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423 results for “spatial use”
Spatial and temporal dynamics of space use by free-ranging domestic dogs Canis familiaris in rural Africa
<p>Variation in the spatial ecology of animals influences the transmission of infections and so understanding host behaviour can improve the control of diseases. Despite the global distribution of free-ranging domestic dogs <i>Canis familiaris</i> and their role as reservoirs for zoonotic diseases, little is known about the dynamics of their space use. We deployed GPS loggers on free-ranging dogs from six villages in rural Chad, and tracked the movements of 174 individuals in the dry season and 151 in the wet season. We calculated 95% and core home ranges using auto-correlated kernel density estimates (AKDE<sub>95</sub> and AKDE<sub>core</sub>), determined the degree to which their movements were predictable, and identified correlates of movement patterns. The median AKDE<sub>95 </sub>range in the dry season was 0.54km<sup>2</sup> and in the wet season was 0.31km<sup>2</sup>, while the median AKDE<sub>core </sub>range in the dry season was 0.08km<sup>2</sup> and in the wet season was 0.04km<sup>2</sup>. Seasonal variation was, in part, related to owner activities. Dogs from hunting households had ranges that were five times larger in the dry season. At least 70% of individuals were more predictably 'at home' (<50m from the household) throughout the day in the dry season. 80% of dogs demonstrated periodicity in activity levels (speed) and just over half the dogs exhibited periodicity in location (repeated space use). In the wet season, dogs mostly exhibited 24-hour cycles in activity and location, with peaks at midday. In the dry season, dogs exhibited both 12 and 24-hour cycles, with either a single peak at midday, or one peak between 6am and 12 noon and a second between 6 and 10pm. Strategies to control canine-mediated zoonoses can be improved by tailoring operations to the local spatial ecology of free-ranging dogs. Interventions using a door-to-door strategy in rural Chad would best conduct operations during the dry season, when access to dogs around their household more reliably exceeds 70% throughout the day. Given the importance of use in hunting for explaining variation in dog space-use, targeting approaches to disease control at the household level on the basis of owner activities offers potential to improve access to dogs.</p>
Data from: Effects of roads and land use on frog distributions across spatial scales and regions in the eastern and central United States
Aim: Understanding the scales over which land use affects animal populations is critical for conservation planning, and it can provide information about the mechanisms that underlie correlations between species distributions and land use. We used a citizen-science database of anuran surveys to examine the relationship between road density, land use, and the distribution of frogs and toads across spatial scales and regions of the United States. Location: Eastern and Central United States Methods: We compiled data on anuran occupancy collected from 1999-2013 across 13 states in the North American Amphibian Monitoring Program, a citizen science survey of calling frogs. These data were indexed to measures of land use within buffers ranging from 300 m to 10 km. Results: The negative effects of road density and development on anuran richness were strongest at the smallest scales (300 – 1000 m), and this pattern was consistent across regions. In contrast, the relationships of anuran richness to agriculture and forest cover were similar across local scales but varied among regions. Richness had a negative relationship with agriculture/ forest loss in the Midwest but a positive relationship with agriculture in the Northeast. Anuran richness was more closely related to primary/secondary road density than to rural road density, and the negative effects of larger roads increased at smaller scales. Individual species differed in the scales over which roads and development affected their distributions, but these differences were not closely related to either body size or movement ability. Main conclusions: This study further refines our understanding of the relationship between roads and amphibian populations and highlights the need for research into the specific mechanisms by which roads affect amphibians. Additionally, we find that relationships between land use and species richness can differ substantially across regions, demonstrating that one should use caution in generalizing from one region to another, even when species composition is similar.
Data from: Species-specific variation in germination rates contributes to spatial coexistence more than adult plant water use in four closely-related annual flowering plants
1. Spatial partitioning is a classic hypothesis to explain plant species coexistence, but evidence linking local environmental variation to spatial sorting, demography, and species' traits is sparse. If co-occurring species' performance is optimized differently along environmental gradients because of trait variation, then spatial variation might facilitate coexistence. 2. We used a system of four naturally co-occurring species of Clarkia (Onagraceae) to ask if distribution patchiness corresponds to variation in two environmental variables that contribute to hydrological variation. We then reciprocally sowed Clarkia into each patch type and measured demographic rates in the absence of congeneric competition. Species sorted in patches along one or both gradients, and in three of the four species, germination rate in the "home" patch was higher than all other patches. 3. Spatially variable germination resulted in the same three species exhibiting the highest population growth rates in their home patches. 4. Species' trait values related to plant water use, as well as indicators of water stress in home patches, differed among species and corresponded to home patch attributes. However, post-germination survival did not vary among species or between patch types, and fecundity did not vary spatially. 5. Synthesis Our research demonstrates the likelihood that within-community spatial heterogeneity affects plant species coexistence, and presents novel evidence that differential performance in space is explained by what happens in the germination stage. Despite the seemingly obvious link between adult plant water-use and variation in the environment, our results distinguish the germination stage as important for spatially variable population performance.
