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Data from: Ant diversity in Neotropical savannas: hierarchical processes acting at multiple spatial scales
1. Understanding what creates and maintains macroscale biodiversity gradients is a central focus of ecological and evolutionary research. Spatial patterns in diversity are driven by a hierarchy of factors operating at multiple scales. Historical and climatic factors drive large-scale patterns of diversity by affecting the size of regional species pools, while habitat heterogeneity or microhabitat characteristics further influence species coexistence at small scales. 2. We tested the degree to which the species-energy, historical factors, habitat heterogeneity and local environment hypotheses explain observed patterns of ant diversity across hierarchical spatial scales. 3. We sampled ground-dwelling ants at 29 sites within a Neotropical savanna region, the Brazilian Cerrado. We measured species density – an abundance-dependent diversity metric – and rarefied species richness – an abundance-independent metric – at spatial scales with varying grain sizes. For each hypothesis, two correlates were used to predict ant diversity patterns: i) species-energy: rainfall and productivity; ii) historical factors: historical variation in rainfall and refugial areas; iii) habitat heterogeneity: heterogeneity in greenness and diversity of land cover; iv) local factors: contents of sand and coarse fragments in the soil. 4. Ant diversity patterns correlated to net primary productivity and to the proportion of coarse fragments in the soil, corroborating the species-energy and local environment hypotheses, respectively. Soil negatively influenced species density, but not rarefied species richness, which was positively influenced by productivity. We found scale dependencies in the effects of soil/productivity on species density; productivity best predicted species density patterns at large scales, since sampling completeness offset the abundance-driven effects of soil. 5. Considering abundance differences may help to discern the mechanisms underlying the relationship between macroscale diversity patterns and its ecological drivers. Plant productivity affected ant diversity independently of abundance, possibly by limiting the size of regional species pools. On the other hand, soil properties had an abundance-dependent effect on ant diversity, indicating a sampling mechanism. Our findings are consistent with predictions of the hierarchical theory of diversity. Large-scale patterns of productivity limit regional diversity, an effect that cascades down to finer spatial scales, where soil properties influence the number of coexisting species.
Data from: Multiple scales of genetic connectivity in a brooding coral on isolated reefs following catastrophic bleaching
Understanding the pattern of connectivity among populations is crucial for the development of realistic and spatially explicit population models in marine systems. Here we analysed variation at eight microsatellite loci to assess the genetic structure and to infer patterns of larval dispersal for a brooding coral, Seriatopora hystrix, at an isolated system of reefs in northern Western Australia. Spatial autocorrelation analyses show that populations are locally subdivided, and that the majority of larvae recruit to within 100 m of their natal colony. Further, a combination of F- and R- statistics showed significant differentiation at larger spatial scales (2–60 km) between sites, and this pattern was clearly not associated with distance. However, Bayesian analysis demonstrated that recruitment has been supplemented by less frequent but recent input of larvae from outside the local area; 2–6% of colonies were excluded from the site at which they were sampled. Individual assignments of these migrants to the most likely populations suggest that the majority of migrants were produced at the only site that was not decimated by a recent and catastrophic coral bleaching event. Furthermore, the only site that recovered to prebleaching levels received most of these immigrants. We conclude that the genetic structure of this brooding coral reflects its highly opportunistic life history, in which prolific, philopatric recruitment is occasionally supplemented by exogenously produced larvae.
