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dc.contributor.authorCasalegno, S
dc.contributor.authorAnderson, K
dc.contributor.authorCox, DTC
dc.contributor.authorHancock, S
dc.contributor.authorGaston, KJ
dc.date.accessioned2017-03-06T09:11:21Z
dc.date.accessioned2017-03-06T10:40:53Z
dc.date.issued2017-04-06
dc.description.abstractThe movements of organisms and the resultant flows of ecosystem services are strongly shaped by landscape connectivity. Studies of urban ecosystems have relied on two-dimensional (2D) measures of greenspace structure to calculate connectivity. It is now possible to explore three-dimensional (3D) connectivity in urban vegetation using waveform lidar technology that measures the full 3D structure of the canopy. Making use of this technology, here we evaluate urban greenspace 3D connectivity, taking into account the full vertical stratification of the vegetation. Using three towns in southern England, UK, all with varying greenspace structures, we describe and compare the structural and functional connectivity using both traditional 2D greenspace models and waveform lidar-generated vegetation strata (namely, grass, shrubs and trees). Measures of connectivity derived from 3D greenspace are lower than those derived from 2D models, as the latter assumes that all vertical vegetation strata are connected, which is rarely true. Fragmented landscapes that have more complex 3D vegetation showed greater functional connectivity and we found highest 2D to 3D functional connectivity biases for short dispersal capacities of organisms (6 m to 16 m). These findings are particularly pertinent in urban systems where the distribution of greenspace is critical for delivery of ecosystem services.en_GB
dc.description.sponsorshipThis work was funded under the NERC Biodiversity and Ecosystem Services Sustainability (BESS) thematic programme for the ‘Fragments Functions and Flows in Urban Ecosystems’ project (Reference: NE/J015237/1; http://bess-urban.group.shef.ac.uk/). The waveform ALS data were acquired by the NERC Airborne Research and Survey Facility (ARSF) and the team from the ARSF Data Analysis Node at Plymouth Marine Laboratory is acknowledged for undertaking initial ALS processing.en_GB
dc.identifier.citationVol. 7, article 45571en_GB
dc.identifier.doi10.1038/srep45571
dc.identifier.urihttp://hdl.handle.net/10871/26237
dc.language.isoenen_GB
dc.publisherNature Researchen_GB
dc.relation.replaces10871/26198
dc.relation.replaceshttp://hdl.handle.net/10871/26198
dc.rights© The Author(s) 2017. Open access. This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/
dc.titleEcological connectivity in the three-dimensional urban green volume using waveform airborne lidaren_GB
dc.typeArticleen_GB
dc.identifier.issn2045-2322
pubs.merge-from10871/26198
pubs.merge-fromhttp://hdl.handle.net/10871/26198
dc.descriptionThis is the final version. Available on open access from Nature Research via the DOI in this record.
dc.identifier.journalScientific Reportsen_GB
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dcterms.dateAccepted2017-02-27
rioxxterms.versionVoR
refterms.dateFCD2017-03-06
refterms.versionFCDAM
refterms.dateFOA2019-04-10T14:28:46Z


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© The Author(s) 2017. Open access. This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/
Except where otherwise noted, this item's licence is described as © The Author(s) 2017. Open access. This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/