cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.3530825.v1
title: Data Paper. Data Paper

description: File List BEF_summary_v2_Aug2008.csv -- File is 545 records, not including header row, and is formatted as a comma separated values. No compression scheme was used. Cells noted with "." indicate that the information is not relevant, not reported, or not available from the study. Cells that are blank mean that the information has yet to be collected (i.e., the data may or may not exist). Description Over the past decade, accelerating rates of species extinction have prompted an increasing number of studies to reduce the number of species experimentally in a variety of ecosystems and examine how this aspect of diversity alters the efficiency by which communities capture biologically essential resources and convert them into new tissue. Here we summarize the results of 164 experiments (reported in 84 publications) that have manipulated the richness of primary producers, herbivores, detritivores, or predators in a variety of terrestrial and aquatic ecosystems and examined how this impacts (1) the standing stock abundance or biomass of the focal trophic group, (2) the abundance or biomass of that trophic group's primary resource(s), and/or (3) the extent to which that trophic group depletes its resource(s). Our summary includes studies that have focused on the top-down effects of diversity; whereby researchers have examined how the richness of trophic group <i>t</i> impacts the consumption of a shared resource, and also studies that have focused on the bottom-up effects of diversity, whereby researchers have examined how the richness of trophic group <i>t</i> impacts the consumption of <i>t</i> by the next highest trophic level. The first portion of the data set provides information about the source of data and relevant aspects of the experimental design, including the spatial and temporal scales at which the work was performed. The second portion gives the magnitude of each response variable, the standard deviation, and the level of replication at each level of species richness manipulated. The third portion of the data set summarizes the magnitude of diversity effects in two ways. First, log ratios are used to compare the response variable in the most diverse polyculture to either the mean of all monocultures or the species having the highest/lowest value in monoculture. Second, data from each level of species richness are fit to three nonlinear functions (log, power, and hyperbolic) to assess which best characterizes the shape of diversity effects. The final portion of the data set summarizes any information that helps parse diversity effects into that attributable to species richness vs. that attributable to changes in species composition across levels of richness. <i>Key words: biodiversity; ecosystem efficiency; ecosystem functioning; ecosystem services; productivity; species richness; trophic efficiency.</i>
DOI: 10.6084/m9.figshare.3530822
title: Data Paper. Data Paper

description: File List 761BiodiversityEffects1974-2004.txt -- File is 761 records, not including header row, Ascii text, tab delimited. No compression scheme was used. Missing data denoted as "NA". Description This metadata set contains 761 effects of biodiversity on ecosystem, community, or population response variables reported in 137 publications from 1974 to June 2004. All data were obtained in experimental studies. Relationships between species richness or other biodiversity measures and response variables are described by their shape, direction, significance, and, if available, the simple (regression) or multiple (analysis of variance) correlation coefficient. We classified response variables into groups according to the ecosystem function or service to which they were related, the trophic level at which they were measured, and several other classification schemes. Similarly, we classified studies into groups according to ecosystem type, experimental system (bottle/pot, greenhouse, field), range and trophic level of biodiversity treatments, and further design variables. Covariates include location of study, number of plots, and number of species used in the biodiversity treatments. Analyses of these metadata have shown, for example, that biodiversity effects on community (and ecosystem) variables are often positive, and those on population variables negative, that stocks are more responsive than rates to biodiversity manipulation, and that bottom-up biodiversity effects in multi-trophic studies are often negative. This metadata set gives a representative overview over the results of a first generation of biodiversity experiments and allows a quantitative assessment of the influence of a number of biological and design variables on the significance and shape of the relationship between biodiversity and response variables. <i>Key words: aquatic ecosystems; biodiversity experiments; design variables; ecosystem functioning; ecosystem services; effect size; significance; terrestrial ecosystems; trophic level.</i>
DOI: 10.6084/m9.figshare.3530819
title: Supplementary Material for: Leveraging Methylome-Environment Interaction to Detect Genetic Determinants of Disease

