cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.3550743
title: Supplement 1. R-code used to simulate occupancy data and to fit the IFM naïve, IFM missing, and IFM robust models.

description: File List 1_MCMC_FUNCTIONS.R<br> 2_IFM_NO_MISSING_MCMC_FUNCTION.R<br> 3_IFM_MISSING_MCMC_FUNCTION.R<br> 4_IFM_ROBUST_MCMC_FUNCTION.R<br> 5_CHAIN_DIAGNOSTICS_FUNCTIONS.R.<br> 6_IFM_SIM_DATA_PREP.R<br> 7_AUDIT_INM.R<br> 8_AUDIT_IFM_MISSING.R<br> 9_AUDIT_IFM_ROBUST.R Description The files in this Supplement are used to fit the three models described in this study (IFM Naïve, IFM Missing, and IFM Robust) and include a program that simulates occupancy data following the Incidence Function Model. The file 1_MCMC_FUNCTIONS.R contains four auxiliary functions used in 2_IFM_NO_MISSING_MCMC_FUNCTION.R, 3_IFM_MISSING_MCMC_FUNCTION.R, and 4_IFM_ROBUST_MCMC_FUNCTION.R. It includes a function that accepts or rejects proposed values in the Metropolis-Hastings algorithm, a function that counts the acceptance rates, a function that counts the number of missing values, and a function that is used when proposing values from a bivariate normal distribution for the parameters gamma and beta. The file 2_IFM_NO_MISSING_MCMC_FUNCTION.R contains the function that uses MCMC to estimate the IFM Naïve. The methods are described in Appendix A. The file 3_IFM_MISSING_MCMC_FUNCTION.R contains the function that uses MCMC to estimate the IFM Missing. The methods are described in Appendix A. The file 4_IFM_ROBUST_MCMC_FUNCTION.R contains the function that uses MCMC to estimate the IFM Robust. It implements the IFM Robust, and the methods are described in Appendix A. The file 5_CHAIN_DIAGNOSTICS_FUNCTIONS.R contains a number of functions used to diagnose the convergence of MCMC chains. In particular, the function coda.create converts the files created by the MCMC functions to a format that can be read by the “coda” R-package. The file 6_IFM_SIM_DATA_PREP.R simulates occupancy data from the Incidence Function Model. These data sets are then used in the subsequent programs. The file 7_AUDIT_INM.R estimates the parameters of the IFM Naïve using data created in 6_IFM_SIM_DATA_PREP.R. The file 8_AUDIT_IFM_MISSING.R estimates the parameters of the IFM Missing using data created in 6_IFM_SIM_DATA_PREP.R. The file 9_AUDIT_IFM_ROBUST.R estimates the parameters of the IFM Robust using data created in 6_IFM_SIM_DATA_PREP.R.
DOI: 10.6084/m9.figshare.3550743.v1
title: Supplement 1. R-code used to simulate occupancy data and to fit the IFM naïve, IFM missing, and IFM robust models.

