cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.3554829.v1
title: Appendix C. A figure depicting the division of the 24-h cycle into five periods: morning twilight, morning, afternoon, evening twilight, and night.

description: A figure depicting the division of the 24-h cycle into five periods: morning twilight, morning, afternoon, evening twilight, and night.
DOI: 10.6084/m9.figshare.3554823
title: Appendix D. Mean activity values for African wild dogs during seven periods of the night for four phases of the lunar cycle: full moon, half moon (waning), half moon (waxing), and new moon.

description: Mean activity values for African wild dogs during seven periods of the night for four phases of the lunar cycle: full moon, half moon (waning), half moon (waxing), and new moon.
DOI: 10.6084/m9.figshare.3554823.v1
title: Appendix D. Mean activity values for African wild dogs during seven periods of the night for four phases of the lunar cycle: full moon, half moon (waning), half moon (waxing), and new moon.

description: Mean activity values for African wild dogs during seven periods of the night for four phases of the lunar cycle: full moon, half moon (waning), half moon (waxing), and new moon.
DOI: 10.6084/m9.figshare.3554820
title: Appendix E. Mean activity values for cheetahs during seven periods of the night for four phases of the lunar cycle: full moon, half moon (waning), half moon (waxing), and new moon.

description: Mean activity values for cheetahs during seven periods of the night for four phases of the lunar cycle: full moon, half moon (waning), half moon (waxing), and new moon.
DOI: 10.6084/m9.figshare.3554820.v1
title: Appendix E. Mean activity values for cheetahs during seven periods of the night for four phases of the lunar cycle: full moon, half moon (waning), half moon (waxing), and new moon.

description: Mean activity values for cheetahs during seven periods of the night for four phases of the lunar cycle: full moon, half moon (waning), half moon (waxing), and new moon.
DOI: 10.6084/m9.figshare.3554814
title: Appendix F. Mean activity values for spotted hyenas during seven periods of the night for four phases of the lunar cycle: full moon, half moon (waning), half moon (waxing), and new moon.

description: Mean activity values for spotted hyenas during seven periods of the night for four phases of the lunar cycle: full moon, half moon (waning), half moon (waxing), and new moon.
DOI: 10.6084/m9.figshare.3554814.v1
title: Appendix F. Mean activity values for spotted hyenas during seven periods of the night for four phases of the lunar cycle: full moon, half moon (waning), half moon (waxing), and new moon.

description: Mean activity values for spotted hyenas during seven periods of the night for four phases of the lunar cycle: full moon, half moon (waning), half moon (waxing), and new moon.
DOI: 10.6084/m9.figshare.3554811
title: Appendix G. Mean activity values for lions during seven periods of the night for four phases of the lunar cycle: full moon, half moon (waning), half moon (waxing), and new moon.

description: Mean activity values for lions during seven periods of the night for four phases of the lunar cycle: full moon, half moon (waning), half moon (waxing), and new moon.
DOI: 10.6084/m9.figshare.3554811.v1
title: Appendix G. Mean activity values for lions during seven periods of the night for four phases of the lunar cycle: full moon, half moon (waning), half moon (waxing), and new moon.

description: Mean activity values for lions during seven periods of the night for four phases of the lunar cycle: full moon, half moon (waning), half moon (waxing), and new moon.
DOI: 10.6084/m9.figshare.3554808
title: Appendix A. Examples of primary-period, two-occasion mark–recapture histories, associated likelihoods, and notation definitions.

description: Examples of primary-period, two-occasion mark–recapture histories, associated likelihoods, and notation definitions.
DOI: 10.6084/m9.figshare.3554808.v1
title: Appendix A. Examples of primary-period, two-occasion mark–recapture histories, associated likelihoods, and notation definitions.

description: Examples of primary-period, two-occasion mark–recapture histories, associated likelihoods, and notation definitions.
DOI: 10.6084/m9.figshare.3554805
title: Appendix B. Historical Palmer Drought Severity Index (PDSI) for Florida, Climate Division 5 (Everglades) from 1997 to 2009.

description: Historical Palmer Drought Severity Index (PDSI) for Florida, Climate Division 5 (Everglades) from 1997 to 2009.
DOI: 10.6084/m9.figshare.3554805.v1
title: Appendix B. Historical Palmer Drought Severity Index (PDSI) for Florida, Climate Division 5 (Everglades) from 1997 to 2009.

