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opencc-zeroJan 2021View details →

Short-Tandem-Repeat (STR) marker set for Eurasian lynx for article: Genetic analysis indicates spatial-dependent patterns of sex-biased dispersal in Eurasian lynx in Finland

Conservation and management of large carnivores requires knowledge of female and male dispersal. Such information is crucial to evaluate the population's status and thus management actions. This knowledge is challenging to obtain, often incomplete and contradictory at times. The size of the target population and the methods applied can bias the results. Also, population history and biological or environmental influences can affect dispersal on different scales within a study area. We have genotyped Eurasian lynx (180 males and 102 females, collected 2003-2017) continuously distributed in southern Finland (~23,000 km2) using 21 short tandem repeats (STR) loci and compared statistical genetic tests to infer local and sex-specific dispersal patterns within and across genetic clusters as well as geographic regions. We tested for sex-specific substructure with individual-based Bayesian assignment tests and spatial autocorrelation analyses. Differences between the sexes in genetic differentiation, relatedness, inbreeding, and diversity were analysed using population-based AMOVA, F-statistics, and assignment indices. Our results showed two different genetic clusters that were spatially structured for females but admixed for males. Similarly, spatial autocorrelation and relatedness was significantly higher in females than males. However, we found weaker sex-specific patterns for the Eurasian lynx when the data were separated in three geographical regions than when divided in the two genetic clusters. Overall, our results suggest male-biased dispersal and female philopatry for the Eurasian lynx in Southern Finland. The female genetic structuring increased from west to east within our study area. In addition, detection of male-biased dispersal was dependent on analytical methods utilized, on whether subtle underlying genetic structuring was considered or not, and the choice of population delineation. Conclusively, we suggest using multiple genetic approaches to study sex-biased dispersal in a continuously distributed species in which population delineation is difficult.

opencc-zeroDec 2016View details →

Data from: Bringing multivariate support to multiscale codependence analysis: assessing the drivers of community structure across spatial scales

1. Multiscale codependence analysis (MCA) quantifies the joint spatial distribution of a pair of variables in order to provide a spatially-explicit assessment of their relationships to one another. For the sake of simplicity, the original definition of MCA only considered a single response variable (e.g. a single species). However, that definition would limit the application of MCA when many response variables are studied jointly, for example when one wants to study the effect of the environment on the spatial organisation of a multi-species community in an explicit manner. 2. In the present paper, we generalize MCA to multiple response variables. We conducted a simulation study to assess the statistical properties (i.e. type I error rate and statistical power) of multivariate MCA (mMCA) and found that it had honest type I error rate and sufficient statistical power for practical purposes, even with modest sample sizes. We also exemplified mMCA by applying it to two ecological data sets. 3. The simulation study confirmed the adequacy of mMCA from a statistical standpoint: it has honest type I error rates and sufficient power to be useful in practice. Using mMCA, we were able to detect variation in fish community structure along the Doubs River (in France), which was associated with large spatial structures in the variation of physical and chemical variables related to water quality. Also, mMCA usefully described the spatial variation of an Oribatid mite community structure associated with a gradient of water content superimposed on various smaller-scale spatial features associated with vegetation cover in the peat blanket surrounding Lac Geai (in Québec, Canada). 4. In addition to demonstrating the soundness of mMCA in theory and practice, we further discuss the strengths and assumptions of mMCA and describe other potential scenarios where it would be helpful to biologists interested in assessing influence of environmental conditions on community structure in a spatially-explicit way.

opencc-zeroJun 2021View details →

Data from: A new digital method of data collection for spatial point pattern analysis in grassland communities

