Data for: Ignoring species availability biases occupancy estimates in single-scale occupancy models

We simulate over 28,000 datasets and saved their model outputs to answer the following three questions: 
(1) what is an adequate sampling design for the multi-scale occupancy model when there are a priori expectations of parameter estimates?, 
(2) what is an adequate sampling design when we have no expectations of parameter estimates?, and 
(3) what is the cost (in terms of bias, accuracy, precision and coverage) in occupancy estimates) if availability is not accounted for? 

Specifically, we simulated data under four scenarios: 
Scenario 1 (n = 10,000): Species availability is constant across sites (but less than one), 
Scenario 2 (n = 9,358): Species availability is heterogenous across sites, 
Scenario 3 (n = 2,815): Species availability is heterogenous across years, and 
Scenario 4 (n = 5,942): Species availability is correlated to their detection probability. 

Then, for each scenario except the first, we analyzed the data using four different estimators: 
(i) constant multi-scale occupancy model, 
(ii) multi-scale occupancy model with a random-effects term in the availability part of the model, 
(iii) constant single-scale occupancy model, and 
(iv) single-scale occupancy model with a random-effects term in the detection part of the model. 
Note the formulation of the random-effects terms included in the models mimicked the way that data were simulated (e.g., if species availability was heterogenous across sites, then a site random-effects term was included in the models). 
The first scenario was analyzed using models (i) and (iii) only. For simplicity, we refer to models (i) and (iii) as ‘constant’ models or 'fixed-effects' models. We refer to models (ii) and (iv) as ‘random-effects’ models.

The summary of simulated data and model estimates are located in four folders, each corresponding to a different simulated scenario:
Scenario 1 (n = 10,000): Folder ModelOutput_Scen1_TwolevelSim = csv files holding data are named Results_TwoLevelAvail_2lev_x.csv 
Scenario 2 (n = 9,358): Folder ModelOutput_Scen2_HeteroSite = csv files holding data are named Results_TwoLevelAvail_Hetero_x.csv
Scenario 3 (n = 2,815): Folder ModelOutput_Scen3_HeteroYear = csv files holding data are named Results_TwoLevelAvail_HeteroSeason_x.csv
Scenario 4 (n = 5,942): Folder ModelOutput_Scen4_Cor = csv files holding data are named Results_TwoLevelAvail_Cor_x.csv

Each row in each of the csv files contains information related to a different simulated dataset and includes information related to: sampling design, true parameter values, and model estimates. Other files in the folder correspond to the entire model output (.rda files), time for model run to complete (time_..csv), and a file indicating whether or not the model run finished (nsim...csv). For more information related to those files, we point the user to the code that generated them:
Scenario 1 (n = 10,000): Scen1_Constant.R
Scenario 2 (n = 9,358): Scen2_HeteroSite.R
Scenario 3 (n = 2,815): Scen3_HeteroYear.R
Scenario 4 (n = 5,942): Scen4_Corr.R

Tags: biota, imperfect-detection-probability, ma-united-states, multi-scale-occupancy-model, pollocks-robust-design, simulated-dataset, site-occupancy-model, temporary-emigration, usgs-91f83b3b-f11b-482e-bd9c-f176dbd37078
Organization: Department of the Interior