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Critically endangered Hooded Vultures (Necrosyrtes monachus Temminck, 1823), like many vulture species globally, are experiencing rapid population declines due to anthropogenic factors such as poisonings, human persecution, trading for belief-based use, and habitat loss/degradation. The Hooded Vulture is widespread across sub-Saharan Africa. Although it is considered one of the most abundant vultures in West Africa, this vulture species is less common in East and southern Africa, with the population at the southern-most edge of the distribution (in South Africa and Eswatini) estimated at only 100–200 mature individuals. The distribution of Hooded Vultures has contracted dramatically in southern Africa, with breeding populations largely confined to protected areas such as the Greater Kruger National Park. This study aimed to investigate the genetic diversity of the southern African range-edge population and assess if the recent contraction in the distribution has resulted in the population experiencing a genetic bottleneck. Sixteen microsatellite loci were amplified for samples collected along the Olifants River in the Greater Kruger National Park (n = 30). The genetic diversity in the South African population was compared to samples (n = 30) collected in Ghana, where Hooded Vultures are more abundant. Contrary to expectations, the South African peripheral Hooded Vulture population showed higher levels of heterozygosity (HO = 0.495) than the Ghanaian population (HO = 0.315). Neither population showed signs of recent bottleneck events when tested using demographic modelling and Approximate Bayesian computation (ABC). However, both populations showed high levels of inbreeding and relatedness. Our results suggest that despite being a small peripheral population, the South African Hooded Vulture population showed a similar level of genetic diversity as individuals sampled from a core population within the species distribution (in Ghana). This study supports the need for Hooded Vulture conservation efforts in the southern African region and highlights the evolutionary importance of range-edge populations. [ABSTRACT FROM AUTHOR]

Abstract Critically endangered Hooded Vultures (Necrosyrtes monachus Temminck, 1823), like many vulture species globally, are experiencing rapid population declines due to anthropogenic factors such as poisonings, human persecution, trading for belief-based use, and habitat loss/degradation. The Hooded Vulture is widespread across sub-Saharan Africa. Although it is considered one of the most abundant vultures in West Africa, this vulture species is less common in East and southern Africa, with the population at the southern-most edge of the distribution (in South Africa and Eswatini) estimated at only 100–200 mature individuals. The distribution of Hooded Vultures has contracted dramatically in southern Africa, with breeding populations largely confined to protected areas such as the Greater Kruger National Park. This study aimed to investigate the genetic diversity of the southern African range-edge population and assess if the recent contraction in the distribution has resulted in the population experiencing a genetic bottleneck. Sixteen microsatellite loci were amplified for samples collected along the Olifants River in the Greater Kruger National Park (n = 30). The genetic diversity in the South African population was compared to samples (n = 30) collected in Ghana, where Hooded Vultures are more abundant. Contrary to expectations, the South African peripheral Hooded Vulture population showed higher levels of heterozygosity (HO = 0.495) than the Ghanaian population (HO = 0.315). Neither population showed signs of recent bottleneck events when tested using demographic modelling and Approximate Bayesian computation (ABC). However, both populations showed high levels of inbreeding and relatedness. Our results suggest that despite being a small peripheral population, the South African Hooded Vulture population showed a similar level of genetic diversity as individuals sampled from a core population within the species distribution (in Ghana). This study supports the need for Hooded Vulture conservation efforts in the southern African region and highlights the evolutionary importance of range-edge populations.

Streptococcus pneumoniae (the pneumococcus) is a globally distributed, human obligate opportunistic bacterial pathogen which, although often carried commensally, is also a significant cause of invasive disease. Apart from multi-drug resistant and virulent clones, the rate and direction of pneumococcal dissemination between different countries remains largely unknown. The ability for the pneumococcus to take a foothold in a country depends on existing population configuration, the extent of vaccine implementation, as well as human mobility since it is a human obligate bacterium. To shed light on its international movement, we used extensive genome data from the Global Pneumococcal Sequencing project and estimated migration parameters between multiple countries in Africa. Data on allele frequencies of polymorphisms at housekeeping-like loci for multiple different lineages circulating in the populations of South Africa, Malawi, Kenya, and The Gambia were used to calculate the fixation index (⁠ F s t ⁠) between countries. We then further used these summaries to fit migration coalescent models with the likelihood-free inference algorithms available in the ELFI software package. Synthetic datawere additionally used to validate the inference approach. Our results demonstrate country-pair specific migration patterns and heterogeneity in the extent of migration between different lineages. Our approach demonstrates that coalescent models can be effectively used for inferring migration rates for bacterial species and lineages provided sufficiently granular population genomics surveillance data. Further, it can demonstrate the connectivity of respiratory disease agents between countries to inform intervention policy in the longer term. [ABSTRACT FROM AUTHOR]

Some taxa of mammals live in water, all of which evolved from land-dwelling ancestors. In the family Mustelidae (Mammalia: Carnivora), most species live on land, while otters, comprising the subfamily Lutrinae, inhabit aquatic environments, which include the almost exclusively aquatic sea otters (Enhydra lutris). Thus, the transition from a terrestrial to an aquatic lifestyle has occurred within this family. Despite potentially different selection pressures on body size in aquatic and terrestrial habitats, no divergence in the evolutionary pattern of body size between otters and other mustelids has previously been shown using models of trait evolution on a phylogeny. We applied models that explicitly incorporated lineage-specific directional selection to the evolution of body mass in living mustelids. Using a simulation-based likelihood and approximate Bayesian computation approach, we demonstrated lineage-specific directional selection for larger body mass in otters, which is distinct from other mustelids. There was no evidence of a difference between sea otters and other otters in the strength of directional selection for larger body mass. Additionally, our analyses supported no difference in the rate at which body mass evolves in both directions between otters and other mustelids. These findings suggest that the evolution of body mass in otters is associated with selective advantages of larger size rather than the relaxation of constraints on body size in aquatic habitats, like other aquatic mammals such as sirenians, cetaceans, and pinnipeds. [ABSTRACT FROM AUTHOR]

