cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.3563976.v1
title: Supplement 1. R script for analyzing the effects of environmental variables, linear features (roads, power lines, rivers) and linear feature combinations on moose step selection.

description: File List moose.r (MD5: eca62b33b44187ca6fe4c82269008b96) Description The R code includes the commands to run a basic ("mcb"), barrier ("mcr"), corridor ("mcor") and a combined ("mco") cox proportional hazard model testing for the effects of environmental variables, linear features (roads, power lines, rivers) and linear feature combinations on moose step selection. The code to calculate QIC values ("QIC.coxph", from Mathieu Basille), a customized predict function for cox proportional hazard models ("prcph2"), the code to reproduce model predictions, graphs and the code for seasonal models is also provided. The code should be run consecutively. The data file to be used with the R-script moose.r to analyze the effects of environmental variables, linear features (roads, power lines, rivers) and linear feature combinations on moose step selection may be obtained from the authors upon request. The data was derived from moose GPS relocation data collected in Central-Norway during 2006-2010. A description of the variables can be found in the R-script moose.r. Please ask the Norwegian Institute for Nature Research (NINA) for consent before using this data for publication. Contact Erling.Solberg@nina.no and Christer.Rolandsen@nina.no.<b></b>
DOI: 10.6084/m9.figshare.3563973
title: Supplement 1. R script for calculating climate stability and departure using convex hulls as was used in main manuscript for comparing the resilience of conservation networks to climate change. Script based on the Qhull program implemented in the R geometry package.

description: File List R script for calculating climate stability: ClimateStability.r (MD5: 47588dbeb7967456168c39efd6d3883c) Description This code uses a convex hull to approximate climate space for a geographical area. In the main text, the code is used to compare networks of protected areas. The code requires raster data describing climate space based on <i>n</i> variables (3 in the example: DFJTmin, JJATmax, CWD) for historic (hst) and future, and a raster of unique cell IDs for planning units within the study area. In the main text this method was used to calculate climate stability for regional networks as a whole (e.g., iNetLU, vNetLU, iNetReg, vNetReg), and also for the networks within individual landscape units. The output is a .csv table with 5 columns: "planning" the ID for the planning unit, "hullv_hst" the historic/present/current hull volume by planning unit, "hullv_fut" the future value according to a GCM scenario, "hulllv_both" the hull created by the union of future and current climate data, and "climstab" the proportion of future climate space that overlaps current climate space (stability if positive), or a unitless measure of departure (negative values). Any use of this code should reference main manuscript, Heller et al. 2015. Targeting climate diversity in conservation planning to build resilience to climate change. Ecosphere. Neither the U.S. Government, the Department of the Interior, nor the USGS, nor any of their employees, contractors, or subcontractors, make any warranty, express or implied, nor assume any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, nor represent that its use would not infringe on privately owned rights.
DOI: 10.6084/m9.figshare.3563973.v1
title: Supplement 1. R script for calculating climate stability and departure using convex hulls as was used in main manuscript for comparing the resilience of conservation networks to climate change. Script based on the Qhull program implemented in the R geometry package.

description: File List R script for calculating climate stability: ClimateStability.r (MD5: 47588dbeb7967456168c39efd6d3883c) Description This code uses a convex hull to approximate climate space for a geographical area. In the main text, the code is used to compare networks of protected areas. The code requires raster data describing climate space based on <i>n</i> variables (3 in the example: DFJTmin, JJATmax, CWD) for historic (hst) and future, and a raster of unique cell IDs for planning units within the study area. In the main text this method was used to calculate climate stability for regional networks as a whole (e.g., iNetLU, vNetLU, iNetReg, vNetReg), and also for the networks within individual landscape units. The output is a .csv table with 5 columns: "planning" the ID for the planning unit, "hullv_hst" the historic/present/current hull volume by planning unit, "hullv_fut" the future value according to a GCM scenario, "hulllv_both" the hull created by the union of future and current climate data, and "climstab" the proportion of future climate space that overlaps current climate space (stability if positive), or a unitless measure of departure (negative values). Any use of this code should reference main manuscript, Heller et al. 2015. Targeting climate diversity in conservation planning to build resilience to climate change. Ecosphere. Neither the U.S. Government, the Department of the Interior, nor the USGS, nor any of their employees, contractors, or subcontractors, make any warranty, express or implied, nor assume any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, nor represent that its use would not infringe on privately owned rights.
DOI: 10.6084/m9.figshare.3563970
title: Supplement 1. Complete raw data set used for the community composition meta-analysis.

