Taphonomy and ecology of modern avifaunal remains from Amboseli Park, Kenya

Paleobiology ◽  
2003 ◽  
Vol 29 (1) ◽  
pp. 52-70 ◽  
Author(s):  
Anna K. Behrensmeyer ◽  
C. Tristan Stayton ◽  
Ralph E. Chapman
Keyword(s):  
Habitat Preferences ◽  
Large Body ◽  
Bird Community ◽  
Skeletal Remains ◽  
Small Sample ◽  
Small Samples ◽  
Scaling Analysis ◽  
Ecological Data ◽  
Small Sample Sizes ◽  
Grassland Habitats

Avian skeletal remains occur in many fossil assemblages, and in spite of small sample sizes and incomplete preservation, they may be a source of valuable paleoecological information. In this paper, we examine the taphonomy of a modern avian bone assemblage and test the relationship between ecological data based on avifaunal skeletal remains and known ecological attributes of a living bird community. A total of 54 modern skeletal occurrences and a sample of 126 identifiable bones from Amboseli Park, Kenya, were analyzed for weathering features and skeletal part preservation in order to characterize preservation features and taphonomic biases. Avian remains, with the exception of ostrich, decay more rapidly than adult mammal bones and rarely reach advanced stages of weathering. Breakage and the percentage of anterior limb elements serve as indicators of taphonomic overprinting that may affect paleoecological signals. Using ecomorphic categories including body weight, diet, and habitat, we compared species in the bone assemblage with the living Amboseli avifauna. The documented bone sample is biased toward large body size, representation of open grassland habitats, and grazing or scavenging diets. In spite of this, multidimensional scaling analysis shows that the small faunal sample (16 out of 364 species) in the pre-fossil bone assemblage accurately represents general features of avian ecospace in Amboseli. This provides a measure of the potential fidelity of paleoecological reconstructions based on small samples of avian remains. In the Cenozoic, the utility of avian fossils is enhanced because bird ecomorphology is relatively well known and conservative through time, allowing back-extrapolations of habitat preferences, diet, etc. based on modern taxa.

scholarly journals G-computation and machine learning for estimating the causal effects of binary exposure statuses on binary outcomes

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Florent Le Borgne ◽  
Arthur Chatton ◽  
Maxime Léger ◽  
Rémi Lenain ◽  
Yohann Foucher
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Statistical Power ◽  
Small Sample ◽  
Causal Effects ◽  
Small Samples ◽  
Support Vector ◽  
Sample Sizes ◽  
Super Learner ◽  
Small Sample Sizes

AbstractIn clinical research, there is a growing interest in the use of propensity score-based methods to estimate causal effects. G-computation is an alternative because of its high statistical power. Machine learning is also increasingly used because of its possible robustness to model misspecification. In this paper, we aimed to propose an approach that combines machine learning and G-computation when both the outcome and the exposure status are binary and is able to deal with small samples. We evaluated the performances of several methods, including penalized logistic regressions, a neural network, a support vector machine, boosted classification and regression trees, and a super learner through simulations. We proposed six different scenarios characterised by various sample sizes, numbers of covariates and relationships between covariates, exposure statuses, and outcomes. We have also illustrated the application of these methods, in which they were used to estimate the efficacy of barbiturates prescribed during the first 24 h of an episode of intracranial hypertension. In the context of GC, for estimating the individual outcome probabilities in two counterfactual worlds, we reported that the super learner tended to outperform the other approaches in terms of both bias and variance, especially for small sample sizes. The support vector machine performed well, but its mean bias was slightly higher than that of the super learner. In the investigated scenarios, G-computation associated with the super learner was a performant method for drawing causal inferences, even from small sample sizes.

