Raw Estimates Research Articles

Joint calibration of array shape and sensor gain/phase for highly deformed arrays using wideband signals

A joint sensor position and gain/phase calibration method for highly deformed arrays is proposed. It applies to the case where the sensor gain is omnidirectional and frequency-invariant, and the sensor phase is directional and frequency-invariant. The method is initialized with the raw estimates of the sensor gains, which are simply obtained from the main diagonal elements of the sample covariance matrix. Then, it alternately updates either the sensor gain estimates or the sensor position and phase estimates while fixing the other. When the sensor gain estimates are available, the steering matrices for all the frequency bins are estimated using the constant modulus method. By combining all these steering matrices, the estimates of the sensor positions and the frequency-invariant sensor phases are straightforwardly derived. Then, the sensor gain estimates are refined for the next iteration by using the orthogonality of the subspaces. To resolve the phase ambiguities due to the large position mismatches of highly deformed arrays, the nominal intersensor distance and the array bending angle are used. The Cramer–Rao lower bounds (CRLBs) for the unknown sensor gain/phase and position are also derived. The performance of the proposed method is evaluated using simulation data and compared with the CRLBs.

Visual-Inertial Guidance With a Plenoptic Camera for Autonomous Underwater Vehicles

This letter demonstrates the feasibility of near realtime, plenoptic-inertial navigation on a low-cost central processing unit (CPU). To enable real-time operation, a standard plenoptic camera was modeled as a system of stereo cameras and triangulation was used to estimate its pose from a minimal set of subaperture images. The relationship between distance and disparity for the simplified model was experimentally validated in an aquatic environment, using a first-generation Lytro camera, and a mean error of 2% of the target distance was obtained. This letter culminates with testing the proposed navigation system on an in-house developed, novel, biologically inspired, autonomous underwater vehicle (AUV), CephaloBot. The test consisted of the AUV rotating around a static object while maintaining a fixed separation distance. The mean position error from the test was 2.5% of the target distance. With the simplified plenoptic model, only 750 ms were required to process the raw plenoptic data and estimate position on an Intel i5 CPU. The processing delay was short enough that the delayed position measurements bounded the effects of sensor drift when fused with an inertial measurement unit using a delayed extended Kalman filter. This result demonstrates the feasibility of plenoptic-inertial navigation on a low-cost CPU.

Valor econômico e determinantes socioculturais dos produtos florestais não madeireiros na Reserva Transnacional W da Biosfera, Benim

This paper examined the economic value of non-timber forest products and their contribution to cash income of people living around the W Transbundary Bioreserve of W in Benin. 148 people among two ethnic groups were interviewed. Data were analysed using the indirect method of opportunity cost and raw margin estimation. Results showed that local people use to harvest mostly five products: one non-marketable product (firewood) and four marketable products (almonds of Vitellaria paradoxa, C.F.Gaertn, seeds and pulp of Parkia biglobosa (Jacq.) R. Br. ex G. Don and leaves of Adansonia digitata L.). The mean contribution of marketable products to surveyed dwellers was estimated at XOF 255,484 ($US 510.968) (standard error: XOF 37,109), representing about 11.46% of the annual cash income per household. Age and sociolinguistic group were the main determinants of non-timber forest products harvesting. Given their value to the communities, those plants should be prioritized for domestication and conservation.

Probing the Effect of Cadence on the Estimates of Photospheric Energy and Helicity Injections in Eruptive Active Region NOAA AR 11158