The imprint left by niche evolution on the variation of biological diversity across spatial and environmental gradients is still debated among ecologists. Furthermore, understanding to what extent dispersal limitation may reinforce or blur such imprint is still a gap in the ecological knowledge. In this article we introduce a simulation approach coupled to approximate Bayesian computation (ABC) that parameterizes both the adaptation rate of species' niche positions over the evolution of a monophyletic lineage and the intensity of dispersal limitation associated with the variation of species alpha diversity among assemblages distributed across spatial and environmental gradients. The analytical tool was implemented in the R package 'mcfly' (www.r‐project.org). We evaluated the statistical performance of the analytical framework using simulated datasets, which confirmed the suitability of the analysis to estimate adaptation rate parameter but showed to be less precise in relation to the dispersal limitation parameter. Also, we found that increased dispersal limitation levels improved the parameterization of the adaptation rate of species' niche positions in simulated datasets. Further, we evaluated the role played by niche evolution and dispersal limitation on species alpha diversity variation of Phyllostomidae bats across the Neotropics. The framework proposed here shed light on the links between niche evolution, dispersal limitation and gradients of biological diversity, and thereby improved our understanding of evolutionary imprints on current biological diversity patterns. [ABSTRACT FROM AUTHOR]

Large-scale networks of phase synchronization are considered to regulate the communication between brain regions fundamental to cognitive function, but the mapping to their structural substrates, i.e., the structure-function relationship, remains poorly understood. Biophysical Network Models (BNMs) have demonstrated the influences of local oscillatory activity and inter-regional anatomical connections in generating alpha-band (8–12 Hz) networks of phase synchronization observed with Electroencephalography (EEG) and Magnetoencephalography (MEG). Yet, the influence of inter-regional conduction delays remains unknown. In this study, we compared a BNM with standard “distance-dependent delays”, which assumes constant conduction velocity, to BNMs with delays specified by two alternative methods accounting for spatially varying conduction velocities, “isochronous delays” and “mixed delays”. We followed the Approximate Bayesian Computation (ABC) workflow, i) specifying neurophysiologically informed prior distributions of BNM parameters, ii) verifying the suitability of the prior distributions with Prior Predictive Checks, iii) fitting each of the three BNMs to alpha-band MEG resting-state data (N = 75) with Bayesian optimization for Likelihood-Free Inference (BOLFI), and iv) choosing between the fitted BNMs with ABC model comparison on a separate MEG dataset (N = 30). Prior Predictive Checks revealed the range of dynamics generated by each of the BNMs to encompass those seen in the MEG data, suggesting the suitability of the prior distributions. Fitting the models to MEG data yielded reliable posterior distributions of the parameters of each of the BNMs. Finally, model comparison revealed the BNM with “distance-dependent delays”, as the most probable to describe the generation of alpha-band networks of phase synchronization seen in MEG. These findings suggest that distance-dependent delays might contribute to the neocortical architecture of human alpha-band networks of phase synchronization. Hence, our study illuminates the role of inter-regional delays in generating the large-scale networks of phase synchronization that might subserve the communication between regions vital to cognition.

Body size correlates with various biological attributes. Hence, its evolution has attracted considerable attention. The family Felidae (Mammalia: Carnivora) has extensive variation in body size among extant species, with a few species being strikingly larger. The body size of extremely large species compared with phylogenetically related species may have evolved through directional selection toward increased size in a particular lineage, although lineage-specific directional selection has not been explicitly incorporated into standard models of body size evolution on a phylogeny. We used simulation-based likelihood and approximate Bayesian computation to examine the effect of lineage-specific directional selection on body mass evolution in extant felids. Our analyses revealed the occurrence of lineage-specific directional selection that favors larger body mass in the genus Panthera (lion, Panthera leo; jaguar, Panthera onca; leopard, Panthera pardus; tiger, Panthera tigris; and snow leopard, Panthera uncia), as well as in the cheetah (Acinonyx jubatus) and puma (Puma concolor). Lineage-specific directional selection was not detected in the clouded leopard (Neofelis nebulosa), which, along with the genus Panthera, diverged early from all other extant felids. The cheetah, puma, and jaguarundi (Puma yagouarondi) compose a monophyletic group in which body size may have decreased during the evolution of the jaguarundi, although our analyses revealed no lineage-specific directional selection for smaller sizes in the species. Directional selection for increased body size shown herein seems to be associated with a lifestyle dependent on relatively large prey. This selection may be accelerated by the features of solitary hunters in felids. [ABSTRACT FROM AUTHOR]

A better understanding of long-term effects of climate and historical anthropogenic changes is needed to define effective conservation measures of endangered forest inhabiting managed landscapes. Diversification and distribution of Mediterranean firs are attributed to the global climate change during the Miocene and Quaternary as well as to the effects of human-driven deforestation. We evaluated the impact of climate change and anthropogenic activities in shaping the genetic diversity and structure of Abies pinsapo Boiss. (Pinaceae), a relict fir endemic from SW Spain. We genotyped a total of 440 individuals from 44 populations by using two different molecular markers (cpSSRs and nSSRs). Overall, low genetic structure was found; however, incipient differentiation appeared within mountain ranges. Analyses suggest that the effects of isolation by distance and lithological or topographical diversity were not enough to structure the populations of the different mountain ranges. The combined role of genetic drift in the small populations and the anthropogenic action associated with forest management has shaped the current genetic pattern of this fir species in the study area. Demographic inference analyses pointed to a very recent synchronic divergence (eleventh–sixteenth century) of the ancestral A. pinsapo population into its current scattered distribution range. Although population bottlenecks were supported by several analyses, the conservation of this endangered species seems not to be limited by lacking genetic diversity, while threats of current climate change and habitat loss must be regarded. [ABSTRACT FROM AUTHOR]

