SimSurvey
SimSurvey simulates and analyzes sample surveys of spatially-correlated, age-structured populations to evaluate sampling designs and estimate stratified means and variances for ecological studies such as fisheries stock assessment.
Key Features:
- Simulation of Age-Structured Populations: Generates simulations of age-structured, spatially-correlated populations with temporal variability to produce realistic virtual populations.
- Flexible Sampling Protocols: Supports built-in and user-defined sampling protocols to test a wide range of survey designs.
- Analysis of Stratified Data: Estimates stratified means and variances from simulated survey data.
- Identification of Bias Sources: Uses simulation to identify unexpected sources of bias in sampling designs.
- Design-Based Solutions Exploration: Enables testing of alternative design-based strategies to mitigate biases or inefficiencies revealed by simulations.
Scientific Applications:
- Ecological spatial-temporal studies: Evaluating population dynamics across space and time for spatially distributed populations.
- Fisheries stock assessment: Optimizing sampling strategies and survey designs for fish stocks, including evaluation of multi-stage sampling scenarios.
- Survey design optimization: Comparing alternative sampling strategies to improve precision and reduce costs associated with correlated metrics.
Methodology:
SimSurvey creates virtual spatially-correlated, age-structured populations from user-specified parameters for age structure, spatial distribution, and temporal changes, applies built-in or user-defined sampling protocols to those simulations, and computes stratified means and variances for statistical evaluation of sampling performance.
Topics
Details
- Programming Languages:
- R
- Added:
- 1/18/2021
- Last Updated:
- 2/18/2021
Operations
Publications
Regular PM, Robertson GJ, Lewis KP, Babyn J, Healey B, Mowbray F. SimSurvey: An R package for comparing the design and analysis of surveys by simulating spatially-correlated populations. PLOS ONE. 2020;15(5):e0232822. doi:10.1371/journal.pone.0232822. PMID:32392233. PMCID:PMC7213729.