We study how the input-data cadence affects the photospheric energy and helicity injection estimates in eruptive NOAA Active Region 11158. We sample the novel 2.25-minute vector magnetogram and Dopplergram data from the Helioseismic and Magnetic Imager (HMI) instrument onboard the Solar Dynamics Observatory (SDO) spacecraft to create input datasets of variable cadences ranging from 2.25 minutes to 24 hours. We employ state-of-the-art data processing, velocity, and electric-field inversion methods for deriving estimates of the energy and helicity injections from these datasets. We find that the electric-field inversion methods that reproduce the observed magnetic-field evolution through the use of Faraday’s law are more stable against variable cadence: the PDFI (PTD-Doppler-FLCT-Ideal, where PTD refers to Poloidal–Toroidal Decomposition, and FLCT to Fourier Local Correlation Tracking) electric-field inversion method produces consistent injection estimates for cadences from 2.25 minutes up to two hours, implying that the photospheric processes acting on time scales below two hours contribute little to the injections, or that they are below the sensitivity of the input data and the PDFI method. On other hand, the electric-field estimate derived from the output of DAVE4VM (Differential Affine Velocity Estimator for Vector Magnetograms), which does not fulfill Faraday’s law exactly, produces significant variations in the energy and helicity injection estimates in the 2.25 minutes – two hours cadence range. We also present a third, novel DAVE4VM-based electric-field estimate, which corrects the poor inductivity of the raw DAVE4VM estimate. This method is less sensitive to the changes of cadence, but it still faces significant issues for the lowest of considered cadences (≥ two hours). We find several potential problems in both PDFI- and DAVE4VM-based injection estimates and conclude that the quality of both should be surveyed further in controlled environments.

The difference between first and second harmonic amplitudes correlates between glottal airflow and neck-surface accelerometer signals during phonation

Miniature high-bandwidth accelerometers on the anterior neck surface are used in laboratory and ambulatory settings to obtain vocal function measures. This study compared the widely applied L1-L2 measure (historically, H1-H2)-the difference between the log-magnitude of the first and second harmonics-computed from the glottal airflow waveform with L1-L2 derived from the raw neck-surface acceleration signal in 79 vocally healthy female speakers. Results showed a significant correlation (r = 0.72) between L1-L2 values estimated from both airflow and accelerometer signals, suggesting that raw accelerometer-based estimates of L1-L2 may be interpreted as reflecting glottal physiological parameters and voice quality attributes during phonation.

Age distribution, trends, and forecasts of under-5 mortality in 31 sub-Saharan African countries: A modeling study.

BackgroundDespite the sharp decline in global under-5 deaths since 1990, uneven progress has been achieved across and within countries. In sub-Saharan Africa (SSA), the Millennium Development Goals (MDGs) for child mortality were met only by a few countries. Valid concerns exist as to whether the region would meet new Sustainable Development Goals (SDGs) for under-5 mortality. We therefore examine further sources of variation by assessing age patterns, trends, and forecasts of mortality rates.Methods and findingsData came from 106 nationally representative Demographic and Health Surveys (DHSs) with full birth histories from 31 SSA countries from 1990 to 2017 (a total of 524 country-years of data). We assessed the distribution of age at death through the following new demographic analyses. First, we used a direct method and full birth histories to estimate under-5 mortality rates (U5MRs) on a monthly basis. Second, we smoothed raw estimates of death rates by age and time by using a two-dimensional P-Spline approach. Third, a variant of the Lee–Carter (LC) model, designed for populations with limited data, was used to fit and forecast age profiles of mortality. We used mortality estimates from the United Nations Inter-agency Group for Child Mortality Estimation (UN IGME) to adjust, validate, and minimize the risk of bias in survival, truncation, and recall in mortality estimation. Our mortality model revealed substantive declines of death rates at every age in most countries but with notable differences in the age patterns over time. U5MRs declined from 3.3% (annual rate of reduction [ARR] 0.1%) in Lesotho to 76.4% (ARR 5.2%) in Malawi, and the pace of decline was faster on average (ARR 3.2%) than that observed for infant (IMRs) (ARR 2.7%) and neonatal (NMRs) (ARR 2.0%) mortality rates. We predict that 5 countries (Kenya, Rwanda, Senegal, Tanzania, and Uganda) are on track to achieve the under-5 sustainable development target by 2030 (25 deaths per 1,000 live births), but only Rwanda and Tanzania would meet both the neonatal (12 deaths per 1,000 live births) and under-5 targets simultaneously. Our predicted NMRs and U5MRs were in line with those estimated by the UN IGME by 2030 and 2050 (they overlapped in 27/31 countries for NMRs and 22 for U5MRs) and by the Institute for Health Metrics and Evaluation (IHME) by 2030 (26/31 and 23/31, respectively). This study has a number of limitations, including poor data quality issues that reflected bias in the report of births and deaths, preventing reliable estimates and predictions from a few countries.ConclusionsTo our knowledge, this study is the first to combine full birth histories and mortality estimates from external reliable sources to model age patterns of under-5 mortality across time in SSA. We demonstrate that countries with a rapid pace of mortality reduction (ARR ≥ 3.2%) across ages would be more likely to achieve the SDG mortality targets. However, the lower pace of neonatal mortality reduction would prevent most countries from achieving those targets: 2 countries would reach them by 2030, 13 between 2030 and 2050, and 13 after 2050.

