[tdwg-content] Name is species concept thinking

Peter DeVries pete.devries at gmail.com
Sun Jun 13 07:24:23 CEST 2010

I think that the problem is that most species descriptions are written a way
that person1 interprets specimenA as conceptB and person2 interprets
specimenA and ConceptC.

This needs to be made more scientific so that one can test what proportions
of specimens actually conform to the description (concept).

These descriptions should be open, world readable and reference-able via a


- Pete

** There also seems to be mismatch between the concept the human identifier
choose (often via a key) and the species description (concept) to which you
are saying their data applies.

On Sat, Jun 12, 2010 at 7:50 PM, Richard Pyle <deepreef at bishopmuseum.org>wrote:

> > 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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Pete DeVries
Department of Entomology
University of Wisconsin - Madison
445 Russell Laboratories
1630 Linden Drive
Madison, WI 53706
GeoSpecies Knowledge Base
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