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35 results for “rarefaction”
Fig. 5. Sample-based rarefaction curve with 95 in Crepuscular and nocturnal hawkmoths (Lepidoptera: Sphingidae) from a fragment of Atlantic rainforest in the state of São Paulo, southeastern Brazil
Fig. 5. Sample-based rarefaction curve with 95% confidence interval and results of the richness estimators (triangle: Jackknife 1; X: Jackknife 2; open circle: ICE; square: Chao 2).
Fig. 3. Rarefaction curves. Solid lines indicate mean and dashed lines indicate 95 in Streetlights attract a broad array of beetle species
Fig. 3. Rarefaction curves. Solid lines indicate mean and dashed lines indicate 95% confidence intervals. Inset shows the full curves for Hg and Na.
Fig. 1. Rarefaction curves, generated with 1,000 in Temporal dynamics of fruit-feeding butterflies (Lepidoptera: Nymphalidae) in two habitats in a seasonal Brazilian environment
Fig. 1. Rarefaction curves, generated with 1,000 randomizations without replacement, based on the MauTau (Sobs, black circles) and "total" estimated (Jackknife 1, gray circles) values. The data of Nymphalidae species richness was acquired from Jul 2012 to Jun 2013 in the Fazenda Água Limpa and the Reserva Ecológica do Roncador, Brasilia, DF.
Fig. 4. Rarefaction curves with extrapolations and 95 in Diversity and microhabitat use of benthic invertebrates in an urban forest stream (Southeastern Brazil)
Fig. 4. Rarefaction curves with extrapolations and 95% of confidence intervals for both the total sampling and each type of microhabitat of benthic invertebrates of Tijuca River, located at the Tijuca Forest, Rio de Janeiro, Brazil.
Data from: When are extinctions simply bad luck? rarefaction as a framework for disentangling selective and stochastic extinctions
1. A key challenge in conservation biology is that not all species are equally likely to go extinct when faced with a disturbance. Traditionally, differences in species extinction risk are considered a form of extinction selectivity, a nanrondom process by which species' extinction risks are associated with their traits. While selectivity clearly contributes to varation in extinction among taxa, it is also clear that rare species are more likely to go extinct than are common species. While obvious, this law of extinction suggests that random chance, operating on species abundance, plays an important role in the extinction process. Unless ecologists and conservation biologists can disentangle random and nonrandom extinction processes, then the prediction and prevention of future extinctions will continue to be an elusive challenge. 2. We suggest that a modified version of a common null model procedure, rarefaction, can be used to disentangle the influence of stochastic species loss from selective nonrandom processes. To this end we applied a rarefaction based null model to three published data sets to characterize the influence of species rarity in driving biodiversity loss following three disturbance events: i) disease-associated bat declines; ii) disease-associated amphibian declines; and iii) habitat loss and invasive species-associated gastropod declines. For each case study, we used rarefaction to generate null expectations of stochastic biodiversity loss and species-specific extinction probabilities. 3. In each of our case studies we find evidence for random and nonrandom (selective) extinctions. Our findings highlight the importance of explicitly considering that some species extinctions are the result of stochastic processes, i.e., bad luck. 4. Policy Implications If there is a first law of extinction, it is that rare species are most likely than common species to go extinct. We suggest that taking this law into account in analyses of extinction risk is critical to the identification of selective extinctions. Our results suggest that rarefaction can be used to identify nonrandom decline, extirpation, and extinction events and provide an important baseline comparison point for future extinction analyses.12-Jul-2019
Taxonomically revised dataset of acanthomorphic acritarch species of the Doushantuo Formation: dataset for rarefaction, NMDS, and network analysis
<p>This is supplementary material for the article entitled "Silicified microfossils from the Ediacaran Doushantuo Formation along a shelf margin-slope-basin transect in Hunan Province, South China, with stratigraphical implications" by Ouyang et al. The dataset contains (1) taxonomic revisions of published acanthomorph specimens from the Doushantuo Formation from 55 previous publications that provide clear, pulished, microfossil images and clearly designated stratigraphic horizons; and updated occurrence data of Doushantuo acanthomorphic acritarchs and certain sphaeromorphic taxa considered as stratigraphically useful (i.e., <em>Schizofusa zangwenlongii</em> Grey, 2005) based on the revised taxonomy, with each occurrence assigned to a specific fossil collection; (2) acritarch relative abundance data from this and six previous publications for rarefaction analysis in this study; (3) presence/absence data of the Doushantuo acritarchs (based on occurrence data in sheet 1) for NMDS and network analysis with R in this study; (4) species abbreviations in (3); (5) species loadings generated by the NMDS analysis and plotted in Fig. 41.3 of this study.</p>
melian009/Mispark: Preliminary analysis mutualistic networks in space, rarefaction, sierra size and sampling individuals, species, and traits
<p>Mutualistic networks in space, morphological traits and colors, or how to put all together to understand rare and common species in rapidly changing landscapes</p>
Estimating total species richness: fitting rarefaction by asymptotic approximation