IPAC: Integrate Pedestrian Dead Reckoning and Computer Vision for Indoor Localization and Tracking

Indoor localization is of great importance in the era of mobile computing. Smartphone-based pedestrian tracking is essential to a wide range of applications in shopping malls, industries, office buildings, and other public places. Current mainstream solutions rely on radio fingerprints and/or inertial sensors to distinguish and track pedestrians. However, these methods suffer from considerable deployment efforts and large accumulative errors. In recent years, the increasing numbers of security surveillance cameras installed in public areas provide a fresh perspective to overcome these drawbacks. However, in the real dynamic environments, fusing camera-based and inertial sensors-based pedestrian tracking is non-trivial due to the low robustness of visual tracking, incorrespondence of identifications and high complexity of computation. This paper presents the design and implementation of iPAC, an integrated inertial sensor and camera-based indoor localization and tracking system that achieves high accuracy in dynamic indoor environments with zero human effort. iPAC designs a robust visual detection and tracking algorithm to differentiate and track pedestrians in dynamic environments. Furthermore, iPAC employs a motion sequence-based matching algorithm to fuse raw estimates from both systems. By doing so, iPAC outputs enhanced accuracy, while overcoming the respective drawbacks of each sub-system. We implement iPAC on commodity smartphones and validate its performance in complex environments (including a laboratory, a classroom building, and a office building). The result shows that iPAC achieves a remarkable detection success rate of 93% and tracking success rate of 95% even suffering from severe line-of-sight blockages.

Evaluating and designing student loan systems: An overview of empirical approaches

To understand and design student loan systems, realistic earnings and/or income projections for current and future graduates are crucial. In this paper, Current Population Survey (CPS) data from the US is used to demonstrate empirical approaches that can be exploited to simulate lifetime income and earnings profiles for graduates which are needed to understand and design effective and sustainable student loan systems. The crucial element in getting this analysis correct is having reliable simulations of the whole distribution of future graduate earnings and income. Typically, in this literature, the repayment burdens (RBs) of student loans are calculated at different quantiles of the graduate income or earnings distribution. Often, unconditional quantile regression (UQR) is used to calculate age–earnings profiles for different quantiles of the income or earnings distribution. The paper shows that this approach has limitations when evaluating student loans and that simple raw quantile estimation by age with some age smoothing is preferable. This approach can also be used when income is censored and recorded in income bands as occurs with relevant data in some countries. The paper shows a simple way of incorporating dynamics utilizing these age–earnings profiles by quantile even when only very short panel data is available. This involves using copula functions. Having reliable dynamic estimates turns out to be important in assessing not only the taxpayer costs of designing an income-contingent loan (ICL) but also for correctly assessing the extent of loan repayment hardship for individuals.

