Alaska Department of Fish and Game
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Gene Conservation Laboratory
Statistics Program for Analyzing Mixtures (SPAM) Software
The mission of the Gene Conservation Laboratory is to apply genetic principles and tools to support the management of commercially important fish and shellfish species in Alaska based on the sustained yield principle.
- Port Moller Test Fishery (updated 7/5/2014)
- Yukon River Chinook Salmon Genetic Baseline Project (updated 7/1/2014)
- Scientists Up their Ability to Track Salmon through DNA 'Fin-Printing (PDF 129 kB)
- Cook Inlet Chinook Salmon Projects
- Cook Inlet Coho Genetic Baseline Project
- Chatham Strait, Icy Strait, and Lynn Canal Sockeye Project
- Alaska Peninsula Chinook Project
- Hatchery Research Project
- Western Alaska Salmon Stock Identification Program
SPAM - Genetic Stock Identification Software
SPAM - What is it?
Statistics Program for Analyzing Mixtures (SPAM) estimates the relative contributions of discrete populations to a mixture sample, solving what is commonly referred to in fisheries as the mixed stock analysis (MSA) or genetic stock identification (GSI) problem. The software was mainly developed here at the Gene Conservation Lab and is available for free.
What can you do with it?
SPAM can run in either Estimation or Simulation mode. In estimation mode, SPAM derives mixture proportion estimates by numerically solving a conditional maximum likelihood problem. The likelihood is conditional on the assumption that the distributions of identifying characteristics are known without error for each population potentially contributing to the mixture. In simulation mode, SPAM samples from a known mixture of baseline populations, then estimates the mixture proportions. This allows one to investigate mixture sample sizes, 'identifiability' of potential populations or population assemblages, etc.
Besides proportion point estimates, SPAM 3.7b can be used to generate confidence intervals, test equality of mixtures that have been independently sampled, conduct likelihood ratio tests to compare competing mixture models, assess potential contribution estimate bias arising from using very large baselines, and investigate changing detection power as a function of mixture sample size.
Mixture Analysis Papers and Posters
What goes well with it? (recommended sites)
Collection of genetic data analysis links.