cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.3563796.v1
title: Supplement 1. Fortran and Matlab files for conducting model calculations described in the main text.

description: File ListSupplement, original calcB7_5curve.for (md5: 4d88e7313e582d7eb41e272f536467cd) B7.inc (md5: 6b4d6683acd1765f93fce9fefd2b7ff3) calcB7_5curve.exe (md5: c46092f613abb3cb6c0b69c735d132ef) B_and_NPP_of_Trantime.m (md5: b02746500c7f393645567eb995a8997c) <br> Supplement, Revision 1 (posted 23 January 2013) calcB7_5curve.for – <b>corrected</b>, (md5: b7233a525e671cf016281c86913aee91) calcB7_5curve.exe – <b>corrected</b>, (md5: 42f4844387a0c2f5d9cfcdaa26c1d8a9) B_and_NPP_of_Trantime.m – <b>corrected</b>, (md5: b31358b099d3038809795cacd1a9ff7a) Description The Fortran code “calcB7_5curve.for” and accompanying include file “B7.inc” contain the model used to test Hypothesis #1 in Lucas and Thompson (2012), i.e., to explore the relationship between phytoplankton biomass and productivity and water depth, and how that relationship changes as a function of benthic grazing rate. The include file, B7.inc, contains and describes all changeable model parameters. We include an executable version of the code “calcB7_5curve.exe”. However, if any changes are made to the include file, the code needs to be recompiled before running in order for changes to take effect. To produce the results presented in Lucas and Thompson (2012), the model was compiled with Compaq Visual Fortran Standard Edition 6.6.a. See Appendix and Table 1 in main manuscript and comments in source code for more detail. The Matlab script “B_and_NPP_of_Trantime.m” contains the model used to test Hypothesis #2 in Lucas and Thompson (2012), i.e., to explore the relationship between phytoplankton biomass and productivity and transport time, and how that relationship changes as a function of benthic grazing rate. In this model, net phytoplankton growth rate, inflowing algal biomass concentration, zooplankton grazing rate, water depth, and cellular C:Chl ratio are specified by the user and assumed uniform and constant. In addition, a range of benthic grazing rates and transport times are specified. This model calculates steady-state habitat averaged phytoplankton biomass and productivity two ways: (1) via analytical expressions provided in the Appendix (see Eqs. 5 and 6) of Lucas and Thompson (2012); (2) via numerical integration along the streamwise habitat axis. This script also plots and compares results in various ways. See comments in script and Appendix and Table 1 in main manuscript for more detail.
DOI: 10.6084/m9.figshare.3563793
title: Supplement 1. Raw survey results and data cleaning/processing scripts for R.

description: File List DataCleaning_20120105.R (md5:7c3b115561b5c687e34c7ffa63896a82)<br><br> DataTrimming_20120105.R (md5:77f0bbe669da955c81d9b5fb2d3e2d3e)<br><br> AdvancedSpreadsheet_20110526.csv (md5:db607c6c3238ffb45effa2c97ec40b9a)<br><br> VariableRenaming_20110711.csv (md5:ac7519d78d3eac3d2ccf82ef94f1af80)<br><br> ParamTable.csv (md5:ff3fa18d83a3a6b860462c2955a5c8c9) Description <i>How the files should be used:</i> Scripts (2) DataCleaning_20120105.R is an R script for cleaning up data prior to analysis. This script removes spaces, substitutes text for codes, removed duplicate schools, and converts questions and answers from the survey into more simple parameter names, without any numbers, spaces, or symbols. This script is heavily annotated to assist the user of the file in understanding what is being done to the data files. The script produces the file cleandata_[date].Rdata, which is called in the file DataTrimming_20120105.R DataTrimming_20120105.R is an R script for trimming extraneous variables not used in final analyses. Some variables are combined as needed and NAs (no answers) are removed. The file is heavily annotated. It produces trimdata_[date].Rdata, which was imported into Excel for summary statistics. Data files (3) AdvancedSpreadsheet_20110526.csv is the output file from the SurveyMonkey online survey tool used for this project. It is a .csv sheet with the complete set of survey data, although some data (e.g., open-ended responses, institution names) are removed to prevent schools and/or instructors from being identifiable. This file is read into DataCleaning_20120105.R for cleaning and editing. VariableRenaming_20110711.csv is called into the DataCleaning_20120105.R script to convert the questions and answers from the survey into simple parameter names, without any numbers, spaces, or symbols. ParamTable.csv is a list of the parameter names used for analysis and the value codes. It can be used to understand outputs from the scripts above (cleandata_[date].Rdata and trimdata_[date].Rdata).
DOI: 10.6084/m9.figshare.3563793.v1
title: Supplement 1. Raw survey results and data cleaning/processing scripts for R.

