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83 results for “species counts”
Fig. 21 in Still counting: new records, nomenclatural notes, and three new species of Phaeogenini (Hymenoptera, Ichneumonidae, Ichneumoninae) from the Afrotropical region
Fig. 21. Distribution of Heterischnus mfongosi Rousse & van Noort, 2013. A. Previous (blue dots) and new records (yellow stars). B. Previous (blue) and new (yellow) country records.
Tree regeneration after fire: Delta 1994 burn surveys, live seedling counts, by species
Data for this study were collected in 2001 and 2002 by Jill Johnstone (University of Alaska Fairbanks) and Eric Kasischke (University of Maryland). Sites were located within the perimeter of the 1994 burn southeast of Delta Junction Alaska, USA, bordering the Alaska Highway to the North and the Gerstle River to the West. Sites were selected from satellite classifications prepared by Eric Kasischke to represent different levels of burn severity and post-fire vegetation canopy greenness (NDVI). Site selection was constrained by road access, and only areas where all trees had been killed by the fire were selected. At each site, a central point was located in an area of visually homogeneous vegetation. Five parallel transects, each 50 m long, were laid out as follows: 1) the first transect started at the central point and followed a randomly-selected compass direction, 2) two additional transects were established parallel to the first, but at a random distance from the central transect up to 25 m distant. Vegetation was sampled in a 2-m wide belt centered on each transect, and soil samples were made at intervals along the transect line. Vegetation measurements included: a) basal diameters of all pre-fire trees greater than 1.3 m in height, b) counts of all post-fire tree seedlings, and c) basal diameters of tree seedlings and willows, measured in a randomly chosen 5x2 m portion of each transect. General notes were made on visual percent cover of different vegetation growth forms at the site. Destructive measurements of tree seedlings and willows made in 2001 were used to develop allometric equations to predict dry biomass from basal diameter. Measurements of soil organic layer depth were made at 5 m intervals with the use of a spade to excavate small chunks of sod. At one randomly-selected sample point per transect, a 10x10 cm sample of the organic layer was collected for bulk density measurements. Bulk density samples were dried in a 60degC oven for 48 hours and then w
Fig. 1 in The Amount And Distribution Of The Red Data Book Bird Wetland Species In The Azov-Black Sea Region Of Ukraine According To The Results Of August Counts 2004-2015
Fig. 1. Number of August Counts in the different wetlands of Azov-Black Sea coast of Ukraine.
Species counts, depth and latitude from NOAA's annual groundfish trawl surveys (1999-2018) in the Northeast Pacific
<p>These data are provided as Supporting Information to the article appearing in Ecology Letters entitled: "Non-linear models of species' responses to environmental and spatial gradients".</p> <p>The article describes a parametric framework for modelling species-environment non-linear relationships, including an R package ('<em>senlm</em>'), available on Github: <a href="https://primer-e.github.io/senlm">https://primer-e.github.io/senlm</a>. The framework has two components: (i) a non-linear parametric mathematical function to model the mean species response along a gradient that allows asymmetry, flattening/peakedness or bimodality; and (ii) a statistical error distribution tailored for ecological data types, allowing intrinsic mean-variance relationships and zero-inflation. The article demonstrates the utility of this model framework, highlighting the flexibility of a range of possible mean functions and a broad range of potential error distributions, in analyses of fish species' abundances along a depth gradient, and how they change over time and at different latitudes.</p> <p>A vignette in R ('Vignette_S1.pdf') and associated R code ('Rcode_S1.txt') that utilise this dataset to demonstrate the <em>senlm</em> modeling framework are available as Supporting Information to the article.</p>
Fig. 14 in Still counting: new records, nomenclatural notes, and three new species of Phaeogenini (Hymenoptera, Ichneumonidae, Ichneumoninae) from the Afrotropical region
Fig. 14. Distribution of Chauvinia ganota Claridge sp. nov. A. Type locality. B. Country record.
Fig. 35 in Still counting: new records, nomenclatural notes, and three new species of Phaeogenini (Hymenoptera, Ichneumonidae, Ichneumoninae) from the Afrotropical region
Fig. 35. Number of known species of Phaeogenini Förster, 1869 per country.
