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194 results for “population abundance”
Fig. 1 in Rodent population cycle as a determinant of gastrointestinal nematode abundance in a low-arctic population of the red fox
Fig. 1. Map showing the sampling sites on Varanger peninsula in northern Norway. Red triangles denote the sites where the 612 red foxes included in the analyses were. sampled. White squares denote sites where rodents were trapped for the purpose of monitoring their population dynamics. Dark areas are sub-arctic birch forest, while areas with different shading of grey show tundra at different altitudes. The meteorological station from which the climate data were derived, is denoted with a blue star. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)
Fig. 2 in Rodent population cycle as a determinant of gastrointestinal nematode abundance in a low-arctic population of the red fox
Fig. 2. Time series of annual climate variables, rodent density and egg counts of gastrointestinal parasites (i.e. number of eggs recorded) in red foxes faeces in Varanger Peninsula. A) The mean summer temperature (̊C) for July, August and September from the weather station in Vardø (see Fig. 1). Horizontal broken lines show the 1960–1990 normal for temperature. B) Rodent density indexed as number of individuals caught per 100 trap nights in early September based on the trapping sites shown in Fig. 1 and number of foxes culled each winter season and local hunter (grey). Note that 2005 represents the foxes culled winter 2005–2006. C) Abundance (mean number of eggs per gram with standard error) of the three parasite species in the annual fox samples. Note the left (red) y-axis represents T. leonina while the right (black) y-axis represents T. canis and U. stenocephala. D) Prevalence (proportion of foxes with parasites, with standard error) of the three parasite species in the annual fox samples. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)
Fig. 2 in Seasonal population abundance of the assembly of solitary wasps and bees (Hymenoptera) according to land-use in Maranhão state, Brazil
Fig. 2. Abundance of solitary bees according to land-use (a), month (b) and interactions between land-use and month (c). Repeated measures ANOVA followed by post hoc Fisher LSD tests (P <0.05). Means ± SE are given.
Fig. 1 in Seasonal population abundance of the assembly of solitary wasps and bees (Hymenoptera) according to land-use in Maranhão state, Brazil
Fig. 1. Abundance of solitary wasps according to land-use (a), month (b) and interaction between land-use and month (c). Repeated measures ANOVA followed by post hoc Fisher LSD tests (P <0.05). Means ± SE are given.
Fig. 3 in Effect of vegetation and abiotic factors on the abundance and population structure of Crocodylus acutus (Cuvier, 1806) in coastal lagoons of Colima, Mexico
Fig. 3. Dendrogram considering the crocodiles observed, water salinity, temperature, depth, and the four vegetation types present. Acronym definitions and characteristics of the sites are given in Table 1.
Fig. 1 in Effect of vegetation and abiotic factors on the abundance and population structure of Crocodylus acutus (Cuvier, 1806) in coastal lagoons of Colima, Mexico
Fig. 1. Selected sites in the study area. Acronym definitions and characteristics of the sites are given in Table 1.
Fig. 2 in Effect of vegetation and abiotic factors on the abundance and population structure of Crocodylus acutus (Cuvier, 1806) in coastal lagoons of Colima, Mexico
Fig. 2. Non-metric Multidimensional Scaling (NMDS) analysis showing the formation of two groups, by taking into account the crocodiles observed, water salinity, temperature, depth, and the four vegetation types present. Acronym definitions and characteristics of the sites are given in Table 1.
Figs 4-8 in Influence of environmental variables on seasonal abundance and relative growth of Macrobrachium amazonicum (Crustacea: Decapoda: Caridea): variations of a continental population
Figs 4-8. Percentage distribution of the independent effect of the abiotic factor on the total abundance (Fig. 4), and on the abundance by demographic category (Figs 5-8) of Macrobrachium amazonicum (Heller, 1862). Grey bars indicate a significant effect (p<0.05), determined by the randomization test. Positive and relative relationships are shown by the bars above and under the horizontal aXis, respectively (EC, conductivity; DO, dissolved oXygen; PI, precipitation; T, water temperature).
