Impacts of imperfect reference data on the apparent accuracy of species presence–absence models and their predictions

Aim To explore the impacts of imperfect reference data on the accuracy of species distribution model predictions. The main focus is on impacts of the quality of reference data (labelling accuracy) and, to a lesser degree, data quantity (sample size) on species presence–absence modelling. Innovation The paper challenges the common assumption that some popular measures of model accuracy and model predictions are prevalence independent. It highlights how imperfect reference data may impact on a study and the actions that may be taken to address problems. Main conclusions The theoretical independence of prevalence of popular accuracy measures, such as sensitivity, specificity, true skills statistics (TSS) and area under the receiver operating characteristic curve (AUC), is unlikely to occur in practice due to reference data error; all of these measures of accuracy, together with estimates of species occurrence, showed prevalence dependency arising through the use of a non‐gold‐standard reference. The number of cases used also had implications for the ability of a study to meet its objectives. Means to reduce the negative effects of imperfect reference data in study design and interpretation are suggested.     

Method to estimate genotype probabilities at individual loci in farm livestock

Summary A Bayesian method to estimate genotype probabilities at a single locus using information on the individual and all its relatives and their mates has been developed. The method uses data over several generations, can deal with large numbers of individuals in large livestock families and allows for missing information. It can be extended to multiple alleles and can be used for autosomal or sex-linked loci. The allele frequencies and the form of expression (dominance, penetrance) must be specified. An algorithm using the method and involving an iterative procedure has been developed to calculate the genotype probabilities for practical use in livestock breeding. The method and algorithm were used to determine the accuracy of estimating genotype probabilities of sires for a female sex-limited trait, such as genetic variants of milk proteins. Data were similated and genotype probabilities estimated for 100 sires (20 replicates) with 3, 6 and 12 female offspring per sire, for different population frequencies, for additive and dominance gene action and for variable genotypic expression. Such simulation is useful in the design of testing systems for the use of information on specific genetic loci in selection.Prepared during a leave at Centre for Genetic Improvement of Livestock, Guelph, Canada     

Sample size and sampling error in geometric morphometric studies of size and shape

Geometric morphometric studies are increasingly becoming common in systematics and palaeontology. The samples in such studies are often small, due to the paucity of material available for analysis. However, very few studies have tried to assess the impact of sampling error on analytical results. Here, this issue is addressed empirically using repeated randomized selection experiments to build progressively smaller samples from an original dataset of ∼400 vervet monkey (Cercopithecus aethiops) skulls. Size and shape parameters (including mean size and shape, size and shape variances, angles of allometric trajectories) that are commonly used in geometric morphometric studies, are estimated first in the original sample and then in the random subsamples. Estimates are then compared to give an indication of what is the minimum desirable sample size for each parameter. Mean size, standard deviation of size and variance of shape are found to be fairly accurate even in relatively small samples. In contrast, mean shapes and angles between static allometric trajectories are strongly affected by sampling error. If confirmed in other groups, our findings may have substantial implications for studies of morphological variation in present and fossil species. By performing rarefaction analyses like those presented in our study, morphometricians can be easily provided with important clues on how a simple but crucial factor like sample size can alter results of their studies.     

Accuracy map of an optical motion capture system with 42 or 21 cameras in a large measurement volume

Optical motion capture is commonly used in biomechanics to measure human kinematics. However, no studies have yet examined the accuracy of optical motion capture in a large capture volume (>100 m³), or how accuracy varies from the center to the extreme edges of the capture volume. This study measured the dynamic 3D errors of an optical motion capture system composed of 42 OptiTrack Prime 41 cameras (capture volume of 135 m³) by comparing the motion of a single marker to the motion reported by a ThorLabs linear motion stage. After spline interpolating the data, it was found that 97% of the capture area had error below 200 μm. When the same analysis was performed using only half (21) of the cameras, 91% of the capture area was below 200 μm of error. The only locations that exceeded this threshold were at the extreme edges of the capture area, and no location had a mean error exceeding 1 mm. When measuring human kinematics with skin-mounted markers, uncertainty of marker placement relative to underlying skeletal features and soft tissue artifact produce errors that are orders of magnitude larger than the errors attributed to the camera system itself. Therefore, the accuracy of this OptiTrack optical motion capture system was found to be more than sufficient for measuring full-body human kinematics with skin-mounted markers in a large capture volume (>100 m³).     

The Effect of Recombination on the Accuracy of Phylogeny Estimation

Phylogenetic studies based on DNA sequences typically ignore the potential occurrence of recombination, which may produce different alignment regions with different evolutionary histories. Traditional phylogenetic methods assume that a single history underlies the data. If recombination is present, can we expect the inferred phylogeny to represent any of the underlying evolutionary histories? We examined this question by applying traditional phylogenetic reconstruction methods to simulated recombinant sequence alignments. The effect of recombination on phylogeny estimation depended on the relatedness of the sequences involved in the recombinational event and on the extent of the different regions with different phylogenetic histories. Given the topologies examined here, when the recombinational event was ancient, or when recombination occurred between closely related taxa, one of the two phylogenies underlying the data was generally inferred. In this scenario, the evolutionary history corresponding to the majority of the positions in the alignment was generally recovered. Very different results were obtained when recombination occurred recently among divergent taxa. In this case, when the recombinational breakpoint divided the alignment in two regions of similar length, a phylogeny that was different from any of the true phylogenies underlying the data was inferred.     