description: File List data.csv (MD5: 2c4ca6b034a419bb8f2d3f5f2ba29c82) Description The data.csv file is a comma separated file. This file contains the complete raw data set used for the community composition meta-analysis. The supporting data includes all plants found in the study area in Plumas Co, CA. Names follow the convention in the latest edition of the Jepson Manual. ** denotes nonnative species. <pre>Fire strategies are defined as the following: </pre> <pre>R = resprouter </pre> <pre>S = obligate seeder </pre> <pre>FS = facultative seeder (grouped with resprouters for analysis)</pre> <pre>I = invader</pre> <pre>A = avoider </pre> <pre>Species with no fire regeneration strategy were omitted from analysis. </pre> <pre>Freq = the frequency of plots where this plant was observed</pre> <pre>Cover = average percent cover in a 16m circular plot </pre> <i>N</i> = The number of plots.
DOI: 10.6084/m9.figshare.3563970.v1
title: Supplement 1. Complete raw data set used for the community composition meta-analysis.

description: File List data.csv (MD5: 2c4ca6b034a419bb8f2d3f5f2ba29c82) Description The data.csv file is a comma separated file. This file contains the complete raw data set used for the community composition meta-analysis. The supporting data includes all plants found in the study area in Plumas Co, CA. Names follow the convention in the latest edition of the Jepson Manual. ** denotes nonnative species. <pre>Fire strategies are defined as the following: </pre> <pre>R = resprouter </pre> <pre>S = obligate seeder </pre> <pre>FS = facultative seeder (grouped with resprouters for analysis)</pre> <pre>I = invader</pre> <pre>A = avoider </pre> <pre>Species with no fire regeneration strategy were omitted from analysis. </pre> <pre>Freq = the frequency of plots where this plant was observed</pre> <pre>Cover = average percent cover in a 16m circular plot </pre> <i>N</i> = The number of plots.
DOI: 10.6084/m9.figshare.3563967
title: Supplement 1. Consolidated LANDIS simulation output.

description: File List out_areas_ha.txt (MD5: f779181d10cffeaea00c9ec292617462) uncert_areas_ha.txt (MD5: 95085cd805323d1d58a902be8e520901) Description out_areas_ha.txt – This file contains data on the amount of suitable area (in hectares) for each LANDIS simulation. Each row constitutes the output from a single simulation. scenario: harvest scenario (1-9) order: the harvest order (1-3) init: the initial forest conditions (1-3) year: the simulated year (1-50 in increments of 5) myso_noct: the hecatres of suitable habitat for Indiana bats during the night myso_diur: the hecatres of suitable habitat for Indiana bats during the day myso_integ: the hecatres of suitable habitat for Indiana bats during the day and night myse_noct: the hecatres of suitable habitat for northern long-eared bats during the night myse_diur: the hecatres of suitable habitat for northern long-eared bats during the day myse_integ: the hecatres of suitable habitat for northern long-eared bats during the day and night Checksum values: out_areas_ha.txt: 892 rows (with headers), 10 columns Column 6 (myso_diur): 38236869<br> Column 10 (myse_integ): 42608524 uncert_areas_ha.txt – This file contains data following the initial uncertainty analysis of model results. Uncertainty analyses are only presented for a single simulation (scenario 5, harvest order 3, initial conditions 3).Values represent when uncertainty is derived from normal (normal) or uniform (uniform) distributions. Simulations without uncertainty (mean) are the results of all simulations under scenario 5 (not just order 3 and initial conditions 3).All results only pertain to simulated year 50. scenario: harvest scenario (1-9) order: the harvest order (1-3) init: the initial forest conditions (1-3) year: the simulated year (1-50 in increments of 5) type: the source of distribution of uncertainty for (normal, uniform or mean [no uncertainty]) myso_noct: the hecatres of suitable habitat for Indiana bats during the night myso_diur: the hecatres of suitable habitat for Indiana bats during the day myso_integ: the hecatres of suitable habitat for Indiana bats during the day and night myse_noct: the hecatres of suitable habitat for northern long-eared bats during the night myse_diur: the hecatres of suitable habitat for northern long-eared bats during the day myse_integ: the hecatres of suitable habitat for northern long-eared bats during the day and night Checksum values: uncert_areas_ha.txt: 20 rows (with headers), 11 columns Column 6 (myso_noct): 975128<br> Column 10 (myse_diur): 1002880
DOI: 10.6084/m9.figshare.3563967.v1
title: Supplement 1. Consolidated LANDIS simulation output.