Implications of Small Samples for Generalization: Adjustments and Rules of Thumb

2016 ◽  
Vol 41 (5) ◽  
pp. 472-505 ◽  
Author(s):  
Elizabeth Tipton ◽  
Kelly Hallberg ◽  
Larry V. Hedges ◽  
Wendy Chan
Keyword(s):  
Observational Studies ◽  
Small Sample ◽  
Average Treatment Effect ◽  
Small Samples ◽  
Sample Sizes ◽  
Random Samples ◽  
Rules Of Thumb ◽  
Large Populations ◽  
Small Sample Sizes ◽  
Combine Information

Background: Policy makers and researchers are frequently interested in understanding how effective a particular intervention may be for a specific population. One approach is to assess the degree of similarity between the sample in an experiment and the population. Another approach is to combine information from the experiment and the population to estimate the population average treatment effect (PATE). Method: Several methods for assessing the similarity between a sample and population currently exist as well as methods estimating the PATE. In this article, we investigate properties of six of these methods and statistics in the small sample sizes common in education research (i.e., 10–70 sites), evaluating the utility of rules of thumb developed from observational studies in the generalization case. Result: In small random samples, large differences between the sample and population can arise simply by chance and many of the statistics commonly used in generalization are a function of both sample size and the number of covariates being compared. The rules of thumb developed in observational studies (which are commonly applied in generalization) are much too conservative given the small sample sizes found in generalization. Conclusion: This article implies that sharp inferences to large populations from small experiments are difficult even with probability sampling. Features of random samples should be kept in mind when evaluating the extent to which results from experiments conducted on nonrandom samples might generalize.

Testing Techniques for Mechanical Characterization of Nanostructured Materials

2003 ◽  
Vol 791 ◽  
Author(s):  
Carl C. Koch ◽  
Ronald O. Scattergood ◽  
K. Linga Murty ◽  
Ramesh K. Guduru ◽  
Gopinath Trichy ◽  
...  
Keyword(s):  
Indentation Test ◽  
Small Sample ◽  
Small Samples ◽  
Punch Test ◽  
Ball Indentation ◽  
Shear Punch Test ◽  
Testing Techniques ◽  
Ball Indentation Test ◽  
Small Sample Sizes

ABSTRACTTesting methods are reviewed that can be applied to the small sample sizes which result from many of the processing routes for preparation of nanocrystalline materials. These include the measurement of elastic properties on small samples; hardness, with emphasis on nanoindentation methods; the miniaturized disk bend test (MDBT); the automated ball indentation test (ABI); the shear punch test; and the use of subsize compression and tensile samples.

Procedures for the Analysis of Differential Item Functioning (DIF) for Small Sample Sizes

2005 ◽  
Vol 28 (3) ◽  
pp. 283-294 ◽  
Author(s):  
Jin-Shei Lai ◽  
Jeanne Teresi ◽  
Richard Gershon
Keyword(s):  
Differential Item Functioning ◽  
Small Sample ◽  
Small Samples ◽  
Sample Sizes ◽  
Education And Health ◽  
Item Functioning ◽  
Related Quality ◽  
Health Related ◽  
Small Sample Sizes

An item with differential item functioning (DIF) displays different statistical properties, conditional on a matching variable. The presence of DIF in measures can invalidate the conclusions of medical outcome studies. Numerous approaches have been developed to examine DIF in many areas, including education and health-related quality of life. There is little consensus in the research community regarding selection of one best method, and most methods require large sample sizes. This article describes some approaches to examine DIF with small samples (e.g., less than 200).

Statistics for Small Groups: The Power of the Pretest

1988 ◽  
Vol 13 (3) ◽  
pp. 142-146 ◽  
Author(s):  
David A. Cole
Keyword(s):  
Statistical Power ◽  
Change Score ◽  
Small Sample ◽  
Analysis Of Covariance ◽  
Small Samples ◽  
Group Differences ◽  
Control Group ◽  
Small Sample Sizes ◽  
Almost All ◽  
Treatment Control Group

In the area of severe-profound retardation, researchers are faced with small sample sizes. The question of statistical power is critical. In this article, three commonly used tests for treatment-control group differences are compared with respect to their relative power: the posttest-only approach, the change-score approach, and an analysis of covariance (ANCOVA) approach. In almost all cases, the ANCOVA approach is the more powerful than the other two, even when very small samples are involved. Finally, a fourth approach involving ANCOVA plus alternate rank assignments is examined and found to be superior even to the ANCOVA approach, especially in small sample cases. Use of slightly more sophisticated statistics in small sample research is recommended.

