Diversified Sampling Research Articles

Multi-agent deep reinforcement learning for trajectory planning in UAVs-assisted mobile edge computing with heterogeneous requirements

In heterogeneous wireless networks, massive user equipments (UEs) generate computing tasks with time-varying heterogeneous requirements. To improve the service quality, this paper formulates a unmanned aerial vehicles (UAVs)-assisted mobile edge computing (MEC) framework for time-varying heterogeneous task requirements. In the framework, the task delay and the number of successfully executed tasks are optimized by jointly controlling the trajectories of multiple UAVs. To address the considered trajectory planning optimization problem, a collaborative multi-agent deep reinforcement learning (MADRL) algorithm is proposed, where each UAV is regarded as a learning agent. First, a counterfactual inference based personalized policy update mechanism is proposed to evaluate the independent policy of agents by comparing the policy with a designed counterfactual policy. Based on this idea, each agent updates a personalized policy from both group and individual interests to improve its cooperation ability in dynamic and complex environments. Then, a diversified experience sampling mechanism is proposed to enhance the efficiency of policy evaluation and update with rich experiences provided by the environment interaction and the modified whale optimization algorithm. Finally, evaluation results demonstrate the superiority and effectiveness of the proposed MADRL algorithm.

Divide-and-conquer the NAS puzzle in resource-constrained federated learning systems

Federated Learning (FL) is a privacy-preserving distributed machine learning approach geared towards applications in edge devices. However, the problem of designing custom neural architectures in federated environments is not tackled from the perspective of overall system efficiency. In this paper, we propose DC-NAS—a divide-and-conquer approach that performs supernet-based Neural Architecture Search (NAS) in a federated system by systematically sampling the search space. We propose a novel diversified sampling strategy that balances exploration and exploitation of the search space by initially maximizing the distance between the samples and progressively shrinking this distance as the training progresses. We then perform channel pruning to reduce the training complexity at the devices further. We show that our approach outperforms several sampling strategies including Hadamard sampling, where the samples are maximally separated. We evaluate our method on the CIFAR10, CIFAR100, EMNIST, and TinyImagenet benchmarks and show a comprehensive analysis of different aspects of federated learning such as scalability, and non-IID data. DC-NAS achieves near iso-accuracy as compared to full-scale federated NAS with 50% fewer resources.

Skyline Fossilized Birth-Death Model is Robust to Violations of Sampling Assumptions in Total-Evidence Dating.

Several total-evidence dating studies under the fossilized birth-death (FBD) model have produced very old age estimates, which are not supported by the fossil record. This phenomenon has been termed "deep root attraction (DRA)." For two specific data sets, involving divergence time estimation for the early radiations of ants, bees, and wasps (Hymenoptera) and of placental mammals (Eutheria), it has been shown that the DRA effect can be greatly reduced by accommodating the fact that extant species in these trees have been sampled to maximize diversity, so-called diversified sampling. Unfortunately, current methods to accommodate diversified sampling only consider the extreme case where it is possible to identify a cut-off time such that all splits occurring before this time are represented in the sampled tree but none of the younger splits. In reality, the sampling bias is rarely this extreme and may be difficult to model properly. Similar modeling challenges apply to the sampling of the fossil record. This raises the question of whether it is possible to find dating methods that are more robust to sampling biases. Here, we show that the skyline FBD (SFBD) process, where the diversification and fossil-sampling rates can vary over time in a piecewise fashion, provides age estimates that are more robust to inadequacies in the modeling of the sampling process and less sensitive to DRA effects. In the SFBD model we consider, rates in different time intervals are either considered to be independent and identically distributed or assumed to be autocorrelated following an Ornstein-Uhlenbeck (OU) process. Through simulations and reanalyses of Hymenoptera and Eutheria data, we show that both variants of the SFBD model unify age estimates under random and diversified sampling assumptions. The SFBD model can resolve DRA by absorbing the deviations from the sampling assumptions into the inferred dynamics of the diversification process over time. Although this means that the inferred diversification dynamics must be interpreted with caution, taking sampling biases into account, we conclude that the SFBD model represents the most robust approach currently available for addressing DRA in total-evidence dating.

