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1,445 results for “species richness.”
Data from: Plastic responses of belowground foraging traits to soil phosphorus-rich patches across 17 coexisting AM tree species in a subtropical forest
<p><span>1. </span><span>Belowground plastic responses to soil nutrient "hot-spots" form a key nutrient foraging strategy of plants coexisting in natural ecosystems. However, it is unclear how plant species differ in these belowground plastic responses and how they co-vary.</span></p> <p><span>2. </span><span>Plastic responses to soil phosphorus (P)-rich patches of absorptive root, mycorrhizal, and exudation traits of 17 co-existing arbuscular mycorrhizal (AM) tree species in a subtropical evergreen broad-leaved forest were investigated using a root bag method.</span></p> <p><span>3. </span><span>There was considerable variation and heterogeneity in species-specific responses to P-rich patches. Negative log response ratios usually occurred for high-cost traits and positive log response ratios for low-cost traits. There were tradeoffs in the plastic responses between root acid phosphatase activity and extraradical hyphal length, which were unaffected by phylogeny, and between root acid phosphatase activity and specific root length. Thicker-rooted species responded to P-rich patches more through root exudation plasticity than mycorrhizal plasticity. Thinner-rooted species relied more on mycorrhizal plasticity.</span></p> <p><span>4. </span><em><span>Synthesis</span></em><span>. Our results revealed diverse P foraging strategies comprising different combinations of plastic adjustments in absorptive root, mycorrhizal, and exudation traits among coexisting AM tree species, which suggest the potential for complementary exploitation of different soil P sources.</span></p>
Data and code for Salminen et al Assessing the relation between geodiversity and species richness in mountain heaths and tundra landscapes
<p>Data and code for Salminen et al Assessing the local-scale relation between geodiversity and species richness in mountain heaths and tundra</p>
Tree species richness and soil organic carbon stock
<p class="MsoNormal"><span>Recently, the perspectives for the stronger persistence of soil organic carbon (SOC) caused by the higher molecular diversity of organic compounds were proposed. Therefore, the effects of tree species richness and composition on the diversity of molecular components of SOC need to be explored. In this study, we collected data on tree species diversity and composition, SOC concentration, chemical composition, litter and fine root properties, and examined the relationships between the richness, composition and functional diversity of tree species, and the evenness of SOC chemical compositions at a molecular level by <sup>13</sup>C nuclear magnetic resonance, across six natural forest types encompassing a diversity gradient, ranging from cold temperate to tropical forests. Across the range, tree species richness correlated to the evenness of SOC chemical components through tree species composition. The negative correlation of evenness of SOC chemical components with tree species composition and the positive correlation of evenness of SOC chemical components with tree functional diversity were found. The positive correlation of the evenness of SOC chemical components with indicator tree species. These findings suggest that the indicator tree species conservation might be preferable to simply increasing tree species richness, for enhancing the potential resistance of SOC to decomposition.</span></p>
Abundance variations within feeding guilds reveal ecological mechanisms behind avian species richness pattern along the elevational gradient of Mount Cameroon
<p><span>Two distinct diversity patterns are observed along tropical elevations: (a) decreasing number of species towards high elevations and (b) a hump-shaped pattern with the peak at mid-elevations. As diversity is likely supported by ecological capacity of the environment, decomposition of the overall richness into ecological facets and considering number of individuals within them is crucial for the proper understanding of richness patterns. We examined abundances of different avian guilds along the forested part of the elevational gradient on Mt. Cameroon. We (a) compared richness and abundance elevational patterns, (b) assessed the effective contribution of multiple guilds to richness and abundance patterns, and (b) assessed to which extent observed abundances of guilds differ from those expected by chance. We sampled birds in 2011–2015 during the dry season at seven elevations (30 m, 350 m, 650 m, 1100 m, 1500 m, 1850 m, 2200 m a.s.l.). For each assemblage, we estimated proportions of species and individuals that use particular diets, foraging modes, and feeding strata. We found that a rather decreasing pattern of species richness turns into a hump-shaped one if we look at the total abundances, implying different mechanisms behind these patterns. The number of species and individuals thus do not seem to be directly related, contrary to 'the more-individuals hypothesis'. Abundances of foliage gleaners at mid-elevations, nectarivores at high elevations, and frugivores at low elevations deviated from random expectations. Our results suggest that parts of ecological space are filled partly separately by bird species and individuals along elevation of Mt. Cameroon.</span></p>
Increasing synchrony opposes stabilizing effects of species richness on terrestrial communities
