cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.3563916.v1
title: Supplement 1. R scripts for fitting the base- and full-model structures to sample data.

description: File List run_base_full_models.R (MD5: adc930d6094b52c44838c1530c662920) base_model.txt (MD5: 36b6b952b0c31b1921cee7a53e48b83a) full_model.txt (MD5: 211e7b9afd6bc402f307653661e168ab) jags_data.Rd (MD5: b10e52de46c11eeb2477960681a631e6) viterbi_funs.R (MD5: 7d6e8db0684738d8ea1722c95b8c7eba) Description The code included in run_base_full_models.R fits the 'base' and 'full' models described in the main text (Table 2). The base model (Model structure 2) is fitted with 5 states and fixed step length (FS). The full model (Model structure 9) is fitted with 6 states and time-varying step length (TS). The script then calls Viterbi_funs.R to estimate the Viterbi sequence of states based upon the posterior state-dependent parameter estimates. Running the script requires jags software (freely available at http://mcmc-jags.sourceforge.net/) and the r package 'R2jags'. Sample data, priors, and example R2jags fitting objects are supplied in jags_data.Rd which can be directly loaded into r. The text files contain the model structures in jags script. To run the script, place all the files into your r working directory.
DOI: 10.6084/m9.figshare.3563907
title: Supplement 1. R script and data to reproduce analyses of big sagebrush regeneration at leading and trailing edges of distribution shifts.

description: File List README_Database.txt (MD5: 2c8929073dc6a5de187db7970acdc78f) dbTables_Schlaepfer_BigSagebrushRegeneration_LeadingTrailingEdges.sqlite3.zip (MD5: 62f6e606c8cfcdb441aa518981676590) Rcode_Schlaepfer_BigSagebrushRegeneration_LeadingTrailingEdges.R (MD5: b1f2270ca68cc645e5a9cbd5f6fb958e) Description README_Database.txt – Metadata explaining tables and fields of the database<br> dbTables_Schlaepfer_BigSagebrushRegeneration_LeadingTrailingEdges.sqlite3.zip – SQL-lite database containing regenerations simulation results required for analyses<br> Rcode_Schlaepfer_BigSagebrushRegeneration_LeadingTrailingEdges.R – R code accessing and analyzing data contained in database
DOI: 10.6084/m9.figshare.3563907.v1
title: Supplement 1. R script and data to reproduce analyses of big sagebrush regeneration at leading and trailing edges of distribution shifts.

description: File List README_Database.txt (MD5: 2c8929073dc6a5de187db7970acdc78f) dbTables_Schlaepfer_BigSagebrushRegeneration_LeadingTrailingEdges.sqlite3.zip (MD5: 62f6e606c8cfcdb441aa518981676590) Rcode_Schlaepfer_BigSagebrushRegeneration_LeadingTrailingEdges.R (MD5: b1f2270ca68cc645e5a9cbd5f6fb958e) Description README_Database.txt – Metadata explaining tables and fields of the database<br> dbTables_Schlaepfer_BigSagebrushRegeneration_LeadingTrailingEdges.sqlite3.zip – SQL-lite database containing regenerations simulation results required for analyses<br> Rcode_Schlaepfer_BigSagebrushRegeneration_LeadingTrailingEdges.R – R code accessing and analyzing data contained in database
DOI: 10.6084/m9.figshare.3563904
title: Supplement 1. Demonstration Mathematica code used to generate the model-based figures for this paper, together with Matlab code used in exploratory analyses.

