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96 results for “rarity”
Fig. 1 in A New Measure Of Conservation Value Combining Rarity And Ecological Diversity: A Case Study With Light Trap Collected Caddisflies (Insecta: Trichoptera)
Fig. 1. The map of Hungary with the position of the sampling sites (filled squares show light traps)
Fig. 3 in A New Measure Of Conservation Value Combining Rarity And Ecological Diversity: A Case Study With Light Trap Collected Caddisflies (Insecta: Trichoptera)
Fig. 3. The diversity (A) and RAR-index (B) of the different aquatic habitats (aquatic habitats with the same letter are not significantly different at p = 0.05 by non-parametric Tukey-test)
Linked collectors and determiners for: Descriptions of a new species and previously unknown males of Nesticus (Araneae: Nesticidae) from caves in Eastern North America, with comments on species rarity.
Natural history specimen data linked to collectors and determiners held within, "Descriptions of a new species and previously unknown males of Nesticus (Araneae: Nesticidae) from caves in Eastern North America, with comments on species rarity". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/a5076314-93fc-409e-b2f4-153872a57b77">https://bionomia.net/dataset/a5076314-93fc-409e-b2f4-153872a57b77</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/a5076314-93fc-409e-b2f4-153872a57b77">https://gbif.org/dataset/a5076314-93fc-409e-b2f4-153872a57b77</a>. Formatted as a Frictionless Data package.
Thermodynamic rarity of metals 2020-2050
<p>Thermodinamic Rarity of metals 2020-2050 of the technologies for the energy and digital transition in Spain. </p> <p>Bulk Metals: Al, Cu, Ni, Mn</p> <p>Technological Metals: Ag, Au, Co, Li, Nd, Dy, Pd, Pt</p>
Figure 4 in The lycaenid butterfly fauna (Lepidoptera) of Cosñipata, Peru: annotated checklist, elevational patterns, and rarity
Figure 4. El Mirador (red marking), a site occupied by territorial males at 1,720m (image courtesy S. Kinyon).
Fig. 5 in Sampling mammalian carnivores in western Thailand: Issues of rarity and detectability
Fig. 5. Model averaged estimates of detection probability for 15 carnivore species by camera trapping plotted as a function of log10(weight in kg). Error bars represent 95% confidence intervals. Fitted line is derived from a second-order polynomial which described the best fit of the data. R2 is the coefficient of determination of the fitted line. The six species with lg(weight) greater than 1.0 are Asiatic golden cat, Asiatic jackal, dhole, sun bear, Asiatic black bear, and tiger, from left to right.
Fig. 4 in Sampling mammalian carnivores in western Thailand: Issues of rarity and detectability
Fig. 4. Model averaged estimates of detection probability across the models shown in Tables 2B and 2C for species functional groups (TRAIT; large vs small and terrestrial vs semi-arboreal behavior). Estimated detection probabilities of each species are also shown for TERRESTRIAL and SEMI-ARBOREAL species. Error bars represent 95% confidence intervals.
Fig. 3 in Sampling mammalian carnivores in western Thailand: Issues of rarity and detectability
Fig. 3. Model averaged estimates of detection probability of four functional groups of carnivores (top row) and for each of 15 species separately. Detection probabilities are estimated as functions of camera placement categories (road [no. cameras = 1], streams [5], trails [14], & trails by streams [tbs; 33]). Error bars represent 95% confidence intervals. Estimates were presented without error bars when standard errors could not be estimated. Estimates with 95% confidence intervals less than zero and/or greater than one indicate a lack of model convergence. n refers to the number of independent photographs used to estimate detection probabilities.
Fig. 2 in Sampling mammalian carnivores in western Thailand: Issues of rarity and detectability
Fig. 2. Species discovery curves generated from rarefaction for mammalian carnivores based on camera trap surveys at four study sites within mosaic forest types of Thung Yai between November 2007 and August 2008. Bars represent 95% confidence intervals of the estimates scaled by survey effort (camera trap nights) combined across all active camera locations (n = 53). Curves were estimated using package BiodiversityR in program R.
Fig. 1 in Sampling mammalian carnivores in western Thailand: Issues of rarity and detectability
Fig. 1. Map of Thung Yai Naresuan Wildlife Sanctuary showing camera trap polygons in four study sites, from south to north, Headquarters (HQ), Tikong (TK), Sesawo (SSW), and Mae Gatha (MGT) between November 2007 and August 2008.