<p><span><strong>Aim</strong>: Ecological theory has predicted that species richness should stabilize communities, with mechanisms including species synchrony and population variability determining the net impacts. While these theories have been supported empirically, results can be sensitive to taxonomic bias as studies are often focused on plants. Trophic differences between consumers and primary producers can lead to varying stabilizing effects of species richness. </span><span>Here, we compared the impact of species richness on community variability in four taxonomic groups: terrestrial birds, mammals, invertebrates, and plants.</span></p> <p><span><strong>Location</strong>: Global</span></p> <p><span><strong>Method</strong>: Using data from 6,763 time series globally (BioTIME) for four terrestrial taxa, we quantified community and population variability and species synchrony based on abundance fluctuations over time.</span></p> <p><span><strong>Results</strong>: Species richness destabilized communities through increasing synchrony and stabilized communities through reducing population variability in all taxa. Such opposing effects weakened net impacts of species richness on communities. Population variability had higher importance relative to synchrony in plant communities. In contrast, synchrony had more comparable (or even higher) importance compared to population variability in animal communities. When synchrony and population variability were not controlled, stabilizing impacts of species richness were detected in plant communities only.</span></p> <p><strong><span>M</span></strong><span><strong>ain conclusions</strong>: </span><span>Our results highlight how species richness drives stabilizing and destabilizing mechanisms simultaneously across all taxa, with strong taxonomic variation in the relative importance of these mechanisms in regulating community variability. This </span><span>questions the generality of previous findings on stabilizing impacts of species richness based on limited taxonomic coverage. Additionally, our results indicate the need to understand how the importance of stabilizing and destabilizing mechanisms differ in determining community variability across organisms and environments.</span></p>
FIG. 4 in Species richness, functional diversity and assemblage structure of insectivorous bats along an elevational gradient in tropical West Africa
FIG. 4. Mean pairwise distances (MPD) of multivariate traits (A and B), forearm length (C and D), greatest skull length (E and F), narrowest breadth of skull (G and H), ear length (I and J) and tail length (K and L) of insectivorous bat assemblages along the Mount Nimba elevational gradient. Observed MPD for each elevation is represented by the blue dots. A blue line of best fit is shown for significant relationships between observed MPD and elevation. The red dots indicate the expected MPD as calculated by 999 randomized community shuffles for figures on the left, and trait shuffles for figures on the right. A red line of best fit is shown for significant relationships between expected MPD and elevation. Instances where observed MPD differs significantly from the expected MPD are indicated by black rings
FIG. 2 in Species richness, functional diversity and assemblage structure of insectivorous bats along an elevational gradient in tropical West Africa
FIG. 2. Quadratic linear regression of species richness of assemblages versus elevation (P = 0.008; species richness = 34.32 - 7.893*elevation + 0.4881*elevation2)
FIG. 5 in Species richness, functional diversity and assemblage structure of insectivorous bats along an elevational gradient in tropical West Africa
FIG. 5. Mean nearest taxon distances (MNTD) of multivariate traits (A and B), forearm length (C and D), greatest skull length (E and F), narrowest breadth of skull (G and H), ear length (I and J) and tail length (K and L) of insectivorous bats along the Mount Nimba elevational gradient. Observed MNTD for each elevation is represented by the green dots. A green line of best fit is shown for significant relationships between observed MNTD and elevation. The red dots indicate the expected MNTD as calculated by 999 randomized community shuffles for figures on the left, and trait shuffles for figures on the right. A red line of best fit is shown for significant relationships between expected MNTD and elevation. Instances where observed MNTD differs significantly from the expected MNTD are indicated by black rings
FIG. 3 in Species richness, functional diversity and assemblage structure of insectivorous bats along an elevational gradient in tropical West Africa
FIG. 3. Dendrogram of bat functional groups present on Mount Nimba. Eight functional groups were identified, each represented by a different colour. See Supplementary Table S1 for full species names
FIG. 1 in Species richness, functional diversity and assemblage structure of insectivorous bats along an elevational gradient in tropical West Africa
FIG. 1. Study sites in Liberia and Guinea (Google Earth, 2015) and their assignment to the eight elevation belts. Key for site colours: red: <500 m; green: 500–600 m; yellow: 601–800 m; blue: 801–900 m; purple: 901–1100 m; black: 1101–1200 m; orange: 1201– 1400 m; white: 1401–1600 m
FIGURE 5 in Unexpected high species richness of Bythotrephes Leydig, 1860 (Branchiopoda: Cladocera: Cercopagididae) in subalpine Austrian lakes, with the description of new taxa
FIGURE 5. Bythotrephes cf. brevimanus Lilljeborg, 1901, females, Lake Halstättersee (A–C, G– I). Bythotrephes sp., females, Lake Wolfgangsee (D–F, J, K). A, D, setae of distal part of first endopodital segment of tl I. B, C, E, F, setae of distal part of second endopodital segment of tl I. G–K, claws of postabdomen and caudal process.