description: File List out_areas_ha.txt (MD5: f779181d10cffeaea00c9ec292617462) uncert_areas_ha.txt (MD5: 95085cd805323d1d58a902be8e520901) Description out_areas_ha.txt – This file contains data on the amount of suitable area (in hectares) for each LANDIS simulation. Each row constitutes the output from a single simulation. scenario: harvest scenario (1-9) order: the harvest order (1-3) init: the initial forest conditions (1-3) year: the simulated year (1-50 in increments of 5) myso_noct: the hecatres of suitable habitat for Indiana bats during the night myso_diur: the hecatres of suitable habitat for Indiana bats during the day myso_integ: the hecatres of suitable habitat for Indiana bats during the day and night myse_noct: the hecatres of suitable habitat for northern long-eared bats during the night myse_diur: the hecatres of suitable habitat for northern long-eared bats during the day myse_integ: the hecatres of suitable habitat for northern long-eared bats during the day and night Checksum values: out_areas_ha.txt: 892 rows (with headers), 10 columns Column 6 (myso_diur): 38236869<br> Column 10 (myse_integ): 42608524 uncert_areas_ha.txt – This file contains data following the initial uncertainty analysis of model results. Uncertainty analyses are only presented for a single simulation (scenario 5, harvest order 3, initial conditions 3).Values represent when uncertainty is derived from normal (normal) or uniform (uniform) distributions. Simulations without uncertainty (mean) are the results of all simulations under scenario 5 (not just order 3 and initial conditions 3).All results only pertain to simulated year 50. scenario: harvest scenario (1-9) order: the harvest order (1-3) init: the initial forest conditions (1-3) year: the simulated year (1-50 in increments of 5) type: the source of distribution of uncertainty for (normal, uniform or mean [no uncertainty]) myso_noct: the hecatres of suitable habitat for Indiana bats during the night myso_diur: the hecatres of suitable habitat for Indiana bats during the day myso_integ: the hecatres of suitable habitat for Indiana bats during the day and night myse_noct: the hecatres of suitable habitat for northern long-eared bats during the night myse_diur: the hecatres of suitable habitat for northern long-eared bats during the day myse_integ: the hecatres of suitable habitat for northern long-eared bats during the day and night Checksum values: uncert_areas_ha.txt: 20 rows (with headers), 11 columns Column 6 (myso_noct): 975128<br> Column 10 (myse_diur): 1002880
DOI: 10.6084/m9.figshare.3563964
title: Supplement 1. Production and biomass data compiled from the literature.

description: File List populations.csv (MD5: fa2c0597d805469ca642465722914e13) assemblages.csv (MD5: 10d4b0e6fd3a89b7c0ae1491eac44ecc) ecosystems_and_biomes.csv (MD5: 818eb15ef8c64c0193a7ef9a26e72b56) Description populations.csv includes the following columns for data sets assigned to the population hierarchical level: entry number, category (e.g., terrestrial plants), data source, species identifier, details from the source, P (g/m²/y), B (g/m²). Blank rows separate analyzed categories. assemblages.csv includes different data columns per analyzed set. For example, grasslands data from Scurlock et al. (2003) inclue two measures of P and two measures of B, whereas other data sets include one of each. See headings at the top of each data set, where blank rows separate analyzed data sets. Otherwise, information provided is similar to that in populations.csv ecosystems_and_biomes.csv includes the following columns for ecosystems and biome data sets: entry number, system dewscriptor (e.g., polar barren), P (g/m²/y), B (g/m²). Sources are listed as subheadings for each data set, and blank rows separate analyzed data sets.
DOI: 10.6084/m9.figshare.3563964.v1
title: Supplement 1. Production and biomass data compiled from the literature.