scholarly journals Seeing distinct groups where there are none: spurious patterns from between-group PCA

2019 ◽  
Author(s):  
Andrea Cardini ◽  
Paul O’Higgins ◽  
F. James Rohlf
Keyword(s):  
Dimensional Space ◽  
Small Sample ◽  
Small Samples ◽  
Sample Sizes ◽  
Large Samples ◽  
Large Numbers ◽  
Group Variation ◽  
Small Sample Sizes ◽  
Analysis Of Variation ◽  
Dimensions Of Variation

AbstractUsing sampling experiments, we found that, when there are fewer groups than variables, between-groups PCA (bgPCA) may suggest surprisingly distinct differences among groups for data in which none exist. While apparently not noticed before, the reasons for this problem are easy to understand. A bgPCA captures the g-1 dimensions of variation among the g group means, but only a fraction of the ∑ni − g dimensions of within-group variation (ni are the sample sizes), when the number of variables, p, is greater than g-1. This introduces a distortion in the appearance of the bgPCA plots because the within-group variation will be underrepresented, unless the variables are sufficiently correlated so that the total variation can be accounted for with just g-1 dimensions. The effect is most obvious when sample sizes are small relative to the number of variables, because smaller samples spread out less, but the distortion is present even for large samples. Strong covariance among variables largely reduces the magnitude of the problem, because it effectively reduces the dimensionality of the data and thus enables a larger proportion of the within-group variation to be accounted for within the g-1-dimensional space of a bgPCA. The distortion will still be relevant though its strength will vary from case to case depending on the structure of the data (p, g, covariances etc.). These are important problems for a method mainly designed for the analysis of variation among groups when there are very large numbers of variables and relatively small samples. In such cases, users are likely to conclude that the groups they are comparing are much more distinct than they really are. Having many variables but just small sample sizes is a common problem in fields ranging from morphometrics (as in our examples) to molecular analyses.

scholarly journals What is the extent of prokaryotic diversity?

2006 ◽  
Vol 361 (1475) ◽  
pp. 2023-2037 ◽  
Author(s):  
Thomas P Curtis ◽  
Ian M Head ◽  
Mary Lunn ◽  
Stephen Woodcock ◽  
Patrick D Schloss ◽  
...  
Keyword(s):  
Microbial Diversity ◽  
Local Community ◽  
Practical Importance ◽  
Real Life ◽  
Small Sample ◽  
Small Samples ◽  
Sample Sizes ◽  
Clone Libraries ◽  
Migration Rates ◽  
Small Sample Sizes

The extent of microbial diversity is an intrinsically fascinating subject of profound practical importance. The term ‘diversity’ may allude to the number of taxa or species richness as well as their relative abundance. There is uncertainty about both, primarily because sample sizes are too small. Non-parametric diversity estimators make gross underestimates if used with small sample sizes on unevenly distributed communities. One can make richness estimates over many scales using small samples by assuming a species/taxa-abundance distribution. However, no one knows what the underlying taxa-abundance distributions are for bacterial communities. Latterly, diversity has been estimated by fitting data from gene clone libraries and extrapolating from this to taxa-abundance curves to estimate richness. However, since sample sizes are small, we cannot be sure that such samples are representative of the community from which they were drawn. It is however possible to formulate, and calibrate, models that predict the diversity of local communities and of samples drawn from that local community. The calibration of such models suggests that migration rates are small and decrease as the community gets larger. The preliminary predictions of the model are qualitatively consistent with the patterns seen in clone libraries in ‘real life’. The validation of this model is also confounded by small sample sizes. However, if such models were properly validated, they could form invaluable tools for the prediction of microbial diversity and a basis for the systematic exploration of microbial diversity on the planet.

scholarly journals Rasch Versus Classical Equating in the Context of Small Sample Sizes

2019 ◽  
Vol 80 (3) ◽  
pp. 499-521
Author(s):  
Ben Babcock ◽  
Kari J. Hodge
Keyword(s):  
Item Difficulty ◽  
Real Data ◽  
Past Research ◽  
Small Sample ◽  
Small Samples ◽  
Sample Sizes ◽  
Certification Exam ◽  
Small Sample Sizes ◽  
The Rasch Model ◽  
Better Than

