Remembrance of things past: modelling the relationship between species' abundances in living communities and death assemblages

Accumulations of dead skeletal material are a valuable archive of past ecological conditions. However, such assemblages are not equivalent to living communities because they mix the remains of multiple generations and are altered by post-mortem processes. The abundance of a species in a death assemblage can be quantitatively modelled by successively integrating the product of an influx time series and a post-mortem loss function (a decay function with a constant half-life). In such a model, temporal mixing increases expected absolute dead abundance relative to average influx as a linear function of half-life and increases variation in absolute dead abundance values as a square-root function of half-life. Because typical abundance distributions of ecological communities are logarithmically distributed, species' differences in preservational half-life would have to be very large to substantially alter species' abundance ranks (i.e. make rare species common or vice-versa). In addition, expected dead abundances increase at a faster rate than their range of variation with increased time averaging, predicting greater consistency in the relative abundance structure of death assemblages than their parent living community.     

历史因素对土壤微生物群落与外来细菌入侵间关系的影响

群落的组成和结构如何影响其可入侵性一直是入侵生态学的研究热点。然而关于群落可入侵性和群落特征间关系的认知却很不统一。采用交叉互换的试验方法,首先将野外采集的两种长期不同施肥土壤(有机肥和化肥)进行灭菌并回接已方和对方的土壤悬液,研究土壤环境(历史非生物因素)和土壤微生物群落(历史生物因素)对重建土壤微生物群落特征的相对贡献。随后将用红色荧光蛋白标记的青枯菌作为外来种接入重建的土壤中,探究不同土壤微生物群落特征对外来细菌存活数量(前期入侵潜力)和存活时间(后期入侵潜力)的影响。结果表明,历史生物因素对重建土壤的原生动物数量、革兰氏阴性与阳性细菌比等群落特征和外来细菌的存活数量有影响;历史非生物因素对土壤微生物活性、细菌物种多样性和功能多样性等群落特征以及外来细菌入侵土壤后总的存活时间有影响;外来细菌入侵前期状况仅与原生动物数量、革兰氏阴性与阳性细菌比相关,而入侵后期的状况则仅与微生物活性、细菌物种多样性和功能多样性相关。总之,外来细菌在土壤中各时期的入侵潜力和土著微生物群落特征的相关性主要取决于二者是否由同种历史影响因素控制。本研究对于阐明生物群落结构与群落可入侵性之间关系,及指导土壤外来病原菌的防控均具有重要意义。     

Bayesian variable selection for latent class models

In this article, we develop a latent class model with class probabilities that depend on subject-specific covariates. One of our major goals is to identify important predictors of latent classes. We consider methodology that allows estimation of latent classes while allowing for variable selection uncertainty. We propose a Bayesian variable selection approach and implement a stochastic search Gibbs sampler for posterior computation to obtain model-averaged estimates of quantities of interest such as marginal inclusion probabilities of predictors. Our methods are illustrated through simulation studies and application to data on weight gain during pregnancy, where it is of interest to identify important predictors of latent weight gain classes.     

A methodological approach to identify cheap and accurate indicators for biodiversity assessment: application to grazing management and two grassland bird species

In response to environmental threats, numerous indicators have been developed to assess the impact of livestock farming systems on the environment. Some of them, notably those based on management practices have been reported to have low accuracy. This paper reports the results of a study aimed at assessing whether accuracy can be increased at a reasonable cost by mixing individual indicators into models. We focused on proxy indicators representing an alternative to the direct impact measurement on two grassland bird species, the lapwing Vanellus vanellus and the redshank Tringa totanus. Models were developed using stepwise selection procedures or Bayesian model averaging (BMA). Sensitivity, specificity, and probability of correctly ranking fields (area under the curve, AUC) were estimated for each individual indicator or model from observational data measured on 252 grazed plots during 2 years. The cost of implementation of each model was computed as a function of the number and types of input variables. Among all management indicators, 50% had an AUC lower than or equal to 0.50 and thus were not better than a random decision. Independently of the statistical procedure, models combining management indicators were always more accurate than individual indicators for lapwings only. In redshanks, models based either on BMA or some selection procedures were non-informative. Higher accuracy could be reached, for both species, with model mixing management and habitat indicators. However, this increase in accuracy was also associated with an increase in model cost. Models derived by BMA were more expensive and slightly less accurate than those derived with selection procedures. Analysing trade-offs between accuracy and cost of indicators opens promising application perspectives as time consuming and expensive indicators are likely to be of low practical utility.     

Breakpoint mapping of a novel de novo translocation t(X;20)(q11.1;p13) by positional cloning and long read sequencing

Disease associated chromosomal rearrangements often have break points located within disease causing genes or in their vicinity. The purpose of this study is to characterize a balanced reciprocal translocation in a girl with intellectual disability and seizures by positional cloning and whole genome sequencing. The translocation was identification by G- banding and confirmed by WCP FISH. Fine mapping using BAC clones and whole genome sequencing using Oxford nanopore long read sequencing technology for a 1.46 X coverage of the genome was done. The positional cloning showed split signals with BAC RP11-943 J20. Long read sequencing analysis of chimeric reads carrying parts of chromosomes X and 20 helped to identify the breakpoints to be in intron 2 of ARHGEF9 gene on Xp11.1 and on 20p13 between RASSF2 and SLC23A2 genes. This is the first report of translocation which successfully delineated to single base resolution using Nanopore sequencing. The genotype-phenotype correlation is discussed.     

Recipient Environment More Important than Community Composition in Determining the Success of an Experimental Sponge Transplant

Human activities have inflicted profound damage upon many ecosystems, and ecologists are now seeking effective means of restoring ecosystems to their natural state. Industrial ports and harbors are highly modified and often depauperate in native fauna. They are typically characterized by poor water quality and modified community composition, both of which may hinder attempts to reintroduce native species. Here, we conducted a field experiment to separate the effects of the recipient environment and community composition on the success of endemic sponge explants in Port Kembla Harbor, NSW, Australia. A reciprocal transplant was conducted between communities originating from six sites that varied in water quality and community composition, enabling us to assess the relative factors simultaneously. A colony of the endemic sponge Tedania anhelans was then inserted into the center of each community, and we quantified the survival, growth, and metal bioaccumulation of sponges over three months. Endemic sponges consistently performed better against resident assemblages when water quality was good. Sponges transplanted to cleaner sites had over double the survivorship and approximately three times the surface area of sponges transplanted to disturbed sites. These patterns were independent of community composition. Bioaccumulation of metals in sponges was correlated with survival; however, other factors such as turbidity may be required to explain sponge mortality at some sites. This study adds to evidence that remediation of the physical and chemical environments may be a prerequisite for biological remediation and demonstrates the value of experimental transplants in assessing restoration potential.     

Multimodel inference in ecology and evolution: challenges and solutions

Information theoretic approaches and model averaging are increasing in popularity, but this approach can be difficult to apply to the realistic, complex models that typify many ecological and evolutionary analyses. This is especially true for those researchers without a formal background in information theory. Here, we highlight a number of practical obstacles to model averaging complex models. Although not meant to be an exhaustive review, we identify several important issues with tentative solutions where they exist (e.g. dealing with collinearity amongst predictors; how to compute model-averaged parameters) and highlight areas for future research where solutions are not clear (e.g. when to use random intercepts or slopes; which information criteria to use when random factors are involved). We also provide a worked example of a mixed model analysis of inbreeding depression in a wild population. By providing an overview of these issues, we hope that this approach will become more accessible to those investigating any process where multiple variables impact an evolutionary or ecological response.