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298 results for “abundance distribution”
Resilin distribution and abundance in Apis mellifera across biological age classes and castes
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Data and code for: Plants with higher dispersal capabilities follow ‘abundant-centre’ distributions but such patterns remain rare in animals
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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>
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>
Dietary abundance distributions: Dominance and diversity in vertebrate diets
<p>Diet composition is among the most important yet least understood dimensions of animal ecology. Inspired by the study of species-abundance distributions (SADs), we tested for generalities in the structure of vertebrate diets by characterizing them as dietary-abundance distributions (DADs). We compiled data on 1167 population-level diets, representing >500 species from 6 vertebrate classes, spanning all continents and oceans. DADs near-universally (92.5%) followed a hollow-curve shape, with scant support for other plausible rank-abundance-distribution shapes. This strong generality is inherently related to, yet incompletely explained by, the SADs of available food taxa. By quantifying dietary generalization as the half-saturation point of the cumulative distribution of dietary abundance (<em>sp50</em>, minimum number of foods required to account for 50% of diet), we found that vertebrate populations are surprisingly specialized: in most populations, fewer than three foods accounted for at least half the diet. Variation in <em>sp50</em> was strongly associated with consumer type, with carnivores being more specialized than herbivores or omnivores. Other methodological (sampling method and effort, taxonomic resolution), biological (body mass, frugivory), and biogeographic (latitude) factors influenced <em>sp50</em> to varying degrees. Future challenges include identifying the mechanisms underpinning the hollow-curve DAD, its generality beyond vertebrates, and the biological determinants of dietary generalization.</p>
Fig. 5 in Abundance And Summer Distribution Of A Local Stock Of Black Sea Bottlenose Dolphins, Tursiops Truncatus (Cetacea, Delphinidae), In Coastal Waters Near Sudak (Ukraine, Crimea)
Fig. 5. Discovery curve as cumulative number of identified dolphins vs. duration of study.
Fig. 3 in Abundance And Summer Distribution Of A Local Stock Of Black Sea Bottlenose Dolphins, Tursiops Truncatus (Cetacea, Delphinidae), In Coastal Waters Near Sudak (Ukraine, Crimea)
Fig. 3. Categories of dorsal fins for photo-identification(a– d, marked; e, f, unmarked).
Fig. 1 in Abundance And Summer Distribution Of A Local Stock Of Black Sea Bottlenose Dolphins, Tursiops Truncatus (Cetacea, Delphinidae), In Coastal Waters Near Sudak (Ukraine, Crimea)
Fig. 1. Area of study in the northern Black Sea.
Data from: The abundance and distributional (in)equalities of forageable street tree resources in Lagos Metropolis, Nigeria
<p>Foraging for wild resources links urban citizens to nature and biodiversity while providing resources important for local livelihoods and culture. However, the abundance and distributional (in)equity of forageable urban tree resources have rarely been examined. Consequently, this study assessed the abundance of forageable street trees and their distribution in Lagos metropolis, Nigeria. During a survey of 32 randomly selected wards across 16 local government areas (LGAs) in the metropolis, 4,017 street trees from 46 species were enumerated. The LGA with the highest number of street trees was Ikeja, with 818 trees, while Lagos Island had the lowest count, with two trees. This disparity in tree numbers could be attributed to variations in human population density within each LGA. Ninety-four percent of the street trees surveyed had at least one documented use and 76 % had two, and thus were potentially forageable. However, the most common species had relatively low forageability scores. Only 5.6 % of the total street tree population was rated as highly forageable, with a usability score of at least 11 out of 15. The most forageable street trees were fruit trees and non-native species. The forageable street trees in the LGAs showed a significant disparity in their distribution, as evidenced by a Gini coefficient of 0.81. Overall, richer neighbourhoods had a higher street tree abundance, richness, and forageability potential. To meet greening and foraging goals and address the current inequitable distribution, we suggest allocating more funds for greening, particularly in low-income neighbourhoods. Further research should evaluate forageable species from other sites to acquire a detailed understanding of the distribution and abundance of forageable resources in Lagos metropolis.</p>
Fig. 6 in Large Herbivore Abundance, Distribution And Winter Pasture Quality In Two Game Farms In North Kazakhstan
Fig. 6. Siberian roe deer wintering concentration places in "Zerenda" game farm.