description: <strong><em>Objective:</em></strong> The association between DNA methylation and a trait of interest may depend on an environmental exposure, and incorrectly accounting for this dependence can lead to a reduction in power of the standard tests used in epigenome-wide association studies. We present the M-ME test to jointly test for the main effect of DNA methylation and methylation-environment interaction. <b><i>Methods:</i></b> Through simulation, we compare the power and type 1 error of the M-ME test to a standard marginal test (M test) and a standard interaction test (ME test) under 1,800 different underlying models. These models allow for methylation-environment correlation and measurement error in the exposure. <b><i>Results:</i></b> In many true underlying models, either the M test or the ME test has very low power, but the M-ME test has optimal or nearly optimal power to detect a DNA methylation effect in all models considered, including those with methylation- environment dependence and measurement error in the exposure. Type 1 error inflation occurs in the tests when the exposure is measured with error and correlated with DNA methylation. <b><i>Conclusion:</i></b> The M-ME test is an attractive choice for studies aiming to detect any DNA methylation association when little is known about the epigenetic associations a priori.
DOI: 10.6084/m9.figshare.3530819.v1
title: Supplementary Material for: Leveraging Methylome-Environment Interaction to Detect Genetic Determinants of Disease

description: <strong><em>Objective:</em></strong> The association between DNA methylation and a trait of interest may depend on an environmental exposure, and incorrectly accounting for this dependence can lead to a reduction in power of the standard tests used in epigenome-wide association studies. We present the M-ME test to jointly test for the main effect of DNA methylation and methylation-environment interaction. <b><i>Methods:</i></b> Through simulation, we compare the power and type 1 error of the M-ME test to a standard marginal test (M test) and a standard interaction test (ME test) under 1,800 different underlying models. These models allow for methylation-environment correlation and measurement error in the exposure. <b><i>Results:</i></b> In many true underlying models, either the M test or the ME test has very low power, but the M-ME test has optimal or nearly optimal power to detect a DNA methylation effect in all models considered, including those with methylation- environment dependence and measurement error in the exposure. Type 1 error inflation occurs in the tests when the exposure is measured with error and correlated with DNA methylation. <b><i>Conclusion:</i></b> The M-ME test is an attractive choice for studies aiming to detect any DNA methylation association when little is known about the epigenetic associations a priori.
DOI: 10.6084/m9.figshare.3530816
title: Supplement 1. Program code for maximum likelihood estimation of neutral model parameters for multiple samples of species abundances using the two-stage approach described in the paper.

description: File List ML-TwoStage.zip -- (all files at once) logKDA1.gp loglikSAD1a.gp MLthetaISADstage2.gp MLthetaISADtwostage.gp Description ML-TwoStage.zip contains files to calculate the parameters that maximize the likelihood of a particular species-abundance data set in multiple samples where dispersal limitation may differ across samples. It runs under PARI/gp which can be downloaded for free from http://pari.math.u-bordeaux.fr/download.html Put all files in the PARI/examples folder. Then open “MLthetaISADtwostage.gp” in any editor and read the instructions (where the species-abundance data should be put) and start the program by typing read("MLthetaISADtwostage.gp") at the PARI/GP prompt, or by \r MLthetaISADtwostage.gp, depending on what version of PARI/GP you are using. The output will be, for each sample: the sample number, the sample size, the number of species in the sample, Fisher's alpha, the θ-value obtained by maximizing the likelihood given by the Ewens sampling formula, the Ewens maximum-likelihood itself, the θ-value obtained by the two-stage approach, the <i>m</i>-value obtained by the two-stage approach, the <i>I</i>-value obtained by the two-stage approach, and the Etienne (2005) likelihood for these parameter values if this sample was considered in isolation. <i>Note</i>: the total likelihood will not be the sum of the sample likelihoods, because the order of the species matters in the multple sample case.
DOI: 10.6084/m9.figshare.3530816.v1
title: Supplement 1. Program code for maximum likelihood estimation of neutral model parameters for multiple samples of species abundances using the two-stage approach described in the paper.