Using error grid analysis to assess blood glucose estimation accuracy in adolescents with type 1 diabetes

ABSTRACTDespite an increase in adolescents’ use of type 1 diabetes (T1D)-related technology to aid in blood glucose (BG) management and insulin administration, adolescents with T1D often estimate their current BG levels to inform management decisions. The present study used error grid analysis to explore adolescents’ BG estimation accuracy and examined the relationship between estimation accuracy and diabetes technology use. Fifty-five adolescents diagnosed with T1D completed the study. Participants estimated their BG level before checking it with a meter, and described their use of diabetes technology. Error grid analyses indicated that most estimates did not have “clinically serious” implications; however, raw estimations differed from actual BG values by an average of 33%. Participants who reported checking their BG more frequently per day with a meter made more accurate BG estimations as measured by both error grid analysis and percentage deviations. Findings underscore the importance of encouraging adolescents with T1D to engage in BG monitoring often. Increased BG monitoring with a meter may increase adolescents’ ability to accurately estimate their BG values. Thus, advanced BG monitoring technology should be used in conjunction with meter checking. Examining BG estimation accuracy with error grid analysis holds promise for better understanding self-management practices.

Prediction of Brain Connectivity Map in Resting-State fMRI Data Using Shrinkage Estimator.

Introduction:In recent years, brain functional connectivity studies are extended using the advanced statistical methods. Functional connectivity is identified by synchronous activation in a spatially distinct region of the brain in resting-state functional Magnetic Resonance Imaging (MRI) data. For this purpose there are several methods such as seed-based correlation analysis based on temporal correlation between different Regions of Interests (ROIs) or between brain’s voxels of prior seed.Methods:In the current study, test-retest Resting State functional MRI (rs-fMRI) data of 21 healthy subjects were analyzed to predict second replication connectivity map using first replication data. A potential estimator is “raw estimator” that uses the first replication data from each subject to predict the second replication connectivity map of the same subject. The second estimator, “mean estimator” uses the average of all sample subjects' connectivity to estimate the correlation map. Shrinkage estimator is made by shrinking raw estimator towards the average connectivity map of all subjects' first replicate. Prediction performance of the second replication correlation map is evaluated by Mean Squared Error (MSE) criteria.Results:By the employment of seed-based correlation analysis and choosing precentral gyrus as the ROI over 21 subjects in the study, on average MSE for raw, mean and shrinkage estimator were 0.2169, 0.1118, and 0.1103, respectively. Also, percent reduction of MSE for shrinkage and mean estimator in comparison with raw estimator is 49.14 and 48.45, respectively.Conclusion:Shrinkage approach has the positive effect on the prediction of functional connectivity. When data has a large between session variability, prediction of connectivity map can be improved by shrinking towards population mean.

Submicron particle number doses in the human respiratory tract: implications for urban traffic and background environments.

The deposition of ambient submicron particles in the different parts of the human respiratory tract (HRT) was, for the first time, estimated for males and females from different age classes (children-adults-seniors) of urban population in the city of Thessaloniki, northern Greece, during the cold and the warm period of the year. Outdoor daily and hourly particle number doses in the different regions of the HRT, i.e., the extra-thoracic (ET), tracheobronchial (TB), and the acinar (AC) regions, were calculated by employing the Multiple-Path Particle Dosimetry (MPPD) model. Because of the absence of information being available for the hygroscopic properties of particles, three different particle hygroscopicity scenarios were considered: (i) non-hygroscopic (i.e., raw model estimations), (ii) nearly hydrophobic, and (iii) hygroscopic particles. When hygroscopic properties were considered, we found a remarkable reduction (up to ~ 55%) in the estimated total particle number doses in comparison to the non-hygroscopic particle scenario. Furthermore, we found that the size distribution pattern of the particle doses within the different parts of the HRT was strongly affected by particles' hygroscopic properties with the non-hygroscopic particle scenario significantly overestimating the particle doses in the sub-100-nm range, while underestimating the doses of larger particles. On the contrary, the deposition density appeared to be negligibly affected by the particles' hygroscopic properties, implying the existence of a possible threshold in the number of particles deposited per airway surface area. Similarly, the lobar particle number deposition fraction was unaffected by the hygroscopic properties of particles, as well as the ambient particle size distribution and the individuals' physiological parameters. The total particle number deposition doses estimated here are within the range of the corresponding values reported for other urban environments. It is hoped that our findings could contribute to better understanding of submicron particle exposure and add to the development of more sufficient methods to evaluate the related health impacts.

