What is he best known for?
The fields of study he is best known for:
- Ecology
- Biodiversity
- Ecosystem
Ben A. Woodcock focuses on Biodiversity, Ecology, Species richness, Ecosystem and Ecosystem services.
He interconnects Canonical analysis, Detrended correspondence analysis, Agronomy and Pollination in the investigation of issues within Biodiversity.
His Species richness study incorporates themes from Agroforestry, Biomass, Grassland, Species diversity and Trophic level.
His Ecosystem study integrates concerns from other disciplines, such as Agriculture, Arable land, Environmental resource management and Wildlife.
His Ecosystem services research includes themes of Habitat conservation, Landscape ecology, Integrated pest management and Pest control.
The concepts of his Habitat study are interwoven with issues in Sampling and Invertebrate.
His most cited work include:
- Biodiversity and resilience of ecosystem functions (491 citations)
- Functional traits as indicators of biodiversity response to land use changes across ecosystems and organisms. (296 citations)
- Country-specific effects of neonicotinoid pesticides on honey bees and wild bees. (286 citations)
What are the main themes of his work throughout his whole career to date?
Ben A. Woodcock mostly deals with Ecology, Biodiversity, Species richness, Habitat and Grassland.
Pollination, Plant community, Ecosystem services, Trophic level and Predation are among the areas of Ecology where Ben A. Woodcock concentrates his study.
His Biodiversity research integrates issues from Species evenness, Agroforestry, Abundance, Agronomy and Buffer strip.
His Species richness research is multidisciplinary, incorporating perspectives in Forb, Biomass, Herbivore, Species diversity and Tussock.
His studies in Habitat integrate themes in fields like Biological dispersal, Butterfly and Arable land.
His Grassland research integrates issues from Invertebrate, Plant species, Restoration ecology and Grazing.
He most often published in these fields:
- Ecology (55.65%)
- Biodiversity (41.13%)
- Species richness (33.87%)
What were the highlights of his more recent work (between 2018-2021)?
- Ecology (55.65%)
- Pollination (16.13%)
- Agriculture (10.48%)
In recent papers he was focusing on the following fields of study:
His main research concerns Ecology, Pollination, Agriculture, Biodiversity and Habitat.
His study in Ecology concentrates on Species richness, Generalist and specialist species and Biological pest control.
His studies deal with areas such as Trophic level and Detritivore as well as Species richness.
His Pollination research focuses on Crop and how it connects with Landscape ecology, Pollen and Abundance.
His studies in Biodiversity integrate themes in fields like Species evenness, Chronosequence, Herbivore, Grassland and Plant community.
His Habitat research includes elements of Spider, Sustainable agriculture and Crop yield.
Between 2018 and 2021, his most popular works were:
- Widespread losses of pollinating insects in Britain. (153 citations)
- Meta-analysis reveals that pollinator functional diversity and abundance enhance crop pollination and yield (57 citations)
- Agrochemicals in the wild: Identifying links between pesticide use and declines of nontarget organisms (16 citations)
In his most recent research, the most cited papers focused on:
- Ecology
- Biodiversity
- Ecosystem
His scientific interests lie mostly in Agriculture, Species richness, Ecology, Ecosystem services and Biodiversity.
His Agriculture research incorporates themes from Global population and Wildlife.
Ecology is represented through his Pollination and Pollinator research.
The various areas that Ben A. Woodcock examines in his Pollinator study include Hoverfly, Habitat and Occupancy.
His Ecosystem services study incorporates themes from Agroforestry, Arable land, Agricultural productivity, Agricultural land and Beneficial insects.
Ben A. Woodcock interconnects Species evenness, Plant community, Climate change and Land management in the investigation of issues within Biodiversity.
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.