description: File List ML-TwoStage.zip -- (all files at once) logKDA1.gp loglikSAD1a.gp MLthetaISADstage2.gp MLthetaISADtwostage.gp Description ML-TwoStage.zip contains files to calculate the parameters that maximize the likelihood of a particular species-abundance data set in multiple samples where dispersal limitation may differ across samples. It runs under PARI/gp which can be downloaded for free from http://pari.math.u-bordeaux.fr/download.html Put all files in the PARI/examples folder. Then open “MLthetaISADtwostage.gp” in any editor and read the instructions (where the species-abundance data should be put) and start the program by typing read("MLthetaISADtwostage.gp") at the PARI/GP prompt, or by \r MLthetaISADtwostage.gp, depending on what version of PARI/GP you are using. The output will be, for each sample: the sample number, the sample size, the number of species in the sample, Fisher's alpha, the θ-value obtained by maximizing the likelihood given by the Ewens sampling formula, the Ewens maximum-likelihood itself, the θ-value obtained by the two-stage approach, the <i>m</i>-value obtained by the two-stage approach, the <i>I</i>-value obtained by the two-stage approach, and the Etienne (2005) likelihood for these parameter values if this sample was considered in isolation. <i>Note</i>: the total likelihood will not be the sum of the sample likelihoods, because the order of the species matters in the multple sample case.
DOI: 10.6084/m9.figshare.3530813
title: Appendix A. Derivation of the expected slope of the log–log species–area relationship, zE, and the expected slope of the log–log species–time relationship, wE.

description: Derivation of the expected slope of the log–log species–area relationship, zE, and the expected slope of the log–log species–time relationship, wE.
DOI: 10.6084/m9.figshare.3530813.v1
title: Appendix A. Derivation of the expected slope of the log–log species–area relationship, zE, and the expected slope of the log–log species–time relationship, wE.

description: Derivation of the expected slope of the log–log species–area relationship, zE, and the expected slope of the log–log species–time relationship, wE.
DOI: 10.6084/m9.figshare.3530810
title: Appendix B. Derivation of uE, the partial derivative of zE and wE with respect to the natural logarithm of time and area, respectively.

description: Derivation of uE, the partial derivative of zE and wE with respect to the natural logarithm of time and area, respectively.
DOI: 10.6084/m9.figshare.3530807
title: Appendix C. Graphics displaying the predictions of the sampling model for five relative abundance distributions (RADs) with different levels of evenness and five values of the replacement rate (R).

description: Graphics displaying the predictions of the sampling model for five relative abundance distributions (RADs) with different levels of evenness and five values of the replacement rate (R).
DOI: 10.6084/m9.figshare.3530810.v1
title: Appendix B. Derivation of uE, the partial derivative of zE and wE with respect to the natural logarithm of time and area, respectively.

description: Derivation of uE, the partial derivative of zE and wE with respect to the natural logarithm of time and area, respectively.
DOI: 10.6084/m9.figshare.3530804
title: Supplement 1. R code for the sampling effect model.

description: File List STAR_samp_model.R - R code for sampling effect model Description The file STAR_samp_model.R is a script file that can be opened in a text editor or within the R program interface. R is a free open source program for statistical analysis that can be downloaded at the following site: http://cran.r-project.org/. This script is provided such that the user can recreate the patterns that were documented and discussed in the manuscript as well as explore other possible parameterizations of the sampling model. Instructions to the user are demarcated in the code by the “#” symbol. The first part of the script defines the size of the species pool:“S” and the different relative abundance distributions (RADs) to be used. Each individual RAD will be assigned to a single column of the matrix,“P.mat”. Two graphical routines are provided to examine the RADs chosen: a rank log-abundance plot and a cumulative probability plot. Next the number of different areas and times the model should be calculated for are defined, this is specified by the variable “ncurves”. Also the different values for the replacement rate, “R”, are defined at this time. Next the two main routines that run the sampling model algorithm are provided. The first algorithm calculates average richness, z, and w values for each combination of a particular value of area, time, R, and RAD. These values will be assigned to the arrays “ExpS”, “Z”, and “W” respectively. The second algorithm calculates the time-by-area interaction array, “U”. The time-by-area interaction was calculated in a separate routine because computation time can be long and we wanted to provide the user the choice of calculating this array or not. Lastly the script provides a variety of graphical procedures for examining the results of the sampling effect model. To generate color figures identical to those displayed in Appendix C the R package “dichromat” must be downloaded from CRAN and loaded into the R interface to create the color scheme. Specifically the script was using R v2.6.2. However, the routines should be reliable in future or past versions of this software.
DOI: 10.6084/m9.figshare.3530804.v1
title: Supplement 1. R code for the sampling effect model.