Spatial domain morphological filtering for interpolation of the Fourier domain

We establish here a method to partially recover the missing frequencies in data acquired through sub-sampling in the Fourier domain. Non-linear open and close operations are applied recursively to the raw spatial image estimated from its sparse Fourier samples. The mean of the open and closed images updates the estimated image after applying each structuring element (SE). The updated Fourier spectra are required at each stage to remain consistent with the known sample values. The SE size is incremented until the values estimated for the missing Fourier coefficients converge. The average image obtained from applying multiple sets of randomly selected SEs forms the final result. A denoising scheme is finally used to supplement the interpolated result. The denoising estimates the alias artefacts that arise from the initial sub-sampling by assuming that a narrow region around the borders of the reconstructed image has constant grey level. Convergence occurs in around 30 iterations that usually requires, in total a little less than one minute on a 64GB RAM, i7 machine. Since the computational complexity of the method is low, it can serve as a reliable initialization for more sophisticated iterative reconstruction schemes. Results for several simulated and real sub-sampled image data show an improvement of about 0.1 in Structural SIMilarity index (SSIM, whose maximum possible value is 1) in comparison to the raw inverse FFT image estimate. This method is more likely to underestimate the missing frequency values.

Low clouds link equilibrium climate sensitivity to hydrological sensitivity

Equilibrium climate sensitivity (ECS) and hydrological sensitivity describe the global mean surface temperature and precipitation responses to a doubling of atmospheric CO2. Despite their connection via the Earth’s energy budget, the physical linkage between these two metrics remains controversial. Here, using a global climate model with a perturbed mean hydrological cycle, we show that ECS and hydrological sensitivity per unit warming are anti-correlated owing to the low-cloud response to surface warming. When the amount of low clouds decreases, ECS is enhanced through reductions in the reflection of shortwave radiation. In contrast, hydrological sensitivity is suppressed through weakening of atmospheric longwave cooling, necessitating weakened condensational heating by precipitation. These compensating cloud effects are also robustly found in a multi-model ensemble, and further constrained using satellite observations. Our estimates, combined with an existing constraint to clear-sky shortwave absorption, suggest that hydrological sensitivity could be lower by 30% than raw estimates from global climate models. The connections between global mean temperature and precipitation responses to CO2 doubling (equilibrium climate and hydrological sensitivity) are driven through low-cloud responses to surface warming, according to MIROC5 perturbation experiments.

Efficient Venn predictors using random forests

Successful use of probabilistic classification requires well-calibrated probability estimates, i.e., the predicted class probabilities must correspond to the true probabilities. In addition, a probabilistic classifier must, of course, also be as accurate as possible. In this paper, Venn predictors, and its special case Venn-Abers predictors, are evaluated for probabilistic classification, using random forests as the underlying models. Venn predictors output multiple probabilities for each label, i.e., the predicted label is associated with a probability interval. Since all Venn predictors are valid in the long run, the size of the probability intervals is very important, with tighter intervals being more informative. The standard solution when calibrating a classifier is to employ an additional step, transforming the outputs from a classifier into probability estimates, using a labeled data set not employed for training of the models. For random forests, and other bagged ensembles, it is, however, possible to use the out-of-bag instances for calibration, making all training data available for both model learning and calibration. This procedure has previously been successfully applied to conformal prediction, but was here evaluated for the first time for Venn predictors. The empirical investigation, using 22 publicly available data sets, showed that all four versions of the Venn predictors were better calibrated than both the raw estimates from the random forest, and the standard techniques Platt scaling and isotonic regression. Regarding both informativeness and accuracy, the standard Venn predictor calibrated on out-of-bag instances was the best setup evaluated. Most importantly, calibrating on out-of-bag instances, instead of using a separate calibration set, resulted in tighter intervals and more accurate models on every data set, for both the Venn predictors and the Venn-Abers predictors.

