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Multispectral canopy reflectance improves spatial distribution models of Amazonian understory species

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dc.contributor.author Van doninc, Jasper
dc.contributor.author Jones, Mirkka M.
dc.contributor.author Zuquim, Gabriela
dc.contributor.author Ruokolainen, Kalle
dc.contributor.author Massaine Moulatlet, Gabriel
dc.contributor.author Cárdenas, Glenda
dc.contributor.author Lehtonen, Samuli
dc.contributor.author Tuomisto, Hanna
dc.date.accessioned 2020-04-10T21:22:55Z
dc.date.available 2020-04-10T21:22:55Z
dc.date.issued 2020
dc.identifier.citation Van, J., Jones, M. M., Zuquim, G., Ruokolainen, K., Moulatlet, G. M., Sirén, A., … Tuomisto, H. (2020). Multispectral canopy reflectance improves spatial distribution models of Amazonian understory species. 23(1), 128–137.doi.org/10.1111/ecog.04729 es
dc.identifier.uri https://doi.org/10.1111/ecog.04729
dc.identifier.uri http://repositorio.ikiam.edu.ec/jspui/handle/RD_IKIAM/347
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. es
dc.publisher Blackwell Publishing Inc. es
dc.relation.ispartofseries PRODUCCIÓN CIENTÍFICA-ARTÍCULOS;A-IKIAM-000248
dc.rights openAccess es
dc.rights Atribución-NoComercial-SinDerivadas 3.0 Estados Unidos de América *
dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/3.0/us/ *
dc.subject Amazonia, es
dc.subject CHELSA es
dc.subject Ferns es
dc.subject Landsat es
dc.subject Remote sensing es
dc.subject , soils es
dc.subject Species distribution modelling es
dc.title Multispectral canopy reflectance improves spatial distribution models of Amazonian understory species es
dc.type Article es


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