what modelling and statistical analysis methods require quantitative and presence-absence data?
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.orgmailto:dschigel@gbif.org Web www.gbif.orghttp://www.gbif.org/ [GBIFlogoNO_TEXT_50%]
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)
Botanic Garden Meise Domein van Bouchout B-1860 Meise Belgium
ORCID: 0000-0002-0596-5376 http://orcid.org/0000-0002-0596-5376
Landline; +32 (0) 226 009 20 ext. 364 FAX: +32 (0) 226 009 45
E-mail: quentin.groom@plantentuinmeise.be Skype name: qgroom Website: www.botanicgarden.be
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 <+45%2035%2032%2014%2085>
Mobile +45 30 45 02 25 <+45%2030%2045%2002%2025>
E-mail dschigel@gbif.org
Web www.gbif.org
[image: GBIFlogoNO_TEXT_50%]
DFFU_IAS mailing list DFFU_IAS@lists.gbif.org https://lists.gbif.org/mailman/listinfo/dffu_ias
participants (2)
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Dmitry Schigel -
Quentin Groom