cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.3520649.v1
title: Appendix B. Non-raptor species English name, taxonomic name, migration guild, and number of individuals observed (abundance) during autumn migration surveys along Lake Superior’s northern coast 2009–2010.

description: Non-raptor species English name, taxonomic name, migration guild, and number of individuals observed (abundance) during autumn migration surveys along Lake Superior’s northern coast 2009–2010.
DOI: 10.6084/m9.figshare.3520646
title: Appendix A. All bird species detected and their abundances from 2011–2013 at the Tallgrass Prairie Preserve, Oklahoma, USA.

description: All bird species detected and their abundances from 2011–2013 at the Tallgrass Prairie Preserve, Oklahoma, USA.
DOI: 10.6084/m9.figshare.3520643
title: Appendix A. Detailed sampling protocols 2011 and 2012.

description: Detailed sampling protocols 2011 and 2012.
DOI: 10.6084/m9.figshare.3520640
title: Appendix B. Maps of experimental soybean fields in Wisconsin and Michigan.

description: Maps of experimental soybean fields in Wisconsin and Michigan.
DOI: 10.6084/m9.figshare.3520643.v1
title: Appendix A. Detailed sampling protocols 2011 and 2012.

description: Detailed sampling protocols 2011 and 2012.
DOI: 10.6084/m9.figshare.3520640.v1
title: Appendix B. Maps of experimental soybean fields in Wisconsin and Michigan.

description: Maps of experimental soybean fields in Wisconsin and Michigan.
DOI: 10.6084/m9.figshare.3520637
title: Appendix C. Figure showing the temporal variation of soybean aphids and natural enemies in 2011 and 2012.

description: Figure showing the temporal variation of soybean aphids and natural enemies in 2011 and 2012.
DOI: 10.6084/m9.figshare.3520637.v1
title: Appendix C. Figure showing the temporal variation of soybean aphids and natural enemies in 2011 and 2012.

description: Figure showing the temporal variation of soybean aphids and natural enemies in 2011 and 2012.
DOI: 10.6084/m9.figshare.3520634
title: Appendix D. Figure of the landscape gradients of our experimental soybean sites in 2011 and 2012.

description: Figure of the landscape gradients of our experimental soybean sites in 2011 and 2012.
DOI: 10.6084/m9.figshare.3520634.v1
title: Appendix D. Figure of the landscape gradients of our experimental soybean sites in 2011 and 2012.

description: Figure of the landscape gradients of our experimental soybean sites in 2011 and 2012.
DOI: 10.6084/m9.figshare.3520631
title: Appendix E. Figure of the yearly variation in natural enemy abundances and soybean yield gain.

description: Figure of the yearly variation in natural enemy abundances and soybean yield gain.
DOI: 10.6084/m9.figshare.3520631.v1
title: Appendix E. Figure of the yearly variation in natural enemy abundances and soybean yield gain.

description: Figure of the yearly variation in natural enemy abundances and soybean yield gain.
DOI: 10.6084/m9.figshare.3520628
title: Appendix F. Table with the bootstrapped standardized and unstandardized path coefficients for the SEM models.

description: Table with the bootstrapped standardized and unstandardized path coefficients for the SEM models.
DOI: 10.6084/m9.figshare.3520628.v1
title: Appendix F. Table with the bootstrapped standardized and unstandardized path coefficients for the SEM models.

description: Table with the bootstrapped standardized and unstandardized path coefficients for the SEM models.
DOI: 10.6084/m9.figshare.3520625
title: Appendix G. Figure showing the nonlinear relationship between habitat diversity and proportion soybean in the landscape.

description: Figure showing the nonlinear relationship between habitat diversity and proportion soybean in the landscape.
DOI: 10.6084/m9.figshare.3520625.v1
title: Appendix G. Figure showing the nonlinear relationship between habitat diversity and proportion soybean in the landscape.

description: Figure showing the nonlinear relationship between habitat diversity and proportion soybean in the landscape.
DOI: 10.6084/m9.figshare.3520622
title: Appendix H. Figure showing the nonlinear relationship between aphid abundance and soybean yield in 2011 and 2012.

description: Figure showing the nonlinear relationship between aphid abundance and soybean yield in 2011 and 2012.
DOI: 10.6084/m9.figshare.3520622.v1
title: Appendix H. Figure showing the nonlinear relationship between aphid abundance and soybean yield in 2011 and 2012.