Data from: Ultra-fine scale spatially-integrated mapping of habitat and occupancy using structure-from-motion
Organisms respond to and often simultaneously modify their environment. While these interactions are apparent at the landscape extent, the driving mechanisms often occur at very fine spatial scales. Structure-from-Motion (SfM), a computer vision technique, allows the simultaneous mapping of organisms and fine scale habitat, and will greatly improve our understanding of habitat suitability, ecophysiology, and the bi-directional relationship between geomorphology and habitat use. SfM can be used to create high-resolution (centimeter-scale) three-dimensional (3D) habitat models at low cost. These models can capture the abiotic conditions formed by terrain and simultaneously record the position of individual organisms within that terrain. While coloniality is common in seabird species, we have a poor understanding of the extent to which dense breeding aggregations are driven by fine-scale active aggregation or limited suitable habitat. We demonstrate the use of SfM for fine-scale habitat suitability by reconstructing the locations of nests in a gentoo penguin colony and fitting models that explicitly account for conspecific attraction. The resulting digital elevation models (DEMs) are used as covariates in an inhomogeneous hybrid point process model. We find that gentoo penguin nest site selection is a function of the topography of the landscape, but that nests are far more aggregated than would be expected based on terrain alone, suggesting a strong role of behavioral aggregation in driving coloniality in this species. This integrated mapping of organisms and fine scale habitat will greatly improve our understanding of fine-scale habitat suitability, ecophysiology, and the complex bi-directional relationship between geomorphology and habitat use.
COLOURATION IN 2.5D LOCAL WAVE FIELD SYNTHESIS USING SPATIAL BANDWIDTH-LIMITATION -- Data
<p>This data set contains stimuli and results from an experiment for human perception of colouration in Stereophonics, Wave Field Synthesis, Near-Field-Compensated Higher-Order Ambisonics and Local Wave Field Synthesis simulated via binaural synthesis using head related impulse responses (HRIRs). The corresponding binaural room scanning (BRS) files for the binaural simulation can be found in the directory `brs`. The employed stimuli is contained in `stimuli`. The Multiple Stimulus with Hidden Reference and Anchor (MUSHRA) ratings are stored in `results` together with a text file to describe the content in more detail. The `analysis` directory includes scripts for the statistical evaluation in the publication. The source code is licensed under GPL v3.</p> <p>Abstract of the Publication:</p> <p>Sound Field Synthesis techniques, such as Wave Field Synthesis aim at a physically accurate reproduction of a desired sound field inside an extended listening area. This area is surrounded by loudspeakers individually driven by their respective driving signals. Due to practical limitations, artefacts impair the synthesis accuracy resulting in a perceivable change in timbre compared to the desired sound field. Recently, an approach for so-called Local Wave Field Synthesis was published which enhances the reproduction accuracy in a limited region by applying a spatial bandwidth limitation in the circular/spherical harmonics domain to the desired sound field. This paper reports on a listening experiment comparing conventional Sound Field Synthesis techniques with the mentioned approach. Also the influence of the different parametrisations for Local Wave Field Synthesis is investigated. The results show that the enhanced reproduction accuracy in Local Wave Field Synthesis leads to an improvement with regard to the perceived colouration.</p>
Data from: Distances and their visualization in studies of spatial-temporal genetic variation using single nucleotide polymorphisms (SNPs)
<p>Distance measures are widely used for examining genetic structure in datasets that comprise many individuals scored for a very large number of attributes. Genotype datasets composed of single nucleotide polymorphisms (SNPs) typically contain bi-allelic scores for tens of thousands if not hundreds of thousands of loci.</p> <p>We examine the application of distance measures to SNP genotypes and sequence tag presence-absences (SilicoDArT) and use real datasets and simulated data to illustrate pitfalls in the application of genetic distances and their visualization.</p> <p>The datasets used to illustrate points in the associated review are provided here together with the R script used to analyse the data. Data are either simulated internal to this script or are SNP data generated as part of other studies and included as compressed binary files readily accessable by reading into R using R base function readRDS(). Refer to the analysis script for examples.</p>