description: File List populations.csv (MD5: fa2c0597d805469ca642465722914e13) assemblages.csv (MD5: 10d4b0e6fd3a89b7c0ae1491eac44ecc) ecosystems_and_biomes.csv (MD5: 818eb15ef8c64c0193a7ef9a26e72b56) Description populations.csv includes the following columns for data sets assigned to the population hierarchical level: entry number, category (e.g., terrestrial plants), data source, species identifier, details from the source, P (g/m²/y), B (g/m²). Blank rows separate analyzed categories. assemblages.csv includes different data columns per analyzed set. For example, grasslands data from Scurlock et al. (2003) inclue two measures of P and two measures of B, whereas other data sets include one of each. See headings at the top of each data set, where blank rows separate analyzed data sets. Otherwise, information provided is similar to that in populations.csv ecosystems_and_biomes.csv includes the following columns for ecosystems and biome data sets: entry number, system dewscriptor (e.g., polar barren), P (g/m²/y), B (g/m²). Sources are listed as subheadings for each data set, and blank rows separate analyzed data sets.
DOI: 10.6084/m9.figshare.3563961
title: Supplement 1. Standard 0/1 NEXUS-PAE input.

description: File List PAE.nex (MD5: fe45a65c0e5ebce184835ce869f2d783) Description The commented list of 257 presumably alien species of LVV and their occurrence coded on 0/1 standard NEXUS-PAE input. File must be executed in PAUP* 4.0 or higher.
DOI: 10.6084/m9.figshare.3563961.v1
title: Supplement 1. Standard 0/1 NEXUS-PAE input.

description: File List PAE.nex (MD5: fe45a65c0e5ebce184835ce869f2d783) Description The commented list of 257 presumably alien species of LVV and their occurrence coded on 0/1 standard NEXUS-PAE input. File must be executed in PAUP* 4.0 or higher.
DOI: 10.6084/m9.figshare.3563949
title: Supplement 1. Data files used to generate occupancy models in the main text.

description: File List Appendix1_data_covariates.csv (MD5: c858dce6372990cf1033a5ff8201d006) Appendix1_data_occupancy.csv (MD5: 684e75646bdbb00309b12cfa5d2d2c4c) Description "Appendix1_data_covariates.csv" contains the values of the covariates used in the occupancy modeling. "Appendix1_data_occupancy.csv" contains the occupancy data for the two fish species in each of the springs. Values of 0 represent instances where the species was not recorded during the survey and values of 1 represent presence of the species during the survey. Missing data are denoted with the symbol "-". Occupancy data 2009–2013 was collected by Adam Kerezsy. Data is published in this manuscript by permission. While we're usually happy to share data, please contact the authors if you'd like to use our data so that we can provide the appropriate citation.
DOI: 10.6084/m9.figshare.3563949.v1
title: Supplement 1. Data files used to generate occupancy models in the main text.

description: File List Appendix1_data_covariates.csv (MD5: c858dce6372990cf1033a5ff8201d006) Appendix1_data_occupancy.csv (MD5: 684e75646bdbb00309b12cfa5d2d2c4c) Description "Appendix1_data_covariates.csv" contains the values of the covariates used in the occupancy modeling. "Appendix1_data_occupancy.csv" contains the occupancy data for the two fish species in each of the springs. Values of 0 represent instances where the species was not recorded during the survey and values of 1 represent presence of the species during the survey. Missing data are denoted with the symbol "-". Occupancy data 2009–2013 was collected by Adam Kerezsy. Data is published in this manuscript by permission. While we're usually happy to share data, please contact the authors if you'd like to use our data so that we can provide the appropriate citation.
DOI: 10.6084/m9.figshare.3563946
title: Supplement 1. R code for simulating long-term soil carbon pools.

description: File List myc_ode.txt (MD5: 177ce2ec5175d7a58be65aca79cba19f) myc_solver.txt (MD5: 117ec7041a594a4ca7201108bc75d0dc) myc_perturbation.txt (MD5: 3642ff50200485b7da497667baa86559) Description The MATLAB script myc_ode.txt contains a function to be called by MATLAB ODE solver including the ODE equations. The MATLAB script myc_solver.txt contains source code used to solve the ODE equations in myc_ode.txt. The MATLAB script myc_perturbation.txt contains perturbation experiments for parameters and derives solutions analytically.
DOI: 10.6084/m9.figshare.3563946.v1
title: Supplement 1. R code for simulating long-term soil carbon pools.

