Evolutionary Models Research Articles

PhyloJunction: a computational framework for simulating, developing, and teaching evolutionary models.

We introduce PhyloJunction, a computational framework designed to facilitate the prototyping, test- ing, and characterization of evolutionary models. PhyloJunction is distributed as an open-source Python library that can be used to implement a variety of models, thanks to its flexible graphical modeling architecture and dedicated model specification language. Model design and use are exposed to users via command-line and graphical interfaces, which integrate the steps of simulating, summarizing, and visualizing data. This paper describes the features of PhyloJunction - which include, but are not limited to, a general implementation of a popular family of phylogenetic diversification models - and, moving forward, how it may be expanded to not only include new models, but to also become a platform for conducting and teaching statistical learning.

Adaptive gene loss in the common bean pan-genome during range expansion and domestication

The common bean (Phaseolus vulgaris L.) is a crucial legume crop and an ideal evolutionary model to study adaptive diversity in wild and domesticated populations. Here, we present a common bean pan-genome based on five high-quality genomes and whole-genome reads representing 339 genotypes. It reveals ~234 Mb of additional sequences containing 6,905 protein-coding genes missing from the reference, constituting 49% of all presence/absence variants (PAVs). More non-synonymous mutations are found in PAVs than core genes, probably reflecting the lower effective population size of PAVs and fitness advantages due to the purging effect of gene loss. Our results suggest pan-genome shrinkage occurred during wild range expansion. Selection signatures provide evidence that partial or complete gene loss was a key adaptive genetic change in common bean populations with major implications for plant adaptation. The pan-genome is a valuable resource for food legume research and breeding for climate change mitigation and sustainable agriculture.

Dynamic Evolution Law of Production Stress Field in Fractured Tight Sandstone Horizontal Wells Considering Stress Sensitivity of Multiple Media

Inter-well frac-hit has become an important challenge in the development of unconventional oil and gas resources such as fractured tight sandstone. Due to the presence of hydraulic fracturing fractures, secondary induced fractures, natural fractures, and other seepage media in real formations, the acquisition of stress fields requires the coupling effect of seepage and stress. In this process, there is also stress sensitivity, which leads to unclear dynamic evolution laws of stress fields and increases the difficulty of the staged multi-cluster fracturing of horizontal wells. The use of a multi-stage stress-sensitive horizontal well production stress field prediction model is an effective means of analyzing the influence of natural fracture parameters, main fracture parameters, and multi-stage stress sensitivity coefficients on the stress field. This article considers multi-stage stress sensitivity and, based on fractured sandstone reservoir parameters, establishes a numerical model for the dynamic evolution of the production stress field in horizontal wells with matrix self-supporting fracture-supported fracture–seepage–stress coupling. The influence of various factors on the production stress field is analyzed. The results show that under constant pressure production, for low-permeability reservoirs, multi-stage stress sensitivity has a relatively low impact on reservoir stress, and the amplitude of principal stress change in the entire fracture length direction is only within the range of 0.27%, with no significant change in stress distribution; The parameters of the main fracture have a significant impact on the stress field, with a variation amplitude of within 2.85%. The ability of stress to diffuse from the fracture tip to the surrounding areas is stronger, and the stress concentration area spreads from an elliptical distribution to a semi-circular distribution. The random natural fracture parameters have a significant impact on pore pressure. As the density and angle of the fractures increase, the pore pressure changes within the range of 3.32%, and the diffusion area of pore pressure significantly increases, making it easy to communicate with the reservoir on both sides of the fractures.

1D -FNO - Residual convolutional neural network autoregressive prediction model for deep water wave train evolution based on high-order spectral (HOS) data

Accurate prediction of deep-water wave dynamics is essential for ocean engineering, meteorology, and maritime safety. Traditional physical models, though effective, face significant computational and time challenges with complex nonlinear dynamics. This study introduces an autoregressive prediction model using a one-dimensional adaptive Fourier Neural Operator-Residual Convolutional Neural Network (1D-FNO-ResNet) to enhance prediction accuracy. We developed a high-precision single-step prediction model capable of capturing wave characteristics such as height and fluid particle velocity. A fine-tuning training strategy based on multi-step prediction errors was proposed for autoregressive evolution. Performance comparisons between the ResNet and FNO-ResNet models were made under various fine-tuning steps. A correction model was also introduced to control error accumulation, improving autoregressive prediction steps. The model's generalization performance was validated with modulated and random wave trains under different conditions, optimizing predictive stability and accuracy. This approach demonstrates efficient 1D deep-water wave evolution prediction, offering a novel deep learning solution for wave dynamics prediction in ocean engineering, supporting wave energy conversion device design and optimization.

