Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
13,117
datasets available to search
ShareScore release 0.9.0
Dataset results
13,117 results for “Maps”
Data from: Landscape connectivity for wildlife: development and validation of multi-species linkage maps
The ability to identify regions of high functional connectivity for multiple wildlife species is of conservation interest with respect to forest management and corridor planning. We present a method that does not require independent, field-collected data, is insensitive to the placement of source and destination sites (nodes) for modeling connectivity, and does not require the selection of a focal species. In the first step of our approach, we created a cost surface that represented permeability of the landscape to movement for a suite of species. We randomly selected nodes around the perimeter of the buffered study area and used circuit theory to connect pairs of nodes. When the buffer was removed, the resulting current density map represented, for each grid cell, the probability of use by moving animals. We found that using nodes that were randomly located around the perimeter of the buffered study area was less biased by node placement than randomly selecting nodes within the study area. We also found that a buffer of ≥ 20% of the study area width was sufficient to remove the effects of node placement on current density. We tested our method by creating a map of connectivity in the Algonquin to Adirondack region in eastern North America, and we validated the map with independently collected data. We found that amphibians and reptiles were more likely to cross roads in areas of high current density, and fishers (Pekania [Martes] pennanti) used areas with high current density within their home ranges. Our approach provides an efficient and cost-effective method of predicting areas with relatively high functional connectivity.
Data from: Mapping wood production in European forests
Wood production is an important forest use, impacting a range of other ecosystem services. However, information on the spatial patterns in wood production is limited and often available only for larger administrative units. In this study, we developed high-resolution wood production maps for European forests. We collected wood production statistics for 29 European countries from 2000 to 2010, as well as comprehensive sets of biophysical and socioeconomic location factors. We used regression analyses to produce maps indicating the harvest likelihood on a 1 × 1 km2 grid. These likelihood maps were validated using national forest inventory plot data. We then disaggregated wood production statistics from larger administrative units to the grid level using the harvest likelihood as weights. We verified the resulting wood production maps by correlating predicted and observed wood production at the level of smaller administrative units not used for generating the wood production maps. We conclude that (i) productivity, tree species composition and terrain ruggedness are the most important location factors that determine the spatial patterns of wood production at the pan-European scale and that (ii) incorporating these location factors substantially improves the results of disaggregating wood production statistics compared to a disaggregation based on forest cover only. Our wood production maps give insight into forest ecosystem service provisioning and can be used to improve the assessment of potentials and costs of woody biomass supply.
Data from: Localizing FST outliers on a QTL map reveals evidence for large genomic regions of reduced gene exchange during speciation-with-gene-flow
Populations that maintain phenotypic divergence in sympatry typically show a mosaic pattern of genomic divergence, requiring a corresponding mosaic of genomic isolation (reduced gene flow). However, mechanisms that could produce the genomic isolation required for divergence-with-gene-flow have barely been explored, apart from the traditional localized effects of selection and reduced recombination near centromeres or inversions. By localizing FST outliers from a genome scan of wild pea aphid host races on a Quantitative Trait Locus (QTL) map of key traits, we test the hypothesis that between-population recombination and gene exchange are reduced over large 'divergence hitchhiking' (DH) regions. As expected under divergence hitchhiking, our map confirms that QTL and divergent markers cluster together in multiple large genomic regions. Under divergence hitchhiking, the nonoutlier markers within these regions should show signs of reduced gene exchange relative to nonoutlier markers in genomic regions where ongoing gene flow is expected. We use this predicted difference among nonoutliers to perform a critical test of divergence hitchhiking. Results show that nonoutlier markers within clusters of FST outliers and QTL resolve the genetic population structure of the two host races nearly as well as the outliers themselves, while nonoutliers outside DH regions reveal no population structure, as expected if they experience more gene flow. These results provide clear evidence for divergence hitchhiking, a mechanism that may dramatically facilitate the process of speciation-with-gene-flow. They also show the power of integrating genome scans with genetic analyses of the phenotypic traits involved in local adaptation and population divergence.