Improved multimer prediction using massive sampling with AlphaFold in CASP15.

AlphaFold2 has revolutionized structure prediction by achieving high accuracy comparable to experimentally determined structures. However, there is still room for improvement, especially for challenging cases like multimers. A key to the success of AlphaFold is its ability to assess and rank its own predictions. Our basic idea for the Wallner group in CASP15 was to exploit this excellent scoring function in AlphaFold by massive sampling. To achieve this goal, we conducted AlphaFold runs using six different settings, using templates, without templates, and with an increased number of recycles for both multimer v1 and v2 weights. In all instances, we enabled dropout layers during inference, allowing for sampling of uncertainty and enhancing the diversity of the generated models. In total, 274 289 models were generated for the 38 targets in CASP15, with a median of 4810 models per target. Of these 38 targets, 10 were high quality, 11 were medium quality, 11 were acceptable, and only 6 were incorrect. The improvement over the baseline method, NBIS-AF2-multimer, is substantial, with the mean DockQ increasing from 0.43 to 0.56, with several targets showing a DockQ score increase of +0.6 units. Remarkable, considering Wallner and NBIS-AF2-multimer were using identical input data. The success can be attributed to the diversified sampling using dropout with different settings and, in particular, the use of multimer v1, which is much more susceptible to sampling compared with v2. The method is available here: http://wallnerlab.org/AFsample/.

Impacts of Taxon-Sampling Schemes on Bayesian Tip Dating Under the Fossilized Birth-Death Process.

Evolutionary timescales can be inferred by molecular-clock analyses of genetic data and fossil evidence. Bayesian phylogenetic methods such as tip dating provide a powerful framework for inferring evolutionary timescales, but the most widely used priors for tree topologies and node times often assume that present-day taxa have been sampled randomly or exhaustively. In practice, taxon sampling is often carried out so as to include representatives of major lineages, such as orders or families. We examined the impacts of different densities of diversified sampling on Bayesian tip dating on unresolved fossilized birth-death (FBD) trees, in which fossil taxa are topologically constrained but their exact placements are averaged out. We used synthetic data generated by simulations of nucleotide sequence evolution, fossil occurrences, and diversified taxon sampling. Our analyses under the diversified-sampling FBD process show that increasing taxon-sampling density does not necessarily improve divergence-time estimates. However, when informative priors were specified for the root age or when tree topologies were fixed to those used for simulation, the performance of tip dating on unresolved FBD trees maintains its accuracy and precision or improves with taxon-sampling density. By exploring three situations in which models are mismatched, we find that including all relevant fossils, without pruning off those that are incompatible with the diversified-sampling FBD process, can lead to underestimation of divergence times. Our reanalysis of a eutherian mammal data set confirms some of the findings from our simulation study, and reveals the complexity of diversified taxon sampling in phylogenomic data sets. In highlighting the interplay of taxon-sampling density and other factors, the results of our study have practical implications for using Bayesian tip dating to infer evolutionary timescales across the Tree of Life. [Bayesian tip dating; eutherian mammals; fossilized birth-death process; phylogenomics; taxon sampling.].

Microbiological Characteristics of Some Stations of Moscow Subway.

