cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.3544190
title: Appendix B. A figure depicting convergence of the proportion of the total number of species accounted for by the four functional groups.

description: A figure depicting convergence of the proportion of the total number of species accounted for by the four functional groups.
DOI: 10.6084/m9.figshare.3544190.v1
title: Appendix B. A figure depicting convergence of the proportion of the total number of species accounted for by the four functional groups.

description: A figure depicting convergence of the proportion of the total number of species accounted for by the four functional groups.
DOI: 10.6084/m9.figshare.3544187
title: Appendix C. One table showing the experimental design and six tables providing the results of statistical analyses.

description: One table showing the experimental design and six tables providing the results of statistical analyses.
DOI: 10.6084/m9.figshare.3544187.v1
title: Appendix C. One table showing the experimental design and six tables providing the results of statistical analyses.

description: One table showing the experimental design and six tables providing the results of statistical analyses.
DOI: 10.6084/m9.figshare.3544184
title: Appendix A. Comparison between PCA results using all measured traits and a subset excluding theoretically interdependent traits.

description: Comparison between PCA results using all measured traits and a subset excluding theoretically interdependent traits.
DOI: 10.6084/m9.figshare.3544184.v1
title: Appendix A. Comparison between PCA results using all measured traits and a subset excluding theoretically interdependent traits.

description: Comparison between PCA results using all measured traits and a subset excluding theoretically interdependent traits.
DOI: 10.6084/m9.figshare.3544178
title: Appendix A. Description of litter chemical characteristics, SOC changes over the incubation period, respiration rate of the combined POM and MOM fraction, and the 14C activity of the younger SOC fraction in mineral soil.

description: Description of litter chemical characteristics, SOC changes over the incubation period, respiration rate of the combined POM and MOM fraction, and the 14C activity of the younger SOC fraction in mineral soil.
DOI: 10.6084/m9.figshare.3544175
title: Appendix A. An example of a meta-analysis.

description: An example of a meta-analysis.
DOI: 10.6084/m9.figshare.3544175.v1
title: Appendix A. An example of a meta-analysis.

description: An example of a meta-analysis.
DOI: 10.6084/m9.figshare.3544169
title: Supplement 1. Computer programs in R and data sets used in the paper.

description: File List R code and comments Details of computer programs (in R) used in the paper. N2O Emission Data for the N<sub>2</sub>O emission Example. rtol Richness tolerance data for the EUSE Example. EUSE_ENV_Data Data for the EUSE example, describing environmental characteristics of each metropolitan area. EUSE_AG_GRASS Data for the EUSE example, providing estimated background agricultural land use information. Description R programs for the examples in the paper and in Appendix A are in this file. The N<sub>2</sub>O emission data set has seven columns: -- TABLE: Please see in attached file. -- The richness tolerance data set has eight columns: -- TABLE: Please see in attached file. -- Regional environmental data set has seven columns: -- TABLE: Please see in attached file. -- Background agricultural data set has five columns: -- TABLE: Please see in attached file. --
DOI: 10.6084/m9.figshare.3544169.v1
title: Supplement 1. Computer programs in R and data sets used in the paper.

description: File List R code and comments Details of computer programs (in R) used in the paper. N2O Emission Data for the N<sub>2</sub>O emission Example. rtol Richness tolerance data for the EUSE Example. EUSE_ENV_Data Data for the EUSE example, describing environmental characteristics of each metropolitan area. EUSE_AG_GRASS Data for the EUSE example, providing estimated background agricultural land use information. Description R programs for the examples in the paper and in Appendix A are in this file. The N<sub>2</sub>O emission data set has seven columns: -- TABLE: Please see in attached file. -- The richness tolerance data set has eight columns: -- TABLE: Please see in attached file. -- Regional environmental data set has seven columns: -- TABLE: Please see in attached file. -- Background agricultural data set has five columns: -- TABLE: Please see in attached file. --
DOI: 10.6084/m9.figshare.3544166
title: Appendix A. Estimating ecological filters with selection ratios and community aggregated traits.

description: Estimating ecological filters with selection ratios and community aggregated traits.
DOI: 10.6084/m9.figshare.3544166.v1
title: Appendix A. Estimating ecological filters with selection ratios and community aggregated traits.