FIGURE 4 in Unexpected high species richness of Bythotrephes Leydig, 1860 (Branchiopoda: Cladocera: Cercopagididae) in subalpine Austrian lakes, with the description of new taxa
FIGURE 4. Bythotrephes longimanus austriacus ssp. nov., females: Lake Irrsee (A–C), Lake Erlaufsee (D–F), Lake Wolfgangsee (G, H). A–H, claws of postabdomen and caudal process.
FIGURE 1 in Unexpected high species richness of Bythotrephes Leydig, 1860 (Branchiopoda: Cladocera: Cercopagididae) in subalpine Austrian lakes, with the description of new taxa
FIGURE 1. Bythotrephes inexpectatus sp. nov., females (A–H) and males (I–K): Lake Toplitzsee (A, B, E, F, I–K) and Lake Altaussee (C, D, G, H). A, general lateral view. B, antennule. C, I, endopodite of thoracic limb of first pair (tl I). D, setae of distal part of first endopodital segment of tl I. E, setae of distal part of second endopodital segment of tl I. F, apical end of distal segment of upper antennal branch (exopodite). G, postabdomen and proximal part of caudal process. H, apical rudimentary setae of caudal process. J, proximal part of distal segment of tl I with clasping hook. K, copulatory appendage.
FIGURE 3 in Unexpected high species richness of Bythotrephes Leydig, 1860 (Branchiopoda: Cladocera: Cercopagididae) in subalpine Austrian lakes, with the description of new taxa
FIGURE 3. Bythotrephes longimanus austriacus ssp. nov., females: Lake Irrsee (A, F–I, K, L), Lake Erlaufsee (C, D, J, M, N), Lake Wolfgangsee (E, O–R). A, endopodite of thoracic limb of first pair (tl I). B–E, setae of distal part of first endopodital segment of tl I. F–R, setae of distal part of second endopodital segment of tl I.
FIGURE 2 in Unexpected high species richness of Bythotrephes Leydig, 1860 (Branchiopoda: Cladocera: Cercopagididae) in subalpine Austrian lakes, with the description of new taxa
FIGURE 2. Bythotrephes inexpectatus sp. nov., females, Lake Toplitzsee (A–C, D, F, I) and Lake Altaussee (E, G, H). A–C, thoracic limbs of second (tl II), third (tl III), and fourth (tl IV) pairs, respectively. D–I, claws of postabdomen and caudal process.
Predicting species richness and diversity using satellite remote sensing and random forest machine learning algorithm
<p><strong>Aims</strong>: Remote sensing approaches could be beneficial for monitoring and compiling essential biodiversity data because it is cost-effective and allows for coverage of large areas over a short period. This study investigated the relationship between multispectral remote sensing data from Landsat 8 and Sentinel 2 and species richness and diversity in mountainous and protected grasslands.</p> <p><strong>Locations</strong>: Golden Gate Highlands National Park, Free State, South Africa. </p> <p><strong>Methods</strong>: In-situ data of plant species composition and cover from 142 plots with 16 releves each were distributed across the study site and used to calculate species richness and Shannon-wiener species diversity index (species diversity. We used a machine-learning random forest algorithm to optimise the prediction of species richness and diversity. The algorithm was used to identify the optimal spectral bands and vegetation indices for estimating species richness and diversity. Subsequently, the selected bands and vegetation indices were used to estimate species richness through random forest regression. </p> <p><strong>Results</strong>: This research found weak relationships between remote sensing vegetation indices and the diversity metrics, but significant relationships were found between some spectral bands and diversity metrics. Moreover, using machine learning random forest, the multispectral datasets exhibited strong predictive powers. In this investigation, for both sensors, near-infrared (NIR) seemed to be the most selected band to explain species diversity in mountainous grasslands.</p> <p><strong>Main</strong> <strong>conclusions</strong>: This finding further ascertains the efficiency of using NIR in vegetation mapping. This research shows that NIR, SAVI and EVI are the most adequate for predicting species richness and diversity in mountainous grasslands with relatively good accuracies.</p>
FIGURE 4 in One sample-three new species: an example of species richness of the genus Symplecis Förster, 1869 (Hymenoptera, Ichneumonidae: Orthocentrinae) in Burundi
FIGURE 4. Symplecis kibiraensis Varga, sp. n. A–B, D–G—holotype female; C—paratype male. A—lateral view of habitus; B–C—frontal view of face; D—dorsal view of mesoscutum; E—dorsal view of propodeum; F—wings; G—dorsal view of metasomal tergites 1–3.
FIGURE 2 in One sample-three new species: an example of species richness of the genus Symplecis Förster, 1869 (Hymenoptera, Ichneumonidae: Orthocentrinae) in Burundi
FIGURE 2. Symplecis aperta Varga, sp. n., holotype female. A—lateral view of habitus; B—frontal view of face; C—dorsal view of mesoscutum; D—dorsal view of propodeum and metasomal tergites 1–2; E—wings.
