Experimental warming shifts coupling of carbon and nitrogen cycles in an alpine meadow

增温对高寒草甸生态系统碳氮循环耦合关系的影响 陆地生态系统碳吸收受土壤氮素可用性的调节。然而,全球变化背景下的不同生态系统组分的碳氮比及其所反映的碳氮循环耦合关系尚不十分清楚。本文运用数据同化的方法,将一个高寒草甸增温试验的14组数据同化到草地生态系统模型中,从而评估了增温如何影响陆地生态系统的碳氮循环耦合关系。研究结果表明,增温提高了土壤氮素的有效性,降低了土壤活性碳库的碳氮比,导致植物对土壤氮的吸收增加。但是由于植物叶片吸收的碳比吸收的氮增加更多,使得叶片中碳氮比增加,而根部的碳输入增加则低于氮的增加,导致根部的碳氮比减少。同时,增温降低了凋落物碳氮比,可能是在土壤高氮有效性的条件下,凋落物氮的固定得到增强;而且增温加速了凋落物的分解。同时增温还增加了慢速土壤有机质的碳氮比,使得该土壤碳库的碳固存潜力增大。由于大多数模型在不同的环境中通常使用相对固定的碳氮比,本研究所发现的气候变暖条件下碳氮比的差异变化可为模型参数化提供一个有效的参考,有利于模型对未来气候变化背景下生态系统碳氮耦合关系响应的预测。     

Molecular dating for phylogenies containing a mix of populations and species by using Bayesian and RelTime approaches

Simultaneous molecular dating of population and species divergences is essential in many biological investigations, including phylogeography, phylodynamics and species delimitation studies. In these investigations, multiple sequence alignments consist of both intra‐ and interspecies samples (mixed samples). As a result, the phylogenetic trees contain interspecies, interpopulation and within‐population divergences. Bayesian relaxed clock methods are often employed in these analyses, but they assume the same tree prior for both inter‐ and intraspecies branching processes and require specification of a clock model for branch rates (independent vs. autocorrelated rates models). We evaluated the impact of a single tree prior on Bayesian divergence time estimates by analysing computer‐simulated data sets. We also examined the effect of the assumption of independence of evolutionary rate variation among branches when the branch rates are autocorrelated. Bayesian approach with coalescent tree priors generally produced excellent molecular dates and highest posterior densities with high coverage probabilities. We also evaluated the performance of a non‐Bayesian method, RelTime, which does not require the specification of a tree prior or a clock model. RelTime's performance was similar to that of the Bayesian approach, suggesting that it is also suitable to analyse data sets containing both populations and species variation when its computational efficiency is needed.     

Eutrophic status influences the impact of pesticide mixtures and predation on Daphnia pulex populations

Pesticides, nutrients, and ecological stressors such as competition or predation co‐occur in freshwater ecosystems impacted by agriculture. The extent to which combinations of these stressors affect aquatic populations and the role of nutrients availability in modulating these responses requires further understanding. In this study, we assessed how pesticides affecting different taxonomic groups and predation influence the response of Daphnia pulex populations under different trophic conditions. An outdoor experiment was designed following a factorial design, with the insecticide chlorpyrifos, the herbicide diuron, and the predation by Notonecta sp. individuals as key stressors. The single impact of each of these stressors, and their binary and tertiary combinations, was evaluated on D. pulex abundance and population structure under mesotrophic and eutrophic conditions for 21 days. Data were analyzed using generalized linear mixed models estimated by means of a novel Bayesian shrinkage technique. Our study shows a significant influence of each of the evaluated stressors on D. pulex abundance; however, the impacts of the herbicide and predation were lower under eutrophic conditions as compared to the mesotrophic ones. We found that binary stressor interactions were generally additive in the mesotrophic scenario, except for the herbicide–predation combination, which resulted in synergistic effects. The impacts of the binary stressor combinations in the eutrophic scenario were classified as antagonistic, except for the insecticide–herbicide combination, which was additive. The tertiary interaction resulted in significant effects on some sampling dates; however, these were rather antagonistic and resembled the most important binary stressor combination in each trophic scenario. Our study shows that the impact of pesticides on freshwater populations depends on the predation pressure, and demonstrates that the combined effect of pesticides and ecological stressors is influenced by the food availability and organism fitness related to the trophic status of freshwater ecosystems.     

A kinematic method for computing the motion of the body centre-of-mass (CoM) during walking: a Bayesian approach

The gait pattern of a particular patient can be altered in a large set of pathologies. Tracking the body centre-of-mass (CoM) during the gait allows a quantitative evaluation of these diseases at comparing the gait with normal patterns. A correct estimation of this variable is still an open question because of its non-linearity and inaccurate location. This paper presents a novel strategy for tracking the CoM, using a biomechanical gait model whose parameters are determined by a Bayesian strategy. A particle filter is herein implemented for predicting the model parameters from a set of markers located at the sacral zone. The present approach is compared with other conventional tracking methods and decreases the calculated root mean squared error in about a 56% in the x-axis and 59% in the y-axis.     