Simple SummaryPublic transport facilities, including subway systems, provide the most suitable conditions for the transfer of microorganisms between people and the environment, contributing to the pathogenic potential of the urban habitat. Investigation of microbiome diversity and description of its characteristic properties, e.g., antibiotic-resistance profiles, leads to understanding of these interactions. In this study, we aimed to conduct an extended analysis of the bioaerosol and surface microbiome of the Moscow subway, using 16S rRNA gene sample sequencing and classical microbiology methods. The microbiomes of two subway stations (Novokosino and Cherkizovskaya) were reconstructed which differ in terms of passenger traffic and duration of exploitation. It was shown that most bacterial genera were ubiquitous; however, the unique genera were presented in aerosol samples. The relatively older Cherkizovskaya station possessed greater diversity in antibiotic resistance among the identified microorganisms compared to Novokosino station. We also provided a comparative analysis of these results with the previously published data, which allowed us to identify the distribution of microorganisms associated with the human microbiome and the environment regardless of the seasonal fluctuations. The obtained results provide valuable information on the diversity of bacterial communities in the Moscow subway, one of the most socially important facilities in metropolitan areas.The subway is one of the most actively used means of transport in the traffic infrastructure of large metropolitan areas. More than seven million passengers use the Moscow subway every day, which promotes the exchange of microorganisms between people and the surrounding subway environment. In this research, a study of the bacterial communities of two Moscow subway stations was conducted and the common subway microbiome was determined. However, there were differences in microbiological and antibiotic-resistance profiles, depending on the station. The station’s operational period since opening correlated with the taxonomic diversity and resistance of the identified bacteria. Moreover, differences between aerosol and surface bacterial communities were found at the two subway stations, indicating the importance of diversified sampling during the microbiome profiling of public areas. In this study, we also compared our data with previously published results obtained for the Moscow subway. Despite sample collection at different stations and seasons, we showed the presence of 15 common genera forming the core microbiome of the Moscow subway, which represents human commensal species, as well as widespread microorganisms from the surrounding environment.

Estimation of maximum recommended therapeutic dose of anti-retroviral drugs using diversified sampling and varied descriptors

article: Estimation of maximum recommended therapeutic dose of anti-retroviral drugs using diversified sampling and varied descriptors - Minerva Biotechnology and Biomolecular Research 2021 December;33(4):210-8 - Minerva Medica - Journals

Federated Learning Based on OPTICS Clustering Optimization

Federated learning (FL) has emerged for solving the problem of data fragmentation and isolation in machine learning based on privacy protection. Each client node uploads the trained model parameter information to the central server based on the local training data, and the central server aggregates the parameter information to achieve the purpose of common training. In the real environment, the distribution of data among nodes is often inconsistent. By analyzing the influence of independent identically distributed data (non‐IID) on the accuracy of FL, it is shown that the accuracy of the model obtained by the traditional FL method is low. Therefore, we proposed the diversified sampling strategies to simulate the non‐IID data situation and came up with the OPTICS (ordering points to identify the clustering structure)‐based clustering optimization federated learning method (OCFL), which solves the problem that the learning accuracy is reduced when the data of different nodes are non‐IID in FL. Experiments indicate that OCFL greatly improves the model accuracy and training speed compared with the traditional FL algorithm.

A systematic review of principal time use research

PurposeTo advance the study of principal time use (PTU), the purpose of this study is to report findings from a systematic review of PTU research. In addition to identifying common findings, this study also examined the supporting evidence and methodologies of PTU studies. From this dual approach, this study specified the evidence that supports claims about PTU, as well as identified areas requiring future examination.Design/methodology/approachA systematic reference review process considered 5,746 potential PTU manuscripts. The inclusion criteria identified 55 studies published between 1920 and 2015. This review synthesized data pertaining to the methodologies and findings of PTU research.FindingsFindings from studies conducted across decades indicated that principals worked extensive hours. Moreover, the workdays of principals consisted of brief and unrelated activities, most often focused on noninstructional tasks. Contrary to common hypotheses, studies indicated that PTU dedicated to administrative tasks exhibited positive correlations with educational outcomes. However, claims about PTU have been derived from samples overrepresented by large urban school districts and limited periods of observation.Practical implicationsFuture studies should implement diversified sampling strategies and extended observation periods. For principal preparation programs, the results indicated an opportunity for increased instruction on time management skills.Originality/valueThis systematic review identifies the overlooked history of the research and specifies the evidence that supports common claims about PTU, which provides empirically derived guidance for future PTU studies.

Adaptive Foraging in Dynamic Environments Using Scale-Free Interaction Networks.