description: File List 1_MCMC_FUNCTIONS.R<br> 2_IFM_NO_MISSING_MCMC_FUNCTION.R<br> 3_IFM_MISSING_MCMC_FUNCTION.R<br> 4_IFM_ROBUST_MCMC_FUNCTION.R<br> 5_CHAIN_DIAGNOSTICS_FUNCTIONS.R.<br> 6_IFM_SIM_DATA_PREP.R<br> 7_AUDIT_INM.R<br> 8_AUDIT_IFM_MISSING.R<br> 9_AUDIT_IFM_ROBUST.R Description The files in this Supplement are used to fit the three models described in this study (IFM Naïve, IFM Missing, and IFM Robust) and include a program that simulates occupancy data following the Incidence Function Model. The file 1_MCMC_FUNCTIONS.R contains four auxiliary functions used in 2_IFM_NO_MISSING_MCMC_FUNCTION.R, 3_IFM_MISSING_MCMC_FUNCTION.R, and 4_IFM_ROBUST_MCMC_FUNCTION.R. It includes a function that accepts or rejects proposed values in the Metropolis-Hastings algorithm, a function that counts the acceptance rates, a function that counts the number of missing values, and a function that is used when proposing values from a bivariate normal distribution for the parameters gamma and beta. The file 2_IFM_NO_MISSING_MCMC_FUNCTION.R contains the function that uses MCMC to estimate the IFM Naïve. The methods are described in Appendix A. The file 3_IFM_MISSING_MCMC_FUNCTION.R contains the function that uses MCMC to estimate the IFM Missing. The methods are described in Appendix A. The file 4_IFM_ROBUST_MCMC_FUNCTION.R contains the function that uses MCMC to estimate the IFM Robust. It implements the IFM Robust, and the methods are described in Appendix A. The file 5_CHAIN_DIAGNOSTICS_FUNCTIONS.R contains a number of functions used to diagnose the convergence of MCMC chains. In particular, the function coda.create converts the files created by the MCMC functions to a format that can be read by the “coda” R-package. The file 6_IFM_SIM_DATA_PREP.R simulates occupancy data from the Incidence Function Model. These data sets are then used in the subsequent programs. The file 7_AUDIT_INM.R estimates the parameters of the IFM Naïve using data created in 6_IFM_SIM_DATA_PREP.R. The file 8_AUDIT_IFM_MISSING.R estimates the parameters of the IFM Missing using data created in 6_IFM_SIM_DATA_PREP.R. The file 9_AUDIT_IFM_ROBUST.R estimates the parameters of the IFM Robust using data created in 6_IFM_SIM_DATA_PREP.R.
DOI: 10.6084/m9.figshare.3550740
title: Appendix A. Tables showing regression analyses of insect biomass in relation to date during the laying and nestling periods and selection gradients on standardized laying date for each year and location.

description: Tables showing regression analyses of insect biomass in relation to date during the laying and nestling periods and selection gradients on standardized laying date for each year and location.
DOI: 10.6084/m9.figshare.3550740.v1
title: Appendix A. Tables showing regression analyses of insect biomass in relation to date during the laying and nestling periods and selection gradients on standardized laying date for each year and location.

description: Tables showing regression analyses of insect biomass in relation to date during the laying and nestling periods and selection gradients on standardized laying date for each year and location.
DOI: 10.6084/m9.figshare.3550737
title: Appendix B. Figures showing seasonal patterns of insect biomass from Alberta, New York, Northwest Territories, Ontario, and Wisconsin.

description: Figures showing seasonal patterns of insect biomass from Alberta, New York, Northwest Territories, Ontario, and Wisconsin.
DOI: 10.6084/m9.figshare.3550737.v1
title: Appendix B. Figures showing seasonal patterns of insect biomass from Alberta, New York, Northwest Territories, Ontario, and Wisconsin.

description: Figures showing seasonal patterns of insect biomass from Alberta, New York, Northwest Territories, Ontario, and Wisconsin.
DOI: 10.6084/m9.figshare.3550734
title: Supplement 1. Dates of laying, hatching, and peak insect biomass used in Table 1.

description: File List Data.txt Description The data.txt file is a tab-separated ascii file. The file contains the raw data used in statistical analysis of laying and hatching dates in relation to the date of peak insect biomass (see Table 1).<br> <br> Column definitions:<br> <br> Loc = location of study site<br> Year = Year of sample<br> InsectTrap = type of insect sampler (suction or tow-net)<br> 25%Laydate= the 25<sup>th</sup> percentile of laying dates (1 = 1 May)<br> MeanLaydate = mean date of laying (1 = 1May)<br> DateMaxBiomass = Date of the maximum insect biomass (1 = 1 May)<br> MeanHatchDate = mean date of hatching (1 = 1 May)<br> <br> Column checksum values:<br> <br> column 1: location, 26 values, none missing<br> column 2: year, 26 values, none missing<br> column 3: type of insect sampler, 26 values, none missing<br> column 4: 25th percentile of laying dates, checksum = 447, 26 values, none missing<br> column 5: mean laying dates, checksum = 547.9, 26 values, none missing<br> column 6: date of maximum insect biomass, checksum = 934, 26 values, none missing<br> column 7: mean hatch date, checksum = 1058.3, 26 values, none missing<br>
DOI: 10.6084/m9.figshare.3550734.v1
title: Supplement 1. Dates of laying, hatching, and peak insect biomass used in Table 1.

