DOI: 10.5061/dryad.fxpnvx0rv
title: Ignoring species availability biases occupancy estimates in single-scale occupancy models

description: 1. Most applications of single-scale occupancy models do not differentiate
 between availability and detectability, even though species availability
 is rarely equal to one. Species availability can be estimated using
 multi-scale occupancy models, and the availability process includes
 elements of species movement, behavior, and phenology. However, for the
 practical application of multi-scale occupancy models, it can be unclear
 what a robust sampling design looks like and what the statistical
 properties of the multi-scale and single-scale occupancy models are when
 availability is less than one. 2. Using simulations, we explore the
 following common questions asked by ecologists during the design phase of
 a field study: (Q1) what is a robust sampling design for the multi-scale
 occupancy model when there are a priori expectations of parameter
 estimates?, (Q2) what is a robust sampling design when we have no
 expectations of parameter estimates?, and (Q3) can a single-scale
 occupancy model with a random effects term adequately absorb the extra
 heterogeneity produced when availability is less than one and provide
 reliable estimates of occupancy probability?. 3. Our results show that
 there is a tradeoff between the number of sites and surveys needed to
 achieve a specified level of acceptable error for occupancy estimates
 using the multi-scale occupancy model. We also document that when species
 availability is low (&lt; 0.40 on the probability scale), then
 single-scale occupancy models underestimate occupancy by as much as 0.40
 on the probability scale, produce overly precise estimates, and provide
 poor parameter coverage. This pattern was observed when a random effects
 term was and was not included in the single-scale occupancy model,
 suggesting that adding a random-effects term does not adequately absorb
 the extra heterogeneity produced by the availability process. In contrast,
 when species availability was high (&gt; 0.60), single-scale occupancy
 models performed similarly to the multi-scale occupancy model. 4. As a
 companion, we provide an RShiny app that allows users to further explore
 our results and sampling designs across a number of different scenarios
 https://gdirenzo.shinyapps.io/multi-scale-occ/. Our results suggest that
 unaccounted for availability can lead to underestimating species
 distributions when using single-scale occupancy models, which can have
 large implications on ecological inference and predictions for
 practitioners, such as those working at the front lines of invasion
 ecology, disease emergence, and species conservation.  
description: These files were written by: G. V. DiRenzo If
 you have any questions, please email: gdirenzo@umass.edu
 <strong>USGS
 Disclaimer</strong> Unless otherwise
 stated, all data, metadata, and related materials are considered to
 satisfy the quality standards relative to the purpose for which the data
 were collected. Although these data and associated metadata have been
 reviewed for accuracy and completeness and approved for release by the
 U.S. Geological Survey (USGS), no warranty expressed or implied is made
 regarding the display or utility of the data for other purposes, nor on
 all computer systems, nor shall the act of distribution constitute any
 such warranty. <strong>README
 objective</strong> This file is
 intended to help navigate through the Data, Software, and Supplemental
 files associated with the manuscript: DiRenzo, G. V., D. A. W. Miller, E.
 H. C. Grant. Ignoring species availability biases occupancy estimates in
 single-scale occupancy models. All data in this
 repository was simulated. We navigate the user to the files where they can
 simulate the data, analyze the data, process the data, and create the
 manuscript figures and tables. With the code listed
 below, any user should be able to reproduce all of the tables and figures
 in the manuscript. In addition, the user should also be
 able to reproduce the RShiny app:
 https://gdirenzo.shinyapps.io/multi-scale-occ/ <strong>Table of
 Contents</strong> 1. Navigate to
 files that generate a spreadsheet with different parameter values and
 survey designs that were used to simulate datasets 2.
 Navigate to files that simulate & analyze data on the cluster when
 availability is constant across sites (Scenario 1) 3.