description: File List STAR_samp_model.R - R code for sampling effect model Description The file STAR_samp_model.R is a script file that can be opened in a text editor or within the R program interface. R is a free open source program for statistical analysis that can be downloaded at the following site: http://cran.r-project.org/. This script is provided such that the user can recreate the patterns that were documented and discussed in the manuscript as well as explore other possible parameterizations of the sampling model. Instructions to the user are demarcated in the code by the “#” symbol. The first part of the script defines the size of the species pool:“S” and the different relative abundance distributions (RADs) to be used. Each individual RAD will be assigned to a single column of the matrix,“P.mat”. Two graphical routines are provided to examine the RADs chosen: a rank log-abundance plot and a cumulative probability plot. Next the number of different areas and times the model should be calculated for are defined, this is specified by the variable “ncurves”. Also the different values for the replacement rate, “R”, are defined at this time. Next the two main routines that run the sampling model algorithm are provided. The first algorithm calculates average richness, z, and w values for each combination of a particular value of area, time, R, and RAD. These values will be assigned to the arrays “ExpS”, “Z”, and “W” respectively. The second algorithm calculates the time-by-area interaction array, “U”. The time-by-area interaction was calculated in a separate routine because computation time can be long and we wanted to provide the user the choice of calculating this array or not. Lastly the script provides a variety of graphical procedures for examining the results of the sampling effect model. To generate color figures identical to those displayed in Appendix C the R package “dichromat” must be downloaded from CRAN and loaded into the R interface to create the color scheme. Specifically the script was using R v2.6.2. However, the routines should be reliable in future or past versions of this software.
DOI: 10.6084/m9.figshare.3530801
title: Appendix A. Summary of model selection process on California Spotted Owl data.

description: Summary of model selection process on California Spotted Owl data.
DOI: 10.6084/m9.figshare.3530801.v1
title: Appendix A. Summary of model selection process on California Spotted Owl data.

description: Summary of model selection process on California Spotted Owl data.
DOI: 10.6084/m9.figshare.3530798
title: Appendix B. Posterior distribution summaries for detection probabilities in the green frog example.

description: Posterior distribution summaries for detection probabilities in the green frog example.
DOI: 10.6084/m9.figshare.3530798.v1
title: Appendix B. Posterior distribution summaries for detection probabilities in the green frog example.

description: Posterior distribution summaries for detection probabilities in the green frog example.
DOI: 10.6084/m9.figshare.3530795
title: Supplement 1. WinBUGS 2.2.0 code for the green frog example.

description: File List Supplement.txt Description WinBUGS 2.2.0 code for implementing species occurrence model with multiple states for green frog example. There were 4 levels of occurrence, chorus indices of size 0–3, and the problem has been coded such that the chorus index of 0 corresponds to level 4 in the WinBUGS code, other index values remain unchanged. A non-detection is therefore recorded as a '4' in the data rather than a '0'.
DOI: 10.6084/m9.figshare.3530792
title: Appendix A. Derivation of the partial differential equation (PDE) for U(w,t).

description: Derivation of the partial differential equation (PDE) for U(w,t).
DOI: 10.6084/m9.figshare.3530789
title: Appendix B. Numerical methods for the stochastic and deterministic models.

description: Numerical methods for the stochastic and deterministic models.
DOI: 10.6084/m9.figshare.3530789.v1
title: Appendix B. Numerical methods for the stochastic and deterministic models.

description: Numerical methods for the stochastic and deterministic models.
DOI: 10.6084/m9.figshare.3530786
title: Appendix A. More methods and results.

description: More methods and results.
DOI: 10.6084/m9.figshare.3530786.v1
title: Appendix A. More methods and results.

