Comparing N-mixture models and GLMMs for relative abundance estimation in a citizen science dataset
- Location
-
Deutsche Nationalbibliothek Frankfurt am Main
- ISSN
-
2045-2322
- Extent
-
Online-Ressource
- Language
-
Englisch
- Notes
-
online resource.
- Bibliographic citation
-
Comparing N-mixture models and GLMMs for relative abundance estimation in a citizen science dataset ; volume:12 ; number:1 ; day:19 ; month:7 ; year:2022 ; pages:1-12 ; date:12.2022
Scientific reports ; 12, Heft 1 (19.7.2022), 1-12, 12.2022
- Creator
-
Goldstein, Benjamin R.
de Valpine, Perry
- Contributor
-
SpringerLink (Online service)
- DOI
-
10.1038/s41598-022-16368-z
- URN
-
urn:nbn:de:101:1-2022092822095576267426
- Rights
-
Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
-
15.08.2025, 7:28 AM CEST
Data provider
This object is provided by:
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Associated
- Goldstein, Benjamin R.
- de Valpine, Perry
- SpringerLink (Online service)
Other Objects (12)
Mixture modeling of transcript abundance classes in natural populations
N-mixture models reliably estimate the abundance of small vertebrates
A dataset for 15N natural abundance of soil ammonia volatilization
Toward reliable estimates of abundance: comparing index methods to assess the abundance of a mammalian predator
Comparing functional traits and abundance of invasive versus native woodwasps
N-mixture models with camera trap imagery produce accurate abundance estimates of ungulates
PARE: A tool for comparing protein abundance and mRNA expression data
Measuring and comparing two kinds of rationalizable opportunity cost in mixture models
How to reach external citizens? Comparing varieties of transnational party infrastructure
Mixture
Citizens’ data afterlives: Practices of dataset inclusion in machine learning for public welfare
A dataset of road-killed vertebrates collected via citizen science from 2014–2020
Mixture modeling of transcript abundance classes in natural populations
N-mixture models reliably estimate the abundance of small vertebrates
A dataset for 15N natural abundance of soil ammonia volatilization
Toward reliable estimates of abundance: comparing index methods to assess the abundance of a mammalian predator
Comparing functional traits and abundance of invasive versus native woodwasps
N-mixture models with camera trap imagery produce accurate abundance estimates of ungulates
PARE: A tool for comparing protein abundance and mRNA expression data
Measuring and comparing two kinds of rationalizable opportunity cost in mixture models
How to reach external citizens? Comparing varieties of transnational party infrastructure
Mixture
Citizens’ data afterlives: Practices of dataset inclusion in machine learning for public welfare
A dataset of road-killed vertebrates collected via citizen science from 2014–2020
Mixture modeling of transcript abundance classes in natural populations
N-mixture models reliably estimate the abundance of small vertebrates
A dataset for 15N natural abundance of soil ammonia volatilization
Toward reliable estimates of abundance: comparing index methods to assess the abundance of a mammalian predator
Comparing functional traits and abundance of invasive versus native woodwasps
N-mixture models with camera trap imagery produce accurate abundance estimates of ungulates
PARE: A tool for comparing protein abundance and mRNA expression data
Measuring and comparing two kinds of rationalizable opportunity cost in mixture models
How to reach external citizens? Comparing varieties of transnational party infrastructure
Mixture
Citizens’ data afterlives: Practices of dataset inclusion in machine learning for public welfare