A variety of methods are commonly used to quantify animal home ranges using location data acquired with telemetry. High-volume location data from global positioning system (GPS) technology provide researchers the opportunity to identify various intensities of use within home ranges, typically quantified through utilization distributions (UDs). However, the wide range of variability evident within UDs constructed with modern home range estimators is often overlooked or ignored during home range comparisons, and challenges may arise when summarizing distributional shifts among multiple UDs. We describe an approach to gain additional insight into home range changes by comparing UDs across isopleths and summarizing comparisons into meaningful results. To demonstrate the efficacy of this approach, we used GPS location data from 16 bighorn sheep (Ovis canadensis) to identify distributional changes before and after habitat alterations, and we discuss advantages in its application when comparing home range size, overlap, and joint-space use. We found a consistent increase in bighorn sheep home range size when measured across home range levels, but that home range overlap and similarity values decreased when examined at increasing core levels. Our results highlight the benefit of conducting multiscale assessments when comparing distributions, and we encourage researchers to expand comparative home range analyses to gain a more comprehensive evaluation of distributional changes and to evaluate comparisons across home range levels.

aData originate from 40 underwater visual transects per zone per year.bEach size distribution was analyzed twice, first using a broad range of size categories (k = 5 after pooling) and second using only two size categories (cA significant difference was considered to exist if p

A notion of data depth is used to measure the centrality/outlyingness of a given point with respect to a given distribution or data cloud. Several depth-based graphical tools and nonparametric tests have been proposed for the comparison of two or more multivariate distributions. This article proposes graphical tools for the comparison of multiple multivariate distributions. These graphical tools can be considered as a generalization of the well-known depth versus depth plot (DD-plot) for the visual comparison of the two multivariate samples. Different types of variations in location, scale, skewness, or kurtosis among distributions give rise to different deviation patterns in the proposed plots. Based on these graphical tools we generalize the statistical tests for two sample location, scale, and homogeneity of species assemblages to the corresponding multi-sample problems. An extensive simulation study reveals that the performances of the proposed tests are superior to that of the existing tests. The proposed graphical tools and tests are illustrated with different simulated and real data sets.

By measures of the difference between median values , the Kolmogorov-Smirnov distance , and the Kullback-Leibler divergence , the cumulative distribution of INT comes closer to EXP than NON does. This is especially apparent in the and values. The three distributions are shown in Fig. 6(C).

This data view contains local government payment transactions for the Local Option Sales Tax as recorded in the State of Iowa’s central accounting system for the Executive Branch.

Local governments receive monthly payments plus a thirteenth reconciliation payment before November 10th. Therefore, when comparing distributions to estimates, it is important to note that the early November payment belongs to the previous fiscal year from a program management perspective (example: the November 8, 2017 payment is the reconciliation payment for FY16).

Little is known about how mutualistic interactions affect the distribution of species richness on broad geographic scales. Because mutualism positively affects the fitness of all species involved in the interaction, one hypothesis is that the richness of species involved should be positively correlated across their range, especially for obligate relationships. Alternatively, if mutualisms are facilitative (e.g., involving multiple mutualistic partners), the distribution of mutualists should not necessarily be related, and patterns in species distributions might be more strongly correlated with environmental factors. In this study, we compared the distributions of plants and vertebrate animals involved in seed-dispersal mutualisms across the United States and Canada. We compiled geographic distributions of plants dispersed by frugivores and scatter-hoarding animals, and compared their distribution of richness to the distribution in disperser richness. We found that the distribution of animal dispersers shows a negative relationship to the distribution of the plants that they disperse, and this is true whether the plants dispersed by frugivores or scatter-hoarders are considered separately or combined. In fact, the mismatch in species richness between plants and the animals that disperse their seeds is dramatic, with plants species richness greatest in the in the eastern United States and the animal species richness greatest in the southwest United States. Environmental factors were corelated with the difference in the distribution of plants and their animal mutualists and likely are more important in the distribution of both plants and animals. This study is the first to describe the broad-scale distribution of seed-dispersing vertebrates and compare the distributions to the plants they disperse. With these data, we can now identify locations that warrant further study either to understand better seed-dispersal mutualisms or the factors that influence the distribution of the plants and animals involved in these mutualisms.