description: File List Figure3.nb (MD5: 20d1058537efec4c066cefc4eb5c5a62) Demo code for Figures 4 and 5.nb (MD5: c01d41b592a5722a95f9d94c4f3114cb) Figure6.nb (MD5: b954500fa5fdb7741bd7861138ca61f3) runmodel.m (MD5: 9a8b5b6e18dee79265f74c36d175075f) Derivsub_ratio.m (MD5: 7fd3899e02de98977cdb02e9faee3013) Description Figure 3.nb – Mathematica code used to generate the isoclines shown in Fig. 3. Demo code for Figures 4 and 5.nb – Example Mathematica code used to generate the equilibria shown in Figs. 4 and 5; to generate the other equilibria, use the appropriate parameter combinations Figure 6.nb – Mathematica code used to generate Fig. 6 and related figures used in exploring the joint effects of the maximum P recycling rate <i>r</i> and the loss rate <i>s</i>. Runmodel.m – Demonstration Matlab code used to explore model results using simulations Derivsub_ratio.m – sub program called by runmodel.m
DOI: 10.6084/m9.figshare.3563901
title: Supplement 1. Recruitment facilitation model with source code and executable file for readers to use.

description: File List Mussel_Recruitment_Model_description_and_source_code_June_2014.txt (MD5: e29281a06751245eda695b7b678b2243) mussel_recruitment_model.exe (MD5: 0eee8f1d904aca658f2accbe8b13d636) Description The Recruitment Facilitation Model allows the user to choose from four distinctly paired rules called "Edge" (recruitment facilitation at patch edge), "No Edge" (recruitment at any location), "Growth" (recruitment with bed growth), and "No Growth" (recruitment without bed growth), creating four possible scenarios. If desired, each of the four rules may be applied to a mussel bed where the user can spatially structure the probability of recruitment success, creating four additional scenarios.
DOI: 10.6084/m9.figshare.3563901.v1
title: Supplement 1. Recruitment facilitation model with source code and executable file for readers to use.

description: File List Mussel_Recruitment_Model_description_and_source_code_June_2014.txt (MD5: e29281a06751245eda695b7b678b2243) mussel_recruitment_model.exe (MD5: 0eee8f1d904aca658f2accbe8b13d636) Description The Recruitment Facilitation Model allows the user to choose from four distinctly paired rules called "Edge" (recruitment facilitation at patch edge), "No Edge" (recruitment at any location), "Growth" (recruitment with bed growth), and "No Growth" (recruitment without bed growth), creating four possible scenarios. If desired, each of the four rules may be applied to a mussel bed where the user can spatially structure the probability of recruitment success, creating four additional scenarios.
DOI: 10.6084/m9.figshare.3563898
title: Supplement 1. Congressional districts and climate change projections.

description: File List landcover.csv (MD5: 96d9e0839642848c691bfbc0715544ba) Description Comma separated value file of congressional districts, the area (ha) of each landcover category within each district, and the projected mean annual temperature increase (ºC) and annual precipitation change (±%) from the ensemble average model.
DOI: 10.6084/m9.figshare.3563898.v1
title: Supplement 1. Congressional districts and climate change projections.

description: File List landcover.csv (MD5: 96d9e0839642848c691bfbc0715544ba) Description Comma separated value file of congressional districts, the area (ha) of each landcover category within each district, and the projected mean annual temperature increase (ºC) and annual precipitation change (±%) from the ensemble average model.
DOI: 10.6084/m9.figshare.3563895
title: Supplement 1. Habitat breadth indices for 22 330 terrestrial vertebrate species.

description: File List Habitat_breadth_index.txt (MD5: a1b4363c509cb7e5204d6578fc059fcd) Description Habitat breadth indices calculated for 22 330 terrestrial vertebrates. Both the multiplicative and additive beta diversity indices are given, calculated within and among classes. Habitat breadth estimated as the total number of habitat subtypes in which a species occurs, based on the 101 habitat subtypes described by the IUCN is also given, as well as the number of habitat categories occupied by each species, with habitat types classified according to Bennett and Owens (2002), constructed by clustering IUCN habitat types into their eight proposed groups.
DOI: 10.6084/m9.figshare.3563892
title: Supplement 1. R scripts and species lists for plants and pollinators.