description: File List DataCleaning_20120105.R (md5:7c3b115561b5c687e34c7ffa63896a82)<br><br> DataTrimming_20120105.R (md5:77f0bbe669da955c81d9b5fb2d3e2d3e)<br><br> AdvancedSpreadsheet_20110526.csv (md5:db607c6c3238ffb45effa2c97ec40b9a)<br><br> VariableRenaming_20110711.csv (md5:ac7519d78d3eac3d2ccf82ef94f1af80)<br><br> ParamTable.csv (md5:ff3fa18d83a3a6b860462c2955a5c8c9) Description <i>How the files should be used:</i> Scripts (2) DataCleaning_20120105.R is an R script for cleaning up data prior to analysis. This script removes spaces, substitutes text for codes, removed duplicate schools, and converts questions and answers from the survey into more simple parameter names, without any numbers, spaces, or symbols. This script is heavily annotated to assist the user of the file in understanding what is being done to the data files. The script produces the file cleandata_[date].Rdata, which is called in the file DataTrimming_20120105.R DataTrimming_20120105.R is an R script for trimming extraneous variables not used in final analyses. Some variables are combined as needed and NAs (no answers) are removed. The file is heavily annotated. It produces trimdata_[date].Rdata, which was imported into Excel for summary statistics. Data files (3) AdvancedSpreadsheet_20110526.csv is the output file from the SurveyMonkey online survey tool used for this project. It is a .csv sheet with the complete set of survey data, although some data (e.g., open-ended responses, institution names) are removed to prevent schools and/or instructors from being identifiable. This file is read into DataCleaning_20120105.R for cleaning and editing. VariableRenaming_20110711.csv is called into the DataCleaning_20120105.R script to convert the questions and answers from the survey into simple parameter names, without any numbers, spaces, or symbols. ParamTable.csv is a list of the parameter names used for analysis and the value codes. It can be used to understand outputs from the scripts above (cleandata_[date].Rdata and trimdata_[date].Rdata).
DOI: 10.6084/m9.figshare.3563790
title: Supplement 1. Presence of OTUs associated with Dryas integrifolia and Salix arctica across 23 sampling sites in the North American Arctic.

description: File List Supplement_Table.1_OTU_PRESENCE.txt (md5: 1b06e7c0b71ca68e40916b1bb99e7076)Description The supplementary table contains presence data (based on presence/absence in a host plant) of all Operational Taxonomic Units (OTUs) identified at ≥97% similarity in ITS region across 23 sampling sites in the North American Arctic. The rows represent the OTUs with their identified taxon name. The columns represent the locations in each subzone (A–E) and the hosts (SA: <i>Salix arctica, </i>DI:<i> Dryas integrifolia</i>). The last column contains the GenBank accession numbers of deposited sequences.
DOI: 10.6084/m9.figshare.3563790.v1
title: Supplement 1. Presence of OTUs associated with Dryas integrifolia and Salix arctica across 23 sampling sites in the North American Arctic.

description: File List Supplement_Table.1_OTU_PRESENCE.txt (md5: 1b06e7c0b71ca68e40916b1bb99e7076)Description The supplementary table contains presence data (based on presence/absence in a host plant) of all Operational Taxonomic Units (OTUs) identified at ≥97% similarity in ITS region across 23 sampling sites in the North American Arctic. The rows represent the OTUs with their identified taxon name. The columns represent the locations in each subzone (A–E) and the hosts (SA: <i>Salix arctica, </i>DI:<i> Dryas integrifolia</i>). The last column contains the GenBank accession numbers of deposited sequences.
DOI: 10.6084/m9.figshare.3563787
title: Supplement 1. Input data required for running simulations in LANDIS-II.

