Evolution Of Bias Research Articles

Measuring the scale of cosmic homogeneity with SDSS-IV DR14 quasars

The quasar sample of the fourteenth data release of the Sloan Digital Sky Survey (SDSS-IV DR14) is used to determine the cosmic homogeneity scale in the redshift range $0.80<z<2.24$. We divide the sample into 4 redshift bins, each one with $N_{\rm q} \geq 19,000$ quasars, spanning the whole redshift coverage of the survey and use two correlation function estimators to measure the scaled counts-in-spheres and its logarithmic derivative, i.e., the fractal correlation dimension, $D_2$. Using the $\Lambda$CDM cosmology as the fiducial model, we first estimate the redshift evolution of quasar bias and then the homogeneity scale of the underlying matter distribution $r_{\rm{hom}}^{\rm{m}}$. We find that $r_{\rm{hom}}^{\rm{m}}$ exhibits a decreasing trend with redshift and that the values obtained are in good agreement with the $\Lambda$CDM prediction over the entire redshift interval studied. We, therefore, conclude that the large-scale homogeneity assumption is consistent with the largest spatial distribution of quasars currently available

Joint analysis of the thermal Sunyaev–Zeldovich effect and 2MASS galaxies: probing gas physics in the local Universe and beyond

We present a first joint analysis of the power spectra of the thermal Sunyaev-Zeldovich (tSZ) effect measured by the Planck and the number density fluctuations of galaxies in the 2MASS redshift survey (2MRS) catalog, including their cross-correlation. Combining these measurements with the cosmic microwave background (CMB) data and CMB lensing of Planck assuming a flat $\Lambda$CDM model, we constrain the mass bias parameter as $B = 1.54 \pm 0.098\;(1\sigma)$ [$(1-b) = 0.649 \pm 0.041$, where $(1-b) \equiv B^{-1}$], i.e., the Planck cluster mass should be $35\%$ lower than the true mass. The mass bias determined by the 2MRS-tSZ cross-power spectrum alone is consistent with that determined by the tSZ auto-power spectrum alone, suggesting that this large mass bias is not due to obvious systematics in the tSZ data. We find that the 2MRS-tSZ cross-power spectrum is more sensitive to less massive halos than the tSZ auto-power spectrum and it significantly improves a constraint on the mass dependence of the mass bias. The redshift dependence is not strongly constrained since the multipole range in which high redshift clusters mainly contribute to the tSZ auto is dominated by the contaminating sources. We conclude that no strong mass or redshift evolution of the mass bias is needed to explain the data.

Suppression of reentrant spin glass and induced zero-field-cooled exchange bias by lattice contraction in NiMnSbAl alloys

In this work, the evolution of magnetic ground state and exchange bias (EB) with Al substitution in Ni50Mn37Sb13-xAlx Heusler alloys was investigated in detail. Al doping reduces the cell volume, enhancing antiferromagnetic interactions between Mn atoms and changing the ground state from “reentrant” spin glass (RSG) to spin glass. The conventional EB increases greatly from 314 Oe for x = 0 to 6682 Oe for x = 10. More interestingly, the zero-field-cooled EB begans to emerge at x = 6 where RSG is surppressed and reaches a maximum value at x = 8. These results imply that tuning cell volume provides an effective way to adjust the strength of exchange interaction and further modulate EB in Ni-Mn-based Heusler alloys.

Star formation history from the cosmic infrared background anisotropies

We present a linear clustering model of cosmic infrared background (CIB) anisotropies at large scales that is used to measure the cosmic star formation rate density up to redshift 6, the effective bias of the CIB, and the mass of dark matter halos hosting dusty star-forming galaxies. This is achieved using the Planck CIB auto- and cross-power spectra (between different frequencies) and CIB × CMB (cosmic microwave background) lensing cross-spectra measurements, as well as external constraints (e.g. on the CIB mean brightness). We recovered an obscured star formation history which agrees well with the values derived from infrared deep surveys and we confirm that the obscured star formation dominates the unobscured formation up to at least z = 4. The obscured and unobscured star formation rate densities are compatible at 1σ at z = 5. We also determined the evolution of the effective bias of the galaxies emitting the CIB and found a rapid increase from ~0.8 at z = 0 to ~8 at z = 4. At 2 &lt; z &lt; 4, this effective bias is similar to that of galaxies at the knee of the mass functions and submillimetre galaxies. This effective bias is the weighted average of the true bias with the corresponding emissivity of the galaxies. The halo mass corresponding to this bias is thus not exactly the mass contributing the most to the star formation density. Correcting for this, we obtained a value of log(Mh/M⊙) = 12.77−0.125+0.128 for the mass of the typical dark matter halo contributing to the CIB at z = 2. Finally, using a Fisher matrix analysis we also computed how the uncertainties on the cosmological parameters affect the recovered CIB model parameters, and find that the effect is negligible.

