What is he best known for?
The fields of study he is best known for:
Daniel C. Reed focuses on Ecology, Kelp, Macrocystis pyrifera, Kelp forest and Biological dispersal.
Pterygophora californica, Algae, Invasive species, Ecosystem and Introduced species are among the areas of Ecology where the researcher is concentrating his efforts.
His biological study spans a wide range of topics, including Annual growth rate and Disturbance.
His work on Macrocystis as part of general Macrocystis pyrifera study is frequently connected to Grazing pressure, therefore bridging the gap between diverse disciplines of science and establishing a new relationship between them.
His studies deal with areas such as Spatial ecology, Climate change, Oceanography and Foundation species as well as Kelp forest.
His Biological dispersal research is multidisciplinary, relying on both Propagule and Ectocarpus siliculosus.
His most cited work include:
- Food Availability, Sea Urchin Grazing, and Kelp Forest Community Structure (366 citations)
- The Effects of Canopy Shadings on Algal Recruitment and Growth in a Giant Kelp Forest (332 citations)
- Herbivory on coral reefs: community structure following mass mortalities of sea urchins (244 citations)
What are the main themes of his work throughout his whole career to date?
Daniel C. Reed spends much of his time researching Ecology, Kelp, Macrocystis pyrifera, Kelp forest and Oceanography.
His research links Biological dispersal with Ecology.
His Kelp study incorporates themes from Sporophyte, Biomass, Algae, Foundation species and Temporal scales.
The study incorporates disciplines such as Primary production, Canopy and Frond in addition to Macrocystis pyrifera.
The concepts of his Kelp forest study are interwoven with issues in Trophic level, Climate change, Reef, Fishery and Spatial ecology.
As part of the same scientific family, Daniel C. Reed usually focuses on Oceanography, concentrating on Organic matter and intersecting with Total organic carbon and Bottom water.
He most often published in these fields:
- Ecology (61.21%)
- Kelp (38.18%)
- Macrocystis pyrifera (34.55%)
What were the highlights of his more recent work (between 2017-2021)?
- Ecology (61.21%)
- Kelp (38.18%)
- Kelp forest (31.52%)
In recent papers he was focusing on the following fields of study:
Daniel C. Reed mainly investigates Ecology, Kelp, Kelp forest, Macrocystis pyrifera and Biomass.
In his papers, Daniel C. Reed integrates diverse fields, such as Ecology and Term.
His research in Kelp intersects with topics in Oceanography, Shore and Remote sensing.
The various areas that he examines in his Kelp forest study include Abundance, Understory, Benthic zone and Foundation species.
Daniel C. Reed interconnects Urea and Primary production in the investigation of issues within Macrocystis pyrifera.
His research integrates issues of Propagule, Species richness, Temperate climate and Temporal scales in his study of Biomass.
Between 2017 and 2021, his most popular works were:
- BioTIME: A database of biodiversity time series for the Anthropocene (113 citations)
- Giant kelp, Macrocystis pyrifera, increases faunal diversity through physical engineering. (41 citations)
- Loss of foundation species: disturbance frequency outweighs severity in structuring kelp forest communities. (26 citations)
In his most recent research, the most cited papers focused on:
- Ecology
- Ecosystem
- Biodiversity
His scientific interests lie mostly in Ecology, Kelp forest, Biomass, Kelp and Species richness.
He studies Ecology, namely Algae.
His work carried out in the field of Kelp forest brings together such families of science as Spatial ecology, Marine ecosystem and Northern Hemisphere, Arctic oscillation.
His Kelp research is mostly focused on the topic Macrocystis pyrifera.
His Macrocystis pyrifera research includes elements of Temperate climate, Primary production, Atmospheric sciences, Standing crop and Temporal scales.
His Species richness study combines topics in areas such as Biodiversity, Anthropocene, Invertebrate and Habitat.
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.