Data from: Genetic relatedness does not retain spatial pattern across multiple spatial scales: dispersal and colonization in the coral, Pocillopora damicornis
Patterns of isolation-by-distance are uncommon in coral populations. Here, we depart from historical trends of large-scale, geographic genetic analyses by scaling down to a single patch reef in Kāne'ohe Bay, Hawai'i, and map and genotype all colonies of the coral, Pocillopora damicornis. Six polymorphic microsatellite loci were used to assess population genetic and clonal structure and to calculate individual colony pairwise relatedness values. Our results point to an inbred, highly clonal reef (between 53 and 116 clonal lineages out of 2352 genotyped colonies) with a very skewed genet frequency distribution (over 70% of the reef was composed of just seven genotypes). Spatial autocorrelation analyses revealed that corals found close together on the reef were more genetically related than corals further apart. Spatial genetic structure disappears, however, as spatial scale increases and then becomes negative at the largest distances. Stratified, random sampling of three neighbouring reefs confirms that reefs are demographically open and inter-reef genetic structuring was not detected. Attributing process to pattern in corals is complicated by their mixed reproductive strategies. Separate autocorrelation analyses, however, show that the spatial distribution of both clones and non-clones contribute to spatial genetic structure. Overall, we demonstrate genetic structure on an intra-reef scale and genetic panmixia on an inter-reef scale indicating that, for P. damicornis, small- and large-scale dispersal processes are likely not the same. By starting from an inter-individual, intra-reef level before scaling up to an inter-reef level, this study demonstrates that isolation-by-distance patterns for the coral P. damicornis are limited to small scales and highlights the importance of investigating genetic patterns and ecological processes at multiple scales.
Data from: Intraspecific trait variation across multiple scales: the leaf economics spectrum in coffee
Understanding species differences in plant functional traits has been critical in developing a mechanistic understanding of terrestrial ecological processes. Greater attention is now being placed on understanding the extent, causes and consequences of intraspecific trait variation (ITV). ITV is especially important in governing ecological processes in cropping systems, where only a small number of species or genotypes exist in high abundances. However, it remains unclear if key principles of trait-based ecology – namely the leaf economics spectrum (LES) – also describe intraspecific variation in crop functional biology. There also remains a need to understand whether ITV within crops is random, or structured across environmental, management-related or biological levels of organization in agroecosystems. We employed a nested design field survey to evaluate ITV in leaf traits in coffee (Coffea arabica), one of the world's most widespread tropical crops. We evaluated ITV in eight physiological, morphological and chemical leaf traits, across five nested categorical levels (sites, management systems, spatial location, plant identity, branch identity). We compared patterns of LES trait covariation in coffee, to interspecific patterns observed across over 700 wild plant species. Patterns of bivariate and multivariate ITV in coffee were broadly consistent with, but considerably weaker than, interspecific patterns associated with the LES, indicating that crops may systematically diverge from global patterns of trait trade-offs observed in wild plants. Physiological traits varied most widely (coefficient of variation (cv) 42–107%), followed by morphological traits (cv = 15–38%) and chemical traits (cv = 3–11%). Physiological ITV was best explained by the site in which a coffee plant was growing (17–55% explained), while ITV for chemical traits was best explained by management treatments within sites (25–36%); morphological ITV was higher even at the individual tree level or branch level and remained largely unexplained. Our results support the hypothesis that artificial selection and high-resource agricultural environments lead crops to systematically deviate from patterns of leaf trait covariation observed across wild plants species. Coupled with an understanding of how different traits vary systematically across multiple levels of biological organization, these findings help integrate ITV into future analyses of agroecosystem structure and function.
Data from: Environmental filtering improves ecological niche models across multiple scales
1. A clear challenge for ecological niche modeling is determining how to best mitigate the effects of sampling bias from commonly collected biodiversity data. Recent approaches have focused on filtering occurrences in overrepresented regions based on geographic or environmental proximity. 2. We tested the efficacy of filtering in geographic and environmental space using occurrence data from four species. Our evaluation strategies examined 14 distance measures in geographic and environmental spaces and eight combinations of environmental variables and their ordinations. This resulted in 78 datasets for each species, which we evaluated using area under the curve (AUC), the difference between training and testing AUC, omission rate, the true skill statistic, and Schoener's D to examine the effects of different filtering schemes. 3. The degree of change produced by filtering on predicted suitability and evaluation statistics increased with increasing range size. Environmental filtering resulted in higher model fit at larger extents and retained more occurrences than geographic filtering. 4. Our results indicate that models should be evaluated using multiple evaluation statistics at multiple thresholds. The use of bin sizes when filtering in environmental space allows for simple comparison between species and filter types and makes for an easily reportable and repeatable distance metric. We specifically recommend that ecological niche models using natural history collection data filter in environmental space with variables derived from permutation importance or the first few axes of a principal components ordination.