Species counts, depth and latitude from NOAA's annual groundfish trawl surveys (1999-2018) in the Northeast Pacific
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Hubbard Brook site, station 10-hectare bird count plot at Hubbard Brook Experimental Forest, study of animal species richness of birds in units of numberPer10Hectares on a yearly timescale
The EcoTrends project was established in 2004 by Dr. Debra Peters (Jornada Basin LTER, USDA-ARS Jornada Experimental Range) and Dr. Ariel Lugo (Luquillo LTER, USDA-FS Luquillo Experimental Forest) to support the collection and analysis of long-term ecological datasets. The project is a large synthesis effort focused on improving the accessibility and use of long-term data. At present, there are ~50 state and federally funded research sites that are participating and contributing to the EcoTrends project, including all 26 Long-Term Ecological Research (LTER) sites and sites funded by the USDA Agriculture Research Service (ARS), USDA Forest Service, US Department of Energy, US Geological Survey (USGS) and numerous universities. Data from the EcoTrends project are available through an exploratory web portal (http://www.ecotrends.info). This web portal enables the continuation of data compilation and accessibility by users through an interactive web application. Ongoing data compilation is updated through both manual and automatic processing as part of the LTER Provenance Aware Synthesis Tracking Architecture (PASTA). The web portal is a collaboration between the Jornada LTER and the LTER Network Office. The following dataset from Hubbard Brook (HBR) contains animal species richness of birds measurements in numberPer10Hectares units and were aggregated to a yearly timescale.
Hymenoptera Species and Counts in a Maryland Forest Clearing Using Multiple Colored Pan Traps
These data present the results of pan trapping for Hymenoptera in the Mid-Atlantic in a forest clearing using multiple pan colors.Pan traps consisted of seven color treatments: blue, fluorescent blue, yellow, fluorescent yellow, red, white, and clear (control) bowls. The species were identified, and the number of each species found in each bowl color in each transect is presented.
Avian point-counts from Rhode Island and Connecticut used to test species distribution models
<p>Spatial-biases are a common feature of presence-absence data from citizen scientists. Spatial thinning can mitigate errors in species distribution models (SDMs) that use these data. When detections or non-detections are rare, however, SDMs may suffer from class imbalance or low sample size of the minority (i.e. rarer) class. Poor predictions can result, the severity of which may vary by modeling technique. To explore the consequences of spatial bias and class imbalance in presence-absence data, we used eBird citizen science data for 102 bird species from the northeastern USA to compare spatial thinning, class balancing, and majority-only thinning (i.e., retaining all samples of the minority class). We created SDMs using two parametric or semi-parametric techniques (generalized linear models and generalized additive models) and two machine-learning techniques (random forest and boosted regression trees). We tested the predictive abilities of these SDMs using an independent and systematically collected reference dataset with a combination of discrimination (area under the receiver operator characteristic curve; true skill statistic; area under the precision-recall curve) and calibration (Brier score; Cohen's kappa) metrics. We found large variation in SDM performance depending on thinning and balancing decisions. Across all species, there was no single best approach, with the optimal choice of thinning and/or balancing depending on modeling technique, performance metric, and the baseline sample prevalence of species in the data. Spatially thinning all the data was often a poor approach, especially for species with baseline sample prevalence < 0.1. For most of these rare species, balancing classes improved model discrimination between presence and absence classes, but hindered model calibration. Baseline sample prevalence, sample size, modeling approach, and the intended application of SDM output – whether discrimination or calibration – should guide decisions about how to thin or balance data, given the considerable influence of these methodological choices on SDM performance. For prognostic applications requiring good model calibration (vis-à-vis discrimination), the match between sample prevalence and true species prevalence may be the overriding feature and warrants further investigation.</p>
Data from: Estimating density for species conservation: comparing camera trap spatial count models to genetic spatial capture-recapture models
Density estimation is integral to the effective conservation and management of wildlife. Camera traps in conjunction with spatial capture-recapture (SCR) models have been used to accurately and precisely estimate densities of "marked" wildlife populations comprising identifiable individuals. The emergence of spatial count (SC) models holds promise for cost-effective density estimation of "unmarked" wildlife populations when individuals are not identifiable. We evaluated model agreement, precision, and survey costs, between i) a fully marked approach using SCR models fit using non-invasive genetic data, and ii) an unmarked approach using SC models fit using camera trap data, for a recovering population of the mesocarnivore fisher (Pekania pennanti). The SCR density estimates ranged from 2.95 to 3.42 (2.18–5.19 95% BCI) fishers 100 km−2. The SC density estimates were influenced by their priors, ranging from 0.95 (0.65–2.95 95% BCI) fishers 100 km−2 for the uninformative model to 3.60 (2.01–7.55 95% BCI) fishers 100 km−2 for the model informed by prior knowledge of a 16 km2 fisher home range. We caution against using strongly informative priors but instead recommend using a range of unweighted prior knowledge. Thin detection data was problematic for both SCR and SC models, potentially producing biased low estimates. The total cost of the genetic survey ($47 610) was two-thirds of the camera trap survey ($77 080), or comparable ($75 746) if genetic sampling effort was increased to include sex and trap-behaviour covariates in SCR models. Density estimation of unmarked populations continues to be a series of trade-offs but as methods improve and integrate, so will our estimates.