The global distribution of plants used by humans datasets: list of utilised species, occurrence data and model outputs at 10 arc-minutes spatial resolution
<p>Datasets and model outputs used to map the global distribution of utilised plants by humans. The folder is composed of two subfolders <em>raw_data</em> and <em>processed_data</em> containing respectively the list of utilised plant species modelled -<em>utilised_plants_species_list.csv</em>-, and their occurrence data -<em>occurrence_data.zip-</em> and predicted distribution -<em>species_proba_per_cell.rds-.</em></p> <p> </p> <ul> <li>The file <em>utilised_plants_species_list.csv</em> in the <em>raw_data</em> folder contains a<strong> </strong>list of 35687 plant species (and hybrids) used by humans and 10 plant use categories with the following 14 fields:</li> </ul> <p><strong>plant_ID:<em> </em></strong>plant identifier number ranging from between 1-35687</p> <p><strong>binomial_acc_name:</strong> binomial accepted name of the plant species</p> <p><strong>author_acc_name</strong>: name of the author(s)</p> <p><strong>is_hybrid:</strong> logical TRUE or FALSE indicating whether the species is an hybrid or not.</p> <p><strong>AnimalFood:</strong> forage and fodder for vertebrate animals only.</p> <p><strong>EnvironmentalUses:</strong> examples include intercrops and nurse crops, ornamentals, barrier hedges, shade plants, windbreaks, soil improvers, plants for revegetation and erosion control, wastewater purifiers, indicators of the presence of metals, pollution, or underground water.</p> <p><strong>Fuels:</strong> charcoal, petroleum substitutes, fuel alcohols, etc. Given the importance of energy plants for people, those were distinguished from Materials.</p> <p><strong>GeneSources:</strong> wild relatives of major crops which may possess traits associated with biotic or abiotic resistance and may be valuable for breeding programs.</p> <p><strong>HumanFood:</strong> food for humans only, including beverages and food additives.</p> <p><strong>InvertebrateFood:</strong> plants consumed by invertebrates used by humans, such as bees, silkworms, lac insects and edible grubs.</p> <p><strong>Materials:</strong> woods, fibers, cork, cane, tannins, latex, resins, gums, waxes, oils, lipids, etc. and their derived products.</p> <p><strong>Medicines:</strong> both human and veterinary.</p> <p><strong>Poisons:</strong> plants which are poisonous to both vertebrates and invertebrates, both accidentally and intentionally, e.g., for hunting and fishing, molluscicides, herbicides, insecticides.</p> <p><strong>SocialsUses:</strong> plants used for social purposes, which cannot be defined as food or medicine, for instance, masticatories, smoking materials, narcotics, hallucinogens and psychoactive drugs, and plants with ritual or religious significance.</p> <p><strong>Totals:</strong> total number of uses recorded for a species</p> <p> </p> <ul> <li>The zipfile <em>occurrence_data.zip</em> in the <em>processed_data</em> folder contains 35687 Comma Separated Values (CSV) files, one for each species, containing curated geographic occurrence records used to build species distribution models with the following 14 fields:</li> </ul> <p><strong>Species:</strong> the binomial accepted name of the species</p> <p><strong>Fullname:</strong> same as species</p> <p><strong>decimalLongitude:</strong> the geographic longitude of the occurrence records of the species in decimal degrees</p> <p><strong>decimalLatitude:</strong> the geographic latitude of the occurrence records of the species in decimal degrees</p> <p><strong>countryCode:</strong> a three-letter standard abbreviation for the country of the occurrence locality</p> <p><strong>coordinateUncertaintyinMeters</strong>: indicator for the accuracy of the coordinate location, described as the radius of a circle around the stated point location</p> <p><strong>year:</strong> year of the observation of the occurrence record of the species</p> <p><strong>individualCount:</strong> the number of individuals present at the time of the observation</p> <p><strong>gbifID:</strong> unique identifier number for the occurrence from the original database</p> <p><strong>basisOfRecords:</strong> the type of the individual record, e.g. observation, physical specimen, fossil, living ex-situ, culture collection specimen</p> <p><strong>institutionCode</strong>: the name of the institution or organization listed as the data publisher on GBIF</p> <p><strong>establishmentMeans:</strong> statement about whether an organism has been introduced to a given place and time through the direct or indirect activity of modern humans</p> <p><strong>is_cultivated_observation:</strong> whether or not an organism is cultivated</p> <p><strong>sourceID:</strong> name of the source database</p> <p> </p> <ul> <li>The file <em>species_proba_per_cell.rds</em> in the <em>processed_data</em> folder is<em> a R Data Serialization </em>(RDS) file containing a data.table object with the following 3 fields:</li> </ul> <p><strong>plant_ID:</strong><em> </em>plant identifier number ranging from between 1-35687</p> <p><strong>proba:</strong> species occurrence probability</p> <p><strong>cell:</strong><em> </em>raster grid cell number between 1-2251762</p> <p>This object can be used in combination with a raster layer to reconstruct the modelled distribution of each species or retrieve species richness and endemism.