Figs 2, 3 in Influence of environmental variables on seasonal abundance and relative growth of Macrobrachium amazonicum (Crustacea: Decapoda: Caridea): variations of a continental population
Figs 2, 3. Percentage of total abundance (Fig. 2) and juveniles, males, non-ovigerous females and ovigerous females (Fig. 3) of Macrobrachium amazonicum (Heller, 1862) along the study period (J, juveniles; M, males; NOF, non-ovigerous female; OF, ovigerous females).
Text-fig. 3 Ternary diagram of the relative abundance (in %) of juvenile, prime adult, and old adult specimens in samples of Castor fiber (data from Table 3). The red dots indicate the Pleistocene samples of Bilzingsleben II (B), Weimar- Ehringsdorf (E), and Weimar-Taubach (T), the black dot represents an extant population from Telemark in Norway (data from Campbell 2009). Abbreviations of zones (after Discamps and Costamagno 2015): JOP – Juveniles-Old-Prime dominated zone, JPO – Juveniles-Prime-Old dominated zone, O – Old dominated zone, P – Prime dominated zone. The diagram shows the position of all three fossil samples in the prime dominated zone. in Mortality Profiles Of Castor And Trogontherium (Mammalia: Rodentia, Castoridae), With Notes On The Site Formation Of The Mid-Pleistocene Hominin Locality Bilzingsleben Ii (Thuringia, Central Germany)
Text-fig. 3 Ternary diagram of the relative abundance (in %) of juvenile, prime adult, and old adult specimens in samples of Castor fiber (data from Table 3). The red dots indicate the Pleistocene samples of Bilzingsleben II (B), Weimar- Ehringsdorf (E), and Weimar-Taubach (T), the black dot represents an extant population from Telemark in Norway (data from Campbell 2009). Abbreviations of zones (after Discamps and Costamagno 2015): JOP – Juveniles-Old-Prime dominated zone, JPO – Juveniles-Prime-Old dominated zone, O – Old dominated zone, P – Prime dominated zone. The diagram shows the position of all three fossil samples in the prime dominated zone.
Estimating the abundance of the critically endangered Baltic Proper harbour porpoise (Phocoena phocoena) population using passive acoustic monitoring
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Dataset of habitat quality does not predict animal population abundance on frequently disturbed landscapes
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Meiobenthos abundance. Long-term variability and dynamics of estuarine meiobenthic populations for North Inlet Estuary, South Carolina, from 1972 to 1992, North Inlet LTER (Reformatted to a Darwin Core Archive)
This data package is formatted as a Darwin Core Archive (DwC-A, event core). For more information on Darwin Core see https://www.tdwg.org/standards/dwc/. This Level 2 data package was derived from the Level 1 data package found here: https://pasta.lternet.edu/package/metadata/eml/edi/350/3, which was derived from the Level 0 data package found here: https://pasta.lternet.edu/package/metadata/eml/knb-lter-nin/6/1. The abstract below was extracted from the Level 0 data package and is included for context: The original purpose of this research was to determine if natural meiobenthic assemblages exhibited continuity over time and to monitor several physical variables to determine if these influenced long-term temporal patterns. The most recent study focused on variation and the relations of meiobenthos abundance with environmental factors over 11 years. Typically marine benthic community studies are limited temporally and the majority of previously published 'longterm' meiofauna results (all taxa) were based on about a year's duration.
