Author

C. K. Glidden

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· 4

Signature Assays, Organisms & Methods

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Approach:machine learning (classical)· 4 Research Focus:ecology & biodiversity· 3 Research Focus:benchmarking· 2 Research Focus:host-pathogen & immune response· 2 Organism:human· 1 Organism:mouse· 1 Organism:rat· 1

Frequent Co-Authors

E. A. Mordecai· 4 shared A. A. Castellanos· 2 shared B. A. Han· 2 shared T. S. Athni· 2 shared A. Chamberlin· 1 shared A. L. Singleton· 1 shared A. M. V. Monteiro· 1 shared A. Murran· 1 shared

Activity by Year

2022-2024 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 24 mutual co-author links

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4 shared papersEcology

E. A. Mordecai

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Author profile →

4 papers

Recent·Most cited

Climate and urbanization drive changes in the habitat suitability of Schistosoma mansoni competent snails in Brazil

C. K. Glidden, A. L. Singleton, A. Chamberlin, R. Tuan, R. G. S. Palasio, R. Calderia, A. M. V. Monteiro, K. M. Lwiza, P. Liu, V. Silva, T. S. Athni, S. H. Sokolow, E. A. Mordecai, G. A. De Leo

The paper uses machine learning and remote sensing to analyze 30 years of snail occurrence data in Brazil, showing climate change and urbanization have altered Biomphalaria habitat suitability.

bioRxiv · Jan 3, 2024 · PreprintEcology

Temperature dependence of mosquitoes: comparing mechanistic and machine learning approaches

T. S. Athni, M. Childs, C. K. Glidden, E. A. Mordecai

This study compares mechanistic and machine learning approaches to estimate mosquito thermal responses, finding strong agreement for thermal minima but less for optima between lab and field data.

bioRxiv · Dec 4, 2023Ecology

Phylogenetic and biogeographical traits predict unrecognized hosts of zoonotic leishmaniasis

C. K. Glidden, A. Murran, R. A. Silva, A. A. Castellanos, B. A. Han, E. A. Mordecai

The paper uses machine learning to predict unrecognized wildlife hosts of zoonotic Leishmania by analyzing biogeography, phylogenetic distance, and environmental traits.

bioRxiv · Oct 11, 2022 · PreprintEcology

Data-Driven Predictions of Potential Leishmania Vectors in Latin America

G. Vadmal, C. K. Glidden, B. A. Han, B. Carvalho, A. A. Castellanos, E. A. Mordecai

This paper uses machine learning to predict potential Leishmania vectors in Latin America based on sandfly traits and environmental factors, identifying two new potential vectors.

bioRxiv · Aug 22, 2022 · PreprintEcology