description: File List R_function.txt (MD5: 3fc8d9510f355a5569ed1adf92c0f9c6) Second_R_function.txt (MD5: 15a475fa05e1bfa615bbb674166a39f8) Pollinator_species_list.csv (MD5: 791e68b4b9ff6ccf79b0831a1695bd89) Plant_species_list.csv (MD5: a958fc22ec3eca1d4d6384dbcda06bc4) Description <b>R_function.txt</b><b>: </b>R function modified from Poisot et al. (2012) used to calculate partitions of interaction turnover according to Novotny (2009). <b>Second_R_function.txt</b><b>:</b> Second R function modified from Poisot et al. (2012), used for comparing more than two matrices <b>Pollinator_species_list.csv</b><b>:</b> Pollinator species list by site. Numbers represent the weeks (1-9) during which the pollinator was present at the site <b>Plant_species_list.csv</b><b>: </b>Plant species list by site, and results for sampling completeness. Numbers represent the weeks (1–9) during which the species was present at the site. S<sub>Obs</sub> is the visiting pollinator richness for each plant species, and S<sub>Est</sub> is the estimated visiting pollinator richness as calculated by the Chao estimator.
DOI: 10.6084/m9.figshare.3563889
title: Supplement 1. R code for piecewise regression.

description: File List R_code_for_piecewise_regression.r (MD5: 56713e387417b7364390e6095dad8fab) Description Piecewise regression is a statistical method for estimationg two slopes in a series of data, one slope before a breakpoint and one slope after the breakpoint. The breakpoint can be fixed or estimated. Fixed breakpoints are based on changes in the environment. However, estimated breakpoints are necessary in competition studies, because an increase in one competitor must occur before the decline in the other competitor(s). Estimated breakpoints can determine, without bias, if the declines occurred after the increase.
DOI: 10.6084/m9.figshare.3563886
title: Supplement 1. BUGS code for implementing the hierarchical spatially-varying threshold model described in the text.

description: File List BUGS_code.txt (MD5: b46c68b26ad9085375d718ceb9d9213b)Description BUGS code for fitting a hierarchical Bayesian threshold model.
DOI: 10.6084/m9.figshare.3563886.v1
title: Supplement 1. BUGS code for implementing the hierarchical spatially-varying threshold model described in the text.

description: File List BUGS_code.txt (MD5: b46c68b26ad9085375d718ceb9d9213b)Description BUGS code for fitting a hierarchical Bayesian threshold model.
DOI: 10.6084/m9.figshare.3563877
title: Supplement 1. EID-ABM pseudocode and Java source code for the EID-ABM.

description: File List eidabm-pseudocode.txt (md5: c034df6b8e38ec2df333e5844e59ba46) eidabm_source_code.zip (md5: 2a482ae7ec765859022d8f1aaf239c76) Description eidabm-pseudocode.txt – Pseudocode for the most important methods in the EID-ABM. eidabm_source_code.zip – Java source code and executable JAR files for the EID-ABM.
DOI: 10.6084/m9.figshare.3563877.v1
title: Supplement 1. EID-ABM pseudocode and Java source code for the EID-ABM.

description: File List eidabm-pseudocode.txt (md5: c034df6b8e38ec2df333e5844e59ba46) eidabm_source_code.zip (md5: 2a482ae7ec765859022d8f1aaf239c76) Description eidabm-pseudocode.txt – Pseudocode for the most important methods in the EID-ABM. eidabm_source_code.zip – Java source code and executable JAR files for the EID-ABM.
DOI: 10.6084/m9.figshare.3563874
title: Supplement 1. Raw stable isotope values in bat fur.

description: File List Raw_stable_isotope_in_bat_fur.txt (MD5: 8139421e6c0c1f7a76831e45796158c8) Description Raw stable isotope values (Δ<sup>13</sup>C, Δ<sup>15</sup>N, and Δ<sup>2</sup>H) in bat fur collected from fatalities found at a wind farm in south-western Alberta, Canada.
DOI: 10.6084/m9.figshare.3563871
title: Supplement 1. R scripts and an example data set for conducting the power analysis simulations described in the main text.