Phillips_etal_EcoEvol_SurveyDataFinalShip, aerial, and telemetry survey data

Detrital zircon age distributions provide robust insights into past sedimentary systems, but these age distributions are often complex and multi-peaked, with sample sizes too small to confidently resolve population distributions. This limited sampling hinders existing quantitative methods for comparing detrital zircon age distributions, which show systematic dependence on the sizes of compared samples. The proliferation of detrital zircon studies motivates the development of more robust quantitative methods. We present the first attempt, to our knowledge, to infer probability model ensembles (PMEs) for samples of detrital zircon ages using a Bayesian method. Our method infers the parent population age distribution from which a sample is drawn, using a Monte Carlo approach to aggregate a representative set of probability models that is consistent with the constraints that the sample data provide. Using the PMEs inferred from sample data, we develop a new estimate of correspondence between detrital zircon populations called Bayesian Population Correlation (BPC). Tests of BPC on synthetic and real detrital zircon age data show that it is nearly independent from sample size bias, unlike existing correspondence metrics. Robust BPC uncertainties can be readily estimated, enhancing interpretive value. When comparing two partially overlapping zircon age populations where the shared proportion of each population is independently varied, BPC results conform almost perfectly to expected values derived analytically from probability theory. This conformity of experimental and analytical results permits direct inference of the shared proportions of two detrital zircon age populations from BPC. We provide MATLAB scripts to facilitate the procedures we describe.

Related article: Bergroth, C., Järv, O., Tenkanen, H., Manninen, M., Toivonen, T., 2022. A 24-hour population distribution dataset based on mobile phone data from Helsinki Metropolitan Area, Finland. Scientific Data 9, 39.

In this dataset:

We present temporally dynamic population distribution data from the Helsinki Metropolitan Area, Finland, at the level of 250 m by 250 m statistical grid cells. Three hourly population distribution datasets are provided for regular workdays (Mon – Thu), Saturdays and Sundays. The data are based on aggregated mobile phone data collected by the biggest mobile network operator in Finland. Mobile phone data are assigned to statistical grid cells using an advanced dasymetric interpolation method based on ancillary data about land cover, buildings and a time use survey. The data were validated by comparing population register data from Statistics Finland for night-time hours and a daytime workplace registry. The resulting 24-hour population data can be used to reveal the temporal dynamics of the city and examine population variations relevant to for instance spatial accessibility analyses, crisis management and planning.

Please cite this dataset as:

Bergroth, C., Järv, O., Tenkanen, H., Manninen, M., Toivonen, T., 2022. A 24-hour population distribution dataset based on mobile phone data from Helsinki Metropolitan Area, Finland. Scientific Data 9, 39. https://doi.org/10.1038/s41597-021-01113-4

Organization of data

The dataset is packaged into a single Zipfile Helsinki_dynpop_matrix.zip which contains following files:

HMA_Dynamic_population_24H_workdays.csv represents the dynamic population for average workday in the study area.

HMA_Dynamic_population_24H_sat.csv represents the dynamic population for average saturday in the study area.

HMA_Dynamic_population_24H_sun.csv represents the dynamic population for average sunday in the study area.

target_zones_grid250m_EPSG3067.geojson represents the statistical grid in ETRS89/ETRS-TM35FIN projection that can be used to visualize the data on a map using e.g. QGIS.

Column names

YKR_ID : a unique identifier for each statistical grid cell (n=13,231). The identifier is compatible with the statistical YKR grid cell data by Statistics Finland and Finnish Environment Institute.

H0, H1 ... H23 : Each field represents the proportional distribution of the total population in the study area between grid cells during a one-hour period. In total, 24 fields are formatted as “Hx”, where x stands for the hour of the day (values ranging from 0-23). For example, H0 stands for the first hour of the day: 00:00 - 00:59. The sum of all cell values for each field equals to 100 (i.e. 100% of total population for each one-hour period)

In order to visualize the data on a map, the result tables can be joined with the target_zones_grid250m_EPSG3067.geojson data. The data can be joined by using the field YKR_ID as a common key between the datasets.

