红松人工林枝叶分解速度及养分动态的研究

修枝作为培育无节良材的重要经营措施已被广泛应用于生产,修落的枝叶能为林地提供大量的有机物,分解后提高林地土壤肥力。为了探明红松人工纯林枝叶分解过程中各种营养元素的变化规律,我们于1984年至1986年在草河     

Herbivore Dung Promotes Plant Litter Decomposition Rate in a Semi-arid Grassland Ecosystem

Ecosystems - The decomposition of plant litter and animal dung is the key processes of nutrient cycling and has been studied in a wide range of grassland ecosystems. However, most studies focus on...     

Estimating Sustainable Harvest in Wolverine Populations Using Logistic Regression

Abstract: Population viability analysis (PVA) is a common tool to evaluate population vulnerability. However, most techniques require reliable estimates of underlying population parameters, which are often difficult to obtain and PVA are, therefore, best used in a qualitative context. Logistic regression is a powerful alternative to traditional PVA methods but has received surprisingly limited attention. Logistic regression fits regression equations to binary output from PVA models at a specific point in time to predict probability of a binary response over a range of parameter values. We used logistic regression on output from stochastic population models to evaluate the relative importance of demographic parameters for wolverine (Gulo gulo) populations and to estimate sustainable harvest in a wolverine population in Alaska. Our analysis indicated that adult survival is the most important demographic parameter to reliably estimate in wolverine populations because it had a greater effect on population persistence than did both fecundity and subadult survival. In accordance with this, harvest rate had a greater effect on population persistence than did any of the other harvest- and migration-related variables we tested. Furthermore, a high proportion of harvested females strengthened the effect of harvest. Hypothetical wolverine populations suffered high probabilities of both extinction and population decline over a range of realistic population sizes and harvest regimes. We suggest that harvested wolverine populations must be regarded as sink populations and that source populations in combination with sufficient dispersal corridors must be secured for any wolverine harvest to be sustainable.     

Merging Spline Approximations with Analysis of Nonlinear Regression Models

The paper outlines a method of analysis of intricically non-linear regression functions. The nonlinear model is first approximated by a cubic spline function. Thereafter, the standard methods of analysis in linear models are applied to obtain estimates and test statistics.     

Parameter Sensitivity Analysis in Electrophysiological Models Using Multivariable Regression

Computational models of electrical activity and calcium signaling in cardiac myocytes are important tools for understanding physiology. The sensitivity of these models to changes in parameters is often not well-understood, however, because parameter evaluation can be a time-consuming, tedious process. I demonstrate here what I believe is a novel method for rapidly determining how changes in parameters affect outputs. In three models of the ventricular action potential, parameters were randomized, repeated simulations were run, important outputs were calculated, and multivariable regression was performed on the collected results. Random parameters included both maximal rates of ion transport and gating variable characteristics. The procedure generated simplified, empirical models that predicted outputs resulting from new sets of input parameters. The linear regression models were quite accurate, despite nonlinearities in the mechanistic models. Moreover, the regression coefficients, which represent parameter sensitivities, were robust, even when parameters were varied over a wide range. Most importantly, a side-by-side comparison of two similar models identified fundamental differences in model behavior, and revealed model predictions that were both consistent with, and inconsistent with, experimental data. This new method therefore shows promise as a tool for the characterization and assessment of computational models. The general strategy may also suggest methods for integrating traditional quantitative models with large-scale data sets obtained using high-throughput technologies.     

Fitting Mixed Poisson Regression Models Using Quasi-Likelihood Methods

This paper is to investigate the use of the quasi-likelihood, extended quasi-likelihood, and pseudo-likelihood approach to estimating and testing the mean parameters with respect to two variance models, M1: φ μ^θ(1+μphis;) and M2: φ μ^θ(1+τ). Simulation was conducted to compare the bias and standard deviation, and type I error of the Wald tests, based on the model-based and robust variance estimates, using the three semi-parametric approaches under four mixed Poisson models, two variance structures, and two sample sizes. All methods perform reasonably well in terms of bias. Type I error of the Wald test, based on either the model-based or robust estimate, tends to be larger than the nominal level when over-dispersion is moderate. The extended quasi-likelihood method with the variance model M1 performs more consistently in terms of the efficiency and controlling the type I error than with the model M2, and better than the pseudo-likelihood approach with either the M1 or M2 model. The model-based estimate seems to perform better than the robust estimate when the sample size is small.     