description: File List Miller_and_Mitchell_Power_Analysis_Code.r (md5: e0161858aaaeac3e81a2c755640b9feb) Tree_BA.csv (md5: e7e52c80094e578260cc7d4bade00208) Description Miller_and_Mitchell_Power_Analysis_Code.R - This script runs a bootstrap power analysis based on a mixed effects model of sample data (plot is the random effect, time and site (park) are fixed effects). The simulation determines power to detect a uniform percentage per sampling cycle change in the value of a metric as a linear trend in a mixed-effects model. The sample sizes tested by the script do not have to be the same as the number of samples in the data file; any desired number of samples will be bootstrapped from the actual data. The script will report power for a uniform trend across all parks (model with no interaction), as well as power for a trend that occurs only at one park (model with an interaction effect, where simulated effect occurs and power is tested for each park in turn). This script requires a comma delimited (.csv) file with the following headings: ID (unique alphanumeric value for each row of data; does not need to be called "ID") Plot (text, not numeric only, e.g.: "ACAD1" not "1") Park (text) Year (year of sample, numeric) Metric (metric to be evaluated, numeric) The data in the file must have two measurements for each plot, with the initial measurement of all plots collected prior to any second measurements (separate data collection cycles). Tree_BA.csv – This is an example of the data sets used for the power analysis in this article. Data sets must be formatted as demonstrated in this data set for the simulation to work properly.
DOI: 10.6084/m9.figshare.3563871.v1
title: Supplement 1. R scripts and an example data set for conducting the power analysis simulations described in the main text.

description: File List Miller_and_Mitchell_Power_Analysis_Code.r (md5: e0161858aaaeac3e81a2c755640b9feb) Tree_BA.csv (md5: e7e52c80094e578260cc7d4bade00208) Description Miller_and_Mitchell_Power_Analysis_Code.R - This script runs a bootstrap power analysis based on a mixed effects model of sample data (plot is the random effect, time and site (park) are fixed effects). The simulation determines power to detect a uniform percentage per sampling cycle change in the value of a metric as a linear trend in a mixed-effects model. The sample sizes tested by the script do not have to be the same as the number of samples in the data file; any desired number of samples will be bootstrapped from the actual data. The script will report power for a uniform trend across all parks (model with no interaction), as well as power for a trend that occurs only at one park (model with an interaction effect, where simulated effect occurs and power is tested for each park in turn). This script requires a comma delimited (.csv) file with the following headings: ID (unique alphanumeric value for each row of data; does not need to be called "ID") Plot (text, not numeric only, e.g.: "ACAD1" not "1") Park (text) Year (year of sample, numeric) Metric (metric to be evaluated, numeric) The data in the file must have two measurements for each plot, with the initial measurement of all plots collected prior to any second measurements (separate data collection cycles). Tree_BA.csv – This is an example of the data sets used for the power analysis in this article. Data sets must be formatted as demonstrated in this data set for the simulation to work properly.
DOI: 10.6084/m9.figshare.3563868
title: Supplement 1. Octave (or Matlab) program for identifying intraguild predation (IGP) motifs within the Atlantis-SE model food web.

description: File List diet_matrix_analysis.m (MD5: f62e3db6ea7bb79c394f53b6bd4e6d8c) Description The Octave (or Matlab) script below identifies which species in the "Southeast Atlantis" food web are potentially part of an intraguild predation (IGP) motif.
DOI: 10.6084/m9.figshare.3563868.v1
title: Supplement 1. Octave (or Matlab) program for identifying intraguild predation (IGP) motifs within the Atlantis-SE model food web.

description: File List diet_matrix_analysis.m (MD5: f62e3db6ea7bb79c394f53b6bd4e6d8c) Description The Octave (or Matlab) script below identifies which species in the "Southeast Atlantis" food web are potentially part of an intraguild predation (IGP) motif.
DOI: 10.6084/m9.figshare.3563865
title: Supplement 1. R functions for fitting the LOBAG and LOBAG-OC models.