description: Estimating ecological filters with selection ratios and community aggregated traits.
DOI: 10.6084/m9.figshare.3544163
title: Supplement 1. R source code for functions and examples of selection ratio analysis on community aggregated traits (SRCAT).

description: File List srcat.R -- R source code to run SRCAT analyses srcat_examples.R -- R source code to run SRCAT examples Description The function srcat() calculates the probability that a site <i>i</i> will select state <i>j</i> from <i>k</i> alternative versions of a discrete trait. If a continuous trait is supplied, a discrete approximation is used to estimate the selective filter imposed by <i>i</i> as a probability function of the trait value. Both estimations are accomplished using data on species (relative) abundance at sites (<b>S</b>), species possession of trait states (<b>W</b>), and the expected availability of species to sites, assuming no trait-based selection (<b>A</b>). Function parameters are described in comments within the script. The script file srcat_examples.R runs several examples of SRCAT analysis using both discrete and continuous traits.
DOI: 10.6084/m9.figshare.3544163.v1
title: Supplement 1. R source code for functions and examples of selection ratio analysis on community aggregated traits (SRCAT).

description: File List srcat.R -- R source code to run SRCAT analyses srcat_examples.R -- R source code to run SRCAT examples Description The function srcat() calculates the probability that a site <i>i</i> will select state <i>j</i> from <i>k</i> alternative versions of a discrete trait. If a continuous trait is supplied, a discrete approximation is used to estimate the selective filter imposed by <i>i</i> as a probability function of the trait value. Both estimations are accomplished using data on species (relative) abundance at sites (<b>S</b>), species possession of trait states (<b>W</b>), and the expected availability of species to sites, assuming no trait-based selection (<b>A</b>). Function parameters are described in comments within the script. The script file srcat_examples.R runs several examples of SRCAT analysis using both discrete and continuous traits.
DOI: 10.6084/m9.figshare.3544157
title: Supplement 2. Data for examples of selection ratio analysis on community aggregated traits (SRCAT).

description: File List pdxherbs.csv -- Herb community data for Portland, Oregon greenspaces Description Data on the relative importance of 76 herb species in 47 forested greenspaces in Portland, Oregon, USA. The data are stored as a comma-separated text (.csv) file. The first row contains species codes and the first column contains site identifiers. The second column contains the variable popdens which is the population density in a 100-m buffer around each site (people/ha). The final 2 rows of the file contain dummy codes (0/1) indicating whether each species is native or nonnative to western Oregon. Checksum values are as follows: -- TABLE: Please see in attached file. --
DOI: 10.6084/m9.figshare.3544157.v1
title: Supplement 2. Data for examples of selection ratio analysis on community aggregated traits (SRCAT).

description: File List pdxherbs.csv -- Herb community data for Portland, Oregon greenspaces Description Data on the relative importance of 76 herb species in 47 forested greenspaces in Portland, Oregon, USA. The data are stored as a comma-separated text (.csv) file. The first row contains species codes and the first column contains site identifiers. The second column contains the variable popdens which is the population density in a 100-m buffer around each site (people/ha). The final 2 rows of the file contain dummy codes (0/1) indicating whether each species is native or nonnative to western Oregon. Checksum values are as follows: -- TABLE: Please see in attached file. --
DOI: 10.6084/m9.figshare.3544154
title: Appendix A. A detailed explanation of how the penalty function was derived.

description: A detailed explanation of how the penalty function was derived.
DOI: 10.6084/m9.figshare.3544151
title: Appendix B. An algorithm to obtain the bootstrap confidence intervals.

description: An algorithm to obtain the bootstrap confidence intervals.
DOI: 10.6084/m9.figshare.3544154.v1
title: Appendix A. A detailed explanation of how the penalty function was derived.

description: A detailed explanation of how the penalty function was derived.
DOI: 10.6084/m9.figshare.3544151.v1
title: Appendix B. An algorithm to obtain the bootstrap confidence intervals.

description: An algorithm to obtain the bootstrap confidence intervals.
DOI: 10.6084/m9.figshare.3544148
title: Appendix A. Diagnostic analyses of the multilevel confirmatory path analysis in Fig. 2.

description: Diagnostic analyses of the multilevel confirmatory path analysis in Fig. 2.
DOI: 10.6084/m9.figshare.3544148.v1
title: Appendix A. Diagnostic analyses of the multilevel confirmatory path analysis in Fig. 2.