Data from: Genome wide association mapping of floral traits in cultivated sunflower (Helianthus annuus)
Floral morphology and pigmentation are both charismatic and economically relevant traits associated with cultivated sunflower (Helianthus annuus L.). Recent work has linked floral morphology and pigmentation to pollinator efficiency and seed yield. Understanding the genetic architecture of such traits is essential for crop improvement, and gives insight into the role of genetic constraints in shaping floral diversity. A diversity panel of 288 sunflower genotypes was phenotyped for a variety of morphological, phenological, and color traits in both a greenhouse and a field setting. Association mapping was performed using 5788 SNP markers using a mixed linear model approach. Several dozen markers across ten linkage groups were significantly associated with variation in morphological and color trait variation. Substantial trait plasticity was observed between greenhouse and field phenotyping, and associations differed between environments. Color traits mapped more strongly than morphology in both settings, with markers together explaining 16% of petal carotenoid content in the greenhouse, and 17% and 24% of variation in disc anthocyanin presence in the field and greenhouse, respectively. Morphological traits like disc size mapped more strongly in the field, with markers together explaining up to 19% of disc size variation. Loci identified here through association mapping within cultivated germplasm differ from those identified through biparental crosses between modern cultivated sunflower and either its wild progenitor or domesticated landraces. Several loci lie within genomic regions involved in domestication. Differences between phenotype expression under greenhouse and field conditions highlight the importance of plasticity in determining floral morphology and pigmentation.
Data from: Admixture mapping in a hybrid zone reveals loci associated with avian feather coloration
Identifying the genetic bases for color patterns has provided important insights into the control and expression of pigmentation and how these characteristics influence fitness. However, much more is known about the genetic bases for traits based on melanin pigments than for traits based on another major class of pigments, carotenoids. Here we use natural admixture in a hybrid zone between Audubon's and myrtle warblers (Setophaga coronata auduboni / S. c. coronata) to identify genomic regions associated with both types of pigmentation. Warblers are known for rapid speciation and dramatic differences in plumage. For each of five plumage coloration traits, we found highly significant associations with multiple SNPs across the genome and these were clustered in discrete regions. Regions near significantly associated SNPs were enriched for genes associated with keratin filaments, fibrils that makeup feathers. A carotenoid-based trait that differs between the taxa—throat color—had more than a dozen genomic regions of association. One cluster of SNPs for this trait overlaps the Scavenger Receptor Class F Member 2 (SCARF2) gene. Other scavenger receptors are presumed to be expressed at target tissues and involved in the selective movement of carotenoids into the target cells, making SCARF2 a plausible new candidate for carotenoid processing. In addition, two melanin-based plumage traits—colors of the eye line and eye spot—show very strong associations with a single genomic region mapping to chromosome 20 in the zebra finch. These findings indicate that only a subset of the genomic regions differentiated between these two warblers are associated with the plumage differences between them and demonstrate the utility of reduced-representation genomic scans in hybrid zones.
Data from: Development of a genomic resource and quantitative trait loci mapping of male calling traits in the lesser wax moth, Achroia grisella
In the study of sexual selection among insects, the Lesser Waxmoth, Achroia grisella (Lepidoptera: Pyralidae), has been one of the more intensively studied species over the past 20 years. Studies have focused on how the male calling song functions in pair formation and on the quantitative genetics of male song characters and female preference for the song. Recent QTL studies have attempted to elucidate the genetic architecture of male song and female preference traits using AFLP markers. We continued these QTL studies using SNP markers derived from an EST library that allowed us to measure both DNA sequence variation and map loci with respect to the lepidopteran genome. We report that the level of sequence variation within A. grisella is typical among other Lepidoptera that have been examined, and that comparison with the Bombyx mori genome shows that macrosynteny is conserved. Our QTL map shows that a QTL for a male song trait, pulse-pair rate, is situated on the Z chromosome, a prediction for sexually selected traits in Lepidoptera. Our findings will be useful for future studies of genetic architecture of this model species and may help identify the genetics associated with the evolution of its novel acoustic communication.