Coesite-bearing garnet xenocrysts from diatexite in the Nordøyane domain, Western Gneiss Region, Norway: Implications for eclogite-melt interaction at ultra-high pressure

Ultra-high-pressure (UHP) metamorphic rocks in the Western Gneiss Region (WGR) of Norway formed in subducted Baltican crust during the Scandian phase of the Caledonian orogeny. In the Nordøyane UHP domain, eclogites locally preserve coesite and microdiamond. Along the north coasts of Haramsøya and Flemsøya, in situ orthopyroxene-eclogite bodies are surrounded by dioritic "diatexite selvages" containing abundant eclogitic enclaves and xenocrysts. In one location eclogite and diatexite are associated with a quartz diorite dyke that is highly contaminated with eclogitic material. The diatexite matrix and dyke have similar plagioclase + quartz + biotite assemblages with abundant embayed and fragmented garnet and clinopyroxene xenocrysts. Coesite is present in some garnet xenocrysts but has not been found in the adjacent eclogite bodies. Pressure-temperature (P-T) estimates from in situ eclogites range from 2.2-3.2 GPa and 810-840 °C, spanning the quartz-coesite transition, with P-T estimates from xenocrysts spanning a wider range. Diorites and retrogressed eclogites record amphibolite-facies conditions, ca. 1.0-1.5 GPa and 700-800 °C. The wide range and large uncertainties in P-T estimates reflect high-temperature re-equilibration and a relative lack of P-sensitive assemblages. Differences in texture, composition, and included assemblages indicate that the xenocrysts were not derived from the nearby eclogite bodies. By implication, their dioritic hosts were not locally derived but must have originated from a different, probably deeper, source. The results are integrated with a numerical model for WGR tectonic evolution to assess the sources of the melts and their possible effects on the exhumation of UHP rocks in western Norway.

Research on the static and dynamic tensile damage mechanism of concrete based on fuzzy sets

The quantitative analysis of microscopic damage mechanisms in concrete and the formulation of damage evolution equations using mathematical theory and computerized tomography (CT) imaging are still emerging fields. The test data from CT scans have yet to be fully exploited. This study proposes a method for quantitatively characterizing mesoscale damage evolution in concrete. By segmenting CT images from static and dynamic tensile tests, we define metrics such as the aggregate rate, mortar rate, and porosity to examine the damage progression under both stress conditions. We then establish a damage evolution model based on porosity changes. Our findings reveal that crack growth in concrete is slower under static tension, with cracks navigating around aggregates towards weaker regions. Conversely, under dynamic loads, cracks propagate more rapidly and directly through aggregates, following paths of quicker energy dispersion. Static loading results in less aggregate damage than does dynamic loading, with a notable increase in porosity and a more varied change in the mortar rate. The damage to the finer components of concrete increases as the crack-adjacent region diminishes. Damage is generally more extensive under static loading than under dynamic loading. The proposed porosity-based damage evolution model effectively describes concrete damage under static tension, leveraging CT technology to enhance the universality of the research and offering a microscopic-level theoretical foundation for studying concrete mechanical properties.

Practical guidelines for Bayesian phylogenetic inference using Markov chain Monte Carlo (MCMC)

Phylogenetic estimation is, and has always been, a complex endeavor. Estimating a phylogenetic tree involves evaluating many possible solutions and possible evolutionary histories that could explain a set of observed data, typically by using a model of evolution. Values for all model parameters need to be evaluated as well. Modern statistical methods involve not just the estimation of a tree, but also solutions to more complex models involving fossil record information and other data sources. Markov chain Monte Carlo (MCMC) is a leading method for approximating the posterior distribution of parameters in a mathematical model. It is deployed in all Bayesian phylogenetic tree estimation software. While many researchers use MCMC in phylogenetic analyses, interpreting results and diagnosing problems with MCMC remain vexing issues to many biologists. In this manuscript, we will offer an overview of how MCMC is used in Bayesian phylogenetic inference, with a particular emphasis on complex hierarchical models, such as the fossilized birth-death (FBD) model. We will discuss strategies to diagnose common MCMC problems and troubleshoot difficult analyses, in particular convergence issues. We will show how the study design, the choice of models and priors, but also technical features of the inference tools themselves can all be adjusted to obtain the best results. Finally, we will also discuss the unique challenges created by the incorporation of fossil information in phylogenetic inference, and present tips to address them.