License Creative Commons Attribution 4.0 International.

Related datasets

Järv, Olle; Tenkanen, Henrikki & Toivonen, Tuuli. (2017). Multi-temporal function-based dasymetric interpolation tool for mobile phone data. Zenodo. https://doi.org/10.5281/zenodo.252612

Tenkanen, Henrikki, & Toivonen, Tuuli. (2019). Helsinki Region Travel Time Matrix [Data set]. Zenodo. http://doi.org/10.5281/zenodo.3247564

Biotic interactions have been rarely included in traditional species distribution models, wherein Joint Species Distribution Models (JSDMs) emerge as a feasible approach to incorporate environmental factors and interspecific interactions simultaneously, making it a powerful tool for analyzing the structure and assembly processes of biotic communities. However, the predictability and statistical robustness of JSDMs are largely unknown because of the lack of research efforts for those newly developed models. This study systematically evaluated the performances of five JSDMs in predicting the occurrence and biomass of multiple species, with a particular focus on diverse characteristics of sampling data, including type of response variables, number of sampling sites, and the number of species included in models. In general, most models yielded satisfactory performances on fitting to observed data and on the estimation of environmental effects; however, they showed less well performances in evaluating species associations, and their predictability had large variations. The JSDMs showed inconsistent performances between the goodness-of-fit and predictability in cross-validation, and the Boral model was relatively robust than others. The predictability of JSDMs was less influenced by sample sizes and substantially improved by incorporating rare species. This study contributes to an appropriate model selection and application of JSDMs.

Data associated with the paper 'Comparing temperature data sources for use in species distribution models: From in-situ logging to remote sensing. Global Ecology and Biogeography' by Lembrechts JJ et al., published in Global Ecology and Biogeography.

Contains a dataset containing all extracted and measured temperature variables for all 106 measurement plots (climatedata), as well as the climate and species data used in the Species Distribution Models (SDMs).

For details on the content of the table, see the readme-file, for details on methodology, see the original paper.

Franchises of business and public service companies constituted over **** of franchises in Russia as of the beginning of 2025. Catering franchises occupied the second-largest share, at ** percent of the total.

Estimate’s comparison between normal distribution (N) and t distribution (t).

We were interested here in particular in conditions where un-modeled effects are present as manifested by the different degradation curve at 45°C. Although all algorithms were given the same amount of information to the degree practical, there were considerable differences in performance. Specifically, the combined Bayesian regression-estimation approach implemented as a RVM-PF framework has significant advantages over conventional methods of RUL estimation like ARIMA and EKF. ARIMA, being a purely data-driven method, does not incorporate any physics of the process into the computation, and hence ends up with wide uncertainty margins that make it unsuitable for long-term predictions. Additionally, it may not be possible to eliminate all non-stationarity from a dataset even after repeated differencing, thus adding to prediction inaccuracy. EKF, though robust against non-stationarity, suffers from the inability to accommodate un-modeled effects and can diverge quickly as shown. We did not explore other variations of the Kalman Filter that might provide better performance such as the unscented Kalman Filter. The Bayesian statistical approach, on the other hand, appears to be well suited to handle various sources of uncertainties since it defines probability distributions over both parameters and variables and integrates out the nuisance terms. Also, it does not simply provide a mean estimate of the time-to-failure; rather it generates a probability distribution over time that best encapsulates the uncertainties inherent in the system model and measurements and in the core concept of failure prediction.