Calibrating stochastic radio channel models to new measurement data is challenging when the likelihood function is intractable. The standard approach to this problem involves sophisticated algorithms for extraction and clustering of multipath components, following which point estimates of the model parameters can be obtained using specialized estimators. We propose a likelihood-free calibration method using approximate Bayesian computation. The method is based on the maximum mean discrepancy, which is a notion of distance between probability distributions. Our method not only by-passes the need to implement any high-resolution or clustering algorithm but is also automatic in that it does not require any additional input or manual preprocessing from the user. It also has the advantage of returning an entire posterior distribution on the value of the parameters, rather than a simple point estimate. We evaluate the performance of the proposed method by fitting two different stochastic channel models, namely the Saleh–Valenzuela model and the propagation graph model, to both simulated and measured data. The proposed method is able to estimate the parameters of both the models accurately in simulations, as well as when applied to 60 GHz indoor measurement data. [ABSTRACT FROM AUTHOR]

Innovation in synthetic biology often still depends on large-scale experimental trial and error, domain expertise, and ingenuity. The application of rational design engineering methods promises to make this more efficient, faster, cheaper, and safer. However, this requires mathematical models of cellular systems. For these models, we then have to determine if they can meet our intended target behavior. Here, we develop two complementary approaches that allow us to determine whether a given molecular circuit, represented by a mathematical model, is capable of fulfilling our design objectives. We discuss algebraic methods that are capable of identifying general principles guaranteeing desired behavior; we provide an overview of Bayesian design approaches that allow us to choose a model that has the highest probability of fulfilling our design objectives from a set of models. We discuss their uses in the context of biochemical adaptation and, then, consider how robustness can and should affect our design approach. [ABSTRACT FROM AUTHOR]

The uncertainties quantification of fiber path parameters is essential for fully developing the variable stiffness composite laminates (VSCL) performance. How to design a reliable fiber placing scheme effectively with a given mechanical requirement is an important issue to be considered. To achieve a feasible solution, a data-driven-based approximate Bayesian computation (ABC) inverse method is suggested. The novelty of this paper lies in three aspects. We employ the auto-encoder to extract the feature vector of the high-dimensional physical field as the form of a summary statistical quantity of the ABC. Back propagation neural network is applied to construct the mapping between the fiber path parameters and the feature vectors to avoid the time-consuming physical field simulation. Finally, a hybrid adaptive nested sampling strategy is proposed by introducing the differential operation to accelerate the sampling process of posterior distribution of fiber path parameters. Two VSCL engineering examples are used to verify the feasibility of the suggested method. The experiment results indicate that the available fiber path parameters with uncertainties can be obtained based on the suggested inverse identification framework. [ABSTRACT FROM AUTHOR]

Unraveling the origin and colonization history of invasive plants is a long-standing challenge in evolutionary and conservation biology. The knowledge of the origin of the invasive plants in Europe is often confounded by limited sampling in the source region. We determined the extent of genetic structuring in the native range and reconstructed the origin and the invasion history of Persian hogweed, Heracleum persicum, into Europe. We used allelic polymorphism of microsatellite markers obtained from 36 Iranian populations from Middle East combined with data from 38 European populations representing the major native and introduced ranges, respectively. Comprehensive sampling in the native range covered 97% of allelic diversity found in the introduced range, and showed that allelic variation, heterozygosity and population differentiation are significantly reduced in the introduced populations. Results from Bayesian structure, neighbor-net, and ordination analyses showed that populations in the native range consist of three distinct genetic clusters: Eastern, Central, and Western. Although we observed high genetic differentiation among these groups, the Western cluster was genetically closer to the Central cluster. Approximate Bayesian Computation (ABC) analysis supports at least three independent origins for European populations. No European population originated from the Eastern cluster within the native range. Danish populations originated from the Central cluster, whereas the UK and Finland populations originated independently from the Western cluster. Norwegian populations originated from UK, and subsequently established the FI-Kar population in Finland. Our results shed light on the complex origin and history of an aggressive invasive plant species in Europe, supporting contribution from multiple genetic lineages in recent ancestry of introduced populations, thus suggesting the potential for ecological diversification within the introduced range. [ABSTRACT FROM AUTHOR]

Secure power system operation relies on accurate steady-state and dynamic system models. It is thus crucial to carefully validate the models in power systems, in particular the generator models. The phasor measurement unit (PMU) technologies provide a low-cost option for generator model validation and parameter calibration without interfering with their operation. In this paper, an empirical parameter sensitivity Gramian based approach is developed to identify the critical parameters from a nonlinear system perspective. We further propose a synchrophasor measurement based generator parameter calibration method by adaptive Approximate Bayesian Computation with a sequential Monte Carlo sampler (A-ABC-SMC) that avoids directly dealing with likelihood functions. We propose adaptive threshold sequence scheme and perturbation kernel function in A-ABC-SMC in order to improve the computational efficiency. The effectiveness of the proposed method is validated for a hydro generator against multiple system events. The simulation results show that the proposed approach can accurately and efficiently estimate the full probabilistic posterior distributions of the generator parameters even when there are gross errors in the parameters’ prior distributions. [ABSTRACT FROM AUTHOR]