Quantitative analysis of biomarkers,drugs and toxins in biological samples by immunoaffinity chromatography coupled to mass spectrometry or tandem mass spectrometry: A focused review of recent applications

Immunoaffinity chromatography (IAC), mass spectrometry and especially tandem mass spectrometry (MS/MS) represent the most efficient and reliable analytical techniques for specific isolation, unequivocal identification and accurate quantification of numerous natural and synthetic substances in biological samples. This review article focuses on the combined use of these outstanding methodologies in basic and clinical research and in life sciences for the quantitative analysis of low- and high-molecular mass biomarkers, drugs and toxins in urine, plasma or serum samples, in tissue and other biologicals systems published in the last decade. The analytes discussed in some detail include the biomarkers of oxidative stress 15(S)-8-iso-prostaglandin F_2α {15(S)-8-iso-PGF_2α} and 3-nitrotyrosine, the major urinary metabolite of the lipid mediators cysteinyl leukotrienes, i.e., the leukotriene E₄ (LTE₄), melatonin, and the major collagen type II neoepitope peptide in human urine.     

遥感探测土地植被覆盖指数的准确度评估

GIS数据的准确度一直是GIS应用的考虑事项。用虚例和实例揭示了土地植被覆盖指数的准确度与图像分类的准确度之间的复杂关系,并进一步用数学方式进行了解释。土地植被覆盖指数的准确度取决于图像分类准确度,但与分类总准确度没有直接关系。用户和产家准确度比总准确度对土地植被覆盖指数的准确度具有更直接的控制,所以在图像分类报告中不应被忽略为了保证土地植被覆盖指数的准确度,某土地类型的用户和产家准确度应该尽可能一致,同时要使得这两个准确度数值越高越好。     

Modeling the two-dimensional accuracy of soccer kicks

In many sports, athletes perform motor tasks that simultaneously require both speed and accuracy for success, such as kicking a ball. Because of the biomechanical trade-off between speed and accuracy, athletes must balance these competing demands. Modelling the optimal compromise between speed and accuracy requires one to quantify how task speed affects the dispersion around a target, a level of experimental detail not previously addressed. Using soccer penalties as a system, we measured two-dimensional kicking error over a range of speeds, target heights, and kicking techniques. Twenty experienced soccer players executed a total of 8466 kicks at two targets (high and low). Players kicked with the side of their foot or the instep at ball speeds ranging from 40% to 100% of their maximum. The inaccuracy of kicks was measured in horizontal and vertical dimensions. For both horizontal and vertical inaccuracy, variance increased as a power function of speed, whose parameter values depended on the combination of kicking technique and target height. Kicking precision was greater when aiming at a low target compared to a high target. Side-foot kicks were more accurate than instep kicks. The centre of the dispersion of shots shifted as a function of speed. An analysis of the covariance between horizontal and vertical error revealed right-footed kickers tended to miss below and to the left of the target or above and to the right, while left-footed kickers tended along the reflected axis. Our analysis provides relationships needed to model the optimal strategy for penalty kickers.     

Full and reduced models for yield trials

Summary Empirical results routinely demonstrate that the reduced Additive Main effects and Multiplicative Interaction (AMMI) model achieves better predictive accuracy for yield trials than does the full treatment means model. It may seem mysterious that treatment means are not the most accurate estimates, but rather that the AMMI model is often more accurate than its data. The statistical explanation involves the Stein effect, whereby a small sacrifice in bias can produce a large gain in accuracy. The corresponding agricultural explanation is somewhat complex, beginning with a yield trial's design and ending with its research purposes and applications. In essence, AMMI selectively recovers pattern related to the treatment design in its model, while selectively relegating noise related to the experimental design in its discarded residual. For estimating the yield of a particular genotype in a particular environment, the AMMI model uses the entire yield trial, rather than only the several replications of this particular trial, as in the treatment means model. This use of more information is the source of AMMI's gain in accuracy.This research was supported by the Rhizobotany Project of the USDA-ARS     

On criteria for evaluating models of absolute risk

Absolute risk is the probability that an individual who is free of a given disease at an initial age, a, will develop that disease in the subsequent interval (a, t]. Absolute risk is reduced by mortality from competing risks. Models of absolute risk that depend on covariates have been used to design intervention studies, to counsel patients regarding their risks of disease and to inform clinical decisions, such as whether or not to take tamoxifen to prevent breast cancer. Several general criteria have been used to evaluate models of absolute risk, including how well the model predicts the observed numbers of events in subsets of the population ("calibration"), and "discriminatory power," measured by the concordance statistic. In this paper we review some general criteria and develop specific loss function-based criteria for two applications, namely whether or not to screen a population to select subjects for further evaluation or treatment and whether or not to use a preventive intervention that has both beneficial and adverse effects. We find that high discriminatory power is much more crucial in the screening application than in the preventive intervention application. These examples indicate that the usefulness of a general criterion such as concordance depends on the application, and that using specific loss functions can lead to more appropriate assessments.