Despite the wide application of meta-analysis in ecology, some of the traditional methods used for meta-analysis may not perform well given the type of data characteristic of ecological meta-analyses. We reviewed published meta-analyses on the ecological impacts of global climate change, evaluating the number of replicates used in the primary studies (ni) and the number of studies or records (k) that were aggregated to calculate a mean effect size. We used the results of the review in a simulation experiment to assess the performance of conventional frequentist and Bayesian meta-analysis methods for estimating a mean effect size and its uncertainty interval. Our literature review showed that ni and k were highly variable, distributions were right-skewed, and were generally small (median ni =5, median k=44). Our simulations show that the choice of method for calculating uncertainty intervals was critical for obtaining appropriate coverage (close to the nominal value of 0.95). When k was low (<40), 95% coverage was achieved by a confidence interval based on the t-distribution that uses an adjusted standard error (the Hartung-Knapp-Sidik-Jonkman, HKSJ), or by a Bayesian credible interval, whereas bootstrap or z-distribution confidence intervals had lower coverage. Despite the importance of the method to calculate the uncertainty interval, 39% of the meta-analyses reviewed did not report the method used, and of the 61% that did, 94% used a potentially problematic method, which may be a consequence of software defaults. In general, for a simple random-effects meta-analysis, the performance of the best frequentist and Bayesian methods were similar for the same combinations of factors (k and mean replication), though the Bayesian approaches had higher than nominal (>95%) coverage for the mean effect when k was very low (k<15). Our literature review suggests that many meta-analyses that used z-distribution or bootstrapping confidence intervals may have over-estimated the statistical significance of their results when the number of studies was low; more appropriate methods need to be adopted in ecological meta-analyses.

Data_Description_ECOG-01477_Comparison of approaches to combine species distribution models based on different sets of predictorsHuman activities were described using different variables: water bodies (WR, LK, IMAR), population (HPd), distance to highways (Dhi) and land-cover. Land cover layers were processed so that the surface area of each class (PAST, SV, OAKM, CM, OAKW, CW, OLG, DHER, FT, IHER, LW, RW, HE, NM, VIN). Latitude and longitude were used as the spatial variables. Topography was represented by three variables: slope (S), degree of southward exposure (SE), and degree of westward exposure (WE), which were derived from digital elevation models. Climate variables (temperature and precipitation) were obtained from the datasets supplied by the Spanish Institute of Meteorology (Agencia Estatal de Meteorología).Data_Description_ECOG-01477.zip

description: ABSTRACT: A global data set of root biomass, rooting profiles, and concentrations nutrients in roots was compiled from the primary literature and used to study distributions of root properties. This data set consists of estimates of fine root biomass and specific area, site characteristics. This data set provides analysis of rooting patterns for terrestrial biomes and compare distributions for various plant functional groups.; abstract: ABSTRACT: A global data set of root biomass, rooting profiles, and concentrations nutrients in roots was compiled from the primary literature and used to study distributions of root properties. This data set consists of estimates of fine root biomass and specific area, site characteristics. This data set provides analysis of rooting patterns for terrestrial biomes and compare distributions for various plant functional groups.