Aim: Species with widespread distributions offer excellent opportunities for investigating recent biogeographical patterns across broad scales. Here, we tested the hypothesis that, due to its intermediate geographical location, NW Africa is pivotal in explaining the phylogeographical patterns of taxa with Mediterranean–Macaronesian distributions using a legume species with short generation times. Location: Mediterranean, with a focus on NW Africa and the Canary Islands. Taxon: Pitch trefoil (Bituminaria bituminosa). Methods: We generated genetic data and performed phylogeographical and demographic analyses at two geographical scales: Mediterranean Basin (MB), using plastid sequences (115 individuals), and Macaronesia, using plastid sequences (182 individuals) and 10 nuclear microsatellite loci (220 individuals). We also performed a literature survey focusing on phylogeographical studies of other circum‐Mediterranean taxa. Results: Northwest Africa was identified as a centre of genetic diversity (19 out of 38 haplotypes) and demographic expansion of B. bituminosa in the MB during the Pleistocene. Our literature review revealed two main phylogeographical patterns in widespread species: pre‐Mediterranean evergreen sclerophylls versus Pleistocene facultative‐deciduous (including Bituminaria) taxa, but on average, both functional groups show a similar, large genetic diversity (~40% of haplotypes) in NW Africa. At the Macaronesian scale, we found that Canarian Bituminaria is composed of two genetic sublineages that coexist and hybridize on the central islands and in the mainland Macaronesian enclave (Anti‐Atlas region). Demographic analyses rejected the progression rule as the model of island colonization but strongly suggested that Anti‐Atlas populations are the result of back‐colonization from the easternmost islands before the Last Glacial Maximum (LGM). Conclusions: Bituminaria displays a pattern of Quaternary eastward expansion in the MB that appears to be paralleled by several members of its functional plant group. Thus, our study reveals a previously undescribed dual role of NW Africa in plant biogeography, acting both as a source of species expansion to the rest of the MB and a LGM refugium of plant populations with a Macaronesian island origin. [ABSTRACT FROM AUTHOR]

Current procedures for inferring population history generally assume complete neutrality—that is, they neglect both direct selection and the effects of selection on linked sites. We here examine how the presence of direct purifying selection and background selection may bias demographic inference by evaluating two commonly-used methods (MSMC and fastsimcoal2), specifically studying how the underlying shape of the distribution of fitness effects and the fraction of directly selected sites interact with demographic parameter estimation. The results show that, even after masking functional genomic regions, background selection may cause the mis-inference of population growth under models of both constant population size and decline. This effect is amplified as the strength of purifying selection and the density of directly selected sites increases, as indicated by the distortion of the site frequency spectrum and levels of nucleotide diversity at linked neutral sites. We also show how simulated changes in background selection effects caused by population size changes can be predicted analytically. We propose a potential method for correcting for the mis-inference of population growth caused by selection. By treating the distribution of fitness effect as a nuisance parameter and averaging across all potential realizations, we demonstrate that even directly selected sites can be used to infer demographic histories with reasonable accuracy. [ABSTRACT FROM AUTHOR]

Comparing divergences across multiple sister population pairs has been a focus in phylogeography since its inception. Initial approaches used organelle genetic data and involved qualitative comparisons of phylogenetic patterns to evaluate hypotheses of shared and variable evolutionary responses. This endeavor has progressed with coalescent model‐based statistical techniques and advances in next‐generation sequencing, yet there remains a need for methods that can exploit aggregated genomic‐scale data within a unified analytical framework. To this end, we introduce the aggregate joint site frequency spectrum (ajSFS) by validating its use within a hierarchical Bayesian framework through several in silico experiments. Subsequently, we applied our method against two published restriction site–associated DNA marker datasets consisting of eight local replicates of a lamprey species pair and six co‐distributed passerine taxon pairs, respectively, with the aim of inferring variability in co‐divergence and co‐migration histories. We found that the lamprey population pairs exhibited temporal synchrony in both co‐divergence and collective secondary contact times, yet an idiosyncratic pattern in secondary migration intensities. In contrast, the bird population pairs displayed thoroughly asynchronous co‐divergence histories. Our results demonstrate that the ajSFS can be exploited for complex and flexible co‐demographic inference, opening up new possibilities for comparative phylogeography and population genomic studies. [ABSTRACT FROM AUTHOR]

Bayesian synthetic likelihood (BSL) is now a well-established method for performing approximate Bayesian parameter estimation for simulation-based models that do not possess a tractable likelihood function. BSL approximates an intractable likelihood function of a carefully chosen summary statistic at a parameter value with a multivariate normal distribution. The mean and covariance matrix of this normal distribution are estimated from independent simulations of the model. Due to the parametric assumption implicit in BSL, it can be preferred to its nonparametric competitor, approximate Bayesian computation, in certain applications where a high-dimensional summary statistic is of interest. However, despite several successful applications of BSL, its widespread use in scientific fields may be hindered by the strong normality assumption. In this paper, we develop a semi-parametric approach to relax this assumption to an extent and maintain the computational advantages of BSL without any additional tuning. We test our new method, semiBSL, on several challenging examples involving simulated and real data and demonstrate that semiBSL can be significantly more robust than BSL and another approach in the literature. [ABSTRACT FROM AUTHOR]

Aim: Phylogeography of fruit trees is challenging due to recurrent exchanges between domesticated and wild populations. Here we tested the eastern refugium hypothesis (ERH) for the carob tree, Ceratonia siliqua, which supports its natural and domestication origins in the eastern Mediterranean and a feral origin in the west. Location: Mediterranean basin. Taxon: Ceratonia siliqua L., Leguminosae. Methods: A phylogenetic reconstruction based on two nuclear and one plastid sequences was performed to estimate the divergence time between the carob tree and its sister species, Ceratonia oreothauma. Variation from four plastid regions and 17 nuclear microsatellite loci were used to decipher genetic structure in the carob tree and to test coalescent‐based models by an Approximate Bayesian computation (ABC) approach. We assessed our hypotheses by examining palaeobotanical records and hindcasting the past distribution of the carob tree at Mid‐Holocene, Last Glacial Maximum (LGM) and Last Interglacial (LIG) using species distribution modelling. Results: The split between C. oreothauma and C. siliqua was estimated at 6.4 Ma, and a first divergence within C. siliqua at 1.3 Ma. After a continuous presence since the Oligocene, Ceratonia was rarely found in the fossil record during the Pleistocene but present in the western and the eastern Mediterranean. Plastid and nuclear markers, characterized by low allelic richness, revealed a strong west‐east genetic structuring. ABC analyses rejected the ERH. Main conclusions: Our study supports a severe population decline during LIG. The strong west–east divergence and the occurrence of four lineages within C. siliqua provided support for a new hypothesis of multiple domestications of the carob tree from native populations throughout the Mediterranean basin. [ABSTRACT FROM AUTHOR]