description: Historical Palmer Drought Severity Index (PDSI) for Florida, Climate Division 5 (Everglades) from 1997 to 2009.
DOI: 10.6084/m9.figshare.3554799
title: Appendix C. Summary of models used to test for parsimony in the probability of detection conditional on breeding state (p) and the probability of correctly classifying an individual Snail Kite's breeding state given it was detected (δ).

description: Summary of models used to test for parsimony in the probability of detection conditional on breeding state (p) and the probability of correctly classifying an individual Snail Kite's breeding state given it was detected (δ).
DOI: 10.6084/m9.figshare.3554799.v1
title: Appendix C. Summary of models used to test for parsimony in the probability of detection conditional on breeding state (p) and the probability of correctly classifying an individual Snail Kite's breeding state given it was detected (δ).

description: Summary of models used to test for parsimony in the probability of detection conditional on breeding state (p) and the probability of correctly classifying an individual Snail Kite's breeding state given it was detected (δ).
DOI: 10.6084/m9.figshare.3554796
title: Appendix D. Probability of detecting a breeding Snail Kite (pB) and correctly classifying it, given it was detected (δB) with 95% CIs for secondary sampling periods from 1996 to 2009.

description: Probability of detecting a breeding Snail Kite (pB) and correctly classifying it, given it was detected (δB) with 95% CIs for secondary sampling periods from 1996 to 2009.
DOI: 10.6084/m9.figshare.3554796.v1
title: Appendix D. Probability of detecting a breeding Snail Kite (pB) and correctly classifying it, given it was detected (δB) with 95% CIs for secondary sampling periods from 1996 to 2009.

description: Probability of detecting a breeding Snail Kite (pB) and correctly classifying it, given it was detected (δB) with 95% CIs for secondary sampling periods from 1996 to 2009.
DOI: 10.6084/m9.figshare.3554793
title: Appendix E. Full set of robust design models used to test for the potential effects of "extreme drought" and experience (young vs. adult) on Snail Kite breeding probabilities (ψ) and the cost of attempting to breed on survival (S) while accounting for the uncertainty associated with the misclassification of breeding status when state assignment is based on behavioral observations.

description: Full set of robust design models used to test for the potential effects of "extreme drought" and experience (young vs. adult) on Snail Kite breeding probabilities (ψ) and the cost of attempting to breed on survival (S) while accounting for the uncertainty associated with the misclassification of breeding status when state assignment is based on behavioral observations.
DOI: 10.6084/m9.figshare.3554793.v1
title: Appendix E. Full set of robust design models used to test for the potential effects of "extreme drought" and experience (young vs. adult) on Snail Kite breeding probabilities (ψ) and the cost of attempting to breed on survival (S) while accounting for the uncertainty associated with the misclassification of breeding status when state assignment is based on behavioral observations.

description: Full set of robust design models used to test for the potential effects of "extreme drought" and experience (young vs. adult) on Snail Kite breeding probabilities (ψ) and the cost of attempting to breed on survival (S) while accounting for the uncertainty associated with the misclassification of breeding status when state assignment is based on behavioral observations.
DOI: 10.6084/m9.figshare.3554790
title: Appendix A. Additional information on study area, site selection, and bird sampling.

description: Additional information on study area, site selection, and bird sampling.
DOI: 10.6084/m9.figshare.3554790.v1
title: Appendix A. Additional information on study area, site selection, and bird sampling.

description: Additional information on study area, site selection, and bird sampling.
DOI: 10.6084/m9.figshare.3554787
title: Appendix B. A numerical example of the use of the species–area relationship equation for creating communities, and information on creating values of Gaussian curve for sensitivity analysis.

description: A numerical example of the use of the species–area relationship equation for creating communities, and information on creating values of Gaussian curve for sensitivity analysis.
DOI: 10.6084/m9.figshare.3554787.v1
title: Appendix B. A numerical example of the use of the species–area relationship equation for creating communities, and information on creating values of Gaussian curve for sensitivity analysis.

description: A numerical example of the use of the species–area relationship equation for creating communities, and information on creating values of Gaussian curve for sensitivity analysis.
DOI: 10.6084/m9.figshare.3554778
title: Supplement 1. The R source code containing the algorithms used for modeling communities and a comma-delimited file containing the bird data set collected in the Atlantic Forest.