The effect of nonlinear environmental feedback on the outcomes of evolutionary dynamics

In this paper, we construct a nonlinear evolutionary game model to analyze the cooperation mechanisms of the population based on a nonlinear relationship among environment and strategies. In the model, replicator dynamics and aspiration dynamics are used to explore the evolutionary outcomes of collective decision, respectively. The results suggest that the environment tends to become progressively more affluent as the number of cooperators increases, if there is a smaller intensity of environmental destruction of defectors. Interestingly, the enriched environments may attract more defectors. Hence, the population requires a higher level of vigilance against plentiful environments in response to the emergence of defectors. As opposed to replicator dynamics, aspiration dynamics can avoid the persistent oscillatory loops due to the level of aspiration. Further, we investigate the effect of complexity between the population strategy and the environment on the evolutionary outcomes. It is found that higher level of complexity can drive the environment closer to a state of affluence, but the population's strategy structure will not be modified. These insights into the relationship between environment and strategies further our understanding of the evolutionary mechanism of population and society.

Energy expansion planning with a human evolutionary model

This study presents a novel method for planning the expansion of transmission lines and energy storage systems while considering the interconnectedness of electricity and gas networks. We developed a two-level stochastic planning model that addresses both the expansion of transmission and battery systems in the electrical grid and the behavior of the gas network. This research explores the challenges and effects of integrating high levels of renewable energy sources while ensuring security within both networks. Our model uses a stochastic mixed-integer non-linear programming approach. To solve this complex model, we applied the Human Evolutionary Model (HEM). We tested our approach on two case studies: a simple 6-node network and the more complex IEEE RTS 24-bus network for the electricity grid, combined with 5-node and 10-node gas networks, respectively. The results demonstrate the effectiveness of our model, particularly in scenarios where connections in the power and gas networks are disrupted, preventing load shedding even when integrated network connections are cut.

The genetics of phenotypic plasticity. XVIII. Developmental limits restrict adaptive plasticity.

After environmental change, the trait evolution needed to rescue a population depends on the functional form of the plastic change (reaction norm) of that trait. Nearly all previous models of plasticity evolution for continuous traits have assumed that the functional form is linear, i.e., no limits on the range of plasticity. This paper examines the effect of developmental limits, modeled as a sigmoidal reaction norm, on evolutionary rescue after an abrupt environmental change and the subsequent evolution of plasticity, including genetic assimilation. We examined four different scenarios: (1) developmental limits only, (2) developmental limits plus a cost of plasticity, (3) developmental limits with developmental noise, and (4) developmental limits plus environmental variation. The probability of evolutionary rescue increased with an increase in phenotypic variation allowed by plastic development. With a smaller limit to the range of the plastic phenotype, the evolution of adaptive plasticity was limited, meaning the evolution of non-plastic genes was necessary. The addition of developmental constraints to the model did not speed up genetic assimilation, suggesting new theory is needed to understand empirical observations. The modeling framework presented here could be extended to different ecological and evolutionary conditions, alternative reaction norm shapes, the evolution of additional reaction norm parameters such as the range or the location of the inflection point on the environmental axis, or other function-valued traits.

Adaptive Evolutionary Computing Ensemble Learning Model for Sentiment Analysis

Standard machine learning and deep learning architectures have been widely used in the field of sentiment analysis, but their performance is unsatisfactory if the input texts are short (e.g., social media posts). Specifically, the accuracy of standard machine learning methods crucially depends on the richness and completeness of the features used to represent the texts, and in the case of short messages, it is often difficult to obtain high-quality features. Conversely, methods based on deep learning can achieve better expressiveness, but these methods are computationally demanding and often suffer from over-fitting. This paper proposes a new adaptive evolutionary computational integrated learning model (AdaECELM) to overcome the problems encountered by traditional machine learning and deep learning models in sentiment analysis for short texts. AdaECELM consists of three phases: feature selection, sub classifier training, and global integration learning. First, a grid search is used for feature extraction and selection of term frequency-inverse document frequency (TF-IDF). Second, cuckoo search (CS) is introduced to optimize the combined hyperparameters in the sub-classifier support vector machine (SVM). Finally, the training set is divided into different feature subsets for sub-classifier training, and then the trained sub-classifiers are integrated and learned using the AdaBoost integrated soft voting method. Extensive experiments were conducted on six real polar sentiment analysis data sets. The results show that the AdaECELM model outperforms the traditional ML comparison methods according to evaluation metrics such as accuracy, precision, recall, and F1-score in all cases, and we report an improvement in accuracy exceeding 4.5%, the second-best competitor.