Data from: The relative influence of habitat amount and configuration on genetic structure across multiple spatial scales
Despite strong interest in understanding how habitat spatial structure shapes the genetics of populations, the relative importance of habitat amount and configuration for patterns of genetic differentiation remains largely unexplored in empirical systems. In this study, we evaluate the relative influence of, and interactions among, the amount of habitat and aspects of its spatial configuration on genetic differentiation in the pitcher plant midge, Metriocnemus knabi. Larvae of this species are found exclusively within the water-filled leaves of pitcher plants (Sarracenia purpurea) in a system that is naturally patchy at multiple spatial scales (i.e., leaf, plant, cluster, peatland). Using generalized linear mixed models and multimodel inference, we estimated effects of the amount of habitat, patch size, interpatch distance, and patch isolation, measured at different spatial scales, on genetic differentiation (FST) among larval samples from leaves within plants, plants within clusters, and clusters within peatlands. Among leaves and plants, genetic differentiation appears to be driven by female oviposition behaviors and is influenced by habitat isolation at a broad (peatland) scale. Among clusters, gene flow is spatially restricted and aspects of both the amount of habitat and configuration at the focal scale are important, as is their interaction. Our results suggest that both habitat amount and configuration can be important determinants of genetic structure and that their relative influence is scale dependent.
SPREAD: A Large-scale, High-fidelity Synthetic Dataset for Multiple Forest Vision Tasks (Part II)
<p><strong>This page only provides the </strong><strong>drone-view image</strong><strong> dataset. </strong></p> <ul> <li><strong>For the ground-level image dataset, please visit <a href="https://zenodo.org/records/13570934" target="_blank" rel="noopener"><em>SPREAD: A Large-scale, High-fidelity Synthetic Dataset for Multiple Forest Vision Tasks (Part I)</em></a>.</strong></li> <li><strong>For the point clouds, please visit <em><a href="https://zenodo.org/records/14228467" target="_blank" rel="noopener">SPREAD: A Large-scale, High-fidelity Synthetic Dataset for Multiple Forest Vision Tasks (Part III)</a>.</em></strong></li> </ul> <p>The dataset contains drone-view RGB images, depth maps and instance segmentation labels collected from different scenes. Data from each scene is stored in a separate .7z file, along with a <code>color_palette.xlsx</code> file, which contains the RGB_id and corresponding RGB values.</p> <p>All files follow the naming convention: <code>{central_tree_id}_{timestamp}</code>, where <code>{central_tree_id}</code> represents the ID of the tree centered in the image, which is typically in a prominent position, and <code>timestamp</code> indicates the time when the data was collected.</p> <p>Specifically, each 7z file includes the following folders:</p> <ul> <li> <p><strong>rgb</strong>: This folder contains the RGB images (PNG) of the scenes and their metadata (TXT). The metadata describes the weather conditions and the world time when the image was captured. An example metadata entry is: <code>Weather:Snow_Blizzard,Hour:10,Minute:56,Second:36</code>.</p> </li> <li> <p><strong>depth_pfm</strong>: This folder contains absolute depth information of the scenes, which can be used to reconstruct the point cloud of the scene through reprojection.</p> </li> <li> <p><strong>instance_segmentation</strong>: This folder stores instance segmentation labels (PNG) for each tree in the scene, along with metadata (TXT) that maps <code>tree_id</code> to <code>RGB_id</code>. The <code>tree_id</code> can be used to look up detailed information about each tree in <code>obj_info_final.xlsx</code>, while the <code>RGB_id</code> can be matched to the corresponding RGB values in <code>color_palette.xlsx</code>. This mapping allows for identifying which tree corresponds to a specific color in the segmentation image.</p> </li> <li> <p><strong>obj_info_final.xlsx</strong>: This file contains detailed information about each tree in the scene, such as position, scale, species, and various parameters, including trunk diameter (in cm), tree height (in cm), and canopy diameter (in cm).