Data from: High resolution spatial mapping of human footprint across Antarctica and its implications for the strategic conservation of avifauna
Human footprint models allow visualization of human spatial pressure across the globe. Up until now, Antarctica has been omitted from global footprint models, due possibly to the lack of a permanent human population and poor accessibility to necessary datasets. Yet Antarctic ecosystems face increasing cumulative impacts from the expanding tourism industry and national Antarctic operator activities, the management of which could be improved with footprint assessment tools. Moreover, Antarctic ecosystem dynamics could be modelled to incorporate human drivers. Here we present the first model of estimated human footprint across predominantly ice-free areas of Antarctica. To facilitate integration into global models, the Antarctic model was created using methodologies applied elsewhere with land use, density and accessibility features incorporated. Results showed that human pressure is clustered predominantly in the Antarctic Peninsula, southern Victoria Land and several areas of East Antarctica. To demonstrate the practical application of the footprint model, it was used to investigate the potential threat to Antarctica's avifauna by local human activities. Relative footprint values were recorded for all 204 of Antarctica's Important Bird Areas (IBAs) identified by BirdLife International and the Scientific Committee on Antarctic Research (SCAR). Results indicated that formal protection of avifauna under the Antarctic Treaty System has been unsystematic and is lacking for penguin and flying bird species in some of the IBAs most vulnerable to human activity and impact. More generally, it is hoped that use of this human footprint model may help Antarctic Treaty Consultative Meeting policy makers in their decision making concerning avifauna protection and other issues including cumulative impacts, environmental monitoring, non-native species and terrestrial area protection.
Data from: Incorporating interspecific competition into species-distribution mapping by upward scaling of small-scale model projections to the landscape
There are a number of overarching questions and debate in the scientific community concerning the importance of biotic interactions in species distribution models at large spatial scales. In this paper, we present a framework for revising the potential distribution of tree species native to the Western Ecoregion of Nova Scotia, Canada, by integrating the long-term effects of interspecific competition into an existing abiotic-factor-based definition of potential species distribution (PSD). The PSD model is developed by combining spatially explicit data of individualistic species' response to normalized incident photosynthetically active radiation, soil water content, and growing degree days. A revised PSD model adds biomass output simulated over a 100-year timeframe with a robust forest gap model and scaled up to the landscape using a forestland classification technique. To demonstrate the method, we applied the calculation to the natural range of 16 target tree species as found in 1,240 provincial forest-inventory plots. The revised PSD model, with the long-term effects of interspecific competition accounted for, predicted that eastern hemlock (Tsuga canadensis), American beech (Fagus grandifolia), white birch (Betula papyrifera), red oak (Quercus rubra), sugar maple (Acer saccharum), and trembling aspen (Populus tremuloides) would experience a significant decline in their original distribution compared with balsam fir (Abies balsamea), black spruce (Picea mariana), red spruce (Picea rubens), red maple (Acer rubrum L.), and yellow birch (Betula alleghaniensis). True model accuracy improved from 64.2% with original PSD evaluations to 81.7% with revised PSD. Kappa statistics slightly increased from 0.26 (fair) to 0.41 (moderate) for original and revised PSDs, respectively.
Data from: A high-density linkage map for Astyanax mexicanus using genotyping-by-sequencing technology
The Mexican tetra, Astyanax mexicanus, is a unique model system consisting of cave-adapted and surface-dwelling morphotypes which diverged >1My ago. This remarkable natural experiment has enabled powerful genetic analyses of cave adaptation. Here, we describe the application of next-generation sequencing technology to the creation of a high-density linkage map. Our map comprises over 2200 markers populating 25 linkage groups constructed from genotypic data generated from a single genotyping-by-sequencing project. We leveraged emergent genomic and transcriptomic resources to anchor hundreds of anonymous Astyanax markers to the genome of the zebrafish (Danio rerio), the most closely related model organism to our study species. This facilitated the identification of 784 distinct connections between our linkage map and the Danio rerio genome, highlighting several regions of conserved genomic architecture between the two species despite ~150My of divergence. Using a Mendelian cave-associated trait as a proof-of-principle, we successfully recovered the genomic position of the albinism locus near the gene Oca2. Further, our map successfully informed the positions of unplaced Astyanax genomic scaffolds within particular linkage groups. This ability to identify the relative location, orientation and linear order of unaligned genomic scaffolds will facilitate ongoing efforts to improve upon the current early draft and assemble future versions of the Astyanax physical genome. Moreover, this improved linkage map will enable higher resolution genetic analyses and catalyze the discovery of the genetic basis for cave-associated phenotypes.
