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135 results for “Sample size”
The impacts of fine-tuning, phylogenetic distance, and sample size on big-data bioacoustics
<p>Vocalizations in animals, particularly birds, are critically important behaviors that influence their reproductive fitness. While recordings of bioacoustic data have been captured and stored in collections for decades, the automated extraction of data from these recordings has only recently been facilitated by artificial intelligence methods. These have yet to be evaluated with respect to accuracy of different automation strategies and features. Here, we use a recently published machine learning framework to extract syllables from ten bird species ranging in their phylogenetic relatedness from 1 to 85 million years, to compare how phylogenetic relatedness influences accuracy. We also evaluate the utility of applying trained models to novel species. Our results indicate that model performance is best on conspecifics, with accuracy progressively decreasing as phylogenetic distance increases between taxa. However, we also find that the application of models trained on multiple distantly related species can improve the overall accuracy to levels near that of training and analyzing a model on the same species. When planning big-data bioacoustics studies, care must be taken in sample design to maximize sample size and minimize human labor without sacrificing accuracy.</p>
Data, sample sizes, and R code for analysis of: Variation in mutation (co)variances
<p>Because of pleiotropy, mutations affect the expression and inheritance of multiple traits and, together with selection, are expected to shape standing genetic covariances between traits and eventual phenotypic divergence between populations. It is therefore important to find if the M matrix, describing mutational variances of each trait and covariances between traits, varies between genotypes. We here estimate the M matrix for six locomotion behavior traits in lines of two genotypes of the nematode <em>Caenorhabditis elegans </em>that accumulated mutations in a nearly-neutral manner for 250 generations. We find significant mutational variance along at least one phenotypic dimension of the M matrices, but neither their size nor their orientation had detectable differences between genotypes. The number of generations of mutation accumulation, or the number of MA lines measured, was likely insufficient to sample enough mutations and detect potentially small differences between the two M matrices. We then tested if the M matrices were similar to one G matrix describing the standing genetic (co)variances of a population derived by the hybridization of several genotypes, including the two measured for M, and domesticated to a lab-defined environment for 140 generations. We found that the M and G were different because the genetic covariances caused by mutational pleiotropy in the two genotypes are smaller than those caused by linkage disequilibrium in the lab population. We further show that M matrices differed in their alignment with the lab population G matrix. If generalized to other founder genotypes of the lab population, these observations indicate that selection does not shape the evolution of the M matrix for locomotion behavior in the short-term of a few tens to hundreds of generations and suggests that the hybridization of <em>C. elegans </em>genotypes allows selection on new phenotypic dimensions of locomotion behavior.</p>
Biases and distribution patterns in hard-bodied microscopic animals (Acari: Halacaridae): Size doesn't matter, but generalism and sampling effort do
<span>Aim</span> <p><span>The interplay between distribution ranges, species traits, and sampling and taxonomic biases remain elusive amongst microscopic animals. This ignorance obscures our understanding of the diversity patterns of a major component of biodiversity. Here, we used marine Halacaridae to explore whether differences between marine provinces can explain their distribution patterns or if differential sampling efforts across regions prevent any macroecological inference. Furthermore, we test if certain functional traits influence their distribution patterns.</span></p> <span>Location</span> <p><span>Europe.</span></p> <span>Results</span> <p><span>Whereas geographical variables provided a better explanation for differences in species composition, sampling effort and distance from marine biological stations accounted for the majority of differences in European Halacaridae richness. Species occurring in more habitats showed broader geographical ranges and accumulated more records. Species traits like body size affected the distribution of halacarid species.</span></p> <span>Main conclusions</span> <p><span>We propose that the sampling effort of halacarid mites in Europe might be explained by two different cognitive biases: the convenience of selecting certain sampling localities compared to others, and the tendency of zoologists to scrutinize habitats where their target organisms are more common.</span></p>
Data from: Overcoming the challenge of small effective sample sizes in home-range estimation
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Estimating required sample sizes for gut content metabarcoding studies of dietary diversity in spiders
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Female fiddler crabs, Austruca lactea (Decapoda: Ocypodidae), adjust their rate of mate sampling based on remaining days until oviposition under a size-dependent temperature constraint
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The impacts of fine-tuning, phylogenetic distance, and sample size on big-data bioacoustics
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Biases and distribution patterns in hard-bodied microscopic animals (Acari: Halacaridae): Size doesn’t matter, but generalism and sampling effort do
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Data from: Sample size guidelines for mapping migration corridors and population distributions using tracking data