Fig. 1 in Large Herbivore Abundance, Distribution And Winter Pasture Quality In Two Game Farms In North Kazakhstan
Fig. 1. Moose wintering concentration places in "Bulandy" game farm.
Fig.4 in Large Herbivore Abundance, Distribution And Winter Pasture Quality In Two Game Farms In North Kazakhstan
Fig.4. Moose wintering concentration places in "Zerenda" game farm.
The effects of progressive land use changes on the distribution, abundance and behavior of vector mosquitoes in Sabah, Malaysia
<b>Description: </b><p>The objectives of this study were:1) To investigate the effects of progressive land use change from pre-development forest, through forest clearing and cultivation to plantation maintenance on occurrence of vector mosquitoes.<br>2) To determine the status of Anopheles donaldi as a vector of malaria in changing land uses.<br>3) To study the seasonality, abundance and behaviour of vector mosquitoes in study areas.<br><br>Methods<br>Study sites:<br>Study areas were located at The SAFE Project field site:<br>1. areas between Maliau Basin Conservation Area (old growth site),<br>2. logged forest sites in the Benta Wawasan area (area undergoing clearing),<br>3. oil palm plantation sites in Benta Wawasan's Silangan Batu Estate (oil palm site)<br><br>Mosquito collection<br>Mosquito samplings (adults and immature stages) were taken at all 3 study areas every alternate month from January 2017 until December 2018. Every sampling month, 2 collectors (n=2) spent 1 night at each study area where all-night human landing collection were carried out at 3 different sampling points for each collector. Collectors performed outdoor landing catches from 18:00 to 06:00. They collected mosquitoes that landed on naked legs with aspirators. Collectors were given prophylaxis prior to the sampling activities. Collected mosquitoes were then placed at hourly intervals inside glass vials. Mosquitoes were morphologically identified using available dichotomous keys the following morning. In every sampling period, meteorological data such as air temperature, relative humidity, atmospheric pressure and wind speed was recorded on hourly basis using a handheld weather station.</p><p><b>Project: </b>This dataset was collected as part of the following SAFE research project: <a href="https://www.safeproject.net/projects/project_view/180"><b>The effects of progressive land use changes on the distribution, abundance and behavior of vector mosquitoes in Sabah, Malaysia</b></a></p><p><b>Funding: </b>These data were collected as part of research funded by: </p><ul><li>Universiti Malaysia Sabah (Studentship)</li></ul><p>This dataset is released under the CC-BY 4.0 licence, requiring that you cite the dataset in any outputs, but has the additional condition that you acknowledge the contribution of these funders in any outputs.</p><p></p><p><b>Permits: </b>These data were collected under permit from the following authorities:</p><ul><li>Sabah Biodiversity Centre (SaBC) (Research licence Local)</li></ul><p></p><p><b>XML metadata: </b>GEMINI compliant metadata for this dataset is available <a href="https://www.safeproject.net/datasets/xml_metadata?id=3475408">here</a></p><p><b>Files: </b>This consists of 1 file: Evyen_Mosquito_data.xlsx</p><p><b>Evyen_Mosquito_data.xlsx</b></p><p>This file contains dataset metadata and 2 data tables:</p><ol><li><p><b>Mosquito_count</b> (described in worksheet Mosquito_count)</p><p>Description: The taxonomic identification of mosquitos caught</p><p>Number of fields: 6</p><p>Number of data rows: 40</p><p>Fields: </p><ul><li><b>Month</b>: month the mosquitos (Field type: categorical)</li><li><b>Species</b>: species ID of mosquitos caught (Field type: taxa)</li><li><b>MB</b>: Number of species caught in the Maliau Basin (Field type: numeric)</li><li><b>LFE</b>: Number of species caught in the LFE safe plot (Field type: numeric)</li><li><b>B_862</b>: Number of species caught in the B fragment SAFE (Field type: numeric)</li><li><b>Total</b>: Total caught per month (Field type: numeric)</li></ul></li><li><p><b>Mosquito_weather</b> (described in worksheet Mosquito_weather)</p><p>Description: The weather conditions of the mosquito samplings days</p><p>Number of fields: 9</p><p>Number of data rows: 203</p><p>Fields: </p><ul><li><b>Date</b>: Date of sampling (Field type: date)</li><li><b>Time</b>: Time of sampling (Field type: time)</li><li><b>Location</b>: Location of sampling (Field type: location)</li><li><b>Temperature</b>: Air tempreture (Field type: numeric)</li><li><b>Humidity</b>: Air humidity (Field type: numeric)</li><li><b>Wind Speed</b>: Wind (Field type: numeric)</li><li><b>Pressure</b>: Atomspheric pressure (Field type: numeric)</li><li><b>No.mosquito collected</b>: Number of mosquitos caught (Field type: numeric)</li><li><b>Notes</b>: Species (Field type: comments)</li></ul></li></ol><p><b>Date range: </b>2017-07-16 to 2018-08-21</p><p><b>Latitudinal extent: </b>4.4300 to 5.0700</p><p><b>Longitudinal extent: </b>116.5800 to 117.8200</p><p><b>Taxonomic coverage: </b><br> All taxon names are validated against the GBIF backbone taxonomy. If a dataset uses a synonym, the accepted usage is shown followed by the dataset usage in brackets. Taxa that cannot be validated, including new species and other unknown taxa, morphospecies, functional groups and taxonomic levels not used in the GBIF backbone are shown in square brackets.</p><div> -  Animalia <br> -  -  Arthropoda <br> -  -  -  Insecta <br> -  -  -  -  Diptera <br> -  -  -  -  -  Culicidae <br> -  -  -  -  -  -  <i>Heizmannia</i> <br> -  -  -  -  -  -  <i>Anopheles</i> <br> -  -  -  -  -  -  -  <i>Anopheles balabacensis</i> <br> -  -  -  -  -  -  -  <i>Anopheles latens</i> <br> -  -  -  -  -  -  <i>Culex</i> <br> -  -  -  -  -  -  -  <i>Culex sitiens</i> <br> -  -  -  -  -  -  -  <i>Culex vishnui</i> <br> -  -  -  -  -  -  <i>Aedes</i> <br> -  -  -  -  -  -  -  <i>Aedes albopictus</i> <br> -  -  -  -  -  -  -  <i>Aedes ganapathi</i> <br></div><p></p>
Figure 1 in Abundance and distribution of eggs and larvae of anchovy (Engraulis encrasicolus, Linnaeus, 1758) and horse mackerel (Trachurus mediterraneus, Steindachner, 1868) on the coasts of the eastern Black Sea
Figure 1. Sampling area.
Figure 3 in Abundance and distribution of eggs and larvae of anchovy (Engraulis encrasicolus, Linnaeus, 1758) and horse mackerel (Trachurus mediterraneus, Steindachner, 1868) on the coasts of the eastern Black Sea
Figure 3. Monthly vertical profiles of salinity.
Figure 5 in Abundance and distribution of eggs and larvae of anchovy (Engraulis encrasicolus, Linnaeus, 1758) and horse mackerel (Trachurus mediterraneus, Steindachner, 1868) on the coasts of the eastern Black Sea
Figure 5. Monthly number and percentage of T. mediterraneus eggs and larvae.
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.
Fig. 1 in South American Sea Lions Otaria flavescens, a good indicator of relative spatial and temporal changes in the distribution and abundance of marine resources?
Fig. 1. Study area showing the location of the rookeries analysed at RÍo Negro Province, Argentina.
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.
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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.