Group interactions are widely observed in nature to optimize a set of critical collective behaviors, most notably sensing and decision making in uncertain environments. Nevertheless, these interactions are commonly modeled using local (proximity) networks, in which individuals interact within a certain spatial range. Recently, other interaction topologies have been revealed to support the emergence of higher levels of scalability and rapid information exchange. One prominent example is scale-free networks. In this study, we aim to examine the impact of scale-free communication when implemented for a swarm foraging task in dynamic environments. We model dynamic (uncertain) environments in terms of changes in food density and analyze the collective response of a simulated swarm with communication topology given by either proximity or scale-free networks. Our results suggest that scale-free networks accelerate the process of building up a rapid collective response to cope with the environment changes. However, this comes at the cost of lower coherence of the collective decision. Moreover, our findings suggest that the use of scale-free networks can improve swarm performance due to two side-effects introduced by using long-range interactions and frequent network regeneration. The former is a topological consequence, while the latter is a necessity due to robot motion. These two effects lead to reduced spatial correlations of a robot's behavior with its neighborhood and to an enhanced opinion mixing, i.e., more diversified information sampling. These insights were obtained by comparing the swarm performance in presence of scale-free networks to scenarios with alternative network topologies, and proximity networks with and without packet loss.

Selective Sampling of Species and Fossils Influences Age Estimates Under the Fossilized Birth-Death Model.

The fossilized birth–death (FBD) model allows the estimation of species divergence times from molecular and fossil information in a coherent framework of diversification and fossil sampling. Some assumptions of the FBD model, however, are difficult to meet in phylogenetic analyses of highly diverse groups. Here, I use simulations to assess the impact of extreme model violations, including diversified sampling of species and the exclusive use of the oldest fossils per clade, on divergence times estimated with the FBD model. My results demonstrate that selective sampling of fossils can produce dramatically overestimated divergence times when the FBD model is used for inference, due to an interplay of underestimates for the model parameters net diversification rate, turnover, and fossil-sampling proportion. In contrast, divergence times estimated with CladeAge, a method that uses information about the oldest fossils per clade together with estimates of sampling and diversification rates, are accurate under these conditions. Practitioners of Bayesian divergence-time estimation should therefore ensure that the dataset conforms to the expectations of the FBD model, or estimates of sampling and diversification rates should be obtained a priori so that CladeAge can be used for the inference.

Active Mini-Batch Sampling Using Repulsive Point Processes

The convergence speed of stochastic gradient descent (SGD) can be improved by actively selecting mini-batches. We explore sampling schemes where similar data points are less likely to be selected in the same mini-batch. In particular, we prove that such repulsive sampling schemes lower the variance of the gradient estimator. This generalizes recent work on using Determinantal Point Processes (DPPs) for mini-batch diversification (Zhang et al., 2017) to the broader class of repulsive point processes. We first show that the phenomenon of variance reduction by diversified sampling generalizes in particular to non-stationary point processes. We then show that other point processes may be computationally much more efficient than DPPs. In particular, we propose and investigate Poisson Disk sampling—frequently encountered in the computer graphics community—for this task. We show empirically that our approach improves over standard SGD both in terms of convergence speed as well as final model performance.

Compositional heterogeneity and outgroup choice influence the internal phylogeny of the ants