 Navigate to files that perform the post-processing for Section II. Q1 in
 the main text of the manuscript 4. Navigate to files
 that perform the post-processing for Section II. Q2 in the main text of
 the manuscript 5. Navigate to files that simulate
 & analyze data on the cluster when availability is heterogenous
 across sites (Scenario 2) 6. Navigate to files that
 simulate & analyze data on the cluster when availability is
 heterogenous across years (Scenario 3) 7. Navigate to
 files that simulate & analyze data on the cluster when
 availability is correlated to detection probability (Scenario 4)
 8. Navigate to files that perform the post-processing for Section
 II. Q3 in the main text of the manuscript 9. List of
 other files/folders in the repo that are not listed here
 10. RShiny app files
 -----------------------------------------------
 1. Navigate to files that generate a spreadsheet with different
 parameter values and survey designs that were used to simulate datasets
 across Scenarios 1 - 4
 -----------------------------------------------
 For code
 that generated the parameter values & sampling designs for
 Scenarios 1 & 2:
 <strong>Software/ParameterCombinations/LHS_parameter_combos.R</strong> This script generates 1 csv file: <em>parameter_combos_TwoLevelAvail4.csv</em> This file can be located in: Data/ParameterCombinations/parameter_combos_TwoLevelAvail4.csv Information related to fields are located in: Data/Metadata/TwoLevel-Metadata 2021 01 24.xml Once the file is open (in MetaData Wizard), navigate to "Entity and Attributes" along the top Then, along the left, there are several "Detailed" tabs - click through them and locate the one with the "Dataset Label" =  parameter_combos_TwoLevelAvail4.cs Briefly, here is a description of each field in the file: parameter_combos_TwoLevelAvail4.csv n.site = Number of sites n.sec.surveys = Number of secondary surveys n.tier.surveys = Number of tertiary surveys psi = True Occupancy probability - on the logit scale availability = True Availability - on the logit scale detection = True Detection probability - on the logit scale For code that generated the parameter values & sampling designs for Scenarios 3 & 4: <strong>Software/ParameterCombinations/LHS_parameter_combos_years.R</strong> This script generates 1 csv file: <em>parameter_combos_TwoLevelAvail_years.csv</em> That file is located: Data/ParameterCombinations/parameter_combos_TwoLevelAvail_years.csv Information related to fields are located in: Data/Metadata/TwoLevel-Metadata 2021 01 24.xml Once the file is open (in MetaData Wizard), navigate to "Entity and Attributes" along the top Then, along the left, there are several "Detailed" tabs - click through them and locate the one with the "Dataset Label" =  parameter_combos_TwoLevelAvail_years.csv Briefly, here is a description of each field in the file: parameter_combos_TwoLevelAvail_years.csv n.site = Number of sites n.sec.surveys = Number of secondary surveys n.tier.surveys = Number of tertiary surveys n.season = Number of years psi = True Occupancy probability - on the logit scale availability = True Availability - on the logit scale detection = True Detection probability - on the logit scale sd = Not used To find a detailed description of the fields in the file parameter_combos_TwoLevelAvail_years.csv, please navigate to: Data/Metadata/TwoLevel-Metadata 2021 01 24.xml This file can either be opened in a text editor or using the USGS software "Metadata wizard" (https://usgs.github.io/fort-pymdwizard/) ----------------------------------------------- 2. Navigate to files that simulate & analyze data on the cluster when availability is constant across sites (Scenario 1) ----------------------------------------------- Data simulated: Availability is constant across sites (but less than 1) Models used to analyze the data: (i) Constant multi-scale occupancy model and (iii) Constant single-scale occupancy model To simulate & analyze the datasets on the cluster: Software/SimulationCode/Scen1_Constant.sh The corresponding R file for the cluster script is: Software/SimulationCode/Scen1_Constant.R ----------------------------------------------- 3. Navigate to files that perform the post-processing for Section II. Q1 in the main text of the manuscript ----------------------------------------------- For code that does the post-processing for Section II. Q1.: <strong>Software/PostProcessing/II. Q1. TwoLevelAvail_SampDesign_ParamEstimate.R</strong> This file generates: <em>Table S2; Figures S1, S2, S3, and S4</em> These files can be located in: Supplemental-Information/Tables/TableS2_ParamCombos.csv Supplemental-Information/Figures/ParameterEstimates/FigS1_ERROR_SampDesign_2v4tertsurvs.pdf Supplemental-Information/Figures/ParameterEstimates/FigS2_WIDTH_SampDesign_2v4tertsurvs.pdf Supplemental-Information/Figures/ParameterEstimates/FigS3_BIAS_SampDesign_2v4tertsurvs.pdf Supplemental-Information/Figures/ParameterEstimates/FigS4_COV_SampDesign_2v4tertsurvs.pdf ----------------------------------------------- 4. Navigate to files that perform the post-processing for Section II. Q2. ----------------------------------------------- For code that does the post-processing for Section II. Q2.: <strong>Software/SamplingDesign/PostProcessing/II. Q2. TwoLevelAvail_GenRec.R</strong> This file generates: <em>Table 1</em> This file can be found in: Supplemental-Information/Tables/Table1_GenSampRec.csv ----------------------------------------------- 5. Navigate to files that simulate & analyze data on the cluster when availability is heterogenous across sites (Scenario 2) ----------------------------------------------- <strong>Scenario 2</strong> Data simulated: Availability is heterogenous across sites Models used: (i) the multi-scale occupancy model with constant availability across sites, (ii) the multi-scale occupancy model with a random site-effects term for availability, (iii) the single-scale occupancy model with constant detection across sites, and (iv) the single-scale occupancy model with a random site-effects term for detection To simulate & analyze the datasets on the cluster: Software/SimulationCode/Scen2_HeteroSite.sh The corresponding R file for the cluster script is: Software/SimulationCode/Scen2_HeteroSite.R ----------------------------------------------- 6. Navigate to files that simulate & analyze data on the cluster when availability is heterogenous across years (Scenario 3) ----------------------------------------------- <strong>Scenario 3</strong> Data simulated: Availability is heterogenous across years Models used: (i) the multi-scale occupancy model with constant availability across years, (ii) the multi-scale occupancy model with a random year-effects term for availability, (iii) the single-scale occupancy model with constant detection across years, and (iv) the single-scale occupancy model with a random year-effects term for detection To simulate & analyze the datasets on the cluster: Software/SimulationCode/Scen3_HeteroYear.sh The corresponding R file for the cluster script is: Software/SimulationCode/Scen3_HeteroYear.R ----------------------------------------------- 7. Navigate to files that simulate & analyze data on the cluster when availability is correlated to detection probability (Scenario 4) ----------------------------------------------- <strong>Scenario 4</strong> Data simulated: Availability correlated to detection probability Models used: (i) the multi-scale occupancy model with constant availability across years, (ii) the multi-scale occupancy model with a random year-effects term for availability, (iii) the single-scale occupancy model with constant detection across years, and (iv) the single-scale occupancy model with a random year-effects term for detection To simulate & analyze the datasets on the cluster: Software/SimulationCode/Scen4_Corr.sh The corresponding R file for the cluster script is: Software/SimulationCode/Scen4_Corr.R ----------------------------------------------- 8. Navigate to files that perform the post-processing for Section II. Q3 in the main text of the manuscript ----------------------------------------------- For code that does the post-processing for Section II. Q3.: <strong>Software/PostProcessing/II. Q3. Scenario 1-4- Model performance.R</strong> This file generates: <em>Figure 2, 3, 4, & 5</em> These files can be located in: Supplemental-Information/Figures/ModComp/Fig2 - Accuracy.pdf Supplemental-Information/Figures/ModComp/Fig3 - Precision.pdf Supplemental-Information/Figures/ModComp/Fig4 - Bias.pdf Supplemental-Information/Figures/ModComp/Fig5 - Coverage.pdf ----------------------------------------------- 9. List of other files/folders in the repo that are not listed here ----------------------------------------------- Folder that holds all of the manuscript figures <strong>Supplemental-Information/Figures/</strong> Main text figures: Supplemental-Information/Figures/ModComp/ Appendix figures: Supplemental-Information/Figures/ParameterEstimates Folder that holds all of the manuscript tables <strong>Supplemental-Information/Tables</strong> Folder that holds all of the model output generated by Scenario 1 <strong>Data/ModelOutput_Scen1_TwolevelSim</strong> Information related to fields are located in: Data/Metadata/TwoLevel-Metadata 2021 01 24.xml Once the file is open (in MetaData Wizard), navigage to "Entity and Attributes" along the top Then, along the left, there are several "Detailed" tabs - click through them and locate the one with the "Dataset Label" = Results_TwoLevelAvail_2lev_x.csv Briefly, here is a description of each field in the file: Results_TwoLevelAvail_2lev_x.csv n.site = Number of sites n.sec.surveys = Number of secondary surveys n.tier.surveys = Number of tertiary surveys psi = True occupancy probability- logit scale availability = True availability- logit scale detection&lt; = True detection probability- logit scale Prop_avail = NA- not used Prop_detect = NA- not used Prop_overall = NA- not used Prop_site = NA- not used TwoLev_alpha.psi_Mean = Multi-scale occupancy model mean estimate of occupancy probability on the logit scale TwoLev_alpha.psi_ylo = Multi-scale occupancy model lower 95% CI estimate of occupancy probability on the logit scale TwoLev_alpha.psi_yhi = Multi-scale occupancy model upper 95% CI estimate of occupancy probability on the logit scale TwoLev_alpha.availability_Mean = Multi-scale occupancy model mean estimate of availability probability on the logit scale TwoLev_alpha.availability_ylo = Multi-scale occupancy model lower 95% CI estimate of availability probability on the logit scale TwoLev_alpha.availability_yhi = Multi-scale occupancy model upper 95% CI estimate of availability probability on the logit scale TwoLev_alpha.p_Mean = Multi-scale occupancy model mean estimate of detection probability on the logit scale TwoLev_alpha.p_ylo = Multi-scale occupancy model lower 95% CI estimate of detection probability on the logit scale TwoLev_alpha.p_yhi = Multi-scale occupancy model upper 95% CI