Data from: Estimating fish population abundance by integrating quantitative data on environmental DNA and hydrodynamic modeling
<p>Molecular analysis of DNA left in the environment, known as environmental DNA (eDNA), has proven to be a powerful and cost-effective approach to infer occurrence of species. Nonetheless, relating measurements of eDNA concentration to population abundance remains difficult because detailed knowledge on the processes that govern spatial and temporal distribution of eDNA should be integrated to reconstruct the underlying distribution and abundance of a target species. In this study, we propose a general framework of abundance estimation for aquatic systems on the basis of spatially replicated measurements of eDNA. The proposed method explicitly accounts for production, transport, and degradation of eDNA by utilizing numerical hydrodynamic models that can simulate the distribution of eDNA concentrations within an aquatic area. It turns out that, under certain assumptions, population abundance can be estimated via a Bayesian inference of a generalized linear model. Application to a Japanese jack mackerel (<em>Trachurus japonicus</em>) population in Maizuru Bay revealed that the proposed method gives an estimate of population abundance comparable to that of a quantitative echo sounder method. Furthermore, the method successfully identified a source of exogenous input of eDNA (a fish market), which may render a quantitative application of eDNA difficult to interpret unless its effect is taken into account. These findings indicate the ability of eDNA to reliably reflect population abundance of aquatic macroorganisms; when the "ecology of eDNA" is adequately accounted for, population abundance can be quantified on the basis of measurements of eDNA concentration.</p>
Data from: Estimating abundance of an open population with an N-mixture model using auxiliary data on animal movements
Accurate assessment of abundance forms a central challenge in population ecology and wildlife management. Many statistical techniques have been developed to estimate population sizes because populations change over time and space, and to correct for the bias resulting from animals that are present in a study area but not observed. The mobility of individuals makes it difficult to design sampling procedures that account for movement into and out of areas with fixed jurisdictional boundaries. Aerial surveys are the gold standard used to obtain data of large mobile species in geographic regions with harsh terrain, but these surveys can be prohibitively expensive and dangerous. Estimating abundance with ground based census methods have practical advantages, but it can be difficult to simultaneously account for temporary emigration and observer error to avoid biased results. Contemporary research in population ecology increasingly relies on telemetry observations of the states and locations of individuals to gain insight on vital rates, animal movements, and population abundance. Analytical models that use observations of movements to improve estimates of abundance have not been developed. Here we build upon existing multi-state mark recapture methods using a hierarchical N-mixture model with multiple sources of data, including telemetry data on locations of individuals, to improve estimates of population sizes. We used a state-space approach to model animal movements to approximate the number of marked animals present within the study area at any observation period, thereby accounting for a frequently changing number of marked individuals. We illustrate the approach using data on a population of elk (Cervus elaphus nelsoni) in Northern Colorado, USA. We demonstrate substantial improvement compared to existing abundance estimation methods and corroborate our results from the ground based surveys with estimates from aerial surveys during the same seasons. We develop a hierarchical Bayesian N-mixture model using multiple sources of data on abundance, movement and survival to estimate the population size of a mobile species that uses remote conservation areas. The model improves accuracy of inference relative to previous methods for estimating abundance of open populations.
Figure 1 in Andean bear (Tremarctos ornatus) population density and relative abundance at the buffer zone of the Chingaza National Natural Park, cordillera oriental of the colombian andes
Figure 1. Natural covers map showing camera traps distribution at 9 grids throughout the study area.
Data from: Abundance models of endemic birds of the Sierra Nevada de Santa Marta, northern South America, suggest small population sizes and dependence on montane elevations
<p>Abundance measures are almost non-existent for several bird species threatened with extinction, particularly range-restricted Neotropical taxa, for which estimating population sizes can be challenging. Here we use data collected over nine years to explore the abundance of 11 endemic birds from the Sierra Nevada de Santa Marta (SNSM), one of Earth's most irreplaceable ecosystems. We established 99 transects in the "Cuchilla de San Lorenzo" Important Bird Area within native forest, early successional vegetation, and areas of transformed vegetation by human activities. A total of 763 bird counts were carried out covering the entire elevation range in the study area (~175–2650 m). We applied hierarchical distance-sampling models to assess elevation- and habitat-related variation in local abundance and obtain values of population density and total and effective population size. Most species were more abundant in the montane elevational range (1800–2650 