Calastri, C, Giergiczny, M, Zedrosser, A et al. (1 more author) (2023) Modelling activity patterns of wild animals - An application of the multiple discrete-continuous extreme value (MDCEV) model. Journal of Choice Modelling, 47. 100415. ISSN 1755-5345
Abstract
Advanced econometric models used in the field of transport or marketing are becoming increasingly sophisticated and able to capture complex decision making and outcomes. In this paper, we apply state-of-the-art discrete-continuous choice models to the field of Ecology, in particular to model activity engagement of the population of Swedish Brown bears. Using data from GPS collars that track wild animals over time, we estimate a Multiple Discrete-Continuous Extreme Value (MDCEV) model to understand activity engagement and duration as a function of both bear characteristics and other external factors. We show that the methodology is not only suitable to address this aim, but also allows us to produce insights into the connection between the animal's age and gender and activity engagement as well as the links with climate variables (temperature and precipitation) and human activity (hunting).
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2023 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Environment (Leeds) > Institute for Transport Studies (Leeds) > ITS: Choice Modelling The University of Leeds > Faculty of Environment (Leeds) > Institute for Transport Studies (Leeds) > ITS: Economics and Discrete Choice (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 04 May 2023 09:27 |
Last Modified: | 25 Jun 2023 23:20 |
Status: | Published |
Publisher: | Elsevier |
Identification Number: | 10.1016/j.jocm.2023.100415 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:198864 |