What is he best known for?
The fields of study he is best known for:
- Ecology
- Ecosystem
- Habitat
Ecology, Habitat, Riparian zone, Community structure and Flood myth are his primary areas of study.
His Ecology and Life history, Ecosystem, Redfield ratio, Phytoplankton and Mesocosm investigations all form part of his Ecology research activities.
His study in Life history is interdisciplinary in nature, drawing from both Adaptation, Entire population and STREAMS.
His Ecosystem research incorporates elements of Taxonomic rank and Metacommunity.
His studies deal with areas such as Biodiversity, Biological dispersal and Predictability as well as Habitat.
He has researched Flood myth in several fields, including Organism, Vital rates, Natural flow and Deciduous.
His most cited work include:
- Adaptation to natural flow regimes (1208 citations)
- Theory, methods and tools for determining environmental flows for riparian vegetation: riparian vegetation‐flow response guilds (265 citations)
- HYDROLOGIC REGIMES AND RIPARIAN FORESTS: A STRUCTURED POPULATION MODEL FOR COTTONWOOD (172 citations)
What are the main themes of his work throughout his whole career to date?
His scientific interests lie mostly in Ecology, Habitat, Ecosystem, Biological dispersal and Species richness.
His Ecology research incorporates themes from Flood myth and STREAMS.
His biological study spans a wide range of topics, including Perennial plant and Taxon.
As a member of one scientific family, David A. Lytle mostly works in the field of Habitat, focusing on Community structure and, on occasion, Regime shift.
His work deals with themes such as Metacommunity, Natural flow, Riparian zone and Zooplankton, which intersect with Ecosystem.
David A. Lytle works mostly in the field of Species richness, limiting it down to topics relating to Abiotic component and, in certain cases, Trophic level.
He most often published in these fields:
- Ecology (74.42%)
- Habitat (32.56%)
- Ecosystem (23.26%)
What were the highlights of his more recent work (between 2015-2021)?
- Ecology (74.42%)
- Ecosystem (23.26%)
- Biodiversity (15.12%)
In recent papers he was focusing on the following fields of study:
His main research concerns Ecology, Ecosystem, Biodiversity, Habitat and Riparian zone.
His Ecology study combines topics from a wide range of disciplines, such as Population growth and STREAMS.
His work carried out in the field of STREAMS brings together such families of science as Drainage basin, Water scarcity, Chironomidae and Arid.
The study incorporates disciplines such as Invertebrate and Metacommunity in addition to Ecosystem.
His work is dedicated to discovering how Biodiversity, Species sorting are connected with Phytoplankton, Zooplankton, Paradox of the plankton and Primary producers and other disciplines.
His Habitat study combines topics in areas such as Carrying capacity, Aquatic ecosystem and Population size.
Between 2015 and 2021, his most popular works were:
- The role of dispersal in river network metacommunities: Patterns, processes, and pathways (125 citations)
- Seasonality and predictability shape temporal species diversity. (122 citations)
- Flow regime alteration degrades ecological networks in riparian ecosystems (83 citations)
In his most recent research, the most cited papers focused on:
- Ecology
- Ecosystem
- Biodiversity
His primary areas of investigation include Ecology, Ecosystem, Biodiversity, Habitat and Community structure.
His Ecology study spans across into subjects like Government, Work, Public administration, Flow management and Public domain.
His work in the fields of River ecosystem overlaps with other areas such as Environmental resource management.
David A. Lytle interconnects Species diversity, Predictability, Seasonality and Temporal scales in the investigation of issues within Habitat.
His work in Community structure addresses issues such as Streamflow, which are connected to fields such as Riparian zone.
His Beta diversity research is multidisciplinary, incorporating elements of Invertebrate, Aquatic ecosystem, Resistance and STREAMS.
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.