m). Habitat-related differences in abundance were only detected for five species, which were more numerous in either early succession, secondary forest, or transformed areas. Inferences of effective population size indicated that at least four endemics likely maintain populations no larger than 15,000–20,000 mature individuals. Estimates of species' area of occupancy and effective population size were lower than most values previously described, a possible consequence of increasing anthropogenic threats. At least four of the endemics exceeded criteria for threatened species listing and a thorough evaluation of their extinction risk should be conducted. Population strongholds for most of the study species were located on the northern and western slopes of the SNSM between 1500–2700 m. We highlight the urgent need for facilitating effective protection of native vegetation in premontane and montane ecosystems to safeguard critical habitats for the SNSM's endemic avifauna. Follow-up studies collecting abundance data across the SNSM are needed to obtain precise range-wide density estimations for all species.</p>
Data from: using camera traps and N-mixture models to estimate population abundance: model selection really matters
<p>Estimating the abundance or density of wildlife populations is a critical part of species conservation and management, but estimates can vary greatly in precision and accuracy according to the data collection and statistical methods, sampling and ecological variation, and sample size. N-mixture models are a common method which has been applied to a wide range of taxa for estimating population abundance from non-invasive data representing the distribution of the species. We used population estimates from an aerial survey of moose and videos from camera traps to assess the sensitivity of N-mixture models to ecological conditions, the spatial scale at which they were measured, the criteria used to define independent detections, and model choice based on the common statistical criterion of parsimony. The most parsimonious N-mixture models were considerably biased, producing implausibly large and considerably imprecise estimates of the abundance of moose. Most of the other models produced estimates of abundance that were ecologically realistic and relatively accurate. The accuracy of population estimates produced by N-mixture models were not overly sensitive to the formulation of models, the scale at which ecological conditions were measured, or the criteria used to define independent detection and by extension sample size. Our results suggest that parsimony was a poor measure of the predictive accuracy of the population estimates produced with the N-mixture model. Collecting and processing data from the aerial survey was less expensive and took less time, but data from camera traps can provide valuable information on behavior of the target species as well as insights into multiple species in the community.</p>
Data from: Range-wide genetic analysis of an endangered bumble bee (Bombus affinis) reveals population structure, isolation by distance, and low colony abundance
<p>Declines in bumblebee species ranges and abundances are documented across multiple continents and have prompted the need for research to aid species recovery and conservation. The rusty patched bumblebee (<em>Bombus affinis</em>) is the first federally-listed bumblebee species in North America. We conducted a range-wide population genetics study of <em>B. affinis</em> from across all extant conservation units to inform conservation efforts. To understand the species' vulnerability and help establish recovery targets, we examined population structure, patterns of genetic diversity, and population differentiation. Additionally, we conducted site-level analysis of colony abundance to inform prioritizing areas for conservation, translocation, and other recovery actions. We find substantial evidence of population structuring along an east-to-west gradient. Putative populations show evidence of isolation by distance, high inbreeding coefficients, and a range wide male diploidy rate of ~15%. Our results suggest the Appalachians represents a genetically distinct cluster with high levels of private alleles and substantial differentiation from the rest of the extant range. Site-level analyses suggest low colony abundance estimates for <em>B. affinis</em> compared to similar datasets of stable, co-occurring species. These results lend genetic support to trends from observational studies suggesting B. affinis has undergone a recent decline and exhibits substantial spatial structure. The low colony abundances observed here suggest caution in overinterpreting the stability of populations even where <em>B. affinis</em> is reliably detected interannually. These results help delineate informed management units, provide context for the potential risks of translocation programs, and can help set clear recovery targets for this and other threatened bumblebee species.</p>
Data & codes for "Changes in abundance and distribution of European forest bird populations depend on biome, ecological specialisation and traits"
