What is he best known for?
The fields of study he is best known for:
Niklas Wahlberg mostly deals with Ecology, Nymphalidae, Evolutionary biology, Biodiversity and Phylogenetics.
His Ecology study frequently involves adjacent topics like Metapopulation.
His Nymphalidae study combines topics in areas such as Zoology, Subfamily and Clade.
The Zoology study combines topics in areas such as Sister group, Aganainae and Monophyly.
He has researched Evolutionary biology in several fields, including Ditrysia, Nymphalis, Systematics, Heliconius and Data set.
His research in Phylogenetics intersects with topics in Domestication and European origin.
His most cited work include:
- Animal biodiversity: An outline of higher-level classification and survey of taxonomic richness (509 citations)
- Order Lepidoptera Linnaeus, 1758. In : Zhang, Z.-Q. (Ed.) Animal biodiversity: An outline of higher-level classification and survey of taxonomic richness (326 citations)
- Nymphalid butterflies diversify following near demise at the Cretaceous/Tertiary boundary (326 citations)
What are the main themes of his work throughout his whole career to date?
The scientist’s investigation covers issues in Ecology, Evolutionary biology, Nymphalidae, Zoology and Phylogenetic tree.
His Ecology study frequently draws connections between related disciplines such as Biological dispersal.
His Evolutionary biology research includes themes of Taxon and Subfamily, Phylogenetics, Gene, Mitochondrial DNA.
His study focuses on the intersection of Nymphalidae and fields such as Systematics with connections in the field of Paraphyly.
His Zoology research integrates issues from Lepidoptera genitalia, Botany and Monophyly.
His Phylogenetic tree study combines topics in areas such as Bayesian probability and Tribe.
He most often published in these fields:
- Ecology (45.49%)
- Evolutionary biology (42.21%)
- Nymphalidae (40.16%)
What were the highlights of his more recent work (between 2018-2021)?
- Evolutionary biology (42.21%)
- Phylogenetic tree (29.51%)
- Genus (18.03%)
In recent papers he was focusing on the following fields of study:
Niklas Wahlberg mainly focuses on Evolutionary biology, Phylogenetic tree, Genus, Ecology and Phylogenetics.
Niklas Wahlberg combines subjects such as Molecular phylogenetics, Taxon, Tribe, Monophyly and Subfamily with his study of Evolutionary biology.
His study looks at the relationship between Phylogenetic tree and topics such as Bayesian probability, which overlap with Papilionoidea and Host plants.
His study in Ecology is interdisciplinary in nature, drawing from both Phylogeography and Biological dispersal.
The various areas that Niklas Wahlberg examines in his Phylogenetics study include Scale, Wolbachia, Mitochondrial DNA and Sexual dimorphism.
As a part of the same scientific study, Niklas Wahlberg usually deals with the Systematics, concentrating on Crown group and frequently concerns with Zoology.
Between 2018 and 2021, his most popular works were:
- Embracing heterogeneity: coalescing the Tree of Life and the future of phylogenomics. (56 citations)
- Priors and Posteriors in Bayesian Timing of Divergence Analyses: The Age of Butterflies Revisited. (35 citations)
- The Global Museum: natural history collections and the future of evolutionary science and public education. (24 citations)
In his most recent research, the most cited papers focused on:
Evolutionary biology, Taxon, Bayesian probability, Data science and Phylogenetic tree are his primary areas of study.
The concepts of his Evolutionary biology study are interwoven with issues in Ennominae, Sawfly, Polyphyly, Paraphyly and Larentiinae.
His Polyphyly research is multidisciplinary, incorporating perspectives in Systematics, Tribe and Molecular phylogenetics.
His Taxon research incorporates themes from Sensu, Clade, Larva, Key and DNA barcoding.
His Bayesian probability research incorporates elements of Host plants and Papilionoidea.
His Phylogenetic tree study integrates concerns from other disciplines, such as Endemism, Phylogenetics, Butterfly and Mitochondrial DNA.
This overview was generated by a machine learning system which analysed the scientist’s
body of work. If you have any feedback, you can
contact us
here.