description: Figure showing the nonlinear relationship between aphid abundance and soybean yield in 2011 and 2012.
DOI: 10.6084/m9.figshare.3520619.v1
title: Appendix A. Definitions of covariates and covariate combinations (groups) used to model survival and recapture probabilities of southern Beaufort Sea polar bears.

description: Definitions of covariates and covariate combinations (groups) used to model survival and recapture probabilities of southern Beaufort Sea polar bears.
DOI: 10.6084/m9.figshare.3520616
title: Appendix B. Southern Beaufort Sea polar bear capture statistics.

description: Southern Beaufort Sea polar bear capture statistics.
DOI: 10.6084/m9.figshare.3520613
title: Appendix C. Model-averaged estimates of recapture probabilities for southern Beaufort Sea polar bears and total model weights associated with recapture probability covariate structures.

description: Model-averaged estimates of recapture probabilities for southern Beaufort Sea polar bears and total model weights associated with recapture probability covariate structures.
DOI: 10.6084/m9.figshare.3520613.v1
title: Appendix C. Model-averaged estimates of recapture probabilities for southern Beaufort Sea polar bears and total model weights associated with recapture probability covariate structures.

description: Model-averaged estimates of recapture probabilities for southern Beaufort Sea polar bears and total model weights associated with recapture probability covariate structures.
DOI: 10.6084/m9.figshare.3520610
title: Appendix D. An analysis of movement data from southern Beaufort Sea polar bears instrumented with radio-collars.

description: An analysis of movement data from southern Beaufort Sea polar bears instrumented with radio-collars.
DOI: 10.6084/m9.figshare.3520607
title: Appendix E. Projected trends in the abundance of southern Beaufort Sea polar bears based on estimated survival rates.

description: Projected trends in the abundance of southern Beaufort Sea polar bears based on estimated survival rates.
DOI: 10.6084/m9.figshare.3520607.v1
title: Appendix E. Projected trends in the abundance of southern Beaufort Sea polar bears based on estimated survival rates.

description: Projected trends in the abundance of southern Beaufort Sea polar bears based on estimated survival rates.
DOI: 10.6084/m9.figshare.3520604
title: Appendix F. The covariate Cap-procliv used to model heterogeneous recapture probabilities of southern Beaufort Sea polar bears.

description: The covariate Cap-procliv used to model heterogeneous recapture probabilities of southern Beaufort Sea polar bears.
DOI: 10.6084/m9.figshare.3520604.v1
title: Appendix F. The covariate Cap-procliv used to model heterogeneous recapture probabilities of southern Beaufort Sea polar bears.

description: The covariate Cap-procliv used to model heterogeneous recapture probabilities of southern Beaufort Sea polar bears.
DOI: 10.6084/m9.figshare.3520601
title: Appendix A. Detailed descriptions of development of multi-scale models with exponentially- and gamma-distributed extirpation time.

description: Detailed descriptions of development of multi-scale models with exponentially- and gamma-distributed extirpation time.
DOI: 10.6084/m9.figshare.3520598
title: Appendix B. Maximum likelihood point estimates for multi-scale model parameters and spread predictions for multi-scale model with gamma-distributed extirpation time.

description: Maximum likelihood point estimates for multi-scale model parameters and spread predictions for multi-scale model with gamma-distributed extirpation time.
DOI: 10.6084/m9.figshare.3520598.v1
title: Appendix B. Maximum likelihood point estimates for multi-scale model parameters and spread predictions for multi-scale model with gamma-distributed extirpation time.

description: Maximum likelihood point estimates for multi-scale model parameters and spread predictions for multi-scale model with gamma-distributed extirpation time.
DOI: 10.6084/m9.figshare.3520595
title: Appendix C. Sensitivity analysis of multi-scale SIR model with exponentially distributed infectious period.

description: Sensitivity analysis of multi-scale SIR model with exponentially distributed infectious period.
DOI: 10.6084/m9.figshare.3520592
title: Appendix D. Aspects neglected by the modeling approach.

description: Aspects neglected by the modeling approach.
DOI: 10.6084/m9.figshare.3520592.v1
title: Appendix D. Aspects neglected by the modeling approach.

description: Aspects neglected by the modeling approach.
DOI: 10.6084/m9.figshare.3520589
title: Supplement 1. Data, R source code, and script files to perform analysis and simulation of multi-scale models.

