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

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).

Files descriptions:

All csv files refer to results from the different models (PAMM, AARs, Linear models, MRPPs) on each iteration of the simulation. One row being one iteration. "results_perfect_detection.csv" refers to the results from the first simulation part with all the observations."results_imperfect_detection.csv" refers to the results from the first simulation part with randomly thinned observations to mimick imperfect detection.

ID_run: identified of the iteration (N: number of sites, D_AB: duration of the effect of A on B, D_BA: duration of the effect of B on A, AB: effect of A on B, BA: effect of B on A, Se: seed number of the iteration).PAMM30: p-value of the PAMM running on the 30-days survey.PAMM7: p-value of the PAMM running on the 7-days survey.AAR1: ratio value for the Avoidance-Attraction-Ratio calculating AB/BA.AAR2: ratio value for the Avoidance-Attraction-Ratio calculating BAB/BB.Harmsen_P: p-value from the linear model with interaction Species1*Species2 from Harmsen et al. (2009).Niedballa_P: p-value from the linear model comparing AB to BA (Niedballa et al. 2021).Karanth_permA: rank of the observed interval duration median (AB and BA undifferenciated) compared to the randomized median distribution, when permuting on species A (Karanth et al. 2017).MurphyAB_permA: rank of the observed AB interval duration median compared to the randomized median distribution, when permuting on species A (Murphy et al. 2021). MurphyBA_permA: rank of the observed BA interval duration median compared to the randomized median distribution, when permuting on species A (Murphy et al. 2021). Karanth_permB: rank of the observed interval duration median (AB and BA undifferenciated) compared to the randomized median distribution, when permuting on species B (Karanth et al. 2017).MurphyAB_permB: rank of the observed AB interval duration median compared to the randomized median distribution, when permuting on species B (Murphy et al. 2021). MurphyBA_permB: rank of the observed BA interval duration median compared to the randomized median distribution, when permuting on species B (Murphy et al. 2021).

"results_int_dir_perf_det.csv" refers to the results from the second simulation part, with all the observations."results_int_dir_imperf_det.csv" refers to the results from the second simulation part, with randomly thinned observations to mimick imperfect detection.ID_run: identified of the iteration (N: number of sites, D_AB: duration of the effect of A on B, D_BA: duration of the effect of B on A, AB: effect of A on B, BA: effect of B on A, Se: seed number of the iteration).p_pamm7_AB: p-value of the PAMM running on the 7-days survey testing for the effect of A on B.p_pamm7_AB: p-value of the PAMM running on the 7-days survey testing for the effect of B on A.AAR1: ratio value for the Avoidance-Attraction-Ratio calculating AB/BA.AAR2_BAB: ratio value for the Avoidance-Attraction-Ratio calculating BAB/BB.AAR2_ABA: ratio value for the Avoidance-Attraction-Ratio calculating ABA/AA.Harmsen_P: p-value from the linear model with interaction Species1*Species2 from Harmsen et al. (2009).Niedballa_P: p-value from the linear model comparing AB to BA (Niedballa et al. 2021).Karanth_permA: rank of the observed interval duration median (AB and BA undifferenciated) compared to the randomized median distribution, when permuting on species A (Karanth et al. 2017).MurphyAB_permA: rank of the observed AB interval duration median compared to the randomized median distribution, when permuting on species A (Murphy et al. 2021). MurphyBA_permA: rank of the observed BA interval duration median compared to the randomized median distribution, when permuting on species A (Murphy et al. 2021). Karanth_permB: rank of the observed interval duration median (AB and BA undifferenciated) compared to the randomized median distribution, when permuting on species B (Karanth et al. 2017).MurphyAB_permB: rank of the observed AB interval duration median compared to the randomized median distribution, when permuting on species B (Murphy et al. 2021). MurphyBA_permB: rank of the observed BA interval duration median compared to the randomized median distribution, when permuting on species B (Murphy et al. 2021).