<h1>1. Selection of European forest bird species and classification of their biome preferences</h1> <p>We selected all species that are related to forest and woodland based on two data sources: Storchová & Hořák (2018) and Tobias et al. (2022), resulting in 107 bird species studied (Data S1). We defined forest bird species as those using environments ranging from closed-canopy forests to more open-canopy woodlands (A. Lehikoinen & Virkkala, 2018; Storchová & Hořák, 2018; Tobias et al., 2022). We determined their biome specialisation using breeding distribution centroids and the overall breeding distribution of each of the species, using the global map of terrestrial ecoregions from Olson et al. (2001) and range data from European Breeding Bird Atlas 1 and 2 (Hagemeijer & Blair, 1997; Keller et al., 2020). We categorised species as Mediterranean, temperate, or boreal based on their predominant biogeographic region. We considered species commonly occurring over several biomes as “generalists”. For instance, we reclassified the two typically boreal species Glaucidium passerinum Linnaeus and Strix uralensis Pallas as “generalists” due to significant range expansions into central and southern Europe in recent decades, therefore no longer restricted to the boreal region. For the complete list of species, biome specialisation, traits, and specialisation indices, refer to Data S1.</p> <h1>2. Changes in abundance and distribution of European forest bird species</h1> <p>We assessed long-term changes in European forest bird populations through two approaches: (i) changes in estimated total European-level species abundance over a 40-year timeframe; and (ii) changes in species spatial distribution over a 30-year timeframe (Fig. 1).</p> <p>We utilized the estimated trends in European-level population size (i.e., the total number of individuals) for each common native European bird species from 1980 to 2017, as reported by Burns et al. (2021). Three species out of the 107 studied forest species were missing in the original manuscript and we used data generated with the same method from 1980 to 2018 from the European assessment, Article 12 (https://nature-art12.eionet.europa.eu/article12/). These abundance trends were calculated by Burns et al. (2021) using multi-sourced annual times series. For each species, they gathered population estimates and trends from each European country as well as European Union (EU)-level population trends. They analysed these data with a Bayesian hierarchical model to reconstruct EU-level smoothed species population time series. The model outputs include an average annual rate of abundance change and an associated 95% credible interval (Burns et al., 2021). Therefore, we did not directly use the average annual rate of abundance change, as this would have led us to consider species with low uncertainty as similar to those with high uncertainty. To account for the uncertainty, we categorised species as (i) declining, i.e., annual rates below one, (ii) increasing, i.e., annual rates above one and (iii) stable, i.e., annual rate whose 95% CI overlap one, i.e., no significant change. To better acknowledge the magnitude of the abundance change, significant changes with rates below 0.98 were labelled as “strongly declining” (i.e., 6.5% of the 107 species), while those above 1.02 were labelled as “strongly increasing” (i.e., 11% of the 107 species). To evaluate the sensitivity of the decision to categorised abundance change data, we also analysed abundance trend as continuous variable (see Supporting Information Fig. S8).</p> <p>To determine changes in species distributions, we used a comparison of species distributions between two periods (i.e., 1985-1988 and 2013-2017) using the European Breeding Bird Atlas 1 and 2 (EBBA 1 & 2; Hagemeijer & Blair, 1997; Howard et al., 2023; Keller et al., 2020). Howard et al. (2023) provided calculations of observed colonisation and extinction areas at a 50 x 50 km resolution across Europe. We measured changes in range as the difference between colonisations and extinctions of each species, with negative values indicating contracting ranges and positive values indicating expanding ranges. Additionally, we calculated the shift in the centre of gravity of the distribution range between the two periods, as a distance (km) along the south-north gradient for each species (Howard et al., 2023).</p> <h1>3. Trait and specialisation data for European forest bird species</h1> <p>We extracted data for six functional traits from several sources (Table 1). (i) The species temperature index (STI)represents the long-term average temperature within the species’ breeding range (A. Lehikoinen et al., 2021). (ii) Diet data during the breeding season were obtained from Storchová & Hořák (2018), classifying species into binary variables as vertebrate carnivorous, invertebrate carnivorous, and herbivores (combining the leaf and seed eaters). Storchová & Hořák (2018) classified species into a diet category when the corresponding food resource represented at least 10% of the species diet throughout the breeding season. Therefore, one species can be in several categories (i.e., omnivores). (iii) We obtained nesting site data from Pearman et al. (2014), classifying species into binary variables as ground nesters, tree hole nesters, or elevated nesters (> 1 m in a tree or shrub). We also included data on (iv) species dependence on old-growth forests (Data S1; mostly from Fraixedas et al. (2015) and Mönkkönen et al. (2014), if present on both references, we classified them as “1” and if only in one reference as “0.5”), (v) migration distance (Howard et al., 2023), and (vi) body mass (Tobias et al., 2022).