</p>
STalign: Alignment of spatial transcriptomics data using diffeomorphic metric mapping
<p>Spatial transcriptomics (ST) technologies enable high throughput gene expression characterization within thin tissue sections. However, comparing spatial observations across sections, samples, and technologies remains challenging. To address this challenge, we developed STalign to align ST datasets in a manner that accounts for partially matched tissue sections and other local non-linear distortions using diffeomorphic metric mapping. We apply STalign to align ST datasets within and across technologies as well as to align ST datasets to a 3D common coordinate framework. We show that STalign achieves high gene expression and cell-type correspondence across matched spatial locations that is significantly improved over landmark-based affine alignments. Applying STalign to align ST datasets of the mouse brain to the 3D common coordinate framework from the Allen Brain Atlas, we highlight how STalign can be used to lift over brain region annotations and enable the interrogation of compositional heterogeneity across anatomical structures. STalign is available as an open-source Python toolkit at <a href="https://github.com/JEFworks-Lab/STalign">https://github.com/JEFworks-Lab/STalign</a> and as supplementary software with additional documentation and tutorials available at <a href="https://jef.works/STalign">https://jef.works/STalign</a>.</p> <p>Here we have included alignment results that were used in performance analysis of STalign:</p> <p>We aligned Slice 2 Replicate 3 to Slice 2 Replicate 2 of the MERFISH mouse coronal brain sections available from Vizgen Data Release V1.0. May 2021 (<a href="https://info.vizgen.com/mouse-brain-map">https://info.vizgen.com/mouse-brain-map</a>).</p> <ul> <li>STalign_S2R3_to_S2R2.csv.gz contains cell ids, original cell centroid positions of S2R3, cell positions of S2R3 after alignment to S2R2 with STalign, cell positions of S2R3 after supervised affine alignment to S2R2, and counts for genes and blanks.</li> <li>STalign_S2R2.csv.gz contains cell ids, cell centroid positions of S2R2 and counts for genes and blanks.</li> </ul> <p>Additionally, we aligned Slice 2 Replicate 3 to a Visium dataset of an FFPE preserved adult mouse brain were obtained from the 10X Datasets website for <em>Spatial Gene Expression Dataset by Space Ranger 1.3.0</em> (<a href="https://www.10xgenomics.com/resources/datasets/adult-mouse-brain-ffpe-1-standard-1-3-0">https://www.10xgenomics.com/resources/datasets/adult-mouse-brain-ffpe-1-standard-1-3-0</a>).</p> <ul> <li>STalign_S2R3_to_Visium.csv.gz contains cell ids, original cell centroid positions of S2R3, cell positions of S2R3 after alignment to Visium H&E staining with STalign, and counts for genes and blanks.</li> </ul> <p>Furthermore, we performed alignments with the 50um resolution 3D Allen Reference Atlas Nissl common coordinate framework, CCF (<a href="https://help.brain-map.org/display/mouseconnectivity/API">https://help.brain-map.org/display/mouseconnectivity/API</a>). We applied STalign to align the Allen CCF to each of the 9 MERFISH slices (3 slice locations with 3 biological replicates) provided by Vizgen. Because the Allen CCF has annotated brain regions, we were able to lift over those brain region annotations to label all cells in the MERFISH datasets.</p> <p>Also, since the STalign mappings from the Allen CCF to the MERFISH slices are invertible, for each slice we can apply the inverse of the mapping to get cell positions in the Allen CCF coordinates.</p> <ul> <li>STalign_SXRX_with_structure_id_name.csv.gz contains cell ids for Slice X Replicate X, original cell centroid positions, cell xyz-coordinates in Allen CCF, brain structure id per cell, brain structure acronym</li> </ul> <p>To evaluate the 3D CCF alignment, we performed unified transcriptional clustering analysis and cell-type annotation. All MERFISH datasets were combined. Transcriptional clustering analysis and cell type annotation was performed using the SCANPY package [version 1.9.1]. Data were normalized to counts per million (scanpy: normalize_total) and log transformed (scanpy: log1p). PCA (scanpy: pca) was computed on the cell by gene matrix. A neighborhood graph of cells using the top 10 PCs and 10 nearest neighbors was created (scanpy: neighbors), and Leiden clustering was performed on this graph (scanpy: leiden) to identify 29 clusters. Differentially expressed genes were extracted from each cluster (scanpy: rank_genes_groups), and cell-types were annotated based on marker genes in each cluster.