Predictions from machine learning ensembles: marine bird distribution and density on Canada’s Pacific coast

Marine ecology progress series

, G. K. A. Harvey¹  , P. C. Paquet¹  C. H. Fox²  *, F. H. Huettmann³  , K. H. Morgan⁴  , R. Williams⁵  , J. Robinson⁶ 

[1] Department of Geography, University of Victoria, Victoria, BC V8W 2Y2, Canada. Raincoast Conservation Foundation, Sidney, BC V8L 3Y3, Canada;Department of Geography, University of Victoria, Victoria, BC V8W 2Y2, Canada. Raincoast Conservation Foundation, Sidney, BC V8L 3Y3, Canada. Present address: Department of Oceanography, Dalhousie University, Halifax, NS B3H 4R2, Canada;EWHALE lab, Biology and Wildlife Departments, Institute of Arctic Biology, University of Alaska-Fairbanks, Fairbanks, AK 99775, USA;Environment and Climate Change Canada, Sidney, BC V8L 4B2, Canada;Oceans Initiative, Seattle, WA 98102, USA;Raincoast Conservation Foundation, Sidney, BC V8L 3Y3, Canada

关键词: Marine birds;    Ensemble models;    Density and distribution estimates;    Line transect survey;    Machine learning;    North Pacific Ocean;   

DOI  :  10.3354/meps12030

学科分类：海洋学与技术

来源: Inter-Research

【 摘 要 】

Increasingly disrupted and altered, the world’s oceans are subject to immense and intensifying anthropogenic pressures. Of the biota inhabiting these ecosystems, marine birds are among the most threatened. For conservation efforts targeting marine birds to be effective, quantitative information relating to their at-sea density and distribution is typically a crucial knowledge component. In this study, we generated predictive machine learning ensemble models for 13 marine bird species and 7 groups (representing 24 additional species) in Canada’s Pacific coast waters, including several species listed under Canada’s Species at Risk Act. Predictive models were based on systematic marine bird line transect survey information collected in spring, summer, and fall on Canada’s Pacific coast (2005-2008). Multiple Covariate Distance Sampling (MCDS) was used to estimate marine bird density along transect segments. Spatial and temporal environmental predictors, including remote sensing information, were used in model ensembles, which were constructed using 4 machine learning algorithms in Salford Systems Predictive Modeler v7.0 (SPM7): Random Forests, TreeNet, Multivariate Adaptive Regression Splines, and Classification and Regression Trees. Predictive models were subsequently combined to generate seasonal and overall predictions of areas important to marine birds based on normalized marine bird species or group richness and densities. Our results employ open access data sharing and are intended to better inform marine bird conservation efforts and management planning on Canada’s Pacific coast and for broader-scale geographic initiatives across North America and elsewhere.

【 授权许可】

Unknown