Equating and scaling in the context of small sample exams, such as credentialing exams for highly specialized professions, has received increased attention in recent research. Investigators have proposed a variety of both classical and Rasch-based approaches to the problem. This study attempts to extend past research by (1) directly comparing classical and Rasch techniques of equating exam scores when sample sizes are small ( N≤ 100 per exam form) and (2) attempting to pool multiple forms’ worth of data to improve estimation in the Rasch framework. We simulated multiple years of a small-sample exam program by resampling from a larger certification exam program’s real data. Results showed that combining multiple administrations’ worth of data via the Rasch model can lead to more accurate equating compared to classical methods designed to work well in small samples. WINSTEPS-based Rasch methods that used multiple exam forms’ data worked better than Bayesian Markov Chain Monte Carlo methods, as the prior distribution used to estimate the item difficulty parameters biased predicted scores when there were difficulty differences between exam forms.

scholarly journals Development of Graphitization of μg-Sized Samples at Lund University

Radiocarbon ◽  
2010 ◽  
Vol 52 (3) ◽  
pp. 1270-1276 ◽  
Author(s):  
J Genberg ◽  
K Stenström ◽  
M Elfman ◽  
M Olsson
Keyword(s):  
Mass Spectrometry ◽  
Detection Limit ◽  
Accelerator Mass Spectrometry ◽  
Atmospheric Aerosol ◽  
Pressure Transducer ◽  
Small Sample ◽  
Small Samples ◽  
Mass Detection ◽  
Small Pressure ◽  
Small Sample Sizes

To be able to successfully measure radiocarbon with accelerator mass spectrometry (AMS) in atmospheric aerosol samples, graphitization of small sample sizes (<50 μg carbon) must provide reproducible results. At Lund University, a graphitization line optimized for small samples has been constructed. Attention has been given to minimize the reduction reactor volume and each reactor is equipped with a very small pressure transducer that enables constant monitoring of the reaction. Samples as small as 25 μg of carbon have been successfully analyzed, and the mass detection limit of the system has probably not been reached.

scholarly journals MaxEnt’s parameter configuration and small samples: Are we paying attention to recommendations?

2016 ◽  
Author(s):  
Narkis S. Morales ◽  
Ignacio C. Fernándezb ◽  
Victoria Baca-Gonzálezd
Keyword(s):  
Species Distribution ◽  
Small Sample ◽  
Small Samples ◽  
Distribution Model ◽  
Environmental Niche ◽  
Sample Sizes ◽  
Environmental Niche Modeling ◽  
Parameter Configuration ◽  
Small Sample Sizes ◽  
Modelling Process

AbstractEnvironmental niche modeling (ENM) is commonly used to develop probabilistic maps of species distribution. Among available ENM techniques, MaxEnt has become one of the most popular tools for modeling species distribution, with hundreds of peer-reviewed articles published each year. MaxEnt’s popularity is mainly due to the use of a graphical interface and automatic parameter configuration capabilities. However, recent studies have shown that using the default automatic configuration may not be always appropriate because it can produce non-optimal models; particularly when dealing with a small number of species presence points. Thus, the recommendation is to evaluate the best potential combination of parameters (feature classes and regularization multiplier) to select the most appropriate model. In this work we reviewed 244 articles from 142 journals between 2013 and 2015 to assess whether researchers are following recommendations to avoid using the default parameter configuration when dealing with small sample sizes, or if they are using MaxEnt as a “black box tool”. Our results show that in only 16% of analyzed articles authors evaluated best feature classes, in 6.9% evaluated best regularization multipliers, and in a meager 3.7% evaluated simultaneously both parameters before producing the definitive distribution model. These results are worrying, because publications are potentially reporting over-complex or over-simplistic models that can undermine the applicability of their results. Of particular importance are studies used to inform policy making. Therefore, researchers, practitioners, reviewers and editors need to be very judicious when dealing with MaxEnt, particularly when the modelling process is based on small sample sizes.

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