Data from: Spatial structure of above-ground biomass limits accuracy of carbon mapping in rainforest but large scale forest inventories can help to overcome
Precise mapping of above-ground biomass (AGB) is a major challenge for the success of REDD+ processes in tropical rainforest. The usual mapping methods are based on two hypotheses: a large and long-ranged spatial autocorrelation and a strong environment influence at the regional scale. However, there are no studies of the spatial structure of AGB at the landscapes scale to support these assumptions. We studied spatial variation in AGB at various scales using two large forest inventories conducted in French Guiana. The dataset comprised 2507 plots (0.4 to 0.5 ha) of undisturbed rainforest distributed over the whole region. After checking the uncertainties of estimates obtained from these data, we used half of the dataset to develop explicit predictive models including spatial and environmental effects and tested the accuracy of the resulting maps according to their resolution using the rest of the data. Forest inventories provided accurate AGB estimates at the plot scale, for a mean of 325 Mg.ha-1. They revealed high local variability combined with a weak autocorrelation up to distances of no more than10 km. Environmental variables accounted for a minor part of spatial variation. Accuracy of the best model including spatial effects was 90 Mg.ha-1 at plot scale but coarse graining up to 2-km resolution allowed mapping AGB with accuracy lower than 50 Mg.ha-1. Whatever the resolution, no agreement was found with available pan-tropical reference maps at all resolutions. We concluded that the combined weak autocorrelation and weak environmental effect limit AGB maps accuracy in rainforest, and that a trade-off has to be found between spatial resolution and effective accuracy until adequate "wall-to-wall" remote sensing signals provide reliable AGB predictions. Waiting for this, using large forest inventories with low sampling rate (<0.5%) may be an efficient way to increase the global coverage of AGB maps with acceptable accuracy at kilometric resolution.
Data from: A microsatellite-based linkage map for song sparrows (Melospiza melodia)
Although linkage maps are important tools in evolutionary biology, their availability for wild populations is limited. The population of song sparrows (Melospiza melodia) on Mandarte Island, Canada, is among the more intensively studied wild animal populations. Its long-term pedigree data, together with extensive genetic sampling, have allowed the study of a range of questions in evolutionary biology and ecology. However, the availability of genetic markers has been limited. We here describe 191 new microsatellite loci, including 160 high-quality polymorphic autosomal, 7 Z-linked and 1 W-linked markers. We used these markers to construct a linkage map for song sparrows with a total sex-averaged map length of 1731 cM and covering 35 linkage groups, and hence, these markers cover most of the 38–40 chromosomes. Female and male map lengths did not differ significantly. We then bioinformatically mapped these loci to the zebra finch (Taeniopygia guttata) genome and found that linkage groups were conserved between song sparrows and zebra finches. Compared to the zebra finch, marker order within small linkage groups was well conserved, whereas the larger linkage groups showed some intrachromosomal rearrangements. Finally, we show that as expected, recombination frequency between linked loci explained the majority of variation in gametic phase disequilibrium. Yet, there was substantial overlap in gametic phase disequilibrium between pairs of linked and unlinked loci. Given that the microsatellites described here lie on 35 of the 38–40 chromosomes, these markers will be useful for studies in this species, as well as for comparative genomics studies with other species.
