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dc.contributor.authorChraibi, Eric
dc.contributor.authorArnold, Haley
dc.contributor.authorLuque, Sandra
dc.contributor.authorDeacon, Amy
dc.contributor.authorMagurran, Anne E.
dc.contributor.authorFéret, Jean-Baptiste
dc.date.accessioned2021-09-29T12:30:01Z
dc.date.available2021-09-29T12:30:01Z
dc.date.issued2021-05-30
dc.identifier274387920
dc.identifier7ec68555-b552-4b58-b271-dd20660c0bc1
dc.identifier000660611100001
dc.identifier85107869711
dc.identifier.citationChraibi , E , Arnold , H , Luque , S , Deacon , A , Magurran , A E & Féret , J-B 2021 , ' A remote sensing approach to understanding patterns of secondary succession in tropical forest ' , Remote Sensing , vol. 13 , no. 11 , 2148 . https://doi.org/10.3390/rs13112148en
dc.identifier.issn2072-4292
dc.identifier.otherBibtex: rs13112148
dc.identifier.otherBibtex: rs13112148
dc.identifier.otherORCID: /0000-0002-0036-2795/work/101217916
dc.identifier.urihttps://hdl.handle.net/10023/24047
dc.descriptionFunding: E. Chraibi and J.-B. Féret acknowledge financial support from Agence Nationale de la Recherche (BioCop project—ANR-17-CE32-0001-01). A.E. Magurran acknowledges support from the Leverhulme Trust (RPG-2019-402).en
dc.description.abstractBiodiversity monitoring and understanding ecological processes on a global scale is a major challenge for biodiversity conservation. Field assessments commonly used to assess patterns of biodiversity and habitat condition are costly, challenging, and restricted to small spatial scales. As ecosystems face increasing anthropogenic pressures, it is important that we find ways to assess patterns of biodiversity more efficiently. Remote sensing has the potential to support understanding of landscape-level ecological processes. In this study, we considered cacao agroforests at different stages of secondary succession, and primary forest in the Northern Range of Trinidad, West Indies. We assessed changes in tree biodiversity over succession using both field data, and data derived from remote sensing. We then evaluated the strengths and limitations of each method, exploring the potential for expanding field data by using remote sensing techniques to investigate landscape-level patterns of forest condition and regeneration. Remote sensing and field data provided different insights into tree species compositional changes, and patterns of alpha- and beta-diversity. The results highlight the potential of remote sensing for detecting patterns of compositional change in forests, and for expanding on field data in order to better understand landscape-level patterns of forest diversity.
dc.format.extent19
dc.format.extent9324001
dc.language.isoeng
dc.relation.ispartofRemote Sensingen
dc.subjectDiversity indicesen
dc.subjectAgroforestryen
dc.subjectSentinel-2en
dc.subjectBiodiversity monitoringen
dc.subjectPatterns of alphaen
dc.subjectBeta-diversityen
dc.subjectGE Environmental Sciencesen
dc.subjectQH301 Biologyen
dc.subjectSD Forestryen
dc.subject3rd-DASen
dc.subject.lccGEen
dc.subject.lccQH301en
dc.subject.lccSDen
dc.titleA remote sensing approach to understanding patterns of secondary succession in tropical foresten
dc.typeJournal articleen
dc.contributor.sponsorThe Leverhulme Trusten
dc.contributor.institutionUniversity of St Andrews. School of Biologyen
dc.contributor.institutionUniversity of St Andrews. Centre for Biological Diversityen
dc.contributor.institutionUniversity of St Andrews. Scottish Oceans Instituteen
dc.contributor.institutionUniversity of St Andrews. Institute of Behavioural and Neural Sciencesen
dc.contributor.institutionUniversity of St Andrews. St Andrews Sustainability Instituteen
dc.contributor.institutionUniversity of St Andrews. Centre for Research into Ecological & Environmental Modellingen
dc.contributor.institutionUniversity of St Andrews. Fish Behaviour and Biodiversity Research Groupen
dc.contributor.institutionUniversity of St Andrews. Marine Alliance for Science & Technology Scotlanden
dc.identifier.doi10.3390/rs13112148
dc.description.statusPeer revieweden
dc.identifier.urlhttps://www.mdpi.com/journal/remotesensing/special_issues/FP_SI_Ecological_Remote_Sensingen
dc.identifier.grantnumberORPG-8061en


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