description: File List Data.txt Description The data.txt file is a tab-separated ascii file. The file contains the raw data used in statistical analysis of laying and hatching dates in relation to the date of peak insect biomass (see Table 1).<br> <br> Column definitions:<br> <br> Loc = location of study site<br> Year = Year of sample<br> InsectTrap = type of insect sampler (suction or tow-net)<br> 25%Laydate= the 25<sup>th</sup> percentile of laying dates (1 = 1 May)<br> MeanLaydate = mean date of laying (1 = 1May)<br> DateMaxBiomass = Date of the maximum insect biomass (1 = 1 May)<br> MeanHatchDate = mean date of hatching (1 = 1 May)<br> <br> Column checksum values:<br> <br> column 1: location, 26 values, none missing<br> column 2: year, 26 values, none missing<br> column 3: type of insect sampler, 26 values, none missing<br> column 4: 25th percentile of laying dates, checksum = 447, 26 values, none missing<br> column 5: mean laying dates, checksum = 547.9, 26 values, none missing<br> column 6: date of maximum insect biomass, checksum = 934, 26 values, none missing<br> column 7: mean hatch date, checksum = 1058.3, 26 values, none missing<br>
DOI: 10.6084/m9.figshare.3550731
title: Appendix A. The existence and stability of equilibria.

description: The existence and stability of equilibria.
DOI: 10.6084/m9.figshare.3550731.v1
title: Appendix A. The existence and stability of equilibria.

description: The existence and stability of equilibria.
DOI: 10.6084/m9.figshare.3550728
title: Appendix A. Seasonal differences in encounter rates of snake and lizard species in the wet–dry tropics.

description: Seasonal differences in encounter rates of snake and lizard species in the wet–dry tropics.
DOI: 10.6084/m9.figshare.3550728.v1
title: Appendix A. Seasonal differences in encounter rates of snake and lizard species in the wet–dry tropics.

description: Seasonal differences in encounter rates of snake and lizard species in the wet–dry tropics.
DOI: 10.6084/m9.figshare.3550722
title: Supplement 1. WinBUGS 1.4 code for hierarchical spatial N-mixture and occupancy models.

description: File List HierarchicalSpatialNmixtureModel.odc - WinBUGS 1.4 code.<br> HierarchicalSpatialOccupancyModel.odc - WinBUGS 1.4 code. Description The file HierarchicalSpatialNmixtureModel.odc contains WinBUGS code, simulated data, and initial values to run an example hierarchical spatial <i>N</i>-mixture model based on count data and modified from (Royle, J. A. 2004. <i>N</i>-mixture models for estimating population size from spatially replicated counts. Biometrics 60:108–115.). Spatial and non-spatial normal random effects are employed for both occupancy probability [which is related to the Poisson parameter lambda = -log(1- psi))] and capture probability. The model employs the hierarchical half-Cauchy prior for random effect standard deviations from Gelman [2006. Prior distributions for variance parameters in hierarchical models (Comment on article by Brown and Draper). Bayesian Analysis 3:515–534).]. The file HierarchicalSpatialOccupancyModel.odc contains WinBUGS code, simulated data, and initial values for a hierarchical spatial occupancy model. The code employs a sequential approach to modeling the binary detection data to avoid specifying a new sampling distribution which would be necessary to directly use the likelihood in MacKenzie et al. (2002, Estimating site occupancy rates when detection probabilities are less than 1. Ecology 83:2248–2255.). The hierarchical half-Cauchy prior for random effect standard deviations is also employed.
DOI: 10.6084/m9.figshare.3550722.v1
title: Supplement 1. WinBUGS 1.4 code for hierarchical spatial N-mixture and occupancy models.