description: File List CountyDurationsScript.R (MD5: 11c986e429c95939119dfba189c36f31)<br> frag_runner.r (MD5: 2f7485ebe2d2ad6f87c731dc1409124f)<br> GammaDistributedCountyDurationsScript.R (MD5: 564c893438c156880dbf7c72bf01f9de)<br> GLMModelFitCaveData.R (MD5: f184fb5e3fae0854c0be7c537548a551)<br> NYPA_caves_infections.csv (MD5: 8e48980a329d637d317af5211ba5e3b4)<br> si_dur.Rdata (MD5: 91473c25d66a90f099d3564e5a87f443)<br> us_data.csv (MD5: 37d75dbf0e9966f305c7fc4ef562abde)<br> us_data_dur.csv (MD5: 6d6e2fea108b8d4ac05b86ff31a54959)<br> WNS-GT-Duration.R (MD5: 688935e135f35abddcb4ff6321a3e7c4) Description CountyDurationsScript.R - R script to calculate county infection durrations assuming exponentialy distributed hibernaculum infection times.<br> frag_runner.r - R script to simulate SIR and SI epidimics<br> GammaDistributedCountyDurationsScript.R - R script to calculate county infection durrations assuming gamma distributed hibernaculum infection times<br> GLMModelFitCaveData.R - R script to parameterize the county model by fitting a GLM to incidence data from New York and Pennsylvania<br> NYPA_caves_infections.csv - Incidence data from Ney Yourk and Pennsylvania used to parameterize the county model<br> si_dur.Rdata - Calculated county infection durration data assuming an SI county model<br> us_data.csv - Data table of cave counts and WNS incidence from Maher et al.<br> us_data_dur.csv - Copy of us_data.csv with added county infection durration column<br> WNS-GT-Duration.R - R function definitions to simulate epidimics
DOI: 10.6084/m9.figshare.3520586
title: Supplement 1. Matlab code for solving optimization problems and create Fig. 3 in the text.

description: File List consletters.m (MD5: 2f9532ab9ff4f24590bca218e26fa674) consconsletter1.m (MD5: 376470467e5c1959d2f1a153456f4129) makebspareshare9.m (MD5: 16c990f61bb4242241c2b2facc674b4c) profit.m (MD5: 2bc2a751b8a3d2d6f4f5dd7a4906cbaa) Description There are 4 files of MATLAB code which will solve the optimization problems presented in Fig. 3 of the manuscript. “consletters.m” This file contains the code to solve the optimization and calls out the following functions. “consconsletter1.m” is the constraint function that is called for the optimization. “makebspareshare9.m” contains the matrixes of coefficients for the yield density curves used in the optimizations. “profit.m” has the function to be maximized. Key variables: X= yield of each parcel(varies between 0–1) a, g, in, in2 are coeffcients used to estimate the yield density curves e = the minimum number of a given species that meets the minimum species constraint. Obi= total production All code is written and solved using the MATLAB optimization toolbox from MATLAB 2013B. We are unsure if the program will work in earlier or later versions.
DOI: 10.6084/m9.figshare.3520583
title: Appendix A. Combinations of probabilities used for each of the input nodes to predict the likelihood of starting seagrass biomass at different impact levels (Step 4) and to determine the effect of starting seagrass biomass on the likelihood of more seagrass biomass (Step 5).

description: Combinations of probabilities used for each of the input nodes to predict the likelihood of starting seagrass biomass at different impact levels (Step 4) and to determine the effect of starting seagrass biomass on the likelihood of more seagrass biomass (Step 5).
DOI: 10.6084/m9.figshare.3520583.v1
title: Appendix A. Combinations of probabilities used for each of the input nodes to predict the likelihood of starting seagrass biomass at different impact levels (Step 4) and to determine the effect of starting seagrass biomass on the likelihood of more seagrass biomass (Step 5).

description: Combinations of probabilities used for each of the input nodes to predict the likelihood of starting seagrass biomass at different impact levels (Step 4) and to determine the effect of starting seagrass biomass on the likelihood of more seagrass biomass (Step 5).
DOI: 10.6084/m9.figshare.3520580
title: Appendix B. Locations and input data used in the case study.

description: Locations and input data used in the case study.
DOI: 10.6084/m9.figshare.3520580.v1
title: Appendix B. Locations and input data used in the case study.

description: Locations and input data used in the case study.
DOI: 10.6084/m9.figshare.3520577
title: Appendix C. Model validation comparing the status (presence or absence) of Zostera muelleri at each of the case study locations predicted by the model with past seagrass distribution maps for Moreton Bay.

description: Model validation comparing the status (presence or absence) of Zostera muelleri at each of the case study locations predicted by the model with past seagrass distribution maps for Moreton Bay.
DOI: 10.6084/m9.figshare.3520577.v1
title: Appendix C. Model validation comparing the status (presence or absence) of Zostera muelleri at each of the case study locations predicted by the model with past seagrass distribution maps for Moreton Bay.