description: File List lobag.R (MD5: 297094eda0d98965c144121e7849ea6b) Description The file lobag.R contains code for four functions used to fit and predict species' ecological niches using the LOBAG and LOBAG-OC models. Functions depend on the kernlab package. Function arguments include covariate data at presence points (LOBAG and LOBAG-OC) and background points (LOBAG only) and various tuning parameters, as described in the main text.
DOI: 10.6084/m9.figshare.3563862
title: Supplement 1. R scripts for conducting clear sky PAR simulations described in the main text.

description: File List Clear Sky PAR Model.txt (MD5: 487aee68581be90fc60476a2c3af1b2d) Description The code included in <i>Clear Sky PAR Model.R</i> allows conducting the model of clear sky PAR used for calibration of PAR sensors as described in the methods section of the main text.
DOI: 10.6084/m9.figshare.3563862.v1
title: Supplement 1. R scripts for conducting clear sky PAR simulations described in the main text.

description: File List Clear Sky PAR Model.txt (MD5: 487aee68581be90fc60476a2c3af1b2d) Description The code included in <i>Clear Sky PAR Model.R</i> allows conducting the model of clear sky PAR used for calibration of PAR sensors as described in the methods section of the main text.
DOI: 10.6084/m9.figshare.3563859
title: Supplement 1. Sample files for replicating the simulations in the main text.

description: File List trapsimulation.zip (MD5: 64ca08cfdd7b95ed27daf3520b6b3b56) DriftFenceSimulatorManual.pdf (MD5: 74cd6e765351de9a1e1254a7c06a274b) arrangement.sdl (MD5: 9087569919359bcaf9efedd378913add) grid.sdl (MD5: 0971fedb87b770f1804ec43f1be72ca7) arrangement.db (MD5: bde4824ab4922ea555851f1f7f0785de) trapsimulation-1.0-SNAPSHOT.jar (MD5: 4e407840e718deac1f457d491497c306) Description Java code in a hierarchical directory structure with installation instructions for the simulations are provided in a zip file (trapsimulation.zip). A user manual (DriftFenceSimulator.pdf) contains an example parameter file to define movement behaviors and fence arrangements, plus R scripts to load and manipulate the simulation results in preparation for analysis or plotting. Sample parameter files (arrangement.sdl, grid.sdl) allow replication the simulations in the main text. A sample output database for fence arrangement test (arrangement.db) allows for further exploration of the impacts of fence arrangements on success rates. An executable Java file (trapsimulation-1.0-SNAPSHOT.jar) allows the simulations in the main text to be replicated and new designs to be tested.
DOI: 10.6084/m9.figshare.3563859.v1
title: Supplement 1. Sample files for replicating the simulations in the main text.

description: File List trapsimulation.zip (MD5: 64ca08cfdd7b95ed27daf3520b6b3b56) DriftFenceSimulatorManual.pdf (MD5: 74cd6e765351de9a1e1254a7c06a274b) arrangement.sdl (MD5: 9087569919359bcaf9efedd378913add) grid.sdl (MD5: 0971fedb87b770f1804ec43f1be72ca7) arrangement.db (MD5: bde4824ab4922ea555851f1f7f0785de) trapsimulation-1.0-SNAPSHOT.jar (MD5: 4e407840e718deac1f457d491497c306) Description Java code in a hierarchical directory structure with installation instructions for the simulations are provided in a zip file (trapsimulation.zip). A user manual (DriftFenceSimulator.pdf) contains an example parameter file to define movement behaviors and fence arrangements, plus R scripts to load and manipulate the simulation results in preparation for analysis or plotting. Sample parameter files (arrangement.sdl, grid.sdl) allow replication the simulations in the main text. A sample output database for fence arrangement test (arrangement.db) allows for further exploration of the impacts of fence arrangements on success rates. An executable Java file (trapsimulation-1.0-SNAPSHOT.jar) allows the simulations in the main text to be replicated and new designs to be tested.
DOI: 10.6084/m9.figshare.3563856
title: Supplement 1. R scripts for simulations, data analysis and manuscript figures.