</p> </li> <li> <p><strong>landscape_info.txt</strong>: This file contains the ground location information within the scene, sampled every 0.5 meters.</p> </li> </ul> <p>For birch_forest, broadleaf_forest, redwood_forest and rainforest, we also provided COCO-format annotation files (.json). Two such files can be found in these datasets:</p> <ul> <li><strong>{name}_coco.json</strong>: This file contains the annotation of each tree in the scene.</li> <li><strong>{name}_filtered.json</strong>: This file is derived from the previous one, but filtering is applied to rule out overlapping instances.</li> </ul> <p>⚠️: 7z files that begin with "<strong>!</strong>" indicate that the RGB values in the images within the <code>instance_segmentation</code> folder cannot be found in <code>color_palette.xlsx</code>. Consequently, this prevents matching the trees in the segmentation images to their corresponding tree information, which may hinder the application of the dataset to certain tasks. This issue is related to a bug in Colossium/AirSim, which has been reported in <a href="https://github.com/microsoft/AirSim/issues/3423" target="_blank" rel="noopener">link1</a> and <a href="https://github.com/microsoft/AirSim/issues/1852" target="_blank" rel="noopener">link2</a>.</p>
Elucidating gene expression patterns across multiple biological contexts through a large-scale investigation of transcriptomic datasets
<p>This contains data for described in detail in our paper, "Elucidating gene expression patterns across multiple biological contexts through a large-scale investigation of transcriptomic datasets" (Figueiredo <em>et al.</em>, 2022) which aims at revealing common and specific biological processes and mechanisms across contexts by identifying transcriptional patterns that are unique to various cell types, tissues, and cell lines, as well as patterns which are consistent across them.</p>
Data from: Genotyping-in-Thousands by sequencing panel development and application to inform kokanee salmon (Oncorhynchus nerka) fisheries management at multiple scales
<p>The ability to differentiate life history variants is vital for estimating fisheries management parameters, yet traditional survey methods can be inaccurate in mixed-stock fisheries. Such is the case for kokanee, the resident freshwater form of sockeye salmon (<i>Oncorhynchus nerka</i>), which exhibits various reproductive ecotypes (stream-, shore-, deep-spawning) that co-occur with each other and/or anadromous <i>O. nerka</i> in some systems across their pan-Pacific distribution. Here, we developed a multi-purpose Genotyping-in-Thousands by sequencing (GT-seq) panel of 288 targeted single nucleotide polymorphisms (SNPs) to enable accurate kokanee stock identification by geographic basin, migratory form, and reproductive ecotype across British Columbia, Canada. The GT-seq panel exhibited high self-assignment accuracy (93.3%) and perfect assignment of individuals not included in the baseline to their geographic basin, migratory form, and reproductive ecotype of origin. The GT-seq panel was subsequently applied to Wood Lake, a valuable mixed-stock fishery, revealing high concordance (>98%) with previous assignments to ecotype using microsatellites and TaqMan<span> </span>SNP genotyping assays, while improving resolution, extending a long-term time-series, and demonstrating the scalability of this approach for this system and others.</p>
Multiple drivers of large‐scale lichen decline in boreal forest canopies
<p>Thin, hair-like lichens (<em>Alectoria, Bryoria, Usnea</em>) form conspicuous epiphyte communities across the boreal biome. These poikilohydric organisms provide important ecosystem functions and are useful indicators of global change. We analyse how environmental drivers influence changes in occurrence and length of these lichens on Norway spruce (<em>Picea abies</em>) over 10 years in managed forests in Sweden using data from >6000 trees. <em>Alectoria</em> and <em>Usnea</em> showed strong declines in southern-central regions, whereas Bryoria declined in northern regions. Overall, relative loss rates across the country ranged from 1.7% per year in <em>Alectoria </em>to 0.5% in <em>Bryoria</em>. These losses contrasted with increased length of <em>Bryoria </em>and <em>Usnea </em>in some regions. Occurrence trajectories (extinction, colonization, presence, absence) on remeasured trees