estimate of detection probability on the logit scale OneLev_alpha.psi_Mean = Single-scale occupancy model mean estimate of occupancy probability on the logit scale OneLev_alpha.psi_ylo = Single-scale occupancy model lower 95% CI estimate of occupancy probability on the logit scale OneLev_alpha.psi_yhi = Single-scale occupancy model upper 95% CI estimate of occupancy probability on the logit scale OneLev_alpha.p_Mean = Single-scale occupancy model mean estimate of detection probability on the logit scale OneLev_alpha.p_ylo = Single-scale occupancy model lower 95% CI estimate of detection probability on the logit scale OneLev_alpha.p_yhi = Single-scale occupancy model upper 95% CI estimate of detection probability on the logit scale Rhat_check.2lev = Rhat check for convergence of the Multi-scale occupancy model. Value = 1 means yes the model converged. Value = 0 or NA means no the model did not converge Rhat_check.1lev = Rhat check for convergence of the Single-scale occupancy model. Value = 1 means yes the model converged.Value = 0 or NA means no the model did not converge Folder that holds all of the model output generated by Scenario 2 <strong>Data/ModelOutput_Scen2_HeteroSite</strong> Information related to fields are located in: Data/Metadata/TwoLevel-Metadata 2021 01 24.xml Once the file is open (in MetaData Wizard), navigate to "Entity and Attributes" along the top Then, along the left, there are several "Detailed" tabs - click through them and locate the one with the "Dataset Label" = Results_TwoLevelAvail_Hetero_x.csv Briefly, here is a description of each field in the file: Results_TwoLevelAvail_Hetero_x.csv n.site = Number of sites n.sec.surveys = Number of secondary surveys n.tier.surveys = Number of tertiary surveys psi = True occupancy probability- logit scale availability = True availability- logit scale detection = True detection probability- logit scale stdev = NA- not used Prop_avail = NA- not used Prop_detect = NA- not used Prop_overall = NA- not used Prop_site = NA- not used TwoLev_Con_alpha.psi_Mean = Multi-scale occupancy model with fixed parameters mean estimate of occupancy probability on the logit scale TwoLev_Con_alpha.psi_ylo = Multi-scale occupancy model with fixed parameters lower 95% CI estimate of occupancy probability on the logit scale TwoLev_Con_alpha.psi_yhi = Multi-scale occupancy model with fixed parameters upper 95% CI estimate of occupancy probability on the logit scale TwoLev_Con_alpha.availability_Mean = Multi-scale occupancy model with fixed parameters mean estimate of availability probability on the logit scale TwoLev_Con_alpha.availability_ylo = Multi-scale occupancy model with fixed parameters lower 95% CI estimate of availability probability on the logit scale TwoLev_Con_alpha.availability_yhi = Multi-scale occupancy model with fixed parameters upper 95% CI estimate of availability probability on the logit scale TwoLev_Con_alpha.p_Mean = Multi-scale occupancy model with fixed parameters mean estimate of detection probability on the logit scale TwoLev_Con_alpha.p_ylo = Multi-scale occupancy model with fixed parameters lower 95% CI estimate of detection probability on the logit scale TwoLev_Con_alpha.p_yhi&lt; = Multi-scale occupancy model with fixed parameters upper 95% CI estimate of detection probability on the logit scale TwoLev_Hetero_alpha.psi_Mean = Multi-scale occupancy model with random-effects parameters mean estimate of occupancy probability on the logit scale TwoLev_Hetero_alpha.psi_ylo = Multi-scale occupancy model with random-effects parameters lower 95% CI estimate of occupancy probability on the logit scale TwoLev_Hetero_alpha.psi_yhi = Multi-scale occupancy model with random-effects parameters upper 95% CI estimate of occupancy probability on the logit scale TwoLev_Hetero_alpha.availability_Mean = Multi-scale occupancy model with random-effects parameters mean estimate of availability probability on the logit scale TwoLev_Hetero_alpha.availability_ylo = Multi-scale occupancy model with random-effects parameters lower 95% CI estimate of availability probability on the logit scale TwoLev_Hetero_alpha.availability_yhi = Multi-scale occupancy model with random-effects parameters upper 95% CI estimate of availability probability on the logit scale TwoLev_Hetero_alpha.p_Mean&lt; = Multi-scale occupancy model with random-effects parameters mean estimate of detection probability on the logit scale TwoLev_Hetero_alpha.p_ylo = Multi-scale occupancy model with random-effects parameters lower 95% CI estimate of detection probability on the logit scale TwoLev_Hetero_alpha.p_yhi = Multi-scale occupancy model with random-effects parameters upper 95% CI estimate of detection probability on the logit scale TwoLev_Hetero_stdev_Mean = Multi-scale occupancy model with random-effects parameters mean estimate of sigma TwoLev_Hetero_stdev_ylo = Multi-scale occupancy model with random-effects parameters lower 95% CI estimate of sigma TwoLev_Hetero_stdev_yhi = Multi-scale occupancy model with random-effects parameters upper 95% CI estimate of sigma OneLev_Con_alpha.psi_Mean = Single-scale occupancy model with fixed effects mean estimate of occupancy probability on the logit scale OneLev_Con_alpha.psi_ylo = Single-scale occupancy model with fixed effects lower 95% CI estimate of occupancy probability on the logit scale OneLev_Con_alpha.psi_yhi = Single-scale occupancy model with fixed effects upper 95% CI estimate of occupancy probability on the logit scale OneLev_Con_alpha.p_Mean = Single-scale occupancy model with fixed effects mean estimate of detection probability on the logit scale OneLev_Con_alpha.p_ylo = Single-scale occupancy model with fixed effects lower 95% CI estimate of detection probability on the logit scale OneLev_Con_alpha.p_yhi = Single-scale occupancy model with fixed effects upper 95% CI estimate of detection probability on the logit scale OneLev_Hetero_alpha.psi_Mean = Single-scale occupancy model with random effects mean estimate of occupancy probability on the logit scale OneLev_Hetero_alpha.psi_ylo = Single-scale occupancy model with random effects lower 95% CI estimate of occupancy probability on the logit scale OneLev_Hetero_alpha.psi_yhi = Single-scale occupancy model with random effects upper 95% CI estimate of occupancy probability on the logit scale OneLev_Hetero_alpha.p_Mean = Single-scale occupancy model with random effects mean estimate of detection probability on the logit scale OneLev_Hetero_alpha.p_ylo = Single-scale occupancy model with random effects lower 95% CI estimate of detection probability on the logit scale OneLev_Hetero_alpha.p_yhi = Single-scale occupancy model with random effects upper 95% CI estimate of detection probability on the logit scale OneLev_Hetero_stdev_Mean = Single-scale occupancy model with random-effects parameters mean estimate of sigma OneLev_Hetero_stdev_ylo = Single-scale occupancy model with random-effects parameters lower 95% CI estimate of sigma OneLev_Hetero_stdev_yhi = Single-scale occupancy model with random-effects parameters upper 95% CI estimate of sigma Rhat_check.2lev.con = Following model run completion, we checked the Rhat values across all parameters for convergence. Specifically, we checked to see if the Rhat vale for each parameter was &lt; 1.1. If all Rhat values were &lt; 1.1, then we assigned a value of 1 in this column. If all the parameters did not converge, then a value of 0 was assigned. There are some cases with a value of NA, which means that the model was not run. This Rhat check is specifically for the Multi-scale occupancy model with fixed effects. Rhat_check.2lev.hetero = Following model run completion, we checked the Rhat values across all parameters for convergence. Specifically, we checked to see if the Rhat vale for each parameter was &lt; 1.1. If all Rhat values were &lt; 1.1, then we assigned a value of 1 in this column. If all the parameters did not converge, then a value of 0 was assigned. There are some cases with a value of NA, which means that the model was not run. This Rhat check is specifically for the Multi-scale occupancy model with random effects. Rhat_check.1lev.con = Following model run completion, we checked the Rhat values across all parameters for convergence. Specifically, we checked to see if the Rhat vale for each parameter was &lt; 1.1. If all Rhat values were &lt; 1.1, then we assigned a value of 1 in this column. If all the parameters did not converge, then a value of 0 was assigned. There are some cases with a value of NA, which means that the model was not run. This Rhat check is specifically for the Site-occupancy model with fixed effects. Rhat_check.1lev.hetero = Following model run completion, we checked the Rhat values across all parameters for convergence. Specifically, we checked to see if the Rhat vale for each parameter was &lt; 1.1. If all Rhat values were &lt; 1.1, then we assigned a value of 1 in this column. If all the parameters did not converge, then a value of 0 was assigned. There are some cases with a value of NA, which means that the model was not run. This Rhat check is specifically for the Site-occupancy model with random effects.  Folder that holds all of the model output generated by Scenario 3 <strong>Data/ModelOutput_Scen3_HeteroYear</strong> Information related to fields are located in: Data/Metadata/TwoLevel-Metadata 2021 01 24.xml Once the file is open (in MetaData Wizard), navigage to "Entity and Attributes" along the top Then, along the left, there are several "Detailed" tabs - click through them and locate the one with the "Dataset Label" = Results_TwoLevelAvail_HeteroSeason_x.csv Briefly, here is a description of each field in the file: Results_TwoLevelAvail_HeteroSeason_x.csv n.site = Number of sites n.sec.surveys = Number of secondary surveys n.tier.surveys = Number of tertiary surveys n.season = Number of seasons psi = True occupancy probability- logit scale availability = True availability- logit scale detection = True detection probability- logit scale sd = NA- not used Prop_avail = NA- not used Prop_detect = NA- not used Prop_overall = NA- not used Prop_site = NA- not used TwoLev_Con_alpha.psi_Mean = Multi-scale occupancy model with fixed parameters mean estimate of occupancy probability on the logit scale TwoLev_Con_alpha.psi_ylo = Multi-scale occupancy model with fixed parameters lower 95% CI estimate of occupancy probability on the logit scale TwoLev_Con_alpha.psi_yhi = Multi-scale occupancy model with fixed parameters upper 95% CI estimate of occupancy probability on the logit scale TwoLev_Con_alpha.availability_Mean = Multi-scale occupancy model with fixed parameters mean estimate of availability probability on the logit scale TwoLev_Con_alpha.availability_ylo = Multi-scale occupancy model with fixed parameters lower 95% CI estimate of availability probability on the logit scale TwoLev_Con_alpha.availability_yhi = Multi-scale occupancy model with fixed parameters upper 95% CI estimate of availability probability on the logit scale