Dark Energy Survey Year-1 results: galaxy mock catalogues for BAO

Mock catalogues are a crucial tool in the analysis of galaxy surveys data, both for the accurate computation of covariance matrices, and for the optimisation of analysis methodology and validation of data sets. In this paper, we present a set of 1800 galaxy mock catalogues designed to match the Dark Energy Survey Year-1 BAO sample (Crocce et al. 2017) in abundance, observational volume, redshift distribution and uncertainty, and redshift dependent clustering. The simulated samples were built upon HALOGEN (Avila et al. 2015) halo catalogues, based on a $2LPT$ density field with an exponential bias. For each of them, a lightcone is constructed by the superposition of snapshots in the redshift range $0.45<z<1.4$. Uncertainties introduced by so-called photometric redshifts estimators were modelled with a \textit{double-skewed-Gaussian} curve fitted to the data. We also introduce a hybrid HOD-HAM model with two free parameters that are adjusted to achieve a galaxy bias evolution $b(z_{\rm ph})$ that matches the data at the 1-$\sigma$ level in the range $0.6<z_{\rm ph}<1.0$. We further analyse the galaxy mock catalogues and compare their clustering to the data using the angular correlation function $ w(\theta)$, the comoving transverse separation clustering $\xi_{\mu<0.8}(s_{\perp})$ and the angular power spectrum $C_\ell$.

An Empirical Planetesimal Belt Radius–Stellar Luminosity Relation

Abstract Resolved observations of millimeter-sized dust, tracing larger planetesimals, have pinpointed the location of 26 Edgeworth–Kuiper Belt analogs. We report that a belt’s distance R to its host star correlates with the star’s luminosity L ⋆, following with a low intrinsic scatter of ∼17%. Remarkably, our Edgeworth–Kuiper Belt in the solar system and the two CO snow lines imaged in protoplanetary disks lie close to this R–L ⋆ relation, suggestive of an intrinsic relationship between protoplanetary disk structures and belt locations. To test the effect of bias on the relation, we use a Monte Carlo approach and simulate uncorrelated model populations of belts. We find that observational bias could produce the slope and intercept of the R–L ⋆ relation but is unable to reproduce its low scatter. We then repeat the simulation taking into account the collisional evolution of belts, following the steady-state model that fits the belt population as observed through infrared excesses. This significantly improves the fit by lowering the scatter of the simulated R–L ⋆ relation; however, this scatter remains only marginally consistent with the one observed. The inability of observational bias and collisional evolution alone to reproduce the tight relationship between belt radius and stellar luminosity could indicate that planetesimal belts form at preferential locations within protoplanetary disks. The similar trend for CO snow line locations would then indicate that the formation of planetesimals or planets in the outer regions of planetary systems is linked to the volatility of their building blocks, as postulated by planet formation models.

The triply-ionized carbon forest from eBOSS: cosmological correlations with quasars in SDSS-IV DR14

We present measurements of the cross-correlation of the triply-ionized carbon (CIV) forest with quasars using Sloan Digital Sky Survey Data Release 14. The study exploits a large sample of new quasars from the first two years of observations by the Extended Baryon Oscillation Spectroscopic Survey (eBOSS). The CIV forest is a weaker tracer of large-scale structure than the Lyα forest, but benefits from being accessible at redshifts z<2 where the quasar number density from eBOSS is high. Our data sample consists of 287,651 CIV forest quasars in the redshift range 1.4<z<3.5 and 387,315 tracer quasars with 1.2<z<3.5. We measure large-scale correlations from CIV absorption occuring in three distinct quasar rest-frame wavelength bands of the spectra referred to as the CIV forest, the SiIV forest and the Lyα forest. From the combined fit to the quasar-CIV cross-correlations for the CIV forest and the SiIV forest, the CIV redshift-space distortion parameter is βCIV=0.27 −0.14 −0.26 +0.16 +0.34 and its combination with the CIV linear transmission bias parameter is bCIV(1+βCIV)=−0.0183 −0.0014 −0.0029 +0.0013 +0.0025 (1σ and 2σ statistical errors) at the mean redshift z=2.00. Splitting the sample at z=2.2 to constrain the bias evolution with redshift yields the power-law exponent γ=0.60±0.63, indicating a significantly weaker redshift-evolution than for the Lyα forest linear transmission bias. Additionally, we demonstrate that CIV absorption has the potential to be used as a probe of baryon acoustic oscillations (BAO). While the current data set is insufficient for a detection of the BAO peak feature, the final quasar samples for redshifts 1.4<z<2.2 from eBOSS and the Dark Energy Spectroscopic Instrument (DESI) are expected to provide measurements of the isotropic BAO scale to ∼7% and ∼3% precision, respectively, at z≃1.6.

