Multispectral canopy reflectance improves spatial distribution models of Amazonian understory species

Show simple item record doninck, Jasper Van Jones, Mirkka M. Zuquim, Gabriela Ruokolainen, Kalle Moulatlet, Gabriel M. Sirén, Anders Cárdenas, Glenda Lehtonen, Samuli Tuomisto, Hanna 2020-06-04T08:24:01Z 2020-06-04T08:24:01Z 2020-01-01
dc.identifier.citation doninck , J V , Jones , M M , Zuquim , G , Ruokolainen , K , Moulatlet , G M , Sirén , A , Cárdenas , G , Lehtonen , S & Tuomisto , H 2020 , ' Multispectral canopy reflectance improves spatial distribution models of Amazonian understory species ' , Ecography , vol. 43 , no. 1 , pp. 128-137 .
dc.identifier.other PURE: 136715941
dc.identifier.other PURE UUID: a520ff0e-e68a-49f9-a3f7-2fe59f501382
dc.identifier.other Bibtex: 4177c84fcc82477695b75f67bc986ec0
dc.identifier.other ORCID: /0000-0003-4159-4506/work/75465598
dc.identifier.other ORCID: /0000-0002-8157-8730/work/75565490
dc.identifier.other WOS: 000491568900001
dc.description.abstract Species distribution models are required for the research and management of biodiversity in the hyperdiverse tropical forests, but reliable and ecologically relevant digital environmental data layers are not always available. We here assess the usefulness of multispectral canopy reflectance (Landsat) relative to climate data in modelling understory plant species distributions in tropical rainforests. We used a large dataset of quantitative fern and lycophyte species inventories across lowland Amazonia as the basis for species distribution modelling (SDM). As predictors, we used CHELSA climatic variables and canopy reflectance values from a recent basin-wide composite of Landsat TM/ETM+ images both separately and in combination. We also investigated how species accumulate over sites when environmental distances were expressed in terms of climatic or surface reflectance variables. When species accumulation curves were constructed such that differences in Landsat reflectance among the selected plots were maximised, species accumulated faster than when climatic differences were maximised or plots were selected in a random order. Sixty-nine species were sufficiently frequent for species distribution modelling. For most of them, adequate SDMs were obtained whether the models were based on CHELSA data only, Landsat data only or both combined. Model performance was not influenced by species’ prevalence or abundance. Adding Landsat-based environmental data layers overall improved the discriminatory capacity of SDMs compared to climate-only models, especially for soil specialist species. Our results show that canopy surface reflectance obtained by multispectral sensors can provide studies of tropical ecology, as exemplified by SDMs, much higher thematic (taxonomic) detail than is generally assumed. Furthermore, multispectral datasets complement the traditionally used climatic layers in analyses requiring information on environmental site conditions. We demonstrate the utility of freely available, global remote sensing data for biogeographical studies that can aid conservation planning and biodiversity management. en
dc.format.extent 10
dc.language.iso eng
dc.relation.ispartof Ecography
dc.rights cc_by
dc.rights.uri info:eu-repo/semantics/openAccess
dc.subject Amazonia
dc.subject CHELSA
dc.subject ferns
dc.subject Landsat
dc.subject remote sensing
dc.subject soils
dc.subject species distribution modelling
dc.subject 1171 Geosciences
dc.title Multispectral canopy reflectance improves spatial distribution models of Amazonian understory species en
dc.type Article
dc.contributor.organization Institute of Biotechnology
dc.contributor.organization Earth Change Observation Laboratory (ECHOLAB)
dc.description.reviewstatus Peer reviewed
dc.relation.issn 0906-7590
dc.rights.accesslevel openAccess
dc.type.version publishedVersion

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