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552 results for “species abundance”
Figure 10. Microplana terrestris and M in Abundance, reproduction, and feeding of three species of British terrestrial planarians: Observations over 4 years
Figure 10. Microplana terrestris and M. scharffi. The time taken for cocoons to hatch after shedding or collection at different dates. Collected cocoons were within 1 day of being shed so that the maximum error in the time taken to hatch is 1 day.
Figure 7 in Abundance, reproduction, and feeding of three species of British terrestrial planarians: Observations over 4 years
Figure 7. Khaki Microplana species. (a) Monthly occurrence (corrected numbers) over 48 months from March 2001 to February 2005: 1 to 5 on the x-axis are years 2001 to 2005; (b) mean¡SE monthly numbers from January (J) to December (D) over 4 years.
Figure 8 in Abundance, reproduction, and feeding of three species of British terrestrial planarians: Observations over 4 years
Figure 8. Kitchen site: cocoons (& and solid line) and hatchlings (• and dashed line) in 2003 and 2004. Mean¡SE values for each month.
Fig. 3 in Relationships between water transparency and abundance of Cynodontidae species in the Bananal floodplain, Mato Grosso, Brazil
Fig. 3. Cluster dendrogram based on relative abundance in biomass of Cynodontidae species and water transparency from 15 sampling sites in the Bananal floodplain, Mato Grosso, Brazil (UPGMA algorithm, Euclidian distance). See Table 1 for site codes.
Fig. 1 in Relationships between water transparency and abundance of Cynodontidae species in the Bananal floodplain, Mato Grosso, Brazil
Fig. 1. Partial map of South America showing the location of the study area in the Bananal floodplain. The detailed map depicts the sampling sites (circles) in the rios Araguaia and Mortes basins. See Table 1 for site codes.
Fig. 2 in Relationships between water transparency and abundance of Cynodontidae species in the Bananal floodplain, Mato Grosso, Brazil
Fig. 2. Linear regression between relative abundance in biomass (a) and number of individuals (b) of Cynodontidae species and water transparency from 15 sampling sites in the Bananal floodplain, Mato Grosso, Brazil.
Fig. 7 in Biology of Isopisthus parvipinnis: an abundant sciaenid species captured bycatch during sea-bob shrimp fishery in Brazil
Fig. 7. Length/weight relationships of the species Isopisthus parvipinnis (n = 990) in Ilhéus, Bahia.
Fig. 6 in Biology of Isopisthus parvipinnis: an abundant sciaenid species captured bycatch during sea-bob shrimp fishery in Brazil
Fig. 6. Estimated length of first maturation (L ) of Isopisthus parvipinnis (n = 990) in Ilhéus, Bahia, Brazil.
Fig. 5 in Biology of Isopisthus parvipinnis: an abundant sciaenid species captured bycatch during sea-bob shrimp fishery in Brazil
Fig. 5. Average length of Isopisthus parvipinnis and confidence interval of 95% (n = 990) across seasons of those individuals captured along the coast of Ilhéus, Bahia, Brazil.
Fig. 4 in Biology of Isopisthus parvipinnis: an abundant sciaenid species captured bycatch during sea-bob shrimp fishery in Brazil
Fig. 4. Number of Isopisthus parvipinnis individuals (n = 990) per length category across those captured along the coast of Ilhéus, Bahia, Brazil.
Fig. 2 in Biology of Isopisthus parvipinnis: an abundant sciaenid species captured bycatch during sea-bob shrimp fishery in Brazil
Fig. 2. Adjusted average of number of Isopisthus parvipinnis individuals captured over the seasons on coast of Ilhéus, Bahia, Brazil.
Fig. 3 in Biology of Isopisthus parvipinnis: an abundant sciaenid species captured bycatch during sea-bob shrimp fishery in Brazil
Fig. 3. Total number of Isopisthus parvipinnis individuals captured per sampling month along the coast of Ilhéus, Bahia, Brazil.
Fig. 1 in Biology of Isopisthus parvipinnis: an abundant sciaenid species captured bycatch during sea-bob shrimp fishery in Brazil
Fig. 1. Map of the studied area (Ilhéus city, Bahia State, Brazil) indicating the sample sites (x) and the isobaths (dashed lines).