description: File List Site_by_species_matrix.csv (md5: 40436d5d0cf1f856df0a24d84bb2ebdc)<br> Codes.r (md5: 65fc434d02b30cfccbc7b0a9b763710e)<br> Codes_sensitivity_analysis.r (md5: 7eb16dfd396f2513d91b27893ca71367)<br> Function_create.prob.r (md5: a30b99e9f10b1728a82bc9b000fc69dd)<br> Function_CreateCommunity.r (md5: 59352753f4649ebd4cbbb5d15df06235) Description The Site_by_species_matrix.csv file is a comma delimited file. It contains the data on presence and absence of Atlantic Forest bird species captured in fragments and continuous sites. Rows contains sites and columns contain species. The Codes.r file is in R programming language. It contains the algorithm used for modeling communities and for analysing the data generated during simulations. The files Site_by_species_matrix.csv, Function_create.prob.r, and Function_CreateCommunity.r are needed to successfully run this code. The code is annotated throughout and written to improve clarity rather than to maximise processing times. Full time required for running all simulations is roughly four days, depending on processor and amount of RAM. The Codes_sensitivity_analysis.r file is in R programming language. It contains the algorithm used for running a sensitivity analysis on the values of Gaussian curve used to create SPEC communities. The files Site_by_species_matrix.csv, Function_create.prob.r, and Function_CreateCommunity.r are needed to successfully run this code. The code presented here was written to improve clarity rather than to maximise processing times. Full time required for running all simulations is roughly four or more days, depending on processor and amount of RAM. The Function_create.prob.r file is in R programming language. It contains the codes used to create the function create.prob, which creates a matrix of sampling probabilities necessary to create SPEC communities. This function is sourced directly from Codes.r and from Codes_sensitivity_analysis.r. The Function_CreateCommunity.r file is in R programming language. It contains the codes used to create the function create.community, which is necessary to create RAND, SFD, and SPEC communities. This function is sourced directly from Codes.r and from Codes_sensitivity_analysis.r, and it requires Function_create.prob.r to create SPEC communities. The function here presented was written to improve clarity and re-writing the function substantially speeds processing time.
DOI: 10.6084/m9.figshare.3554778.v1
title: Supplement 1. The R source code containing the algorithms used for modeling communities and a comma-delimited file containing the bird data set collected in the Atlantic Forest.

description: File List Site_by_species_matrix.csv (md5: 40436d5d0cf1f856df0a24d84bb2ebdc)<br> Codes.r (md5: 65fc434d02b30cfccbc7b0a9b763710e)<br> Codes_sensitivity_analysis.r (md5: 7eb16dfd396f2513d91b27893ca71367)<br> Function_create.prob.r (md5: a30b99e9f10b1728a82bc9b000fc69dd)<br> Function_CreateCommunity.r (md5: 59352753f4649ebd4cbbb5d15df06235) Description The Site_by_species_matrix.csv file is a comma delimited file. It contains the data on presence and absence of Atlantic Forest bird species captured in fragments and continuous sites. Rows contains sites and columns contain species. The Codes.r file is in R programming language. It contains the algorithm used for modeling communities and for analysing the data generated during simulations. The files Site_by_species_matrix.csv, Function_create.prob.r, and Function_CreateCommunity.r are needed to successfully run this code. The code is annotated throughout and written to improve clarity rather than to maximise processing times. Full time required for running all simulations is roughly four days, depending on processor and amount of RAM. The Codes_sensitivity_analysis.r file is in R programming language. It contains the algorithm used for running a sensitivity analysis on the values of Gaussian curve used to create SPEC communities. The files Site_by_species_matrix.csv, Function_create.prob.r, and Function_CreateCommunity.r are needed to successfully run this code. The code presented here was written to improve clarity rather than to maximise processing times. Full time required for running all simulations is roughly four or more days, depending on processor and amount of RAM. The Function_create.prob.r file is in R programming language. It contains the codes used to create the function create.prob, which creates a matrix of sampling probabilities necessary to create SPEC communities. This function is sourced directly from Codes.r and from Codes_sensitivity_analysis.r. The Function_CreateCommunity.r file is in R programming language. It contains the codes used to create the function create.community, which is necessary to create RAND, SFD, and SPEC communities. This function is sourced directly from Codes.r and from Codes_sensitivity_analysis.r, and it requires Function_create.prob.r to create SPEC communities. The function here presented was written to improve clarity and re-writing the function substantially speeds processing time.
DOI: 10.6084/m9.figshare.3554775
title: Appendix A. Study site and sampling information.