</p> <ul> <li>STalign_celltypeannotations_merfishslices_v2.csv.gz contains for all nine slices cell ids and cell type annotations</li> </ul> <p>This updated (v2) cell-type annotation file contains a new column with simplified cell-types. Briefly, we fixed typos, standardized lower case/upper case formats, merged subclasses of each cell-types. For example, subclasses of astrocytes­­, which are originally labeled as “Astrocytes”, “Astrocytes(1)”, “Astrocytes(2)”, “Astrocytes(3)”, are all labeled as “Astrocytes” in the added column.</p> <p>Note: Cell ids may have been mutated from original string of numbers through reading and writing across programming languages that handle numbers with different precision. If using R to read the files shared here, one can find the cells in STalign_celltypeannotations_merfishslices_v2.csv.gz that correspond with STalign_SXRX_with_structure_id_name.csv.gz when cell ids are formatted as a double in scientific notation, which is how R will read the file automatically.</p>
Mesosphere/lower thermosphere 3-dimensional spatially resolved winds observed by Chinese multistatic meteor radar network using the newly developed VVP method
<p>This dataset supports the article "Mesosphere/lower thermosphere 3-dimensional spatially resolved winds observed by Chinese multistatic meteor radar network using the newly developed VVP method" . The data are provided in MatLab format.</p>
Data for Characterizing the spatial signal of environmental DNA in river systems using a community ecology approach
<p>Environmental DNA (eDNA) is gaining a growing popularity among scientists but its applicability to biodiversity research and management remains limited in river systems by the lack of knowledge about the spatial extent of the downstream transport of eDNA.</p> <p>Here, we assessed the ability of eDNA inventories to retrieve spatial patterns of fish assemblages along two large and species rich Neotropical rivers. We first examined overall community variation with distance through the distance decay of similarity and compared this pattern to capture-based samples. We then considered previous knowledge on individual species distributions, and compared it to the eDNA inventories for a set of 53 species.</p> <p>eDNA collected from 28 sites in the Maroni and 25 sites in the Oyapock rivers permitted to retrieve a decline of species similarity with distance between sites. The distance decay of similarity derived from eDN<span>A </span>was similar, and even more pronounced, than that obtained with capture-based methods (gil-nets). In addition, the species upstream-downstream distribution range derived from eDNA matched to the known distribution of most species.</p> <p>Our results demonstrate that environmental DNA does not represent an integrative measure of biodiversity across the whole upstream river basin but provide a relevant picture of local fish assemblages. Importantly, the spatial signal gathered from eDNA was therefore comparable to that gathered with local capture based methods, which describes fish fauna over a few hundred metres.</p>
Fine-scale spatial segregation in a pelagic seabird driven by differential use of tidewater glacier fronts
<div class="WordSection1"> <p><span><span>In colonially breeding marine predators, individual movements and colonial segregation are influenced by seascape characteristics. Tidewater glacier fronts are important features of the Arctic seascape and are often described as foraging hotspots. Albeit their documented importance for wildlife, little is known about their structuring effect on arctic predator movements and space use. In this study, we tested the hypothesis that tidewater glacier fronts can influence marine bird foraging patterns and drive spatial segregation among adjacent colonies. We analysed movements of black-legged kittiwakes (<i>Rissa tridactyla</i>) in a glacial fjord by tracking breeding individuals from five colonies. Although breeding kittiwakes were observed to travel up to <i>ca</i>. 280 km from the colony, individuals were more likely to use glacier fronts located closer to their colony and rarely used glacier fronts located farther away than 18 km. Such variation in the use of glacier fronts created fine-scale spatial segregation among the four closest (<i>ca</i>. 7 km distance on average) kittiwake colonies. Overall, our results support the hypothesis that spatially predictable foraging patches like glacier fronts can have strong structuring effects on predator movements and can modulate the magnitude of intercolonial spatial segregation in central-place foragers.</span></span></p> </div> <p> </p>
