dc.contributor.author | Isaac, Nick J.B. | |
dc.contributor.author | OHara, Robert Brian | |
dc.date.accessioned | 2021-03-09T11:03:43Z | |
dc.date.available | 2021-03-09T11:03:43Z | |
dc.date.created | 2021-01-18T11:24:14Z | |
dc.date.issued | 2019 | |
dc.identifier.issn | 0169-5347 | |
dc.identifier.uri | https://hdl.handle.net/11250/2732344 | |
dc.description.abstract | With the expansion in the quantity and types of biodiversity data being collected, there is a need to find ways to combine these different sources to provide cohesive summaries of species’ potential and realized distributions in space and time. Recently, model-based data integration has emerged as a means to achieve this by combining datasets in ways that retain the strengths of each. We describe a flexible approach to data integration using point process models, which provide a convenient way to translate across ecological currencies. We highlight recent examples of large-scale ecological models based on data integration and outline the conceptual and technical challenges and opportunities that arise. | en_US |
dc.language.iso | eng | en_US |
dc.publisher | Cell Press | en_US |
dc.rights | Navngivelse 4.0 Internasjonal | * |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/deed.no | * |
dc.title | Data Integration for Large-Scale Models of Species Distributions | en_US |
dc.type | Peer reviewed | en_US |
dc.type | Journal article | en_US |
dc.description.version | publishedVersion | en_US |
dc.source.journal | Trends in Ecology & Evolution | en_US |
dc.identifier.doi | 10.1016/j.tree.2019.08.006 | |
dc.identifier.cristin | 1873057 | |
dc.description.localcode | © 2019 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). | en_US |
cristin.ispublished | true | |
cristin.fulltext | original | |
cristin.qualitycode | 1 | |