Climate Model Biases and Modification of the Climate Change Signal by Intensity-Dependent Bias Correction

AbstractClimate change impact research and risk assessment require accurate estimates of the climate change signal (CCS). Raw climate model data include systematic biases that affect the CCS of high-impact variables such as daily precipitation and wind speed. This paper presents a novel, general, and extensible analytical theory of the effect of these biases on the CCS of the distribution mean and quantiles. The theory reveals that misrepresented model intensities and probability of nonzero (positive) events have the potential to distort raw model CCS estimates. We test the analytical description in a challenging application of bias correction and downscaling to daily precipitation over alpine terrain, where the output of 15 regional climate models (RCMs) is reduced to local weather stations. The theoretically predicted CCS modification well approximates the modification by the bias correction method, even for the station–RCM combinations with the largest absolute modifications. These results demonstrate that the CCS modification by bias correction is a direct consequence of removing model biases. Therefore, provided that application of intensity-dependent bias correction is scientifically appropriate, the CCS modification should be a desirable effect. The analytical theory can be used as a tool to 1) detect model biases with high potential to distort the CCS and 2) efficiently generate novel, improved CCS datasets. The latter are highly relevant for the development of appropriate climate change adaptation, mitigation, and resilience strategies. Future research needs to focus on developing process-based bias corrections that depend on simulated intensities rather than preserving the raw model CCS.

Permeation and diffusion tests for estimation of durability of fibreconcretes with dense aggregate and recycled concrete

Mechanical and deformational properties of concrete are markedly improved by the addition of long structural fibres. This addition, however, not always improves the durability of concrete. One important factor for an estimation of concrete durability is to assess the permeation and diffusion characteristics of the surface layer. The research was performed by means of the TORRENT permeability method and the method for determination of the non-steady-state migration coefficient of chlorides in concrete – NORDTEST. The addition of long polypropylene fibres contributes to increasing the permeability of the surface layer for air and chloride solution, but no too significantly. The higher permeability was determined in concrete with concrete recyclate and polypropylene fibres. The measured results by both used methods are partially comparable and can be used for raw estimation of durability of concretes. The higher values of permeability indicate the expected lower durability of tested concretes.

Accounting for differences in species frequency distributions when calculating beta diversity in the fossil record

Abstract Beta diversity is a measure of the taxonomic differentiation between habitats/localities within an assemblage, and is normally calculated as a set of pairwise taxonomic “distances” between the localities. Due to incomplete sampling, beta diversity estimates for fossil assemblages will always be higher than the true value. However, the difference between the observed and true distances will vary greatly depending on differences in the shape of the relative abundance distribution. Using simulations, it is shown that incomplete sampling of a homogenous fauna with more even relative abundances of taxa produces higher beta diversity than one with very few extremely common taxa. A new procedure is proposed for calculating beta diversity in the fossil record, whereby the observed distances are compared to the distance obtained by randomly resampling a homogenous fauna with the same abundance distribution. This procedure is named Relative Abundance Corrected beta diversity. Simulations indicate that this method outperforms raw beta diversity estimates, providing beta diversity estimates closer to the true values even when the assumptions of the method are violated. This method is applied to an empirical dataset: Permian–Triassic tetrapods from the Karoo Supergroup of South Africa. Increased provincialism is found across the Permian–Triassic boundary in contrast to uncorrected beta diversity patterns.