description: File List century-succession-inputs.txt -- Input file for the Century Extension (md5: 0e97574bc7fd4ea324ceb55104f459cb) dynamic-fire-inputs.txt -- Input file for the Dynamic Fire and Fuels Extension (md5: 3c2fdb229a61b87b1f8f31da3e374ab2) Description The supplements are designed to provide the reader with a list of input parameters that we used to run the simulation. The first file provides a list of the input parameters for the Century Extension. The second file is a list of all the input parameters for the Dynamic Fire and Fuels Extension.
DOI: 10.6084/m9.figshare.3563787.v1
title: Supplement 1. Input data required for running simulations in LANDIS-II.

description: File List century-succession-inputs.txt -- Input file for the Century Extension (md5: 0e97574bc7fd4ea324ceb55104f459cb) dynamic-fire-inputs.txt -- Input file for the Dynamic Fire and Fuels Extension (md5: 3c2fdb229a61b87b1f8f31da3e374ab2) Description The supplements are designed to provide the reader with a list of input parameters that we used to run the simulation. The first file provides a list of the input parameters for the Century Extension. The second file is a list of all the input parameters for the Dynamic Fire and Fuels Extension.
DOI: 10.6084/m9.figshare.3563784
title: Supplement 1. R script and input data file for creating multilevel models using the forest herb data set described in the main text.

description: File List MLM.R (md5: 432ae9cbc2a4a0ff70c8a2c3e5d7de05) MLM_dataset.csv (md5: 6519956fea79f678e665ee50fdcba301) Description The code included in MLM.R uses the forest herb data in MLM_dataset.csv to create multilevel models, analyze the significance of random effects, and compare the best multilevel model to CCA, RDA, and NMDS using the same data.
DOI: 10.6084/m9.figshare.3563781
title: Supplement 1. R scripts for analyses summarized in main text.

description: File List Grace_etal_SEMguidelines_supplement.txt Description The code presented in “Grace_etal_SEMguidelines_supplement.txt” documents the data and scripts used to perform the analyses summarized in the main text of the article. Included are scripts for both Bayesian and frequentist estimation as well as a scripts for evaluating model structure. Note that the .txt file can be opened in R.
DOI: 10.6084/m9.figshare.3563778
title: Supplement 1. Coordinates for sampled municipalities and the dates of tap water sample collection.

description: File List Coordinates.txt Description The Coordinates.txt file is a tab-separated file. It contains the city, state, latitude, and longitude of sampled municipalities as well as the month and year of tap water collection.
DOI: 10.6084/m9.figshare.3563775
title: Supplement 1. Delphi/Pascal scripts for simulating and parameterizing individual-based movement models.

description: File List Hypercube_21_8_3x10e6_10e5.txt<br> IN_real_bas.txt<br> IN_real_galera.txt<br> IN_real_galera98.txt<br> IN_real_monteria.txt<br> IN_real_palas.txt<br> IN_real_Plantilla.txt<br> IN_real_quin.txt<br> IN_real_sierrecica.txt<br> MOD1_Definitions.txt<br> MOD1_Demographics.txt<br> MOD1_Dispersal.txt<br> MOD1_Main.txt<br> MOD1_Movement.txt<br> MOD1_Parameterization.txt<br> MOD1_Utils.txt<br> MOD2_Definitions.txt<br> MOD2_Demographics.txt<br> MOD2_Dispersal.txt<br> MOD2_Main.txt<br> MOD2_Movement.txt<br> MOD2_Parametrization.txt<br> MOD2_Utils.txt<br> usos4bas10m.txt<br> usos4gal10m.txt<br> usos4gal98_10m.txt<br> usos4pal10m.txt<br> usos4quin10m.txt<br> usos4sierr10m.txt Description Indivual-based models were developed in Delphi programming language. Programs in Delphi are divided in units, each one represented by a .txt file (in Delphi, with extension '.pas'). Our programs have six units with name starting with 'MOD_'. These units use external information: - Radiotraking record of radiotracked individuals (observed patterns). One file per location. Name: 'IN_….txt'<br> - Parameterizations that will be tested. One file pr program. Name: 'Hypercube_….txt'<br> - Habitat maps of the study areas (matrix of values). Name: 'usos4….txt' We present two programs: - Model1 (MOD1_) is the main simulation model that yields for each parameterization, <br> for each observed individual i and summary statistic j two indices m_ij and v_ij <br> that quantify the deviance between the summary statistics of the observed and simulated data considering observation error and process stochasticity <br> (see also Fig. D1 and Appendix D for details). <br> The dimensions (cols × rows) of the matrix result of this program = (n × 8 × 2) × (NPARM) where n × 8 × 2 means number of individuals × 8 summary statistics × 2 indices (mij, vij), and NPARM means the number of parameterizations tested. - Model2 (MOD2_) is a modified version of Model1 that assesses the inter-individual variability and the stochasticity due to the sampling protocol. Model2 describes, for all pairs of individuals, noise in the observed data with respect to the mean and standard deviation of the summary statistic j (m_j_kl, v_j_kl, see Appendices for details). Starting from these values, threshold values tmj, tvj are calculated.
DOI: 10.6084/m9.figshare.3563775.v1
title: Supplement 1. Delphi/Pascal scripts for simulating and parameterizing individual-based movement models.