Low-Carbon Supply Chain Decision-Making and CSR Strategy Evolution Analysis Considering Heterogeneous Consumer Preferences

Decision-making regarding the low-carbon supply chain, considering corporate social responsibility (CSR) and the heterogeneous preferences of consumers, has become an urgent topic to be explored. This paper explores the decision-making problem of a low-carbon supply chain considering the heterogeneous preferences of consumers under different CSR situations, analyzes the influence of important parameters on each equilibrium solution, compares the size relationship of each equilibrium solution under different CSR situations, and verifies the conclusions obtained through numerical simulation. Then, based on the obtained equilibrium solution, a CSR evolutionary game model of the low-carbon supply chain is constructed, and the evolutionary stability strategies of the two sides on the CSR game are solved. Finally, the evolutionary trajectory of the game system is intuitively presented using a simulation method, and the influence of the main parameters on the evolutionary trends of the two sides is analyzed. The findings are as follows: (1) When both manufacturers and retailers undertake CSR, the retail price and wholesale price are their lowest, while carbon emission reduction, total market demand, manufacturer utility, retailer utility, and supply chain total utility are the highest. (2) When a company undertakes CSR, carbon emission reduction, total market demand, manufacturer utility, retailer utility, and supply chain total utility all increase with the increase in the CSR degree of the company and the ratio of the potential scale of low-carbon consumers to the potential scale of ordinary consumers. (3) The evolutionary stability strategy for both manufacturers and retailers is to undertake CSR. In addition, the initial proportion of manufacturers and retailers that undertake CSR, the low-carbon preference of low-carbon consumers, and the increase in the ratio of the potential scale of low-carbon consumers to the potential scale of ordinary consumers can encourage both members of the supply chain to undertake CSR.

Beyond head and wings: Unveiling influence of diet, body size, and phylogeny on the evolution of the femur in phyllostomid bats.

Phyllostomidae, the most diverse family of Neotropical bats, encompass 230 species with varied dietary habits and food acquisition methods. Their feeding niche diversification has shaped skull and wing morphologies through natural selection, reflecting food processing and flight strategies. Yet, evolution of bat hindlimbs, especially in phyllostomids, remains little understood. Previous studies highlighted the femur's morphology as a key to understanding the evolution of quadrupedalism in yangochiropteran bats, including the adept walking observed in vampire bats (Desmodontinae). Here, we aimed to describe the femoral morphological variation in Phyllostomidae, correlating this with body size and assessing the effects of phylogenetic history, dietary habits, and hindlimb usage. Analyzing 15 femoral traits from 45 species across 9 subfamilies through phylogenetically informed methods, we discovered a significant phylogenetic structure in femoral morphology. Allometric analysis indicated that body mass accounts for about 85% of the variance in phyllostomid femoral size and about 11% in femoral shape. Relatively smaller femurs showed to be typical in Stenodermatinae, Lonchophyllinae, and Glossophaginae, in contrast to the larger femurs of Phyllostominae, Desmodontinae, Micronycterinae, and Lonchorrhininae. Furthermore, extensive femur shape variation was detected, with the most distinct morphologies in vampire bats, followed by frugivorous species. Adaptive evolutionary models related to diet more effectively explained variations in femoral relative size and shape than stochastic models. Contrary to the conventional belief of limited functional demand on bat femurs, our findings suggest that femoral morphology is significantly influenced by functional demands associated with diet and food capture, in addition to being partially structured by body size and shared evolutionary history.