description: Diagnostic analyses of the multilevel confirmatory path analysis in Fig. 2.
DOI: 10.6084/m9.figshare.3544139
title: Appendix A. Locations of Arisaema triphyllum study populations.

description: Locations of Arisaema triphyllum study populations.
DOI: 10.6084/m9.figshare.3544139.v1
title: Appendix A. Locations of Arisaema triphyllum study populations.

description: Locations of Arisaema triphyllum study populations.
DOI: 10.6084/m9.figshare.3544136
title: Appendix B. Methods and results for developing nondestructive biomass estimation from aboveground plant size for Arisaema.

description: Methods and results for developing nondestructive biomass estimation from aboveground plant size for Arisaema.
DOI: 10.6084/m9.figshare.3544136.v1
title: Appendix B. Methods and results for developing nondestructive biomass estimation from aboveground plant size for Arisaema.

description: Methods and results for developing nondestructive biomass estimation from aboveground plant size for Arisaema.
DOI: 10.6084/m9.figshare.3544133
title: Appendix C. Effect size analysis of deer exclusion on unpalatable plant size for five focal species measured in paired deer access/deer exclusion plots.

description: Effect size analysis of deer exclusion on unpalatable plant size for five focal species measured in paired deer access/deer exclusion plots.
DOI: 10.6084/m9.figshare.3544133.v1
title: Appendix C. Effect size analysis of deer exclusion on unpalatable plant size for five focal species measured in paired deer access/deer exclusion plots.

description: Effect size analysis of deer exclusion on unpalatable plant size for five focal species measured in paired deer access/deer exclusion plots.
DOI: 10.6084/m9.figshare.3544130
title: Appendix D. Statistical methods and results of analysis of independent abiotic variables, table of mean abiotic values used in principal components analysis, and MANOVA results for compaction data from experimental sites.

description: Statistical methods and results of analysis of independent abiotic variables, table of mean abiotic values used in principal components analysis, and MANOVA results for compaction data from experimental sites.
DOI: 10.6084/m9.figshare.3544130.v1
title: Appendix D. Statistical methods and results of analysis of independent abiotic variables, table of mean abiotic values used in principal components analysis, and MANOVA results for compaction data from experimental sites.

description: Statistical methods and results of analysis of independent abiotic variables, table of mean abiotic values used in principal components analysis, and MANOVA results for compaction data from experimental sites.
DOI: 10.6084/m9.figshare.3544127
title: Appendix E. Results of the stepwise backward regression.

description: Results of the stepwise backward regression.
DOI: 10.6084/m9.figshare.3544127.v1
title: Appendix E. Results of the stepwise backward regression.

description: Results of the stepwise backward regression.
DOI: 10.6084/m9.figshare.3544124
title: Appendix A. A figure showing that FDiv and FEve do not estimate functional dispersion.

description: A figure showing that FDiv and FEve do not estimate functional dispersion.
DOI: 10.6084/m9.figshare.3544121
title: Appendix B. Simulation results with presence–absence data.

description: Simulation results with presence–absence data.
DOI: 10.6084/m9.figshare.3544118
title: Appendix C. A list of criteria for functional diversity indices tested on FDis.

description: A list of criteria for functional diversity indices tested on FDis.
DOI: 10.6084/m9.figshare.3544112
title: Appendix D. A table listing the main features of the "dbFD" function.

description: A table listing the main features of the "dbFD" function.
DOI: 10.6084/m9.figshare.3544109
title: Appendix A. Information on methodology and parameters used for a "host-community" model.

description: Information on methodology and parameters used for a "host-community" model.
DOI: 10.6084/m9.figshare.3544109.v1
title: Appendix A. Information on methodology and parameters used for a "host-community" model.

description: Information on methodology and parameters used for a "host-community" model.
DOI: 10.6084/m9.figshare.3544106
title: Appendix B. Results of sensitivity tests on the parameters used for the "host-community" model.

description: Results of sensitivity tests on the parameters used for the "host-community" model.
DOI: 10.6084/m9.figshare.3544106.v1
title: Appendix B. Results of sensitivity tests on the parameters used for the "host-community" model.

description: Results of sensitivity tests on the parameters used for the "host-community" model.
DOI: 10.6084/m9.figshare.3544094
title: Appendix A. A continuous time Kalman Filter/smoother for Argos locations.