description: File List Hypercube_21_8_3x10e6_10e5.txt<br> IN_real_bas.txt<br> IN_real_galera.txt<br> IN_real_galera98.txt<br> IN_real_monteria.txt<br> IN_real_palas.txt<br> IN_real_Plantilla.txt<br> IN_real_quin.txt<br> IN_real_sierrecica.txt<br> MOD1_Definitions.txt<br> MOD1_Demographics.txt<br> MOD1_Dispersal.txt<br> MOD1_Main.txt<br> MOD1_Movement.txt<br> MOD1_Parameterization.txt<br> MOD1_Utils.txt<br> MOD2_Definitions.txt<br> MOD2_Demographics.txt<br> MOD2_Dispersal.txt<br> MOD2_Main.txt<br> MOD2_Movement.txt<br> MOD2_Parametrization.txt<br> MOD2_Utils.txt<br> usos4bas10m.txt<br> usos4gal10m.txt<br> usos4gal98_10m.txt<br> usos4pal10m.txt<br> usos4quin10m.txt<br> usos4sierr10m.txt Description Indivual-based models were developed in Delphi programming language. Programs in Delphi are divided in units, each one represented by a .txt file (in Delphi, with extension '.pas'). Our programs have six units with name starting with 'MOD_'. These units use external information: - Radiotraking record of radiotracked individuals (observed patterns). One file per location. Name: 'IN_….txt'<br> - Parameterizations that will be tested. One file pr program. Name: 'Hypercube_….txt'<br> - Habitat maps of the study areas (matrix of values). Name: 'usos4….txt' We present two programs: - Model1 (MOD1_) is the main simulation model that yields for each parameterization, <br> for each observed individual i and summary statistic j two indices m_ij and v_ij <br> that quantify the deviance between the summary statistics of the observed and simulated data considering observation error and process stochasticity <br> (see also Fig. D1 and Appendix D for details). <br> The dimensions (cols × rows) of the matrix result of this program = (n × 8 × 2) × (NPARM) where n × 8 × 2 means number of individuals × 8 summary statistics × 2 indices (mij, vij), and NPARM means the number of parameterizations tested. - Model2 (MOD2_) is a modified version of Model1 that assesses the inter-individual variability and the stochasticity due to the sampling protocol. Model2 describes, for all pairs of individuals, noise in the observed data with respect to the mean and standard deviation of the summary statistic j (m_j_kl, v_j_kl, see Appendices for details). Starting from these values, threshold values tmj, tvj are calculated.
DOI: 10.6084/m9.figshare.3563772
title: Supplement 1. Matlab source code for performing TVARSS(p), TVVARSS(1), and SETARSS(p) model fitting.

