热带林茎流收集及计算方法探索

热带林茎流收集及计算方法探索周光益,吴仲民,李意德,陈步峰（中国林科院热带林业研究所,广州５１０５２０）ＣｏｌｌｅｃｔｉｏｎａｎｄＣａｌｃｕｌａｔｉｏｎＭｅｔｈｏｄｓｆｏｒＳｔｅｍｆｌｏｗｉｎＴｒｏｐｉｃａｌＦｏｒｅｓｔ．￥ＺｈｏｕＧｕａｎｇｙｉ;Ｗ...     

Estimating regional carbon stocks and spatially covarying edaphic factors using soil maps at three scales

Most estimates of regional and global soil carbon stocks are based on extrapolations of mean soil C contents for broad categories of soil or vegetation types. Uncertainties exist in both the estimates of mean soil C contents and the area over which each mean should be extrapolated. Geographic information systems now permit spatially referenced estimates of soil C at finer scales of resolution than were previously practical. We compared estimates of total soil C stocks of the state of Maine using three methods: (1) multiplying the area of the state by published means of soil C for temperate forests and for Spodosols; (2) calculating areas of inclusions of soil taxa in the 1:5,000,000 FAO/UNESCO Soils Map of the World and multiplying those areas by selected mean carbon contents; and (3) calculating soil C for each soil series and map unit in the 1:250,000 State Soil Geographic Data Base (STATSGO) and summing these estimates for the entire state. The STATSGO estimate of total soil C was between 23% and 49% higher than the common coarse scale extrapolations, primarily because STATSGO included data on Histosols, which cover less than 5% of the area of the state, but which constitute over one-third of the soil C. Spodosols cover about 65% of the state, but contribute less than 39% of the soil C. Estimates of total soil C in Maine based on the FAO map agreed within 8% of the STATSGO estimate for one possible matching of FAO soil taxa with data on soil C, but another plausible matching overestimated soil C stocks. We also compared estimates from the 1:250,000 STATSGO database and from the 1:20,000 Soil Survey Geographic Data Base (SSURGO) for a 7.5 minute quadrangle within the state. SSURGO indicated 13% less total soil C than did STATSGO, largely because the attribute data on depths of soil horizons in SSURGO are more specific for this locality. Despite localized differences, the STATSGO database offers promise of scaling up county soil survey data to regional scales because it includes attribute data and estimates of areal coverage of C-rich inclusions within map units. The spatially referenced data also permit examination of covariation of soil C stocks with soil properties thought to affect stabilization of soil C. Clay content was a poor predictor of soil C in Maine, but drainage class covaried significantly with soil C across the state.     

Isozyme variation in germplasm accessions of the wild oat Avena sterilis L.

Summary Optimal exploitation of crop genetic resources requires a knowledge of the range and structure of the variation present in the gene pool of interest. Avena sterilis L., the cultivated oat progenitor, contains a store of genetic diversity that is readily accessible to the oat breeder. The objectives of the present paper were: (1) to evaluate isozyme polymorphisms in a sample of A. sterilis accessions from the U.S. National Small Grains Collection, (2) to analyze the distribution of isozyme diversity across the geographic range of the accessions, (3) to classify the accessions into groups based on isozyme variation, and (4) to suggest strategies for efficient sampling of this germplasm collection. One thousand and five accessions from 23 countries and 679 collection sites were screened for variation using 23 enzyme systems. Due to limited information about the genetic relationship among individual members of families of isozymes in hexaploid oat species, data were recorded solely for band presence. The frequencies of bands in accessions from the various countries were used to calculate the probability of genotypic identity (I_x.y), the probability of a unique genotype (U_x.y), and an adjusted polymorphic index (H_x). Accessions from Turkey and Lebanon had the largest polymorphic index values, Turkish and Moroccan accessions displayed the greatest numbers of bands. Accessions from Iran, Turkey, Iraq, and Lebanon had the largest mean probabilities of containing unique genotypes. Based on isozyme data, Turkey appeared to represent the center of diversity in this germplasm collection. Band frequencies calculated among countries were used in a principal component analysis. Accessions from Israel and Morocco clustered together; accessions from Iran, Iraq, Turkey, and Ethiopia formed another group; and Algerian accessions formed an outlying group. Several isozyme bands had a regional distribution. These results suggested that choosing accessions from countries based on their groupings in the principal component analysis should secure a greater range of diversity than sampling from the collection at random. Cluster analyses based on Jaccard's distances calculated for all pairwise combinations of the 1005 accessions revealed six broad genetic groups of accessions. Groups 1 and 6 contained accessions from many countries and encompassed half of all accessions. Groups 2 and 4 were heavily populated by accessions from Israel and Morocco. Groups 3 and 5 were composed almost exclusively of accessions from Iran, Iraq, and Turkey. By selecting representative accessions from these six groups, oat breeders could most effectively sample the range of genetic variation in this A. sterilis collection.     

Bayesian analysis of linkage between genetic markers and quantitative trait loci. I. Prior knowledge

Summary Prior information on gene effects at individual quantitative trait loci (QTL) and on recombination rates between marker loci and QTL is derived. The prior distribution of QTL gene effects is assumed to be exponential with major effects less likely than minor ones. The prior probability of linkage between a marker and another single locus is a function of the number and length of chromosomes, and of the map function relating recombination rate to genetic distance among loci. The prior probability of linkage between a marker locus and a quantitative trait depends additionally on the number of detectable QTL, which may be determined from total additive genetic variance and minimum detectable QTL effect. The use of this prior information should improve linkage tests and estimates of QTL effects.     

