Hi Dmitry,
here is a link to one of my papers that requires present/absence data. Of course absence is implied by the lack of presence. This paper uses kriging for the analysis.
https://peerj.com/articles/77/
Regards
Quentin



Dr. Quentin Groom
(Botany and Information Technology)

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Domein van Bouchout
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Belgium


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On 6 April 2017 at 15:48, Dmitry Schigel <dschigel@gbif.org> wrote:

Hello Task Groups, Science Committee

cc comms, Andrea,

 

You agreed to have your mailing lists active, and I’d like to ask for your help in one (surely easy for you) matter:

 

Could you please name a few important modelling and statistical analyses methods where quantitative and presence-absence data makes a difference, in contrast with presence-only?

GEO BON and GBIF are preparing the joint campaign on mobilization of sampling event data, to grow content in this segment: https://demo.gbif.org/dataset/search?type=SAMPLING_EVENT

Since the number of such dataset is still very low, we cannot yet demonstrate the academic reuse. Instead, we would like to be clear about analytical potential of such data.

 

In other words, what kind of analyses and modeling you can do with ecological data, and can’t with museum /  isolated observation data?

Why should one share quantitative and presence-absence data through GBIF? Which uses and analyses will it enable?

 

I appreciate if you just reply with a few names of such methods, we will google the rest and summarize.

Comments also welcome, if you have time.

 

Thank you in advance.

Dmitry

 

Dmitry Schigel, PhD

Scientific Officer

Global Biodiversity Information Facility (GBIF)
Secretariat

Universitetsparken 15
DK-2100 Copenhagen Ø

DENMARK

 

Office                  +45 35 32 14 85

Mobile                +45 30 45 02 25

E-mail                  dschigel@gbif.org

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