Eco-evolutionary causes and consequences of rarity in plants: a meta-analysis
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Data and code from: Functional rarity of plants in German hay meadows - patterns on the species level and mismatches with community species richness
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Data from: Spatial patterns and rarity of the white-phased 'Spirit Bear' allele reveals gaps in habitat protection
<p>Preserving genetic and phenotypic diversity can help safeguard not only biodiversity but also cultural and economic values.</p> <p>Here, we present data that emerged from Indigenous-led research at the intersection of evolution and ecology to support conservation planning of a culturally salient, economically valuable, and rare phenotypic variant. We addressed three conservation objectives for the white-phased 'Spirit bear' polymorphism, a rare and endemic white-coated phenotype of black bear (Ursus americanus) in Kitasoo/Xai'xais and Gitga'at Territories and beyond in coastal British Columbia, Canada. First, we used non-invasively collected hair samples (n = 385 bears over ~18,000 km<sup>2</sup>) to assess the spatial variation in the frequency of the allele that controls the white-coloured morph (mc1r). Second, we compared our observed allele frequencies at mc1r with those expected under Hardy-Weinberg equilibrium. Finally, we examined how well current protected areas in the region aligned with spatial hotspots of Spirit bear alleles.</p> <p>We found that landscape-level allele frequency was lower than previously reported. For example, our systematic sampling estimated a frequency of 0.25 (95% CI 0.13-0.41) on Gribbell Island compared with the previously reported estimate of 0.56. Also, in contrast with previous reports, we failed to detect a statistically significant departure from Hardy-Weinberg equilibrium at mc1r, which calls into question the previously-posited role of homozygote gene flow, heterozygote disadvantage, and positive assortative mating in the maintenance of this polymorphism. Finally, we found a discrepancy between the placement of protected areas and the 90th percentile hotspots (upper 10% of all estimated values) of Spirit bear alleles, with ~50% of hotspots falling outside of protected areas.</p> <p>These results provide new insight into hypotheses related to the maintenance of this rare polymorphism, and directly relevant information to support evidence-based opportunities for Indigenous Nations of the area to attend to gaps in conservation planning.</p>
Data from: Rarity does not limit genetic variation or preclude subpopulation structure in the geographically restricted desert forb Astragalus lentiginosus var. piscinensis
Premise of the study: Characteristics of rare taxa include small population sizes and limited geographical ranges. The genetic consequences of rarity are poorly understood for most taxa. A small geographical range could result in reduced opportunity for isolation by distance or environment, thereby limiting genetic structure and variation, but few studies explore genetic structure at small spatial scales with sufficient resolution to test this hypothesis. Moreover, few comparative genetic studies exist among infrataxa differing in rarity. Here, we compare genetic variation among varieties of Astragalus lentiginosus differing in range size. Additionally, we ask if genetic structure exists in A. l. var. piscinensis, a rare taxon consisting of several thousand individuals that persist on ~8 km2 of alkaline soil. Methods: We compared genetic variation among 11 varieties of A. lentiginosus differing in range size using a genotyping by sequencing (GBS) approach, which generated 11,475 single nucleotide polymorphisms (SNPs). We characterized genetic structure among subpopulations of A. l. var. piscinensis using a second GBS dataset of 7,274 SNPs and explored associations between genetic structure and environmental variation. Key results: We found no association between genetic variation and range size among varieties of A. lentiginosus. Additionally, despite the extremely small range of A. l. var. piscinensis, we report a well-defined genetic structure among subpopulations associated with microhabitat variation in soil composition. Conclusion: Our results suggest that fine scale genetic structure may exist within other rare Astragalus taxa and that rarity does not preclude the maintenance of genetic diversity in this genus. In compliance with data protection regulations, please contact the publication office if you would like to have your personal information removed from the database.