description: A continuous time Kalman Filter/smoother for Argos locations.
DOI: 10.6084/m9.figshare.3544094.v1
title: Appendix A. A continuous time Kalman Filter/smoother for Argos locations.

description: A continuous time Kalman Filter/smoother for Argos locations.
DOI: 10.6084/m9.figshare.3544085
title: Appendix B. Calculating empirical error covariance matrices from calibration data.

description: Calculating empirical error covariance matrices from calibration data.
DOI: 10.6084/m9.figshare.3544085.v1
title: Appendix B. Calculating empirical error covariance matrices from calibration data.

description: Calculating empirical error covariance matrices from calibration data.
DOI: 10.6084/m9.figshare.3544082
title: Appendix B. :Simulation experiment.

description: :Simulation experiment.
DOI: 10.6084/m9.figshare.3544082.v1
title: Appendix B. :Simulation experiment.

description: :Simulation experiment.
DOI: 10.6084/m9.figshare.3544079
title: Appendix B. Diagnostics for the Kalman Filter.

description: Diagnostics for the Kalman Filter.
DOI: 10.6084/m9.figshare.3544079.v1
title: Appendix B. Diagnostics for the Kalman Filter.

description: Diagnostics for the Kalman Filter.
DOI: 10.6084/m9.figshare.3544076
title: Supplement. Demonstration of Kalman filtering smoothing and confidence interval calibration.

description: File List kf_algorithm.R Description This file contains R-code which demonstrates the Kalman filter and smoother and construction of CIC curves. No estimation of parameters is shown, however, the negative log-likelihood is calculated and so this code could be modified to be used for mMaximum-likelihood estimation of parameters. Note that estimation routines used in practice are best coded in a compiled language (e.g., C/Fortran) for optimal speed. Modifications to the code below will allow for parameter estimation, but it is likely to be quite slow. The function sim.track generates some simulated data from a bivariate random walk. For simplicity the simulation gives a regular time series of positions. Also demonstrated is the construction of confidence interval calibration (CIC) curves. In this case because the correct parameter values are used the CIC curve is the best possible case. By tinkering with the parameter values in the error covariance, the user will be able to see how the CIC curves change. To run the code open an R session and source the code then run the kfdemo function:<br> &gt; source('kfalgorithm.R')<br> &gt; kfdemo() <br>
DOI: 10.6084/m9.figshare.3544076.v1
title: Supplement. Demonstration of Kalman filtering smoothing and confidence interval calibration.

description: File List kf_algorithm.R Description This file contains R-code which demonstrates the Kalman filter and smoother and construction of CIC curves. No estimation of parameters is shown, however, the negative log-likelihood is calculated and so this code could be modified to be used for mMaximum-likelihood estimation of parameters. Note that estimation routines used in practice are best coded in a compiled language (e.g., C/Fortran) for optimal speed. Modifications to the code below will allow for parameter estimation, but it is likely to be quite slow. The function sim.track generates some simulated data from a bivariate random walk. For simplicity the simulation gives a regular time series of positions. Also demonstrated is the construction of confidence interval calibration (CIC) curves. In this case because the correct parameter values are used the CIC curve is the best possible case. By tinkering with the parameter values in the error covariance, the user will be able to see how the CIC curves change. To run the code open an R session and source the code then run the kfdemo function:<br> &gt; source('kfalgorithm.R')<br> &gt; kfdemo() <br>
DOI: 10.6084/m9.figshare.3544073
title: Appendix A. Numerical simulations for Type I error and power of the five ANOVA models: method and results.

description: Numerical simulations for Type I error and power of the five ANOVA models: method and results.
DOI: 10.6084/m9.figshare.3544073.v1
title: Appendix A. Numerical simulations for Type I error and power of the five ANOVA models: method and results.

description: Numerical simulations for Type I error and power of the five ANOVA models: method and results.
DOI: 10.6084/m9.figshare.3544070
title: Appendix B. Tables of simulation results.

description: Tables of simulation results.
DOI: 10.6084/m9.figshare.3544070.v1
title: Appendix B. Tables of simulation results.

description: Tables of simulation results.
DOI: 10.6084/m9.figshare.3544067
title: Appendix C. Tests of S and T in the presence of a significant interaction.

