10.5061/DRYAD.FXPNVX0RV
DiRenzo, Graziella
0000-0001-5264-4762
U.S. Geological Survey
Miller, David
Pennsylvania State University
Grant, Evan
U.S. Geological Survey
Ignoring species availability biases occupancy estimates in single-scale
occupancy models
Dryad
dataset
2021
imperfect detection probability
multi-scale occupancy modeling
Pollock’s robust design
site-occupancy model
temporary emigration
single-level occupancy model
FOS: Biological sciences
en
https://doi.org/10.5281/zenodo.6214643
https://doi.org/10.5281/zenodo.6214645
4628618349 bytes
6
CC0 1.0 Universal (CC0 1.0) Public Domain Dedication
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 (< 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 (> 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.
These files were written by: G. V. DiRenzo If you have any questions,
please email: gdirenzo@umass.edu USGS Disclaimer 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. README objective 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/ Table of
Contents 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:
Software/ParameterCombinations/LHS_parameter_combos.R This script
generates 1 csv file: parameter_combos_TwoLevelAvail4.csv 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: Software/ParameterCombinations/LHS_parameter_combos_years.R
This script generates 1 csv file: parameter_combos_TwoLevelAvail_years.csv
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.: Software/PostProcessing/II.
Q1. TwoLevelAvail_SampDesign_ParamEstimate.R This file generates: Table
S2; Figures S1, S2, S3, and S4 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.: Software/SamplingDesign/PostProcessing/II. Q2. TwoLevelAvail_GenRec.R This file generates: Table 1 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) ----------------------------------------------- Scenario 2 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) ----------------------------------------------- Scenario 3 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) ----------------------------------------------- Scenario 4 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.: Software/PostProcessing/II. Q3. Scenario 1-4- Model performance.R This file generates: Figure 2, 3, 4, & 5 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 Supplemental-Information/Figures/ Main text figures: Supplemental-Information/Figures/ModComp/ Appendix figures: Supplemental-Information/Figures/ParameterEstimates Folder that holds all of the manuscript tables Supplemental-Information/Tables Folder that holds all of the model output generated by Scenario 1 Data/ModelOutput_Scen1_TwolevelSim 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< = 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 Data/ModelOutput_Scen2_HeteroSite 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< = 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 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 Data/ModelOutput_Scen3_HeteroYear 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) Data/ModelOutput_Scen4_Cor 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< 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 Software/Models 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: Data/Rshiny-app/ 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 Software/Rshiny-app/app.R Helper file used by the RShiny app Software/Rshiny-app/helpers.R Folder with the datasets used by the RShiny app Data/Rshiny-app/ The code to generate the RShiny-app datasets is located: Software/PostProcessing/RShiny-code End README