Respective roles of remote and local wind stress forcings in the development of warm SST errors in the South-Eastern Tropical Atlantic in a coupled high-resolution model

Processes involved in the development of the warm sea surface temperature (SST) bias in the Tropical South-Eastern Atlantic (SETA) in a high resolution (HR) version of the CNRM-CM model are evaluated based on full-field initialized seasonal hindcasts starting at 1 February of each year for 2000–2009. Whereas the initial SST growth is likely associated with local atmospheric forcing, its further development is due to remote oceanic processes. A mixed layer heat budget analysis in SETA indicates a spurious warm horizontal advection observed as far as south of 25°S that appears at the beginning of March. It is associated with an erroneous oceanic mean state at the equator resulting from the mean equatorial westerly wind bias. A sensitivity experiment with corrected wind stress over the equatorial region suggests that the remote forcing explains about 57% of the SETA SST bias in March–May. Comparison with a lower resolution (LR) version of the model reveals that in general similar processes are responsible for the SST bias in both models. A strong reduction of the bias in the HR model is observed only over the near-coastal Southern Benguela region due to a better representation of atmospheric and oceanic processes controlling the coastal upwelling. Overall, the results of the inter-comparison of the SETA SST bias evolution in different sensitivity experiments performed in this study can be interpreted in terms of the relative contributions of (erroneous) warm horizontal advection, associated with equatorial forcing, and cold horizontal advection, associated with local offshore Ekman transport.

Cross-correlation redshift calibration without spectroscopic calibration samples in DES Science Verification Data

Galaxy cross-correlations with high-fidelity redshift samples hold the potential to precisely calibrate systematic photometric redshift uncertainties arising from the unavailability of complete and representative training and validation samples of galaxies. However, application of this technique in the Dark Energy Survey (DES) is hampered by the relatively low number density, small area, and modest redshift overlap between photometric and spectroscopic samples. We propose instead using photometric catalogs with reliable photometric redshifts for photo-z calibration via cross-correlations. We verify the viability of our proposal using redMaPPer clusters from the Sloan Digital Sky Survey (SDSS) to successfully recover the redshift distribution of SDSS spectroscopic galaxies. We demonstrate how to combine photo-z with cross-correlation data to calibrate photometric redshift biases while marginalizing over possible clustering bias evolution in either the calibration or unknown photometric samples. We apply our method to DES Science Verification (DES SV) data in order to constrain the photometric redshift distribution of a galaxy sample selected for weak lensing studies, constraining the mean of the tomographic redshift distributions to a statistical uncertainty of $\Delta z \sim \pm 0.01$. We forecast that our proposal can in principle control photometric redshift uncertainties in DES weak lensing experiments at a level near the intrinsic statistical noise of the experiment over the range of redshifts where redMaPPer clusters are available. Our results provide strong motivation to launch a program to fully characterize the systematic errors from bias evolution and photo-z shapes in our calibration procedure.

Comparison of the linear bias models in the light of the Dark Energy Survey

The evolution of the linear and scale independent bias, based on the most popular dark matter bias models within the $\Lambda$CDM cosmology, is confronted to that of the Dark Energy Survey (DES) Luminous Red Galaxies (LRGs). Applying a $\chi^2$ minimization procedure between models and data we find that all the considered linear bias models reproduce well the LRG bias data. The differences among the bias models are absorbed in the predicted mass of the dark-matter halo in which LRGs live and which ranges between $\sim 6 \times 10^{12} h^{-1} M_{\odot}$ and $1.4 \times 10^{13} h^{-1} M_{\odot}$, for the different bias models. Similar results, reaching however a maximum value of $\sim 2\times 10^{13} h^{-1} M_{\odot}$, are found by confronting the SDSS (2SLAQ) Large Red Galaxies clustering with theoretical clustering models, which also include the evolution of bias. This later analysis also provides a value of $\Omega_{m}=0.30\pm 0.01$, which is in excellent agreement with recent joint analyses of different cosmological probes and the reanalysis of the Planck data.