TwoLev_Con_alpha.p_Mean = Multi-scale occupancy model with fixed parameters mean estimate of detection probability on the logit scale TwoLev_Con_alpha.p_ylo = Multi-scale occupancy model with fixed parameters lower 95% CI estimate of detection probability on the logit scale TwoLev_Con_alpha.p_yhi = Multi-scale occupancy model with fixed parameters upper 95% CI estimate of detection probability on the logit scale TwoLev_Hetero_alpha.psi_Mean = Multi-scale occupancy model with random-effects parameters mean estimate of occupancy probability on the logit scale TwoLev_Hetero_alpha.psi_ylo = Multi-scale occupancy model with random-effects parameters lower 95% CI estimate of occupancy probability on the logit scale TwoLev_Hetero_alpha.psi_yhi = Multi-scale occupancy model with random-effects parameters upper 95% CI estimate of occupancy probability on the logit scale TwoLev_Hetero_alpha.availability_Mean = Multi-scale occupancy model with random-effects parameters mean estimate of availability probability on the logit scale TwoLev_Hetero_alpha.availability_ylo = Multi-scale occupancy model with random-effects parameters lower 95% CI estimate of availability probability on the logit scale TwoLev_Hetero_alpha.availability_yhi = Multi-scale occupancy model with random-effects parameters upper 95% CI estimate of availability probability on the logit scale TwoLev_Hetero_alpha.p_Mean = Multi-scale occupancy model with random-effects parameters mean estimate of detection probability on the logit scale TwoLev_Hetero_alpha.p_ylo = Multi-scale occupancy model with random-effects parameters lower 95% CI estimate of detection probability on the logit scale TwoLev_Hetero_alpha.p_yhi = Multi-scale occupancy model with random-effects parameters upper 95% CI estimate of detection probability on the logit scale TwoLev_Hetero_stdev_Mean = Multi-scale occupancy model with random-effects parameters mean estimate of sigma TwoLev_Hetero_stdev_ylo = Multi-scale occupancy model with random-effects parameters lower 95% CI estimate of sigma TwoLev_Hetero_stdev_yhi = Multi-scale occupancy model with random-effects parameters upper 95% CI estimate of sigma OneLev_Con_alpha.psi_Mean = Single-scale occupancy model with fixed effects mean estimate of occupancy probability on the logit scale OneLev_Con_alpha.psi_ylo = Single-scale occupancy model with fixed effects lower 95% CI estimate of occupancy probability on the logit scale OneLev_Con_alpha.psi_yhi = Single-scale occupancy model with fixed effects upper 95% CI estimate of occupancy probability on the logit scale OneLev_Con_alpha.p_Mean = Single-scale occupancy model with fixed effects lower 95% CI estimate of detection probability on the logit scale OneLev_Con_alpha.p_ylo = Single-scale occupancy model with fixed effects lower 95% CI estimate of detection probability on the logit scale OneLev_Con_alpha.p_yhi = Single-scale occupancy model with fixed effects upper 95% CI estimate of detection probability on the logit scale OneLev_Hetero_alpha.psi_Mean = Single-scale occupancy model with random effects mean estimate of occupancy probability on the logit scale OneLev_Hetero_alpha.psi_ylo = Single-scale occupancy model with random effects lower 95% CI estimate of occupancy probability on the logit scale OneLev_Hetero_alpha.psi_yhi = Single-scale occupancy model with random effects upper 95% CI estimate of occupancy probability on the logit scale OneLev_Hetero_alpha.p_Mean = Single-scale occupancy model with random effects mean estimate of detection probability on the logit scale OneLev_Hetero_alpha.p_ylo = Single-scale occupancy model with random effects lower 95% CI estimate of detection probability on the logit scale OneLev_Hetero_alpha.p_yhi = Single-scale occupancy model with random effects upper 95% CI estimate of detection probability on the logit scale OneLev_Hetero_stdev_Mean = Single-scale occupancy model with random-effects parameters mean estimate of sigma OneLev_Hetero_stdev_ylo = Single-scale occupancy model with random-effects parameters lower 95% CI estimate of sigma OneLev_Hetero_stdev_yhi = Single-scale occupancy model with random-effects parameters upper 95% CI estimate of sigma Rhat_check.2lev.con = Following model run completion, we checked the Rhat values across all parameters for convergene. Specifically, we checked to see if the Rhat vale for each parameter was &lt; 1.1. If all Rhat values were &lt; 1.1, then we assigned a value of 1 in this column. If all the parameters did not converge, then a value of 0 was assigned. There are some cases with a value of NA, which means that the model was not run. This Rhat check is specifically for the Multi-scale occupancy model with fixed effects. Rhat_check.2lev.hetero = Following model run completion, we checked the Rhat values across all parameters for convergene. Specifically, we checked to see if the Rhat vale for each parameter was &lt; 1.1. If all Rhat values were &lt; 1.1, then we assigned a value of 1 in this column. If all the parameters did not converge, then a value of 0 was assigned. There are some cases with a value of NA, which means that the model was not run. This Rhat check is specifically for the Multi-scale occupancy model with random effects. Rhat_check.1lev.con = Following model run completion, we checked the Rhat values across all parameters for convergene. Specifically, we checked to see if the Rhat vale for each parameter was &lt; 1.1. If all Rhat values were &lt; 1.1, then we assigned a value of 1 in this column. If all the parameters did not converge, then a value of 0 was assigned. There are some cases with a value of NA, which means that the model was not run. This Rhat check is specifically for the Site-occupancy model with fixed effects. Rhat_check.1lev.hetero = Following model run completion, we checked the Rhat values across all parameters for convergene. Specifically, we checked to see if the Rhat vale for each parameter was &lt; 1.1. If all Rhat values were &lt; 1.1, then we assigned a value of 1 in this column. If all the parameters did not converge, then a value of 0 was assigned. There are some cases with a value of NA, which means that the model was not run. This Rhat check is specifically for the Site-occupancy model with random effects.  