Vegetation survey data to understand drivers of plant rarity
<p>Determining the drivers of plant rarity is a major challenge in ecology. Analysing spatial associations between different plant species can provide an exploratory avenue for understanding the ecological drivers of plant rarity. Here, we examined the different types of spatial associations between rare and common plants to determine if they influence the occurrence patterns of rare species. We completed vegetation surveys at 86 sites in woodland, forest, and heath communities in south-east Australia. We also examined two different rarity measures to quantify how categorisation criteria affected our results. Rare species were more likely to have positive associations with both rare and common species across all three vegetation communities. However, common species had positive or negative associations with rare and other common species, depending on the vegetation community in which they occurred. Rare species were positively associated with species diversity in forest communities. In woodland communities, rare species were associated negatively with species diversity but positively associated with species evenness. Rare species with high habitat specificity were more clustered spatially than expected by chance. Efforts to understand the drivers of plant rarity should use rarity definitions that consider habitat specificity. Our findings suggest that examining spatial associations between plants can help understand the drivers of plant rarity.</p>
Cross-scale drivers of woody plant species commonness and rarity in the Brazilian drylands
<p><strong>Aim</strong>: <span>Locally abundant species are typically widespread, while locally scarce species are geographically restricted – the </span>so-called abundance-occupancy relationships (AORs)<span>. AORs help explain the drivers of species rarity and community assembly</span>, but little is known about how variation around such relationships is driven by species traits and niche-based processes, particularly in tropical woody plants. We<span> tested the hypothesis that AORs in tropical dryland woody plants are positive and mediated by niche and functional traits along environmental gradients.</span></p> <p><strong><span>Location</span></strong><span>: The Caatinga dry forest and Cerrado savannah, Brazil.</span></p> <p><strong><span>Methods</span></strong><span>: We aggregated abundance and occurrence data into grid-cells representing local (10-km) to landscape scales (50-km). We calculated species mean relative abundance at occupied grid-cells (local abundance) and the proportion of grid-cells occupied (occupancy), and estimated their niche breadth and marginality along multivariate environmental gradients. </span></p> <p><strong><span>Results</span></strong><span>: AORs were positive but weak at different scales in both regions due to some locally abundant but geographically restricted species, with most species being both locally and geographically rare. Cross-species variation in local abundance was largely unpredictable, but occupancy was strongly driven by niche and functional traits, with a prominent negative effect of niche marginality. Geographically restricted species were associated with rare habitats,</span><span> such as wetter and less intensively used habitats. Large seeds and abiotic dispersal favoured occupancy in Caatinga at small and large spatial scales, respectively, whereas species with conservative leaves were more widespread across scales in Cerrado. </span></p> <p><strong><span>Main conclusions</span></strong><span>: Woody plants in dry tropical biotas exhibit weak AORs, likely related to low habitat availability and dispersal limitation. Caatinga and Cerrado emerge as environmentally structured at multiple spatial scales, with several habitat-specialist rare species bearing specific regenerative and resource-use traits and relying on conditions threatened by climate change and land-use intensification. </span>Examining AORs through the lens of niche, functional traits and spatial scales enables mapping patterns and drivers of species commonness and rarity, enhancing understanding of species assembly and providing tools for biodiversity conservation.</p>
20220516-DOLA: Agent adapt ontologies to agree on decision taking. Introducing rarity index for agents to consider before adapting.
This archive contains the results of a multi-agent simulation experiment [1] carried out with Lazy lavender [2] environment.<br><br>Experiment Label: 20220516-DOLA<br><br>Experiment design: Agent adapt ontologies to agree on decision taking. Introducing rarity index for agents to consider before adapting.<br><br>Experiment setting: Agents learn decision trees (transformed into ontologies); get income from environment; adapt by splitting their leaf nodes<br><br>Hypotheses: Success rate converges to 1. Improve the average accuracy at the end of the experiment.<br><br>Detailed information can be found in index.html or notebook.ipynb.<br><br>[1] <a href="https://sake.re/20220516-DOLA">https://sake.re/20220516-DOLA</a><br>[2] <a href="https://gitlab.inria.fr/moex/lazylav/">https://gitlab.inria.fr/moex/lazylav/</a><br><br>
Fig. 4 in A New Measure Of Conservation Value Combining Rarity And Ecological Diversity: A Case Study With Light Trap Collected Caddisflies (Insecta: Trichoptera)
Fig. 4. The relationship between diversity (D) and rarity (RAR-index) of the samples
Figure 3 in The lycaenid butterfly fauna (Lepidoptera) of Cosñipata, Peru: annotated checklist, elevational patterns, and rarity
Figure 3. The Cosñipata Valley, view from 1,650m, looking towards the northeast.
Figure 2 in The lycaenid butterfly fauna (Lepidoptera) of Cosñipata, Peru: annotated checklist, elevational patterns, and rarity
Figure 2. Location of major sampling sites. The numbers reference Table 1.
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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.