description: File List HierarchicalSpatialNmixtureModel.odc - WinBUGS 1.4 code.<br> HierarchicalSpatialOccupancyModel.odc - WinBUGS 1.4 code. Description The file HierarchicalSpatialNmixtureModel.odc contains WinBUGS code, simulated data, and initial values to run an example hierarchical spatial <i>N</i>-mixture model based on count data and modified from (Royle, J. A. 2004. <i>N</i>-mixture models for estimating population size from spatially replicated counts. Biometrics 60:108–115.). Spatial and non-spatial normal random effects are employed for both occupancy probability [which is related to the Poisson parameter lambda = -log(1- psi))] and capture probability. The model employs the hierarchical half-Cauchy prior for random effect standard deviations from Gelman [2006. Prior distributions for variance parameters in hierarchical models (Comment on article by Brown and Draper). Bayesian Analysis 3:515–534).]. The file HierarchicalSpatialOccupancyModel.odc contains WinBUGS code, simulated data, and initial values for a hierarchical spatial occupancy model. The code employs a sequential approach to modeling the binary detection data to avoid specifying a new sampling distribution which would be necessary to directly use the likelihood in MacKenzie et al. (2002, Estimating site occupancy rates when detection probabilities are less than 1. Ecology 83:2248–2255.). The hierarchical half-Cauchy prior for random effect standard deviations is also employed.
DOI: 10.6084/m9.figshare.3550719
title: Appendix A. Subset of a priori hypotheses used to predict the presence of each ungulate species on the landscape at Karongwe Game Reserve, South Africa.

description: Subset of a priori hypotheses used to predict the presence of each ungulate species on the landscape at Karongwe Game Reserve, South Africa.
DOI: 10.6084/m9.figshare.3550719.v1
title: Appendix A. Subset of a priori hypotheses used to predict the presence of each ungulate species on the landscape at Karongwe Game Reserve, South Africa.

description: Subset of a priori hypotheses used to predict the presence of each ungulate species on the landscape at Karongwe Game Reserve, South Africa.
DOI: 10.6084/m9.figshare.3550716
title: Appendix B. Model-averaged parameter estimates for top AIC-ranked models predicting ungulate distribution on the landscape in Karongwe Game Reserve, South Africa.

description: Model-averaged parameter estimates for top AIC-ranked models predicting ungulate distribution on the landscape in Karongwe Game Reserve, South Africa.
DOI: 10.6084/m9.figshare.3550716.v1
title: Appendix B. Model-averaged parameter estimates for top AIC-ranked models predicting ungulate distribution on the landscape in Karongwe Game Reserve, South Africa.

description: Model-averaged parameter estimates for top AIC-ranked models predicting ungulate distribution on the landscape in Karongwe Game Reserve, South Africa.
DOI: 10.6084/m9.figshare.3550713
title: Appendix C. Mean cross-validated Spearman-rank correlations (rs) between the probability of occurrence and bin ranks for each species across sub-samples (n = 5).

description: Mean cross-validated Spearman-rank correlations (rs) between the probability of occurrence and bin ranks for each species across sub-samples (n = 5).
DOI: 10.6084/m9.figshare.3550710
title: Appendix A. Nitrogen excretion estimates by species and stream for the model: log10 (NH4+-N excretion) ~ log10 (wet mass).

description: Nitrogen excretion estimates by species and stream for the model: log10 (NH4+-N excretion) ~ log10 (wet mass).
DOI: 10.6084/m9.figshare.3550707
title: Appendix B. Phosphorus excretion estimates by species and stream for the model: log10 (SRP excretion) ~ log10 (wet mass).

description: Phosphorus excretion estimates by species and stream for the model: log10 (SRP excretion) ~ log10 (wet mass).
DOI: 10.6084/m9.figshare.3550707.v1
title: Appendix B. Phosphorus excretion estimates by species and stream for the model: log10 (SRP excretion) ~ log10 (wet mass).

description: Phosphorus excretion estimates by species and stream for the model: log10 (SRP excretion) ~ log10 (wet mass).
DOI: 10.6084/m9.figshare.3550704
title: Appendix C. Model selection criteria for N and P excretion rates and N:P excretion ratio.

