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68 results for “species abundance distributions”
Native and invasive species abundance distributions in lakes at North Temperate Lakes LTER 1979-2010
These data were compiled from multiple sources. We collated data on the abundance or density of aquatic invasive and native species sampled in more than 20 sites using the same methods. To control for sampling methodology and allow comparisons among native and invasive species, we only included data where both invasive and native species from a taxonomic group were sampled using the same methods across multiple sites. Exceptions were made to include rusty crayfish (Orconectes rusticus) in its native range and zebra mussel (Dreissena polymorpha) data.
Fig. 3 in Observations On Species Abundance Distribution In Fly Collections
Fig. 3. Frequency polygons of 2-moving averaged frequencies. X and Y refer to the artificial collection containing the first 46 319 individuals and the last 46 524 individuals in the combined collection
Fig. 4 in Observations On Species Abundance Distribution In Fly Collections
Fig. 4. Further frequency polygons of 2-moving averaged frequencies. A, B and C refer to artificial collections mentioned in the text. Details see at Fig. 1.
Fig. 2 in Observations On Species Abundance Distribution In Fly Collections
Fig. 2. Frequency polygons of 2-moving averaged frequencies relating to combined collections. The 2003–2004 polygon is shifted in the figure by two abundance classes to the right. Details see at Fig. 1.
Рис. 2. Распределение Значений биомассы и численности Macoma balthica по станциЯм отбора проб. Fig. 2. Distribution of the Macoma balthica biomass and abundance values at sampling stations. in Species composition and distribution of bivalve mollusks in plankton and benthos in Nevelsky Strait in summer
Рис. 2. Распределение Значений биомассы и численности Macoma balthica по станциЯм отбора проб. Fig. 2. Distribution of the Macoma balthica biomass and abundance values at sampling stations.
Fig. 1. Photographs taken with a in Diptilomiopus floridanus (Acari: Eriophyoidea: Diptilomiopidae): its distribution and relative abundance with other eriophyoid species on dooryard, varietal block, and commercial citrus in Florida
Fig. 1. Photographs taken with a scanning electron microscope of the new species of Diptilomiopus floridanus Craemer & Amrine on Florida citrus. (A) Dorsal view of prodorsum, legs, and well developed chelicerae. (B) Dorsal view of the mite. (C) Lateral view of the mite. (D) Dorso–lateral view of the mite with extended, downward gnathosome.
Figure 2. – Mean abundance per 750 m2 in Changes in distribution patterns of two vulnerable fish species (Epinephelus marginatus and Sciaena umbra) in the Scandola marine reserve (Corsica, NW Mediterranean): a possible effect of increased boat tourism
Figure 2. – Mean abundance per 750 m2 (± SE) of the dusky grouper Epinephelus marginatus (A) and the brown meagre Sciaena umbra (B) according to protection level at Scandola in 2012 and 2018. IR: integral reserve, BZ: buffer zone, UP: unprotected zone. Interannual difference are indicated for each protection level, *: significant at p <0.05, ns: not significant.
Figure 3 in Spatiotemporal distribution, abundance, and species-environment relationships of Scyphozoa (Cnidaria) species in Hisarönü, Marmaris, and Fethiye bays (Muğla, Turkey
Figure 3. RDA ordination plot for Scyphozoa species, environmental parameters, sampling months, and stations. Sampling stations in RDA plot indicated with □: Hisarönü 1; ■: Hisarönü 2; O: Marmaris 1; ●: Marmaris 2; ◇: Marmaris 3; ◆: Marmaris 4; ×: Marmaris 5; ∆: Fethiye 1; △: Fethiye 2; ▲: Fethiye 3. Scyphozoa species indicated by the following abbreviations: Aa: Aurelia aurita; Ct: Cotylorhiza tuberculata; Ca: Cassiopea andromeda. Sampling months in RDA plot indicated with: 1: September 2011; 2: October 2011; 3: November 2011; 4: December 2011; 5: January 2012; 6: February 2012; 7: March 2012; 8: April 2012; 9: May 2012; 10: June 2012; 11: July 2012; 12: August 2012; 13: September 2012; 14: October 2012. See Table 4 for abbreviations of environmental variables.
Figure 15-16. Rank abundance chart for Phanaeini species. 15 in Diversity and distribution of the scarab beetle tribe Phanaeini in the northern states of the Brazilian Northeast (Coleoptera: Scarabaeidae: Scarabaeinae)
Figure 15-16. Rank abundance chart for Phanaeini species. 15) Recorded in Ceará during February-June 2008. Light grey indicates specimens observed in Atlantic forest; dark grey indicates specimens observed in caatinga. 16) Recorded in Maranhão during February and May 2008. All specimens were broadly observed in cerrado habitat (see species accounts for details).
Species distribution and abundance modelling with dynamicSDM: a case study analysis of the red-billed quelea (Quelea quelea).