Identity-by-Descent Matrix Decomposition Using Latent Ancestral Allele Models

Genetic linkage and association studies are empowered by proper modeling of relatedness among individuals. Such relatedness can be inferred from marker and/or pedigree information. In this study, the genetic relatedness among n inbred individuals at a particular locus is expressed as an n × n square matrix Q. The elements of Q are identity-by-descent probabilities, that is, probabilities that two individuals share an allele descended from a common ancestor. In this representation the definition of the ancestral alleles and their number remains implicit. For human inspection and further analysis, an explicit representation in terms of the ancestral allele origin and the number of alleles is desirable. To this purpose, we decompose the matrix Q by a latent class model with K classes (latent ancestral alleles). Let P be an n × K matrix with assignment probabilities of n individuals to K classes constrained such that every element is nonnegative and each row sums to 1. The problem then amounts to approximating Q by PP^T, while disregarding the diagonal elements. This is not an eigenvalue problem because of the constraints on P. An efficient algorithm for calculating P is provided. We indicate the potential utility of the latent ancestral allele model. For representative locus-specific Q matrices constructed for a set of maize inbreds, the proposed model recovered the known ancestry.HIGH-THROUGHPUT techniques allow extensive genotyping of individuals for thousands of SNP markers (Gibbs et al. 2003) and thereby provide accurate information about the genetic diversity within a population at many chromosomal loci. If two individuals within this population carry the same DNA sequence at a locus, and this sequence can be traced to the same common ancestor, the individuals are said to be identical by descent (IBD) for this segment (Chapman and Thompson 2003). Quite often, however, the ancestral source of a chromosomal segment is ambiguous and thus IBD relationships between haplotypes are given as probabilities. Various methods have been described to estimate the IBD probability of pairs of chromosomal segments (Meuwissen and Goddard 2001; Leutenegger et al. 2003). When pedigree relationships are known, these can be included to estimate IBD probabilities (Wang et al. 1995; Heath 1997; George et al. 2000; Meuwissen and Goddard 2000; Besnier and Carlborg 2007).In quantitative genetic analysis we seek to find and characterize associations between the large number of SNPs that are now available for many organisms and phenotypic variation for traits of interest (e.g., grain yield and time to flowering). Many current methods developed for this purpose make use of IBD information. For example, a locus-specific matrix of IBD probabilities can be incorporated into restricted maximum-likelihood (REML) procedures for fine mapping quantitative trait loci (Bink and Meuwissen 2004) as well as for marker-based genetic evaluation (Fernando and Grossman 1989) using mixed models. The IBD matrix takes the role of a covariance matrix in the REML procedure.Other approaches, however, require that chromosome segments (also referred to here as haplotypes or alleles) are assigned to independent ancestors. These approaches include regression approaches with genetic predictors (Malosetti et al. 2006) and Bayesian oligo-allelic approaches that sample the ancestral origin of each chromosomal segment (Heath 1997; Uimari and Sillanpaa 2001; Bink et al. 2008a). In the IBD matrix representation the ancestral alleles and their number remain implicit. For these approaches, the locus-specific matrix of IBD probabilities must therefore be decomposed into a matrix that links the chromosomal segments to independent ancestral alleles. This decomposition is addressed in this article.The individuals that we consider in this article are inbred. For n inbred individuals the IBD matrix at a given chromosomal position is thus n × n, because there is no need to distinguish between identical chromosomes. In diploid, outbred populations, each individual would be represented by two haplotypes (alleles) and the matrix would be 2n × 2n (Fernando and Grossman 1989). This is feasible if any phase ambiguity can be resolved. From now on, the term “individual” thus means chromosomal segment or haplotype. Analogously, ancestor will be shorthand for ancestral allele (ancestral haplotype).We propose two models of IBD matrix decomposition, a simple threshold model (TIBD) and a more sophisticated latent ancestral allele model (LAAM), that provide (1) an estimate of the number of independent ancestral alleles, (2) a concise, easy-to-interpret, summary of the relatedness, (3) an explicit (probabilistic) representation of the descent of alleles, and (4) the ability to sample alleles for each individual from a set of ancestral alleles in such a way that the probability that a pair of individuals shares the same allele corresponds to their IBD probability.The last two features of the model are essential for its use in Bayesian oligo-allelic approaches to quantitative trait locus (QTL) analysis (Uimari and Sillanpaa 2001; Bink et al. 2008a).     