A major objective of plant ecology research is to determine the underlying processes responsible for the observed spatial distribution patterns of plant species. Plants can be approximated as points in space for this purpose, and thus, spatial point pattern analysis has become increasingly popular in ecological research. The basic piece of data for point pattern analysis is a point location of an ecological object in some study region. Therefore, point pattern analysis can only be performed if data can be collected. However, due to the lack of a convenient sampling method, a few previous studies have used point pattern analysis to examine the spatial patterns of grassland species. This is unfortunate because being able to explore point patterns in grassland systems has widespread implications for population dynamics, community-level patterns and ecological processes. In this study, we develop a new method to measure individual coordinates of species in grassland communities. This method records plant growing positions via digital picture samples that have been sub-blocked within a geographical information system (GIS). Here, we tested out the new method by measuring the individual coordinates of Stipa grandis in grazed and ungrazed S. grandis communities in a temperate steppe ecosystem in China. Furthermore, we analyzed the pattern of S. grandis by using the pair correlation function g(r) with both a homogeneous Poisson process and a heterogeneous Poisson process. Our results showed that individuals of S. grandis were overdispersed according to the homogeneous Poisson process at 0-0.16 m in the ungrazed community, while they were clustered at 0.19 m according to the homogeneous and heterogeneous Poisson processes in the grazed community. These results suggest that competitive interactions dominated the ungrazed community, while facilitative interactions dominated the grazed community. In sum, we successfully executed a new sampling method, using digital photography and a Geographical Information System, to collect experimental data on the spatial point patterns for the populations in this grassland community.

opencc-by-4.0Nov 2023View details →

Data from "FICTURE: Scalable segmentation-free analysis of sub-micron resolution spatial transcriptomics"

Open the record for dataset details and reuse information.

opencc-zeroDec 2023View details →

Data for: The meta-analysis of the effects of spatial sampling bias correction on presence only species distribution models

This dataset contains information extracted from 70 studies identified through a systematic review of the peer-reviewed literature (Web of Science and SCOPUS databases both searched on the 13/02/2023) to evaluate the effect of spatial sampling bias correction methods in presence-only species distribution models.

opencc-by-4.0Mar 2024View details →

Causal HRSI Dataset: Human-Robot Spatial Interaction Dataset for Causal Analysis from Mobile Platforms

<h2>Causal HRSI Dataset: Human-Robot Spatial Interaction Dataset for Causal Analysis from Mobile Platforms</h2> <div>The dataset captures a Human-Robot Spatial Interaction (HRSI) scenario between a person and the TIAGo robot. It focuses specifically on human-goal and human-robot spatial interaction in an indoor environment, captured from the perspective of a 3D Velodyne VLP-16 LiDAR mounted on the TIAGo robot. It includes:</div> <ul> <li>rosbags containing: Velodyne LiDAR point clound, robot and human state (position, orientation and velocities);</li> <li>CSV files containing trajectories of the person and the robot generated by post-processing the rosbags;</li> <li>the map of the environment extracted from the TIAGo robot.</li> </ul> 15 participants took part in the experiment, with the dataset capturing 5 minutes of HRSI motion for each participant. <h3>Experiment Description</h3> The experiment and data collection occurred in a laboratory room of the University of Lincoln (UK), measuring 5 x 8.2m.  Fifteen participants (6 females, aged between 25 and 55) took part in the experiment. Seven of them were used to work with a robot. They were required to walk between four goal positions and avoid the robot if a cross occurs. A predefined rectangular path was set for the TIAGo robot to navigate along the room and generate frequent interactions with the participants. The experimental procedure can be described as follows. Each participant started from one of the four target positions. The next target position was randomly chosen by the participant, who then started moving towards it. Upon reaching the goal position, the participant stopped there and randomly chose the next goal, repeating the process for 5 minutes. In this experimental setting, the robot was considered by the participant as an obstacle to avoid while walking towards their target positions. <h3>Directory Structure</h3> Dataset | |____Map: folder containing the map of the environment extracted from the TIAGo robot | |____RosBags: forder containing the rosbag for each partipant |        |____A1.bag |        |____A2.bag |        |____A3.bag |        |____A4.bag |        |____A5.bag |        |____A6.bag |        |____A7.bag |        |____A8.bag |        |____A9.bag |        |____A10.bag |        |____A11.bag |        |____A12.bag |        |____A13.bag |        |____A14.bag |        |____A15.bag | |____Trajectories: postprocessed trajectories extracted for the rosbag files           |____A1_traj.csv          |____A2_traj.csv          |____A3_traj.csv          |____A4_traj.csv          |____A5_traj.csv          |____A6_traj.csv          |____A7_traj.csv          |____A8_traj.csv          |____A9_traj.csv          |____A10_traj.csv          |____A11_traj.csv          |____A12_traj.csv          |____A13_traj.csv          |____A14_traj.csv          |____A15_traj.csv