<p><strong>GBIF_all_aves_2000_2020.csv</strong><br> A dataset containing e-Bird sampling events for all bird species across southern Africa between 2000-2020 (Fink et al., 2021, GBIF, 2021). <br> <br> Fink, D., T. Auer, A. Johnston, M. Strimas-Mackey, O. Robinson, S. Ligocki, W. Hochachka, L. Jaromczyk, C. Wood, I. Davies, M. Iliff, L. Seitz. 2021. eBird Status and Trends, Data Version: 2020; Released: 2021. Cornell Lab of Ornithology, Ithaca, New York. \doi{10.2173/ebirdst.2020}<br> GBIF.org (12 July 2021) GBIF Occurrence Download \doi{10.15468/dl.ppcu6q}</p> <p><strong>RBQ_full_analysis.R</strong></p> <p>An R script for the generation of dynamic species distribution and abundance models for nomadic bird, the red-billed quelea (<em>Quelea quelea</em>) using dynamicSDM package functions. </p> <p><strong>Unfiltered_quelea_occurrence.csv</strong><br> A dataset containing species occurrence and abundance records for the bird species, the red-billed quelea (<em>Quelea quelea</em>) between 1976-2021 (GBIF 2021 & GBIF 2022 & sources listed in Table 1). <br> <br> GBIF.org (12 July 2021) GBIF Occurrence Download \doi{10.15468/dl.ppcu6q}<br> <br> GBIF.org (25 July 2022) GBIF Occurrence Download \doi{10.15468/dl.k2kftv}<br> </p> <p><strong>Table S1. </strong>Red-billed quelea (<em>Quelea quelea</em>) occurrence and abundance data sources.</p> <table align="left"> <tbody> <tr> <td> <p><strong>Data type</strong></p> </td> <td> <p><strong>Sources</strong></p> </td> </tr> <tr> <td> <p><strong>Control operation </strong></p> </td> <td> <ul> <li>Information Core for Southern African Migrant Pests (ICOSAMP, 2001-2005).</li> <li>Centre for Overseas Pest Research (COPR), Natural History Museum, Tring.</li> <li>Botswana Ministries of Agriculture.</li> <li>Mozambique Ministry of Agriculture</li> </ul> </td> </tr> <tr> <td> <p><strong>Citizen science</strong></p> </td> <td> <ul> <li>Global Biodiversity Information Facility, including iNaturalist, eBird, South Africa Bird Atlas Project (SABAP) and South Africa Bird Ringing Unit (SAFRING) sources.</li> </ul> </td> </tr> <tr> <td> <p><strong>Independent research</strong></p> </td> <td> <ul> <li>EXCEL File "NfA-yearposRAC" (unpublished data set complied by R. A. Cheke, 2010).</li> </ul> </td> </tr> </tbody> </table>
Fig. 3 in Unveiling global species abundance distributions
Fig. 3 | The temporal change in our statistical understanding of gSADs. a, The final 20-year rolling window gSAD for each of ten example classes with the best fit overlaid for the log-series, negative binomial and Poisson log-normal distributions.b, Yearly goodness of fit (correlation) of each distribution for each 20-year rolling window gSAD.Example classes from top to bottom: Actinopterygii, Amphibia,Arachnida, Aves, Bivalvia, Cephalopoda, Cycadopsida, Insecta, Liliopsida and Mammalia.
Fig. 4 in Unveiling global species abundance distributions
Fig. 4 | How the relative position of the veil corresponds to species richness and the number of individuals in a class. a–c, The proportion of the gSAD uncovered,assuming a Poisson log-normal distribution, and its relationship to observed species richness/number of observations (a), number of observations (b) and species richness (c). To aid in visualizing the patterns, the red dashed line represents a fit from geom_smooth() and the shaded grey area represents the 95% confidence interval around that fit.
Fig. 1 in Unveiling global species abundance distributions
Fig. 1 | Conceptual scheme illustrating the Poisson sampling of a community with species abundances described by a gamma or a log-normal distribution. Two types of gSAD—gamma (left) and log-normal distribution (right) are shown at the top.Each distribution represents the probability f of a species having a given abundance λ, with the gamma distribution having parameters k (shape) and θ (scale) and the log-normal distribution having parameters μ (mean) and σ (standard deviation), and Γ() representing the gamma function.In the middle, sampling of the gSAD with the probability of each species having a given number of individuals sampled described by a Poisson distribution is illustrated. The mean abundance of each species sampled is randomly taken from the SAD.We exemplify two samples of different sizes, where different symbols denote individuals of different species. The bottom graphs show that: if the global abundances have a log-normal distribution, the mixture distribution of abundances in the sample is a Poisson log-normal; if the global abundances follow a gamma distribution the resulting mixture distribution is a negative binomial but in the limit k→0, we obtain the Fisher log-series.
