Authors: Evans, R, Lea, M-A, Hindell, MA
Subject Terms: predator-prey relationships, trophic interactions, boosted regression trees / BRT, foraging, hotspot, marine heatwaves, seabirds, species-distribution modelling
Relation: Evans, R, Lea, M-A orcid:0000-0001-8318-9299 and Hindell, MA orcid:0000-0002-7823-7185 2021 , 'Predicting the distribution of foraging seabirds during a period of heightened environmental variability' , Ecological Applications, vol. 31, no. 5 , pp. 1-20 , doi: https://doi.org/10.1002/eap.2343 .
Authors: Wege, M, Nico de Bruyn, PJ, Hindell, MA, Lea, M-A, Bester, MN
Subject Terms: Arctocephalus, boosted regression tree, foraging behaviour, foraging segregation, machine learning, Marion Island, niche, sympatry
File Description: application/pdf
Relation: https://eprints.utas.edu.au/31766/1/135124%20-%20Preferred,%20small-scale%20foraging%20areas%20of%20two%20Southern%20Ocean%20fur%20seal%20species.pdf; Wege, M, Nico de Bruyn, PJ, Hindell, MA orcid:0000-0002-7823-7185 , Lea, M-A orcid:0000-0001-8318-9299 and Bester, MN 2019 , 'Preferred, small-scale foraging areas of two Southern Ocean fur seal species are not determined by habitat characteristics' , BMC Ecology, vol. 19 , pp. 1-14 , doi: https://doi.org/10.1186/s12898-019-0252-x .
Availability:
https://doi.org/10.1186/s12898-019-0252-x
https://eprints.utas.edu.au/31766/
https://eprints.utas.edu.au/31766/1/135124%20-%20Preferred,%20small-scale%20foraging%20areas%20of%20two%20Southern%20Ocean%20fur%20seal%20species.pdf
Authors: Fountain-Jones, NM, Machado, G, Carver, SS, Packer, C, Recamonde-Mendoza, M, Craft, ME
Subject Terms: boosted regression trees, disease ecology, gradient boosting models, machine learning, model-agnostic methods, random forests, serology, support vector machines
Relation: Fountain-Jones, NM, Machado, G, Carver, SS orcid:0000-0002-3579-7588 , Packer, C, Recamonde-Mendoza, M and Craft, ME 2019 , 'How to make more from exposure data? an integrated machine learning pipeline to predict pathogen exposure' , Journal of Animal Ecology, vol. 88, no. 10 , pp. 1447-1461 , doi: https://doi.org/10.1111/1365-2656.13076 .
Availability: https://eprints.utas.edu.au/31610/