opencc-by-4.0Nov 2024View details →

Dataset on Spatial Analysis and Clustering of Deforestation in the Amazon Biome: Spatio-Temporal Patterns and Priority Areas

The dataset was developed with the aim of facilitating the development of a methodology to identify and evaluate deforestation patterns and trends in the Amazon. This innovative method combines deforestation alerts from the Real-Time Deforestation Detection System (DETER) with detailed information on various land categories, including environmental protection areas, settlements, rural properties, undesignated public forests, indigenous lands, and conservation units. The integration of this robust data allowed for the precise identification of areas at risk of deforestation, significantly strengthening monitoring and control activities aimed at combating deforestation in the Amazon region.   Spatial resolution The data are available with a spatial resolution of 25 x 25 km (625 km²) and cover the Amazon biome.   Temporal resolution  Period of observed data: 2017 and 2021   Coordinate reference system  Geographic Coordinate System with Datum SIRGAS 2000 (EPSG:5880)   Data format Data is provided as Shapefile.   Dataset usage  It is free to use, but please make sure to cite the repository and our paper properly if you use this dataset.   Publication & further information For additional scenario information, please contact Francisco Gilney Silva Bezerra (franciscogilney@gmail.com).

opencc-by-4.0Jun 2023View details →

Mitigating autocorrelation during spatially resolved transcriptomics data analysis

Here we include the marmoset brain and mouse gut STARmap data introduced in the corresponding manuscript, "Mitigating autocorrelation during spatially resolved transcriptomics data analysis". We also include the mouse brain STARmap PLUS data that was used to demonstrate cross-species spatial integration and was previously published in Shi, He, Zhou et al. 2022.

opencc-zeroDec 2021View details →

Spatial personalities: a meta-analysis of consistent individual differences in spatial behavior

Individual variation in behavior, particularly consistent among-individual differences (i.e., personality), has important ecological and evolutionary implications for population and community dynamics, trait divergence, and patterns of speciation. Nevertheless, individual variation in spatial behaviors, such as home range behavior, movement characteristics, or habitat use has yet to be incorporated into the concepts or methodologies of ecology and evolutionary biology. To evaluate evidence for the existence of consistent among-individual differences in spatial behavior – which we refer to as "spatial personality" – we performed a meta-analysis of 200 repeatability estimates of home range size, movement metrics, and habitat use. We found that the existence of spatial personality is a general phenomenon, with consistently high repeatability (r) across classes of spatial behavior (r = 0.67 - 0.82), taxa (r = 0.31 - 0.79), and time between repeated measurements (r = 0.54 - 0.74). These results suggest: (1) repeatable spatial behavior may either be a cause or consequence of the environment experienced and lead to spatial personalities that may limit the ability of individuals to behaviorally adapt to changing landscapes; (2) interactions between spatial phenotypes and environmental conditions could result in differential reproduction, survival, and dispersal, suggesting that among-individual variation may facilitate population-level adaptation; (3) spatial patterns of species' distributions and spatial population dynamics may be better understood by shifting from a mean field analytical approach towards methods that account for spatial personalities and their associated fitness and ecological dynamics.

opencc-zeroDec 2021View details →

Data from: The effects of human-altered habitat spatial pattern on frugivory and seed dispersal: a global meta-analysis