Degradation modeling and remaining life prediction for a multi-component system under triple uncertainties

As equipment systems are gradually moving toward large-scale and complex that comprise multi-components, the correlation between components significantly impacts the system’s degradation. Meanwhile, the system degradation process is subject to aleatory uncertainty due to inherent fluctuations, epistemic uncertainty since insufficient information, and measurement uncertainty affected by measurement noise. In this paper, a system-level RUL prediction method based on deep belief network, self-organizing map neural network and uncertain random process is proposed by combining the advantages of deep learning in extracting degraded features with the advantages of statistical-based models in quantifying uncertainty. Firstly, the monitoring variables that characterize the equipment degradation trend are selected based on multi-sensor monitoring information of a single component. Deep belief network is utilized to extract component-level deep hidden degradation features. Subsequently, a system-level health index construction method combining mutual information and self-organizing map neural network is proposed according to correlation analysis between components. Finally, a health index evolution model is constructed to predict system RUL by integrating the advantages of the multi-phase degradation model based on uncertain random processes quantifying multiple uncertainties. The superiority of the proposed method is verified through the C-MAPSS dataset provided by NASA.

Coupled fates of Earth's mantle and core: Early sluggish-lid tectonics and a long-lived geodynamo.

Conventional Earth evolution models are unable to simultaneously reproduce two fundamental observations: the mantle's secular temperature record and a long-lived geodynamo before inner core nucleation. Today, plate tectonics efficiently cools the mantle, but if assumed to operate throughout Earth's history, past mantle temperature and plate motion become unrealistically high. Through coupled core-mantle modeling that self-consistently predicts multiple mantle convection regimes, we show that over most of the Precambrian, Earth likely operated in a distinct "sluggish-lid" tectonic mode, characterized by partial decoupling between the lithosphere and mantle. This dominant early regime is due to a hotter Earth and the presence of the asthenosphere. This mode regulates the core-mantle boundary heat flow, which powers the geodynamo before inner core nucleation. Both sluggish-lid tectonics and a long-lived dynamo demonstrate the inextricably connected paths of the core-mantle system. Moreover, our simulations simultaneously satisfy diverse geological observations and are consistent with emerging interpretations of such records.

Designing and applying a methodology to assess sperm cell viability and DNA damage in a model amphipod

Sperm quality is defined as the sperm cell ability to successfully fertilize eggs and allow normal embryo development⁠. Few studies explore sperm quality using aquatic invertebrates. Parhyale hawaiensis is a marine amphipod with a circumtropical distribution and considered a model for evolution, development, and ecotoxicological studies. We aimed to develop a methodology to collect sperm cells of P. hawaiensis and evaluate their viability and DNA damage (comet assay). We directly exposed the sperm cells to different mutagenic agents to optimize/develop the protocols. Then, as a proof of concept, we exposed the males to mutagenic compounds (EMS, benzo[a]pyrene (BaP), azo and anthraquinone dyes) at non-lethal concentrations verified by the proposed viability test and analyzed their sperm cells for DNA damage (comet assay). Organisms exposed to EMS presented a clear concentration response in the DNA damage response. We also showed that BaP was able to induce a statistically significant increase in DNA damage of the sperm cells. For the two dyes, although DNA damage increased, statistically differences were not observed. We believe we successfully developed a test to detect genotoxicity of chemicals in sperm cells using an invertebrate model. The protocol for sperm cell viability needs to be further explored with different chemicals to verify its utility as a toxicity endpoint. The developed genotoxicity test has the advantages to employ organisms that are easily cultivated in reduced space, use simple laboratory resources and reduced amount of material and reagents. Positive responses with this model could be used to disclose new germ cell mutagen candidates which could be further confirmed in vertebrates' systems.

CHAOS. VIII. Far-ultraviolet Spectra of M101 and the Impact of Wolf–Rayet Stars* ∗Based on observations made with the NASA/ESA Hubble Space Telescope, obtained from the Data Archive at the Space Telescope Science Institute, which is operated by the Association of Universities for Research in Astronomy, Inc., under NASA contract NAS 5-26555.

We investigate the stellar and nebular properties of nine H ii regions in the spiral galaxy M101 with far-ultraviolet (FUV; ∼900–2000 Å) and optical (∼3200–10,000 Å) spectra. We detect significant C iii] λλ1907,1909 nebular emission in seven regions, but O iii] λ1666 only in the lowest-metallicity region. We produce new analytic functions of the carbon ionization correction factors as a function of metallicity in order to perform a preliminary C/O abundance analysis. The FUV spectra also contain numerous stellar emission and P-Cygni features that we fit with luminosity-weighted combinations of single-burst Starburst99 and BPASS models. We find that the best-fit Starburst99 models closely match the observed very-high-ionization P-Cygni features, requiring very hot, young (≲3 Myr), metal-enriched massive stars. The youngest stellar populations are strongly correlated with broad He ii emission, nitrogen Wolf–Rayet (WR) FUV and optical spectral features, and enhanced N/O gas abundances. Thus, the short-lived WR phase may be driving excess emission in several N P-Cygni wind features (λ955, λ991, λ1720) that bias the stellar continuum fits to higher metallicities relative to the gas-phase metallicities. Accurate characterization of these H ii regions requires additional inclusion of WR stars in the stellar population synthesis models. Our FUV spectra demonstrate that the ∼900–1200 Å FUV can provide a strong test bed for future WR atmosphere and evolution models.