Scripts files description:1_Functions: R script containing the functions: - MRPP from Karanth et al. (2017) adapted here for time efficiency. - MRPP from Murphy et al. (2021) adapted here for time efficiency. - Version of the ct_to_recurrent() function from the recurrent package adapted to process parallized on the simulation datasets. - The simulation() function used to simulate two species observations with reciprocal effect on each other.2_Simulations: R script containing the parameters definitions for all iterations (for the two parts of the simulations), the simulation paralellization and the random thinning mimicking imperfect detection.3_Approaches comparison: R script containing the fit of the different models tested on the simulated data.3_1_Real data comparison: R script containing the fit of the different models tested on the real data example from Murphy et al. 2021.4_Graphs: R script containing the code for plotting results from the simulation part and appendices.5_1_Appendix - Check for similarity between codes for Karanth et al 2017 method: R script containing Karanth et al. (2017) and Murphy et al. (2021) codes lines and the adapted version for time-efficiency matter and a comparison to verify similarity of results.5_2_Appendix - Multi-response procedure permutation difference: R script containing R code to test for difference of the MRPPs approaches according to the species on which permutation are done.

Comparison between the independent bivariate normal distribution and mixture normal distribution in Fig 1.

Studies estimating species’ distributions require information about animal locations in space and time. Location data can be collected using surveys within a predetermined frame of reference (i.e., Eulerian sampling) or from animal-borne tracking devices (i.e., Lagrangian sampling). Integration of observations obtained from Eulerian and Lagrangian perspectives can provide insights into animal movement and habitat use. However, contemporaneous data from both perspectives are rarely available, making examination of biases of each sampling approach difficult to quantify. We compared distributions of a mobile seabird observed concurrently from ship, aerial, and satellite tag surveys during May, June, and July 2012 in the northern California Current. We calculated utilization distributions to quantify and compare variability in common murre (Uria aalge) space use and examine how sampling perspective and platform influence observed patterns. Spatial distributions of murres were similar in May, regardless of sampling perspective. Greatest density distributions occurred in coastal waters off southern Washington and northern Oregon, near large murre colonies and the mouth of the Columbia River. Density distributions of murres estimated from ship and aerial surveys in June and July were similar to those observed in May, whereas distributions of satellite-tagged murres in June and July indicated northward movement into British Columbia, Canada, resulting in different patterns observed from Eulerian and Lagrangian perspectives. These results suggest that the population of murres observed in the northern California Current during spring and summer includes relatively stationary individuals attending breeding colonies and non-stationary, vagile adults and subadults. Given the expected growth of telemetry studies and advances in survey technology (e.g., unmanned aerial systems), these results highlight the importance of considering methodological approaches, spatial extent, and synopticity of distribution data sets prior to integrating data from different sampling perspectives.

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).

SQRT(S)=53 GEV. INCLUSIVE DISTRIBUTIONS FROM P P AND PBAR P COLLISIONS AT THE CERN ISR WITH THE SPLIT-FIELD MAGNET.

Comparison of rate, type, and distribution of disability between the RA and control groups.

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The World Bank, the World Inequality Database (WID), and the Luxembourg Income Study (LIS) are all sources of data on poverty and inequality. They differ in terms of the income measure they use, the countries they cover, and the frequency of their data updates.

The World Bank uses a measure of income after taxes and transfers, which is called disposable income. It covers a wide range of countries, but the data is not updated as frequently as the data from the other two sources. The WID uses a measure of net national income after taxes, which is called net national income per adult. It covers a smaller range of countries than the World Bank, but the data is updated more frequently. The LIS uses a measure of disposable household income per capita. It covers a smaller range of countries than the World Bank or the WID, but the data is very detailed and goes back further in time. In general, the LIS data is considered to be the most reliable source of data on poverty and inequality. However, the World Bank and WID data are also useful, especially for countries that are not covered by the LIS.

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).

In 2023, there were an estimated *********** blood donor deferrals in the United States. The most common reason for donor deferral was low hemoglobin/hematocrit, which accounted for **** percent of all deferrals that year. This graph shows the distribution of blood donor deferrals in the United States in 2021 and 2023, by reason.