Seed dispersal by frugivorous animals is important for plant mobility, regeneration, and persistence. Human-caused landscape change is thought to disrupt seed dispersal, but evidence is scarce. We performed a comprehensive meta-analysis on the effects of habitat spatial pattern on frugivory and seed dispersal. We found 233 effects from 71 studies. At a patch or local scale, altered habitat spatial pattern was measured as declining patch size, increasing patch isolation, or habitat edge (vs. interior). At a landscape scale it was measured as declining amount of habitat, increasing mean patch isolation, increasing number of patches, or increasing habitat edge in the landscape. We found overall negative effects of altered habitat spatial pattern on: (i) the quantity of frugivory or seed dispersal, (ii) the number of species involved in a plant-frugivore interaction, and (iii) seed dispersal distance. Moderator variable analysis was only possible for the first of these. It revealed negative responses of the quantity of frugivory or seed dispersal to habitat loss at both the local scale (declining patch size), and the landscape scale (declining habitat amount), but little evidence for a response to habitat edge at either scale. In addition, altered habitat spatial pattern reduced the quantity of frugivory or seed dispersal more strongly in temperate than tropical areas. Finally, the few-recorded effects of landscape-scale fragmentation per se (increasing patch density or edge density) on the quantity of frugivory or seed dispersal were mixed and weak. Our meta-analysis reinforces the notion that habitat loss is a major threat to frugivory and seed dispersal by animals, and reveals an insufficiency of studies of the effects of habitat fragmentation per se. Thus, based on the current literature, we conclude that maintaining and increasing habitat amount is vital for maintaining seed dispersal by frugivorous animals.

opencc-zeroMay 2022View details →

Dispersal increases spatial synchrony of populations but has weak effects on population variability: a meta-analysis

The effects of dispersal on spatial synchrony and population variability have been well documented in theoretical research, and a growing number of empirical tests have been performed. Yet a synthesis is still lacking. Here, we conducted a meta-analysis of relevant experiments and examined how dispersal affected spatial synchrony and temporal population variability across scales. Our analyses showed that dispersal generally promoted spatial synchrony, and such effects increased with dispersal rate and decreased with environmental correlation among patches. The synchronizing effect of dispersal, however, was only detected when spatial synchrony was measured using the correlation-based index, but not for the covariance-based index. In contrast to theoretical predictions, the effect of dispersal on local population variability was generally non-significant, except when environment correlation among patch was negative and/or experimental period was long. At the regional scale, while low dispersal stabilized metapopulation dynamics, high dispersal led to destabilization. Overall, the sign and strength of dispersal effects on spatial synchrony and population variability were modulated by taxa, environmental heterogeneity, type of perturbations, patch number, and experimental length. Our synthesis demonstrates that dispersal can substantially affect the dynamics of spatially distributed populations, but its effects are context dependent on abiotic and biotic factors.

opencc-by-4.0Apr 2024View details →

Dataset for Spatial Variations in the Osteocyte Lacuno-canalicular Network Density and Analysis of the Connectomic Parameters

This dataset is a representative case of the loaded tibia of a C57BL/6 mouse at the mid-shaft. The image pixel size is 0.303 by 0.303 um, and the z-depth is 0.296 um.  To generate, analyse, and quantify the osteocyte lacuno-canalicular network, it requires 'Tool for Image and Network Analysis (TINA)' which can be acqruied from https://gitlab.mpikg.mpg.de/rummler/TINA.git. A demonstration has been included on using TINA.

opencc-by-4.0May 2024View details →

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> 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. 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. <h2>File Naming Convention:</h2> <code>SYSTEM_PROGRAMMEMATERIAL_AZIMUTH_ELEVATION_-26LUFS.wav</code> - <code>SYSTEM</code>: The spatial analysis and synthesis method used (e.g., BSDM-6OM1-Omni, HO-SIRR, SDM-em32, etc.) - <code>PROGRAMMEMATERIAL</code>: The anechoic audio sample used (Bongo, Speech, Orchestra) - <code>AZIMUTH</code>: The azimuth angle of the sound source (e.g., 0, 30, 45, 90, 135) - <code>ELEVATION</code>: The elevation angle of the sound source (e.g., 0, 45) <h2>Audio File Specifications:</h2> - Format: WAV - Sample Rate: 48 kHz - Bit Depth: 32-bit - Loudness Normalization: -26 LUFS To use these stimuli, simply load the desired WAV file into your audio playback software. For more information about the study, please refer to the full paper. 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.