Evolution of bias and sample size in postoperative pain management trials after hip and knee arthroplasty.

Bias (systematic error) and small trial sample size (random error) may induce imprecise and exaggerated treatment effects in randomised controlled trials (RCTs). To avoid this, SPIRIT- and CONSORT-guidelines, and Cochrane Collaboration bias recommendations were developed. We investigated risk of bias and trial sample size development over time in postoperative pain trials. This study was based on data from two systematic reviews regarding pain management after total hip arthroplasty (THA) or total knee arthroplasty (TKA). RCTs of analgesic interventions with a comparator control group were included. Primary outcomes were risk of bias and trial sample size developments over time. We calculated cumulated bias scores ranging from 0 to 14 based on Cochrane's seven bias domains (0 = low; 1 = unclear, 2 = high). Developments were evaluated with run and control charts. Further, we compared data from trials published between 1990-1999 and 2010-2016. We included 171 trials published between 1989 and 2016. Overall, the summarised risk of bias decreased, mainly due to better randomization and allocation concealment. Visual inspection suggested an on-going improvement that started around 2007. Trial sample size did not change significantly. For trials published between 1990-1999 and 2010-2016 adequate reporting increased from 36% to 75% for random sequence generation, from 9% to 38% for allocation concealment and from 27% to 52% for blinding of participants/personnel. Risk of bias for RCTs regarding postoperative pain management after THA and TKA has decreased from 2007 to 2016, mainly due to better randomization and allocation concealment. Deficiencies remain. Thus, reporting according to validated guidelines is essential. Sample sizes did not change significantly.

Evolution of Synonymous Codon Usage Bias in West African and Central African Strains of Monkeypox Virus.

The evolution of bias in synonymous codon usage in chosen monkeypox viral genomes and the factors influencing its diversification have not been reported so far. In this study, various trends associated with synonymous codon usage in chosen monkeypox viral genomes were investigated, and the results are reported. Identification of factors that influence codon usage in chosen monkeypox viral genomes was done using various codon usage indices, such as the relative synonymous codon usage, the effective number of codons, and the codon adaptation index. The Spearman rank correlation analysis and a correspondence analysis were used for correlating various factors with codon usage. The results revealed that mutational pressure due to compositional constraints, gene expression level, and selection at the codon level for utilization of putative optimal codons are major factors influencing synonymous codon usage bias in monkeypox viral genomes. A cluster analysis of relative synonymous codon usage values revealed a grouping of more virulent strains as one major cluster (Central African strains) and a grouping of less virulent strains (West African strains) as another major cluster, indicating a relationship between virulence and synonymous codon usage bias. This study concluded that a balance between the mutational pressure acting at the base composition level and the selection pressure acting at the amino acid level frames synonymous codon usage bias in the chosen monkeypox viruses. The natural selection from the host does not seem to have influenced the synonymous codon usage bias in the analyzed monkeypox viral genomes.

Discrepancies in the Climatology and Trends of Cloud Cover in Global and Regional Climate Models for the Mediterranean Region

AbstractThe present study aims at comparing total cloud cover (TCC) as simulated by regional climate models (RCM) from CORDEX project with the same variable as simulated by the driving global climate models (GCM), which are part of the fifth phase of the Climate Model Intercomparison Project ensemble. The comparison is performed for the Mediterranean region, and for the 1971–2005 period, when results from the “historical” scenario can also be compared with two data sets of ground‐based cloud observations. We work with 14 modeling results (resolution, 0.11° × 0.11°), which are a combination of five GCMs and five RCMs. In general, RCMs improve only very slightly the climatic estimation of TCC when compared with observations. Indeed, not all RCMs behave the same, and some indicators (monthly evolution of the relative bias) show an enhancement, while other indices (overall mean bias and annual range difference) improve only very slightly with respect to GCMs. Changes in the estimate of TCC in summer might be the most relevant value added by RCMs, as these should describe in a more proper way several mesoscale processes, which play a more relevant role in summer. Noticeably, RCMs are unable to capture the observed decadal trend in TCC. Thus, TCC simulated by RCMs is almost stable, in contradiction with observations and GCMs, which both show statistically significant decreasing trends in the Mediterranean area. This result is somewhat unsatisfactory, as if RCMs cannot reproduce past trends in TCC, their skill in projecting TCC into the future may be questioned.