Estimating Treatment Effects of Longitudinal Designs using Regression Models on Propensity Scores

Summary We derive regression estimators that can compare longitudinal treatments using only the longitudinal propensity scores as regressors. These estimators, which assume knowledge of the variables used in the treatment assignment, are important for reducing the large dimension of covariates for two reasons. First, if the regression models on the longitudinal propensity scores are correct, then our estimators share advantages of correctly specified model‐based estimators, a benefit not shared by estimators based on weights alone. Second, if the models are incorrect, the misspecification can be more easily limited through model checking than with models based on the full covariates. Thus, our estimators can also be better when used in place of the regression on the full covariates. We use our methods to compare longitudinal treatments for type II diabetes mellitus.     

Effects of Calcium on the Rate and Extent of Litter Decomposition in a Northern Hardwood Forest

Cross-site syntheses of litter decomposition studies have shown that litter calcium (Ca) concentration may have a role in controlling the extent of decomposition of tree foliage. We used an ongoing watershed CaSiO₃ addition experiment at the Hubbard Brook Experimental Forest in New Hampshire, USA, to test the hypotheses that increased Ca in litter would have no effect on the initial rates of litter decay but would increase the extent or completeness (limit value) of foliar litter decomposition. We tested these hypotheses with a 6-year litter decomposition experiment using foliar litter of four tree species that are prominent at this site and in the Northern Hardwood forest type of North America: sugar maple (Acer saccharum Marsh), American beech (Fagus grandifolia Ehrh.), yellow birch (Betula alleghaniensis Britt.), and white ash (Fraxinus americana L.). The experiment used a reciprocal transplant design with the Ca-treated watershed and a control site providing two sources of litter and two placement sites. The litter from the Ca-treated site was 10–92% higher in Ca concentration, depending on species, than the litter from the control site. After about 3 years of decomposition, the Ca concentrations in the litter reflected the placement of the litter (that is, the site in which it was incubated) rather than the source of the litter. The source of the litter had no significant effect on measures of initial decomposition rate, cumulative mass loss (6 years), or limit value. However, the placement of the litter had a highly significant effect on extent of decomposition. Some litter types responded more than others; in particular, beech litter placed in the Ca-treated site had a significantly higher limit value, indicating more complete decomposition, and maple litter in the Ca-treated site had a marginally higher limit value. These results indicate that Ca may influence the extent of litter decomposition, but it is the Ca at the incubation site rather than the initial litter Ca that matters most. The results also suggest that loss of Ca from the soil due to decades of acid deposition at this site may have impeded late-stage litter decomposition, possibly leading to greater soil C storage, especially in forest stands with a substantial component of beech. Likewise, de-acidification may lead to a reduction in soil C.     

Bacterial Diversity in Agricultural Soils during Litter Decomposition

Denaturing gradient gel electrophoresis (DGGE) of amplified fragments of genes coding for 16S rRNA was used to study the development of bacterial communities during decomposition of crop residues in agricultural soils. Ten strains were tested, and eight of these strains produced a single band. Furthermore, a mixture of strains yielded distinguishable bands. Thus, DGGE DNA band patterns were used to estimate bacterial diversity. A field experiment performed with litter in nylon bags was used to evaluate the bacterial diversity during the decomposition of readily degradable rye and more refractory wheat material in comparable luvisols and cambisols in northern, central, and southern Germany. The amount of bacterial DNA in the fresh litter was small. The DNA content increased rapidly after the litter was added to the soil, particularly in the rapidly decomposing rye material. Concurrently, diversity indices, such as the Shannon-Weaver index, evenness, and equitability, which were calculated from the number and relative abundance (intensity) of the bacterial DNA bands amplified from genes coding for 16S rRNA, increased during the course of decomposition. This general trend was not significant for evenness and equitability at any time. The indices were higher for the more degradation-resistant wheat straw than for the more easily decomposed rye grass. Thus, the DNA band patterns indicated that there was increasing bacterial diversity as decomposition proceeded and substrate quality decreased. The bacterial diversity differed for the sites in northern, central, and southern Germany, where the same litter material was buried in the soil. This shows that in addition to litter type climate, vegetation, and indigenous microbes in the surrounding soil affected the development of the bacterial communities in the litter.     