Knowledge of the internal phylogeny and evolutionary history of ants (Formicidae), the world’s most species-rich clade of eusocial organisms, has dramatically improved since the advent of molecular phylogenetics. A number of relationships at the subfamily level, however, remain uncertain. Key unresolved issues include placement of the root of the ant tree of life and the relationships among the so-called poneroid subfamilies. Here we assemble a new data set to attempt a resolution of these two problems and carry out divergence dating, focusing on the age of the root node of crown Formicidae. For the phylogenetic analyses we included data from 110 ant species, including the key species Martialis heureka. We focused taxon sampling on non-formicoid lineages of ants to gain insight about deep nodes in the ant phylogeny. For divergence dating we retained a subset of 62 extant taxa and 42 fossils in order to approximate diversified sampling in the context of the fossilized birth-death process. We sequenced 11 nuclear gene fragments for a total of ∼7.5 kb and investigated the DNA sequence data for the presence of among-taxon compositional heterogeneity, a property known to mislead phylogenetic inference, and for its potential to affect the rooting of the ant phylogeny. We found sequences of the Leptanillinae and several outgroup taxa to be rich in adenine and thymine (51% average AT content) compared to the remaining ants (45% average). To investigate whether this heterogeneity could bias phylogenetic inference we performed outgroup removal experiments, analysis of compositionally homogeneous sites, and a simulation study. We found that compositional heterogeneity indeed appears to affect the placement of the root of the ant tree but has limited impact on more recent nodes. Our findings have implications for outgroup choice in phylogenetics, which should be made not only on the basis of close relationship to the ingroup, but should also take into account sequence divergence and other properties relative to the ingroup. We put forward a hypothesis regarding the rooting of the ant phylogeny, in which Martialis and the Leptanillinae together constitute a clade that is sister to all other ants. After correcting for compositional heterogeneity this emerges as the best-supported hypothesis of relationships at deep nodes in the ant tree. The results of our divergence dating under the fossilized birth-death process and diversified sampling suggest that the crown Formicidae originated during the Albian or Aptian ages of the Lower Cretaceous (103–124 Ma). In addition, we found support for monophyletic poneroids comprising the subfamilies Agroecomyrmecinae, Amblyoponinae, Apomyrminae, Paraponerinae, Ponerinae, and Proceratiinae, and well-supported relationships among these subfamilies except for the placement of Proceratiinae and (Amblyoponinae + Apomyrminae). Our phylogeny also highlights the non-monophyly of several ant genera, including Protanilla and Leptanilla in the Leptanillinae, Proceratium in the Proceratiinae, and Cryptopone, Euponera, and Mesoponera within the Ponerinae.

Closing the gap between rocks and clocks using total-evidence dating.

Total-evidence dating (TED) allows evolutionary biologists to incorporate a wide range of dating information into a unified statistical analysis. One might expect this to improve the agreement between rocks and clocks but this is not necessarily the case. We explore the reasons for such discordance using a mammalian dataset with rich molecular, morphological and fossil information. There is strong conflict in this dataset between morphology and molecules under standard stochastic models. This causes TED to push divergence events back in time when using inadequate models or vague priors, a phenomenon we term ‘deep root attraction’ (DRA). We identify several causes of DRA. Failure to account for diversified sampling results in dramatic DRA, but this can be addressed using existing techniques. Inadequate morphological models also appear to be a major contributor to DRA. The major reason seems to be that current models do not account for dependencies among morphological characters, causing distorted topology and branch length estimates. This is particularly problematic for huge morphological datasets, which may contain large numbers of correlated characters. Finally, diversification and fossil sampling priors that do not incorporate all the available background information can contribute to DRA, but these priors can also be used to compensate for DRA. Specifically, we show that DRA in the mammalian dataset can be addressed by introducing a modest extra penalty for ghost lineages that are unobserved in the fossil record, for instance by assuming rapid diversification, rare extinction or high fossil sampling rate; any of these assumptions produces highly congruent divergence time estimates with a minimal gap between rocks and clocks. Under these conditions, fossils have a stabilizing influence on divergence time estimates and significantly increase the precision of those estimates, which are generally close to the dates suggested by palaeontologists.This article is part of the themed issue ‘Dating species divergences using rocks and clocks’.

Total-Evidence Dating under the Fossilized Birth-Death Process.

Bayesian total-evidence dating involves the simultaneous analysis of morphological data from the fossil record and morphological and sequence data from recent organisms, and it accommodates the uncertainty in the placement of fossils while dating the phylogenetic tree. Due to the flexibility of the Bayesian approach, total-evidence dating can also incorporate additional sources of information. Here, we take advantage of this and expand the analysis to include information about fossilization and sampling processes. Our work is based on the recently described fossilized birth–death (FBD) process, which has been used to model speciation, extinction, and fossilization rates that can vary over time in a piecewise manner. So far, sampling of extant and fossil taxa has been assumed to be either complete or uniformly at random, an assumption which is only valid for a minority of data sets. We therefore extend the FBD process to accommodate diversified sampling of extant taxa, which is standard practice in studies of higher-level taxa. We verify the implementation using simulations and apply it to the early radiation of Hymenoptera (wasps, ants, and bees). Previous total-evidence dating analyses of this data set were based on a simple uniform tree prior and dated the initial radiation of extant Hymenoptera to the late Carboniferous (309 Ma). The analyses using the FBD prior under diversified sampling, however, date the radiation to the Triassic and Permian (252 Ma), slightly older than the age of the oldest hymenopteran fossils. By exploring a variety of FBD model assumptions, we show that it is mainly the accommodation of diversified sampling that causes the push toward more recent divergence times. Accounting for diversified sampling thus has the potential to close the long-discussed gap between rocks and clocks. We conclude that the explicit modeling of fossilization and sampling processes can improve divergence time estimates, but only if all important model aspects, including sampling biases, are adequately addressed.

