> That
said modeling relationships between taxonomic publications where
> the
authors actually read the original species description, reviewed
> the
type specimens, and thought about the actual species conscription
is
appropriate.
This is the sort of things the Meta-Authorities would take
into account when
selecting a "follow-this-treatment" Usage-Instance for
the preferred
treatment of a name.
> Also consider that a large proportion of specimens are
misidentified,
> and it occurs to me that modeling things like species
occurrences as
> if they are Puma concolor (Linnaeus, 1771) sensu
stricto is probably
> not appropriate. At best they are something like
(Felis concolor /
> Puma concolor) with some significant level of
error.
GNA can't helpw ith that directly -- but it can help
indirectly. Imagine a
service that takes ever specimen in a given
collection's database, and runs
it against a mapping service as I
described in the previous message. I can
easily imagine a GIS-based
algorithm that finds "outliers" -- that is
occurrence records that appear
to be outside the distribution based on the
occurrence records from other
sources. A clver/robust such algorithm could
probably even discern
whether the outlier likely represented a range
extension (e.g.
poorly-known species, plausible extansion), vs. a
misidentification
(e.g., well-known species and/or common
misidentification).
This
would lead to a set of flagged records from the collection that might
be
misidentified.
Rich
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