Por favor, use este identificador para citar o enlazar este ítem: http://repositorio.ikiam.edu.ec/jspui/handle/RD_IKIAM/347
Título : Multispectral canopy reflectance improves spatial distribution models of Amazonian understory species
Autor : Van doninc, Jasper
Jones, Mirkka M.
Zuquim, Gabriela
Ruokolainen, Kalle
Massaine Moulatlet, Gabriel
Cárdenas, Glenda
Lehtonen, Samuli
Tuomisto, Hanna
Palabras clave : Amazonia,
Remote sensing
, soils
Species distribution modelling
Fecha de publicación : 2020
Editorial : Blackwell Publishing Inc.
Citación : 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
Resumen : 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.
URI : https://doi.org/10.1111/ecog.04729
Aparece en las colecciones: ARTÍCULOS CIENTÍFICOS

Ficheros en este ítem:
Fichero Descripción Tamaño Formato  
A-IKIAM-000248.pdfMultispectral canopy reflectance improves spatial distribution models of Amazonian understory species1,92 MBAdobe PDFVista previa

Este ítem está sujeto a una licencia Creative Commons Licencia Creative Commons Creative Commons