QFASA Robustness to Assumption Violations: Computer Code

Quantitative fatty acid signature analysis (QFASA; Iverson et al. 2004. Ecological Monographs 74:211-235) has become a common method of estimating diet composition, especially for marine mammals, but the performance of the method has received limited investigation. This software was developed to compare the bias of several QFASA estimators using computer simulation and develop recommendations regarding estimator selection (Bromaghin et al. 2015. Assessing the robustness of quantitative fatty acid signature analysis to assumption violations. Methods in Ecology and Evolution (publication expected in late 2015 or early 2016).

Data and Resources

Field Value
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identifier USGS:ASC132
metadata_type geospatial
modified 20201125
publisher U.S. Geological Survey
publisher_hierarchy Department of the Interior > U.S. Geological Survey
resource-type Dataset
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Groups
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  • National Provider
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Tags
  • aitchison
  • aitchison-distance-measure
  • amerigeo
  • amerigeoss
  • bias
  • biota
  • ckan
  • diet-composition
  • diet-estimation
  • estimator-robustness
  • geo
  • geoss
  • kullback-leibler
  • kullback-leibler-distance
  • linear-mixing-model
  • measure
  • model-assumption-violations
  • national
  • north-america
  • numerical-optimization
  • predator-prey
  • qfasa
  • simulation
  • united-states
  • usgs-asc132
isopen False
license_id notspecified
license_title License not specified
maintainer U.S. Geological Survey Alaska Science Center
maintainer_email ascweb@usgs.gov
metadata_created 2025-11-20T21:32:47.283270
metadata_modified 2025-11-20T21:32:47.283274
notes Quantitative fatty acid signature analysis (QFASA; Iverson et al. 2004. Ecological Monographs 74:211-235) has become a common method of estimating diet composition, especially for marine mammals, but the performance of the method has received limited investigation. This software was developed to compare the bias of several QFASA estimators using computer simulation and develop recommendations regarding estimator selection (Bromaghin et al. 2015. Assessing the robustness of quantitative fatty acid signature analysis to assumption violations. Methods in Ecology and Evolution (publication expected in late 2015 or early 2016).
num_resources 2
num_tags 25
title QFASA Robustness to Assumption Violations: Computer Code