description: More methods and results.
DOI: 10.6084/m9.figshare.3530783
title: Appendix A. Flow speed calibration of respirometry chambers.

description: Flow speed calibration of respirometry chambers.
DOI: 10.6084/m9.figshare.3530783.v1
title: Appendix A. Flow speed calibration of respirometry chambers.

description: Flow speed calibration of respirometry chambers.
DOI: 10.6084/m9.figshare.3530780
title: Appendix B. Multivariate regression analysis of the combined effects of light and flow on protein content and reproductive output.

description: Multivariate regression analysis of the combined effects of light and flow on protein content and reproductive output.
DOI: 10.6084/m9.figshare.3530780.v1
title: Appendix B. Multivariate regression analysis of the combined effects of light and flow on protein content and reproductive output.

description: Multivariate regression analysis of the combined effects of light and flow on protein content and reproductive output.
DOI: 10.6084/m9.figshare.3530777
title: Appendix C. Sensitivity of niche boundaries to light acclimation.

description: Sensitivity of niche boundaries to light acclimation.
DOI: 10.6084/m9.figshare.3530777.v1
title: Appendix C. Sensitivity of niche boundaries to light acclimation.

description: Sensitivity of niche boundaries to light acclimation.
DOI: 10.6084/m9.figshare.3530774
title: Appendix A. Figures showing study sites, species–area relationships for each site, and the estimates of S () for the mobile assemblages.

description: Figures showing study sites, species–area relationships for each site, and the estimates of S () for the mobile assemblages.
DOI: 10.6084/m9.figshare.3530774.v1
title: Appendix A. Figures showing study sites, species–area relationships for each site, and the estimates of S () for the mobile assemblages.

description: Figures showing study sites, species–area relationships for each site, and the estimates of S () for the mobile assemblages.
DOI: 10.6084/m9.figshare.3530771
title: Genetic Maps

description: Rutgers combined genetic and physical map and HapMap genetic map
DOI: 10.6084/m9.figshare.3530771.v1
title: Genetic Maps

description: Rutgers combined genetic and physical map and HapMap genetic map
DOI: 10.6084/m9.figshare.3530768
title: Appendix B. Tables showing estimated S and accuracy for shores in three regions of the coast and for gastropod assemblages in two bays.

description: Tables showing estimated S and accuracy for shores in three regions of the coast and for gastropod assemblages in two bays.
DOI: 10.6084/m9.figshare.3530768.v1
title: Appendix B. Tables showing estimated S and accuracy for shores in three regions of the coast and for gastropod assemblages in two bays.

description: Tables showing estimated S and accuracy for shores in three regions of the coast and for gastropod assemblages in two bays.
DOI: 10.6084/m9.figshare.3530765
title: Index Files

description: Index Files
DOI: 10.6084/m9.figshare.3530765.v1
title: Index Files

description: Index Files
DOI: 10.6084/m9.figshare.3530759
title: Appendix A. A map of the study sites.

description: A map of the study sites.
DOI: 10.6084/m9.figshare.3530759.v1
title: Appendix A. A map of the study sites.

description: A map of the study sites.
DOI: 10.6084/m9.figshare.3530756
title: Appendix B. Source, spatial resolution, and temporal resolution of ocean climate and ocean productivity variables.

description: Source, spatial resolution, and temporal resolution of ocean climate and ocean productivity variables.
DOI: 10.6084/m9.figshare.3530756.v1
title: Appendix B. Source, spatial resolution, and temporal resolution of ocean climate and ocean productivity variables.

description: Source, spatial resolution, and temporal resolution of ocean climate and ocean productivity variables.
DOI: 10.6084/m9.figshare.3530753
title: Appendix C. Time periods over which ocean climate and ocean productivity variables were averaged for each population.

description: Time periods over which ocean climate and ocean productivity variables were averaged for each population.
DOI: 10.6084/m9.figshare.3530750
title: Appendix D. Rankings of mean and variance of local climate and ocean productivity variables at each study site relative to other study sites.

description: Rankings of mean and variance of local climate and ocean productivity variables at each study site relative to other study sites.