The Chilean GNSS Network: Current Status and Progress toward Early Warning Applications

Chile is one of the world’s most seismically active regions and is therefore extensively studied by the earthquake sciences. The great length of the country hosts a variety of measurement systems allowing for the characterization of earthquake processes over a wide range of timescales and in different phases of the seismic cycle. Starting in the early 1990s, several research groups began to deploy continuously operating geodetic networks in Chile, forming the core of the modern network of Global Navigation Satellite Systems (GNSS) receivers used to monitor geodynamics from the southern tip of the Americas to the central Andes. Today, the Centro Sismologico Nacional (CSN) of the Universidad de Chile maintains and improves this network, increasing its coverage and spatial density while greatly reducing solution latency. We present the status of the GNSS network, its data streams, and the real‐time analysis system used to support real‐time modeling of earthquakes. The system takes 2 s, on average, to collect raw data, estimate positions, and stream results. Such low latency is essential to enabling early warning of earthquakes and tsunamis in Chile.

How has our knowledge of dinosaur diversity through geologic time changed through research history?

Assessments of dinosaur macroevolution at any given time can be biased by the historical publication record. Recent studies have analysed patterns in dinosaur diversity that are based on secular variations in the numbers of published taxa. Many of these have employed a range of approaches that account for changes in the shape of the taxonomic abundance curve, which are largely dependent on databases compiled from the primary published literature. However, how these ‘corrected’ diversity patterns are influenced by the history of publication remains largely unknown. Here, we investigate the influence of publication history between 1991 and 2015 on our understanding of dinosaur evolution using raw diversity estimates and shareholder quorum subsampling for the three major subgroups: Ornithischia, Sauropodomorpha, and Theropoda. We find that, while sampling generally improves through time, there remain periods and regions in dinosaur evolutionary history where diversity estimates are highly volatile (e.g. the latest Jurassic of Europe, the mid-Cretaceous of North America, and the Late Cretaceous of South America). Our results show that historical changes in database compilation can often substantially influence our interpretations of dinosaur diversity. ‘Global’ estimates of diversity based on the fossil record are often also based on incomplete, and distinct regional signals, each subject to their own sampling history. Changes in the record of taxon abundance distribution, either through discovery of new taxa or addition of existing taxa to improve sampling evenness, are important in improving the reliability of our interpretations of dinosaur diversity. Furthermore, the number of occurrences and newly identified dinosaurs is still rapidly increasing through time, suggesting that it is entirely possible for much of what we know about dinosaurs at the present to change within the next 20 years.

Deep time diversity of metatherian mammals: implications for evolutionary history and fossil-record quality

AbstractDespite a global fossil record, Metatheria are now largely restricted to Australasia and South America. Most metatherian paleodiversity studies to date are limited to particular subclades, time intervals, and/or regions, and few consider uneven sampling. Here, we present a comprehensive new data set on metatherian fossil occurrences (Barremian to end Pliocene). These data are analyzed using standard rarefaction and shareholder quorum subsampling (including a new protocol for handling Lagerstätte-like localities).Global metatherian diversity was lowest during the Cretaceous, and increased sharply in the Paleocene, when the South American record begins. Global and South American diversity rose in the early Eocene then fell in the late Eocene, in contrast to the North American pattern. In the Oligocene, diversity declined in the Americas, but this was more than offset by Oligocene radiations in Australia. Diversity continued to decrease in Laurasia, with final representatives in North America (excluding the later entry ofDidelphis virginiana) and Europe in the early Miocene, and Asia in the middle Miocene. Global metatherian diversity appears to have peaked in the early Miocene, especially in Australia. Following a trough in the late Miocene, the Pliocene saw another increase in global diversity. By this time, metatherian biogeographic distribution had essentially contracted to that of today.Comparison of the raw and sampling-corrected diversity estimates, coupled with evaluation of “coverage” and number of prolific sites, demonstrates that the metatherian fossil record is spatially and temporally extremely patchy. Therefore, assessments of macroevolutionary patterns based on the raw fossil record (as in most previous studies) are inadvisable.