Datasets used for the spatial mode-based calibration (SMoC) paper
<p>Datasets for a journal paper "Spatial mode-based calibration (SMoC) of forecast precipitation fields from numerical weather prediction models".</p> <p>In the SMoC paper, the performance of SMoC is evaluated by applying it to forecasts of substantive precipitation events over the Brisbane Drainage Basin in eastern Australia. This repository provides datasets of precipitation forecasts (3 years) and observations (33 years) at a grid spacing of 0.05° x 0.05° and on a daily basis for this case study: (1) Forecasts: "3-year forecast data.nc"; (2) Corresponding observations: "3-year observation data.nc"; (3) Long-term observations: "30-year observation data.nc". These three files are compressed into "Forecast and observation data.zip".</p> <p>Author name: Pengcheng Zhao. Affiliation: Department of Infrastructure Engineering, Faculty of Engineering and Information Technology, The University of Melbourne. Email: pengcheng@student.unimelb.edu.au.</p>
comspat: an R package to analyze within-community spatial organization using species combinations
<p>The diversity of species combinations observable in sampling units reflects a species' uneven distribution and preference for specific abiotic and biotic conditions – a phenomenon most commonly expressed in terms of ecological assembly rules of plant communities and other sessile organisms (e.g., subtidal algae, invertebrates and coral reefs). We present comspat, a new R package that uses grid or transect data sets to measure the number of realized (observed) species combinations (NRC) and the Shannon diversity of realized species combinations (compositional diversity; CD) as a function of spatial scale. NRC and CD represent two measures from a model family developed by Pál Juhász-Nagy based on Information Theory. Classical Shannon diversity measures biodiversity based on the number and relative abundance of species, whereas the specific version of Shannon diversity presented here characterizes biodiversity and provides information on species coexistence relationships; both measures operate at fine-scale within the sampling unit or within the community. comspat offers two commonly applied null models, complete spatial randomness and random shift, to disentangle the textural, intraspecific, and interspecific effects on the observed spatial patterns. Combined, these models assist users in detecting and interpreting spatial associations and inferring assembly mechanisms. Our open-sourced package provides a vignette that describes the method and reproduces the figures from this paper to help users contextualize and apply functions to their data.</p>
Data from: Prey encounters and spatial memory influence use of foraging patches in a marine central place forager
<p class="MsoNormal">Given the patchiness and long-term predictability of marine resources, memory of high-quality foraging grounds is expected to provide fitness advantages for central place foragers. However, it remains challenging to characterise how marine predators integrate memory with recent prey encounters to adjust fine-scale movement and use of foraging patches. Here, we used two months of movement data from harbour seals (<em>Phoca vitulina</em>) to quantify the repeatability in foraging patches as a proxy for memory. We then integrated these data into analyses of fine-scale movement and underwater behaviour to test how both spatial memory and prey encounter rates influenced the seals' Area Restricted Search (ARS) behaviour. Specifically, we used one month's GPS data from 29 individuals to build spatial memory maps of searched areas, and archived accelerometry data from a subset of five individuals to detect prey catch attempts, a proxy for prey encounters. Individuals were highly consistent in the areas they visited over two consecutive months. Hidden Markov Models showed that both spatial memory and prey encounters increased the probability of seals initiating ARS. These results provide evidence that predators use memory to adjust their fine scale movement and this ability should be accounted for in movement models.</p>
Data from: Modeling spatiotemporal abundance and movement dynamics using an integrated spatial capture-recapture movement model
