Predictive modelling of beaver habitats using machine learning
department or school: Arts, Faculty of
department or school: Geography, Department of
data provider: DSpace
degree campus: UBCV
creator: Matechuk, Landen
issued: 2024-08-15T16:23:55Z
issued: 2024
related degree: Master of Science - MSc
degree grantor: University of British Columbia
description: Beavers inhabit and alter freshwater ecosystems through the construction of beaver dams, lodges, ponds and canals. Beaver-controlled stream systems exhibit increased sediment and water storage, creating pools and important habitats for fish and invertebrates. However, determining historic and or suitable beaver habitats poses a challenge due to factors such as trapping, land use disturbances, and climate change. As interest in restoring riverine and stream ecosystems grows, the reintroduction of beavers and construction of beaver dam analogs—man-made structures designed to mimic natural beaver dams—are increasingly being used to rehabilitate impaired riverine habitats. A mapping approach that identifies suitable topographic and environmental conditions associated with successful beaver habitats would therefore be a useful guide for future freshwater ecosystem restoration efforts. In this study, beaver dam locations were identified and used to train a random forest machine learning model to assess stream, wetland and river beaver habitat suitability. The model uses provincially available topographic, biological, and hydrologic variables to test their importance for predicting the presence or absence of beaver dams. This procedure produced a measure of habitat suitability with an accuracy score of 97%. The research discussed here can contribute to informed decision-making regarding the reintroduction of beavers and the restoration of stream systems, promoting ecological balance and enhancing the natural state of riverine ecosystems.
aggregated CHO: https://circle.library.ubc.ca/rest/handle/2429/88940
note: Predictive Modelling of Beaver Habitats Using Machine Learning by Landen Matechuk B.Sc., The University of Victoria, 2020 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE in THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (Geography) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) August 2024 © Landen Matechuk, 2024ii The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, the thesis entitled: Predictive Modelling of Beaver Habitats Using Machine Learning submitted by Landen Matechuk in partial fulfilment of the requirements for the degree of Master of Science in Geography Examining Committee: Marwan Hassan, Professor, Department of Geography, University of British Columbia. Supervisor Mike Miles, Principal, M. Miles and Associates Ltd. Supervisory Committee Member Dave Reid, Senior Restoration Biologist, District of Fisheries and Oceans Canada Additional Examiner iii Abstract Beavers inhabit and alter freshwater ecosystems through the construction of beaver dams, lodges, ponds and canals. Beaver-controlled stream systems exhibit increased sediment and water storage, creating pools and important habitats for fish and invertebrates. However, determining historic and or suitable beaver habitats poses a challenge due to factors such as trapping, land use disturbances, and climate change. As interest in restoring riverine and stream ecosystems grows, the reintroduction of beavers and construction of beaver dam analogs—man-made structures designed to mimic natural beaver dams—are increasingly being used to rehabilitate impaired riverine habitats. A mapping approach that identifies suitable topographic and environmental conditions associated with successful beaver habitats would therefore be a useful guide for future freshwater ecosystem restoration efforts. In this study, beaver dam locations were identified and used to train a random forest m…
has type: Thesis/Dissertation
date issued: 2024-11
is shown at: 10.14288/1.0445073
language: eng
degree discipline: Geography
provider: Vancouver : University of British Columbia Library
publisher: University of British Columbia
rights: Attribution-NonCommercial-NoDerivatives 4.0 International
rights URI: http://creativecommons.org/licenses/by-nc-nd/4.0/
scholar level: Graduate
contributor: Hassan, Marwan A.
type: Text
identifier URI: http://hdl.handle.net/2429/88940