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Data from: Dealing with assumptions and sampling bias in the estimation of effective population size: A case study in an amphibian population
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A test of Bergmann’s rule in the Early Triassic: Latitude, body size, and sampling in Lystrosaurus
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Data, sample sizes, and R code for analysis of: Variation in mutation (co)variances
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Taxonomic similarity does not predict necessary sample size for ex situ conservation: a comparison among five genera
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All Insect Savanna Sweepnet Sampling 2004:Trophic Structure: Insect Species Diversity, Abundance and Body Size
The goal of this study was to examine the populations of insects in prairies and savannas. Most of the prairies had developed after being abandoned from agriculture, but none of the savannas had been cultivated. The history of burning varied between sites. The main sampling for this study was conducted in 1992 by the lead investigators: John Haarstad, Evan Siemann, and David Tilman. Insects were sampled via sweep-net sampling, pitfalls and ant scent plates throughout the growing season in each of 49 grassland fields and savannas. In total, 89,596 individuals of 1,167 species were captured and enumerated. Body size was measured for a subset of grasshoppers collected. In 2004, John Haarstad conducted two similar studies by identifying and enumerating all insects collected in sweepnet samples taken in old fields (prairies) as part of E014 grasshopper studies and from sweepnet samples taken in savannas.
Old Field All Arthropod Sweepnet Sampling 2004 :Trophic Structure: Insect Species Diversity, Abundance and Body Size
The goal of this study was to examine the populations of insects in prairies and savannas. Most of the prairies had developed after being abandoned from agriculture, but none of the savannas had been cultivated. The history of burning varied between sites. The main sampling for this study was conducted in 1992 by the lead investigators: John Haarstad, Evan Siemann, and David Tilman. Insects were sampled via sweep-net sampling, pitfalls and ant scent plates throughout the growing season in each of 49 grassland fields and savannas. In total, 89,596 individuals of 1,167 species were captured and enumerated. Body size was measured for a subset of grasshoppers collected. In 2004, John Haarstad conducted two similar studies by identifying and enumerating all insects collected in sweepnet samples taken in old fields (prairies) as part of E014 grasshopper studies and from sweepnet samples taken in savannas.
Examination of sample size determination in integration studies based on the coefficient of variation (ICV)
<p>Although there are various indices available for calculating morphological integration, the integration coefficient of variation (ICV) is most suited for assessing magnitudes of integration within and between morphological variance/covariance (V/CV) matrices. However, it is currently not known what the effects of varying sample sizes are on the reliable estimation of distributions of ICV scores.<b> </b>In this regard, the effects of varying sample size on ICV was examined by simulating parameter V/CV matrices with varying underlying magnitudes of average trait correlation (r<sup>2</sup>). ICV distributions were generated using a trait resampling protocol for various sample sizes (11 through 150) within various parameter r<sup>2 </sup>values. Next, empirical r<sup>2</sup> values were calculated based on data from 22 skeletal elements of 40 <i>Macaca fascicularis</i> specimens to examine whether the results from the simulation corresponded to real biological data. Mean ICV scores of various sample sizes were compared using Mann-Whitney U tests to examine which minimum sample sizes are required to reliably calculate mean ICV.<b> </b>Mann-Whitney U test results based on the simulated data showed that a sample size of 51 may be sufficient even for relatively low r<sup>2 </sup>values of 0.05. The empirical macaque data showed that 30‒40 individuals may be sufficient to reliably calculate mean ICV scores across skeletal elements.<b> </b>Our results correspond closely with previous assessments by Cheverud and colleagues that argued that a sample size of 40 is necessary to accurately estimate the structure of V/CV matrices.</p>
Data from: Effects of growth rate, size, and light availability on tree survival across life stages: a demographic analysis accounting for missing values and small sample sizes
Background: Plant survival is a key factor in forest dynamics and survival probabilities often vary across life stages. Studies specifically aimed at assessing tree survival are unusual and so data initially designed for other purposes often need to be used; such data are more likely to contain errors than data collected for this specific purpose. Results: We investigate the survival rates of ten tree species in a dataset designed to monitor growth rates. As some individuals were not included in the census at some time points we use capture-mark-recapture methods both to allow us to account for missing individuals, and to estimate relocation probabilities. Growth rates, size, and light availability were included as covariates in the model predicting survival rates. The study demonstrates that tree mortality is best described as constant between years and size-dependent at early life stages and size independent at later life stages for most species of UK hardwood. We have demonstrated that even with a twenty-year dataset it is possible to discern variability both between individuals and between species. Conclusions: Our work illustrates the potential utility of the method applied here for calculating plant population dynamics parameters in time replicated datasets with small sample sizes and missing individuals without any loss of sample size, and including explanatory covariates.
