[MARMAM] New QFASA Diet Estimation Publication

Bromaghin, Jeffrey jbromaghin at usgs.gov
Wed Jul 26 10:56:21 PDT 2017


Colleagues,



I am pleased to announce a publication that presents a new clustering
method to detect hidden structure in fatty acid signature data and
potentially improve estimates of consumer diet composition using
quantitative fatty acid signature analysis, a method commonly used in
studies of marine mammals.



Citation:

Bromaghin, J. F., S. M. Budge, and G. W. Thiemann.  2017. Detect and
exploit hidden structure in fatty acid signature data. *Ecosphere* 8(7):
e01896.

Available open access at
http://onlinelibrary.wiley.com/doi/10.1002/ecs2.1896/full



Abstract:

Estimates of predator diet composition are essential to our understanding
of their ecology.  Although several methods of estimating diet are
practiced, methods based on biomarkers have become increasingly common.
Quantitative fatty acid signature analysis (QFASA) is a popular method that
continues to be refined and extended.  QFASA is based on differences in the
signatures of prey types, often species, which are recognized and
designated by investigators.  Similarly, predator signatures may be
structured by known factors such as sex or age class, and the season or
region of sample collection.  The recognized structure in signature data
inherently influences QFASA results in important and typically beneficial
ways.  However, predator and prey signatures may contain additional, hidden
structure that investigators either choose not to incorporate into an
analysis or of which they are unaware, being caused by unknown ecological
mechanisms.  Hidden structure also influences QFASA results, most often
negatively.  We developed a new method to explore signature data for hidden
structure, called divisive magnetic clustering (DIMAC).  Our DIMAC approach
is based on the same distance measure used in diet estimation, closely
linking methods of data exploration and parameter estimation, and it does
not require data transformation or distributional assumptions, as do many
multivariate ordination methods in common use.  We investigated the
potential benefits of the DIMAC method to detect and subsequently exploit
hidden structure in signature data using two prey signature libraries with
quite different characteristics.  We found that the existence of hidden
structure in prey signatures can increase the confusion between prey types
and thereby reduce the accuracy and precision of QFASA diet estimates.
Conversely, the detection and exploitation of hidden structure represents a
potential opportunity to improve predator diet estimates and may lead to
new insights into the ecology of either predator or prey.  The DIMAC
algorithm is implemented in the R diet estimation package qfasar.



Regards,

Jeff


-----------------------------------------------
Jeffrey F. Bromaghin, PhD
Research Statistician
USGS Alaska Science Center
Marine Ecosystems Office
4210 University Drive
Anchorage, AK 99508
907-786-7086
jbromaghin at usgs.gov
*http://alaska.usgs.gov/science/biology/quantitative_ecology/index.php
<http://alaska.usgs.gov/science/biology/quantitative_ecology/index.php>*
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