One major challenge of using the phylogenetic comparative method (PCM) is the analysis of the evolution of interrelated continuous and discrete traits in a single multivariate statistical framework. In addition, more intricate parameters such as branch-specific directional selection have rarely been integrated into such multivariate PCM frameworks. Here, originally motivated to analyze the complex evolutionary trajectories of group size (continuous variable) and social systems (discrete variable) in African subterranean rodents, we develop a flexible approach using approximate Bayesian computation (ABC). Specifically, our multivariate ABC-PCM method allows the user to flexibly model an underlying latent evolutionary function between continuous and discrete traits. The ABC-PCM also simultaneously incorporates complex evolutionary parameters such as branch-specific selection. This study highlights the flexibility of ABC-PCMs in analyzing the evolution of phenotypic traits interrelated in a complex manner. [ABSTRACT FROM AUTHOR]

Under the United States Endangered Species Act, a species is granted protection if it is in danger of extinction throughout all or a significant portion of its range. Since 2016, the United States Fish and Wildlife Service has adopted a more analytical approach to determining significant portion of its range. Termed Species Status Assessment (SSA), this approach addresses whether loss of individuals from a portion of its range will influence at least one of the conservation biology principles of redundancy (ability to withstand catastrophic events), resiliency (ability to withstand stochastic events), and representation (ability to adapt over time to long-term changes in the environment). Using Sicklefin Redhorse (Moxostoma sp.), we illustrate the use of genetic data to evaluate each SSA metric. We sampled (n = 382) Sicklefin Redhorse from three major river basins throughout its contemporary distribution and estimated genetic parameters using ten microsatellite markers. Using STRUCTURE analyses, we showed that redundancy was three, but our approximate Bayesian computation analysis revealed that this value could be reduced to two if admixture, due to anthropogenic stressors of the 1900s, continues. We used estimates of effective population size (Ne) to measure resiliency and representation and found that all populations showed resiliency and representation with Ne ≥ 479. Genetic monitoring of the Little Tennessee and Tuckasegee populations will be necessary to assess the future status of redundancy for this species. Any reduction in redundancy would warrant further ESA evaluation. [ABSTRACT FROM AUTHOR]

• Compared methods to specify delays in Biophysical Network Models (BNMs). • BNM with "distance-dependent" conduction delays more probable than alternatives. • BNMs with biologically informed prior distributions generate dynamics seen in MEG. • Fitting BNMs yields reliable posterior distributions informed by MEG data (N = 75). Large-scale networks of phase synchronization are considered to regulate the communication between brain regions fundamental to cognitive function, but the mapping to their structural substrates, i.e. , the structure-function relationship, remains poorly understood. Biophysical Network Models (BNMs) have demonstrated the influences of local oscillatory activity and inter-regional anatomical connections in generating alpha-band (8–12 Hz) networks of phase synchronization observed with Electroencephalography (EEG) and Magnetoencephalography (MEG). Yet, the influence of inter-regional conduction delays remains unknown. In this study, we compared a BNM with standard "distance-dependent delays", which assumes constant conduction velocity, to BNMs with delays specified by two alternative methods accounting for spatially varying conduction velocities, "isochronous delays" and "mixed delays". We followed the Approximate Bayesian Computation (ABC) workflow, i) specifying neurophysiologically informed prior distributions of BNM parameters, ii) verifying the suitability of the prior distributions with Prior Predictive Checks, iii) fitting each of the three BNMs to alpha-band MEG resting-state data (N = 75) with Bayesian optimization for Likelihood-Free Inference (BOLFI), and iv) choosing between the fitted BNMs with ABC model comparison on a separate MEG dataset (N = 30). Prior Predictive Checks revealed the range of dynamics generated by each of the BNMs to encompass those seen in the MEG data, suggesting the suitability of the prior distributions. Fitting the models to MEG data yielded reliable posterior distributions of the parameters of each of the BNMs. Finally, model comparison revealed the BNM with "distance-dependent delays", as the most probable to describe the generation of alpha-band networks of phase synchronization seen in MEG. These findings suggest that distance-dependent delays might contribute to the neocortical architecture of human alpha-band networks of phase synchronization. Hence, our study illuminates the role of inter-regional delays in generating the large-scale networks of phase synchronization that might subserve the communication between regions vital to cognition. [ABSTRACT FROM AUTHOR]

We developed a novel approximate Bayesian computation (ABC) framework, ABCDP, which produces differentially private (DP) and approximate posterior samples. Our framework takes advantage of the sparse vector technique (SVT), widely studied in the differential privacy literature. SVT incurs the privacy cost only when a condition (whether a quantity of interest is above/below a threshold) is met. If the condition is sparsely met during the repeated queries, SVT can drastically reduce the cumulative privacy loss, unlike the usual case where every query incurs the privacy loss. In ABC, the quantity of interest is the distance between observed and simulated data, and only when the distance is below a threshold can we take the corresponding prior sample as a posterior sample. Hence, applying SVT to ABC is an organic way to transform an ABC algorithm to a privacy-preserving variant with minimal modification, but yields the posterior samples with a high privacy level. We theoretically analyzed the interplay between the noise added for privacy and the accuracy of the posterior samples. We apply ABCDP to several data simulators and show the efficacy of the proposed framework.