Evaluation of five strategies for obtaining a core subset from a large genetic resource collection of durum wheat

The use of plant genetic resources contained in a large collection may be enhanced by specifying subsamples, called core samples. Five strategies for selecting a core sample from a collection of 3000 durum wheat accessions were applied and evaluated using four qualitative and eight quantitative spike characters. Each of the following strategies generated about 500 accessions for the core sample: random, random-systematic according to chronology of entry of the accessions into the collection, stratified by countryof-origin, stratified by log frequency by country-of-origin, and stratified by canonical variables. The first three strategies produced samples representative of the whole collection, but the remaining two produced the desired effect of increasing frequencies from less-represented countries-of-origin for several characters. The stratified canonical sample increased phenotypic variances. The quality of core samples is dependent upon good passport and evaluation data to partition the collection. The multivariate approach is extremely useful, but requires considerable data from the whole collection. Ecogeographic origin may be used in the absence of evaluation data on several characters to select useful core samples.     

Ecological and evolutionary consequences of selective interspecific information use

Recent work has shown that animals frequently use social information from individuals of their own species as well as from other species; however, the ecological and evolutionary consequences of this social information use remain poorly understood. Additionally, information users may be selective in their social information use, deciding from whom and how to use information, but this has been overlooked in an interspecific context. In particular, the intentional decision to reject a behaviour observed via social information has received less attention, although recent work has indicated its presence in various taxa. Based on existing literature, we explore in which circumstances selective interspecific information use may lead to different ecological and coevolutionary outcomes between two species, such as explaining observed co-occurrences of putative competitors. The initial ecological differences and the balance between the costs of competition and the benefits of social information use potentially determine whether selection may lead to trait divergence, convergence or coevolutionary arms race between two species. We propose that selective social information use, including adoption and rejection of behaviours, may have far-reaching fitness consequences, potentially leading to community-level eco-evolutionary outcomes. We argue that these consequences of selective interspecific information use may be much more widespread than has thus far been considered.     

Mechanisms of group-hunting in vertebrates

Group-hunting is ubiquitous across animal taxa and has received considerable attention in the context of its functions. By contrast much less is known about the mechanisms by which grouping predators hunt their prey. This is primarily due to a lack of experimental manipulation alongside logistical difficulties quantifying the behaviour of multiple predators at high spatiotemporal resolution as they search, select, and capture wild prey. However, the use of new remote-sensing technologies and a broadening of the focal taxa beyond apex predators provides researchers with a great opportunity to discern accurately how multiple predators hunt together and not just whether doing so provides hunters with a per capita benefit. We incorporate many ideas from collective behaviour and locomotion throughout this review to make testable predictions for future researchers and pay particular attention to the role that computer simulation can play in a feedback loop with empirical data collection. Our review of the literature showed that the breadth of predator:prey size ratios among the taxa that can be considered to hunt as a group is very large (<10⁰ to >10²). We therefore synthesised the literature with respect to these predator:prey ratios and found that they promoted different hunting mechanisms. Additionally, these different hunting mechanisms are also related to particular stages of the hunt (search, selection, capture) and thus we structure our review in accordance with these two factors (stage of the hunt and predator:prey size ratio). We identify several novel group-hunting mechanisms which are largely untested, particularly under field conditions, and we also highlight a range of potential study organisms that are amenable to experimental testing of these mechanisms in connection with tracking technology. We believe that a combination of new hypotheses, study systems and methodological approaches should help push the field of group-hunting in new directions.     

Semiparametric estimation of the transformation model by leveraging external aggregate data in the presence of population heterogeneity

Leveraging information in aggregate data from external sources to improve estimation efficiency and prediction accuracy with smaller scale studies has drawn a great deal of attention in recent years. Yet, conventional methods often either ignore uncertainty in the external information or fail to account for the heterogeneity between internal and external studies. This article proposes an empirical likelihood-based framework to improve the estimation of the semiparametric transformation models by incorporating information about the t-year subgroup survival probability from external sources. The proposed estimation procedure incorporates an additional likelihood component to account for uncertainty in the external information and employs a density ratio model to characterize population heterogeneity. We establish the consistency and asymptotic normality of the proposed estimator and show that it is more efficient than the conventional pseudopartial likelihood estimator without combining information. Simulation studies show that the proposed estimator yields little bias and outperforms the conventional approach even in the presence of information uncertainty and heterogeneity. The proposed methodologies are illustrated with an analysis of a pancreatic cancer study.     

Elastic priors to dynamically borrow information from historical data in clinical trials

Use of historical data and real-world evidence holds great potential to improve the efficiency of clinical trials. One major challenge is to effectively borrow information from historical data while maintaining a reasonable type I error and minimal bias. We propose the elastic prior approach to address this challenge. Unlike existing approaches, this approach proactively controls the behavior of information borrowing and type I errors by incorporating a well-known concept of clinically significant difference through an elastic function, defined as a monotonic function of a congruence measure between historical data and trial data. The elastic function is constructed to satisfy a set of prespecified criteria such that the resulting prior will strongly borrow information when historical and trial data are congruent, but refrain from information borrowing when historical and trial data are incongruent. The elastic prior approach has a desirable property of being information borrowing consistent, that is, asymptotically controls type I error at the nominal value, no matter that historical data are congruent or not to the trial data. Our simulation study that evaluates the finite sample characteristic confirms that, compared to existing methods, the elastic prior has better type I error control and yields competitive or higher power. The proposed approach is applicable to binary, continuous, and survival endpoints.