</p> <p>Finally, we extracted and developed seven species specialisation indices. (i) We used an overall specialisation index based on multiple traits (i.e., temperature, diet, foraging behaviour and substrate, habitat, and nesting site), and (ii) a nesting specialisation index, both obtained from Morelli et al. (2019). Both indices represent species specialization based on the dispersion of trait preferences for each species: e.g., nesting specialism equal 0 for species that nest in all habitat type and equal 1 for species that nest in only one habitat type). They are both calculated using the Gini index of inequality, which measures overall dispersion across, e.g., all traits for the overall specialization, based on data from Pearman et al. (2014) and Storchová & Hořák (2018). For additional information, see Morelli et al. (2019). We also used (iii) the diet specialisation index, (iv) the species distribution range during the breeding season (hereafter “breeding range area”) and (v) the climatic niche breadth from Reif et al. (2016). The diet specialisation index was calculated as the coefficient of variation for diet preferences for each species, where high values denotes specialized species (Reif et al., 2016). The breeding range area was evaluated as the number of 50-km squares in the distribution maps in Europe occupied by each species during the reproduction period, and is based on EBBA 1 (Hagemeijer & Blair, 1997). The climatic niche breadth was calculated as the difference between the 5% hottest and the 5% coldest mean temperature between April and June in which each species occurs, using EBBA 1 (Hagemeijer & Blair, 1997; Reif et al., 2016).</p> <p>Additionally, (vi) we calculated a broadleaf forest specialisation index based on binary forest habitat preferences (Storchová & Hořák, 2018), assigning values of one for species found only in broadleaf forests; zero for those in coniferous forests, and 0.5 for those found in both. Lastly, (vii) we created a forest specialisation index based on the species habitat preferences (Storchová & Hořák, 2018). The forest specialisation index was calculated as the mean of species affinity across habitats. We used increasing habitat weights along a gradient of tree dominance: open habitats as 1, shrubland as 1.5, woodland as 2 (i.e., species associated with habitats structured by trees in lower density than in forest), forest generalist (found in both coniferous and broadleaf dense forests) as 3, and forest specialist (found only either in coniferous or broadleaf dense forests) as 4. For instance, the index value for species occurring either in shrubland, woodland or both broadleaf and coniferous forests is 2.167.</p> <h1>4. Data analysis</h1> <p>Data analyses were conducted with R software version 4.4.1. (R Core Team, 2024). Given the non-independence of species due to their genetic relatedness, we accounted for interspecific phylogenetic distance in all models. We constructed the phylogenetic tree for the 107 European forest bird species using ‘rotl’ and ‘ape’ R-packages (Michonneau et al., 2022; Paradis et al., 2023). We used rotl as an interface with the "Open Tree of Life", employing tol_induced_subtree R-function to generate the phylogenetic tree and compute.brlen R-function to set branch lengths using Grafen’s computation. We generated separate phylogenetic trees for boreal (17), temperate (15), Mediterranean (16) and “generalist” (59) species to perform biome-specific analysis (see Supplementary Information, Figs. S1 & S2).</p> <p>To investigate the effects of functional traits and specialisation indices on abundance, range changes, and distribution shift, we used two regression methods. All methods were based on the relationships between a measure of change and a functional trait or specialisation index. Our sample unit is an individual forest bird species (i.e., one value for each species, either abundance or range change, or distribution shift). Abundance change was a categorical variable (i.e., strong decline – decline – stable – increase – strong increase), while range change (i.e., difference between colonisation and extinction) and distribution shift (i.e., south-north shift) were continuous variables. Therefore, to study abundance changes, we used proportional-odds linear mixed effects model using (Phylo)clmm R-function from the ‘ordinal’ R-package (Christensen, 2022). Interspecific phylogenetic relatedness was included as a random effect, reflecting the correlation between species based on phylogenetic distances (see also Hagge et al. (2021) and Seibold et al. (2015)). For distribution changes, we employed phylogenetic generalised least squares regression (PGLS) using the gls R-function from the ‘nlme’ R-package (Pinheiro et al., 2023). The phylogenetic correlation structure was integrated into PGLS using Pagel’s lambda parameter (λ; Pagel (1999)) a widely used measured of phylogenetic signal strength (see, e.g., Hagge et al., 2021; Triviño et al., 2013).</p> <p>Furthermore, we included latitude, a key driver of bird communities at broad scales (Luoto et al., 2007), as a fixed covariable (centroid latitude of the species’ breeding distribution) in all global models (i.e., species from all biomes together), except for the STI model due to strong correlation. For biome-specific analysis, we included latitude only in boreal species models for range change and distribution shift, as it significantly improved model fit (ΔAIC < -2). We did not add latitude for models specific to temperate, Mediterranean, and generalist species since it did not improve model fits (ΔAIC > -2). Additionally, we included breeding range area in range change and distribution shift models, assuming that species with larger ranges would exhibit larger shifts. We scaled predictors to a mean of 0 and standard deviation of 1 to facilitate effect size comparisons. We adjusted p-values using the Holm method (for n=3) to account for multiple testing of traits and specialisation indices on three response variables.</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.