Availability of code | Introduction | Survey protocol | Data input | Moose data | Block area | Stratum information | Sightability model. | Extracting from AerialSurvey | Reading coefficients of the model | Creation of VegCoverClass variable | Key variables. | Dealing with missing and zero values | Group information | Covariate information | Blocks with no observed groups. | Block information | A single block in a stratum | Analysis | Analysis not adjusting for sightability | Within a stratum | Density | Abundance | Bull to Cow and other ratios | Across all strata | The MoosePopR() function. | Sample Analyses | Density of all moose | Abundance of all moose | Number of bulls and number of cows | Final object | Adjusting for sightability | The SightabilityPopR() function | Dealing with missing values in the sightability covariates | Domain estimation | Domain at PSU (block) level; domain population information known | Example | Domain at PSU (block) level; domain population information unknown. | Domain at group level; domain population information known | Domain at group level; domain population information unknown. | References

Introduction | Modified Horvitz-Thompson estimator | Variance estimators | Package description and example applications | Future work

Introduction | Implementation | References

1 Availability of code | 2 Introduction | 3 Types of stratification | 3.1 Across sampling-unit stratification | 3.2 Within sampling-unit stratification (domain estimation) | 3.3 Combined designs | 3.4 CAUTIONS | 4 Data structures that allow for both types of stratification | 4.1 Stratum Totals | 4.2 Sampling unit information | 4.3 Way point (group) information | 4.4 Sightability Model | 5 Example | 5.1 Survey Design | 5.2 Data Values | 5.3 Uncorrected Estimation | 5.4 Corrected for sightability estimation | 5.5 SCF < 1 - Why does this happen? | 5.6 Adjusting data for SCF using MoosePopR() | 6 Final estimates | 6.1 Abundance of all moose - corrected for sightability and domain stratification | 6.2 Density of all moose - corrected for sightability and domain stratification | 6.3 Bulls:100 Cows - corrected for sightability and domain stratification | 7 Summary and Recommendations | 7.1 Survey Design | 7.2 Data structures | 7.3 Include schematic of design in report | 7.4 Analysis | 8 References

Interpolating run early and late | 1 Introduction | 1.1 Fit with the current data. | 1.2 Forcing the run curve to zero.

Diagonal Case | 1 Location of vignette source and code. | 2 Introduction | 3 Experimental Protocol | 4 Example of basic BTSPAS fit. | 4.1 Reading in the data | 4.2 Preliminary screening of the data | 4.3 Fitting the basic BTSPAS diagonal model | 4.4 The output from the basic fit | 5 Fixing p’s | 5.1 Reading in the data | 5.2 Fitting the BTSPAS diagonal model and fixing p.  | 5.3 The output from the fit | 6 Using covariates to model the p’s | 6.1 Reading in the data | 6.2 Fitting the BTSPAS diagonal model and fixing p and covariates for p.  | 6.3 The output from the fit | 7 Using covariates to model the p’s - prior information about beta coefficients | 7.1 Reading in the data | 7.2 Fitting the BTSPAS diagonal model with no covariates | 7.3 Fitting the BTSPAS diagonal model with using log(flow) and default priors | 7.4 Fitting the BTSPAS diagonal model with using log(flow) and weak prior | 7.5 Fitting the BTSPAS diagonal model with using log(flow) and strong prior | 7.6 Comparing the results of the fits | 8 Adding structure to the p’s with a spline. | 8.1 Reading in the data | 8.2 Fitting the BTSPAS diagonal model and a spline for p.  | 8.3 The output from the fit | 9 References

Diagonal Case - Multiple Stocks/Ages | 1 Location of vignette source and code. | 2 Introduction | 2.1 Fixing values of \(p\) or using covariates. | 3 Wild and Hatchery Chinook | 3.1 Reading in the data | 3.2 Fitting the BTSPAS diagonal model | 3.3 The output from the fit | 4 Wild and Hatchery Chinook with YoY and Age 1 fish. | 4.1 Reading in the data | 4.2 Fitting the BTSPAS diagonal model | 4.3 The output from the fit | 5 Wild and Hatchery Steelhead with YoY and Age 1 fish. | 5.1 Reading in the data | 5.2 Fitting the BTSPAS diagonal model | 5.3 The output from the fit | 6 References

Non-Diagonal Case | 1 Location of vignette source and code. | 2 Introduction | 2.1 Fixing values of \(p\) or using covariates. | 3 Example of basic non-diagonal BTSPAS fit. | 3.1 Reading in the data | 3.2 Preliminary screening of the data | 4 Fitting the BTSPAS non-diagonal model with a log-normal movement distribution. | 4.1 The output from the fit | 5 Fitting the BTSPAS non-diagonal model with a non-parametric movement distribution. | 5.1 The output from the fit | 6 Prior information on the movement probabilities. | 6.1 The output from the fit | 7 References

Non-Diagonal Fall-Back Model | 1 Location of vignette source and code. | 2 Introduction | 2.1 Experimental set-up | 2.2 Fall-back information | 2.3 Fixing values of \(p\) or using covariates. | 3 Example of non-diagonal model with fall-back. | 3.1 Reading in the data | 3.2 Fitting the BTSPAS non-diagonal model with fall-back model with a non-parametric movement distribution. | 3.3 The output from the fit | 4 References

Bias caused by incomplete sampling | 1 Location of vignette source and code. | 2 Introduction | 2.1 Experimental setup | 2.2 Generation of population | 3 Analysis of dataset stratified to the 3-day strata. | 3.1 Pooled Petersen estimator | 3.2 BTSPAS on the full dataset | 3.3 Exploring the output | 4 Analysis at the 6-day stratum level. | 4.1 Pooled Petersen estimator | 4.2 BTSPAS on the pooled dataset | 4.3 Exploring the output | 5 References