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.

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