opencc-by-4.0Jun 2024View details →

An Interior-Penalty Scheme for a Discontinuous Galerkin Isogeometric Analysis Spatial Discretization of the Self-Adjoint Angular Flux Form of the Multi-Group Neutron Transport Equation

This repository holds all of the raw data generated by my (Modern) Fortran code for a paper "An Interior-Penalty Scheme for a Discontinuous Galerkin Isogeometric Analysis Spatial Discretization of the Self-Adjoint Angular Flux Form of the Multi-Group Neutron Transport Equation". The (Modern) Fortran code solves the SAAF form of the multi-group neutron transport equation using novel NURBS-based, IGA spatial discretisations.

Fig. 1 in Multi-Criteria Decision Analysis as a tool to extract fishing footprints: application to small scale fisheries and implications for management in the context of the Maritime Spatial Planning Directive

Fig. 1: Map of the study area.

opencc-by-4.0Jun 2024View details →

Partitioning of water and CO2 fluxes at NEON sites into soil and plant components: a five-year dataset for spatial and temporal analysis

This dataset includes estimates of transpiration, evaporation, soil respiration, and plant net photosynthesis obtained using five partitioning approaches. Flux components are available at 47 NEON sites over a period of five years. Additional meteorological inputs and water-use efficiency data are also included.

opencc-by-4.0Aug 2023View details →

Code and data for spatial and temporal magnitude clustering analysis

Code used for performing spatial and temporal seismic magnitude clustering analysis.  Includes documentation (README.txt) with steps on how to implement the code. The public datasets used for this study can be accessed at the following locations:  <ul> <li>Southern California Catalog:  <ul> <li>SCEDC (2013): Southern California Earthquake Center. Caltech.Dataset. doi:<a href="https://dx.doi.org/10.7909/C3WD3xH1">10.7909/C3WD3xH1</a></li> </ul> </li> <li>Northern California Catalog: <ul> <li>NCEDC (2014), Northern California Earthquake Data Center. UC Berkeley Seismological Laboratory. Dataset. doi:10.7932/NCEDC.</li> </ul> </li> <li>Mixed-mode Laboratory Catalog: <ul> <li>Lin, Qing, et al. "Opening and mixed mode fracture processes in a quasi-brittle material via digital imaging." Engineering Fracture Mechanics 131 (2014): 176-193.</li> </ul> </li> <li>ETAS Code: <ul> <li>Leila Mizrahi, Shyam Nandan, Stefan Wiemer 2021; Embracing Data Incompleteness for Better Earthquake Forecasting. (Section 3.1) Journal of Geophysical Research: Solid Earth; doi: <a href="https://doi.org/10.1029/2021JB022379">https://doi.org/10.1029/2021JB022379</a></li> </ul> </li> </ul>

Fig. 6 in Multi-Criteria Decision Analysis as a tool to extract fishing footprints: application to small scale fisheries and implications for management in the context of the Maritime Spatial Planning Directive

Fig. 6: Scatterplot matrices for Large (A) and Linear (B) FM functions.

Fig. 8 in Multi-Criteria Decision Analysis as a tool to extract fishing footprints: application to small scale fisheries and implications for management in the context of the Maritime Spatial Planning Directive

Fig. 8: High values of the FP c index as estimated by Local Moran's I test.

Fig. 5 in Multi-Criteria Decision Analysis as a tool to extract fishing footprints: application to small scale fisheries and implications for management in the context of the Maritime Spatial Planning Directive

Fig. 5: Spatial representation of coastal vessels activity indexes (Ac).