Data from: Impact of ecological redundancy on the performance of machine learning classifiers in vegetation mapping
Vegetation maps are models of the real vegetation patterns and are considered important tools in conservation and management planning. Maps created through traditional methods can be expensive and time‐consuming, thus, new more efficient approaches are needed. The prediction of vegetation patterns using machine learning shows promise, but many factors may impact on its performance. One important factor is the nature of the vegetation–environment relationship assessed and ecological redundancy. We used two datasets with known ecological redundancy levels (strength of the vegetation–environment relationship) to evaluate the performance of four machine learning (ML) classifiers (classification trees, random forests, support vector machines, and nearest neighbor). These models used climatic and soil variables as environmental predictors with pretreatment of the datasets (principal component analysis and feature selection) and involved three spatial scales. We show that the ML classifiers produced more reliable results in regions where the vegetation–environment relationship is stronger as opposed to regions characterized by redundant vegetation patterns. The pretreatment of datasets and reduction in prediction scale had a substantial influence on the predictive performance of the classifiers. The use of ML classifiers to create potential vegetation maps shows promise as a more efficient way of vegetation modeling. The difference in performance between areas with poorly versus well‐structured vegetation–environment relationships shows that some level of understanding of the ecology of the target region is required prior to their application. Even in areas with poorly structured vegetation–environment relationships, it is possible to improve classifier performance by either pretreating the dataset or reducing the spatial scale of the predictions.
Data from: Tree-centric mapping of forest carbon density from airborne laser scanning and hyperspectral data
Forests are a major component of the global carbon cycle, and accurate estimation of forest carbon stocks and fluxes is important in the context of anthropogenic global change. Airborne laser scanning (ALS) data sets are increasingly recognized as outstanding data sources for high-fidelity mapping of carbon stocks at regional scales. We develop a tree-centric approach to carbon mapping, based on identifying individual tree crowns (ITCs) and species from airborne remote sensing data, from which individual tree carbon stocks are calculated. We identify ITCs from the laser scanning point cloud using a region-growing algorithm and identifying species from airborne hyperspectral data by machine learning. For each detected tree, we predict stem diameter from its height and crown-width estimate. From that point on, we use well-established approaches developed for field-based inventories: above-ground biomasses of trees are estimated using published allometries and summed within plots to estimate carbon density. We show this approach is highly reliable: tests in the Italian Alps demonstrated a close relationship between field- and ALS-based estimates of carbon stocks (r2 = 0·98). Small trees are invisible from the air, and a correction factor is required to accommodate this effect. An advantage of the tree-centric approach over existing area-based methods is that it can produce maps at any scale and is fundamentally based on field-based inventory methods, making it intuitive and transparent. Airborne laser scanning, hyperspectral sensing and computational power are all advancing rapidly, making it increasingly feasible to use ITC approaches for effective mapping of forest carbon density also inside wider carbon mapping programs like REDD++.
Data from: Range-wide spatial mapping reveals convergent character displacement of bird song
A long-held view in evolutionary biology is that character displacement generates divergent phenotypes in closely related coexisting species to avoid the costs of hybridisation or ecological competition, whereas an alternative possibility is that signals of dominance or aggression may instead converge to facilitate coexistence among ecological competitors. Although this counter-intuitive process⎯termed convergent agonistic character displacement⎯is supported by recent theoretical and empirical studies, the extent to which it drives spatial patterns of trait evolution at continental scales remains unclear. By modeling variation in song structure of two ecologically similar species of Hypocnemis antbird across Western Amazonia, we show that their territorial signals converge such that trait similarity peaks in the sympatric zone, where intense interspecific territoriality between these taxa has previously been demonstrated. We also use remote sensing data to show that signal convergence is not explained by environmental gradients and is thus unlikely to evolve by sensory drive (i.e. acoustic adaptation to the sound transmission properties of habitats). Our results suggest that agonistic character displacement driven by interspecific competition can generate spatial patterns opposite to those predicted by classic character displacement theory, and highlight the potential role of social selection in shaping geographical variation in signal phenotypes of ecological competitors.