<p>Animal movement is a fundamental ecological process affecting the survival and reproduction of individuals, the structure of populations, and the dynamics of communities. Methods to quantify animal movement and spatiotemporal abundances, however, are generally separate and thus omit linkages between individual-level and population-level processes. We describe an integrated spatial capture-recapture (SCR) movement model to jointly estimate (1) the number and distribution of individuals in a defined spatial region and (2) movement of those individuals through time. We applied our model to a study of polar bears (Ursus maritimus) in a 28,125 km<sup>2</sup> survey area of the eastern Chukchi Sea, USA in 2015 that incorporated capture-recapture and telemetry data. In simulation studies, the model provided unbiased estimates of movement, abundance, and detection parameters using a bivariate normal random walk and correlated random walk movement process. Our case study provided detailed evidence of directional movement persistence for both male and female bears, where individuals regularly traversed areas larger than the survey area during the 36-day study period. Scaling from individual- to population-level inferences, we found that densities varied from < 0.75 bears/625 km<sup>2</sup> grid cell/day in nearshore cells to 1.6–2.5 bears/grid cell/day for cells surrounded by sea ice. Daily abundance estimates ranged from 53–69 bears, with no trend across days. The cumulative number of unique bears that used the survey area increased through time due to movements into and out of the area, resulting in an estimated 171 individuals using the survey area during the study (95% credible interval 124–250). Abundance estimates were similar to a previous multi-year integrated population model using capture-recapture and telemetry data (2008–2016; Regehr et al. 2018). Overall, the SCR-movement model successfully quantified both individual- and population-level space use, including the effects of landscape characteristics on movement, abundance, and detection, while linking the movement and abundance processes to directly estimate density within a prescribed spatial region and temporal period. Integrated SCR-movement models provide a generalizable approach to incorporate greater movement realism into population dynamics and link movement to emergent properties including spatiotemporal densities and abundances.</p>
Supplementary code and data for: Inferring differential subcellular localisation in comparative spatial proteomics using BANDLE
<p>This repository contains code and data to reproduce the figures in the manuscript: Inferring differential subcellular localisation in comparative spatial proteomics using BANDLE.</p> <p>Please refer to the readme in the repository. </p>
Using deep convolutional neural networks to forecast spatial patterns of Amazonian deforestation: supporting data and outputs
<p class="MsoNormal"><strong>1. </strong>Tropical forests are subject to diverse deforestation pressures while their conservation is essential to achieve global climate goals. Predicting the location of deforestation is challenging due to the complexity of the natural and human systems involved but accurate and timely forecasts could enable effective planning and on-the-ground enforcement practices to curb deforestation rates. New computer vision technologies based on deep learning can be applied to the increasing volume of Earth observation data to generate novel insights and make predictions with unprecedented accuracy.</p> <p class="MsoNormal"><strong>2. </strong>Here, we demonstrate the ability of deep convolutional neural networks (CNNs) to learn spatiotemporal patterns of deforestation from a limited set of freely available global data layers, including multispectral satellite imagery, the Hansen maps of annual forest change (2001-2020) and the ALOS PALSAR digital surface model, to forecast deforestation (2021). We designed four model architectures, based on 2D CNNs, 3D CNNs, and Convolutional Long Short-Term Memory (ConvLSTM) Recurrent Neural Networks (RNNs), to produce spatial maps that indicate the risk to each forested pixel (~30 m) in the landscape of becoming deforested within the next year. They were trained and tested on data from two ~80,000 km<sup>2</sup> tropical forest regions in the Southern Peruvian Amazon.</p> <p class="MsoNormal"><strong>3.</strong><strong> </strong><span>The networks could predict the location of future forest loss to a high degree of accuracy (F</span><sub>1 </sub><span>= 0.58-0.71). Our best performing model (3D CNN) had the highest pixel-wise accuracy (F</span><sub>1 </sub><span>= 0.71) when validated on 2020 forest loss (2014-2019 training). Visual interpretation of the mapped forecasts indicated that the network could automatically discern the drivers of forest loss from the input data. For example, pixels around new access routes (e.g. roads) were assigned high risk whereas this was not the case for recent, concentrated natural loss events (e.g. remote landslides).</span></p> <p class="MsoNormal"><strong>4.</strong><strong> </strong>CNNs can harness limited time-series data to predict near-future deforestation patterns, an important step in harnessing the growing volume of satellite remote sensing data to curb global deforestation. The modelling framework can be readily applied to any tropical forest location and used by governments and conservation organisations to prevent deforestation and plan protected areas.</p>