description: Study site and sampling information.
DOI: 10.6084/m9.figshare.3554775.v1
title: Appendix A. Study site and sampling information.

description: Study site and sampling information.
DOI: 10.6084/m9.figshare.3554772
title: Appendix B. Apparent survival models and estimates from Cormack-Jolly-Seber analysis.

description: Apparent survival models and estimates from Cormack-Jolly-Seber analysis.
DOI: 10.6084/m9.figshare.3554772.v1
title: Appendix B. Apparent survival models and estimates from Cormack-Jolly-Seber analysis.

description: Apparent survival models and estimates from Cormack-Jolly-Seber analysis.
DOI: 10.6084/m9.figshare.3554769
title: Appendix C. Pradel recruitment models.

description: Pradel recruitment models.
DOI: 10.6084/m9.figshare.3554769.v1
title: Appendix C. Pradel recruitment models.

description: Pradel recruitment models.
DOI: 10.6084/m9.figshare.3554766
title: Appendix D. Pradel population growth rate models and parameter estimates.

description: Pradel population growth rate models and parameter estimates.
DOI: 10.6084/m9.figshare.3554766.v1
title: Appendix D. Pradel population growth rate models and parameter estimates.

description: Pradel population growth rate models and parameter estimates.
DOI: 10.6084/m9.figshare.3554754
title: Appendix A. Replication principle for the expected species richness when expected sample coverage is standardized.

description: Replication principle for the expected species richness when expected sample coverage is standardized.
DOI: 10.6084/m9.figshare.3554742
title: Appendix B. An unbiased estimator for the expected sample coverage in coverage-based rarefaction.

description: An unbiased estimator for the expected sample coverage in coverage-based rarefaction.
DOI: 10.6084/m9.figshare.3554733
title: Appendix C. A bootstrap method to construct the confidence interval of the expected species richness at a given coverage.

description: A bootstrap method to construct the confidence interval of the expected species richness at a given coverage.
DOI: 10.6084/m9.figshare.3554730
title: Appendix D. Theoretical proof of the proposed unbiased algorithm in coverage-based rarefaction.

description: Theoretical proof of the proposed unbiased algorithm in coverage-based rarefaction.
DOI: 10.6084/m9.figshare.3554730.v1
title: Appendix D. Theoretical proof of the proposed unbiased algorithm in coverage-based rarefaction.

description: Theoretical proof of the proposed unbiased algorithm in coverage-based rarefaction.
DOI: 10.6084/m9.figshare.3554727
title: Appendix E. Coverage-based extrapolation.

description: Coverage-based extrapolation.
DOI: 10.6084/m9.figshare.3554724
title: Appendix F. Beetle species frequency data (Janzen 1973a,b) and computational details for two examples.

description: Beetle species frequency data (Janzen 1973a,b) and computational details for two examples.
DOI: 10.6084/m9.figshare.3554721
title: Appendix G. Comparisons of size- and coverage-based species accumulation curves.

description: Comparisons of size- and coverage-based species accumulation curves.
DOI: 10.6084/m9.figshare.3554718
title: Appendix H. Some basic theoretical properties of coverage-based species accumulation curves.

description: Some basic theoretical properties of coverage-based species accumulation curves.
DOI: 10.6084/m9.figshare.3554715
title: Appendix A. Formal description of a Bayesian mixed-effects model in which the probability of an excess zero is functionally linked to mean counts of snapper from baited underwater video deployments in a marine reserve monitoring program.

description: Formal description of a Bayesian mixed-effects model in which the probability of an excess zero is functionally linked to mean counts of snapper from baited underwater video deployments in a marine reserve monitoring program.
DOI: 10.6084/m9.figshare.3554715.v1
title: Appendix A. Formal description of a Bayesian mixed-effects model in which the probability of an excess zero is functionally linked to mean counts of snapper from baited underwater video deployments in a marine reserve monitoring program.

description: Formal description of a Bayesian mixed-effects model in which the probability of an excess zero is functionally linked to mean counts of snapper from baited underwater video deployments in a marine reserve monitoring program.
DOI: 10.6084/m9.figshare.3554712
title: Appendix B. A table presenting summary statistics for the posterior distributions of estimated parameters.

