Predictions from machine learning ensembles: Marine bird distribution and density on Canada’s Pacific coast
| dc.contributor.author | Fox, Caroline Hazel | |
| dc.contributor.author | Huettmann, F. H. | |
| dc.contributor.author | Harvey, G. K. A. | |
| dc.contributor.author | Morgan, K. H. | |
| dc.contributor.author | Robinson, J. | |
| dc.contributor.author | Williams, R. | |
| dc.contributor.author | Paquet, Paul C. | |
| dc.date.accessioned | 2018-08-02T18:03:40Z | |
| dc.date.available | 2018-08-02T18:03:40Z | |
| dc.date.copyright | 2017 | en_US |
| dc.date.issued | 2017 | |
| dc.description.abstract | 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. | en_US |
| dc.description.reviewstatus | Reviewed | en_US |
| dc.description.scholarlevel | Faculty | en_US |
| dc.description.sponsorship | Support for marine bird surveys by Raincoast Conservation Foundation (RCF) and subsequent data analysis was provided by the Gordon and Betty Moore Foundation, the Marisla Foundation, the McLean Foundation, the Bullitt Foundation, Mountain Equipment Co-op, Patagonia, the Conservation Alliance, the Vancouver Foundation, the Russell Family Foundation, Environment and Climate Change Canada (ECCC), and RCF donors, volunteers, and others. H. Krajewsky, M. Price, and other marine bird observers and survey members are acknowledged for their contributions. We also thank D. Kawai for his contributions to data preparation and P. O'Hara and N. Serra-Sogas for the study hexagons and advice on environmental variables. Salford Systems Ltd provided SPM7 for this research via the EWHALE lab license to F.H. C.H.F was supported by an NSERC IRDF postdoctoral fellowship, G.K.H. and J.R. by RCF and the ECCC Science Horizons program, P. C. P. by RCF, and K.M. by ECCC. F.H. appreciates the support by UAF for the EWHALE lab, as well as S. Linke, H. Berrios Alvarez, and the project team of co-authors. | en_US |
| dc.identifier.citation | Fox, C.H.; Huettmann, F.H.; Harvey, G.K.A.; Morgan, K.H.; Robinson, J.; Williams, R.; & Paquet, P.C. (2017). Predictions from machine learning ensembles: Marine bird distribution and density on Canada’s Pacific coast. Marine Ecology Progress Series, 566, 199-216. https://doi.org/10.3354/meps12030 | en_US |
| dc.identifier.uri | https://doi.org/10.3354/meps12030 | |
| dc.identifier.uri | http://hdl.handle.net/1828/9824 | |
| dc.language.iso | en | en_US |
| dc.publisher | Marine Ecology Progress Series | en_US |
| dc.subject | marine birds | |
| dc.subject | ensemble models | |
| dc.subject | density and distribution estimates | |
| dc.subject | line transect survey | |
| dc.subject | machine learning | |
| dc.subject | North Pacific Ocean | |
| dc.subject.department | Department of Geography | |
| dc.title | Predictions from machine learning ensembles: Marine bird distribution and density on Canada’s Pacific coast | en_US |
| dc.type | Article | en_US |