SESMG scenario-files of the study "Model-based run-time and memory reduction for a mixed-use multi-energy system model with high spatial resolution"
<p>This dataset contains model scenario-files belonging to the publication "Model-based run-time and memory reduction for a mixed-use multi-energy system model with high spatial resolution".</p> <p>The individual scenarios can be executed and evaluated with the "Spreadsheet Energy System Model Generator" (<a href="https://github.com/chrklemm/SESMG">SESMG</a>) <a href="https://github.com/chrklemm/SESMG/tree/v0.4.0rc1">v0.4.0rc1</a></p> <p>The respective file names indicate to which model run mentioned in the main study the scenario-files belong. For model runs for which no sepparate scenario file exists, the scenario "reference.xlsx" with adjusted SESMG settings was used.</p> <p> </p>
Rendered Stimuli for "Spatial Analysis and Synthesis Methods: Subjective and Objective Evaluations Using Various Microphone Arrays in the Auralization of a Critical Listening Room"
<h2>Rendered Stimuli from the Subjective Evaluation</h2> <p>This archive (<code>Stimuli.zip</code>) contains the rendered stimuli used in the subjective evaluation of various spatial analysis and synthesis methods, as described in the paper "Spatial Analysis and Synthesis Methods: Subjective and Objective Evaluations Using Various Microphone Arrays in the Auralization of a Critical Listening Room" by Alan Pawlak, Hyunkook Lee, Aki Mäkivirta, and Thomas Lund.</p> <p>The stimuli are provided to improve the reproducibility of the study and to allow readers to listen to the same audio samples used in the subjective evaluation.</p> <h2>File Naming Convention:</h2> <p><code>SYSTEM_PROGRAMMEMATERIAL_AZIMUTH_ELEVATION_-26LUFS.wav</code></p> <p>- <code>SYSTEM</code>: The spatial analysis and synthesis method used (e.g., BSDM-6OM1-Omni, HO-SIRR, SDM-em32, etc.)<br>- <code>PROGRAMMEMATERIAL</code>: The anechoic audio sample used (Bongo, Speech, Orchestra)<br>- <code>AZIMUTH</code>: The azimuth angle of the sound source (e.g., 0, 30, 45, 90, 135)<br>- <code>ELEVATION</code>: The elevation angle of the sound source (e.g., 0, 45)</p> <h2>Audio File Specifications:</h2> <p>- Format: WAV<br>- Sample Rate: 48 kHz<br>- Bit Depth: 32-bit<br>- Loudness Normalization: -26 LUFS</p> <p>To use these stimuli, simply load the desired WAV file into your audio playback software.</p> <p>For more information about the study, please refer to the full paper.</p> <p>Pawlak, A., Lee, H., Mäkivirta, A. and Lund, T., 2024. Spatial Analysis and Synthesis Methods: Subjective and Objective Evaluations Using Various Microphone Arrays in the Auralization of a Critical Listening Room.</p>
Data used in manuscript Spatial modelling of local-scale biogenic and anthropogenic carbon dioxide emissions in Helsinki
<p>This data set includes data used to develop and evaluate carbon dioxide emission modelling component in the Surface Urban Energy and Water balance Scheme (SUEWS). The data files are:</p> <ol> <li>CO2_Model_Parameter_Fitting.zip contains m-files (Matlab) used to calculate parameters for photosynthesis modelling <ul> <li>F_pho_data.mat includes meteorological and EC data used to fit photosynthesis model parameters in Kumpula</li> <li>FitKumpulaData.m calculates the model parameters in Kumpula</li> <li>FitViikkiData.m calculates the model parameters in Viikki</li> <li>Other m-files needed by the above two codes</li> </ul> </li> <li>Data.zip contains measured data used to develop and evaluate SUEWS <ul> <li>KumpulaData2012.txt and TorniData2012.txt include eddy covariance data measured at the two sites in Helsinki</li> <li>SMEARIII_meteorology_2016MM_30.m meteorological data used to fit model parameters in Viikki street trees (see 00 ReadMe_SMEARIII_Meteorology.TXT for details)</li> <li>Viikki_SWC_2016.txt measured soil moisture from Viikki in 2016</li> <li>Kumpula_2016_HH_RLAI6_Output.out is SPP output used to fit model parameters in Viikki street trees</li> </ul> </li> <li>SUEWS_EC_Site_Model_runs: SUEWS input and output files for Kumpula and Torni model runs</li> <li>SpatialRun_input.zip: SUEWS input files for the spatial model run</li> <li>spatmatHel_final.mat: SUEWS output files for spatial model run in mat-format</li> </ol>
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.