correlated best with temperature, rain, nitrogen deposition, and stand age in multinomial logistic regression models. Our analysis strongly suggests that industrial forestry, in combination with nitrogen, is the main driver of lichen declines. Logging of forests with long continuity of tree cover, short rotation cycles, substrate limitation and low light in dense forests are harmful for lichens. Nitrogen deposition has decreased but is apparently still sufficiently high to prevent recovery. Warming correlated with occurrence trajectories of <em>Alectoria</em> and <em>Bryoria</em>, likely by altering hydration regimes and increasing respiration during autumn/winter. The large-scale lichen decline on an important host has cascading effects on biodiversity and function of boreal forest canopies. Forest management must apply a broad spectrum of methods, including uneven-aged continuous cover forestry and retention of large patches, to secure the ecosystem functions of these important canopy components under future climates. Our findings highlight interactions among drivers of lichen decline (forestry, nitrogen, climate), functional traits (dispersal, lichen colour, sensitivity to nitrogen, water storage), and population processes (extinction/colonization).</p>
Unravelling mechanisms of protein and lipid oxidation in mayonnaise at multiple length scales
<p>raw data</p>
Data from: Tree diversity across multiple scales and environmental heterogeneity promote ecosystem multifunctionality in a large temperate forest region
<p><strong>Aim</strong>: Biodiversity across different scales provides multidimensional insurance for ecosystem functioning. Although the effects of biodiversity on ecosystem multifunctionality are well recorded in local communities, they remain poorly understood across scales (from local to larger spatial scales). This study evaluates how multiple attributes of biodiversity maintain ecosystem multifunctionality from local to regional scales, across diverse environmental gradients.</p> <p><strong>Location</strong>: North-eastern China.</p> <p><strong>Time period</strong>: 2017.</p> <p><strong>Major taxa studied</strong>: Woody plants.</p> <p><strong>Methods</strong>: We define multifunctionality using both averaged and modified multiple threshold approaches. Multiple dimensions of biodiversity across varying spatial scales were measured within the framework of Hill‒Chao numbers. Using variance decomposition, linear mixed models, and structural equation modeling, we explored how multiple attributes of tree diversity at varying spatial scales affect multifunctionality, and how these relationships are modulated by environmental drivers.<br>Results: We found that both α- and β-diversity are critical for regional community multifunctionality, while the relationships between species, functional, and phylogenetic diversity and multifunctionality decoupled across spatial scales and thresholds of ecosystem functioning. Phylogenetic β-diversity and species α-diversity are respectively more important for promoting high and moderate threshold multifunctionality (e.g., EMFT90 and EMFT50) in regional communities. Environmental drivers typically have stronger effects than biodiversity on multifunctionality. Soil and climatic conditions had either direct effects on multifunctionality, or indirect ones mediated by species α-diversity. Environmental heterogeneity is important for high threshold multifunctionality, exerting directly and indirectly through phylogenetic β-diversity. Latitude not only directly influences multifunctionality but also modulates it through species α-diversity and phylogenetic β-diversity.</p> <p><strong>Main conclusions</strong>: This study underscores the positive effects of biodiversity on multifunctionality across multiple dimensions. Based on our findings, we conclude that any design of a forested landscape that is aimed at maximizing multifunctionality should consider maintaining high local diversity as well as forest community heterogeneity at varying scales.</p>
Figure 9. Fouldenia dorsal fins. Unlabelled scale bars equal 1 in Styracopterid (Actinopterygii) ontogeny and the multiple origins of post-Hangenberg deep-bodied fishes
Figure 9. Fouldenia dorsal fins. Unlabelled scale bars equal 1 cm. A, NMS 1980.40.30; B, NMS 1980.40.27; C, NMS 1965.4.3; D, NMS 1980.40.31; E, NHM P61548; F, NHM P61549.