Scyphozoan species abundance and water parameters in the Klang Strait during June 2010-December 2011
<p>This is the raw (unprocessed) datasets for scyphozoan jellyfish abundance (catch per unit effort: no. of individual/net) and environmental water parameters (salinity, temperature, pH, DO, turbidity, current speed) collected during the monthly sampling routine (June 2010-December 2011) and 24-hour sampling series conducted during the dry period of the southwest monsoon (June-July 2011) and wet period of the northeast monsoon (November-December 2011) at the Klang Strait, Malaysia.</p> <p>Jellyfish samples were collected during daylight flood tide occasion of the full moon on a monthly basis for 19 months (June 2010-December 2011). Sampling times were fixed at approximately six hours during the flood tide. To investigate the effect of rainfall (dry/wet period), moon phases, diel cycle and tide cycle on scyphozoan abundance, two additional sampling series were conducted on the driest period (June – July, SWM) and wettest period (November – December, NEM) of the year based on past data (Malaysian Meteorological Department). At each dry and wet period, jellyfish were collected at approximately 6-hourly sampling intervals over a 24-hour cycle every week for a month. The samplings thus covered all daily tides in the tide cycle (two flood tides and two ebb tides), day and night (diel cycle), and four consecutive moon phases (1<sup>st </sup>quarter moon, full moon, 3<sup>rd</sup> quarter moon, new moon). The number of bag nets (as replicates) set during samplings were highly dependent on weather condition and ranged from 8 to 20 nets per sampling occasion. All jellyfish from the bag nets were instantly examined and photographed in the field for identification using the keys and descriptions of Rizman-Idid et al. (2016), Jarms and Morandini (2019), and Syazwan et al. (2020b).</p> <p>Physical water parameters were obtained monthly at the surface layer (0.5 - 1.0 m depth from the surface) of the water column during day time (monthly sampling series), and at approximately two-hourly intervals over 24 hours (24-hour sampling series).</p> <p>Legends: SWM = southwest monsoon; IN = inter-monsoon; NEM = northeast monsoon; D = dry period; W = wet period; 1Q = 1<sup>st</sup> quarter moon; FM = full moon; 3Q = 3<sup>rd</sup> quarter moon; NM = new moon; D = day; N = night; F = flood tide; E = ebb tide</p>
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>
Input data, species level results and code accompanying paper: Drivers of the changing abundance of European birds at two spatial scales
<p>This repository contains the input data, species level results and code associated with the paper: <strong>Drivers of the changing abundance of European birds at two spatial scales. </strong></p>
Most rotifer species have positive responses in abundance caused by the increase in nitrogen, phosphorus and chlorophyll-a in reservoirs using TITAN analysis
<p>Some zooplanktonic species change their abundances according to the primary productivity increases in freshwater lentic environments. Here, we aimed to i) evaluate the concentration threshold of variables related to eutrophication (nitrogen, phosphorus<br> and chlorophyll-a concentrations) that alter the frequency of occurrence and relative abundance of rotifer species, and ii) analyze which Rotifera species are related positively or negatively to the increase in these variables. The rotifer community<br> structure was studied in fifteen reservoirs in La Plata River Basin, the second largest in South America, relating its abundance with nitrogen, phosphorus and chlorophyll-a values using the Threshold Indicator Rate Analysis (TITAN). Seventy-one rotifer<br> species were registered in the reservoirs, and six species were considered as indicators of changes in their frequency of occurrence and relative abundance with points of change of 1.118 µg.L -1 , 22.44 µg.L -1 and 3.89 µg.L -1 of the nitrogen, phosphorus and<br> chlorophyll-a concentrations, respectively. Species with positive responses to the increase in nutrients were Keratella tropica, Plationus patulus, Filinia terminalis and Synchaeta oblonga, and negative responses Conochilus unicornis and Synchaeta stylata,<br> typical of oligotrophic reservoirs. The only species that presented the same response for all variable concentrations was Keratella tropica, which is a very common species in South America. Our results reinforce the assumption that some rotifer species are good<br> indicators to the variables related to the trophic level in reservoirs. The script and study data are attached to this database.</p>
Data for: A model of ecological abundance: Terrestrial species inventories
<p>Counts of species in ecological samples are important for two reasons: they tell us about community assembly processes and they form the basis of species diversity estimates. Previous models of count distributions are either complex, widely rejected, not grounded in population dynamics, or not able to predict high unevenness. I present a new one-parameter model assuming that individual counts track the geometric series. The series' governing parameter <em>p</em> is set to vary randomly among species. Communities differ only in the centering of the distribution of <em>p</em>. To find the probability distribution, a vector of evenly-spaced initial values called q is drawn from the range 0 to 1. Values are then scaled by (1) transforming each q into the odds <em>o</em> = <em>q</em>/(<em>1 – q</em>), (2) multiplying each o by a fitted parameter <em>m</em>, and (3) back-computing each <em>p</em> as <em>m o</em>/(<em>m o </em>+ <em>1</em>). This skews the values to match the centering of the actual counts. The distribution is consistent with a population dynamics model in which the number of offspring produced in each interval by each species is distributed geometrically, rising with the number of adults. Large-scale surveys of corals, fishes, butterflies, and trees are consistent with the distribution, as are local-scale inventories of trees and assorted vertebrate and insect groups. Each local survey is used to predict counts within biogeographically and taxonomically matched surveys. When only decisive differences are considered, the model's predictions outperform those of each rival in at least 86% of all pairwise comparisons. The new distribution's estimates haves no substantial sample size bias. Thus, it is preferable to other species diversity estimation methods in the frequent cases where it is a good fit to count data.</p>
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.
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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.