description: File List TVARSS.m -- Matlab source code to run TVARSS(p) model fitting -- (MD5: a84eab62b227b7e74cd28f42ec144e45) <br> TVVAR1SS.m -- Matlab source code to run TVVARSS(1) model fitting -- (MD5: 7395757e2c19dfe261a3a3422231af6f) <br> SETARSS.m -- Matlab source code to run SETARSS(p) model fitting -- (MD5: a39cf09301a343a622a8cc2834493236) <br> timevaryingfunct_24Apr12.m -- Matlab source code called by TVARSS -- (MD5: aa488ed19ac261db895e1565487b1816) <br> VAR1funct_24Apr12.m -- Matlab source code called by TVVARSS -- (MD5: 9070310f4eb934f3f4f5c0b7090c02b1) <br> twopointfunct_25Nov11.m -- Matlab source code called by SETARSS -- (MD5: 66becb86164b59403b3152b5868d3a18) <br> anneal.m -- Matlab source code for simulated annealing (from Joachim Vandekerckhove) -- (MD5: 70672ad1d98fcf004cea433bbcbc214d)<br> IvesDakos_Fig2.m -- Matlab source code to produce Fig. 2 -- (MD5: 946a820ebea42355170aebdb3b759c04) <br> IvesDakos_Fig4.m -- Matlab source code to produce Fig. 4 -- (MD5: 0864b27fcdd01571340dc7fbf61a2161) <br> IvesDakos_Fig5.m -- Matlab source code to produce Fig. 5 -- (MD5: fae8fc7fb7e9cde13f36b6291b46a9ca) <br> IvesDakos_Fig6.m -- Matlab source code to produce Fig. 6 -- (MD5: 63b775992103040060c44389337ec70c) <br> ASS_May_tomin.m -- Matlab source code generating the May harvesting model -- (MD5: 0f2941acf30fe7f84a08d20926e007a9) <br> ASS_NB_tomin.m -- Matlab source code generating the Nicholson-Bailey model -- (MD5: 67dbcc602da7d02b947f4bb11cb80270) Description The functions TVARSS.m, TVVAR1SS.m, and SETARSS.m perform statistical analyses for TVARSS, TVVARSS, and SETARSS models. The code IvesDakos_FigX.m produces Fig. <i>X</i> in the manuscript. The remaining code is called by the other programs.
DOI: 10.6084/m9.figshare.3563769
title: Supplement 1. C++ code used to model and map water isotope ratios and accompanying documentation.

description: File List IsoMAP_geostats_Ecosphere2012.pdf -- (MD5: d1227b8a0e9397d0dff56858ceac4d08)<br> also available here as downloadable ASCII text: code.txt -- (MD5: 4cdcd3fc748e2a9e6f8f925a73ff2c71) IsoMAP_geostats_Ecosphere2012.exe -- (MD5: 7e9723837ec7bf2a9cce5f2bc681249a) IsoMAP_geostats_documentation.pdf -- (MD5: 10af687b7dc203313b5344116d689c17) Description The file IsoMAP_geostats_Ecosphere2012.pdf contains source code that can be downloaded, modified if desired, and compiled for use in developing statistical and geostatistical models and maps of water isotope ratios or other environmental parameters. The methods implemented in this code and input/output file descriptions are described in the file IsoMAP_geostats_documentation.pdf. IsoMAP_geostats_Ecosphere2012.exe is a pre-complied version of the code used to conduct the analyses presented in our paper. It was compiled on a PC (Dell Optiplex 760 with Intel Core 2 Duo processor) running Windows XP (Service Pack 3) using the Borland Turbo C++ compiler (version 10).
DOI: 10.6084/m9.figshare.3563769.v1
title: Supplement 1. C++ code used to model and map water isotope ratios and accompanying documentation.