Folder that holds all of the model output generated by Scenario 4 (item 7 in this list) <strong>Data/ModelOutput_Scen4_Cor</strong> Information related to fields are located in: Data/Metadata/TwoLevel-Metadata 2021 01 24.xml Once the file is open (in MetaData Wizard), navigage to "Entity and Attributes" along the top Then, along the left, there are several "Detailed" tabs - click through them and locate the one with the "Dataset Label" = Results_TwoLevelAvail_Cor_x.csv Briefly, here is a description of each field in the file: Results_TwoLevelAvail_Cor_x.csv n.site = Number of sites n.sec.surveys = Number of secondary surveys n.tier.surveys = Number of tertiary surveys n.season = Number of seasons psi = True occupancy probability- logit scale availability = True availability- logit scale detection = True detection probability- logit scale sd = NA- not used stdev = NA- not used u.cor = NA- not used v.cor = NA- not used Prop_avail = NA- not used Prop_detect = NA- not used Prop_overall = NA- not used Prop_site = NA- not used TwoLev_Con_alpha.psi_Mean = Multi-scale occupancy model with fixed parameters mean estimate of occupancy probability on the logit scale TwoLev_Con_alpha.psi_ylo = Multi-scale occupancy model with fixed parameters lower 95% CI estimate of occupancy probability on the logit scale TwoLev_Con_alpha.psi_yhi = Multi-scale occupancy model with fixed parameters upper 95% CI estimate of occupancy probability on the logit scale TwoLev_Con_alpha.availability_Mean = Multi-scale occupancy model with fixed parameters mean estimate of availability probability on the logit scale TwoLev_Con_alpha.availability_ylo = Multi-scale occupancy model with fixed parameters lower 95% CI estimate of availability probability on the logit scale TwoLev_Con_alpha.availability_yhi = Multi-scale occupancy model with fixed parameters upper 95% CI estimate of availability probability on the logit scale TwoLev_Con_alpha.p_Mean = Multi-scale occupancy model with fixed parameters mean estimate of detection probability on the logit scale TwoLev_Con_alpha.p_ylo = Multi-scale occupancy model with fixed parameters lower 95% CI estimate of detection probability on the logit scale TwoLev_Con_alpha.p_yhi = Multi-scale occupancy model with fixed parameters upper 95% CI estimate of detection probability on the logit scale TwoLev_HeteroCor_alpha.psi_Mean = Multi-scale occupancy model with random-effects and correlation mean estimate of occupancy probability on the logit scale TwoLev_HeteroCor_alpha.psi_ylo = Multi-scale occupancy model with random-effects and correlation lower 95% CI estimate of occupancy probability on the logit scale TwoLev_HeteroCor_alpha.psi_yhi = Multi-scale occupancy model with random-effects and correlation upper 95% CI estimate of occupancy probability on the logit scale TwoLev_HeteroCor_alpha.availability_Mean = Multi-scale occupancy model with random-effects and correlation mean estimate of availability probability on the logit scale TwoLev_HeteroCor_alpha.availability_ylo = Multi-scale occupancy model with random-effects and correlation lower 95% CI estimate of availability probability on the logit scale TwoLev_HeteroCor_alpha.availability_yhi = Multi-scale occupancy model with random-effects and correlation upper 95% CI estimate of availability probability on the logit scale TwoLev_HeteroCor_u.cor_Mean = Multi-scale occupancy model with random-effects and correlation mean estimate of intercept value for detection part of the model on the logit scale TwoLev_HeteroCor_u.cor_ylo = Multi-scale occupancy model with random-effects and correlation lower 95% CI estimate of intercept value for detection part of the model on the logit scale TwoLev_HeteroCor_u.cor_yhi = Multi-scale occupancy model with random-effects and correlation upper 95% CI estimate of intercept value for detection part of the model on the logit scale TwoLev_HeteroCor_v.cor_Mean = Multi-scale occupancy model with random-effects and correlation mean estimate of slope value for detection part of the model on the logit scale TwoLev_HeteroCor_v.cor_ylo = Multi-scale occupancy model with random-effects and correlation lower 95% CI estimate of slope value for detection part of the model on the logit scale TwoLev_HeteroCor_v.cor_yhi = Multi-scale occupancy model with random-effects and correlation upper 95% CI estimate of slope value for detection part of the model on the logit scale TwoLev_HeteroCor_stdev_Mean = Multi-scale occupancy model with random-effects and correlation mean estimate of sigma TwoLev_HeteroCor_stdev_ylo = Multi-scale and correlation model with random-effects parameters upper 95% CI estimate of sigma TwoLev_HeteroCor_stdev_yhi = Multi-scale and correlation model with random-effects parameters upper 95% CI