Spatial covariation of fish population vital rates in a stream network

Animal populations are spatially structured in heterogeneous landscapes, in which local patches with differing vital rates are connected by dispersal of individuals to varying degrees. Although there is evidence that vital rates differ among local populations, much less is understood about how vital rates covary among local patches in spatially heterogeneous landscapes. In this study, we conducted a nine-year annual mark–recapture survey to characterize spatial covariation of survival and growth for two Japanese native salmonids, white-spotted charr Salvelinus leucomaenis japonicus and red-spotted masu salmon Oncorhynchus masou ishikawae, in a headwater stream network composed of distinctly different tributary and mainstem habitats. Spatial structure of survival and growth differed by species and age class, but results provided support for negative covariation between vital rates, where survival was higher in the tributary habitat but growth was higher in the mainstem habitat. Thus, neither habitat was apparently more important than the other, and local habitats with complementary vital rates may make this spatially structured population less vulnerable to environmental change (i.e. portfolio effect). Despite the spatial structure of vital rates and possibilities that fish can exploit spatially distributed resources, movement of fish was limited due partly to a series of low-head dams that prevented upstream movement of fish in the study area. This study shows that spatial structure of vital rates can be complex and depend on species and age class, and this knowledge is likely paramount to elucidating dynamics of spatially structured populations.     

Adjustment for exploratory cut-off selection in randomized clinical trials with survival endpoint

Defining the target population based on predictive biomarkers plays an important role during clinical development. After establishing a relationship between a biomarker candidate and response to treatment in exploratory phases, a subsequent confirmatory trial ideally involves only subjects with high potential of benefiting from the new compound. In order to identify those subjects in case of a continuous biomarker, a cut-off is needed. Usually, a cut-off is chosen that resulted in a subgroup with a large observed treatment effect in an exploratory trial. However, such a data-driven selection may lead to overoptimistic expectations for the subsequent confirmatory trial. Treatment effect estimates, probability of success, and posterior probabilities are useful measures for deciding whether or not to conduct a confirmatory trial enrolling the biomarker-defined population. These measures need to be adjusted for selection bias. We extend previously introduced Approximate Bayesian Computation techniques for adjustment of subgroup selection bias to a time-to-event setting with cut-off selection. Challenges in this setting are that treatment effects become time-dependent and that subsets are defined by the biomarker distribution. Simulation studies show that the proposed method provides adjusted statistical measures which are superior to naïve Maximum Likelihood estimators as well as simple shrinkage estimators.     

Bayesian design of biosimilars clinical programs involving multiple therapeutic indications

In this paper, we propose a Bayesian design framework for a biosimilars clinical program that entails conducting concurrent trials in multiple therapeutic indications to establish equivalent efficacy for a proposed biologic compared to a reference biologic in each indication to support approval of the proposed biologic as a biosimilar. Our method facilitates information borrowing across indications through the use of a multivariate normal correlated parameter prior (CPP), which is constructed from easily interpretable hyperparameters that represent direct statements about the equivalence hypotheses to be tested. The CPP accommodates different endpoints and data types across indications (eg, binary and continuous) and can, therefore, be used in a wide context of models without having to modify the data (eg, rescaling) to provide reasonable information-borrowing properties. We illustrate how one can evaluate the design using Bayesian versions of the type I error rate and power with the objective of determining the sample size required for each indication such that the design has high power to demonstrate equivalent efficacy in each indication, reasonably high power to demonstrate equivalent efficacy simultaneously in all indications (ie, globally), and reasonable type I error control from a Bayesian perspective. We illustrate the method with several examples, including designing biosimilars trials for follicular lymphoma and rheumatoid arthritis using binary and continuous endpoints, respectively.     

A Bayesian multi-risks survival (MRS) model in the presence of double censorings

Semi-competing risks data include the time to a nonterminating event and the time to a terminating event, while competing risks data include the time to more than one terminating event. Our work is motivated by a prostate cancer study, which has one nonterminating event and two terminating events with both semi-competing risks and competing risks present as well as two censoring times. In this paper, we propose a new multi-risks survival (MRS) model for this type of data. In addition, the proposed MRS model can accommodate noninformative right-censoring times for nonterminating and terminating events. Properties of the proposed MRS model are examined in detail. Theoretical and empirical results show that the estimates of the cumulative incidence function for a nonterminating event may be biased if the information on a terminating event is ignored. A Markov chain Monte Carlo sampling algorithm is also developed. Our methodology is further assessed using simulations and also an analysis of the real data from a prostate cancer study. As a result, a prostate-specific antigen velocity greater than 2.0 ng/mL per year and higher biopsy Gleason scores are positively associated with a shorter time to death due to prostate cancer.