In 2024, around 52 percent of people living with HIV worldwide were in Eastern and Southern Africa. Furthermore, around 17 percent of people living with HIV were in the region of Asia and the Pacific.

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

The graph shows the number of publications with a given number of citations of ^. The data are presented on a logarithmic scale for the sake of clarity.

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.

Existing methods for calculating directional relations in polygons (i.e. the directional similarity model, the cone-based model, and the modified cone-based model) were compared to human perceptions of change through an online survey. The results from this survey provide the first empirical validation of computational approaches to calculating directional relations in polygonal spatial data. We have found that while the evaluated methods generally agreed with each other, they varied in their alignment with human perceptions of directional relations. Specifically, translation transformations of the target and reference polygons showed greatest discrepancy to human perceptions and across methods. The online survey was developed using Qualtrics Survey Software, and participants were recruited via online messaging on social media (i.e., Twitter) with hashtags related to geographic information science. In total sixty-one individuals responded to the survey. This survey consisted of nine questions. For the first question, participants indicated how many years they have worked with GIS and/or spatial data. For the remaining eight questions, participants ranked pictorial database scenes according to degrees of their match to query scenes. Each of these questions represented a test case that Goyal and Egenhofer (2001) used to empirically evaluate the directional similarity model; participants were randomly presented with four of these questions. The query scenes were created using ArcMap and contained a pair of reference and target polygons. The database scenes were generated by gradually changing the geometry of the target polygon within each query scene. The relations between the target and reference polygon varied by the type of movement, the scaling change of the polygon, and changes in rotation. The scenarios were varied in order to capture a representative range of variability in polygon movements and changes in real world data. The R statistical computing environment was used to determine the similarity value that corresponds with each database scene based on the directional similarity model, the cone-based model, and the modified cone-based model. Using the survey responses, the frequency of first, second, third, etc. ranks were calculated for each database scene. Weight variables were multiplied by the frequencies to create an overall rank based on participant responses. A rank of one was weighted as a five, a rank of two was weighted as a four, and so on. Spearman’s rank-order correlation was used to measure the strength and direction of association between the rank determined using the three models and the rank determined using participant responses.

Comparison of subject distributions and patient characteristics between upper and lower halves in the cluster of top 15 apparent lipid species.

Aim: The boundaries of species distributions are often shaped by natural barriers such as mountains and rivers, but species distribution models usually fail to include these constraints. We tested several approaches that include barriers as explanatory variables in species distribution models. Location: Africa and South America. Time period: Current Major taxa studied: Primates Methods: We modelled the ranges of pairs of species separated by a river taking into account three explanatory components: the environment (ecosystems, topo-hydrography, climate, human pressure), the spatial structure shaped by history and population dynamics (using a trend-surface approach), and rivers as naturals barriers to dispersal (using a binary cis-trans variable that describes both sides of the river). To assess how the addition of a spatial structure and the barrier could improve distribution models, we used a nested approach by comparing models based on: a) the environment; b) the environment and the spatial structure; and c) the environment, the spatial structure and the river. These models were constructed using the favourability functions. Results: There was a decreased occurrence of high-favourability values in the opposite side of the rivers in models that included the spatial structure of distributions, compared to models based on environment alone. This decrease was more marked when the description of the spatial structure was made more flexible. However, model performance was significantly improved by the inclusion of cis-trans variables that identified areas on the opposite side as totally unfavourable. Main conclusions: The performance of distribution models can improve by the use of approaches that describe barriers. Although adding the location of geographic units in relation to a river appears to be the most accurate way to define the presence of a barrier, defining this variable may be challenging. A suitable alternative is to analyse the spatial structure of distributions using a flexible approach.

Data and Code associated with the paper entitled "Hot to Model Intermittent Water Supply: Comparing Modelling Choices and Their Impact on Inequality" Description of contents and instructions on how to use this dataset are in the file README.md located in the root path

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.