description: File List myc_ode.txt (MD5: 177ce2ec5175d7a58be65aca79cba19f) myc_solver.txt (MD5: 117ec7041a594a4ca7201108bc75d0dc) myc_perturbation.txt (MD5: 3642ff50200485b7da497667baa86559) Description The MATLAB script myc_ode.txt contains a function to be called by MATLAB ODE solver including the ODE equations. The MATLAB script myc_solver.txt contains source code used to solve the ODE equations in myc_ode.txt. The MATLAB script myc_perturbation.txt contains perturbation experiments for parameters and derives solutions analytically.
DOI: 10.6084/m9.figshare.3563934
title: Full Archive

description: Full Archive
DOI: 10.6084/m9.figshare.3563934.v1
title: Full Archive

description: Full Archive
DOI: 10.6084/m9.figshare.3563928
title: Supplement 1. R codes for calculating population growth rate (lambda) and 95% confidence limits described in the main text.

description: File List Mead's_milkweed_lambda_with_CI_burn.R (MD5: 8cecc81cb0f92ebd62950a478ce1c9a1) Mead's_milkweed_lambda_with_CI_cross.R (MD5: 31438524f215f758aff42ba02f995194) Description The R code included in "Mead's milkweed lambda with CI burn.R" and "Mead's milkweed lambda with CI cross.R" includes transition matrices and calculates population growth rate (lambda) and 95% confidence limits for the transition matrices described in the main text.
DOI: 10.6084/m9.figshare.3563922
title: Supplement 1. Code developed to measure riparian buffer width, vegetation height and canopy cover along stream reaches.

description: File List <b>All files at once:</b> Code.zip funCalcStreamLength.m funCalcTransectBufferWidth2.m funCalculateMEI.m funCalculateNewXY.m funCalculatePointsAlongLine.m funCalculatePointsAlongLine_Reverse.m funCalculatepointsAlongLine_Reverse_TransectCenter.m funCalculateRectangleVertices.m funFindStreamGrdLidarExtent.m funFindStreamVeglidarExtent.m funPrctile.m funSampleLidar.m funSampleLidar_forestClass.m funSplitStreams.m ProduceFinalTransectData_ForAnalysis.m SampleStreambufferWidth_Mar31_2012_Oct2014_Publish.m Slidefun.m Description The ProduceFinalTransectData_ForAnalysis.m and SampleStreambufferWidth_Mar31_2012_Oct2014_Publish.m files are the core code used to process all of the LiDAR data presented in this manuscript. All other code included in this supplement are functions that are called within the two main scripts. Please be sure to save all code files in the same directory. The inputs to both scripts are detailed in the top matter of the scripts. First, run SampleStreambufferWidth_Mar31_2012_Oct2014_Publish.m. The inputs for this script include ground and non-ground lidar point clouds saved in the "lidarData" directory and a streams data saved in x,y,z format. Please see the code for documentation and examples of all inputs. Other input variables can be defined in the top matter of the code or you may choose to use the default values. All final output will be saved in the "finalResults" folder – included as an empty directory in this supplement. This script will do several things. First it will segment out your stream data into 1,000.01m long reaches. Analyzing smaller segments of stream reduces the file processing sizes and time. Next, the code will use the stream segments to calculate transects within which the lidar data will be analyzed. The final output will be data for each transect that includes its width, and vegetation structure within moving windows as defined in the algorithm. The second script, ProduceFinalTransectData_ForAnalysis.m, takes all of the output from the first script and generate a final matrix that can be used to perform final statistical analysis. This code uses the results in the finalResults folder.
DOI: 10.6084/m9.figshare.3563928.v1
title: Supplement 1. R codes for calculating population growth rate (lambda) and 95% confidence limits described in the main text.

description: File List Mead's_milkweed_lambda_with_CI_burn.R (MD5: 8cecc81cb0f92ebd62950a478ce1c9a1) Mead's_milkweed_lambda_with_CI_cross.R (MD5: 31438524f215f758aff42ba02f995194) Description The R code included in "Mead's milkweed lambda with CI burn.R" and "Mead's milkweed lambda with CI cross.R" includes transition matrices and calculates population growth rate (lambda) and 95% confidence limits for the transition matrices described in the main text.
DOI: 10.6084/m9.figshare.3563916
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.