Temporal evolution of the central fixation bias in scene viewing.

When watching the image of a natural scene on a computer screen, observers initially move their eyes toward the center of the image-a reliable experimental finding termed central fixation bias. This systematic tendency in eye guidance likely masks attentional selection driven by image properties and top-down cognitive processes. Here, we show that the central fixation bias can be reduced by delaying the initial saccade relative to image onset. In four scene-viewing experiments we manipulated observers' initial gaze position and delayed their first saccade by a specific time interval relative to the onset of an image. We analyzed the distance to image center over time and show that the central fixation bias of initial fixations was significantly reduced after delayed saccade onsets. We additionally show that selection of the initial saccade target strongly depended on the first saccade latency. A previously published model of saccade generation was extended with a central activation map on the initial fixation whose influence declined with increasing saccade latency. This extension was sufficient to replicate the central fixation bias from our experiments. Our results suggest that the central fixation bias is generated by default activation as a response to the sudden image onset and that this default activation pattern decreases over time. Thus, it may often be preferable to use a modified version of the scene viewing paradigm that decouples image onset from the start signal for scene exploration to explicitly reduce the central fixation bias.

Assessment of Digital Image Correlation Measurement Accuracy in the Ultimate Error Regime: Improved Models of Systematic and Random Errors

The literature contains many studies on assessment of DIC uncertainties, particularly in the ultimate error regime, when the shape function used to describe the material transformation perfectly matches the actual transformation. For pure sub-pixel translations, bias and random errors obtained for experimental or synthetic images show more complex evolution versus the fractional part of displacement than those predicted by the existing theoretical models. Indeed, small deviations arise, mainly around integer values of imposed displacements for noisy images, and they are interpreted as the unrepresentativeness of the underlying hypotheses of the theoretical models. In a first step, differences between imposed and measured displacements are analysed: random error is independent of fractional displacement, and systematic error does not decrease for values close to integer displacements whatever the noise level. Therefore, new prediction models are proposed based on the analysis of identified phenomena from synthetic speckle-pattern 8-bit images. The statistical approach used in this paper generalizes the methods proposed in the literature and mimics the experimental methodology usually used for displacement measurements performed in different subsets in the same image. Two closed-form expressions for the systematic and random errors and a linear interpolation scheme are developed. These models, depending only on image properties and the imposed displacement, are built with a very limited number of parameters. It is then possible to predict the evolution of bias and random errors from one to four images.

Comparative study of the hydroxyapatite coatings prepared with/without substrate bias

Abstract The aim of this paper is to prepare the hydroxyapatite by Ion Beam Assisted Deposition and to investigate in terms of its elemental and phase composition, roughness and in vitro corrosion resistance. The coatings were prepared with and without applying bias on substrate, in order to find the effect of bias on the chemical, structural, morphological and anti-corrosive properties. The biased coatings exhibited Ca/P ratio closer to the value of the stoichiometric HAP (1.67). The phase composition is not affected by the bias evolution. The adhesion of both coatings is still satisfactory for biomedical applications, irrespective of the bias. Hydroxyapatite deposited without bias presented the best corrosion resistance in SBF at 37 °C, probably due to its smooth surface and low porosity. Moreover, this coating proved to have the highest protection ability at the SBF corrosive attack.

Association between mean and interannual equatorial Indian Ocean subsurface temperature bias in a coupled model

In the present study the association between mean and interannual subsurface temperature bias over the equatorial Indian Ocean (EIO) is investigated during boreal summer (June through September; JJAS) in the National Centers for Environmental Prediction (NCEP) Climate Forecast System (CFSv2) hindcast. Anomalously high subsurface warm bias (greater than 3 °C) over the eastern EIO (EEIO) region is noted in CFSv2 during summer, which is higher compared to other parts of the tropical Indian Ocean. Prominent eastward current bias in the upper 100 m over the EIO region induced by anomalous westerly winds is primarily responsible for subsurface temperature bias. The eastward currents transport warm water to the EEIO and is pushed down to subsurface due to downwelling. Thus biases in both horizontal and vertical currents over the EIO region support subsurface warm bias. The evolution of systematic subsurface warm bias in the model shows strong interannual variability. These maximum subsurface warming episodes over the EEIO are mainly associated with La Nina like forcing. Strong convergence of low level winds over the EEIO and Maritime continent enhanced the westerly wind bias over the EIO during maximum warming years. This low level convergence of wind is induced by the bias in the gradient in the mean sea level pressure with positive bias over western EIO and negative bias over EEIO and parts of western Pacific. Consequently, changes in the atmospheric circulation associated with La Nina like conditions affected the ocean dynamics by modulating the current bias thereby enhancing the subsurface warm bias over the EEIO. It is identified that EEIO subsurface warming is stronger when La Nina co-occurred with negative Indian Ocean Dipole events as compared to La Nina only years in the model. Ocean general circulation model (OGCM) experiments forced with CFSv2 winds clearly support our hypothesis that ocean dynamics influenced by westerly winds bias is primarily responsible for the strong subsurface warm bias over the EEIO. This study advocates the importance of understanding the ability of the models in representing the large scale air–sea interactions over the tropics and their impact on ocean biases for better monsoon forecast.