description: Model validation comparing the status (presence or absence) of Zostera muelleri at each of the case study locations predicted by the model with past seagrass distribution maps for Moreton Bay.
DOI: 10.6084/m9.figshare.3520574
title: Appendix A. An example straying matrix.

description: An example straying matrix.
DOI: 10.6084/m9.figshare.3520574.v1
title: Appendix A. An example straying matrix.

description: An example straying matrix.
DOI: 10.6084/m9.figshare.3520571
title: Appendix B. Simulation input parameters and default values.

description: Simulation input parameters and default values.
DOI: 10.6084/m9.figshare.3520571.v1
title: Appendix B. Simulation input parameters and default values.

description: Simulation input parameters and default values.
DOI: 10.6084/m9.figshare.3520568
title: Appendix C. Sensitivity illustration with alternative parameter values.

description: Sensitivity illustration with alternative parameter values.
DOI: 10.6084/m9.figshare.3520568.v1
title: Appendix C. Sensitivity illustration with alternative parameter values.

description: Sensitivity illustration with alternative parameter values.
DOI: 10.6084/m9.figshare.3520565
title: Appendix D. Example simulated time series from alternative conservation scenarios.

description: Example simulated time series from alternative conservation scenarios.
DOI: 10.6084/m9.figshare.3520565.v1
title: Appendix D. Example simulated time series from alternative conservation scenarios.

description: Example simulated time series from alternative conservation scenarios.
DOI: 10.6084/m9.figshare.3520562
title: Appendix E. An illustration of the correlation between populations.

description: An illustration of the correlation between populations.
DOI: 10.6084/m9.figshare.3520562.v1
title: Appendix E. An illustration of the correlation between populations.

description: An illustration of the correlation between populations.
DOI: 10.6084/m9.figshare.3520559
title: Supplement 1. Recreating the analyses with the metafolio R package.

description: File List suppl-1.pdf (MD5: f3e14b1163639994a3dc9eaf0f11d2f8) Description suppl-1.pdf describes how to install and load the metafolio R package and includes R code to run the analyses in the paper.
DOI: 10.6084/m9.figshare.3520559.v1
title: Supplement 1. Recreating the analyses with the metafolio R package.

description: File List suppl-1.pdf (MD5: f3e14b1163639994a3dc9eaf0f11d2f8) Description suppl-1.pdf describes how to install and load the metafolio R package and includes R code to run the analyses in the paper.
DOI: 10.6084/m9.figshare.3520556
title: Appendix A. Species contributions to standing biomass and biomass dynamics.

description: Species contributions to standing biomass and biomass dynamics.
DOI: 10.6084/m9.figshare.3520556.v1
title: Appendix A. Species contributions to standing biomass and biomass dynamics.

description: Species contributions to standing biomass and biomass dynamics.
DOI: 10.6084/m9.figshare.3520553
title: Supplement 1. Data on tree dynamics during secondary succession and wood specific gravity in northeastern Costa Rica.

description: File List Chazdon_data.txt (MD5: 05f10c673d870ea6e647a56b3ea7b67d) Permanent plot data Chazdon_meta.txt (MD5: 90757c3a3ffd54b096105814f2296c86) Information on the plots WSG_data.txt (MD5: 210cd495964e565b31d2efb1a0135bb8) Data on wood specific gravity Description This supplement includes the raw data of the permanent sample plots (Chazdon_data.txt), along with information on the characteristics of the plots (Chazdon_meta.txt), as well as data on wood specific gravity (WSG) for 92 tropical tree species from La Selva, Costa Rica. <i>Note:</i> Robin Chazdon should be contacted before using the data set, to sign a data-use agreement, and to receive the latest version of the data, as updates and corrections (e.g., to species names) are continuously made. Additional data on WSG is available from Plourde et al. (<i>in press</i>). Average, species-specific WSG was measured as the ratio of dry mass to green volume, from cores or discs of trees ranging from 5–60 cm DBH for each species. Green volume was measured with the water displacement method, then wood samples were dried at 103–105°C for 24–48 hours before dry mass was determined (Plourde et al., <i>in press</i>). See Plourde et al. (<i>in press</i>) for more details on the methodology. Additional information on some of the columns in Chazdon_data.txt: StemID – The first number is the site code. The three digits after the first hyphen refer to the subplot number (1-100). The digits after the second hyphen indicate the number of an individual tree within each subplot. If multiple stems are separated below 1.3 m height they are marked separately. Each StemID is unique. DBH.1997-2013 – Diameter at 1.3 m height, or above buttresses or stem irregularities for each year. DBH2.2012-2013 – Second diameter measurement higher in the stem when the point of measurement was changed because of the formation of buttresses or stem irregularities.
DOI: 10.6084/m9.figshare.3520553.v1
title: Supplement 1. Data on tree dynamics during secondary succession and wood specific gravity in northeastern Costa Rica.