Data from: Mapping resistance to Alternaria cucumerina in Cucumis melo
Infection with Alternaria cucumerina causes Alternaria leaf blight (ALB), a disease characterized by lesion formation on leaves, leading to substantial yield and quality losses in Cucumis melo (melon). While fungicides are effective against ALB, reduction in the frequency of application would be economically and environmentally beneficial. Resistant melon lines have been identified but the genetic basis of this resistance has not been determined. A saturated melon genetic map was constructed with markers developed through genotyping-by sequencing of an MR-1 (resistant) by Ananas Yokneum (susceptible Israeli cultivar) a recombinant inbred line (RIL) population (F6-F10; N=82) derived from single seed descent of a F2 population from a cross between the ALB resistant parent MR-1 and the ALB susceptible parent Ananas Yokneum. The population was evaluated for A. cucumerina resistance with an augmented block greenhouse study using inoculation with the wounded-leaf method. Multiple quantitative trait loci (QTL) mapping identified two QTL that explained 33.9% of variation in lesion area. Several candidate genes within range of these QTL were identified using the C. melo v3.5 genome. Markers linked to these QTL will be used to accelerate efforts to breed melon cultivars resistant to ALB.
Data from: Deciphering the genomic architecture of the stickleback brain with a novel multi-locus gene-mapping approach
Quantitative traits important to organismal function and fitness, such as brain size, are presumably controlled by many small-effect loci. Deciphering the genetic architecture of such traits with traditional quantitative trait locus (QTL) mapping methods is challenging. Here, we investigated the genetic architecture of brain size (and the size of five different brain parts) in nine-spined sticklebacks (Pungitius pungitius) with the aid of novel multi-locus QTL mapping approaches based on a de-biased LASSO method. Apart from having more statistical power to detect QTL and reduced rate of false positives than conventional QTL mapping approaches, the developed methods can handle large marker panels and provide estimates of genomic heritability. Single-locus analyses of an F2-interpopulation cross with 239 individuals and 15 198 fully informative single nucleotide polymorphisms (SNPs) uncovered 79 QTL associated with variation in stickleback brain size traits. Many of these loci were in strong linkage disequilibrium (LD) with each other, and consequently, a multi-locus mapping of individual SNPs, accounting for LD structure in the data, recovered only four significant QTL. However, a multi-locus mapping of SNPs grouped by linkage group (LG) identified 14 LGs (1-6 depending on the trait) that influence variation in brain traits. For instance, 17.6% of the variation in relative brain size was explainable by cumulative effects of SNPs distributed over six LGs, whereas 42% of the variation was accounted for by all 21 LGs. Hence, the results suggest that variation in stickleback brain traits is influenced by many small-effect loci. Apart from suggesting moderately heritable (h2 ≈ 0.15-0.42) multifactorial genetic architecture of brain traits, the results highlight the challenges in identifying the loci contributing to variation in quantitative traits. Nevertheless, the results demonstrate that the novel QTL mapping approach developed here has distinctive advantages over the traditional QTL mapping methods in analyses of dense marker panels.
Data from: Topographic mapping of the interfaces between human and aquatic mosquito habitats to enable barrier-targeting of interventions against malaria vectors
Geophysical topographic metrics of local water-accumulation potential are freely available and have long been known as high resolution predictors of where aquatic habitats for immature Anopheles mosquitoes are most abundant, resulting in elevated densities of adult malaria vectors and human infection burden. Using existing entomological and epidemiological survey data, here we illustrate how topography can also be used to map out the interfaces between wet, unoccupied valleys and dry, densely populated uplands, where malaria vector densities and infection risk are focally exacerbated. These topographically identifiable geophysical boundaries experience disproportionately high vector densities and malaria transmission risk because this is where Anopheles mosquitoes first encounter humans when they search for blood after emerging or ovipositing in the valleys. Geophysical topographic indicators accounted for 67% of variance for vector density but only 43% for infection prevalence, so they could enable very selective targeting of interventions against the former but not the latter (targeting ratios of 9.0 versus 1.5 to 1, respectively). So in addition to being useful for targeting larval source management to wet valleys, geophysical topographic indicators may also be used to selectively target adult Anopheles mosquitoes with insecticidal residual sprays, fencing, vapour emanators or space sprays to barrier areas along their fringes.