description: Model selection criteria for N and P excretion rates and N:P excretion ratio.
DOI: 10.6084/m9.figshare.3550704.v1
title: Appendix C. Model selection criteria for N and P excretion rates and N:P excretion ratio.

description: Model selection criteria for N and P excretion rates and N:P excretion ratio.
DOI: 10.6084/m9.figshare.3550701
title: Appendix D. Best statistical models (based on lowest corrected Akaike information criterion value) for N and P excretion rates and N:P excretion ratio when points at minimum detection limit are included.

description: Best statistical models (based on lowest corrected Akaike information criterion value) for N and P excretion rates and N:P excretion ratio when points at minimum detection limit are included.
DOI: 10.6084/m9.figshare.3550701.v1
title: Appendix D. Best statistical models (based on lowest corrected Akaike information criterion value) for N and P excretion rates and N:P excretion ratio when points at minimum detection limit are included.

description: Best statistical models (based on lowest corrected Akaike information criterion value) for N and P excretion rates and N:P excretion ratio when points at minimum detection limit are included.
DOI: 10.6084/m9.figshare.3550698
title: Appendix A. Expanded materials and methods.

description: Expanded materials and methods.
DOI: 10.6084/m9.figshare.3550698.v1
title: Appendix A. Expanded materials and methods.

description: Expanded materials and methods.
DOI: 10.6084/m9.figshare.3550695
title: Appendix B. Bayesian Metabolic Model description and example fits to data.

description: Bayesian Metabolic Model description and example fits to data.
DOI: 10.6084/m9.figshare.3550695.v1
title: Appendix B. Bayesian Metabolic Model description and example fits to data.

description: Bayesian Metabolic Model description and example fits to data.
DOI: 10.6084/m9.figshare.3550692
title: Appendix C. Diel δ13C-DIC data from Pick Creek before and after salmon spawning.

description: Diel δ13C-DIC data from Pick Creek before and after salmon spawning.
DOI: 10.6084/m9.figshare.3550692.v1
title: Appendix C. Diel δ13C-DIC data from Pick Creek before and after salmon spawning.

description: Diel δ13C-DIC data from Pick Creek before and after salmon spawning.
DOI: 10.6084/m9.figshare.3550689
title: Appendix D. Table of ecosystem metabolic rates for three salmon streams in southwestern Alaska before, during, and after salmon in 2008 and 2009.

description: Table of ecosystem metabolic rates for three salmon streams in southwestern Alaska before, during, and after salmon in 2008 and 2009.
DOI: 10.6084/m9.figshare.3550689.v1
title: Appendix D. Table of ecosystem metabolic rates for three salmon streams in southwestern Alaska before, during, and after salmon in 2008 and 2009.

description: Table of ecosystem metabolic rates for three salmon streams in southwestern Alaska before, during, and after salmon in 2008 and 2009.
DOI: 10.6084/m9.figshare.3550686
title: Appendix E. The 2008 seasonal trends in PAR and water temperature at Pick Creek, Alaska.

description: The 2008 seasonal trends in PAR and water temperature at Pick Creek, Alaska.
DOI: 10.6084/m9.figshare.3550683
title: Appendix A. Details of the stage-structured model with growth variability.

description: Details of the stage-structured model with growth variability.
DOI: 10.6084/m9.figshare.3550686.v1
title: Appendix E. The 2008 seasonal trends in PAR and water temperature at Pick Creek, Alaska.

description: The 2008 seasonal trends in PAR and water temperature at Pick Creek, Alaska.
DOI: 10.6084/m9.figshare.3550683.v1
title: Appendix A. Details of the stage-structured model with growth variability.

description: Details of the stage-structured model with growth variability.
DOI: 10.6084/m9.figshare.3550680
title: Appendix B. Discretization of juvenile dynamics with growth variability using the master equation.

description: Discretization of juvenile dynamics with growth variability using the master equation.
DOI: 10.6084/m9.figshare.3550680.v1
title: Appendix B. Discretization of juvenile dynamics with growth variability using the master equation.

description: Discretization of juvenile dynamics with growth variability using the master equation.
DOI: 10.6084/m9.figshare.3550677
title: Appendix C. Relation between coefficient of variation and diffusion coefficient for models 1 and 2.