description: File List Chazdon_data.txt (MD5: 05f10c673d870ea6e647a56b3ea7b67d) Permanent plot data Chazdon_meta.txt (MD5: 90757c3a3ffd54b096105814f2296c86) Information on the plots WSG_data.txt (MD5: 210cd495964e565b31d2efb1a0135bb8) Data on wood specific gravity Description This supplement includes the raw data of the permanent sample plots (Chazdon_data.txt), along with information on the characteristics of the plots (Chazdon_meta.txt), as well as data on wood specific gravity (WSG) for 92 tropical tree species from La Selva, Costa Rica. <i>Note:</i> Robin Chazdon should be contacted before using the data set, to sign a data-use agreement, and to receive the latest version of the data, as updates and corrections (e.g., to species names) are continuously made. Additional data on WSG is available from Plourde et al. (<i>in press</i>). Average, species-specific WSG was measured as the ratio of dry mass to green volume, from cores or discs of trees ranging from 5–60 cm DBH for each species. Green volume was measured with the water displacement method, then wood samples were dried at 103–105°C for 24–48 hours before dry mass was determined (Plourde et al., <i>in press</i>). See Plourde et al. (<i>in press</i>) for more details on the methodology. Additional information on some of the columns in Chazdon_data.txt: StemID – The first number is the site code. The three digits after the first hyphen refer to the subplot number (1-100). The digits after the second hyphen indicate the number of an individual tree within each subplot. If multiple stems are separated below 1.3 m height they are marked separately. Each StemID is unique. DBH.1997-2013 – Diameter at 1.3 m height, or above buttresses or stem irregularities for each year. DBH2.2012-2013 – Second diameter measurement higher in the stem when the point of measurement was changed because of the formation of buttresses or stem irregularities.
DOI: 10.6084/m9.figshare.3520550
title: Appendix A. Sample expert opinion survey on the probability of eradication of aquatic non-indigenous species.

description: Sample expert opinion survey on the probability of eradication of aquatic non-indigenous species.
DOI: 10.6084/m9.figshare.3520547
title: Supplement 1. R code and the data set necessary to conduct the Random Forest analysis.

description: File List dreissena_in_lakes_of_belarus.csv (MD5: 3dc2d2f89af3064223358983c785771d) r_script_random_forest.R (MD5: af1295890d60bc832955e940889e4575) Description This Supplementary material contains two files necessary to fully reproduce the results obtained using the Random Forest classifier. The first of these files, dreissena_in_lakes_of_belarus.csv, is a plain text table that has 553 records, each described with the following variables: 1. Lake_Code: numeric codes uniquely identifying each lake (for reference only, not used in analysis explicitely). 2. ZMpresence: indicator of whether a lake is infested with zebra mussel (0 – for non-infested, 1 – for infested). 3. LAREA: lake area 4. LVOL: lake volume 5. MAXD: maximal depth 6. AVED: average depth 7. SPECWATSHED: specific watershed (i.e., drainage area) 8. TRANSP: Secci depth 9. COLOR: water color 10. pH: water pH 11. HCO3: HCO3 content 12. SO4: SO4 content 13. Cl: CL content 14. Ca: Ca content 15. Mg: Mg content 16. TDS: total dissolved solids 17: Fe: Fe content 18. Si: Si content 19. NH4: NH4 content 20. NO2: NO2 content 21. PO4: PO4 content 22. PermOx: permanganate oxydizability 23. N: latitude (decimal degree) 24: E: longitude (decimal degree) Missing values in the data set are denoted as NA. The second file, r_script_random_forest.R, loads the data into R (assuming that the file dreissena_in_lakes_of_belarus.csv is stored in the current R working directory), fits the Random Forest model, and plots the results. The analysis relies on three add-on packages: caret, geosphere, randomForest, and ggplot2. All these packages are assumed to be already installed on the user's computer (if not, they can be freely downloaded from the Comprehensive R Archive Network, cran.r-project.org, or installed directly from within R using the following command: install.packages(c("caret", "geosphere", "randomForest", "ggplot2"))).