Despite the huge body of research on osteogenic differentiation and bone tissue engineering, the translation potential of in vitro results still does not match the effort employed. One reason might be that the protocols used for in vitro research have inherent pitfalls. The synthetic glucocorticoid dexamethasone is commonly used in protocols for trilineage differentiation of human bone marrow mesenchymal stromal cells (hBMSCs). However, in the case of osteogenic commitment, dexamethasone has the main pitfall of inhibiting terminal osteoblast differentiation, and its pro-adipogenic effect is well known. In this work, we aimed to clarify the role of dexamethasone in the osteogenesis of hBMSCs, with a particular focus on off-target differentiation. The results showed that dexamethasone does induce osteogenic differentiation by inhibiting SOX9 expression, but not directly through RUNX2 upregulation as it is commonly thought. Rather, PPARG is concomitantly and strongly upregulated, leading to the formation of adipocyte-like cells within osteogenic cultures. Limiting the exposure to dexamethasone to the first week of differentiation did not affect the mineralization potential. Gene expression levels of RUNX2, SOX9, and PPARG were simulated using approximate Bayesian computation based on a simplified theoretical model, which was able to reproduce the observed experimental trends but with a different range of responses, indicating that other factors should be integrated to fully understand how dexamethasone influences cell fate. In summary, this work provides evidence that current in vitro differentiation protocols based on dexamethasone do not represent a good model, and further research is warranted in this field.

Abstract Background Estimating the variability in isolation times across co-distributed taxon pairs that may have experienced the same allopatric isolating mechanism is a core goal of comparative phylogeography. The use of hierarchical Approximate Bayesian Computation (ABC) and coalescent models to infer temporal dynamics of lineage co-diversification has been a contentious topic in recent years. Key issues that remain unresolved include the choice of an appropriate prior on the number of co-divergence events (Ψ), as well as the optimal strategies for data summarization. Methods Through simulation-based cross validation we explore the impact of the strategy for sorting summary statistics and the choice of prior on Ψ on the estimation of co-divergence variability. We also introduce a new setting (β) that can potentially improve estimation of Ψ by enforcing a minimal temporal difference between pulses of co-divergence. We apply this new method to three empirical datasets: one dataset each of co-distributed taxon pairs of Panamanian frogs and freshwater fishes, and a large set of Neotropical butterfly sister-taxon pairs. Results We demonstrate that the choice of prior on Ψ has little impact on inference, but that sorting summary statistics yields substantially more reliable estimates of co-divergence variability despite violations of assumptions about exchangeability. We find the implementation of β improves estimation of Ψ, with improvement being most dramatic given larger numbers of taxon pairs. We find equivocal support for synchronous co-divergence for both of the Panamanian groups, but we find considerable support for asynchronous divergence among the Neotropical butterflies. Conclusions Our simulation experiments demonstrate that using sorted summary statistics results in improved estimates of the variability in divergence times, whereas the choice of hyperprior on Ψ has negligible effect. Additionally, we demonstrate that estimating the number of pulses of co-divergence across co-distributed taxon-pairs is improved by applying a flexible buffering regime over divergence times. This improves the correlation between Ψ and the true variability in isolation times and allows for more meaningful interpretation of this hyperparameter. This will allow for more accurate identification of the number of temporally distinct pulses of co-divergence that generated the diversification pattern of a given regional assemblage of sister-taxon-pairs.

Highlights • Reanalysis based Approximate Bayesian computation (ABC) method is developed to address inverse heat conduction problem (IHCP). • A none-parametric population Monte Carlo method are proposed to improve the convergence rate of the ABC and reduce the size of samples. • To address the static IHCP, superposition principle based heat conduction solver is utilized. • To address the nonlinear dynamic IHCP, reanalysis based dynamic heat conduction solver is proposed. Abstract Bayesian approach has been widely used in inverse heat conduction problem (IHCP). However, due to either computationally prohibitive or analytically unavailable, its likelihood function is always intractable. In this study, to circumvent the intractable likelihood function, an approximate Bayesian computation (ABC) is extended to IHCP. However, massive expensive forward simulations are needed. It might lead to prohibited computational cost. In order to improve the efficiency of the ABC-IHCP, two strategies are proposed in this study. At first, in order to improve the convergence rate of ABC and reduce the number of samples, a none-parametric population Monte Carlo (NPMC) is proposed to determine the decreasing tolerance value adaptively. Secondly, in order to save the expensive computational cost of heat conduction simulation, the fast computational techniques are utilized. Based on the characteristics of the linear and nonlinear heat transfer problems, two heat conduction solvers are developed, respectively. The linear solver is based on superposition principle. As for the nonlinear problem, the fast and accurate reanalysis solver is suggested. Finally, the accuracy and efficiency of the suggested methods are verified with two numerical examples. [ABSTRACT FROM AUTHOR]

Simulation-based Bayesian inference methods are useful when the statistical model of interest does not possess a computationally tractable likelihood function. One such likelihood-free method is approximate Bayesian computation (ABC), which approximates the likelihood of a carefully chosen summary statistic via model simulation and nonparametric density estimation. ABC is known to suffer a curse of dimensionality with respect to the size of the summary statistic. When the model summary statistic is roughly normally distributed in regions of the parameter space of interest, Bayesian synthetic likelihood (BSL), which uses a normal likelihood approximation for a summary statistic, is a useful method that can be more computationally efficient than ABC. However, BSL requires estimation of the covariance matrix of the summary statistic for each proposed parameter, which requires a large number of simulations to estimate precisely using the sample covariance matrix when the summary statistic is high dimensional. In this article, we propose to use the graphical lasso to provide a sparse estimate of the precision matrix. This approach can estimate the covariance matrix accurately with significantly fewer model simulations. We discuss the nontrivial issue of tuning parameter choice in the context of BSL and demonstrate on several complex applications that our method, which we call BSLasso, provides significant improvements in computational efficiency whilst maintaining the ability to produce similar posterior distributions to BSL. The BSL and BSLasso methods applied to the examples of this article are implemented in the package in R, which is available on the Comprehensive R Archive Network. Supplemental materials for this article are available online. [ABSTRACT FROM AUTHOR]