Data from: Informative plot sizes in presence-absence sampling of forest floor vegetation
1. Plant communities are attracting increased interest in connection with forest and landscape inventories due to society's interest in ecosystem services. However, the acquisition of accurate information about plant communities poses several methodological challenges. Here we investigate the use of presence-absence sampling with the aim to monitor state and change of plant density. We study what plot sizes are informative, i.e. the estimators should have as high precision as possible. 2. Plant occurrences were modeled through different Poisson processes and tests were developed for assessing the plausibility of the model assumptions. Optimum plot sizes were determined by minimizing the variance of the estimators. While state estimators of similar kind as ours have been proposed in previous studies, our tests and change estimation procedures are new. 3. We found that the most informative plot size for state estimation is 1.6 divided by the plant density, i.e. if the true density is 1 plant per square meter the optimum plot size is 1.6 square meters. This is in accordance with previous findings. More importantly, the most informative plot size for change estimation was smaller and depended on the change patterns. We provide theoretical results as well as some empirical results based on data from the Swedish National Forest Inventory. 4. Use of too small or too large plots resulted in poor precision of the density (and density change) estimators. As a consequence, a range of different plot sizes would be required for jointly monitoring both common and rare plants using presence-absence sampling in monitoring programmes.
Data from: Heritability estimates from genome wide relatedness matrices in wild populations: application to a passerine, using a small sample size
Genomic developments have empowered the investigation of heritability in wild populations directly from genome wide relatedness matrices (GRM). Such GRM based approaches can in particular be used to improve or substitute approaches based on social pedigree (PED-social). However, measuring heritability from GRM in the wild has not been widely applied yet, especially using small samples and in non-model species. Here, we estimated heritability for four quantitative traits (tarsus length, wing length, bill length and body mass), using PED-social and a pedigree corrected by genetic data (PED-corrected) and GRM from a small sample (n = 494) of blue tits from natural populations in Corsica genotyped at nearly 50,000 filtered SNPs derived from RAD-seq. We also measured genetic correlations among traits and we performed chromosome partitioning. Heritability estimates were slightly higher when using GRM compared to PED-social, and PED-corrected yielded intermediate values, suggesting a minor underestimation of heritability in PED-social due to incorrect pedigree links, including extra-pair paternity, and to lower information content than the GRM. Genetic correlations among traits were similar between PED-social and GRM but credible intervals were very large in both cases, suggesting a lack of power for this small dataset. Although a positive linear relationship was found between the number of genes per chromosomes and the chromosome heritability for tarsus length, chromosome partitioning similarly showed a lack of power for the three other traits. We discuss the usefulness and limitations of the quantitative genetic inferences based on genomic data in small samples from wild populations.
Data from: Hierarchical distance sampling to estimate population sizes of common lizards across a desert ecoregion
1) Multi-species wildlife monitoring across large geographical regions is important for effective conservation planning in response to expected impacts from climate change and land use. Unlike many species of birds, mammals, and amphibians which can be efficiently sampled using automated sensors including cameras and sound recorders, reptiles are often much more challenging to detect, in part because of their typically cryptic behavior and generally small body sizes. Although many lizard species are more active during the day which makes them easier to detect using visual encounter surveys, they may be unavailable for sampling during certain periods of the day or year due to their sensitivity to temperature. 2) In recognition of these sampling challenges, we demonstrate application of a recent innovation in distance sampling that adjusts for temporary emigration between repeat survey visits. We used transect surveys to survey lizards at 229 sites throughout the Mojave Desert in California, USA, 2016. 3) We estimated a total population size of 80 million (90% CI: 64–97 million) for the three most common species of lizards across this 66,830 km2 ecoregion. We mapped how density at the 1-km2 scale was predicted to vary with vegetation cover and human development. We validated these results against independent surveys from the southern portion of our study area. 4) Our methods and results demonstrate how multi-species monitoring programs spanning arid ecoregions can better incorporate information about reptiles.
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.