Amphidecta calliomma is a butterfly species that occurs in Colombia, Bolivia, Peru, Venezuela, Ecuador, Panama and Brazil (in the states of Mato Grosso, Mato Grosso do Sul, Rondônia and Pará). Here, we present a new occurrence of A. calliomma in the Carajás National Forest (Pará, eastern Amazon), expanding the known distribution of the species. We also provide Species Distribution Model comparing the contribution of the new occurrence to species area of occurrence projections, supporting future field research. The projections reveal an expansion of area of occurrence for A. calliomma located mainly in the southeast portion of Amazon Forest. Despite its wide distribution, the small number of records of A. calliomma may indicate that the species has a low detectability in surveys. This study provides support for new surveys and reduces the knowledge gap about A. calliomma, thus supporting its conservation. Methods Sampling From 05 to 14 November 2019, we conducted a campaign to collect frugivorous butterflies in the Carajás National Forest (southwestern Pará state, Brazil). Butterflies were collected using Van Someren-Rydon traps baited with a mixture of banana and beer (instead of sugarcane), which was fermented for 48 hours, following methodologies adapted from Uehara-Prado et al. (2005) and Freitas et al. (2014). The individuals captured in the traps were collected (SISBIO license number: 68977-1) and identified based on literature resources and with the help of the website “Butterflies of America” (https://www.butterfliesofamerica.com/L/Nymphalidae.htm, accessed in November 2020) (Warren et al., 2013). After identification and preparation, the specimen of A. calliomma was incorporated into the entomological collection of the Museu Paraense Emílio Goeldi (MPEG.HLE 04045043) (MPEG, Pará, Brazil). Occurrence records In addition to field collection, we retrieved data from Global Biodiversity Information Facility (GBIF; www.gbif.org, accessed in November 2022; DOI: https://doi.org/10.15468/dl.kgbph8) and SpeciesLink (https://specieslink.net/, accessed in November 2022) and from published articles, totaling 52 records. We also removed duplicate and non-georeferenced data. We removed inconsistencies using a conservative pipeline (Gomes et al., 2018). Thus, our final database totaled 16 occurrence records (11 from the digital databases, 4 from articles and 1 occurrence from our field collections) (Supporting Information Table 1). Climate information We downloaded climate data with a resolution of 10 arc-minutes (~ 18 km x 18 km) from the WorldClim database version 2.1 (www.worldclim.org, accessed in November 2022). We focused on non-correlated climate data, based on ecological relevance. Butterflies are highly sensitive to climate as warm temperatures can stimulate their flight muscles efficiency and wind is a key component for flying animals and precipitation affects species richness (Turner et al. 1987; Checa et al. 2019). We downloaded and tested for correlation (coefficient threshold |ρ| < 0.7) seven historical climate variables: precipitation, water vapor pressure, solar radiation, wind speed, maximum temperature, minimum temperature and average temperature. Species Distribution Model We used an algorithm based on maximum entropy (MaxEnt) to produce models of species potential distribution to estimate A. calliomma area of occurrence (AOO) (Phillips et al., 2004; IUCN, 2022). We followed Gomes et al. (2019) and used background information to calibrate MaxEnt predictions based on data of tree species from Amazon forest since most of the occurrences of the A. calliomma are located in this biome. Background data is a sample from the study area used to characterize its environmental conditions (Phillips et al., 2009). Distribution modelling methods using background data generally outperformed those using presence-absence or pseudo-absence information, especially when modelling mobile species (Fernandez et al., 2022). Also, background information methods are more flexible, producing more realistic and less over-fitted predictions (Peterson et al., 2011). Since A. calliomma has little occurrence information available, we used a more flexible approach to understand the general distribution pattern of the species. We used product, threshold and hinge features of MaxEnt (Boucher-Lalonde et al., 2012; Merow et al., 2013). To evaluate the models, we used a null model approach (Raes & Steege, 2007). We tested the predictive performance of the A. calliomma models as estimated by the area under the ROC curve (AUC) against the predictive performance of 99 null models generated using the same number of occurrences of A. calliomma generated randomly. If the AUC of the models scores higher than the 95th best null models, this means that the chance of a model generated randomly showing a better performance is less than five percent. The models were converted in binary maps by using the 10th percentile training presence threshold, which omits the regions with environmental suitability lower than the lowest 10% of occurrence records (Gomes et al., 2018). We then clipped the binary maps by using the extent of occupancy (EOO) of the species plus a buffer of 300 km, based on the notion that the EOO is restricted by dispersal capabilities (Gaston, 2009; De Ro et al., 2021). We estimated A. calliomma AOO using the new occurrence sampled and comparing with the AOO estimation with no new occurrence. All calculations and analyses were performed with R version 3.6.3, including the R packages raster (Hijmans & van Etten, 2016), rgdal (Bivand, Keitt, & Rowlingson, 2017), gstat (Pebesma & Heuvelink, 2016), dismo (Hijmans et al., 2016), rJava (Urbanek, 2017) and SDMTools (VanDerWal et al., 2019).

PLAB = 120 TO 280 GEV. Forward produced protons and anti-protons in deep inelastic scattering from the EMC at CERN. Numerical values of data supplied by C. Benchouk.

SLAC-PEP. HRS Collaboration. Measurement of charged particle multiplicity distributions in E+ E- annihilation at 29 GeV. This data are a 2JET sample selected with Sphericity & lt;0.25 and Aplanarity & lt;0.10 to remove events with hard gluon radiation. Individual forward and backward jets are selected as being either side of a plane perpendicular to the Thrust axis. The multiplicity results show good agreement with binomial distribution. The rapidity gap distribution is exponential with slope equal to the mean rapidity density. Data are compared with other hadronic data. Numerical values of figure 3 supplied by M.Derrick.