<p class="MsoNormal"><span>Estimating the number of species in a community is important for assessments of biodiversity. Previous species richness estimators are mainly based on non-parametric approaches. Although parametric asymptotic models have been applied, they received limited attention due to specific limitations. Here, we introduce parametric models fitting the probability-based rarefied species richness curve that allow us to estimate the 'Total Expected Species' (TES) in a community based on species' abundance data. We develop two approaches to calculate TES (termed 'TESa' and 'TESb'), based on two slightly different mathematical assumptions regarding Expected Species (ES) models. We provide R functions to calculate both these estimation approaches and their standard deviation. The function also enables users to visualize the estimation. We test the performance of TESa, TESb and their average (TESab) across simulated and empirical data, and compare their bias, precision and accuracy with other, commonly used, non-parametric species richness estimators; the bias-corrected (bc-)Chao1 and the Abundance-based Coverage Estimator (ACE). Simulation reveals that in small samples, TESa shows a tendency to over-estimate and TESb to under-estimate overall species richness. TESab performs well in bias, precision and accuracy when compared to (bc-)Chao1 and ACE estimators. Results from empirical data shows that the variance generated from TES estimates is comparable to that for (bc-)Chao1 and ACE. Our study demonstrates that rarefaction theory in combination with parametric approximation models provides a valuable new approach to estimate the species richness of incompletely sampled communities. <a name="_Hlk114347649"></a>Robust estimates are likely to be obtained where the observed number of species is greater than half of the TES estimation. When the ratio of TESa to the observed richness is >> 2, we suggest the use of TESb or TESab. Although more comprehensive comparisons with other estimators are suggested, we encourage researchers to consider the TES approach in their biodiversity studies as a complement to current existing estimators.</span></p>
rarefaction and extrapolation of sample coverage based on sample-based abundance data
<p>#R code.txt is the R code for plotting figures and constructing Tables</p> <p>#bciabun1010.txt is the species_by_plot matrix that BCI forest Plot is divided by a plot with size 10m*<em>10m</em></p> <p><em>#bciabun2020.txt is the species_by_plot matrix that BCI forest Plot is divided by a plot with size 20m*</em>20m</p> <p><em>#bciabun5050.txt is the species_by_plot matrix that BCI forest Plot is divided by a plot with size 50m*5</em>0m</p> <p>#fus10.txt is the species_by_plot matrix that Fushan forest Plot is divided by a plot with size 10m*<em>10m</em></p> <p><em>#fus20.txt is the species_by_plot matrix that Fushan forest Plot is divided by a plot with size 20m*</em>20m</p> <p>#fus50.txt is the species_by_plot matrix that Fushan forest Plot is divided by a plot with size 50m*<em>50m</em></p> <p><em>#lhc10.txt is the species_by_plot matrix that Lianhuachi forest Plot is divided by a plot with size 10m*</em>10m</p> <p>#lhc20.txt is the species_by_plot matrix that Lianhuachi forest Plot is divided by a plot with size 20m*20m</p> <p>#lhc50.txt is the species_by_plot matrix that Lianhuachi forest Plot is divided by a plot with size 50m*50m</p>
Estimating total species richness: fitting rarefaction by asymptotic approximation
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Data from: When are extinctions simply bad luck? rarefaction as a framework for disentangling selective and stochastic extinctions
Open the record for dataset details and reuse information.
Taxonomically revised dataset of acanthomorphic acritarch species of the Doushantuo Formation: dataset for rarefaction, NMDS, and network analysis
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Data from: Microvascular rarefaction in the sinoatrial node
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Phyton scripts to generate Figure 9.1: Rarefaction curves of twenty biggest genera (i.e., having the most number of available genomes) from the global analysis of 1.2 million BGCs.
<p>Phyton scripts to generate Figure 9.1: Rarefaction curves of twenty biggest genera (i.e., having the most number of available genomes) from the global analysis of 1.2 million BGCs.</p>
Effects of Change in Blood Pressure on Retinal Capillary Rarefaction in Patients With Arterial Hypertension
ClinicalTrials.gov study NCT06098300. IPD Sharing: Not stated. Countries: 1. Publications: 31.
Effect of Aerobic EXercise on MiCroVAscular RarefacTION in Chinese Mild HyperteNsive Patients（EXCAVATION-CHN1）
ClinicalTrials.gov study NCT02817204. IPD Sharing: NO. Countries: 1. Publications: 1.
Data from: Retinal capillary rarefaction in patients with untreated mild-moderate hypertension
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Fig. 52. Species estimation–extrapolated rarefaction curve. X in The millipede genus Leucogeorgia Verhoeff, 1930 in the Caucasus, with descriptions of eleven new species, erection of a new monotypic genus and notes on the tribe Leucogeorgiini (Diplopoda: Julida: Julidae)
Fig. 52. Species estimation–extrapolated rarefaction curve. X-axis: cave location with species of Leucogeorgiini (each cave–species record counts separately); Y-axis: estimated number of species.
Data from: Retinal capillary rarefaction in patients with type 2 diabetes mellitus
Purpose: In diabetes mellitus type 2, capillary rarefaction plays a pivotal role in the pathogenesis of end-organ damage. We investigated retinal capillary density in patients with early disease. Methods: This cross-sectional study compares retinal capillary rarefaction determined by intercapillary distance (ICD) and capillary area (CapA), measured non-invasively and in vivo by scanning laser Doppler flowmetry, in 73 patients with type 2 diabetes, 55 healthy controls and 134 individuals with hypertension stage 1 or 2. Results: In diabetic patients, ICD was greater (23.2±5.5 vs 20.2±4.2, p = 0.013) and CapA smaller (1592±595 vs 1821±652, p = 0.019) than in healthy controls after adjustment for differences in cardiovascular risk factors between the groups. Compared to hypertensive patients, diabetic individuals showed no difference in ICD (23.1±5.8, p = 0.781) and CapA (1556±649, p = 0.768). Conclusion: In the early stage of diabetes type 2, patients showed capillary rarefaction compared to healthy individuals.
Figure 2. Extrapolated rarefaction curve with 95 in The native bee fauna of the Palouse Prairie (Hymenoptera: Apoidea)
Figure 2. Extrapolated rarefaction curve with 95% confidence intervals based on all collected nonHemihalictus bees. Vertical line indicates the actual number of collected non-Hemihalictus bees.
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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)
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DANDI Archive for NWB datasets
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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.