What is he best known for?
The fields of study he is best known for:
- Ecology
- IUCN Red List
- Habitat
His scientific interests lie mostly in Ecology, Biodiversity, Camera trap, Range and Predation.
His research in Ecology intersects with topics in Population density and Extinction.
His Biodiversity research incorporates themes from Global network and Environmental resource management.
His Camera trap research includes themes of Abundance, Abundance estimation, Range and Image sensor.
Chris Carbone usually deals with Range and limits it to topics linked to Habitat and Species richness, Allometry and Zoology.
His work in the fields of Predation, such as Carnivore, Order Carnivora and Predator, intersects with other areas such as Vertebrate and Energy requirement.
His most cited work include:
- PanTHERIA: a species‐level database of life history, ecology, and geography of extant and recently extinct mammals (980 citations)
- Energetic constraints on the diet of terrestrial carnivores (445 citations)
- Energetic constraints on the diet of terrestrial carnivores (445 citations)
What are the main themes of his work throughout his whole career to date?
The scientist’s investigation covers issues in Ecology, Habitat, Camera trap, Predation and Foraging.
Ecology is represented through his Range, Abundance, Species richness, Biodiversity and Carnivore research.
Many of his research projects under Biodiversity are closely connected to Biological data with Biological data, tying the diverse disciplines of science together.
His Habitat research includes elements of Protected area and Macroecology.
His Camera trap study incorporates themes from Remote sensing, Abundance estimation and Computer vision.
In his study, Order Carnivora is strongly linked to Invertebrate, which falls under the umbrella field of Predation.
He most often published in these fields:
- Ecology (71.17%)
- Habitat (16.22%)
- Camera trap (15.32%)
What were the highlights of his more recent work (between 2016-2020)?
- Ecology (71.17%)
- Habitat (16.22%)
- Biodiversity (15.32%)
In recent papers he was focusing on the following fields of study:
His primary areas of investigation include Ecology, Habitat, Biodiversity, Camera trap and Range.
Ecology and Population density are commonly linked in his work.
Chris Carbone combines subjects such as Protected area and Macroecology with his study of Habitat.
Chris Carbone has included themes like Rainforest, Ecosystem and Scale in his Biodiversity study.
His biological study spans a wide range of topics, including Artificial neural network, Artificial intelligence, Giraffa camelopardalis, Sympatric speciation and Computer vision.
In the subject of general Predation, his work in Predator, Carnivore and Crocuta crocuta is often linked to Context, thereby combining diverse domains of study.
Between 2016 and 2020, his most popular works were:
- Scaling-up camera traps: monitoring the planet's biodiversity with networks of remote sensors (145 citations)
- Mammalian species abundance across a gradient of tropical land-use intensity: A hierarchical multi-species modelling approach (44 citations)
- Global drivers of population density in terrestrial vertebrates (26 citations)
In his most recent research, the most cited papers focused on:
- Ecology
- IUCN Red List
- Habitat
Chris Carbone spends much of his time researching Ecology, Predation, Abundance, Habitat and Biodiversity.
Chris Carbone undertakes multidisciplinary studies into Ecology and Context in his work.
His work on Carnivore as part of general Predation research is frequently linked to Scaling, thereby connecting diverse disciplines of science.
His research in Abundance intersects with topics in Population density, Competition, Leopardus, Leopardus wiedii and Guild.
His work deals with themes such as Zoology, Venom, Predator and Macroecology, which intersect with Habitat.
His Convention on Biological Diversity study, which is part of a larger body of work in Biodiversity, is frequently linked to Metadata, Diversity and Citizen science, bridging the gap between disciplines.
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.