FIGURE 4 in Photophore counts in the deep-sea commercial shrimp Aristeus alcocki Ramadan, 1938 (Crustacea: Decapoda: Aristeidae), with a revised key to the Indo-West Pacific species of the genus
FIGURE 4. Neighbor-joining (NJ) tree from partial sequences of mitochondrial COI gene of the Indo-West Pacific species of Aristeus. Numbers at nodes are bootstrap values and only values>50 are shown.?: indicates possible misidentification in GenBank and reexamination of voucher specimens is necessary.
FIGURE 3. Aristeus alcocki Ramadan, 1938 in Photophore counts in the deep-sea commercial shrimp Aristeus alcocki Ramadan, 1938 (Crustacea: Decapoda: Aristeidae), with a revised key to the Indo-West Pacific species of the genus
FIGURE 3. Aristeus alcocki Ramadan, 1938, Sakthikulangara fishing harbor, southwestern India, female cl 52.6 mm (DABFUK). A, left pereiopod I, chela to distal part of merus; B, left pereiopod II, chela to distal part of merus; C, left pereiopod III, chela to distal part of merus; D, left pereiopod IV, dactylus to distal part of merus; E, left pereiopod V, dactylus to distal part of merus. Dots representing photophores. Scale = 2 mm.
FIGURE 2. A, C in Photophore counts in the deep-sea commercial shrimp Aristeus alcocki Ramadan, 1938 (Crustacea: Decapoda: Aristeidae), with a revised key to the Indo-West Pacific species of the genus
FIGURE 2. A, C, Aristeus alcocki Ramadan, 1938, Sakthikulangara fishing harbor, southwestern India, A, female cl 41.4 mm (DABFUK), C, female cl 52.6 mm (DABFUK)); B, D, A. virilis (Bate, 1881), Dasi fishing port, northeastern Taiwan, female cl 38.8 mm (NTOU M02048). A, B, branchial chamber of thoracic segment VI and VII (corresponding to pereiopods III and IV), a= arthobranch, e = epipods, p = pleurobranch, P = pereiopod; C, D, pterygostomian region of carapace. Scales: A, B = 2 mm, C, D = 5 mm.
FIGURE 1. Aristeus alcocki Ramadan, 1938 in Photophore counts in the deep-sea commercial shrimp Aristeus alcocki Ramadan, 1938 (Crustacea: Decapoda: Aristeidae), with a revised key to the Indo-West Pacific species of the genus
FIGURE 1. Aristeus alcocki Ramadan, 1938, Sakthikulangara fishing harbor, southwestern India, female cl 41.4 mm (DABFUK), atypical in having only two instead of three dorsal rostral teeth.
FIGURE 4 in On The Use Of 10-Minute Point Counts And 10-Species Lists For Surveying Birds In Lowland Atlantic Forests In Southeastern Brazil
FIGURE 4: Species accumulation curves of bird species surveyed with 10-species lists and 10-minute point counts as a function of the number of days in lowland Atlantic Forests in the municipalities of Bertioga and Santos, state of São Paulo, southeastern Brazil.
FIGURE 2 in On The Use Of 10-Minute Point Counts And 10-Species Lists For Surveying Birds In Lowland Atlantic Forests In Southeastern Brazil
FIGURE 2: Species accumulation curves and estimation curves using the Chao1 estimator for bird communities in the municipalities of Bertioga and Santos, state of São Paulo, southeastern Brazil, using MacKinnon lists (one sample = one 10-species list) and point counts (one sample = one point count). Each curve represents the average values of 50 randomizations of the sampling order.
FIGURE 1 in Counting counts: revised estimates of numbers of accepted species of flowering plants, seed plants, vascular plants and land plants with a review of other recent estimates
FIGURE 1. Accepted names at species rank as a function of all published species names for selected seed plant families (all published in WCSP).
FIGURE 10 in Ten new species of Sticta and counting: Colombia as a hot spot for unrecognized diversification in a conspicuous macrolichen genus
FIGURE 10. Sticta tunjensis (holotype collection). A. Upper side of thallus. B. Lower side of thallus. C. Lower surface enlarged. D. Detail of lower surface. E–G. Detail of marginal isidia from upper and lower side. G. Young lobe tip showing cilia. H. Microscopic section through cyphella showing basal membrane with papillose cells. Scale in A–D = 5 mm, in E and F = 1 mm, in G = 0.5 mm, in H = 10 µm.
FIGURE 9 in Ten new species of Sticta and counting: Colombia as a hot spot for unrecognized diversification in a conspicuous macrolichen genus
FIGURE 9. Sticta silverstonii (holotype collection). A. Thallus in herbarium showing surface structure. B. Lower surface showing large, contiguous cyphellae. C. Upper surface with apothecia. D. Microscopic section through cyphella showing basal membrane with papillose cells. E. Microscopic section of lower surface showing primary and secondary tomentum. Scale in A and B = 10 mm, in C = 0.5 mm, in D = 5 µm, in E = 20 µm.
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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
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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.