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41 results for “estimated species richness”
Data from: Acoustic indices estimate breeding bird species richness with daily and seasonally variable effectiveness in lowland temperate Białowieża forest
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FIGURE 5. Species richness estimations slopes. Jack 2s in A protocol for online documentation of spider biodiversity inventories applied to a Mexican tropical wet forest (Araneae, Araneomorphae)
FIGURE 5. Species richness estimations slopes. Jack 2s indicates the slope average for these estimations based on incidence (BAT) and abundance (Bat and Estimates). Same averages were calculated for Jack 1s and Chao 1 and 2 estimation slopes. Column indicated with (*) correspond to the slope value for pitfalls under Chao's estimations.
Data from: Breaking down the lithification bias: the effect of preferential sampling of larger specimens on the estimate of species richness, evenness, and average specimen size
Lithification, the transition of unconsolidated sediments to fully indurated rocks, can potentially bias estimates of species richness, evenness, and body size distribution derived from fossil assemblages. Fossil collections made from well-indurated rocks consistently exhibit lower species richness, lower evenness, and a specimen size distribution skewed towards larger specimens relative to collections made from unconsolidated sediments, even when collections are drawn from the same assemblage. This phenomenon is known as the lithification bias. While the bias itself has been demonstrated empirically, much less attention has been paid to its causes. Proposed causes include taphonomic processes (e.g., destruction of small specimens during early diagenesis) or methodological differences (e.g., sieving vs. counting specimens on outcrops, bedding surfaces, or mechanically split surfaces). Here we investigate the potential effects of preferential intersection that could also result in a methodologically related bias: the preferential sampling of larger specimens relative to smaller ones when fossils are counted on rock surfaces. We used an analog model to simulate preferential intersection (fossil collection via splitting fossiliferous rock) and compare the results to a random draw model that approximates the effects of sieving. The model was parameterized using nine different combinations of species abundance and species size distributions. The results show that, with rare exceptions, species richness is 5–23% lower, evenness 5-25% lower, and average specimen size 24–150% higher in preferential intersection than in random draw simulations. We conclude that preferential intersection can impose a significant bias independent of other mechanisms (e.g., preferential destruction of smaller specimens during diagenetic or sampling processes), that the magnitude of this bias is partially dependent on the species abundance and size distributions, and that this bias alone does not fully account for empirically observed lithification bias on species richness (i.e., other sources of bias are also at work).
Figure 1 in Application of species-richness estimators for the assessment of earthworm diversity
Figure 1. Performance of eight species-richness estimators (dashed lines) for earthworms sampling data set: ACE; ICE; Chao 1; Chao 2; Jack 1; Jack 2; Bootstrap; Michaelis–Menten asymptote, and the species accumulation curve (solid lines).
Data from: The devil is in the detail: estimating species richness, density, and relative abundance of tropical island herpetofauna
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Data used to estimate the influence of habitat protection, habitat heterogeneity, and periodic flooding on species richness and abundance of waterbirds of the lower Paraná River, Argentina
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Data from: Breaking down the lithification bias: the effect of preferential sampling of larger specimens on the estimate of species richness, evenness, and average specimen size
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Data from: How many dinosaur species were there? Fossil bias and true richness estimated using a Poisson sampling model
The fossil record is a rich source of information about biological diversity in the past. However, the fossil record is not only incomplete but has also inherent biases due to geological, physical, chemical and biological factors. Our knowledge of past life is also biased because of differences in academic and amateur interests and sampling efforts. As a result, not all individuals or species that lived in the past are equally likely to be discovered at any point in time or space. To reconstruct temporal dynamics of diversity using the fossil record, biased sampling must be explicitly taken into account. Here, we introduce an approach that uses the variation in the number of times each species is observed in the fossil record to estimate both sampling bias and true richness. We term our technique TRiPS (True Richness estimated using a Poisson Sampling model) and explore its robustness to violation of its assumptions via simulations. We then venture to estimate sampling bias and absolute species richness of dinosaurs in the geological stages of the Mesozoic. Using TRiPS, we estimate that 1936 (1543–2468) species of dinosaurs roamed the Earth during the Mesozoic. We also present improved estimates of species richness trajectories of the three major dinosaur clades: the sauropodomorphs, ornithischians and theropods, casting doubt on the Jurassic–Cretaceous extinction event and demonstrating that all dinosaur groups are subject to considerable sampling bias throughout the Mesozoic.