Abstract Using genome-wide SNP data obtained from high-throughput techniques based on double digest restriction site-associated DNA sequencing (ddRAD-seq), we elucidated the migration history and genetic diversity of the Japanese population of the ecologically important brown seaweed Sargassum thunbergii (Mertens ex Roth) Kuntze. STRUCTURE and NeighborNet analyses showed a clear genetic differentiation among populations of four geographic regions: Kyushu (POP1); Sea of Japan (POP2); Hokkaido and Tohoku (POP3); and Pacific coast from Kyushu to Kanto (POP4). Approximate Bayesian Computation (ABC) analysis indicated that POP4 diverged first, followed by the separation between POP2 (the largest effective population size) and POP3; POP1 was the last to form, shaped by the mixture of POP2 (73%) and POP4 (27%). High genetic diversity was detected in POP1 and POP2, whereas low genetic diversity was detected in POP3 and POP4. These results indicated that S. thunbergii populations of Kyushu and the Sea of Japan might have been maintained as large and stable populations gathered different lineages from China, Korea and/or Japan. Highlights • ddRADseq analysis obtains SNP data for the phylogeographic analysis. • A clear genetic differentiation among populations of four geographic regions. • Kyushu and the Sea of Japan have been maintained as large and stable populations. [ABSTRACT FROM AUTHOR]

Abstract: Linking pattern to process across spatial and temporal scales has been a key goal of the field of biogeography. In January 2017, the 8th biennial conference of the International Biogeography Society sponsored a symposium on Building up biogeography—process to pattern that aimed to review progress towards this goal. Here we present a summary of the symposium, in which we identified promising areas of current research and suggested future research directions. We focus on (1) emerging types of data such as behavioural observations and ancient DNA, (2) how to better incorporate historical data (such as fossils) to move beyond what we term “footprint measures” of past dynamics and (3) the role that novel modelling approaches (e.g. maximum entropy theory of ecology and approximate Bayesian computation) and conceptual frameworks can play in the unification of disciplines. We suggest that the gaps separating pattern and process are shrinking, and that we can better bridge these aspects by considering the dimensions of space and time simultaneously. [ABSTRACT FROM AUTHOR]

Abstract: This study investigates Bayesian signature‐domain inference of hydrological models using Approximate Bayesian Computation (ABC) algorithms, and compares it to “traditional” time‐domain inference. Our focus is on the quantification of predictive uncertainty in the streamflow time series and on understanding the information content of particular combinations of signatures. A combination of synthetic and real data experiments using conceptual rainfall‐runoff models is employed. Synthetic experiments demonstrate: (i) the general consistency of signature and time‐domain inferences, (ii) the ability to estimate streamflow error model parameters (reliably quantify streamflow uncertainty) even when calibrating in the signature domain, and (iii) the potential robustness of signature‐domain inference when the (probabilistic) hydrological model is misspecified (e.g., by unaccounted timing errors). The experiments also suggest limitations of the signature‐domain approach in terms of information loss when general (nonsufficient) statistics are used, and increased computational costs incurred by the ABC implementation. Real data experiments confirm the viability of Bayesian signature‐domain inference and its general consistency with time‐domain inference in terms of predictive uncertainty quantification. In addition, we demonstrate the utility of the flashiness index for the estimation of streamflow error parameters, and show that signatures based on the Flow Duration Curve alone are insufficient to calibrate parameters controlling streamflow dynamics. Overall, the study further establishes signature‐domain inference (implemented using ABC) as a promising method for comparing the information content of hydrological signatures, for prediction under data‐scarce conditions, and, under certain circumstances, for mitigating the impact of deficiencies in the formulation of the predictive model. [ABSTRACT FROM AUTHOR]

Abstract: This study considers Bayesian calibration of hydrological models using streamflow signatures and its implementation using Approximate Bayesian Computation (ABC). If the modeling objective is to predict streamflow time series and associated uncertainty, a probabilistic model of streamflow must be specified but the inference equations must be developed in the signature domain. However, even starting from simple probabilistic models of streamflow time series, working in the signature domain makes the likelihood function difficult or impractical to evaluate (in particular, as it is unavailable in closed form). This challenge can be tackled using ABC, a general class of numerical algorithms for sampling from conditional distributions, such as (but not limited to) Bayesian posteriors given any calibration data. Using ABC does not avoid the requirement of Bayesian inference to specify a probability model of the data, but rather exchanges the requirement to evaluate the pdf of this model (needed to evaluate the likelihood function) by the requirement to sample model output realizations. For this reason ABC is attractive for inference in the signature domain. We clarify poorly understood aspects of ABC in the hydrological literature, including similarities and differences between ABC and GLUE, and comment on previous applications of ABC in hydrology. An error analysis of ABC approximation errors and their dependence on the tolerance is presented. An empirical case study is used to illustrate the impact of omitting the specification of a probabilistic model (and instead using a deterministic model within the ABC algorithm), and the impact of a coarse ABC tolerance. [ABSTRACT FROM AUTHOR]