estimate of sigma OneLev_Con_alpha.psi_Mean = Site-occupancy model with fixed effects mean estimate of occupancy probability on the logit scale OneLev_Con_alpha.psi_ylo = Site-occupancy model with fixed effects lower 95% CI estimate of occupancy probability on the logit scale OneLev_Con_alpha.psi_yhi = Site-occupancy model with fixed effects upper 95% CI estimate of occupancy probability on the logit scale OneLev_Con_alpha.p_Mean = Site-occupancy model with fixed effects mean estimate of detection probability on the logit scale OneLev_Con_alpha.p_ylo = Site-occupancy model with fixed effects lower 95% CI estimate of detection probability on the logit scale OneLev_Con_alpha.p_yhi = Site-occupancy model with fixed effects upper 95% CI estimate of detection probability on the logit scale OneLev_Hetero_alpha.psi_Mean = Site-occupancy model with random effects mean estimate of occupancy probability on the logit scale OneLev_Hetero_alpha.psi_ylo = Site-occupancy model with random effects lower 95% CI estimate of occupancy probability on the logit scale OneLev_Hetero_alpha.psi_yhi = Site-occupancy model with random effects upper 95% CI estimate of occupancy probability on the logit scale OneLev_Hetero_alpha.p_Mean = Site-occupancy model with random effects mean estimate of detection probability on the logit scale OneLev_Hetero_alpha.p_ylo = Site-occupancy model with random effects lower 95% CI estimate of detection probability on the logit scale OneLev_Hetero_alpha.p_yhi = Site-occupancy model with random effects upper 95% CI estimate of detection probability on the logit scale OneLev_Hetero_stdev_Mean = Site-occupancy model with random-effects parameters mean estimate of sigma OneLev_Hetero_stdev_ylo = Site-occupancy model with random-effects parameters lower 95% CI estimate of sigma OneLev_Hetero_stdev_yhi = Site-occupancy model with random-effects parameters upper 95% CI estimate of sigma correlation = NA- not used&lt; Rhat_check.2lev.con = Following model run completion, we checked the Rhat values across all parameters for convergene. Specifically, we checked to see if the Rhat vale for each parameter was &lt; 1.1. If all Rhat values were &lt; 1.1, then we assigned a value of 1 in this column. If all the parameters did not converge, then a value of 0 was assigned. There are some cases with a value of NA, which means that the model was not run. This Rhat check is specifically for the Multi-scale occupancy model with fixed effects. Rhat_check.2lev.hetero = Following model run completion, we checked the Rhat values across all parameters for convergene. Specifically, we checked to see if the Rhat vale for each parameter was &lt; 1.1. If all Rhat values were &lt; 1.1, then we assigned a value of 1 in this column. If all the parameters did not converge, then a value of 0 was assigned. There are some cases with a value of NA, which means that the model was not run. This Rhat check is specifically for the Multi-scale occupancy model with random and correlation. Rhat_check.1lev.con = Following model run completion, we checked the Rhat values across all parameters for convergene. Specifically, we checked to see if the Rhat vale for each parameter was &lt; 1.1. If all Rhat values were &lt; 1.1, then we assigned a value of 1 in this column. If all the parameters did not converge, then a value of 0 was assigned. There are some cases with a value of NA, which means that the model was not run. This Rhat check is specifically for the Site-occupancy model with fixed effects. Rhat_check.1lev.hetero = Following model run completion, we checked the Rhat values across all parameters for convergene. Specifically, we checked to see if the Rhat vale for each parameter was &lt; 1.1. If all Rhat values were &lt; 1.1, then we assigned a value of 1 in this column. If all the parameters did not converge, then a value of 0 was assigned. There are some cases with a value of NA, which means that the model was not run. This Rhat check is specifically for the Site-occupancy model with random effects. Folder that holds all of the JAGS models <strong>Software/Models</strong> Metadata information (can be read with the MetaData Wizard app from USGS; https://usgs.github.io/fort-pymdwizard/) Data/Metadata/TwoLevel-Metadata 2021 01 24.xml Data used in the RShiny app are located in: <strong>Data/Rshiny-app/</strong> The names of fields & explanations for these files can be found in: all_dat_20210201.rds = Software/PostProcessingCode/RShiny-code/II. Q1. Rshiny-dataset.R GenSampRec_20210201.rds = Software/PostProcessingCode/RShiny-code/II. Q2. Rshiny-dataset.R Comparison_scen1_20210203.rds = Software/PostProcessingCode/RShiny-code/II. Q3. Scenario 1. Rshiny-dataset.R Comparison_scen2_20210203.rds = Software/PostProcessingCode/RShiny-code/II. Q3. Scenario 2. Rshiny-dataset.R Comparison_scen3_20210203.rds = Software/PostProcessingCode/RShiny-code/II. Q3. Scenario 3. Rshiny-dataset.R Comparison_scen4_20210203.rds = Software/PostProcessingCode/RShiny-code/II. Q3. Scenario 4. Rshiny-dataset.R ----------------------------------------------- 10. RShiny app files ----------------------------------------------- File that holds the code for the RShiny app <strong>Software/Rshiny-app/app.R</strong> Helper file used by the RShiny app <strong>Software/Rshiny-app/helpers.R</strong> Folder with the datasets used by the RShiny app <strong>Data/Rshiny-app/</strong> The code to generate the RShiny-app datasets is located: <strong>Software/PostProcessing/RShiny-code</strong> End README