Figure 2 in Application of species-richness estimators for the assessment of earthworm diversity
Figure 2. Ordination of localities according to non-metric multidimensional scaling.
Figure 8 from: DeWalt R, Cao Y, Tweddale T, Grubbs S, Hinz L, Pessino M, Robinson J (2012) Ohio USA stoneflies (Insecta, Plecoptera): species richness estimation, distribution of functional niche traits, drainage affiliations, and relationships to other states. ZooKeys 178: 1-26. https://doi.org/10.3897/zookeys.178.2616
Figure 8 - Non–parametric Multi–Dimensional Scaling of Ohio Plecoptera assemblages associated with HUC6 drainages. Axis 1 vs Axis 3.
Figure 2 from: DeWalt R, Cao Y, Tweddale T, Grubbs S, Hinz L, Pessino M, Robinson J (2012) Ohio USA stoneflies (Insecta, Plecoptera): species richness estimation, distribution of functional niche traits, drainage affiliations, and relationships to other states. ZooKeys 178: 1-26. https://doi.org/10.3897/zookeys.178.2616
Figure 2 - Pre-European settlement vegetation percentage cover for Ohio (from Ohio Department of Natural Resources 2003).
Figure 7 from: DeWalt R, Cao Y, Tweddale T, Grubbs S, Hinz L, Pessino M, Robinson J (2012) Ohio USA stoneflies (Insecta, Plecoptera): species richness estimation, distribution of functional niche traits, drainage affiliations, and relationships to other states. ZooKeys 178: 1-26. https://doi.org/10.3897/zookeys.178.2616
Figure 7 - A–B Sampling intensity, drainage area, unique locations, and species richness relationships for HUC6 drainages A Sampling intensity for HUC6 drainages B Species richness vs. number of unique locations in HUC6 drainage areas.
Data from: How many dinosaur species were there? Fossil bias and true richness estimated using a Poisson sampling model
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Data from: Estimating species richness using environmental DNA
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Multi-species occupancy models as robust estimators of community richness
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Figure 9 from: DeWalt R, Cao Y, Tweddale T, Grubbs S, Hinz L, Pessino M, Robinson J (2012) Ohio USA stoneflies (Insecta, Plecoptera): species richness estimation, distribution of functional niche traits, drainage affiliations, and relationships to other states. ZooKeys 178: 1-26. https://doi.org/10.3897/zookeys.178.2616
Figure 9 - Comparison of Ohio Plecoptera assemblage with Midwest states/provinces.
Figure 5 from: DeWalt R, Cao Y, Tweddale T, Grubbs S, Hinz L, Pessino M, Robinson J (2012) Ohio USA stoneflies (Insecta, Plecoptera): species richness estimation, distribution of functional niche traits, drainage affiliations, and relationships to other states. ZooKeys 178: 1-26. https://doi.org/10.3897/zookeys.178.2616
Figure 5 - Species richness of Ohio Plecoptera in 5 increment occurrence classes.
Figure 4 from: DeWalt R, Cao Y, Tweddale T, Grubbs S, Hinz L, Pessino M, Robinson J (2012) Ohio USA stoneflies (Insecta, Plecoptera): species richness estimation, distribution of functional niche traits, drainage affiliations, and relationships to other states. ZooKeys 178: 1-26. https://doi.org/10.3897/zookeys.178.2616
Figure 4 - Singleton and doubleton species richness.
Figure 1 from: DeWalt R, Cao Y, Tweddale T, Grubbs S, Hinz L, Pessino M, Robinson J (2012) Ohio USA stoneflies (Insecta, Plecoptera): species richness estimation, distribution of functional niche traits, drainage affiliations, and relationships to other states. ZooKeys 178: 1-26. https://doi.org/10.3897/zookeys.178.2616
Figure 1 - HUC6 drainages and point locations for Ohio Plecoptera collections.
Figure 6 from: DeWalt R, Cao Y, Tweddale T, Grubbs S, Hinz L, Pessino M, Robinson J (2012) Ohio USA stoneflies (Insecta, Plecoptera): species richness estimation, distribution of functional niche traits, drainage affiliations, and relationships to other states. ZooKeys 178: 1-26. https://doi.org/10.3897/zookeys.178.2616
Figure 6 - Species richness of Ohio Plecoptera families.
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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.