Abstract: Stochastic computer simulations are often the only practical way of answering questions relating to ecological management. However, due to their complexity, such models are difficult to calibrate and evaluate. Approximate Bayesian Computation (ABC) offers an increasingly popular approach to this problem, widely applied across a variety of fields. However, ensuring the accuracy of ABC's estimates has been difficult. Here, we obtain more accurate estimates by incorporating estimation of error into the ABC protocol. We show how this can be done where the data consist of repeated measures of the same quantity and errors may be assumed to be normally distributed and independent. We then derive the correct acceptance probabilities for a probabilistic ABC algorithm, and update the coverage test with which accuracy is assessed. We apply this method, which we call error‐calibrated ABC, to a toy example and a realistic 14‐parameter simulation model of earthworms that is used in environmental risk assessment. A comparison with exact methods and the diagnostic coverage test show that our approach improves estimation of parameter values and their credible intervals for both models. [ABSTRACT FROM AUTHOR]

Selected reaction monitoring (SRM) has become one of the main methods for low-mass-range-targeted proteomics by mass spectrometry (MS). However, in most SRM-MS biomarker validation studies, the sample size is very small, and in particular smaller than the number of proteins measured in the experiment. Moreover, the data can be noisy due to a low number of ions detected per peptide by the instrument. In this article, those issues are addressed by a model-based Bayesian method for classification of SRM-MS data. The methodology is likelihood-free, using approximate Bayesian computation implemented via a Markov chain Monte Carlo procedure and a kernel-based Optimal Bayesian Classifier. Extensive experimental results demonstrate that the proposed method outperforms classical methods such as linear discriminant analysis and 3NN, when sample size is small, dimensionality is large, the data are noisy, or a combination of these. [ABSTRACT FROM AUTHOR]

Admixture between differentiated populations is considered to be a powerful mechanism stimulating the invasive success of some introduced species. It is generally facilitated through multiple introductions; however, the importance of admixture prior to introduction has rarely been considered. We assess the likelihood that the invasive Ambrosia artemisiifolia populations of Europe and Australia developed through multiple introductions or were sourced from a historical admixture zone within native North America. To do this, we combine large genomic and sampling data sets analysed with approximate Bayesian computation and random forest scenario evaluation to compare single and multiple invasion scenarios with pre- and postintroduction admixture simultaneously. We show the historical admixture zone within native North America originated before global invasion of this weed and could act as a potential source of introduced populations. We provide evidence supporting the hypothesis that the invasive populations established through multiple introductions from the native range into Europe and subsequent bridgehead invasion into Australia. We discuss the evolutionary mechanisms that could promote invasiveness and evolutionary potential of alien species from bridgehead invasions and admixed source populations. [ABSTRACT FROM AUTHOR]

Isolation and demographic history are key factors that affect lineage divergence of tree species in topographic complex areas, such as the Qinghai-Tibet Plateau (QTP), yet few studies have evaluated these factors in a coalescent-based modeling framework. In the present study, we surveyed ten nuclear DNA sequence loci (nDNA) and six nuclear microsatellite loci (nSSR) for an endangered conifer, Cupressus chengiana, throughout its natural range in the eastern QTP. BARRIER analyses revealed a strong genetic barrier between Gansu and Sichuan populations of C. chengiana, and isolation with migration models detected limited gene flow between them, supporting the division of this species into two evolutionary significant units (ESUs). Two independent coalescent-based approaches suggest a Quaternary divergence between ESUs, their consensus age range ((0.09-) 0.59-1.53 (-2.71) Mya) largely overlaps with the time period when the largest glaciation occurred in the QTP. Both demographic inferences, IMa2 and DIYABC, suggest that both ESUs may have experienced a bottleneck or population contraction event during the late Quaternary. A documented massive recent anthropogenic habitat loss and fragmentation may have led to further decrease of the natural distribution of this conifer. We propose that the conservation and management of both natural stands and plantations of C. chengiana should be reconsidered in the light of our findings. [ABSTRACT FROM AUTHOR]

Background: Estimating the variability in isolation times across co-distributed taxon pairs that may have experienced the same allopatric isolating mechanism is a core goal of comparative phylogeography. The use of hierarchical Approximate Bayesian Computation (ABC) and coalescent models to infer temporal dynamics of lineage co-diversification has been a contentious topic in recent years. Key issues that remain unresolved include the choice of an appropriate prior on the number of co-divergence events (Ø), as well as the optimal strategies for data summarization. Methods: Through simulation-based cross validation we explore the impact of the strategy for sorting summary statistics and the choice of prior on Ψ on the estimation of co-divergence variability. We also introduce a new setting (β) that can potentially improve estimation of Ψ by enforcing a minimal temporal difference between pulses of co-divergence. We apply this new method to three empirical datasets: one dataset each of co-distributed taxon pairs of Panamanian frogs and freshwater fishes, and a large set of Neotropical butterfly sister-taxon pairs. Results: We demonstrate that the choice of prior on Ψ has little impact on inference, but that sorting summary statistics yields substantially more reliable estimates of co-divergence variability despite violations of assumptions about exchangeability. We find the implementation of β improves estimation of Ψ, with improvement being most dramatic given larger numbers of taxon pairs. We find equivocal support for synchronous co-divergence for both of the Panamanian groups, but we find considerable support for asynchronous divergence among the Neotropical butterflies. Conclusions: Our simulation experiments demonstrate that using sorted summary statistics results in improved estimates of the variability in divergence times, whereas the choice of hyperprior on Ψ has negligible effect. Additionally, we demonstrate that estimating the number of pulses of co-divergence across co-distributed taxonpairs is improved by applying a flexible buffering regime over divergence times. This improves the correlation between Ψ and the true variability in isolation times and allows for more meaningful interpretation of this hyperparameter. This will allow for more accurate identification of the number of temporally distinct pulses of codivergence that generated the diversification pattern of a given regional assemblage of sister-taxon-pairs. [ABSTRACT FROM AUTHOR]