description: A table presenting summary statistics for the posterior distributions of estimated parameters.
DOI: 10.6084/m9.figshare.3554712.v1
title: Appendix B. A table presenting summary statistics for the posterior distributions of estimated parameters.

description: A table presenting summary statistics for the posterior distributions of estimated parameters.
DOI: 10.6084/m9.figshare.3554709
title: Appendix C. A demonstration of various potential relationships between the mean of the count distribution (λ) and the probability of an excess zero (π) under the general form of the linked model, logit(π) = γ0 + γ1log(λ).

description: A demonstration of various potential relationships between the mean of the count distribution (λ) and the probability of an excess zero (π) under the general form of the linked model, logit(π) = γ0 + γ1log(λ).
DOI: 10.6084/m9.figshare.3554709.v1
title: Appendix C. A demonstration of various potential relationships between the mean of the count distribution (λ) and the probability of an excess zero (π) under the general form of the linked model, logit(π) = γ0 + γ1log(λ).

description: A demonstration of various potential relationships between the mean of the count distribution (λ) and the probability of an excess zero (π) under the general form of the linked model, logit(π) = γ0 + γ1log(λ).
DOI: 10.6084/m9.figshare.3554706
title: Supplement 1. R and OpenBUGS code and data for fitting a linked zero-inflated negative binomial model applied to counts of legally sized snapper from a marine reserve monitoring program.

description: File List Linked_zero-inflated_model_for snapper.R (md5: 531da87c73774928b5d5814cf397bcf3) Description This Supplement provides code and data for applying a linked zero-inflated negative binomial model to counts of legally sized snapper. Both the analysis and data are provided as R (R Development Core Team 2012) code in one file. It is necessary to have installed the R library R2OpenBUGS (Sturtz et al. 2005) and the OpenBUGS software for this code to work. The model coded below may superficially appear to be different to the model described in Appendix A. This is because the model below was parameterized using hierarchical centering (Browne 2004), which may improve convergence of the MCMC chains. The models are equivalent. Zero-inflated distributions are essentially mixture distributions. Parameterizing the likelihood of such a model by utilizing a latent Bernoulli parameter for zero-inflation (e.g. the "data-augmentation" approach used by Ghosh et al. 2006), can render the plug-in estimates of the Deviance Information Criterion (DIC) inappropriate (Lawson and Clark 2002). For the models presented in this paper, we used the "zeros trick" and specified explicitly the likelihood of the models (rather than using the built-in distributions available within OpenBUGS), thus marginalizing over observation-level latent parameters. We found this approach to have several advantages over the alternative. First, avoiding the use of observation- level latent parameters meant that the plug-in DIC could be expected to be perform well as a model selection criterion (Millar 2009). Second, we found that the models ran more successfully when specified in this way, encountering fewer errors and achieving better mixing of the MCMC chains.
DOI: 10.6084/m9.figshare.3554703
title: Appendix A. Photo showing examples of Tetrix subulata pygmy grasshopper color morphs.

description: Photo showing examples of Tetrix subulata pygmy grasshopper color morphs.
DOI: 10.6084/m9.figshare.3554703.v1
title: Appendix A. Photo showing examples of Tetrix subulata pygmy grasshopper color morphs.

description: Photo showing examples of Tetrix subulata pygmy grasshopper color morphs.
DOI: 10.6084/m9.figshare.3554700
title: Appendix B. Description of outdoor enclosures.

description: Description of outdoor enclosures.
DOI: 10.6084/m9.figshare.3554700.v1
title: Appendix B. Description of outdoor enclosures.

description: Description of outdoor enclosures.
DOI: 10.6084/m9.figshare.3554697
title: Appendix C. Table showing information of location, biotopes, and reproductive capacity of the five Tetrix subulata source populations used to construct founder groups.

description: Table showing information of location, biotopes, and reproductive capacity of the five Tetrix subulata source populations used to construct founder groups.
DOI: 10.6084/m9.figshare.3554697.v1
title: Appendix C. Table showing information of location, biotopes, and reproductive capacity of the five Tetrix subulata source populations used to construct founder groups.

description: Table showing information of location, biotopes, and reproductive capacity of the five Tetrix subulata source populations used to construct founder groups.