description: Tests of S and T in the presence of a significant interaction.
DOI: 10.6084/m9.figshare.3544067.v1
title: Appendix C. Tests of S and T in the presence of a significant interaction.

description: Tests of S and T in the presence of a significant interaction.
DOI: 10.6084/m9.figshare.3544064
title: Appendix D. Indicator species of the Trichoptera example.

description: Indicator species of the Trichoptera example.
DOI: 10.6084/m9.figshare.3544064.v1
title: Appendix D. Indicator species of the Trichoptera example.

description: Indicator species of the Trichoptera example.
DOI: 10.6084/m9.figshare.3544061
title: Supplement 1. The STI package (source code and compiled libraries for Windows and Mac OS X), an R language library for the analysis of the main factors space and time and the interaction in space–time studies using permutation tests.

description: File List The zipped folder STI_package.zip -- contains the STI R-language package: STI_1.0_source.tar.gz -- Folder containing the source code of the package STI_1.0_win.zip -- package compiled for Windows STI_1.0_mac.tar.gz -- package compiled for Mac OS X Description The R-language STI package performs two-way ANOVA to test space–time interaction in the absence of replication. This Supplement contains the package STI (source code), an R-language library for the analysis of the main factors space and time, and the interaction, in space–time studies, using permutation tests. The functions correspond to the six models presented in Fig. 1 of the paper. The downloadable files comprise the source code as well as packages compiled for Windows and Mac OS X.
DOI: 10.6084/m9.figshare.3544061.v1
title: Supplement 1. The STI package (source code and compiled libraries for Windows and Mac OS X), an R language library for the analysis of the main factors space and time and the interaction in space–time studies using permutation tests.

description: File List The zipped folder STI_package.zip -- contains the STI R-language package: STI_1.0_source.tar.gz -- Folder containing the source code of the package STI_1.0_win.zip -- package compiled for Windows STI_1.0_mac.tar.gz -- package compiled for Mac OS X Description The R-language STI package performs two-way ANOVA to test space–time interaction in the absence of replication. This Supplement contains the package STI (source code), an R-language library for the analysis of the main factors space and time, and the interaction, in space–time studies, using permutation tests. The functions correspond to the six models presented in Fig. 1 of the paper. The downloadable files comprise the source code as well as packages compiled for Windows and Mac OS X.
DOI: 10.6084/m9.figshare.3544058
title: Appendix A. Additional genetic methods and amplification conditions.

description: Additional genetic methods and amplification conditions.
DOI: 10.6084/m9.figshare.3544058.v1
title: Appendix A. Additional genetic methods and amplification conditions.

description: Additional genetic methods and amplification conditions.
DOI: 10.6084/m9.figshare.3544055
title: Appendix B. Definitions for newly derived topographic morphology metrics.

description: Definitions for newly derived topographic morphology metrics.
DOI: 10.6084/m9.figshare.3544055.v1
title: Appendix B. Definitions for newly derived topographic morphology metrics.

description: Definitions for newly derived topographic morphology metrics.
DOI: 10.6084/m9.figshare.3544052
title: Appendix C. Full table of model results (expanded from Table 2).

description: Full table of model results (expanded from Table 2).
DOI: 10.6084/m9.figshare.3544052.v1
title: Appendix C. Full table of model results (expanded from Table 2).

description: Full table of model results (expanded from Table 2).
DOI: 10.6084/m9.figshare.3544049
title: Appendix D. Color version of Fig. 1 from text and maps of important variables retained in Random Forests models.

description: Color version of Fig. 1 from text and maps of important variables retained in Random Forests models.
DOI: 10.6084/m9.figshare.3544046
title: Appendix A. Sampling and experimental sites at Kaikoura and Banks Peninsula on the east coast, and Nine- and Twelve-Mile Beaches on the west coast of southern New Zealand.

description: Sampling and experimental sites at Kaikoura and Banks Peninsula on the east coast, and Nine- and Twelve-Mile Beaches on the west coast of southern New Zealand.
DOI: 10.6084/m9.figshare.3544046.v1
title: Appendix A. Sampling and experimental sites at Kaikoura and Banks Peninsula on the east coast, and Nine- and Twelve-Mile Beaches on the west coast of southern New Zealand.

description: Sampling and experimental sites at Kaikoura and Banks Peninsula on the east coast, and Nine- and Twelve-Mile Beaches on the west coast of southern New Zealand.