Figure 8. Fouldenia paired fins. Unlabelled scale bars equal 1 in Styracopterid (Actinopterygii) ontogeny and the multiple origins of post-Hangenberg deep-bodied fishes
Figure 8. Fouldenia paired fins. Unlabelled scale bars equal 1 cm. A, GSE 2143 pectoral; B, 1980.40.30 pectoral; C, 1984.67.61 pectoral; D, NMS 1984.67.61 pelvic; E, GLAHM V8327 pectoral; F, NHM P13183 pectoral; G, NHM P14564 pectoral; H, NMS 1984.67.65 pectoral; I, NHM 1984.67.65 pelvic; J, NHM P61002 pectoral; K, NMS 1984.67.64 pectoral.
Figure 10. Fouldenia anal fins. Unlabelled scale bars equal 1 in Styracopterid (Actinopterygii) ontogeny and the multiple origins of post-Hangenberg deep-bodied fishes
Figure 10. Fouldenia anal fins. Unlabelled scale bars equal 1 cm. A, NMS 1980.40.30; B, GSE 2187; C, NMS 1965.4.3 (part); D, NMS 1965.4.3 (counterpart); E, NMS 1984.67.61; F, NMS 1980.40.31; G, NMS P61549; H, NHM P61002; I, NMS 1984.67.62; J, NHM P61548.
Figure 2. Styracopterus paired fins. Unlabelled scale bars equal 1 in Styracopterid (Actinopterygii) ontogeny and the multiple origins of post-Hangenberg deep-bodied fishes
Figure 2. Styracopterus paired fins. Unlabelled scale bars equal 1 cm. A, GSE 8731 pectoral; B, NMS 1891.53.50 pectoral; C, NMS 1891.53.51; D, GSE 5672 pectoral; E, GSE 5664 pectoral; F, GSE 5663 pectoral; G, NHM P1663 pelvic; H, GSE 8731 pelvic; I, GSE 5663 pelvic.
When 'good' is not good enough: a retrospective Rasch analysis study of the Berg Balance Scale for persons with Multiple Sclerosis
<p>Raw data associated with the scientific publication "When ‘good’ is not good enough: a retrospective Rasch analysis study of the Berg Balance Scale for persons with Multiple Sclerosis".</p>
Data for: Consistently heterogeneous structures observed at multiple spatial scales across fire-intact reference sites
<p>Geospatial polygons representing fire-suppressed control sites against which fire-intact reference sites were compared in Chamberlain et al. (2023). Control sites represent areas with 1) no record of fire history, 2) no record of late 20th century or early 21st century timber management, and 3) no "Fast Change" detected by the Landscape Change Monitoring System dataset. All sites are predominantly within the yellow pine and mixed-conifer zone of California's Sierra Nevada, USA. Polygon boundaries were defined using the NHDPlusV2 catchments, and were manually reshaped using aerial imagery to ensure that polygons were > 100 ha, represented primarily forested areas, and excluded major roads, infrastructure, and major rock outcrops.</p> <p>Detailed description of the methods used to produce this dataset provided in:<br> Chamberlain, C.P., Cova, G.R., Cansler, C.A., North, M.P., Meyer, M.D., Jeronimo, S.M.A., Kane, V.R., 2023. Consistently heterogeneous structures observed at multiple spatial scales across fire-intact reference sites. Forest Ecology and Management.</p>
Unidimensional Self-Efficacy Scale for Multiple Sclerosis (USE-MS) Turkish Adaptation
ClinicalTrials.gov study NCT06231030. IPD Sharing: NO. Countries: 1. Publications: 4.
Multiple drivers of large‐scale lichen decline in boreal forest canopies
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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
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.