Data from: Mass production of SNP markers in a nonmodel passerine bird through RAD sequencing and contig mapping to the zebra finch genome
Here, we present an adaptation of restriction-site-associated DNA sequencing (RAD-seq) to the Illumina HiSeq2000 technology that we used to produce SNP markers in very large quantities at low cost per unit in the Réunion grey white-eye (Zosterops borbonicus), a nonmodel passerine bird species with no reference genome. We sequenced a set of six pools of 18–25 individuals using a single sequencing lane. This allowed us to build around 600 000 contigs, among which at least 386 000 could be mapped to the zebra finch (Taeniopygia guttata) genome. This yielded more than 80 000 SNPs that could be mapped unambiguously and are evenly distributed across the genome. Thus, our approach provides a good illustration of the high potential of paired-end RAD sequencing of pooled DNA samples combined with comparative assembly to the zebra finch genome to build large contigs and characterize vast numbers of informative SNPs in nonmodel passerine bird species in a very efficient and cost-effective way.
Data from: Uncovering spatial variation in acoustic environments using sound mapping
Animals select and use habitats based on environmental features relevant to their ecology and behavior. For animals that use acoustic communication, the sound environment itself may be a critical feature, yet acoustic characteristics are not commonly measured when describing habitats and as a result, how habitats vary acoustically over space and time is poorly known. Such considerations are timely, given worldwide increases in anthropogenic noise combined with rapidly accumulating evidence that noise hampers the ability of animals to detect and interpret natural sounds. Here, we used microphone arrays to record the sound environment in three terrestrial habitats (forest, prairie, and urban) under ambient conditions and during experimental noise introductions. We mapped sound pressure levels (SPLs) over spatial scales relevant to diverse taxa to explore spatial variation in acoustic habitats and to evaluate the number of microphones needed within arrays to capture this variation under both ambient and noisy conditions. Even at small spatial scales and over relatively short time spans, SPLs varied considerably, especially in forest and urban habitats, suggesting that quantifying and mapping acoustic features could improve habitat descriptions. Subset maps based on input from 4, 8, 12 and 16 microphones differed slightly (< 2 dBA/pixel) from those based on full arrays of 24 microphones under ambient conditions across habitats. Map differences were more pronounced with noise introductions, particularly in forests; maps made from only 4-microphones differed more (> 4 dBA/pixel) from full maps than the remaining subset maps, but maps with input from eight microphones resulted in smaller differences. Thus, acoustic environments varied over small spatial scales and variation could be mapped with input from 4–8 microphones. Mapping sound in different environments will improve understanding of acoustic environments and allow us to explore the influence of spatial variation in sound on animal ecology and behavior.
Data from: Ensemble approach for potential habitat mapping of invasive Prosopis in Turkana, Kenya
Aim: Prosopis spp. are an invasive alien plant species native to the Americas and well adapted to thrive in arid environments. In Kenya, several remote‐sensing studies conclude that the genus is well established throughout the country and is rapidly in‐ vading new areas. This research aims to model the potential habitat of Prosopis spp. by using an ensemble model consisting of four species distribution models. Furthermore, environmental and expert knowledge‐based variables are assessed. Location: Turkana County, Kenya. Methods: We collected and assessed a large number of environmental and expert knowl‐ edge‐based variables through variable correlation, collinearity, and bias tests. The varia‐ bles were used for an ensemble model consisting of four species distribution models: (a) logistic regression, (b) maximum entropy, (c) random forest, and (d) Bayesian networks. The models were evaluated through a block cross‐validation providing statistical measures. Results: The best predictors for Prosopis spp. habitat are distance from water and built‐up areas, soil type, elevation, lithology, and temperature seasonality. All species distribution models achieved high accuracies while the ensemble model achieved the highest scores. Highly and moderately suitable Prosopis spp. habitat covers 6% and 9% of the study area, respectively. Main conclusions: Both ensemble and individual models predict a high risk of continued invasion, confirming local observations and conceptions. Findings are valuable to stake‐ holders for managing invaded area, protecting areas at risk, and to raise awareness.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.