Methods for Estimating Population Density in Data-Limited Areas: Evaluating Regression and Tree-Based Models in Peru

Obtaining accurate small area estimates of population is essential for policy and health planning but is often difficult in countries with limited data. In lieu of available population data, small area estimate models draw information from previous time periods or from similar areas. This study focuses on model-based methods for estimating population when no direct samples are available in the area of interest. To explore the efficacy of tree-based models for estimating population density, we compare six different model structures including Random Forest and Bayesian Additive Regression Trees. Results demonstrate that without information from prior time periods, non-parametric tree-based models produced more accurate predictions than did conventional regression methods. Improving estimates of population density in non-sampled areas is important for regions with incomplete census data and has implications for economic, health and development policies.     

Estimating the Total Rate of DNA Replication Using Branching Processes

Increasing the knowledge of various cell cycle kinetic parameters, such as the length of the cell cycle and its different phases, is of considerable importance for several purposes including tumor diagnostics and treatment in clinical health care and a deepened understanding of tumor growth mechanisms. Of particular interest as a prognostic factor in different cancer forms is the S phase, during which DNA is replicated. In the present paper, we estimate the DNA replication rate and the S phase length from bromodeoxyuridine-DNA flow cytometry data. The mathematical analysis is based on a branching process model, paired with an assumed gamma distribution for the S phase duration, with which the DNA distribution of S phase cells can be expressed in terms of the DNA replication rate. Flow cytometry data typically contains rather large measurement variations, however, and we employ nonparametric deconvolution to estimate the underlying DNA distribution of S phase cells; an estimate of the DNA replication rate is then provided by this distribution and the mathematical model.     

Long-Term Grazing Accelerated Litter Decomposition in Northern Temperate Grasslands

Livestock grazing affects plant community composition, diversity, and carbon (C) and nutrient cycling in grasslands. Grazing leads to plant communities that have higher relative abundance of grazing-tolerant species, which in turn may alter the chemical composition of biomass and subsequent litter decomposition rates. To better understand the effects of long-term grazing and associated vegetation shifts on biogeochemical cycling in northern temperate grasslands of western Canada, we studied litter decomposition over 18 months at 15 locations, stratified across the Mixed-grass Prairie, Central Parkland, and Foothills Fescue natural subregions. At each location, we examined decomposition in an area exposed to grazing and an area where cattle were excluded. We used litterbags containing leaf litter from seven major grass species representing different grazing tolerances and included a local source of community litter from each study site and cellulose paper as standards. Decomposition was affected by litter types, with litter from grazing-tolerant species such as Poa pratensis and Bouteloua gracilis having faster decomposition rates compared to grazing-intolerant species, supporting the hypothesis that changes in vegetation composition due to grazing influences biogeochemical cycling by modifying litter decomposition in grasslands. Litter decomposition was also overall most rapid in the cool–wet Foothills Fescue, followed by the temperate mesic Central Parkland, and slowest in the warmer–drier Mixed-grass Prairie. Combined with known grazing-induced changes in grassland composition, these findings indicate that livestock grazing may accelerate litter decomposition rates in the more mesic Foothills Fescue and parkland regions, but not the more arid Mixed-grass Prairie. Overall, this study elucidates the role of livestock grazing and its associated effects on litter decomposition and ecosystem processes in northern grassland ecosystems.     

Leaf Litter Mixtures Alter Microbial Community Development: Mechanisms for Non-Additive Effects in Litter Decomposition

