Author

C. S. Boyd

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Ecology· 3

Signature Assays, Organisms & Methods

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Research Focus:ecology & biodiversity· 3 Assay:imaging· 2 Approach:statistical model· 2 Research Focus:benchmarking· 1 Research Focus:deconvolution· 1 Approach:deep learning· 1 Approach:machine learning (classical)· 1 Assay:metagenomics· 1

Frequent Co-Authors

B. W. Allred· 3 shared D. E. Naugle· 3 shared J. D. Maestas· 3 shared K. W. Davies· 3 shared M. O. Jones· 3 shared A. R. Kleinhesselink· 2 shared J. T. Smith· 2 shared B. T. Bestelmeyer· 1 shared

Activity by Year

2020-2021 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 39 mutual co-author links

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3 shared papersEcology

B. W. Allred

Mutual co-authors in cluster

Author profile →

3 papers

Recent·Most cited

Where there's smoke, there's fuel: dynamic vegetation data improve predictions of wildfire hazard in the Great Basin

J. T. Smith, B. W. Allred, C. S. Boyd, K. W. Davies, M. O. Jones, A. R. Kleinhesselink, J. D. Maestas, D. E. Naugle

This study uses dynamic vegetation data and machine learning to predict wildfire hazard in the Great Basin, finding that fire probability is strongly linked to vegetation biomass, bare ground cover, and drought indices.

bioRxiv · Jun 25, 2021Ecology

The elevational ascent and spread of exotic annual grass dominance in the Great Basin, USA

J. T. Smith, B. W. Allred, C. S. Boyd, K. W. Davies, M. O. Jones, J. D. Maestas, S. L. Morford, D. E. Naugle, A. R. Kleinhesselink

The paper quantifies the rapid expansion of exotic annual grass dominance in the Great Basin, showing an eight-fold increase in area and spread into higher elevations and north-facing slopes.

bioRxiv · Jan 5, 2021Ecology

Improving Landsat predictions of rangeland fractional cover with multitask learning and uncertainty

B. W. Allred, B. T. Bestelmeyer, C. S. Boyd, C. Brown, K. W. Davies, L. M. Ellsworth, T. A. Erickson, S. D. Fuhlendorf, T. V. Griffiths, V. Jansen, M. O. Jones, J. Karl, J. D. Maestas, J. J. Maynard, S. E. McCord, , D. R. Uden

The paper improves rangeland fractional cover predictions using multitask learning and provides pixel-level uncertainty estimates, enhancing management tools with better accuracy and confidence measures.

bioRxiv · Jun 10, 2020Ecology