Individual investors and local bias in the UK, 1870–1935

AbstractThis article examines the long‐run evolution of local bias by UK investors between the 1870s and the 1930s. It uses a large sample of nearly 30,000 shareholders based on 197 sets of share records, a large and representative database of the investor population across sectors and time. It investigates the structure and the evolution of local investment preference between shareholders and the companies in which they invested, as measured by the distance between where they lived and corporate headquarters. The study offers evidence of strong initial local investment preference, which declined over time for non‐Londoners, but remained strong for Londoners until the 1930s. Local investment preference of security holders was related to the size of the board of directors and, for wealthy investors, was related to the age of the firm. For large firms, local networks between investors and directors appear stronger when director shareholdings and voting rights were important. This study supports the analytical hypothesis of local informal trust networks between investors and directors as a means to overcome informational asymmetries and weak legal protection, and provides evidence that local preference was a means to curb insider opportunism and private benefits of control.

The clustering of galaxies in the completed SDSS-III Baryon Oscillation Spectroscopic Survey: angular clustering tomography and its cosmological implications

We investigate the cosmological implications of studying galaxy clustering using a tomographic approach applied to the final BOSS DR12 galaxy sample, including both auto- and cross-correlation functions between redshift shells. We model the signal of the full shape of the angular correlation function, $\omega(\theta)$, in redshift bins using state-of-the-art modelling of non-linearities, bias and redshift-space distortions. We present results on the redshift evolution of the linear bias of BOSS galaxies, which cannot be obtained with traditional methods for galaxy-clustering analysis. We also obtain constraints on cosmological parameters, combining this tomographic analysis with measurements of the cosmic microwave background (CMB) and type Ia supernova (SNIa). We explore a number of cosmological models, including the standard $\Lambda$CDM model and its most interesting extensions, such as deviations from $w_\rm{DE} = -1$, non-minimal neutrino masses, spatial curvature and deviations from general relativity using the growth-index $\gamma$ parametrisation. These results are, in general, comparable to the most precise present-day constraints on cosmological parameters, and show very good agreement with the standard model. In particular, combining CMB, $\omega(\theta)$ and SNIa, we find a value of $w_\rm{DE}$ consistent with $-1$ to a precision better than 5\% when it is assumed to be constant in time, and better than 6\% when we also allow for a spatially-curved Universe.

The Evolution of LINE-1 in Vertebrates.

The abundance and diversity of the LINE-1 (L1) retrotransposon differ greatly among vertebrates. Mammalian genomes contain hundreds of thousands L1s that have accumulated since the origin of mammals. A single group of very similar elements is active at a time in mammals, thus a single lineage of active families has evolved in this group. In contrast, non-mammalian genomes (fish, amphibians, reptiles) harbor a large diversity of concurrently transposing families, which are all represented by very small number of recently inserted copies. Why the pattern of diversity and abundance of L1 is so different among vertebrates remains unknown. To address this issue, we performed a detailed analysis of the evolution of active L1 in 14 mammals and in 3 non-mammalian vertebrate model species. We examined the evolution of base composition and codon bias, the general structure, and the evolution of the different domains of L1 (5′UTR, ORF1, ORF2, 3′UTR). L1s differ substantially in length, base composition, and structure among vertebrates. The most variation is found in the 5′UTR, which is longer in amniotes, and in the ORF1, which tend to evolve faster in mammals. The highly divergent L1 families of lizard, frog, and fish share species-specific features suggesting that they are subjected to the same functional constraints imposed by their host. The relative conservation of the 5′UTR and ORF1 in non-mammalian vertebrates suggests that the repression of transposition by the host does not act in a sequence-specific manner and did not result in an arms race, as is observed in mammals.