To what extent microbial community composition can explain variability in ecosystem processes remains an open question in ecology. Microbial decomposer communities can change during litter decomposition due to biotic interactions and shifting substrate availability. Though relative abundance of decomposers may change due to mixing leaf litter, linking these shifts to the non-additive patterns often recorded in mixed species litter decomposition rates has been elusive, and links community composition to ecosystem function. We extracted phospholipid fatty acids (PLFAs) from single species and mixed species leaf litterbags after 10 and 27 months of decomposition in a mixed conifer forest. Total PLFA concentrations were 70% higher on litter mixtures than single litter types after 10 months, but were only 20% higher after 27 months. Similarly, fungal-to-bacterial ratios differed between mixed and single litter types after 10 months of decomposition, but equalized over time. Microbial community composition, as indicated by principal components analyses, differed due to both litter mixing and stage of litter decomposition. PLFA biomarkers a15∶0 and cy17∶0, which indicate gram-positive and gram-negative bacteria respectively, in particular drove these shifts. Total PLFA correlated significantly with single litter mass loss early in decomposition but not at later stages. We conclude that litter mixing alters microbial community development, which can contribute to synergisms in litter decomposition. These findings advance our understanding of how changing forest biodiversity can alter microbial communities and the ecosystem processes they mediate.     

Litter Decomposition in a California Annual Grassland: Interactions Between Photodegradation and Litter Layer Thickness

In annual grasslands that experience a mediterranean-type climate, the synchrony between plant senescence and peak solar radiation over summer results in high litter sun exposure. We examined the decomposition of both shaded and sun-exposed litter over summer and inferred the effects of photodegradation from changes in mass loss and litter chemistry. The carry-over effects of summer litter exposure on wet season decomposition were also assessed, and the attenuation of photodegradation with litter layer thickness was used to estimate the proportion of grass litter lignin susceptible to photodegradation under different treatments of a factorial global change experiment. Over summer, mass loss from grass and forb litter exposed to ambient sunlight ranged from 8% to 10%, whereas lignin decreased in grass litter by approximately 20%. After one year of decomposition, mass losses from grass leaves exposed to sunlight over summer were more than double the mass losses from summer-shaded leaves. When shade litter layer thickness was varied, mass losses over summer for all treatments were also approximately 8%; however, lignin decreased significantly only in the low shade treatments (0–64 g m⁻² of shade litter). Aboveground production of annual grasses nearly quadrupled in response to the combined effects of N addition, elevated atmospheric CO₂, increased precipitation and warming. The estimated proportion of grass litter lignin experiencing full photodegradation ranged from 100% under ambient conditions to 31–62% in plots receiving the combined global change treatments. These results reveal an important role of sun exposure over summer in accelerating litter decomposition in these grasslands and provide evidence that future changes in the quantity of litter deposition may modulate the influence of photodegradation integrated across the litter layer.     

Spatial Variability of Plant Litter Decomposition in Stream Networks: from Litter Bags to Watersheds

The decomposition of plant litter plays a fundamental role in the cycling of carbon and nutrients and is driven by complex interactions of biological and physical controls, yet little is known about its variability and controls across spatial scales. Here we address the indirect effects of riparian canopy cover on litter decomposition and decomposers and their variability within a set of hierarchical scales (watershed, stream segments and reaches) controlling for confounding factors that could co-vary with canopy cover (for example, temperature and nutrients), in high-altitude subtropical streams. Total, microbial and invertebrate-driven decomposition rates were approximately 1.4–6.6 times higher in closed-canopy than in open-canopy watersheds. Riparian canopy cover accounted for 62–69% of total variability of decomposition rates and indirectly (via light availability and litter inputs) promoted fungal facilitation of shredders through leaf litter conditioning. In contrast to what we expected, much of the spatial variability in the decomposition occurred at smaller scale (4–20% of total variability among reaches versus <1% among watersheds) and coincided with the greatest variability in shredder abundance and fungal biomass (70 and 17% among reaches, respectively). We conclude that riparian canopy cover may be an important control of natural variability of litter decomposition at the watershed scale through its effects on fungal decomposers and shredder consumption. We also provide evidence of higher reach and minor watershed variability of litter decomposition in stream networks. Our results point to the importance of identifying the sources of natural variability of decomposition and how they interact within and among spatial scales.     

Prediction Intervals in Nonlinear Regression

By treating the nonlinear model as if it were linear in the parameterization θ in the neighbourhood of the least squares estimate θ, we construct two-sided nominally-q-prediction intervals by applying the usual linear model theory. The derivation of the truncated series expansion of the expected coverage of the prediction intervals at a feasible value of the parameter vector is described. The quadratic approximation of the expected coverage is then obtained for a two-parameter nonlinear model. Finally we show how we may construct the prediction intervals when a certain type of nonlinear transformation of the parameter vector has been applied.