Vascular plant species richness in Poland version 1.1
<p>It is slightly modified, in the results of reviewers' comments, version of the "Vascular plant species richness in Poland version 1.0" dataset. The changes involve file names and their organization within the dataset.</p> <p>Poland has a long tradition of geobotanical studies. However, outputs of this research have never been used for mapping vascular plant species richness at a larger spatial scale. Here we presented the results of joining and harmonization data from distribution atlas of vascular plants in Poland (Zając and Zając 2001, 2019), and Polish Vegetation Database (Kącki and Śliwiński 2012) to obtain a comprehensive data set on vascular plant species richness in a 10 x 10 km square grid, covering the territory of entire Poland. The presented data set is based on the recent version of the both above-mentioned data sources, provided for harmonization in 2020. The species were classified according to their origin, conservation status, and frequency of occurrences. The 10x10 km spatial grid was prepared by Komsta (2016) and Verey (2017). We used the grid system downloaded from:<a href="https://worldbig.org/atpol/"> https://worldbig.org/atpol</a>, and clipped it to the study area extent.</p> <p>Kącki, Z. and Śliwiński, M., 2012. The Polish Vegetation Database: structure, resources and development. Acta societatis botanicorum Poloniae, 81(2). DOI: 10.5586/asbp.2012.014</p> <p>Komsta, Ł., 2016. ATPOL geobotanical grid revisited-a proposal of coordinate conversion algorithms. Annales Universitatis Mariae Curie-Skłodowska. Sectio E, Agricultura, 71(1), pp.31-37.</p> <p>Verey, M (2017). Teoretyczna analiza i praktyczne konsekwencje przyjęcia modelowej siatki ATPOL jako odwzorowania stożkowego definiującego konwersję współrzędnych płaskich na elipsoidę WGS 84. Fragmenta Floristica et Geobotanica Polonica, 24(2), 469-488.</p> <p>Zając A. (1978) Atlas of distribution of vascular plants in Poland (ATPOL). Taxon, 481-484. <a href="https://doi.org/10.2307/1219899">https://doi.org/10.2307/1219899</a></p> <p>Zając A., Zając, M. (2001) Atlas rozmieszczenia roślin naczyniowych w Polsce. Nakładem Pracowni Chorologii Komputerowej Instytutu Botaniki Uniwersytetu Jagiellońskiego, Kraków</p> <p>Zając, A., & Zając, M. (2019). Distribution atlas of vascular plants in Poland: appendix. Institute of Botany, Jagiellonian University.–Kraków.</p> <p> </p> <p><strong>This dataset consists: </strong></p> <p><strong>Files_description - </strong>file with a description of the data stored.</p> <p><strong>Taxa_list.</strong> The nomenclature according to Euro+Med PlantBase (Euro+Med.) and operational taxonomical units (OTUs) used for analysis and mapping in the project. For simplification, the taxonomical operational units are called ‘species’.</p> <p><strong>Taxa_status</strong>. The species affinity to taxonomic units (family, genera), status in Polish flora (native, archeophytes, neophytes), conservation status (Red List species), and frequency of their distribution (rare, moderate and common). </p> <p><strong>Species_richness. </strong>Statistics on species richness and frequency in species groups for 10 × 10 km ATPOL squares. The names of squares according to original names in the ATPOL project (Zając 1978). The sampling bias (SB) shows adequately sampled squares labelled with 1, while squares with 0 are those with low sampling effort. Cross-boundary squares (CBS) denoted by 1 are squares with more than 80% of the area within the terrestrial territory of Poland, while squares with CBS of 0 are those with 80% or less of the area within the terrestrial territory of Poland. The detail information about the particular columns is shown in ‘Files_description’ and ‘Taxa_status’ files.</p> <p><strong>Map_data</strong>. A shapefile with squares geospatial locations, codes of their names, and data on species richness and frequency in species groups. The map is registered in WGS 84 coordinate reference system (EPSG code 4326). The abbreviations and square names used in ‘dbf’ file are the same as those used in ‘Species_richness’ file.</p> <p> </p>
Fig. 4 in Geographic variation in the relationship between large-scale environmental determinants and bat species richness
Fig. 4. Spatial variation in the partial coefficients of determination (R2) for three environmental hypotheses and their shared effects. The coefficients are: energy (E), heterogeneity (H), seasonality (S), shared contribution between energy and heterogeneity (E: H), energy and seasonality (E: S), heterogeneity and seasonality (H: S), and the contribution shared among energy, heterogeneity and seasonality (E: H: S).
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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.