Community care workers, poor referral networks and consumption of personal resources in rural South Africa.

Although home-based care (HBC) programs are widely implemented throughout Africa, their success depends on the existence of an enabling environment, including a referral system and supply of essential commodities. The objective of this study was to explore the current state of client referral patterns and practices by community care workers (CCWs), in an evolving environment of one rural South African sub-district. Using a participant triangulation approach, in-depth qualitative interviews were conducted with 17 CCWs, 32 HBC clients and 32 primary caregivers (PCGs). An open-ended interview guide was used for data collection. Participants were selected from comprehensive lists of CCWs and their clients, using a diversified criterion-based sampling method. Three independent researchers coded three sets of data – CCWs, Clients and PCGs, for referral patterns and practices of CCWs. Referrals from clinics and hospitals to HBC occurred infrequently, as only eight (25%) of the 32 clients interviewed were formally referred. Community care workers showed high levels of commitment and personal investment in supporting their clients to use the formal health care system. They went to the extent of using their own personal resources. Seven CCWs used their own money to ensure client access to clinics, and eight gave their own food to ensure treatment adherence. Community care workers are essential in linking clients to clinics and hospitals and to promote the appropriate use of medical services, although this effort frequently necessitated consumption of their own personal resources. Therefore, risk protection strategies are urgently needed so as to ensure sustainability of the current work performed by HBC organizations and the CCW volunteers.

Likelihood Inference of Non-Constant Diversification Rates with Incomplete Taxon Sampling

Large-scale phylogenies provide a valuable source to study background diversification rates and investigate if the rates have changed over time. Unfortunately most large-scale, dated phylogenies are sparsely sampled (fewer than 5% of the described species) and taxon sampling is not uniform. Instead, taxa are frequently sampled to obtain at least one representative per subgroup (e.g. family) and thus to maximize diversity (diversified sampling). So far, such complications have been ignored, potentially biasing the conclusions that have been reached. In this study I derive the likelihood of a birth-death process with non-constant (time-dependent) diversification rates and diversified taxon sampling. Using simulations I test if the true parameters and the sampling method can be recovered when the trees are small or medium sized (fewer than 200 taxa). The results show that the diversification rates can be inferred and the estimates are unbiased for large trees but are biased for small trees (fewer than 50 taxa). Furthermore, model selection by means of Akaike's Information Criterion favors the true model if the true rates differ sufficiently from alternative models (e.g. the birth-death model is recovered if the extinction rate is large and compared to a pure-birth model). Finally, I applied six different diversification rate models – ranging from a constant-rate pure birth process to a decreasing speciation rate birth-death process but excluding any rate shift models – on three large-scale empirical phylogenies (ants, mammals and snakes with respectively 149, 164 and 41 sampled species). All three phylogenies were constructed by diversified taxon sampling, as stated by the authors. However only the snake phylogeny supported diversified taxon sampling. Moreover, a parametric bootstrap test revealed that none of the tested models provided a good fit to the observed data. The model assumptions, such as homogeneous rates across species or no rate shifts, appear to be violated.

