赖光谱特征引起的误判。这将为神农架川金丝猴栖息地保护与恢复提供更精确的数据依据。     

Dynamic conditionally linear mixed models for longitudinal data

We develop a new class of models, dynamic conditionally linear mixed models, for longitudinal data by decomposing the within-subject covariance matrix using a special Cholesky decomposition. Here 'dynamic' means using past responses as covariates and 'conditional linearity' means that parameters entering the model linearly may be random, but nonlinear parameters are nonrandom. This setup offers several advantages and is surprisingly similar to models obtained from the first-order linearization method applied to nonlinear mixed models. First, it allows for flexible and computationally tractable models that include a wide array of covariance structures; these structures may depend on covariates and hence may differ across subjects. This class of models includes, e.g., all standard linear mixed models, antedependence models, and Vonesh-Carter models. Second, it guarantees the fitted marginal covariance matrix of the data is positive definite. We develop methods for Bayesian inference and motivate the usefulness of these models using a series of longitudinal depression studies for which the features of these new models are well suited.