description: Relation between coefficient of variation and diffusion coefficient for models 1 and 2.
DOI: 10.6084/m9.figshare.3550674
title: Appendix D. Notes on numerical bifurcation of the Daphnia-algae system with growth variability.

description: Notes on numerical bifurcation of the Daphnia-algae system with growth variability.
DOI: 10.6084/m9.figshare.3550677.v1
title: Appendix C. Relation between coefficient of variation and diffusion coefficient for models 1 and 2.

description: Relation between coefficient of variation and diffusion coefficient for models 1 and 2.
DOI: 10.6084/m9.figshare.3550674.v1
title: Appendix D. Notes on numerical bifurcation of the Daphnia-algae system with growth variability.

description: Notes on numerical bifurcation of the Daphnia-algae system with growth variability.
DOI: 10.6084/m9.figshare.3550671
title: Appendix E. Resonant period of quasi-cycles for models 1 and 2.

description: Resonant period of quasi-cycles for models 1 and 2.
DOI: 10.6084/m9.figshare.3550671.v1
title: Appendix E. Resonant period of quasi-cycles for models 1 and 2.

description: Resonant period of quasi-cycles for models 1 and 2.
DOI: 10.6084/m9.figshare.3550668
title: Appendix A. Model rescaling, parameter interpretation, and equilibrium analysis.

description: Model rescaling, parameter interpretation, and equilibrium analysis.
DOI: 10.6084/m9.figshare.3550668.v1
title: Appendix A. Model rescaling, parameter interpretation, and equilibrium analysis.

description: Model rescaling, parameter interpretation, and equilibrium analysis.
DOI: 10.6084/m9.figshare.3550665
title: Appendix A. Locations and sample sizes for eastern North American adult monarchs captured during the fall migration and overwintering period.

description: Locations and sample sizes for eastern North American adult monarchs captured during the fall migration and overwintering period.
DOI: 10.6084/m9.figshare.3550665.v1
title: Appendix A. Locations and sample sizes for eastern North American adult monarchs captured during the fall migration and overwintering period.

description: Locations and sample sizes for eastern North American adult monarchs captured during the fall migration and overwintering period.
DOI: 10.6084/m9.figshare.3550662
title: Appendix B. Map of volunteer participants in two citizen science monarch butterfly programs, Project Monarch Health and Monarch Larva Monitoring Project.

description: Map of volunteer participants in two citizen science monarch butterfly programs, Project Monarch Health and Monarch Larva Monitoring Project.
DOI: 10.6084/m9.figshare.3550662.v1
title: Appendix B. Map of volunteer participants in two citizen science monarch butterfly programs, Project Monarch Health and Monarch Larva Monitoring Project.

description: Map of volunteer participants in two citizen science monarch butterfly programs, Project Monarch Health and Monarch Larva Monitoring Project.
DOI: 10.6084/m9.figshare.3550659
title: Appendix C. Generalized linear model results for the effects of year, region, breeding-season phase, and host density on parasite prevalence from Project Monarch Health and Monarch Larva Monitoring Project from 2006–2009.

description: Generalized linear model results for the effects of year, region, breeding-season phase, and host density on parasite prevalence from Project Monarch Health and Monarch Larva Monitoring Project from 2006–2009.
DOI: 10.6084/m9.figshare.3550659.v1
title: Appendix C. Generalized linear model results for the effects of year, region, breeding-season phase, and host density on parasite prevalence from Project Monarch Health and Monarch Larva Monitoring Project from 2006–2009.

description: Generalized linear model results for the effects of year, region, breeding-season phase, and host density on parasite prevalence from Project Monarch Health and Monarch Larva Monitoring Project from 2006–2009.
DOI: 10.6084/m9.figshare.3550656
title: Appendix A. Laboratory and field data on cannibalism in Geocoris pallens, and predation rates of Zelus renardii on G. pallens.

description: Laboratory and field data on cannibalism in Geocoris pallens, and predation rates of Zelus renardii on G. pallens.