Geo-economic Quandaries and Educational Exclusion in the Context of pastoralists The Case of Dasanech Children in Southern Omo, Ethiopia

The intent of this study was to examine the status of educational exclusion of pastoral Dasanech children in the primary schools of South Omo Zone, and identifying the major geo-economic impediments in their educational inclusion. To serve this purpose, descriptive survey research method was employed. The data were gathered from the sample of students, teachers, and educated personnel from Dasanech ethnic group, Parent-Teacher Association members, tribal chiefs and ethnically Dasanech school dropouts. Applying diversified sampling techniques drew these respondents. Apart from document analysis, information was solicited from the sample respondents through questionnaire, Interview and Focus Group Discussion. Mixed method procedures of Sequential Explanatory Strategy were used to analyze and interpret both the quantitative and qualitative data. The findings indicated that the status of educational exclusion of pastoral nomad Dasanechs’ children in the primary school of the south Omo zone is low as compared to other ethnic groups. Whereas, the dropout rate of children from primary is high above the regional average. Based on the findings, geo-economic predicaments, like poverty, conflict, parents’ illiteracy, nomadism, high demand of child labor and problem of health have been found to be the major geo-economic constraints. Formulation of specific policy and strategy for pastoral children education, school location mapping, flexible academic calendar, construction of semi-sedentary education centers and boarding schools, and introducing mobile tent schools, promoting participatory conflict management approaches were recommended. DOI: 10.5901/mjss.2013.v4n13p305

Inferring Speciation and Extinction Rates under Different Sampling Schemes

The birth-death process is widely used in phylogenetics to model speciation and extinction. Recent studies have shown that the inferred rates are sensitive to assumptions about the sampling probability of lineages. Here, we examine the effect of the method used to sample lineages. Whereas previous studies have assumed random sampling (RS), we consider two extreme cases of biased sampling: "diversified sampling" (DS), where tips are selected to maximize diversity and "cluster sampling (CS)," where sample diversity is minimized. DS appears to be standard practice, for example, in analyses of higher taxa, whereas CS may occur under special circumstances, for example, in studies of geographically defined floras or faunas. Using both simulations and analyses of empirical data, we show that inferred rates may be heavily biased if the sampling strategy is not modeled correctly. In particular, when a diversified sample is treated as if it were a random or complete sample, the extinction rate is severely underestimated, often close to 0. Such dramatic errors may lead to serious consequences, for example, if estimated rates are used in assessing the vulnerability of threatened species to extinction. Using Bayesian model testing across 18 empirical data sets, we show that DS is commonly a better fit to the data than complete, random, or cluster sampling (CS). Inappropriate modeling of the sampling method may at least partly explain anomalous results that have previously been attributed to variation over time in birth and death rates.

Prospects of barcoding the Italian wild dendroflora: oaks reveal severe limitations to tracking species identity

DNA barcoding may be particularly important in influencing ecology, economic issues, and the fundamental crisis facing biodiversity as a standardized, species-level identification tool for taxonomy assessment. Trees play important roles in the conservation of many land ecosystems, the wood trade, and the definition of biogeographical processes; nevertheless, peculiar biological, evolutionary and taxonomical features will probably constitute an intriguing challenge to barcoders. We examined whether four marker regions (trnh-psba, rbcL, rpoc1, matK) proposed by the Consortium for the Barcode of Life (CBOL) matched species taxonomy in a preliminary tree biodiversity survey of Italian forested land. Our objective was to provide a test of future in situ applications of DNA barcodes by evaluating the efficacy of species discrimination under the criteria of uniformity of methods and natural co-occurrence of the species in the main forest ecosystems. Fifty-two species were included in a floristic study. We obtained 73% total discrimination success, with trnH-psbA as the best performing marker and oaks as the least responsive plants to the markers used. A further taxon-based study of Quercus (thirty specimens, 12 species) revealed that this genus is refractory to barcoding (0% discrimination success), a probable consequence of low variation rate at the plastid genome level, hybridization, and the incidence of biogeography. We conclude that some species-rich tree genera in small geographical regions may prove exceptionally difficult to barcode. Until more efficient markers are developed, we recommend that improved and diversified sampling (multiple locations of sympatric and co-occurring congenerics) be embraced as a timely and important goal for the precise assessment of haplotype specificity to facilitate the productive application of barcoding in practice.