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1,445 results for “species richness.”
Data from: Species richness and phylogenetic diversity of seed plants across vegetation zones of Mount Kenya, East Africa
Mount Kenya is of ecological importance in tropical east Africa due to the dramatic gradient in vegetation types that can be observed from low to high elevation zones. However, species richness and phylogenetic diversity of this mountain have not been well studied. Here, we surveyed distribution patterns for a total of 1,335 seed plants of this mountain and calculated species richness and phylogenetic diversity across seven vegetation zones. We also measured phylogenetic structure using the net relatedness index (NRI) and the nearest species index (NTI). Our results show that lower montane wet forest has the highest level of species richness, density, and phylogenetic diversity of woody plants, while lower montane dry forest has the highest level of species richness, density, and phylogenetic diversity in herbaceous plants. In total plants, NRI and NTI of four forest zones were smaller than three alpine zones. In woody plants, lower montane wet forest and upper montane forest have overdispersed phylogenetic structures. In herbaceous plants, NRI of Afro‐alpine zone and nival zone are smaller than those of bamboo zone, upper montane forest, and heath zone. We suggest that compared to open dry forest, humid forest has fewer herbaceous plants because of the closed canopy of woody plants. Woody plants may have climate‐dominated niches, whereas herbaceous plants may have edaphic and microhabitat‐dominated niches. We also proposed lower and upper montane forests with high species richness or overdispersed phylogenetic structures as the priority areas in conservation of Mount Kenya and other high mountains in the Eastern Afro‐montane biodiversity hotspot regions.
Figure 2 in Conservation gaps identification through patterns of species richness established from species niche models of mammals in a sector of Chaco Seco ecoregion
Figure 2. Binary maps of potential distribution of (A) chacoan peccary, (B) cougar, (C) brown brocket deer, (D) collared peccary and (E) anteater. The gray pixels indicate the places of presence of the species.
Figure 1 in Conservation gaps identification through patterns of species richness established from species niche models of mammals in a sector of Chaco Seco ecoregion
Figure 1. Study area. Geometric figures of different colors indicating the sites of presence of the selected mammalian species used for the distribution models.
Figure 3 in Conservation gaps identification through patterns of species richness established from species niche models of mammals in a sector of Chaco Seco ecoregion
Figure 3. Response graphs of habitat suitability (ordinate axis) according to the explanatory variables that intervened in the adjustment of the model for cougar (A, B, C). Precipitation is expressed in mm and altitude in meters. Source of bioclimatic variables (bio), site https://www.worldclim.org/data/bioclim.html.
Stochastic dispersal shapes the spatial pattern of species richness in mountain landscapes
<p class="MsoNormal"><strong><span>Aim<a name="OLE_LINK3"></a>: </span></strong><span><span>Biogeographers have begun to address the problem of species distribution patterns in three-dimensional space. A key question is: What patterns of species richness would arise on the three-dimensional surface of a landscape under minimal biological assumptions? Recently, a theory called "Landscape Elevational Connectivity" (LEC) has been developed, which measures how topography and geomorphology drive biodiversity patterns. Here, we tested the predictive ability of LEC for spatial patterns of species richness for the first time.</span></span></p> <p class="MsoNormal"><span><strong><span>Location: </span></strong></span><span><span>The Tibetan Plateau.</span></span></p> <p class="MsoNormal"><span><strong><span>Methods:</span></strong></span><span><span> We </span><span>used the "stacked species distribution models" (S-SDMs) approach to</span></span><span><span> estimate the empirical spatial distribution pattern of bird species richness on the Tibetan Plateau based on online species occurrence data and expert maps, and we compared this estimated distribution with the predictions of LEC.</span></span></p> <p class="MsoNormal"><span><strong><span>Results: </span></strong></span><span><span>We found a high correlation between the LEC null model and observed bird species richness in the biodiversity hotspot on the southeast edge of the Tibetan Plateau (Spearman's correlation, <em>r</em><span>s</span> = 0.746, 95% CI: 0.744-0.748). On a wider scale, LEC was better correlated with species richness in regions higher net primary productivity than in regions with lower net primary productivity.</span></span></p> <p class="MsoNormal"><span><strong><span>Main conclusions:</span></strong></span><span><span> <a name="OLE_LINK31"></a>Our results suggest that the impact of stochastic processes on the spatial distribution pattern of species richness may have been routinely underestimated, especially in regions with rich resources and high species richness. We conclude that it would be fruitful to reconsider the contribution of deterministic factors to the distribution pattern of species richness, especially in mountain landscapes, by applying LEC as a null model.</span></span></p>
Figure 13 from: Ivković M, Ćevid J, Horvat B, Sinclair BJ (2017) Aquatic dance flies (Diptera, Empididae, Clinocerinae and Hemerodromiinae) of Greece: species richness, distribution and description of five new species. ZooKeys 724: 53-100. https://doi.org/10.3897/zookeys.724.21415
Figure 13 Species richness of aquatic Empididae genera from Greece.
Figure 1 from: Ivković M, Ćevid J, Horvat B, Sinclair BJ (2017) Aquatic dance flies (Diptera, Empididae, Clinocerinae and Hemerodromiinae) of Greece: species richness, distribution and description of five new species. ZooKeys 724: 53-100. https://doi.org/10.3897/zookeys.724.21415
Figure 1 Sampling sites of aquatic Empididae recorded from Greece (see Table 1 for codes).
FIGURE 3 in Palaeoecology and sea level changes: Decline of mammal species richness during late Quaternary island formation in the Montebello Islands, north-western Australia
FIGURE 3. Google Earth© (2020) image of Campbell Island within the Montebello Island group.
Figure 1 from: Sublett CA, Cook JL, Janovec JP (2019) Species richness and community composition of sphingid moths (Lepidoptera: Sphingidae) along an elevational gradient in southeastern Peru. Zoologia 36: 1-11. https://doi.org/10.3897/zoologia.36.e32938
Figure 1 Collection sites ordered by elevation.
Figure 2 in Species richness and diversity of butterflies (Insecta: Lepidoptera) of Ganga Lake, Itanagar Wildlife Sanctuary, Arunachal Pradesh, India
Figure 2. Family-wise number of species of butterflies.
Figure 4 in Species richness and diversity of butterflies (Insecta: Lepidoptera) of Ganga Lake, Itanagar Wildlife Sanctuary, Arunachal Pradesh, India
Figure 4. Habitat-wise number of butterflies species.
Data and Code for: Landscape-level synergistic and antagonistic effects among conservation measures drive wild bee densities and species richness
<p>Data and R code for 'Landscape-level synergistic and antagonistic effects among conservation measures drive wild bee densities and species richness'.</p> <p>Information about the files can be found in the README file.</p>
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.
Figure 3 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 3 - Ohio Plecoptera species richness, actual vs. predicted.
Figure 1 from: Esqueda-González M, Ríos-Jara E, Galván-Villa C, Rodríguez-Zaragoza F (2014) Species composition, richness, and distribution of marine bivalve molluscs in Bahía de Mazatlán, México. ZooKeys 399: 43-69. https://doi.org/10.3897/zookeys.399.6256
Figure 1 - Study area and sampling sites at Bahía de Mazatlán, México.
Figure 8 from: Caterino MS, Tishechkin AK (2016) Spatial and environmental correlates of species richness and turnover patterns in European cryptocephaline and chrysomeline beetles. ZooKeys 557: 59-77. https://doi.org/10.3897/zookeys.557.7087
Figure 8 - Map showing all collecting records for Megalocraerus spp.
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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
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.