Orbits and Dynamical Masses for Six Binary Systems in the Hyades Cluster

We report long-baseline interferometric observations with the CHARA Array that resolve six previously known double-lined spectroscopic binary systems in the Hyades cluster, with orbital periods ranging from 3 to 358 days: HD 27483, HD 283882, HD 26874, HD 27149, HD 30676, and HD 28545. We combine those observations with new and existing radial-velocity measurements, to infer the dynamical masses for the components as well as the orbital parallaxes. For most stars, the masses are determined to be better than 1%. Our work significantly increases the number of systems with mass determinations in the cluster. We find that, while current models of stellar evolution for the age and metallicity of the Hyades are able to reproduce the overall shape of the empirical mass–luminosity relation, they overestimate the V-band fluxes by about 0.1 mag between 0.5 and 1.4 M ⊙. The disagreement is smaller in H, and near zero in K, and depends somewhat on the model. We also make use of the TESS light curves to estimate rotation periods for our targets, and detect numerous flares in one of them (HD 283882), estimating an average flaring rate of 0.44 events per day.

Ab initio framework for nuclear scattering and reactions induced by light projectiles

A quantitative and predictive microscopic theoretical framework that can describe reactions induced by α particles (4He nuclei) and heavier projectiles is currently lacking. Such a framework would contribute to reducing uncertainty in the modeling of stellar evolution and nucleosynthesis and provide the basis for achieving a comprehensive understanding of the phenomenon of nuclear clustering (the organization of protons and neutrons into distinct substructures within a nucleus). We have developed an efficient and general configuration-interaction framework for the description of low-energy reactions and clustering in light nuclei. The new formalism takes full advantage of powerful second-quantization techniques, enabling the description of α-α scattering and an exploration of clustering in the exotic 12Be nucleus. We find that the 4He(α,α)4He differential cross section computed with non-locally regulated chiral interactions is in good agreement with experimental data. Our results for 12Be indicate the presence of strongly mixed helium-cluster states consistent with a molecular-like picture surviving far above the 6He+6He threshold, and reveal the strong influence of neutron decay in both the 12Be spectrum and in the 6He(6He,α)8He cross section. We expect that this approach will enable the description of helium burning cross sections and provide insight on how three-nucleon forces influence the emergence of clustering in nuclei.

New data on the crown proteid Bishara backa from the Upper Cretaceous (Bostobe Formation) of Kazakhstan: implications for early evolution and palaeobiogeography of proteidae

ABSTRACT We provide a detailed redescription of all available material for the geologically oldest proteid salamander Bishara backa from the Upper Cretaceous (Santonian – lower Campanian) Bostobe Formation in Kazakhstan, consisting an atlantal centrum (neotype of Bishara backa) and a trunk vertebra. Bishara backa is characterised by the following combination of characters: large, dorsoventrally compressed and almost flat anterior cotyles of atlas enclosing an angle about 230°; anterior cotylar surfaces are continuous medially across lateral surfaces of small, dorsally placed intercotylar tubercle; ventrolateral ridges are alar-like; atlantal transverse processes are bicipitate; oblique anterior alar processes and lateral fossae on atlas present; transverse processes on trunk vertebra are bicipitate and divergent; alar processes on trunk vertebra are prominent; anterodorsal ridge on trunk vertebra is present; neural arch of trunk vertebra with high neural spine and grooves on its posterior face; two large subcentral foramina on ventral surface of the centrum. Our phylogenetic analyses finds Bishara backa as a crown proteid and a basal member of Eurasian clade of crown proteids (Bishara+Proteus+Mioproteus). We propose a model of proteid evolution that includes three main episodes of radiations with the last two taking place during the Late Cretaceous and the Palaeocene – Eocene thermal maximum.