Integrating presence-only and detection/non-detection data to estimate distributions and expected abundance of difficult-to-monitor species on a landscape-scale
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Future abundance and distribution of key bird species for pathogen transmission in the Netherlands
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Species Abundance Distributions (SADs) for local tree communities in 1-ha forest plots on 20 tropical islands in the Indo-Pacific region
<p>Species abundance distributions (SADs) characterise the distribution of individuals among species. This dataset was used to investigate the relative importance of disturbance regime (tropical cyclone regime) and island geography (the area and isolation of islands) on the shape of SADs.</p>
Hierarchical multi-grain models improve descriptions of species' environmental associations, distribution, and abundance
<p>The characterization of species' environmental niches and spatial distribution predictions based on them are now central to much of ecology and conservation, but implicitly requires decisions about the appropriate spatial scale (i.e. <i>grain</i>) of analysis. Ecological theory and empirical evidence suggest that range-resident species respond to their environment at two characteristic, hierarchical spatial grains: (i) <i>response grain</i>, the (relatively fine) grain at which an individual uses environmental resources, and (ii) <i>occupancy grain</i>,<i> </i>the (relatively coarse) grain equivalent to a typical home range. We use a multi-grain (MG) occupancy model, aided by fine-grain remotely sensed imagery, to simultaneously estimate species-environment associations at both grains, conduct grain optimization to measure response grain, and apply this analysis framework to an example species: a medium-sized bird (<i>Tockus deckeni</i>) in a heterogeneous East African landscape. Based on home range analysis of movement data, we calculate an occupancy grain of 1km for <i>T. deckeni</i>. Using a grain optimization procedure across 32 grains from 10m to 500m, we identify 60m as the most strongly supported response grain for a suite of environmental variables, slightly coarser than opportunistic behavioral observations would have suggested. Validation confirms that the accuracy of the optimized MG occupancy model substantially exceeds that of equivalent single-grain (SG) occupancy models. We further use a simulation approach to assess the potential impacts of accounting for the multi-scale structure of species' environmental requirements on estimates of population size. We find that the more strongly supported MG approach consistently predicts a minimum population sizes in the study landscape that is much lower than that provided by the SG model. This suggests that SG approaches commonly used in conservation applications could lead to overly optimistic abundance and population estimates and that the MG approach may be more appropriate for supporting species conservation goals. More generally, we conclude that multi-grain approaches of the sort presented, and increasingly enabled by growing high-resolution remotely sensed data, hold great promise for offering a more mechanistic framework for assessing the appropriate grain(s) for population monitoring and management and enable more reliable estimates of abundances and species' distributions.</p>
Figure 1 in Spatiotemporal distribution, abundance, and species-environment relationships of Scyphozoa (Cnidaria) species in Hisarönü, Marmaris, and Fethiye bays (Muğla, Turkey
Figure 1. Sampling stations on the coast of Muğla.
Data from: The biogeographical patterns of species richness and abundance distribution in stream diatoms are driven by climate and water chemistry
In this inter-continental study of stream diatoms, we asked three important but still unresolved ecological questions: 1) What factors drive the biogeography of species richness and species abundance distribution (SAD); 2) Are climate-related hypotheses, which have dominated the research on the latitudinal and altitudinal diversity gradients, adequate in explaining spatial biotic variability; and 3) Is the SAD response to the environment independent of richness? We tested a number of climatic theories and hypotheses (i.e., the species-energy and the metabolic theory; and the energy variability and the climatic tolerance hypothesis) but found no support for any of these concepts as the relationships of richness with explanatory variables were non-existent, weak or unexpected. Instead, we demonstrated that diatom richness and SAD evenness generally increased with temperature seasonality and at mid- to high total phosphorus concentrations. The spatial patterns of diatom richness and the SAD—mainly longitudinal in the US, but latitudinal in Finland—were defined primarily by the covariance of climate and water chemistry with space. The SAD was not entirely controlled by richness, emphasizing its utility for ecological research. Thus, we found support for the operation of both climate and water chemistry mechanisms in structuring diatom communities, which underscores their complex response to the environment and the necessity for novel predictive frameworks.
Data from: Odonate species occupancy frequency distribution and abundance – occupancy relationship patterns in temporal and permanent water bodies in a subtropical area
<p>This paper investigates species richness and species occupancy frequency distributions (SOFD) as well as patterns of abundance-occupancy relationship (SAOR) in Odonata (dragonflies and damselflies) in a subtropical area. A total of 82 species and 1983 individuals were noted from 73 permanent and temporal water bodies (lakes and ponds) in the Pampa biome in southern Brazil. Odonate species occupancy ranged from 1 to 54. There were few widely distributed generalist species and several specialist species with a restricted distribution. About 70% of the species occurred in less than 10% of the water bodies, yielding a surprisingly high number of rare species, often making up the majority of the communities. No difference in species richness was found between temporal and permanent water bodies. Both temporal and permanent water bodies had odonate assemblages that fitted best with the unimodal satellite SOFD pattern. It seems that unimodal satellite SOFD pattern frequently occurred in the aquatic habitats. The SAOR pattern was positive and did not differ between permanent and temporal water bodies. Our results are consistent with a niche-based model rather than a metapopulation dynamics model.</p>
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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.