description: File List Resource_competition_simulations.R (MD5: 252c212ecc079584192f9d89e836dd0f) Resource_competition_deterministic_results.R (MD5: 786ca62c9b4887ba959ccfade7982ee6) Resource_competition_complete_data_analysis_and_manuscript_figures.R (MD5: 40a3ab795483cece902a4395ee1645b8) Description The code included in Resource_competition_simulations.R allows conduction the simulation as described in the Methods of the main text. The code in Resource_competition_deterministic_results.R solves the deterministic system of equations for the parameter combinations used in the simulations. The code in Resource_competition_data_analysis_and_manuscript_figures.R carries out analysis of the tail distribution of extinction times and the boosted regression tree models on the output from Resource_competition_simulations.R and Resource_competition_deterministic_results.R. Code constructing the manuscript figures from these analyses is also included in this file.
DOI: 10.6084/m9.figshare.3563856.v1
title: Supplement 1. R scripts for simulations, data analysis and manuscript figures.

description: File List Resource_competition_simulations.R (MD5: 252c212ecc079584192f9d89e836dd0f) Resource_competition_deterministic_results.R (MD5: 786ca62c9b4887ba959ccfade7982ee6) Resource_competition_complete_data_analysis_and_manuscript_figures.R (MD5: 40a3ab795483cece902a4395ee1645b8) Description The code included in Resource_competition_simulations.R allows conduction the simulation as described in the Methods of the main text. The code in Resource_competition_deterministic_results.R solves the deterministic system of equations for the parameter combinations used in the simulations. The code in Resource_competition_data_analysis_and_manuscript_figures.R carries out analysis of the tail distribution of extinction times and the boosted regression tree models on the output from Resource_competition_simulations.R and Resource_competition_deterministic_results.R. Code constructing the manuscript figures from these analyses is also included in this file.
DOI: 10.6084/m9.figshare.3563853
title: Supplement 1. Soil and site properties along four transects as described in the main text.

description: File List Soil_and_Site_properties.txt (MD5: c1689581af48bd6bbb82724b7c93180f) Description This tab-delineated file is best viewed in Excel. Rows represent individual soil samples locations, and each column represents a site or soil property.
DOI: 10.6084/m9.figshare.3563853.v1
title: Supplement 1. Soil and site properties along four transects as described in the main text.

description: File List Soil_and_Site_properties.txt (MD5: c1689581af48bd6bbb82724b7c93180f) Description This tab-delineated file is best viewed in Excel. Rows represent individual soil samples locations, and each column represents a site or soil property.
DOI: 10.6084/m9.figshare.3563850
title: Supplement 1. Raw invertebrate density (count/g of litter) for all invertebrates encountered in soil samples on control and treatment plots during the time period of the study (2007–2009) separated by year (Year 1, Year 2), treatment of plot (C, T), and time interval sampled (T0–T5) for further analysis as needed.

description: File List Best_Welsh_2013.txt (MD5: 17197a3338e77a2ff0f726f3896c1957) Description The data included in Best_Welsh_2013.txt enables readers to analyze any assortment of invertebrate density values across: year, time interval, control/treatment (or any combination therein) including any breakdown of invertebrate taxa; individual taxa (family or order), functional group, or size classes. These data may be analyzed using R, SAS, NCSS, or any other preferred statistical software.
DOI: 10.6084/m9.figshare.3563850.v1
title: Supplement 1. Raw invertebrate density (count/g of litter) for all invertebrates encountered in soil samples on control and treatment plots during the time period of the study (2007–2009) separated by year (Year 1, Year 2), treatment of plot (C, T), and time interval sampled (T0–T5) for further analysis as needed.

description: File List Best_Welsh_2013.txt (MD5: 17197a3338e77a2ff0f726f3896c1957) Description The data included in Best_Welsh_2013.txt enables readers to analyze any assortment of invertebrate density values across: year, time interval, control/treatment (or any combination therein) including any breakdown of invertebrate taxa; individual taxa (family or order), functional group, or size classes. These data may be analyzed using R, SAS, NCSS, or any other preferred statistical software.