description: File List IsoMAP_geostats_Ecosphere2012.pdf -- (MD5: d1227b8a0e9397d0dff56858ceac4d08)<br> also available here as downloadable ASCII text: code.txt -- (MD5: 4cdcd3fc748e2a9e6f8f925a73ff2c71) IsoMAP_geostats_Ecosphere2012.exe -- (MD5: 7e9723837ec7bf2a9cce5f2bc681249a) IsoMAP_geostats_documentation.pdf -- (MD5: 10af687b7dc203313b5344116d689c17) Description The file IsoMAP_geostats_Ecosphere2012.pdf contains source code that can be downloaded, modified if desired, and compiled for use in developing statistical and geostatistical models and maps of water isotope ratios or other environmental parameters. The methods implemented in this code and input/output file descriptions are described in the file IsoMAP_geostats_documentation.pdf. IsoMAP_geostats_Ecosphere2012.exe is a pre-complied version of the code used to conduct the analyses presented in our paper. It was compiled on a PC (Dell Optiplex 760 with Intel Core 2 Duo processor) running Windows XP (Service Pack 3) using the Borland Turbo C++ compiler (version 10).
DOI: 10.6084/m9.figshare.3563766
title: Supplement 1. R package for conducting the simulations described in the main text.

description: File List simpsi_1.1.tar.gz -- (MD5: 33f82ba813a98b6cc5b0bf56d6b276bd) simpsi_1.1.zip -- (MD5: 895ccf64f96d49978c32b468bd686999) simpsi-manual.pdf -- (MD5: da0a009057855112950fd1851a5329c6) Description Code to conduct the simulations underlying Figs. 3–6 in the main text is provided as an R package. The source code for this package is in simpsi_1.1.tar.gz and a Windows binary is in simpsi_1.1.zip (note that the latter should be installed as a package in R using the “Install package(s) from local zip files” menu option). The source code in simpsi_1.1.tar.gz comprises files circle.square.overlap.R, occpsi.R, runsimDDD.R, runsimHRDD.R, runsimRPSD.R, and runsimSR.R. Details of usage are provided in the help (.Rd) files, collated in simpsi-manual.pdf. Each group of simulations has its own ‘runsim’ function, as indicated on Page 2 of the manual. Actual code to perform the simulations is given as “Not run” code in each Examples section (“Not run” is used to skip these lines during automatic checking, to save time). Source code for each function may be viewed from within R once the package has been installed. The R code uses the other R packages ‘secr’, ‘abind’, and ‘spatstat’; these should be downloaded from the CRAN website (http://cran.r-project.org).
DOI: 10.6084/m9.figshare.3563766.v1
title: Supplement 1. R package for conducting the simulations described in the main text.

description: File List simpsi_1.1.tar.gz -- (MD5: 33f82ba813a98b6cc5b0bf56d6b276bd) simpsi_1.1.zip -- (MD5: 895ccf64f96d49978c32b468bd686999) simpsi-manual.pdf -- (MD5: da0a009057855112950fd1851a5329c6) Description Code to conduct the simulations underlying Figs. 3–6 in the main text is provided as an R package. The source code for this package is in simpsi_1.1.tar.gz and a Windows binary is in simpsi_1.1.zip (note that the latter should be installed as a package in R using the “Install package(s) from local zip files” menu option). The source code in simpsi_1.1.tar.gz comprises files circle.square.overlap.R, occpsi.R, runsimDDD.R, runsimHRDD.R, runsimRPSD.R, and runsimSR.R. Details of usage are provided in the help (.Rd) files, collated in simpsi-manual.pdf. Each group of simulations has its own ‘runsim’ function, as indicated on Page 2 of the manual. Actual code to perform the simulations is given as “Not run” code in each Examples section (“Not run” is used to skip these lines during automatic checking, to save time). Source code for each function may be viewed from within R once the package has been installed. The R code uses the other R packages ‘secr’, ‘abind’, and ‘spatstat’; these should be downloaded from the CRAN website (http://cran.r-project.org).
DOI: 10.6084/m9.figshare.3563763
title: Supplement 1. Annotated SAS code for random-effects resource selection models described in this paper.

description: File List Supplement_Annotated_SAS_code_Dzialak_et_al.sas -- (MD5: 8ac1ab31f6592777a2dde8c0a3b1352d) Description Annotated SAS code for random-effects resource selection models described in this paper.
DOI: 10.6084/m9.figshare.3563763.v1
title: Supplement 1. Annotated SAS code for random-effects resource selection models described in this paper.

description: File List Supplement_Annotated_SAS_code_Dzialak_et_al.sas -- (MD5: 8ac1ab31f6592777a2dde8c0a3b1352d) Description Annotated SAS code for random-effects resource selection models described in this paper.