What is she best known for?
The fields of study she is best known for:
- Ecology
- Ecosystem
- Biodiversity
Her scientific interests lie mostly in Restoration ecology, Fire regime, Forest structure, Ecosystem and Ecology.
Her Restoration ecology study frequently intersects with other fields, such as Old-growth forest.
Her Forest structure study combines topics from a wide range of disciplines, such as Agroforestry, Forestry and Quercus gambelii.
Her Agroforestry research is multidisciplinary, incorporating perspectives in Ponderosa pine forest and Ecosystem health.
Her Ecosystem study integrates concerns from other disciplines, such as Range and Environmental resource management.
In the subject of general Ecology, her work in Thinning is often linked to Context, thereby combining diverse domains of study.
Her most cited work include:
- Southwestern Ponderosa Forest Structure: Changes Since Euro-American Settlement (693 citations)
- DETERMINING REFERENCE CONDITIONS FOR ECOSYSTEM MANAGEMENT OF SOUTHWESTERN PONDEROSA PINE FORESTS (561 citations)
- Restoring Ecosystem Health in Ponderosa Pine Forests of the Southwest (535 citations)
What are the main themes of her work throughout her whole career to date?
Her primary areas of study are Ecology, Forestry, Restoration ecology, Silviculture and Experimental forest.
Her work is connected to Understory, Fire regime, Herbivore, Species richness and Ecosystem, as a part of Ecology.
Her research integrates issues of National park, Canyon, Old-growth forest and Fire ecology in her study of Fire regime.
The Forestry study combines topics in areas such as Range, Forest ecology, Forest structure and Vegetation.
Margaret M. Moore combines subjects such as Forest management, Agroforestry, Forest restoration, Agronomy and Thinning with her study of Restoration ecology.
Her Thinning study incorporates themes from Forest floor and Prescribed burn.
She most often published in these fields:
- Ecology (46.99%)
- Forestry (39.76%)
- Restoration ecology (31.33%)
What were the highlights of her more recent work (between 2012-2021)?
- Ecology (46.99%)
- Restoration ecology (31.33%)
- Vegetation (14.46%)
In recent papers she was focusing on the following fields of study:
Her primary areas of investigation include Ecology, Restoration ecology, Vegetation, Adaptive value and Vital rates.
Her Species richness, Competition and Regeneration study in the realm of Ecology interacts with subjects such as Specific leaf area and Tree.
Her studies in Species richness integrate themes in fields like Grazing, Relative species abundance, Forb and Understory.
Her Restoration ecology research includes elements of Parent material, Soil water and Ecosystem, Terrestrial ecosystem.
Her Ecosystem research integrates issues from Quercus gambelii, Common spatial pattern, Spatial ecology, Dendrochronology and Abiotic component.
The concepts of her Vegetation study are interwoven with issues in Seedling, Subshrub and Foundation species.
Between 2012 and 2021, her most popular works were:
- The hierarchy of predictability in ecological restoration: are vegetation structure and functional diversity more predictable than community composition? (36 citations)
- Evaluating unmanned aerial vehicle images for estimating forest canopy fuels in a ponderosa pine stand (34 citations)
- Survival rates indicate that correlations between community-weighted mean traits and environments can be unreliable estimates of the adaptive value of traits. (30 citations)
In her most recent research, the most cited papers focused on:
- Ecology
- Ecosystem
- Biodiversity
Margaret M. Moore mainly investigates Competition, Ecology, Remote sensing, Cover and Drone.
Her Competition research includes themes of Vital rates, Soil fertility and Adaptive value.
Her work on Perennial plant, Range, Vegetation and Ecosystem as part of general Ecology study is frequently connected to Trait, therefore bridging the gap between diverse disciplines of science and establishing a new relationship between them.
Her work in the fields of Lidar overlaps with other areas such as Fire behavior.
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